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Updated ml module interfaces and documentation

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This commit is contained in:
Maksim Shabunin
2015-02-11 13:24:14 +03:00
parent da383e65e2
commit 79e8f0680c
32 changed files with 1403 additions and 1528 deletions
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60
modules/ml/src/lr.cpp
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@@ -60,31 +60,41 @@ using namespace std;
namespace cv {
namespace ml {
LogisticRegression::Params::Params(double learning_rate,
int iters,
int method,
int normlization,
int reg,
int batch_size)
class LrParams
{
alpha = learning_rate;
num_iters = iters;
norm = normlization;
regularized = reg;
train_method = method;
mini_batch_size = batch_size;
term_crit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, num_iters, alpha);
}
public:
LrParams()
{
alpha = 0.001;
num_iters = 1000;
norm = LogisticRegression::REG_L2;
train_method = LogisticRegression::BATCH;
mini_batch_size = 1;
term_crit = TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, num_iters, alpha);
}
double alpha; //!< learning rate.
int num_iters; //!< number of iterations.
int norm;
int train_method;
int mini_batch_size;
TermCriteria term_crit;
};
class LogisticRegressionImpl : public LogisticRegression
{
public:
LogisticRegressionImpl(const Params& pms)
: params(pms)
{
}
LogisticRegressionImpl() { }
virtual ~LogisticRegressionImpl() {}
CV_IMPL_PROPERTY(double, LearningRate, params.alpha)
CV_IMPL_PROPERTY(int, Iterations, params.num_iters)
CV_IMPL_PROPERTY(int, Regularization, params.norm)
CV_IMPL_PROPERTY(int, TrainMethod, params.train_method)
CV_IMPL_PROPERTY(int, MiniBatchSize, params.mini_batch_size)
CV_IMPL_PROPERTY(TermCriteria, TermCriteria, params.term_crit)
virtual bool train( const Ptr<TrainData>& trainData, int=0 );
virtual float predict(InputArray samples, OutputArray results, int) const;
virtual void clear();
@@ -103,7 +113,7 @@ protected:
bool set_label_map(const Mat& _labels_i);
Mat remap_labels(const Mat& _labels_i, const map<int, int>& lmap) const;
protected:
Params params;
LrParams params;
Mat learnt_thetas;
map<int, int> forward_mapper;
map<int, int> reverse_mapper;
@@ -111,9 +121,9 @@ protected:
Mat labels_n;
};
Ptr<LogisticRegression> LogisticRegression::create(const Params& params)
Ptr<LogisticRegression> LogisticRegression::create()
{
return makePtr<LogisticRegressionImpl>(params);
return makePtr<LogisticRegressionImpl>();
}
bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
@@ -312,7 +322,7 @@ double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels
theta_b = _init_theta(Range(1, n), Range::all());
multiply(theta_b, theta_b, theta_c, 1);
if(this->params.regularized > 0)
if(params.norm != REG_NONE)
{
llambda = 1;
}
@@ -367,7 +377,7 @@ Mat LogisticRegressionImpl::compute_batch_gradient(const Mat& _data, const Mat&
m = _data.rows;
n = _data.cols;
if(this->params.regularized > 0)
if(params.norm != REG_NONE)
{
llambda = 1;
}
@@ -439,7 +449,7 @@ Mat LogisticRegressionImpl::compute_mini_batch_gradient(const Mat& _data, const
Mat data_d;
Mat labels_l;
if(this->params.regularized > 0)
if(params.norm != REG_NONE)
{
lambda_l = 1;
}
@@ -570,7 +580,6 @@ void LogisticRegressionImpl::write(FileStorage& fs) const
fs<<"alpha"<<this->params.alpha;
fs<<"iterations"<<this->params.num_iters;
fs<<"norm"<<this->params.norm;
fs<<"regularized"<<this->params.regularized;
fs<<"train_method"<<this->params.train_method;
if(this->params.train_method == LogisticRegression::MINI_BATCH)
{
@@ -592,7 +601,6 @@ void LogisticRegressionImpl::read(const FileNode& fn)
this->params.alpha = (double)fn["alpha"];
this->params.num_iters = (int)fn["iterations"];
this->params.norm = (int)fn["norm"];
this->params.regularized = (int)fn["regularized"];
this->params.train_method = (int)fn["train_method"];
if(this->params.train_method == LogisticRegression::MINI_BATCH)