--- /dev/null
+///////////////////////////////////////////////////////////////////////////////////////
+// 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.
+
+// This is a implementation of the Logistic Regression algorithm in C++ in OpenCV.
+
+// AUTHOR:
+// Rahul Kavi rahulkavi[at]live[at]com
+
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// #
+// # You are free to use, change, or redistribute the code in any way you wish for
+// # non-commercial purposes, but please maintain the name of the original author.
+// # This code comes with no warranty of any kind.
+
+// # Logistic Regression ALGORITHM
+
+
+// License Agreement
+// For Open Source Computer Vision Library
+
+// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
+// Copyright (C) 2008-2011, Willow Garage Inc., 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:
+
+// * Redistributions of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+
+// * Redistributions 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 the copyright holders 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.
+
+#include "precomp.hpp"
+
+using namespace cv;
+using namespace std;
+
+CvLR_TrainParams::CvLR_TrainParams()
+{
+ term_crit = CvTermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 10000, 0.001);
+}
+
+CvLR_TrainParams::CvLR_TrainParams(double _alpha, int _num_iters, int _norm, int _regularized, int _train_method, int _minibatchsize):
+ alpha(_alpha), num_iters(_num_iters), norm(_norm), regularized(_regularized), train_method(_train_method), minibatchsize(_minibatchsize)
+///////////////////////////////////////////////////
+// CvLR_TrainParams::CvLR_TrainParams(double _alpha, int _num_iters, int _norm, int _debug, int _regularized, int _train_method, int _minibatchsize):
+// alpha(_alpha), num_iters(_num_iters), norm(_norm), debug(_debug), regularized(_regularized), train_method(_train_method), minibatchsize(_minibatchsize)
+///////////////////////////////////////////////////
+{
+ term_crit = CvTermCriteria(TermCriteria::COUNT + TermCriteria::EPS, num_iters, 0.001);
+}
+
+CvLR_TrainParams::~CvLR_TrainParams()
+{
+
+}
+
+CvLR::CvLR()
+{
+ default_model_name = "my_lr";
+ // set_default_params();
+}
+
+
+CvLR::CvLR(const cv::Mat& _data, const cv::Mat& _labels, const CvLR_TrainParams& _params)
+{
+ this->params = _params;
+ default_model_name = "my_lr";
+ train(_data, _labels);
+}
+
+CvLR::~CvLR()
+{
+ clear();
+}
+
+
+bool CvLR::train(const cv::Mat& _data_i, const cv::Mat& _labels_i)
+{
+ CV_Assert( !_labels_i.empty() && !_data_i.empty());
+
+ // check the number of colums
+ CV_Assert( _labels_i.cols == 1);
+
+ if(_labels_i.cols != 1)
+ {
+ cv::error(Error::StsBadArg, "_labels_i should be a column matrix", "cv::ml::CvLR::train", __FILE__, __LINE__);
+ }
+ // check data type.
+ // data should be of floating type CV_32FC1
+
+ if((_data_i.type() != CV_32FC1) || (_labels_i.type() != CV_32FC1))
+ {
+ cv::error(Error::StsBadArg, "train: data and labels must be a floating point matrix", "cv::ml::CvLR::train", __FILE__, __LINE__);
+ }
+
+ bool ok = false;
+
+ cv::Mat labels;
+
+ //CvLR::set_label_map(_labels_i);
