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Enable sequences for sparse input. 

Extend sequence and frame packers to support sparse input.
    	Create a tightly filled layout and base packing off of it.
    	Add a number of unit test for sequence packing (both sparse and
    	dense, no randomization, using CNTK text format for input).
This commit is contained in:
Alexey Reznichenko 2016-04-07 14:34:42 +02:00
Родитель 2667dec5ba
Коммит 14de27d114
83 изменённых файлов: 19514 добавлений и 874 удалений
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2
.gitignore поставляемый
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@ -182,5 +182,5 @@ Source/CNTK/buildinfo.h
Source/CNTK/buildinfo.h$$
# Unit test output
Tests/UnitTests/ReaderTests/Control/*_Output.txt
Tests/UnitTests/ReaderTests/Control/**/*_Output.txt
Tests/UnitTests/NetworkTests/Output/

2
Makefile
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@ -235,7 +235,7 @@ READER_SRC =\
$(SOURCEDIR)/Readers/ReaderLib/SequencePacker.cpp \
$(SOURCEDIR)/Readers/ReaderLib/BpttPacker.cpp \
$(SOURCEDIR)/Readers/ReaderLib/PackerBase.cpp \
$(SOURCEDIR)/Readers/ReaderLib/SampleModePacker.cpp \
$(SOURCEDIR)/Readers/ReaderLib/FramePacker.cpp \
COMMON_SRC =\
$(SOURCEDIR)/Common/Config.cpp \

6
Source/Readers/CNTKTextFormatReader/CNTKTextFormatReader.cpp
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@ -10,6 +10,7 @@
#include "BlockRandomizer.h"
#include "NoRandomizer.h"
#include "TextParser.h"
#include "SequencePacker.h"
namespace Microsoft { namespace MSR { namespace CNTK {
@ -30,8 +31,8 @@ CNTKTextFormatReader::CNTKTextFormatReader(MemoryProviderPtr provider,
m_deserializer = shared_ptr<IDataDeserializer>(new TextParser<double>(configHelper));
}
size_t window = configHelper.GetRandomizationWindow();
TransformerPtr randomizer;
size_t window = configHelper.GetRandomizationWindow();
if (window > 0)
{
// Verbosity is a general config parameter, not specific to the text format reader.
@ -66,7 +67,8 @@ void CNTKTextFormatReader::StartEpoch(const EpochConfiguration& config)
}
m_transformer->StartEpoch(config);
m_packer = std::make_shared<SampleModePacker>(
// TODO: add "frameMode" config paramter
m_packer = std::make_shared<SequencePacker>(
m_provider,
m_transformer,
config.m_minibatchSizeInSamples,

4
Source/Readers/CNTKTextFormatReader/CNTKTextFormatReader.h
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@ -7,7 +7,7 @@
#include "TextParser.h"
#include "Reader.h"
#include "SampleModePacker.h"
#include "Packer.h"
namespace Microsoft { namespace MSR { namespace CNTK {
@ -36,7 +36,7 @@ private:
TransformerPtr m_transformer;
// Packer.
SampleModePackerPtr m_packer;
PackerPtr m_packer;
// Memory provider (TODO: this will possibly change in the near future.)
MemoryProviderPtr m_provider;

2
Source/Readers/CNTKTextFormatReader/Indexer.cpp
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@ -105,7 +105,7 @@ void Indexer::Build()
{
return;
}
if (m_maxChunkSize > 0)
{
auto fileSize = filesize(m_file);

4
Source/Readers/CNTKTextFormatReader/TextConfigHelper.cpp
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@ -24,7 +24,7 @@ TextConfigHelper::TextConfigHelper(const ConfigParameters& config)
}
const ConfigParameters& input = config(L"input");
if (input.empty())
{
RuntimeError("CNTKTextFormatReader configuration contains an empty input section");
@ -121,7 +121,7 @@ TextConfigHelper::TextConfigHelper(const ConfigParameters& config)
{
m_randomizationWindow = config(L"randomize");
}
}
}
else
{
m_randomizationWindow = randomizeAuto;

47
Source/Readers/CNTKTextFormatReader/TextParser.cpp
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@ -4,12 +4,14 @@
//
#include "stdafx.h"
#include <cfloat>
#include <inttypes.h>
#include <cfloat>
#include <limits>
#include "Indexer.h"
#include "TextParser.h"
#include "TextReaderConstants.h"
#undef max // max is defined in minwindef.h
#define isSign(c) ((c == '-' || c == '+'))
#define isE(c) ((c == 'e' || c == 'E'))
@ -190,11 +192,11 @@ void TextParser<ElemType>::GetSequencesForChunk(size_t chunkId, std::vector<Sequ
const auto& index = m_indexer->GetIndex();
const auto& chunk = index[chunkId];
result.reserve(chunk.m_sequences.size());
for (auto const& s : chunk.m_sequences)
{
result.push_back(
{
{
s.m_id,
s.m_numberOfSamples,
s.m_chunkId,
@ -305,20 +307,13 @@ void TextParser<ElemType>::LoadChunk(TextChunkPtr& chunk, const ChunkDescriptor&
SparseInputStreamBuffer* input = reinterpret_cast<SparseInputStreamBuffer*>(sequenceData[j].get());
auto data = make_shared<SparseSequenceData>();
data->m_data = input->m_buffer.data();
// TODO: will be refactored once SparseSequenceData is updated.
data->m_indices.resize(input->m_nnzCounts.size());
auto from = input->m_indices.begin();
for (size_t k = 0; k < input->m_nnzCounts.size(); ++k)
{
auto& indices = data->m_indices[k];
auto nnzCount = input->m_nnzCounts[k];
indices.reserve(nnzCount);
copy(from, from + nnzCount, indices.begin());
from += nnzCount;
}
data->m_indices = input->m_indices.data();
data->m_nnzCounts.reserve(input->m_nnzCounts.size());
copy(input->m_nnzCounts.begin(), input->m_nnzCounts.end(), back_inserter(data->m_nnzCounts));
data->m_totalNnzCount = input->m_totalNnzCount;
data->m_chunk = chunk;
data->m_id = sequenceDescriptor.m_id;
data->m_numberOfSamples = input->m_nnzCounts.size();
sequencePtrs[j] = data;
}
}
@ -535,7 +530,7 @@ bool TextParser<ElemType>::ReadRow(SequenceBuffer& sequence, size_t& bytesToRead
{
SparseInputStreamBuffer* data = reinterpret_cast<SparseInputStreamBuffer*>(sequence[id].get());
vector<ElemType>& values = data->m_buffer;
vector<size_t>& indices = data->m_indices;
vector<IndexType>& indices = data->m_indices;
assert(values.size() == indices.size());
size_t size = values.size();
if (!ReadSparseSample(values, indices, bytesToRead))
@ -556,8 +551,11 @@ bool TextParser<ElemType>::ReadRow(SequenceBuffer& sequence, size_t& bytesToRead
SkipToNextInput(bytesToRead);
continue;
}
assert(values.size() == indices.size());
++data->m_numberOfSamples;
data->m_nnzCounts.push_back(values.size() - size);
IndexType count = static_cast<IndexType>(values.size() - size);
data->m_nnzCounts.push_back(count);
data->m_totalNnzCount += count;
}
found |= true;
@ -732,7 +730,7 @@ bool TextParser<ElemType>::ReadDenseSample(vector<ElemType>& values, size_t samp
}
template <class ElemType>
bool TextParser<ElemType>::ReadSparseSample(std::vector<ElemType>& values, std::vector<size_t>& indices, size_t& bytesToRead)
bool TextParser<ElemType>::ReadSparseSample(std::vector<ElemType>& values, std::vector<IndexType>& indices, size_t& bytesToRead)
{
size_t index;
ElemType value;
@ -761,6 +759,17 @@ bool TextParser<ElemType>::ReadSparseSample(std::vector<ElemType>& values, std::
{
// bail out.
return false;
}
if (index > numeric_limits<IndexType>::max())
{
if (m_traceLevel >= Warning)
{
fprintf(stderr,
"WARNING: sparse index value(%" PRIu64 ") exceeds the maximum allowed "
" value (%" PRIu64 ")\n", index, (size_t)numeric_limits<IndexType>::max());
}
// bail out.
return false;
}
// an index must be followed by a delimiter
@ -790,7 +799,7 @@ bool TextParser<ElemType>::ReadSparseSample(std::vector<ElemType>& values, std::
}
values.push_back(value);
indices.push_back(index);
indices.push_back(static_cast<IndexType>(index));
}
IncrementNumberOfErrorsOrDie();

7
Source/Readers/CNTKTextFormatReader/TextParser.h
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@ -61,8 +61,9 @@ private:
// of NNZ counts (one for each sample).
struct SparseInputStreamBuffer : InputStreamBuffer
{
std::vector<size_t> m_indices;
std::vector<size_t> m_nnzCounts;
IndexType m_totalNnzCount = 0;
std::vector<IndexType> m_indices;
std::vector<IndexType> m_nnzCounts;
};
// A sequence buffer is a vector that contains an input buffer for each input stream.
@ -138,7 +139,7 @@ private:
bool ReadDenseSample(std::vector<ElemType>& values, size_t sampleSize, size_t& bytesToRead);
// Reads sparse sample values and corresponging indices into the provided vectors.
bool ReadSparseSample(std::vector<ElemType>& values, std::vector<size_t>& indices, size_t& bytesToRead);
bool ReadSparseSample(std::vector<ElemType>& values, std::vector<IndexType>& indices, size_t& bytesToRead);
// Reads one whole row (terminated by a row delimiter) of samples
bool ReadRow(SequenceBuffer& sequence, size_t& bytesToRead);

4
Source/Readers/ExperimentalHTKMLFReader/HTKMLFReader.cpp
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@ -11,8 +11,8 @@
#include "ConfigHelper.h"
#include "Bundler.h"
#include "StringUtil.h"
#include "FramePacker.h"
#include "SequencePacker.h"
#include "SampleModePacker.h"
#include "BpttPacker.h"
#include "BlockRandomizer.h"
#include "NoRandomizer.h"
@ -164,7 +164,7 @@ void HTKMLFReader::StartEpoch(const EpochConfiguration& config)
switch (m_packingMode)
{
case PackingMode::sample:
m_packer = std::make_shared<SampleModePacker>(
m_packer = std::make_shared<FramePacker>(
m_provider,
m_randomizer,
config.m_minibatchSizeInSamples,

52
Source/Readers/ExperimentalHTKMLFReader/MLFDataDeserializer.cpp
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@ -4,12 +4,16 @@
//
#include "stdafx.h"
#include <inttypes.h>
#include <limits>
#include "MLFDataDeserializer.h"
#include "ConfigHelper.h"
#include "../HTKMLFReader/htkfeatio.h"
#include "../HTKMLFReader/msra_mgram.h"
#include "latticearchive.h"
#undef max // max is defined in minwindef.h
namespace Microsoft { namespace MSR { namespace CNTK {
static float s_oneFloat = 1.0;
@ -49,6 +53,12 @@ MLFDataDeserializer::MLFDataDeserializer(CorpusDescriptorPtr corpus, const Confi
config.CheckLabelType();
size_t dimension = config.GetLabelDimension();
if (dimension > numeric_limits<IndexType>::max())
{
RuntimeError("Label dimension (%" PRIu64 ") exceeds the maximum allowed "
"value (%" PRIu64 ")\n", dimension, (size_t)numeric_limits<IndexType>::max());
}
// TODO: Similarly to the old reader, currently we assume all Mlfs will have same root name (key)
// restrict MLF reader to these files--will make stuff much faster without having to use shortened input files
@ -154,11 +164,16 @@ MLFDataDeserializer::MLFDataDeserializer(CorpusDescriptorPtr corpus, const Confi
// Initializing array of labels.
m_categories.reserve(dimension);
m_categoryIndices.reserve(dimension);
for (size_t i = 0; i < dimension; ++i)
{
SparseSequenceDataPtr category = std::make_shared<SparseSequenceData>();
category->m_indices.resize(1);
category->m_indices[0] = std::vector<size_t>{ m_categories.size() };
m_categoryIndices.push_back(static_cast<IndexType>(i));
category->m_indices = &(m_categoryIndices[i]);
category->m_nnzCounts.resize(1);
category->m_nnzCounts[0] = 1;
category->m_totalNnzCount = 1;
category->m_numberOfSamples = 1;
if (m_elementType == ElementType::tfloat)
{
category->m_data = &s_oneFloat;
@ -230,13 +245,25 @@ ChunkPtr MLFDataDeserializer::GetChunk(size_t chunkId)
template <class ElemType>
struct MLFSequenceData : SparseSequenceData
{
std::vector<ElemType> m_nonZero;
std::vector<ElemType> m_values;
std::unique_ptr<IndexType[]> m_indicesPtr;
MLFSequenceData(size_t numberOfSamples)
: m_nonZero(numberOfSamples, 1)
MLFSequenceData(size_t numberOfSamples) :
m_values(numberOfSamples, 1),
m_indicesPtr(new IndexType[numberOfSamples])
{
if (numberOfSamples > numeric_limits<IndexType>::max())
{
RuntimeError("Number of samples in an MLFSequence (%" PRIu64 ") "
"exceeds the maximum allowed value (%" PRIu64 ")\n",
numberOfSamples, (size_t)numeric_limits<IndexType>::max());
}
m_nnzCounts.resize(numberOfSamples, static_cast<IndexType>(1));
m_numberOfSamples = numberOfSamples;
m_data = m_nonZero.data();
m_totalNnzCount = static_cast<IndexType>(numberOfSamples);
m_indices = m_indicesPtr.get();
m_data = m_values.data();
}
};
@ -251,7 +278,8 @@ void MLFDataDeserializer::GetSequenceById(size_t sequenceId, std::vector<Sequenc
else
{
// Packing labels for the utterance into sparse sequence.
size_t numberOfSamples = m_utteranceIndex[sequenceId + 1] - m_utteranceIndex[sequenceId];
size_t startFrameIndex = m_utteranceIndex[sequenceId];
size_t numberOfSamples = m_utteranceIndex[sequenceId + 1] - startFrameIndex;
SparseSequenceDataPtr s;
if (m_elementType == ElementType::tfloat)
{
@ -263,13 +291,11 @@ void MLFDataDeserializer::GetSequenceById(size_t sequenceId, std::vector<Sequenc
s = std::make_shared<MLFSequenceData<double>>(numberOfSamples);
}
size_t startFrameIndex = m_utteranceIndex[sequenceId];
s->m_indices.reserve(s->m_numberOfSamples);
for (size_t i = startFrameIndex; i < m_utteranceIndex[sequenceId + 1]; i++)
for (size_t i = 0; i < numberOfSamples; i++)
{
size_t label = m_classIds[m_frames[i].m_index];
s->m_indices.push_back(std::vector<size_t> { label });
size_t frameIndex = startFrameIndex + i;
size_t label = m_classIds[m_frames[frameIndex].m_index];
s->m_indices[i] = static_cast<IndexType>(label);
}
result.push_back(s);
}

4
Source/Readers/ExperimentalHTKMLFReader/MLFDataDeserializer.h
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@ -69,6 +69,10 @@ private:
// We do no allocate data for all input sequences, only returning a pointer to existing category.
std::vector<SparseSequenceDataPtr> m_categories;
// A list of category indices
// (a list of numbers from 0 to N, where N = (number of categories -1))
std::vector<IndexType> m_categoryIndices;
// Flag that indicates whether a single speech frames should be exposed as a sequence.
bool m_frameMode;
};

2
Source/Readers/ImageReader/ImageConfigHelper.cpp
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@ -62,6 +62,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
features->m_id = 0;
features->m_name = msra::strfun::utf16(featureSection.ConfigName());
features->m_sampleLayout = std::make_shared<TensorShape>(ImageDimensions(w, h, c).AsTensorShape(m_dataFormat));
features->m_storageType = StorageType::dense;
m_streams.push_back(features);
ConfigParameters label = config(labelNames[0]);
@ -71,6 +72,7 @@ namespace Microsoft { namespace MSR { namespace CNTK {
labelSection->m_id = 1;
labelSection->m_name = msra::strfun::utf16(label.ConfigName());
labelSection->m_sampleLayout = std::make_shared<TensorShape>(labelDimension);
labelSection->m_storageType = StorageType::dense;
m_streams.push_back(labelSection);
m_mapPath = config(L"file");

24
Source/Readers/ImageReader/ImageDataDeserializer.cpp
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@ -4,10 +4,12 @@
//
#include "stdafx.h"
#include <inttypes.h>
#include <opencv2/opencv.hpp>
#include <numeric>
#include <limits>
#include "ImageDataDeserializer.h"
#include "ImageConfigHelper.h"
#include <inttypes.h>
namespace Microsoft { namespace MSR { namespace CNTK {
@ -27,19 +29,28 @@ template <class TElement>
class TypedLabelGenerator : public ImageDataDeserializer::LabelGenerator
{
public:
TypedLabelGenerator() : m_value(1)
TypedLabelGenerator(size_t labelDimension) : m_value(1), m_indices(labelDimension)
{
if (labelDimension > numeric_limits<IndexType>::max())
{
RuntimeError("Label dimension (%" PRIu64 ") exceeds the maximum allowed "
"value (%" PRIu64 ")\n", labelDimension, (size_t)numeric_limits<IndexType>::max());
}
iota(m_indices.begin(), m_indices.end(), 0);
}
virtual void CreateLabelFor(size_t classId, SparseSequenceData& data) override
{
data.m_indices.resize(1);
data.m_indices[0] = std::vector<size_t>{ classId };
data.m_nnzCounts.resize(1);
data.m_nnzCounts[0] = 1;
data.m_totalNnzCount = 1;
data.m_data = &m_value;
data.m_indices = &(m_indices[classId]);
}
private:
TElement m_value;
vector<IndexType> m_indices;
};
// Used to keep track of the image. Accessed only using DenseSequenceData interface.
@ -98,6 +109,7 @@ public:
SparseSequenceDataPtr label = std::make_shared<SparseSequenceData>();
label->m_chunk = shared_from_this();
m_parent.m_labelGenerator->CreateLabelFor(imageSequence.m_classId, *label);
label->m_numberOfSamples = 1;
result.push_back(label);
}
};
@ -122,11 +134,11 @@ ImageDataDeserializer::ImageDataDeserializer(const ConfigParameters& config)
if (label->m_elementType == ElementType::tfloat)
{
m_labelGenerator = std::make_shared<TypedLabelGenerator<float>>();
m_labelGenerator = std::make_shared<TypedLabelGenerator<float>>(labelDimension);
}
else if (label->m_elementType == ElementType::tdouble)
{
m_labelGenerator = std::make_shared<TypedLabelGenerator<double>>();
m_labelGenerator = std::make_shared<TypedLabelGenerator<double>>(labelDimension);
}
else
{

3
Source/Readers/ImageReader/ImageReader.cpp
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@ -11,6 +11,7 @@
#include "BlockRandomizer.h"
#include "NoRandomizer.h"
#include "ImageDataDeserializer.h"
#include "FramePacker.h"
#include <omp.h>
namespace Microsoft { namespace MSR { namespace CNTK {
@ -81,7 +82,7 @@ void ImageReader::StartEpoch(const EpochConfiguration& config)
}
m_transformer->StartEpoch(config);
m_packer = std::make_shared<SampleModePacker>(
m_packer = std::make_shared<FramePacker>(
m_provider,
m_transformer,
config.m_minibatchSizeInSamples,

4
Source/Readers/ImageReader/ImageReader.h
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@ -7,7 +7,7 @@
#include "Reader.h"
#include "ImageTransformers.h"
#include "SampleModePacker.h"
#include "Packer.h"
namespace Microsoft { namespace MSR { namespace CNTK {
@ -37,7 +37,7 @@ private:
TransformerPtr m_transformer;
// Packer.
SampleModePackerPtr m_packer;
PackerPtr m_packer;
// Seed for the random generator.
unsigned int m_seed;

41
Source/Readers/ReaderLib/BpttPacker.cpp
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@ -19,7 +19,7 @@ using namespace std;
class Slot
{
public:
Slot() : m_length(0), m_sampleCursor(0)
Slot() : m_length(0), m_sampleCursor(0), m_sampleOffset(0)
{}
// Checks if slot is empty.
@ -54,6 +54,7 @@ public:
{
assert(!m_sequences.empty());
m_sampleCursor = 0;
m_sampleOffset = 0;
m_length -= m_sequences.front()->m_numberOfSamples;
m_sequences.pop_front();
}
@ -61,6 +62,12 @@ public:
// Contains the current sample cursor in the first sequence(m_sequences.front()) of the slot.
size_t m_sampleCursor;
// offset of the current sample into the data region of the first sequence.
// For dense input, this is just (sample cursor) x (sample size in bytes).
// For sparse input, this is (element size in bytes) x (sum of nnz counts
// of all preceding samples).
size_t m_sampleOffset;
private:
// Prepared sequences.
deque<SequenceDataPtr> m_sequences;
@ -107,6 +114,12 @@ BpttPacker::BpttPacker(
: PackerBase(memoryProvider, transformer, minibatchSize, streams),
m_truncationSize(truncationSize)
{
auto sparseOutput = find_if(m_outputStreamDescriptions.begin(), m_outputStreamDescriptions.end(), [](const StreamDescriptionPtr& s){ return s->m_storageType == StorageType::sparse_csc; });
if (sparseOutput != m_outputStreamDescriptions.end())
{
RuntimeError("Sparse output is not supported in BPTT mode.");
}
// Estimating the number of parallel sequences to pack (slots) from the minibatch size and truncation size.
m_numParallelSequences = max(1, (int)floor(m_minibatchSize / m_truncationSize));
@ -114,9 +127,8 @@ BpttPacker::BpttPacker(
for (int i = 0; i < m_outputStreamDescriptions.size(); ++i)
{
const auto& stream = m_outputStreamDescriptions[i];
m_streamBufferSizes.push_back(m_numParallelSequences * m_truncationSize * GetSampleSize(stream));
m_streamBuffers.push_back(AllocateBuffer(m_numParallelSequences * m_truncationSize, GetSampleSize(stream)));
auto& buffer = m_streamBuffers[i];
buffer.Resize(m_numParallelSequences * m_truncationSize * GetSampleSize(stream));
m_sequenceBufferPerStream.push_back(make_shared<SequenceBuffer>(m_numParallelSequences));
m_currentLayouts.push_back(make_shared<MBLayout>());
}
@ -150,7 +162,7 @@ Minibatch BpttPacker::ReadMinibatch()
}
StreamMinibatchPtr m = make_shared<StreamMinibatch>();
m->m_data = m_streamBuffers[streamIndex].get();
m->m_data = m_streamBuffers[streamIndex].m_data.get();
m->m_layout = m_currentLayouts[streamIndex];
result.m_data.push_back(m);
}
@ -215,19 +227,28 @@ void BpttPacker::PackSlot(size_t streamIndex, size_t slotIndex)
// Fill in the data from the first sequence in the slot.
auto data = slot.FrontSequence();
// Get buffer destination for the current sample.
void* destination = m_streamBuffers[streamIndex].get() + strideSize * currentTimestep + slotIndex * sampleSize;
assert(destination >= m_streamBuffers[streamIndex].get());
assert(destination < m_streamBuffers[streamIndex].get() + m_streamBufferSizes[streamIndex]);
auto& buffer = m_streamBuffers[streamIndex];
auto offset = strideSize * currentTimestep + slotIndex * sampleSize;
assert(offset >= 0 && offset < buffer.m_size);
char* destination = buffer.m_data.get() + offset;
// Pack the sample.
if (storageType == StorageType::dense)
{
PackDenseSample(destination, data, slot.m_sampleCursor, elementSize, sampleSize);
assert(slot.m_sampleOffset == slot.m_sampleCursor * sampleSize);
PackDenseSample(destination, data, slot.m_sampleOffset, sampleSize);
slot.m_sampleOffset += sampleSize;
}
else
{
assert(storageType == StorageType::sparse_csc);
PackSparseSample(destination, data, slot.m_sampleCursor, elementSize, sampleSize);
// TODO: make type casts members of the SparseSequenceData
SparseSequenceDataPtr sparseSequence = static_pointer_cast<SparseSequenceData>(data);
assert(slot.m_sampleCursor < sparseSequence->m_nnzCounts.size());
PackSparseSampleAsDense(destination, sparseSequence, slot.m_sampleCursor,
slot.m_sampleOffset, sampleSize, elementSize);
slot.m_sampleOffset += sparseSequence->m_nnzCounts[slot.m_sampleCursor];
assert(slot.m_sampleOffset <= sparseSequence->m_totalNnzCount);
}
slot.m_sampleCursor++;

13
Source/Readers/ReaderLib/BpttPacker.h
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@ -38,6 +38,12 @@ private:
// Packs a slot into the data buffer.
void PackSlot(size_t streamIndex, size_t slotIndex);
virtual MBLayoutPtr CreateMBLayout(const StreamBatch& batch)
{
UNUSED(batch);
NOT_IMPLEMENTED;
}
// Parallel number of sequences to pack.
// Calculated based on the truncation size and minibatch size in samples.
size_t m_numParallelSequences;
@ -53,13 +59,6 @@ private:
// Layout per stream.
// TODO: currently assume that layout is the same between different streams, this will change.
std::vector<MBLayoutPtr> m_currentLayouts;
// Buffers for allocated data.
std::vector<std::shared_ptr<char>> m_streamBuffers;
// Size of allocated buffers, m_streamBuffers.size() == m_streamBufferSizes.size().
// TODO: this will disappear after Alexey's merge (StreamBuffer class).
std::vector<size_t> m_streamBufferSizes;
};
typedef std::shared_ptr<BpttPacker> BpttPackerPtr;

11
Source/Readers/ReaderLib/DataDeserializer.h
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@ -40,19 +40,20 @@ typedef std::shared_ptr<SequenceDescription> SequenceDescriptionPtr;
// Currently CNTK supports dense and sparse sequences (csc).
// The storageType in the corresponding stream description identifies what type of SequenceData
// data deserializer or transformer can provide provides.
// TODO: add type casts (As<T>() or AsRef<>() or AsPtr<>()) to subclasses as members here.
struct SequenceDataBase
{
SequenceDataBase() : m_data(nullptr), m_numberOfSamples(0) { }
SequenceDataBase() : m_id(0), m_numberOfSamples(0), m_data(nullptr) {}
virtual ~SequenceDataBase() = default;
// Sequence id.
size_t m_id;
size_t m_numberOfSamples; // Number of samples in the sequence
ChunkPtr m_chunk;
// A non-owned pointer. The actual size is provided for particular sequences,
// i.e. see DenseSequenceData, or SparseSequenceData.
void* m_data;
size_t m_numberOfSamples; // Number of samples in the sequence
};
typedef std::shared_ptr<SequenceDataBase> SequenceDataPtr;
@ -72,7 +73,11 @@ typedef std::shared_ptr<DenseSequenceData> DenseSequenceDataPtr;
// All samples in the sequence should have the same layout.
struct SparseSequenceData : SequenceDataBase
{
std::vector<std::vector<size_t>> m_indices;
IndexType* m_indices; // an index for every value in the m_data array
std::vector<IndexType> m_nnzCounts; // nnz count for each sample in the sequence
IndexType m_totalNnzCount; // sum of all nzzCounts of all samples
// Using IndexType for both properties above since the nnzCount should fit inside
// the index type (in CSC format, the last value in the column index array == nnzCount)
};
typedef std::shared_ptr<SparseSequenceData> SparseSequenceDataPtr;

29
Source/Readers/ReaderLib/FramePacker.cpp Normal file
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@ -0,0 +1,29 @@
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#define _CRT_SECURE_NO_WARNINGS
#define _SCL_SECURE_NO_WARNINGS
#include <algorithm>
#include "FramePacker.h"
namespace Microsoft { namespace MSR { namespace CNTK {
MBLayoutPtr FramePacker::CreateMBLayout(const StreamBatch& batch)
{
auto violation = find_if(batch.begin(), batch.end(), [](const SequenceDataPtr& s){ return s->m_numberOfSamples > 1; });
if (violation != batch.end())
{
RuntimeError("Detected a non-frame sequence of size %d in frame mode.",
(int)(*violation)->m_numberOfSamples);
}
// Creating the minibatch layout.
MBLayoutPtr pMBLayout = make_shared<MBLayout>();
pMBLayout->InitAsFrameMode(batch.size());
return pMBLayout;
}
} } }

32
Source/Readers/ReaderLib/FramePacker.h Normal file
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@ -0,0 +1,32 @@
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#pragma once
#include "SequencePacker.h"
namespace Microsoft { namespace MSR { namespace CNTK {
//A packer optimized for the case of single-frame sequences.
class FramePacker : public SequencePacker
{
public:
FramePacker(
MemoryProviderPtr memoryProvider,
TransformerPtr transformer,
size_t minibatchSize,
const std::vector<StreamDescriptionPtr>& streams) :
SequencePacker(memoryProvider, transformer, minibatchSize, streams)
{
}
private:
MBLayoutPtr CreateMBLayout(const StreamBatch& batch) override;
};
typedef std::shared_ptr<FramePacker> FramePackerPtr;
} } }

2
Source/Readers/ReaderLib/MemoryProvider.h
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@ -22,6 +22,8 @@ public:
// Frees contiguous storage.
virtual void Free(void* ptr) = 0;
// TODO: add Resize function.
virtual ~MemoryProvider() { }
};

76
Source/Readers/ReaderLib/PackerBase.cpp
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@ -13,33 +13,36 @@ namespace Microsoft { namespace MSR { namespace CNTK {
using namespace std;
// TODO: this should be handled by the memory provider
void PackerBase::StreamBuffer::Resize(size_t newSize)
{
m_size = newSize;
m_data.reset(reinterpret_cast<char*>(m_memoryProvider->Alloc(1, newSize)),
[this](char* p)
{
m_memoryProvider->Free(p);
});
}
PackerBase::PackerBase(MemoryProviderPtr memoryProvider,
TransformerPtr transformer,
size_t minibatchSize,
const vector<StreamDescriptionPtr>& streams)
: m_memoryProvider(memoryProvider),
const std::vector<StreamDescriptionPtr>& streams) :
m_transformer(transformer),
m_minibatchSize(minibatchSize),
m_outputStreamDescriptions(streams)
{
m_inputStreamDescriptions = m_transformer->GetStreamDescriptions();
assert(m_inputStreamDescriptions.size() == m_outputStreamDescriptions.size());
assert(m_inputStreamDescriptions.size() != 0);
// Currently do not support sparse output.
// TODO: Will be supported in the future.
auto sparseOutput = find_if(m_outputStreamDescriptions.begin(), m_outputStreamDescriptions.end(), [](const StreamDescriptionPtr& s){ return s->m_storageType == StorageType::sparse_csc; });
if (sparseOutput != m_outputStreamDescriptions.end())
{
RuntimeError("Sparse sequences are currently not supported.");
}
assert(m_inputStreamDescriptions.size() == m_outputStreamDescriptions.size());
if (m_minibatchSize == 0)
{
LogicError("Minibatch size cannot be zero.");
}
m_streamBuffers.reserve(m_outputStreamDescriptions.size());
// Sanity checks:
for (size_t i = 0; i < m_outputStreamDescriptions.size(); ++i)
{
@ -51,39 +54,18 @@ PackerBase::PackerBase(MemoryProviderPtr memoryProvider,
assert(stream->m_name == m_inputStreamDescriptions[i]->m_name);
assert(stream->m_id == m_inputStreamDescriptions[i]->m_id);
assert(GetSampleSize(m_inputStreamDescriptions[i]) == GetSampleSize(stream));
if (m_inputStreamDescriptions[i]->m_storageType == StorageType::dense &&
stream->m_storageType == StorageType::sparse_csc)
{
RuntimeError("Dense to sparse re-packing requested for stream '%ls' is not supported.",
stream->m_name.c_str());
}
m_streamBuffers.push_back(StreamBuffer(memoryProvider));
}
}
// Packs a sparse sample as dense.
void PackerBase::PackSparseSample(void* destination, SequenceDataPtr sequence, size_t sample, size_t elementSize, size_t sampleSize)
{
// Because the sample is sparse firstly prepare the buffer and set everything to zero.
memset(destination, 0, sampleSize);
SparseSequenceDataPtr s = static_pointer_cast<SparseSequenceData>(sequence);
const auto& rowIndexes = s->m_indices[sample];
size_t nonZeroCount = rowIndexes.size();
// Iterate through non zero elements and copy them to the corresponding place using their index.
// Sample is a sparse vector encoded as csc: m_data points to the array of non zero elements,
// m_indices[sample] stores the non-zero row indexes for the sample.
for (size_t nonZeroIndex = 0; nonZeroIndex < nonZeroCount; ++nonZeroIndex)
{
memcpy(
(char*)destination + rowIndexes[nonZeroIndex] * elementSize,
(const char*)(s->m_data) + nonZeroIndex * elementSize,
elementSize);
}
}
// Packs a dense sample as dense.
void PackerBase::PackDenseSample(void* destination, SequenceDataPtr sequence, size_t sample, size_t elementSize, size_t sampleSize)
{
UNUSED(elementSize);
// Because the sample is dense - simply copying it to the output.
memcpy(destination, (char*)(sequence->m_data) + sample * sampleSize, sampleSize);
}
// Gets samples size in bytes.
size_t PackerBase::GetSampleSize(StreamDescriptionPtr stream)
{
@ -92,12 +74,4 @@ size_t PackerBase::GetSampleSize(StreamDescriptionPtr stream)
return stream->m_sampleLayout->GetNumElements() * elementSize;
}
// Allocates a buffer for the specified number of elements and a given size of an element.
shared_ptr<char> PackerBase::AllocateBuffer(size_t numElements, size_t elementSize)
{
return shared_ptr<char>(
reinterpret_cast<char*>(m_memoryProvider->Alloc(elementSize, numElements)),
[this](char* p) { m_memoryProvider->Free(p); });
}
}}}
}}}

