Remove runtime errors due to a column of zeros in W, H.

Multiplicative Algorithm/cSNMF.py

@@ -22,7 +22,11 @@ def N(A): return m(A, NONZEROS)       # NONZEROS is a global variable.
 def sparsity_metric(H):
     def sparsity_column(column):
         n = len(column)
-        ratio = np.linalg.norm(column, 1)/np.linalg.norm(column, 2)
+        if sum(column) == 0:
+            ratio = 1
+            logger.info('Column of zeros in H')
+        else:
+            ratio = np.linalg.norm(column, 1)/np.linalg.norm(column, 2)
         return (math.sqrt(n) - ratio)/(math.sqrt(n) - 1)
     return np.mean([sparsity_column(column) for column in H.T])
 
@@ -34,7 +38,10 @@ def calculate_error(D, W, H):
 def spikeyness(W):
     def spikeyness_column(column):
         trend = W[:52*24*7, column].reshape(52,24*7)
-        return np.mean(np.nanstd(trend, axis=0))/np.nanmean(W[:, column])
+        if np.mean(W[:, column]) == 0:
+            return np.nan
+        else:
+            return np.mean(np.nanstd(trend, axis=0))/np.mean(W[:, column])
         
     spikey_mean = np.mean([spikeyness_column(sig) for sig in range(config.RANK)])
     spikey_std = np.std([spikeyness(sig) for sig in range(config.RANK)])