+ set_label_map(_labels_i);
+ int num_classes = this->forward_mapper.size();
+
+ // add a column of ones
+ cv::Mat data_t = cv::Mat::zeros(_data_i.rows, _data_i.cols+1, CV_32F);
+ vconcat(cv::Mat(_data_i.rows, 1, _data_i.type(), Scalar::all(1.0)), data_t.col(0));
+ for (int i=1;i<data_t.cols;i++)
+ {
+ vconcat(_data_i.col(i-1), data_t.col(i));
+ }
+
+ if(num_classes < 2)
+ {
+ cv::error(Error::StsBadArg, "train: data should have atleast 2 classes", "cv::ml::CvLR::train", __FILE__, __LINE__);
+ }
+
+ if(_labels_i.rows != _data_i.rows)
+ {
+ cv::error(Error::StsBadArg, "train: number of rows in data and labels should be the equal", "cv::ml::CvLR::train", __FILE__, __LINE__);
+ }
+
+
+ cv::Mat thetas = cv::Mat::zeros(num_classes, data_t.cols, CV_32F);
+ cv::Mat init_theta = cv::Mat::zeros(data_t.cols, 1, CV_32F);
+
+ cv::Mat labels_l = remap_labels(_labels_i, this->forward_mapper);
+ cv::Mat new_local_labels;
+
+ int ii=0;
+
+ if(num_classes == 2)
+ {
+ //data_t.convertTo(data, CV_32F);
+ labels_l.convertTo(labels, CV_32F);
+
+ //cv::Mat new_theta = CvLR::compute_batch_gradient(data, labels, init_theta);
+ cv::Mat new_theta = compute_batch_gradient(data_t, labels, init_theta);
+
+ thetas = new_theta.t();
+ }
+
+ else
+ {
+ /* take each class and rename classes you will get a theta per class
+ as in multi class class scenario, we will have n thetas for n classes */
+ ii = 0;
+
+ for(map<int,int>::iterator it = this->forward_mapper.begin(); it != this->forward_mapper.end(); ++it)
+ {
+ new_local_labels = (labels_l == it->second)/255;
+ // cout<<"processing class "<<it->second<<endl;
+ // data_t.convertTo(data, CV_32F);
+ new_local_labels.convertTo(labels, CV_32F);
+ // cout<<"initial theta: "<<init_theta<<endl;
+
+ cv::Mat new_theta = compute_batch_gradient(data_t, labels, init_theta);
+
+ // cout<<"learnt theta: "<<new_theta<<endl;
+ hconcat(new_theta.t(), thetas.row(ii));
+ ii += 1;
+ }
+
+ }
+
+ this->learnt_thetas = thetas.clone();
+ if( cvIsNaN( (double)cv::sum(this->learnt_thetas)[0] ) )
+ {
+ cv::error(Error::StsBadArg, "train: check training parameters. Invalid training classifier","cv::ml::CvLR::train", __FILE__, __LINE__);
+ }
+
+ ok = true;
+
+ return ok;
+}
+
+float CvLR::predict(const Mat& _data)
+{
+ cv::Mat pred_labs;
+ pred_labs = cv::Mat::zeros(1,1, _data.type());
+
+ if(_data.rows >1)
+ {
+ cv::error(Error::StsBadArg, "predict: _data should have only 1 row", "cv::ml::CvLR::predict", __FILE__, __LINE__);
+ }
+
+
+ predict(_data, pred_labs);
+
+ return static_cast<float>(pred_labs.at<int>(0,0));
+}
+
+float CvLR::predict(const cv::Mat& _data, cv::Mat& _pred_labs)
+{
+ /* returns a class of the predicted class
+ class names can be 1,2,3,4, .... etc */
+ cv::Mat thetas;
+
+ // check if learnt_mats array is populated
+ if(this->learnt_thetas.total()<=0)
+ {
+ cv::error(Error::StsBadArg, "predict: classifier should be trained first", "cv::ml::CvLR::predict", __FILE__, __LINE__);
+ }
+ if(_data.type() != CV_32F)
+ {
+ cv::error(Error::StsBadArg, "predict: _data must be of floating type","cv::ml::CvLR::predict",__FILE__, __LINE__);
+ }
+
+ // add a column of ones