78
Source/Readers/ReaderLib/PackerBase.h
Просмотреть файл

@ -17,20 +17,47 @@ namespace Microsoft { namespace MSR { namespace CNTK {
class PackerBase : public Packer
{
protected:
struct StreamBuffer
{
size_t m_size; // buffer size in bytes.
// Memory provider.
// TODO: Should possibly switch to matrices here.
MemoryProviderPtr m_memoryProvider;
std::shared_ptr<char> m_data; // contiguous array of data.
StreamBuffer(MemoryProviderPtr m_memoryProvider) :
m_size(0), m_memoryProvider(m_memoryProvider), m_data(nullptr)
{
}
void Resize(size_t newSize);
};
PackerBase(MemoryProviderPtr memoryProvider,
TransformerPtr transformer,
size_t minibatchSize,
const std::vector<StreamDescriptionPtr>& streams);
TransformerPtr transformer,
size_t minibatchSize,
const std::vector<StreamDescriptionPtr>& streams);
typedef std::vector<SequenceDataPtr> StreamBatch;
protected:
std::shared_ptr<char> AllocateBuffer(size_t numElements, size_t elementSize);
size_t GetSampleSize(StreamDescriptionPtr stream);
void PackSparseSample(void* destination, SequenceDataPtr sequence, size_t sample, size_t elementSize, size_t sampleSize);
void PackDenseSample(void* destination, SequenceDataPtr sequence, size_t sample, size_t elementSize, size_t sampleSize);
// Memory provider.
// TODO: Should possibly switch to matrices here.
MemoryProviderPtr m_memoryProvider;
// Packs a sparse sample as dense:
// - 0-fills a region of sampleSize bytes in the block of memory pointed to by destination;
// - copies non-zero values of the required sample (given by sampleIndex) from the data
// portion of the source sequence to the destination block of memory, where each value is placed
// at the offset equal to value index * elementSize. sampleOffset specifies the offset of the
// first value from the given sample in the sequence data/indices array (sampleOffset is equal
// to the sum of non-zero value counts of all preceding samples).
void PackSparseSampleAsDense(char* destination, SparseSequenceDataPtr sequence,
size_t sampleIndex, size_t sampleOffset, size_t sampleSize, size_t elementSize);
// Packs a dense sample as dense. Copies sampleSize bytes staring at the sampleOffset from
// the data portion of the source sequence to the destination block of memory. sampleOffset
// specifies the offset of the first value from the given sample in the sequence data/ array
// (sampleOffset is equal to the sum of sample sizes of all preceding samples).
void PackDenseSample(char* destination, SequenceDataPtr sequence, size_t sampleOffset, size_t sampleSize);
TransformerPtr m_transformer;
// Input stream descriptions provided by the transformer.
@ -39,8 +66,39 @@ protected:
// Output stream descriptions expected by the network.
std::vector<StreamDescriptionPtr> m_inputStreamDescriptions;
// Buffers for allocated data.
std::vector<StreamBuffer> m_streamBuffers;
// Minibatch size in samples.
size_t m_minibatchSize;
};
inline void PackerBase::PackSparseSampleAsDense(char* destination, SparseSequenceDataPtr sequence,
size_t sampleIndex, size_t sampleOffset, size_t sampleSize, size_t elementSize)
{
//The sample is sparse, first, need to zero out the buffer.
memset(destination, 0, sampleSize);
// Get the nnz count of the sample.
size_t nonZeroCount = sequence->m_nnzCounts[sampleIndex];
// In a sparse sequence, m_data points to the array of non zero elements,
// m_indices stores the corresponding indices for each element.
// Iterate through non zero elements and copy from m_data them into the
// destination at the offset given by the corresponding row index (m_index).
for (size_t nonZeroIndex = 0; nonZeroIndex < nonZeroCount; ++nonZeroIndex)
{
auto sourceOffset = sampleOffset + nonZeroIndex;
auto elementIndex = sequence->m_indices[sourceOffset];
auto destinationOffset = elementIndex * elementSize;
assert(destinationOffset < sampleSize);
const auto* source = (const char*)(sequence->m_data) + (sourceOffset)* elementSize;
memcpy(destination + destinationOffset, source, elementSize);
}
}
inline void PackerBase::PackDenseSample(char* destination, SequenceDataPtr sequence, size_t sampleOffset, size_t sampleSize)
{
// Because the sample is dense - simply copying it to the output.
memcpy(destination, (const char*)(sequence->m_data) + sampleOffset, sampleSize);
}
}}}

2
Source/Readers/ReaderLib/Reader.h
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@ -12,6 +12,8 @@
namespace Microsoft { namespace MSR { namespace CNTK {
typedef GPUSPARSE_INDEX_TYPE IndexType;
typedef std::shared_ptr<TensorShape> TensorShapePtr;
struct MBLayout;

4
Source/Readers/ReaderLib/ReaderLib.vcxproj
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@ -92,7 +92,7 @@
<ClInclude Include="CudaMemoryProvider.h" />
<ClInclude Include="DataDeserializer.h" />
<ClInclude Include="ElementTypeUtils.h" />
<ClInclude Include="SampleModePacker.h" />
<ClInclude Include="FramePacker.h" />
<ClInclude Include="HeapMemoryProvider.h" />
<ClInclude Include="MemoryProvider.h" />
<ClInclude Include="Reader.h" />
@ -106,7 +106,7 @@
<ClCompile Include="NoRandomizer.cpp" />
<ClCompile Include="BlockRandomizer.cpp" />
<ClCompile Include="PackerBase.cpp" />
<ClCompile Include="SampleModePacker.cpp" />
<ClCompile Include="FramePacker.cpp" />
<ClCompile Include="ReaderShim.cpp" />
<ClCompile Include="SequencePacker.cpp" />
<ClCompile Include="SequenceRandomizer.cpp" />

20
Source/Readers/ReaderLib/ReaderLib.vcxproj.filters
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@ -34,9 +34,6 @@
<ClInclude Include="DataDeserializerBase.h">
<Filter>Deserializers</Filter>
</ClInclude>
<ClInclude Include="SampleModePacker.h">
<Filter>Packers</Filter>
</ClInclude>
<ClInclude Include="Bundler.h">
<Filter>Deserializers</Filter>
</ClInclude>
@ -58,10 +55,13 @@
<ClInclude Include="SequencePacker.h">
<Filter>Packers</Filter>
</ClInclude>
<ClInclude Include="BpttPacker.h">
<ClInclude Include="PackerBase.h">
<Filter>Packers</Filter>
</ClInclude>
<ClInclude Include="PackerBase.h">
<ClInclude Include="FramePacker.h">
<Filter>Packers</Filter>
</ClInclude>
<ClInclude Include="BpttPacker.h">
<Filter>Packers</Filter>
</ClInclude>
</ItemGroup>
@ -72,9 +72,6 @@
<ClCompile Include="ReaderShim.cpp">
<Filter>Utils</Filter>
</ClCompile>
<ClCompile Include="SampleModePacker.cpp">
<Filter>Packers</Filter>
</ClCompile>
<ClCompile Include="Bundler.cpp">
<Filter>Deserializers</Filter>
</ClCompile>
@ -90,10 +87,13 @@
<ClCompile Include="SequencePacker.cpp">
<Filter>Packers</Filter>
</ClCompile>
<ClCompile Include="BpttPacker.cpp">
<ClCompile Include="PackerBase.cpp">
<Filter>Packers</Filter>
</ClCompile>
<ClCompile Include="PackerBase.cpp">
<ClCompile Include="FramePacker.cpp">
<Filter>Packers</Filter>
</ClCompile>
<ClCompile Include="BpttPacker.cpp">
<Filter>Packers</Filter>
</ClCompile>
</ItemGroup>

81
Source/Readers/ReaderLib/ReaderShim.cpp
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@ -10,6 +10,8 @@
#ifdef _WIN32
#include <objbase.h>
#endif
#include <sstream>
#include "Basics.h"
#define DATAREADER_EXPORTS // creating the exports here
@ -82,9 +84,31 @@ void ReaderShim<ElemType>::StartDistributedMinibatchLoop(
});
}
string EnumerateInputs(const map<wstring, size_t> &nameToStreamId)
{
// TODO use boost::algorithm::join, boost::adapters::transformed, make this a generic function
std::stringstream str;
bool first = true;
for (auto s : nameToStreamId)
{
str << (first ? "" : ", ");
auto name = msra::strfun::utf8(s.first);
str << '\"' << name.c_str() << '\"';
first = false;
}
return str.str();
}
template <class ElemType>
bool ReaderShim<ElemType>::GetMinibatch(StreamMinibatchInputs& matrices)
{
// TODO: verify that the set of matrix names is identical
// to the set of reader input names. Warn if it's a subset, throw
// if it's a superset.
if (m_endOfEpoch)
{
return false;
@ -119,28 +143,61 @@ bool ReaderShim<ElemType>::GetMinibatch(StreamMinibatchInputs& matrices)
// Copy returned minibatch to the matrices.
for (const auto& mx : matrices)
{
assert(m_nameToStreamId.find(mx.first) != m_nameToStreamId.end());
if (m_nameToStreamId.find(mx.first) == m_nameToStreamId.end())
{
string inputNames = EnumerateInputs(m_nameToStreamId);
RuntimeError("Could not map input '%ls' to the reader. Reader outputs only [%s].",
mx.first.c_str(), inputNames.c_str());
}
size_t streamId = m_nameToStreamId[mx.first];
const auto& stream = minibatch.m_data[streamId];
m_layout->CopyFrom(stream->m_layout);
size_t columnNumber = m_layout->GetNumCols();
size_t rowNumber = m_streams[streamId]->m_sampleLayout->GetNumElements();
auto* data = reinterpret_cast<const ElemType*>(stream->m_data);
matrices.GetInputMatrix<ElemType>(mx.first).SetValue(rowNumber, columnNumber, mx.second->GetDeviceId(), const_cast<ElemType*>(data), matrixFlagNormal);
m_layout = stream->m_layout;
size_t sampleSize = m_streams[streamId]->m_sampleLayout->GetNumElements();
auto& matrix = matrices.GetInputMatrix<ElemType>(mx.first);
FillMatrixFromStream(m_streams[streamId]->m_storageType, &matrix, sampleSize, stream);
}
}
m_prefetchTask = std::async(m_launchType, [this]()
if (!m_endOfEpoch)
{
return m_reader->ReadMinibatch();
});
m_prefetchTask = std::async(m_launchType, [this]()
{
return m_reader->ReadMinibatch();
});
}
return !minibatch.m_data.empty();
}
template <class ElemType>
void ReaderShim<ElemType>::FillMatrixFromStream(StorageType type, Matrix<ElemType>* matrix, size_t numRows, const StreamMinibatchPtr& stream)
{
size_t numCols = stream->m_layout->GetNumCols();
if (type == StorageType::dense)
{
auto data = reinterpret_cast<const ElemType*>(stream->m_data);
matrix->SetValue(numRows, numCols, matrix->GetDeviceId(), const_cast<ElemType*>(data), matrixFlagNormal);
}
else if (type == StorageType::sparse_csc)
{
// In the sparse case the m_data layout is identical to CUDA's CSC layout
// (see http://docs.nvidia.com/cuda/cusparse/#compressed-sparse-column-format-csc).
size_t* data = reinterpret_cast<size_t*>(stream->m_data);
size_t nnzCount = *data;
ElemType* values = reinterpret_cast<ElemType*>(data + 1);
IndexType* rows = reinterpret_cast<IndexType*>(values + nnzCount);
IndexType* columns = reinterpret_cast<IndexType*>(rows + nnzCount);
matrix->SetMatrixFromCSCFormat(columns, rows, values, nnzCount, numRows, numCols);
}
else
{
RuntimeError("Storage type %d is not supported.", (int)type);
}
}
template <class ElemType>
bool ReaderShim<ElemType>::DataEnd() { return false; } // Note: Return value never used.

2
Source/Readers/ReaderLib/ReaderShim.h
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@ -63,6 +63,8 @@ private:
std::map<std::wstring, size_t> m_nameToStreamId;
std::vector<StreamDescriptionPtr> m_streams;
launch m_launchType;
void FillMatrixFromStream(StorageType type, Matrix<ElemType>* matrix, size_t numRows, const StreamMinibatchPtr& stream);
};
}}}

148
Source/Readers/ReaderLib/SampleModePacker.cpp
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@ -1,148 +0,0 @@
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#define _CRT_SECURE_NO_WARNINGS
#define _SCL_SECURE_NO_WARNINGS
#include "SampleModePacker.h"
#include "ElementTypeUtils.h"
namespace Microsoft { namespace MSR { namespace CNTK {
SampleModePacker::SampleModePacker(
MemoryProviderPtr memoryProvider,
TransformerPtr transformer,
size_t minibatchSize,
const std::vector<StreamDescriptionPtr>& streams) : m_transformer(transformer),
m_minibatchSize(minibatchSize),
m_outputStreams(streams),
m_minibatchLayout(std::make_shared<MBLayout>()),
m_memoryProvider(memoryProvider)
{
m_inputStreams = m_transformer->GetStreamDescriptions();
assert(m_inputStreams.size() == m_outputStreams.size());
assert(
std::find_if(
m_outputStreams.begin(),
m_outputStreams.end(),
[](const StreamDescriptionPtr& s)
{
return s->m_storageType == StorageType::sparse_csc;
}) == m_outputStreams.end());
assert(m_minibatchSize > 0);
for (int i = 0; i < m_outputStreams.size(); ++i)
{
const auto& stream = m_outputStreams[i];
// Input and output should match in everything except for sparse/dense.
assert(stream->m_elementType == ElementType::tfloat || stream->m_elementType == ElementType::tdouble);
assert(stream->m_name == m_inputStreams[i]->m_name);
assert(stream->m_id == m_inputStreams[i]->m_id);
assert(GetSampleSize(m_inputStreams[i]) == GetSampleSize(stream));
m_streamBuffers.push_back(
AllocateBuffer(m_minibatchSize * stream->m_sampleLayout->GetNumElements(), GetSizeByType(stream->m_elementType)));
}
}
Minibatch SampleModePacker::ReadMinibatch()
{
auto sequences = m_transformer->GetNextSequences(m_minibatchSize);
Minibatch minibatch(sequences.m_endOfEpoch);
if (sequences.m_data.empty())
{
return minibatch;
}
assert(m_streamBuffers.size() == sequences.m_data.size());
// For each sequence iterating thru all the streams with this sequence id and copying to the buffer.
for (size_t streamIndex = 0; streamIndex < sequences.m_data.size(); streamIndex++)
{
const auto& streamSequences = sequences.m_data[streamIndex];
// Iterating for sequences inside the batch of sequences.
for (size_t sequenceIndex = 0; sequenceIndex < sequences.m_data[streamIndex].size(); ++sequenceIndex)
{
CopySequenceToBuffer(streamSequences[sequenceIndex], streamIndex, sequenceIndex);
}
}
// Creating output minibatch with shared layout between all streams.
m_minibatchLayout->InitAsFrameMode(sequences.m_data.front().size());
for (int i = 0; i < m_outputStreams.size(); ++i)
{
auto stream = std::make_shared<StreamMinibatch>();
stream->m_data = m_streamBuffers[i].get();
stream->m_layout = m_minibatchLayout;
minibatch.m_data.push_back(stream);
}
return minibatch;
}
size_t SampleModePacker::GetSampleSize(StreamDescriptionPtr stream)
{
assert(stream != nullptr);
size_t elementSize = GetSizeByType(stream->m_elementType);
return stream->m_sampleLayout->GetNumElements() * elementSize;
}
void SampleModePacker::CopySequenceToBuffer(SequenceDataPtr sample, size_t streamIndex, size_t sampleIndex)
{
// In framemode sequence just contains a single sample.
size_t sampleSize = GetSampleSize(m_inputStreams[streamIndex]);
auto sampleData = reinterpret_cast<const char*>(sample->m_data);
const auto& stream = m_inputStreams[streamIndex];
auto elementSize = GetSizeByType(stream->m_elementType);
auto buffer = m_streamBuffers[streamIndex].get();
if (stream->m_storageType == StorageType::dense)
{
// Expect single sample.
assert(reinterpret_cast<DenseSequenceData&>(*sample).m_numberOfSamples == 1);
// Copying the sequence to its position in the buffer. Effectivly a buffer contains concatenation of samples for a stream.
std::copy(sampleData, sampleData + sampleSize, buffer + sampleIndex * sampleSize);
}
else if (stream->m_storageType == StorageType::sparse_csc)
{
const auto& data = reinterpret_cast<SparseSequenceData&>(*sample);
// Expect single sample.
assert(data.m_indices.size() == 1);
// Currently sparse data has to be unpacked to the dense one. Possibly can be done later
// in the network or as a transformation.
// Fill it in with zeros.
std::fill(buffer + sampleIndex * sampleSize, buffer + (sampleIndex + 1) * sampleSize, 0);
// Copy the non zero data to the buffer.
size_t nonZeroCount = data.m_indices[0].size();
for (size_t nonZeroIndex = 0; nonZeroIndex < nonZeroCount; ++nonZeroIndex)
{
size_t rowIndex = data.m_indices[0][nonZeroIndex];
char* destination = buffer + sampleIndex * sampleSize + rowIndex * elementSize;
std::copy(sampleData + nonZeroIndex * elementSize, sampleData + (nonZeroIndex + 1) * elementSize, destination);
}
}
else
{
RuntimeError("Storage type %d is not supported.", (int)m_inputStreams[streamIndex]->m_storageType);
}
}
std::shared_ptr<char> SampleModePacker::AllocateBuffer(size_t numElements, size_t elementSize)
{
return std::shared_ptr<char>(
reinterpret_cast<char*>(m_memoryProvider->Alloc(elementSize, numElements)),
[this](char* p)
{
m_memoryProvider->Free(p);
});
}
} } }

43
Source/Readers/ReaderLib/SampleModePacker.h
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@ -1,43 +0,0 @@
//
// Copyright (c) Microsoft. All rights reserved.
// Licensed under the MIT license. See LICENSE.md file in the project root for full license information.
//
#pragma once
#include "Reader.h"
#include "MemoryProvider.h"
#include "Transformer.h"
#include "Packer.h"
namespace Microsoft { namespace MSR { namespace CNTK {
// A sample packer that densely packs samples in parallel for GPU consumptions.
class SampleModePacker : public Packer
{
public:
SampleModePacker(
MemoryProviderPtr memoryProvider,
TransformerPtr transformer,
size_t minibatchSize,
const std::vector<StreamDescriptionPtr>& streams);
virtual Minibatch ReadMinibatch() override;
private:
std::shared_ptr<char> AllocateBuffer(size_t numElements, size_t elementSize);
size_t GetSampleSize(StreamDescriptionPtr stream);
void CopySequenceToBuffer(SequenceDataPtr sample, size_t streamIndex, size_t sampleIndex);
MemoryProviderPtr m_memoryProvider;
TransformerPtr m_transformer;
std::vector<StreamDescriptionPtr> m_outputStreams;
std::vector<StreamDescriptionPtr> m_inputStreams;
std::vector<std::shared_ptr<char>> m_streamBuffers;
MBLayoutPtr m_minibatchLayout;
size_t m_minibatchSize;
};
typedef std::shared_ptr<SampleModePacker> SampleModePackerPtr;
} } }

294
Source/Readers/ReaderLib/SequencePacker.cpp
Просмотреть файл

@ -6,114 +6,270 @@
#define _CRT_SECURE_NO_WARNINGS
#define _SCL_SECURE_NO_WARNINGS
#include <numeric>
#include <inttypes.h>
#include "SequencePacker.h"
#include "ElementTypeUtils.h"
namespace Microsoft { namespace MSR { namespace CNTK {
SequencePacker::SequencePacker(
MemoryProviderPtr memoryProvider,
TransformerPtr transformer,
size_t minibatchSize,
const std::vector<StreamDescriptionPtr>& streams) : PackerBase(memoryProvider, transformer, minibatchSize, streams)
MBLayoutPtr SequencePacker::CreateMBLayout(const StreamBatch& batch)
{
for (int i = 0; i < m_outputStreamDescriptions.size(); ++i)
vector<MBLayout::SequenceInfo> infos;
for (size_t index = 0; index < batch.size(); ++index)
{
m_streamBufferSizes.push_back(0);
m_streamBuffers.push_back(nullptr);
MBLayout::SequenceInfo info;
info.seqId = index;
info.tBegin = 0;
info.tEnd = batch[index]->m_numberOfSamples;
infos.push_back(info);
}
vector<pair<size_t, size_t>> placement;
vector<size_t> rowAllocations;
// Creating the minibatch layout.
MBLayoutPtr pMBLayout = make_shared<MBLayout>();
pMBLayout->InitAsPackedSequences(infos, placement, rowAllocations);
return pMBLayout;
}
// Reading minibatch.
Minibatch SequencePacker::ReadMinibatch()
{
assert(m_streamBufferSizes.size() == m_streamBuffers.size());
const auto sequences = m_transformer->GetNextSequences(m_minibatchSize);
auto sequences = m_transformer->GetNextSequences(m_minibatchSize);
const auto& batch = sequences.m_data;
Minibatch minibatch(sequences.m_endOfEpoch);
if (sequences.m_data.empty())
if (batch.empty())
{
return minibatch;
}
// For each stream packing the minibatch.
minibatch.m_data.reserve(sequences.m_data.size());
for (size_t streamIndex = 0; streamIndex < sequences.m_data.size(); ++streamIndex)
assert(m_outputStreamDescriptions.size() == batch.size());
for (int streamIndex = 0; streamIndex < batch.size(); ++streamIndex)
{
minibatch.m_data.push_back(PackStreamMinibatch(sequences.m_data[streamIndex], streamIndex));
const auto& streamBatch = batch[streamIndex];
const auto& type = m_outputStreamDescriptions[streamIndex]->m_storageType;
auto pMBLayout = (type == StorageType::dense) ?
PackDenseStream(streamBatch, streamIndex) : PackSparseStream(streamBatch, streamIndex);
auto& buffer = m_streamBuffers[streamIndex];
auto streamMinibatch = std::make_shared<StreamMinibatch>();
streamMinibatch->m_data = buffer.m_data.get();
streamMinibatch->m_layout = pMBLayout;
minibatch.m_data.push_back(streamMinibatch);
}
return minibatch;
}
StreamMinibatchPtr SequencePacker::PackStreamMinibatch(const std::vector<SequenceDataPtr>& sequences, size_t streamId)
MBLayoutPtr SequencePacker::PackDenseStream(const StreamBatch& batch, size_t streamIndex)
{
// Create sequence info for each sequences that we have got from the transformer.
std::vector<MBLayout::SequenceInfo> inputSequences;
for (size_t index = 0; index < sequences.size(); ++index)
assert(m_outputStreamDescriptions[streamIndex]->m_storageType == StorageType::dense);
const auto& stream = m_inputStreamDescriptions[streamIndex];
auto& buffer = m_streamBuffers[streamIndex];
size_t sampleSize = GetSampleSize(stream);
auto pMBLayout = CreateMBLayout(batch);
size_t requiredSize = pMBLayout->GetNumCols() * sampleSize;
if (buffer.m_size < requiredSize)
{
MBLayout::SequenceInfo info;
// In each minibatch sequence ids should be unique.
// They have to match between different input streams in the same minibatch.
// We are using sequence index in the set of received sequences.
// TODO: should we use m_key as sequence id and pass it with data?
info.seqId = index;
info.tBegin = 0;
info.tEnd = sequences[index]->m_numberOfSamples;
inputSequences.push_back(info);
buffer.Resize(requiredSize);
}
std::vector<std::pair<size_t, size_t>> placement;
std::vector<size_t> rowAllocations;
auto elementSize = GetSizeByType(stream->m_elementType);
// Creating the minibatch layout.
MBLayoutPtr layout = std::make_shared<MBLayout>();
layout->InitAsPackedSequences(inputSequences, placement, rowAllocations);
const auto& sequenceInfos = pMBLayout->GetAllSequences();
// Allocating necessary data buffer for the stream.
size_t sampleSize = GetSampleSize(m_inputStreamDescriptions[streamId]);
size_t totalNumberOfSamplesInBytes = layout->GetNumCols() * sampleSize;
if (m_streamBufferSizes[streamId] < totalNumberOfSamplesInBytes)
// Iterate over sequences in the layout, copy samples from the
// source sequences into the buffer (at appropriate offsets).
for (const auto& sequenceInfo : sequenceInfos)
{
m_streamBuffers[streamId] = AllocateBuffer(layout->GetNumCols(), sampleSize);
m_streamBufferSizes[streamId] = totalNumberOfSamplesInBytes;
}
// Packing the actual data.
StorageType storageType = m_inputStreamDescriptions[streamId]->m_storageType;
size_t elementSize = GetSizeByType(m_inputStreamDescriptions[streamId]->m_elementType);
const auto& packedSequences = layout->GetAllSequences();
char* streamBuffer = m_streamBuffers[streamId].get();
for (const auto& sequence : packedSequences)
{
if (sequence.seqId == GAP_SEQUENCE_ID)
continue;
const auto& data = sequences[sequence.seqId];
// Packing the sequence
for (size_t sampleIndex = 0; sampleIndex < sequence.GetNumTimeSteps(); ++sampleIndex)
// skip gaps
if (sequenceInfo.seqId == GAP_SEQUENCE_ID)
{
char* destination = streamBuffer + layout->GetColumnIndex(sequence, sampleIndex) * sampleSize;
if (storageType == StorageType::dense)
continue;
}
const auto& sequence = batch[sequenceInfo.seqId];
size_t numSamples = sequence->m_numberOfSamples;
assert(numSamples == sequenceInfo.GetNumTimeSteps());
char* bufferPtr = buffer.m_data.get();
// Iterate over all samples in the sequence, keep track of the sample offset (which is especially
// important for sparse input, where offset == number of preceding nnz elements).
for (size_t sampleIndex = 0, sampleOffset = 0; sampleIndex < numSamples; ++sampleIndex)
{
// Compute the offset into the destination buffer, using the layout information
// to get the column index corresponding to the given sample.
auto destinationOffset = pMBLayout->GetColumnIndex(sequenceInfo, sampleIndex) * sampleSize;
// verify that there's enough space left in the buffer to fit a full sample.
assert(destinationOffset <= buffer.m_size - sampleSize);
auto* destination = bufferPtr + destinationOffset;
if (stream->m_storageType == StorageType::dense)
{
PackDenseSample(destination, data, sampleIndex, elementSize, sampleSize);
// verify that the offset (an invariant for dense).
assert(sampleOffset == sampleIndex * sampleSize);
PackDenseSample(destination, sequence, sampleOffset, sampleSize);
sampleOffset += sampleSize;
}
else // sparse
else if (stream->m_storageType == StorageType::sparse_csc)
{
assert(storageType == StorageType::sparse_csc);
PackSparseSample(destination, data, sampleIndex, elementSize, sampleSize);
// TODO: make type casts members of the SparseSequenceData
SparseSequenceDataPtr sparseSequence = static_pointer_cast<SparseSequenceData>(sequence);
// make sure that the sequence meta-data is correct.
assert(numSamples == sparseSequence->m_nnzCounts.size());
PackSparseSampleAsDense(destination, sparseSequence, sampleIndex, sampleOffset, sampleSize, elementSize);
// move the offset by nnz count of the sample.
sampleOffset += sparseSequence->m_nnzCounts[sampleIndex];
// verify that the offset is within the bounds (less or equal
// to the total nnz count of the sequence).
assert(sampleOffset <= sparseSequence->m_totalNnzCount);
}
else
{
RuntimeError("Storage type %d is not supported.", (int)stream->m_storageType);
}
}
}
// Ok, minibatch is ready, give it out.
StreamMinibatchPtr result = std::make_shared<StreamMinibatch>();
result->m_data = m_streamBuffers[streamId].get();
result->m_layout = layout;
return result;
return pMBLayout;
}
MBLayoutPtr SequencePacker::PackSparseStream(const StreamBatch& batch, size_t streamIndex)
{
assert(m_outputStreamDescriptions[streamIndex]->m_storageType == StorageType::sparse_csc);
// compute the aggregate nnz count of all the sequence in the batch.
size_t nnzCount = 0;
for (const auto& sequence : batch)
{
SparseSequenceDataPtr sparseSequence = static_pointer_cast<SparseSequenceData>(sequence);
nnzCount += sparseSequence->m_totalNnzCount;
}
if (nnzCount > numeric_limits<IndexType>::max())
{
RuntimeError("Minibatch NNZ count (%" PRIu64 ") exceeds the maximum allowed "
"value (%" PRIu64 ")\n", nnzCount, (size_t)numeric_limits<IndexType>::max());
}
const auto& stream = m_inputStreamDescriptions[streamIndex];
assert(stream->m_storageType == StorageType::sparse_csc);
auto elementSize = GetSizeByType(stream->m_elementType);
auto indexSize = sizeof(IndexType);
auto pMBLayout = CreateMBLayout(batch);
// Compute the required buffer size:
// size of nnz type + nnz * (size of the element type) + nnz * (size of the row index type) +
// (number of columns + 1) * (size of the column index type).
size_t requiredSize =
sizeof(nnzCount) +
nnzCount * (elementSize + indexSize) +
indexSize * (pMBLayout->GetNumCols() + 1);
auto& buffer = m_streamBuffers[streamIndex];
if (buffer.m_size < requiredSize)
{
buffer.Resize(requiredSize);
}
auto* destination = buffer.m_data.get();
// insert the nnzCount as the first element in the buffer.
memcpy(destination, &nnzCount, sizeof(nnzCount));
// create two pointers to the memory blocks inside the buffer,
// one for data portion and anther -- for indices.
auto* dataDst = destination + sizeof(nnzCount);
auto* indicesDst = dataDst + elementSize* nnzCount;
// column index for the current sample (= number of nnz value packed so far).
IndexType columnOffset = 0;
// a vector to store column index for each sample in the resulting (packed) matrix.
vector<IndexType> sparseColumnIndices;
// a vector to keep track of the offsets into each input sequence,
// there an offset is the number of nnz values packed so far. Current sample
// values/indices start of the offset position in the sequence data/index array
vector<IndexType> sequenceOffsets(batch.size(), 0);
vector<MBLayout::SequenceInfo> sequenceInfos(pMBLayout->GetAllSequences());
// sort the vector in ascending order of the parallel sequence index.
sort(sequenceInfos.begin(), sequenceInfos.end(),
[](const MBLayout::SequenceInfo& a, const MBLayout::SequenceInfo& b){ return a.s < b.s; });
// Iterate over the all time steps in the layout (total number of samples/columns
// in a parallel sequence), traversing the layout in horizontal direction.
for (auto timeStep = 0; timeStep < pMBLayout->GetNumTimeSteps(); ++timeStep)
{
// For each time step, iterate over all sequences in the minibatch,
// traversing the layout in vertical direction.
for (const auto& sequenceInfo : sequenceInfos)
{
// skip the sequence if it does not intersect with the time step
if (timeStep < sequenceInfo.tBegin || timeStep >= sequenceInfo.tEnd)
{
continue;
}
// store the offset of the current column )...
sparseColumnIndices.push_back(columnOffset);
auto seqId = sequenceInfo.seqId;
if (seqId == GAP_SEQUENCE_ID)
{
continue;
}
// compute the index of the sample inside the sequence.
size_t sampleIndex = timeStep - sequenceInfo.tBegin;
const auto& sequence = batch[seqId];
// make sure the index less than the sequence length in samples.
assert(sampleIndex < sequence->m_numberOfSamples);
auto& sequenceOffset = sequenceOffsets[seqId];
SparseSequenceDataPtr sparseSequence = static_pointer_cast<SparseSequenceData>(sequence);
IndexType nnz = sparseSequence->m_nnzCounts[sampleIndex];
// compute the sample offset in bytes.
size_t sampleOffset = sequenceOffset * elementSize;
// copy all nzz values from source sequence into the buffer.
const auto* dataSrc = reinterpret_cast<const char*>(sequence->m_data) + sampleOffset;
memcpy(dataDst, dataSrc, nnz * elementSize);
dataDst += nnz * elementSize; // advance the destination pointer
// copy all nzz value indices from source sequence into the buffer.
const auto* indicesSrc = sparseSequence->m_indices + sequenceOffset;
memcpy(indicesDst, indicesSrc, nnz * indexSize);
indicesDst += nnz * indexSize; // advance the destination pointer
sequenceOffset += nnz;
columnOffset += nnz;
}
}
// at this point each element in sequenceOffsets should be equal to the total
// nnz count of the respective sequence and the sum of all elements - to the
// overall nnz count.
assert(accumulate(sequenceOffsets.begin(), sequenceOffsets.end(), 0) == nnzCount);
// check the distance between data and index destination pointers.
assert(indicesDst == dataDst + nnzCount * indexSize);
// after we packed all samples, the column offset must be equal to the total nnz count.
assert(columnOffset == nnzCount);
sparseColumnIndices.push_back(columnOffset);
// check that the number of column indices == N + 1 (where N is the number of
// column in the packed matrix)
assert((pMBLayout->GetNumCols() + 1) == sparseColumnIndices.size());
// verify that there's enough space in the buffer for the array of column indices.
assert(indicesDst + sparseColumnIndices.size()*indexSize <= destination + requiredSize);
// copy column indices into the buffer.
memcpy(indicesDst, sparseColumnIndices.data(), sparseColumnIndices.size() * indexSize);
return pMBLayout;
}
}}}

27
Source/Readers/ReaderLib/SequencePacker.h
Просмотреть файл

@ -5,14 +5,12 @@
#pragma once
#include "Reader.h"
#include "MemoryProvider.h"
#include "Transformer.h"
#include "PackerBase.h"
namespace Microsoft { namespace MSR { namespace CNTK {
// A sequence packer that packs dense or sparse samples in dense minibatch for parallel GPU consumption.
// This packer generates minibatches containing full sequences packed for
// efficient (concurrent) consumption on a GPU.
class SequencePacker : public PackerBase
{
public:
@ -20,19 +18,22 @@ public:
MemoryProviderPtr memoryProvider,
TransformerPtr transformer,
size_t minibatchSize,
const std::vector<StreamDescriptionPtr>& streams);
const std::vector<StreamDescriptionPtr>& streams) :
PackerBase(memoryProvider, transformer, minibatchSize, streams)
{
}
virtual Minibatch ReadMinibatch() override;
private:
// Auxiliary packing functions.
// Packs sequences from a particular stream into a minibatch.
StreamMinibatchPtr PackStreamMinibatch(const std::vector<SequenceDataPtr>& sequences, size_t streamId);
protected:
virtual MBLayoutPtr PackDenseStream(const StreamBatch& batch, size_t streamIndex);
// Buffers for allocated data.
std::vector<std::shared_ptr<char>> m_streamBuffers;
// Size of allocated buffers, m_streamBuffers.size() == m_streamBufferSizes.size().
std::vector<size_t> m_streamBufferSizes;
virtual MBLayoutPtr PackSparseStream(const StreamBatch& batch, size_t streamIndex);
// Given a number of sequences, creates an MB layout that is used to guide
// the actual packing.
virtual MBLayoutPtr CreateMBLayout(const StreamBatch& batch);
};
typedef std::shared_ptr<SequencePacker> SequencePackerPtr;

