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Reworked ML logistic regression implementation, initial version

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This commit is contained in:
Maksim Shabunin
2014-08-14 19:01:45 +04:00
parent 71770eb790
commit 3e26086f82
4 changed files with 214 additions and 312 deletions
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117
modules/ml/src/lr.cpp
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@@ -55,55 +55,72 @@
#include "precomp.hpp"
using namespace cv;
using namespace cv::ml;
using namespace std;
LogisticRegressionParams::LogisticRegressionParams()
namespace cv {
namespace ml {
LogisticRegression::Params::Params(double learning_rate,
int iters,
int method,
int normlization,
int reg,
int batch_size)
{
term_crit = cv::TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 1000, 0.001);
alpha = 0.001;
num_iters = 1000;
norm = LogisticRegression::REG_L2;
regularized = 1;
train_method = LogisticRegression::BATCH;
mini_batch_size = 1;
}
LogisticRegressionParams::LogisticRegressionParams( double learning_rate, int iters, int train_algo = LogisticRegression::BATCH, int normlization = LogisticRegression::REG_L2, int reg = 1, int mb_size = 5)
{
term_crit = cv::TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, iters, learning_rate);
alpha = learning_rate;
num_iters = iters;
norm = normlization;
regularized = reg;
train_method = train_algo;
mini_batch_size = mb_size;
train_method = method;
mini_batch_size = batch_size;
term_crit = cv::TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, num_iters, alpha);
}
LogisticRegression::LogisticRegression(const LogisticRegressionParams& pms)
class LogisticRegressionImpl : public LogisticRegression
{
default_model_name = "my_lr";
this->params = pms;
}
public:
LogisticRegressionImpl(const Params& pms)
: params(pms)
{
}
virtual ~LogisticRegressionImpl() {}
LogisticRegression::LogisticRegression(cv::InputArray data, cv::InputArray labels, const LogisticRegressionParams& pms)
virtual bool train( const Ptr<TrainData>& trainData, int=0 );
virtual float predict(InputArray samples, OutputArray results, int) const;
virtual void clear();
virtual void write(FileStorage& fs) const;
virtual void read(const FileNode& fn);
virtual cv::Mat get_learnt_thetas() const;
virtual int getVarCount() const { return learnt_thetas.cols; }
virtual bool isTrained() const { return !learnt_thetas.empty(); }
virtual bool isClassifier() const { return true; }
virtual String getDefaultModelName() const { return "opencv_ml_lr"; }
protected:
cv::Mat calc_sigmoid(const cv::Mat& data) const;
double compute_cost(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta);
cv::Mat compute_batch_gradient(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta);
cv::Mat compute_mini_batch_gradient(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta);
bool set_label_map(const cv::Mat& _labels_i);
cv::Mat remap_labels(const cv::Mat& _labels_i, const map<int, int>& lmap) const;
protected:
Params params;
cv::Mat learnt_thetas;
map<int, int> forward_mapper;
map<int, int> reverse_mapper;
cv::Mat labels_o;
cv::Mat labels_n;
};
Ptr<LogisticRegression> LogisticRegression::create(const Params& params)
{
default_model_name = "my_lr";
this->params = pms;
train(data, labels);
return makePtr<LogisticRegressionImpl>(params);
}
LogisticRegression::~LogisticRegression()
bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
{
clear();
}
bool LogisticRegression::train(cv::InputArray data_ip, cv::InputArray labels_ip)
{
clear();
cv::Mat _data_i = data_ip.getMat();
cv::Mat _labels_i = labels_ip.getMat();
cv::Mat _data_i = trainData->getSamples();
