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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv { namespace ml {
ParamGrid::ParamGrid() { minVal = maxVal = 0.; logStep = 1; }
ParamGrid::ParamGrid(double _minVal, double _maxVal, double _logStep)
{
CV_TRACE_FUNCTION();
minVal = std::min(_minVal, _maxVal);
maxVal = std::max(_minVal, _maxVal);
logStep = std::max(_logStep, 1.);
}
Ptr<ParamGrid> ParamGrid::create(double minval, double maxval, double logstep) {
return makePtr<ParamGrid>(minval, maxval, logstep);
}
bool StatModel::empty() const { return !isTrained(); }
int StatModel::getVarCount() const { return 0; }
bool StatModel::train( const Ptr<TrainData>&, int )
{
CV_TRACE_FUNCTION();
CV_Error(CV_StsNotImplemented, "");
return false;
}
bool StatModel::train( InputArray samples, int layout, InputArray responses )
{
CV_TRACE_FUNCTION();
return train(TrainData::create(samples, layout, responses));
}
class ParallelCalcError : public ParallelLoopBody
{
private:
const Ptr<TrainData>& data;
bool &testerr;
Mat &resp;
const StatModel &s;
vector<double> &errStrip;
public:
ParallelCalcError(const Ptr<TrainData>& d, bool &t, Mat &_r,const StatModel &w, vector<double> &e) :
data(d),
testerr(t),
resp(_r),
s(w),
errStrip(e)
{
}
virtual void operator()(const Range& range) const CV_OVERRIDE
{
int idxErr = range.start;
CV_TRACE_FUNCTION_SKIP_NESTED();
Mat samples = data->getSamples();
Mat weights=testerr? data->getTestSampleWeights() : data->getTrainSampleWeights();
int layout = data->getLayout();
Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx();
const int* sidx_ptr = sidx.ptr<int>();
bool isclassifier = s.isClassifier();
Mat responses = data->getResponses();
int responses_type = responses.type();
double err = 0;
const float* sw = weights.empty() ? 0 : weights.ptr<float>();
for (int i = range.start; i < range.end; i++)
{
int si = sidx_ptr ? sidx_ptr[i] : i;
double sweight = sw ? static_cast<double>(sw[i]) : 1.;
Mat sample = layout == ROW_SAMPLE ? samples.row(si) : samples.col(si);
float val = s.predict(sample);
float val0 = (responses_type == CV_32S) ? (float)responses.at<int>(si) : responses.at<float>(si);
if (isclassifier)
err += sweight * fabs(val - val0) > FLT_EPSILON;
else
err += sweight * (val - val0)*(val - val0);
if (!resp.empty())
resp.at<float>(i) = val;
}
errStrip[idxErr]=err ;
};
ParallelCalcError& operator=(const ParallelCalcError &) {
return *this;
};
};
float StatModel::calcError(const Ptr<TrainData>& data, bool testerr, OutputArray _resp) const
{
CV_TRACE_FUNCTION_SKIP_NESTED();
Mat samples = data->getSamples();
Mat sidx = testerr ? data->getTestSampleIdx() : data->getTrainSampleIdx();
Mat weights = testerr ? data->getTestSampleWeights() : data->getTrainSampleWeights();
int n = (int)sidx.total();
bool isclassifier = isClassifier();
Mat responses = data->getResponses();
if (n == 0)
{
n = data->getNSamples();
weights = data->getTrainSampleWeights();
testerr =false;
}
if (n == 0)
return -FLT_MAX;
Mat resp;
if (_resp.needed())
resp.create(n, 1, CV_32F);
double err = 0;
vector<double> errStrip(n,0.0);
ParallelCalcError x(data, testerr, resp, *this,errStrip);
parallel_for_(Range(0,n),x);
for (size_t i = 0; i < errStrip.size(); i++)
err += errStrip[i];
float weightSum= weights.empty() ? n: static_cast<float>(sum(weights)(0));
if (_resp.needed())
resp.copyTo(_resp);
return (float)(err/ weightSum * (isclassifier ? 100 : 1));
}
/* Calculates upper triangular matrix S, where A is a symmetrical matrix A=S'*S */
static void Cholesky( const Mat& A, Mat& S )
{
CV_TRACE_FUNCTION();
CV_Assert(A.type() == CV_32F);
S = A.clone();
cv::Cholesky ((float*)S.ptr(),S.step, S.rows,NULL, 0, 0);
S = S.t();
for (int i=1;i<S.rows;i++)
for (int j=0;j<i;j++)
S.at<float>(i,j)=0;
}
/* Generates <sample> from multivariate normal distribution, where <mean> - is an
average row vector, <cov> - symmetric covariation matrix */
void randMVNormal( InputArray _mean, InputArray _cov, int nsamples, OutputArray _samples )
{
CV_TRACE_FUNCTION();
// check mean vector and covariance matrix
Mat mean = _mean.getMat(), cov = _cov.getMat();
int dim = (int)mean.total(); // dimensionality
CV_Assert(mean.rows == 1 || mean.cols == 1);
CV_Assert(cov.rows == dim && cov.cols == dim);
mean = mean.reshape(1,1); // ensure a row vector
// generate n-samples of the same dimension, from ~N(0,1)
_samples.create(nsamples, dim, CV_32F);
Mat samples = _samples.getMat();
randn(samples, Scalar::all(0), Scalar::all(1));
// decompose covariance using Cholesky: cov = U'*U
// (cov must be square, symmetric, and positive semi-definite matrix)
Mat utmat;
Cholesky(cov, utmat);
// transform random numbers using specified mean and covariance
for( int i = 0; i < nsamples; i++ )
{
Mat sample = samples.row(i);
sample = sample * utmat + mean;
}
}
}}
/* End of file */