54 lines
1.2 KiB
Python
54 lines
1.2 KiB
Python
#!/usr/bin/python
|
|
# -*- coding: utf-8 -*-
|
|
|
|
"""
|
|
=========================================================
|
|
The Iris Dataset
|
|
=========================================================
|
|
This data sets consists of 3 different types of irises'
|
|
(Setosa, Versicolour, and Virginica) petal and sepal
|
|
length, stored in a 150x4 numpy.ndarray
|
|
|
|
The rows being the samples and the columns being:
|
|
Sepal Length, Sepal Width, Petal Length and Petal Width.
|
|
|
|
The below plot uses the first two features.
|
|
See
|
|
`here <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_
|
|
for more information on this dataset.
|
|
"""
|
|
print __doc__
|
|
|
|
|
|
# Code source: Gael Varoqueux
|
|
# Modified for Documentation merge by Jaques Grobler
|
|
# License: BSD
|
|
|
|
import pylab as pl
|
|
from sklearn import datasets
|
|
|
|
# import some data to play with
|
|
iris = datasets.load_iris()
|
|
X = iris.data[:, :2] # we only take the first two features.
|
|
Y = iris.target
|
|
|
|
x_min, x_max = X[:,0].min() - .5, X[:,0].max() + .5
|
|
y_min, y_max = X[:,1].min() - .5, X[:,1].max() + .5
|
|
|
|
pl.figure(1, figsize=(4, 3))
|
|
pl.clf()
|
|
pl.set_cmap(pl.cm.Paired)
|
|
|
|
# Plot also the training points
|
|
pl.scatter(X[:,0], X[:,1], c=Y)
|
|
pl.xlabel('Sepal length')
|
|
pl.ylabel('Sepal width')
|
|
|
|
pl.xlim(x_min, x_max)
|
|
pl.ylim(y_min, y_max)
|
|
pl.xticks(())
|
|
pl.yticks(())
|
|
|
|
pl.show()
|
|
|