Test_LSTM_Model

"""# Test Stock Market"""

testcolumns = ['Open', 'High', 'Low', 'Close', 'Volume']
testdata = pd.read_csv('test_stock_prices.csv', usecols=testcolumns)
testdata.shape

log_testdata = np.log(testdata)   # log() == loge()
plt.plot(log_testdata)
plt.show()

print(log_testdata.columns)

log_testdata = log_testdata.replace([np.inf, -np.inf], np.nan)
log_testdata = log_testdata.fillna(log_data.mean())

MinMax_Scaler = MinMaxScaler()
MinMax_feature_transform = MinMax_Scaler.fit_transform(log_testdata, range(0, 1))
#MinMax_feature_transform = pd.DataFrame(MinMax_feature_transform, columns=log_testdata.columns, index=log_testdata.index)

test_data = MinMax_feature_transform.reshape(log_testdata.shape[0], 1, log_testdata.shape[1])
print(test_data.shape)

test_lstm_prediction = model.predict(test_data)
print(len(test_lstm_prediction))

print(test_lstm_prediction)

test_lstm_prediction = np.exp(test_lstm_prediction)
print(test_lstm_prediction)

x = 0
for i in test_lstm_prediction:
  print(pd.to_datetime(i, unit='s'))
  x=x+1
print(x)