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lstm.py
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lstm.py
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import time
import warnings
import numpy as np
from numpy import newaxis
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
warnings.filterwarnings("ignore")
def load_data(filename, seq_len, normalise_window):
f = open(filename, 'rb').read()
data = f.split('\n')
sequence_length = seq_len + 1
result = []
for index in range(len(data) - sequence_length):
result.append(data[index: index + sequence_length])
if normalise_window:
result = normalise_windows(result)
result = np.array(result)
row = round(0.9 * result.shape[0])
train = result[:row, :]
np.random.shuffle(train)
x_train = train[:, :-1]
y_train = train[:, -1]
x_test = result[row:, :-1]
y_test = result[row:, -1]
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
return [x_train, y_train, x_test, y_test]
def normalise_windows(window_data):
normalised_data = []
for window in window_data:
normalised_window = [((float(p) / float(window[0])) - 1) for p in window]
normalised_data.append(normalised_window)
return normalised_data
def build_model(layers):
model = Sequential()
model.add(LSTM(
input_dim=layers[0],
output_dim=layers[1],
return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(
layers[2],
return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(
output_dim=layers[3]))
model.add(Activation("linear"))
start = time.time()
model.compile(loss="mse", optimizer="rmsprop")
print "Compilation Time : ", time.time() - start
return model
def predict_point_by_point(model, data):
#Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time
predicted = model.predict(data)
predicted = np.reshape(predicted, (predicted.size,))
return predicted
def predict_sequence_full(model, data, window_size):
#Shift the window by 1 new prediction each time, re-run predictions on new window
curr_frame = data[0]
predicted = []
for i in xrange(len(data)):
predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0)
return predicted
def predict_sequences_multiple(model, data, window_size, prediction_len):
#Predict sequence of 50 steps before shifting prediction run forward by 50 steps
prediction_seqs = []
for i in xrange(len(data)/prediction_len):
curr_frame = data[i*prediction_len]
predicted = []
for j in xrange(prediction_len):
predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0])
curr_frame = curr_frame[1:]
curr_frame = np.insert(curr_frame, [window_size-1], predicted[-1], axis=0)
prediction_seqs.append(predicted)
return prediction_seqs