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630 lines
19 KiB
C++
630 lines
19 KiB
C++
///////////////////////////////////////////////////////////////////////////////////////
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
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// AUTHOR:
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// Rahul Kavi rahulkavi[at]live[at]com
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// # You are free to use, change, or redistribute the code in any way you wish for
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// # non-commercial purposes, but please maintain the name of the original author.
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// # This code comes with no warranty of any kind.
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// #
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// # You are free to use, change, or redistribute the code in any way you wish for
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// # non-commercial purposes, but please maintain the name of the original author.
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// # This code comes with no warranty of any kind.
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// # Logistic Regression ALGORITHM
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// License Agreement
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// For Open Source Computer Vision Library
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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#include "precomp.hpp"
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using namespace std;
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namespace cv {
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namespace ml {
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class LrParams
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{
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public:
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LrParams()
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{
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alpha = 0.001;
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num_iters = 1000;
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norm = LogisticRegression::REG_L2;
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train_method = LogisticRegression::BATCH;
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mini_batch_size = 1;
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term_crit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, num_iters, alpha);
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}
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double alpha; //!< learning rate.
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int num_iters; //!< number of iterations.
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int norm;
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int train_method;
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int mini_batch_size;
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TermCriteria term_crit;
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};
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class LogisticRegressionImpl : public LogisticRegression
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{
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public:
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LogisticRegressionImpl() { }
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virtual ~LogisticRegressionImpl() {}
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CV_IMPL_PROPERTY(double, LearningRate, params.alpha)
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CV_IMPL_PROPERTY(int, Iterations, params.num_iters)
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CV_IMPL_PROPERTY(int, Regularization, params.norm)
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CV_IMPL_PROPERTY(int, TrainMethod, params.train_method)
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CV_IMPL_PROPERTY(int, MiniBatchSize, params.mini_batch_size)
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CV_IMPL_PROPERTY(TermCriteria, TermCriteria, params.term_crit)
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virtual bool train( const Ptr<TrainData>& trainData, int=0 );
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virtual float predict(InputArray samples, OutputArray results, int flags=0) const;
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virtual void clear();
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virtual void write(FileStorage& fs) const;
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virtual void read(const FileNode& fn);
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virtual Mat get_learnt_thetas() const { return learnt_thetas; }
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virtual int getVarCount() const { return learnt_thetas.cols; }
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virtual bool isTrained() const { return !learnt_thetas.empty(); }
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virtual bool isClassifier() const { return true; }
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virtual String getDefaultName() const { return "opencv_ml_lr"; }
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protected:
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Mat calc_sigmoid(const Mat& data) const;
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double compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
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void compute_gradient(const Mat& _data, const Mat& _labels, const Mat &_theta, const double _lambda, Mat & _gradient );
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Mat batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
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Mat mini_batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
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bool set_label_map(const Mat& _labels_i);
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Mat remap_labels(const Mat& _labels_i, const map<int, int>& lmap) const;
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protected:
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LrParams params;
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Mat learnt_thetas;
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map<int, int> forward_mapper;
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map<int, int> reverse_mapper;
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Mat labels_o;
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Mat labels_n;
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};
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Ptr<LogisticRegression> LogisticRegression::create()
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{
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return makePtr<LogisticRegressionImpl>();
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}
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bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
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{
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// return value
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bool ok = false;
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clear();
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Mat _data_i = trainData->getSamples();
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Mat _labels_i = trainData->getResponses();
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// check size and type of training data
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CV_Assert( !_labels_i.empty() && !_data_i.empty());
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if(_labels_i.cols != 1)
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{
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CV_Error( CV_StsBadArg, "labels should be a column matrix" );
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}
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if(_data_i.type() != CV_32FC1 || _labels_i.type() != CV_32FC1)
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{
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CV_Error( CV_StsBadArg, "data and labels must be a floating point matrix" );
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}
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if(_labels_i.rows != _data_i.rows)
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{
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CV_Error( CV_StsBadArg, "number of rows in data and labels should be equal" );
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}
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// class labels
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set_label_map(_labels_i);
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Mat labels_l = remap_labels(_labels_i, this->forward_mapper);
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int num_classes = (int) this->forward_mapper.size();
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if(num_classes < 2)
