# --------------------------------------------- newtrain=pca.fit_transform(train)
# ------------------------------------------- print pca.explained_variance_ratio_
# ----------------------------------- print np.sum(pca.explained_variance_ratio_)
# ------------------------------------------------------ print np.shape(newtrain)
# ------------------------------------------------------------------------------
# ------------------------------------------------------------------------------
# -------------------------------------------------------------- train = newtrain
##############################################Training with rbf############################################################################
ff = mysvc.training_regress()
best = ff.svmrbf(train, trainlabel, -4, 4, -4, 4, 10)
#
# # Test with SVM
svtt = mysvc.test()
output = svtt.test_regression(test, testlabel, best)
###############################################Train the model_10CV ##################################################
# -------------------------------------------------- ff = mysvc.training_manCV()#
# ------------------------------------------------------------------------------
# df = ff.trainSVC(train, trainlabel, 'poly', Cmin=-10, Cmax=10, numC=21, rmin=-10, rmax=10, numr=21, degree = 4)
# ------------------------------------------------------------------------------
# df.to_csv('/home/peng/git/Machine_learning_for_reliability_analysis/Test_1/Results/poly_pca6_cm_10CV_d4_n10_p10_21.csv', header = True)
################################################ ####