+ cv::Mat data_t = cv::Mat::zeros(_data.rows, _data.cols+1, CV_32F);
+ for (int i=0;i<data_t.cols;i++)
+ {
+ if(i==0)
+ {
+ vconcat(cv::Mat(_data.rows, 1, _data.type(), Scalar::all(1.0)), data_t.col(i));
+ continue;
+ }
+ vconcat(_data.col(i-1), data_t.col(i));
+ }
+
+ this->learnt_thetas.convertTo(thetas, CV_32F);
+
+ CV_Assert(thetas.rows > 0);
+
+ double min_val;
+ double max_val;
+
+ Point min_loc;
+ Point max_loc;
+
+ cv::Mat labels;
+ cv::Mat labels_c;
+ cv::Mat temp_pred;
+
+ cv::Mat pred_m = cv::Mat::zeros(data_t.rows, thetas.rows, _data.type());
+
+ if(thetas.rows == 1)
+ {
+ temp_pred = calc_sigmoid(data_t*thetas.t());
+ CV_Assert(temp_pred.cols==1);
+ // if greater than 0.5, predict class 0 or predict class 1
+ temp_pred = (temp_pred>0.5)/255;
+ temp_pred.convertTo(labels_c, CV_32S);
+ }
+
+ else
+ {
+ for(int i = 0;i<thetas.rows;i++)
+ {
+ // temp_pred = CvLR::calc_sigmoid(data_t * thetas.row(i).t());
+ temp_pred = calc_sigmoid(data_t * thetas.row(i).t());
+ cv::vconcat(temp_pred, pred_m.col(i));
+ }
+
+ for(int i = 0;i<pred_m.rows;i++)
+ {
+ temp_pred = pred_m.row(i);
+ minMaxLoc( temp_pred, &min_val, &max_val, &min_loc, &max_loc, Mat() );
+ labels.push_back(max_loc.x);
+ }
+
+ labels.convertTo(labels_c, CV_32S);
+ }
+
+ _pred_labs = remap_labels(labels_c, this->reverse_mapper);
+
+ // convert _pred_labs to integer type
+
+ _pred_labs.convertTo(_pred_labs, CV_32S);
+
+ return 0.0;
+}
+
+cv::Mat CvLR::calc_sigmoid(const Mat& data)
+{
+ cv::Mat dest;
+ cv::exp(-data, dest);
+ return 1.0/(1.0+dest);
+}
+
+double CvLR::compute_cost(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta)
+{
+
+ int llambda = 0;
+ int m;
+ int n;
+
+ double cost = 0;
+ double rparameter = 0;
+
+ cv::Mat gradient;
+ cv::Mat theta_b;
+ cv::Mat theta_c;
+
+ m = _data.rows;
+ n = _data.cols;
+
+ gradient = cv::Mat::zeros( _init_theta.rows, _init_theta.cols, _init_theta.type());
+
+ theta_b = _init_theta(Range(1, n), Range::all());
+
+ cv::multiply(theta_b, theta_b, theta_c, 1);
+
+ if(this->params.regularized > 0)
+ {
+ llambda = 1;
+ }
+
+ if(this->params.norm == CvLR::REG_L1)
+ {
+ rparameter = (llambda/(2*m)) * cv::sum(theta_b)[0];
+ }
+ else
+ {
+ // assuming it to be L2 by default
+ rparameter = (llambda/(2*m)) * cv::sum(theta_c)[0];
+ }
+
+
+ // cv::Mat d_a = LogisticRegression::CvLR::calc_sigmoid(_data* _init_theta);
+ cv::Mat d_a = calc_sigmoid(_data* _init_theta);
+
+
+ cv::log(d_a, d_a);
+ cv::multiply(d_a, _labels, d_a);
+
+ // cv::Mat d_b = 1 - LogisticRegression::CvLR::calc_sigmoid(_data * _init_theta);
+ cv::Mat d_b = 1 - calc_sigmoid(_data * _init_theta);
+ cv::log(d_b, d_b);
+ cv::multiply(d_b, 1-_labels, d_b);
+
+ cost = (-1.0/m) * (cv::sum(d_a)[0] + cv::sum(d_b)[0]);
+ cost = cost + rparameter;
+
+ return cost;
+}
+
+cv::Mat CvLR::compute_batch_gradient(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta)
+{
+ // implements batch gradient descent
+
+ if(this->params.alpha<=0)
+ {
+ cv::error(Error::StsBadArg, "compute_batch_gradient: check training parameters for the classifier","cv::ml::CvLR::compute_batch_gradient", __FILE__, __LINE__);
+ }
+
+ if(this->params.num_iters <= 0)