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{
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CV_Error( CV_StsBadArg, "data should have atleast 2 classes" );
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}
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// add a column of ones to the data (bias/intercept term)
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Mat data_t;
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hconcat( cv::Mat::ones( _data_i.rows, 1, CV_32F ), _data_i, data_t );
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// coefficient matrix (zero-initialized)
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Mat thetas;
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Mat init_theta = Mat::zeros(data_t.cols, 1, CV_32F);
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// fit the model (handles binary and multiclass cases)
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Mat new_theta;
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Mat labels;
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if(num_classes == 2)
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{
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labels_l.convertTo(labels, CV_32F);
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if(this->params.train_method == LogisticRegression::BATCH)
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new_theta = batch_gradient_descent(data_t, labels, init_theta);
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else
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new_theta = mini_batch_gradient_descent(data_t, labels, init_theta);
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thetas = new_theta.t();
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}
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else
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{
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/* take each class and rename classes you will get a theta per class
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as in multi class class scenario, we will have n thetas for n classes */
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thetas.create(num_classes, data_t.cols, CV_32F);
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Mat labels_binary;
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int ii = 0;
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for(map<int,int>::iterator it = this->forward_mapper.begin(); it != this->forward_mapper.end(); ++it)
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{
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// one-vs-rest (OvR) scheme
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labels_binary = (labels_l == it->second)/255;
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labels_binary.convertTo(labels, CV_32F);
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if(this->params.train_method == LogisticRegression::BATCH)
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new_theta = batch_gradient_descent(data_t, labels, init_theta);
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else
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new_theta = mini_batch_gradient_descent(data_t, labels, init_theta);
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hconcat(new_theta.t(), thetas.row(ii));
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ii += 1;
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}
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}
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// check that the estimates are stable and finite
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this->learnt_thetas = thetas.clone();
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if( cvIsNaN( (double)sum(this->learnt_thetas)[0] ) )
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{
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CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
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}
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// success
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ok = true;
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return ok;
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}
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float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, int flags) const
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{
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// check if learnt_mats array is populated
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if(!this->isTrained())
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{
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CV_Error( CV_StsBadArg, "classifier should be trained first" );
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}
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// coefficient matrix
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Mat thetas;
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if ( learnt_thetas.type() == CV_32F )
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{
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thetas = learnt_thetas;
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}
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else
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{
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this->learnt_thetas.convertTo( thetas, CV_32F );
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}
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CV_Assert(thetas.rows > 0);
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// data samples
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Mat data = samples.getMat();
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if(data.type() != CV_32F)
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{
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CV_Error( CV_StsBadArg, "data must be of floating type" );
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}
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// add a column of ones to the data (bias/intercept term)
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Mat data_t;
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hconcat( cv::Mat::ones( data.rows, 1, CV_32F ), data, data_t );
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CV_Assert(data_t.cols == thetas.cols);
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// predict class labels for samples (handles binary and multiclass cases)
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Mat labels_c;
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Mat pred_m;
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Mat temp_pred;
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if(thetas.rows == 1)
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{
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// apply sigmoid function
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temp_pred = calc_sigmoid(data_t * thetas.t());
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CV_Assert(temp_pred.cols==1);
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pred_m = temp_pred.clone();
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// if greater than 0.5, predict class 0 or predict class 1
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temp_pred = (temp_pred > 0.5f) / 255;
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temp_pred.convertTo(labels_c, CV_32S);
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}
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else
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{
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// apply sigmoid function
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pred_m.create(data_t.rows, thetas.rows, data.type());
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for(int i = 0; i < thetas.rows; i++)
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{
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temp_pred = calc_sigmoid(data_t * thetas.row(i).t());
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vconcat(temp_pred, pred_m.col(i));
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}
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// predict class with the maximum output
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Point max_loc;
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Mat labels;
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for(int i = 0; i < pred_m.rows; i++)
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{
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temp_pred = pred_m.row(i);
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minMaxLoc( temp_pred, NULL, NULL, NULL, &max_loc );
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labels.push_back(max_loc.x);
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}
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labels.convertTo(labels_c, CV_32S);
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}
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// return label of the predicted class. class names can be 1,2,3,...