0
Tests/EndToEndTests/Speech/DNN/WriteCommand/run-test Executable file → Normal file
Просмотреть файл

330
Tests/UnitTests/ReaderTests/CNTKTextFormatReaderTests.cpp
Просмотреть файл

@ -19,17 +19,17 @@ struct CNTKTextFormatReaderFixture : ReaderFixture
BOOST_FIXTURE_TEST_SUITE(ReaderTestSuite, CNTKTextFormatReaderFixture)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReaderSimple)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_Simple_dense)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReaderSimple_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReaderSimple_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReaderSimple_Output.txt",
"Simple_Test",
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/Simple_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/Simple_dense_Output.txt",
"Simple",
"reader",
1000, // epoch size
250, // mb size
10, // num epochs
10, // num epochs
1,
1,
0,
@ -37,13 +37,13 @@ BOOST_AUTO_TEST_CASE(CNTKTextFormatReaderSimple)
};
BOOST_AUTO_TEST_CASE(CNTKTextFormatReaderMNIST)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_MNIST_dense)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReaderMNIST_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReaderMNIST_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReaderMNIST_Output.txt",
"MNIST_Test",
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/MNIST_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/MNIST_dense_Output.txt",
"MNIST",
"reader",
1000, // epoch size
1000, // mb size
@ -54,13 +54,14 @@ BOOST_AUTO_TEST_CASE(CNTKTextFormatReaderMNIST)
1);
};
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x1_1)
// 1 single sample sequence
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_1x1_1_dense)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader1x1_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReader1x1_1_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReader1x1_1_Output.txt",
"1x1_Test",
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/1x1_1_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/1x1_1_dense_Output.txt",
"1x1",
"reader",
1, // epoch size
1, // mb size
@ -71,13 +72,14 @@ BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x1_1)
1);
};
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x1_2)
// 1 sequence with 2 samples
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_1x1_2_dense)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader1x1_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReader1x1_2_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReader1x1_2_Output.txt",
"1x1_Test",
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/1x1_2_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/1x1_2_dense_Output.txt",
"1x1",
"reader",
2, // epoch size
1, // mb size
@ -88,30 +90,32 @@ BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x1_2)
1);
};
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x100_1)
// 1 sequence with 10 samples
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_1x10_dense)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader1x100_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReader1x100_1_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReader1x100_1_Output.txt",
"1x100_Test",
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/1x10_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/1x10_dense_Output.txt",
"1x10",
"reader",
10, // epoch size
1, // mb size
10, // num epochs
1,
10, // mb size
1, // num epochs
1,
0, // no labels
0,
1);
};
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x10_MI_2)
// 10 identical single sample sequences
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_10x1_MI_2_dense)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader1x10_MI_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReader1x10_MI_2_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReader1x10_MI_2_Output.txt",
"1x10_MI_Test",
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/10x1_MI_2_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/10x1_MI_2_dense_Output.txt",
"10x1_MI",
"reader",
7, // epoch size
3, // mb size
@ -122,30 +126,14 @@ BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x10_MI_2)
1);
};
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x100_2)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader1x100_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReader1x100_2_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReader1x100_2_Output.txt",
"1x100_Test",
"reader",
5, // epoch size
3, // mb size
4, // num epochs
1,
1,
0,
1);
};
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x10_MI_1)
// 10 identical single sample sequences
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_1x10_MI_1_dense)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader1x10_MI_Config.cntk",
testDataPath() + "/Control/CNTKTextFormatReader1x10_MI_1_Control.txt",
testDataPath() + "/Control/CNTKTextFormatReader1x10_MI_1_Output.txt",
"1x10_MI_Test",
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/10x1_MI_1_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/10x1_MI_1_dense_Output.txt",
"10x1_MI",
"reader",
10, // epoch size
1, // mb size
@ -156,6 +144,234 @@ BOOST_AUTO_TEST_CASE(CNTKTextFormatReader1x10_MI_1)
1);
};
// 10 sequences with 10 samples each (no randomization)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_10x10_dense)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/10x10_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/10x10_dense_Output.txt",
"10x10",
"reader",
100, // epoch size
100, // mb size
1, // num epochs
1,
0, // no labels
0,
1);
};
// 100 identical single sample sequences
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_100x1_1_dense)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/100x1_1_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/100x1_1_dense_Output.txt",
"100x1",
"reader",
10, // epoch size
1, // mb size
10, // num epochs
1,
1,
0,
1);
};
// 100 identical single sample sequences
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_100x1_2_dense)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/100x1_2_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/100x1_2_dense_Output.txt",
"100x1",
"reader",
5, // epoch size
3, // mb size
4, // num epochs
1,
1,
0,
1);
};
// 50 sequences with up to 20 samples each (508 samples in total)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_50x20_jagged_sequences_dense)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader/dense.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/50x20_jagged_sequences_dense.txt",
testDataPath() + "/Control/CNTKTextFormatReader/50x20_jagged_sequences_dense_Output.txt",
"50x20_jagged_sequences",
"reader",
508, // epoch size
508, // mb size
1, // num epochs
1,
0,
0,
1);
};
// 1 single sample sequence
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_1x1_sparse)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/1x1_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/1x1_sparse_Output.txt",
"1x1",
"reader",
1, // epoch size
1, // mb size
1, // num epochs
1,
0, // no labels
0,
1,
true);
};
// 1 sequence with 2 samples
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_1x2_sparse)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/1x2_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/1x2_sparse_Output.txt",
"1x2",
"reader",
2, // epoch size
2, // mb size
1, // num epochs
1,
0, // no labels
0,
1,
true);
};
// 1 sequence with 10 samples
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_1x10_sparse)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/1x10_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/1x10_sparse_Output.txt",
"1x10",
"reader",
10, // epoch size
10, // mb size
1, // num epochs
1,
0, // no labels
0,
1,
true);
};
// 10 sequences with 10 samples each (no randomization)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_10x10_sparse)
{
HelperRunReaderTest<double>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/10x10_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/10x10_sparse_Output.txt",
"10x10",
"reader",
100, // epoch size
100, // mb size
1, // num epochs
1,
0, // no labels
0,
1,
true);
};
// 3 sequences with 5 samples for each of 3 input stream (no randomization)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_3x5_MI_sparse)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/3x5_MI_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/3x5_MI_sparse_Output.txt",
"3x5_MI",
"reader",
15, // epoch size
15, // mb size
1, // num epochs
3,
0, // no labels
0,
1,
true);
};
// 20 sequences with 10 samples for each of 3 input stream with
// random number of values in each sample (no randomization)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_20x10_MI_jagged_samples_sparse)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/20x10_MI_jagged_samples_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/20x10_MI_jagged_samples_sparse_Output.txt",
"20x10_MI_jagged_samples",
"reader",
200, // epoch size
200, // mb size
1, // num epochs
3,
0, // no labels
0,
1,
true);
};
// 50 sequences with up to 20 samples each (536 samples in total)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_50x20_jagged_sequences_sparse)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/50x20_jagged_sequences_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/50x20_jagged_sequences_sparse_Output.txt",
"50x20_jagged_sequences",
"reader",
564, // epoch size
564, // mb size
1, // num epochs
1,
0,
0,
1,
true);
};
// 50 sequences with up to 20 samples each and up to 20 values in each sample
// (4887 samples in total)
BOOST_AUTO_TEST_CASE(CNTKTextFormatReader_100x100_jagged_sparse)
{
HelperRunReaderTest<float>(
testDataPath() + "/Config/CNTKTextFormatReader/sparse.cntk",
testDataPath() + "/Control/CNTKTextFormatReader/100x100_jagged_sparse.txt",
testDataPath() + "/Control/CNTKTextFormatReader/100x100_jagged_sparse_Output.txt",
"100x100_jagged",
"reader",
4887, // epoch size
4887, // mb size
1, // num epochs
1,
0,
0,
1,
true);
};
BOOST_AUTO_TEST_SUITE_END()
} } } }

8
Tests/UnitTests/ReaderTests/Common/ReaderTestHelper.h
Просмотреть файл

@ -124,7 +124,7 @@ struct ReaderFixture
void OutputMatrix(Matrix<ElemType>& matrix, const MBLayout& layout, ofstream& outputFile)
{
if (matrix.GetMatrixType() == MatrixType::SPARSE)
{
{
matrix.SwitchToMatrixType(MatrixType::DENSE, MatrixFormat::matrixFormatDense, true);
}
@ -145,11 +145,11 @@ struct ReaderFixture
}
for (auto j = 0; j < numRows; j++)
{
{
auto idx = i*numRows + j;
outputFile << pItem[idx] << ((j + 1) == numRows ? "\n" : " ");
}
}
}
}
// Helper function to write the Reader's content to a file.
@ -323,4 +323,4 @@ struct ReaderFixture
};
}
}}}
}}}

270
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReader/dense.cntk Normal file
Просмотреть файл

@ -0,0 +1,270 @@
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
traceLevel = 1
1x1 = [
precision = "double"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains a single frame
# (a sequence with a single sample)
file = "CNTKTextFormatReader/1x1_dense.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F"
dim = 10000
format = "dense"
]
]
]
]
1x2 = [
precision = "double"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains a single sequence with two samples
file = "CNTKTextFormatReader/1x2_dense.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 100
format = "dense"
]
]
]
]
1x10 = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains a single sequence with ten samples
file = "CNTKTextFormatReader/1x10_dense.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 10
format = "dense"
]
]
]
]
10x1_MI = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains the same sequence
# (with a single per-input sample from a number of feature/label inputs)
# repeated 10 times,
file = "CNTKTextFormatReader/10x1_MI_dense.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
chunkSizeInBytes = 50000 # ~ 1/2 input size
numChunksToCache = 1
input = [
features1 = [
alias = "F1"
dim = 1
format = "dense"
]
features2 = [
alias = "F2"
dim = 10
format = "dense"
]
features3 = [
alias = "F3"
dim = 100
format = "dense"
]
features4 = [
alias = "F4"
dim = 1000
format = "dense"
]
labels1 = [
alias = "L1"
dim = 1
format = "dense"
]
labels2 = [
alias = "L2"
dim = 10
format = "dense"
]
labels3 = [
alias = "L3"
dim = 100
format = "dense"
]
]
]
]
10x10 = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains ten sequence with ten samples each
file = "CNTKTextFormatReader/10x10_dense.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 5
format = "dense"
]
]
]
]
100x1 = [
precision = "double"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains the same sequence (with a single sample)
# repeated 100 times. Also, it uses UTF-8 encoding and contains
# BOM value.
file = "CNTKTextFormatReader/100x1_dense.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
chunkSizeInBytes = 10000 # ~ 3 full sequences (30 samples)
numChunksToCache = 5
input = [
features = [
alias = "F"
dim = 100
format = "dense"
]
labels = [
alias = "L"
dim = 100
format = "dense"
]
]
]
]
MNIST = [
precision = "double"
reader = [
readerType = "CNTKTextFormatReader"
file = "CNTKTextFormatReader/MNIST_dense.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
chunkSizeInBytes = 10000 # should be enough for ~ 10 samples.
numChunksToCache = 10
input = [
features = [
alias = "F"
dim = 784
format = "dense"
]
labels = [
alias = "L"
dim = 10
format = "dense"
]
]
]
]
Simple = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
file = "CNTKTextFormatReader/Simple_dense.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F"
dim = 2
format = "dense"
]
labels = [
alias = "L"
dim = 2
format = "dense"
]
]
]
]
50x20_jagged_sequences = [
precision = "double"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains 50 sequence with *up to* 20 samples each
file = "CNTKTextFormatReader/50x20_jagged_sequences_dense.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 3
format = "dense"
]
]
]
]

208
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReader/sparse.cntk Normal file
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@ -0,0 +1,208 @@
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
traceLevel = 1
1x1 = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains a single frame
# (a sequence with a single sample)
file = "CNTKTextFormatReader/1x1_sparse.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 10000
format = "sparse"
]
]
]
]
1x2 = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains a single sequence with two samples
file = "CNTKTextFormatReader/1x2_sparse.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 1000
format = "sparse"
]
]
]
]
1x10 = [
precision = "double"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains a single sequence with ten samples
file = "CNTKTextFormatReader/1x10_sparse.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 100
format = "sparse"
]
]
]
]
10x10 = [
precision = "double"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains ten sequences with ten samples each
file = "CNTKTextFormatReader/10x10_sparse.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 100
format = "sparse"
]
]
]
]
3x5_MI = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains three sequences with five samples
# coming from three input streams
file = "CNTKTextFormatReader/3x5_MI_sparse.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features1 = [
alias = "F0"
dim = 2
format = "sparse"
]
features2 = [
alias = "F1"
dim = 20
format = "sparse"
]
features3 = [
alias = "F2"
dim = 200
format = "sparse"
]
]
]
]
20x10_MI_jagged_samples = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains 20 sequences with 10 samples
# coming from 3 input streams. Number of values in each sparse
# sample is chosen randomly (< dim)
file = "CNTKTextFormatReader/20x10_MI_jagged_samples_sparse.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features1 = [
alias = "F0"
dim = 2
format = "sparse"
]
features2 = [
alias = "F1"
dim = 20
format = "sparse"
]
features3 = [
alias = "F2"
dim = 200
format = "sparse"
]
]
]
]
50x20_jagged_sequences = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains 50 sequence with *up to* 20 samples each
file = "CNTKTextFormatReader/50x20_jagged_sequences_sparse.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 100
format = "sparse"
]
]
]
]
100x100_jagged = [
precision = "float"
reader = [
readerType = "CNTKTextFormatReader"
# Training file contains 50 sequence with *up to* 20 samples each
file = "CNTKTextFormatReader/100x100_jagged_sparse.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F0"
dim = 20
format = "sparse"
]
]
]
]

52
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReader1x100_Config.cntk
Просмотреть файл

@ -1,52 +0,0 @@
RootDir = .
ModelDir = "models"
command = "1x100_Test"
precision = "double"
modelPath = "$ModelDir$/CNTKTextFormatReader1x100_Model.dnn"
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
outputNodeNames = "ScaledLogLikelihood"
traceLevel = 1
#######################################
# CONFIG (1x100, Fixed LR) #
#######################################
1x100_Test = [
# Parameter values for the reader
reader = [
# reader to use
readerType = "CNTKTextFormatReader"
# Training file contains the same sequence (with a single sample)
# repeated 100 times. Also, it uses UTF-8 encoding and contains
# BOM value.
file = "$RootDir$/CNTKTextFormatReader1x100_Train.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
chunkSizeInBytes = 10000 # ~ 3 full sequences (30 samples)
numChunksToCache = 5
input = [
features = [
alias = "F"
dim = 100
format = "dense"
]
labels = [
alias = "L"
dim = 100
format = "dense"
]
]
]
]

77
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReader1x10_MI_Config.cntk
Просмотреть файл

@ -1,77 +0,0 @@
RootDir = .
ModelDir = "models"
command = "1x10_MI_Test"
precision = "float"
modelPath = "$ModelDir$/CNTKTextFormatReader1x10_MI_Model.dnn"
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
outputNodeNames = "ScaledLogLikelihood"
traceLevel = 1
#######################################
# CONFIG (1x10_MI, Fixed LR) #
#######################################
1x10_MI_Test = [
# Parameter values for the reader
reader = [
# reader to use
readerType = "CNTKTextFormatReader"
# Training file contains the same sequence
# (with a single per-input sample from a number of feature/label inputs)
# repeated 10 times,
file = "$RootDir$/CNTKTextFormatReader1x10_MI_Train.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
chunkSizeInBytes = 50000 # ~ 1/2 input size
numChunksToCache = 1
input = [
features1 = [
alias = "F1"
dim = 1
format = "dense"
]
features2 = [
alias = "F2"
dim = 10
format = "dense"
]
features3 = [
alias = "F3"
dim = 100
format = "dense"
]
features4 = [
alias = "F4"
dim = 1000
format = "dense"
]
labels1 = [
alias = "L1"
dim = 1
format = "dense"
]
labels2 = [
alias = "L2"
dim = 10
format = "dense"
]
labels3 = [
alias = "L3"
dim = 100
format = "dense"
]
]
]
]

41
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReader1x1_Config.cntk
Просмотреть файл

@ -1,41 +0,0 @@
RootDir = .
ModelDir = "models"
command = "1x1_Test"
precision = "double"
modelPath = "$ModelDir$/CNTKTextFormatReader1x1_Model.dnn"
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
outputNodeNames = "ScaledLogLikelihood"
traceLevel = 1
#######################################
# CONFIG (1x1, Fixed LR) #
#######################################
1x1_Test = [
# Parameter values for the reader
reader = [
# reader to use
readerType = "CNTKTextFormatReader"
# Training file contains a single frame
# (a sequence with a single sample)
file = "$RootDir$/CNTKTextFormatReader1x1_Train.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F"
dim = 10000
format = "dense"
]
]
]
]

49
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReaderMNIST_Config.cntk
Просмотреть файл

@ -1,49 +0,0 @@
RootDir = .
ModelDir = "models"
command = "MNIST_Test"
precision = "double"
modelPath = "$ModelDir$/CNTKTextFormatReaderMNIST_Model.dnn"
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
outputNodeNames = "ScaledLogLikelihood"
traceLevel = 1
#######################################
# CONFIG (MNIST, Fixed LR) #
#######################################
MNIST_Test = [
# Parameter values for the reader
reader = [
# reader to use
readerType = "CNTKTextFormatReader"
file = "$RootDir$/CNTKTextFormatReaderMNIST_Train.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
chunkSizeInBytes = 10000 # should be enough for ~ 10 samples.
numChunksToCache = 10
input = [
features = [
alias = "F"
dim = 784
format = "dense"
]
labels = [
alias = "L"
dim = 10
format = "dense"
]
]
]
]

51
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReaderMultiInput_Config.cntk
Просмотреть файл

@ -1,51 +0,0 @@
RootDir = .
ModelDir = "models"
command = "1x100_Test"
precision = "double"
modelPath = "$ModelDir$/CNTKTextFormatReader1x100_Model.dnn"
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
outputNodeNames = "ScaledLogLikelihood"
traceLevel = 1
#######################################
# CONFIG (1x100, Fixed LR) #
#######################################
1x100_Test = [
# Parameter values for the reader
reader = [
# reader to use
readerType = "CNTKTextFormatReader"
# Training file contains the same sequence (with a single sample)
# repeated 100 times
file = "$RootDir$/CNTKTextFormatReader1x100_Train.txt"
randomize = "auto"
#maxErrors = 0 -- default value
traceLevel = 1
chunkSize = 10000 # ~ 3 full sequences (30 samples)
chunkCacheSize = 5
input = [
features = [
alias = "F"
dim = 100
format = "dense"
]
labels = [
alias = "L"
dim = 100
format = "dense"
]
]
]
]

46
Tests/UnitTests/ReaderTests/Config/CNTKTextFormatReaderSimple_Config.cntk
Просмотреть файл

@ -1,46 +0,0 @@
RootDir = .
ModelDir = "models"
command = "Simple_Test"
precision = "float"
modelPath = "$ModelDir$/CNTKTextFormatReaderSimple_Model.dnn"
# deviceId = -1 for CPU, >= 0 for GPU devices
deviceId = -1
outputNodeNames = "ScaledLogLikelihood"
traceLevel = 1
#######################################
# CONFIG (Simple, Fixed LR) #
#######################################
Simple_Test = [
# Parameter values for the reader
reader = [
# reader to use
readerType = "CNTKTextFormatReader"
file = "$RootDir$/CNTKTextFormatReaderSimple_Train.txt"
randomize = "none"
#maxErrors = 0 -- default value
traceLevel = 1
input = [
features = [
alias = "F"
dim = 2
format = "dense"
]
labels = [
alias = "L"
dim = 2
format = "dense"
]
]
]
]

4887
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/100x100_jagged_sparse.txt Normal file
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Разница между файлами не показана из-за своего большого размера Загрузить разницу

0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader1x100_1_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/100x1_1_dense.txt
Просмотреть файл

0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader1x100_2_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/100x1_2_dense.txt
Просмотреть файл

100
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/10x10_dense.txt Normal file
Просмотреть файл

@ -0,0 +1,100 @@
1.49457 0.00834684 -0.000576041 -1.97058 45345.1
34.4335 -0.0363218 0.000237977 1.45302e+007 2.51278
-0.00126554 0.0878471 -0.824936 70.9996 -2.04745
48010.8 -1.85651 -0.00019822 -0.989896 3.09719e+015
-58.8919 14.5061 -220.532 -3.18108e-006 -1.00405
-0.422416 -5.68569e+006 -0.331459 2029.06 0.792
-0.527352 -0.143901 -4328.55 0.0279332 0.000419555
-0.663195 6.96224e-006 1.02905 -1.02775 318390
-1.00511 32.4822 0.444447 0.0224129 3.72643e+008
-0.000149884 -0.669034 0.283698 0.885162 -0.438219
-0.872504 0.0320481 -0.025001 -3.82937 -58.1164
0.00740398 0.286219 -0.482441 -3.45117 -6.63441
-26.6818 0.0133339 -0.237047 2.57868 -7.38979
0.00231155 -1.68273 17.0301 -1.02165 0.0893058
-2.90814e-005 2.19885 2.13569e-011 12.3718 1.34559e-006
-2.60258 -61.1632 0.112698 0.057651 -0.00653793
-4.47624 -11.2465 2.26046 0.202971 -8.76779
334454 -7.30348e-006 672.522 2.01404e-005 -0.00355033
-1.95018 -0.420012 0.000138377 -25.016 4.94127
-0.317257 -3.65197 0.20975 -0.063178 528.867
-0.0155679 -0.181901 -0.0161843 -0.0895696 2.28351
0.0125528 -4622.61 -0.155361 -53.4073 3.1381e-007
4.71896 -0.0201569 9.54099e-006 -0.223647 -0.00882129
-5.45707 1.21262 2489 0.20885 -0.28008
60.0508 1.66303 -70.6264 0.00913118 -0.386433
1.25211 -0.742191 -0.18171 29.0904 -4.20734e-008
1.19192 0.0983695 -0.003452 2.52805e+006 0.665687
0.824399 -1.26805 43.799 0.0988672 -76.6798
0.673966 0.21328 31.112 -2287.61 -1.08539
0.0282505 -0.078076 2.32624 0.264178 -5.81853
0.0116508 613.265 -7.50127e-006 -0.928704 -200.45
-1066.1 0.34007 3.20311e+009 0.00837805 -54298
1.96573 -0.000713883 1.4749e+009 0.594539 -1.80666e-005
-1.5907 -3781.25 -39.348 1.5536e-007 -1.0436
0.0791082 106.532 162091 -1.34017 -1.43435e+012
-26.2418 0.00610587 0.00951123 -1.13034e+010 0.0737757
-1.14361 -0.0094751 -0.392415 11262.8 -4.47319e-005
0.263427 6.87102 -2.03319 9.66175 -1.43072e-009
0.812754 0.152538 0.0163725 -5.82876 -0.0389674
0.484065 12.9358 -0.253202 0.000293108 -1.06904
0.969043 0.0484433 4.27538 -1.59307 66821.4
0.000248914 1162.51 0.85182 0.0680728 -1.72421
0.0892114 0.000161074 -4.06434 1.91506 -0.514226
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100
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/10x10_sparse.txt Normal file
Просмотреть файл

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0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader1x10_MI_1_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/10x1_MI_1_dense.txt
Просмотреть файл

0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader1x10_MI_2_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/10x1_MI_2_dense.txt
Просмотреть файл

10
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/1x10_dense.txt Normal file
Просмотреть файл

@ -0,0 +1,10 @@
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-0.00923309 -211.488 2.27874e-005 0.000116783 966.248 -0.402776 0.0225629 -1.46072e+008 0.0724741 3.71899
0.0547737 0.697928 -4.43214 1.05633 -0.343985 135.835 -0.0030006 6.06558 -25.5058 2.16231
-0.477001 -0.00520275 -1.65596 -1.20112 -0.000103034 1.99208e-005 -0.284429 8411.77 -1.43145 -0.000683784
5.66203 0.995731 -0.421539 0.928061 -10717.7 -1.00966 -0.792171 1.40365 -2.74474e-007 131.182
1.09727e-014 -1.1983 -6.4148e-007 19.4354 -2.14661e-005 -0.0109343 -0.110837 0.935603 -0.638487 -1.82358
0.113024 -0.105877 0.0164807 -0.000127929 -0.842283 0.00176853 -8.44495 3159.91 6.74958e-005 0.561992
-50.181 -27.6954 -0.144307 1.19575 -1.61622 -0.000727732 -5250.77 2346.56 10106.4 0.677379
1.33732 0.0842088 -877.47 1.64622e-006 1.13396 25.944 -0.991924 -4.74796 -9526.69 3.60882e-005
5.48539 -0.239072 -3.38083 -0.971194 -2.17082 -1.25458 -1.54563e+010 9.20801 29424.3 0.00303663

10
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/1x10_sparse.txt Normal file
Просмотреть файл

@ -0,0 +1,10 @@
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0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader1x1_1_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/1x1_1_dense.txt
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0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader1x1_2_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/1x1_2_dense.txt
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1
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/1x1_sparse.txt Normal file
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Различия файлов скрыты, потому что одна или несколько строк слишком длинны

2
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/1x2_dense.txt Normal file
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2
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/1x2_sparse.txt Normal file
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600
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45
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/3x5_MI_sparse.txt Normal file
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508
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/50x20_jagged_sequences_dense.txt Normal file
Просмотреть файл

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564
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/50x20_jagged_sequences_sparse.txt Normal file
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0 0 0 0 0 0 0 0 0 -1.20029e+007 0 0 0 0 0 0 0 0 0 0 17660.4 0 0 0 0 0 0 0.411761 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.31929 0 0 0 11.4635 0 0 0 0 0 0 0 1365.05 0 0 0 0 0 0 0 0 0 0.917387 83.5339 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0226889 0 0 0 0 0 0 0 0 0 0.424619 0

0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReaderMNIST_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/MNIST_dense.txt
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0
Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReaderSimple_Control.txt → Tests/UnitTests/ReaderTests/Control/CNTKTextFormatReader/Simple_dense.txt
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4817
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/100x100_jagged_sequences_sparse.txt Normal file
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Разница между файлами не показана из-за своего большого размера Загрузить разницу

4887
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/100x100_jagged_sparse.txt Normal file
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Разница между файлами не показана из-за своего большого размера Загрузить разницу

0
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader1x100_Train.txt → Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/100x1_dense.txt
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100
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/10x10_dense.txt Normal file
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@ -0,0 +1,100 @@
0|F0 1.49457 0.00834684 -0.000576041 -1.97058 45345.1
0|F0 -0.872504 0.0320481 -0.025001 -3.82937 -58.1164
0|F0 -0.0155679 -0.181901 -0.0161843 -0.0895696 2.28351
0|F0 0.0116508 613.265 -7.50127e-006 -0.928704 -200.45
0|F0 0.969043 0.0484433 4.27538 -1.59307 66821.4
0|F0 -47.6772 -0.0606534 -1.71303 -29.0837 -15.4355
0|F0 4.7636e-012 49.8844 0.364283 -0.273914 -131207
0|F0 1.20458 2.14771e-005 1.52218 -3.49902 0.00666104
0|F0 0.682768 6.14392 3.15247 -11.8844 0.00864247
0|F0 -7.87624e-005 -8.22891 -10.6072 1.55724 0.116211
1|F0 34.4335 -0.0363218 0.000237977 1.45302e+007 2.51278
1|F0 0.00740398 0.286219 -0.482441 -3.45117 -6.63441
1|F0 0.0125528 -4622.61 -0.155361 -53.4073 3.1381e-007
1|F0 -1066.1 0.34007 3.20311e+009 0.00837805 -54298
1|F0 0.000248914 1162.51 0.85182 0.0680728 -1.72421
1|F0 -2.24579 -347.892 0.784575 3.98604e-006 -1582.06
1|F0 -0.0244184 -5.02058 -1.91253 -0.164098 -0.340998
1|F0 -28.9989 9.51279e-011 5.02861 0.0838921 4.22884
1|F0 -220937 -29.1413 0.0286711 -0.7617 0.00485614
1|F0 -4.66648 -10.7744 -302842 -7.60194 5.83576
2|F0 -0.00126554 0.0878471 -0.824936 70.9996 -2.04745
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2|F0 4.71896 -0.0201569 9.54099e-006 -0.223647 -0.00882129
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2|F0 0.0892114 0.000161074 -4.06434 1.91506 -0.514226
2|F0 6.0328e-006 0.00125139 -1.03017 133.431 -1.6135
2|F0 -0.170677 0.810714 -1364.3 -1.73386 -0.224098
2|F0 -1.06006 31.8025 -0.139635 1.01279 -0.217705
2|F0 1.95443 37.9848 -0.429973 6.96966e+006 -4.37643e-005
2|F0 1.96695e-013 0.305191 1.17544 -1.74764 -1455.39
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3|F0 0.00231155 -1.68273 17.0301 -1.02165 0.0893058
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3|F0 -1.5907 -3781.25 -39.348 1.5536e-007 -1.0436
3|F0 -81036.9 -0.0125482 53.6556 0.000714525 1.28129e+020
3|F0 1.25217 1.31559 0.0474235 -11.5754 0.0022026
3|F0 0.0852062 2959.13 2.31898e+007 -0.00280945 -23.7699
3|F0 -3.89276 4.33202 1.45266e-006 -0.118594 -0.743047
3|F0 -0.912855 614.571 -1875.84 0.002058 2.99321e-008
3|F0 -0.759856 -0.0506138 -0.000830346 5.8925e+008 139.526
4|F0 -58.8919 14.5061 -220.532 -3.18108e-006 -1.00405
4|F0 -2.90814e-005 2.19885 2.13569e-011 12.3718 1.34559e-006
4|F0 60.0508 1.66303 -70.6264 0.00913118 -0.386433
4|F0 0.0791082 106.532 162091 -1.34017 -1.43435e+012
4|F0 -42.9949 -12.6998 0.000390951 -0.503459 1848.83
4|F0 -157858 -0.000280383 792215 1.63331 -29056.2
4|F0 -1.15356 3.35842e+007 -7.40231 0.063909 82.8868
4|F0 3.22323 1.04704 -0.60966 -0.0965849 0.0106335
4|F0 4.10623e+008 19.6502 -1.81502 3.15734 -0.00327652
4|F0 1.39343 -0.000845484 -185.998 19.4213 -5.05967
5|F0 -0.422416 -5.68569e+006 -0.331459 2029.06 0.792
5|F0 -2.60258 -61.1632 0.112698 0.057651 -0.00653793
5|F0 1.25211 -0.742191 -0.18171 29.0904 -4.20734e-008
5|F0 -26.2418 0.00610587 0.00951123 -1.13034e+010 0.0737757
5|F0 -0.970701 -0.0327877 -335.077 0.268597 1.45984e+007
5|F0 5.40336 4.09947e+008 0.00960347 75.6248 1.57951
5|F0 6.35186 7.47084 -10.1179 -1.39103 5.06044
5|F0 -0.0629689 7.41512 -272.704 2.87126 -20639.6
5|F0 -0.316764 0.511471 3.65374 0.891453 -6.31102e-008
5|F0 -0.0188709 -69.3322 4.6503 0.208609 1.80824
6|F0 -0.527352 -0.143901 -4328.55 0.0279332 0.000419555
6|F0 -4.47624 -11.2465 2.26046 0.202971 -8.76779
6|F0 1.19192 0.0983695 -0.003452 2.52805e+006 0.665687
6|F0 -1.14361 -0.0094751 -0.392415 11262.8 -4.47319e-005
6|F0 -2.28565e-006 2.21447 0.0376055 -0.274032 0.0237592
6|F0 0.408187 0.879523 -0.0975374 -4.37881 0.496285
6|F0 -156.937 -0.94888 -0.362383 4.09705 -0.00150142
6|F0 0.784943 0.316427 -1.46679e-007 -1.0333 -0.00869571
6|F0 0.969403 0.397685 -7.25061 5.89234 94.8506
6|F0 -7.90599e-009 0.720738 -2430.58 0.0355956 0.580232
7|F0 -0.663195 6.96224e-006 1.02905 -1.02775 318390
7|F0 334454 -7.30348e-006 672.522 2.01404e-005 -0.00355033
7|F0 0.824399 -1.26805 43.799 0.0988672 -76.6798
7|F0 0.263427 6.87102 -2.03319 9.66175 -1.43072e-009
7|F0 -1.3672 10.443 776521 -2.46975 0.00146144
7|F0 -0.0590974 -0.93626 -0.18737 3.01403e+011 3.89229
7|F0 -20356.8 -0.0372209 1.67516 0.0252612 186.454
7|F0 1.23918 -2.69063e-005 -0.00170681 -0.97649 -6.92066e-005
7|F0 -6.42472e-005 -19.1344 0.309452 -6.00614 0.649334
7|F0 0.000722519 0.979686 5.37938 -0.753472 -0.184087
8|F0 -1.00511 32.4822 0.444447 0.0224129 3.72643e+008
8|F0 -1.95018 -0.420012 0.000138377 -25.016 4.94127
8|F0 0.673966 0.21328 31.112 -2287.61 -1.08539
8|F0 0.812754 0.152538 0.0163725 -5.82876 -0.0389674
8|F0 -0.90614 -3.31145 -0.271313 1.63005 -3.5226
8|F0 0.210133 0.104607 -0.256539 -0.0044804 51.9094
8|F0 -0.000245073 1.12171 -2095.23 1.06563 0.0528527
8|F0 4.34688e-007 -2.55712 -0.926795 -2.87923 0.438687
8|F0 0.0119994 -54.8988 1.06007e-013 -5220.13 0.27824
8|F0 -0.113206 -0.00690726 -0.855945 -0.16179 -1.51302
9|F0 -0.000149884 -0.669034 0.283698 0.885162 -0.438219
9|F0 -0.317257 -3.65197 0.20975 -0.063178 528.867
9|F0 0.0282505 -0.078076 2.32624 0.264178 -5.81853
9|F0 0.484065 12.9358 -0.253202 0.000293108 -1.06904
9|F0 3.86917 -1.11441 4.95705e-008 27.3111 18.0369
9|F0 -9.97863e-005 -34915.1 1.79147 -0.719583 -0.0119458
9|F0 1.05567 174.746 3.45397 -0.0617324 -1.29628
9|F0 1.78451 1.20184 -0.410693 79.4053 0.650425
9|F0 -2.65818 -2.21723 3.2137 -990.056 -1.29628
9|F0 -8.19191e+006 -0.854059 -0.45603 -4.64811 1.87054

100
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/10x10_sparse.txt Normal file
Просмотреть файл

