vnl_matrix <double> MCLR_SM::Normalize_Feature_Matrix(vnl_matrix<double> feats)
{
mbl_stats_nd stats;
for(int i = 0; i<feats.rows() ; ++i)
{
vnl_vector<double> temp_row = feats.get_row(i);
stats.obs(temp_row);
}
std_vec = stats.sd();
mean_vec = stats.mean();
//The last column is the training column
for(int i = 0; i<feats.columns() ; ++i)
{
vnl_vector<double> temp_col = feats.get_column(i);
if(std_vec(i) > 0)
{
for(int j =0; j<temp_col.size() ; ++j)
temp_col[j] = (temp_col[j] - mean_vec(i))/std_vec(i) ;
}
feats.set_column(i,temp_col);
}
return feats;
}
/// \brief Unscented transform of process Sigma points
void UnscentedKalmanFilter::utf(vnl_matrix<double> X, vnl_vector<double> u,
vnl_vector<double> &y, vnl_matrix<double> &Y, vnl_matrix<double> &P, vnl_matrix<double> &Y1)
{
// determine number of sigma points
unsigned int L = X.cols();
// zero output matrices
y.fill(0.0); Y.fill(0.0);
// transform the sigma points and put them as columns in a matrix Y
for( int k = 0; k < L; k++ )
{
vnl_vector<double> xk = X.get_column(k);
vnl_vector<double> yk(N);
f(xk,u,yk);
Y.set_column(k,yk);
// add each transformed point to the weighted mean
y = y + Wm.get(0,k)*yk;
}
// create a matrix with each column being the weighted mean
vnl_matrix<double> Ymean(N,L);
for( int k = 0; k < L; k++ )
Ymean.set_column(k,y);
// set the matrix of difference vectors
Y1 = Y-Ymean;
// calculate the covariance matrix output
vnl_matrix<double> WC(L,L,0.0);
WC.set_diagonal(Wc.get_row(0));
P = Y1*WC*Y1.transpose();
}
vnl_matrix <double> Normalize_Feature_Matrix(vnl_matrix<double> feats)
{
mbl_stats_nd stats;
for(int i = 0; i<feats.rows() ; ++i)
{
vnl_vector<double> temp_row = feats.get_row(i);
stats.obs(temp_row);
}
vnl_vector<double> std_vec = stats.sd();
vnl_vector<double> mean_vec = stats.mean();
for(int i = 0; i<feats.columns()-3 ; ++i)
{
vnl_vector<double> temp_col = feats.get_column(i);
if(std_vec(i) > 0)
{
for(int j =0; j<temp_col.size() ; ++j)
temp_col[j] = (temp_col[j] - mean_vec(i))/std_vec(i) ;
}
feats.set_column(i,temp_col);
}
return feats;
}
void normalize(vnl_matrix<double>& x,
vnl_vector<double>& centroid, double& scale) {
int n = x.rows();
if (n==0) return;
int d = x.cols();
centroid.set_size(d);
vnl_vector<double> col;
for (int i = 0; i < d; ++i) {
col = x.get_column(i);
centroid(i) = col.mean();
}
for (int i = 0; i < n; ++i) {
x.set_row(i, x.get_row(i) - centroid);
}
scale = x.frobenius_norm() / sqrt(double(n));
x = x / scale;
}
vnl_matrix <double> MCLR_SM::Normalize_Feature_Matrix_1(vnl_matrix<double> feats, vnl_vector<double> vector_1, vnl_vector<double> vector_2)
{
std_vec = vector_1;
mean_vec = vector_2;
//The last column is the training column
for(int i = 0; i<feats.columns() ; ++i)
{
vnl_vector<double> temp_col = feats.get_column(i);
if(std_vec(i) > 0)
{
for(int j =0; j<temp_col.size() ; ++j)
temp_col[j] = (temp_col[j] - mean_vec(i))/std_vec(i) ;
}
feats.set_column(i,temp_col);
}
return feats;
}
