From 9b224aafc1d9aff7b27e95f47c97e5ed1c3d4cc7 Mon Sep 17 00:00:00 2001
From: Fabian Pedregosa <fabian.pedregosa@inria.fr>
Date: Tue, 14 Dec 2010 19:37:33 +0100
Subject: [PATCH] Add type info to docstrings.
---
scikits/learn/linear_model/logistic.py | 32 ++++++++++++++++++++++----
1 file changed, 27 insertions(+), 5 deletions(-)
diff --git a/scikits/learn/linear_model/logistic.py b/scikits/learn/linear_model/logistic.py
index bd37a2fdaa..e2e7eef452 100644
--- a/scikits/learn/linear_model/logistic.py
+++ b/scikits/learn/linear_model/logistic.py
@@ -62,15 +62,26 @@ class LogisticRegression(BaseLibLinear, ClassifierMixin):
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,
@@ -78,11 +89,22 @@ class LogisticRegression(BaseLibLinear, ClassifierMixin):
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))
--
GitLab