_classification.py 文件源码

def __new__(self, time, y, x, clf='lda', cvtype=None, clfArg={},
                cvArg={}):

        self.y = np.ravel(y)
        self.time = time

        # Define clf if it's not defined :
        if isinstance(clf, (int, str)):
            clf = defClf(y, clf=clf, **clfArg)
        self.clf = clf

        # Define cv if it's not defined :
        if isinstance(cvtype, str) and (cvtype is not None):
            cvtype = defCv(y, cvtype=cvtype, rep=1, **cvArg)
        self.cv = cvtype
        if isinstance(cvtype, list):
            cvtype = cvtype[0]
        # Check the size of x:
        x = np.atleast_3d(x)
        npts, ntrials = len(time), len(y)
        if x.shape[0] is not npts:
            raise ValueError('First dimension of x must be '+str(npts))
        if x.shape[1] is not ntrials:
            raise ValueError('Second dimension of x must be '+str(ntrials))

        da = np.zeros([npts, npts])
        # Training dimension
        for k in range(npts):
            xx = x[k, ...]
            # Testing dimension
            for i in range(npts):
                xy = x[i, ...]
                # If cv is defined, do a cv on the diagonal
                if (k == i) and (cvtype is not None):
                    da[i, k] = _cvscore(xx, y, clf, self.cv.cvr[0])[0]/100
                # If cv is not defined, let the diagonal at zero
                elif (k == i) and (cvtype is None):
                    pass
                else:
                    da[i, k] = accuracy_score(y, clf.fit(xx, y).predict(xy))
        return 100*da