55b6fb811938351d42929228ccbb0281183d8cf6,Python/phate/phate.py,PHATE,von_neumann_entropy,#PHATE#Any#,488

Before Change

            t_max = np.floor(t_max / 2).astype(np.int16)
        _, eigenvalues, _ = svd(self.diff_op)
        entropy = []
        eigenvalues_t = np.copy(eigenvalues)
        for _ in range(t_max):
            prob = eigenvalues_t / np.sum(eigenvalues_t)
            prob = prob + np.finfo(float).eps
            entropy.append(-np.sum(prob * np.log(prob)))
            eigenvalues_t = eigenvalues_t * eigenvalues
        entropy = np.array(entropy)

After Change

            t_max = np.floor(t_max / 2).astype(np.int16)
            t = np.arange(t_max) * 2 + 1
        else:
            t = np.arange(t_max)

        return t, compute_von_neumann_entropy(self.diff_op, t_max=t_max)

    def optimal_t(self, t_max=100, plot=False, ax=None):

Instances