コード例 #1
0
ファイル: test_glmnet.py プロジェクト: esc/PyMVPA
def test_glmnet_c_sensitivities():
    data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)

    # use GLMNET on binary problem
    clf = GLMNET_C()
    clf.train(data)

    # now ask for the sensitivities WITHOUT having to pass the dataset
    # again
    sens = clf.get_sensitivity_analyzer(force_train=False)(None)

    #failUnless(sens.shape == (data.nfeatures,))
    assert_equal(sens.shape, (len(data.UT), data.nfeatures))
コード例 #2
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def test_glmnet_c_sensitivities():
    data = normal_feature_dataset(perlabel=10, nlabels=2, nfeatures=4)

    # use GLMNET on binary problem
    clf = GLMNET_C()
    clf.train(data)

    # now ask for the sensitivities WITHOUT having to pass the dataset
    # again
    sens = clf.get_sensitivity_analyzer(force_training=False)()

    #failUnless(sens.shape == (data.nfeatures,))
    assert_equal(sens.shape, (len(data.UT), data.nfeatures))
コード例 #3
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ファイル: test_glmnet.py プロジェクト: esc/PyMVPA
def test_glmnet_c():
    # define binary prob
    data = datasets['dumb2']

    # use GLMNET on binary problem
    clf = GLMNET_C()
    clf.ca.enable('estimates')

    clf.train(data)

    # test predictions
    pre = clf.predict(data.samples)

    assert_array_equal(pre, data.targets)
コード例 #4
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def test_glmnet_c():
    # define binary prob
    data = datasets['dumb2']

    # use GLMNET on binary problem
    clf = GLMNET_C()
    clf.ca.enable('estimates')

    clf.train(data)

    # test predictions
    pre = clf.predict(data.samples)

    assert_array_equal(pre, data.targets)