Fixed a bug inNormalizedRegressorNc.

NormalizedRegressorNc was sometimes misbehaving due to trying to calculate log(0). Fixed this by adding a small error term to each training example before taking their log error.
donlnz · May 6, 2015 · 4f2f85d · 4f2f85d
1 parent cf0c9a4
commit 4f2f85d
Show file tree

Hide file tree

Showing 2 changed files with 6 additions and 2 deletions.
diff --git a/nonconformist/__init__.py b/nonconformist/__init__.py
@@ -6,6 +6,6 @@
 
 # Authors: Henrik Linusson
 
-__version__ = '1.2.0'
+__version__ = '1.2.1'
 
 __all__ = ['icp', 'nc', 'acp']
diff --git a/nonconformist/nc.py b/nonconformist/nc.py
@@ -446,7 +446,9 @@ def __init__(self,
 
 	def fit(self, x, y):
 		super(NormalizedRegressorNc, self).fit(x, y)
-		log_err = np.log(np.abs(self._underlying_predict(x) - y))
+		err = np.abs(self._underlying_predict(x) - y)
+		err += 0.00001 # Add a small error to each sample to avoid log(0)
+		log_err = np.log(err)
 		self.normalizer.fit(x, log_err)
 
 	def calc_nc(self, x, y):
@@ -464,6 +466,8 @@ def calc_nc(self, x, y):
 		else:
 			self.beta_ = self.beta
 
+		print(norm)
+
 		return self.err_func(prediction, y, norm, self.beta_)
 
 	def predict(self, x, nc, significance=None):