# Recurrent Neural Network

# Part 1 - Data Preprocessing


# Importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Importing the training set

dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
training_data = dataset_train.iloc[:,1:2].values

# Feature Scaling

from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler()
training_set_scaled = sc.fit_transform(training_data)

# Creating a data structure with 60 timesteps and 1 output
X_train = []
y_train = []
for i in range(60,len(training_set_scaled)):
    X_train.append(training_set_scaled[i-60:i,0])
    y_train.append(training_set_scaled[i,0])
X_train,y_train =  np.array(X_train),np.array(y_train)    

# Reshaping
X_train = np.reshape(X_train,(X_train.shape[0],X_train.shape[1],1))

# Part 2 - Building the RNN

# Importing the Keras libraries and packages

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout

# Initialising the RNN
regressor =  Sequential()
# Adding the first LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50,return_sequences=True,input_shape = (X_train.shape[1],1)))
regressor.add(Dropout(0.2))
# Adding a second LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50,return_sequences=True))
regressor.add(Dropout(0.2))
# Adding a third LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50,return_sequences=True))
regressor.add(Dropout(0.2))
# Adding a fourth LSTM layer and some Dropout regularisation
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
# Adding the output layer
regressor.add(Dense(units=1))
# Compiling the RNN
regressor.compile(optimizer = 'adam',loss = 'mean_squared_error')
# Fitting the RNN to the Training set
regressor.fit(X_train,y_train,epochs=100,batch_size=32)


# Part 3 - Making the predictions and visualising the results

# Getting the real stock price of 2017
datadet_test =pd.read_csv('Google_Stock_Price_Test.csv')
real_stock_price = datadet_test.iloc[:,1:2].values

# Getting the predicted stock price of 2017
dataset_total = pd.concat((dataset_train['Open'],datadet_test['Open']),axis=0)
inputs = dataset_total[len(dataset_total)-len(datadet_test)-60:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)

X_test = []
for i in range(60,80):
    X_test.append(inputs[i-60:i,0])
X_test =  np.array(X_test)   

X_test = np.reshape(X_test,(X_test.shape[0],X_test.shape[1],1))
predicted_stock_price = regressor.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)

plt.plot(real_stock_price,color='red',label = 'real')
plt.plot(predicted_stock_price,color='green',label ='prediction')
plt.xlabel('TIme')
plt.ylabel('Google Stock Price')
plt.show()
