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Improve BigO calculations, no bug, not part of the build

This commit is contained in:
bob%bclary.com 2005-11-24 03:25:06 +00:00
Родитель 86fd5c16a9
Коммит c82c184d94
3 изменённых файлов: 177 добавлений и 119 удалений
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99
js/tests/ecma_3/shell.js
Просмотреть файл

@ -200,7 +200,6 @@ function currentFunc()
}
/*
Calculate the "order" of a set of data points {X: [], Y: []}
by computing successive "derivatives" of the data until
@ -211,18 +210,27 @@ function BigO(data)
var order = 0;
var origLength = data.X.length;
while (data.X.length > 1)
while (data.X.length > 2)
{
var lr = new LinearRegression(data);
order++;
if (lr.SE < 0.01)
if (lr.b > 1e-6)
{
// only increase the order if the slope
// is "great" enough
order++;
}
if (lr.r > 0.98 || lr.Syx < 1 || lr.b < 1e-6)
{
// terminate if close to a line lr.r
// small error lr.Syx
// small slope lr.b
break;
}
data = dataDeriv(data);
}
if (order == origLength - 1)
if (2 == origLength - order)
{
order = Number.POSITIVE_INFINITY;
}
@ -230,14 +238,12 @@ function BigO(data)
function LinearRegression(data)
{
// http://www.bearcave.com/misl/misl_tech/wavelets/stat/
/*
X0,X1...; Y0,Y1,...
b = sum( (Xi - Xavg)(Yi - Yavg) ) / sum ( (Xi - Xavg)*(Xi - Xavg) )
a = Yavg - b * Xavg
stddev*stddev = (sum( (Yi - Yavg) * (Yi - Yavg) ) -
b * sum( (Xi - Xavg)*(Yi - Yavg) ) ) /
sum ( (Xi - Xavg) * (Xi - Xavg) )
y = a + bx
for data points (Xi, Yi); 0 <= i < n
b = (n*SUM(XiYi) - SUM(Xi)*SUM(Yi))/(n*SUM(Xi*Xi) - SUM(Xi)*SUM(Xi))
a = (SUM(Yi) - b*SUM(Xi))/n
*/
var i;
@ -245,46 +251,59 @@ function BigO(data)
{
throw 'LinearRegression: data point length mismatch';
}
if (data.X.length < 2)
if (data.X.length < 3)
{
throw 'LinearRegression: data point length < 2';
}
var length = data.X.length;
var n = data.X.length;
var X = data.X;
var Y = data.Y;
this.Xavg = 0;
this.Yavg = 0;
for (i = 0; i < length; i++)
var SUM_X = 0;
var SUM_XY = 0;
var SUM_XX = 0;
var SUM_Y = 0;
var SUM_YY = 0;
for (i = 0; i < n; i++)
{
this.Xavg += X[i];
this.Yavg += Y[i];
}
this.Xavg /= length;
this.Yavg /= length;
var sumXdiffYdiff = 0;
var sumXdiffsquared = 0;
var sumYdiffsquared = 0;
for (i = 0; i < length; i++)
{
var xDiff = (X[i] - this.Xavg);
var yDiff = (Y[i] - this.Yavg);
sumXdiffYdiff += xDiff * yDiff;
sumXdiffsquared += xDiff * xDiff;
sumYdiffsquared += yDiff * yDiff;
SUM_X += X[i];
SUM_XY += X[i]*Y[i];
SUM_XX += X[i]*X[i];
SUM_Y += Y[i];
SUM_YY += Y[i]*Y[i];
}
this.b = sumXdiffYdiff / sumXdiffsquared;
this.a = this.Yavg - this.b * this.Xavg;
// standard deviation of regression
this.sigma = Math.sqrt( (sumYdiffsquared - this.b * sumXdiffYdiff) /
(length - 2));
// standard error in b
this.SE = this.sigma / Math.sqrt(sumXdiffsquared);
this.b = (n * SUM_XY - SUM_X * SUM_Y)/(n * SUM_XX - SUM_X * SUM_X);
this.a = (SUM_Y - this.b * SUM_X)/n;
this.Xavg = SUM_X/n;
this.Yavg = SUM_Y/n;
var SUM_Ydiff2 = 0;
var SUM_Xdiff2 = 0;
var SUM_XdiffYdiff = 0;
for (i = 0; i < n; i++)
{
var Ydiff = Y[i] - this.Yavg;
var Xdiff = X[i] - this.Xavg;
SUM_Ydiff2 += Ydiff * Ydiff;
SUM_Xdiff2 += Xdiff * Xdiff;
SUM_XdiffYdiff += Xdiff * Ydiff;
}
var Syx2 = (SUM_Ydiff2 - Math.pow(SUM_XdiffYdiff/SUM_Xdiff2, 2))/(n - 2);
var r2 = Math.pow((n*SUM_XY - SUM_X * SUM_Y), 2) /
((n*SUM_XX - SUM_X*SUM_X)*(n*SUM_YY-SUM_Y*SUM_Y));
this.Syx = Math.sqrt(Syx2);
this.r = Math.sqrt(r2);
}
function dataDeriv(data)

