diff --git a/betacal/beta_calibration.py b/betacal/beta_calibration.py
index e8208e7..2ff89c6 100644
--- a/betacal/beta_calibration.py
+++ b/betacal/beta_calibration.py
@@ -30,7 +30,7 @@ def _beta_calibration(df, y, sample_weight=None):
         lr = LogisticRegression(C=99999999999)
         lr.fit(x, y, sample_weight)
         coefs = lr.coef_[0]
-        a = 0
+        a = None
         b = coefs[0]
     elif coefs[1] < 0:
         x = x[:, 0].reshape(-1, 1)
@@ -38,13 +38,14 @@ def _beta_calibration(df, y, sample_weight=None):
         lr.fit(x, y, sample_weight)
         coefs = lr.coef_[0]
         a = coefs[0]
-        b = 0
+        b = None
     else:
         a = coefs[0]
         b = coefs[1]
     inter = lr.intercept_[0]
 
-    m = minimize_scalar(lambda mh: np.abs(b*np.log(1.-mh)-a*np.log(mh)-inter),
+    a_, b_ = a or 0, b or 0
+    m = minimize_scalar(lambda mh: np.abs(b_*np.log(1.-mh)-a_*np.log(mh)-inter),
                         bounds=[0, 1], method='Bounded').x
     map = [a, b, m]
     return map, lr
@@ -112,9 +113,9 @@ def predict(self, S):
         x = np.hstack((df, 1. - df))
         x = np.log(x)
         x[:, 1] *= -1
-        if self.map_[0] == 0:
+        if self.map_[0] == None:
             x = x[:, 1].reshape(-1, 1)
-        elif self.map_[1] == 0:
+        elif self.map_[1] == None:
             x = x[:, 0].reshape(-1, 1)
 
         return self.lr_.predict_proba(x)[:, 1]