+ {
+ cv::error(Error::StsBadArg,"compute_batch_gradient: number of iterations cannot be zero or a negative number","cv::ml::CvLR::compute_batch_gradient",__FILE__,__LINE__);
+ }
+
+ int llambda = 0;
+ ///////////////////////////////////////////////////
+ double ccost;
+ ///////////////////////////////////////////////////
+ int m, n;
+
+ cv::Mat pcal_a;
+ cv::Mat pcal_b;
+ cv::Mat pcal_ab;
+ cv::Mat gradient;
+ cv::Mat theta_p = _init_theta.clone();
+
+ // cout<<"_data size "<<_data.rows<<", "<<_data.cols<<endl;
+ // cout<<"_init_theta size "<<_init_theta.rows<<", "<<_init_theta.cols<<endl;
+
+ m = _data.rows;
+ n = _data.cols;
+
+ if(this->params.regularized > 0)
+ {
+ llambda = 1;
+ }
+
+ for(int i = 0;i<this->params.num_iters;i++)
+ {
+ ccost = compute_cost(_data, _labels, theta_p);
+
+ if( cvIsNaN( ccost ) )
+ {
+ cv::error(Error::StsBadArg, "compute_batch_gradient: check training parameters. Invalid training classifier","cv::ml::CvLR::compute_batch_gradient", __FILE__, __LINE__);
+ }
+
+ ///////////////////////////////////////////////////
+ // cout<<"calculated cost: "<<ccost<<endl;
+ // if(this->params.debug == 1 && i%(this->params.num_iters/2)==0) //
+ // {
+ // cout<<"iter: "<<i<<endl;
+ // cout<<"cost: "<<ccost<<endl;
+ // cout<<"alpha"<<this->params.alpha<<endl;
+ // cout<<"num_iters"<<this->params.num_iters<<endl;
+ // cout<<"norm"<<this->params.norm<<endl;
+ // cout<<"debug"<<this->params.debug<<endl;
+ // cout<<"regularized"<<this->params.regularized<<endl;
+ // cout<<"train_method"<<this->params.train_method<<endl;
+ // }
+ ///////////////////////////////////////////////////
+
+ pcal_b = calc_sigmoid((_data*theta_p) - _labels);
+
+ pcal_a = (static_cast<double>(1/m)) * _data.t();
+
+ gradient = pcal_a * pcal_b;
+
+ pcal_a = calc_sigmoid(_data*theta_p) - _labels;
+
+ pcal_b = _data(Range::all(), Range(0,1));
+
+ cv::multiply(pcal_a, pcal_b, pcal_ab, 1);
+
+ gradient.row(0) = ((float)1/m) * sum(pcal_ab)[0];
+
+
+ pcal_b = _data(Range::all(), Range(1,n));
+
+ //cout<<"for each training data entry"<<endl;
+ for(int ii = 1;ii<gradient.rows;ii++)
+ {
+ pcal_b = _data(Range::all(), Range(ii,ii+1));
+
+ cv::multiply(pcal_a, pcal_b, pcal_ab, 1);
+
+ gradient.row(ii) = (1.0/m)*cv::sum(pcal_ab)[0] + (llambda/m) * theta_p.row(ii);
+ }
+
+ theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
+ //cout<<"updated theta_p"<<endl;
+ }
+
+ return theta_p;
+}
+
+
+cv::Mat CvLR::compute_mini_batch_gradient(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta)
+{
+ // implements batch gradient descent
+
+ int lambda_l = 0;
+ double ccost;
+
+ int m, n;
+
+ int j = 0;
+ int size_b = this->params.minibatchsize;
+
+ // if(this->minibatchsize == 0)
+ // {
+ // cv::error(Error::StsDivByZero, "compute_mini_batch_gradient: set CvLR::MINI_BATCH value to a non-zero number (and less than number of samples in a given class) ", "cv::ml::CvLR::compute_mini_batch_gradient", __FILE__, __LINE__);
+ // }
+
+ if(this->params.minibatchsize <= 0 || this->params.alpha == 0)
+ {
+ cv::error(Error::StsBadArg, "compute_mini_batch_gradient: check training parameters for the classifier","cv::ml::CvLR::compute_mini_batch_gradient", __FILE__, __LINE__);