cv::Mat _labels_i = trainData->getResponses();
CV_Assert( !_labels_i.empty() && !_data_i.empty());
@@ -194,13 +211,12 @@ bool LogisticRegression::train(cv::InputArray data_ip, cv::InputArray labels_ip)
return ok;
}
void LogisticRegression::predict( cv::InputArray _ip_data, cv::OutputArray _output_predicted_labels ) const
float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, int) const
{
/* returns a class of the predicted class
class names can be 1,2,3,4, .... etc */
cv::Mat thetas, data, pred_labs;
data = _ip_data.getMat();
data = samples.getMat();
// check if learnt_mats array is populated
if(this->learnt_thetas.total()<=0)
@@ -266,19 +282,20 @@ void LogisticRegression::predict( cv::InputArray _ip_data, cv::OutputArray _outp
pred_labs = remap_labels(labels_c, this->reverse_mapper);
// convert pred_labs to integer type
pred_labs.convertTo(pred_labs, CV_32S);
pred_labs.copyTo(_output_predicted_labels);
pred_labs.copyTo(results);
// TODO: determine
return 0;
}
cv::Mat LogisticRegression::calc_sigmoid(const Mat& data)
cv::Mat LogisticRegressionImpl::calc_sigmoid(const cv::Mat& data) const
{
cv::Mat dest;
cv::exp(-data, dest);
return 1.0/(1.0+dest);
}
double LogisticRegression::compute_cost(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta)
double LogisticRegressionImpl::compute_cost(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta)
{
int llambda = 0;
int m;
int n;
@@ -328,7 +345,7 @@ double LogisticRegression::compute_cost(const cv::Mat& _data, const cv::Mat& _la
return cost;
}
cv::Mat LogisticRegression::compute_batch_gradient(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta)
cv::Mat LogisticRegressionImpl::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)
@@ -397,7 +414,7 @@ cv::Mat LogisticRegression::compute_batch_gradient(const cv::Mat& _data, const c
return theta_p;
}
cv::Mat LogisticRegression::compute_mini_batch_gradient(const cv::Mat& _data, const cv::Mat& _labels, const cv::Mat& _init_theta)
cv::Mat LogisticRegressionImpl::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;
@@ -488,7 +505,7 @@ cv::Mat LogisticRegression::compute_mini_batch_gradient(const cv::Mat& _data, co
return theta_p;
}
bool LogisticRegression::set_label_map(const cv::Mat& _labels_i)
bool LogisticRegressionImpl::set_label_map(const cv::Mat &_labels_i)
{
// this function creates two maps to map user defined labels to program friendly labels two ways.
int ii = 0;
@@ -522,7 +539,7 @@ bool LogisticRegression::set_label_map(const cv::Mat& _labels_i)
return ok;
}
cv::Mat LogisticRegression::remap_labels(const Mat& _labels_i, const std::map<int, int>& lmap)
cv::Mat LogisticRegressionImpl::remap_labels(const cv::Mat& _labels_i, const map<int, int>& lmap) const
{
cv::Mat labels;
_labels_i.convertTo(labels, CV_32S);
@@ -538,14 +555,14 @@ cv::Mat LogisticRegression::remap_labels(const Mat& _labels_i, const std::map<in
return new_labels;
}
void LogisticRegression::clear()
void LogisticRegressionImpl::clear()
{
this->learnt_thetas.release();
this->labels_o.release();
this->labels_n.release();
}
void LogisticRegression::write(FileStorage& fs) const
void LogisticRegressionImpl::write(FileStorage& fs) const
{
// check if open
if(fs.isOpened() == 0)
@@ -568,7 +585,7 @@ void LogisticRegression::write(FileStorage& fs) const
fs<<"o_labels"<<this->labels_o;
}
void LogisticRegression::read(const FileNode& fn )
void LogisticRegressionImpl::read(const FileNode& fn)
{
// check if empty
if(fn.empty())
@@ -598,8 +615,12 @@ void LogisticRegression::read(const FileNode& fn )
}
}
const cv::Mat LogisticRegression::get_learnt_thetas() const
cv::Mat LogisticRegressionImpl::get_learnt_thetas() const
{
return this->learnt_thetas;
}
}
}
/* End of file. */