update samples from Release-60 as a part of SDK release
This commit is contained in:
amlrelsa-ms
Родитель
0ab8b141ee
Коммит
5b684070e1
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@ -103,7 +103,7 @@
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"source": [
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"import azureml.core\n",
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"\n",
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"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
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"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
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"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
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]
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},
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@ -1,500 +0,0 @@
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1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796
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0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116
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0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696
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1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 1.231 1.234
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0 0.897 -1.703 -1.306 1.022 -0.729 0.836 0.859 -0.333 2.173 1.336 -0.965 0.972 2.215 0.671 1.021 -1.439 0.000 0.493 -2.019 -0.289 0.000 0.805 0.930 0.984 1.430 2.198 1.934 1.684
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||||
0 0.756 1.126 -0.945 2.355 -0.555 0.889 0.800 1.440 0.000 0.585 0.271 0.631 2.215 0.722 1.744 1.051 0.000 0.618 0.924 0.698 1.551 0.976 0.864 0.988 0.803 0.234 0.822 0.911
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||||
0 1.141 -0.741 0.953 1.478 -0.524 1.197 -0.871 1.689 2.173 0.875 1.321 -0.518 1.107 0.540 0.037 -0.987 0.000 0.879 1.187 0.245 0.000 0.888 0.701 1.747 1.358 2.479 1.491 1.223
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1 0.606 -0.936 -0.384 1.257 -1.162 2.719 -0.600 0.100 2.173 3.303 -0.284 1.561 1.107 0.689 1.786 -0.326 0.000 0.780 -0.532 1.216 0.000 0.936 2.022 0.985 1.574 4.323 2.263 1.742
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1 0.603 0.429 -0.279 1.448 1.301 1.008 2.423 -1.295 0.000 0.452 1.305 0.533 0.000 1.076 1.011 1.256 2.548 2.021 1.260 -0.343 0.000 0.890 0.969 1.281 0.763 0.652 0.827 0.785
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||||
0 1.171 -0.962 0.521 0.841 -0.315 1.196 -0.744 -0.882 2.173 0.726 -1.305 1.377 1.107 0.643 -1.790 -1.264 0.000 1.257 0.222 0.817 0.000 0.862 0.911 0.987 0.846 1.293 0.899 0.756
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1 1.392 -0.358 0.235 1.494 -0.461 0.895 -0.848 1.549 2.173 0.841 -0.384 0.666 1.107 1.199 2.509 -0.891 0.000 1.109 -0.364 -0.945 0.000 0.693 2.135 1.170 1.362 0.959 2.056 1.842
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1 1.024 1.076 -0.886 0.851 1.530 0.673 -0.449 0.187 1.087 0.628 -0.895 1.176 2.215 0.696 -0.232 -0.875 0.000 0.411 1.501 0.048 0.000 0.842 0.919 1.063 1.193 0.777 0.964 0.807
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||||
1 0.890 -0.760 1.182 1.369 0.751 0.696 -0.959 -0.710 1.087 0.775 -0.130 -1.409 2.215 0.701 -0.110 -0.739 0.000 0.508 -0.451 0.390 0.000 0.762 0.738 0.998 1.126 0.788 0.940 0.790
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||||
1 0.460 0.537 0.636 1.442 -0.269 0.585 0.323 -1.731 2.173 0.503 1.034 -0.927 0.000 0.928 -1.024 1.006 2.548 0.513 -0.618 -1.336 0.000 0.802 0.831 0.992 1.019 0.925 1.056 0.833
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1 0.364 1.648 0.560 1.720 0.829 1.110 0.811 -0.588 0.000 0.408 1.045 1.054 2.215 0.319 -1.138 1.545 0.000 0.423 1.025 -1.265 3.102 1.656 0.928 1.003 0.544 0.327 0.670 0.746
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1 0.525 -0.096 1.206 0.948 -1.103 1.519 -0.582 0.606 2.173 1.274 -0.572 -0.934 0.000 0.855 -1.028 -1.222 0.000 0.578 -1.000 -1.725 3.102 0.896 0.878 0.981 0.498 0.909 0.772 0.668
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0 0.536 -0.821 -1.029 0.703 1.113 0.363 -0.711 0.022 1.087 0.325 1.503 1.249 2.215 0.673 1.041 -0.401 0.000 0.480 2.127 1.681 0.000 0.767 1.034 0.990 0.671 0.836 0.669 0.663
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||||
1 1.789 -0.583 1.641 0.897 0.799 0.515 -0.100 -1.483 0.000 1.101 0.031 -0.326 2.215 1.195 0.001 0.126 2.548 0.768 -0.148 0.601 0.000 0.916 0.921 1.207 1.069 0.483 0.934 0.795
|
||||
1 1.332 -0.571 0.986 0.580 1.508 0.582 0.634 -0.746 1.087 1.084 -0.964 -0.489 0.000 0.785 0.274 0.343 2.548 0.779 0.721 1.489 0.000 1.733 1.145 0.990 1.270 0.715 0.897 0.915
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||||
0 1.123 0.629 -1.708 0.597 -0.882 0.752 0.195 1.522 2.173 1.671 1.515 -0.003 0.000 0.778 0.514 0.139 1.274 0.801 1.260 1.600 0.000 1.495 0.976 0.988 0.676 0.921 1.010 0.943
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||||
0 1.816 -0.515 0.171 0.980 -0.454 0.870 0.202 -1.399 2.173 1.130 1.066 -1.593 0.000 0.844 0.735 1.275 2.548 1.125 -1.133 0.348 0.000 0.837 0.693 0.988 1.112 0.784 1.009 0.974
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1 0.364 0.694 0.445 1.862 0.159 0.963 -1.356 1.260 1.087 0.887 -0.540 -1.533 2.215 0.658 -2.544 -1.236 0.000 0.516 -0.807 0.039 0.000 0.891 1.004 0.991 1.092 0.976 1.000 0.953
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||||
1 0.790 -1.175 0.475 1.846 0.094 0.999 -1.090 0.257 0.000 1.422 0.854 1.112 2.215 1.302 1.004 -1.702 1.274 2.557 -0.787 -1.048 0.000 0.890 1.429 0.993 2.807 0.840 2.248 1.821
|
||||
1 0.765 -0.500 -0.603 1.843 -0.560 1.068 0.007 0.746 2.173 1.154 -0.017 1.329 0.000 1.165 1.791 -1.585 0.000 1.116 0.441 -0.886 0.000 0.774 0.982 0.989 1.102 0.633 1.178 1.021
|
||||
1 1.407 1.293 -1.418 0.502 -1.527 2.005 -2.122 0.622 0.000 1.699 1.508 -0.649 2.215 1.665 0.748 -0.755 0.000 2.555 0.811 1.423 1.551 7.531 5.520 0.985 1.115 1.881 4.487 3.379
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||||
1 0.772 -0.186 -1.372 0.823 -0.140 0.781 0.763 0.046 2.173 1.128 0.516 1.380 0.000 0.797 -0.640 -0.134 2.548 2.019 -0.972 -1.670 0.000 2.022 1.466 0.989 0.856 0.808 1.230 0.991
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||||
1 0.546 -0.954 0.715 1.335 -1.689 0.783 -0.443 -1.735 2.173 1.081 0.185 -0.435 0.000 1.433 -0.662 -0.389 0.000 0.969 0.924 1.099 0.000 0.910 0.879 0.988 0.683 0.753 0.878 0.865
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||||
1 0.596 0.276 -1.054 1.358 1.355 1.444 1.813 -0.208 0.000 1.175 -0.949 -1.573 0.000 0.855 -1.228 -0.925 2.548 1.837 -0.400 0.913 0.000 0.637 0.901 1.028 0.553 0.790 0.679 0.677
|
||||
0 0.458 2.292 1.530 0.291 1.283 0.749 -0.930 -0.198 0.000 0.300 -1.560 0.990 0.000 0.811 -0.176 0.995 2.548 1.085 -0.178 -1.213 3.102 0.891 0.648 0.999 0.732 0.655 0.619 0.620
|
||||
0 0.638 -0.575 -1.048 0.125 0.178 0.846 -0.753 -0.339 1.087 0.799 -0.727 1.182 0.000 0.888 0.283 0.717 0.000 1.051 -1.046 -1.557 3.102 0.889 0.871 0.989 0.884 0.923 0.836 0.779
|
||||
1 0.434 -1.119 -0.313 2.427 0.461 0.497 0.261 -1.177 2.173 0.618 -0.737 -0.688 0.000 1.150 -1.237 -1.652 2.548 0.757 -0.054 1.700 0.000 0.809 0.741 0.982 1.450 0.936 1.086 0.910
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||||
1 0.431 -1.144 -1.030 0.778 -0.655 0.490 0.047 -1.546 0.000 1.583 -0.014 0.891 2.215 0.516 0.956 0.567 2.548 0.935 -1.123 -0.082 0.000 0.707 0.995 0.995 0.700 0.602 0.770 0.685
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||||
1 1.894 0.222 1.224 1.578 1.715 0.966 2.890 -0.013 0.000 0.922 -0.703 -0.844 0.000 0.691 2.056 1.039 0.000 0.900 -0.733 -1.240 3.102 1.292 1.992 1.026 0.881 0.684 1.759 1.755
|
||||
0 0.985 -0.316 0.141 1.067 -0.946 0.819 -1.177 1.307 2.173 1.080 -0.429 0.557 1.107 1.726 1.435 -1.075 0.000 1.100 1.547 -0.647 0.000 0.873 1.696 1.179 1.146 1.015 1.538 1.270
|
||||
0 0.998 -0.187 -0.236 0.882 0.755 0.468 0.950 -0.439 2.173 0.579 -0.550 -0.624 0.000 1.847 1.196 1.384 1.274 0.846 1.273 -1.072 0.000 1.194 0.797 1.013 1.319 1.174 0.963 0.898
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||||
0 0.515 0.246 -0.593 1.082 1.591 0.912 -0.623 -0.957 2.173 0.858 0.418 0.844 0.000 0.948 2.519 1.599 0.000 1.158 1.385 -0.095 3.102 0.973 1.033 0.988 0.998 1.716 1.054 0.901
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||||
0 0.919 -1.001 1.506 1.389 0.653 0.507 -0.616 -0.689 2.173 0.808 0.536 -0.467 2.215 0.496 2.187 -0.859 0.000 0.822 0.807 1.163 0.000 0.876 0.861 1.088 0.947 0.614 0.911 1.087
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||||
0 0.794 0.051 1.477 1.504 -1.695 0.716 0.315 0.264 1.087 0.879 -0.135 -1.094 2.215 1.433 -0.741 0.201 0.000 1.566 0.534 -0.989 0.000 0.627 0.882 0.974 0.807 1.130 0.929 0.925
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||||
1 0.455 -0.946 -1.175 1.453 -0.580 0.763 -0.856 0.840 0.000 0.829 1.223 1.174 2.215 0.714 0.638 -0.466 0.000 1.182 0.223 -1.333 0.000 0.977 0.938 0.986 0.713 0.714 0.796 0.843
|
||||
1 0.662 -0.296 -1.287 1.212 -0.707 0.641 1.457 0.222 0.000 0.600 0.525 -1.700 2.215 0.784 -0.835 -0.961 2.548 0.865 1.131 1.162 0.000 0.854 0.877 0.978 0.740 0.734 0.888 0.811
|
||||
0 0.390 0.698 -1.629 1.888 0.298 0.990 1.614 -1.572 0.000 1.666 0.170 0.719 2.215 1.590 1.064 -0.886 1.274 0.952 0.305 -1.216 0.000 1.048 0.897 1.173 0.891 1.936 1.273 1.102
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||||
0 1.014 0.117 1.384 0.686 -1.047 0.609 -1.245 -0.850 0.000 1.076 -1.158 0.814 1.107 1.598 -0.389 -0.111 0.000 0.907 1.688 -1.673 0.000 1.333 0.866 0.989 0.975 0.442 0.797 0.788
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||||
0 1.530 -1.408 -0.207 0.440 -1.357 0.902 -0.647 1.325 1.087 1.320 -0.819 0.246 1.107 0.503 1.407 -1.683 0.000 1.189 -0.972 -0.925 0.000 0.386 1.273 0.988 0.829 1.335 1.173 1.149
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||||
1 1.689 -0.590 0.915 2.076 1.202 0.644 -0.478 -0.238 0.000 0.809 -1.660 -1.184 0.000 1.227 -0.224 -0.808 2.548 1.655 1.047 -0.623 0.000 0.621 1.192 0.988 1.309 0.866 0.924 1.012
|
||||
0 1.102 0.402 -1.622 1.262 1.022 0.576 0.271 -0.269 0.000 0.591 0.495 -1.278 0.000 1.271 0.209 0.575 2.548 0.941 0.964 -0.685 3.102 0.989 0.963 1.124 0.857 0.858 0.716 0.718
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||||
0 2.491 0.825 0.581 1.593 0.205 0.782 -0.815 1.499 0.000 1.179 -0.999 -1.509 0.000 0.926 0.920 -0.522 2.548 2.068 -1.021 -1.050 3.102 0.874 0.943 0.980 0.945 1.525 1.570 1.652
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||||
0 0.666 0.254 1.601 1.303 -0.250 1.236 -1.929 0.793 0.000 1.074 0.447 -0.871 0.000 0.991 1.059 -0.342 0.000 1.703 -0.393 -1.419 3.102 0.921 0.945 1.285 0.931 0.462 0.770 0.729
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||||
0 0.937 -1.126 1.424 1.395 1.743 0.760 0.428 -0.238 2.173 0.846 0.494 1.320 2.215 0.872 -1.826 -0.507 0.000 0.612 1.860 1.403 0.000 3.402 2.109 0.985 1.298 1.165 1.404 1.240
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||||
1 0.881 -1.086 -0.870 0.513 0.266 2.049 -1.870 1.160 0.000 2.259 -0.428 -0.935 2.215 1.321 -0.655 -0.449 2.548 1.350 -1.766 -0.108 0.000 0.911 1.852 0.987 1.167 0.820 1.903 1.443
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0 0.410 0.835 -0.819 1.257 1.112 0.871 -1.737 -0.401 0.000 0.927 0.158 1.253 0.000 1.183 0.405 -1.570 0.000 0.807 -0.704 -0.438 3.102 0.932 0.962 0.987 0.653 0.315 0.616 0.648
|
||||
1 0.634 0.196 -1.679 1.379 -0.967 2.260 -0.273 1.114 0.000 1.458 1.070 -0.278 1.107 1.195 0.110 -0.688 2.548 0.907 0.298 -1.359 0.000 0.949 1.129 0.984 0.675 0.877 0.938 0.824
|
||||
1 0.632 -1.254 1.201 0.496 -0.106 0.235 2.731 -0.955 0.000 0.615 -0.805 0.600 0.000 0.633 -0.934 1.641 0.000 1.407 -0.483 -0.962 1.551 0.778 0.797 0.989 0.578 0.722 0.576 0.539
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0 0.714 1.122 1.566 2.399 -1.431 1.665 0.299 0.323 0.000 1.489 1.087 -0.861 2.215 1.174 0.140 1.083 2.548 0.404 -0.968 1.105 0.000 0.867 0.969 0.981 1.039 1.552 1.157 1.173
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1 0.477 -0.321 -0.471 1.966 1.034 2.282 1.359 -0.874 0.000 1.672 -0.258 1.109 0.000 1.537 0.604 0.231 2.548 1.534 -0.640 0.827 0.000 0.746 1.337 1.311 0.653 0.721 0.795 0.742
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1 1.351 0.460 0.031 1.194 -1.185 0.670 -1.157 -1.637 2.173 0.599 -0.823 0.680 0.000 0.478 0.373 1.716 0.000 0.809 -0.919 0.010 1.551 0.859 0.839 1.564 0.994 0.777 0.971 0.826
|
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1 0.520 -1.442 -0.348 0.840 1.654 1.273 -0.760 1.317 0.000 0.861 2.579 -0.791 0.000 1.779 0.257 -0.703 0.000 2.154 1.928 0.457 0.000 1.629 3.194 0.992 0.730 1.107 2.447 2.747
|
||||
0 0.700 -0.308 0.920 0.438 -0.879 0.516 1.409 1.101 0.000 0.960 0.701 -0.049 2.215 1.442 -0.416 -1.439 2.548 0.628 1.009 -0.364 0.000 0.848 0.817 0.987 0.759 1.421 0.937 0.920
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||||
1 0.720 1.061 -0.546 0.798 -1.521 1.066 0.173 0.271 1.087 1.453 0.114 1.336 1.107 0.702 0.616 -0.367 0.000 0.543 -0.386 -1.301 0.000 0.653 0.948 0.989 1.031 1.500 0.965 0.790
|
||||
1 0.735 -0.416 0.588 1.308 -0.382 1.042 0.344 1.609 0.000 0.926 0.163 -0.520 1.107 1.050 -0.427 1.159 2.548 0.834 0.613 0.948 0.000 0.848 1.189 1.042 0.844 1.099 0.829 0.843
|
||||
1 0.777 -0.396 1.540 1.608 0.638 0.955 0.040 0.918 2.173 1.315 1.116 -0.823 0.000 0.781 -0.762 0.564 2.548 0.945 -0.573 1.379 0.000 0.679 0.706 1.124 0.608 0.593 0.515 0.493
|
||||
1 0.934 0.319 -0.257 0.970 -0.980 0.726 0.774 0.731 0.000 0.896 0.038 -1.465 1.107 0.773 -0.055 -0.831 2.548 1.439 -0.229 0.698 0.000 0.964 1.031 0.995 0.845 0.480 0.810 0.762
|
||||
0 0.461 0.771 0.019 2.055 -1.288 1.043 0.147 0.261 2.173 0.833 -0.156 1.425 0.000 0.832 0.805 -0.491 2.548 0.589 1.252 1.414 0.000 0.850 0.906 1.245 1.364 0.850 0.908 0.863
|
||||
1 0.858 -0.116 -0.937 0.966 1.167 0.825 -0.108 1.111 1.087 0.733 1.163 -0.634 0.000 0.894 0.771 0.020 0.000 0.846 -1.124 -1.195 3.102 0.724 1.194 1.195 0.813 0.969 0.985 0.856
|
||||
0 0.720 -0.335 -0.307 1.445 0.540 1.108 -0.034 -1.691 1.087 0.883 -1.356 -0.678 2.215 0.440 1.093 0.253 0.000 0.389 -1.582 -1.097 0.000 1.113 1.034 0.988 1.256 1.572 1.062 0.904
|
||||
1 0.750 -0.811 -0.542 0.985 0.408 0.471 0.477 0.355 0.000 1.347 -0.875 -1.556 2.215 0.564 1.082 -0.724 0.000 0.793 -0.958 -0.020 3.102 0.836 0.825 0.986 1.066 0.924 0.927 0.883
|
||||
0 0.392 -0.468 -0.216 0.680 1.565 1.086 -0.765 -0.581 1.087 1.264 -1.035 1.189 2.215 0.986 -0.338 0.747 0.000 0.884 -1.328 -0.965 0.000 1.228 0.988 0.982 1.135 1.741 1.108 0.956
|
||||
1 0.434 -1.269 0.643 0.713 0.608 0.597 0.832 1.627 0.000 0.708 -0.422 0.079 2.215 1.533 -0.823 -1.127 2.548 0.408 -1.357 -0.828 0.000 1.331 1.087 0.999 1.075 1.015 0.875 0.809
|
||||
0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184
|
||||
1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822
|
||||
1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948
|
||||
0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885
|
||||
1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871
|
||||
1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794
|
||||
0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679
|
||||
1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903
|
||||
1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840
|
||||
0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909
|
||||
1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892
|
||||
1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836
|
||||
1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886
|
||||
1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661
|
||||
1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883
|
||||
1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783
|
||||
1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838
|
||||
0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106
|
||||
0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895
|
||||
0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352
|
||||
1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836
|
||||
1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645
|
||||
1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866
|
||||
0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706
|
||||
0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750
|
||||
0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720
|
||||
0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006
|
||||
1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826
|
||||
1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908
|
||||
0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041
|
||||
0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898
|
||||
1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823
|
||||
0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498
|
||||
0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062
|
||||
0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863
|
||||
1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358
|
||||
0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655
|
||||
0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089
|
||||
1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806
|
||||
0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779
|
||||
1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832
|
||||
1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713
|
||||
1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977
|
||||
0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749
|
||||
1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926
|
||||
0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150
|
||||
0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107
|
||||
1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791
|
||||
1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781
|
||||
0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692
|
||||
1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824
|
||||
0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754
|
||||
0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969
|
||||
0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786
|
||||
1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791
|
||||
1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798
|
||||
1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777
|
||||
0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687
|
||||
0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806
|
||||
1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694
|
||||
1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906
|
||||
1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770
|
||||
1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656
|
||||
1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785
|
||||
0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827
|
||||
0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745
|
||||
0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650
|
||||
1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489
|
||||
1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876
|
||||
0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041
|
||||
1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242
|
||||
0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769
|
||||
1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078
|
||||
1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623
|
||||
1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137
|
||||
1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589
|
||||
0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816
|
||||
1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638
|
||||
0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057
|
||||
0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774
|
||||
1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526
|
||||
1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958
|
||||
0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902
|
||||
0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707
|
||||
1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740
|
||||
1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949
|
||||
0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815
|
||||
1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474
|
||||
0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183
|
||||
1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715
|
||||
0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094
|
||||
1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777
|
||||
0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008
|
||||
1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807
|
||||
1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879
|
||||
1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798
|
||||
0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661
|
||||
0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870
|
||||
1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963
|
||||
1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705
|
||||
1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866
|
||||
0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280
|
||||
1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866
|
||||
1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878
|
||||
0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676
|
||||
0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608
|
||||
1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726
|
||||
0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578
|
||||
0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059
|
||||
1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939
|
||||
0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035
|
||||
0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145
|
||||
0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276
|
||||
0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466
|
||||
1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776
|
||||
0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278
|
||||
0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934
|
||||
0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430
|
||||
1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841
|
||||
1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906
|
||||
0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325
|
||||
1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786
|
||||
0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950
|
||||
0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762
|
||||
0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861
|
||||
0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851
|
||||
0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079
|
||||
1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724
|
||||
1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744
|
||||
0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912
|
||||
0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012
|
||||
0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665
|
||||
1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034
|
||||
0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095
|
||||
0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221
|
||||
1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777
|
||||
1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917
|
||||
1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771
|
||||
0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395
|
||||
1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865
|
||||
1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890
|
||||
1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815
|
||||
0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131
|
||||
0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154
|
||||
1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717
|
||||
0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775
|
||||
1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893
