本文整理汇总了C++中vnl_matrix::set_column方法的典型用法代码示例。如果您正苦于以下问题：C++ vnl_matrix::set_column方法的具体用法？C++ vnl_matrix::set_column怎么用？C++ vnl_matrix::set_column使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类vnl_matrix的用法示例。
在下文中一共展示了vnl_matrix::set_column方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: sigmas
/// \brief Create Sigma point to represent a normal distribution
void UnscentedKalmanFilter::sigmas(vnl_vector<double> x, vnl_matrix<double> P, double c, vnl_matrix<double> &X)
{
// Copy matrix P to A
vnl_matrix<double> A = P;
// Compute the Cholesky decomposition of P
vnl_cholesky chol(A,vnl_cholesky::verbose);
A = chol.upper_triangle();
// Apply the weight c
A = c*A.transpose();
// Create matrix Y with copies of reference point x
vnl_matrix<double> Y(N,N);
for( int i = 0; i<N; i++ )
Y.set_column(i,x);
// Add and subtract A from Y
vnl_matrix<double> YpA = Y + A;
vnl_matrix<double> YmA = Y - A;
// Set columns of X with Sigma points
X.set_column(0,x);
for( int i = 0; i<N; i++ )
{
X.set_column(i+1,YpA.get_column(i));
X.set_column(i+1+N,YmA.get_column(i));
}
}示例2:
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;
}示例3: utf
/// \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();
}示例4:
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;
}