def main():
data = load_dataCSV()
look_back = 28
jump=4
train_data, test_data = dp.rescale_data(data)
trainX, trainY = dp.create_dataset(train_data, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX, testY = dp.create_dataset(test_data, look_back)
model = mod.getModel(look_back)
model.fit(
trainX,
trainY,
batch_size=128,
nb_epoch=300,
validation_split=0.10)
pred,perfs=mod.testModel(model,testX,testY,jump,look_back)
actual_test_data=test_data[len(test_data)-len(pred):]
print("\n Average Covarance between predicted and actual prices on only predicted days:")
print(np.mean(perfs))
print("\n Covarance between predicted and actual prices on all days:")
print(np.cov(actual_test_data,pred)[1][0])
plt.figure(3)
plt.plot(actual_test_data)
plt.figure(4)
plt.plot(pred)
mod.saveModel(model,'lstm3')
def main():
data = load_dataCSV()
look_back = 28
jump = 4
train_data, test_data = dp.rescale_data(data)
trainX, trainY = dp.create_dataset(train_data, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX, testY = dp.create_dataset(test_data, look_back)
savedModel = load_model('lstm3.h5')
pred, perfs = mod.testModel(savedModel, testX, testY, jump, look_back)
actual_test_data = test_data[len(test_data) - len(pred):]
print(
"\n Average Covarance between predicted and actual prices on only predicted days:"
)
print(np.mean(perfs))
print("\n Covarance between predicted and actual prices on all days:")
print(np.cov(actual_test_data, pred)[1][0])