spatialfilters.py 文件源码

def fit(self, X, y):
        """Train xdawn spatial filters.

        Parameters
        ----------
        X : ndarray, shape (n_trials, n_channels, n_samples)
            ndarray of trials.
        y : ndarray shape (n_trials, 1)
            labels corresponding to each trial.

        Returns
        -------
        self : Xdawn instance
            The Xdawn instance.
        """
        Nt, Ne, Ns = X.shape

        self.classes_ = (numpy.unique(y) if self.classes is None else
                         self.classes)

        # FIXME : too many reshape operation
        tmp = X.transpose((1, 2, 0))
        Cx = numpy.matrix(self.estimator(tmp.reshape(Ne, Ns * Nt)))

        self.evokeds_ = []
        self.filters_ = []
        self.patterns_ = []
        for c in self.classes_:
            # Prototyped responce for each class
            P = numpy.mean(X[y == c, :, :], axis=0)

            # Covariance matrix of the prototyper response & signal
            C = numpy.matrix(self.estimator(P))

            # Spatial filters
            evals, evecs = eigh(C, Cx)
            evecs = evecs[:, numpy.argsort(evals)[::-1]]  # sort eigenvectors
            evecs /= numpy.apply_along_axis(numpy.linalg.norm, 0, evecs)
            V = evecs
            A = numpy.linalg.pinv(V.T)
            # create the reduced prototyped response
            self.filters_.append(V[:, 0:self.nfilter].T)
            self.patterns_.append(A[:, 0:self.nfilter].T)
            self.evokeds_.append(numpy.dot(V[:, 0:self.nfilter].T, P))

        self.evokeds_ = numpy.concatenate(self.evokeds_, axis=0)
        self.filters_ = numpy.concatenate(self.filters_, axis=0)
        self.patterns_ = numpy.concatenate(self.patterns_, axis=0)
        return self