MusicEmotionMachine.py 文件源码

def rbf_kernel(self, x, y, gamma):
    """
    Custom sigmoid kernel function, similarities of vectors using a radial basis function kernel
    :param x: array of input vectors
    :param y: array of input vectors
    :param gamma: reach factor
    :returns:
      - rbfk: radial basis of the kernel's inner product
    """     
        mat1 = np.mat(x) #convert to readable matrices
        mat2 = np.mat(y)                                                                                                
        trnorms1 = np.mat([(v * v.T)[0, 0] for v in mat1]).T #norm matrices
        trnorms2 = np.mat([(v * v.T)[0, 0] for v in mat2]).T                                                                                
        k1 = trnorms1 * np.mat(np.ones((mat2.shape[0], 1), dtype=np.float64)).T #dot products of y and y transposed and x and x transposed   
        k2 = np.mat(np.ones((mat1.shape[0], 1), dtype=np.float64)) * trnorms2.T          

        rbfk = k1 + k2 #sum products together
        rbfk -= 2 * np.mat(mat1 * mat2.T) #dot product of x and y transposed                                         
        rbfk *= - 1./(2 * np.power(gamma, 2)) #radial basis
        np.exp(rbfk,rbfk)
        return np.array(rbfk)