conditionally_positive_definite_rbf.py 文件源码

def fit(self, Y):
        """
        Generates the RBF coefficients to fit a set of given data values Y for centers self.centers
        :param Y: A set of dependent data values corresponding to self.centers
        :return: Void, sets the self.coefs values
        """
        kernel_matrix = self.EvaluateCentersKernel()
        kernel_matrix[np.isinf(kernel_matrix)] = 0 # TODO: Is there a better way to avoid the diagonal?
        monomial_basis = poly.GetMonomialBasis(self.dimension, self.poly_degree)
        poly_matrix = poly.BuildPolynomialMatrix(monomial_basis, self.centers.transpose()) # TODO: Probably remove transpose requirement
        poly_shape = np.shape(poly_matrix)
        # Get the number of columns, as we need to make an np.zeros((num_cols,num_cols))
        num_cols = poly_shape[1]
        num_rbf_coefs = len(self.centers)
        zero_mat = np.zeros((num_cols,num_cols))
        upper_matrix = np.hstack((kernel_matrix, poly_matrix))
        lower_matrix = np.hstack((poly_matrix.transpose(),zero_mat))
        rbf_matrix = np.vstack((upper_matrix,lower_matrix))
        Y = np.concatenate((Y,np.zeros((num_cols)))) # Extend with zeros for the polynomial annihilation
        self.coefs = sl.solve(rbf_matrix, Y, sym_pos=False)