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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
+import matplotlib.pyplot as plt
+
+
+warnings.filterwarnings("ignore")
+
+def plot_results_multiple(predicted_data, true_data, prediction_len):
+    fig = plt.figure(facecolor='white')
+    ax = fig.add_subplot(111)
+    ax.plot(true_data, label='True Data')
+    print 'yo'
+    #Pad the list of predictions to shift it in the graph to it's correct start
+    for i, data in enumerate(predicted_data):
+        padding = [None for p in xrange(i * prediction_len)]
+        plt.plot(padding + data, label='Prediction')
+        plt.legend()
+    plt.show()
+
+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[:int(row), :]
+    np.random.shuffle(train)
+    x_train = train[:, :-1]
+    y_train = train[:, -1]
+    x_test = result[int(row):, :-1]
+    y_test = result[int(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