99
js/tests/js1_5/shell.js
Просмотреть файл

@ -210,18 +210,27 @@ function BigO(data)
var order = 0;
var origLength = data.X.length;
while (data.X.length > 1)
while (data.X.length > 2)
{
var lr = new LinearRegression(data);
order++;
if (lr.SE < 0.01)
if (lr.b > 1e-6)
{
// only increase the order if the slope
// is "great" enough
order++;
}
if (lr.r > 0.98 || lr.Syx < 1 || lr.b < 1e-6)
{
// terminate if close to a line lr.r
// small error lr.Syx
// small slope lr.b
break;
}
data = dataDeriv(data);
}
if (order == origLength - 1)
if (2 == origLength - order)
{
order = Number.POSITIVE_INFINITY;
}
@ -229,14 +238,12 @@ function BigO(data)
function LinearRegression(data)
{
// http://www.bearcave.com/misl/misl_tech/wavelets/stat/
/*
X0,X1...; Y0,Y1,...
b = sum( (Xi - Xavg)(Yi - Yavg) ) / sum ( (Xi - Xavg)*(Xi - Xavg) )
a = Yavg - b * Xavg
stddev*stddev = (sum( (Yi - Yavg) * (Yi - Yavg) ) -
b * sum( (Xi - Xavg)*(Yi - Yavg) ) ) /
sum ( (Xi - Xavg) * (Xi - Xavg) )
y = a + bx
for data points (Xi, Yi); 0 <= i < n
b = (n*SUM(XiYi) - SUM(Xi)*SUM(Yi))/(n*SUM(Xi*Xi) - SUM(Xi)*SUM(Xi))
a = (SUM(Yi) - b*SUM(Xi))/n
*/
var i;
@ -244,46 +251,59 @@ function BigO(data)
{
throw 'LinearRegression: data point length mismatch';
}
if (data.X.length < 2)
if (data.X.length < 3)
{
throw 'LinearRegression: data point length < 2';
}
var length = data.X.length;
var n = data.X.length;
var X = data.X;
var Y = data.Y;
this.Xavg = 0;
this.Yavg = 0;
for (i = 0; i < length; i++)
var SUM_X = 0;
var SUM_XY = 0;
var SUM_XX = 0;
var SUM_Y = 0;
var SUM_YY = 0;
for (i = 0; i < n; i++)
{
this.Xavg += X[i];
this.Yavg += Y[i];
}
this.Xavg /= length;
this.Yavg /= length;
var sumXdiffYdiff = 0;
var sumXdiffsquared = 0;
var sumYdiffsquared = 0;
for (i = 0; i < length; i++)
{
var xDiff = (X[i] - this.Xavg);
var yDiff = (Y[i] - this.Yavg);
sumXdiffYdiff += xDiff * yDiff;
sumXdiffsquared += xDiff * xDiff;
sumYdiffsquared += yDiff * yDiff;
SUM_X += X[i];
SUM_XY += X[i]*Y[i];
SUM_XX += X[i]*X[i];
SUM_Y += Y[i];
SUM_YY += Y[i]*Y[i];
}
this.b = sumXdiffYdiff / sumXdiffsquared;
this.a = this.Yavg - this.b * this.Xavg;
// standard deviation of regression
this.sigma = Math.sqrt( (sumYdiffsquared - this.b * sumXdiffYdiff) /
(length - 2));
// standard error in b
this.SE = this.sigma / Math.sqrt(sumXdiffsquared);
this.b = (n * SUM_XY - SUM_X * SUM_Y)/(n * SUM_XX - SUM_X * SUM_X);
this.a = (SUM_Y - this.b * SUM_X)/n;
this.Xavg = SUM_X/n;
this.Yavg = SUM_Y/n;
var SUM_Ydiff2 = 0;
var SUM_Xdiff2 = 0;
var SUM_XdiffYdiff = 0;
for (i = 0; i < n; i++)
{
var Ydiff = Y[i] - this.Yavg;
var Xdiff = X[i] - this.Xavg;
SUM_Ydiff2 += Ydiff * Ydiff;
SUM_Xdiff2 += Xdiff * Xdiff;
SUM_XdiffYdiff += Xdiff * Ydiff;
}
var Syx2 = (SUM_Ydiff2 - Math.pow(SUM_XdiffYdiff/SUM_Xdiff2, 2))/(n - 2);
var r2 = Math.pow((n*SUM_XY - SUM_X * SUM_Y), 2) /
((n*SUM_XX - SUM_X*SUM_X)*(n*SUM_YY-SUM_Y*SUM_Y));
this.Syx = Math.sqrt(Syx2);
this.r = Math.sqrt(r2);
}
function dataDeriv(data)
@ -313,7 +333,6 @@ function BigO(data)
}
/* JavaScriptOptions
encapsulate the logic for setting and retrieving the values
of the javascript options.

