fixed compatibility issues to support python 3

.gitignore

@@ -0,0 +1,2 @@
+.ipynb_checkpoints
+__pycache__

lstm.py

@@ -14,10 +14,9 @@ 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)]
+        padding = [None for p in range(i * prediction_len)]
         plt.plot(padding + data, label='Prediction')
         plt.legend()
     plt.show()
@@ -76,7 +75,7 @@ def build_model(layers):
 
     start = time.time()
     model.compile(loss="mse", optimizer="rmsprop")
-    print "Compilation Time : ", time.time() - start
+    print("Compilation Time : ", time.time() - start)
     return model
 
 def predict_point_by_point(model, data):
@@ -89,7 +88,7 @@ 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)):
+    for i in range(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)
@@ -98,10 +97,10 @@ def predict_sequence_full(model, data, window_size):
 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):
+    for i in range(len(data) // prediction_len):
         curr_frame = data[i*prediction_len]
         predicted = []
-        for j in xrange(prediction_len):
+        for j in range(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)