|
||||
0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901
|
||||
0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769
|
||||
1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828
|
||||
0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748
|
||||
1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599
|
||||
0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120
|
||||
1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833
|
||||
0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051
|
||||
1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126
|
||||
1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881
|
||||
1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135
|
||||
1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797
|
||||
0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612
|
||||
0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556
|
||||
0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789
|
||||
1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708
|
||||
0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878
|
||||
0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087
|
||||
0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846
|
||||
1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028
|
||||
0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994
|
||||
0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868
|
||||
1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913
|
||||
0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291
|
||||
0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123
|
||||
1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878
|
||||
0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511
|
||||
0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076
|
||||
0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112
|
||||
1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046
|
||||
1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009
|
||||
1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891
|
||||
1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140
|
||||
0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744
|
||||
1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238
|
||||
0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306
|
||||
0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758
|
||||
1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172
|
||||
1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992
|
||||
1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786
|
||||
1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922
|
||||
0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609
|
||||
1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351
|
||||
1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836
|
||||
0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252
|
||||
1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122
|
||||
0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042
|
||||
0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662
|
||||
1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626
|
||||
1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920
|
||||
1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059
|
||||
1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722
|
||||
1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941
|
||||
0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072
|
||||
1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053
|
||||
0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310
|
||||
0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847
|
||||
0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873
|
||||
0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183
|
||||
1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807
|
||||
0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936
|
||||
1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013
|
||||
1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822
|
||||
1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803
|
||||
0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176
|
||||
0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714
|
||||
0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815
|
||||
0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918
|
||||
0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056
|
||||
0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513
|
||||
0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773
|
||||
0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799
|
||||
1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813
|
||||
1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978
|
||||
1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743
|
||||
1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817
|
||||
1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875
|
||||
0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078
|
||||
1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619
|
||||
1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726
|
||||
0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950
|
||||
0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860
|
||||
0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034
|
||||
0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846
|
||||
0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920
|
||||
1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668
|
||||
0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313
|
||||
0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671
|
||||
1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224
|
||||
1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779
|
||||
0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851
|
||||
1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860
|
||||
0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958
|
||||
1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671
|
||||
1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127
|
||||
1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807
|
||||
1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823
|
||||
1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872
|
||||
1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061
|
||||
1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833
|
||||
1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780
|
||||
0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143
|
||||
1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709
|
||||
1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858
|
||||
1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091
|
||||
0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838
|
||||
0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661
|
||||
0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729
|
||||
0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878
|
||||
1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838
|
||||
1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792
|
||||
0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702
|
||||
0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068
|
||||
1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931
|
||||
0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522
|
||||
0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807
|
||||
1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054
|
||||
1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606
|
||||
1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961
|
||||
0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308
|
||||
0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146
|
||||
1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709
|
||||
0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180
|
||||
1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868
|
||||
1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779
|
||||
0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213
|
||||
1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168
|
||||
1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238
|
||||
0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891
|
||||
0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561
|
||||
1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911
|
||||
0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303
|
||||
1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094
|
||||
1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825
|
||||
1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995
|
||||
1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151
|
||||
1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280
|
||||
1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893
|
||||
1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043
|
||||
0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226
|
||||
1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851
|
||||
0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733
|
||||
0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764
|
||||
0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028
|
||||
1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774
|
||||
1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661
|
||||
1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776
|
||||
0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675
|
||||
1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931
|
||||
1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715
|
||||
0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889
|
||||
1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886
|
||||
0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087
|
||||
0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760
|
||||
0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006
|
||||
1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811
|
||||
0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176
|
||||
0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096
|
||||
0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721
|
||||
1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732
|
||||
1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874
|
||||
1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753
|
||||
0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883
|
||||
0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018
|
||||
1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806
|
||||
0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472
|
||||
0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827
|
||||
1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835
|
||||
1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826
|
||||
1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640
|
||||
1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764
|
||||
1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025
|
||||
1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223
|
||||
1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169
|
||||
1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003
|
||||
1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908
|
||||
1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836
|
||||
0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833
|
||||
0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294
|
||||
1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845
|
||||
1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510
|
||||
0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445
|
||||
1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926
|
||||
1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933
|
||||
1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853
|
||||
0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678
|
||||
1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038
|
||||
1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197
|
||||
1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746
|
||||
0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772
|
||||
1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983
|
||||
1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759
|
||||
1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825
|
||||
1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776
|
||||
0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892
|
||||
0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110
|
||||
1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762
|
||||
0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756
|
||||
1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831
|
||||
1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748
|
||||
0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756
|
||||
1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993
|
||||
1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881
|
||||
1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786
|
||||
0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983
|
||||
0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983
|
||||
1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867
|
||||
0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937
|
||||
0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293
|
||||
1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105
|
||||
0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949
|
||||
1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895
|
||||
0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722
|
||||
1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809
|
||||
0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007
|
||||
1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717
|
||||
0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656
|
||||
0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856
|
||||
1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832
|
||||
1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753
|
||||
0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954
|
||||
1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874
|
||||
0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848
|
||||
1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518
|
||||
1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787
|
||||
1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834
|
||||
0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722
|
||||
1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040
|
||||
0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758
|
||||
1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854
|
||||
1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828
|
||||
1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745
|
||||
0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701
|
||||
1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215
|
||||
1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837
|
||||
1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867
|
||||
0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119
|
||||
1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914
|
||||
0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373
|
||||
1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623
|
||||
1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133
|
||||
0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079
|
||||
0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919
|
||||
1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651
|
||||
0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207
|
||||
0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660
|
||||
1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951
|
||||
1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910
|
||||
0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715
|
||||
1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026
|
||||
0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239
|
||||
1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696
|
||||
0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662
|
||||
1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910
|
||||
0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050
|
||||
0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258
|
||||
1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884
|
||||
0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026
|
||||
1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119
|
||||
1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926
|
||||
0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079
|
||||
1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674
|
||||
0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733
|
||||
0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795
|
||||
0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329
|
||||
0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161
|
||||
0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200
|
||||
1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875
|
||||
0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071
|
||||
1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993
|
||||
1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866
|
||||
1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978
|
||||
1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982
|
||||
1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853
|
||||
1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122
|
||||
1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719
|
||||
1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739
|
||||
0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690
|
||||
1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643
|
||||
0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142
|
||||
1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758
|
||||
1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823
|
||||
0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258
|
||||
1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836
|
||||
0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349
|
||||
0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961
|
||||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,500 +0,0 @@
|
|||
1 0.644 0.247 -0.447 0.862 0.374 0.854 -1.126 -0.790 2.173 1.015 -0.201 1.400 0.000 1.575 1.807 1.607 0.000 1.585 -0.190 -0.744 3.102 0.958 1.061 0.980 0.875 0.581 0.905 0.796
|
||||
0 0.385 1.800 1.037 1.044 0.349 1.502 -0.966 1.734 0.000 0.966 -1.960 -0.249 0.000 1.501 0.465 -0.354 2.548 0.834 -0.440 0.638 3.102 0.695 0.909 0.981 0.803 0.813 1.149 1.116
|
||||
0 1.214 -0.166 0.004 0.505 1.434 0.628 -1.174 -1.230 1.087 0.579 -1.047 -0.118 0.000 0.835 0.340 1.234 2.548 0.711 -1.383 1.355 0.000 0.848 0.911 1.043 0.931 1.058 0.744 0.696
|
||||
1 0.420 1.111 0.137 1.516 -1.657 0.854 0.623 1.605 1.087 1.511 -1.297 0.251 0.000 0.872 -0.368 -0.721 0.000 0.543 0.731 1.424 3.102 1.597 1.282 1.105 0.730 0.148 1.231 1.234
|
||||
0 0.897 -1.703 -1.306 1.022 -0.729 0.836 0.859 -0.333 2.173 1.336 -0.965 0.972 2.215 0.671 1.021 -1.439 0.000 0.493 -2.019 -0.289 0.000 0.805 0.930 0.984 1.430 2.198 1.934 1.684
|
||||
0 0.756 1.126 -0.945 2.355 -0.555 0.889 0.800 1.440 0.000 0.585 0.271 0.631 2.215 0.722 1.744 1.051 0.000 0.618 0.924 0.698 1.551 0.976 0.864 0.988 0.803 0.234 0.822 0.911
|
||||
0 1.141 -0.741 0.953 1.478 -0.524 1.197 -0.871 1.689 2.173 0.875 1.321 -0.518 1.107 0.540 0.037 -0.987 0.000 0.879 1.187 0.245 0.000 0.888 0.701 1.747 1.358 2.479 1.491 1.223
|
||||
1 0.606 -0.936 -0.384 1.257 -1.162 2.719 -0.600 0.100 2.173 3.303 -0.284 1.561 1.107 0.689 1.786 -0.326 0.000 0.780 -0.532 1.216 0.000 0.936 2.022 0.985 1.574 4.323 2.263 1.742
|
||||
1 0.603 0.429 -0.279 1.448 1.301 1.008 2.423 -1.295 0.000 0.452 1.305 0.533 0.000 1.076 1.011 1.256 2.548 2.021 1.260 -0.343 0.000 0.890 0.969 1.281 0.763 0.652 0.827 0.785
|
||||
0 1.171 -0.962 0.521 0.841 -0.315 1.196 -0.744 -0.882 2.173 0.726 -1.305 1.377 1.107 0.643 -1.790 -1.264 0.000 1.257 0.222 0.817 0.000 0.862 0.911 0.987 0.846 1.293 0.899 0.756
|
||||
1 1.392 -0.358 0.235 1.494 -0.461 0.895 -0.848 1.549 2.173 0.841 -0.384 0.666 1.107 1.199 2.509 -0.891 0.000 1.109 -0.364 -0.945 0.000 0.693 2.135 1.170 1.362 0.959 2.056 1.842
|
||||
1 1.024 1.076 -0.886 0.851 1.530 0.673 -0.449 0.187 1.087 0.628 -0.895 1.176 2.215 0.696 -0.232 -0.875 0.000 0.411 1.501 0.048 0.000 0.842 0.919 1.063 1.193 0.777 0.964 0.807
|
||||
1 0.890 -0.760 1.182 1.369 0.751 0.696 -0.959 -0.710 1.087 0.775 -0.130 -1.409 2.215 0.701 -0.110 -0.739 0.000 0.508 -0.451 0.390 0.000 0.762 0.738 0.998 1.126 0.788 0.940 0.790
|
||||
1 0.460 0.537 0.636 1.442 -0.269 0.585 0.323 -1.731 2.173 0.503 1.034 -0.927 0.000 0.928 -1.024 1.006 2.548 0.513 -0.618 -1.336 0.000 0.802 0.831 0.992 1.019 0.925 1.056 0.833
|
||||
1 0.364 1.648 0.560 1.720 0.829 1.110 0.811 -0.588 0.000 0.408 1.045 1.054 2.215 0.319 -1.138 1.545 0.000 0.423 1.025 -1.265 3.102 1.656 0.928 1.003 0.544 0.327 0.670 0.746
|
||||
1 0.525 -0.096 1.206 0.948 -1.103 1.519 -0.582 0.606 2.173 1.274 -0.572 -0.934 0.000 0.855 -1.028 -1.222 0.000 0.578 -1.000 -1.725 3.102 0.896 0.878 0.981 0.498 0.909 0.772 0.668
|
||||
0 0.536 -0.821 -1.029 0.703 1.113 0.363 -0.711 0.022 1.087 0.325 1.503 1.249 2.215 0.673 1.041 -0.401 0.000 0.480 2.127 1.681 0.000 0.767 1.034 0.990 0.671 0.836 0.669 0.663
|
||||
1 1.789 -0.583 1.641 0.897 0.799 0.515 -0.100 -1.483 0.000 1.101 0.031 -0.326 2.215 1.195 0.001 0.126 2.548 0.768 -0.148 0.601 0.000 0.916 0.921 1.207 1.069 0.483 0.934 0.795
|
||||
1 1.332 -0.571 0.986 0.580 1.508 0.582 0.634 -0.746 1.087 1.084 -0.964 -0.489 0.000 0.785 0.274 0.343 2.548 0.779 0.721 1.489 0.000 1.733 1.145 0.990 1.270 0.715 0.897 0.915
|
||||
0 1.123 0.629 -1.708 0.597 -0.882 0.752 0.195 1.522 2.173 1.671 1.515 -0.003 0.000 0.778 0.514 0.139 1.274 0.801 1.260 1.600 0.000 1.495 0.976 0.988 0.676 0.921 1.010 0.943
|
||||
0 1.816 -0.515 0.171 0.980 -0.454 0.870 0.202 -1.399 2.173 1.130 1.066 -1.593 0.000 0.844 0.735 1.275 2.548 1.125 -1.133 0.348 0.000 0.837 0.693 0.988 1.112 0.784 1.009 0.974
|
||||
1 0.364 0.694 0.445 1.862 0.159 0.963 -1.356 1.260 1.087 0.887 -0.540 -1.533 2.215 0.658 -2.544 -1.236 0.000 0.516 -0.807 0.039 0.000 0.891 1.004 0.991 1.092 0.976 1.000 0.953
|
||||
1 0.790 -1.175 0.475 1.846 0.094 0.999 -1.090 0.257 0.000 1.422 0.854 1.112 2.215 1.302 1.004 -1.702 1.274 2.557 -0.787 -1.048 0.000 0.890 1.429 0.993 2.807 0.840 2.248 1.821
|
||||
1 0.765 -0.500 -0.603 1.843 -0.560 1.068 0.007 0.746 2.173 1.154 -0.017 1.329 0.000 1.165 1.791 -1.585 0.000 1.116 0.441 -0.886 0.000 0.774 0.982 0.989 1.102 0.633 1.178 1.021
|
||||
1 1.407 1.293 -1.418 0.502 -1.527 2.005 -2.122 0.622 0.000 1.699 1.508 -0.649 2.215 1.665 0.748 -0.755 0.000 2.555 0.811 1.423 1.551 7.531 5.520 0.985 1.115 1.881 4.487 3.379
|
||||
1 0.772 -0.186 -1.372 0.823 -0.140 0.781 0.763 0.046 2.173 1.128 0.516 1.380 0.000 0.797 -0.640 -0.134 2.548 2.019 -0.972 -1.670 0.000 2.022 1.466 0.989 0.856 0.808 1.230 0.991
|
||||
1 0.546 -0.954 0.715 1.335 -1.689 0.783 -0.443 -1.735 2.173 1.081 0.185 -0.435 0.000 1.433 -0.662 -0.389 0.000 0.969 0.924 1.099 0.000 0.910 0.879 0.988 0.683 0.753 0.878 0.865
|
||||
1 0.596 0.276 -1.054 1.358 1.355 1.444 1.813 -0.208 0.000 1.175 -0.949 -1.573 0.000 0.855 -1.228 -0.925 2.548 1.837 -0.400 0.913 0.000 0.637 0.901 1.028 0.553 0.790 0.679 0.677
|
||||
0 0.458 2.292 1.530 0.291 1.283 0.749 -0.930 -0.198 0.000 0.300 -1.560 0.990 0.000 0.811 -0.176 0.995 2.548 1.085 -0.178 -1.213 3.102 0.891 0.648 0.999 0.732 0.655 0.619 0.620
|
||||
0 0.638 -0.575 -1.048 0.125 0.178 0.846 -0.753 -0.339 1.087 0.799 -0.727 1.182 0.000 0.888 0.283 0.717 0.000 1.051 -1.046 -1.557 3.102 0.889 0.871 0.989 0.884 0.923 0.836 0.779
|
||||
1 0.434 -1.119 -0.313 2.427 0.461 0.497 0.261 -1.177 2.173 0.618 -0.737 -0.688 0.000 1.150 -1.237 -1.652 2.548 0.757 -0.054 1.700 0.000 0.809 0.741 0.982 1.450 0.936 1.086 0.910
|
||||
