54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
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"""
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=========================================
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Density Estimation for a Gaussian mixture
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=========================================
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Plot the density estimation of a mixture of two Gaussians. Data is
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generated from two Gaussians with different centers and covariance
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matrices.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib.colors import LogNorm
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from sklearn import mixture
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n_samples = 300
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# generate random sample, two components
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np.random.seed(0)
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# generate spherical data centered on (20, 20)
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shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 20])
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# generate zero centered stretched Gaussian data
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C = np.array([[0.0, -0.7], [3.5, 0.7]])
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stretched_gaussian = np.dot(np.random.randn(n_samples, 2), C)
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# concatenate the two datasets into the final training set
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X_train = np.vstack([shifted_gaussian, stretched_gaussian])
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# fit a Gaussian Mixture Model with two components
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clf = mixture.GaussianMixture(n_components=2, covariance_type="full")
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clf.fit(X_train)
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# display predicted scores by the model as a contour plot
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x = np.linspace(-20.0, 30.0)
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y = np.linspace(-20.0, 40.0)
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X, Y = np.meshgrid(x, y)
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XX = np.array([X.ravel(), Y.ravel()]).T
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Z = -clf.score_samples(XX)
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Z = Z.reshape(X.shape)
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CS = plt.contour(
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X, Y, Z, norm=LogNorm(vmin=1.0, vmax=1000.0), levels=np.logspace(0, 3, 10)
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)
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CB = plt.colorbar(CS, shrink=0.8, extend="both")
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plt.scatter(X_train[:, 0], X_train[:, 1], 0.8)
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plt.title("Negative log-likelihood predicted by a GMM")
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plt.axis("tight")
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plt.show()
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