RFCT Simpler code

Author: luispedro

ch08/corrneighbours.py

@@ -15,8 +15,8 @@
 
 def predict(otrain):
     binary = (otrain > 0)
-    norm = NormalizePositive()
-    train = norm.fit_transform(otrain.T).T
+    norm = NormalizePositive(axis=1)
+    train = norm.fit_transform(otrain)
 
     dists = distance.pdist(binary, 'correlation')
     dists = distance.squareform(dists)
@@ -37,9 +37,9 @@ def predict(otrain):
                 n //= 2
                 n += 1
                 revs = revs[:n]
-                filled[u,m] = revs.mean()
+                filled[u,m] = np.mean(revs)
 
-    return norm.inverse_transform(filled.T).T
+    return norm.inverse_transform(filled)
 
 def main(transpose_inputs=False):
     train, test = get_train_test(random_state=12)

ch08/norm.py

@@ -2,37 +2,50 @@
 
 class NormalizePositive(object):
 
+    def __init__(self, axis=0):
+        self.axis = axis
+
     def fit(self, features, y=None):
-        # count features that are greater than zero in axis 0:
+        # count features that are greater than zero in axis `self.axis`:
+        if self.axis == 1:
+            features = features.T
         binary = (features > 0)
-        count0 = binary.sum(axis=0)
+        count = binary.sum(axis=0)
 
         # to avoid division by zero, set zero counts to one:
-        count0[count0 == 0] = 1.
+        count[count == 0] = 1.
 
-        self.mean = features.sum(axis=0)/count0
+        self.mean = features.sum(axis=0)/count
 
         # Compute variance by average squared difference to the mean, but only
         # consider differences where binary is True (i.e., where there was a
         # true rating):
         diff = (features - self.mean) * binary
         diff **= 2
         # regularize the estimate of std by adding 0.1
-        self.std = np.sqrt(0.1 + diff.sum(axis=0)/count0)
+        self.std = np.sqrt(0.1 + diff.sum(axis=0)/count)
         return self
 
     def transform(self, features):
+        if self.axis == 1:
+            features = features.T
         binary = (features > 0)
         features = features - self.mean
         features /= self.std
         features *= binary
+        if self.axis == 1:
+            features = features.T
         return features
 
     def inverse_transform(self, features, copy=True):
         if copy:
             features = features.copy()
+        if self.axis == 1:
+            features = features.T
         features *= self.std
         features += self.mean
+        if self.axis == 1:
+            features = features.T
         return features
 
     def fit_transform(self, features):