1 0.431 -1.144 -1.030 0.778 -0.655 0.490 0.047 -1.546 0.000 1.583 -0.014 0.891 2.215 0.516 0.956 0.567 2.548 0.935 -1.123 -0.082 0.000 0.707 0.995 0.995 0.700 0.602 0.770 0.685
|
||||
1 1.894 0.222 1.224 1.578 1.715 0.966 2.890 -0.013 0.000 0.922 -0.703 -0.844 0.000 0.691 2.056 1.039 0.000 0.900 -0.733 -1.240 3.102 1.292 1.992 1.026 0.881 0.684 1.759 1.755
|
||||
0 0.985 -0.316 0.141 1.067 -0.946 0.819 -1.177 1.307 2.173 1.080 -0.429 0.557 1.107 1.726 1.435 -1.075 0.000 1.100 1.547 -0.647 0.000 0.873 1.696 1.179 1.146 1.015 1.538 1.270
|
||||
0 0.998 -0.187 -0.236 0.882 0.755 0.468 0.950 -0.439 2.173 0.579 -0.550 -0.624 0.000 1.847 1.196 1.384 1.274 0.846 1.273 -1.072 0.000 1.194 0.797 1.013 1.319 1.174 0.963 0.898
|
||||
0 0.515 0.246 -0.593 1.082 1.591 0.912 -0.623 -0.957 2.173 0.858 0.418 0.844 0.000 0.948 2.519 1.599 0.000 1.158 1.385 -0.095 3.102 0.973 1.033 0.988 0.998 1.716 1.054 0.901
|
||||
0 0.919 -1.001 1.506 1.389 0.653 0.507 -0.616 -0.689 2.173 0.808 0.536 -0.467 2.215 0.496 2.187 -0.859 0.000 0.822 0.807 1.163 0.000 0.876 0.861 1.088 0.947 0.614 0.911 1.087
|
||||
0 0.794 0.051 1.477 1.504 -1.695 0.716 0.315 0.264 1.087 0.879 -0.135 -1.094 2.215 1.433 -0.741 0.201 0.000 1.566 0.534 -0.989 0.000 0.627 0.882 0.974 0.807 1.130 0.929 0.925
|
||||
1 0.455 -0.946 -1.175 1.453 -0.580 0.763 -0.856 0.840 0.000 0.829 1.223 1.174 2.215 0.714 0.638 -0.466 0.000 1.182 0.223 -1.333 0.000 0.977 0.938 0.986 0.713 0.714 0.796 0.843
|
||||
1 0.662 -0.296 -1.287 1.212 -0.707 0.641 1.457 0.222 0.000 0.600 0.525 -1.700 2.215 0.784 -0.835 -0.961 2.548 0.865 1.131 1.162 0.000 0.854 0.877 0.978 0.740 0.734 0.888 0.811
|
||||
0 0.390 0.698 -1.629 1.888 0.298 0.990 1.614 -1.572 0.000 1.666 0.170 0.719 2.215 1.590 1.064 -0.886 1.274 0.952 0.305 -1.216 0.000 1.048 0.897 1.173 0.891 1.936 1.273 1.102
|
||||
0 1.014 0.117 1.384 0.686 -1.047 0.609 -1.245 -0.850 0.000 1.076 -1.158 0.814 1.107 1.598 -0.389 -0.111 0.000 0.907 1.688 -1.673 0.000 1.333 0.866 0.989 0.975 0.442 0.797 0.788
|
||||
0 1.530 -1.408 -0.207 0.440 -1.357 0.902 -0.647 1.325 1.087 1.320 -0.819 0.246 1.107 0.503 1.407 -1.683 0.000 1.189 -0.972 -0.925 0.000 0.386 1.273 0.988 0.829 1.335 1.173 1.149
|
||||
1 1.689 -0.590 0.915 2.076 1.202 0.644 -0.478 -0.238 0.000 0.809 -1.660 -1.184 0.000 1.227 -0.224 -0.808 2.548 1.655 1.047 -0.623 0.000 0.621 1.192 0.988 1.309 0.866 0.924 1.012
|
||||
0 1.102 0.402 -1.622 1.262 1.022 0.576 0.271 -0.269 0.000 0.591 0.495 -1.278 0.000 1.271 0.209 0.575 2.548 0.941 0.964 -0.685 3.102 0.989 0.963 1.124 0.857 0.858 0.716 0.718
|
||||
0 2.491 0.825 0.581 1.593 0.205 0.782 -0.815 1.499 0.000 1.179 -0.999 -1.509 0.000 0.926 0.920 -0.522 2.548 2.068 -1.021 -1.050 3.102 0.874 0.943 0.980 0.945 1.525 1.570 1.652
|
||||
0 0.666 0.254 1.601 1.303 -0.250 1.236 -1.929 0.793 0.000 1.074 0.447 -0.871 0.000 0.991 1.059 -0.342 0.000 1.703 -0.393 -1.419 3.102 0.921 0.945 1.285 0.931 0.462 0.770 0.729
|
||||
0 0.937 -1.126 1.424 1.395 1.743 0.760 0.428 -0.238 2.173 0.846 0.494 1.320 2.215 0.872 -1.826 -0.507 0.000 0.612 1.860 1.403 0.000 3.402 2.109 0.985 1.298 1.165 1.404 1.240
|
||||
1 0.881 -1.086 -0.870 0.513 0.266 2.049 -1.870 1.160 0.000 2.259 -0.428 -0.935 2.215 1.321 -0.655 -0.449 2.548 1.350 -1.766 -0.108 0.000 0.911 1.852 0.987 1.167 0.820 1.903 1.443
|
||||
0 0.410 0.835 -0.819 1.257 1.112 0.871 -1.737 -0.401 0.000 0.927 0.158 1.253 0.000 1.183 0.405 -1.570 0.000 0.807 -0.704 -0.438 3.102 0.932 0.962 0.987 0.653 0.315 0.616 0.648
|
||||
1 0.634 0.196 -1.679 1.379 -0.967 2.260 -0.273 1.114 0.000 1.458 1.070 -0.278 1.107 1.195 0.110 -0.688 2.548 0.907 0.298 -1.359 0.000 0.949 1.129 0.984 0.675 0.877 0.938 0.824
|
||||
1 0.632 -1.254 1.201 0.496 -0.106 0.235 2.731 -0.955 0.000 0.615 -0.805 0.600 0.000 0.633 -0.934 1.641 0.000 1.407 -0.483 -0.962 1.551 0.778 0.797 0.989 0.578 0.722 0.576 0.539
|
||||
0 0.714 1.122 1.566 2.399 -1.431 1.665 0.299 0.323 0.000 1.489 1.087 -0.861 2.215 1.174 0.140 1.083 2.548 0.404 -0.968 1.105 0.000 0.867 0.969 0.981 1.039 1.552 1.157 1.173
|
||||
1 0.477 -0.321 -0.471 1.966 1.034 2.282 1.359 -0.874 0.000 1.672 -0.258 1.109 0.000 1.537 0.604 0.231 2.548 1.534 -0.640 0.827 0.000 0.746 1.337 1.311 0.653 0.721 0.795 0.742
|
||||
1 1.351 0.460 0.031 1.194 -1.185 0.670 -1.157 -1.637 2.173 0.599 -0.823 0.680 0.000 0.478 0.373 1.716 0.000 0.809 -0.919 0.010 1.551 0.859 0.839 1.564 0.994 0.777 0.971 0.826
|
||||
1 0.520 -1.442 -0.348 0.840 1.654 1.273 -0.760 1.317 0.000 0.861 2.579 -0.791 0.000 1.779 0.257 -0.703 0.000 2.154 1.928 0.457 0.000 1.629 3.194 0.992 0.730 1.107 2.447 2.747
|
||||
0 0.700 -0.308 0.920 0.438 -0.879 0.516 1.409 1.101 0.000 0.960 0.701 -0.049 2.215 1.442 -0.416 -1.439 2.548 0.628 1.009 -0.364 0.000 0.848 0.817 0.987 0.759 1.421 0.937 0.920
|
||||
1 0.720 1.061 -0.546 0.798 -1.521 1.066 0.173 0.271 1.087 1.453 0.114 1.336 1.107 0.702 0.616 -0.367 0.000 0.543 -0.386 -1.301 0.000 0.653 0.948 0.989 1.031 1.500 0.965 0.790
|
||||
1 0.735 -0.416 0.588 1.308 -0.382 1.042 0.344 1.609 0.000 0.926 0.163 -0.520 1.107 1.050 -0.427 1.159 2.548 0.834 0.613 0.948 0.000 0.848 1.189 1.042 0.844 1.099 0.829 0.843
|
||||
1 0.777 -0.396 1.540 1.608 0.638 0.955 0.040 0.918 2.173 1.315 1.116 -0.823 0.000 0.781 -0.762 0.564 2.548 0.945 -0.573 1.379 0.000 0.679 0.706 1.124 0.608 0.593 0.515 0.493
|
||||
1 0.934 0.319 -0.257 0.970 -0.980 0.726 0.774 0.731 0.000 0.896 0.038 -1.465 1.107 0.773 -0.055 -0.831 2.548 1.439 -0.229 0.698 0.000 0.964 1.031 0.995 0.845 0.480 0.810 0.762
|
||||
0 0.461 0.771 0.019 2.055 -1.288 1.043 0.147 0.261 2.173 0.833 -0.156 1.425 0.000 0.832 0.805 -0.491 2.548 0.589 1.252 1.414 0.000 0.850 0.906 1.245 1.364 0.850 0.908 0.863
|
||||
1 0.858 -0.116 -0.937 0.966 1.167 0.825 -0.108 1.111 1.087 0.733 1.163 -0.634 0.000 0.894 0.771 0.020 0.000 0.846 -1.124 -1.195 3.102 0.724 1.194 1.195 0.813 0.969 0.985 0.856
|
||||
0 0.720 -0.335 -0.307 1.445 0.540 1.108 -0.034 -1.691 1.087 0.883 -1.356 -0.678 2.215 0.440 1.093 0.253 0.000 0.389 -1.582 -1.097 0.000 1.113 1.034 0.988 1.256 1.572 1.062 0.904
|
||||
1 0.750 -0.811 -0.542 0.985 0.408 0.471 0.477 0.355 0.000 1.347 -0.875 -1.556 2.215 0.564 1.082 -0.724 0.000 0.793 -0.958 -0.020 3.102 0.836 0.825 0.986 1.066 0.924 0.927 0.883
|
||||
0 0.392 -0.468 -0.216 0.680 1.565 1.086 -0.765 -0.581 1.087 1.264 -1.035 1.189 2.215 0.986 -0.338 0.747 0.000 0.884 -1.328 -0.965 0.000 1.228 0.988 0.982 1.135 1.741 1.108 0.956
|
||||
1 0.434 -1.269 0.643 0.713 0.608 0.597 0.832 1.627 0.000 0.708 -0.422 0.079 2.215 1.533 -0.823 -1.127 2.548 0.408 -1.357 -0.828 0.000 1.331 1.087 0.999 1.075 1.015 0.875 0.809
|
||||
0 0.828 -1.803 0.342 0.847 -0.162 1.585 -1.128 -0.272 2.173 1.974 0.039 -1.717 0.000 0.900 0.764 -1.741 0.000 1.349 -0.079 1.035 3.102 0.984 0.815 0.985 0.780 1.661 1.403 1.184
|
||||
1 1.089 -0.350 -0.747 1.472 0.792 1.087 -0.069 -1.192 0.000 0.512 -0.841 -1.284 0.000 2.162 -0.821 0.545 2.548 1.360 2.243 -0.183 0.000 0.977 0.628 1.725 1.168 0.635 0.823 0.822
|
||||
1 0.444 0.451 -1.332 1.176 -0.247 0.898 0.194 0.007 0.000 1.958 0.576 -1.618 2.215 0.584 1.203 0.268 0.000 0.939 1.033 1.264 3.102 0.829 0.886 0.985 1.265 0.751 1.032 0.948
|
||||
0 0.629 0.114 1.177 0.917 -1.204 0.845 0.828 -0.088 0.000 0.962 -1.302 0.823 2.215 0.732 0.358 -1.334 2.548 0.538 0.582 1.561 0.000 1.028 0.834 0.988 0.904 1.205 1.039 0.885
|
||||
1 1.754 -1.259 -0.573 0.959 -1.483 0.358 0.448 -1.452 0.000 0.711 0.313 0.499 2.215 1.482 -0.390 1.474 2.548 1.879 -1.540 0.668 0.000 0.843 0.825 1.313 1.315 0.939 1.048 0.871
|
||||
1 0.549 0.706 -1.437 0.894 0.891 0.680 -0.762 -1.568 0.000 0.981 0.499 -0.425 2.215 1.332 0.678 0.485 1.274 0.803 0.022 -0.893 0.000 0.793 1.043 0.987 0.761 0.899 0.915 0.794
|
||||
0 0.475 0.542 -0.987 1.569 0.069 0.551 1.543 -1.488 0.000 0.608 0.301 1.734 2.215 0.277 0.499 -0.522 0.000 1.375 1.212 0.696 3.102 0.652 0.756 0.987 0.828 0.830 0.715 0.679
|
||||
1 0.723 0.049 -1.153 1.300 0.083 0.723 -0.749 0.630 0.000 1.126 0.412 -0.384 0.000 1.272 1.256 1.358 2.548 3.108 0.777 -1.486 3.102 0.733 1.096 1.206 1.269 0.899 1.015 0.903
|
||||
1 1.062 0.296 0.725 0.285 -0.531 0.819 1.277 -0.667 0.000 0.687 0.829 -0.092 0.000 1.158 0.447 1.047 2.548 1.444 -0.186 -1.491 3.102 0.863 1.171 0.986 0.769 0.828 0.919 0.840
|
||||
0 0.572 -0.349 1.396 2.023 0.795 0.577 0.457 -0.533 0.000 1.351 0.701 -1.091 0.000 0.724 -1.012 -0.182 2.548 0.923 -0.012 0.789 3.102 0.936 1.025 0.985 1.002 0.600 0.828 0.909
|
||||
1 0.563 0.387 0.412 0.553 1.050 0.723 -0.992 -0.447 0.000 0.748 0.948 0.546 2.215 1.761 -0.559 -1.183 0.000 1.114 -0.251 1.192 3.102 0.936 0.912 0.976 0.578 0.722 0.829 0.892
|
||||
1 1.632 1.577 -0.697 0.708 -1.263 0.863 0.012 1.197 2.173 0.498 0.990 -0.806 0.000 0.627 2.387 -1.283 0.000 0.607 1.290 -0.174 3.102 0.916 1.328 0.986 0.557 0.971 0.935 0.836
|
||||
1 0.562 -0.360 0.399 0.803 -1.334 1.443 -0.116 1.628 2.173 0.750 0.987 0.135 1.107 0.795 0.298 -0.556 0.000 1.150 -0.113 -0.093 0.000 0.493 1.332 0.985 1.001 1.750 1.013 0.886
|
||||
1 0.987 0.706 -0.492 0.861 0.607 0.593 0.088 -0.184 0.000 0.802 0.894 1.608 2.215 0.782 -0.471 1.500 2.548 0.521 0.772 -0.960 0.000 0.658 0.893 1.068 0.877 0.664 0.709 0.661
|
||||
1 1.052 0.883 -0.581 1.566 0.860 0.931 1.515 -0.873 0.000 0.493 0.145 -0.672 0.000 1.133 0.935 1.581 2.548 1.630 0.695 0.923 3.102 1.105 1.087 1.713 0.948 0.590 0.872 0.883
|
||||
1 2.130 -0.516 -0.291 0.776 -1.230 0.689 -0.257 0.800 2.173 0.730 -0.274 -1.437 0.000 0.615 0.241 1.083 0.000 0.834 0.757 1.613 3.102 0.836 0.806 1.333 1.061 0.730 0.889 0.783
|
||||
1 0.742 0.797 1.628 0.311 -0.418 0.620 0.685 -1.457 0.000 0.683 1.774 -1.082 0.000 1.700 1.104 0.225 2.548 0.382 -2.184 -1.307 0.000 0.945 1.228 0.984 0.864 0.931 0.988 0.838
|
||||
0 0.311 -1.249 -0.927 1.272 -1.262 0.642 -1.228 -0.136 0.000 1.220 -0.804 -1.558 2.215 0.950 -0.828 0.495 1.274 2.149 -1.672 0.634 0.000 1.346 0.887 0.981 0.856 1.101 1.001 1.106
|
||||
0 0.660 -1.834 -0.667 0.601 1.236 0.932 -0.933 -0.135 2.173 1.373 -0.122 1.429 0.000 0.654 -0.034 -0.847 2.548 0.711 0.911 0.703 0.000 1.144 0.942 0.984 0.822 0.739 0.992 0.895
|
||||
0 3.609 -0.590 0.851 0.615 0.455 1.280 0.003 -0.866 1.087 1.334 0.708 -1.131 0.000 0.669 0.480 0.092 0.000 0.975 0.983 -1.429 3.102 1.301 1.089 0.987 1.476 0.934 1.469 1.352
|
||||
1 0.905 -0.403 1.567 2.651 0.953 1.194 -0.241 -0.567 1.087 0.308 -0.384 -0.007 0.000 0.608 -0.175 -1.163 2.548 0.379 0.941 1.662 0.000 0.580 0.721 1.126 0.895 0.544 1.097 0.836
|
||||
1 0.983 0.255 1.093 0.905 -0.874 0.863 0.060 -0.368 0.000 0.824 -0.747 -0.633 0.000 0.614 0.961 1.052 0.000 0.792 -0.260 1.632 3.102 0.874 0.883 1.280 0.663 0.406 0.592 0.645
|
||||
1 1.160 -1.027 0.274 0.460 0.322 2.085 -1.623 -0.840 0.000 1.634 -1.046 1.182 2.215 0.492 -0.367 1.174 0.000 0.824 -0.998 1.617 0.000 0.943 0.884 1.001 1.209 1.313 1.034 0.866
|
||||
0 0.299 0.028 -1.372 1.930 -0.661 0.840 -0.979 0.664 1.087 0.535 -2.041 1.434 0.000 1.087 -1.797 0.344 0.000 0.485 -0.560 -1.105 3.102 0.951 0.890 0.980 0.483 0.684 0.730 0.706
|
||||
0 0.293 1.737 -1.418 2.074 0.794 0.679 1.024 -1.457 0.000 1.034 1.094 -0.168 1.107 0.506 1.680 -0.661 0.000 0.523 -0.042 -1.274 3.102 0.820 0.944 0.987 0.842 0.694 0.761 0.750
|
||||
0 0.457 -0.393 1.560 0.738 -0.007 0.475 -0.230 0.246 0.000 0.776 -1.264 -0.606 2.215 0.865 -0.731 -1.576 2.548 1.153 0.343 1.436 0.000 1.060 0.883 0.988 0.972 0.703 0.758 0.720
|
||||
0 0.935 -0.582 0.240 2.401 0.818 1.231 -0.618 -1.289 0.000 0.799 0.544 -0.228 2.215 0.525 -1.494 -0.969 0.000 0.609 -1.123 1.168 3.102 0.871 0.767 1.035 1.154 0.919 0.868 1.006
|
||||
1 0.902 -0.745 -1.215 1.174 -0.501 1.215 0.167 1.162 0.000 0.896 1.217 -0.976 0.000 0.585 -0.429 1.036 0.000 1.431 -0.416 0.151 3.102 0.524 0.952 0.990 0.707 0.271 0.592 0.826
|
||||
1 0.653 0.337 -0.320 1.118 -0.934 1.050 0.745 0.529 1.087 1.075 1.742 -1.538 0.000 0.585 1.090 0.973 0.000 1.091 -0.187 1.160 1.551 1.006 1.108 0.978 1.121 0.838 0.947 0.908
|
||||
0 1.157 1.401 0.340 0.395 -1.218 0.945 1.928 -0.876 0.000 1.384 0.320 1.002 1.107 1.900 1.177 -0.462 2.548 1.122 1.316 1.720 0.000 1.167 1.096 0.989 0.937 1.879 1.307 1.041
|
||||
0 0.960 0.355 -0.152 0.872 -0.338 0.391 0.348 0.956 1.087 0.469 2.664 1.409 0.000 0.756 -1.561 1.500 0.000 0.525 1.436 1.728 3.102 1.032 0.946 0.996 0.929 0.470 0.698 0.898
|
||||
1 1.038 0.274 0.825 1.198 0.963 1.078 -0.496 -1.014 2.173 0.739 -0.727 -0.151 2.215 1.035 -0.799 0.398 0.000 1.333 -0.872 -1.498 0.000 0.849 1.033 0.985 0.886 0.936 0.975 0.823
|
||||
0 0.490 0.277 0.318 1.303 0.694 1.333 -1.620 -0.563 0.000 1.459 -1.326 1.140 0.000 0.779 -0.673 -1.324 2.548 0.860 -1.247 0.043 0.000 0.857 0.932 0.992 0.792 0.278 0.841 1.498
|
||||
0 1.648 -0.688 -1.386 2.790 0.995 1.087 1.359 -0.687 0.000 1.050 -0.223 -0.261 2.215 0.613 -0.889 1.335 0.000 1.204 0.827 0.309 3.102 0.464 0.973 2.493 1.737 0.827 1.319 1.062
|
||||
0 1.510 -0.662 1.668 0.860 0.280 0.705 0.974 -1.647 1.087 0.662 -0.393 -0.225 0.000 0.610 -0.996 0.532 2.548 0.464 1.305 0.102 0.000 0.859 1.057 1.498 0.799 1.260 0.946 0.863
|
||||
1 0.850 -1.185 -0.117 0.943 -0.449 1.142 0.875 -0.030 0.000 2.223 -0.461 1.627 2.215 0.767 -1.761 -1.692 0.000 1.012 -0.727 0.639 3.102 3.649 2.062 0.985 1.478 1.087 1.659 1.358
|
||||
0 0.933 1.259 0.130 0.326 -0.890 0.306 1.136 1.142 0.000 0.964 0.705 -1.373 2.215 0.546 -0.196 -0.001 0.000 0.578 -1.169 1.004 3.102 0.830 0.836 0.988 0.837 1.031 0.749 0.655
|
||||
0 0.471 0.697 1.570 1.109 0.201 1.248 0.348 -1.448 0.000 2.103 0.773 0.686 2.215 1.451 -0.087 -0.453 2.548 1.197 -0.045 -1.026 0.000 0.793 1.094 0.987 0.851 1.804 1.378 1.089
|
||||
1 2.446 -0.701 -1.568 0.059 0.822 1.401 -0.600 -0.044 2.173 0.324 -0.001 1.344 2.215 0.913 -0.818 1.049 0.000 0.442 -1.088 -0.005 0.000 0.611 1.062 0.979 0.562 0.988 0.998 0.806
|
||||
0 0.619 2.029 0.933 0.528 -0.903 0.974 0.760 -0.311 2.173 0.825 0.658 -1.466 1.107 0.894 1.594 0.370 0.000 0.882 -0.258 1.661 0.000 1.498 1.088 0.987 0.867 1.139 0.900 0.779
|
||||
1 0.674 -0.131 -0.362 0.518 -1.574 0.876 0.442 0.145 1.087 0.497 -1.526 -1.704 0.000 0.680 2.514 -1.374 0.000 0.792 -0.479 0.773 1.551 0.573 1.198 0.984 0.800 0.667 0.987 0.832
|
||||
1 1.447 1.145 -0.937 0.307 -1.458 0.478 1.264 0.816 1.087 0.558 1.015 -0.101 2.215 0.937 -0.190 1.177 0.000 0.699 0.954 -1.512 0.000 0.877 0.838 0.990 0.873 0.566 0.646 0.713
|
||||
1 0.976 0.308 -0.844 0.436 0.610 1.253 0.149 -1.585 2.173 1.415 0.568 0.096 2.215 0.953 -0.855 0.441 0.000 0.867 -0.650 1.643 0.000 0.890 1.234 0.988 0.796 2.002 1.179 0.977
|
||||
0 0.697 0.401 -0.718 0.920 0.735 0.958 -0.172 0.168 2.173 0.872 -0.097 -1.335 0.000 0.513 -1.192 -1.710 1.274 0.426 -1.637 1.368 0.000 0.997 1.227 1.072 0.800 1.013 0.786 0.749
|
||||
1 1.305 -2.157 1.740 0.661 -0.912 0.705 -0.516 0.759 2.173 0.989 -0.716 -0.300 2.215 0.627 -1.052 -1.736 0.000 0.467 -2.467 0.568 0.000 0.807 0.964 0.988 1.427 1.012 1.165 0.926
|
||||
0 1.847 1.663 -0.618 0.280 1.258 1.462 -0.054 1.371 0.000 0.900 0.309 -0.544 0.000 0.331 -2.149 -0.341 0.000 1.091 -0.833 0.710 3.102 1.496 0.931 0.989 1.549 0.115 1.140 1.150
|
||||
0 0.410 -0.323 1.069 2.160 0.010 0.892 0.942 -1.640 2.173 0.946 0.938 1.314 0.000 1.213 -1.099 -0.794 2.548 0.650 0.053 0.056 0.000 1.041 0.916 1.063 0.985 1.910 1.246 1.107
|
||||
1 0.576 1.092 -0.088 0.777 -1.579 0.757 0.271 0.109 0.000 0.819 0.827 -1.554 2.215 1.313 2.341 -1.568 0.000 2.827 0.239 -0.338 0.000 0.876 0.759 0.986 0.692 0.457 0.796 0.791
|
||||
1 0.537 0.925 -1.406 0.306 -0.050 0.906 1.051 0.037 0.000 1.469 -0.177 -1.320 2.215 1.872 0.723 1.158 0.000 1.313 0.227 -0.501 3.102 0.953 0.727 0.978 0.755 0.892 0.932 0.781
|
||||
0 0.716 -0.065 -0.484 1.313 -1.563 0.596 -0.242 0.678 2.173 0.426 -1.909 0.616 0.000 0.885 -0.406 -1.343 2.548 0.501 -1.327 -0.340 0.000 0.470 0.728 1.109 0.919 0.881 0.665 0.692
|
||||
1 0.624 -0.389 0.128 1.636 -1.110 1.025 0.573 -0.843 2.173 0.646 -0.697 1.064 0.000 0.632 -1.442 0.961 0.000 0.863 -0.106 1.717 0.000 0.825 0.917 1.257 0.983 0.713 0.890 0.824
|
||||
0 0.484 2.101 1.714 1.131 -0.823 0.750 0.583 -1.304 1.087 0.894 0.421 0.559 2.215 0.921 -0.063 0.282 0.000 0.463 -0.474 -1.387 0.000 0.742 0.886 0.995 0.993 1.201 0.806 0.754
|
||||
0 0.570 0.339 -1.478 0.528 0.439 0.978 1.479 -1.411 2.173 0.763 1.541 -0.734 0.000 1.375 0.840 0.903 0.000 0.965 1.599 0.364 0.000 0.887 1.061 0.992 1.322 1.453 1.013 0.969
|
||||
0 0.940 1.303 1.636 0.851 -1.732 0.803 -0.030 -0.177 0.000 0.480 -0.125 -0.954 0.000 0.944 0.709 0.296 2.548 1.342 -0.418 1.197 3.102 0.853 0.989 0.979 0.873 0.858 0.719 0.786
|
||||
1 0.599 0.544 -0.238 0.816 1.043 0.857 0.660 1.128 2.173 0.864 -0.624 -0.843 0.000 1.159 0.367 0.174 0.000 1.520 -0.543 -1.508 0.000 0.842 0.828 0.984 0.759 0.895 0.918 0.791
|
||||
1 1.651 1.897 -0.914 0.423 0.315 0.453 0.619 -1.607 2.173 0.532 -0.424 0.209 1.107 0.369 2.479 0.034 0.000 0.701 0.217 0.984 0.000 0.976 0.951 1.035 0.879 0.825 0.915 0.798
|
||||
1 0.926 -0.574 -0.763 0.285 1.094 0.672 2.314 1.545 0.000 1.124 0.415 0.809 0.000 1.387 0.270 -0.949 2.548 1.547 -0.631 -0.200 3.102 0.719 0.920 0.986 0.889 0.933 0.797 0.777
|
||||
0 0.677 1.698 -0.890 0.641 -0.449 0.607 1.754 1.720 0.000 0.776 0.372 0.782 2.215 0.511 1.491 -0.480 0.000 0.547 -0.341 0.853 3.102 0.919 1.026 0.997 0.696 0.242 0.694 0.687
|
||||
0 1.266 0.602 0.958 0.487 1.256 0.709 0.843 -1.196 0.000 0.893 1.303 -0.594 1.107 1.090 1.320 0.354 0.000 0.797 1.846 1.139 0.000 0.780 0.896 0.986 0.661 0.709 0.790 0.806
|
||||
1 0.628 -0.616 -0.329 0.764 -1.150 0.477 -0.715 1.187 2.173 1.250 0.607 1.026 2.215 0.983 -0.023 -0.583 0.000 0.377 1.344 -1.015 0.000 0.744 0.954 0.987 0.837 0.841 0.795 0.694
|
||||
1 1.035 -0.828 -1.358 1.870 -1.060 1.075 0.130 0.448 2.173 0.660 0.697 0.641 0.000 0.425 1.006 -1.035 0.000 0.751 1.055 1.364 3.102 0.826 0.822 0.988 0.967 0.901 1.077 0.906
|
||||
1 0.830 0.265 -0.150 0.660 1.105 0.592 -0.557 0.908 2.173 0.670 -1.419 -0.671 0.000 1.323 -0.409 1.644 2.548 0.850 -0.033 -0.615 0.000 0.760 0.967 0.984 0.895 0.681 0.747 0.770
|
||||
1 1.395 1.100 1.167 1.088 0.218 0.400 -0.132 0.024 2.173 0.743 0.530 -1.361 2.215 0.341 -0.691 -0.238 0.000 0.396 -1.426 -0.933 0.000 0.363 0.472 1.287 0.922 0.810 0.792 0.656
|
||||
1 1.070 1.875 -1.298 1.215 -0.106 0.767 0.795 0.514 1.087 0.401 2.780 1.276 0.000 0.686 1.127 1.721 2.548 0.391 -0.259 -1.167 0.000 1.278 1.113 1.389 0.852 0.824 0.838 0.785
|
||||
0 1.114 -0.071 1.719 0.399 -1.383 0.849 0.254 0.481 0.000 0.958 -0.579 0.742 0.000 1.190 -0.140 -0.862 2.548 0.479 1.390 0.856 0.000 0.952 0.988 0.985 0.764 0.419 0.835 0.827
|
||||
0 0.714 0.376 -0.568 1.578 -1.165 0.648 0.141 0.639 2.173 0.472 0.569 1.449 1.107 0.783 1.483 0.361 0.000 0.540 -0.790 0.032 0.000 0.883 0.811 0.982 0.775 0.572 0.760 0.745
|
||||
0 0.401 -1.731 0.765 0.974 1.648 0.652 -1.024 0.191 0.000 0.544 -0.366 -1.246 2.215 0.627 0.140 1.008 2.548 0.810 0.409 0.429 0.000 0.950 0.934 0.977 0.621 0.580 0.677 0.650
|
||||
1 0.391 1.679 -1.298 0.605 -0.832 0.549 1.338 0.522 2.173 1.244 0.884 1.070 0.000 1.002 0.846 -1.345 2.548 0.783 -2.464 -0.237 0.000 4.515 2.854 0.981 0.877 0.939 1.942 1.489
|
||||
1 0.513 -0.220 -0.444 1.699 0.479 1.109 0.181 -0.999 2.173 0.883 -0.335 -1.716 2.215 1.075 -0.380 1.352 0.000 0.857 0.048 0.147 0.000 0.937 0.758 0.986 1.206 0.958 0.949 0.876
|
||||
0 1.367 -0.388 0.798 1.158 1.078 0.811 -1.024 -1.628 0.000 1.504 0.097 -0.999 2.215 1.652 -0.860 0.054 2.548 0.573 -0.142 -1.401 0.000 0.869 0.833 1.006 1.412 1.641 1.214 1.041
|
||||
1 1.545 -0.533 -1.517 1.177 1.289 2.331 -0.370 -0.073 0.000 1.295 -0.358 -0.891 2.215 0.476 0.756 0.985 0.000 1.945 -0.016 -1.651 3.102 1.962 1.692 1.073 0.656 0.941 1.312 1.242
|
||||
0 0.858 0.978 -1.258 0.286 0.161 0.729 1.230 1.087 2.173 0.561 2.670 -0.109 0.000 0.407 2.346 0.938 0.000 1.078 0.729 -0.658 3.102 0.597 0.921 0.982 0.579 0.954 0.733 0.769
|
||||
1 1.454 -1.384 0.870 0.067 0.394 1.033 -0.673 0.318 0.000 1.166 -0.763 -1.533 2.215 2.848 -0.045 -0.856 2.548 0.697 -0.140 1.134 0.000 0.931 1.293 0.977 1.541 1.326 1.201 1.078
|
||||
1 0.559 -0.913 0.486 1.104 -0.321 1.073 -0.348 1.345 0.000 0.901 -0.827 -0.842 0.000 0.739 0.047 -0.415 2.548 0.433 -1.132 1.268 0.000 0.797 0.695 0.985 0.868 0.346 0.674 0.623
|
||||
1 1.333 0.780 -0.964 0.916 1.202 1.822 -0.071 0.742 2.173 1.486 -0.399 -0.824 0.000 0.740 0.568 -0.134 0.000 0.971 -0.070 -1.589 3.102 1.278 0.929 1.421 1.608 1.214 1.215 1.137
|
||||
1 2.417 0.631 -0.317 0.323 0.581 0.841 1.524 -1.738 0.000 0.543 1.176 -0.325 0.000 0.827 0.700 0.866 0.000 0.834 -0.262 -1.702 3.102 0.932 0.820 0.988 0.646 0.287 0.595 0.589
|
||||
0 0.955 -1.242 0.938 1.104 0.474 0.798 -0.743 1.535 0.000 1.356 -1.357 -1.080 2.215 1.320 -1.396 -0.132 2.548 0.728 -0.529 -0.633 0.000 0.832 0.841 0.988 0.923 1.077 0.988 0.816
|
||||
1 1.305 -1.918 0.391 1.161 0.063 0.724 2.593 1.481 0.000 0.592 -1.207 -0.329 0.000 0.886 -0.836 -1.168 2.548 1.067 -1.481 -1.440 0.000 0.916 0.688 0.991 0.969 0.550 0.665 0.638
|
||||
0 1.201 0.071 -1.123 2.242 -1.533 0.702 -0.256 0.688 0.000 0.967 0.491 1.040 2.215 1.271 -0.558 0.095 0.000 1.504 0.676 -0.383 3.102 0.917 1.006 0.985 1.017 1.057 0.928 1.057
|
||||
0 0.994 -1.607 1.596 0.774 -1.391 0.625 -0.134 -0.862 2.173 0.746 -0.765 -0.316 2.215 1.131 -0.320 0.869 0.000 0.607 0.826 0.301 0.000 0.798 0.967 0.999 0.880 0.581 0.712 0.774
|
||||
1 0.482 -0.467 0.729 1.419 1.458 0.824 0.376 -0.242 0.000 1.368 0.023 1.459 2.215 0.826 0.669 -1.079 2.548 0.936 2.215 -0.309 0.000 1.883 1.216 0.997 1.065 0.946 1.224 1.526
|
||||
1 0.383 1.588 1.611 0.748 1.194 0.866 -0.279 -0.636 0.000 0.707 0.536 0.801 2.215 1.647 -1.155 0.367 0.000 1.292 0.303 -1.681 3.102 2.016 1.581 0.986 0.584 0.684 1.107 0.958
|
||||
0 0.629 0.203 0.736 0.671 -0.271 1.350 -0.486 0.761 2.173 0.496 -0.805 -1.718 0.000 2.393 0.044 -1.046 1.274 0.651 -0.116 -0.541 0.000 0.697 1.006 0.987 1.069 2.317 1.152 0.902
|
||||
0 0.905 -0.564 -0.570 0.263 1.096 1.219 -1.397 -1.414 1.087 1.164 -0.533 -0.208 0.000 1.459 1.965 0.784 0.000 2.220 -1.421 0.452 0.000 0.918 1.360 0.993 0.904 0.389 2.118 1.707
|
||||
1 1.676 1.804 1.171 0.529 1.175 1.664 0.354 -0.530 0.000 1.004 0.691 -1.280 2.215 0.838 0.373 0.626 2.548 1.094 1.774 0.501 0.000 0.806 1.100 0.991 0.769 0.976 0.807 0.740
|
||||
1 1.364 -1.936 0.020 1.327 0.428 1.021 -1.665 -0.907 2.173 0.818 -2.701 1.303 0.000 0.716 -0.590 -1.629 2.548 0.895 -2.280 -1.602 0.000 1.211 0.849 0.989 1.320 0.864 1.065 0.949
|
||||
0 0.629 -0.626 0.609 1.828 1.280 0.644 -0.856 -0.873 2.173 0.555 1.066 -0.640 0.000 0.477 -1.364 -1.021 2.548 1.017 0.036 0.380 0.000 0.947 0.941 0.994 1.128 0.241 0.793 0.815
|
||||
1 1.152 -0.843 0.926 1.802 0.800 2.493 -1.449 -1.127 0.000 1.737 0.833 0.488 0.000 1.026 0.929 -0.990 2.548 1.408 0.689 1.142 3.102 1.171 0.956 0.993 2.009 0.867 1.499 1.474
|
||||
0 2.204 0.081 0.008 1.021 -0.679 2.676 0.090 1.163 0.000 2.210 -1.686 -1.195 0.000 1.805 0.891 -0.148 2.548 0.450 -0.502 -1.295 3.102 6.959 3.492 1.205 0.908 0.845 2.690 2.183
|
||||
1 0.957 0.954 1.702 0.043 -0.503 1.113 0.033 -0.308 0.000 0.757 -0.363 -1.129 2.215 1.635 0.068 1.048 1.274 0.415 -2.098 0.061 0.000 1.010 0.979 0.992 0.704 1.125 0.761 0.715
|
||||
0 1.222 0.418 1.059 1.303 1.442 0.282 -1.499 -1.286 0.000 1.567 0.016 -0.164 2.215 0.451 2.229 -1.229 0.000 0.660 -0.513 -0.296 3.102 2.284 1.340 0.985 1.531 0.314 1.032 1.094
|
||||
1 0.603 1.675 -0.973 0.703 -1.709 1.023 0.652 1.296 2.173 1.078 0.363 -0.263 0.000 0.734 -0.457 -0.745 1.274 0.561 1.434 -0.042 0.000 0.888 0.771 0.984 0.847 1.234 0.874 0.777
|
||||
0 0.897 0.949 -0.848 1.115 -0.085 0.522 -1.267 -1.418 0.000 0.684 -0.599 1.474 0.000 1.176 0.922 0.641 2.548 0.470 0.103 0.148 3.102 0.775 0.697 0.984 0.839 0.358 0.847 1.008
|
||||
1 0.987 1.013 -1.504 0.468 -0.259 1.160 0.476 -0.971 2.173 1.266 0.919 0.780 0.000 0.634 1.695 0.233 0.000 0.487 -0.082 0.719 3.102 0.921 0.641 0.991 0.730 0.828 0.952 0.807
|
||||
1 0.847 1.581 -1.397 1.629 1.529 1.053 0.816 -0.344 2.173 0.895 0.779 0.332 0.000 0.750 1.311 0.419 2.548 1.604 0.844 1.367 0.000 1.265 0.798 0.989 1.328 0.783 0.930 0.879
|
||||
1 0.805 1.416 -1.327 0.397 0.589 0.488 0.982 0.843 0.000 0.664 -0.999 0.129 0.000 0.624 0.613 -0.558 0.000 1.431 -0.667 -1.561 3.102 0.959 1.103 0.989 0.590 0.632 0.926 0.798
|
||||
0 1.220 -0.313 -0.489 1.759 0.201 1.698 -0.220 0.241 2.173 1.294 1.390 -1.682 0.000 1.447 -1.623 -1.296 0.000 1.710 0.872 -1.356 3.102 1.198 0.981 1.184 0.859 2.165 1.807 1.661
|
||||
0 0.772 -0.611 -0.549 0.465 -1.528 1.103 -0.140 0.001 2.173 0.854 -0.406 1.655 0.000 0.733 -1.250 1.072 0.000 0.883 0.627 -1.132 3.102 0.856 0.927 0.987 1.094 1.013 0.938 0.870
