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Deleted functions makeTrainData() and makeTestData() in test_svmsgd.cpp.

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Added function makeData() in test_svmsgd.cpp.
This commit is contained in:
Marina Noskova
2016-02-25 16:57:03 +03:00
parent 74c87a26a5
commit d484893839
3 changed files with 33 additions and 48 deletions
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8
modules/ml/include/opencv2/ml.hpp
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@@ -1626,10 +1626,10 @@ public:
* stepDecreasingPower = 1;
* termCrit.maxCount = 100000;
* termCrit.epsilon = 0.00001;
* @param svmsgdType is the type of SVMSGD classifier. Legal values are SvmsgdType::SGD and SvmsgdType::ASGD.
* Recommended value is SvmsgdType::ASGD (by default).
* @param marginType is the type of margin constraint. Legal values are MarginType::SOFT_MARGIN and MarginType::HARD_MARGIN.
* Default value is MarginType::SOFT_MARGIN.
* @param svmsgdType is the type of SVMSGD classifier. Legal values are SVMSGD::SvmsgdType::SGD and SVMSGD::SvmsgdType::ASGD.
* Recommended value is SVMSGD::SvmsgdType::ASGD (by default).
* @param marginType is the type of margin constraint. Legal values are SVMSGD::MarginType::SOFT_MARGIN and SVMSGD::MarginType::HARD_MARGIN.
* Default value is SVMSGD::MarginType::SOFT_MARGIN.
*/
CV_WRAP virtual void setOptimalParameters(int svmsgdType = SVMSGD::ASGD, int marginType = SVMSGD::SOFT_MARGIN) = 0;

12
modules/ml/src/svmsgd.cpp
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@@ -142,6 +142,7 @@ void SVMSGDImpl::normalizeSamples(Mat &samples, Mat &average, float &multiplier)
int samplesCount = samples.rows;
average = Mat(1, featuresCount, samples.type());
CV_Assert(average.type() == CV_32FC1);
for (int featureIndex = 0; featureIndex < featuresCount; featureIndex++)
{
average.at<float>(featureIndex) = static_cast<float>(mean(samples.col(featureIndex))[0]);
@@ -170,11 +171,11 @@ void SVMSGDImpl::makeExtendedTrainSamples(const Mat &trainSamples, Mat &extended
cv::hconcat(normalizedTrainSamples, onesCol, extendedTrainSamples);
}
void SVMSGDImpl::updateWeights(InputArray _sample, bool firstClass, float stepSize, Mat& weights)
void SVMSGDImpl::updateWeights(InputArray _sample, bool positive, float stepSize, Mat& weights)
{
Mat sample = _sample.getMat();
int response = firstClass ? 1 : -1; // ensure that trainResponses are -1 or 1
int response = positive ? 1 : -1; // ensure that trainResponses are -1 or 1
if ( sample.dot(weights) * response > 1)
{
@@ -197,6 +198,7 @@ float SVMSGDImpl::calcShift(InputArray _samples, InputArray _responses) const
Mat trainResponses = _responses.getMat();
CV_Assert(trainResponses.type() == CV_32FC1);
for (int samplesIndex = 0; samplesIndex < trainSamplesCount; samplesIndex++)
{
Mat currentSample = trainSamples.row(samplesIndex);
@@ -261,7 +263,7 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
RNG rng(0);
CV_Assert (params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS);
CV_Assert ((params.termCrit.type & TermCriteria::COUNT || params.termCrit.type & TermCriteria::EPS) && (trainResponses.type() == CV_32FC1));
int maxCount = (params.termCrit.type & TermCriteria::COUNT) ? params.termCrit.maxCount : INT_MAX;
double epsilon = (params.termCrit.type & TermCriteria::EPS) ? params.termCrit.epsilon : 0;
@@ -300,7 +302,7 @@ bool SVMSGDImpl::train(const Ptr<TrainData>& data, int)
weights_ = extendedWeights(roi);
weights_ *= multiplier;
CV_Assert(params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN);
CV_Assert((params.marginType == SOFT_MARGIN || params.marginType == HARD_MARGIN) && (extendedWeights.type() == CV_32FC1));
if (params.marginType == SOFT_MARGIN)
{
@@ -332,7 +334,7 @@ float SVMSGDImpl::predict( InputArray _samples, OutputArray _results, int ) cons
else
{
CV_Assert( nSamples == 1 );
results = Mat(1, 1, CV_32F, &result);
results = Mat(1, 1, CV_32FC1, &result);
}
for (int sampleIndex = 0; sampleIndex < nSamples; sampleIndex++)