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Mat pred_labs = remap_labels(labels_c, this->reverse_mapper);
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pred_labs.convertTo(pred_labs, CV_32S);
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// return either the labels or the raw output
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if ( results.needed() )
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{
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if ( flags & StatModel::RAW_OUTPUT )
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{
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pred_m.copyTo( results );
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}
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else
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{
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pred_labs.copyTo(results);
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}
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}
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return ( pred_labs.empty() ? 0.f : static_cast<float>(pred_labs.at<int>(0)) );
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}
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Mat LogisticRegressionImpl::calc_sigmoid(const Mat& data) const
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{
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Mat dest;
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exp(-data, dest);
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return 1.0/(1.0+dest);
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}
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double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
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{
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int llambda = 0;
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int m;
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int n;
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double cost = 0;
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double rparameter = 0;
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Mat theta_b;
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Mat theta_c;
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Mat d_a;
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Mat d_b;
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m = _data.rows;
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n = _data.cols;
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theta_b = _init_theta(Range(1, n), Range::all());
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if (params.norm != REG_DISABLE)
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{
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llambda = 1;
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}
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if(this->params.norm == LogisticRegression::REG_L1)
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{
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rparameter = (llambda/(2*m)) * sum(theta_b)[0];
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}
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else
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{
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// assuming it to be L2 by default
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multiply(theta_b, theta_b, theta_c, 1);
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rparameter = (llambda/(2*m)) * sum(theta_c)[0];
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}
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d_a = calc_sigmoid(_data * _init_theta);
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log(d_a, d_a);
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multiply(d_a, _labels, d_a);
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// use the fact that: log(1 - sigmoid(x)) = log(sigmoid(-x))
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d_b = calc_sigmoid(- _data * _init_theta);
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log(d_b, d_b);
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multiply(d_b, 1-_labels, d_b);
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cost = (-1.0/m) * (sum(d_a)[0] + sum(d_b)[0]);
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cost = cost + rparameter;
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if(cvIsNaN( cost ) == 1)
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{
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CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
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}
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return cost;
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}
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struct LogisticRegressionImpl_ComputeDradient_Impl : ParallelLoopBody
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{
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const Mat* data;
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const Mat* theta;
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const Mat* pcal_a;
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Mat* gradient;
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double lambda;
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LogisticRegressionImpl_ComputeDradient_Impl(const Mat& _data, const Mat &_theta, const Mat& _pcal_a, const double _lambda, Mat & _gradient)
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: data(&_data)
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, theta(&_theta)
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, pcal_a(&_pcal_a)
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, gradient(&_gradient)
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, lambda(_lambda)
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{
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}
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void operator()(const cv::Range& r) const
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{
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const Mat& _data = *data;
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const Mat &_theta = *theta;
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Mat & _gradient = *gradient;
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const Mat & _pcal_a = *pcal_a;
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const int m = _data.rows;
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Mat pcal_ab;
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for (int ii = r.start; ii<r.end; ii++)
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{
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Mat pcal_b = _data(Range::all(), Range(ii,ii+1));
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multiply(_pcal_a, pcal_b, pcal_ab, 1);
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_gradient.row(ii) = (1.0/m)*sum(pcal_ab)[0] + (lambda/m) * _theta.row(ii);
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}
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}
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};
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void LogisticRegressionImpl::compute_gradient(const Mat& _data, const Mat& _labels, const Mat &_theta, const double _lambda, Mat & _gradient )
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{
