From 490fdb3d9c36fad04036e91d3af316a4dd6a4bca Mon Sep 17 00:00:00 2001 From: Kazuki Adachi Date: Sun, 8 Sep 2024 12:41:12 +0900 Subject: [PATCH] minor update on docstring --- ignite/metrics/hsic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ignite/metrics/hsic.py b/ignite/metrics/hsic.py index e144ed8ac43..5e6adbbc084 100644 --- a/ignite/metrics/hsic.py +++ b/ignite/metrics/hsic.py @@ -18,14 +18,14 @@ class HSIC(Metric): + \frac{\mathbf{1}^\top \tilde{\mathbf{K}} \mathbf{11}^\top \tilde{\mathbf{L}} \mathbf{1}}{(B-1)(B-2)} -\frac{2}{B-2}\mathbf{1}^\top \tilde{\mathbf{K}}\tilde{\mathbf{L}} \mathbf{1} \right] - where :math:`\tilde{\mathbf{K}}` and :math:`\tilde{\mathbf{L}}` are the Gram matrices of + where :math:`B` is the batch size, and :math:`\tilde{\mathbf{K}}` and :math:`\tilde{\mathbf{L}}` are the Gram matrices of the Gaussian RBF kernel with their diagonal entries being set to zero. HSIC measures non-linear statistical independence between features :math:`X` and :math:`Y`. HSIC becomes zero if and only if :math:`X` and :math:`Y` are independent. This metric computes the unbiased estimator of HSIC proposed in - `Song et al., 2012 `_ + `Song et al. (2012) `_ for each batch and accumulates the average. Each batch must contain at least four samples.