print('ANN: %f'%score_ann)
print('LSTM: %f'%score_lstm)
print('NN: %f'%score_nn)
print('MLP: %f'%score_mlp)
###########Graphs####################
plt.figure(facecolor='w', edgecolor='k')
plt.xlabel('No. of Days')
plt.ylabel('USD/%s scaled'%cur)
plt.plot(y_test)
plt.plot(y_pred_svr)
plt.plot(y_pred_test_ann)
plt.plot(y_pred_test_lstm)
plt.plot(y_pred_nn)
plt.plot(y_pred_mlp)
plt.legend(['Actual Price','SVR Prediction','ANN Prediction','LSTM Prediction','NN Prediction','MLP Prediction'])
plt.grid(True)
plt.show()
if __name__ == '__main__':
df = pd.read_csv('Historical Data.csv',usecols=[0,1,2,3,4,5,6,7,8,9,10,11],parse_dates=['Date'])
num_cores = mp.cpu_count()
pool = Pool(os.cpu_count())
print("No. of Cores : %d \n"%num_cores)
mcp(df)
result = pool.map(mcp(df), df)
pool.close()
pool.join()
pool.clear()
