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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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