|
||||
1 1.910 0.771 0.828 0.231 1.267 1.398 1.455 -0.295 2.173 0.837 -2.564 0.770 0.000 0.540 2.189 1.287 0.000 1.345 1.311 -1.151 0.000 0.861 0.869 0.984 1.359 1.562 1.105 0.963
|
||||
1 0.295 0.832 1.399 1.222 -0.517 2.480 0.013 1.591 0.000 2.289 0.436 0.287 2.215 1.995 -0.367 -0.409 1.274 0.375 1.367 -1.716 0.000 1.356 2.171 0.990 1.467 1.664 1.855 1.705
|
||||
1 1.228 0.339 -0.575 0.417 1.474 0.480 -1.416 -1.498 2.173 0.614 -0.933 -0.961 0.000 1.189 1.690 1.003 0.000 1.690 -1.065 0.106 3.102 0.963 1.147 0.987 1.086 0.948 0.930 0.866
|
||||
0 2.877 -1.014 1.440 0.782 0.483 1.134 -0.735 -0.196 2.173 1.123 0.084 -0.596 0.000 1.796 -0.356 1.044 2.548 1.406 1.582 -0.991 0.000 0.939 1.178 1.576 0.996 1.629 1.216 1.280
|
||||
1 2.178 0.259 1.107 0.256 1.222 0.979 -0.440 -0.538 1.087 0.496 -0.760 -0.049 0.000 1.471 1.683 -1.486 0.000 0.646 0.695 -1.577 3.102 1.093 1.070 0.984 0.608 0.889 0.962 0.866
|
||||
1 0.604 0.592 1.295 0.964 0.348 1.178 -0.016 0.832 2.173 1.626 -0.420 -0.760 0.000 0.748 0.461 -0.906 0.000 0.728 0.309 -1.269 1.551 0.852 0.604 0.989 0.678 0.949 1.021 0.878
|
||||
0 0.428 -1.352 -0.912 1.713 0.797 1.894 -1.452 0.191 2.173 2.378 2.113 -1.190 0.000 0.860 2.174 0.949 0.000 1.693 0.759 1.426 3.102 0.885 1.527 1.186 1.090 3.294 4.492 3.676
|
||||
0 0.473 0.485 0.154 1.433 -1.504 0.766 1.257 -1.302 2.173 0.414 0.119 0.238 0.000 0.805 0.242 -0.691 2.548 0.734 0.749 0.753 0.000 0.430 0.893 1.137 0.686 0.724 0.618 0.608
|
||||
1 0.763 -0.601 0.876 0.182 -1.678 0.818 0.599 0.481 2.173 0.658 -0.737 -0.553 0.000 0.857 -1.138 -1.435 0.000 1.540 -1.466 -0.447 0.000 0.870 0.566 0.989 0.728 0.658 0.821 0.726
|
||||
0 0.619 -0.273 -0.143 0.992 -1.267 0.566 0.876 -1.396 2.173 0.515 0.892 0.618 0.000 0.434 -0.902 0.862 2.548 0.490 -0.539 0.549 0.000 0.568 0.794 0.984 0.667 0.867 0.597 0.578
|
||||
0 0.793 0.970 0.324 0.570 0.816 0.761 -0.550 1.519 2.173 1.150 0.496 -0.447 0.000 0.925 0.724 1.008 1.274 1.135 -0.275 -0.843 0.000 0.829 1.068 0.978 1.603 0.892 1.041 1.059
|
||||
1 0.480 0.364 -0.067 1.906 -1.582 1.397 1.159 0.140 0.000 0.639 0.398 -1.102 0.000 1.597 -0.668 1.607 2.548 1.306 -0.797 0.288 3.102 0.856 1.259 1.297 1.022 1.032 1.049 0.939
|
||||
0 0.514 1.304 1.490 1.741 -0.220 0.648 0.155 0.535 0.000 0.562 -1.016 0.837 0.000 0.863 -0.780 -0.815 2.548 1.688 -0.130 -1.545 3.102 0.887 0.980 1.309 1.269 0.654 1.044 1.035
|
||||
0 1.225 0.333 0.656 0.893 0.859 1.037 -0.876 1.603 1.087 1.769 0.272 -0.227 2.215 1.000 0.579 -1.690 0.000 1.385 0.471 -0.860 0.000 0.884 1.207 0.995 1.097 2.336 1.282 1.145
|
||||
0 2.044 -1.472 -0.294 0.392 0.369 0.927 0.718 1.492 1.087 1.619 -0.736 0.047 2.215 1.884 -0.101 -1.540 0.000 0.548 -0.441 1.117 0.000 0.798 0.877 0.981 0.750 2.272 1.469 1.276
|
||||
0 1.037 -0.276 0.735 3.526 1.156 2.498 0.401 -0.590 1.087 0.714 -1.203 1.393 2.215 0.681 0.629 1.534 0.000 0.719 -0.355 -0.706 0.000 0.831 0.857 0.988 2.864 2.633 1.988 1.466
|
||||
1 0.651 -1.218 -0.791 0.770 -1.449 0.610 -0.535 0.960 2.173 0.380 -1.072 -0.031 2.215 0.415 2.123 -1.100 0.000 0.776 0.217 0.420 0.000 0.986 1.008 1.001 0.853 0.588 0.799 0.776
|
||||
0 1.586 -0.409 0.085 3.258 0.405 1.647 -0.674 -1.519 0.000 0.640 -1.027 -1.681 0.000 1.452 -0.444 -0.957 2.548 0.927 -0.017 1.215 3.102 0.519 0.866 0.992 0.881 0.847 1.018 1.278
|
||||
0 0.712 0.092 -0.466 0.688 1.236 0.921 -1.217 -1.022 2.173 2.236 -1.167 0.868 2.215 0.851 -1.892 -0.753 0.000 0.475 -1.216 -0.383 0.000 0.668 0.758 0.988 1.180 2.093 1.157 0.934
|
||||
0 0.419 0.471 0.974 2.805 0.235 1.473 -0.198 1.255 1.087 0.931 1.083 -0.712 0.000 1.569 1.358 -1.179 2.548 2.506 0.199 -0.842 0.000 0.929 0.991 0.992 1.732 2.367 1.549 1.430
|
||||
1 0.667 1.003 1.504 0.368 1.061 0.885 -0.318 -0.353 0.000 1.438 -1.939 0.710 0.000 1.851 0.277 -1.460 2.548 1.403 0.517 -0.157 0.000 0.883 1.019 1.000 0.790 0.859 0.938 0.841
|
||||
1 1.877 -0.492 0.372 0.441 0.955 1.034 -1.220 -0.846 1.087 0.952 -0.320 1.125 0.000 0.542 0.308 -1.261 2.548 1.018 -1.415 -1.547 0.000 1.280 0.932 0.991 1.273 0.878 0.921 0.906
|
||||
0 1.052 0.901 1.176 1.280 1.517 0.562 -1.150 -0.079 2.173 1.228 -0.308 -0.354 0.000 0.790 -1.492 -0.963 0.000 0.942 -0.672 -1.588 3.102 1.116 0.902 0.988 1.993 0.765 1.375 1.325
|
||||
1 0.518 -0.254 1.642 0.865 0.725 0.980 0.734 0.023 0.000 1.448 0.780 -1.736 2.215 0.955 0.513 -0.519 0.000 0.365 -0.444 -0.243 3.102 0.833 0.555 0.984 0.827 0.795 0.890 0.786
|
||||
0 0.870 0.815 -0.506 0.663 -0.518 0.935 0.289 -1.675 2.173 1.188 0.005 0.635 0.000 0.580 0.066 -1.455 2.548 0.580 -0.634 -0.199 0.000 0.852 0.788 0.979 1.283 0.208 0.856 0.950
|
||||
0 0.628 1.382 0.135 0.683 0.571 1.097 0.564 -0.950 2.173 0.617 -0.326 0.371 0.000 1.093 0.918 1.667 2.548 0.460 1.221 0.708 0.000 0.743 0.861 0.975 1.067 1.007 0.843 0.762
|
||||
0 4.357 0.816 -1.609 1.845 -1.288 3.292 0.726 0.324 2.173 1.528 0.583 -0.801 2.215 0.605 0.572 1.406 0.000 0.794 -0.791 0.122 0.000 0.967 1.132 1.124 3.602 2.811 2.460 1.861
|
||||
0 0.677 -1.265 1.559 0.866 -0.618 0.823 0.260 0.185 0.000 1.133 0.337 1.589 2.215 0.563 -0.830 0.510 0.000 0.777 0.117 -0.941 3.102 0.839 0.763 0.986 1.182 0.649 0.796 0.851
|
||||
0 2.466 -1.838 -1.648 1.717 1.533 1.676 -1.553 -0.109 2.173 0.670 -0.666 0.284 0.000 0.334 -2.480 0.316 0.000 0.366 -0.804 -1.298 3.102 0.875 0.894 0.997 0.548 0.770 1.302 1.079
|
||||
1 1.403 0.129 -1.307 0.688 0.306 0.579 0.753 0.814 1.087 0.474 0.694 -1.400 0.000 0.520 1.995 0.185 0.000 0.929 -0.504 1.270 3.102 0.972 0.998 1.353 0.948 0.650 0.688 0.724
|
||||
1 0.351 1.188 -0.360 0.254 -0.346 1.129 0.545 1.691 0.000 0.652 -0.039 -0.258 2.215 1.089 0.655 0.472 2.548 0.554 -0.493 1.366 0.000 0.808 1.045 0.992 0.570 0.649 0.809 0.744
|
||||
0 1.875 -0.013 -0.128 0.236 1.163 0.902 0.426 0.590 2.173 1.251 -1.210 -0.616 0.000 1.035 1.534 0.912 0.000 1.944 1.789 -1.691 0.000 0.974 1.113 0.990 0.925 1.120 0.956 0.912
|
||||
0 0.298 0.750 -0.507 1.555 1.463 0.804 1.200 -0.665 0.000 0.439 -0.829 -0.252 1.107 0.770 -1.090 0.947 2.548 1.165 -0.166 -0.763 0.000 1.140 0.997 0.988 1.330 0.555 1.005 1.012
|
||||
0 0.647 0.342 0.245 4.340 -0.157 2.229 0.068 1.170 2.173 2.133 -0.201 -1.441 0.000 1.467 0.697 -0.532 1.274 1.457 0.583 -1.640 0.000 0.875 1.417 0.976 2.512 2.390 1.794 1.665
|
||||
1 1.731 -0.803 -1.013 1.492 -0.020 1.646 -0.541 1.121 2.173 0.459 -1.251 -1.495 2.215 0.605 -1.711 -0.232 0.000 0.658 0.634 -0.068 0.000 1.214 0.886 1.738 1.833 1.024 1.192 1.034
|
||||
0 0.515 1.416 -1.089 1.697 1.426 1.414 0.941 0.027 0.000 1.480 0.133 -1.595 2.215 1.110 0.752 0.760 2.548 1.062 0.697 -0.492 0.000 0.851 0.955 0.994 1.105 1.255 1.175 1.095
|
||||
0 1.261 0.858 1.465 0.757 0.305 2.310 0.679 1.080 2.173 1.544 2.518 -0.464 0.000 2.326 0.270 -0.841 0.000 2.163 0.839 -0.500 3.102 0.715 0.825 1.170 0.980 2.371 1.527 1.221
|
||||
1 1.445 1.509 1.471 0.414 -1.285 0.767 0.864 -0.677 2.173 0.524 1.388 0.171 0.000 0.826 0.190 0.121 2.548 0.572 1.691 -1.603 0.000 0.870 0.935 0.994 0.968 0.735 0.783 0.777
|
||||
1 0.919 -0.264 -1.245 0.681 -1.722 1.022 1.010 0.097 2.173 0.685 0.403 -1.351 0.000 1.357 -0.429 1.262 1.274 0.687 1.021 -0.563 0.000 0.953 0.796 0.991 0.873 1.749 1.056 0.917
|
||||
1 0.293 -2.258 -1.427 1.191 1.202 0.394 -2.030 1.438 0.000 0.723 0.596 -0.024 2.215 0.525 -1.678 -0.290 0.000 0.788 -0.824 -1.029 3.102 0.821 0.626 0.976 1.080 0.810 0.842 0.771
|
||||
0 3.286 0.386 1.688 1.619 -1.620 1.392 -0.009 0.280 0.000 1.179 -0.776 -0.110 2.215 1.256 0.248 -1.114 2.548 0.777 0.825 -0.156 0.000 1.026 1.065 0.964 0.909 1.249 1.384 1.395
|
||||
1 1.075 0.603 0.561 0.656 -0.685 0.985 0.175 0.979 2.173 1.154 0.584 -0.886 0.000 1.084 -0.354 -1.004 2.548 0.865 1.224 1.269 0.000 1.346 1.073 1.048 0.873 1.310 1.003 0.865
|
||||
1 1.098 -0.091 1.466 1.558 0.915 0.649 1.314 -1.182 2.173 0.791 0.073 0.351 0.000 0.517 0.940 1.195 0.000 1.150 1.187 -0.692 3.102 0.866 0.822 0.980 1.311 0.394 1.119 0.890
|
||||
1 0.481 -1.042 0.148 1.135 -1.249 1.202 -0.344 0.308 1.087 0.779 -1.431 1.581 0.000 0.860 -0.860 -1.125 0.000 0.785 0.303 1.199 3.102 0.878 0.853 0.988 1.072 0.827 0.936 0.815
|
||||
0 1.348 0.497 0.318 0.806 0.976 1.393 -0.152 0.632 2.173 2.130 0.515 -1.054 0.000 0.908 0.062 -0.780 0.000 1.185 0.687 1.668 1.551 0.720 0.898 0.985 0.683 1.292 1.320 1.131
|
||||
0 2.677 -0.420 -1.685 1.828 1.433 2.040 -0.718 -0.039 0.000 0.400 -0.873 0.472 0.000 0.444 0.340 -0.830 2.548 0.431 0.768 -1.417 3.102 0.869 0.917 0.996 0.707 0.193 0.728 1.154
|
||||
1 1.300 0.586 -0.122 1.306 0.609 0.727 -0.556 -1.652 2.173 0.636 0.720 1.393 2.215 0.328 1.280 -0.390 0.000 0.386 0.752 -0.905 0.000 0.202 0.751 1.106 0.864 0.799 0.928 0.717
|
||||
0 0.637 -0.176 1.737 1.322 -0.414 0.702 -0.964 -0.680 0.000 1.054 -0.461 0.889 2.215 0.861 -0.267 0.225 0.000 1.910 -1.888 1.027 0.000 0.919 0.899 1.186 0.993 1.109 0.862 0.775
|
||||
1 0.723 -0.104 1.572 0.428 -0.840 0.655 0.544 1.401 2.173 1.522 -0.154 -0.452 2.215 0.996 0.190 0.273 0.000 1.906 -0.176 0.966 0.000 0.945 0.894 0.990 0.981 1.555 0.988 0.893
|
||||
0 2.016 -0.570 1.612 0.798 0.441 0.334 0.191 -0.909 0.000 0.939 0.146 0.021 2.215 0.553 -0.444 1.156 2.548 0.781 -1.545 -0.520 0.000 0.922 0.956 1.528 0.722 0.699 0.778 0.901
|
||||
0 1.352 -0.707 1.284 0.665 0.580 0.694 -1.040 -0.899 2.173 0.692 -2.048 0.029 0.000 0.545 -2.042 1.259 0.000 0.661 -0.808 -1.251 3.102 0.845 0.991 0.979 0.662 0.225 0.685 0.769
|
||||
1 1.057 -1.561 -0.411 0.952 -0.681 1.236 -1.107 1.045 2.173 1.288 -2.521 -0.521 0.000 1.361 -1.239 1.546 0.000 0.373 -1.540 0.028 0.000 0.794 0.782 0.987 0.889 0.832 0.972 0.828
|
||||
0 1.118 -0.017 -1.227 1.077 1.256 0.714 0.624 -0.811 0.000 0.800 0.704 0.387 1.107 0.604 0.234 0.986 0.000 1.306 -0.456 0.094 3.102 0.828 0.984 1.195 0.987 0.672 0.774 0.748
|
||||
1 0.602 2.201 0.212 0.119 0.182 0.474 2.130 1.270 0.000 0.370 2.088 -0.573 0.000 0.780 -0.725 -1.033 0.000 1.642 0.598 0.303 3.102 0.886 0.988 0.985 0.644 0.756 0.651 0.599
|
||||
0 1.677 -0.844 1.581 0.585 0.887 1.012 -2.315 0.752 0.000 1.077 0.748 -0.195 0.000 0.718 0.832 -1.337 1.274 1.181 -0.557 -1.006 3.102 1.018 1.247 0.988 0.908 0.651 1.311 1.120
|
||||
1 1.695 0.259 1.224 1.344 1.067 0.718 -1.752 -0.215 0.000 0.473 0.991 -0.993 0.000 0.891 1.285 -1.500 2.548 0.908 -0.131 0.288 0.000 0.945 0.824 0.979 1.009 0.951 0.934 0.833
|
||||
0 0.793 0.628 0.432 1.707 0.302 0.919 1.045 -0.784 0.000 1.472 0.175 -1.284 2.215 1.569 0.155 0.971 2.548 0.435 0.735 1.625 0.000 0.801 0.907 0.992 0.831 1.446 1.082 1.051
|
||||
1 0.537 -0.664 -0.244 1.104 1.272 1.154 0.394 1.633 0.000 1.527 0.963 0.559 2.215 1.744 0.650 -0.912 0.000 1.097 0.730 -0.368 3.102 1.953 1.319 1.045 1.309 0.869 1.196 1.126
|
||||
1 0.585 -1.469 1.005 0.749 -1.060 1.224 -0.717 -0.323 2.173 1.012 -0.201 1.268 0.000 0.359 -0.567 0.476 0.000 1.117 -1.124 1.557 3.102 0.636 1.281 0.986 0.616 1.289 0.890 0.881
|
||||
1 0.354 -1.517 0.667 2.534 -1.298 1.020 -0.375 1.254 0.000 1.119 -0.060 -1.538 2.215 1.059 -0.395 -0.140 0.000 2.609 0.199 -0.778 1.551 0.957 0.975 1.286 1.666 1.003 1.224 1.135
|
||||
1 0.691 -1.619 -1.380 0.361 1.727 1.493 -1.093 -0.289 0.000 1.447 -0.640 1.341 0.000 1.453 -0.617 -1.456 1.274 1.061 -1.481 -0.091 0.000 0.744 0.649 0.987 0.596 0.727 0.856 0.797
|
||||
0 1.336 1.293 -1.359 0.357 0.067 1.110 -0.058 -0.515 0.000 0.976 1.498 1.207 0.000 1.133 0.437 1.053 2.548 0.543 1.374 0.171 0.000 0.764 0.761 0.984 0.827 0.553 0.607 0.612
|
||||
0 0.417 -1.111 1.661 2.209 -0.683 1.931 -0.642 0.959 1.087 1.514 -2.032 -0.686 0.000 1.521 -0.539 1.344 0.000 0.978 -0.866 0.363 1.551 2.813 1.850 1.140 1.854 0.799 1.600 1.556
|
||||
0 1.058 0.390 -0.591 0.134 1.149 0.346 -1.550 0.186 0.000 1.108 -0.999 0.843 1.107 1.124 0.415 -1.514 0.000 1.067 -0.426 -1.000 3.102 1.744 1.050 0.985 1.006 1.010 0.883 0.789
|
||||
1 1.655 0.253 1.216 0.270 1.703 0.500 -0.006 -1.418 2.173 0.690 -0.350 0.170 2.215 1.045 -0.924 -0.774 0.000 0.996 -0.745 -0.123 0.000 0.839 0.820 0.993 0.921 0.869 0.725 0.708
|
||||
0 1.603 -0.850 0.564 0.829 0.093 1.270 -1.113 -1.155 2.173 0.853 -1.021 1.248 2.215 0.617 -1.270 1.733 0.000 0.935 -0.092 0.136 0.000 1.011 1.074 0.977 0.823 1.269 1.054 0.878
|
||||
0 1.568 -0.792 1.005 0.545 0.896 0.895 -1.698 -0.988 0.000 0.608 -1.634 1.705 0.000 0.826 0.208 0.618 1.274 2.063 -1.743 -0.520 0.000 0.939 0.986 0.990 0.600 0.435 1.033 1.087
|
||||
0 0.489 -1.335 -1.102 1.738 1.028 0.628 -0.992 -0.627 0.000 0.652 -0.064 -0.215 0.000 1.072 0.173 -1.251 2.548 1.042 0.057 0.841 3.102 0.823 0.895 1.200 1.164 0.770 0.837 0.846
|
||||
1 1.876 0.870 1.234 0.556 -1.262 1.764 0.855 -0.467 2.173 1.079 1.351 0.852 0.000 0.773 0.383 0.874 0.000 1.292 0.829 -1.228 3.102 0.707 0.969 1.102 1.601 1.017 1.112 1.028
|
||||
0 1.033 0.407 -0.374 0.705 -1.254 0.690 -0.231 1.502 2.173 0.433 -2.009 -0.057 0.000 0.861 1.151 0.334 0.000 0.960 -0.839 1.299 3.102 2.411 1.480 0.982 0.995 0.377 1.012 0.994
|
||||
0 1.092 0.653 -0.801 0.463 0.426 0.529 -1.055 0.040 0.000 0.663 0.999 1.255 1.107 0.749 -1.106 1.185 2.548 0.841 -0.745 -1.029 0.000 0.841 0.743 0.988 0.750 1.028 0.831 0.868
|
||||
1 0.799 -0.285 -0.011 0.531 1.392 1.063 0.854 0.494 2.173 1.187 -1.065 -0.851 0.000 0.429 -0.296 1.072 0.000 0.942 -1.985 1.172 0.000 0.873 0.693 0.992 0.819 0.689 1.131 0.913
|
||||
0 0.503 1.973 -0.377 1.515 -1.514 0.708 1.081 -0.313 2.173 1.110 -0.417 0.839 0.000 0.712 -1.153 1.165 0.000 0.675 -0.303 -0.930 1.551 0.709 0.761 1.032 0.986 0.698 0.963 1.291
|
||||
0 0.690 -0.574 -1.608 1.182 1.118 0.557 -2.243 0.144 0.000 0.969 0.216 -1.383 1.107 1.054 0.888 -0.709 2.548 0.566 1.663 -0.550 0.000 0.752 1.528 0.987 1.408 0.740 1.290 1.123
|
||||
1 0.890 1.501 0.786 0.779 -0.615 1.126 0.716 1.541 2.173 0.887 0.728 -0.673 2.215 1.216 0.332 -0.020 0.000 0.965 1.828 0.101 0.000 0.827 0.715 1.099 1.088 1.339 0.924 0.878
|
||||
0 0.566 0.883 0.655 1.600 0.034 1.155 2.028 -1.499 0.000 0.723 -0.871 0.763 0.000 1.286 -0.696 -0.676 2.548 1.134 -0.113 1.207 3.102 4.366 2.493 0.984 0.960 0.962 1.843 1.511
|
||||
0 1.146 1.086 -0.911 0.838 1.298 0.821 0.127 -0.145 0.000 1.352 0.474 -1.580 2.215 1.619 -0.081 0.675 2.548 1.382 -0.748 0.127 0.000 0.958 0.976 1.239 0.876 1.481 1.116 1.076
|
||||
0 1.739 -0.326 -1.661 0.420 -1.705 1.193 -0.031 -1.212 2.173 1.783 -0.442 0.522 0.000 1.064 -0.692 0.027 0.000 1.314 0.359 -0.037 3.102 0.968 0.897 0.986 0.907 1.196 1.175 1.112
|
||||
1 0.669 0.194 -0.703 0.657 -0.260 0.899 -2.511 0.311 0.000 1.482 0.773 0.974 2.215 3.459 0.037 -1.299 1.274 2.113 0.067 1.516 0.000 0.740 0.871 0.979 1.361 2.330 1.322 1.046
|
||||
1 1.355 -1.033 -1.173 0.552 -0.048 0.899 -0.482 -1.287 2.173 1.422 -1.227 0.390 1.107 1.937 -0.028 0.914 0.000 0.849 -0.230 -1.734 0.000 0.986 1.224 1.017 1.051 1.788 1.150 1.009
|
||||
1 0.511 -0.202 1.029 0.780 1.154 0.816 0.532 -0.731 0.000 0.757 0.517 0.749 2.215 1.302 0.289 -1.188 0.000 0.584 1.211 -0.350 0.000 0.876 0.943 0.995 0.963 0.256 0.808 0.891
|
||||
1 1.109 0.572 1.484 0.753 1.543 1.711 -0.145 -0.746 1.087 1.759 0.631 0.845 2.215 0.945 0.542 0.003 0.000 0.378 -1.150 -0.044 0.000 0.764 1.042 0.992 1.045 2.736 1.441 1.140
|
||||
0 0.712 -0.025 0.553 0.928 -0.711 1.304 0.045 -0.300 0.000 0.477 0.720 0.969 0.000 1.727 -0.474 1.328 1.274 1.282 2.222 1.684 0.000 0.819 0.765 1.023 0.961 0.657 0.799 0.744
|
||||
1 1.131 -0.302 1.079 0.901 0.236 0.904 -0.249 1.694 2.173 1.507 -0.702 -1.128 0.000 0.774 0.565 0.284 2.548 1.802 1.446 -0.192 0.000 3.720 2.108 0.986 0.930 1.101 1.484 1.238
|
||||
0 1.392 1.253 0.118 0.864 -1.358 0.922 -0.447 -1.243 1.087 1.969 1.031 0.774 2.215 1.333 -0.359 -0.681 0.000 1.099 -0.257 1.473 0.000 1.246 0.909 1.475 1.234 2.531 1.449 1.306
|
||||
0 1.374 2.291 -0.479 1.339 -0.243 0.687 2.345 1.310 0.000 0.467 1.081 0.772 0.000 0.656 1.155 -1.636 2.548 0.592 0.536 -1.269 3.102 0.981 0.821 1.010 0.877 0.217 0.638 0.758
|
||||
1 0.401 -1.516 0.909 2.738 0.519 0.887 0.566 -1.202 0.000 0.909 -0.176 1.682 0.000 2.149 -0.878 -0.514 2.548 0.929 -0.563 -1.555 3.102 1.228 0.803 0.980 1.382 0.884 1.025 1.172
|
||||
1 0.430 -1.589 1.417 2.158 1.226 1.180 -0.829 -0.781 2.173 0.798 1.400 -0.111 0.000 0.939 -0.878 1.076 2.548 0.576 1.335 -0.826 0.000 0.861 0.970 0.982 1.489 1.308 1.015 0.992
|
||||
1 1.943 -0.391 -0.840 0.621 -1.613 2.026 1.734 1.025 0.000 0.930 0.573 -0.912 0.000 1.326 0.847 -0.220 1.274 1.181 0.079 0.709 3.102 1.164 1.007 0.987 1.094 0.821 0.857 0.786
|
||||
1 0.499 0.436 0.887 0.859 1.509 0.733 -0.559 1.111 1.087 1.011 -0.796 0.279 2.215 1.472 -0.510 -0.982 0.000 1.952 0.379 -0.733 0.000 1.076 1.358 0.991 0.589 0.879 1.068 0.922
|
||||
0 0.998 -0.407 -1.711 0.139 0.652 0.810 -0.331 -0.721 0.000 0.471 -0.533 0.442 0.000 0.531 -1.405 0.120 2.548 0.707 0.098 -1.176 1.551 1.145 0.809 0.988 0.529 0.612 0.562 0.609
|
||||
1 1.482 0.872 0.638 1.288 0.362 0.856 0.900 -0.511 1.087 1.072 1.061 -1.432 2.215 1.770 -2.292 -1.547 0.000 1.131 1.374 0.783 0.000 6.316 4.381 1.002 1.317 1.048 2.903 2.351
|
||||
1 2.084 -0.422 1.289 1.125 0.735 1.104 -0.518 -0.326 2.173 0.413 -0.719 -0.699 0.000 0.857 0.108 -1.631 0.000 0.527 0.641 -1.362 3.102 0.791 0.952 1.016 0.776 0.856 0.987 0.836
|
||||
0 0.464 0.674 0.025 0.430 -1.703 0.982 -1.311 -0.808 2.173 1.875 1.060 0.821 2.215 0.954 -0.480 -1.677 0.000 0.567 0.702 -0.939 0.000 0.781 1.076 0.989 1.256 3.632 1.652 1.252
|
||||
1 0.457 -1.944 -1.010 1.409 0.931 1.098 -0.742 -0.415 0.000 1.537 -0.834 0.945 2.215 1.752 -0.287 -1.269 2.548 0.692 -1.537 -0.223 0.000 0.801 1.192 1.094 1.006 1.659 1.175 1.122
|
||||
0 3.260 -0.943 1.737 0.920 1.309 0.946 -0.139 -0.271 2.173 0.994 -0.952 -0.311 0.000 0.563 -0.136 -0.881 0.000 1.236 -0.507 0.906 1.551 0.747 0.869 0.985 1.769 1.034 1.179 1.042
|
||||
0 0.615 -0.778 0.246 1.861 1.619 0.560 -0.943 -0.204 2.173 0.550 -0.759 -1.342 2.215 0.578 0.076 -0.973 0.000 0.939 0.035 0.680 0.000 0.810 0.747 1.401 0.772 0.702 0.719 0.662
|
||||
1 2.370 -0.064 -0.237 1.737 0.154 2.319 -1.838 -1.673 0.000 1.053 -1.305 -0.075 0.000 0.925 0.149 0.318 1.274 0.851 -0.922 0.981 3.102 0.919 0.940 0.989 0.612 0.598 1.219 1.626
|
||||
1 1.486 0.311 -1.262 1.354 -0.847 0.886 -0.158 1.213 2.173 1.160 -0.218 0.239 0.000 1.166 0.494 0.278 2.548 0.575 1.454 -1.701 0.000 0.429 1.129 0.983 1.111 1.049 1.006 0.920
|
||||
1 1.294 1.587 -0.864 0.487 -0.312 0.828 1.051 -0.031 1.087 2.443 1.216 1.609 2.215 1.167 0.813 0.921 0.000 1.751 -0.415 0.119 0.000 1.015 1.091 0.974 1.357 2.093 1.178 1.059
|
||||
1 0.984 0.465 -1.661 0.379 -0.554 0.977 0.237 0.365 0.000 0.510 0.143 1.101 0.000 1.099 -0.662 -1.593 2.548 1.104 -0.197 -0.648 3.102 0.925 0.922 0.986 0.642 0.667 0.806 0.722
|
||||
1 0.930 -0.009 0.047 0.667 1.367 1.065 -0.231 0.815 0.000 1.199 -1.114 -0.877 2.215 0.940 0.824 -1.583 0.000 1.052 -0.407 -0.076 1.551 1.843 1.257 1.013 1.047 0.751 1.158 0.941
|
||||
0 0.767 -0.011 -0.637 0.341 -1.437 1.438 -0.425 -0.450 2.173 1.073 -0.718 1.341 2.215 0.633 -1.394 0.486 0.000 0.603 -1.945 -1.626 0.000 0.703 0.790 0.984 1.111 1.848 1.129 1.072
|
||||
1 1.779 0.017 0.432 0.402 1.022 0.959 1.480 1.595 2.173 1.252 1.365 0.006 0.000 1.188 -0.174 -1.107 0.000 1.181 0.518 -0.258 0.000 1.057 0.910 0.991 1.616 0.779 1.158 1.053
|
||||
0 0.881 0.630 1.029 1.990 0.508 1.102 0.742 -1.298 2.173 1.565 1.085 0.686 2.215 2.691 1.391 -0.904 0.000 0.499 1.388 -1.199 0.000 0.347 0.861 0.997 0.881 1.920 1.233 1.310
|
||||
0 1.754 -0.266 0.389 0.347 -0.030 0.462 -1.408 -0.957 2.173 0.515 -2.341 -1.700 0.000 0.588 -0.797 1.355 2.548 0.608 0.329 -1.389 0.000 1.406 0.909 0.988 0.760 0.593 0.768 0.847
|
||||
0 1.087 0.311 -1.447 0.173 0.567 0.854 0.362 0.584 0.000 1.416 -0.716 -1.211 2.215 0.648 -0.358 -0.692 1.274 0.867 -0.513 0.206 0.000 0.803 0.813 0.984 1.110 0.491 0.921 0.873
|
||||
0 0.279 1.114 -1.190 3.004 -0.738 1.233 0.896 1.092 2.173 0.454 -0.374 0.117 2.215 0.357 0.119 1.270 0.000 0.458 1.343 0.316 0.000 0.495 0.540 0.988 1.715 1.139 1.618 1.183
|
||||
1 1.773 -0.694 -1.518 2.306 -1.200 3.104 0.749 0.362 0.000 1.871 0.230 -1.686 2.215 0.805 -0.179 -0.871 1.274 0.910 0.607 -0.246 0.000 1.338 1.598 0.984 1.050 0.919 1.678 1.807
|
||||
0 0.553 0.683 0.827 0.973 -0.706 1.488 0.149 1.140 2.173 1.788 0.447 -0.478 0.000 0.596 1.043 1.607 0.000 0.373 -0.868 -1.308 1.551 1.607 1.026 0.998 1.134 0.808 1.142 0.936
|
||||
1 0.397 1.101 -1.139 1.688 0.146 0.972 0.541 1.518 0.000 1.549 -0.873 -1.012 0.000 2.282 -0.151 0.314 2.548 1.174 0.033 -1.368 0.000 0.937 0.776 1.039 1.143 0.959 0.986 1.013
|
||||
1 0.840 1.906 -0.959 0.869 0.576 0.642 0.554 -1.351 0.000 0.756 0.923 -0.823 2.215 1.251 1.130 0.545 2.548 1.513 0.410 1.073 0.000 1.231 0.985 1.163 0.812 0.987 0.816 0.822
|
||||
1 0.477 1.665 0.814 0.763 -0.382 0.828 -0.008 0.280 2.173 1.213 -0.001 1.560 0.000 1.136 0.311 -1.289 0.000 0.797 1.091 -0.616 3.102 1.026 0.964 0.992 0.772 0.869 0.916 0.803
|
||||
0 2.655 0.020 0.273 1.464 0.482 1.709 -0.107 -1.456 2.173 0.825 0.141 -0.386 0.000 1.342 -0.592 1.635 1.274 0.859 -0.175 -0.874 0.000 0.829 0.946 1.003 2.179 0.836 1.505 1.176
|
||||
0 0.771 -1.992 -0.720 0.732 -1.464 0.869 -1.290 0.388 2.173 0.926 -1.072 -1.489 2.215 0.640 -1.232 0.840 0.000 0.528 -2.440 -0.446 0.000 0.811 0.868 0.993 0.995 1.317 0.809 0.714
|
||||
0 1.357 1.302 0.076 0.283 -1.060 0.783 1.559 -0.994 0.000 0.947 1.212 1.617 0.000 1.127 0.311 0.442 2.548 0.582 -0.052 1.186 1.551 1.330 0.995 0.985 0.846 0.404 0.858 0.815
|
||||
0 0.442 -0.381 -0.424 1.244 0.591 0.731 0.605 -0.713 2.173 0.629 2.762 1.040 0.000 0.476 2.693 -0.617 0.000 0.399 0.442 1.486 3.102 0.839 0.755 0.988 0.869 0.524 0.877 0.918
|
||||
0 0.884 0.422 0.055 0.818 0.624 0.950 -0.763 1.624 0.000 0.818 -0.609 -1.166 0.000 1.057 -0.528 1.070 2.548 1.691 -0.124 -0.335 3.102 1.104 0.933 0.985 0.913 1.000 0.863 1.056
|
||||
0 1.276 0.156 1.714 1.053 -1.189 0.672 -0.464 -0.030 2.173 0.469 -2.483 0.442 0.000 0.564 2.580 -0.253 0.000 0.444 -0.628 1.080 1.551 5.832 2.983 0.985 1.162 0.494 1.809 1.513
|
||||
0 1.106 -0.556 0.406 0.573 -1.400 0.769 -0.518 1.457 2.173 0.743 -0.352 -0.010 0.000 1.469 -0.550 -0.930 2.548 0.540 1.236 -0.571 0.000 0.962 0.970 1.101 0.805 1.107 0.873 0.773
|
||||
0 0.539 -0.964 -0.464 1.371 -1.606 0.667 -0.160 0.655 0.000 0.952 0.352 -0.740 2.215 0.952 0.007 1.123 0.000 1.061 -0.505 1.389 3.102 1.063 0.991 1.019 0.633 0.967 0.732 0.799
|
||||
1 0.533 -0.989 -1.608 0.462 -1.723 1.204 -0.598 -0.098 2.173 1.343 -0.460 1.632 2.215 0.577 0.221 -0.492 0.000 0.628 -0.073 0.472 0.000 0.518 0.880 0.988 1.179 1.874 1.041 0.813
|
||||
1 1.024 1.075 -0.795 0.286 -1.436 1.365 0.857 -0.309 2.173 0.804 1.532 1.435 0.000 1.511 0.722 1.494 0.000 1.778 0.903 0.753 1.551 0.686 0.810 0.999 0.900 1.360 1.133 0.978
|
||||
1 2.085 -0.269 -1.423 0.789 1.298 0.281 1.652 0.187 0.000 0.658 -0.760 -0.042 2.215 0.663 0.024 0.120 0.000 0.552 -0.299 -0.428 3.102 0.713 0.811 1.130 0.705 0.218 0.675 0.743
|
||||
1 0.980 -0.443 0.813 0.785 -1.253 0.719 0.448 -1.458 0.000 1.087 0.595 0.635 1.107 1.428 0.029 -0.995 0.000 1.083 1.562 -0.092 0.000 0.834 0.891 1.165 0.967 0.661 0.880 0.817
|
||||
1 0.903 -0.733 -0.980 0.634 -0.639 0.780 0.266 -0.287 2.173 1.264 -0.936 1.004 0.000 1.002 -0.056 -1.344 2.548 1.183 -0.098 1.169 0.000 0.733 1.002 0.985 0.711 0.916 0.966 0.875
|
||||
0 0.734 -0.304 -1.175 2.851 1.674 0.904 -0.634 0.412 2.173 1.363 -1.050 -0.282 0.000 1.476 -1.603 0.103 0.000 2.231 -0.718 1.708 3.102 0.813 0.896 1.088 0.686 1.392 1.033 1.078
|
||||
1 1.680 0.591 -0.243 0.111 -0.478 0.326 -0.079 -1.555 2.173 0.711 0.714 0.922 2.215 0.355 0.858 1.682 0.000 0.727 1.620 1.360 0.000 0.334 0.526 1.001 0.862 0.633 0.660 0.619
|
||||
1 1.163 0.225 -0.202 0.501 -0.979 1.609 -0.938 1.424 0.000 1.224 -0.118 -1.274 0.000 2.034 1.241 -0.254 0.000 1.765 0.536 0.237 3.102 0.894 0.838 0.988 0.693 0.579 0.762 0.726
|
||||
0 1.223 1.232 1.471 0.489 1.728 0.703 -0.111 0.411 0.000 1.367 1.014 -1.294 1.107 1.524 -0.414 -0.164 2.548 1.292 0.833 0.316 0.000 0.861 0.752 0.994 0.836 1.814 1.089 0.950
|
||||
0 0.816 1.637 -1.557 1.036 -0.342 0.913 1.333 0.949 2.173 0.812 0.756 -0.628 2.215 1.333 0.470 1.495 0.000 1.204 -2.222 -1.675 0.000 1.013 0.924 1.133 0.758 1.304 0.855 0.860
|
||||
0 0.851 -0.564 -0.691 0.692 1.345 1.219 1.014 0.318 0.000 1.422 -0.262 -1.635 2.215 0.531 1.802 0.008 0.000 0.508 0.515 -1.267 3.102 0.821 0.787 1.026 0.783 0.432 1.149 1.034
|
||||
0 0.800 -0.599 0.204 0.552 -0.484 0.974 0.413 0.961 2.173 1.269 -0.984 -1.039 2.215 0.380 -1.213 1.371 0.000 0.551 0.332 -0.659 0.000 0.694 0.852 0.984 1.057 2.037 1.096 0.846
|
||||
