예제 #1
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def test_reproj_2():
    pca = KPCA(n_components=2)
    pca.fit(X1)
    exp = pca.transform(X1[1, None])
    assert_almost_equal(pca.X_projected_[1, None], exp, decimal=2)
예제 #2
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def test_fail_array_transform():
    pca = KPCA(n_components=2)
    pca.fit(X1)
    with pytest.raises(ValueError):
        pca.transform(X1[1])
예제 #3
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plt.xlabel('PC1')
plt.yticks([])
#plt.show()
plt.savefig('../figs/tutorial/mlxtendex1_4.png')
plt.close()
 # Feature space linearly seperable at x=0, also data entirely 1-D, forming horizontal line.

 #subspace like this can then be used as input in generalised classification models eg logistic regression.

import matplotlib.pyplot as plt
from sklearn.datasets import make_moons

#New data, two crescents, now using 100 samples per crescent,using random state seed of 5.
X2, y2 = make_moons(n_samples=200, random_state=5)
#Transform new dataset according to previous KPCA parameters.
X2_kpca = kpca.transform(X2)

#Initial data
plt.scatter(X_kpca[y==0, 0], X_kpca[y==0, 1],
            color='red', marker='o', alpha=0.5, label='fit data')
plt.scatter(X_kpca[y==1, 0], X_kpca[y==1, 1],
            color='blue', marker='^', alpha=0.5, label='fit data')

#New data projected onto new component axes
plt.scatter(X2_kpca[y2==0, 0], X2_kpca[y2==0, 1],
            color='orange', marker='v',
            alpha=0.2, label='new data')
plt.scatter(X2_kpca[y2==1, 0], X2_kpca[y2==1, 1],
            color='cyan', marker='s',
            alpha=0.2, label='new data')
예제 #4
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def test_fail_array_transform():
    pca = KPCA(n_components=2)
    pca.fit(X1)
    pca.transform(X1[1])
예제 #5
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def test_reproj_2():
    pca = KPCA(n_components=2)
    pca.fit(X1)
    exp = pca.transform(X1[1, None])
    assert_almost_equal(pca.X_projected_[1, None], exp, decimal=2)
예제 #6
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def test_fail_array_transform():
    pca = KPCA(n_components=2)
    pca.fit(X1)
    exp = pca.transform(X1[1])
예제 #7
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def test_fail_array_transform():
    pca = KPCA(n_components=2)
    pca.fit(X1)
    with pytest.raises(ValueError):
        pca.transform(X1[1])