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- LogisticRegressionImpl::predict() was changed to return the predicted value and to only write to the OutputArray results if specified (no longer segfaults).

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- Refactored batch and mini_batch training to use a common gradient computation function (removed duplicate code).
- Altered the cost computation so that NAN is not computed unnecessarily.
- Greatly simplified (and sped up) the code that appends a column of 1s to the data.
- Minor code cleanup.

Removed unused variables.

Added cast to float to remove warning
This commit is contained in:
Abe Friesen 2015-11-02 16:16:49 -08:00
parent 979f88f6db
commit 9c6ff4d955
1 changed files with 90 additions and 114 deletions
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204
modules/ml/src/lr.cpp
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@ -108,8 +108,9 @@ public:
protected:
Mat calc_sigmoid(const Mat& data) const;
double compute_cost(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
Mat compute_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
Mat compute_mini_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
void compute_gradient(const Mat& _data, const Mat& _labels, const Mat &_theta, const double _lambda, Mat & _gradient );
Mat batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
Mat mini_batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta);
bool set_label_map(const Mat& _labels_i);
Mat remap_labels(const Mat& _labels_i, const map<int, int>& lmap) const;
protected:
@ -156,13 +157,8 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
int num_classes = (int) this->forward_mapper.size();
// add a column of ones
Mat data_t = Mat::zeros(_data_i.rows, _data_i.cols+1, CV_32F);
vconcat(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));
}
Mat data_t;
hconcat( cv::Mat::ones( _data_i.rows, 1, CV_32F ), _data_i, data_t );
if(num_classes < 2)
{
@ -188,9 +184,9 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
{
labels_l.convertTo(labels, CV_32F);
if(this->params.train_method == LogisticRegression::BATCH)
new_theta = compute_batch_gradient(data_t, labels, init_theta);
new_theta = batch_gradient_descent(data_t, labels, init_theta);
else
new_theta = compute_mini_batch_gradient(data_t, labels, init_theta);
new_theta = mini_batch_gradient_descent(data_t, labels, init_theta);
thetas = new_theta.t();
}
else
@ -204,9 +200,9 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
new_local_labels = (labels_l == it->second)/255;
new_local_labels.convertTo(labels, CV_32F);
if(this->params.train_method == LogisticRegression::BATCH)
new_theta = compute_batch_gradient(data_t, labels, init_theta);
new_theta = batch_gradient_descent(data_t, labels, init_theta);
else
new_theta = compute_mini_batch_gradient(data_t, labels, init_theta);
new_theta = mini_batch_gradient_descent(data_t, labels, init_theta);
hconcat(new_theta.t(), thetas.row(ii));
ii += 1;
}
@ -221,13 +217,15 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
return ok;
}
float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, int) const
float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, int flags) const
{
/* returns a class of the predicted class
class names can be 1,2,3,4, .... etc */
Mat thetas, data, pred_labs;
data = samples.getMat();
const bool rawout = flags & StatModel::RAW_OUTPUT;
// check if learnt_mats array is populated
if(this->learnt_thetas.total()<=0)
{
@ -239,18 +237,17 @@ float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, i
}
// add a column of ones
Mat data_t = Mat::zeros(data.rows, data.cols+1, CV_32F);
for (int i=0;i<data_t.cols;i++)
{
if(i==0)
{
vconcat(Mat(data.rows, 1, data.type(), Scalar::all(1.0)), data_t.col(i));
continue;
}
vconcat(data.col(i-1), data_t.col(i));
}
Mat data_t;
hconcat( cv::Mat::ones( data.rows, 1, CV_32F ), data, data_t );
this->learnt_thetas.convertTo(thetas, CV_32F);
if ( learnt_thetas.type() == CV_32F )
{
thetas = learnt_thetas;
}
else
{
this->learnt_thetas.convertTo( thetas, CV_32F );
}
CV_Assert(thetas.rows > 0);
@ -292,9 +289,21 @@ float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, i
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(results);
// TODO: determine
return 0;
// return either the labels or the raw output
if ( results.needed() )
{
if ( rawout )
{
pred_m.copyTo( results );
}
else
{
pred_labs.copyTo(results);
}
}
return ( pred_labs.empty() ? 0.f : (float) pred_labs.at< int >( 0 ) );
}
Mat LogisticRegressionImpl::calc_sigmoid(const Mat& data) const
@ -320,7 +329,6 @@ double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels
n = _data.cols;
theta_b = _init_theta(Range(1, n), Range::all());
multiply(theta_b, theta_b, theta_c, 1);
