From df0cbb369e5df6eae26f753e052f8d9958e0e425 Mon Sep 17 00:00:00 2001
From: Olivier Grisel <olivier.grisel@ensta.org>
Date: Mon, 13 Dec 2010 17:38:12 +0100
Subject: [PATCH] more formatting in SGD reST and fixed docstest broken by last
checkin :(
---
doc/modules/sgd.rst | 13 +++++++------
1 file changed, 7 insertions(+), 6 deletions(-)
diff --git a/doc/modules/sgd.rst b/doc/modules/sgd.rst
index d6f21ae9ea..7993e8fb22 100644
--- a/doc/modules/sgd.rst
+++ b/doc/modules/sgd.rst
@@ -68,12 +68,12 @@ After being fitted, the model can then be used to predict new values::
array([ 1.])
SGD fits a linear model to the training data. The member `coef_` holds
-the model parameters:
+the model parameters::
>>> clf.coef_
array([ 9.90090187, 9.90090187])
-Member `intercept_` holds the intercept (aka offset or bias):
+Member `intercept_` holds the intercept (aka offset or bias)::
>>> clf.intercept_
array(-9.9900299301496904)
@@ -81,7 +81,7 @@ Member `intercept_` holds the intercept (aka offset or bias):
Whether or not the model should use an intercept, i.e. a biased
hyperplane, is controlled by the parameter `fit_intercept`.
-To get the signed distance to the hyperplane use `decision_function`:
+To get the signed distance to the hyperplane use `decision_function`::
>>> clf.decision_function([[2., 2.]])
array([ 29.61357756])
@@ -100,10 +100,11 @@ probability estimates.
In the case of binary classification and `loss="log"` you get a
probability estimate P(y=C|x) using `predict_proba`, where `C` is the
-largest class label:
+largest class label::
- >>> clf = SGDClassifier(loss="log").fit(X, y) >>>
- clf.predict_proba([[1., 1.]]) array([ 0.99999949])
+ >>> clf = SGDClassifier(loss="log").fit(X, y)
+ >>> clf.predict_proba([[1., 1.]])
+ array([ 0.99999949])
The concrete penalty can be set via the `penalty` parameter. `SGD`
supports the following penalties:
--
GitLab