コード例 #1
0
ファイル: bench_pca.py プロジェクト: fabianp/ml-benchmarks
def bench_pymvpa():
#
#       .. PyMVPA ..
#
    from mvpa.mappers.mdp_adaptor import PCAMapper as MVPA_PCA
    from mvpa.datasets import dataset_wizard
    start = datetime.now()
    clf = MVPA_PCA(output_dim=n_components)
    data = dataset_wizard(samples=X)
    clf.train(data)
    return datetime.now() - start
コード例 #2
0
ファイル: bench_pca.py プロジェクト: bdholt1/ml-benchmarks
def bench_pymvpa(X, y, T, valid):
#
#       .. PyMVPA ..
#
    from mvpa.mappers.mdp_adaptor import PCAMapper
    from mvpa.datasets import dataset_wizard
    start = datetime.now()
    clf = PCAMapper(output_dim=n_components)
    data = dataset_wizard(samples=X)
    clf.train(data)
    delta = datetime.now() - start
    ev = explained_variance(X, clf.proj.T).sum()
    return ev, delta
コード例 #3
0
def bench_pymvpa(X, y, T, valid):
    #
    #       .. PyMVPA ..
    #
    from mvpa.mappers.mdp_adaptor import PCAMapper
    from mvpa.datasets import dataset_wizard
    start = datetime.now()
    clf = PCAMapper(output_dim=n_components)
    data = dataset_wizard(samples=X)
    clf.train(data)
    delta = datetime.now() - start
    ev = explained_variance(X, clf.proj.T).sum()
    return ev, delta
コード例 #4
0
ファイル: test_mdp.py プロジェクト: arokem/PyMVPA
def test_pcamapper():
    # data: 40 sample feature line in 20d space (40x20; samples x features)
    ndlin = Dataset(np.concatenate([np.arange(40)
                               for i in range(20)]).reshape(20,-1).T)

    pm = PCAMapper()
    # train PCA
    assert_raises(mdp.NodeException, pm.train, ndlin)
    ndlin.samples = ndlin.samples.astype('float')
    ndlin_noise = ndlin.copy()
    ndlin_noise.samples += np.random.random(size=ndlin.samples.shape)
    # we have no variance for more than one PCA component, hence just one
    # actual non-zero eigenvalue
    assert_raises(mdp.NodeException, pm.train, ndlin)
    pm.train(ndlin_noise)
    assert_equal(pm.proj.shape, (20, 20))
    # now project data into PCA space
    p = pm.forward(ndlin.samples)
    assert_equal(p.shape, (40, 20))
    # check that the mapped data can be fully recovered by 'reverse()'
    assert_array_almost_equal(pm.reverse(p), ndlin)
コード例 #5
0
ファイル: test_mdp.py プロジェクト: heqing-psychology/PyMVPA
def test_pcamapper():
    # data: 40 sample feature line in 20d space (40x20; samples x features)
    ndlin = Dataset(
        np.concatenate([np.arange(40) for i in range(20)]).reshape(20, -1).T)

    pm = PCAMapper()
    # train PCA
    assert_raises(mdp.NodeException, pm.train, ndlin)
    ndlin.samples = ndlin.samples.astype('float')
    ndlin_noise = ndlin.copy()
    ndlin_noise.samples += np.random.random(size=ndlin.samples.shape)
    # we have no variance for more than one PCA component, hence just one
    # actual non-zero eigenvalue
    assert_raises(mdp.NodeException, pm.train, ndlin)
    pm.train(ndlin_noise)
    assert_equal(pm.proj.shape, (20, 20))
    # now project data into PCA space
    p = pm.forward(ndlin.samples)
    assert_equal(p.shape, (40, 20))
    # check that the mapped data can be fully recovered by 'reverse()'
    assert_array_almost_equal(pm.reverse(p), ndlin)
コード例 #6
0
from mvpa.mappers.svd import SVDMapper
from mvpa.mappers.mdp_adaptor import ICAMapper, PCAMapper
from mvpa import cfg

center = [10, 20]
axis_range = 7


##REF: Name was automagically refactored
def plot_proj_dir(p):
    pl.plot([0, p[0, 0]], [0, p[0, 1]], linewidth=3, hold=True, color='y')
    pl.plot([0, p[1, 0]], [0, p[1, 1]], linewidth=3, hold=True, color='k')


mappers = {
    'PCA': PCAMapper(),
    'SVD': SVDMapper(),
    'ICA': ICAMapper(alg='CuBICA'),
}
datasets = [
    noisy_2d_fx(100, lambda x: x, [lambda x: x], center, noise_std=0.5),
    noisy_2d_fx(50,
                lambda x: x, [lambda x: x, lambda x: -x],
                center,
                noise_std=0.5),
    noisy_2d_fx(50,
                lambda x: x, [lambda x: x, lambda x: 0],
                center,
                noise_std=0.5),
]