local_transition.py 文件源码

def _cov(self, indices, n):
        if len(indices) > 1:
            surrounding_indices = indices[n, 1:]
            nearest_vector_deltas = (self.X_arr[surrounding_indices]
                                     - self.X_arr[n])
            local_weights = self.w[surrounding_indices]
        else:
            nearest_vector_deltas = sp.absolute(self.X_arr)
            local_weights = sp.array([1])
        cov = smart_cov(nearest_vector_deltas,
                        local_weights / local_weights.sum())
        if sp.absolute(cov.sum()) == 0:
            for k in range(cov.shape[0]):
                cov[k, k] = sp.absolute(self.X_arr[0, k])
        return cov * self.scaling