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const int m = _data.rows;
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Mat pcal_a, pcal_b, pcal_ab;
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const Mat z = _data * _theta;
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CV_Assert( _gradient.rows == _theta.rows && _gradient.cols == _theta.cols );
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pcal_a = calc_sigmoid(z) - _labels;
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pcal_b = _data(Range::all(), Range(0,1));
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multiply(pcal_a, pcal_b, pcal_ab, 1);
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_gradient.row(0) = ((float)1/m) * sum(pcal_ab)[0];
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//cout<<"for each training data entry"<<endl;
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LogisticRegressionImpl_ComputeDradient_Impl invoker(_data, _theta, pcal_a, _lambda, _gradient);
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cv::parallel_for_(cv::Range(1, _gradient.rows), invoker);
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}
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Mat LogisticRegressionImpl::batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
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{
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// implements batch gradient descent
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if(this->params.alpha<=0)
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{
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CV_Error( CV_StsBadArg, "check training parameters (learning rate) for the classifier" );
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}
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if(this->params.num_iters <= 0)
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{
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CV_Error( CV_StsBadArg, "number of iterations cannot be zero or a negative number" );
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}
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int llambda = 0;
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int m;
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Mat theta_p = _init_theta.clone();
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Mat gradient( theta_p.rows, theta_p.cols, theta_p.type() );
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m = _data.rows;
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if (params.norm != REG_DISABLE)
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{
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llambda = 1;
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}
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for(int i = 0;i<this->params.num_iters;i++)
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{
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// this seems to only be called to ensure that cost is not NaN
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compute_cost(_data, _labels, theta_p);
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compute_gradient( _data, _labels, theta_p, llambda, gradient );
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theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
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}
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return theta_p;
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}
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Mat LogisticRegressionImpl::mini_batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
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{
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// implements batch gradient descent
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int lambda_l = 0;
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int m;
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int j = 0;
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int size_b = this->params.mini_batch_size;
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if(this->params.mini_batch_size <= 0 || this->params.alpha == 0)
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{
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CV_Error( CV_StsBadArg, "check training parameters for the classifier" );
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}
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if(this->params.num_iters <= 0)
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{
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CV_Error( CV_StsBadArg, "number of iterations cannot be zero or a negative number" );
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}
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Mat theta_p = _init_theta.clone();
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Mat gradient( theta_p.rows, theta_p.cols, theta_p.type() );
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Mat data_d;
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Mat labels_l;
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if (params.norm != REG_DISABLE)
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{
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lambda_l = 1;
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}
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for(int i = 0;i<this->params.term_crit.maxCount;i++)
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{
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if(j+size_b<=_data.rows)
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{
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data_d = _data(Range(j,j+size_b), Range::all());
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labels_l = _labels(Range(j,j+size_b),Range::all());
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}
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else
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{
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data_d = _data(Range(j, _data.rows), Range::all());
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labels_l = _labels(Range(j, _labels.rows),Range::all());
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}
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|
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m = data_d.rows;
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|
|
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// this seems to only be called to ensure that cost is not NaN
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compute_cost(data_d, labels_l, theta_p);
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|
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compute_gradient(data_d, labels_l, theta_p, lambda_l, gradient);
|
|
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theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
|
|
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j += this->params.mini_batch_size;
|
|
|
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// if parsed through all data variables
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if (j >= _data.rows) {
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|
j = 0;
|
|
}
|
|
}
|
|
return theta_p;
|
|
}
|
|
|
|
bool LogisticRegressionImpl::set_label_map(const Mat &_labels_i)
|
|
{
|
|
// this function creates two maps to map user defined labels to program friendly labels two ways.
