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76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
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"""
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Small helpers for code that is not shown in the notebooks
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"""
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from sklearn import neighbors, datasets, linear_model
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import pylab as pl
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import numpy as np
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from matplotlib.colors import ListedColormap
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# Create color maps for 3-class classification problem, as with iris
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cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
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cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
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def plot_iris_knn():
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iris = datasets.load_iris()
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X = iris.data[:, :2] # we only take the first two features. We could
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# avoid this ugly slicing by using a two-dim dataset
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y = iris.target
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knn = neighbors.KNeighborsClassifier(n_neighbors=5)
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knn.fit(X, y)
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x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1
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y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1
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xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),
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np.linspace(y_min, y_max, 100))
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Z = knn.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()
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pl.pcolormesh(xx, yy, Z, cmap=cmap_light)
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# Plot also the training points
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pl.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
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pl.xlabel('sepal length (cm)')
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pl.ylabel('sepal width (cm)')
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pl.axis('tight')
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def plot_polynomial_regression():
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rng = np.random.RandomState(0)
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x = 2*rng.rand(100) - 1
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f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9
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y = f(x) + .4 * rng.normal(size=100)
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x_test = np.linspace(-1, 1, 100)
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pl.figure()
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pl.scatter(x, y, s=4)
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X = np.array([x**i for i in range(5)]).T
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X_test = np.array([x_test**i for i in range(5)]).T
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regr = linear_model.LinearRegression()
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regr.fit(X, y)
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pl.plot(x_test, regr.predict(X_test), label='4th order')
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X = np.array([x**i for i in range(10)]).T
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X_test = np.array([x_test**i for i in range(10)]).T
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regr = linear_model.LinearRegression()
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regr.fit(X, y)
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pl.plot(x_test, regr.predict(X_test), label='9th order')
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pl.legend(loc='best')
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pl.axis('tight')
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pl.title('Fitting a 4th and a 9th order polynomial')
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pl.figure()
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pl.scatter(x, y, s=4)
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pl.plot(x_test, f(x_test), label="truth")
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pl.axis('tight')
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pl.title('Ground truth (9th order polynomial)')
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