Fisher.py 文件源码

def transform(self, X):
        """
        Project the data so as to maximize class separation (large separation
        between projected class means and small variance within each class).

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]

        Returns
        -------
        X_new : array, shape = [n_samples, n_components_found_]
        """

        #X = np.asarray(X)
        #ts = time.time()
        k = self._get_kernel(X, self.X_fit_)
        #if self.print_timing: print 'KernelFisher.transform: k took', time.time() - ts

        #ts = time.time()
        z = np.inner(self.Z, (k-self.K_mean) ).T
        #if self.print_timing: print 'KernelFisher.transform: z took', time.time() - ts

        return z