@ -0,0 +1,100 @@
0|F0 1:21217.5 37:91.0471 39:0.790628 54:-0.650826 71:-0.244843
|F0 6:-0.448316 12:2.18789 15:0.00360779 18:-2.04612 55:-1.12455
|F0 26:245.792 39:1.79593e-009 58:956.186 67:0.252758 91:0.204157
|F0 37:-89.6133 58:565194 69:-0.77792 71:2.84126 88:-169.94
|F0 41:1.38299 44:0.0147381 60:1.26413e+006 68:-0.0132043 83:120.062
|F0 23:2.52209 46:3.60457e-009 49:3.48218 77:4.1996e+009 91:-0.0279232
|F0 68:6.34261 72:0.533866 76:-1259.67 83:-18.7257 97:1.08442e+018
|F0 5:349.919 24:0.310971 33:-0.122988 38:0.0250344 46:-1.15575
|F0 25:-1435.19 47:7.48929e-005 53:0.451339 76:81.7535 77:0.5353
|F0 1:1.31546e-005 33:-41.7554 35:0.0180013 63:1.13638 67:-1.21977
1|F0 5:1.52436 21:1215.67 48:0.000404926 80:8.75152 99:-1.82926e+008
|F0 6:24343.3 9:-0.0970564 17:0.0389332 26:60307.9 52:-0.0738893
|F0 22:3.35521 30:-6.21837 47:-171.227 71:6.77203 84:-379767
|F0 5:1.28433 16:-2.86171 45:-168.756 54:-1.09466 97:-4.05418
|F0 20:662.909 33:-6.60208 44:-0.00943021 69:1.09303 93:0.422082
|F0 18:-0.221594 31:-0.0631396 32:0.0474814 65:2.18666 97:1.00376
|F0 6:38.5778 21:0.15673 44:-0.141979 74:434242 88:0.371762
|F0 16:7.21781e-007 18:8.28905e+022 37:1.15788e-005 63:244.29 73:-1.0725e+007
|F0 58:-10.3758 66:-0.0315116 91:85.802 92:0.265798 94:1.09662
|F0 37:0.0987281 53:-0.0771098 56:-0.273038 75:0.434354 94:-19.0887
2|F0 12:-0.00118587 14:-763467 19:-2.55535e-005 46:-3.77503e-006 96:22.0499
|F0 10:-1.20155 20:-0.343113 39:1.97604 41:-1.63327 96:-3.40832
|F0 13:-0.00879617 47:162.483 55:1.78128e-005 66:-0.0565789 85:-0.0132914
|F0 21:8.08189 43:3.5445 46:0.000444645 84:7.0402 97:-2.20464e-007
|F0 18:0.174552 19:-0.156138 38:-0.000279254 83:-6.49441 99:246.167
|F0 6:5.5524 14:15.6594 29:0.056835 66:386.739 70:-0.0874713
|F0 14:-2.73452 26:3.9767 32:-0.0196428 68:-0.990362 88:264909
|F0 6:229.495 7:2.2485 12:-5.56194 16:-23917.9 28:-0.0631997
|F0 39:-52.9409 64:0.849102 76:-3.00825 89:-0.572818 90:-0.0504843
|F0 13:6.53126e-005 39:-1.2449 51:-0.0344398 52:3.48542 71:0.137182
3|F0 3:1.15038e-007 27:0.00498057 39:-2.76006 70:1.09679 90:-14.6997
|F0 17:3.01983 44:-0.0153732 58:-24307.6 70:-5.31181 72:0.503159
|F0 2:-2.17426 44:0.014149 56:-3904.81 85:-18.4934 93:-0.761717
|F0 5:-11.0577 20:1.51336 48:-0.485089 51:-0.486464 84:-1.00797
|F0 13:0.218989 17:751.884 77:-4.84921e+013 95:4.55828e-009 99:-0.0136968
|F0 3:-0.742045 29:1.88362 30:22.4135 32:9.37944e+006 68:0.0153907
|F0 57:-0.0472903 69:0.785473 74:-0.000158791 89:-0.00225914 96:2.96233e+006
|F0 0:-4.60434e-007 4:-1.60093 61:-12.8244 73:0.000257511 93:-16731.6
|F0 9:4.0492e-009 12:2.32313 52:-0.309584 73:19.1851 77:0.345846
|F0 10:0.146846 12:-0.0981386 22:0.327063 58:0.573324 70:-0.00384631
4|F0 15:-0.00013568 18:-0.515841 27:-0.714833 55:-2.20645e-010 85:-0.0168298
|F0 23:-0.275195 32:-1.95197 44:2.64502 48:3.91785 92:-1.15199
|F0 1:1.01067 38:-80482.3 51:4.806 53:-63.7435 99:0.00907129
|F0 39:1.97567 41:0.000186998 44:-0.0105051 58:-0.000977385 92:-19148.3
|F0 3:-3.62148 4:-7.52245 6:0.215141 49:-38125.1 91:-2.23548
|F0 3:-0.705182 24:-3.76387 30:-3.38332e-005 33:0.701911 72:317.986
|F0 25:61.0585 40:0.0660382 54:-0.102338 64:-0.0209578 88:0.0120236
|F0 0:-3.72392 18:-0.000213131 33:1.51968 43:-21.4965 50:-2869.35
|F0 6:-101261 17:-0.755782 39:0.627529 46:-2.66768 71:-613.316
|F0 16:6.30232e-009 36:-0.706723 49:0.0204439 61:3.97729e+008 75:0.118528
5|F0 15:4.95528 37:1.94826 79:-0.117214 83:12.8091 89:-0.0195542
|F0 14:2.9182e-005 15:58.9868 47:1.52779 53:-0.0686748 86:-7.19769
|F0 1:-3.15219 6:175.267 18:0.044177 27:1.34933 33:-0.906306
|F0 18:0.00569362 58:1.52731 61:3.11065 63:-1.62589 77:-2522.34
|F0 21:3.9699 39:1.67472 70:0.0838068 74:-67052.6 99:0.156013
|F0 5:-1.03909 20:3.04248e-012 49:0.287964 64:0.133736 87:0.921448
|F0 13:-0.00329803 30:-2232.74 39:0.188321 77:10.2808 82:4.10255
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|F0 0:1.6136 9:-9.05271e-006 10:-0.394786 11:-0.0046185 44:-0.0568206
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6|F0 14:-0.213847 21:99.2227 45:-0.00255111 47:0.27426 68:-39.9079
|F0 26:-19.5486 29:117600 34:-0.996809 53:-0.000444418 60:-6.08699
|F0 15:0.000146258 37:-0.462283 50:0.142981 68:-2.02465 75:9.70607e-012
|F0 36:544558 43:0.00895814 61:0.0671549 62:0.111508 86:-2.64515
|F0 6:0.0672265 9:-4.4543 40:-0.934335 54:-0.220365 58:4.73157
|F0 15:-1.21151 28:-1.42847 68:0.581116 83:0.972768 90:0.00174279
|F0 21:-1.35243 42:-3.06287 64:1.26899 83:47.9782 96:3.08279e-005
|F0 24:-2.57645 27:-0.801796 65:0.000599281 67:-0.320385 71:52423.3
|F0 2:8.0866 35:-13.0947 58:-0.125298 62:-62.8388 70:-0.943184
|F0 26:-0.000107744 46:1.13914 81:1.52762e+007 91:45.188 92:1.42975e-007
7|F0 17:5.68971e-007 24:-69.8495 34:0.547031 71:0.000466435 91:-0.325405
|F0 4:0.647069 6:0.747577 10:-0.593517 33:-0.0165467 80:-0.231547
|F0 23:3.72569e+006 60:-0.0347214 75:-4836.31 85:7.22908 93:3.62018
|F0 2:-0.151862 22:0.221818 31:0.634038 86:794875 90:-0.0517851
|F0 30:-1250.04 39:74.4841 68:-0.204782 70:6.70477e+006 85:0.171666
|F0 11:33.2148 22:-1.07169e-009 37:0.616642 67:51.4898 78:0.472122
|F0 0:-0.6632 26:-0.596279 38:-0.800497 70:0.806035 85:-1264.52
|F0 63:-0.152469 72:251.105 73:-0.0532549 93:0.232568 96:-1446.49
|F0 11:228.63 24:-0.00130588 29:-1.33236 34:0.0442224 98:-3.82727
|F0 0:-5008.94 15:3.05467 21:3.11551e-006 34:-1.62613 68:-1.54903e-009
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|F0 15:0.0304201 45:-137214 77:4.2189 89:-3.24348 99:0.427023
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|F0 63:113.403 64:9.80999e-006 67:-3.52283 74:0.653956 86:0.341021
|F0 4:-274.356 17:0.0588896 49:-0.118275 59:0.00204628 87:1.24441
9|F0 22:0.496833 26:1.31092 41:-5.21439 44:-3.43357 90:-5.46214
|F0 77:0.271707 79:-526.125 84:-0.000303229 95:-0.718097 97:-0.373873
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|F0 28:-0.0486176 53:-1.52951e+014 63:-1.29667e-005 95:-1.13236 96:-0.0433288
|F0 32:-4.98377e+006 43:0.889032 78:9.89495e+007 85:-0.69554 89:0.0112599
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|F0 11:0.0587552 14:-0.0397998 38:2.39289e-006 59:1.04348e-006 61:-1.08084
|F0 7:1.91353 9:-0.559483 38:-7.57996e-006 52:-0.498374 87:3.47359e+009
|F0 9:-2.76586e+008 28:1.22559 30:-27.2123 47:-0.00510742 83:-1.23038

0
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader1x10_MI_Train.txt → Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/10x1_MI_dense.txt
Просмотреть файл

10
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/1x10_dense.txt Normal file
Просмотреть файл

@ -0,0 +1,10 @@
0|F0 -4.73443 3.35042 -3.55349 -2.34185 -3.23589e+009 0.00388636 0.177933 0.622771 0.0548931 -11.997
0|F0 -0.00923309 -211.488 2.27874e-005 0.000116783 966.248 -0.402776 0.0225629 -1.46072e+008 0.0724741 3.71899
0|F0 0.0547737 0.697928 -4.43214 1.05633 -0.343985 135.835 -0.0030006 6.06558 -25.5058 2.16231
0|F0 -0.477001 -0.00520275 -1.65596 -1.20112 -0.000103034 1.99208e-005 -0.284429 8411.77 -1.43145 -0.000683784
0|F0 5.66203 0.995731 -0.421539 0.928061 -10717.7 -1.00966 -0.792171 1.40365 -2.74474e-007 131.182
0|F0 1.09727e-014 -1.1983 -6.4148e-007 19.4354 -2.14661e-005 -0.0109343 -0.110837 0.935603 -0.638487 -1.82358
0|F0 0.113024 -0.105877 0.0164807 -0.000127929 -0.842283 0.00176853 -8.44495 3159.91 6.74958e-005 0.561992
0|F0 -50.181 -27.6954 -0.144307 1.19575 -1.61622 -0.000727732 -5250.77 2346.56 10106.4 0.677379
0|F0 1.33732 0.0842088 -877.47 1.64622e-006 1.13396 25.944 -0.991924 -4.74796 -9526.69 3.60882e-005
0|F0 5.48539 -0.239072 -3.38083 -0.971194 -2.17082 -1.25458 -1.54563e+010 9.20801 29424.3 0.00303663

10
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/1x10_sparse.txt Normal file
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@ -0,0 +1,10 @@
0|F0 8:-7.12567e-005 25:0.086489 31:-0.00518394 35:0.0385633 40:-22.7516 49:1.19651e-009 63:3.56729 80:-5.44507e-005 92:-0.00653453 93:1.00077
0|F0 5:-0.419294 11:148.789 22:2.01403 46:-0.0177482 48:0.195094 52:1.05312e-005 62:-1.17075e-006 67:-0.0887719 88:-3.73782 95:-156.026
0|F0 4:-0.000101065 7:1.1448e-007 8:1.161e-005 17:-1.63807e-007 22:8.85136e-005 34:5093.78 40:-0.667472 45:-0.86494 51:0.0534191 97:-0.248578
0|F0 6:-0.763373 32:-0.00269538 34:-3.24301 36:1.45044 37:-29.7595 41:2.04 45:0.454407 64:-0.000786576 68:2.89249e+008 87:0.991217
0|F0 2:0.0105674 6:-0.000220819 15:-762.949 34:-0.000159567 38:0.701409 52:0.102579 62:7.13734 72:-0.266471 79:9.28407e-008 91:-1.06821
0|F0 1:-140732 17:-0.836618 27:-0.881427 28:-0.0249542 40:-0.343582 42:-0.737997 44:-7.91759 46:-0.00717859 67:-0.00452021 76:495613
0|F0 12:23.8605 24:1.72907 32:0.193678 43:0.353706 59:0.0683971 64:0.447483 72:-0.00215714 75:0.027438 86:-1.65804e+014 99:-1.42585
0|F0 4:-3.43767 15:0.817789 25:1.08495 33:3.898 51:-5.10043e-005 63:-172260 76:-0.00432985 87:-0.211593 92:-1.72907e+022 94:-12.0355
0|F0 2:3.06881 5:1.44268 24:-0.000483701 26:-1.50881 55:5.43877 71:-0.241945 74:-2.92654 77:0.428757 81:0.17718 91:1.29097
0|F0 1:0.607103 6:0.00178492 16:0.00745216 36:2.73652e-007 37:-10.7082 60:9.09292 62:-0.426152 84:-19.0693 97:-1.74872e-019 98:1.54048

0
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader1x1_Train.txt → Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/1x1_dense.txt
Просмотреть файл

1
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/1x1_sparse.txt Normal file
Просмотреть файл

Различия файлов скрыты, потому что одна или несколько строк слишком длинны

2
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/1x2_dense.txt Normal file
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@ -0,0 +1,2 @@
0|F0 -1656.31 -6.3423e+015 -6.85769e-005 6.46013e-006 0.000276641 -0.331415 489.221 0.61788 -65.1809 -1.01697e+009 0.0208253 -2.13636e+010 -1.24241 -0.217954 -1.66783 -72.4772 -7.53744 -8.94653 51.5084 -6.337 -96.6259 -7.57374 0.953721 1.35582 0.915705 1.68791 7.92942e+007 12.8006 -0.0050151 0.106066 0.853345 -0.850407 -0.469603 2.16637e+011 -0.00389841 -6575.97 0.378731 -5.87584e+016 0.00585251 0.256933 2.54647 2.58768e+008 0.105463 -0.121969 0.940521 -2.53895e+009 -0.971114 0.620701 0.0327572 -1.12667 2.65088e-006 -2.60201 2.69619 0.0371924 -0.00627525 0.828215 -1.28202 -1.24248e+013 -6.89387e-012 -104958 -927.665 -389.802 -0.080627 -0.00700765 7.81438e-006 -1.05373 6.44746 17.8758 9.92757e+014 3.54649 -0.284593 0.850295 -28.3745 -242.237 -0.00837951 1.94622 -2.51439 5326.62 -70259.1 0.0206078 0.778735 -0.000937335 64.6139 -3.17452e+007 0.0211986 -3.60812 -1.21259 2.44935 0.704363 0.0111079 -0.623167 -276.462 2.74568e-006 -1.41297e-008 -5.69838e-011 -0.00157465 -0.597312 0.221347 -0.00331784 -5.91255e-006
0|F0 0.0554096 -2.26884 -2.99806 -5564.63 -9.51588 4.71425e+006 -1.70561 -0.0127928 -1083.04 -0.305039 3.31177 -0.727932 -31.7077 2.75502 -0.00653739 -0.00575842 0.000244794 9.1839e-005 61770.4 0.0441308 -6169.79 -0.500892 0.000573641 0.686744 -0.575914 -0.995114 -12.2449 0.000111782 -3.89836 2.38027 2.31334 2286.18 0.00263857 -157237 -1.04737 -7.63654e-006 13.155 -0.0957251 -1.01765 0.0112652 -0.117083 -1.19654e-005 -0.204877 -78730.1 -0.000224817 53137.1 -37.0537 -0.00258735 -1.5633 28.8135 181031 16.0368 1.29491 -0.122245 -0.327041 -0.943098 -0.0796123 1.39725 52949.7 -0.0022673 -1.30148 0.23154 5.7648e-005 3.28758 -0.989104 -57.8733 -3.05273e+007 -0.330071 0.0158875 -0.13162 3944.96 0.034768 -0.162856 -1.28965 0.404299 -0.026375 60.8576 -5.51724 -0.711459 -0.159011 0.127763 -3.26957e+010 0.178524 16.313 0.0139529 -0.029208 2.91796e+012 -5467.69 0.723105 -1.29352 1559.34 -3.4805e+007 -1.01519 102.201 -0.761165 -4.03341e-006 -111.412 -6.71196e-006 -0.00127695 -0.832362

2
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/1x2_sparse.txt Normal file
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@ -0,0 +1,2 @@
0|F0 14:-0.0497287 37:-4.9518e+007 48:9.31078e-007 64:0.994672 68:9.82698 82:-5.05464 85:-0.228525 93:0.0505301 101:-0.28889 106:-1.52827e-010 107:0.0620024 117:-0.827963 129:6.74867 130:-0.0028759 131:331.212 153:0.0115524 160:0.00335002 161:-0.311657 166:-5.0728 169:-0.129302 184:1.78592 189:0.535469 219:-1042.82 222:0.619832 233:0.0408041 234:1.42028e-005 235:-3.26946 237:0.265816 253:3.71731e-005 258:-3.67945e-005 273:0.394788 276:-217.251 278:-4.1177 279:1.05331 282:-150.522 285:-8.12683e-005 318:24.3352 324:-3.27455 336:-4.96978e-008 344:25.9978 356:0.0422829 364:-0.00901259 380:67.0582 402:1.27023 423:1.69945e-011 457:1663.81 461:-10.2123 468:-1.51165e-006 470:-11.589 478:4.63021 480:31.7858 490:2057.67 509:0.196966 512:-27.8374 516:-1.29145 527:0.054297 540:0.660942 549:-0.909693 558:0.0203407 590:-2.09273 597:0.228308 607:35.6564 609:0.675635 634:-5.58718e-005 657:1.11256 665:8.98246 666:0.779308 671:-1.83973e-006 672:-0.35388 702:-5162.98 703:3.84452 707:1.33649e+009 720:-61.1266 736:-63.2216 739:-0.867044 753:0.00181776 769:-0.395385 771:-1.64453 774:-26.8359 776:-0.0994628 778:-1.53292 793:-0.0674824 794:-0.138019 798:0.442764 799:14.3982 804:-4.42079 813:0.153539 830:-13287.2 856:1.23052 863:8.76659e-006 866:-1.96006 875:2.36518 887:0.209151 898:-0.96478 936:46.4298 945:-927.051 971:3232.96 981:-1.75963 991:0.603258 999:-4825.68
0|F0 3:0.0406016 12:0.147808 18:-0.45427 25:8.36254e-005 32:-0.135701 45:-170.77 55:-4.13275 58:-2350.15 71:-2.58044e-005 107:-2.47315e+006 129:-0.877495 170:0.00253156 183:1352.78 205:0.00178977 217:0.0815379 230:-19.1276 237:-6.98035e-009 259:-1.5634 260:-0.103516 278:0.00112823 295:-0.000132366 297:-0.0409087 303:-1.8788e+010 305:-2.0839 307:-0.870501 308:-2.60834e+016 310:-5403.34 314:0.619335 336:-0.00058016 342:-0.98792 357:-8.3688 359:0.00974121 362:0.239254 369:-143.337 400:-0.0680307 402:6.19588 411:1.88133 431:-0.882975 442:125.469 472:0.13402 474:-8766.92 478:-13.6055 493:-0.594603 515:13.7473 526:-0.349323 531:9.53251e-005 535:-57299.4 539:-1.32079 542:-0.382187 563:-0.963374 572:-2.7699e-005 581:0.0120864 582:0.0945888 583:-99.8962 588:3.362e-010 593:-12.7194 596:66.231 601:2.70209e-005 615:29.6081 621:9.50949 625:0.132759 628:4.24984 634:3.46109e-007 641:1.29441 662:213.747 666:-0.282204 668:0.0259879 671:2.76983 680:1.08141 682:0.574576 690:94.7908 726:-10.4348 745:-530.957 755:-0.340363 764:0.160673 771:-3972.7 783:2.10248 799:5.6306 805:-3915.6 808:-1.10437 834:3.28159 846:-1.01947 851:-1.80078e+010 855:1.80745 861:0.732124 868:-453.176 889:-0.25064 900:0.181623 909:-1.10073 911:66.5594 915:0.839725 918:-163.21 922:7.0434 923:6.69835e+006 932:0.0882939 936:-10.4911 957:251033 967:1.85956e-012 975:-0.572939 996:-0.000505966

