63 lines
1.3 KiB
Python
63 lines
1.3 KiB
Python
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#!/usr/bin/python
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# -*- coding: utf-8 -*-
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"""
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=========================================================
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Logit function
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=========================================================
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Show in the plot is how the logistic regression would, in this
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synthetic dataset, classify values as either 0 or 1,
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i.e. class one or two, using the logit-curve.
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"""
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print __doc__
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# Code source: Gael Varoqueux
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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 scikits.learn import linear_model
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# this is our test set, it's just a straight line with some
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# gaussian noise
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xmin, xmax = -5, 5
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n_samples = 100
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np.random.seed(0)
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X = np.random.normal(size=n_samples)
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y = (X > 0).astype(np.float)
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X[X>0] *= 4
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X += .3*np.random.normal(size=n_samples)
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X = X[:, np.newaxis]
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# run the classifier
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clf = linear_model.LogisticRegression(C=1e5)
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clf.fit(X, y)
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# and plot the result
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pl.figure(1, figsize=(4, 3))
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pl.clf()
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pl.scatter(X.ravel(), y, color='black', zorder=20)
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X_test = np.linspace(-5, 10, 300)
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def model(x):
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return 1/(1+np.exp(-x))
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loss = model(X_test*clf.coef_ + clf.intercept_).ravel()
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pl.plot(X_test, loss, color='blue', linewidth=3)
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ols = linear_model.LinearRegression()
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ols.fit(X, y)
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pl.plot(X_test, ols.coef_*X_test + ols.intercept_, linewidth=1)
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pl.axhline(.5, color='.5')
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pl.ylabel('y')
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pl.xlabel('X')
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pl.xticks(())
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pl.yticks(())
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pl.ylim(-.25, 1.25)
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pl.xlim(-4, 10)
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pl.show()
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