Add type info to docstrings.

Author: Fabian Pedregosa

scikits/learn/linear_model/logistic.py

@@ -62,27 +62,49 @@ def __init__(self, penalty='l2', dual=False, eps=1e-4, C=1.0,
             dual=dual, loss='lr', eps=eps, C=C,
             fit_intercept=fit_intercept)
 
-    def predict_proba(self, T):
+    def predict_proba(self, X):
         """
         Probability estimates.
 
         The returned estimates for all classes are ordered by the
         label of classes.
+
+        Parameters
+        ----------
+        X : array-like, shape = [n_samples, n_features]
+
+        Returns
+        -------
+        T : array-like, shape = [n_samples, n_classes]
+            Returns the probability of the sample for each class in
+            the model, where classes are ordered by arithmetical
+            order.
         """
-        T = np.asanyarray(T, dtype=np.float64, order='C')
-        probas = _liblinear.predict_prob_wrap(T, self.raw_coef_,
+        X = np.asanyarray(X, dtype=np.float64, order='C')
+        probas = _liblinear.predict_prob_wrap(X, self.raw_coef_,
                                       self._get_solver_type(),
                                       self.eps, self.C,
                                       self.class_weight_label,
                                       self.class_weight, self.label_,
                                       self._get_bias())
         return probas[:,np.argsort(self.label_)]
 
-    def predict_log_proba(self, T):
+    def predict_log_proba(self, X):
         """
         Log of Probability estimates.
 
         The returned estimates for all classes are ordered by the
         label of classes.
+
+        Parameters
+        ----------
+        X : array-like, shape = [n_samples, n_features]
+
+        Returns
+        -------
+        X : array-like, shape = [n_samples, n_classes]
+            Returns the log-probabilities of the sample for each class in
+            the model, where classes are ordered by arithmetical
+            order.
         """
-        return np.log(self.predict_proba(T))
+        return np.log(self.predict_proba(X))