0 0.744 -0.071 -0.255 0.638 0.512 1.125 0.407 0.844 2.173 0.860 -0.481 -0.677 0.000 1.102 0.181 -1.194 0.000 1.011 -1.081 -1.713 3.102 0.854 0.862 0.982 1.111 1.372 1.042 0.920
|
||||
1 0.400 1.049 -0.625 0.880 -0.407 1.040 2.150 -1.359 0.000 0.747 -0.144 0.847 2.215 0.560 -1.829 0.698 0.000 1.663 -0.668 0.267 0.000 0.845 0.964 0.996 0.820 0.789 0.668 0.668
|
||||
0 1.659 -0.705 -1.057 1.803 -1.436 1.008 0.693 0.005 0.000 0.895 -0.007 0.681 1.107 1.085 0.125 1.476 2.548 1.214 1.068 0.486 0.000 0.867 0.919 0.986 1.069 0.692 1.026 1.313
|
||||
0 0.829 -0.153 0.861 0.615 -0.548 0.589 1.077 -0.041 2.173 1.056 0.763 -1.737 0.000 0.639 0.970 0.725 0.000 0.955 1.227 -0.799 3.102 1.020 1.024 0.985 0.750 0.525 0.685 0.671
|
||||
1 0.920 -0.806 -0.840 1.048 0.278 0.973 -0.077 -1.364 2.173 1.029 0.309 0.133 0.000 1.444 1.484 1.618 1.274 1.419 -0.482 0.417 0.000 0.831 1.430 1.151 1.829 1.560 1.343 1.224
|
||||
1 0.686 0.249 -0.905 0.343 -1.731 0.724 -2.823 -0.901 0.000 0.982 0.303 1.312 1.107 1.016 0.245 0.610 0.000 1.303 -0.557 -0.360 3.102 1.384 1.030 0.984 0.862 1.144 0.866 0.779
|
||||
0 1.603 0.444 0.508 0.586 0.401 0.610 0.467 -1.735 2.173 0.914 0.626 -1.019 0.000 0.812 0.422 -0.408 2.548 0.902 1.679 1.490 0.000 1.265 0.929 0.990 1.004 0.816 0.753 0.851
|
||||
1 0.623 0.780 -0.203 0.056 0.015 0.899 0.793 1.326 1.087 0.803 1.478 -1.499 2.215 1.561 1.492 -0.120 0.000 0.904 0.795 0.137 0.000 0.548 1.009 0.850 0.924 0.838 0.914 0.860
|
||||
0 1.654 -2.032 -1.160 0.859 -1.583 0.689 -1.965 0.891 0.000 0.646 -1.014 -0.288 2.215 0.630 -0.815 0.402 0.000 0.638 0.316 0.655 3.102 0.845 0.879 0.993 1.067 0.625 1.041 0.958
|
||||
1 0.828 -1.269 -1.203 0.744 -0.213 0.626 -1.017 -0.404 0.000 1.281 -0.931 1.733 2.215 0.699 -0.351 1.287 0.000 1.251 -1.171 0.197 0.000 0.976 1.186 0.987 0.646 0.655 0.733 0.671
|
||||
1 0.677 0.111 1.090 1.580 1.591 1.560 0.654 -0.341 2.173 0.794 -0.266 0.702 0.000 0.823 0.651 -1.239 2.548 0.730 1.467 -1.530 0.000 1.492 1.023 0.983 1.909 1.022 1.265 1.127
|
||||
1 0.736 0.882 -1.060 0.589 0.168 1.663 0.781 1.022 2.173 2.025 1.648 -1.292 0.000 1.240 0.924 -0.421 1.274 1.354 0.065 0.501 0.000 0.316 0.925 0.988 0.664 1.736 0.992 0.807
|
||||
1 1.040 -0.822 1.638 0.974 -0.674 0.393 0.830 0.011 2.173 0.770 -0.140 -0.402 0.000 0.294 -0.133 0.030 0.000 1.220 0.807 0.638 0.000 0.826 1.063 1.216 1.026 0.705 0.934 0.823
|
||||
1 0.711 0.602 0.048 1.145 0.966 0.934 0.263 -1.589 2.173 0.971 -0.496 -0.421 1.107 0.628 -0.865 0.845 0.000 0.661 -0.008 -0.565 0.000 0.893 0.705 0.988 0.998 1.339 0.908 0.872
|
||||
1 0.953 -1.651 -0.167 0.885 1.053 1.013 -1.239 0.133 0.000 1.884 -1.122 1.222 2.215 1.906 -0.860 -1.184 1.274 1.413 -0.668 -1.647 0.000 1.873 1.510 1.133 1.050 1.678 1.246 1.061
|
||||
1 0.986 -0.892 -1.380 0.917 1.134 0.950 -1.162 -0.469 0.000 0.569 -1.393 0.215 0.000 0.320 2.667 1.712 0.000 1.570 -0.375 1.457 3.102 0.925 1.128 1.011 0.598 0.824 0.913 0.833
|
||||
1 1.067 0.099 1.154 0.527 -0.789 1.085 0.623 -1.602 2.173 1.511 -0.230 0.022 2.215 0.269 -0.377 0.883 0.000 0.571 -0.540 -0.512 0.000 0.414 0.803 1.022 0.959 2.053 1.041 0.780
|
||||
0 0.825 -2.118 0.217 1.453 -0.493 0.819 0.313 -0.942 0.000 2.098 -0.725 1.096 2.215 0.484 1.336 1.458 0.000 0.482 0.100 1.163 0.000 0.913 0.536 0.990 1.679 0.957 1.095 1.143
|
||||
1 1.507 0.054 1.120 0.698 -1.340 0.912 0.384 0.015 1.087 0.720 0.247 -0.820 0.000 0.286 0.154 1.578 2.548 0.629 1.582 -0.576 0.000 0.828 0.893 1.136 0.514 0.632 0.699 0.709
|
||||
1 0.610 1.180 -0.993 0.816 0.301 0.932 0.758 1.539 0.000 0.726 -0.830 0.248 2.215 0.883 0.857 -1.305 0.000 1.338 1.009 -0.252 3.102 0.901 1.074 0.987 0.875 1.159 1.035 0.858
|
||||
1 1.247 -1.360 1.502 1.525 -1.332 0.618 1.063 0.755 0.000 0.582 -0.155 0.473 2.215 1.214 -0.422 -0.551 2.548 0.838 -1.171 -1.166 0.000 2.051 1.215 1.062 1.091 0.725 0.896 1.091
|
||||
0 0.373 -0.600 1.291 2.573 0.207 0.765 -0.209 1.667 0.000 0.668 0.724 -1.499 0.000 1.045 -0.338 -0.754 2.548 0.558 -0.469 0.029 3.102 0.868 0.939 1.124 0.519 0.383 0.636 0.838
|
||||
0 0.791 0.336 -0.307 0.494 1.213 1.158 0.336 1.081 2.173 0.918 1.289 -0.449 0.000 0.735 -0.521 -0.969 0.000 1.052 0.499 -1.188 3.102 0.699 1.013 0.987 0.622 1.050 0.712 0.661
|
||||
0 1.321 0.856 0.464 0.202 0.901 1.144 0.120 -1.651 0.000 0.803 0.577 -0.509 2.215 0.695 -0.114 0.423 2.548 0.621 1.852 -0.420 0.000 0.697 0.964 0.983 0.527 0.659 0.719 0.729
|
||||
0 0.563 2.081 0.913 0.982 -0.533 0.549 -0.481 -1.730 0.000 0.962 0.921 0.569 2.215 0.731 1.184 -0.679 1.274 0.918 0.931 -1.432 0.000 1.008 0.919 0.993 0.895 0.819 0.810 0.878
|
||||
1 1.148 0.345 0.953 0.921 0.617 0.991 1.103 -0.484 0.000 0.970 1.978 1.525 0.000 1.150 0.689 -0.757 2.548 0.517 0.995 1.245 0.000 1.093 1.140 0.998 1.006 0.756 0.864 0.838
|
||||
1 1.400 0.128 -1.695 1.169 1.070 1.094 -0.345 -0.249 0.000 1.224 0.364 -0.036 2.215 1.178 0.530 -1.544 0.000 1.334 0.933 1.604 0.000 0.560 1.267 1.073 0.716 0.780 0.832 0.792
|
||||
0 0.330 -2.133 1.403 0.628 0.379 1.686 -0.995 0.030 1.087 2.071 0.127 -0.457 0.000 4.662 -0.855 1.477 0.000 2.072 -0.917 -1.416 3.102 5.403 3.074 0.977 0.936 1.910 2.325 1.702
|
||||
0 0.989 0.473 0.968 1.970 1.368 0.844 0.574 -0.290 2.173 0.866 -0.345 -1.019 0.000 1.130 0.605 -0.752 0.000 0.956 -0.888 0.870 3.102 0.885 0.886 0.982 1.157 1.201 1.100 1.068
|
||||
1 0.773 0.418 0.753 1.388 1.070 1.104 -0.378 -0.758 0.000 1.027 0.397 -0.496 2.215 1.234 0.027 1.084 2.548 0.936 0.209 1.677 0.000 1.355 1.020 0.983 0.550 1.206 0.916 0.931
|
||||
0 0.319 2.015 1.534 0.570 -1.134 0.632 0.124 0.757 0.000 0.477 0.598 -1.109 1.107 0.449 0.438 -0.755 2.548 0.574 -0.659 0.691 0.000 0.440 0.749 0.985 0.517 0.158 0.505 0.522
|
||||
0 1.215 1.453 -1.386 1.276 1.298 0.643 0.570 -0.196 2.173 0.588 2.104 0.498 0.000 0.617 -0.296 -0.801 2.548 0.452 0.110 0.313 0.000 0.815 0.953 1.141 1.166 0.547 0.892 0.807
|
||||
1 1.257 -1.869 -0.060 0.265 0.653 1.527 -0.346 1.163 2.173 0.758 -2.119 -0.604 0.000 1.473 -1.133 -1.290 2.548 0.477 -0.428 -0.066 0.000 0.818 0.841 0.984 1.446 1.729 1.211 1.054
|
||||
1 1.449 0.464 1.585 1.418 -1.488 1.540 0.942 0.087 0.000 0.898 0.402 -0.631 2.215 0.753 0.039 -1.729 0.000 0.859 0.849 -1.054 0.000 0.791 0.677 0.995 0.687 0.527 0.703 0.606
|
||||
1 1.084 -1.997 0.900 1.333 1.024 0.872 -0.864 -1.500 2.173 1.072 -0.813 -0.421 2.215 0.924 0.478 0.304 0.000 0.992 -0.398 -1.022 0.000 0.741 1.085 0.980 1.221 1.176 1.032 0.961
|
||||
0 1.712 1.129 0.125 1.120 -1.402 1.749 0.951 -1.575 2.173 1.711 0.445 0.578 0.000 1.114 0.234 -1.011 0.000 1.577 -0.088 0.086 3.102 2.108 1.312 1.882 1.597 2.009 1.441 1.308
|
||||
0 0.530 0.248 1.622 1.450 -1.012 1.221 -1.154 -0.763 2.173 1.698 -0.586 0.733 0.000 0.889 1.042 1.038 1.274 0.657 0.008 0.701 0.000 0.430 1.005 0.983 0.930 2.264 1.357 1.146
|
||||
1 0.921 1.735 0.883 0.699 -1.614 0.821 1.463 0.319 1.087 1.099 0.814 -1.600 2.215 1.375 0.702 -0.691 0.000 0.869 1.326 -0.790 0.000 0.980 0.900 0.988 0.832 1.452 0.816 0.709
|
||||
0 2.485 -0.823 -0.297 0.886 -1.404 0.989 0.835 1.615 2.173 0.382 0.588 -0.224 0.000 1.029 -0.456 1.546 2.548 0.613 -0.359 -0.789 0.000 0.768 0.977 1.726 2.007 0.913 1.338 1.180
|
||||
1 0.657 -0.069 -0.078 1.107 1.549 0.804 1.335 -1.630 2.173 1.271 0.481 0.153 1.107 1.028 0.144 -0.762 0.000 1.098 0.132 1.570 0.000 0.830 0.979 1.175 1.069 1.624 1.000 0.868
|
||||
1 2.032 0.329 -1.003 0.493 -0.136 1.159 -0.224 0.750 1.087 0.396 0.546 0.587 0.000 0.620 1.805 0.982 0.000 1.236 0.744 -1.621 0.000 0.930 1.200 0.988 0.482 0.771 0.887 0.779
|
||||
0 0.524 -1.319 0.634 0.471 1.221 0.599 -0.588 -0.461 0.000 1.230 -1.504 -1.517 1.107 1.436 -0.035 0.104 2.548 0.629 1.997 -1.282 0.000 2.084 1.450 0.984 1.084 1.827 1.547 1.213
|
||||
1 0.871 0.618 -1.544 0.718 0.186 1.041 -1.180 0.434 2.173 1.133 1.558 -1.301 0.000 0.452 -0.595 0.522 0.000 0.665 0.567 0.130 3.102 1.872 1.114 1.095 1.398 0.979 1.472 1.168
|
||||
1 3.308 1.037 -0.634 0.690 -0.619 1.975 0.949 1.280 0.000 0.826 0.546 -0.139 2.215 0.635 -0.045 0.427 0.000 1.224 0.112 1.339 3.102 1.756 1.050 0.992 0.738 0.903 0.968 1.238
|
||||
0 0.588 2.104 -0.872 1.136 1.743 0.842 0.638 0.015 0.000 0.481 0.928 1.000 2.215 0.595 0.125 1.429 0.000 0.951 -1.140 -0.511 3.102 1.031 1.057 0.979 0.673 1.064 1.001 0.891
|
||||
0 0.289 0.823 0.013 0.615 -1.601 0.177 2.403 -0.015 0.000 0.258 1.151 1.036 2.215 0.694 0.553 -1.326 2.548 0.411 0.366 0.106 0.000 0.482 0.562 0.989 0.670 0.404 0.516 0.561
|
||||
1 0.294 -0.660 -1.162 1.752 0.384 0.860 0.513 1.119 0.000 2.416 0.107 -1.342 0.000 1.398 0.361 -0.350 2.548 1.126 -0.902 0.040 1.551 0.650 1.125 0.988 0.531 0.843 0.912 0.911
|
||||
0 0.599 -0.616 1.526 1.381 0.507 0.955 -0.646 -0.085 2.173 0.775 -0.533 1.116 2.215 0.789 -0.136 -1.176 0.000 2.449 1.435 -1.433 0.000 1.692 1.699 1.000 0.869 1.119 1.508 1.303
|
||||
1 1.100 -1.174 -1.114 1.601 -1.576 1.056 -1.343 0.547 2.173 0.555 0.367 0.592 2.215 0.580 -1.862 -0.914 0.000 0.904 0.508 -0.444 0.000 1.439 1.105 0.986 1.408 1.104 1.190 1.094
|
||||
1 2.237 -0.701 1.470 0.719 -0.199 0.745 -0.132 -0.737 1.087 0.976 -0.227 0.093 2.215 0.699 0.057 1.133 0.000 0.661 0.573 -0.679 0.000 0.785 0.772 1.752 1.235 0.856 0.990 0.825
|
||||
1 0.455 -0.880 -1.482 1.260 -0.178 1.499 0.158 1.022 0.000 1.867 -0.435 -0.675 2.215 1.234 0.783 1.586 0.000 0.641 -0.454 -0.409 3.102 1.002 0.964 0.986 0.761 0.240 1.190 0.995
|
||||
1 1.158 -0.778 -0.159 0.823 1.641 1.341 -0.830 -1.169 2.173 0.840 -1.554 0.934 0.000 0.693 0.488 -1.218 2.548 1.042 1.395 0.276 0.000 0.946 0.785 1.350 1.079 0.893 1.267 1.151
|
||||
1 0.902 -0.078 -0.055 0.872 -0.012 0.843 1.276 1.739 2.173 0.838 1.492 0.918 0.000 0.626 0.904 -0.648 2.548 0.412 -2.027 -0.883 0.000 2.838 1.664 0.988 1.803 0.768 1.244 1.280
|
||||
1 0.649 -1.028 -1.521 1.097 0.774 1.216 -0.383 -0.318 2.173 1.643 -0.285 -1.705 0.000 0.911 -0.091 0.341 0.000 0.592 0.537 0.732 3.102 0.911 0.856 1.027 1.160 0.874 0.986 0.893
|
||||
1 1.192 1.846 -0.781 1.326 -0.747 1.550 1.177 1.366 0.000 1.196 0.151 0.387 2.215 0.527 2.261 -0.190 0.000 0.390 1.474 0.381 0.000 0.986 1.025 1.004 1.392 0.761 0.965 1.043
|
||||
0 0.438 -0.358 -1.549 0.836 0.436 0.818 0.276 -0.708 2.173 0.707 0.826 0.392 0.000 1.050 1.741 -1.066 0.000 1.276 -1.583 0.842 0.000 1.475 1.273 0.986 0.853 1.593 1.255 1.226
|
||||
1 1.083 0.142 1.701 0.605 -0.253 1.237 0.791 1.183 2.173 0.842 2.850 -0.082 0.000 0.724 -0.464 -0.694 0.000 1.499 0.456 -0.226 3.102 0.601 0.799 1.102 0.995 1.389 1.013 0.851
|
||||
0 0.828 1.897 -0.615 0.572 -0.545 0.572 0.461 0.464 2.173 0.393 0.356 1.069 2.215 1.840 0.088 1.500 0.000 0.407 -0.663 -0.787 0.000 0.950 0.965 0.979 0.733 0.363 0.618 0.733
|
||||
0 0.735 1.438 1.197 1.123 -0.214 0.641 0.949 0.858 0.000 1.162 0.524 -0.896 2.215 0.992 0.454 -1.475 2.548 0.902 1.079 0.019 0.000 0.822 0.917 1.203 1.032 0.569 0.780 0.764
|
||||
0 0.437 -2.102 0.044 1.779 -1.042 1.231 -0.181 -0.515 1.087 2.666 0.863 1.466 2.215 1.370 0.345 -1.371 0.000 0.906 0.363 1.611 0.000 1.140 1.362 1.013 3.931 3.004 2.724 2.028
|
||||
1 0.881 1.814 -0.987 0.384 0.800 2.384 1.422 0.640 0.000 1.528 0.292 -0.962 1.107 2.126 -0.371 -1.401 2.548 0.700 0.109 0.203 0.000 0.450 0.813 0.985 0.956 1.013 0.993 0.774
|
||||
1 0.630 0.408 0.152 0.194 0.316 0.710 -0.824 -0.358 2.173 0.741 0.535 -0.851 2.215 0.933 0.406 1.148 0.000 0.523 -0.479 -0.625 0.000 0.873 0.960 0.988 0.830 0.921 0.711 0.661
|
||||
1 0.870 -0.448 -1.134 0.616 0.135 0.600 0.649 -0.622 2.173 0.768 0.709 -0.123 0.000 1.308 0.500 1.468 0.000 1.973 -0.286 1.462 3.102 0.909 0.944 0.990 0.835 1.250 0.798 0.776
|
||||
0 1.290 0.552 1.330 0.615 -1.353 0.661 0.240 -0.393 0.000 0.531 0.053 -1.588 0.000 0.675 0.839 -0.345 1.274 1.597 0.020 0.536 3.102 1.114 0.964 0.987 0.783 0.675 0.662 0.675
|
||||
1 0.943 0.936 1.068 1.373 0.671 2.170 -2.011 -1.032 0.000 0.640 0.361 -0.806 0.000 2.239 -0.083 0.590 2.548 1.224 0.646 -1.723 0.000 0.879 0.834 0.981 1.436 0.568 0.916 0.931
|
||||
1 0.431 1.686 -1.053 0.388 1.739 0.457 -0.471 -0.743 2.173 0.786 1.432 -0.547 2.215 0.537 -0.413 1.256 0.000 0.413 2.311 -0.408 0.000 1.355 1.017 0.982 0.689 1.014 0.821 0.715
|
||||
0 1.620 -0.055 -0.862 1.341 -1.571 0.634 -0.906 0.935 2.173 0.501 -2.198 -0.525 0.000 0.778 -0.708 -0.060 0.000 0.988 -0.621 0.489 3.102 0.870 0.956 1.216 0.992 0.336 0.871 0.889
|
||||
1 0.549 0.304 -1.443 1.309 -0.312 1.116 0.644 1.519 2.173 1.078 -0.303 -0.736 0.000 1.261 0.387 0.628 2.548 0.945 -0.190 0.090 0.000 0.893 1.043 1.000 1.124 1.077 1.026 0.886
|
||||
0 0.412 -0.618 -1.486 1.133 -0.665 0.646 0.436 1.520 0.000 0.993 0.976 0.106 2.215 0.832 0.091 0.164 2.548 0.672 -0.650 1.256 0.000 0.695 1.131 0.991 1.017 0.455 1.226 1.087
|
||||
0 1.183 -0.084 1.644 1.389 0.967 0.843 0.938 -0.670 0.000 0.480 0.256 0.123 2.215 0.437 1.644 0.491 0.000 0.501 -0.416 0.101 3.102 1.060 0.804 1.017 0.775 0.173 0.535 0.760
|
||||
0 1.629 -1.486 -0.683 2.786 -0.492 1.347 -2.638 1.453 0.000 1.857 0.208 0.873 0.000 0.519 -1.265 -1.602 1.274 0.903 -1.102 -0.329 1.551 6.892 3.522 0.998 0.570 0.477 2.039 2.006
|
||||
1 2.045 -0.671 -1.235 0.490 -0.952 0.525 -1.252 1.289 0.000 1.088 -0.993 0.648 2.215 0.975 -0.109 -0.254 2.548 0.556 -1.095 -0.194 0.000 0.803 0.861 0.980 1.282 0.945 0.925 0.811
|
||||
0 0.448 -0.058 -0.974 0.945 -1.633 1.181 -1.139 0.266 2.173 1.118 -0.761 1.502 1.107 1.706 0.585 -0.680 0.000 0.487 -1.951 0.945 0.000 2.347 1.754 0.993 1.161 1.549 1.414 1.176
|
||||
0 0.551 0.519 0.448 2.183 1.293 1.220 0.628 -0.627 2.173 1.019 -0.002 -0.652 0.000 1.843 -0.386 1.042 2.548 0.400 -1.102 -1.014 0.000 0.648 0.792 1.049 0.888 2.132 1.262 1.096
|
||||
0 1.624 0.488 1.403 0.760 0.559 0.812 0.777 -1.244 2.173 0.613 0.589 -0.030 2.215 0.692 1.058 0.683 0.000 1.054 1.165 -0.765 0.000 0.915 0.875 1.059 0.821 0.927 0.792 0.721
|
||||
1 0.774 0.444 1.257 0.515 -0.689 0.515 1.448 -1.271 0.000 0.793 0.118 0.811 1.107 0.679 0.326 -0.426 0.000 1.066 -0.865 -0.049 3.102 0.960 1.046 0.986 0.716 0.772 0.855 0.732
|
||||
1 2.093 -1.240 1.615 0.918 -1.202 1.412 -0.541 0.640 1.087 2.019 0.872 -0.639 0.000 0.672 -0.936 0.972 0.000 0.896 0.235 0.212 0.000 0.810 0.700 1.090 0.797 0.862 1.049 0.874
|
||||
1 0.908 1.069 0.283 0.400 1.293 0.609 1.452 -1.136 0.000 0.623 0.417 -0.098 2.215 1.023 0.775 1.054 1.274 0.706 2.346 -1.305 0.000 0.744 1.006 0.991 0.606 0.753 0.796 0.753
|
||||
0 0.403 -1.328 -0.065 0.901 1.052 0.708 -0.354 -0.718 2.173 0.892 0.633 1.684 2.215 0.999 -1.205 0.941 0.000 0.930 1.072 -0.809 0.000 2.105 1.430 0.989 0.838 1.147 1.042 0.883
|
||||
0 1.447 0.453 0.118 1.731 0.650 0.771 0.446 -1.564 0.000 0.973 -2.014 0.354 0.000 1.949 -0.643 -1.531 1.274 1.106 -0.334 -1.163 0.000 0.795 0.821 1.013 1.699 0.918 1.118 1.018
|
||||
1 1.794 0.123 -0.454 0.057 1.489 0.966 -1.190 1.090 1.087 0.539 -0.535 1.035 0.000 1.096 -1.069 -1.236 2.548 0.659 -1.196 -0.283 0.000 0.803 0.756 0.985 1.343 1.109 0.993 0.806
|
||||
0 1.484 -2.047 0.813 0.591 -0.295 0.923 0.312 -1.164 2.173 0.654 -0.316 0.752 2.215 0.599 1.966 -1.128 0.000 0.626 -0.304 -1.431 0.000 1.112 0.910 1.090 0.986 1.189 1.350 1.472
|
||||
0 0.417 -2.016 0.849 1.817 0.040 1.201 -1.676 -1.394 0.000 0.792 0.537 0.641 2.215 0.794 -1.222 0.187 0.000 0.825 -0.217 1.334 3.102 1.470 0.931 0.987 1.203 0.525 0.833 0.827
|
||||
1 0.603 1.009 0.033 0.486 1.225 0.884 -0.617 -1.058 0.000 0.500 -1.407 -0.567 0.000 1.476 -0.876 0.605 2.548 0.970 0.560 1.092 3.102 0.853 1.153 0.988 0.846 0.920 0.944 0.835
|
||||
1 1.381 -0.326 0.552 0.417 -0.027 1.030 -0.835 -1.287 2.173 0.941 -0.421 1.519 2.215 0.615 -1.650 0.377 0.000 0.606 0.644 0.650 0.000 1.146 0.970 0.990 1.191 0.884 0.897 0.826
|
||||
1 0.632 1.200 -0.703 0.438 -1.700 0.779 -0.731 0.958 1.087 0.605 0.393 -1.376 0.000 0.670 -0.827 -1.315 2.548 0.626 -0.501 0.417 0.000 0.904 0.903 0.998 0.673 0.803 0.722 0.640
|
||||
1 1.561 -0.569 1.580 0.329 0.237 1.059 0.731 0.415 2.173 0.454 0.016 -0.828 0.000 0.587 0.008 -0.291 1.274 0.597 1.119 1.191 0.000 0.815 0.908 0.988 0.733 0.690 0.892 0.764
|
||||
1 2.102 0.087 0.449 1.164 -0.390 1.085 -0.408 -1.116 2.173 0.578 0.197 -0.137 0.000 1.202 0.917 1.523 0.000 0.959 -0.832 1.404 3.102 1.380 1.109 1.486 1.496 0.886 1.066 1.025
|
||||
1 1.698 -0.489 -0.552 0.976 -1.009 1.620 -0.721 0.648 1.087 1.481 -1.860 -1.354 0.000 1.142 -1.140 1.401 2.548 1.000 -1.274 -0.158 0.000 1.430 1.130 0.987 1.629 1.154 1.303 1.223
|
||||
1 1.111 -0.249 -1.457 0.421 0.939 0.646 -2.076 0.362 0.000 1.315 0.796 -1.436 2.215 0.780 0.130 0.055 0.000 1.662 -0.834 0.461 0.000 0.920 0.948 0.990 1.046 0.905 1.493 1.169
|
||||
1 0.945 0.390 -1.159 1.675 0.437 0.356 0.261 0.543 1.087 0.574 0.838 1.599 2.215 0.496 -1.220 -0.022 0.000 0.558 -2.454 1.440 0.000 0.763 0.983 1.728 1.000 0.578 0.922 1.003
|
||||
1 2.076 0.014 -1.314 0.854 -0.306 3.446 1.341 0.598 0.000 2.086 0.227 -0.747 2.215 1.564 -0.216 1.649 2.548 0.965 -0.857 -1.062 0.000 0.477 0.734 1.456 1.003 1.660 1.001 0.908
|
||||
1 1.992 0.192 -0.103 0.108 -1.599 0.938 0.595 -1.360 2.173 0.869 -1.012 1.432 0.000 1.302 0.850 0.436 2.548 0.487 1.051 -1.027 0.000 0.502 0.829 0.983 1.110 1.394 0.904 0.836
|
||||
0 0.460 1.625 1.485 1.331 1.242 0.675 -0.329 -1.039 1.087 0.671 -1.028 -0.514 0.000 1.265 -0.788 0.415 1.274 0.570 -0.683 -1.738 0.000 0.725 0.758 1.004 1.024 1.156 0.944 0.833
|
||||
0 0.871 0.839 -1.536 0.428 1.198 0.875 -1.256 -0.466 1.087 0.684 -0.768 0.150 0.000 0.556 -1.793 0.389 0.000 0.942 -1.126 1.339 1.551 0.624 0.734 0.986 1.357 0.960 1.474 1.294
|
||||
1 0.951 1.651 0.576 1.273 1.495 0.834 0.048 -0.578 2.173 0.386 -0.056 -1.448 0.000 0.597 -0.196 0.162 2.548 0.524 1.649 1.625 0.000 0.737 0.901 1.124 1.014 0.556 1.039 0.845
|
||||
1 1.049 -0.223 0.685 0.256 -1.191 2.506 0.238 -0.359 0.000 1.510 -0.904 1.158 1.107 2.733 -0.902 1.679 2.548 0.407 -0.474 -1.572 0.000 1.513 2.472 0.982 1.238 0.978 1.985 1.510
|
||||
0 0.455 -0.028 0.265 1.286 1.373 0.459 0.331 -0.922 0.000 0.343 0.634 0.430 0.000 0.279 -0.084 -0.272 0.000 0.475 0.926 -0.123 3.102 0.803 0.495 0.987 0.587 0.211 0.417 0.445
|
||||
1 2.074 0.388 0.878 1.110 1.557 1.077 -0.226 -0.295 2.173 0.865 -0.319 -1.116 2.215 0.707 -0.835 0.722 0.000 0.632 -0.608 -0.728 0.000 0.715 0.802 1.207 1.190 0.960 1.143 0.926
|
||||
1 1.390 0.265 1.196 0.919 -1.371 1.858 0.506 0.786 0.000 1.280 -1.367 -0.720 2.215 1.483 -0.441 -0.675 2.548 1.076 0.294 -0.539 0.000 1.126 0.830 1.155 1.551 0.702 1.103 0.933
|
||||
1 1.014 -0.079 1.597 1.038 -0.281 1.135 -0.722 -0.177 2.173 0.544 -1.475 -1.501 0.000 1.257 -1.315 1.212 0.000 0.496 -0.060 1.180 1.551 0.815 0.611 1.411 1.110 0.792 0.846 0.853
|
||||
0 0.335 1.267 -1.154 2.011 -0.574 0.753 0.618 1.411 0.000 0.474 0.748 0.681 2.215 0.608 -0.446 -0.354 2.548 0.399 1.295 -0.581 0.000 0.911 0.882 0.975 0.832 0.598 0.580 0.678
|
||||
1 0.729 -0.189 1.182 0.293 1.310 0.412 0.459 -0.632 0.000 0.869 -1.128 -0.625 2.215 1.173 -0.893 0.478 2.548 0.584 -2.394 -1.727 0.000 2.016 1.272 0.995 1.034 0.905 0.966 1.038
|
||||
1 1.225 -1.215 -0.088 0.881 -0.237 0.600 -0.976 1.462 2.173 0.876 0.506 1.583 2.215 0.718 1.228 -0.031 0.000 0.653 -1.292 1.216 0.000 0.838 1.108 0.981 1.805 0.890 1.251 1.197
|
||||
1 2.685 -0.444 0.847 0.253 0.183 0.641 -1.541 -0.873 2.173 0.417 2.874 -0.551 0.000 0.706 -1.431 0.764 0.000 1.390 -0.596 -1.397 0.000 0.894 0.829 0.993 0.789 0.654 0.883 0.746
|
||||
0 0.638 -0.481 0.683 1.457 -1.024 0.707 -1.338 1.498 0.000 0.980 0.518 0.289 2.215 0.964 -0.531 -0.423 0.000 0.694 -0.654 -1.314 3.102 0.807 1.283 1.335 0.658 0.907 0.797 0.772
|
||||
1 1.789 -0.765 -0.732 0.421 -0.020 1.142 -1.353 1.439 2.173 0.725 -1.518 -1.261 0.000 0.812 -2.597 -0.463 0.000 1.203 -0.120 1.001 0.000 0.978 0.673 0.985 1.303 1.400 1.078 0.983
|
||||
1 0.784 -1.431 1.724 0.848 0.559 0.615 -1.643 -1.456 0.000 1.339 -0.513 0.040 2.215 0.394 -2.483 1.304 0.000 0.987 0.889 -0.339 0.000 0.732 0.713 0.987 0.973 0.705 0.875 0.759
|
||||
1 0.911 1.098 -1.289 0.421 0.823 1.218 -0.503 0.431 0.000 0.775 0.432 -1.680 0.000 0.855 -0.226 -0.460 2.548 0.646 -0.947 -1.243 1.551 2.201 1.349 0.985 0.730 0.451 0.877 0.825
|
||||
1 0.959 0.372 -0.269 1.255 0.702 1.151 0.097 0.805 2.173 0.993 1.011 0.767 2.215 1.096 0.185 0.381 0.000 1.001 -0.205 0.059 0.000 0.979 0.997 1.168 0.796 0.771 0.839 0.776
|
||||
0 0.283 -1.864 -1.663 0.219 1.624 0.955 -1.213 0.932 2.173 0.889 0.395 -0.268 0.000 0.597 -1.083 -0.921 2.548 0.584 1.325 -1.072 0.000 0.856 0.927 0.996 0.937 0.936 1.095 0.892
|
||||
0 2.017 -0.488 -0.466 1.029 -0.870 3.157 0.059 -0.343 2.173 3.881 0.872 1.502 1.107 3.631 1.720 0.963 0.000 0.633 -1.264 -1.734 0.000 4.572 3.339 1.005 1.407 5.590 3.614 3.110
|
||||
1 1.088 0.414 -0.841 0.485 0.605 0.860 1.110 -0.568 0.000 1.152 -0.325 1.203 2.215 0.324 1.652 -0.104 0.000 0.510 1.095 -1.728 0.000 0.880 0.722 0.989 0.977 0.711 0.888 0.762
|
||||
0 0.409 -1.717 0.712 0.809 -1.301 0.701 -1.529 -1.411 0.000 1.191 -0.582 0.438 2.215 1.147 0.813 -0.571 2.548 1.039 0.543 0.892 0.000 0.636 0.810 0.986 0.861 1.411 0.907 0.756
|
||||
1 1.094 1.577 -0.988 0.497 -0.149 0.891 -2.459 1.034 0.000 0.646 0.792 -1.022 0.000 1.573 0.254 -0.053 2.548 1.428 0.190 -1.641 3.102 4.322 2.687 0.985 0.881 1.135 1.907 1.831
|
||||
1 0.613 1.993 -0.280 0.544 0.931 0.909 1.526 1.559 0.000 0.840 1.473 -0.483 2.215 0.856 0.352 0.408 2.548 1.058 1.733 -1.396 0.000 0.801 1.066 0.984 0.639 0.841 0.871 0.748
|
||||
0 0.958 -1.202 0.600 0.434 0.170 0.783 -0.214 1.319 0.000 0.835 -0.454 -0.615 2.215 0.658 -1.858 -0.891 0.000 0.640 0.172 -1.204 3.102 1.790 1.086 0.997 0.804 0.403 0.793 0.756
|
||||
1 1.998 -0.238 0.972 0.058 0.266 0.759 1.576 -0.357 2.173 1.004 -0.349 -0.747 2.215 0.962 0.490 -0.453 0.000 1.592 0.661 -1.405 0.000 0.874 1.086 0.990 1.436 1.527 1.177 0.993
|
||||
1 0.796 -0.171 -0.818 0.574 -1.625 1.201 -0.737 1.451 2.173 0.651 0.404 -0.452 0.000 1.150 -0.652 -0.120 0.000 1.008 -0.093 0.531 3.102 0.884 0.706 0.979 1.193 0.937 0.943 0.881
|
||||
1 0.773 1.023 0.527 1.537 -0.201 2.967 -0.574 -1.534 2.173 2.346 -0.307 0.394 2.215 1.393 0.135 -0.027 0.000 3.015 0.187 0.516 0.000 0.819 1.260 0.982 2.552 3.862 2.179 1.786
|
||||
0 1.823 1.008 -1.489 0.234 -0.962 0.591 0.461 0.996 2.173 0.568 -1.297 -0.410 0.000 0.887 2.157 1.194 0.000 2.079 0.369 -0.085 3.102 0.770 0.945 0.995 1.179 0.971 0.925 0.983
|
||||
0 0.780 0.640 0.490 0.680 -1.301 0.715 -0.137 0.152 2.173 0.616 -0.831 1.668 0.000 1.958 0.528 -0.982 2.548 0.966 -1.551 0.462 0.000 1.034 1.079 1.008 0.827 1.369 1.152 0.983
|
||||
1 0.543 0.801 1.543 1.134 -0.772 0.954 -0.849 0.410 1.087 0.851 -1.988 1.686 0.000 0.799 -0.912 -1.156 0.000 0.479 0.097 1.334 0.000 0.923 0.597 0.989 1.231 0.759 0.975 0.867
|
||||
0 1.241 -0.014 0.129 1.158 0.670 0.445 -0.732 1.739 2.173 0.918 0.659 -1.340 2.215 0.557 2.410 -1.404 0.000 0.966 -1.545 -1.120 0.000 0.874 0.918 0.987 1.001 0.798 0.904 0.937
|
||||
0 1.751 -0.266 -1.575 0.489 1.292 1.112 1.533 0.137 2.173 1.204 -0.414 -0.928 0.000 0.879 1.237 -0.415 2.548 1.479 1.469 0.913 0.000 2.884 1.747 0.989 1.742 0.600 1.363 1.293
|
||||
1 1.505 1.208 -1.476 0.995 -0.836 2.800 -1.600 0.111 0.000 2.157 1.241 1.110 2.215 1.076 2.619 -0.913 0.000 1.678 2.204 -1.575 0.000 0.849 1.224 0.990 1.412 0.976 1.271 1.105
|
||||
0 0.816 0.611 0.779 1.694 0.278 0.575 -0.787 1.592 2.173 1.148 1.076 -0.831 2.215 0.421 1.316 0.632 0.000 0.589 0.452 -1.466 0.000 0.779 0.909 0.990 1.146 1.639 1.236 0.949
|
||||
1 0.551 -0.808 0.330 1.188 -0.294 0.447 -0.035 -0.993 0.000 0.432 -0.276 -0.481 2.215 1.959 -0.288 1.195 2.548 0.638 0.583 1.107 0.000 0.832 0.924 0.993 0.723 0.976 0.968 0.895
|
||||
0 1.316 -0.093 0.995 0.860 -0.621 0.593 -0.560 -1.599 2.173 0.524 -0.318 -0.240 2.215 0.566 0.759 -0.368 0.000 0.483 -2.030 -1.104 0.000 1.468 1.041 1.464 0.811 0.778 0.690 0.722
|
||||
1 1.528 0.067 -0.855 0.959 -1.464 1.143 -0.082 1.023 0.000 0.702 -0.763 -0.244 0.000 0.935 -0.881 0.206 2.548 0.614 -0.831 1.657 3.102 1.680 1.105 0.983 1.078 0.559 0.801 0.809
|
||||
0 0.558 -0.833 -0.598 1.436 -1.724 1.316 -0.661 1.593 2.173 1.148 -0.503 -0.132 1.107 1.584 -0.125 0.380 0.000 1.110 -1.216 -0.181 0.000 1.258 0.860 1.053 0.790 1.814 1.159 1.007
|
||||
1 0.819 0.879 1.221 0.598 -1.450 0.754 0.417 -0.369 2.173 0.477 1.199 0.274 0.000 1.073 0.368 0.273 2.548 1.599 2.047 1.690 0.000 0.933 0.984 0.983 0.788 0.613 0.728 0.717
|
||||
0 0.981 -1.007 0.489 0.923 1.261 0.436 -0.698 -0.506 2.173 0.764 -1.105 -1.241 2.215 0.577 -2.573 -0.036 0.000 0.565 -1.628 1.610 0.000 0.688 0.801 0.991 0.871 0.554 0.691 0.656
|
||||
0 2.888 0.568 -1.416 1.461 -1.157 1.756 -0.900 0.522 0.000 0.657 0.409 1.076 2.215 1.419 0.672 -0.019 0.000 1.436 -0.184 -0.980 3.102 0.946 0.919 0.995 1.069 0.890 0.834 0.856
|
||||
