HypercubeKernel.py 文件源码

def kernel(self, X, Y=None):
        """
        Computes the hypercube kerpy k(x,y)=tanh(gamma)^d(x,y), where d is the
        Hamming distance between x and y

        X - 2d numpy.bool8 array, samples on right left side
        Y - 2d numpy.bool8 array, samples on left hand side.
            Can be None in which case its replaced by X
        """

        if not type(X) is numpy.ndarray:
            raise TypeError("X must be numpy array")

        if not len(X.shape) == 2:
            raise ValueError("X must be 2D numpy array")

        if not X.dtype == numpy.bool8:
            raise ValueError("X must be boolean numpy array")

        if not Y is None:
            if not type(Y) is numpy.ndarray:
                raise TypeError("Y must be None or numpy array")

            if not len(Y.shape) == 2:
                raise ValueError("Y must be None or 2D numpy array")

            if not Y.dtype == numpy.bool8:
                raise ValueError("Y must be boolean numpy array")

            if not X.shape[1] == Y.shape[1]:
                raise ValueError("X and Y must have same dimension if Y is not None")

        # un-normalise normalised hamming distance in both cases
        if Y is None:
            K = tanh(self.gamma) ** squareform(pdist(X, 'hamming') * X.shape[1])
        else:
            K = tanh(self.gamma) ** (cdist(X, Y, 'hamming') * X.shape[1])

        return K