fall back to the original method name and adds element_wise_product method

SnShine · SnShine · commit 4075725a829e · 2016-03-16T21:18:32.000+05:30
diff --git a/learning.py b/learning.py
@@ -447,7 +447,7 @@ def predict(example):
         for layer in learned_net[1:]:
             for node in layer:
                 inc = [n.value for n in node.inputs]
-                in_val = sum_dotproduct(inc, node.weights)
+                in_val = dotproduct(inc, node.weights)
                 node.value = node.activation(in_val)
 
         # Hypothesis
@@ -530,7 +530,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches):
             for layer in net[1:]:
                 for node in layer:
                     inc = [n.value for n in node.inputs]
-                    in_val = sum_dotproduct(inc, node.weights)
+                    in_val = dotproduct(inc, node.weights)
                     node.value = node.activation(in_val)
 
             # Initialize delta
@@ -554,7 +554,7 @@ def BackPropagationLearner(dataset, net, learning_rate, epoches):
                      for k in range(h_units)]
 
                 delta[i] = [(layer[j].value) * (1 - layer[j].value) *
-                            sum_dotproduct(w[j], delta[i+1])
+                            dotproduct(w[j], delta[i+1])
                             for j in range(h_units)]
 
             #  Update weights
@@ -591,7 +591,7 @@ def predict(example):
         for layer in learned_net[1:]:
             for node in layer:
                 inc = [n.value for n in node.inputs]
-                in_val = sum_dotproduct(inc, node.weights)
+                in_val = dotproduct(inc, node.weights)
                 node.value = node.activation(in_val)
 
         # Hypothesis
diff --git a/probability.py b/probability.py
@@ -557,11 +557,11 @@ def forward(HMM, fv, ev):
                                 scalar_vector_product(fv[1], HMM.transition_model[1]))
     sensor_dist = HMM.sensor_dist(ev)
 
-    return(normalize(dotproduct(sensor_dist, prediction)))
+    return(normalize(element_wise_product(sensor_dist, prediction)))
 
 def backward(HMM, b, ev):
     sensor_dist = HMM.sensor_dist(ev)
-    prediction = dotproduct(sensor_dist, b)
+    prediction = element_wise_product(sensor_dist, b)
 
     return(normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]),
                                  scalar_vector_product(prediction[1], HMM.transition_model[1]))))
@@ -582,7 +582,7 @@ def forward_backward(HMM, ev, prior):
     for i in range(1, t+ 1):
         fv[i] = forward(HMM, fv[i- 1], ev[i])
     for i in range(t, -1, -1):
-        sv[i- 1] = normalize(dotproduct(fv[i], b))
+        sv[i- 1] = normalize(element_wise_product(fv[i], b))
         b = backward(HMM, b, ev[i])
         bv.append(b)
 
diff --git a/tests/test_utils.py b/tests/test_utils.py
@@ -83,8 +83,8 @@ def test_histogram():
                                                             (7, 1), (5, 1)]
 
 
-def test_sum_dotproduct():
-    assert sum_dotproduct([1, 2, 3], [1000, 100, 10]) == 1230
+def test_dotproduct():
+    assert dotproduct([1, 2, 3], [1000, 100, 10]) == 1230
 
 
 def test_vector_add():
diff --git a/utils.py b/utils.py
@@ -162,13 +162,13 @@ def histogram(values, mode=0, bin_function=None):
         return sorted(bins.items())
 
 
-def sum_dotproduct(X, Y):
+def dotproduct(X, Y):
     """Return the sum of the element-wise product of vectors x and y."""
     return sum(x * y for x, y in zip(X, Y))
 
 
-def dotproduct(X, Y):
-    """Return element-wise product of vectors x and y"""
+def element_wise_product(X, Y):
+    """Return vector as an element-wise product of vectors x and y"""
     assert len(X) == len(Y)
     return(list(x * y for x, y in zip(X, Y)))
 
