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How to use the pyriemann.estimation.HankelCovariances function in pyriemann

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github alexandrebarachant / pyRiemann / tests / test_estimation.py View on Github external
def test_Hankelcovariances():
    """Test Hankel Covariances"""
    x = np.random.randn(2, 3, 100)
    cov = HankelCovariances()
    cov.fit(x)
    cov.fit_transform(x)
    assert_equal(cov.get_params(), dict(estimator='scm', delays=4))

    cov = HankelCovariances(delays=[1, 2])
    cov.fit(x)
    cov.fit_transform(x)
github alexandrebarachant / pyRiemann / tests / test_estimation.py View on Github external
def test_Hankelcovariances():
    """Test Hankel Covariances"""
    x = np.random.randn(2, 3, 100)
    cov = HankelCovariances()
    cov.fit(x)
    cov.fit_transform(x)
    assert_equal(cov.get_params(), dict(estimator='scm', delays=4))

    cov = HankelCovariances(delays=[1, 2])
    cov.fit(x)
    cov.fit_transform(x)
github alexandrebarachant / decoding-brain-challenge-2016 / generate_models.py View on Github external
LogisticRegression('l2'))

# Induced activity models

baseclf = make_pipeline(ElectrodeSelection(10, metric=dict(mean='logeuclid',
                                                           distance='riemann')),
                        TangentSpace('riemann'),
                        LogisticRegression('l1'))

array_clfs['Cosp'] = make_pipeline(CospCovariances(fs=1000, window=32,
                                                   overlap=0.95, fmax=300,
                                                   fmin=1),
                                   CospBoostingClassifier(baseclf))

array_clfs['HankelCov'] = make_pipeline(DownSampler(2),
                                        HankelCovariances(delays=[2, 4, 8, 12, 16], estimator='oas'),
                                        TangentSpace('logeuclid'),
                                        LogisticRegression('l1'))

array_clfs['CSSP'] = make_pipeline(HankelCovariances(delays=[2, 4, 8, 12, 16],
                                                     estimator='oas'),
                                   CSP(30),
                                   LogisticRegression('l1'))

patients = dataframe1.PatientID.values

index = array_clfs.keys() + ['Ensemble']
columns = ['p1', 'p2', 'p3', 'p4']
res_acc = pd.DataFrame(index=index, columns=columns)
res_auc = pd.DataFrame(index=index, columns=columns)
github alexandrebarachant / decoding-brain-challenge-2016 / cross_validation_challenge.py View on Github external
baseclf = make_pipeline(ElectrodeSelection(10, metric=dict(mean='logeuclid',
                                                           distance='riemann')),
                        TangentSpace('riemann'),
                        LogisticRegression('l1'))

array_clfs['Cosp'] = make_pipeline(CospCovariances(fs=1000, window=32,
                                                   overlap=0.95, fmax=300,
                                                   fmin=1),
                                   CospBoostingClassifier(baseclf))

array_clfs['HankelCov'] = make_pipeline(DownSampler(2),
                                        HankelCovariances(delays=[2, 4, 8, 12, 16], estimator='oas'),
                                        TangentSpace('logeuclid'),
                                        LogisticRegression('l1'))

array_clfs['CSSP'] = make_pipeline(HankelCovariances(delays=[2, 4, 8, 12, 16],
                                                     estimator='oas'),
                                   CSP(30),
                                   LogisticRegression('l1'))

patients = dataframe1.PatientID.values

index = array_clfs.keys() + ['Ensemble']
columns = ['p1', 'p2', 'p3', 'p4']
res_acc = pd.DataFrame(index=index, columns=columns)
res_auc = pd.DataFrame(index=index, columns=columns)


for p in np.unique(patients):
    print('Patient %s' % p)
    clfs = deepcopy(array_clfs)
    ix = patients == p
github alexandrebarachant / decoding-brain-challenge-2016 / cross_validation_paper.py View on Github external
baseclf = make_pipeline(ElectrodeSelection(10, metric=dict(mean='logeuclid',
                                                           distance='riemann')),
                        TangentSpace('riemann'),
                        LogisticRegression('l1'))

array_clfs['Cosp'] = make_pipeline(CospCovariances(fs=1000, window=32,
                                                   overlap=0.95, fmax=300,
                                                   fmin=1),
                                   CospBoostingClassifier(baseclf))

array_clfs['HankelCov'] = make_pipeline(DownSampler(2),
                                        HankelCovariances(delays=[2, 4, 8, 12, 16], estimator='oas'),
                                        TangentSpace('logeuclid'),
                                        LogisticRegression('l1'))

array_clfs['CSSP'] = make_pipeline(HankelCovariances(delays=[2, 4, 8, 12, 16],
                                                     estimator='oas'),
                                   CSP(30),
                                   LogisticRegression('l1'))

patients = dataframe1.PatientID.values

index = array_clfs.keys() + ['Ensemble']
columns = ['ca', 'de', 'fp', 'ja', 'mv', 'wc', 'zt']
res_acc = pd.DataFrame(index=index, columns=columns)
res_auc = pd.DataFrame(index=index, columns=columns)

fnames = glob('./fhpred/data/*/*.mat')

for fname in fnames:
    data = loadmat(fname)
    p = fname[-18:-16]
github alexandrebarachant / decoding-brain-challenge-2016 / cross_validation_challenge.py View on Github external
LogisticRegression('l2'))

