MIN Add axis= for explicitness

Author: luispedro

ch08/norm.py

@@ -5,19 +5,19 @@ class NormalizePositive(object):
 deffit(self, X, y=None):
 # count features that are greater than zero in axis 0:
 binary= (X>0)
-count0=binary.sum(0)
+count0=binary.sum(axis=0)
 
 # to avoid division by zero, set zero counts to one:
 count0+= (count0==0)
 
-self.mean=X.sum(0)/count0
+self.mean=X.sum(axis=0)/count0
 
 # 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= (X-self.mean) *binary
 diff**=2
-self.std=np.sqrt(0.1+diff.sum(0)/count0)
+self.std=np.sqrt(0.1+diff.sum(axis=0)/count0)
 returnself
 
 deffit_transform(self, X):