From d7c07abbc50b536531a5f622a089690d5ff5faa3 Mon Sep 17 00:00:00 2001
From: Gael Varoquaux <gael.varoquaux@normalesup.org>
Date: Tue, 20 Apr 2010 22:47:50 +0000
Subject: [PATCH] ENH/DOC: Add an example doing classification on digits.
git-svn-id: https://scikit-learn.svn.sourceforge.net/svnroot/scikit-learn/trunk@669 22fbfee3-77ab-4535-9bad-27d1bd3bc7d8
---
examples/plot_digits_classification.py | 52 ++++++++++++++++++++++++++
1 file changed, 52 insertions(+)
create mode 100644 examples/plot_digits_classification.py
diff --git a/examples/plot_digits_classification.py b/examples/plot_digits_classification.py
new file mode 100644
index 0000000000..3972ec0483
--- /dev/null
+++ b/examples/plot_digits_classification.py
@@ -0,0 +1,52 @@
+"""
+================================
+Recognizing hand-written digits
+================================
+
+An example showing how the scikit-learn can be used to recognize images of
+hand-written digits.
+
+"""
+# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
+# License: Simplified BSD
+
+# Standard scientific Python imports
+import pylab as pl
+
+# The digits dataset
+from scikits.learn import datasets
+digits = datasets.load_digits()
+
+# The data that we are interesting in is made of 8x8 images of digits,
+# let's have a look at the first 3 images. We know which digit they
+# represent: it is given in the 'target' of the dataset.
+for index, (image, label) in enumerate(zip(digits.images, digits.target)[:4]):
+ pl.subplot(2, 4, index+1)
+ pl.imshow(image, cmap=pl.cm.gray_r)
+ pl.title('Training: %i' % label)
+
+# To apply an classifier on this data, we need to flatten the image, to
+# turn the data in a (samples, feature) matrix:
+n_features = len(digits.images)
+data = digits.images.reshape((n_features, -1))
+
+# Import a classifier:
+from scikits.learn import svm
+classifier = svm.SVC()
+
+# We learn the digits on the first half of the digits
+classifier.fit(data[:n_features/2], digits.target[:n_features/2])
+
+# Now predict the value of the digit on the second half:
+predicted = classifier.predict(data[n_features/2:])
+
+for index, (image, prediction) in enumerate(zip(
+ digits.images[n_features/2:],
+ predicted
+ )[:4]):
+ pl.subplot(2, 4, index+5)
+ pl.imshow(image, cmap=pl.cm.gray_r)
+ pl.title('Prediction: %i' % prediction)
+
+
+pl.show()
--
GitLab