98
js/tests/js1_6/shell.js
Просмотреть файл

@ -211,18 +211,27 @@ function BigO(data)
var order = 0;
var origLength = data.X.length;
while (data.X.length > 1)
while (data.X.length > 2)
{
var lr = new LinearRegression(data);
order++;
if (lr.SE < 0.01)
if (lr.b > 1e-6)
{
// only increase the order if the slope
// is "great" enough
order++;
}
if (lr.r > 0.98 || lr.Syx < 1 || lr.b < 1e-6)
{
// terminate if close to a line lr.r
// small error lr.Syx
// small slope lr.b
break;
}
data = dataDeriv(data);
}
if (order == origLength - 1)
if (2 == origLength - order)
{
order = Number.POSITIVE_INFINITY;
}
@ -230,14 +239,12 @@ function BigO(data)
function LinearRegression(data)
{
// http://www.bearcave.com/misl/misl_tech/wavelets/stat/
/*
X0,X1...; Y0,Y1,...
b = sum( (Xi - Xavg)(Yi - Yavg) ) / sum ( (Xi - Xavg)*(Xi - Xavg) )
a = Yavg - b * Xavg
stddev*stddev = (sum( (Yi - Yavg) * (Yi - Yavg) ) -
b * sum( (Xi - Xavg)*(Yi - Yavg) ) ) /
sum ( (Xi - Xavg) * (Xi - Xavg) )
y = a + bx
for data points (Xi, Yi); 0 <= i < n
b = (n*SUM(XiYi) - SUM(Xi)*SUM(Yi))/(n*SUM(Xi*Xi) - SUM(Xi)*SUM(Xi))
a = (SUM(Yi) - b*SUM(Xi))/n
*/
var i;
@ -245,46 +252,59 @@ function BigO(data)
{
throw 'LinearRegression: data point length mismatch';
}
if (data.X.length < 2)
if (data.X.length < 3)
{
throw 'LinearRegression: data point length < 2';
}
var length = data.X.length;
var n = data.X.length;
var X = data.X;
var Y = data.Y;
this.Xavg = 0;
this.Yavg = 0;
for (i = 0; i < length; i++)
var SUM_X = 0;
var SUM_XY = 0;
var SUM_XX = 0;
var SUM_Y = 0;
var SUM_YY = 0;
for (i = 0; i < n; i++)
{
this.Xavg += X[i];
this.Yavg += Y[i];
}
this.Xavg /= length;
this.Yavg /= length;
var sumXdiffYdiff = 0;
var sumXdiffsquared = 0;
var sumYdiffsquared = 0;
for (i = 0; i < length; i++)
{
var xDiff = (X[i] - this.Xavg);
var yDiff = (Y[i] - this.Yavg);
sumXdiffYdiff += xDiff * yDiff;
sumXdiffsquared += xDiff * xDiff;
sumYdiffsquared += yDiff * yDiff;
SUM_X += X[i];
SUM_XY += X[i]*Y[i];
SUM_XX += X[i]*X[i];
SUM_Y += Y[i];
SUM_YY += Y[i]*Y[i];
}
this.b = sumXdiffYdiff / sumXdiffsquared;
this.a = this.Yavg - this.b * this.Xavg;
// standard deviation of regression
this.sigma = Math.sqrt( (sumYdiffsquared - this.b * sumXdiffYdiff) /
(length - 2));
// standard error in b
this.SE = this.sigma / Math.sqrt(sumXdiffsquared);
this.b = (n * SUM_XY - SUM_X * SUM_Y)/(n * SUM_XX - SUM_X * SUM_X);
this.a = (SUM_Y - this.b * SUM_X)/n;
this.Xavg = SUM_X/n;
this.Yavg = SUM_Y/n;
var SUM_Ydiff2 = 0;
var SUM_Xdiff2 = 0;
var SUM_XdiffYdiff = 0;
for (i = 0; i < n; i++)
{
var Ydiff = Y[i] - this.Yavg;
var Xdiff = X[i] - this.Xavg;
SUM_Ydiff2 += Ydiff * Ydiff;
SUM_Xdiff2 += Xdiff * Xdiff;
SUM_XdiffYdiff += Xdiff * Ydiff;
}
var Syx2 = (SUM_Ydiff2 - Math.pow(SUM_XdiffYdiff/SUM_Xdiff2, 2))/(n - 2);
var r2 = Math.pow((n*SUM_XY - SUM_X * SUM_Y), 2) /
((n*SUM_XX - SUM_X*SUM_X)*(n*SUM_YY-SUM_Y*SUM_Y));
this.Syx = Math.sqrt(Syx2);
this.r = Math.sqrt(r2);
}
function dataDeriv(data)