def pds(self, init_width, max_width, n_folds_cal, n_max_components_cal):
y_m_cal = deal_4_1(self.y_m_cal, self.i)
y_s_test = deal_4_1(self.y_s_test, self.i)
y_s_cal = deal_4_1(self.y_s_cal, self.i)
X_m_std, y_m_std, X_s_std, y_s_std = getStd(self.X_m_cal, self.X_s_cal,
y_m_cal, y_s_cal, self.m)
pds = PDS(self.X_m_cal, self.X_s_cal, y_m_cal, X_m_std, X_s_std,
self.X_s_test, y_s_test, init_width, max_width)
# print n_folds_cal , self.max_components , "n_fold_cal_max_components"
Trans_matrix_list = pds.transform3(n_folds_cal, n_max_components_cal)
best_width, best_trans_matrix, coefs_B_cal, err_list, RMSECV_list, comp_best = pds.cv_window(
Trans_matrix_list, self.max_folds, self.max_components)
print best_width, "best_width"
y_predict, RMSEP = pds.predict(best_trans_matrix, coefs_B_cal,
self.X_s_test, y_s_test)
ycal_predict, RMSEC = pds.predict(best_trans_matrix, coefs_B_cal,
self.X_s_cal, y_s_cal)
print RMSEC, RMSEP, "RMSECP"
# best_width, best_trans_matrix, coefs_B_cal, err_list, RMSECV_cal, comp_best_cal = pds.cv_window(Trans_matrix_list, n_folds_cal, max_components_cal)
return RMSEC, best_width, RMSEP, y_predict, RMSECV_list, comp_best, ycal_predict
def pds(init_width,max_width ,n_folds_cal , n_max_components_cal):
RMSEP_list = []
for i in range(5 , 31 , 1):
print i
dataImport = Dataset_Import(type = 0, std_num = 5 , bool = 1)
X_master , X_slave , y = dataImport.dataset_return()
num = int(X_master.shape[0]*0.8)
X_m_cal, y_m_cal, X_s_cal, y_s_cal, X_s_test, y_s_test, X_m_test, y_m_test , X_m_std , y_m_std , X_s_std , y_s_std = Dataset_KS_split_std(X_master , X_slave , y , num, std_num = i )
max_folds = 10
max_components = 15
pds=PDS(X_m_cal,X_s_cal,y_m_cal,X_m_std,X_s_std,X_s_test,y_s_test,init_width,max_width)
# print n_folds_cal , self.max_components , "n_fold_cal_max_components"
Trans_matrix_list=pds.transform3(n_folds_cal,n_max_components_cal)
best_width,best_trans_matrix,coefs_B_cal,err_list, RMSECV_list,comp_best=pds.cv_window(Trans_matrix_list,max_folds , max_components)
print best_width , "best_width"
y_predict,RMSEP=pds.predict(best_trans_matrix, coefs_B_cal,X_s_test,y_s_test)
ycal_predict,RMSEC=pds.predict(best_trans_matrix, coefs_B_cal,X_s_cal,y_s_cal)
RMSEP_list.append(RMSEP)
print RMSEP , "RMSECP"
# best_width, best_trans_matrix, coefs_B_cal, err_list, RMSECV_cal, comp_best_cal = pds.cv_window(Trans_matrix_list, n_folds_cal, max_components_cal)
print RMSEP_list