__init__.py 文件源码

def kde(self):
        """ Calculate kernel density estimator distribution function """

        plot = True

        # Input data
        x_data = np.linspace(-math.pi, math.pi, 1000)

        # Kernels, centered at input data points
        kernels = []

        for datapoint in self.data:
            # Make the basis function as a von mises PDF
            kernel = self.vonMisesPDF(x_data, mu=datapoint)
            kernels.append(kernel)

        # Handle weights
        if len(self.weights) > 0:
            kernels = np.asarray(kernels)
            weighted_kernels = np.multiply(kernels, self.weights[:, None])
        else:
            weighted_kernels = kernels

        # Normalize pdf
        vmkde = np.sum(weighted_kernels, axis=0)
        vmkde = vmkde / np.trapz(vmkde, x=x_data)

        self.fn = interp1d(x_data, vmkde)