Method __init__

dscribe/kernels/rematchkernel.py:44–86

Args: alpha(float): Parameter controlling the entropic penalty. Values close to zero approach the best-match solution and values towards infinity approach the average kernel. threshold(float): Convergence threshold used in the

(
        self,
        alpha=0.1,
        threshold=1e-6,
        metric="linear",
        gamma=None,
        degree=3,
        coef0=1,
        kernel_params=None,
        normalize_kernel=True,
    )

    """

    def __init__(
        self,
        alpha=0.1,
        threshold=1e-6,
        metric="linear",
        gamma=None,
        degree=3,
        coef0=1,
        kernel_params=None,
        normalize_kernel=True,
    ):
        """
        Args:
            alpha(float): Parameter controlling the entropic penalty. Values
                close to zero approach the best-match solution and values
                towards infinity approach the average kernel.
            threshold(float): Convergence threshold used in the
                Sinkhorn-algorithm.
            metric(string or callable): The pairwise metric used for
                calculating the local similarity. Accepts any of the sklearn
                pairwise metric strings (e.g. "linear", "rbf", "laplacian",
                "polynomial") or a custom callable. A callable should accept
                two arguments and the keyword arguments passed to this object
                as kernel_params, and should return a floating point number.
            gamma(float): Gamma parameter for the RBF, laplacian, polynomial,
                exponential chi2 and sigmoid kernels. Interpretation of the
                default value is left to the kernel; see the documentation for
                sklearn.metrics.pairwise. Ignored by other kernels.
            degree(float): Degree of the polynomial kernel. Ignored by other
                kernels.
            coef0(float): Zero coefficient for polynomial and sigmoid kernels.
                Ignored by other kernels.
            kernel_params(mapping of string to any): Additional parameters
                (keyword arguments) for kernel function passed as callable
                object.
            normalize_kernel(boolean): Whether to normalize the final global
                similarity kernel. The normalization is achieved by dividing each
                kernel element :math:`K_{ij}` with the factor
                :math:`\sqrt{K_{ii}K_{jj}}`
        """
        self.alpha = alpha
        self.threshold = threshold
        super().__init__(metric, gamma, degree, coef0, kernel_params, normalize_kernel)

    def get_global_similarity(self, localkernel):
        """