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test
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@ -405,7 +405,9 @@ def kmeans(data, kernel=sklearn.cluster.KMeans()):
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return centers, predictions
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return centers, predictions
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def pca(data, kernel = sklearn.decomposition.PCA(n_components=2)):
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def pca(data, n_components = None, copy = True, whiten = False, svd_solver = ’auto’, tol = 0.0, iterated_power = ’auto’, random_state = None):
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kernel = sklearn.decomposition.PCA()
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return kernel.fit_transform(data)
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return kernel.fit_transform(data)
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