From ea7ad29123735a625d620b5ea28fdfef6f2f42e7 Mon Sep 17 00:00:00 2001
From: Jaques Grobler <jaquesgrobler@gmail.com>
Date: Mon, 14 Oct 2013 18:28:07 +0200
Subject: [PATCH] sort out plot_iris vs plot_svm_iris
---
.../statistical_inference/supervised_learning.rst | 7 ++++---
examples/svm/plot_iris.py | 10 +++++++++-
2 files changed, 13 insertions(+), 4 deletions(-)
diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
index 6ce49a71b1..66fddfee35 100644
--- a/doc/tutorial/statistical_inference/supervised_learning.rst
+++ b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -439,9 +439,10 @@ the separating line (less regularization).
|svm_margin_unreg| |svm_margin_reg|
============================= ==============================
-.. image:: ../../auto_examples/svm/images/plot_svm_iris_1.png
- :target: ../../auto_examples/svm/plot_svm_iris.html
- :scale: 83
+.. topic:: Example:
+
+ - :ref:`example_svm_plot_iris.py`
+
SVMs can be used in regression --:class:`SVR` (Support Vector Regression)--, or in
classification --:class:`SVC` (Support Vector Classification).
diff --git a/examples/svm/plot_iris.py b/examples/svm/plot_iris.py
index 1c33ae2810..fa57d0127a 100644
--- a/examples/svm/plot_iris.py
+++ b/examples/svm/plot_iris.py
@@ -46,15 +46,23 @@ for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
pl.subplot(2, 2, i + 1)
+ pl.subplots_adjust(wspace=0.4, hspace=0.4)
+
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
pl.contourf(xx, yy, Z, cmap=pl.cm.Paired)
- pl.axis('off')
# Plot also the training points
pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)
+ pl.xlabel('Sepal length')
+ pl.ylabel('Sepal width')
+
+ pl.xlim(xx.min(), xx.max())
+ pl.ylim(yy.min(), yy.max())
+ pl.xticks(())
+ pl.yticks(())
pl.title(titles[i])
--
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