X_test_features = np.column_stack((X_test_features_1_ltr_tent,X_test_features_1_rtl_tent,X_test_features_1_ttb_tent,X_test_features_1_btt_tent,X_test_features_0_ltr_tent,X_test_features_0_rtl_tent,X_test_features_0_btt_tent,X_test_features_0_ttb_tent))
### Ridge Model
ridge_model = RidgeClassifier().fit(X_train_features, y_train)
tent_train_accuracy_ridge[t] = ridge_model.score(X_train_features, y_train)
tent_test_accuracy_ridge[t] = ridge_model.score(X_test_features, y_test)
### SVM Model
c = 5
svm_model = SVC(kernel='rbf', C = c).fit(X_train_features, y_train)
tent_train_accuracy_svm[t] = svm_model.score(X_train_features, y_train)
tent_test_accuracy_svm[t] = svm_model.score(X_test_features, y_test)
### Adaptive Features
d = 35
X_train_features_0_ltr_gmm, X_test_features_0_ltr_gmm = adaptive_features(zero_dim_ltr_train, zero_dim_ltr_test, model="gmm", y_train=y_train, d = d)
X_train_features_0_rtl_gmm, X_test_features_0_rtl_gmm = adaptive_features(zero_dim_rtl_train, zero_dim_rtl_test, model="gmm", y_train=y_train, d = d)
X_train_features_0_ttb_gmm, X_test_features_0_ttb_gmm = adaptive_features(zero_dim_ttb_train, zero_dim_ttb_test, model="gmm", y_train=y_train, d = d)
X_train_features_0_btt_gmm, X_test_features_0_btt_gmm = adaptive_features(zero_dim_btt_train, zero_dim_btt_test, model="gmm", y_train=y_train, d = d)
X_train_features_1_ltr_gmm, X_test_features_1_ltr_gmm = adaptive_features(one_dim_ltr_train, one_dim_ltr_test, model="gmm", y_train=y_train, d = d)
X_train_features_1_rtl_gmm, X_test_features_1_rtl_gmm = adaptive_features(one_dim_rtl_train, one_dim_rtl_test, model="gmm", y_train=y_train, d = d)
X_train_features_1_ttb_gmm, X_test_features_1_ttb_gmm = adaptive_features(one_dim_ttb_train, one_dim_ttb_test, model="gmm", y_train=y_train, d = d)
X_train_features_1_btt_gmm, X_test_features_1_btt_gmm = adaptive_features(one_dim_btt_train, one_dim_btt_test, model="gmm", y_train=y_train, d = d)
X_train_features = np.column_stack((X_train_features_1_ltr_gmm,X_train_features_1_rtl_gmm,X_train_features_1_ttb_gmm,X_train_features_1_btt_gmm,X_train_features_0_ltr_gmm,X_train_features_0_rtl_gmm,X_train_features_0_btt_gmm,X_train_features_0_ttb_gmm))
X_test_features = np.column_stack((X_test_features_1_ltr_gmm,X_test_features_1_rtl_gmm,X_test_features_1_ttb_gmm,X_test_features_1_btt_gmm,X_test_features_0_ltr_gmm,X_test_features_0_rtl_gmm,X_test_features_0_btt_gmm,X_test_features_0_ttb_gmm))
ridge_model = RidgeClassifier().fit(X_train_features, y_train)
gmm_train_accuracy_ridge[t] = ridge_model.score(X_train_features, y_train)
gmm_test_accuracy_ridge[t] = ridge_model.score(X_test_features, y_test)
X_test_features_Z1_tent, X_test_features_H1_tent,
X_test_features_S1_tent, X_test_features_V1_tent))
### Ridge Model
ridge_model = RidgeClassifier().fit(X_train_features, y_train)
tent_train_accuracy_ridge[k] = ridge_model.score(X_train_features, y_train)
tent_test_accuracy_ridge[k] = ridge_model.score(X_test_features, y_test)
### SVM Model
c = 5
svm_model = SVC(kernel='rbf', C=c).fit(X_train_features, y_train)
tent_train_accuracy_svm[k] = svm_model.score(X_train_features, y_train)
tent_test_accuracy_svm[k] = svm_model.score(X_test_features, y_test)
### CDER Adaptive
X_train_features_R0_cder, X_test_features_R0_cder = adaptive_features(
R0_train_sample, R0_test_sample, "cder", y_train)
X_train_features_G0_cder, X_test_features_G0_cder = adaptive_features(
G0_train_sample, G0_test_sample, "cder", y_train)
X_train_features_B0_cder, X_test_features_B0_cder = adaptive_features(
B0_train_sample, B0_test_sample, "cder", y_train)
X_train_features_X0_cder, X_test_features_X0_cder = adaptive_features(
X0_train_sample, X0_test_sample, "cder", y_train)
X_train_features_Y0_cder, X_test_features_Y0_cder = adaptive_features(
Y0_train_sample, Y0_test_sample, "cder", y_train)
X_train_features_Z0_cder, X_test_features_Z0_cder = adaptive_features(
Z0_train_sample, Z0_test_sample, "cder", y_train)
X_train_features_H0_cder, X_test_features_H0_cder = adaptive_features(
H0_train_sample, H0_test_sample, "cder", y_train)
X_train_features_S0_cder, X_test_features_S0_cder = adaptive_features(
S0_train_sample, S0_test_sample, "cder", y_train)
X_train_features_V0_cder, X_test_features_V0_cder = adaptive_features(
X_test_features = np.column_stack(
(X_test_features_0_tent, X_test_features_1_tent))
### Ridge Model
ridge_model = RidgeClassifier().fit(X_train_features, y_train)
tent_train_accuracy_ridge[k] = ridge_model.score(X_train_features, y_train)
tent_test_accuracy_ridge[k] = ridge_model.score(X_test_features, y_test)
### SVM Model
c = 1
svm_model = SVC(kernel='rbf', C=c).fit(X_train_features, y_train)
tent_train_accuracy_svm[k] = svm_model.score(X_train_features, y_train)
tent_test_accuracy_svm[k] = svm_model.score(X_test_features, y_test)
### Adaptive Features
X_train_features_1_gmm, X_test_features_1_gmm = adaptive_features(
X_dgm1_train, X_dgm1_test, "cder", y_train)
X_train_features_0_gmm, X_test_features_0_gmm = adaptive_features(
X_dgm0_train, X_dgm0_test, "cder", y_train)
X_train_features = np.column_stack(
(X_train_features_0_gmm, X_train_features_1_gmm))
X_test_features = np.column_stack(
(X_test_features_0_gmm, X_test_features_1_gmm))
ridge_model = RidgeClassifier().fit(X_train_features, y_train)
gmm_train_accuracy_ridge[k] = ridge_model.score(X_train_features, y_train)
gmm_test_accuracy_ridge[k] = ridge_model.score(X_test_features, y_test)
c = 1
svm_model = SVC(kernel='rbf', C=c).fit(X_train_features, y_train)
gmm_train_accuracy_svm[k] = svm_model.score(X_train_features, y_train)
gmm_test_accuracy_svm[k] = svm_model.score(X_test_features, y_test)