68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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Classifiers Comparison
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=========================================================
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A Comparison of a K-nearest-neighbours, Logistic Regression and a Linear SVC
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classifying the `iris <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_
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dataset.
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"""
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print __doc__
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# Code source: Gael Varoqueux
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# Modified for Documentation merge by Jaques Grobler
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# License: BSD
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import numpy as np
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import pylab as pl
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from sklearn import neighbors, datasets, linear_model, svm
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features.
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Y = iris.target
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h = .02 # step size in the mesh
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classifiers = dict(
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knn=neighbors.KNeighborsClassifier(),
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logistic=linear_model.LogisticRegression(C=1e5),
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svm=svm.LinearSVC(C=1e5, loss='l1'),
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)
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fignum = 1
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# we create an instance of Neighbours Classifier and fit the data.
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for name, clf in classifiers.iteritems():
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clf.fit(X, Y)
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# Plot the decision boundary. For that, we will asign a color to each
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# point in the mesh [x_min, m_max]x[y_min, y_max].
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x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
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np.arange(y_min, y_max, h))
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Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
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# Put the result into a color plot
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Z = Z.reshape(xx.shape)
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pl.figure(fignum, figsize=(4, 3))
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pl.pcolormesh(xx, yy, Z, cmap=pl.cm.Paired)
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# Plot also the training points
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pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)
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pl.xlabel('Sepal length')
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pl.ylabel('Sepal width')
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pl.xlim(xx.min(), xx.max())
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pl.ylim(yy.min(), yy.max())
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pl.xticks(())
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pl.yticks(())
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fignum += 1
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pl.show()
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