+ }
+
+ if(this->params.num_iters <= 0)
+ {
+ cv::error(Error::StsBadArg,"compute_mini_batch_gradient: number of iterations cannot be zero or a negative number","cv::ml::CvLR::compute_mini_batch_gradient",__FILE__,__LINE__);
+ }
+
+ cv::Mat pcal_a;
+ cv::Mat pcal_b;
+ cv::Mat pcal_ab;
+ cv::Mat gradient;
+ cv::Mat theta_p = _init_theta.clone();
+ cv::Mat data_d;
+ cv::Mat labels_l;
+
+ if(this->params.regularized > 0)
+ {
+ lambda_l = 1;
+ }
+
+ for(int i = 0;this->params.term_crit.max_iter;i++)
+ {
+ if(j+size_b<=_data.rows)
+ {
+ data_d = _data(Range(j,j+size_b), Range::all());
+ labels_l = _labels(Range(j,j+size_b),Range::all());
+ }
+ else
+ {
+ data_d = _data(Range(j, _data.rows), Range::all());
+ labels_l = _labels(Range(j, _labels.rows),Range::all());
+ }
+
+ m = data_d.rows;
+ n = data_d.cols;
+
+ ccost = compute_cost(data_d, labels_l, theta_p);
+
+
+ if( cvIsNaN( ccost ) == 1)
+ {
+ cv::error(Error::StsBadArg, "compute_mini_batch_gradient: check training parameters. Invalid training classifier","cv::ml::CvLR::compute_mini_batch_gradient", __FILE__, __LINE__);
+ }
+
+ ///////////////////////////////////////////////////
+ // if(this->params.debug == 1 && i%(this->params.term_crit.max_iter/2)==0)
+ // {
+ // cout<<"iter: "<<i<<endl;
+ // cout<<"cost: "<<ccost<<endl;
+ // cout<<"alpha"<<this->params.alpha<<endl;
+ // cout<<"num_iters"<<this->params.num_iters<<endl;
+ // cout<<"norm"<<this->params.norm<<endl;
+ // cout<<"debug"<<this->params.debug<<endl;
+ // cout<<"regularized"<<this->params.regularized<<endl;
+ // cout<<"train_method"<<this->params.train_method<<endl;
+ // cout<< "minibatchsize"<<this->params.minibatchsize<<endl;
+ // }
+ ///////////////////////////////////////////////////
+
+ pcal_b = calc_sigmoid((data_d*theta_p) - labels_l);
+
+ pcal_a = (static_cast<double>(1/m)) * data_d.t();
+
+ gradient = pcal_a * pcal_b;
+
+ pcal_a = calc_sigmoid(data_d*theta_p) - labels_l;
+
+ pcal_b = data_d(Range::all(), Range(0,1));
+
+ cv::multiply(pcal_a, pcal_b, pcal_ab, 1);
+
+ gradient.row(0) = ((float)1/m) * sum(pcal_ab)[0];
+
+ pcal_b = data_d(Range::all(), Range(1,n));
+
+ for(int k = 1;k<gradient.rows;k++)
+ {
+ pcal_b = data_d(Range::all(), Range(k,k+1));
+
+ cv::multiply(pcal_a, pcal_b, pcal_ab, 1);
+
+ gradient.row(k) = (1.0/m)*cv::sum(pcal_ab)[0] + (lambda_l/m) * theta_p.row(k);
+ }
+
+
+ theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
+
+ j+=this->params.minibatchsize;
+
+ if(j+size_b>_data.rows)
+ {
+ // if parsed through all data variables
+ break;
+ }
+ }
+
+ return theta_p;
+}
+
+
+std::map<int, int> CvLR::get_label_map(const cv::Mat& _labels_i)
+{
+ // this function creates two maps to map user defined labels to program friendsly labels
+ // two ways.
+
+ cv::Mat labels;
+ int ii = 0;
+
+ _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;
+ 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 this->forward_mapper;
+}
+
+bool CvLR::set_label_map(const cv::Mat& _labels_i)
+{
+ // this function creates two maps to map user defined labels to program friendsly labels
+ // two ways.