200
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/20x10_MI_jagged_samples_sparse.txt Normal file
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@ -0,0 +1,200 @@
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1|F0 |F1 0:8926.09 2:0.00733386 3:-8.53884e+006 6:0.270891 7:-0.00306527 8:0.0497752 10:-2252.7 12:-8.47053 13:-2.89227 14:0.00663997 15:387.084 16:1.50195 19:-0.0948466|F2 0:-0.00153565 9:24.6203 11:1.08911 12:-133.344 16:0.0283833 18:-9859.62 19:-1.04332e+007 20:-0.873117 21:0.00273759 23:34510 24:11.4596 27:-0.900792 31:-0.0306263 32:-25.0423 34:-0.327183 37:-0.258036 43:-6.67633e-007 51:-935.84 54:9.59773e-005 61:-1110.61 62:-1.01887 64:-3.34501 66:0.00266317 67:-21.3412 71:0.241631 72:0.0630496 73:-1.26724e-008 74:-0.0872422 76:-110.723 77:0.000219706 78:0.711425 80:1.31853e-007 82:9475.58 83:-0.48231 84:1.06081 85:-9.24642 87:41.5691 88:357495 91:2.38606e-021 92:-7.24916 95:14.6914 97:-0.0154217 99:0.0125791 102:-0.00577366 103:-0.999153 104:-8.73696 106:1.47322e-007 107:6.87783 108:2.21948 110:-34.3971 112:-0.00018325 114:-8.80533e-010 115:0.00201505 118:4.8514 119:0.2395 120:65.8906 121:0.46319 123:1.74138 124:-0.000358314 126:0.675261 128:0.000832033 129:-0.0249725 136:-6.61374e-006 137:0.000776602 139:-7.6822e-011 140:4.2129 141:-0.0376868 142:-0.0301933 145:-15.095 146:1.36033 148:298.819 149:-0.719366 154:6543.84 155:12.3978 158:0.176792 159:0.124603 160:-2.4775e-014 162:-13114.5 164:-0.00363772 165:-41.5154 166:-1.88023 168:-2.38889 170:1.43141e-005 171:-123173 172:-0.038522 173:6.8365e-005 174:3244.85 178:0.145188 180:1.42278e+013 182:-0.00595661 186:0.0503526 190:-0.709248 191:-1.20472 192:-2.23311 194:-0.364116 195:1.47773 197:1.27818
1|F0 |F1 0:1.96439 3:1.11637 4:-0.00212023 5:0.190432 6:-0.7473 11:0.0893586 12:-0.101199 13:-0.000427769 14:-0.540761 15:0.222086 16:22.4364 17:0.60743 18:1.91092|F2 1:1.04747 12:-0.0809671 19:-60.9766 21:26244.7 24:-1.39899 29:-1.11781 31:-0.95603 33:0.0066659 36:3.4464e-006 37:230.9 41:-0.00145123 44:-0.384272 45:-2.92393 48:-47856 50:0.761876 51:38.168 56:793.028 58:0.296005 60:-60.8564 61:-0.905902 62:0.101296 64:0.640926 65:1.31979e-005 68:11.0623 69:33.0304 70:0.310457 71:-0.0366395 72:1.00795 73:-5.36855 75:-1.28782 76:-138514 78:-0.127929 81:-1.09988 83:-4.5992 85:-0.560578 86:0.00320355 88:-0.00723091 89:-83.7521 90:1172.29 91:0.424101 92:-6.41756e-008 93:-211.241 94:4.7954e-005 96:-0.0268512 97:-0.0085279 99:-0.780869 100:0.64037 101:-1.33936 104:966.046 105:4.67288 106:0.0682693 109:0.188246 110:40.3832 111:1.67164e-011 112:0.0462577 115:8.31101 119:-598.35 123:-0.215069 124:-2891.92 128:55.5483 131:-1.51459 132:-103894 133:2.18537 135:2.75223e-013 137:-4.08771 138:-0.161616 139:-2.13729e-008 141:-1.02259 143:-11.4269 144:-3236.81 146:0.00224382 152:-0.629673 155:4.60829 159:1.24144 162:-3937.91 164:-157.065 168:-59.5082 169:-0.0203126 170:-0.156289 171:-153199 174:-1.02804 175:-1.06169 176:-1.072 177:-7.71198e-009 178:0.00883221 180:14360.5 181:-0.390718 182:2.46844 185:-0.821858 189:19.8326 190:-23.5784 192:51.6656 193:0.0776172 195:-3891.92 196:2.903e+008 198:0.317568 199:2.30905
1|F0 |F1 0:547969 2:-31.5077 5:-0.115652 7:-2.1377 9:-0.493105 10:2.64246 11:39.2365 12:-1.6506 14:-5.43342e+007 15:-0.0710003 16:-8.28089e-015 17:0.720373 19:1.21273|F2 0:-10199.9 2:-0.289958 3:-1.21416 4:-2.10065 5:3.83963e+006 6:-0.204041 7:0.558806 10:-9.8881 12:-0.910076 15:-759.393 16:-1.61802 22:-3.03022e+010 23:0.713563 29:0.156705 30:0.122488 32:-0.00210394 33:0.169123 35:-1.32638e-008 36:-0.0832999 39:-43521.7 41:27.328 42:-0.393868 43:-0.408103 46:-0.138106 47:-302.2 48:-4.13971 49:3.02027e-006 50:1.895 53:0.724104 54:155 56:2.63005 59:-1773.25 62:3.82438 65:-1.14514 66:-51971.7 67:-4.61023 70:1.1039 71:-1.23281 74:0.00573241 75:7309.2 78:-0.00154423 79:0.719233 80:-594.06 81:0.895447 82:-953.937 90:-0.93141 91:-0.00102792 92:0.000282368 93:5.16582 95:2.4866 96:-0.0710345 97:15.8288 98:-1.23797 101:2.43175e-011 102:7.97028 103:-0.0140784 112:-2.044 113:-11135.6 114:0.0441808 115:75.2707 116:330.231 117:-8.20434 118:-17.8315 123:-1858.85 124:4.86624 125:0.0389351 126:-0.619088 127:6.3853 131:-7.19117 132:0.282162 133:-2.22404 134:-5.06234 136:-0.0530242 138:0.0766949 139:-7.55474 141:21.4578 142:68.2237 146:-37.7516 150:-0.044435 151:1.76542 154:-991.171 156:-2.89711 159:-2.29674 163:35.985 165:-0.318493 168:0.0265139 169:-4.72493e+006 172:-1.11491 174:-0.190792 175:-0.0544925 177:39.5636 179:9.29684 184:-4.19111 185:1.11206 193:-3.75797 195:53.9879 197:0.373238
2|F0 |F1 0:2.4105e+007 3:-1.07631 18:6.20023e-008|F2 3:-0.519465 4:-0.401094 5:0.0893909 6:0.0399461 7:3.16358 8:1.7162 12:-2.82712e-007 14:-1.14089 16:0.2595 17:1.97434 20:-1.366 21:-2.25158e+009 22:-7046.9 25:-45.8429 27:0.0133935 30:0.766395 31:0.589311 36:0.444754 37:-1.4894e-005 40:3.14625e+006 42:2.56411e-005 43:-1.29856 47:-920.244 49:0.0556854 55:0.36472 56:2.03984 58:-215.243 59:-0.0426617 60:-0.131186 61:-0.160204 62:7.06264 63:-0.167932 64:-2.63457e+006 65:122.967 66:0.000697157 67:-0.0108692 68:0.00151376 69:84926.6 70:0.19152 72:-0.469122 74:-8.98468e-005 75:2.81506 76:-0.976198 77:-0.954498 80:0.000360674 81:4.2098e-013 82:0.350476 85:-0.147631 86:0.0446649 87:-0.901213 88:0.214974 89:0.61433 90:68.7112 92:-2.9973 93:0.451875 94:0.00503317 95:-0.218055 97:-23.199 98:-1.80917e+009 101:-2803.68 102:8.87007e+018 103:8.71925e+007 105:6173.13 109:-9.88729 110:-0.0806085 111:-7024.22 113:0.235939 117:2.50908e-007 118:1.72378e-005 119:0.0030293 120:-16199.8 122:0.875577 123:6.80155e-010 124:-6.61503 125:-0.0025691 126:0.391795 131:-7.51216e+009 132:-4.19386e-007 134:-1.40362e-006 135:-0.000237263 136:-2.62497e+006 138:-752.84 139:-0.00419463 141:-1.72976 142:-259.276 144:0.281437 145:-35036.1 147:1.81702 148:0.0135201 151:5.11499e-005 152:9.45926e-009 154:-311.86 158:8.76609e-005 160:0.0381037 162:-1.27301 164:-0.708709 165:-3.31048 167:-2.01075 168:-9.7157 170:-25.0753 172:47.3033 175:793.121 176:912323 179:-8.22149e-006 180:0.535878 181:-36.109 182:-1.164 185:0.00159607 186:-0.834376 188:99.2834 189:735.452 190:1728.91 191:3.974 192:-0.791771 194:-1.07378 195:69630.4 199:-1.32515
2|F0 |F1 3:0.365809 9:6.57238 18:-29436.2|F2 0:0.000158628 3:-0.945848 4:-3.77374 6:-793197 7:-0.798877 10:0.72542 12:2.66292e-006 15:0.33336 19:-3034.21 22:-0.126439 24:-0.259395 25:34.44 28:-630305 29:0.0103861 30:1377.64 31:0.0325632 32:-2419.85 33:0.00164461 34:0.0312535 37:1.4384 39:4012.93 43:-4.5023 45:-0.0574593 46:1.81097e+008 47:3.76276 49:-13.2638 51:-0.100399 54:-13.6276 55:-0.938668 58:-5.33525e-006 60:-6.53511e+013 61:-0.229087 62:1.58377 63:611.218 64:7.03063e-007 67:-2.43233 68:1.75532 69:0.0194539 73:2.48098 74:-0.33058 76:-5.81831 77:-848.201 80:6.75158e+006 81:6.50254e-008 83:1.12162 87:-0.126301 88:-5296.06 89:0.774635 91:0.0271273 93:0.0348628 94:1.07373 96:0.675131 97:1.37928 98:-2.11533 99:48842.5 100:0.0877658 101:-1955.57 103:40.4572 104:194128 106:-0.92887 107:-0.00618398 108:-2.27227 110:0.0963247 111:0.580862 113:-2.2159 114:-0.986123 116:-812.13 117:-4.40835 119:0.446197 121:8.15035e-012 123:1.23142 125:-2.68741 127:0.792382 129:-0.354394 131:-0.00760697 132:1.04482 133:-0.274543 134:1.00104 135:-0.000413409 136:1.86919e-005 137:-0.263426 139:-0.386845 140:661517 141:5.22399e-005 142:-2.79662 143:-0.0267731 146:6.01763e+008 147:0.924826 148:378306 151:0.000162964 152:-0.137473 154:-1.39556 155:-0.001574 156:1.02796 157:-0.718267 158:66.8876 167:-0.892905 171:-10.1555 172:0.124664 173:5.77455 175:0.0026298 177:2.60561 178:-0.00619927 179:1.79489 180:0.169146 181:69.2621 182:-1.08706 187:-0.336006 188:0.0715727 189:0.0903945 190:0.000480141 191:8.28319 192:-1.21016e+007 194:-1.32764 197:0.0951045 198:0.348023 199:-1.91544e+011
2|F0 |F1 0:3.03698e+006 1:0.179787 12:127.218|F2 0:50.5693 1:-3.11543 2:-229.103 3:0.00141914 5:-341.87 7:1.06269 9:-2.08097 11:15.1068 13:-0.0485893 17:-0.394772 20:0.0148876 22:1.0093 23:0.064303 26:-0.589267 29:-0.387121 30:-1.68803 32:-2672.86 36:-8.57245e-005 37:0.0523567 38:7426.72 43:-1.49823e-010 44:0.356673 45:3.42232e+008 46:-0.358754 47:1.03607 49:2.5028 51:-6.72103 52:-0.0270183 54:-1.71469 55:13.5531 57:-0.239975 58:-68.2381 59:-2.09577 60:6.48754 61:-18.5392 62:2.15082e+008 66:2.94164e-005 68:-35258.3 69:-15565.3 71:-576.478 72:1.24891 74:-0.0206604 75:-80.4167 79:-1.13816 80:0.297054 81:7.32507 82:0.550213 83:0.00164128 84:-0.000112185 85:-277925 87:-0.207606 89:4.16905e-005 91:1.35449 92:1.22072 93:-0.764702 94:2.41855 95:0.822804 98:-13.1797 99:-1.44148 100:1.27555 104:-576.131 105:-0.00137894 106:-42.8115 109:6.44955e+010 110:-3.49435e+019 111:-0.94305 113:-3021.33 114:0.524028 115:1.32432 116:0.982252 117:-0.693129 119:-1.08952 120:-7427.68 122:-1.67706 125:-574271 126:2.50382e-006 127:5696.73 131:-3512.96 133:0.439872 136:2.3665 137:-0.745974 138:0.232659 140:9.29823 141:-616.071 142:-4.84701e+010 143:-5.19596e-006 144:6.86119 146:1.30455e-006 148:-0.00254364 150:36.7447 152:0.0190303 153:-3.83635 154:0.0591526 155:-0.110087 157:0.263567 158:-45.7788 160:-2.54315 161:-0.000258556 162:-0.000341615 165:-0.0108328 167:-326.279 168:5553.46 169:0.0333531 170:-95.7896 172:0.0396923 173:0.0312478 174:1.12699 176:-14318.2 183:0.177624 184:85326.7 186:-191335 188:-0.00267372 190:-0.000305202 193:-2.24455 194:1.05764 198:0.464712 199:-0.224028
2|F0 |F1 12:5.17901e+013 15:0.235855 19:0.115005|F2 0:0.211028 1:-91.995 2:-0.00192035 3:-0.00907981 4:-0.188683 5:-313660 6:-34.2432 7:2.80673e-011 9:-0.226155 10:0.462995 11:4.07817 12:-11.4413 14:-1.01534 15:-5.87935 16:32133.6 17:-21.1772 20:2.05261 22:-8.16825e-006 28:-0.510361 30:0.177849 36:-11.2018 38:-0.989317 39:0.00828874 40:-0.473568 44:-0.0793867 45:-0.486946 48:5.95796e-005 51:0.00526515 53:0.016194 54:-1.7008 55:-2.52309 56:-0.837863 57:-3983.33 59:-1.06287 61:27976.9 62:38470 64:-0.00329663 65:-0.591564 67:-0.258807 68:1.0282 69:-2.20485 71:1.12462e-006 73:-1.20244 74:0.0145903 76:-57.07 77:0.0357402 78:-8.0658 79:-77.652 81:-1.19096 82:2.36276 83:-1.83816 86:0.000189762 87:-11.5261 89:-577456 90:-0.000245612 93:-25.5875 94:-0.000245337 95:-4.52186 100:0.52767 101:0.0246322 103:-39.3971 105:0.00632994 106:-0.968873 111:9.09995 112:-2.16965 113:20.102 114:121.534 115:18264.5 118:-2.91973e-006 122:-0.301654 125:-33300.7 126:1.10832e+009 127:1.6248 128:0.121525 130:0.218364 131:7.74113e+006 133:12996.1 134:-64.2795 141:-0.0331537 143:-0.099627 144:0.703847 146:-7.76498 150:-284.719 152:0.502994 153:-0.274163 155:-1.53625 156:630007 157:-22136.4 159:-0.000511938 161:73.9844 162:1.75924 163:0.0601794 164:0.565599 166:6.00767 167:0.728893 168:4.73917 169:3.12244 170:-0.624614 171:-41.9555 172:0.962475 173:1.66033 174:-0.0457261 175:-2.71032e-007 179:-4025.75 180:2071.07 181:1.15064 182:-1.01272 184:-0.511359 189:-5466.38 190:1.31637 191:10.7961 193:-148.556 194:-0.189949 196:4.26354e-006 197:0.350277 198:0.0756317 199:0.00194162
2|F0 |F1 0:-8.96855 2:0.567021 11:1.03956|F2 3:6.89977e-006 4:1984.45 6:0.00124508 7:1.42656e+011 8:0.00082346 10:0.487874 12:3.71061e+011 14:-6.36847 16:-0.0253485 17:-0.140641 19:-0.503459 23:6.83296e+020 24:-8.00896e-007 25:-5.19574 27:712.223 28:1.79482 29:-25.4668 31:0.00110019 32:-0.0684417 33:1.17568 36:-1.01787e+009 37:1787.68 39:-0.98716 40:-5.54867 41:-1.27754 43:3.52085e+006 44:1.58313 47:0.173789 49:-0.157091 50:0.00846728 52:1.27273e-014 53:126.834 54:-9.60996 55:529.802 57:-1.2652 58:-12.6823 62:0.525901 65:0.967232 66:0.0105611 67:346046 68:1.413e-007 71:-0.543902 74:0.440159 76:373.658 78:1.10618 80:-21.9414 81:-0.981782 82:0.103331 83:9.60872 84:-70.8491 89:-1.40057 92:-19.0409 93:-0.223957 94:-1.41175 95:36.4786 96:-0.0387057 97:-0.865137 99:-15.556 101:0.417225 105:1.82937 108:0.0555729 109:-0.982357 112:0.00775655 113:2.7365e-006 114:0.00204053 115:-2.2356 116:-0.0109508 117:-2.74698e+012 118:-642456 119:-4.49982e-022 123:0.153462 124:-11210.5 126:0.0772424 128:2.20097 129:-31.2345 131:0.000665267 132:1.44015 134:5376.7 136:-0.563817 139:7.0517e-005 140:0.0443025 141:0.0196206 146:-40.7053 147:1.10132 148:-559.841 149:8506.59 152:-1.3104 153:83.2068 154:-0.196793 155:-19.3911 156:-0.146988 158:1.16512 161:0.00110814 165:0.597561 167:-12600.8 168:212.772 169:0.772144 170:0.00440132 171:-1.6246 172:-4527.55 175:0.229091 176:-6.14427 177:0.000420672 179:-5.65546 180:-0.00400185 181:0.321378 182:9.7213 184:-1.42177 185:-0.80769 187:-0.000313291 189:9.93615 191:42.9561 193:7008.59 194:-3.77284e+010 195:0.765737 197:8462.39 198:8071.52
2|F0 |F1 7:-0.00171914 9:-170154 15:1.31793e-005|F2 2:0.959552 6:3.16017e-008 7:-1.70899 8:0.38789 9:-8.17604 10:5.45078e-007 11:1.57458 13:0.00604576 16:-1.84044e+009 17:-29.7133 18:0.127223 19:-18.3007 22:2.08092 24:-0.111296 25:224.411 26:0.237742 27:0.732592 28:-1.11445 29:314.964 30:7089.79 32:0.0620398 36:485.932 38:-0.988208 41:1.01162 42:0.00503012 43:-0.150526 45:-1.56831 51:1.66164 53:-0.150457 54:-0.906929 56:23333.4 59:1090.85 61:-7.2773 63:-2.572 67:3.29627 68:-0.115384 69:5537.63 70:-0.000266303 71:0.795773 72:-0.00565107 73:1.18516 74:-19440.1 77:-3.5699 78:-0.775174 79:-0.107004 80:2.05959 81:-5057.8 82:-13634.4 85:-0.971279 86:0.136841 87:-5.17472 88:0.146014 89:-0.724226 90:-27.734 91:-0.00424272 92:-2.85203 93:-0.153908 94:-1.05695 95:44.1353 97:-0.000616555 98:0.0697019 99:23.0007 100:260354 101:0.000680415 104:1.22233 108:-1206.54 110:-0.130075 111:3.12598 112:9513.36 113:-0.170651 114:1.27494e-006 115:2.49741e-007 116:0.445656 117:-15.9119 118:-18.9369 120:4330.24 123:-7.79412e+009 124:-0.0134003 126:-2.13958e+015 127:0.646061 129:-0.474964 130:10.1573 131:0.587531 132:-0.0103777 135:-0.000502657 136:-0.441454 137:21.5694 140:-0.000592933 141:1.73123e-009 146:27.8602 148:0.205752 149:15.563 150:-0.00526412 151:-43.1651 154:-1.04359 155:5272.71 156:124137 157:-2670.81 161:-8.57923e-007 163:-2.76555 164:513.732 165:-0.0518468 166:-0.0363278 167:-9.90664 169:-84.018 170:0.0102009 175:0.157421 177:1.13651 180:-25231.2 182:-1.9517 183:61.7688 190:-1.52598e+009 192:162175 194:0.0428059 196:0.0652639 197:-0.00214187 199:-0.0682437
2|F0 |F1 4:-0.444238 9:0.000634661 19:-3.86674|F2 0:136.217 1:0.718988 2:-0.00742166 3:-3.74584 4:8207.18 6:8.05442e-007 7:-0.0911637 9:1.6293 11:-29415.4 12:1.97936 13:0.039758 14:-0.114373 16:0.0514172 17:2064.43 20:-209646 21:-1.11224 22:0.151776 23:0.447939 26:0.0427135 32:0.00372513 36:-366.147 37:0.742273 39:0.0790938 41:-10.917 42:-10.2522 44:-0.521019 45:0.95578 48:5.688e-021 49:360.28 50:-1.04334e-007 51:-76.1423 52:-0.00185392 54:-0.00082487 56:0.00667814 57:-0.000149682 59:-0.00285886 61:-2.73385e-013 65:5300.48 67:-86497.2 68:-0.0146403 69:-0.0214615 70:-22566.4 72:1.24044 76:0.748282 79:-0.663854 80:-0.193544 81:8.7188 82:-0.00211026 83:0.529304 85:0.0167067 88:-8.37216e-006 89:3.05712e-005 91:-19.9884 94:-0.294341 95:-0.000148823 100:9.33225 101:-4.09554 104:-0.258231 107:-27.6399 110:-0.327639 111:0.424121 112:2.24653 116:-0.0202493 117:-0.898852 118:-3.79428 123:1.59978 124:1.96075e-006 125:0.737636 126:1.97847e+006 127:-0.277414 128:0.0888978 131:-0.0963037 133:0.160863 134:1.09162 135:0.71014 136:-1103.07 137:-16.6018 138:-13.2905 142:1.4802e-010 146:-52972.5 148:-0.909566 149:-18.5889 150:-0.000512491 151:-2.12189e-007 153:214.66 156:3.8351 159:-0.0076813 160:-2.25304 161:-3.02947 162:24.5553 164:-1.99816 165:801.194 166:0.0501642 168:-3.64021e-005 169:3.12454e+009 172:-2.516e+006 173:-0.523752 176:-0.213866 177:-0.0295971 178:-3.16268e-006 180:504.007 181:-12.9909 182:-0.00467017 183:118.811 184:0.0315441 186:-1.15914 188:0.000382024 189:4.78409 190:0.955524 191:-0.101432 192:4.9893 193:0.411865 194:4.22923 195:2847.35 196:-0.000276064 198:-1.48698e+006 199:-349.927
2|F0 |F1 6:6.63625 12:-3572.53 18:-0.956032|F2 2:0.588207 3:0.494428 5:1.07719 6:-2.75506 7:-61.8761 9:1.05074 11:192087 13:1.83443e-007 14:0.00215385 18:0.0191695 19:-0.0727239 20:17.0155 21:0.013587 23:5.6505 24:6.35106 25:-5.01896e+011 26:205.845 27:-1.00083 28:-0.321868 29:1.00096 31:-0.715842 33:-0.364372 35:1.21305e+006 36:-0.606182 37:-0.000378547 42:0.719587 45:1.21923 47:-0.1023 48:-1.3377 50:0.0301271 51:-1.71724 52:-1.06322 53:-168.55 54:0.0223318 55:-0.00456291 58:171.914 63:-1.15893e+006 66:-0.225538 67:-19.1599 68:4.16549 70:-0.884288 72:0.637902 73:0.236592 76:1.39023 79:-0.670186 80:45.2648 81:-3.37457e-010 82:214.06 83:0.0276957 84:-1.7292 87:-0.627354 88:-0.93624 91:-0.000673559 92:-0.730279 93:0.912672 94:0.0201048 95:-0.374857 96:0.0487458 97:0.161462 99:1.47198 100:3.5766 103:37.0632 105:0.0483848 106:0.482018 107:1.13249 109:-0.302864 110:0.451661 112:-0.662107 114:-2.13421 115:-3218.45 117:0.097681 118:-2.46143 119:-1.40597 120:4.04108 121:9.56603e-005 122:2.08604e+007 124:4.39723e+010 125:-22.945 126:-0.904301 129:280.567 130:-13.7167 134:-0.474521 135:429.66 138:2.0367 139:-5.41508 140:-1.18534e-011 141:1.44624 144:-0.0191631 146:-0.0156118 149:-0.00998723 150:0.000207698 152:-0.166985 153:0.19635 154:0.424537 156:-0.0351839 157:0.0328104 159:9.854e+006 160:1.23362 161:-0.00180756 167:-0.419307 168:-0.737796 169:0.0462137 171:-0.612125 172:93186.6 175:3.87697 177:9.30343e+006 178:-0.0132711 180:-0.811275 181:1.32485 183:-1.36656 186:-213.367 188:8.59083e+006 189:0.559568 191:4.80331 192:2049.62 195:-4.06051 196:-0.373176
2|F0 |F1 0:312777 2:-0.00268825 10:0.865712|F2 0:0.0464022 1:-0.00235116 2:-3.83059 3:151.18 4:8.11726e+006 5:0.000526431 6:0.541739 8:3186.22 9:-2.7492 11:-15.5367 12:-6.90814e+009 15:12.0081 17:1.02504 29:0.087303 30:0.784567 32:1.30888 34:-1.07292e+009 35:0.000312467 36:-4.83435 38:3673.36 39:-3609.47 40:-11.3572 41:1.25725 42:-1.83361 44:-121897 45:-0.499493 46:-1.59107e-005 48:0.690087 50:-1188.55 53:-1.3722 54:-0.23262 55:0.488819 56:-0.206231 57:-94.4632 58:0.235188 60:287.409 62:0.131654 63:0.0259438 64:7766.56 67:0.972045 68:-8.80356 70:-408.941 71:2.71533 76:-28.6402 77:2.55174e+009 79:-0.142238 81:0.0510085 82:0.977309 83:0.370262 86:-1.08807 87:-5.87223 88:-0.00300408 89:41.6468 91:-8.43647e+007 94:0.562531 98:-17.2126 99:3.84091e-022 100:4147.19 102:31.6514 107:0.061256 109:0.729517 110:-4.80544 112:-26.1203 115:2.14106 116:-430142 117:5.95925e-005 119:2.23799 120:-0.70084 122:0.18091 123:280682 125:-22.0531 127:3.73866e+008 130:496.831 132:0.0243829 135:-7.7606 136:-21.4183 137:0.192192 138:2.84822 139:10400.4 140:-0.018526 142:606.866 143:715.909 145:-0.0152023 147:-0.0180125 148:-1172.38 150:21.1167 151:0.255411 152:7.18674 153:0.0132084 155:-0.22916 159:1.1716 160:-0.00907376 161:3.20003 162:0.0573367 163:-1.87307 164:16.4528 165:-4.85293e+008 166:0.105013 167:-0.000128152 168:-4145.19 169:-9.87938 175:2.02118e+008 176:3391.14 178:0.381839 180:-0.969409 181:0.508713 182:0.205845 184:-1.03244e+007 185:0.000333032 186:1.35373 188:-2.48076e+008 189:-129.188 191:0.00295154 192:0.357778 194:587.144 195:0.357074 196:-1.93988
2|F0 |F1 0:-1.05613 3:-4.48836 4:-0.00322049|F2 0:1.8857 1:249.511 2:-3.59419 3:-1.56971 5:-0.0306724 7:-4.70468e-008 8:0.264523 11:-14.4197 14:0.0381016 21:-0.425697 22:-5.57684 23:8950.19 24:-1.10017 25:3.78126 27:-1.92224 28:1.64927 29:0.194854 31:8.75714e-010 32:-0.0856851 33:31.7524 34:0.963505 35:-0.961296 37:1384.66 38:1.14115 43:-1.06838 45:-0.135727 46:42.6858 47:0.0115817 48:-0.000343592 50:1.16322 51:-0.928526 53:-99.5379 54:2.64141 56:5.96306e-005 57:2.95868 60:-29.2662 61:6.18815e-007 62:0.169405 63:1.55461 64:-0.00834932 66:-0.977519 68:0.00318408 69:-0.0116543 70:0.00185411 71:-3.95674e+007 72:-6.2516e-006 73:-0.00201122 76:-3.42532 79:-0.421164 81:-60.3105 86:1.50265e-008 87:-0.213511 88:-868.582 90:0.152577 92:32.0873 93:-76.0181 94:-19.1287 95:0.50483 97:90.1005 99:-2.48176e-007 102:0.231553 103:0.518287 104:4.40464 105:-17.103 106:690556 107:-1.02836e-011 109:-1.0183e-007 110:1.13927 111:0.277513 112:-0.0116116 113:-35.0652 115:-11167.8 116:-1.01746 119:-2.02767e-007 120:-3.33266e-011 122:1.02561e-009 130:-4.55132e+007 131:-964.092 132:0.511718 133:-0.270634 134:-0.00096002 139:-1.06748 141:-2.22764 142:0.558857 143:3.01543e+006 146:0.152441 148:-133142 150:-50 151:177.581 152:1.65332e-011 154:10.4132 155:1.12729 159:-4.0719 160:0.514701 168:1.47073e-008 169:-17868.1 170:-0.00451958 171:0.0163752 173:0.795877 175:0.946681 176:0.25076 178:-12.0522 181:0.767468 182:2.61328 183:-0.31766 184:-0.850055 185:1.6356 186:2.21692 187:-91.7965 188:-0.000551086 189:82.2611 193:-1.05795 195:-18392.7 196:-120.791 197:0.0148847 198:-192.774 199:-0.448625
3|F0 |F1 0:30.2357 3:-2.14389e+011 4:80.77 5:50.416 6:-67.4224 9:0.694533 10:5.12377 12:-2.62306 14:0.998554 17:0.572404 18:171.373 19:-3.20347|F2 0:0.130167 1:-0.690912 2:-0.0281402 3:79.5661 4:50.1902 5:-0.762651 7:-0.303893 9:-0.0101838 10:-0.332672 11:-27.0342 12:1.58407 16:5.75053 17:-0.842294 18:0.00445153 19:5.24137e+011 20:0.500103 21:-0.265619 22:-34.265 23:54.2079 25:7.11212e+007 26:0.224227 28:1.98201 29:-8.48686 30:-53.8186 31:-0.222818 32:-4.22676e-005 33:-1807.75 36:0.412665 38:0.195287 39:0.000604125 41:0.589621 42:-292.115 44:6.57666 45:0.489365 46:-2.08749 47:-15.1749 49:-4.73106 50:0.0415063 51:-35137.1 52:-0.157262 54:-1.74913 55:-0.00700351 56:15.3138 57:-2.45992 60:-458.625 62:0.00017365 66:-1.63987 67:0.416677 68:-0.288215 70:127789 71:63.1826 72:-1.82831e-006 73:-2.0052 74:-1090.51 75:0.0639422 76:7.26855 77:0.981686 78:-0.399273 79:0.0144527 81:-2.28651 82:-1.01235e+015 83:-351.449 84:0.0231572 85:8.45245e-006 86:18516.6 90:-0.000138923 91:0.427497 92:-5.59752 93:-6704.43 96:0.527374 99:-0.0691311 101:-0.515361 103:164.775 105:0.000182494 107:1.0263e+008 108:-0.000370013 110:0.020676 112:1.56975 113:-0.182635 114:-24903 116:-52.5779 117:-0.00369886 119:-9.91255 122:-0.272779 123:6.78829 125:-0.145666 126:0.00191246 127:-125.351 128:1.29778 129:178797 130:-0.00127405 132:586.69 133:2.16915 136:-0.437536 137:1.55495 138:0.191648 139:2.00253 140:-0.450829 142:-0.132292 144:3.34271 145:-2.73302 148:-106.162 152:0.00755137 154:-3.46004 155:1.00529 160:-4.57613e-005 162:23.9211 166:1.06427 167:-3635.16 169:-0.205918 171:0.170285 172:1.19809 173:0.0499154 174:-5.41928 175:-1638.87 178:-4.34008 179:2.87901 180:0.362556 181:0.281917 182:1.39593 183:0.562188 184:-1.99202 185:70.8728 188:-0.602972 189:0.150347 191:-118.296 192:-0.381879 193:2.55131 194:0.271692 195:-2.63805 196:-0.788747 197:-3.27326e-005 198:-0.143446
3|F0 |F1 1:260.592 2:-0.24324 3:-1.32219e-014 4:3.02696e+011 6:0.01022 8:-1391.48 9:0.0800984 10:0.00483263 11:-0.840838 15:-5.74706 17:5.91108 18:3.57991|F2 1:0.236977 2:-906.674 3:-0.0429575 4:0.00114203 6:-2.437 7:-4.1445e-013 9:1.31232e-007 11:525165 12:0.92502 13:7.02365e-006 16:12338.6 17:-3.55792e-006 18:-0.00605212 19:9.53575e+010 20:-0.416757 22:-1.69104 24:10524.1 26:-0.0736838 27:31.2765 28:302586 31:22.6904 33:-0.83959 34:-1.42529 35:-0.0782853 37:-0.0013344 38:2.13692 41:-0.803283 46:0.269033 47:-0.983918 48:0.471825 49:-1.14682 50:-8.95677 52:-1954.15 54:-2.39598 55:3.80846e-005 56:0.0541115 57:0.748698 59:-0.0923515 62:22.6335 63:0.0513847 64:1.17443 65:-202738 67:18.7346 69:-5.02539 70:-2.43775e-005 71:21.7109 73:-0.000238858 75:87.3059 76:-0.128542 77:-0.00946846 79:0.155816 81:-15.292 82:343.662 83:3.13925e-008 84:0.000976601 85:-1.5832 87:67042.7 89:0.646923 90:-1.18589 92:0.00587404 93:0.055039 94:-2.45161 95:19.5387 96:-0.144421 97:-0.640325 98:-68.0443 99:137.744 103:15.6409 104:-10.6358 105:2.22978e-012 107:114.083 108:-63.2841 111:0.000426613 112:-0.848774 113:-1.15375 114:-0.126101 115:-0.885412 116:-0.340794 117:0.977666 120:-1.40718 122:0.00351735 123:1.97415 128:0.053235 130:1.27356 131:0.0368275 132:-13.4939 133:0.00315511 134:-0.00621894 139:0.175979 141:1.41086 142:9.51886 143:-0.620852 144:1.09905 146:0.44818 147:0.346821 148:-0.154797 149:1462.93 150:-0.006512 151:-438.761 152:-0.419211 153:1.46901 154:5.18952e+013 156:164956 157:1.40673 158:77.4038 159:54.0846 160:-1.65373 161:0.000126278 163:-1.57038 165:0.410705 166:-0.635167 167:-727.493 168:0.15391 169:-0.000173243 171:0.124071 174:-0.0201847 175:-177017 177:1.49927 178:0.120153 179:-1.32814 181:-0.270819 182:-0.000433528 184:-0.242051 185:6.89176e-009 187:7.0509 188:-0.000783689 189:-0.996655 190:1.50434 191:-0.000740595 194:-0.336107 196:-3510.75 197:0.0236877 198:3.28593e-005
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3|F0 |F1 0:8.82802e-006 2:1.05324 4:1.22186 5:-0.774566 6:542.741 10:2188.85 12:-77.0241 15:1.93985 16:0.0486362 17:-0.902785 18:1.82361 19:125.919|F2 0:-0.337242 1:0.18985 3:-0.00839788 4:-5.37442e-008 5:-4.2049 8:16.7269 9:-0.0247808 10:-0.0729837 11:-2.59042 14:-0.0534344 16:0.0319978 17:-1.02143e-006 18:-2794.67 20:-1.26807 22:-0.000965329 23:-0.0177182 24:-3.7607 25:-0.805572 26:-2.59596e+015 27:3.75134 31:1.42631e+016 32:-0.550239 33:8.29234e-007 34:-3.19542e-008 37:0.895607 39:-6.03478e+007 42:1.43472 43:-108992 45:5.39478e-008 46:-2.78949 47:-20.5019 48:1.31309 49:0.000388427 50:-1.49403 51:1.12066 52:1736.04 53:0.862857 54:633.53 55:-0.32744 57:0.00334496 58:-0.437312 59:0.823789 60:0.990544 64:-6.45218 65:3.02889 66:1.06721 71:0.258994 72:-1.24862 73:2474.62 74:-462.808 75:-39.351 77:-0.768907 79:11.4501 81:-0.838018 83:-1.87946e+008 84:1.36317 85:-0.00343116 86:0.000590442 87:-0.01978 88:-8.9336 89:9.86682e-006 90:0.00018269 92:-0.207731 93:-0.160659 94:-0.000122952 95:1.04443 96:-7.77971 99:-0.0121392 100:1042.74 101:-2.1638 102:2.56089e-008 103:0.00696494 104:0.175078 107:-4618.07 108:3.02189 109:-26.3165 113:-5.49218 116:-0.0367032 118:0.275221 120:-0.0807036 125:0.00210621 127:-504.667 128:-9.577 129:-1.09866 130:1.39783e-016 137:0.237138 138:0.00285205 139:2.00666 140:-1.24918e-006 141:10.8712 142:-0.000251451 143:-0.382192 144:2.75084 145:5.48458e+010 147:-10.8662 149:-0.28 150:-4.5977 151:-9.04402 153:-7.77654 154:-0.487306 157:0.661435 161:1.21252 162:-0.150053 163:-0.0227947 164:0.00129043 165:8.2673e+008 166:-0.263779 168:-2.47423e-006 169:-0.825479 170:0.663452 171:-17.7082 172:-1513.54 173:-0.549526 175:1336.19 176:14.0333 177:-3.31646 178:1.34125 180:1.79842e-005 181:-0.74901 183:-1.19484 184:814130 185:0.149941 186:-1.66085e-007 187:23.4682 188:-1.10763 189:23364.3 190:-5.43258 192:-0.111987 193:0.000972323 194:0.00866091 195:-5.00353 196:5.25945 197:-1.56978
3|F0 |F1 0:-0.20716 1:6.0596e-007 2:-0.377959 4:1.10313e-008 5:3.16336e+009 7:0.93398 8:1139.88 9:1.27475e-008 10:0.467845 11:0.333331 12:-3.42556e+010 14:215.611|F2 0:-1061.57 3:-303772 5:-0.0346511 7:8.2248 11:0.402395 14:-14.6834 15:-0.0061523 16:1.28989 17:0.513373 18:-1.16587 20:-1.36114 21:0.962951 22:-0.079799 23:2.82346e-006 24:1.18281e-005 25:-0.709064 26:-77.971 27:1.0688 28:4.79734 29:-10357 30:-1027.93 31:7.06776e-005 32:2.5843 33:0.0919785 35:-0.00985216 36:-2.23618e-009 37:57.0656 38:0.666859 39:1.39066 40:-2.71409 41:9405.28 42:3.49162 44:-3.05417 45:0.0137248 46:-2.62683 47:-0.0150511 48:174386 51:-1.29798 56:1.17758e+007 57:-0.000871202 58:0.953312 59:-0.980784 61:1.83462e-009 63:0.622579 65:-3.70928e+012 66:-0.103025 67:0.413606 69:1.44201 70:-1.04133 71:20.6424 72:9.59703 73:0.0086304 74:-0.297098 75:14.7785 76:-560.386 77:-0.220397 78:-0.00201496 79:276354 80:-2.10743 81:0.150242 83:-0.000561132 86:2.67258 87:-159.744 88:1.30401 95:-22.1739 96:-88999.5 97:-0.198875 99:0.484801 100:-0.521785 102:0.718019 103:2794.43 104:0.351173 106:-0.00155212 107:-15.0223 108:-0.44324 109:0.723031 110:-106.042 111:-436.733 112:0.659714 113:99531.7 114:4.72171e-007 115:2.21713 117:0.0860055 118:-0.00129114 121:-1.9024 124:-0.336307 126:0.000197264 127:0.0101178 129:0.00454489 130:1.89294 135:-1.01068 136:3.03836 137:-18.6064 139:0.373042 141:0.223433 142:-1.38825 143:1.15088 144:50.3501 146:-0.0125343 147:-7.0515e-009 148:0.220211 149:1.02781 150:0.474576 151:3.83988 152:10.3214 153:-0.00538429 158:118.533 159:-0.496778 160:0.0619072 163:-15.4473 164:-2.13481e-007 167:-1.12956e-006 168:6.23988e-005 169:-16.0941 170:-0.0233821 172:3.47052 173:1859.16 175:-1.04424 177:-0.0629848 181:-1.18447 185:-1.20348 186:0.00140159 187:-3.31293 188:-0.795571 189:11.0483 190:10.365 191:-69.0105 192:-5140.17 193:2.30736 194:15.6575 195:-461.879 197:2.09478e-006 198:2.28149e-008
3|F0 |F1 0:0.00233785 2:-1.01223 3:6.24939 4:-3.3975 5:-6.13925e-010 8:423.55 9:-1.77063 10:121073 15:-1.04583 16:14447 17:-6.56274e-007 19:1611.31|F2 1:0.357836 2:-0.614628 6:-5.1522 8:1.38531 9:-0.6928 10:-2.16489e-007 11:-1.87024 12:4.88544 13:-1.98365 14:-1.12147 15:-0.768404 16:0.022328 17:4.16024 18:7058.99 20:1.55548 21:-2.66208 23:-662.078 25:1.08104 26:-12.897 27:-1.08605e-005 28:0.244428 29:2.98914e+007 30:-0.298958 31:-0.279343 32:3.89405e-006 35:1.94407 36:-222.453 37:0.463829 38:-1.29628 39:-0.560652 40:-1.07981 42:-0.638883 43:-33.5024 44:0.997767 47:6.33815 48:-0.000295253 52:0.827362 54:-0.00841323 55:-1.09291e-005 57:-0.390297 58:2574.34 59:-5.52018e-005 62:-4.58095e-006 64:6.55319 66:-1.21176 67:-0.125401 68:0.101382 70:-0.0871855 71:-5.53913 73:4.39085e-008 75:0.771792 76:-0.000459256 77:2.61568 80:-12833 81:5822.99 86:-5.78385 87:-10.2295 88:-0.0234456 90:62.3571 91:-0.169673 92:0.000604097 93:146672 94:2.94055 95:1.96872 96:-1.06104 97:-7.51158 99:0.630386 100:0.00395315 102:-1.82036 106:857.616 107:0.000217346 109:-36.1534 110:3.20256 113:0.0373366 114:-0.225035 115:0.179871 116:-7.9297e-009 117:-0.00383592 119:3.72705e+007 120:-4945.31 121:3.39639 123:1.05585 124:-0.050894 125:-0.657121 127:-43.1571 130:1.20112e-011 131:-2.31297 132:54.4377 133:0.954486 134:1.19349 138:0.157295 139:-14562.9 141:8.10998e-009 145:0.0586956 146:23.7826 148:143205 149:-27.5653 150:1.30967e-016 151:34.5487 152:0.22079 157:2.39796 158:0.442692 159:-0.898163 160:-1100.22 161:-0.268131 162:-1.15277 163:-0.529878 164:2.4011 165:9.12156e+008 167:40.1787 171:-2.05499e-007 173:2.42326 174:-439.651 175:-1.79381 176:18.0477 177:-0.00347743 179:0.0322318 180:-82.1677 181:5.71245e-010 183:1.07741e-005 184:0.0382021 187:0.975036 188:-2.97846 189:-0.0896833 190:-1.24275 191:-13.2207 192:-0.00122845 193:1.89188 194:755.505 195:0.906967 196:4.1715 198:20.899 199:0.00023222
3|F0 |F1 0:1.32185 1:-189.557 2:5.19858 5:24538 6:5766.91 7:4.0143e-007 8:-0.00468009 9:1.36467 11:4.15196e+008 14:-0.182591 16:-0.000135511 18:5.74627e-011|F2 0:266.978 1:-0.00842076 2:-0.626101 3:-0.0796962 4:-1.18169 5:-215.545 6:-4.2884e+010 7:74.3798 8:-0.440045 9:-2.02906 10:-7.50327e+006 13:3.66991e+007 14:-0.00128191 15:-2.16226e+009 18:2.11761 20:1.48255e-007 21:0.016671 23:-3.71475 30:0.11129 31:-0.346176 32:-14.7177 34:-0.249232 35:5.39943 36:0.632936 37:1.15587 38:-0.000288477 39:2.03099e+009 40:-0.939927 41:0.910386 42:-658655 46:1.06199 47:-0.0256988 49:-0.867462 50:0.265436 56:1.01472 57:230.028 59:-0.802019 60:16.0519 61:1.2344 62:0.0610934 63:-0.493302 65:0.000806413 66:0.000574134 67:0.0011049 68:-0.00016781 69:9.93984e-011 70:0.0216308 71:0.00782665 72:251.872 76:-8.42554e+007 77:14.5544 78:5.55593 79:0.0762518 80:0.29937 83:4.83462e+006 84:-0.658729 86:0.000503407 87:1.55498 88:-181652 89:-0.172347 91:-21.2522 93:-1.01927 95:-0.795501 96:-23.2721 99:1.03617 101:-1.07591e+009 102:-3.18739e-007 104:1.16181 105:14.1541 107:0.824718 108:1.29145 110:173.665 111:0.045119 112:-3.0818 113:0.00931608 114:-21126.4 115:-0.742104 116:0.918467 118:-0.00977026 120:18.7897 122:-933.456 123:3.50601e-005 125:-0.0179487 126:6.45301 129:-0.0614098 130:-2.309 131:-13.1056 132:2.62829e+010 133:4.29158 134:0.370141 135:0.966783 138:-1.19623 141:1.26936 142:-0.0602554 143:-0.138516 144:-211087 147:0.240228 148:0.339164 149:-0.21168 150:-0.361103 151:-7.99837e-011 152:-0.109943 155:-0.353167 156:-0.948114 157:0.537503 159:22.4813 162:0.0314754 163:1.78417 164:-0.245303 165:-0.393456 166:-0.293166 167:0.0376177 168:-172.581 169:-57910.3 170:67.2699 171:823.694 172:2.96029 173:-0.0676058 175:4.0138 178:34.9971 179:0.000905937 180:55.5194 181:1.35706 182:-3.23541 185:-0.000349764 186:-0.338609 189:1.42743e-017 190:0.311264 191:8.38014e-017 192:39.0868 194:-1.4884 196:2.96431 198:-0.352012
3|F0 |F1 0:9.04277e-006 1:9.37894 2:-3.0406e-006 4:-9.94031e-006 5:-0.00398811 6:0.167096 7:-10585.1 9:-1.47773 10:1.22392e-007 12:0.713236 17:-7426.81 18:-0.790828|F2 0:-1.60595e-009 1:-105.675 2:-30.9544 3:0.461806 4:-0.0177327 5:-322.664 6:-0.000598789 9:-2.06163 10:0.036471 12:1.11191 14:1.60016e-007 17:3704.98 18:-0.75809 19:8.03288 21:4.20979 22:0.664081 23:3.87026e-007 25:-0.142699 26:-233994 27:-12.2474 29:-0.123085 30:-2.1454 32:6.23576e+008 35:-106.821 36:0.000106574 38:-4.15451 39:0.000156414 40:-2.8325 46:0.163097 47:-6.33921 48:-0.0817476 49:0.173379 50:0.000354344 51:186.365 52:1.20716 53:-125.139 54:-47256.9 55:-3192.87 56:0.662116 57:100.635 59:-5.56469 60:0.0018426 61:-0.144616 62:-0.127679 63:-1.44126 64:-0.499888 66:-3.12777 67:-0.741374 68:3.51993 69:225.717 70:-0.293038 73:-3.34051e-005 74:-3.91802e+006 75:1.00711 76:196.087 77:2.12233e-007 78:0.0229017 79:-0.408007 82:0.241505 83:0.000154102 84:-15.1282 87:0.105047 88:0.56901 89:-6.27399 90:4914.97 91:-1.05922 92:-1.00885 93:1.4856 95:6.96852e-009 97:-0.000889628 101:0.00107514 103:42.1545 105:1.41407 109:-0.922517 111:-0.0657218 112:0.741141 113:0.122633 116:-0.161955 117:1.28235e-006 118:-30364 119:0.674723 120:0.219182 122:1.20853e-005 123:1.73958 126:3.26183 127:0.00741074 129:0.28959 130:10.6099 131:4.76121 133:0.0122324 134:0.37503 135:3.4204 136:108.693 137:0.997059 138:-2.26146 140:0.04813 141:-1.45025 142:-2.90051 143:-4.13586 146:-9.59477 148:0.0355479 151:-4.85401 153:0.246246 154:-3.21259e-005 155:484.489 156:51.4257 158:0.139641 159:-1.48679 160:-0.000636359 161:6.62321e-006 162:-0.0222415 163:2.46895 164:1.28754 165:-1.7285e-005 166:-55.122 167:19926.4 170:4.20462e-007 172:-3.03333 175:33.794 177:-0.269555 179:-7.59014 181:9.97603 183:2.57601 184:-2.16583 185:-6.90619e-017 186:0.994855 187:-0.000192009 189:-840.56 192:0.306009 195:9.98963e-016 197:-0.3075 198:-11.7182 199:-1.02439
3|F0 |F1 1:0.607208 2:30288.6 3:0.0357528 4:976.087 6:0.00019386 7:0.959067 8:-0.14297 10:-0.471828 13:1863.85 14:1151.78 15:0.0451741 18:-14.1585|F2 0:-3.97331e-005 1:0.308464 2:0.0593678 3:-1.6433 4:4.92116e-007 6:945347 7:-2733.63 9:0.215818 11:-25.3426 13:0.00676818 15:-1.2369 16:-30.1312 18:-1.50931 19:61.3871 20:-265.014 21:-0.000390115 22:-3.02779 23:1.90168 24:-224596 27:0.338796 31:-1.01911 32:-0.886472 34:1.85057 35:2.7672e-005 36:-53630.3 38:-0.327306 39:1.02402 40:3.61215e+008 41:-0.347096 43:-0.140506 44:0.128006 45:-0.329671 46:-29.2171 47:-0.487686 48:86.6408 51:-26.537 52:-0.349791 53:0.0803374 54:-4.06233e-005 55:7.99664e-007 56:1.48254e-019 58:-59.2926 59:-0.0272575 60:-2.6821 62:0.180722 63:1.71573 65:2.36726 66:-1.91915 69:4.42005e-009 70:-0.904408 72:-1.23808 73:-4.76269e-007 74:-0.00305618 75:-0.178976 76:-1.85213 77:2.82279 82:-6620.4 83:-3.13977 85:0.902258 86:-8.13709e+010 89:-0.882745 91:0.000582679 92:-0.971389 95:0.0763549 99:0.000163578 100:0.931029 102:-0.270759 103:-9.49017 105:0.302792 106:-0.995595 109:0.733665 110:0.503451 112:0.0274134 113:-2.68371 114:0.0205662 115:6.84816e-005 116:8424.43 117:0.847053 118:0.472776 119:-0.0543852 120:1.63636 121:-0.182098 123:-0.0855495 124:-1.89262 125:-1.17315 127:0.0214224 128:-425.766 129:-34999.4 130:-0.0612376 131:1.13404 132:11.3221 135:-0.0293087 136:-5.58644 140:1.21188 142:5.6966e-006 143:0.486099 144:21.5401 145:-4.07099 146:0.943619 148:2.39915e+006 149:0.00344658 151:-1.16477 152:-0.988182 154:-0.821819 155:1.1824 157:-4.07122e+013 158:21.8353 159:-0.492891 162:18.98 163:-0.832352 164:8.92928e-006 165:2.2455 169:-9.93458e-010 170:213.389 173:36.9506 174:-3.93185e-010 175:5.17051e-010 176:0.0662226 178:5.73049 179:-0.573827 180:0.140781 181:6.66779e+006 182:-2.48305e-010 183:-34429.3 184:46.4277 185:-0.0088546 186:0.523687 189:2.41183e-005 192:-0.00164408 193:-1.24635 194:3.46512e-005 195:-0.337317 196:-0.0654755
4|F0 1:0.223934|F1 1:0.210301 4:-0.00213233 5:-1.47196 7:0.0451997 11:-0.12997 13:-0.960469 15:2.7194 16:1.28417e-013 17:-3.69782|F2 3:0.37081 7:-0.00134463 11:1.11985 14:-40.0796 27:0.000577613 30:-543200 38:1.58463e-005 43:-1.4741 45:0.0030787 48:0.971954 50:5.34465 53:0.00355439 56:0.190438 64:9.93092 78:-14219.5 81:1.37793e-009 86:4.01602 94:5.78033e-006 100:-0.884242 104:0.526945 108:-0.0654765 112:1.01473 113:-0.154914 115:0.0165244 116:-0.129255 124:-0.126097 145:-2.28688e-006 146:-0.0282262 155:-1.08333 159:4.62805e-006 164:-20.9932 166:1.76666e+008 170:6.73357e+006 179:-5.12452 180:-1.24402 185:0.909513 191:-1.33478 192:-0.198948 196:31336.1
4|F0 0:-1.01253e-010|F1 3:0.0040765 4:899.115 5:-30.5382 6:-0.000229801 8:-11575.8 11:-689.734 12:3.13642 13:1.03021 15:87.8711|F2 0:1.80182e+007 3:-3655.24 11:363.845 17:-5.48106e+017 28:3.54287e+007 31:0.388954 35:-0.758378 40:0.0544505 46:1.26169e-008 51:-167.776 58:-137.543 68:-0.245091 72:-0.214706 75:-0.657255 85:0.486533 86:2.66905 88:-0.0138244 91:0.468414 92:-0.000904668 94:-0.25481 99:3.33777 106:-6.11069 108:5.11947 118:0.402212 119:-1.73843e-007 128:0.296467 133:32.7306 134:148.887 135:-0.654096 136:-1.48038e+010 141:-1.01718 143:6.15749 150:0.145794 160:0.456135 169:9.93991e-006 181:19.9761 192:0.000142751 194:0.0563433 197:-5.82784
4|F0 1:-0.967242|F1 0:0.565965 1:-0.294439 2:4611.82 5:0.955512 9:-7.34988 10:-7.70627 11:18.6495 17:-0.00101263 18:0.821649|F2 3:-0.000313974 5:0.265691 8:-1.02512 11:-0.0010099 15:2.71414 19:1.05147e-005 23:1.08732 24:-1.43413 33:43048.9 38:0.211043 39:-1.04202 44:4306.17 54:-0.0371561 60:0.195716 76:-6319.12 80:-3.9862 82:0.523535 84:-1.21498 85:-0.206485 110:-156.261 115:-2.11383e-008 125:-0.0500535 126:0.0468595 131:-0.00308969 137:-0.0424857 143:-35359.3 148:2.32562 151:-0.867494 153:-1.32305 155:1.19088 157:0.00847172 167:-0.00372275 170:0.0285096 174:2.06992 180:-2.55605 188:0.111815 192:-0.00937141 194:650.94 196:-1.65167e+006
4|F0 0:0.539337|F1 0:0.459083 3:1.08858e-015 4:-5.81669e-005 8:-0.00306665 9:6.57603e-006 10:-3.32511e-007 12:2.45481 13:-3.29914 14:0.302455|F2 3:0.172892 5:-0.00241212 12:-2.63135 15:0.630109 17:-0.0488326 22:-1.92246e-010 25:-0.179498 26:0.169106 28:-4.20787 32:-0.00958315 42:-372.185 46:1.27888 53:-0.692477 55:-0.330284 56:-0.231877 58:9.26039 63:-4.85399 72:1.14827 77:-1.42394 87:0.385639 88:1887.88 91:7.38944 94:-0.457937 105:-0.185024 125:-5.81119e+007 133:1.08398e-005 135:0.420419 136:-8.8206 138:0.000399087 152:0.766265 154:-1.09762 158:-0.215535 171:-0.000154025 172:0.145802 173:-0.00129535 175:0.877357 184:-1.97645e-005 195:0.00879553 197:-0.721665
4|F0 1:0.90939|F1 1:0.27222 2:-0.00151893 4:-0.00215098 10:0.456487 11:0.51191 15:-0.0232884 16:-0.86717 18:-0.0597464 19:-3.05819|F2 0:0.000815346 1:0.760686 8:3.00663e-005 18:-208.638 32:-135.65 41:1422.11 48:-8.24671 54:8.30127e-008 56:0.541178 64:-0.0251722 66:0.0423874 79:0.470876 84:0.167985 85:-118.912 86:-4453.51 88:-0.288932 91:29646.6 94:0.0031841 102:1.91793 109:-0.0245708 110:-32.8033 111:0.0219218 112:-1.24596 119:11723.4 127:-3.55922 134:-0.110509 136:-17.8166 153:-0.36494 156:859.744 163:-0.315101 173:2.99456e+010 176:6.65115 179:1.09391 182:-0.968548 187:2.56908e-008 190:-0.0316565 192:2.79019e-015 196:-532.952 199:-1.07163
4|F0 1:32.0799|F1 6:0.0727488 7:-16.8371 8:3.36792e+009 9:5.92394e+007 11:0.26718 14:3.04553e-015 15:-0.110415 16:-3.42032e+007 19:-0.359479|F2 6:2841.2 8:0.876143 9:-0.00106105 10:-27975.8 11:3.47443 18:-0.46378 19:-1443.72 25:0.00633462 30:-56.86 48:0.197767 49:1.86385 50:-10375.8 58:-0.000219889 60:-1.43691 62:-0.674809 77:0.0160479 83:1.41621e-014 85:-4.23867e-005 88:0.0043944 95:-0.455007 100:5.648 102:-0.0806234 110:1.3735e-005 112:1.2151 114:-0.871645 115:0.00947673 116:1.51883e-019 118:0.465878 122:-1.33515 123:66.7469 124:6.85881 126:2239.12 133:10662.8 149:0.532658 155:-0.185115 169:-480.995 183:1.97569 184:1.05455 191:2.21713e-006
4|F0 1:1803.64|F1 0:-0.332953 1:-117.355 3:-0.447236 7:-1.44936 8:0.400409 14:13.3279 15:-0.188155 16:18.1216 17:2.37057|F2 4:0.694683 12:4709.13 14:-254.104 18:0.111194 54:0.0449542 57:4.66236 60:-0.0968232 64:-0.0319804 65:1.61728 70:68.1085 74:-6.2371 75:-0.00440496 77:1.8285 79:-0.0130149 81:-170.36 88:5911.33 92:0.0992864 97:-0.348087 106:3.2566 107:-126.072 110:3.41036e+006 114:-5.01935e-005 120:1.12577e+006 121:-16.183 126:0.956575 129:-105.535 130:-1405.57 131:2.61134e+006 139:-10149.5 140:398347 142:1.00144 157:1.58327 163:0.00676259 164:777.703 171:32.3336 181:0.993317 183:0.0118631 198:0.000469883 199:-0.0658697
4|F0 1:-0.000674771|F1 2:0.00528737 7:2.63063 8:23.9343 9:-2.85986e-009 11:94.8294 12:-0.0164569 15:-217.206 16:-2.85199e+007 17:0.888353|F2 2:0.613225 8:5.0465e-005 10:0.0719352 27:0.00133172 38:0.567418 39:-0.504595 53:-0.0940549 54:-0.000328116 56:128.996 62:-42447.3 70:-1.30567 72:1.4911 73:2.92649 78:-1.12665e-008 81:-0.0174286 84:0.000256477 85:0.0660458 86:1.42974e-009 87:-0.0260901 89:-4.47862 96:-0.0781786 102:0.45371 106:0.333204 108:10963.7 110:0.148101 121:-1.05022 126:399.958 141:-6.91375 142:260.451 150:0.629763 152:-0.0247856 153:-852229 159:2.52776 163:-9.61691 165:0.750984 182:-2.02607e+006 185:1.05745 188:3.43269e+007 192:0.988582
4|F0 0:0.190959|F1 0:-1.39247 1:-7.39797 5:-1.21196e+007 6:-0.889188 9:-0.266253 11:0.000668985 13:-0.0834279 14:2.40446e+009 15:-0.077386|F2 7:0.558495 13:31696.3 14:-0.873845 18:-0.836727 19:3.85012e+009 24:-2.3084 27:-1.90102e+006 29:0.869484 35:82.6815 36:-0.0036389 37:-5.97677e-008 48:-0.0351055 49:16.8561 50:-1.9847 52:3609.21 60:-5.35649 61:11.7941 63:-0.286459 72:-69.8438 79:-6.6186e-009 93:1.17152 95:0.763338 111:367.47 117:0.21104 118:0.0749418 120:756.467 130:0.727547 155:9.21685 160:0.580053 161:-0.549483 171:5.13495 172:-44.9825 178:-14.0868 184:-1349.15 187:-16.2442 193:-4.40392e-005 195:1.02577 197:-0.00677452 198:126553
4|F0 0:-0.316877|F1 0:306714 1:-73.2181 6:-0.137038 7:0.00494895 13:-1.27333e-005 14:-2.0302e-013 16:-6.54295 17:-1.30631 18:-1.03199e+009|F2 0:1.03135 2:-19.71 3:-2.08012 13:-0.526983 19:-1.82773e-006 20:-0.0240389 26:-4.91013e+007 34:-0.534467 37:0.00183804 39:-902415 42:53393.5 56:-0.00197687 57:0.00211763 58:-2.37361 69:-0.000128696 70:0.000311744 72:0.247062 83:-0.0592846 87:-0.777054 92:-1.00161 95:-0.924817 98:0.0121728 100:5.77711 106:4.30208 108:-7.37475 111:2.52066e+008 120:-1.096e-008 127:0.439731 152:2.21349e+013 153:-4.36132e+006 163:-0.514437 167:0.266482 168:-5.29224 179:0.137975 189:0.130732 192:0.349801 194:0.178937 196:570.725 197:1.05526
5|F0 |F1 2:0.92112 3:-0.657275 4:-0.0777576 5:170.084 9:0.0158627 10:-1.02625e+006 11:-34.2423 12:1.53607e-008 14:0.229766 15:-9.16903e+006 17:-5.5398|F2 6:-0.487001 9:-0.567543 11:3.85901 12:-3.97849e-007 14:-0.00210109 20:2.33973 34:-3.33722e+016 37:-0.035537 38:0.172401 42:-0.000479334 45:-0.000117554 53:-0.68137 78:0.982697 81:0.894468 97:-0.138573 99:-0.00455841 102:-0.000469585 108:28509.9 117:-0.0145867 118:0.0012255 137:-7.75734 138:-1.24769 141:-3386.1 155:1.01703 157:-1.54868 172:-3.65987e-018 189:-6.04814 191:0.369401 196:0.861169 197:-8.3379
5|F0 |F1 1:9.43246e-008 2:5.04061 3:-0.746483 5:19.446 6:3.20842 9:-1.00163 10:42.6653 12:-1.79596e+008 14:-0.0377962 18:-377407 19:-0.140286|F2 3:-112.215 4:29.8328 21:17.2442 28:64.8297 30:-0.274437 40:-6.01475e+007 51:1574.49 58:6.32708 59:0.0391367 67:-0.431072 70:4.55197e-011 71:0.00417965 72:-0.935414 77:-0.143894 105:1.5275 109:0.0657234 121:0.0134084 123:-6.03759e-005 124:0.623554 127:-0.000104588 134:1.55635 139:-0.00770896 148:-1.39023e-005 157:-58.5058 162:146.638 183:5.93023 184:0.647538 185:-25.2899 187:7.85728e-006 199:-0.135166
5|F0 |F1 1:1.61854e-009 2:297.744 4:-0.000653503 5:4.26092 8:1.22091 9:1.38025e-007 10:12.0425 11:-3.33423e-005 13:-1.47907 15:-0.302678 17:-0.630448|F2 3:-9.6664e-009 7:0.947535 14:0.012842 16:-0.0149433 22:-27.5205 28:1.01749 42:-5.59614 47:-0.00257036 48:2.6591 56:0.000905244 57:-0.00345618 66:-0.179097 71:-8600.75 90:0.129947 100:2.87943e-007 101:0.207695 106:-0.693222 119:0.000496298 124:6500.76 127:-7.63916 130:0.292051 134:0.3807 142:-1.4252 154:-0.0072766 156:0.0105318 168:0.674266 169:5.00717e-008 170:-1391.79 171:0.0103878 174:3.40062e+014
5|F0 |F1 0:1.01925 1:0.798067 2:-290245 7:-1.80519 8:9.9508 10:110.805 11:-2.53257 12:-0.000264487 13:-0.746762 14:3.34237 16:0.0361359|F2 10:2.57678 12:-98.2803 16:-0.000109446 24:-2.96549 28:-0.0235446 35:-2.08013 72:0.0403834 78:1.47196 96:-201.656 98:-0.923423 110:-0.726147 114:0.0540244 116:-992.609 122:-91552.1 124:-3.32309e+013 131:4.45436 137:0.000619827 155:-0.0486708 159:-2.82046 161:0.407582 163:-0.433764 165:10.9659 167:-0.534443 168:2.53062e-009 173:-234.296 181:0.0117217 185:-5.99098 186:3.26582 193:-0.949676 195:0.106583
5|F0 |F1 2:9.34676 3:-0.657045 4:-2.59382e-009 5:-36820.9 6:-1524.56 7:-1.06494e+007 8:-1813.47 11:5.8877e-006 14:556.849 15:108.417 18:-4.41684e-013|F2 5:3.49613 14:-3.79219e-005 16:-0.0617161 18:-0.995634 19:0.000378926 34:-1.24988 36:580.463 41:18016 58:-0.66262 63:-0.0464858 73:21.6418 77:2.83114 90:-24.5384 95:2.74845 102:353862 104:0.0339791 107:-23029 110:-694.997 124:1.2655 127:1.29386 137:-2.06707e+007 141:-0.00683479 143:65.0286 147:-3.01045e-007 152:0.000194724 156:-0.265037 158:1.01849 170:1.84738 178:0.890075 186:0.0263988
5|F0 |F1 0:0.00564205 2:0.940944 3:12.4893 6:-1.10366e-007 7:19558.2 8:1.28024e-008 10:1.87006 11:19201.4 15:0.147853 18:5332.21 19:-1.21914|F2 13:-0.162168 23:-1.81523e-005 24:-0.100206 25:-0.368583 50:4.80469 55:-0.0304044 63:0.0040841 74:-0.0643165 76:-0.87667 83:0.776015 88:0.18862 98:0.00102381 101:0.0244927 103:1993.77 111:21.9875 112:-8.50067 113:-3956.87 122:-0.332118 126:0.254254 131:0.0537834 136:-0.142632 151:-0.00159899 153:-9.51767 164:7.32465e+006 169:4.11728e-006 172:-2.32007 183:1.42213 188:9.66271 194:-0.000220921 197:-9.21275e-009
5|F0 |F1 0:-22759 3:0.0883466 8:-0.0126845 9:-0.142634 10:-0.507136 11:-1154.24 12:23.1483 13:14.1165 14:1.00058 15:-3503.62 18:1.24745|F2 1:-4.42553e-006 23:-0.0805889 25:0.0187697 31:-0.037516 33:-0.773508 34:7.1638 44:-1.70543e-008 51:-0.00256606 52:1.60197e+011 59:-1.00266e-007 62:0.602179 67:-31566.7 76:1.87627e-006 77:-1.33133 79:-132.566 91:-0.0194297 97:-0.905199 112:0.00239277 137:-2.61411e-009 143:0.422423 144:1.5012 146:0.180324 150:-44597.8 159:-39.5357 171:0.0168384 172:9.81217 188:-0.689823 189:-0.249183 191:-3.64721e-006 199:-1.09957
5|F0 |F1 2:-0.0386723 3:6.3982e-005 4:-0.311914 5:-1188.55 12:-6.68897e+010 13:-0.000865043 14:10.528 15:7.9587e+006 16:-0.00131006 18:2.17918e-005 19:0.35833|F2 3:-0.0258645 5:-0.163573 21:71.1915 28:61995.6 29:-12.2987 30:-1.0701 32:-1.45869e-005 40:-0.00052465 49:14.7087 55:-2.31823 60:-0.0334143 64:1.22423 68:-0.785743 72:-0.921205 76:-0.0637383 89:0.0104604 96:-2.06757e-006 97:419487 100:-1.43749 105:99528.9 118:1.41269e+010 121:0.463479 128:0.0973641 129:-0.147791 144:-2.51944e-006 168:0.000236654 169:-21.5667 176:1.11681e-006 180:1.57929e-010 192:0.000101263
5|F0 |F1 1:-0.89631 2:-0.557292 3:314.127 4:9.99468 5:1.4547e+010 8:1.30038 9:0.0828783 11:-0.990921 15:2087.79 16:-6.28734 18:-0.0138495|F2 1:0.0719686 6:0.0622178 17:-4.74236 18:0.000426813 38:0.165006 39:-1.2573 48:-0.00992488 55:-1.96501 56:119.75 63:-0.0174201 71:-0.0338234 81:1.23888 82:-1.18696 91:1.12672 94:7.07052 95:-0.181113 98:1.60062 100:-0.447291 102:-1.99943 103:12.114 107:0.0578001 138:1.82674 145:0.992068 149:2.39624 151:-0.415588 157:0.246357 159:0.988394 171:0.223056 191:-0.754078 199:-399.296
5|F0 |F1 0:-1.00065e-009 2:-0.155507 4:-0.115327 6:0.225812 7:-0.998895 8:-10.3922 12:161.116 14:0.00408232 17:0.271057 18:-0.900803 19:5.03528e-005|F2 13:6.30789 14:1.1656e-005 17:-2.76494 22:810.953 23:1.32822 26:247205 28:-0.0151835 48:0.0031024 57:-0.958204 70:-0.0947137 76:-318277 85:-1.83636 86:-0.490528 100:-1.80304 103:-12.2992 105:2.94653 107:1.71165 112:0.0191165 114:-0.838602 128:-1.79465 129:-4.76347e+009 135:0.912832 137:0.0286104 149:-0.956607 150:0.308662 152:19.3702 155:1.53728e+007 169:-3.04323 179:-0.00292387 182:0.704793
6|F0 |F1 0:-0.662433 4:0.975837 7:-1.40137 10:-2.4182 13:-2185.5 14:-5.5329e-006 16:-14.0253 17:8.33816e-006 18:-0.000483103|F2 1:140.165 4:0.843247 7:0.961304 10:0.000706342 12:4646.32 26:-1.5151e+011 28:3.15431 29:560.549 30:1.35439 31:621618 32:0.456524 35:-2.73489 37:3.32039 49:9.07723e+006 56:-0.0093962 60:0.0935737 65:-0.000676653 69:-1.23429 73:0.0630831 76:0.088457 79:-0.220428 85:0.000355508 86:-25.7791 88:-0.000765877 93:-1.22707e-005 96:7.0345e-005 97:-0.0032763 106:2.19859e+010 108:2.69586 109:3.73462e-006 114:-0.724836 125:-0.000121203 126:-7.33267e+007 128:-3.54294 136:569503 141:-0.731896 148:-0.0011827 149:-0.727104 152:-0.000443493 159:-1.53622 161:-0.0111614 163:-0.323093 164:3589.96 166:918.537 167:0.0664919 178:-14.2724 180:4046.32 184:-5.80393e-007 185:0.655349 186:-78.1497 187:4.28477e+008 191:-0.000567172 195:0.247805 199:2450.32
6|F0 |F1 1:0.49806 4:0.186078 6:-0.74578 8:-585.387 11:0.0462862 13:-0.254091 15:10.4307 17:0.13429 18:-0.219154|F2 2:-7.25629 5:-0.224176 6:0.717345 9:0.969952 10:-7.97927e-008 11:-3.83214 14:-868.985 15:0.886801 23:43.4682 32:-10.1791 37:3.69648e-008 42:28.9751 44:-329.68 50:0.0148484 51:0.76266 56:-2.63018e-007 58:-0.224652 61:206.101 67:0.00478491 68:0.223533 69:-57.4354 73:0.473952 79:0.0724805 80:-16.5412 85:0.137253 88:-0.751167 92:-5.63966e+007 93:57259.4 108:-0.000946438 117:23472.9 120:0.0386271 124:-1.10687 126:-14.0358 127:-208.052 130:6.58092e-010 132:-34.969 135:397.187 140:-3.9787 142:23.9736 152:1.15914 154:972.486 155:-7.88667e+010 158:-197.881 163:0.802822 165:-3.51772 171:133.638 177:-0.17763 179:1.00139 180:-4.24659 185:5.97134e-008 190:-0.126675 191:-0.180457 192:0.494933 195:-0.00143149
6|F0 |F1 1:0.287689 2:-0.919725 5:-0.993242 6:1.63326 7:-167.636 12:0.173918 14:0.00699431 17:167.694 19:-0.220217|F2 1:1.07585 6:-2.42301e-010 7:-3.34022 10:0.194741 11:10.5575 12:-795.498 13:1.05089 14:9.06367 31:1.80372 39:-708.063 44:-708.623 45:-3.83649 47:-0.706337 50:0.663991 52:6.11672 54:-0.000443329 57:-4382.5 59:-1.04397 60:-1.54291 67:-17.2362 68:0.0850149 71:1.18177 73:-2.80729e-005 76:2885.38 83:3.41272 92:0.394304 96:-0.000307938 97:1.70222 98:2.10881 103:3.29405e+014 106:0.196096 110:3.15301e+008 114:-17.8673 115:0.577079 116:1.50191e-010 119:-2.35041 129:-0.068432 132:-5.18107 138:-0.0852058 140:-0.392516 144:-0.0611572 145:-11311.7 157:-994013 160:0.0335021 161:-1.20825 166:0.185728 167:-0.0115359 168:0.0956208 175:-0.360392 179:-4.6873 185:5.10702 186:278.534 187:0.444342 191:1.03969
6|F0 |F1 1:-5.78175 3:-2.47424e+006 5:7.58506 6:-0.339983 9:-36.2963 13:-1.44598 17:15.981 18:2884.88 19:-2.70394|F2 1:3.10133e+009 2:0.992539 3:3.68162 4:-1.6685e+009 6:1.70802 23:2.29718 37:3.52183e+006 38:2.1197 41:0.266659 43:-0.0142805 44:-0.0348279 45:0.0150019 47:1.01783 50:35.8821 51:-204.768 53:0.0478799 56:-1.71453 59:-4.54931e-006 61:0.0140646 65:1.19666 72:-0.160581 76:-124.075 77:0.823624 79:-0.0556866 90:-0.0494673 91:13.4125 102:22.3872 105:-2.28882e+006 106:3.71024 109:0.297037 110:8.58493 114:-0.224879 118:1.60906 119:-0.0983444 123:168.577 128:-0.948116 133:-373.117 135:1.83308 137:-8.60838e-007 146:122867 152:-11396.5 154:-0.000225252 167:-5.18072 168:-1.36055 170:-5.89373 177:-0.0151528 179:-23368.3 182:-85.5356 184:-22.0241 185:946.244 187:-862.151 189:-1.87696 194:-0.30921 199:18.6079
6|F0 |F1 3:-0.340246 5:-0.00813074 7:137.51 8:1.12175e+007 9:28.4723 10:-7.79345e+006 11:-0.00140777 13:0.0767425 16:0.00192744|F2 1:0.000739041 3:-0.220742 7:-1.17041 9:-0.0168614 15:-0.0279399 16:-0.0226425 21:-1.14587 22:18.1494 27:0.4224 29:6.376e-007 30:0.00459372 36:-0.0322093 37:-5.35134 39:-13.2667 44:-45.1347 47:8867.65 49:4.04518e+007 60:1.53579 70:-92.4436 72:-3.00586 73:-0.000520796 80:-2.22873e-009 83:-1.05425e+010 86:8.48425 87:1.85823 92:0.121695 95:0.359185 101:46.6186 106:0.537861 107:0.101379 111:-0.55777 119:-5.88701 120:2.31084 125:-1923.16 132:-0.507551 135:-1.79488e-014 137:-0.118243 150:-2.17788e-006 151:0.747177 152:-3.17742 154:-5.16396e-006 156:-94.7516 157:1.09163 158:-0.0107382 159:17.9605 164:-669115 165:-6.22495 169:-0.178868 173:-0.00235143 178:10558.9 184:-7.62502e-006 194:168.394 195:0.0199086 196:0.176895
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6|F0 |F1 2:27.593 3:4.33813e-006 5:-0.814811 6:-1.21481e-008 7:-1.2696e+015 8:49318.3 13:-0.0219047 14:-0.455266 17:-1.26454|F2 1:7.13185e-006 3:1.77114 4:1.35813e-007 7:-0.923353 11:-30.0366 12:-6.23105 18:0.735543 22:-7.57014e-007 25:0.00135004 26:0.0132337 27:-870.701 28:-0.109464 45:0.0176468 46:3.2291 51:2.88175e+007 53:249.705 68:0.25094 69:18.3114 70:-621.88 77:-5.52045 80:0.486674 82:5.03061 85:2.18488 86:7.93227e+007 91:0.0451452 93:2.21585 95:2.83416 97:-1.15072 99:-0.128952 104:-2.62406e-006 106:0.00106084 113:-2.36469e-005 117:-0.629383 122:-2.35372e+007 123:-0.000695329 131:3.40373e-006 134:0.583762 140:-0.239893 142:-0.604982 145:6.37076 152:-0.0690428 156:0.0117471 160:36369.3 162:-0.606705 165:-0.794167 171:0.481234 172:0.072796 173:4.02711 174:0.0018735 176:62510.9 181:-0.37496 188:0.831728 193:230.578 199:-0.00731867
