60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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Logistic Regression 3-class Classifier
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=========================================================
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Show below is a logistic-regression classifiers decision boundaries on the
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first two dimensions (sepal length and width) of the `iris
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<https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The datapoints
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are colored according to their labels.
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"""
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print(__doc__)
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# Code source: Gaël Varoquaux
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# Modified for documentation by Jaques Grobler
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# License: BSD 3 clause
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.linear_model import LogisticRegression
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from sklearn import datasets
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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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logreg = LogisticRegression(C=1e5)
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# Create an instance of Logistic Regression Classifier and fit the data.
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logreg.fit(X, Y)
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# Plot the decision boundary. For that, we will assign a color to each
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# point in the mesh [x_min, x_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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h = .02 # step size in the mesh
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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Z = logreg.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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plt.figure(1, figsize=(4, 3))
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plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
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# Plot also the training points
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plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired)
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plt.xlabel('Sepal length')
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plt.ylabel('Sepal width')
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.xticks(())
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plt.yticks(())
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plt.show()
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