+
+ int ii = 0;
+ cv::Mat labels;
+ bool ok = false;
+
+ this->labels_o = cv::Mat(0,1, CV_8U);
+ this->labels_n = cv::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;
+ }
+ ok = true;
+
+ return ok;
+}
+
+cv::Mat CvLR::remap_labels(const Mat& _labels_i, std::map<int, int> lmap)
+{
+ cv::Mat labels;
+ _labels_i.convertTo(labels, CV_32S);
+
+ cv::Mat new_labels = cv::Mat::zeros(labels.rows, labels.cols, labels.type());
+
+ CV_Assert( lmap.size() > 0 );
+
+ for(int i =0;i<labels.rows;i++)
+ {
+ new_labels.at<int>(i,0) = lmap[labels.at<int>(i,0)];
+ }
+
+ return new_labels;
+}
+
+
+bool CvLR::set_default_params()
+{
+ // set default parameters for the Logisitic Regression classifier
+ this->params.alpha = 1.0;
+ this->params.term_crit.max_iter = 10000;
+ this->params.norm = CvLR::REG_L2;
+ ///////////////////////////////////////////////////
+ // this->params.debug = 1;
+ ///////////////////////////////////////////////////
+ this->params.regularized = 1;
+ this->params.train_method = CvLR::MINI_BATCH;
+ this->params.minibatchsize = 10;
+
+ return true;
+}
+
+void CvLR::clear()
+{
+ this->learnt_thetas.release();
+ this->labels_o.release();
+ this->labels_n.release();
+}
+
+void CvLR::read( CvFileStorage* fs, CvFileNode* node )
+{
+ CvMat *newData;
+ CvMat *o_labels;
+ CvMat *n_labels;
+
+
+ this->params.alpha = cvReadRealByName(fs, node,"alpha", 1.0);
+ this->params.num_iters = cvReadIntByName(fs, node,"iterations", 1000);
+ this->params.norm = cvReadIntByName(fs, node,"norm", 1);
+ // this->params.debug = cvReadIntByName(fs, node,"debug", 1);
+ this->params.regularized = cvReadIntByName(fs, node,"regularized", 1);
+ this->params.train_method = cvReadIntByName(fs, node,"train_method", 0);
+
+ if(this->params.train_method == CvLR::MINI_BATCH)
+ {
+ this->params.minibatchsize = cvReadIntByName(fs, node,"mini_batch_size", 1);
+ }
+
+ newData = (CvMat*)cvReadByName( fs, node, "learnt_thetas" );
+ o_labels = (CvMat*)cvReadByName( fs, node, "o_labels" );
+ n_labels = (CvMat*)cvReadByName( fs, node, "n_labels" );
+
+ this->learnt_thetas = cv::Mat(newData->rows, newData->cols, CV_32F, newData->data.db);
+ this->labels_o = cv::Mat(o_labels->rows, o_labels->cols, CV_32S, o_labels->data.ptr);
+ this->labels_n = cv::Mat(n_labels->rows, n_labels->cols, CV_32S, n_labels->data.ptr);
+
+ 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);
+ }
+
+}
+
+void CvLR::write( CvFileStorage* fs, const char* name ) const
+{
+ string desc = "Logisitic Regression Classifier";
+
+ cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_LR );
+
+ cvWriteString( fs, "classifier", desc.c_str());
+ cvWriteReal(fs,"alpha",this->params.alpha);
+ cvWriteInt(fs,"iterations",this->params.num_iters);
+ cvWriteInt(fs,"norm",this->params.norm);
+ // cvWriteInt(fs,"debug",this->params.debug);
+ cvWriteInt(fs,"regularized",this->params.regularized);
+ cvWriteInt(fs,"train_method",this->params.train_method);
+
+ if(this->params.train_method == CvLR::MINI_BATCH)
+ {
+ cvWriteInt(fs,"mini_batch_size",this->params.minibatchsize);
+ }
+
+ CvMat mat_learnt_thetas = this->learnt_thetas;
+ CvMat o_labels = this->labels_o;
+ CvMat n_labels = this->labels_n;
+
+ cvWrite(fs, "learnt_thetas", &mat_learnt_thetas );
+ cvWrite(fs, "n_labels", &n_labels);
+ cvWrite(fs, "o_labels", &o_labels);
+
+ cvEndWriteStruct(fs);
+}
+
+
+cv::Mat CvLR::get_learnt_mat()
+{
+ return this->learnt_thetas;
+}
+
+/* End of file. */
projects / profile / ivi / opencv.git / commitdiff