/** @brief oscistep
*
* @todo: calculate 2nn output
*/
void SDController::oscistep()
{
// calculate output values
double activityO1=w11_*o1_+w12_*o2_+theta1_+input1_;
double activityO2=w21_*o1_+theta2_+input2_;
o1_= sigmoid_(activityO1);
o2_= sigmoid_(activityO2);
// if targetinput is called, inputs must be reseted to zero
input1_ = input2_ = 0;
}
double Alignment::sumSigmoid_(DBL_MATRIX& x0, DBL_MATRIX& rt_vec, double alpha)
{
if(x0.columnCount() != 1){
MSPP_LOG(logERROR) << "Alignment::sumSigmoid_(): Wrong dimensionality of input - x0 must be a column vector!" << std::endl;
}
mspp_precondition(x0.columnCount() == 1 , "Alignment::sumSigmoid_(): Wrong dimensionality of input - x0 must be a column vector!");
int rt_size = rt_vec.rowCount();
DBL_MATRIX x0mat (rt_size,x0.rowCount(),0.) ;
//fill each line in x0mat with x0
for(int k = 0; k < rt_size; k++){
for(int l = 0; l < x0.rowCount(); l++){
x0mat(k,l) = x0(l,0);
}
}
DBL_MATRIX delta = rt_vec - x0mat;
//return value. increases in loop (sum)
double value = 0;
//go through all vectors in rt_vec
for(int i = 0; i < rt_size; i++){
double prod = -1;
double mprod = -1;
int count = 0; /*WEIGHTS*/
//build products over the dimensions
for(int j = 0; j < rt_vec.columnCount(); j++){
if(rt_vec(i,j) != -1.){
count++; /*WEIGHTS*/
double dij = delta(i,j);
prod *= sigmoid_(dij,alpha);
mprod *= sigmoid_(-dij,alpha);
}
}
value += prod + mprod;
//value += double(count)/double(rt_vec.columnCount()) * (prod + mprod); /*WEIGHTS*/
}
return value;
}
DBL_MATRIX Alignment::sigmoid_(DBL_MATRIX& delta, double alpha)
{
if(delta.columnCount() != 1 ){
MSPP_LOG(logERROR) << "Alignment::sigmoid_(): Wrong dimensionality of input - delta must be a column vector!" << std::endl;
}
mspp_precondition(delta.columnCount() == 1 , "Alignment::sigmoid_(): Wrong dimensionality of input - delta must be a column vector!");
unsigned int size = delta.rowCount();
//create result vector of correct length
DBL_MATRIX sig (size,1);
//fill result vector
for(unsigned int i = 0; i < size; i++){
//calculate each entry with 1-dimensional sigmoid_
sig(i,0) = sigmoid_(delta(i,0), alpha);
}
return sig;
}
void Logit::batch_grad_descent_(Mat &_theta_, Mat &_X_, Mat &_y_, Mat &_Ones_, Mat &_grad_, const double &_alpha_, const double &_lambda_)
{
Mat _z_ = _X_ * _theta_;
Mat temp;
sigmoid_(_z_);
_grad_.row(0).col(0) = ((double) (1 / (double)(_y_.rows))) * ((double)(sum( _z_ - _y_ ).val[0]));
transpose(_X_.colRange(1, _X_.cols) , temp);
_grad_.rowRange(1, _grad_.rows) = ( (1 / (double)(_y_.rows)) * (temp * (_z_ - _y_) ) ) + ( (_lambda_ / (double)(_y_.rows)) * _theta_.rowRange(1, _theta_.rows));
/*if (j == 1)
{
cout<< _theta_.row(0).col(0) << endl;
for (int i = 1; i < _theta_.rows; i++)
{
cout<< _theta_.row(i).col(0) << endl;
}
}*/
}
void Logit::Predict_(Mat &_X_, Mat &_theta_, Mat &_pred_)
{
for (int i = 0; i < _X_.rows; i++)
{
//Mat t = _X_.row(i);
Mat _check_ = _X_.row(i) * _theta_;
sigmoid_(_check_);
float k = _check_.at<float>(0 , 0);
//cout<< k << endl;
if (k >= 0.5)
{
_pred_.row(i).col(0) = 1.0;
}
else
{
_pred_.row(i).col(0) = 0.0;
}
}
}
