final kfold added

cnn.py

@@ -124,4 +124,62 @@ def run_nn(input_, output_, n_experiences, params):
 	print("Average accuracy: ", total_acc)
 	print("Average area under the curve: ", total_auc)
 
+	return total_acc, total_auc, X_train_kfold, X_test_kfold, validation_Y_one_hot
+
+
+def run_kfold(X_train, X_test, y_train, y_test, params):
+	c, b, e = params
+
+
+	for i in range(N_SPLITS):
+		# change the labels from categorical to one-hot encoding
+	    y_train[i] = to_categorical(y_train[i], num_classes = 4)
+	    y_test[i] = to_categorical(y_test[i], num_classes = 4)
+
+	    #convert input to np.array
+	    X_train[i] = np.array(X_train[i])
+	    X_test[i] = np.array(X_test[i])
+
+	    #convert each element of the train and test set into a matrix of size 30x1(?)
+	    X_train[i] = X_train[i].reshape(-1, 30, 1)
+	    X_test[i] = X_test[i].reshape(-1, 30, 1)
+
+	    #convert the data from an int8 format to a float32 type
+	    X_train[i] = X_train[i].astype('float32')
+	    X_test[i] = X_test[i].astype('float32')
+
+	# defining keras model
+	model = m.model_architecture(c)
+	#compile the keras model
+	model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
+
+
+	total_acc = 0
+	total_auc = 0
+	bs, ep = m.choose_batch_epochs(b,e)
+
+
+	for i in range(N_SPLITS):
+		#train the model
+		model.fit(X_train[i], y_train[i], batch_size = bs, epochs = ep, verbose = 1, validation_data = (X_test[i], y_test[i]))
+
+		#calculate accuracy
+		_,accuracy = model.evaluate(X_test[i], y_test[i], verbose = 0)
+		total_acc += accuracy
+		print("t_set = " + str(i))
+		print('Test accuracy:', accuracy)
+
+
+		# calculate area under the curve
+		y_pred = model.predict(X_validation, batch_size = bs)
+		fpr, tpr, auc = ra.roc_auc(N_CLASSES, validation_Y_one_hot, y_pred)
+		total_auc += auc
+		print("Area under the curve:", auc)
+
+
+	total_acc = total_acc/(N_SPLITS)
+	total_auc = total_acc/(N_SPLITS)
+	print("Average accuracy: ", total_acc)
+	print("Average area under the curve: ", total_auc)
+
 	return total_acc, total_auc