REF/TST: fix tests for alpha rescaled

statsmodels · Oct 6, 2012 · 4b39c9e · 4b39c9e
1 parent f4b913f
commit 4b39c9e
Showing 1 changed file with 5 additions and 5 deletions.
diff --git a/statsmodels/discrete/tests/test_discrete.py b/statsmodels/discrete/tests/test_discrete.py
@@ -393,7 +393,7 @@ class TestProbitL1(CheckLikelihoodModelL1):
     def setupClass(cls):
         data = sm.datasets.spector.load()
         data.exog = sm.add_constant(data.exog, prepend=True)
-        alpha = np.array([0.1, 0.2, 0.3, 10]) / data.exog.shape[0]
+        alpha = np.array([0.1, 0.2, 0.3, 10]) #/ data.exog.shape[0]
         cls.res1 = Probit(data.endog, data.exog).fit_regularized(
             method="l1", alpha=alpha, disp=0, trim_mode='auto',
             auto_trim_tol=0.02, acc=1e-10, maxiter=1000)
@@ -413,7 +413,7 @@ def setupClass(cls):
         anes_exog = anes_data.exog
         anes_exog = sm.add_constant(anes_exog, prepend=False)
         mlogit_mod = sm.MNLogit(anes_data.endog, anes_exog)
-        alpha = 10. * np.ones((mlogit_mod.J - 1, mlogit_mod.K)) / anes_exog.shape[0]
+        alpha = 10. * np.ones((mlogit_mod.J - 1, mlogit_mod.K)) #/ anes_exog.shape[0]
         alpha[-1,:] = 0
         cls.res1 = mlogit_mod.fit_regularized(
                 method='l1', alpha=alpha, trim_mode='auto', auto_trim_tol=0.02,
@@ -428,7 +428,7 @@ class TestLogitL1(CheckLikelihoodModelL1):
     def setupClass(cls):
         data = sm.datasets.spector.load()
         data.exog = sm.add_constant(data.exog, prepend=True)
-        cls.alpha = 3 * np.array([0., 1., 1., 1.]) / data.exog.shape[0]
+        cls.alpha = 3 * np.array([0., 1., 1., 1.]) #/ data.exog.shape[0]
         cls.res1 = Logit(data.endog, data.exog).fit_regularized(
             method="l1", alpha=cls.alpha, disp=0, trim_mode='size',
             size_trim_tol=1e-5, acc=1e-10, maxiter=1000)
@@ -450,7 +450,7 @@ def setupClass(self):
     def test_cvxopt_versus_slsqp(self):
         #Compares resutls from cvxopt to the standard slsqp
         if has_cvxopt:
-            self.alpha = 3. * np.array([0, 1, 1, 1.]) / self.data.endog.shape[0]
+            self.alpha = 3. * np.array([0, 1, 1, 1.]) #/ self.data.endog.shape[0]
             res_slsqp = Logit(self.data.endog, self.data.exog).fit_regularized(
                 method="l1", alpha=self.alpha, disp=0, acc=1e-10, maxiter=1000,
                 trim_mode='auto')
@@ -471,7 +471,7 @@ def setupClass(cls):
         cls.alphas = np.array(
                    [[0.1, 0.1, 0.1, 0.1],
                     [0.4, 0.4, 0.5, 0.5],
-                    [0.5, 0.5, 1, 1]]) / data.exog.shape[0]
+                    [0.5, 0.5, 1, 1]]) #/ data.exog.shape[0]
         cls.res1 = DiscreteL1()
         cls.res1.sweep()