# Recurrent Neural Network



# Part 1 - Veri Önİşleme

# Kütüphaneleri Yukleme
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Eğitim setini yükleme
dataset_train = pd.read_csv('Google_Stock_Price_Train.csv')
training_set = dataset_train.iloc[:, 1:2].values

# Özellikleri ölçeklendirme
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)

# 60 timesteps and 1 çıktı veri yapısını oluşturma
X_train = []
y_train = []
for i in range(60, 1258):
    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)

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



# Part 2 - RNN Oluşturma

# Keras kütüphanelerini yükleme
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout

#  RNN Başlama
regressor = Sequential()

# 1. LSTM layer i ekleme ve Dropout düzenlemesi
regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
regressor.add(Dropout(0.2))

# 2. LSTM layer i ekleme ve Dropout düzenlemesi
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

# 3. LSTM layer i ekleme ve Dropout düzenlemesi
regressor.add(LSTM(units = 50, return_sequences = True))
regressor.add(Dropout(0.2))

# 4. LSTM layer i ekleme ve Dropout düzenlemesi
regressor.add(LSTM(units = 50))
regressor.add(Dropout(0.2))

# Çıktı layer i ekleme
regressor.add(Dense(units = 1))

#  RNN dereleme
regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')

# RNN i Eğitim setine fit etme
regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)



# Part 3 - Tahmin üretme ve görselleştirme

# 2017 Hisse Bedeli alma
dataset_test = pd.read_csv('Google_Stock_Price_Test.csv')
real_stock_price = dataset_test.iloc[:, 1:2].values

# Tahmin edilen 2017 Hisse Bedeli alma
dataset_total = pd.concat((dataset_train['Open'], dataset_test['Open']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_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)

# Sonuçları görselleştirme
plt.plot(real_stock_price, color = 'red', label = 'Gerçek Google Hisse Bedeli')
plt.plot(predicted_stock_price, color = 'blue', label = 'Tahmin Edilen Google Hisse Bedeli')
plt.title('Google Hisse Bedeli Tahmini')
plt.xlabel('Zaman')
plt.ylabel('Google Hisse Bedeli')
plt.legend()
plt.show()
