bug fix ...

data/gan.py

@@ -578,18 +578,20 @@ class Predict(GNet):
                                 # if we are dealing with numeric values only we can perform a simple marginal sum against the indexes
                                 # The code below will insure we have some acceptable cardinal relationships between id and synthetic values
                                 #
-                                df =   pd.DataFrame(np.round(f).astype(np.int32))
+                                
+                                df =   pd.DataFrame(np.round(f)).astype(np.int32)
                                 
                                 
                                 p = 0 not in df.sum(axis=1).values
                                 x = df.sum(axis=1).values
                                 
-                                if np.divide( np.sum(x), x.size)  > .9 or p and np.sum(x) == x.size and x.size == self.values.size:
+                                if np.divide( np.sum(x), x.size)  > .9 or p and np.sum(x) == x.size :
                                         ratio.append(np.divide( np.sum(x), x.size))
                                         found.append(df)
                                         if i == CANDIDATE_COUNT:
                                                 break
                                 else:
+
                                         continue
                                         
                         # i = df.T.index.astype(np.int32) #-- These are numeric pseudonyms
@@ -597,9 +599,13 @@ class Predict(GNet):
                         #
                         # In case we are dealing with actual values like diagnosis codes we can perform 
                         #
-                        
+                        _index = [found.index(item) for item in found if item.shape[1] == len(self.values)]
+                        if not _index :
                                 INDEX = np.random.choice(np.arange(len(found)),1)[0]
                                 INDEX = ratio.index(np.max(ratio))
+                        else:
+                                INDEX = _index[0]
+
                         
                         df = found[INDEX]
                         columns = self.ATTRIBUTES['synthetic'] if isinstance(self.ATTRIBUTES['synthetic'],list)else [self.ATTRIBUTES['synthetic']]