accuracy_score(Y_test[:, 1], Y_test_mlp_class))
print('The F1 of training set: ', f1_score(Y_train[:, 1], Y_train_mlp_class))
print('The F1 of testing set:', f1_score(Y_test[:, 1], Y_test_mlp_class))
#%% Results on whole line
pred_tr_step = 1
'''prediction on gather'''
#print('CWT-CNN:')
#Y_pred_cwtc = f.pred(modelcwtc, s1,s1_labels, tw,tinc,method='cwt',
# trace_step=pred_tr_step, dt=dt, freq_index=freq_index)
print('CNN:')
Y_pred_cnn = f.pred(modelcnn,
s1,
s1_labels,
tw,
tinc,
method='cnn',
trace_step=pred_tr_step,
dt=dt)
#print('DNN:')
#Y_pred_dnn = f.pred(modeldnn, s1,s1_labels, tw,tinc, method='dnn',
# trace_step=pred_tr_step, dt=dt)
print('MLP: ')
Y_pred = f.pred(mlp,
s1,
s1_labels,
tw,
tinc,
method='mlp',
trace_step=pred_tr_step,
dt=dt)
Y_test_mlp_class = f.to_classes(Y_test_mlp[:,1], 0.5)
print('The accuracy of training set: ',
accuracy_score(Y_train[:,1], Y_train_mlp_class))
print('The accuracy of testing set:',
accuracy_score(Y_test[:,1], Y_test_mlp_class))
print('The F1 of training set: ', f1_score(Y_train[:,1],Y_train_mlp_class))
print('The F1 of testing set:', f1_score(Y_test[:,1],Y_test_mlp_class))
#%% Classification results on whole line
pred_tr_step = 1
'''prediction on gather'''
print('CWT-CNN:')
Y_pred_cwtc = f.pred(modelcwtc, s1,s1_labels, tw,tinc,method='cwt',
trace_step=pred_tr_step, dt=dt, freq_index=freq_index)
print('CNN:')
Y_pred_cnn = f.pred(modelcnn, s1,s1_labels, tw,tinc, method='cnn',
trace_step=pred_tr_step, dt=dt )
#print('DNN:')
#Y_pred_dnn = f.pred(modeldnn, s1,s1_labels, tw,tinc, method='dnn',
# trace_step=pred_tr_step, dt=dt)
print('MLP: ')
Y_pred = f.pred(mlp, s1,s1_labels, tw,tinc, method='mlp',
trace_step=pred_tr_step, dt=dt )
#%
'''plot the result on gather'''
fig, ax = f.labels_plot(Y_pred_cwtc)
ax.set_yticks(np.arange(0,int(nt/tw),2.5)-0.5)
ax.set_yticklabels( ( (np.arange(0,int(nt/tw),2.5)-0.5)*tw+tw/2 )*dt )