|
|
int ii = 0;
|
|
Mat labels;
|
|
|
|
this->labels_o = Mat(0,1, CV_8U);
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|
this->labels_n = Mat(0,1, CV_8U);
|
|
|
|
_labels_i.convertTo(labels, CV_32S);
|
|
|
|
for(int i = 0;i<labels.rows;i++)
|
|
{
|
|
this->forward_mapper[labels.at<int>(i)] += 1;
|
|
}
|
|
|
|
for(map<int,int>::iterator it = this->forward_mapper.begin(); it != this->forward_mapper.end(); ++it)
|
|
{
|
|
this->forward_mapper[it->first] = ii;
|
|
this->labels_o.push_back(it->first);
|
|
this->labels_n.push_back(ii);
|
|
ii += 1;
|
|
}
|
|
|
|
for(map<int,int>::iterator it = this->forward_mapper.begin(); it != this->forward_mapper.end(); ++it)
|
|
{
|
|
this->reverse_mapper[it->second] = it->first;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
Mat LogisticRegressionImpl::remap_labels(const Mat& _labels_i, const map<int, int>& lmap) const
|
|
{
|
|
Mat labels;
|
|
_labels_i.convertTo(labels, CV_32S);
|
|
|
|
Mat new_labels = Mat::zeros(labels.rows, labels.cols, labels.type());
|
|
|
|
CV_Assert( !lmap.empty() );
|
|
|
|
for(int i =0;i<labels.rows;i++)
|
|
{
|
|
new_labels.at<int>(i,0) = lmap.find(labels.at<int>(i,0))->second;
|
|
}
|
|
return new_labels;
|
|
}
|
|
|
|
void LogisticRegressionImpl::clear()
|
|
{
|
|
this->learnt_thetas.release();
|
|
this->labels_o.release();
|
|
this->labels_n.release();
|
|
}
|
|
|
|
void LogisticRegressionImpl::write(FileStorage& fs) const
|
|
{
|
|
// check if open
|
|
if(fs.isOpened() == 0)
|
|
{
|
|
CV_Error(CV_StsBadArg,"file can't open. Check file path");
|
|
}
|
|
writeFormat(fs);
|
|
string desc = "Logisitic Regression Classifier";
|
|
fs<<"classifier"<<desc.c_str();
|
|
fs<<"alpha"<<this->params.alpha;
|
|
fs<<"iterations"<<this->params.num_iters;
|
|
fs<<"norm"<<this->params.norm;
|
|
fs<<"train_method"<<this->params.train_method;
|
|
if(this->params.train_method == LogisticRegression::MINI_BATCH)
|
|
{
|
|
fs<<"mini_batch_size"<<this->params.mini_batch_size;
|
|
}
|
|
fs<<"learnt_thetas"<<this->learnt_thetas;
|
|
fs<<"n_labels"<<this->labels_n;
|
|
fs<<"o_labels"<<this->labels_o;
|
|
}
|
|
|
|
void LogisticRegressionImpl::read(const FileNode& fn)
|
|
{
|
|
// check if empty
|
|
if(fn.empty())
|
|
{
|
|
CV_Error( CV_StsBadArg, "empty FileNode object" );
|
|
}
|
|
|
|
this->params.alpha = (double)fn["alpha"];
|
|
this->params.num_iters = (int)fn["iterations"];
|
|
this->params.norm = (int)fn["norm"];
|
|
this->params.train_method = (int)fn["train_method"];
|
|
|
|
if(this->params.train_method == LogisticRegression::MINI_BATCH)
|
|
{
|
|
this->params.mini_batch_size = (int)fn["mini_batch_size"];
|
|
}
|
|
|
|
fn["learnt_thetas"] >> this->learnt_thetas;
|
|
fn["o_labels"] >> this->labels_o;
|
|
fn["n_labels"] >> this->labels_n;
|
|
|
|
for(int ii =0;ii<labels_o.rows;ii++)
|
|
{
|
|
this->forward_mapper[labels_o.at<int>(ii,0)] = labels_n.at<int>(ii,0);
|
|
this->reverse_mapper[labels_n.at<int>(ii,0)] = labels_o.at<int>(ii,0);
|
|
}
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
/* End of file. */
|