import numpy

import matplotlib.pyplot as plt

import pandas

import math

from keras.models import Sequential

from keras.layers import Dense, LSTM, Dropout

from sklearn.preprocessing import MinMaxScaler

from sklearn.metrics import mean_squared_error

import pandas as pd

import scipy.io as sio

from os import listdir

from os.path import isfile, join

import numpy as np

import keras

from sklearn.metrics import accuracy_score

number_of_classes = 4

def change(x): #Для получения чисел от 0 до 3

    answer = np.zeros((np.shape(x)[0]))

    for i in range(np.shape(x)[0]):

        max_value = max(x[i, :])

        max_index = list(x[i, :]).index(max_value)

        answer[i] = max_index

    return answer.astype(np.int)

mypath = 'training2017/'

onlyfiles = [f for f in listdir(mypath) if (isfile(join(mypath, f)) and f[0] == 'A')]

bats = [f for f in onlyfiles if f[7] == 'm']

mats = [f for f in bats if (np.shape(sio.loadmat(mypath + f)['val'])[1] >= 9000)]

check = np.shape(sio.loadmat(mypath + mats[0])['val'])[1]

X = np.zeros((len(mats), check))

for i in range(len(mats)):

    X[i, :] = sio.loadmat(mypath + mats[i])['val'][0, :9000]

target_train = np.zeros((len(mats), 1))

Train_data = pd.read_csv(mypath + 'REFERENCE.csv', sep=',', header=None, names=None)

for i in range(len(mats)):

    if Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'N':

        target_train[i] = 0

    elif Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'A':

        target_train[i] = 1

    elif Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'O':

        target_train[i] = 2

    else:

        target_train[i] = 3

Label_set = np.zeros((len(mats), number_of_classes))

for i in range(np.shape(target_train)[0]):

    dummy = np.zeros((number_of_classes))

    dummy[int(target_train[i])] = 1

    Label_set[i, :] = dummy

# scaler = MinMaxScaler(feature_range=(0, 1))

# X = scaler.fit_transform(X)

train_len = 0.9

X_train = X[:int(train_len*len(mats)), :]

Y_train = Label_set[:int(train_len*len(mats)), :]

X_val = X[int(train_len*len(mats)):, :]

Y_val = Label_set[int(train_len*len(mats)):, :]

# reshape input to be [samples, time steps, features]

X_train = numpy.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))

X_val = numpy.reshape(X_val, (X_val.shape[0], 1, X_val.shape[1]))

# create and fit the LSTM network

batch_size = 64

model = Sequential()

model.add(LSTM(512, return_sequences=True, input_shape=(1, check)))

#model.add(Dropout(0.25))

model.add(LSTM(256, return_sequences=True))

#model.add(Dropout(0.25))

model.add(LSTM(128, return_sequences=True))

#model.add(Dropout(0.25))

model.add(LSTM(64, return_sequences=True))

#model.add(Dropout(0.25))

model.add(LSTM(32))

model.add(Dense(number_of_classes, activation='softmax'))

early_stopping = keras.callbacks.EarlyStopping(monitor='val_acc', min_delta=0, patience=50, verbose=1, mode='auto')

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

model.fit(X_train, Y_train, epochs=250, batch_size=batch_size, validation_data=(X_val, Y_val), verbose=2, shuffle=False, callbacks=[early_stopping])

# model.save('Keras_models/my_model_' + str(i) + '_' + str(j) + '_' + str() + '.h5')

predictions = model.predict(X_val)

score = accuracy_score(change(Y_val), change(predictions))

print(score)

# Data[i - starti, j - starti] = str(format(score, '.5f'))

# Output = pd.DataFrame(Data)

# name = str(batch_size) + '.csv'

# Output.to_csv(path_or_buf='Keras_models/' + name, index=None, header=None)