1 0.522 1.805 -0.963 1.136 0.418 0.727 -0.195 -1.695 2.173 0.309 2.559 -0.178 0.000 0.521 1.794 0.919 0.000 0.788 0.174 -0.406 3.102 0.555 0.729 1.011 1.385 0.753 0.927 0.832
|
||||
1 0.793 -0.162 -1.643 0.634 0.337 0.898 -0.633 1.689 0.000 0.806 -0.826 -0.356 2.215 0.890 -0.142 -1.268 0.000 1.293 0.574 0.725 0.000 0.833 1.077 0.988 0.721 0.679 0.867 0.753
|
||||
0 1.298 1.098 0.280 0.371 -0.373 0.855 -0.306 -1.186 0.000 0.977 -0.421 1.003 0.000 0.978 0.956 -1.249 2.548 0.735 0.577 -0.037 3.102 0.974 1.002 0.992 0.549 0.587 0.725 0.954
|
||||
1 0.751 -0.520 -1.653 0.168 -0.419 0.878 -1.023 -1.364 2.173 1.310 -0.667 0.863 0.000 1.196 -0.827 0.358 0.000 1.154 -0.165 -0.360 1.551 0.871 0.950 0.983 0.907 0.955 0.959 0.874
|
||||
0 1.730 0.666 -1.432 0.446 1.302 0.921 -0.203 0.621 0.000 1.171 -0.365 -0.611 1.107 0.585 0.807 1.150 0.000 0.415 -0.843 1.311 0.000 0.968 0.786 0.986 1.059 0.371 0.790 0.848
|
||||
1 0.596 -1.486 0.690 1.045 -1.344 0.928 0.867 0.820 2.173 0.610 0.999 -1.329 2.215 0.883 -0.001 -0.106 0.000 1.145 2.184 -0.808 0.000 2.019 1.256 1.056 1.751 1.037 1.298 1.518
|
||||
1 0.656 -1.993 -0.519 1.643 -0.143 0.815 0.256 1.220 1.087 0.399 -1.184 -1.458 0.000 0.738 1.361 -1.443 0.000 0.842 0.033 0.293 0.000 0.910 0.891 0.993 0.668 0.562 0.958 0.787
|
||||
1 1.127 -0.542 0.645 0.318 -1.496 0.661 -0.640 0.369 2.173 0.992 0.358 1.702 0.000 1.004 0.316 -1.109 0.000 1.616 -0.936 -0.707 1.551 0.875 1.191 0.985 0.651 0.940 0.969 0.834
|
||||
0 0.916 -1.423 -1.490 1.248 -0.538 0.625 -0.535 -0.174 0.000 0.769 -0.389 1.608 2.215 0.667 -1.138 -1.738 1.274 0.877 -0.019 0.482 0.000 0.696 0.917 1.121 0.678 0.347 0.647 0.722
|
||||
1 2.756 -0.637 -1.715 1.331 1.124 0.913 -0.296 -0.491 0.000 0.983 -0.831 0.000 2.215 1.180 -0.428 0.742 0.000 1.113 0.005 -1.157 1.551 1.681 1.096 1.462 0.976 0.917 1.009 1.040
|
||||
0 0.755 1.754 0.701 2.111 0.256 1.243 0.057 -1.502 2.173 0.565 -0.034 -1.078 1.107 0.529 1.696 -1.090 0.000 0.665 0.292 0.107 0.000 0.870 0.780 0.990 2.775 0.465 1.876 1.758
|
||||
1 0.593 -0.762 1.743 0.908 0.442 0.773 -1.357 -0.768 2.173 0.432 1.421 1.236 0.000 0.579 0.291 -0.403 0.000 0.966 -0.309 1.016 3.102 0.893 0.743 0.989 0.857 1.030 0.943 0.854
|
||||
1 0.891 -1.151 -1.269 0.504 -0.622 0.893 -0.549 0.700 0.000 0.828 -0.825 0.154 2.215 1.083 0.632 -1.141 0.000 1.059 -0.557 1.526 3.102 2.117 1.281 0.987 0.819 0.802 0.917 0.828
|
||||
1 2.358 -0.248 0.080 0.747 -0.975 1.019 1.374 1.363 0.000 0.935 0.127 -1.707 2.215 0.312 -0.827 0.017 0.000 0.737 1.059 -0.327 0.000 0.716 0.828 1.495 0.953 0.704 0.880 0.745
|
||||
0 0.660 -0.017 -1.138 0.453 1.002 0.645 0.518 0.703 2.173 0.751 0.705 -0.592 2.215 0.744 -0.909 -1.596 0.000 0.410 -1.135 0.481 0.000 0.592 0.922 0.989 0.897 0.948 0.777 0.701
|
||||
1 0.718 0.518 0.225 1.710 -0.022 1.888 -0.424 1.092 0.000 4.134 0.185 -1.366 0.000 1.415 1.293 0.242 2.548 2.351 0.264 -0.057 3.102 0.830 1.630 0.976 1.215 0.890 1.422 1.215
|
||||
1 1.160 0.203 0.941 0.594 0.212 0.636 -0.556 0.679 2.173 1.089 -0.481 -1.008 1.107 1.245 -0.056 -1.357 0.000 0.587 1.007 0.056 0.000 1.106 0.901 0.987 0.786 1.224 0.914 0.837
|
||||
1 0.697 0.542 0.619 0.985 1.481 0.745 0.415 1.644 2.173 0.903 0.495 -0.958 2.215 1.165 1.195 0.346 0.000 1.067 -0.881 -0.264 0.000 0.830 1.025 0.987 0.690 0.863 0.894 0.867
|
||||
0 1.430 0.190 -0.700 0.246 0.518 1.302 0.660 -0.247 2.173 1.185 -0.539 1.504 0.000 1.976 -0.401 1.079 0.000 0.855 -0.958 -1.110 3.102 0.886 0.953 0.993 0.889 1.400 1.376 1.119
|
||||
1 1.122 -0.795 0.202 0.397 -1.553 0.597 -1.459 -0.734 2.173 0.522 1.044 1.027 2.215 0.783 -1.243 1.701 0.000 0.371 1.737 0.199 0.000 1.719 1.176 0.988 0.723 1.583 1.063 0.914
|
||||
0 1.153 0.526 1.236 0.266 0.001 1.139 -1.236 -0.585 2.173 1.337 -0.215 -1.356 2.215 1.780 1.129 0.902 0.000 1.608 -0.391 -0.161 0.000 1.441 1.633 0.990 1.838 1.516 1.635 1.373
|
||||
1 0.760 1.012 0.758 0.937 0.051 0.941 0.687 -1.247 2.173 1.288 -0.743 0.822 0.000 1.552 1.782 -1.533 0.000 0.767 1.349 0.168 0.000 0.716 0.862 0.988 0.595 0.359 0.697 0.623
|
||||
1 1.756 -1.469 1.395 1.345 -1.595 0.817 0.017 -0.741 2.173 0.483 -0.008 0.293 0.000 1.768 -0.663 0.438 1.274 1.202 -1.387 -0.222 0.000 1.022 1.058 0.992 1.407 1.427 1.356 1.133
|
||||
0 0.397 0.582 -0.758 1.260 -1.735 0.889 -0.515 1.139 2.173 0.973 1.616 0.460 0.000 1.308 1.001 -0.709 2.548 0.858 0.995 -0.231 0.000 0.749 0.888 0.979 1.487 1.804 1.208 1.079
|
||||
0 0.515 -0.984 0.425 1.114 -0.439 1.999 0.818 1.561 0.000 1.407 0.009 -0.380 0.000 1.332 0.230 0.397 0.000 1.356 -0.616 -1.057 3.102 0.978 1.017 0.990 1.118 0.862 0.835 0.919
|
||||
1 1.368 -0.921 -0.866 0.842 -0.598 0.456 -1.176 1.219 1.087 0.419 -1.974 -0.819 0.000 0.791 -1.640 0.881 0.000 1.295 -0.782 0.442 3.102 0.945 0.761 0.974 0.915 0.535 0.733 0.651
|
||||
0 2.276 0.134 0.399 2.525 0.376 1.111 -1.078 -1.571 0.000 0.657 2.215 -0.900 0.000 1.183 -0.662 -0.508 2.548 1.436 -0.517 0.960 3.102 0.569 0.931 0.993 1.170 0.967 0.879 1.207
|
||||
0 0.849 0.907 0.124 0.652 1.585 0.715 0.355 -1.200 0.000 0.599 -0.892 1.301 0.000 1.106 1.151 0.582 0.000 1.895 -0.279 -0.568 3.102 0.881 0.945 0.998 0.559 0.649 0.638 0.660
|
||||
1 2.105 0.248 -0.797 0.530 0.206 1.957 -2.175 0.797 0.000 1.193 0.637 -1.646 2.215 0.881 1.111 -1.046 0.000 0.872 -0.185 1.085 1.551 0.986 1.343 1.151 1.069 0.714 2.063 1.951
|
||||
1 1.838 1.060 1.637 1.017 1.370 0.913 0.461 -0.609 1.087 0.766 -0.461 0.303 2.215 0.724 -0.061 0.886 0.000 0.941 1.123 -0.745 0.000 0.858 0.847 0.979 1.313 1.083 1.094 0.910
|
||||
0 0.364 1.274 1.066 1.570 -0.394 0.485 0.012 -1.716 0.000 0.317 -1.233 0.534 2.215 0.548 -2.165 0.762 0.000 0.729 0.169 -0.318 3.102 0.892 0.944 1.013 0.594 0.461 0.688 0.715
|
||||
1 0.503 1.343 -0.031 1.134 -1.204 0.590 -0.309 0.174 2.173 0.408 2.372 -0.628 0.000 1.850 0.400 1.147 2.548 0.664 -0.458 -0.885 0.000 1.445 1.283 0.989 1.280 1.118 1.127 1.026
|
||||
0 1.873 0.258 0.103 2.491 0.530 1.678 0.644 -1.738 2.173 1.432 0.848 -1.340 0.000 0.621 1.323 -1.316 0.000 0.628 0.789 -0.206 1.551 0.426 0.802 1.125 0.688 1.079 1.338 1.239
|
||||
1 0.826 -0.732 1.587 0.582 -1.236 0.495 0.757 -0.741 2.173 0.940 1.474 0.354 2.215 0.474 1.055 -1.657 0.000 0.415 1.758 0.841 0.000 0.451 0.578 0.984 0.757 0.922 0.860 0.696
|
||||
0 0.935 -1.614 -0.597 0.299 1.223 0.707 -0.853 -1.026 0.000 0.751 0.007 -1.691 0.000 1.062 -0.125 0.976 2.548 0.877 1.275 0.646 0.000 0.962 1.074 0.980 0.608 0.726 0.741 0.662
|
||||
1 0.643 0.542 -1.285 0.474 -0.366 0.667 -0.446 1.195 2.173 1.076 0.145 -0.126 0.000 0.970 -0.661 0.394 1.274 1.218 -0.184 -1.722 0.000 1.331 1.019 0.985 1.192 0.677 0.973 0.910
|
||||
0 0.713 0.164 1.080 1.427 -0.460 0.960 -0.152 -0.940 2.173 1.427 -0.901 1.036 1.107 0.440 -1.269 -0.194 0.000 0.452 1.932 -0.532 0.000 1.542 1.210 1.374 1.319 1.818 1.220 1.050
|
||||
0 0.876 -0.463 -1.224 2.458 -1.689 1.007 -0.752 0.398 0.000 2.456 -1.285 -0.152 1.107 1.641 1.838 1.717 0.000 0.458 0.194 0.488 3.102 4.848 2.463 0.986 1.981 0.974 2.642 2.258
|
||||
1 0.384 -0.275 0.387 1.403 -0.994 0.620 -1.529 1.685 0.000 1.091 -1.644 1.078 0.000 0.781 -1.311 0.326 2.548 1.228 -0.728 -0.633 1.551 0.920 0.854 0.987 0.646 0.609 0.740 0.884
|
||||
0 0.318 -1.818 -1.008 0.977 1.268 0.457 2.451 -1.522 0.000 0.881 1.351 0.461 2.215 0.929 0.239 -0.380 2.548 0.382 -0.613 1.330 0.000 1.563 1.193 0.994 0.829 0.874 0.901 1.026
|
||||
1 0.612 -1.120 1.098 0.402 -0.480 0.818 0.188 1.511 0.000 0.800 -0.253 0.977 0.000 1.175 0.271 -1.289 1.274 2.531 0.226 -0.409 3.102 0.889 0.947 0.979 1.486 0.940 1.152 1.119
|
||||
1 0.587 -0.737 -0.228 0.970 1.119 0.823 0.184 1.594 0.000 1.104 0.301 -0.818 2.215 0.819 0.712 -0.560 0.000 2.240 -0.419 0.340 3.102 1.445 1.103 0.988 0.715 1.363 1.019 0.926
|
||||
0 1.030 -0.694 -1.638 0.893 -1.074 1.160 -0.766 0.485 0.000 1.632 -0.698 -1.142 2.215 1.050 -1.092 0.952 0.000 1.475 0.286 0.125 3.102 0.914 1.075 0.982 0.732 1.493 1.219 1.079
|
||||
1 2.142 0.617 1.517 0.387 -0.862 0.345 1.203 -1.014 2.173 0.609 1.092 0.275 0.000 1.331 0.582 -0.183 2.548 0.557 1.540 -1.642 0.000 0.801 0.737 1.060 0.715 0.626 0.749 0.674
|
||||
0 1.076 0.240 -0.246 0.871 -1.241 0.496 0.282 0.746 2.173 1.095 -0.648 1.100 2.215 0.446 -1.756 0.764 0.000 0.434 0.788 -0.991 0.000 1.079 0.868 1.047 0.818 0.634 0.795 0.733
|
||||
0 1.400 0.901 -1.617 0.625 -0.163 0.661 -0.411 -1.616 2.173 0.685 0.524 0.425 0.000 0.881 -0.766 0.312 0.000 0.979 0.255 -0.667 3.102 0.898 1.105 1.253 0.730 0.716 0.738 0.795
|
||||
0 3.302 1.132 1.051 0.658 0.768 1.308 0.251 -0.374 1.087 1.673 0.015 -0.898 0.000 0.688 -0.535 1.363 1.274 0.871 1.325 -1.583 0.000 1.646 1.249 0.995 1.919 1.288 1.330 1.329
|
||||
0 1.757 0.202 0.750 0.767 -0.362 0.932 -1.033 -1.366 0.000 1.529 -1.012 -0.771 0.000 1.161 -0.287 0.059 0.000 2.185 1.147 1.099 3.102 0.795 0.529 1.354 1.144 1.491 1.319 1.161
|
||||
0 1.290 0.905 -1.711 1.017 -0.695 1.008 -1.038 0.693 2.173 1.202 -0.595 0.187 0.000 1.011 0.139 -1.607 0.000 0.789 -0.613 -1.041 3.102 1.304 0.895 1.259 1.866 0.955 1.211 1.200
|
||||
1 1.125 -0.004 1.694 0.373 0.329 0.978 0.640 -0.391 0.000 1.122 -0.376 1.521 2.215 0.432 2.413 -1.259 0.000 0.969 0.730 0.512 3.102 0.716 0.773 0.991 0.624 0.977 0.981 0.875
|
||||
0 1.081 0.861 1.252 1.621 1.474 1.293 0.600 0.630 0.000 1.991 -0.090 -0.675 2.215 0.861 1.105 -0.201 0.000 1.135 2.489 -1.659 0.000 1.089 0.657 0.991 2.179 0.412 1.334 1.071
|
||||
1 0.652 -0.294 1.241 1.034 0.490 1.033 0.551 -0.963 2.173 0.661 1.031 -1.654 2.215 1.376 -0.018 0.843 0.000 0.943 -0.329 -0.269 0.000 1.085 1.067 0.991 1.504 0.773 1.135 0.993
|
||||
1 1.408 -1.028 -1.018 0.252 -0.242 0.465 -0.364 -0.200 0.000 1.466 0.669 0.739 1.107 1.031 0.415 -1.468 2.548 0.457 -1.091 -1.722 0.000 0.771 0.811 0.979 1.459 1.204 1.041 0.866
|
||||
1 0.781 -1.143 -0.659 0.961 1.266 1.183 -0.686 0.119 2.173 1.126 -0.064 1.447 0.000 0.730 1.430 -1.535 0.000 1.601 0.513 1.658 0.000 0.871 1.345 1.184 1.058 0.620 1.107 0.978
|
||||
1 1.300 -0.616 1.032 0.751 -0.731 0.961 -0.716 1.592 0.000 2.079 -1.063 -0.271 2.215 0.475 0.518 1.695 1.274 0.395 -2.204 0.349 0.000 1.350 0.983 1.369 1.265 1.428 1.135 0.982
|
||||
1 0.833 0.809 1.657 1.637 1.019 0.705 1.077 -0.968 2.173 1.261 0.114 -0.298 1.107 1.032 0.017 0.236 0.000 0.640 -0.026 -1.598 0.000 0.894 0.982 0.981 1.250 1.054 1.018 0.853
|
||||
1 1.686 -1.090 -0.301 0.890 0.557 1.304 -0.284 -1.393 2.173 0.388 2.118 0.513 0.000 0.514 -0.015 0.891 0.000 0.460 0.547 0.627 3.102 0.942 0.524 1.186 1.528 0.889 1.015 1.122
|
||||
1 0.551 0.911 0.879 0.379 -0.796 1.154 -0.808 -0.966 0.000 1.168 -0.513 0.355 2.215 0.646 -1.309 0.773 0.000 0.544 -0.283 1.301 3.102 0.847 0.705 0.990 0.772 0.546 0.790 0.719
|
||||
1 1.597 0.793 -1.119 0.691 -1.455 0.370 0.337 1.354 0.000 0.646 -1.005 0.732 2.215 1.019 0.040 0.209 0.000 0.545 0.958 0.239 3.102 0.962 0.793 0.994 0.719 0.745 0.812 0.739
|
||||
0 1.033 -1.193 -0.452 0.247 0.970 0.503 -1.424 1.362 0.000 1.062 -0.416 -1.156 2.215 0.935 -0.023 0.555 2.548 0.410 -1.766 0.379 0.000 0.590 0.953 0.991 0.717 1.081 0.763 0.690
|
||||
1 0.859 -1.004 1.521 0.781 -0.993 0.677 0.643 -0.338 2.173 0.486 0.409 1.283 0.000 0.679 0.110 0.285 0.000 0.715 -0.735 -0.157 1.551 0.702 0.773 0.984 0.627 0.633 0.694 0.643
|
||||
0 0.612 -1.127 1.074 1.225 -0.426 0.927 -2.141 -0.473 0.000 1.290 -0.927 -1.085 2.215 1.183 1.981 -1.687 0.000 2.176 0.406 -1.581 0.000 0.945 0.651 1.170 0.895 1.604 1.179 1.142
|
||||
1 0.535 0.321 -1.095 0.281 -0.960 0.876 -0.709 -0.076 0.000 1.563 -0.666 1.536 2.215 0.773 -0.321 0.435 0.000 0.682 -0.801 -0.952 3.102 0.711 0.667 0.985 0.888 0.741 0.872 0.758
|
||||
1 0.745 1.586 1.578 0.863 -1.423 0.530 1.714 1.085 0.000 1.174 0.679 1.015 0.000 1.158 0.609 -1.186 2.548 1.851 0.832 -0.248 3.102 0.910 1.164 0.983 0.947 0.858 0.928 0.823
|
||||
0 0.677 -1.014 -1.648 1.455 1.461 0.596 -2.358 0.517 0.000 0.800 0.849 -0.743 2.215 1.024 -0.282 -1.004 0.000 1.846 -0.977 0.378 3.102 2.210 1.423 0.982 1.074 1.623 1.417 1.258
|
||||
1 0.815 -1.263 0.057 1.018 -0.208 0.339 -0.347 -1.646 2.173 1.223 0.600 -1.658 2.215 1.435 0.042 0.926 0.000 0.777 1.698 -0.698 0.000 1.022 1.058 1.000 0.784 0.477 0.886 0.836
|
||||
0 3.512 -1.094 -0.220 0.338 -0.328 1.962 -1.099 1.544 1.087 1.461 -1.305 -0.922 2.215 1.219 -1.289 0.400 0.000 0.731 0.155 1.249 0.000 1.173 1.366 0.993 2.259 2.000 1.626 1.349
|
||||
0 0.904 1.248 0.325 0.317 -1.624 0.685 -0.538 1.665 2.173 0.685 -2.145 -1.106 0.000 0.632 -1.460 1.017 0.000 1.085 -0.182 0.162 3.102 0.885 0.801 0.989 0.930 0.904 1.012 0.961
|
||||
Разница между файлами не показана из-за своего большого размера
Загрузить разницу
|
|
@ -1,270 +0,0 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Copyright (c) Microsoft Corporation. All rights reserved. \n",
|
||||
"Licensed under the MIT License."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Use LightGBM Estimator in Azure Machine Learning\n",
|
||||
"In this notebook we will demonstrate how to run a training job using LightGBM Estimator. [LightGBM](https://lightgbm.readthedocs.io/en/latest/) is a gradient boosting framework that uses tree based learning algorithms. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Prerequisites\n",
|
||||
"This notebook uses azureml-contrib-gbdt package, if you don't already have the package, please install by uncommenting below cell."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install azureml-contrib-gbdt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Workspace, Run, Experiment\n",
|
||||
"import shutil, os\n",
|
||||
"from azureml.widgets import RunDetails\n",
|
||||
"from azureml.contrib.gbdt import LightGBM\n",
|
||||
"from azureml.train.dnn import Mpi\n",
|
||||
"from azureml.core.compute import AmlCompute, ComputeTarget\n",
|
||||
"from azureml.core.compute_target import ComputeTargetException"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"If you are using an AzureML Compute Instance, you are all set. Otherwise, go through the [configuration.ipynb](../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML Workspace"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Set up machine learning resources"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ws = Workspace.from_config()\n",
|
||||
"\n",
|
||||
"print('Workspace name: ' + ws.name, \n",
|
||||
" 'Azure region: ' + ws.location, \n",
|
||||
" 'Subscription id: ' + ws.subscription_id, \n",
|
||||
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cluster_vm_size = \"STANDARD_DS14_V2\"\n",
|
||||
"cluster_min_nodes = 0\n",
|
||||
"cluster_max_nodes = 20\n",
|
||||
"cpu_cluster_name = 'TrainingCompute2' \n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" cpu_cluster = AmlCompute(ws, cpu_cluster_name)\n",
|
||||
" if cpu_cluster and type(cpu_cluster) is AmlCompute:\n",
|
||||
" print('found compute target: ' + cpu_cluster_name)\n",
|
||||
"except ComputeTargetException:\n",
|
||||
" print('creating a new compute target...')\n",
|
||||
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = cluster_vm_size, \n",
|
||||
" vm_priority = 'lowpriority', \n",
|
||||
" min_nodes = cluster_min_nodes, \n",
|
||||
" max_nodes = cluster_max_nodes)\n",
|
||||
" cpu_cluster = ComputeTarget.create(ws, cpu_cluster_name, provisioning_config)\n",
|
||||
" \n",
|
||||
" # can poll for a minimum number of nodes and for a specific timeout. \n",
|
||||
" # if no min node count is provided it will use the scale settings for the cluster\n",
|
||||
" cpu_cluster.wait_for_completion(show_output=True, min_node_count=None, timeout_in_minutes=20)\n",
|
||||
" \n",
|
||||
" # For a more detailed view of current Azure Machine Learning Compute status, use get_status()\n",
|
||||
" print(cpu_cluster.get_status().serialize())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"From this point, you can either upload training data file directly or use Datastore for training data storage\n",
|
||||
"## Upload training file from local"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scripts_folder = \"scripts_folder\"\n",
|
||||
"if not os.path.isdir(scripts_folder):\n",
|
||||
" os.mkdir(scripts_folder)\n",
|
||||
"shutil.copy('./train.conf', os.path.join(scripts_folder, 'train.conf'))\n",
|
||||
"shutil.copy('./binary0.train', os.path.join(scripts_folder, 'binary0.train'))\n",
|
||||
"shutil.copy('./binary1.train', os.path.join(scripts_folder, 'binary1.train'))\n",
|
||||
"shutil.copy('./binary0.test', os.path.join(scripts_folder, 'binary0.test'))\n",
|
||||
"shutil.copy('./binary1.test', os.path.join(scripts_folder, 'binary1.test'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
|
||||
"validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
|
||||
"lgbm = LightGBM(source_directory=scripts_folder, \n",
|
||||
" compute_target=cpu_cluster, \n",
|
||||
" distributed_training=Mpi(),\n",
|
||||
" node_count=2,\n",
|
||||
" lightgbm_config='train.conf',\n",
|
||||
" data=training_data_list,\n",
|
||||
" valid=validation_data_list\n",
|
||||
" )\n",
|
||||
"experiment_name = 'lightgbm-estimator-test'\n",
|
||||
"experiment = Experiment(ws, name=experiment_name)\n",
|
||||
"run = experiment.submit(lgbm, tags={\"test public docker image\": None})\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Use data reference"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core.datastore import Datastore\n",
|
||||
"from azureml.data.data_reference import DataReference\n",
|
||||
"datastore = ws.get_default_datastore()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"datastore.upload(src_dir='.',\n",
|
||||
" target_path='.',\n",
|
||||
" show_progress=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"training_data_list=[\"binary0.train\", \"binary1.train\"]\n",
|
||||
"validation_data_list = [\"binary0.test\", \"binary1.test\"]\n",
|
||||
"lgbm = LightGBM(source_directory='.', \n",
|
||||
" compute_target=cpu_cluster, \n",
|
||||
" distributed_training=Mpi(),\n",
|
||||
" node_count=2,\n",
|
||||
" inputs=[datastore.as_mount()],\n",
|
||||
" lightgbm_config='train.conf',\n",
|
||||
" data=training_data_list,\n",
|
||||
" valid=validation_data_list\n",
|
||||
" )\n",
|
||||
"experiment_name = 'lightgbm-estimator-test'\n",
|
||||
"experiment = Experiment(ws, name=experiment_name)\n",
|
||||
"run = experiment.submit(lgbm, tags={\"use datastore.as_mount()\": None})\n",
|
||||
"RunDetails(run).show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"run.wait_for_completion(show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# uncomment below and run if compute resources are no longer needed\n",
|
||||
"# cpu_cluster.delete() "
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "jingywa"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.6",
|
||||
"language": "python",
|
||||
"name": "python36"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
name: lightgbm-example
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- azureml-contrib-gbdt
|
||||
- azureml-widgets
|
||||
- azureml-core
|
||||
|
|
@ -1,111 +0,0 @@
|
|||
# task type, support train and predict
|
||||
task = train
|
||||
|
||||
# boosting type, support gbdt for now, alias: boosting, boost
|
||||
boosting_type = gbdt
|
||||
|
||||
# application type, support following application
|
||||
# regression , regression task
|
||||
# binary , binary classification task
|
||||
# lambdarank , lambdarank task
|
||||
# alias: application, app
|
||||
objective = binary
|
||||
|
||||
# eval metrics, support multi metric, delimite by ',' , support following metrics
|
||||
# l1
|
||||
# l2 , default metric for regression
|
||||
# ndcg , default metric for lambdarank
|
||||
# auc
|
||||
# binary_logloss , default metric for binary
|
||||
# binary_error
|
||||
metric = binary_logloss,auc
|
||||
|
||||
# frequence for metric output
|
||||
metric_freq = 1
|
||||
|
||||
# true if need output metric for training data, alias: tranining_metric, train_metric
|
||||
is_training_metric = true
|
||||
|
||||
# number of bins for feature bucket, 255 is a recommend setting, it can save memories, and also has good accuracy.
|
||||
max_bin = 255
|
||||
|
||||
# training data
|
||||
# if exsting weight file, should name to "binary.train.weight"
|
||||
# alias: train_data, train
|
||||
data = binary.train
|
||||
|
||||
# validation data, support multi validation data, separated by ','
|
||||
# if exsting weight file, should name to "binary.test.weight"
|
||||
# alias: valid, test, test_data,
|
||||
valid_data = binary.test
|
||||
|
||||
# number of trees(iterations), alias: num_tree, num_iteration, num_iterations, num_round, num_rounds
|
||||
num_trees = 100
|
||||
|
||||
# shrinkage rate , alias: shrinkage_rate
|
||||
learning_rate = 0.1
|
||||
|
||||
# number of leaves for one tree, alias: num_leaf
|
||||
num_leaves = 63
|
||||
|
||||
# type of tree learner, support following types:
|
||||
# serial , single machine version
|
||||
# feature , use feature parallel to train
|
||||
# data , use data parallel to train
|
||||
# voting , use voting based parallel to train
|
||||
# alias: tree
|
||||
tree_learner = feature
|
||||
|
||||
# number of threads for multi-threading. One thread will use one CPU, defalut is setted to #cpu.
|
||||
# num_threads = 8
|
||||
|
||||
# feature sub-sample, will random select 80% feature to train on each iteration
|
||||
# alias: sub_feature
|
||||
feature_fraction = 0.8
|
||||
|
||||
# Support bagging (data sub-sample), will perform bagging every 5 iterations
|
||||
bagging_freq = 5
|
||||
|
||||
# Bagging farction, will random select 80% data on bagging
|
||||
# alias: sub_row
|
||||
bagging_fraction = 0.8
|
||||
|
||||
# minimal number data for one leaf, use this to deal with over-fit
|
||||
# alias : min_data_per_leaf, min_data
|
||||
min_data_in_leaf = 50
|
||||
|
||||
# minimal sum hessians for one leaf, use this to deal with over-fit
|
||||
min_sum_hessian_in_leaf = 5.0
|
||||
|
||||
# save memory and faster speed for sparse feature, alias: is_sparse
|
||||
is_enable_sparse = true
|
||||
|
||||
# when data is bigger than memory size, set this to true. otherwise set false will have faster speed
|
||||
# alias: two_round_loading, two_round
|
||||
use_two_round_loading = false
|
||||
|
||||
# true if need to save data to binary file and application will auto load data from binary file next time
|
||||
# alias: is_save_binary, save_binary
|
||||
is_save_binary_file = false
|
||||
|
||||
# output model file
|
||||
output_model = LightGBM_model.txt
|
||||
|
||||
# support continuous train from trained gbdt model
|
||||
# input_model= trained_model.txt
|
||||
|
||||
# output prediction file for predict task
|
||||
# output_result= prediction.txt
|
||||
|
||||
# support continuous train from initial score file
|
||||
# input_init_score= init_score.txt
|
||||
|
||||
|
||||
# number of machines in parallel training, alias: num_machine
|
||||
num_machines = 2
|
||||
|
||||
# local listening port in parallel training, alias: local_port
|
||||
local_listen_port = 12400
|
||||
|
||||
# machines list file for parallel training, alias: mlist
|
||||
machine_list_file = mlist.txt
|
||||
|
|
@ -230,6 +230,15 @@ You may check the version of tensorflow and uninstall as follows
|
|||
2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
|
||||
3) If the listed version is a not a supported version, `pip uninstall tensorflow` in the command shell and enter y for confirmation.
|
||||
|
||||
## KeyError: 'brand' when running AutoML on local compute or Azure Databricks cluster**
|
||||
If a new environment was created after 10 June 2020 using SDK 1.7.0 or lower, training may fail with the above error due to an update in the py-cpuinfo package. (Environments created on or before 10 June 2020 are unaffected, as well as experiments run on remote compute as cached training images are used.) To work around this issue, either of the two following steps can be taken:
|
||||
|
||||
1) Update the SDK version to 1.8.0 or higher (this will also downgrade py-cpuinfo to 5.0.0):
|
||||
`pip install --upgrade azureml-sdk[automl]`
|
||||
|
||||
2) Downgrade the installed version of py-cpuinfo to 5.0.0:
|
||||
`pip install py-cpuinfo==5.0.0`
|
||||
|
||||
## Remote run: DsvmCompute.create fails
|
||||
There are several reasons why the DsvmCompute.create can fail. The reason is usually in the error message but you have to look at the end of the error message for the detailed reason. Some common reasons are:
|
||||
1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.` Note that underscore is not allowed in the name.