# Induced activity models

baseclf = make_pipeline(ElectrodeSelection(10, metric=dict(mean='logeuclid',
                                                           distance='riemann')),
                        TangentSpace('riemann'),
                        LogisticRegression('l1'))

array_clfs['Cosp'] = make_pipeline(CospCovariances(fs=1000, window=32,
                                                   overlap=0.95, fmax=300,
                                                   fmin=1),
                                   CospBoostingClassifier(baseclf))

array_clfs['HankelCov'] = make_pipeline(DownSampler(2),
                                        HankelCovariances(delays=[2, 4, 8, 12, 16], estimator='oas'),
                                        TangentSpace('logeuclid'),
                                        LogisticRegression('l1'))

array_clfs['CSSP'] = make_pipeline(HankelCovariances(delays=[2, 4, 8, 12, 16],
                                                     estimator='oas'),
                                   CSP(30),
                                   LogisticRegression('l1'))

patients = dataframe1.PatientID.values

index = array_clfs.keys() + ['Ensemble']
columns = ['p1', 'p2', 'p3', 'p4']
res_acc = pd.DataFrame(index=index, columns=columns)
res_auc = pd.DataFrame(index=index, columns=columns)
github alexandrebarachant / decoding-brain-challenge-2016 / generate_models.py View on Github external
baseclf = make_pipeline(ElectrodeSelection(10, metric=dict(mean='logeuclid',
                                                           distance='riemann')),
                        TangentSpace('riemann'),
                        LogisticRegression('l1'))

array_clfs['Cosp'] = make_pipeline(CospCovariances(fs=1000, window=32,
                                                   overlap=0.95, fmax=300,
                                                   fmin=1),
                                   CospBoostingClassifier(baseclf))

array_clfs['HankelCov'] = make_pipeline(DownSampler(2),
                                        HankelCovariances(delays=[2, 4, 8, 12, 16], estimator='oas'),
                                        TangentSpace('logeuclid'),
                                        LogisticRegression('l1'))

array_clfs['CSSP'] = make_pipeline(HankelCovariances(delays=[2, 4, 8, 12, 16],
                                                     estimator='oas'),
                                   CSP(30),
                                   LogisticRegression('l1'))

patients = dataframe1.PatientID.values

index = array_clfs.keys() + ['Ensemble']
columns = ['p1', 'p2', 'p3', 'p4']
res_acc = pd.DataFrame(index=index, columns=columns)
res_auc = pd.DataFrame(index=index, columns=columns)


for p in np.unique(patients):

    clfs = deepcopy(array_clfs.values())
github alexandrebarachant / decoding-brain-challenge-2016 / cross_validation_paper.py View on Github external
LogisticRegression('l2'))

# Induced activity models

baseclf = make_pipeline(ElectrodeSelection(10, metric=dict(mean='logeuclid',
                                                           distance='riemann')),
                        TangentSpace('riemann'),
                        LogisticRegression('l1'))

array_clfs['Cosp'] = make_pipeline(CospCovariances(fs=1000, window=32,
                                                   overlap=0.95, fmax=300,
                                                   fmin=1),
                                   CospBoostingClassifier(baseclf))

array_clfs['HankelCov'] = make_pipeline(DownSampler(2),
                                        HankelCovariances(delays=[2, 4, 8, 12, 16], estimator='oas'),
                                        TangentSpace('logeuclid'),
                                        LogisticRegression('l1'))

array_clfs['CSSP'] = make_pipeline(HankelCovariances(delays=[2, 4, 8, 12, 16],
                                                     estimator='oas'),
                                   CSP(30),
                                   LogisticRegression('l1'))

patients = dataframe1.PatientID.values

index = array_clfs.keys() + ['Ensemble']
columns = ['ca', 'de', 'fp', 'ja', 'mv', 'wc', 'zt']
res_acc = pd.DataFrame(index=index, columns=columns)
res_auc = pd.DataFrame(index=index, columns=columns)

fnames = glob('./fhpred/data/*/*.mat')

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Machine learning for multivariate data with Riemannian geometry

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