bg fix : approximation

pipeline.py

@@ -169,14 +169,24 @@ class Components :
 			#
 			# @TODO: create bins?
 			r = np.random.dirichlet(values+.001) #-- dirichlet doesn't work on values with zeros
+			_sd = values[values > 0].std()
+			_me = values[values > 0].mean()
 			x = []
 			_type = values.dtype
 			for index in np.arange(values.size) :
 				
 				if np.random.choice([0,1],1)[0] :
 					value = values[index] + (values[index] * r[index])
+					
 				else :
 					value = values[index] - (values[index] * r[index])
+				#
+				# randomly shifting the measurements 
+				if np.random.choice([0,1],1)[0] and _me > _sd:
+					if np.random.choice([0,1],1)[0] :
+						value = value * np.divide(_me,_sd)
+					else:
+						value = value + (np.divide(_me,_sd))
 				value = int(value) if _type == int else np.round(value,2)
 				x.append( value)
 			np.random.shuffle(x)
@@ -305,7 +315,7 @@ class Components :
 						if real_df[_col].unique().size > 0 :
 							
 
-							_df[_col] = self.approximate(real_df[_col])
+							_df[_col] = self.approximate(real_df[_col].values)
 							_approx[_col] = {
 								"io":{"min":_df[_col].min().astype(float),"max":_df[_col].max().astype(float),"mean":_df[_col].mean().astype(float),"sd":_df[_col].values.std().astype(float),"missing": _df[_col].where(_df[_col] == -1).dropna().count().astype(float),"zeros":_df[_col].where(_df[_col] == 0).dropna().count().astype(float)},
 								"real":{"min":real_df[_col].min().astype(float),"max":real_df[_col].max().astype(float),"mean":real_df[_col].mean().astype(float),"sd":real_df[_col].values.std().astype(float),"missing": real_df[_col].where(_df[_col] == -1).dropna().count().astype(float),"zeros":real_df[_col].where(_df[_col] == 0).dropna().count().astype(float)}