add part of opencv

Lib/opencv/sources/modules/ml/src/inner_functions.cpp

@@ -0,0 +1,222 @@
+/*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>& trainData, int )
+{
+    CV_TRACE_FUNCTION();
+    CV_Assert(!trainData.empty());
+    CV_Error(CV_StsNotImplemented, "");
+    return false;
+}
+
+bool StatModel::train( InputArray samples, int layout, InputArray responses )
+{
+    CV_TRACE_FUNCTION();
+    CV_Assert(!samples.empty());
+    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();
+    CV_Assert(!data.empty());
+    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 */