Python MDM.transform方法代码示例

# 需要导入模块: from pyriemann.classification import MDM [as 别名]
# 或者: from pyriemann.classification.MDM import transform [as 别名]
def test_MDM_transform():
    """Test transform of MDM"""
    covset = generate_cov(100,3)
    labels = np.array([0,1]).repeat(50)
    mdm = MDM(metric='riemann')
    mdm.fit(covset,labels)
    mdm.transform(covset)

# 需要导入模块: from pyriemann.classification import MDM [as 别名]
# 或者: from pyriemann.classification.MDM import transform [as 别名]
def test_MDM_predict():
    """Test prediction of MDM"""
    covset = generate_cov(100, 3)
    labels = np.array([0, 1]).repeat(50)
    mdm = MDM(metric='riemann')
    mdm.fit(covset, labels)
    mdm.predict(covset)

    # test fit_predict
    mdm = MDM(metric='riemann')
    mdm.fit_predict(covset, labels)

    # test transform
    mdm.transform(covset)

    # predict proba
    mdm.predict_proba(covset)

    # test n_jobs
    mdm = MDM(metric='riemann', n_jobs=2)
    mdm.fit(covset, labels)
    mdm.predict(covset)

# 需要导入模块: from pyriemann.classification import MDM [as 别名]
# 或者: from pyriemann.classification.MDM import transform [as 别名]
class DistanceCalculatorRafal(BaseEstimator, TransformerMixin):

    """Distance Calulator Based on MDM Rafal style."""

    def __init__(self, metric_mean='logeuclid', metric_dist=['riemann'],
                 n_jobs=12, subsample=10):
        """Init."""
        self.metric_mean = metric_mean
        self.metric_dist = metric_dist
        self.n_jobs = n_jobs
        self.subsample = subsample

    def fit(self, X, y):
        """Fit."""
        self.mdm = MDM(metric=self.metric_mean, n_jobs=self.n_jobs)
        labels = y[::self.subsample]
        pCalcMeans = partial(mean_covariance, metric=self.metric_mean)
        pool = Pool(processes=6)
        mc1 = pool.map(pCalcMeans, [X[labels[:, i] == 1] for i in range(6)])
        pool.close()
        pool = Pool(processes=6)
        mc0 = pool.map(pCalcMeans, [X[labels[:, i] == 0] for i in range(6)])
        pool.close()
        self.mdm.covmeans = mc1 + mc0
        return self

    def transform(self, X, y=None):
        """Transform."""
        feattr = []
        for metric in self.metric_dist:
            self.mdm.metric_dist = metric
            feat = self.mdm.transform(X)
            # substract distance of the class 0
            feat = feat[:, 0:6] - feat[:, 6:]
            feattr.append(feat)
        feattr = np.concatenate(feattr, axis=1)
        feattr[np.isnan(feattr)] = 0
        return feattr

    def fit_transform(self, X, y):
        """Fit and transform."""
        self.fit(X, y)
        return self.transform(X)

# 需要导入模块: from pyriemann.classification import MDM [as 别名]
# 或者: from pyriemann.classification.MDM import transform [as 别名]
class DistanceCalculatorAlex(BaseEstimator, TransformerMixin):

    """Distance Calulator Based on MDM."""

    def __init__(self, metric_mean='logeuclid', metric_dist=['riemann'],
                 n_jobs=7, subsample=10):
        """Init."""
        self.metric_mean = metric_mean
        self.metric_dist = metric_dist
        self.n_jobs = n_jobs
        self.subsample = subsample

    def fit(self, X, y):
        """Fit."""
        self.mdm = MDM(metric=self.metric_mean, n_jobs=self.n_jobs)
        labels = np.squeeze(create_sequence(y.T)[::self.subsample])
        self.mdm.fit(X, labels)
        return self

    def transform(self, X, y=None):
        """Transform."""
        feattr = []
        for metric in self.metric_dist:
            self.mdm.metric_dist = metric
            feat = self.mdm.transform(X)
            # substract distance of the class 0
            feat = feat[:, 1:] - np.atleast_2d(feat[:, 0]).T
            feattr.append(feat)
        feattr = np.concatenate(feattr, axis=1)
        feattr[np.isnan(feattr)] = 0
        return feattr

    def fit_transform(self, X, y):
        """Fit and transform."""
        self.fit(X, y)
        return self.transform(X)

# 需要导入模块: from pyriemann.classification import MDM [as 别名]
# 或者: from pyriemann.classification.MDM import transform [as 别名]
class ERPDistance(BaseEstimator, TransformerMixin):

    """ERP distance cov estimator.

    This transformer estimates Riemannian distance for ERP covariance matrices.
    After estimation of special form ERP covariance matrices using the ERP
    transformer, a MDM [1] algorithm is used to compute Riemannian distance.

    References:
    [1] A. Barachant, S. Bonnet, M. Congedo and C. Jutten, "Multiclass
    Brain-Computer Interface Classification by Riemannian Geometry," in IEEE
    Transactions on Biomedical Engineering, vol. 59, no. 4, p. 920-928, 2012
    """

    def __init__(self, window=500, nfilters=3, subsample=1, metric='riemann',
                 n_jobs=1):
        """Init."""
        self.window = window
        self.nfilters = nfilters
        self.subsample = subsample
        self.metric = metric
        self.n_jobs = n_jobs
        self._fitted = False

    def fit(self, X, y):
        """fit."""
        # Create ERP and get cov mat
        self.ERP = ERP(self.window, self.nfilters, self.subsample)
        train_cov = self.ERP.fit_transform(X, y)
        labels_train = self.ERP.labels_train

        # Add rest epochs
        rest_cov = self._get_rest_cov(X, y)
        train_cov = np.concatenate((train_cov, rest_cov), axis=0)
        labels_train = np.concatenate((labels_train, [0] * len(rest_cov)))

        # fit MDM
        self.MDM = MDM(metric=self.metric, n_jobs=self.n_jobs)
        self.MDM.fit(train_cov, labels_train)
        self._fitted = True
        return self

    def transform(self, X, y=None):
        """Transform."""
        test_cov = self.ERP.transform(X)
        dist = self.MDM.transform(test_cov)
        dist = dist[:, 1:] - np.atleast_2d(dist[:, 0]).T
        return dist

    def update_subsample(self, old_sub, new_sub):
        """update subsampling."""
        if self._fitted:
            self.ERP.update_subsample(old_sub, new_sub)

    def _get_rest_cov(self, X, y):
        """Sample rest epochs from data and compute the cov mat."""
        ix = np.where(np.diff(y[:, 0]) == 1)[0]
        rest = []
        offset = - self.window
        for i in ix:
            start = i + offset - self.window
            stop = i + offset
            rest.append(self.ERP.erp_cov(X[slice(start, stop)].T))
        return np.array(rest)