//
// メンバーシップを更新
//
bool Fuzzy::updateMembership () {
// i番目の点とj番目のクラスタの中心点の距離(norm)を格納
cv::Mat matrix_norm_one_xi_minus_cj
= cv::Mat::zeros (number_clusters_, number_points_, CV_32FC1);
//
// 距離の初期化
//
for (unsigned int i = 0 ; i < number_points_; i++)
for (unsigned int j = 0; j < number_clusters_; j++)
matrix_norm_one_xi_minus_cj.at<float> (j, i) = 0.0;
for (unsigned int i = 0 ; i < number_points_; i++) {
// 各クラスタからの距離を計算
cv::Mat point = rows_.row (i);
for (unsigned int j = 0; j < number_clusters_; j++) {
cv::Mat center = centroids_.row (j);
matrix_norm_one_xi_minus_cj.at<float> (j, i)
= calc_dist (point, center, dist_type_);
}
}
float coeff;
for (unsigned int i = 0 ; i < number_points_; i++)
for (unsigned int j = 0; j < number_clusters_; j++){
coeff = 0.0;
for (unsigned int k = 0; k < number_clusters_; k++) {
// if (matrix_norm_one_xi_minus_cj.at<float> (j, i) == 0) {
// coeff += pow (0, 2.0 / (fuzziness_ - 1.0));
// } else if (matrix_norm_one_xi_minus_cj.at<float> (k, i) == 0) {
// coeff += pow (1000000.0, 2.0 / (fuzziness_ - 1.0));
// } else {
coeff +=
pow ( (matrix_norm_one_xi_minus_cj.at<float> (j, i) /
matrix_norm_one_xi_minus_cj.at<float> (k, i)) ,
2.0 / (fuzziness_ - 1.0) );
}
// if (coeff == 0) {
// new_membership_.at<float> (i, j) = 1.0;
// } else {
new_membership_.at<float> (i, j) = 1.0 / coeff;
// }
}
if (!can_stop() ){
// 終了しない場合は更新
membership_ = new_membership_.clone ();
return false;
}
return true;
}
bool Fuzzy::updateMembership(){
Matrix matrix_norm_one_xi_minus_cj(number_points_, number_clusters_);
for (unsigned int i = 0 ; i < number_points_; i++)
for (unsigned int j = 0; j < number_clusters_; j++)
matrix_norm_one_xi_minus_cj(i, j) = 0.0;
// for each point
for (unsigned int i = 0 ; i < number_points_; i++)
// for each cluster
for (unsigned int j = 0; j < number_clusters_; j++)
// for each feature
for (unsigned int f = 0; f < size_of_a_point_; f++){
// x_i-c_j x_i c_j
matrix_norm_one_xi_minus_cj(i, j) +=
pow(abs(rows_(i, f) - (*p_centroids_)(j, f)),2);
//std::cout << "matrix_norm_one_xi_minus_cj("<<i<<","<<j<<")="
// << "rows_("<<i<<", "<<f<<")=" << rows_(i, f)
// << " (*p_centroids_)("<<j<<", "<<f
// <<")=" << (*p_centroids_)(j, f)
// << std::endl;
}
/******************************* Step 8 ***************************************/
double coeff;
// for each point
for (unsigned int i = 0 ; i < number_points_; i++)
// for each cluster
for (unsigned int j = 0; j < number_clusters_; j++){
// for each cluster
coeff = 0.0;
for (unsigned int k = 0; k < number_clusters_; k++)
coeff+=
pow ( (matrix_norm_one_xi_minus_cj(i, j) /
matrix_norm_one_xi_minus_cj(i, k)) ,
2 / (m_ - 1) );
(*p_new_membership_)(j, i) = 1 / coeff;
}
/******************************* Step 9 ***************************************/
//std::cout << "New membership " << (*p_new_membership_) << std::endl;
if (!can_stop()){
(*p_membership_) = (*p_new_membership_); // switch matrices
return false;
}
return true;
}