6|F0 |F1 0:0.972484 2:-0.656511 3:0.00123022 4:-0.0311524 7:-0.804292 8:0.0113775 10:-1.72658 17:-1562.4 19:3.4264|F2 1:-0.0348822 3:-1.29038 6:-0.333534 11:15.0463 15:-0.000178848 19:-239.869 25:227.004 26:-0.59917 32:-910139 43:-0.0851418 45:-2.17254 49:406.819 52:-1.67644 55:487452 63:0.00170478 65:-1.00964 69:1.17735 78:-4.11524 80:-0.0332746 81:0.050022 84:-0.0104782 90:-0.13483 95:62.9414 97:-0.684109 101:147.536 108:-1.75177e-009 110:0.107513 111:0.00150734 114:1.25353 117:-9.12979 120:0.000670187 121:-117.68 126:220.861 127:2.65138 131:0.00034357 132:-230.956 134:0.163873 135:-75.5352 137:-0.367156 143:60.6956 147:-2246.85 148:-0.705386 155:62.0373 157:7813.53 158:-6.68227 160:-0.801034 167:12320.6 172:5.20941 175:8.60755e+009 182:1.79438 185:-2.1191e-005 188:5.34517 195:-0.0202304 196:0.000273678
6|F0 |F1 1:57.167 2:-778.319 9:117.129 11:7.8157 12:20.0065 13:-7527.84 15:0.235698 18:0.000404488 19:-1.16545|F2 0:1.00418 12:1.73426 18:-9.42621e+008 26:1.65538e+006 30:3.89951 32:32.5914 33:0.386656 37:-268.404 39:1.3303 42:0.0126876 45:0.00418917 47:-2.69613 52:-0.685397 62:-0.00160432 71:0.041605 74:0.194329 75:-2.57333e-008 77:-1.55838 82:1.96019e+006 84:-0.849846 86:73.1178 87:-0.0739692 91:-2120.5 94:-0.000588078 96:-7.36633 99:0.0548081 103:-0.149978 105:-0.0133009 113:-0.00396192 115:-3.76077e+012 116:0.509626 118:0.741422 119:-12.8821 129:-0.00291554 133:-549785 134:0.801709 135:-5.10082e+016 139:-2.62338 143:-31.6627 148:-81432.6 150:-1.53604e+007 151:573.603 156:1.68464e+006 160:-562.667 164:-1.2469 166:-6.95154 167:1.229 168:0.000377879 173:-6.91421 181:-1.19219 182:-0.677924 189:-43825.8 194:10.9211 197:11.6263
6|F0 |F1 0:-0.460379 7:-9.04388 8:7.97399e-005 9:0.510505 10:-183639 12:0.137621 16:-0.032782 18:1.48536e+009 19:0.968944|F2 6:138.38 11:0.00181328 13:-663.037 17:-0.140707 18:-575.044 22:0.0088633 23:2.65217e+006 37:516.896 38:-1.06841 42:0.574456 46:0.0481101 48:-112.815 50:-0.0520459 52:-0.0397208 53:-0.000314874 54:-0.186502 59:-0.00948306 61:172099 62:0.000271415 68:-1.34447e+007 77:-2.86361 78:-2.56898e+006 83:-1.66234 88:2.20648 92:-1574.65 102:-0.634998 110:-0.034221 112:8.5068 119:11744 121:785.914 123:-1.08468 124:982491 130:-6.13057e-006 133:-0.253455 136:-0.0996448 137:7.79383 138:677559 139:-0.448581 140:-25.8127 141:3.29434 142:-0.980417 143:1.82484 151:-0.343598 159:-0.623788 160:-131842 162:-0.988389 164:-7.97488 171:-26.6429 172:-1.15719 175:-3.13487 178:0.0124804 186:1322.61 192:953365 199:2.9098
7|F0 1:-6.74357e-005|F1 0:247509 3:-0.732627 5:-7.95403 8:65.8289 9:0.000176462 13:-8.61489e+007 15:0.747222|F2 8:0.38875 9:74.5263 46:2.11643e-006 51:-0.000191642 80:-0.101373 125:6.74717 127:2449.09 133:-1.1397 158:1.02331
7|F0 1:-0.895486|F1 1:-59048.4 2:-638.358 3:-0.155972 6:-1.26322e+006 13:-0.106366 15:-25.5794 18:-0.793099|F2 6:1.42522 7:-4.41041e-009 17:-0.380863 49:48.2695 60:0.0732489 96:0.016877 143:-1.15186 172:-0.000735899 187:-3.93946e+015
7|F0 0:0.0120627|F1 0:-0.179036 5:0.11369 9:8.51403e+011 10:17724.8 14:0.689616 16:8751.29 18:-9.24394|F2 31:-0.721818 39:-4.35828 44:4832.52 48:-0.0281794 66:0.00960419 121:-7.80594e-008 147:-0.241502 152:-0.289913 167:-170.547
7|F0 0:-0.000617953|F1 0:4.87347 1:-1.09503 2:0.0766348 7:2.46508 8:-2147.76 11:9431.67 15:0.526509|F2 21:0.273167 39:3.4848e-007 65:-0.00010388 74:90.7567 76:-0.0364142 139:0.0026295 142:-0.0284529 178:15738.3 190:0.790809
7|F0 1:-0.191023|F1 3:6.6856 4:-11.3527 6:1.87729 8:-1.86862 12:125.722 15:0.0608231 17:-1.13598|F2 3:558.189 10:-0.715273 92:2.40341e+018 112:0.00472426 116:-0.654687 134:0.826631 156:1.36134 160:17.1747 164:0.672455
7|F0 1:0.00157284|F1 1:-3.66382 5:-25063.9 7:-0.117419 8:-127.821 9:28762.5 13:3.85641e+006 18:0.460165|F2 67:0.0285842 68:-0.00277143 88:-1.28255 122:-8.2033 128:-0.685859 145:-45532.6 164:-0.0402775 173:0.00255755 195:-72079.4
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7|F0 0:0.000535056|F1 2:-5555.14 3:1.71275 10:-0.309596 11:-0.696602 12:-0.097848 13:0.000505704 14:-0.708721|F2 38:-785.937 45:-158.298 86:0.529938 126:0.0784603 131:-0.23951 132:-0.146449 155:0.024392 164:650.658 174:-2.23662
7|F0 0:0.000252593|F1 0:-41.258 3:148.92 4:-424705 8:0.0511072 11:149.476 17:2328.39 19:-0.184084|F2 17:-3.57344 70:-88.6829 76:-0.014473 84:0.00553628 92:13.0925 123:1.34316 156:0.586796 175:-0.000255954 186:0.0159665
7|F0 1:-155.362|F1 2:0.387137 4:291.26 8:1.35085 13:-0.0509954 15:-0.454409 16:-40.012 17:104628|F2 16:0.953213 50:1.20169e-011 58:-0.000298809 83:-0.00900348 100:-0.732242 120:33.6041 124:0.242728 129:-0.42935 145:-10.0103
8|F0 |F1 0:3386.49 1:-6.48784 4:157603 5:3.06774 6:-14.2324 7:1.31706e-007 8:-0.576583 9:-3.10729 10:2818.27 11:5.81156 12:-2.06851 13:0.240816 14:1.12259 15:-14.5339 16:0.0687568 18:0.305266 19:701.426|F2 5:81.0834 93:24.9808 116:1.01593 132:12900.3 164:6.86807e-010
8|F0 |F1 0:0.000292579 1:0.531784 2:26140.5 3:35.5997 4:0.00104428 5:-1.57646e-006 6:-0.803263 7:-0.00236919 8:-1.00384 9:15491.2 10:-187.021 11:0.335505 12:-592.222 14:-0.00052097 15:-0.239002 17:0.0310406 19:-1.46568|F2 49:-1.10307 71:2880.53 114:-0.0109807 128:-2.07689e-005 188:1.55158
8|F0 |F1 0:-0.00148945 1:27.6463 2:-1699.74 3:0.0524633 5:0.0328805 6:1.18935e-018 8:-0.673155 9:-2.96453 10:-2.44216e+006 11:-0.792878 12:0.00135974 13:5.96032e-010 15:-3202.57 16:0.000125091 17:1.29247e-009 18:0.0265557 19:0.000264298|F2 11:69.4912 19:0.646252 92:-3.61312e+011 169:0.218102 194:-0.877375
8|F0 |F1 0:-0.802896 1:0.0485242 2:0.000319074 4:2.00057 6:2.49766 7:-0.000161801 8:0.00291662 10:4.21971 11:-0.936864 12:0.360054 13:-0.000408439 14:-0.161082 15:-2.95384 16:-0.603892 17:-27.8976 18:2.1817 19:0.642755|F2 71:-0.063988 118:-1.47847e-006 134:0.729047 136:0.935636 171:-0.0587937
8|F0 |F1 0:-3.33003 1:7.53409 2:-1324.57 3:0.437592 4:-0.609681 5:1.42854 6:-0.254169 7:0.00684275 8:-4.38515e-005 9:-3.30608 11:0.0739748 12:37.7428 13:-0.000173101 14:27.403 16:-0.549541 18:165.019 19:-0.343835|F2 28:161.207 43:-0.792227 80:-1.64839 154:0.000292267 167:0.123021
8|F0 |F1 0:2.11368 1:-1.0887 2:-5.33987 3:0.853682 5:0.0101903 6:-0.99775 7:-2.46346 8:-4244.37 9:-2.35266 10:1.994 11:-439.14 12:0.00756459 13:0.392763 15:-0.721593 16:0.0352201 17:-2.29742 19:-0.179179|F2 75:-0.245204 97:-12.3461 100:-0.804212 171:0.262807 190:6.68843e+009
8|F0 |F1 1:0.0902073 2:-20.8033 4:7.93402 6:0.443671 7:-0.823984 8:-0.24322 9:-26.0543 10:-1130.69 11:7.00339 12:-1154.52 13:-2.89109 14:-0.92409 15:29.1787 16:-0.00695172 17:-0.202341 18:1.31741 19:0.703156|F2 13:0.0739933 36:-7.35279 67:-4.55405e+016 75:0.0540431 113:0.020615
8|F0 |F1 0:0.000133701 1:-1.84602e-006 2:-0.922123 4:-1.04718 5:3.14341 6:0.726042 7:0.98741 8:59.2315 9:-3.02725 10:2.97172 11:8237.7 14:16.5381 15:9.88889e+007 16:-0.056815 17:0.001506 18:-28.2597 19:381684|F2 16:-1.38863 55:-0.10126 100:-0.440018 133:-0.00109997 158:63.0801
8|F0 |F1 0:3.0157 1:0.0791436 2:1.08924 4:176.287 5:-4.78722 7:-0.220733 8:0.00102675 9:-1.08878e-006 11:-0.000889368 12:-7.57117 13:0.119479 14:-1.03666 15:-1.37379 16:-0.160422 17:7.37482 18:4.64772e-005 19:-0.442078|F2 61:0.00814504 67:3.41737 84:2.89328 138:56.9455 176:-31.4901
8|F0 |F1 0:0.58242 1:0.00639108 2:-0.000452465 3:1.76776 4:82.3297 5:2730.81 6:-1.94084e+006 7:-1.06452 8:0.651752 9:219.712 10:-0.52602 11:0.595098 12:1.09453e-006 13:0.232492 17:0.175289 18:-0.627355 19:-1.41093|F2 29:-244.582 148:50.2233 168:0.794816 175:-7.61432e-009 193:-0.0128127
9|F0 1:-0.0609682|F1 3:50463.7 6:-2.48479 9:775716 16:4.77612e-008 17:1.07753 19:1.0682|F2 3:-0.208671 11:-0.38998 13:0.659669 29:-0.77991 34:-3.67536e-008 43:0.00027279 49:-125768 55:5.14116e-007 56:24.0641 57:1.56679 58:-0.143926 61:0.0177754 65:-54.969 92:-966.966 93:1.11084 97:-537.065 105:0.000349227 118:509636 123:-1.67753 126:17.7858 130:-0.962723 133:-1.26533e-009 135:86971.6 140:-1.96825e-010 156:-0.185068 158:182.131 166:-54.141 168:1.23061 191:-1.5936 195:-0.921497
9|F0 1:-0.530616|F1 0:1744.45 2:-8333.18 3:0.385156 4:8.91664e-013 9:2144.9 15:5.81497|F2 1:117.781 15:-2.45185 26:-0.000716817 33:0.00392308 42:-0.107416 54:-7.99592e+006 62:-0.01231 63:0.972457 69:-28.4427 74:-0.0567405 87:2.95372 89:-0.637971 99:-1.7752 117:-102.404 121:-2.43038 130:-0.508919 134:1.19607e-005 139:0.339764 145:-4.0409 147:-1.54116 156:1.078 161:-0.000107115 163:-0.36658 168:-127.346 175:-0.000336902 176:0.0076181 177:1.1347 180:-122.12 182:26.8031 188:-5.85605e+006
9|F0 1:-1.58476e+016|F1 1:-48574.8 3:45.9041 5:-1.94304e-009 9:1.76993 14:-3.48186 17:1.04941|F2 0:-3.59138 5:-2.38579e-007 8:3.62174e-006 16:-4.20695 24:0.0790671 35:0.473209 36:-0.0311259 38:1.61136 47:20.2373 51:-2.92895 62:-0.15662 65:0.897604 70:0.000270394 79:-82.8308 85:0.918405 86:0.0632818 89:-0.924774 93:1.96738 95:73.3785 100:-0.850826 105:-2.95141 133:-0.0945886 134:2572.8 141:-0.125802 152:-0.639607 160:114499 161:-26.6729 163:-134.524 167:5077.29 187:792.304
9|F0 1:-432.644|F1 1:-365.05 3:-0.827769 10:-0.935341 14:-0.00631687 15:1.49213 19:-4.91719e-006|F2 2:4.94892e+006 16:-0.534504 28:-3.25714e+008 32:-2696.36 37:0.0245014 52:0.382164 56:-33.6153 59:1.67473 62:-0.00312636 63:1.37718 75:3.12524 80:6.05628e+008 83:-0.0811858 90:2892.03 100:0.000176886 101:-0.22379 115:-11.7576 120:0.0213163 121:2.23793 127:0.00198781 138:-0.575012 143:0.0422125 148:-59414.9 153:196.573 156:0.0113089 157:-895.802 175:117.638 177:-0.366839 186:0.961035 199:-0.0433156
9|F0 0:-165.002|F1 0:-0.95315 7:-12641.5 8:-0.411283 9:0.000140432 12:1.43793 17:-737463|F2 13:85.8563 24:22.886 70:302.579 71:-10.5862 72:-0.000634344 77:988.659 83:-5.6143e-011 90:0.527938 98:-0.000219276 110:-1.36473 116:11.5667 126:-1.20808 127:-0.32174 128:-5185.65 130:-133.475 134:28178.3 144:-3.06542e-005 146:0.00132234 156:0.000217661 157:1.71691 158:-0.000378474 160:11608.9 161:-0.0481816 165:-0.0236006 175:30.2371 180:64.8602 187:1.03 188:106.193 198:0.151163 199:2.34962
9|F0 1:0.00214989|F1 1:0.151718 7:0.610211 10:-623.428 12:1.04348e+006 14:0.862402 17:0.830613|F2 2:-3.18721 23:-0.521266 35:-1308.7 45:772.188 49:-32.1683 50:-2.64322 57:-0.386635 58:-1.63732 63:40020 77:3.22613e-006 83:6185.03 86:-7.43946 89:0.975541 91:-0.236625 92:-8255.25 100:-0.897282 105:-1.45001 116:-2.5015 124:0.97204 128:0.0309367 145:1370.01 148:-2.22257 155:0.00759762 156:0.000548944 159:-1.89924 163:0.562761 168:2.0134e-005 180:0.204828 187:-7.46273 198:-76.2207
9|F0 0:-0.00196834|F1 3:-0.626097 5:0.245765 9:0.0549233 11:3.27512 14:-0.00159932 15:0.00127071|F2 7:0.0478383 8:-0.354346 24:4.7787e+007 31:149.393 33:11.2118 35:-1.86239 37:-0.108816 38:31.476 43:-0.975135 50:0.0086868 51:0.0769297 54:-3.32118 56:-1112.17 71:0.69078 72:-9.92792 88:-1.46683 94:-0.605301 98:-2.76004 100:6.83554e+006 106:14.4786 112:184.958 114:-26527.5 125:-5.89718e-007 133:0.243953 156:0.0420315 157:0.00532952 161:1344.04 193:-0.848098 194:138608 196:0.70327
9|F0 0:-1.40791|F1 1:-0.264599 7:0.215119 8:-0.130817 14:-4.10832 18:0.0323186 19:-0.523184|F2 9:-761.342 22:0.0651532 26:-0.192485 34:-2.40773 35:-4.13 47:1.19194 54:1.09576e-005 60:-23.4129 72:3.19504 75:29.3484 77:-3.71369e+006 79:0.335419 81:0.0136361 89:0.00221801 101:-42162.2 105:-1.80845 117:-1.83379e-005 121:0.000846289 131:-74843.5 136:0.228545 147:2.98538 150:-0.732757 152:0.987537 155:14.6037 162:12767.2 168:-250114 172:7.34812e-005 177:-0.099732 188:2.34288e-006 191:-6.97979e-012
9|F0 0:-0.000168604|F1 0:-409.417 7:-3771.27 8:-0.000249035 10:0.872189 11:-0.0542446 18:-0.000314065|F2 19:-1.12065 25:-5.31179 42:0.140727 45:-3.10245e-005 48:-1.54568 54:0.00859234 55:49605.2 69:-0.00033768 77:-7.53384e-008 78:0.0481347 82:-0.696019 90:-1.22605 102:-0.771326 105:-2.95673e+010 114:-938343 127:0.350363 133:51.2324 136:1.67483 137:0.714718 142:0.901922 146:2911.34 150:1.53346 152:3.53264 153:0.0162788 156:-1.2335 159:-5.93652 163:3.8236e+007 171:0.422375 177:-1.34778 198:0.174639
9|F0 1:-0.0153012|F1 5:-0.884284 9:1.99179e-009 10:-1.01228 12:-0.000248138 13:12.2226 16:-4009.74|F2 7:-3.13223e-007 8:0.154144 15:-1.27977 32:0.00209021 36:91.8368 46:-8.99306e-006 50:1.31231 58:173.433 59:94644 67:-1.06157e+006 68:3.78353 70:0.00735787 76:-0.232863 81:-0.0919065 100:-1.70681 102:-6.40348e-010 103:-0.0386215 107:1.30117 114:-389.865 119:-0.558134 124:4.78507 133:0.000362497 141:0.0541708 151:3.54472 155:-0.00401234 159:381.531 176:-64.846 178:1.81109 186:6.57131e-008 197:-6.85374e-006
10|F0 |F1 1:-0.0394854 4:-0.258445 7:-11.4629 8:832.44 9:1.01645 10:0.966215 11:-896.416 13:148.319 14:0.121717|F2 0:-0.0485871 3:-0.513062 5:-5.22711 6:-6.41367 7:-713.871 8:-7.76713e-016 9:0.00319113 14:63.4644 15:11.98 18:-0.197858 19:-0.00131175 20:3.54203 21:-54.293 22:342456 23:28.7152 24:0.000111701 27:0.0125595 29:-1.06733 31:113.252 33:-100022 36:0.235754 45:0.0468776 46:-0.0471262 49:-0.000627024 50:0.00162977 51:-168.87 54:0.0112727 59:-0.591593 63:-14.3832 68:0.15794 69:-0.00579632 71:18.5442 72:-0.118511 78:0.421482 80:-5.87563 83:-0.800335 84:1.84526 89:-0.0164513 92:53951.6 93:181.031 94:-0.201051 98:0.541002 103:0.146543 104:-1.27197 105:1.48173 106:-0.235184 107:-1.74944e-007 108:-0.157485 109:0.0144961 110:-0.000831078 113:59.5644 115:-0.04566 118:0.190265 119:3.49883e+006 120:-0.876992 121:1.10077 128:0.0548204 129:0.00037182 130:0.0467324 133:-35.4975 135:-137.516 136:-0.0634779 138:-0.129542 139:0.00174614 140:-1.55094e-022 141:-69760.6 142:-1.09253 143:-0.0701253 145:-0.00101215 146:324.349 147:414023 149:-0.965765 150:-0.0264324 153:53.9917 164:-2.23116 170:-2.06996 171:0.00037581 174:-1.00289 175:-15075.2 178:-0.000226934 179:-42.6301 180:-0.808762 181:-1.41452 182:-2.37953 184:4.13497 186:323.288 188:1.77932 190:-0.394143 191:3.42782 192:18.6457 193:-2.09708e+007 197:-2.20515
10|F0 |F1 2:0.0771178 4:-5.09535 5:-0.000795601 8:7.98205e-014 10:0.010523 11:-0.364085 15:1.36437 17:2.6478e+008 18:-0.758067|F2 4:-0.00619822 6:1.65895e+008 8:-5.29067 9:3.05585 10:1.09222 11:-0.484488 12:-9.35034 17:-411.645 19:-0.0629943 21:0.00307774 28:-0.00140843 30:-0.00206813 31:-0.0259304 32:18.4082 33:-15.1147 34:-0.249807 36:20.2515 37:0.0571373 38:0.0640881 39:-4.74709e-007 40:2.36991 41:26.1228 46:-4.13403e-011 49:1.02398 51:-0.267199 52:0.104292 54:-2.48928 60:-990.015 61:0.782329 62:-5.28901e-008 64:-2.53944e-006 68:-3459.12 69:-36033 70:-1.13717e-008 75:10.2583 78:1.38434e-007 79:1.09236 81:-0.826825 82:-9.72179e-006 85:-202.452 86:-0.328171 88:-0.552499 91:-2.16125 93:26.7376 94:192.799 95:1214.39 101:16.0966 102:0.99087 103:-0.10904 104:-0.0124318 107:0.393222 108:3606.3 112:-0.59988 114:-1.38325 116:-0.0118851 118:3.65849 125:117.986 126:1.15609 131:0.0481646 132:-1.37362e+007 133:-4.03162 134:6.81767e+006 135:0.000562338 138:-1.00569 139:24.0942 141:16.2954 142:-0.377672 144:0.786365 147:-0.985787 148:-5.79295e-006 149:-6997.51 150:1.04899 153:-2.01338 156:-1.76202 157:-3.27969e-010 160:-1.1248 161:1.26717e-009 163:1.58407e-009 168:-1256.66 169:2.01782 170:-0.360589 172:-16.6636 178:-0.00866793 179:488125 181:-169169 182:15.1301 183:-0.957648 185:-0.118463 189:7.2947e-008 192:5.04518 197:-11.2729 199:-0.0122568
10|F0 |F1 2:0.942023 3:-129.93 7:-38.364 8:9.98796e-009 9:-0.000732567 11:0.223815 12:-0.503977 15:1.03922 17:-2.17851e+010|F2 0:-1.41557 1:-43299.5 3:-6.58111 4:1.06531 5:0.0142028 9:-2.36078 10:-1.32191 13:2.42722 17:2.40517e-011 18:25937.3 19:-5.22363e+006 22:0.631914 28:-1.15985e-008 29:-9.91526 31:10.4943 32:-213.835 33:-1.5023e+007 34:-0.93079 35:-0.109317 36:2.15279 37:1.45184 38:2.2997e-006 40:-4.01166 42:0.00615607 48:-1072.26 50:10.2971 59:1.89518 62:2.75803e+009 67:-782894 68:-0.00513905 69:-7.7822 71:2.24171 75:2391.01 76:4.22121 77:-0.89964 79:-7.9349 81:1.02303 86:-1.70297 89:-0.000249483 91:-9.22165 94:-4.8581 99:0.712818 101:-0.0442832 102:-0.504789 103:-1.52725e+007 105:-0.796445 106:1.47315 107:2.15379e-005 109:-1.19318 111:-9.13169 112:-416.827 113:-1.03233 114:-0.00627089 118:0.266656 120:-1.06049 124:0.00213266 128:0.0527542 129:-0.00428803 130:7.75035 131:0.521529 137:-4.37108 139:1.1424 141:-2.11672 144:0.235448 145:-0.000455093 146:55.2527 148:-4.01173e-008 151:-0.331335 152:-7.13905e-007 153:32.4644 155:-59032.7 157:0.000116405 158:-834.813 160:1.06726 162:-0.000498437 166:-4.37226e+006 169:0.133447 174:1.45323e-005 175:-5.85725 176:-0.53764 179:2.49339 181:3.25022 185:0.0661377 186:-0.887234 187:-0.000149722 188:0.155643 189:-18.5893 190:-0.00126105 192:-0.0283317 193:-0.150309 195:-0.175564 197:0.2363
10|F0 |F1 0:0.229128 1:1.26064 4:-107.997 5:-1.59678 7:0.540602 8:-7.16541e-005 14:0.59563 17:8.08491 19:2.11532e-009|F2 5:0.279849 6:-30.6944 8:2.0661 12:0.466399 13:-10367.1 14:1.13001 16:2.99925 17:1.27921 19:1.08466 21:0.0927214 22:-57386.1 24:1.93313 26:-0.246607 28:-260048 31:-0.000515348 32:-140.179 34:0.827843 37:-4.35857e-007 40:4.12502 43:1.10067 45:-0.00104543 48:2.05681 49:-185.4 50:2.44299 52:3825.62 54:-0.000190569 56:-0.000124368 57:-11.4873 58:9116.43 59:-0.503228 61:-0.388473 62:0.070765 64:0.0360652 65:-2.73902 70:0.00697019 71:1.41633e-005 72:-2.66839 74:5.88907e-006 77:0.328034 79:5.27125 80:4.67287 81:-1.73309e-006 84:10.8458 88:-1.00615 95:-10.4644 98:-0.428017 99:4.19625e+010 100:-19.1579 101:1.58961 102:1.24092 103:-0.973193 106:0.0316823 110:-1.8359e-008 112:-0.000161077 113:0.136524 114:0.00371727 122:54.6134 124:-1.25537e+007 127:1.23082 128:-0.000236454 131:-0.788274 132:-0.722345 133:-1.84504e-005 136:1.47171e-006 138:306681 143:-1.49406 146:0.0853341 147:0.42537 152:-0.0368566 155:-3.10938e+013 159:-9.04208 162:-1.02124 163:5.06301e-005 164:-0.437501 170:0.720914 172:-153.449 173:2.34884 174:-0.0387474 176:-3.37128e-008 177:0.228707 179:-0.184897 180:111702 181:-0.00862161 183:-9.97973e-005 184:309.605 185:0.0611567 188:2.5054 191:9758.82 194:2.19992e-008 195:2.2282e+008 196:-0.00780839 198:-0.0460118
10|F0 |F1 0:18.0528 1:0.0351819 9:1037.32 11:-0.425322 13:-0.0326109 15:-1.28408 16:-7.29494e+012 17:-0.23104 18:-10.5359|F2 0:3.99061e-005 2:-3.45263 3:5337.42 10:107515 15:6.90658 16:-0.0745047 17:1.0794 18:-2.01885 19:2.75147 23:-1.30077 25:619.7 26:0.506937 27:22.7408 29:-2081.13 32:0.00338651 35:2017.27 38:-0.000129346 39:3.81387 42:-0.0274396 43:-8.56192e-005 46:1.82321e-009 48:0.1685 50:-511.589 58:-0.578519 60:-0.0371253 62:-2.23896 64:0.418432 66:-9.36836e+006 71:-0.000211958 74:2.72155e+012 77:-4.87564 78:0.0230709 81:-440.502 82:0.0691556 83:-0.262483 85:856841 89:-0.191214 90:-2.94332 91:2.08345 93:-0.0231745 100:-6.30083 101:-0.732318 102:-0.670599 103:1.76511e+007 104:0.483626 105:-131573 107:3.90557 110:0.0342182 113:11.4293 114:-0.000183909 115:-13.5658 116:-1.5502e+008 117:0.962351 118:-2.22768 120:-0.77541 121:-6.59944 122:-1.32116 124:1.63852 125:-0.500895 126:0.256434 128:-1.60037 133:0.0456078 135:0.0158861 136:0.224089 137:-18.3116 139:6.03579e+008 140:-58.4212 143:-2491.39 146:-0.0055438 151:0.852302 153:614430 154:-6.95083e-007 156:-11325.4 157:-5.66701 159:1.02615 162:-0.111314 163:1351.23 164:0.573123 165:0.251787 168:0.990276 169:-20.0831 171:-0.0782708 176:183946 182:-3.41344e-006 184:0.000232058 186:-0.000104882 187:-3.06631 188:393.885 193:0.886912 194:21.2668 197:1.34484 199:9.48445e-006
10|F0 |F1 1:-6.4919e-006 5:5451.53 9:0.518797 10:-1.25493e+013 11:-0.000894434 12:2.37887 15:14.258 18:41.7051 19:3.18999e-006|F2 0:-19.939 1:6.99439 3:44.2012 5:34.2338 9:0.0650443 10:-119561 11:-2.83743 15:-8.82483 18:-28.9262 20:-0.202317 23:-1.64103e+008 26:13.5248 27:0.746915 28:-1.08826 31:-121483 32:0.531584 34:-0.000762312 36:-1.14259e-009 38:5.30129 40:0.0546576 44:-2.28452 45:-2.70842e+006 47:-0.0105446 52:-1.52325 53:-3.14885e-006 54:0.0084976 57:-3.07398e-007 61:-2.11373 62:-0.00660491 63:722.412 64:283.266 66:-0.969987 69:-51.4105 70:-4.12373 72:-57.0336 75:-0.216757 77:1.09314e+024 78:0.0185534 83:-5.61228 84:0.00536778 85:-0.000690017 86:-2422.59 88:-3.52303 89:-62.2097 91:-2.1288 92:-795730 93:-22.2624 96:-0.146442 97:49.762 99:-0.596914 102:-1.49038e+006 103:0.00100002 111:-1.50865e-008 113:-1.20566 121:-0.564516 122:-0.00946141 124:0.118892 125:-0.00228771 126:-5028.44 128:-809868 129:-14972.7 131:-64995.3 132:-1.37519e+009 137:0.0873621 142:-1.75296 146:-2.04826 148:-1.24951 150:-0.0035554 158:-2006.93 161:41.755 162:-3.53949 163:-11.5405 166:0.421072 170:-2.33516 173:1.22845 174:1.49796 175:506.302 176:8.99187e-005 177:-8.80842 178:0.000229417 180:-0.109278 181:-9.59409e+008 182:0.0372369 185:0.771693 186:-3.62541e+006 187:-0.0528365 190:-0.648596 193:-0.435844 194:-0.125136 196:-2.47835 197:2.74232 198:0.0022575
10|F0 |F1 1:-1.04173 4:5.39725e-008 5:35945.2 7:0.0654059 8:-0.00179895 10:-6.78221 11:-0.383053 14:-27730.4 17:2.14012e-005|F2 0:-0.622548 3:-1.82796 6:4.04621 8:2.5799 9:-0.0225957 10:0.180156 11:1.33295e+011 15:-0.716191 17:224.521 19:125.398 20:-1.09619 22:-0.462793 23:-0.00509201 25:4.62877 26:0.0117063 27:0.429276 29:2.43153e-008 30:0.565211 31:5.6268 35:-1.77957e+007 41:6.02004 44:74.0823 47:-0.5923 48:-0.00168164 49:-1949.51 50:0.0107669 52:0.148605 58:0.179574 59:2.63243 65:0.152284 66:-0.410878 67:-1.62306 68:0.0320537 72:2.27466 76:-0.44822 77:-3.89926 78:2.17743 79:0.336285 81:6.28053 82:0.0242315 83:0.881593 84:-0.00524238 92:-1.13986e-012 93:-1484.7 97:-1.38538e-007 98:6.68998e-007 101:7020.78 102:0.741573 104:7.32992e-009 108:-450.141 109:-19.123 113:1.24495 115:0.566455 118:-4.94963e+007 119:0.714756 122:3.93793 125:-434.663 126:408.283 128:0.175566 129:211311 133:-385.419 134:3.47671 135:-5.35428e-008 136:0.733146 137:18.3912 141:0.119825 146:-0.0421207 147:0.947315 148:1.79437 150:-54374.4 152:-0.374267 153:0.215935 157:-0.126154 158:2.35766e-009 161:128.327 162:-4.50803 164:-1.21114 169:-1.11738 170:2.49723 172:3856.92 173:-4.36197 178:16.4803 180:-0.447215 181:-4.18257 184:-0.000611547 188:-0.64754 189:1043.96 190:1.09143 195:-54.3349 196:-1.53159 197:-0.127889 199:0.00877125
10|F0 |F1 1:-0.00960166 3:25.1023 8:-2.49677e-006 10:0.048706 11:-6.92177 12:-268287 13:-0.686321 17:1.10658 19:0.0390898|F2 0:9.44912 1:8080.79 3:0.0108671 9:-0.027278 10:2347.2 12:4.84503 13:-127.84 15:-245.031 16:0.904772 18:5023.92 26:-9.93317 28:-8.61106e-010 33:-200.896 35:-0.127146 36:836.431 38:-0.980897 39:0.0227262 40:-0.000617114 42:3.78076e+010 43:-0.32319 47:-7.22255e-007 48:1.04677 49:-9.34627 50:485.422 52:0.00209009 53:-5299.25 54:0.55444 55:2.02668 56:0.00894164 58:1.76061e-010 65:0.0527081 66:-0.00408377 67:0.593521 68:0.251477 84:0.914627 86:0.966304 87:4.53765e-005 90:67.1848 93:-1.12313 94:0.915087 98:1.54899 100:-0.000383401 102:-1.7106e+008 103:1.00635 104:-7.10579e-013 105:-47.9531 107:1.95318 112:0.0336912 113:0.000565425 114:2.16901 115:-17.2829 117:-1.60919e+011 120:1.54541 122:52.5083 126:-0.0280563 127:-1.12686 128:37.8809 131:0.215156 135:0.137625 137:1687.87 139:-142.358 142:0.209392 143:1.02673 144:-58.499 145:3.95764 146:160.66 147:1.26971 149:-1.01942 150:-9.07862 152:9.6385e-005 153:0.0166848 155:-2.85021 156:-0.0239403 159:-0.0350815 160:-40.854 163:0.0248944 166:-3.16661 167:0.00775266 168:-6.80888e+015 169:-4.42453e+006 170:-137.874 172:-3.28886e-008 175:-24471.8 176:0.12425 177:-8.80127e-017 178:3.09018e+006 183:0.689724 185:-0.63529 190:-0.616271 195:-0.920472 196:-1.03017 199:0.404773
10|F0 |F1 0:2.87319 2:-5145.28 3:0.717055 9:-2.34559 10:-0.00236834 12:0.693282 15:-0.0254614 17:0.0135465 19:0.000337992|F2 0:0.0949265 3:-0.887303 4:0.0965457 5:1.64829 6:-1.07503 8:0.119829 9:3.19937 10:0.000252901 11:0.551937 16:-1.4326e+006 17:-206.895 18:2.45027 19:1.21808 20:0.985118 27:-38.7114 28:-135.559 29:278.931 30:1.68492e-006 33:-0.140647 34:-52.2642 35:-6.70305e+020 36:-1.39999e-009 38:1.35106 40:-0.342266 46:4.00203e-005 48:0.778894 50:34301 52:-9.89385 56:-7.09833 58:14995.7 60:1.36941 62:-13.6185 66:-0.0893831 67:-0.0125219 68:-2.85362e-006 71:1.10408 73:-0.288506 79:0.0289736 80:583.953 81:-0.087957 82:134826 83:0.000192273 85:-1.06227 88:-4.01201e+006 89:-1.05924 90:-0.0030685 91:-177.788 93:-1.61398 94:0.0118641 95:-1.7703 97:-99.3996 98:-62.8907 101:0.103507 102:0.370039 105:-146022 107:-0.538279 111:0.9312 118:-0.765607 119:0.0052935 121:0.0525397 122:-11.5301 124:1636.87 125:2.70836e-007 126:1.07143 127:0.00138871 132:-0.68352 136:-0.0056831 139:0.364877 140:0.0204029 141:44.0985 142:-1.68435e-006 143:-0.147792 144:-20.7085 145:0.0294442 147:-0.00102421 154:-8.67037e+017 160:-0.0253146 162:11.2698 165:1.13025 166:1.07407 167:-0.402418 170:612.614 174:-8.80383 175:1.12468 176:1.12264 178:9.06242 185:135196 186:1.05233 189:3.93431 193:1.30932e-007 194:1.02337 197:-1936.1
10|F0 |F1 3:1.70567 6:24.7625 7:1.01134 8:-1.96838 9:1.27617e+011 10:-2.28179e-005 13:2.23959e-007 15:1076.25 19:-6.45006|F2 1:-3.57168 2:0.00638481 5:1.62404 6:-2.10432e-005 11:0.000715533 12:-0.0686591 13:-2.25243 16:-0.491414 18:-0.0439187 19:-0.0516831 22:-0.000910916 24:598.398 25:-39.5218 27:-0.000922826 36:1.11623 40:-0.000455402 41:24506 44:-4.35629e-007 49:0.243959 54:3.92324e+006 57:1.55498 58:-1.03363 60:-0.138825 64:26.1735 65:23.8382 67:-0.148405 69:5.55219e-007 71:-4.83724 75:-0.476215 77:-87.5862 79:-2184.44 80:0.487993 81:0.246401 84:-0.657244 85:-0.142977 88:-0.0163254 91:-75.9021 95:-0.0877978 100:-3.64555e-005 104:0.00227993 107:-0.771811 109:-0.352366 110:-0.000255081 111:-1.18841 113:-0.322024 114:-4.8457 115:2665.86 116:0.0527355 117:0.0377477 118:1.28774 119:-205.785 121:1.09454 122:1.91939 125:-0.35602 126:-102.492 128:-0.00124704 130:0.0238317 132:6.36734e-010 133:0.23017 136:-0.123679 138:-1.01082e-006 139:0.740388 140:-0.562604 141:-0.0385171 143:4.88604e+007 145:28.8682 146:-2.52301e+010 147:-3.84549e+006 149:13809.7 150:80.2625 154:-0.000748432 156:26596.7 157:2.31074 160:-6.70052 165:3.829 169:-0.000989132 172:-1.45244 173:-1.02227 177:0.625219 180:68.8932 184:-1.32035 186:2.9021 187:0.999808 188:0.266695 189:0.663282 190:-15047.1 191:-0.653848 192:1.14737 194:-0.0181094 196:7.84447 197:-14.5742 199:-0.608686
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12|F0 1:0.0702653|F1 0:-300.261 1:-12.0725 2:2.86638 3:29.5932 4:322.617 5:-1.12948e-006 7:163.52 8:-2.17262e+010 9:-41.757 10:15.8287 11:2790.35 13:-0.628347 14:-2.42742 15:-1.74276 17:-8.67866e+008 18:0.0670043 19:-0.97871|F2 0:0.00093249 1:3.16822e-008 5:1.03077 6:1.15087 11:-0.0302128 12:-2.12063 15:2.02231e+009 16:-1.98416 17:5.46777e-005 18:0.699353 20:-0.582367 22:0.931659 24:-2.47847 27:-0.112955 29:9.10433 30:-1599.6 31:-1.40844e-005 33:-2.0265 34:0.461553 36:0.129408 38:3.82475 39:2.19927 40:-0.160539 44:-6.48432e-005 45:-1.48767 47:4.82801e-005 50:0.39449 51:268.542 53:0.154361 54:2.09488 56:-50.2004 57:-11.0317 58:-1.08001 59:-2.41282e+008 60:0.654872 61:-21165.2 62:-2.6418 63:-0.0144409 65:-1.4826 67:26.8636 68:-0.94308 69:1.04757 70:0.00556879 71:0.000300587 72:-0.784331 74:-3.20657 75:1.53288e-009 77:147.724 78:-0.000123356 80:3.03279e-010 81:1.07451 82:-0.000256194 84:-5.04859 86:-2.16085 87:0.00038134 90:1.05014 92:2670.51 93:-25.8732 94:1.15083e-009 99:-0.0347739 102:131.42 104:-4.14078 107:-0.693284 108:-5.02469e-008 109:3.09533e-006 110:1.15532 111:5.96245 112:28.9793 113:5.14629 117:0.538361 118:-0.000247095 120:-1.1295 121:0.772733 122:1.02099 124:-25.7729 125:-7.718e-005 127:0.540862 129:-6.89606 131:-0.075005 132:1.41734 137:1.03015 138:5.26244 140:-0.105869 141:-313.452 142:-0.916461 143:-0.00242246 144:-6.11927e-005 145:4.17037 147:41.2394 148:-26.5295 150:-0.0993189 151:-6108.35 153:3.98624e-005 155:4.06288 156:1.07209 157:165.819 158:0.0455341 160:-0.039088 161:-0.880125 165:-0.0509014 166:0.283864 170:181.13 171:-0.841477 172:2.12018 173:-0.564104 174:-3.24735 176:6.91984e+012 177:-1.83656 178:0.734144 180:-0.453173 181:-1.66769e+009 183:-2.14101 184:-1.85393e-011 185:-6.49803 186:476.664 189:-181.668 190:-0.000465536 191:-0.906082 194:2.83734 197:-0.505132 198:41.374
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12|F0 1:0.0013786|F1 0:-21.4684 1:-1.3372 2:0.192368 3:-0.298384 4:-3.09557 5:1.23022 7:200.896 8:0.210456 9:-0.652981 10:-412.966 12:-0.000144797 13:-0.221821 14:1.51663 15:-454.624 16:1.97655 17:0.152034 19:2.44794e+006|F2 0:-0.000224042 2:3.36167 4:197.405 6:-0.00892038 7:1.89425e+006 8:-62.9216 10:-1.1137 11:1.83793 12:-8.01901e+012 14:1.21339e+012 16:9.64534e-005 18:-0.488011 19:1.28385 20:0.00322968 23:-1.95233 28:2.56188 32:2.43132e-005 34:2.65947 37:-5.59804 38:-0.00112668 39:4.32927 41:3.70261e+012 43:0.000531813 44:-0.170477 45:-2.81277 47:-1.58301 48:-0.0317336 49:1.01345 50:-0.681325 51:0.00824614 55:1.07236 56:-1.08642 57:-0.00384276 58:-0.230444 60:0.333236 62:-3.48252 63:-1.35332 64:-1.14478 65:-0.00122787 66:-33311.6 67:-4499.52 68:9.38588 71:-0.0172058 72:0.0477909 74:-5.3545 75:20.3044 77:7.75922 78:1.01826 79:-2.02707e-007 80:22.6127 81:2.17644e-006 82:0.108992 84:-6.77879 85:-3.25236e-012 86:206.598 89:-9.7898e-006 91:-1253.07 92:1.24837e+008 93:-0.00298957 94:-0.00843953 98:0.2638 100:4.25477 102:0.570665 104:-2.97465e-005 105:0.0244309 108:-3.39693 110:0.00984148 113:7820.59 114:10.464 117:1.55721 120:-0.116095 122:-443.025 123:0.0849274 124:0.00682642 125:12.4688 129:-64.0557 130:-2.32078 132:-1.01451e+012 133:-129.38 134:-5.0725e-005 135:0.258608 136:1.31347e+006 141:1.95802 143:-0.00093422 145:1.41574 146:-0.373654 147:-1.20327e-010 148:205.652 149:-4.54975e+006 150:-0.0125013 151:-1.72497 154:-0.379136 155:3.00001e+006 156:-0.36222 158:1.68208 159:-0.239786 160:-0.58071 161:117198 162:165.608 164:0.00138067 165:-43.2197 166:0.0141139 167:2.44731e-013 168:0.0408527 169:0.0154773 170:0.83342 171:-0.148482 175:0.405398 176:0.231962 178:15.046 182:-0.753182 183:0.745317 184:-5.92538 185:0.16909 190:-0.725639 192:-15.0906 193:-0.00123296 194:-0.360387 196:-2.03465 197:-0.0840757 198:-0.0172577
12|F0 1:1.72637|F1 0:-25.3996 1:-3.69083e-009 2:1.23101 3:-4.27957 4:13038.4 6:55006.6 7:118814 8:0.00777767 9:0.0021609 10:-367.697 11:0.517583 12:-0.480066 13:-0.00247911 15:0.855898 16:-4.70127 18:-0.292227 19:-0.0360347|F2 3:-0.0454167 5:-5.25957e-006 6:5.2142 8:-0.0309312 10:-2.10612e-005 12:1.07276 16:-510359 18:-754.491 19:-1.45973 22:0.0613822 24:1.45582 26:-0.399588 27:-180325 29:3.45003 30:-2.22787e-005 32:47.5198 33:-0.302075 34:0.31055 35:-1617.48 36:-193978 37:-3.57099e-009 38:-28.4139 39:0.30547 40:-7.74087e+007 41:-1.34321e-006 43:-0.000288551 44:-8.88682e-007 47:0.909563 48:-277.422 49:-2.8848e-006 50:0.0682612 51:0.0494738 55:-1.08522 56:-896.972 57:-0.710941 59:-10.9699 61:-4.84614e-011 62:-1.67633 63:5.66784 72:-0.515119 74:480.505 76:-61.4999 78:-1122.1 80:27.0785 81:-8.25485 83:-102.262 86:0.436354 88:-3733.81 89:-0.00504869 90:1.26584 91:2.14585e-011 92:290.119 94:379101 95:1.07839 96:-4.47741e-011 97:-1.51775 98:9.70706 99:-3063.01 100:5.7898 102:14.031 103:-0.0493958 105:0.0913455 106:-19344.1 107:-16.4838 108:1.88327 109:1.92461 111:1334.71 112:0.00546331 113:0.00425762 116:131.413 119:1.60182 121:1.7061 122:-1.46464 124:-0.211201 125:-0.893066 126:1.31161 129:-0.0116751 134:-1.60841 135:0.0964874 136:-1.03109 137:-0.295163 138:-2.72759 140:2.2536 141:-0.392882 142:0.350012 145:5533.5 146:10962.9 147:1.59612 152:0.227321 156:0.0808011 157:-1.44736 158:-41.376 159:-0.52071 160:1.27714 161:-1.65866 163:2.92783 164:1.58762e+006 165:-0.00105731 167:6.37436e-007 168:-0.000800509 172:66.7469 173:-0.0232373 174:0.089202 175:2.05059 176:4.35569 177:-1.44121e+012 180:-0.440125 181:144.677 182:-2.58206e+011 184:-1.25546 185:-2.64972e-010 186:0.0815873 187:1.0119e-006 188:-2.87003 190:-769.531 191:-2.63384 193:-0.0591524 195:2.6198 196:0.0234793 197:511.434 198:-1.34162
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12|F0 0:-5187.93|F1 0:-6.27552e+009 1:0.163633 2:59.146 3:-5024.74 4:-0.89182 6:174141 7:-0.469147 8:66.9349 10:2.3887e-006 11:3.60771 12:7.37349 13:29173.3 14:-1.12844 16:0.0642668 17:2.02192e-007 18:0.0388596 19:2.01269e+006|F2 0:0.0165583 1:32.3227 2:-24557.7 3:24.0667 4:5.5986e-005 6:4.33452 9:-0.317432 14:0.646018 15:0.000148113 16:-18611.9 18:6.14436e-007 19:0.0293023 20:-0.789155 21:2.70316e+006 23:0.377194 26:-2.17295 27:-0.0621441 28:0.0153933 31:36878.5 33:-1.20555e-013 34:5.88274e-008 37:0.00689759 38:-31760.8 39:-3.15196 41:0.0236606 44:13598 45:0.0244095 46:0.00935737 47:-3.43561e-007 48:0.104963 51:4995.03 54:8.20681 56:-12.8675 57:-28.0162 58:3.92508 59:-0.310475 60:-450.212 64:-0.219374 65:-3.55133 68:2115.8 71:0.0202783 73:0.0754552 74:-13.3462 76:-0.019524 77:7.85183 78:-22.3575 80:1.37198 81:-0.000783031 82:0.391764 83:58.0413 84:-39978.9 85:-62905 86:-0.968436 87:-0.172807 88:13456.1 89:0.479815 92:0.313641 93:-0.028788 94:-8.62854e+013 95:5.12054 96:4.72477e+007 97:-0.0830349 98:0.102754 99:3.69413 101:21.6795 102:0.500718 104:1.04157 105:0.790826 106:0.887596 109:-0.0001385 111:-4866.96 113:819.53 114:6.5238 116:-1.10885e+019 117:-0.96333 119:1.97334e+007 120:-47.7931 122:12356.9 123:-0.448601 124:-0.799675 126:-1.85014 128:6.37533 129:0.0123667 131:0.877281 135:0.402509 136:7.44564 139:73.1032 140:-0.954486 141:0.0199009 142:0.00163912 143:-522353 145:-42.5317 149:-5.02484e-007 152:-0.0676255 153:-0.305819 154:-7.15644 155:18398.1 156:-1.22107 158:-0.0279146 159:34.5926 160:-0.00026664 161:-0.000854793 162:0.32593 164:-0.0468923 165:2.33522 166:9.08909e-014 168:-0.000227707 170:2.18409 171:-8.1272 174:29.3554 177:3.97703 178:-0.00178959 179:0.761716 180:0.508315 182:2.11085 187:-0.18895 191:-0.000993072 192:-2.94109 193:-0.073633 197:-0.0142453 199:-0.349234
12|F0 1:-0.0200268|F1 1:-2.70598 3:-0.0581639 4:36.4132 6:140.927 7:-0.182711 8:-0.314325 9:-1.24852 10:-7.18678 11:-12217.1 12:1.17134 13:23.929 14:3.85897e+006 15:-0.00797717 16:0.576634 17:3718.87 18:-0.946385 19:-3.31754e-005|F2 0:1.6268e-006 1:-0.0952369 4:31.4982 5:2.9124 9:-0.00856206 11:0.229099 14:1.01335 15:-22.0899 17:-0.0444322 19:4.00043e-005 21:-9.22363 22:-1.65977 23:0.0942352 24:-0.688414 25:0.00499485 26:0.0764815 30:-0.0348066 31:-0.186324 32:2.63329 33:-141330 34:-132.936 37:0.315408 38:-6.62381e-005 40:9721.98 41:0.000112145 44:0.0080578 46:0.686421 47:-0.132423 48:0.268404 49:0.0217506 50:2.34379 53:0.803658 54:24.5065 55:-0.0167076 57:3.93565 60:0.0119655 61:-2.65427 62:2.99944 64:1.81986 65:20.8244 66:113.63 67:3.62821 71:-65081.9 75:-190.484 76:1.19821 77:-3.33817 78:-7.72161e-008 82:-0.173543 83:-0.468471 85:0.000344783 86:2.11343 87:424.441 91:2.05564 92:-0.00457734 95:-199.537 97:0.597536 98:-0.603048 99:-7.46195 100:-1.56067 102:-1.82778 103:6.24088 104:1.27735e-005 105:0.90408 106:-0.0215542 107:-2.92433 109:-17.424 110:-129844 111:3.62897e-011 112:-1.63218 114:0.0124917 115:-0.00255369 117:3.9815 118:-20.4022 119:5.77687 123:19909.7 125:1.31413e-008 126:0.00286812 128:6.74267e-019 129:21.8493 130:-7.48416 134:4.93358 135:-2.30395e-009 136:-0.00962926 141:-3.53977 142:3.94805e+006 144:-0.964989 146:1.36894 147:-0.876547 148:1.31771 149:0.0493067 152:0.671205 153:-1.46252 155:-1.87258e-009 156:-0.14947 157:-3.64835e+013 158:-3.02769 162:-2.80665e-009 163:-6.18766e-005 165:6.36541e-005 167:-0.0205849 168:106.464 169:423.475 171:-2.58537 172:0.0646626 173:-212.612 174:-16.1677 176:1.03424 178:-1.23114 179:-3.02872e+009 180:-257.194 183:-3.68065 185:-1.5437 186:3.06958 187:2.82492e-012 188:12.3637 189:7.26791e+006 191:0.000216151 192:-10.0133 196:-0.184806 197:-127.42 198:-0.000689327
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13|F0 0:1.00877|F1 2:7.24238e-005 4:-0.0287737 9:0.779243 12:-0.170349 17:-0.824929|F2 0:-15.8137 2:-8.2397e+008 3:-1.32274e-010 7:-0.484115 10:14776.6 11:0.0354328 13:798249 15:-0.151137 18:0.000730041 23:-3.33584 25:18.5485 29:-129.765 32:-0.0731675 36:2.49861 40:-1.86179 52:-2.22648e-007 53:3.14651 60:0.0571451 61:0.000436458 63:-1.84401 64:0.230229 86:6.0511e+006 89:-9.56108 94:8.91647 95:-475.878 100:1.17719e+013 103:-0.598002 106:9.61246e-008 109:-8.68472e-006 111:-2535.99 114:-1.19544 119:0.000916623 124:-2.65427e-007 129:0.0375397 133:8.18541 135:-2.53366 138:-1.00293 139:-0.373275 153:-63.0504 159:2.32259 160:0.923067 161:2.91516e-006 162:-2.70695 163:-0.873647 166:-0.119337 173:-17.1857 177:-0.156852 181:-33110.3 188:44.5371 193:1.10357
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13|F0 0:314.942|F1 1:3.60737 3:-0.0460356 5:-6.40148e-010 15:-0.420554 16:0.164205|F2 0:10.9431 5:1.51168 6:-0.417716 7:0.441188 8:0.0478391 9:0.432017 10:4.04196 12:1.58846 13:0.0436589 21:4.96801 31:70872 36:-5.06341 43:36258.5 47:-0.00699453 52:0.00209772 53:0.000172011 59:45824.8 64:-17523.8 65:0.956382 73:3.71958e-006 78:-0.223199 82:-0.0278389 84:172.87 98:0.0350598 99:2.23071e-011 103:-0.0025139 106:0.00692971 111:0.167219 114:2378.82 117:-1.04657 118:1.42417e-012 124:-1.00568e+007 125:-3.04062 130:-0.172068 133:-116.271 144:3.56377 152:-0.890216 155:1.38636 157:-1.00117 160:-1.06714e+010 164:-0.187318 174:40.4541 176:6708.34 188:2.67666e-005 190:-2.97792 192:0.134073 193:7873.72 195:0.420618 196:3.32705 197:-0.542669
13|F0 0:10.4433|F1 7:-0.0855588 8:3.28816 10:1.98055 13:0.286464 14:-3.6631|F2 2:-35.906 3:-2.57774 5:-0.00605424 7:-0.635139 14:-23.5433 17:0.54699 26:0.888044 35:-0.0027402 36:-23.1418 40:-1.25802 45:0.771128 47:-1.28042e-005 53:2.93967e+010 56:0.973754 58:-0.0850147 61:1.39839 63:0.140746 64:763.354 72:-5.776 78:969.13 82:9.07396 86:-0.0109141 92:-129.483 101:0.714837 104:3375.86 112:1.03787 123:0.162075 132:0.82096 135:-1046.08 137:0.0970259 138:0.277869 139:-0.00368975 141:-55.3884 143:1.0361 145:-0.447215 154:22.287 155:3.44992e+008 156:3.96266 165:1.97397 166:0.794445 168:15.6123 171:0.192014 175:-2007.15 180:-0.627167 181:-16606.5 182:4097.77 184:0.396443 189:-1.5301 194:7.74537e+007 197:0.0159215
13|F0 1:65437.3|F1 3:-2.00557e-008 6:0.111743 7:0.059 8:-0.215606 17:-0.706254|F2 2:13.8218 4:0.498969 7:1.04088e+011 8:-1.38465 9:-0.982808 13:1.29495 14:0.583355 18:-0.207817 19:-0.0344984 21:-1.58294 25:432735 35:13.9738 43:0.00120883 48:-1.1925 74:-10.6893 77:-0.410788 78:1.93798 82:-3.31477e-014 84:0.301544 85:-33.4432 92:-0.739385 93:2820.77 99:-8247.23 107:-0.840174 113:0.490426 116:-3481.06 125:-0.0150418 126:0.012847 127:-8.57253e-008 128:62.9696 129:1.71434e+006 131:-12.9681 133:-184316 143:-3.95934 144:1.13792e+008 150:33.3776 151:0.101009 153:974.907 156:6.0406 158:-0.297544 159:1.66765e-007 160:3.53287 172:0.0143393 177:-0.0451011 181:114.809 184:-51.3476 186:815274 187:-0.0417297 189:8.95602e-008 197:-2584.8
13|F0 0:-1.03135|F1 2:75.8147 5:-0.00116549 7:0.769634 8:1.18101 13:-14.771|F2 2:0.521834 3:-2.29416 5:-0.916695 8:16.7972 9:-0.174179 24:22.1483 25:1.43731 26:-0.0292719 27:2.21209e-006 29:-0.00657127 31:-0.628443 32:8182.21 33:-9.55826 37:-0.0249984 41:1.41963 47:6.98639 49:-0.687784 58:-1.25341 62:1.62824 63:0.296506 64:-0.548923 71:-2.85684 75:-1.42975 80:1.06384 97:0.0728725 100:0.000542548 103:-0.426514 114:3.30862e-007 122:-0.735682 127:-0.0363726 129:-0.502606 132:2.97351 142:-0.241279 149:-0.00684691 151:-1.9231 155:-1.80963 158:7.41491 161:-1.35398 166:-8.92432 168:1.94874e-011 169:2.00534 170:-197826 173:-0.0793847 180:-1.12128e-007 182:-3.05383 187:1.89446 191:0.00039792 192:0.201252 195:-0.571182 197:7.71659
13|F0 0:0.0593242|F1 3:1.51573 13:1.56065 16:0.332282 17:7.20428e+017 18:1.77123e-017|F2 3:4.01112 6:-12.0957 7:0.000126926 11:-23.7886 16:-2.11818 17:-1.11356 18:-204.074 22:8.22477 24:0.0399639 30:25.9075 34:1.04357e-005 36:14170.7 37:0.11915 41:0.00452782 43:-2.88857 44:-0.96713 55:-10.4816 56:-9.05009e-005 58:-299.931 69:-0.0236522 74:-0.537096 75:-0.221735 79:-0.907789 84:-243.026 85:-7.68426e-010 90:0.62296 92:10.3949 93:-10.1165 95:1.61626 99:5.43008 100:-2.16544 104:-0.812009 108:-93123.7 112:-47.6498 123:0.838245 124:-1.52277 137:-34.4664 140:0.00807556 145:-0.452721 146:0.000585723 157:-0.0104066 167:0.450532 171:1.30289e-006 174:1.94225 175:-17.0289 177:-46183.6 184:-0.441173 189:-224.085 190:-1.22777 194:-24.4364
13|F0 1:1.11145|F1 2:0.93003 4:0.980871 12:5.49042e-009 16:-0.0307699 17:3.19791e-005|F2 4:1.4282 7:21.0479 9:23723.4 11:0.000673579 18:1.00684e+007 19:0.0143551 20:-1.0355 31:-0.151624 39:1.00143e-008 40:2.62831 42:-0.919136 43:3.39387 47:2.89657 49:7.79639 50:-0.554072 53:282.14 56:1.52197 74:-0.901507 77:0.0022334 92:-299858 95:0.000995926 96:-0.0147132 97:0.883551 100:-5.01416 114:-0.599179 122:-149.502 130:-9.98208e-006 137:2.36817 138:0.12114 140:1.03727 142:-3.26765e-007 143:0.204517 144:-1.67639 149:-0.385983 150:16.4137 151:0.6732 154:-0.831127 159:3.14733e+009 161:3.98122 166:-0.00339759 167:2.06545 174:2.87117 176:-309.062 177:0.0300493 178:-1.33932 181:-5.8881 186:-7.08758 188:12.9882 193:-0.0972459 194:-0.356262
14|F0 1:-1.27065e+006|F1 0:-0.000609799 4:-7017.68 8:0.00579882 11:-2.94393e-009 12:3.23442 13:-0.939042 15:-7.10573e-007 17:0.624975 18:-28.6552 19:-2998.87|F2 1:96.1436 5:-0.635273 6:60.1666 12:243.064 16:-0.243614 17:4.30644 19:0.533638 21:-6.46908 25:-9.38164e-008 27:0.00162915 32:0.122997 36:4.50308e+008 43:2.70742 46:-1.30289 49:11.0873 55:1.37245e-009 69:3.41225 76:63.6594 85:-0.995376 90:909814 98:-0.901667 99:7.07598e-008 112:-1.17342 114:-0.184765 121:175.219 126:4.68876 134:-5.15341 141:-30.3094 144:-425.467 154:5.28755 159:0.0118726 160:0.0349579 171:-2.67172 173:-0.00205333 176:-0.489689 177:0.431193 185:-0.374764 191:-0.00066936 197:-0.00333763
14|F0 0:-0.00923712|F1 0:-5.22762 1:496.575 2:-1.08849 3:-1.01161 4:-0.514821 10:59.7932 11:1.78593 12:-10503.8 16:-3.67711 19:-2.84744|F2 5:2.65781 7:-11624.4 8:-0.955347 9:-5.40577e+006 16:4.34102 27:-50225.2 35:-0.551071 40:0.811441 41:-0.000388193 45:0.963741 53:0.0262986 55:-0.204965 56:-1.65434 57:-17213.2 65:-110.541 72:0.0516775 77:-1.22265 79:7.99993e-022 88:0.0152546 92:0.0372007 94:58504.6 100:-0.498879 101:2.15557 103:1.29809 104:0.918764 111:-0.000282591 116:-0.00245581 130:-0.900568 140:0.619297 141:-19.0562 146:1.01667 160:22583.1 163:-15371.4 179:10.6469 180:-0.0135248 183:0.00010542 195:-0.0336815 197:9.11457 199:0.000768293
14|F0 0:0.583743|F1 0:-1.02253 4:-0.140876 6:0.0019175 7:-21.8726 10:-0.0511022 12:-2.09421 13:-1.02113 14:-0.000308469 18:-1193.7 19:1.6181e+007|F2 11:1.47405 20:-1.1309 24:0.321812 29:0.248917 42:0.00831412 46:-0.419063 54:1.00053 58:4.04148 63:0.868937 64:-179200 65:0.0522066 68:0.773634 75:-1.02368 76:-0.104655 85:-0.487441 90:0.168096 93:1.90912e-008 95:-0.00800765 96:49000 98:249.24 104:-7.03653 105:-1.6366 110:-685.431 116:4.59324 120:-0.000323429 124:-2.14653 129:-0.031693 132:0.00219742 135:-36.8743 137:-0.00966214 139:0.677265 147:-178.04 153:-1749.61 154:-10.6357 159:-30.1993 163:1.06175 164:6228.85 166:-1.1047 198:-0.150428
14|F0 0:-12.5289|F1 1:-0.824208 3:0.570165 5:0.558155 8:3.09906e+006 9:-103.33 10:-7.8279 11:419.04 14:-0.00249785 15:-0.565591 16:-0.115052|F2 2:0.349416 11:0.146624 13:0.171891 32:-1.00667 34:-2.0581 36:-4.96999e+009 48:0.0103484 61:-149.157 64:0.799413 68:-18.7791 75:0.904466 83:0.115369 86:2085.16 91:1.15178e-007 92:1.07002e+009 101:-0.858983 104:-1.05796 106:0.176286 114:-0.00243787 120:-0.124716 123:-0.0511423 127:0.000352022 129:-1.11323 134:1.82619 137:-0.000444003 145:-0.00647107 146:0.0463246 152:-0.229979 160:0.180187 166:-0.669975 169:0.175809 170:-849.489 177:-0.15619 178:-0.000445053 185:4.33681 188:0.601668 191:0.134102 197:0.010103 199:-11813.1
14|F0 1:-0.129631|F1 1:-1.20417 2:-0.000100598 4:-5.06082e-006 7:-9.29091 8:-2.7635 10:0.104737 13:-4.32619e-005 15:7.58441 17:-1108.34 18:-12782|F2 1:-8.81938e-005 2:-0.00628994 7:-1.43119e-005 14:1.2408 15:0.867351 17:102.236 21:4.53221 23:1.42764 24:-0.974354 28:-1.93892e-015 32:-1.41969 47:0.703761 49:-0.158015 60:7.47446 64:-0.0646538 69:-0.0292957 70:0.823866 74:1.81334 80:-3.9775e+006 90:0.975828 96:1.82177e-005 110:8.53754 112:-0.776859 118:-0.00373489 121:-0.00152015 128:-629.674 136:0.073638 140:6.07955 142:-32.7236 150:-0.0826094 155:-0.073789 159:2.14954 179:0.00887442 180:-0.072026 183:-13.0467 184:0.500347 188:0.357588 195:0.00372981 199:14.2038
14|F0 0:0.697011|F1 0:-4.72579e-006 2:-2.26758 3:-0.451046 4:0.00622822 7:-5.16025 9:1.0209 11:1.0274 14:0.192392 15:-1.60179e-007 17:3.48231|F2 0:1.63159 15:-0.587371 19:-1.4493 20:-1.46718 26:-0.00184415 27:1.43322e-007 30:-1.38425e-006 34:0.434887 40:-0.0179087 43:-0.86296 45:3.40518 46:-6.46971e-006 50:2.29826 54:7.89693 55:2.39342 70:-0.140579 88:-196426 96:-0.00447933 97:-0.000652232 98:-0.15382 100:17.8212 102:9.13293 104:1.50267e-005 118:-2.06402 126:-0.410073 128:1020.25 129:-0.898471 136:0.120432 137:0.451912 138:-0.000831325 144:-1.36747 145:-0.330247 148:9.8537 150:-1.37676 151:3.12161e+008 154:88.2952 170:-4158.33 173:-1.13359 197:33.2447
14|F0 0:0.0682127|F1 0:0.113465 1:2.66396 2:0.00164291 5:28700.6 8:-1.13643e-005 9:0.167006 10:2086.33 11:-305.768 12:3.41841e+009 16:4.78624|F2 4:0.102307 10:-126.97 15:-13.6458 24:-1.77015 26:-2.43407 44:0.646084 55:-301.755 56:0.656914 61:0.0696498 64:-1.04749 65:-0.4283 70:5318.63 78:-0.0301212 82:9.79972 84:0.193052 90:-12.5124 91:-6.10197e-006 98:1.39163 107:1.71203 110:-4.19172 112:-0.568334 113:-0.67101 114:1.73331 121:0.0182253 129:2.1155 134:-0.424495 140:-4.39724e+007 144:301.571 145:-4.44673e-006 146:1.61754 148:2.78148e-006 151:0.93469 152:-0.0914883 158:8.09835 174:32.607 180:29.9878 189:5.15888 192:-46.6584 198:2.75923e-008
14|F0 1:-96676.1|F1 2:2948.32 3:-6.49939 4:3.12248 6:-3.55115e-007 7:-0.000483664 8:-23.6231 9:0.94659 16:1.08572 17:-487.549 19:0.357411|F2 1:-0.0383063 3:2.8378 6:-4.13885e-007 19:-0.903253 27:-0.000377133 28:1.96196 38:0.147865 39:2.52363 40:-9.69638e-007 44:27.5435 49:-0.00242854 57:-0.79007 59:-71.1217 64:-1.29032 68:12.7497 72:-78.7521 75:8.46924e+006 77:11.7362 78:4.31059 79:-1.41865 82:0.117935 102:-0.693544 108:0.0947908 114:-1.21774 119:1.55635 120:0.000389552 139:0.10411 141:-0.0601495 146:321.504 159:-0.478141 165:2.59379e-006 172:0.0448354 174:26642.3 177:2.93103e+013 178:-0.998844 185:10.4103 190:-0.00337115 193:-2.39102e-010 199:0.0333606
14|F0 0:-0.0718749|F1 0:0.00374727 1:0.506313 3:-0.399515 6:0.0846644 7:150.058 8:0.823732 9:1.87141e-006 10:-0.209059 14:39.2743 15:0.402796|F2 1:0.242172 4:-5.76134 5:-36.3324 8:8.00713e+009 9:0.550242 11:8.24495 18:0.514437 19:-22802.1 23:-16.1907 34:4.06539e-007 40:-37.8148 47:0.795367 48:0.0936285 49:0.353126 50:3.08016 58:-0.027696 68:1.25453 70:88.1034 82:-1.72383 84:-0.710874 91:-15.4229 96:-0.796006 101:72.2574 102:-1.15641 105:-0.259172 110:-24.8329 123:-4.89778 128:0.314804 133:85.506 138:-0.919864 143:5.5794 152:1.50514e-005 154:-0.00161921 161:17272.8 167:0.561896 178:2.13693 185:-1807.78 193:1.14285 198:-0.0349945
14|F0 0:0.0375441|F1 1:-0.31126 2:0.287457 3:-1.8029e-008 6:-31.2021 7:3.89798e+009 10:1.92816e-006 13:-2.16502 14:10.2401 16:-583.373 19:1.86206|F2 2:0.986532 3:-0.0130036 11:-2.71754 15:0.0175168 19:34.0387 22:-0.388738 28:681.669 32:4.21554e+008 35:-0.61792 40:-7.50254e-007 42:-0.00160916 50:-4.20862e-012 67:-0.958697 71:0.778368 75:-34.4299 80:2.9958 87:9.10368e-007 89:0.413962 97:-0.627776 101:-0.765612 109:-0.518798 113:-0.215096 122:0.067136 124:-0.207164 130:-0.0584747 131:2.26288 132:-3.2402e-005 138:-198.471 144:0.216036 160:1.40497e-012 163:-18.373 164:0.843693 175:-0.0110595 180:0.358632 181:-0.0207051 184:-0.217446 190:2.39787e+007 198:53.8746 199:1.07545
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15|F0 |F1 0:0.00016084 4:2.3351 5:-321307 6:0.841529 13:-2.90215e+006 14:-16.5817 15:-14.1734 16:-0.634187 17:-4.98461 18:0.536116 19:0.00117136|F2 0:-0.211022 2:-0.576738 4:1.38683 5:0.0157746 6:0.000184984 9:-4.58324e-005 10:6.40552e-005 13:-0.184319 15:0.00695715 19:3.69549e+007 21:-0.749678 22:1400.87 23:-1.61096e-005 24:1.00829e-006 26:-62.3986 27:-0.00326728 28:-661.41 29:-0.08963 30:24.4599 34:6.15084 35:-1303.89 38:-1.86617e-006 39:-0.00545269 40:0.000125114 41:4.95858 42:16.2117 43:-0.000459335 45:4.51325 46:2.16367 47:28.818 49:-0.0216637 50:14.3644 51:1159 52:-30975.2 53:-2841.01 55:-2470.25 58:-0.0942596 59:-59514.7 61:0.052263 62:0.139159 63:-1.61487e+013 64:-1.89385e-008 66:-0.940867 68:-9.43697 69:-0.0245472 73:228.73 74:3.87012 75:-12813.3 78:0.00302726 81:0.0640742 82:-0.000643911 83:0.0110889 84:-2294.79 85:0.00712476 86:-0.0285096 87:3.85849 88:123.589 90:16.1138 91:-2.76409 92:-0.0434809 95:0.503438 96:-4.02761e-011 98:-4.97637 99:2.38234e+008 100:0.000176717 101:0.00872486 104:0.157167 105:0.309396 107:-20.4633 110:-1.20149e-005 111:0.993933 113:4.03124 114:-1070.68 115:-0.334695 116:5.65759 118:-32.5006 119:-1.94948 121:-18.9271 125:0.11969 126:-8.59073 127:-0.646262 128:0.570494 130:2.15801e+006 131:-12663.4 132:4.87878 133:0.467693 134:0.211563 136:7.58759e+007 137:-3.92566e+007 141:-0.25382 142:-0.00486549 143:0.304113 144:-1.32046e-006 145:0.0224042 146:0.000645186 149:-117.509 150:-0.0140764 152:-0.947609 153:-0.141718 155:-0.48804 157:5.78671e-013 158:37.9154 159:256.766 160:4.55973 161:1.56466 163:1.24672 164:-8.45146e+006 165:-9.41641 166:-7.9603 168:1716.77 170:4.34579e-005 172:-19.6696 173:-33.0672 174:0.165425 175:-0.91997 176:-156.298 178:0.742514 179:-0.299011 180:1.77797 181:-0.186913 184:0.187336 186:-7.3481 187:-0.0234431 188:2.66062e-006 189:2.3213 191:-30.1209 192:-0.0366411 194:0.00144511 197:0.309983 198:-1.77186e-008 199:2.72882
16|F0 |F1 |F2 4:-0.440807 5:-0.00922122 6:37.9746 16:-3.39692 26:-0.0327204 30:-0.127356 32:-1.74666 41:-9.16023e-014 42:1.24047 47:977.556 54:2.16134e+011 57:0.0756962 71:-0.308063 73:-0.309805 84:-15.2386 85:-67.6352 88:-0.417319 89:-0.0194614 96:-8.85278e+013 98:5.03907 106:0.841157 109:-4.58585 110:0.792839 112:-1.35741 114:-0.00675677 115:16.68 128:2.37696 129:0.583212 141:-3.57986 143:-0.970313 146:-0.484164 164:-1.19208 166:-0.56248 174:-0.0772997 184:15.3984 185:-2.35575e-005 188:-1.95815e-010 190:0.264646 192:-1.12885 194:-284.096 195:0.620602
16|F0 |F1 |F2 0:2.63523 1:-1.58279 5:-6.78806 10:-1.50837 17:-8642.98 18:0.000132568 29:0.374649 36:0.0245078 41:1.10785 44:0.753422 46:0.236826 53:-5671.38 55:543.054 63:0.0632683 67:-2.90342 68:-0.620599 82:0.766523 90:-0.0290939 92:0.0443699 112:1.99035e-006 114:7.64606 118:0.0374123 121:0.201087 126:26.3705 127:-0.113147 140:0.664604 141:-4.76737e+010 143:5.55241 148:0.0295105 151:0.560204 154:-0.00116071 156:-51.0783 159:109.569 174:-3.39445 175:-0.140016 176:-1.97535e-012 182:0.535478 184:-0.123183 185:0.00535891 192:-14.0139 195:-9.3341
16|F0 |F1 |F2 3:0.805373 7:-0.616315 12:7.96577 15:-0.000381836 20:1.99242 24:-36.9623 26:4.76172 28:9.80305e-013 37:-1.20237 39:0.0288101 43:-1.63936e-005 46:9.85671e-007 57:1.1472 64:1.40166 75:-17.0074 78:-0.000296958 80:-1.94739 81:1.10607 87:2.51596 89:0.261966 91:-495.363 99:-2.28283e-005 126:-87.9869 128:3.00528 132:1.77599 141:-0.178928 143:3.06224e-006 145:-0.565992 152:-5.92072 153:4.92426 155:0.00101049 160:2.76588 163:-2.04174e+008 164:2640.66 170:-0.000367642 173:-2.06379 178:1317.65 179:-0.00802535 181:-2168.37 183:-3.70057 198:-1.42189
16|F0 |F1 |F2 8:0.333817 16:0.953154 23:312.886 24:-0.714184 30:-0.288627 34:9.91053e+007 37:-4927.77 38:803.766 40:-1.07678e-005 41:-1.48148 46:0.38899 48:0.0838392 52:0.262424 58:-0.149193 73:0.00124782 75:8.4887e-005 76:-0.00112656 78:0.00395721 83:2.12178 85:-3.79148 92:19.1218 107:112574 117:2.86991 120:-0.00753958 140:-5.37487e-005 146:1.69733e-006 150:10.8409 153:0.0329825 155:-0.556278 164:1.35411 167:-20.7417 170:-0.138778 173:-0.830417 180:-8.34289 184:-2.18556 187:-2.28328 189:0.990638 191:-2.0757e-007 192:-3772.98 193:-0.269118 199:-2.09693
16|F0 |F1 |F2 12:-1.04533 16:-0.0230121 17:-0.0122211 20:-1.46033 24:-0.545664 27:4.2869 29:-1.15773 34:186.834 35:-0.00883055 37:2.33103 41:-0.0170856 43:-1.6447 47:0.00660999 57:2.45732 59:0.929793 71:112.466 87:-4.84341e-006 97:-1.33587 100:0.00168654 107:1.4092 110:-0.441721 112:0.00392082 125:887.261 130:0.151477 134:-50382.3 136:0.217145 137:3.20575e+009 138:-0.00633751 144:2.95986e+006 146:2.2817 148:-0.0126636 151:0.63225 155:365.186 156:-0.575951 158:-4.59452 167:2.55996 177:3.72396e-008 179:1.10093 180:3.25892 182:-0.377414 186:-0.00129834
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15
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/3x5_MI_sparse.txt Normal file
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@ -0,0 +1,15 @@
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1|F0 1:1.22095|F1 0:-0.00737828 4:-0.0144601 7:-0.543235|F2 14:1.54776e-009 27:-0.662915 35:0.536497 46:-0.34541 61:0.296044 88:9.98352 103:1.3163 134:-7.61135 164:-1.03857 185:-0.304898
1|F0 1:-1.42519|F1 4:-2.33726e+009 10:-0.00143175 13:8.48106e-006|F2 4:-5.60599 15:-0.00796245 62:0.00715194 105:0.000809338 116:-7.11907 122:4.71727 124:-1.81108 126:0.0783529 154:-0.219359 173:0.0148204
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2|F0 1:-4.07308e-005|F1 3:1.24088 15:49.3155 16:-78.4441|F2 16:0.0485689 44:-1.51917 63:-145469 81:-0.0277713 87:1.30877 117:-0.0171553 120:0.417027 132:-0.409782 172:-1.03724 192:-0.169038