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ dependencies:
|
|||
- pandas>=0.22.0,<=0.23.4
|
||||
- py-xgboost<=0.90
|
||||
- conda-forge::fbprophet==0.5
|
||||
- holidays==0.9.11
|
||||
- pytorch::pytorch=1.4.0
|
||||
- cudatoolkit=10.1.243
|
||||
|
||||
|
|
@ -26,6 +27,5 @@ dependencies:
|
|||
- azureml-pipeline
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.1.8
|
||||
- pyarrow==0.17.0
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ dependencies:
|
|||
- pandas>=0.22.0,<=0.23.4
|
||||
- py-xgboost<=0.90
|
||||
- conda-forge::fbprophet==0.5
|
||||
- holidays==0.9.11
|
||||
- pytorch::pytorch=1.4.0
|
||||
- cudatoolkit=9.0
|
||||
|
||||
|
|
@ -27,5 +28,4 @@ dependencies:
|
|||
- azureml-pipeline
|
||||
- pytorch-transformers==1.0.0
|
||||
- spacy==2.1.8
|
||||
- pyarrow==0.17.0
|
||||
- https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz
|
||||
|
|
|
|||
|
|
@ -105,7 +105,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -362,7 +362,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -93,7 +93,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -232,7 +232,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -97,7 +97,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -88,7 +88,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -390,7 +390,7 @@
|
|||
"hidePrompt": false
|
||||
},
|
||||
"source": [
|
||||
"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required."
|
||||
"We will now run the experiment, starting with 10 iterations of model search. The experiment can be continued for more iterations if more accurate results are required. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -87,7 +87,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -461,7 +461,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"For brevity we have created the function called run_forecast. It submits the test data to the best model and run the estimation on the selected compute target."
|
||||
"For brevity we have created the function called run_forecast. It submits the test data to the best model and run the estimation on the selected compute target. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -97,7 +97,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -94,7 +94,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -142,15 +142,15 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"TIME_COLUMN_NAME = 'date'\n",
|
||||
"GRAIN_COLUMN_NAME = 'grain'\n",
|
||||
"TIME_SERIES_ID_COLUMN_NAME = 'time_series_id'\n",
|
||||
"TARGET_COLUMN_NAME = 'y'\n",
|
||||
"\n",
|
||||
"def get_timeseries(train_len: int,\n",
|
||||
" test_len: int,\n",
|
||||
" time_column_name: str,\n",
|
||||
" target_column_name: str,\n",
|
||||
" grain_column_name: str,\n",
|
||||
" grains: int = 1,\n",
|
||||
" time_series_id_column_name: str,\n",
|
||||
" time_series_number: int = 1,\n",
|
||||
" freq: str = 'H'):\n",
|
||||
" \"\"\"\n",
|
||||
" Return the time series of designed length.\n",
|
||||
|
|
@ -161,9 +161,8 @@
|
|||
" :type test_len: int\n",
|
||||
" :param time_column_name: The desired name of a time column.\n",
|
||||
" :type time_column_name: str\n",
|
||||
" :param\n",
|
||||
" :param grains: The number of grains.\n",
|
||||
" :type grains: int\n",
|
||||
" :param time_series_number: The number of time series in the data set.\n",
|
||||
" :type time_series_number: int\n",
|
||||
" :param freq: The frequency string representing pandas offset.\n",
|
||||
" see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
|
||||
" :type freq: str\n",
|
||||
|
|
@ -174,14 +173,14 @@
|
|||
" data_train = [] # type: List[pd.DataFrame]\n",
|
||||
" data_test = [] # type: List[pd.DataFrame]\n",
|
||||
" data_length = train_len + test_len\n",
|
||||
" for i in range(grains):\n",
|
||||
" for i in range(time_series_number):\n",
|
||||
" X = pd.DataFrame({\n",
|
||||
" time_column_name: pd.date_range(start='2000-01-01',\n",
|
||||
" periods=data_length,\n",
|
||||
" freq=freq),\n",
|
||||
" target_column_name: np.arange(data_length).astype(float) + np.random.rand(data_length) + i*5,\n",
|
||||
" 'ext_predictor': np.asarray(range(42, 42 + data_length)),\n",
|
||||
" grain_column_name: np.repeat('g{}'.format(i), data_length)\n",
|
||||
" time_series_id_column_name: np.repeat('ts{}'.format(i), data_length)\n",
|
||||
" })\n",
|
||||
" data_train.append(X[:train_len])\n",
|
||||
" data_test.append(X[train_len:])\n",
|
||||
|
|
@ -197,8 +196,8 @@
|
|||
" test_len=n_test_periods,\n",
|
||||
" time_column_name=TIME_COLUMN_NAME,\n",
|
||||
" target_column_name=TARGET_COLUMN_NAME,\n",
|
||||
" grain_column_name=GRAIN_COLUMN_NAME,\n",
|
||||
" grains=2)"
|
||||
" time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
|
||||
" time_series_number=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -228,7 +227,7 @@
|
|||
"whole_data = X_train.copy()\n",
|
||||
"target_label = 'y'\n",
|
||||
"whole_data[target_label] = y_train\n",
|
||||
"for g in whole_data.groupby('grain'): \n",
|
||||
"for g in whole_data.groupby('time_series_id'): \n",
|
||||
" plt.plot(g[1]['date'].values, g[1]['y'].values, label=g[0])\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()"
|
||||
|
|
@ -297,7 +296,7 @@
|
|||
"source": [
|
||||
"## Create the configuration and train a forecaster <a id=\"train\"></a>\n",
|
||||
"First generate the configuration, in which we:\n",
|
||||
"* Set metadata columns: target, time column and grain column names.\n",
|
||||
"* Set metadata columns: target, time column and time-series id column names.\n",
|
||||
"* Validate our data using cross validation with rolling window method.\n",
|
||||
"* Set normalized root mean squared error as a metric to select the best model.\n",
|
||||
"* Set early termination to True, so the iterations through the models will stop when no improvements in accuracy score will be made.\n",
|
||||
|
|
@ -313,11 +312,11 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"lags = [1,2,3]\n",
|
||||
"max_horizon = n_test_periods\n",
|
||||
"forecast_horizon = n_test_periods\n",
|
||||
"time_series_settings = { \n",
|
||||
" 'time_column_name': TIME_COLUMN_NAME,\n",
|
||||
" 'grain_column_names': [ GRAIN_COLUMN_NAME ],\n",
|
||||
" 'max_horizon': max_horizon,\n",
|
||||
" 'time_series_id_column_names': [ TIME_SERIES_ID_COLUMN_NAME ],\n",
|
||||
" 'forecast_horizon': forecast_horizon ,\n",
|
||||
" 'target_lags': lags\n",
|
||||
"}"
|
||||
]
|
||||
|
|
@ -326,7 +325,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Run the model selection and training process."
|
||||
"Run the model selection and training process. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -482,7 +481,7 @@
|
|||
"# use forecast_quantiles function, not the forecast() one\n",
|
||||
"y_pred_quantiles = fitted_model.forecast_quantiles(X_test)\n",
|
||||
"\n",
|
||||
"# quantile forecasts returned in a Dataframe along with the time and grain columns \n",
|
||||
"# quantile forecasts returned in a Dataframe along with the time and time series id columns \n",
|
||||
"y_pred_quantiles"
|
||||
]
|
||||
},
|
||||
|
|
@ -492,7 +491,7 @@
|
|||
"source": [
|
||||
"#### Destination-date forecast: \"just do something\"\n",
|
||||
"\n",
|
||||
"In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
|
||||
"In some scenarios, the X_test is not known. The forecast is likely to be weak, because it is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the forecast horizon from training."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -519,7 +518,7 @@
|
|||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per grain, so each grain can have a different forecast origin. \n",
|
||||
"The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per time-series, so each time-series can have a different forecast origin. \n",
|
||||
"\n",
|
||||
"The part of data before the forecast origin is the **prediction context**. To provide the context values the model needs when it looks back, we pass definite values in `y_test` (aligned with corresponding times in `X_test`)."
|
||||
]
|
||||
|
|
@ -536,13 +535,13 @@
|
|||
" test_len=4,\n",
|
||||
" time_column_name=TIME_COLUMN_NAME,\n",
|
||||
" target_column_name=TARGET_COLUMN_NAME,\n",
|
||||
" grain_column_name=GRAIN_COLUMN_NAME,\n",
|
||||
" grains=2)\n",
|
||||
" time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
|
||||
" time_series_number=2)\n",
|
||||
"\n",
|
||||
"# end of the data we trained on\n",
|
||||
"print(X_train.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
|
||||
"print(X_train.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
|
||||
"# start of the data we want to predict on\n",
|
||||
"print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
|
||||
"print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -569,7 +568,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
|
||||
"How should we read that eror message? The forecast origin is at the last time the model saw an actual value of `y` (the target). That was at the end of the training data! The model is attempting to forecast from the end of training data. But the requested forecast periods are past the forecast horizon. We need to provide a define `y` value to establish the forecast origin.\n",
|
||||
"\n",
|
||||
"We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
|
||||
]
|
||||
|
|
@ -584,7 +583,7 @@
|
|||
"\n",
|
||||
" \"\"\"\n",
|
||||
" This function will take the full dataset, and create the query\n",
|
||||
" to predict all values of the grain from the `forecast_origin`\n",
|
||||
" to predict all values of the time series from the `forecast_origin`\n",
|
||||
" forward for the next `horizon` horizons. Context from previous\n",
|
||||
" `lookback` periods will be included.\n",
|
||||
"\n",
|
||||
|
|
@ -654,8 +653,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(X_context.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"print(X_context.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"print(X_away.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
|
||||
"X_context.tail(5)"
|
||||
]
|
||||
},
|
||||
|
|
@ -685,7 +684,7 @@
|
|||
"n_lookback_periods = max(lags)\n",
|
||||
"lookback = pd.DateOffset(hours=n_lookback_periods)\n",
|
||||
"\n",
|
||||
"horizon = pd.DateOffset(hours=max_horizon)\n",
|
||||
"horizon = pd.DateOffset(hours=forecast_horizon)\n",
|
||||
"\n",
|
||||
"# now make the forecast query from context (refer to figure)\n",
|
||||
"X_pred, y_pred = make_forecasting_query(fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME,\n",
|
||||
|
|
@ -701,7 +700,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Note that the forecast origin is at 17:00 for both grains, and periods from 18:00 are to be forecast."
|
||||
"Note that the forecast origin is at 17:00 for both time-series, and periods from 18:00 are to be forecast."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -716,7 +715,7 @@
|
|||
"# show the forecast aligned\n",
|
||||
"X_show = xy_away.reset_index()\n",
|
||||
"# without the generated features\n",
|
||||
"X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
|
||||
"X_show[['date', 'time_series_id', 'ext_predictor', '_automl_target_col']]\n",
|
||||
"# prediction is in _automl_target_col"
|
||||
]
|
||||
},
|
||||
|
|
@ -724,14 +723,14 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Forecasting farther than the maximum horizon <a id=\"recursive forecasting\"></a>\n",
|
||||
"When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified maximum horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
|
||||
"## Forecasting farther than the forecast horizon <a id=\"recursive forecasting\"></a>\n",
|
||||
"When the forecast destination, or the latest date in the prediction data frame, is farther into the future than the specified forecast horizon, the `forecast()` function will still make point predictions out to the later date using a recursive operation mode. Internally, the method recursively applies the regular forecaster to generate context so that we can forecast further into the future. \n",
|
||||
"\n",
|
||||
"To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the maximum horizon given at training time.\n",
|
||||
"To illustrate the use-case and operation of recursive forecasting, we'll consider an example with a single time-series where the forecasting period directly follows the training period and is twice as long as the forecasting horizon given at training time.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first max-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next max-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
|
||||
"Internally, we apply the forecaster in an iterative manner and finish the forecast task in two interations. In the first iteration, we apply the forecaster and get the prediction for the first forecast-horizon periods (y_pred1). In the second iteraction, y_pred1 is used as the context to produce the prediction for the next forecast-horizon periods (y_pred2). The combination of (y_pred1 and y_pred2) gives the results for the total forecast periods. \n",
|
||||
"\n",
|
||||
"A caveat: forecast accuracy will likely be worse the farther we predict into the future since errors are compounded with recursive application of the forecaster.\n",
|
||||
"\n",
|
||||
|
|
@ -745,16 +744,17 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# generate the same kind of test data we trained on, but with a single grain/time-series and test period twice as long as the max_horizon\n",
|
||||
"# generate the same kind of test data we trained on, but with a single time-series and test period twice as long\n",
|
||||
"# as the forecast_horizon.\n",
|
||||
"_, _, X_test_long, y_test_long = get_timeseries(train_len=n_train_periods,\n",
|
||||
" test_len=max_horizon*2,\n",
|
||||
" test_len=forecast_horizon*2,\n",
|
||||
" time_column_name=TIME_COLUMN_NAME,\n",
|
||||
" target_column_name=TARGET_COLUMN_NAME,\n",
|
||||
" grain_column_name=GRAIN_COLUMN_NAME,\n",
|
||||
" grains=1)\n",
|
||||
" time_series_id_column_name=TIME_SERIES_ID_COLUMN_NAME,\n",
|
||||
" time_series_number=1)\n",
|
||||
"\n",
|
||||
"print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
|
||||
"print(X_test_long.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
|
||||
"print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].min())\n",
|
||||
"print(X_test_long.groupby(TIME_SERIES_ID_COLUMN_NAME)[TIME_COLUMN_NAME].max())"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -775,8 +775,8 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"# What forecast() function does in this case is equivalent to iterating it twice over the test set as the following. \n",
|
||||
"y_pred1, _ = fitted_model.forecast(X_test_long[:max_horizon])\n",
|
||||
"y_pred_all, _ = fitted_model.forecast(X_test_long, np.concatenate((y_pred1, np.full(max_horizon, np.nan))))\n",
|
||||
"y_pred1, _ = fitted_model.forecast(X_test_long[:forecast_horizon])\n",
|
||||
"y_pred_all, _ = fitted_model.forecast(X_test_long, np.concatenate((y_pred1, np.full(forecast_horizon, np.nan))))\n",
|
||||
"np.array_equal(y_pred_all, y_pred_long)"
|
||||
]
|
||||
},
|
||||
|
|
@ -785,7 +785,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"#### Confidence interval and distributional forecasts\n",
|
||||
"AutoML cannot currently estimate forecast errors beyond the maximum horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the maximum horizon. "
|
||||
"AutoML cannot currently estimate forecast errors beyond the forecast horizon set during training, so the `forecast_quantiles()` function will return missing values for quantiles not equal to 0.5 beyond the forecast horizon. "
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -801,7 +801,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Similarly with the simple senarios illustrated above, forecasting farther than the max horizon in other senarios like 'multiple grain', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
|
||||
"Similarly with the simple senarios illustrated above, forecasting farther than the forecast horizon in other senarios like 'multiple time-series', 'Destination-date forecast', and 'forecast away from the training data' are also automatically handled by the `forecast()` function. "
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
|
|||
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|
|
@ -82,7 +82,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -428,7 +428,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"You can now submit a new training run. Depending on the data and number of iterations this operation may take several minutes.\n",
|
||||
"Information from each iteration will be printed to the console."
|
||||
"Information from each iteration will be printed to the console. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -96,7 +96,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -98,7 +98,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -92,7 +92,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
@ -233,7 +233,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while."
|
||||
"Call the `submit` method on the experiment object and pass the run configuration. Execution of remote runs is asynchronous. Depending on the data and the number of iterations this can run for a while. Validation errors and current status will be shown when setting `show_output=True` and the execution will be synchronous."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -13,32 +13,45 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Automated ML on Azure Databricks\n",
|
||||
"We support installing AML SDK as library from GUI. When attaching a library follow this https://docs.databricks.com/user-guide/libraries.html and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
|
||||
"\n",
|
||||
"In this example we use the scikit-learn's <a href=\"http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset\" target=\"_blank\">digit dataset</a> to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
|
||||
"2. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"3. Configure Automated ML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using Azure Databricks.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Viewing the engineered names for featurized data and featurization summary for all raw features.\n",
|
||||
"7. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"Before running this notebook, please follow the <a href=\"https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks\" target=\"_blank\">readme for using Automated ML on Azure Databricks</a> for installing necessary libraries to your cluster."
|
||||
"**install azureml-sdk with Automated ML**\n",
|
||||
"* Source: Upload Python Egg or PyPi\n",
|
||||
"* PyPi Name: `azureml-sdk[automl]`\n",
|
||||
"* Select Install Library"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"We support installing AML SDK with Automated ML as library from GUI. When attaching a library follow <a href=\"https://docs.databricks.com/user-guide/libraries.html\" target=\"_blank\">this link</a> and add the below string as your PyPi package. You can select the option to attach the library to all clusters or just one cluster.\n",
|
||||
"# AutoML : Classification with Local Compute on Azure DataBricks\n",
|
||||
"\n",
|
||||
"**azureml-sdk with automated ml**\n",
|
||||
"* Source: Upload Python Egg or PyPi\n",
|
||||
"* PyPi Name: `azureml-sdk[automl]`\n",
|
||||
"* Select Install Library"
|
||||
"In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
|
||||
"2. Create an `Experiment` in an existing `Workspace`.\n",
|
||||
"3. Configure AutoML using `AutoMLConfig`.\n",
|
||||
"4. Train the model using AzureDataBricks.\n",
|
||||
"5. Explore the results.\n",
|
||||
"6. Test the best fitted model.\n",
|
||||
"\n",
|
||||
"Prerequisites:\n",
|
||||
"Before running this notebook, please follow the readme for installing necessary libraries to your cluster."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Register Machine Learning Services Resource Provider\n",
|
||||
"Microsoft.MachineLearningServices only needs to be registed once in the subscription. To register it:\n",
|
||||
"Start the Azure portal.\n",
|
||||
"Select your All services and then Subscription.\n",
|
||||
"Select the subscription that you want to use.\n",
|
||||
"Click on Resource providers\n",
|
||||
"Click the Register link next to Microsoft.MachineLearningServices"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -145,31 +158,8 @@
|
|||
" resource_group = resource_group)\n",
|
||||
"\n",
|
||||
"# Persist the subscription id, resource group name, and workspace name in aml_config/config.json.\n",
|
||||
"ws.write_config()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a Folder to Host Sample Projects\n",
|
||||
"Finally, create a folder where all the sample projects will be hosted."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"sample_projects_folder = './sample_projects'\n",
|
||||
"\n",
|
||||
"if not os.path.isdir(sample_projects_folder):\n",
|
||||
" os.mkdir(sample_projects_folder)\n",
|
||||
" \n",
|
||||
"print('Sample projects will be created in {}.'.format(sample_projects_folder))"
|
||||
"ws.write_config()\n",
|
||||
"write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -178,7 +168,7 @@
|
|||
"source": [
|
||||
"## Create an Experiment\n",
|
||||
"\n",
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -191,6 +181,7 @@
|
|||
"import os\n",
|
||||
"import random\n",
|
||||
"import time\n",
|
||||
"import json\n",
|
||||
"\n",
|
||||
"from matplotlib import pyplot as plt\n",
|
||||
"from matplotlib.pyplot import imshow\n",
|
||||
|
|
@ -212,7 +203,6 @@
|
|||
"source": [
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-local-classification'\n",
|
||||
"project_folder = './sample_projects/automl-local-classification'\n",
|
||||
"\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"\n",
|
||||
|
|
@ -222,94 +212,11 @@
|
|||
"output['Workspace Name'] = ws.name\n",
|
||||
"output['Resource Group'] = ws.resource_group\n",
|
||||
"output['Location'] = ws.location\n",
|
||||
"output['Project Directory'] = project_folder\n",
|
||||
"output['Experiment Name'] = experiment.name\n",
|
||||
"pd.set_option('display.max_colwidth', -1)\n",
|
||||
"pd.DataFrame(data = output, index = ['']).T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Diagnostics\n",
|
||||
"\n",
|
||||
"Opt-in diagnostics for better experience, quality, and security of future releases."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.telemetry import set_diagnostics_collection\n",
|
||||
"set_diagnostics_collection(send_diagnostics = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Registering Datastore"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
|
||||
"\n",
|
||||
"The code below registers a datastore pointing to a publicly readable blob container."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Datastore\n",
|
||||
"\n",
|
||||
"datastore_name = 'demo_training'\n",
|
||||
"container_name = 'digits' \n",
|
||||
"account_name = 'automlpublicdatasets'\n",
|
||||
"Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = datastore_name, \n",
|
||||
" container_name = container_name, \n",
|
||||
" account_name = account_name,\n",
|
||||
" overwrite = True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Below is an example on how to register a private blob container\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = 'example_datastore', \n",
|
||||
" container_name = 'example-container', \n",
|
||||
" account_name = 'storageaccount',\n",
|
||||
" account_key = 'accountkey'\n",
|
||||
")\n",
|
||||
"```\n",
|
||||
"The example below shows how to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_data_lake(\n",
|
||||
" workspace = ws,\n",
|
||||
" datastore_name = 'example_datastore',\n",
|
||||
" store_name = 'adlsstore',\n",
|
||||
" tenant_id = 'tenant-id-of-service-principal',\n",
|
||||
" client_id = 'client-id-of-service-principal',\n",
|
||||
" client_secret = 'client-secret-of-service-principal'\n",
|
||||
")\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -323,9 +230,7 @@
|
|||
"source": [
|
||||
"Automated ML takes a `TabularDataset` as input.\n",
|
||||
"\n",
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
|
||||
"\n",
|
||||
"You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -334,13 +239,12 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.data.datapath import DataPath\n",
|
||||
"\n",
|
||||
"datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
|
||||
"\n",
|
||||
"X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
|
||||
"y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
|
||||
"example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
|
||||
"dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
|
||||
"dataset.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -357,16 +261,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_train.take(5).to_pandas_dataframe()"
|
||||
"training_data = dataset.drop_columns(columns=['FBI Code'])\n",
|
||||
"label = 'Primary Type'"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -384,14 +280,11 @@
|
|||
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
|
||||
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
|
||||
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
|
||||
"|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
|
||||
"|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -404,15 +297,13 @@
|
|||
" debug_log = 'automl_errors.log',\n",
|
||||
" primary_metric = 'AUC_weighted',\n",
|
||||
" iteration_timeout_minutes = 10,\n",
|
||||
" iterations = 3,\n",
|
||||
" preprocess = True,\n",
|
||||
" iterations = 5,\n",
|
||||
" n_cross_validations = 10,\n",
|
||||
" max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" spark_context=sc, #databricks/spark related\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
" training_data=training_data,\n",
|
||||
" label_column_name=label)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -433,26 +324,6 @@
|
|||
"local_run = experiment.submit(automl_config, show_output = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Continue experiment"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_run.continue_experiment(iterations=2,\n",
|
||||
" X=X_train, \n",
|
||||
" y=y_train,\n",
|
||||
" spark_context=sc,\n",
|
||||
" show_output=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -475,14 +346,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"displayHTML(\"<a href={} target='_blank'>Your experiment in Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following will show the child runs and waits for the parent run to complete."
|
||||
"displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -503,6 +367,7 @@
|
|||
"metricslist = {}\n",
|
||||
"for run in children:\n",
|
||||
" properties = run.get_properties()\n",
|
||||
" #print(properties)\n",
|
||||
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)} \n",
|
||||
" metricslist[int(properties['iteration'])] = metrics\n",
|
||||
"\n",
|
||||
|
|
@ -514,9 +379,11 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Retrieve the Best Model after the above run is complete \n",
|
||||
"## Deploy\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
|
||||
"### Retrieve the Best Model\n",
|
||||
"\n",
|
||||
"Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -525,71 +392,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"best_run, fitted_model = local_run.get_output()\n",
|
||||
"print(best_run)\n",
|
||||
"print(fitted_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Best Model Based on Any Other Metric after the above run is complete based on the child run\n",
|
||||
"Show the run and the model that has the smallest `log_loss` value:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lookup_metric = \"log_loss\"\n",
|
||||
"best_run, fitted_model = local_run.get_output(metric = lookup_metric)\n",
|
||||
"print(best_run)\n",
|
||||
"print(fitted_model)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View the engineered names for featurized data\n",
|
||||
"Below we display the engineered feature names generated for the featurized data using the preprocessing featurization."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fitted_model.named_steps['datatransformer'].get_engineered_feature_names()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### View the featurization summary\n",
|
||||
"Below we display the featurization that was performed on different raw features in the user data. For each raw feature in the user data, the following information is displayed:-\n",
|
||||
"- Raw feature name\n",
|
||||
"- Number of engineered features formed out of this raw feature\n",
|
||||
"- Type detected\n",
|
||||
"- If feature was dropped\n",
|
||||
"- List of feature transformations for the raw feature"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Get the featurization summary as a list of JSON\n",
|
||||
"featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
|
||||
"# View the featurization summary as a pandas dataframe\n",
|
||||
"pd.DataFrame.from_records(featurization_summary)"
|
||||
"best_run, fitted_model = local_run.get_output()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -607,11 +410,13 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"blob_location = \"https://{}.blob.core.windows.net/{}\".format(account_name, container_name)\n",
|
||||
"X_test = pd.read_csv(\"{}./X_valid.csv\".format(blob_location), header=0)\n",
|
||||
"y_test = pd.read_csv(\"{}/y_valid.csv\".format(blob_location), header=0)\n",
|
||||
"images = pd.read_csv(\"{}/images.csv\".format(blob_location), header=None)\n",
|
||||
"images = np.reshape(images.values, (100,8,8))"
|
||||
"dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
|
||||
"\n",
|
||||
"df_test = dataset_test.to_pandas_dataframe()\n",
|
||||
"df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
|
||||
"\n",
|
||||
"y_test = df_test[['Primary Type']]\n",
|
||||
"X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -628,35 +433,9 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Randomly select digits and test.\n",
|
||||
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
|
||||
" print(index)\n",
|
||||
" predicted = fitted_model.predict(X_test[index:index + 1])[0]\n",
|
||||
" label = y_test.values[index]\n",
|
||||
" title = \"Label value = %d Predicted value = %d \" % (label, predicted)\n",
|
||||
" fig = plt.figure(3, figsize = (5,5))\n",
|
||||
" ax1 = fig.add_axes((0,0,.8,.8))\n",
|
||||
" ax1.set_title(title)\n",
|
||||
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
|
||||
" display(fig)"
|
||||
"fitted_model.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"When deploying an automated ML trained model, please specify _pippackages=['azureml-sdk[automl]']_ in your CondaDependencies.\n",
|
||||
"\n",
|
||||
"Please refer to only the **Deploy** section in this notebook - <a href=\"https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-with-deployment\" target=\"_blank\">Deployment of Automated ML trained model</a>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -689,10 +468,10 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.5"
|
||||
"version": "3.6.8"
|
||||
},
|
||||
"name": "auto-ml-classification-local-adb",
|
||||
"notebookId": 587284549713154
|
||||
"notebookId": 1275190406842063
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@
|
|||
"source": [
|
||||
"# AutoML : Classification with Local Compute on Azure DataBricks with deployment to ACI\n",
|
||||
"\n",
|
||||
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"In this example we use the scikit-learn's to showcase how you can use AutoML for a simple classification problem.\n",
|
||||
"\n",
|
||||
"In this notebook you will learn how to:\n",
|
||||
"1. Create Azure Machine Learning Workspace object and initialize your notebook directory to easily reload this object from a configuration file.\n",
|
||||
|
|
@ -164,30 +164,6 @@
|
|||
"write_config(path=\"/databricks/driver/aml_config/\",file_name=<alias_conf.cfg>)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a Folder to Host Sample Projects\n",
|
||||
"Finally, create a folder where all the sample projects will be hosted."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"sample_projects_folder = './sample_projects'\n",
|
||||
"\n",
|
||||
"if not os.path.isdir(sample_projects_folder):\n",
|
||||
" os.mkdir(sample_projects_folder)\n",
|
||||
" \n",
|
||||
"print('Sample projects will be created in {}.'.format(sample_projects_folder))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -229,7 +205,6 @@
|
|||
"source": [
|
||||
"# Choose a name for the experiment and specify the project folder.\n",
|
||||
"experiment_name = 'automl-local-classification'\n",
|
||||
"project_folder = './sample_projects/automl-local-classification'\n",
|
||||
"\n",
|
||||
"experiment = Experiment(ws, experiment_name)\n",
|
||||
"\n",
|
||||
|
|
@ -239,94 +214,11 @@
|
|||
"output['Workspace Name'] = ws.name\n",
|
||||
"output['Resource Group'] = ws.resource_group\n",
|
||||
"output['Location'] = ws.location\n",
|
||||
"output['Project Directory'] = project_folder\n",
|
||||
"output['Experiment Name'] = experiment.name\n",
|
||||
"pd.set_option('display.max_colwidth', -1)\n",
|
||||
"pd.DataFrame(data = output, index = ['']).T"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Diagnostics\n",
|
||||
"\n",
|
||||
"Opt-in diagnostics for better experience, quality, and security of future releases."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.telemetry import set_diagnostics_collection\n",
|
||||
"set_diagnostics_collection(send_diagnostics = True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Registering Datastore"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Datastore is the way to save connection information to a storage service (e.g. Azure Blob, Azure Data Lake, Azure SQL) information to your workspace so you can access them without exposing credentials in your code. The first thing you will need to do is register a datastore, you can refer to our [python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore?view=azure-ml-py) on how to register datastores. __Note: for best security practices, please do not check in code that contains registering datastores with secrets into your source control__\n",
|
||||
"\n",
|
||||
"The code below registers a datastore pointing to a publicly readable blob container."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from azureml.core import Datastore\n",
|
||||
"\n",
|
||||
"datastore_name = 'demo_training'\n",
|
||||
"container_name = 'digits' \n",
|
||||
"account_name = 'automlpublicdatasets'\n",
|
||||
"Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = datastore_name, \n",
|
||||
" container_name = container_name, \n",
|
||||
" account_name = account_name,\n",
|
||||
" overwrite = True\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Below is an example on how to register a private blob container\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_blob_container(\n",
|
||||
" workspace = ws, \n",
|
||||
" datastore_name = 'example_datastore', \n",
|
||||
" container_name = 'example-container', \n",
|
||||
" account_name = 'storageaccount',\n",
|
||||
" account_key = 'accountkey'\n",
|
||||
")\n",
|
||||
"```\n",
|
||||
"The example below shows how to register an Azure Data Lake store. Please make sure you have granted the necessary permissions for the service principal to access the data lake.\n",
|
||||
"```python\n",
|
||||
"datastore = Datastore.register_azure_data_lake(\n",
|
||||
" workspace = ws,\n",
|
||||
" datastore_name = 'example_datastore',\n",
|
||||
" store_name = 'adlsstore',\n",
|
||||
" tenant_id = 'tenant-id-of-service-principal',\n",
|
||||
" client_id = 'client-id-of-service-principal',\n",
|
||||
" client_secret = 'client-secret-of-service-principal'\n",
|
||||
")\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -340,9 +232,7 @@
|
|||
"source": [
|
||||
"Automated ML takes a `TabularDataset` as input.\n",
|
||||
"\n",
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
|
||||
"\n",
|
||||
"You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
|
||||
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -351,13 +241,12 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
|
||||
"from azureml.core.dataset import Dataset\n",
|
||||
"from azureml.data.datapath import DataPath\n",
|
||||
"\n",
|
||||
"datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
|
||||
"\n",
|
||||
"X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
|
||||
"y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
|
||||
"example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
|
||||
"dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
|
||||
"dataset.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -374,16 +263,8 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train.take(5).to_pandas_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_train.take(5).to_pandas_dataframe()"
|
||||
"training_data = dataset.drop_columns(columns=['FBI Code'])\n",
|
||||
"label = 'Primary Type'"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -401,14 +282,11 @@
|
|||
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
|
||||
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
|
||||
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**spark_context**|Spark Context object. for Databricks, use spark_context=sc|\n",
|
||||
"|**max_concurrent_iterations**|Maximum number of iterations to execute in parallel. This should be <= number of worker nodes in your Azure Databricks cluster.|\n",
|
||||
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
|
||||
"|**y**|(sparse) array-like, shape = [n_samples, ], [n_samples, n_classes]<br>Multi-class targets. An indicator matrix turns on multilabel classification. This should be an array of integers.|\n",
|
||||
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
|
||||
"|**preprocess**|set this to True to enable pre-processing of data eg. string to numeric using one-hot encoding|\n",
|
||||
"|**exit_score**|Target score for experiment. It is associated with the metric. eg. exit_score=0.995 will exit experiment after that|"
|
||||
"|**n_cross_validations**|Number of cross validation splits.|\n",
|
||||
"|**training_data**|Input dataset, containing both features and label column.|\n",
|
||||
"|**label_column_name**|The name of the label column.|"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -422,14 +300,12 @@
|
|||
" primary_metric = 'AUC_weighted',\n",
|
||||
" iteration_timeout_minutes = 10,\n",
|
||||
" iterations = 5,\n",
|
||||
" preprocess = True,\n",
|
||||
" n_cross_validations = 10,\n",
|
||||
" max_concurrent_iterations = 2, #change it based on number of worker nodes\n",
|
||||
" verbosity = logging.INFO,\n",
|
||||
" spark_context=sc, #databricks/spark related\n",
|
||||
" X = X_train, \n",
|
||||
" y = y_train,\n",
|
||||
" path = project_folder)"
|
||||
" training_data=training_data,\n",
|
||||
" label_column_name=label)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -475,13 +351,6 @@
|
|||
"displayHTML(\"<a href={} target='_blank'>Azure Portal: {}</a>\".format(local_run.get_portal_url(), local_run.id))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The following will show the child runs and waits for the parent run to complete."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
|
|
@ -651,11 +520,13 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"blob_location = \"https://{}.blob.core.windows.net/{}\".format(account_name, container_name)\n",
|
||||
"X_test = pd.read_csv(\"{}./X_valid.csv\".format(blob_location), header=0)\n",
|
||||
"y_test = pd.read_csv(\"{}/y_valid.csv\".format(blob_location), header=0)\n",
|
||||
"images = pd.read_csv(\"{}/images.csv\".format(blob_location), header=None)\n",
|
||||
"images = np.reshape(images.values, (100,8,8))"
|
||||
"dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
|
||||
"\n",
|
||||
"df_test = dataset_test.to_pandas_dataframe()\n",
|
||||
"df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
|
||||
"\n",
|
||||
"y_test = df_test[['Primary Type']]\n",
|
||||
"X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -672,20 +543,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import json\n",
|
||||
"# Randomly select digits and test.\n",
|
||||
"for index in np.random.choice(len(y_test), 2, replace = False):\n",
|
||||
" print(index)\n",
|
||||
" test_sample = json.dumps({'data':X_test[index:index + 1].values.tolist()})\n",
|
||||
" predicted = aci_service.run(input_data = test_sample)\n",
|
||||
" label = y_test.values[index]\n",
|
||||
" predictedDict = json.loads(predicted)\n",
|
||||
" title = \"Label value = %d Predicted value = %s \" % ( label,predictedDict['result'][0]) \n",
|
||||
" fig = plt.figure(3, figsize = (5,5))\n",
|
||||
" ax1 = fig.add_axes((0,0,.8,.8))\n",
|
||||
" ax1.set_title(title)\n",
|
||||
" plt.imshow(images[index], cmap = plt.cm.gray_r, interpolation = 'nearest')\n",
|
||||
" display(fig)"
|
||||
"fitted_model.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -703,7 +561,7 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"myservice.delete()"
|
||||
"aci_service.delete()"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -741,7 +599,7 @@
|
|||
"version": "3.6.8"
|
||||
},
|
||||
"name": "auto-ml-classification-local-adb",
|
||||
"notebookId": 2733885892129020
|
||||
"notebookId": 3772036807853791
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
|
|
|
|||
|
|
@ -1,5 +0,0 @@
|
|||
name: register-model-deploy-local-advanced
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- scikit-learn
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
name: register-model-deploy-local
|
||||
dependencies:
|
||||
- pip:
|
||||
- azureml-sdk
|
||||
- scikit-learn
|
||||
|
|
@ -172,7 +172,7 @@
|
|||
"source": [
|
||||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'],\n",
|
||||
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n",
|
||||
" pip_packages=['azureml-defaults'])\n",
|
||||
"\n",
|
||||
"with open(\"myenv.yml\",\"w\") as f:\n",
|
||||
|
|
@ -465,7 +465,7 @@
|
|||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "shipatel"
|
||||
"name": "gopalv"
|
||||
}
|
||||
],
|
||||
"kernelspec": {
|
||||
|
|
|
|||
|
|
@ -262,8 +262,7 @@
|
|||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'azureml-dataprep'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"# Note: this is to pin the scikit-learn and pandas versions to be same as notebook.\n",
|
||||
|
|
@ -401,8 +400,7 @@
|
|||
"run_config.environment.docker.enabled = True\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'azureml-dataprep'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -7,5 +7,5 @@ dependencies:
|
|||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- sklearn-pandas
|
||||
- azureml-dataprep
|
||||
- azureml-dataset-runtime
|
||||
- ipywidgets
|
||||
|
|
|
|||
|
|
@ -267,8 +267,7 @@
|
|||
"run_config.environment.python.user_managed_dependencies = False\n",
|
||||
"\n",
|
||||
"azureml_pip_packages = [\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-core', 'azureml-telemetry',\n",
|
||||
" 'azureml-interpret', 'azureml-dataprep'\n",
|
||||
" 'azureml-defaults', 'azureml-contrib-interpret', 'azureml-telemetry', 'azureml-interpret'\n",
|
||||
"]\n",
|
||||
" \n",
|
||||
"\n",
|
||||
|
|
@ -423,13 +422,6 @@
|
|||
"Deploy Model and ScoringExplainer"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -563,13 +555,6 @@
|
|||
"1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n",
|
||||
"1. [Inferencing time: deploy a locally-trained keras model and explainer](./train-explain-model-keras-locally-and-deploy.ipynb)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
|
|
@ -7,6 +7,6 @@ dependencies:
|
|||
- matplotlib
|
||||
- azureml-contrib-interpret
|
||||
- sklearn-pandas
|
||||
- azureml-dataprep
|
||||
- azureml-dataset-runtime
|
||||
- azureml-core
|
||||
- ipywidgets
|
||||
|
|
|
|||
|
|
@ -342,7 +342,7 @@
|
|||
"## Running a few steps in parallel\n",
|
||||
"Here we are looking at a simple scenario where we are running a few steps (all involving PythonScriptStep) in parallel. Running nodes in **parallel** is the default behavior for steps in a pipeline.\n",
|
||||
"\n",
|
||||
"We already have one step defined earlier. Let's define few more steps."
