be95ff167e1ea0195e32beb0618228d18a4098eb,Python/phate/phate.py,PHATE,calculate_potential,#PHATE#Any#Any#,603

Before Change

        diff_potential : array-like, shape=[n_samples, n_samples]
            The diffusion potential fit on the input data
        
        tic = time.time()
        if self.verbose:
            print("Calculating diffusion potential...")

        // diffused diffusion operator
        diff_op_t = np.linalg.matrix_power(diff_op, t)

        if self.potential_method == "log":
            // handling small values
            diff_op_t = diff_op_t + 1e-3
            self.diff_potential = -1 * np.log(diff_op_t)
        elif self.potential_method == "sqrt":
            self.diff_potential = np.sqrt(diff_op_t)
        else:
            raise ValueError("Allowable "potential_method" values: "log" or "
                             ""sqrt". "{}" was passed.".format(
                                 self.potential_method))
        if self.verbose:
            print("Calculated diffusion potential in {:.2f} seconds.".format(
                time.time() - tic))

    def von_neumann_entropy(self, t_max=100):
        Calculate Von Neumann Entropy

        Determines the Von Neumann entropy of the diffusion affinities

After Change

            raise ValueError("Allowable "potential_method" values: "log" or "
                             ""sqrt". "{}" was passed.".format(
                                 self.potential_method))
        log_complete("diffusion potential")

    def von_neumann_entropy(self, t_max=100):
        Calculate Von Neumann Entropy

Instances