508
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/50x20_jagged_sequences_dense.txt Normal file
Просмотреть файл

@ -0,0 +1,508 @@
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30|F0 10414 -3.96915e+008 -0.000939743
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31|F0 41.3671 -1.912 -0.00926271
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31|F0 -1.07368 0.000603628 28.3946
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31|F0 -7.48406e-006 18.2823 0.00358694
32|F0 -0.841786 -0.679143 357256
32|F0 1.28332 -0.27335 -9.74604
32|F0 -0.00255979 3.66945e-007 0.378034
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32|F0 2717.15 -0.35506 5.09577e-007
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32|F0 -4.36134e-013 -8.63774 0.122179
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33|F0 2.22958e-010 -30.9881 5.17553
34|F0 0.74774 -0.4401 0.000607662
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34|F0 -2.65571 1.46705e+009 -0.133617
34|F0 -0.654117 -12.7569 70313.3
34|F0 144.822 37.3605 51.8916
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35|F0 16.4811 -4.31254e-017 -6.49237e+008
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37|F0 1.64249e+006 -0.249404 -1.56903
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37|F0 0.73927 1.96332 35.8082
37|F0 -0.0361525 1.04258 -4.39886
37|F0 182.2 0.0024594 -9.81058
37|F0 2.74614 1.31784 0.00068034
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38|F0 1.61095 1.06473 -1148.49
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39|F0 -2.07275 -302.686 16.0116
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41|F0 -5.3577e-007 -4.94369e-005 1.07161
41|F0 1.61752 -0.358136 1.37374
41|F0 142.756 -0.0242057 -0.840408
41|F0 -0.00872021 1.73303 2.2132
41|F0 18.8369 0.00210057 1.48974
41|F0 12.8441 1.38147 -0.0880267
41|F0 0.00061158 0.540068 -25.0447
41|F0 -0.0204324 -209.162 -2.12462
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45|F0 -0.0057562 0.209739 0.00599587
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45|F0 0.464026 0.00377044 409.77
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45|F0 -317405 -0.000119958 -2.84294e+007
45|F0 0.01109 -1.1001 0.0718887
45|F0 7017.2 -1.93939 0.276136
45|F0 0.103032 189.971 0.393897
45|F0 -8860.93 0.303152 -2017.92
45|F0 -0.291923 -13.7012 0.807359
45|F0 -8.83228e-013 4804.25 0.030872
46|F0 -3.69095 11.5356 -1.90857e-006
46|F0 -235639 1.23335 -1880.65
46|F0 0.113996 -0.187991 0.252594
46|F0 -59.7627 0.210411 5094.27
46|F0 7.66814e-013 -0.000148686 -129.436
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46|F0 4.95297e+007 2.4075e+011 1.79844
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46|F0 4.56924e+010 -0.00282875 -0.00165706
46|F0 4.53817e+006 0.306818 8.24263e+006
47|F0 0.631151 -27.0408 4.41943
47|F0 -0.0330179 -0.246969 8646.17
48|F0 -0.0572814 0.394795 0.661676
48|F0 -0.279358 0.774334 -0.0791044
48|F0 12.1647 -1.07851 -2.75982
48|F0 1.12269e-005 0.000667084 0.0124071
48|F0 -20.8505 -48.3981 -1.5488
48|F0 -196.546 31.0336 0.00380951
48|F0 -0.0622801 -3.8769 4.35048
48|F0 -0.481762 0.2774 0.0347257
48|F0 -4.07572 -2.70841 3.97817e+021
48|F0 0.859323 -663659 3.94669e-008
48|F0 0.347577 141.169 -2.82237
48|F0 1.4896 0.0213098 -0.717211
48|F0 -114.235 2.10917 2.75298
48|F0 350.172 2.57129 -0.00081635
48|F0 476.151 -0.0406223 2.52303
49|F0 -9.91001 0.00411611 0.823563
49|F0 0.18766 -0.000597838 0.336529
49|F0 0.000277787 -3.02344e-005 -0.945752
49|F0 1.28724e+015 161.543 -6.53909
49|F0 -0.386447 245.852 -1.37546
49|F0 -2.51648 -0.517552 -0.999326
49|F0 -0.959335 20025.8 0.0740442
49|F0 -110005 2.76944 160.879
49|F0 2.13328e+011 -0.00083935 0.73609
49|F0 -0.0162761 -0.961298 -6.61829
49|F0 1.19302 -6.48571e-010 0.432093
49|F0 6.86115e-007 0.000112199 -0.00314953
49|F0 0.0493277 -1.63446 0.938208