61
modules/ml/test/test_svmsgd.cpp
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@@ -62,8 +62,7 @@ public:
private:
virtual void run( int start_from );
static float decisionFunction(const Mat &sample, const Mat &weights, float shift);
void makeTrainData(Mat weights, float shift);
void makeTestData(Mat weights, float shift);
void makeData(int samplesCount, Mat weights, float shift, RNG rng, Mat &samples, Mat & responses);
void generateSameBorders(int featureCount);
void generateDifferentBorders(int featureCount);
@@ -108,46 +107,28 @@ void CV_SVMSGDTrainTest::generateDifferentBorders(int featureCount)
}
}
void CV_SVMSGDTrainTest::makeTrainData(Mat weights, float shift)
float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift)
{
return static_cast<float>(sample.dot(weights)) + shift;
}
void CV_SVMSGDTrainTest::makeData(int samplesCount, Mat weights, float shift, RNG rng, Mat &samples, Mat & responses)
{
int datasize = 100000;
int featureCount = weights.cols;
RNG rng(0);
cv::Mat samples = cv::Mat::zeros(datasize, featureCount, CV_32FC1);
samples.create(samplesCount, featureCount, CV_32FC1);
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
{
rng.fill(samples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
}
cv::Mat responses = cv::Mat::zeros(datasize, 1, CV_32FC1);
for (int sampleIndex = 0; sampleIndex < datasize; sampleIndex++)
responses.create(samplesCount, 1, CV_32FC1);
for (int i = 0 ; i < samplesCount; i++)
{
responses.at<float>(sampleIndex) = decisionFunction(samples.row(sampleIndex), weights, shift) > 0 ? 1.f : -1.f;
responses.at<float>(i) = decisionFunction(samples.row(i), weights, shift) > 0 ? 1.f : -1.f;
}
data = TrainData::create(samples, cv::ml::ROW_SAMPLE, responses);
}
void CV_SVMSGDTrainTest::makeTestData(Mat weights, float shift)
{
int testSamplesCount = 100000;
int featureCount = weights.cols;
cv::RNG rng(42);
testSamples.create(testSamplesCount, featureCount, CV_32FC1);
for (int featureIndex = 0; featureIndex < featureCount; featureIndex++)
{
rng.fill(testSamples.col(featureIndex), RNG::UNIFORM, borders[featureIndex].first, borders[featureIndex].second);
}
testResponses.create(testSamplesCount, 1, CV_32FC1);
for (int i = 0 ; i < testSamplesCount; i++)
{
testResponses.at<float>(i) = decisionFunction(testSamples.row(i), weights, shift) > 0 ? 1.f : -1.f;
}
}
CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDataType _type, double _precision)
@@ -169,13 +150,16 @@ CV_SVMSGDTrainTest::CV_SVMSGDTrainTest(const Mat &weights, float shift, TrainDat
CV_Error(CV_StsBadArg, "Unknown train data type");
}
makeTrainData(weights, shift);
makeTestData(weights, shift);
}
RNG rng(0);
float CV_SVMSGDTrainTest::decisionFunction(const Mat &sample, const Mat &weights, float shift)
{
return static_cast<float>(sample.dot(weights)) + shift;
Mat trainSamples;
Mat trainResponses;
int trainSamplesCount = 10000;
makeData(trainSamplesCount, weights, shift, rng, trainSamples, trainResponses);
data = TrainData::create(trainSamples, cv::ml::ROW_SAMPLE, trainResponses);
int testSamplesCount = 100000;
makeData(testSamplesCount, weights, shift, rng, testSamples, testResponses);
}
void CV_SVMSGDTrainTest::run( int /*start_from*/ )
@@ -205,7 +189,6 @@ void CV_SVMSGDTrainTest::run( int /*start_from*/ )
}
}
void makeWeightsAndShift(int featureCount, Mat &weights, float &shift)
{
weights.create(1, featureCount, CV_32FC1);
@@ -253,7 +236,7 @@ TEST(ML_SVMSGD, trainSameScale100)
float shift = 0;
makeWeightsAndShift(featureCount, weights, shift);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE);
CV_SVMSGDTrainTest test(weights, shift, CV_SVMSGDTrainTest::UNIFORM_SAME_SCALE, 0.02);
test.safe_run();
}