|
||||
"We already have one step defined earlier. Let's define few more steps. For step3, we are using customized conda-dependency, and job might fail when \"azureml-defaults\" (or other meta package) is not in pip-package list. We need to be aware if we are not using any of the meta packages (azureml-sdk, azureml-defaults, azureml-core), and we recommend installing \"azureml-defaults\"."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -637,7 +637,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.7"
|
||||
"version": "3.6.2"
|
||||
},
|
||||
"order_index": 1,
|
||||
"tags": [
|
||||
|
|
|
|||
|
|
@ -544,7 +544,7 @@
|
|||
"metadata": {
|
||||
"authors": [
|
||||
{
|
||||
"name": "sanpil"
|
||||
"name": "nagaur"
|
||||
}
|
||||
],
|
||||
"category": "tutorial",
|
||||
|
|
|
|||
|
|
@ -40,7 +40,7 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"import azureml.core\n",
|
||||
"from azureml.core import Workspace, Datastore, Experiment\n",
|
||||
"from azureml.core import Workspace, Datastore, Experiment, Dataset\n",
|
||||
"from azureml.core.compute import AmlCompute\n",
|
||||
"from azureml.core.compute import ComputeTarget\n",
|
||||
"\n",
|
||||
|
|
@ -109,7 +109,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"## Building Pipeline Steps with Inputs and Outputs\n",
|
||||
"As mentioned earlier, a step in the pipeline can take data as input. This data can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline."
|
||||
"A step in the pipeline can take [dataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py) as input. This dataset can be a data source that lives in one of the accessible data locations, or intermediate data produced by a previous step in the pipeline."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -118,13 +118,20 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reference the data uploaded to blob storage using DataReference\n",
|
||||
"# Uploading data to the datastore\n",
|
||||
"data_path = def_blob_store.upload_files([\"./20news.pkl\"], target_path=\"20newsgroups\", overwrite=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Reference the data uploaded to blob storage using file dataset\n",
|
||||
"# Assign the datasource to blob_input_data variable\n",
|
||||
"blob_input_data = DataReference(\n",
|
||||
" datastore=def_blob_store,\n",
|
||||
" data_reference_name=\"test_data\",\n",
|
||||
" path_on_datastore=\"20newsgroups/20news.pkl\")\n",
|
||||
"print(\"DataReference object created\")"
|
||||
"blob_input_data = Dataset.File.from_files(data_path).as_named_input(\"test_data\")\n",
|
||||
"print(\"Dataset created\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -142,8 +149,8 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Define a Step that consumes a datasource and produces intermediate data.\n",
|
||||
"In this step, we define a step that consumes a datasource and produces intermediate data.\n",
|
||||
"#### Define a Step that consumes a dataset and produces intermediate data.\n",
|
||||
"In this step, we define a step that consumes a dataset and produces intermediate data.\n",
|
||||
"\n",
|
||||
"**Open `train.py` in the local machine and examine the arguments, inputs, and outputs for the script. That will give you a good sense of why the script argument names used below are important.** \n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -138,7 +138,7 @@
|
|||
"\n",
|
||||
"mnist_data = Datastore.register_azure_blob_container(ws, \n",
|
||||
" datastore_name=datastore_name, \n",
|
||||
" container_name= container_name, \n",
|
||||
" container_name=container_name, \n",
|
||||
" account_name=account_name,\n",
|
||||
" overwrite=True)"
|
||||
]
|
||||
|
|
@ -181,6 +181,13 @@
|
|||
"input_mnist_ds = Dataset.File.from_files(path=path_on_datastore, validate=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The input dataset can be specified as a pipeline parameter, so that you can pass in new data when rerun the PRS pipeline."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
|
|
@ -199,15 +206,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"### Intermediate/Output Data\n",
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
|
||||
"\n",
|
||||
"**Constructing PipelineData**\n",
|
||||
"- name: [Required] Name of the data item within the pipeline graph\n",
|
||||
"- datastore_name: Name of the Datastore to write this output to\n",
|
||||
"- output_name: Name of the output\n",
|
||||
"- output_mode: Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
|
||||
"- output_path_on_compute: For \"upload\" mode, the path to which the module writes this output during execution\n",
|
||||
"- output_overwrite: Flag to overwrite pre-existing data"
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -218,9 +217,7 @@
|
|||
"source": [
|
||||
"from azureml.pipeline.core import Pipeline, PipelineData\n",
|
||||
"\n",
|
||||
"output_dir = PipelineData(name=\"inferences\", \n",
|
||||
" datastore=def_data_store, \n",
|
||||
" output_path_on_compute=\"mnist/results\")"
|
||||
"output_dir = PipelineData(name=\"inferences\", datastore=def_data_store)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -273,11 +270,11 @@
|
|||
"from azureml.core.model import Model\n",
|
||||
"\n",
|
||||
"# register downloaded model \n",
|
||||
"model = Model.register(model_path = \"models/\",\n",
|
||||
" model_name = \"mnist-prs\", # this is the name the model is registered as\n",
|
||||
" tags = {'pretrained': \"mnist\"},\n",
|
||||
" description = \"Mnist trained tensorflow model\",\n",
|
||||
" workspace = ws)"
|
||||
"model = Model.register(model_path=\"models/\",\n",
|
||||
" model_name=\"mnist-prs\", # this is the name the model is registered as\n",
|
||||
" tags={'pretrained': \"mnist\"},\n",
|
||||
" description=\"Mnist trained tensorflow model\",\n",
|
||||
" workspace=ws)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -298,11 +295,7 @@
|
|||
" \n",
|
||||
"\n",
|
||||
"#### Dependencies\n",
|
||||
"Helper scripts or Python/Conda packages required to run the entry script or model.\n",
|
||||
"\n",
|
||||
"The deployment configuration for the compute target that hosts the deployed model. This configuration describes things like memory and CPU requirements needed to run the model.\n",
|
||||
"\n",
|
||||
"These items are encapsulated into an inference configuration and a deployment configuration. The inference configuration references the entry script and other dependencies. You define these configurations programmatically when you use the SDK to perform the deployment. You define them in JSON files when you use the CLI."
|
||||
"Helper scripts or Python/Conda packages required to run the entry script."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -332,7 +325,10 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"### Specify the environment to run the script\n",
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment."
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n",
|
||||
"* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n",
|
||||
"\n",
|
||||
"If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -345,7 +341,7 @@
|
|||
"from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n",
|
||||
"\n",
|
||||
"batch_conda_deps = CondaDependencies.create(pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n",
|
||||
" \"azureml-core\", \"azureml-dataprep[fuse]\"])\n",
|
||||
" \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
|
||||
"batch_env = Environment(name=\"batch_environment\")\n",
|
||||
"batch_env.python.conda_dependencies = batch_conda_deps\n",
|
||||
"batch_env.docker.enabled = True\n",
|
||||
|
|
@ -504,7 +500,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"### View the prediction results per input image\n",
|
||||
"In the score.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows"
|
||||
"In the digit_identification.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called *inferences*. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -514,19 +510,14 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import shutil\n",
|
||||
"import tempfile\n",
|
||||
"\n",
|
||||
"# remove previous run results, if present\n",
|
||||
"shutil.rmtree(\"mnist_results\", ignore_errors=True)\n",
|
||||
"batch_run = pipeline_run.find_step_run(parallelrun_step.name)[0]\n",
|
||||
"batch_output = batch_run.get_output_data(output_dir.name)\n",
|
||||
"\n",
|
||||
"batch_run = next(pipeline_run.get_children())\n",
|
||||
"batch_output = batch_run.get_output_data(\"inferences\")\n",
|
||||
"batch_output.download(local_path=\"mnist_results\")\n",
|
||||
"\n",
|
||||
"for root, dirs, files in os.walk(\"mnist_results\"):\n",
|
||||
" for file in files:\n",
|
||||
" if file.endswith('mnist_outputs.txt'):\n",
|
||||
" result_file = os.path.join(root,file)\n",
|
||||
"target_dir = tempfile.mkdtemp()\n",
|
||||
"batch_output.download(local_path=target_dir)\n",
|
||||
"result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
|
||||
"df.columns = [\"Filename\", \"Prediction\"]\n",
|
||||
|
|
|
|||
|
|
@ -168,15 +168,7 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"### Intermediate/Output Data\n",
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.\n",
|
||||
"\n",
|
||||
"**Constructing PipelineData**\n",
|
||||
"- name: [Required] Name of the data item within the pipeline graph\n",
|
||||
"- datastore_name: Name of the Datastore to write this output to\n",
|
||||
"- output_name: Name of the output\n",
|
||||
"- output_mode: Specifies \"upload\" or \"mount\" modes for producing output (default: mount)\n",
|
||||
"- output_path_on_compute: For \"upload\" mode, the path to which the module writes this output during execution\n",
|
||||
"- output_overwrite: Flag to overwrite pre-existing data"
|
||||
"Intermediate data (or output of a Step) is represented by [PipelineData](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py) object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -250,11 +242,7 @@
|
|||
" \n",
|
||||
"\n",
|
||||
"#### Dependencies\n",
|
||||
"Helper scripts or Python/Conda packages required to run the entry script or model.\n",
|
||||
"\n",
|
||||
"The deployment configuration for the compute target that hosts the deployed model. This configuration describes things like memory and CPU requirements needed to run the model.\n",
|
||||
"\n",
|
||||
"These items are encapsulated into an inference configuration and a deployment configuration. The inference configuration references the entry script and other dependencies. You define these configurations programmatically when you use the SDK to perform the deployment. You define them in JSON files when you use the CLI.\n",
|
||||
"Helper scripts or Python/Conda packages required to run the entry script.\n",
|
||||
"\n",
|
||||
"## Print inferencing script"
|
||||
]
|
||||
|
|
@ -286,7 +274,11 @@
|
|||
"metadata": {},
|
||||
"source": [
|
||||
"### Specify the environment to run the script\n",
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment."
|
||||
"Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.\n",
|
||||
"* Always include **azureml-core** and **azureml-dataset-runtime\\[fuse\\]** in the pip package list to make ParallelRunStep run properly.\n",
|
||||
"* For TabularDataset, add **pandas** as `run(mini_batch)` uses `pandas.DataFrame` as mini_batch type.\n",
|
||||
"\n",
|
||||
"If you're using custom image (`batch_env.python.user_managed_dependencies = True`), you need to install the package to your image."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -299,7 +291,7 @@
|
|||
"from azureml.core.runconfig import CondaDependencies\n",
|
||||
"\n",
|
||||
"predict_conda_deps = CondaDependencies.create(pip_packages=[\"scikit-learn==0.20.3\",\n",
|
||||
" \"azureml-core\", \"azureml-dataprep[pandas,fuse]\"])\n",
|
||||
" \"azureml-core\", \"azureml-dataset-runtime[pandas,fuse]\"])\n",
|
||||
"\n",
|
||||
"predict_env = Environment(name=\"predict_environment\")\n",
|
||||
"predict_env.python.conda_dependencies = predict_conda_deps\n",
|
||||
|
|
@ -438,19 +430,14 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import shutil\n",
|
||||
"import tempfile\n",
|
||||
"\n",
|
||||
"shutil.rmtree(\"iris_results\", ignore_errors=True)\n",
|
||||
"prediction_run = pipeline_run.find_step_run(distributed_csv_iris_step.name)[0]\n",
|
||||
"prediction_output = prediction_run.get_output_data(output_folder.name)\n",
|
||||
"\n",
|
||||
"prediction_run = next(pipeline_run.get_children())\n",
|
||||
"prediction_output = prediction_run.get_output_data(\"inferences\")\n",
|
||||
"prediction_output.download(local_path=\"iris_results\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"for root, dirs, files in os.walk(\"iris_results\"):\n",
|
||||
" for file in files:\n",
|
||||
" if file.endswith('iris_outputs.txt'):\n",
|
||||
" result_file = os.path.join(root,file)\n",
|
||||
"target_dir = tempfile.mkdtemp()\n",
|
||||
"prediction_output.download(local_path=target_dir)\n",
|
||||
"result_file = os.path.join(target_dir, prediction_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
|
||||
"\n",
|
||||
"# cleanup output format\n",
|
||||
"df = pd.read_csv(result_file, delimiter=\" \", header=None)\n",
|
||||
|
|
|
|||
|
|
@ -120,7 +120,7 @@
|
|||
"\n",
|
||||
"def download_model(model_name):\n",
|
||||
" # downloaded models from https://pytorch.org/tutorials/advanced/neural_style_tutorial.html are kept here\n",
|
||||
" url=\"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n",
|
||||
" url = \"https://pipelinedata.blob.core.windows.net/styletransfer/saved_models/\" + model_name\n",
|
||||
" local_path = os.path.join(model_dir, model_name)\n",
|
||||
" urllib.request.urlretrieve(url, local_path)"
|
||||
]
|
||||
|
|
@ -415,7 +415,7 @@
|
|||
"parallel_cd.add_conda_package(\"torchvision\")\n",
|
||||
"parallel_cd.add_conda_package(\"pillow<7\") # needed for torchvision==0.4.0\n",
|
||||
"parallel_cd.add_pip_package(\"azureml-core\")\n",
|
||||
"parallel_cd.add_pip_package(\"azureml-dataprep[fuse]\")\n",
|
||||
"parallel_cd.add_pip_package(\"azureml-dataset-runtime[fuse]\")\n",
|
||||
"\n",
|
||||
"styleenvironment = Environment(name=\"styleenvironment\")\n",
|
||||
"styleenvironment.python.conda_dependencies=parallel_cd\n",
|
||||
|
|
@ -541,8 +541,8 @@
|
|||
"outputs": [],
|
||||
"source": [
|
||||
"def download_video(run, target_dir=None):\n",
|
||||
" stitch_run = run.find_step_run(\"stitch\")[0]\n",
|
||||
" port_data = stitch_run.get_output_data(\"output_video\")\n",
|
||||
" stitch_run = run.find_step_run(stitch_video_step.name)[0]\n",
|
||||
" port_data = stitch_run.get_output_data(output_video.name)\n",
|
||||
" port_data.download(target_dir, show_progress=True)"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -459,7 +459,7 @@
|
|||
" entry_script='tf_mnist.py',\n",
|
||||
" use_gpu=True,\n",
|
||||
" framework_version='2.0',\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -724,7 +724,7 @@
|
|||
" entry_script='tf_mnist.py',\n",
|
||||
" framework_version='2.0',\n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -285,7 +285,7 @@
|
|||
" distributed_training=Mpi(),\n",
|
||||
" framework_version='1.13', \n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ dependencies:
|
|||
- azureml-widgets
|
||||
- keras
|
||||
- tensorflow-gpu==1.13.2
|
||||
- horovod==0.16.1
|
||||
- horovod==0.19.1
|
||||
- matplotlib
|
||||
- pandas
|
||||
- fuse
|
||||
|
|
|
|||
|
|
@ -442,12 +442,12 @@
|
|||
"\n",
|
||||
"# set up environment\\n\n",
|
||||
"env = Environment('my_env')\n",
|
||||
"# ensure latest azureml-dataprep and other required packages installed in the environment\n",
|
||||
"# ensure latest azureml-dataset-runtime and other required packages installed in the environment\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['keras',\n",
|
||||
" 'azureml-sdk',\n",
|
||||
" 'tensorflow==2.0.0',\n",
|
||||
" 'matplotlib',\n",
|
||||
" 'azureml-dataprep[pandas,fuse]'])\n",
|
||||
" 'azureml-dataset-runtime[pandas,fuse]'])\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies = cd"
|
||||
]
|
||||
|
|
|
|||
|
|
@ -300,7 +300,7 @@
|
|||
" script_params=script_params,\n",
|
||||
" entry_script='tf_mnist_with_checkpoint.py',\n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -411,7 +411,7 @@
|
|||
" entry_script='tf_mnist_with_checkpoint.py',\n",
|
||||
" resume_from=model_location,\n",
|
||||
" use_gpu=True,\n",
|
||||
" pip_packages=['azureml-dataprep[pandas,fuse]'])"
|
||||
" pip_packages=['azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -582,7 +582,7 @@
|
|||
" rl_framework=Ray(),\n",
|
||||
" \n",
|
||||
" # Additional pip packages to install\n",
|
||||
" pip_packages = ['azureml-dataprep[fuse,pandas]'])"
|
||||
" pip_packages = ['azureml-dataset-runtime[fuse,pandas]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
FROM mcr.microsoft.com/azureml/base:openmpi3.1.2-ubuntu18.04
|
||||
FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04:20200423.v1
|
||||
|
||||
RUN apt-get update && apt-get install -y --no-install-recommends \
|
||||
python-opengl \
|
||||
|
|
@ -11,7 +11,7 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
|
|||
RUN conda install -y conda=4.7.12 python=3.6.2 && conda clean -ay && \
|
||||
pip install --no-cache-dir \
|
||||
azureml-defaults \
|
||||
azureml-dataprep[fuse,pandas] \
|
||||
azureml-dataset-runtime[fuse,pandas] \
|
||||
azureml-contrib-reinforcementlearning \
|
||||
gputil \
|
||||
cloudpickle==1.3.0 \
|
||||
|
|
|
|||
|
|
@ -100,7 +100,7 @@
|
|||
"\n",
|
||||
"# Check core SDK version number\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using SDK version 1.10.0, you are currently running version\", azureml.core.VERSION)"
|
||||
"print(\"This notebook was created using SDK version 1.11.0, you are currently running version\", azureml.core.VERSION)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -449,7 +449,7 @@
|
|||
" compute_target=compute_target, \n",
|
||||
" entry_script='keras_mnist.py',\n",
|
||||
" framework_version='2.0', \n",
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataprep[pandas,fuse]','matplotlib'])"
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataset-runtime[pandas,fuse]','matplotlib'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -730,7 +730,7 @@
|
|||
" compute_target=compute_target,\n",
|
||||
" entry_script='keras_mnist.py',\n",
|
||||
" framework_version='2.0',\n",
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataprep[pandas,fuse]','matplotlib'])"
|
||||
" pip_packages=['keras<=2.3.1','azureml-dataset-runtime[pandas,fuse]','matplotlib'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -309,7 +309,7 @@
|
|||
"conda_env = Environment(\"conda-env\")\n",
|
||||
"conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['scikit-learn',\n",
|
||||
" 'azureml-sdk',\n",
|
||||
" 'azureml-dataprep[pandas,fuse]>=1.1.21'])"
|
||||
" 'azureml-dataset-runtime[pandas,fuse]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -289,7 +289,7 @@
|
|||
"conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-sdk',\n",
|
||||
" 'azureml-contrib-dataset',\n",
|
||||
" 'torch','torchvision',\n",
|
||||
" 'azureml-dataprep[pandas]'])"
|
||||
" 'azureml-dataset-runtime[pandas]'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
|
|
@ -230,7 +230,7 @@
|
|||
"source": [
|
||||
"# set up the compute environment to install required packages\n",
|
||||
"conda = CondaDependencies.create(\n",
|
||||
" pip_packages=['azureml-sdk','azureml-dataprep[fuse,pandas]'],\n",
|
||||
" pip_packages=['azureml-sdk','azureml-dataset-runtime[fuse,pandas]'],\n",
|
||||
" pin_sdk_version=False)\n",
|
||||
"\n",
|
||||
"conda.set_pip_option('--pre')\n",
|
||||
|
|
@ -302,8 +302,8 @@
|
|||
"source": [
|
||||
"# set up training step with Tensorflow estimator\n",
|
||||
"est = TensorFlow(entry_script='train.py',\n",
|
||||
" source_directory=script_folder, \n",
|
||||
" pip_packages = ['azureml-sdk','keras','numpy','scikit-learn', 'matplotlib'],\n",
|
||||
" source_directory=script_folder,\n",
|
||||
" pip_packages = ['azureml-sdk', 'keras<=2.3.1', 'tensorflow==2.1.0', 'numpy','scikit-learn', 'matplotlib'],\n",
|
||||
" compute_target=compute_target)\n",
|
||||
"\n",
|
||||
"est_step = EstimatorStep(name='train step',\n",
|
||||
|
|
|
|||
|
|
@ -297,7 +297,7 @@
|
|||
" entry_script='train_iris.py', \n",
|
||||
" # pass dataset object as an input with name 'titanic'\n",
|
||||
" inputs=[dataset.as_named_input('iris')],\n",
|
||||
" pip_packages=['azureml-dataprep[fuse]', 'packaging'],\n",
|
||||
" pip_packages=['azureml-dataset-runtime[fuse]', 'packaging'],\n",
|
||||
" compute_target=compute_target) "
|
||||
]
|
||||
},
|
||||
|
|
@ -493,7 +493,7 @@
|
|||
"\n",
|
||||
"conda_env = Environment('conda-env')\n",
|
||||
"conda_env.python.conda_dependencies = CondaDependencies.create(pip_packages=['azureml-core',\n",
|
||||
" 'azureml-dataprep[pandas,fuse]',\n",
|
||||
" 'azureml-dataset-runtime[pandas,fuse]',\n",
|
||||
" 'scikit-learn',\n",
|
||||
" 'packaging'])"
|
||||
]
|
||||
|
|
|
|||
1
index.md
1
index.md
|
|
@ -98,7 +98,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
|
|||
| [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) | | | | | | |
|
||||
| [fairlearn-azureml-mitigation](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/fairness/fairlearn-azureml-mitigation.ipynb) | | | | | | |
|
||||
| [upload-fairness-dashboard](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/fairness/upload-fairness-dashboard.ipynb) | | | | | | |
|
||||
| [lightgbm-example](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/gbdt/lightgbm/lightgbm-example.ipynb) | | | | | | |
|
||||
| [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) | | | | | | |
|
||||
| [auto-ml-continuous-retraining](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb) | | | | | | |
|
||||
| [auto-ml-forecasting-beer-remote](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb) | | | | | | |
|
||||
|
|
|
|||
|
|
@ -102,7 +102,7 @@
|
|||
"source": [
|
||||
"import azureml.core\n",
|
||||
"\n",
|
||||
"print(\"This notebook was created using version 1.10.0 of the Azure ML SDK\")\n",
|
||||
"print(\"This notebook was created using version 1.11.0 of the Azure ML SDK\")\n",
|
||||
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
|
||||
]
|
||||
},
|
||||
|
|
|
|||
|
|
@ -413,7 +413,7 @@
|
|||
"\n",
|
||||
"In this tutorial, the target is AmlCompute. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the dataset.\n",
|
||||
"\n",
|
||||
"First, create the environment that contains: the scikit-learn library, azureml-dataprep required for accessing the dataset, and azureml-defaults which contains the dependencies for logging metrics. The azureml-defaults also contains the dependencies required for deploying the model as a web service later in the part 2 of the tutorial.\n",
|
||||
"First, create the environment that contains: the scikit-learn library, azureml-dataset-runtime required for accessing the dataset, and azureml-defaults which contains the dependencies for logging metrics. The azureml-defaults also contains the dependencies required for deploying the model as a web service later in the part 2 of the tutorial.\n",
|
||||
"\n",
|
||||
"Once the environment is defined, register it with the Workspace to re-use it in part 2 of the tutorial."
|
||||
]
|
||||
|
|
@ -429,7 +429,7 @@
|
|||
"\n",
|
||||
"# to install required packages\n",
|
||||
"env = Environment('tutorial-env')\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies = cd\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -65,7 +65,7 @@
|
|||
"\n",
|
||||
"# to install required packages\n",
|
||||
"env = Environment('tutorial-env')\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies = cd\n",
|
||||
"\n",
|
||||
|
|
|
|||
|
|
@ -81,8 +81,8 @@
|
|||
"from azureml.core.conda_dependencies import CondaDependencies\n",
|
||||
"\n",
|
||||
"# to install required packages\n",
|
||||
"env = Environment('tutorial-env')\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults', 'azure-storage-blob', 'encrypted-inference==0.9'], conda_packages = ['scikit-learn==0.22.1'])\n",
|
||||
"env = Environment('tutorial-encryption-env')\n",
|
||||
"cd = CondaDependencies.create(pip_packages=['azureml-dataset-runtime[pandas,fuse]', 'azureml-defaults', 'azure-storage-blob', 'encrypted-inference==0.9'], conda_packages = ['scikit-learn==0.22.1'])\n",
|
||||
"\n",
|
||||
"env.python.conda_dependencies = cd\n",
|
||||
"\n",
|
||||
|
|
@ -282,7 +282,7 @@
|
|||
"ws = Workspace.from_config()\n",
|
||||
"model = Model(ws, 'sklearn_mnist')\n",
|
||||
"\n",
|
||||
"myenv = Environment.get(workspace=ws, name=\"tutorial-env\")\n",
|
||||
"myenv = Environment.get(workspace=ws, name=\"tutorial-encryption-env\")\n",
|
||||
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)\n",
|
||||
"\n",
|
||||
"service_name = 'sklearn-mnist-svc-' + str(uuid.uuid4())[:4]\n",
|
||||
|
|
|
|||
|
|
@ -124,9 +124,7 @@
|
|||
"\n",
|
||||
"input_images = Dataset.File.from_files((batchscore_blob, \"batchscoring/images/\"))\n",
|
||||
"label_ds = Dataset.File.from_files((batchscore_blob, \"batchscoring/labels/\"))\n",
|
||||
"output_dir = PipelineData(name=\"scores\", \n",
|
||||
" datastore=def_data_store, \n",
|
||||
" output_path_on_compute=\"batchscoring/results\")"
|
||||
"output_dir = PipelineData(name=\"scores\", datastore=def_data_store)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -142,15 +140,13 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"input_images = input_images.register(workspace = ws, name = \"input_images\")\n",
|
||||
"label_ds = label_ds.register(workspace = ws, name = \"label_ds\", create_new_version=True)"
|
||||
"input_images = input_images.register(workspace=ws, name=\"input_images\")\n",
|
||||
"label_ds = label_ds.register(workspace=ws, name=\"label_ds\", create_new_version=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"## Download and register the model"
|
||||
]
|
||||
|
|
@ -277,7 +273,7 @@
|
|||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Before running the pipeline, you create an object that defines the python environment and dependencies needed by your script `batch_scoring.py`. The main dependency required is Tensorflow, but you also install `azureml-defaults` for background processes from the SDK. Create a `RunConfiguration` object using the dependencies, and also specify Docker and Docker-GPU support."
|
||||
"Before running the pipeline, you create an object that defines the python environment and dependencies needed by your script `batch_scoring.py`. The main dependency required is Tensorflow, but you also install `azureml-core` and `azureml-dataset-runtime[fuse]` for background processes from the SDK. Create a `RunConfiguration` object using the dependencies, and also specify Docker and Docker-GPU support."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -291,7 +287,7 @@
|
|||
"from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n",
|
||||
"\n",
|
||||
"cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.15.2\",\n",
|
||||
" \"azureml-core\", \"azureml-dataprep[fuse]\"])\n",
|
||||
" \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n",
|
||||
"\n",
|
||||
"env = Environment(name=\"parallelenv\")\n",
|
||||
"env.python.conda_dependencies=cd\n",
|
||||
|
|
@ -319,6 +315,7 @@
|
|||
" entry_script=\"batch_scoring.py\",\n",
|
||||
" source_directory=\"scripts\",\n",
|
||||
" output_action=\"append_row\",\n",
|
||||
" append_row_file_name=\"parallel_run_step.txt\",\n",
|
||||
" mini_batch_size=\"20\",\n",
|
||||
" error_threshold=1,\n",
|
||||
" compute_target=compute_target,\n",
|
||||
|
|
@ -424,15 +421,15 @@
|
|||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"batch_run = next(pipeline_run.get_children())\n",
|
||||
"batch_output = batch_run.get_output_data(\"scores\")\n",
|
||||
"batch_output.download(local_path=\"inception_results\")\n",
|
||||
"\n",
|
||||
"import pandas as pd\n",
|
||||
"for root, dirs, files in os.walk(\"inception_results\"):\n",
|
||||
" for file in files:\n",
|
||||
" if file.endswith(\"parallel_run_step.txt\"):\n",
|
||||
" result_file = os.path.join(root,file)\n",
|
||||
"import tempfile\n",
|
||||
"\n",
|
||||
"batch_run = pipeline_run.find_step_run(batch_score_step.name)[0]\n",
|
||||
"batch_output = batch_run.get_output_data(output_dir.name)\n",
|
||||
"\n",
|
||||
"target_dir = tempfile.mkdtemp()\n",
|
||||
"batch_output.download(local_path=target_dir)\n",
|
||||
"result_file = os.path.join(target_dir, batch_output.path_on_datastore, parallel_run_config.append_row_file_name)\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(result_file, delimiter=\":\", header=None)\n",
|
||||
"df.columns = [\"Filename\", \"Prediction\"]\n",
|
||||
|
|
|
|||
Загрузка…
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