if (params.norm != REG_DISABLE)
{
@ -334,31 +342,63 @@ double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels
else
{
// assuming it to be L2 by default
multiply(theta_b, theta_b, theta_c, 1);
rparameter = (llambda/(2*m)) * sum(theta_c)[0];
}
d_a = calc_sigmoid(_data* _init_theta);
d_a = calc_sigmoid(_data * _init_theta);
log(d_a, d_a);
multiply(d_a, _labels, d_a);
d_b = 1 - calc_sigmoid(_data * _init_theta);
// use the fact that: log(1 - sigmoid(x)) = log(sigmoid(-x))
d_b = calc_sigmoid(- _data * _init_theta);
log(d_b, d_b);
multiply(d_b, 1-_labels, d_b);
cost = (-1.0/m) * (sum(d_a)[0] + sum(d_b)[0]);
cost = cost + rparameter;
if(cvIsNaN( cost ) == 1)
{
CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
}
return cost;
}
Mat LogisticRegressionImpl::compute_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
void LogisticRegressionImpl::compute_gradient(const Mat& _data, const Mat& _labels, const Mat &_theta, const double _lambda, Mat & _gradient )
{
const int m = _data.rows;
Mat pcal_a, pcal_b, pcal_ab;
const Mat z = _data * _theta;
CV_Assert( _gradient.rows == _theta.rows && _gradient.cols == _theta.cols );
pcal_a = calc_sigmoid(z) - _labels;
pcal_b = _data(Range::all(), Range(0,1));
multiply(pcal_a, pcal_b, pcal_ab, 1);
_gradient.row(0) = ((float)1/m) * sum(pcal_ab)[0];
//cout<<"for each training data entry"<<endl;
for(int ii = 1;ii<_gradient.rows;ii++)
{
pcal_b = _data(Range::all(), Range(ii,ii+1));
multiply(pcal_a, pcal_b, pcal_ab, 1);
_gradient.row(ii) = (1.0/m)*sum(pcal_ab)[0] + (_lambda/m) * _theta.row(ii);
}
}
Mat LogisticRegressionImpl::batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
{
// implements batch gradient descent
if(this->params.alpha<=0)
{
CV_Error( CV_StsBadArg, "check training parameters for the classifier" );
CV_Error( CV_StsBadArg, "check training parameters (learning rate) for the classifier" );
}
if(this->params.num_iters <= 0)
@ -367,15 +407,10 @@ Mat LogisticRegressionImpl::compute_batch_gradient(const Mat& _data, const Mat&
}
int llambda = 0;
double ccost;
int m, n;
Mat pcal_a;
Mat pcal_b;
Mat pcal_ab;
Mat gradient;
int m;
Mat theta_p = _init_theta.clone();
Mat gradient( theta_p.rows, theta_p.cols, theta_p.type() );
m = _data.rows;
n = _data.cols;
if (params.norm != REG_DISABLE)
{
@ -384,50 +419,21 @@ Mat LogisticRegressionImpl::compute_batch_gradient(const Mat& _data, const Mat&
for(int i = 0;i<this->params.num_iters;i++)
{
ccost = compute_cost(_data, _labels, theta_p);
// this seems to only be called to ensure that cost is not NaN
compute_cost(_data, _labels, theta_p);
if( cvIsNaN( ccost ) )
{
CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
}
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));
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));
multiply(pcal_a, pcal_b, pcal_ab, 1);
gradient.row(ii) = (1.0/m)*sum(pcal_ab)[0] + (llambda/m) * theta_p.row(ii);
}
compute_gradient( _data, _labels, theta_p, llambda, gradient );
theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
}
return theta_p;
}
Mat LogisticRegressionImpl::compute_mini_batch_gradient(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
Mat LogisticRegressionImpl::mini_batch_gradient_descent(const Mat& _data, const Mat& _labels, const Mat& _init_theta)
{
// implements batch gradient descent
int lambda_l = 0;
double ccost;
int m, n;
int m;
int j = 0;
int size_b = this->params.mini_batch_size;
@ -441,11 +447,8 @@ Mat LogisticRegressionImpl::compute_mini_batch_gradient(const Mat& _data, const
CV_Error( CV_StsBadArg, "number of iterations cannot be zero or a negative number" );
}
Mat pcal_a;
Mat pcal_b;
Mat pcal_ab;
Mat gradient;
Mat theta_p = _init_theta.clone();
Mat gradient( theta_p.rows, theta_p.cols, theta_p.type() );
Mat data_d;
Mat labels_l;
@ -468,46 +471,19 @@ Mat LogisticRegressionImpl::compute_mini_batch_gradient(const Mat& _data, const
}
m = data_d.rows;
n = data_d.cols;
ccost = compute_cost(data_d, labels_l, theta_p);
// this seems to only be called to ensure that cost is not NaN
compute_cost(data_d, labels_l, theta_p);
if( cvIsNaN( ccost ) == 1)
{
CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
}
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));
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));
multiply(pcal_a, pcal_b, pcal_ab, 1);
gradient.row(k) = (1.0/m)*sum(pcal_ab)[0] + (lambda_l/m) * theta_p.row(k);
}
compute_gradient(data_d, labels_l, theta_p, lambda_l, gradient);
theta_p = theta_p - ( static_cast<double>(this->params.alpha)/m)*gradient;
j+=this->params.mini_batch_size;
j += this->params.mini_batch_size;
if(j+size_b>_data.rows)
{
// if parsed through all data variables
break;
// if parsed through all data variables
if (j >= _data.rows) {
j = 0;
}
}
return theta_p;