564
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/50x20_jagged_sequences_sparse.txt Normal file
Просмотреть файл

@ -0,0 +1,564 @@
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0
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReaderMNIST_Train.txt → Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/MNIST_dense.txt
Просмотреть файл

0
Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReaderSimple_Train.txt → Tests/UnitTests/ReaderTests/Data/CNTKTextFormatReader/Simple_dense.txt
Просмотреть файл

33
Tests/UnitTests/ReaderTests/ReaderTests.vcxproj
Просмотреть файл

@ -148,12 +148,15 @@
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@ -168,11 +171,12 @@
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<Text Include="Control\ImageReaderZip_Control.txt" />
<Text Include="Control\UCIFastReaderSimpleDataLoop_Control.txt" />
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@ -188,11 +192,8 @@
<Image Include="Data\images\red.jpg" />
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<None Include="Config\CNTKTextFormatReaderMNIST_Config.cntk" />
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<None Include="Config\ImageReaderBadLabel_Config.cntk" />
<None Include="Config\ImageReaderBadMap_Config.cntk" />
<None Include="Config\ImageReaderLabelOutOfRange_Config.cntk" />

108
Tests/UnitTests/ReaderTests/ReaderTests.vcxproj.filters
Просмотреть файл

@ -52,6 +52,15 @@
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<UniqueIdentifier>{eb1dd93b-a95c-4576-a18c-7cfb0ee7001f}</UniqueIdentifier>
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@ -183,39 +192,6 @@
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@ -225,6 +201,51 @@
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@ -250,21 +271,6 @@
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@ -274,5 +280,11 @@
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