From 7826a4b86e38100ac642601518a864671647c066 Mon Sep 17 00:00:00 2001 From: James Chapman Date: Wed, 17 Nov 2021 08:31:08 +0000 Subject: [PATCH] Removing the slow constrained option for Elastic CCA. Now uses maxvar by default and sumcor for lasso where valid --- examples/plot_kernel_cca.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/examples/plot_kernel_cca.py b/examples/plot_kernel_cca.py index 3f661d66..581c4476 100644 --- a/examples/plot_kernel_cca.py +++ b/examples/plot_kernel_cca.py @@ -36,8 +36,11 @@ def my_kernel(X, Y, param=0): return np.random.normal(0, param) -kernel_custom = KCCA(latent_dims=latent_dims, kernel=[my_kernel, my_kernel], - kernel_params=[{'param': 1}, {'param': 1}]).fit((X, Y)) +kernel_custom = KCCA( + latent_dims=latent_dims, + kernel=[my_kernel, my_kernel], + kernel_params=[{"param": 1}, {"param": 1}], +).fit((X, Y)) # %% # Linear @@ -90,5 +93,8 @@ def my_kernel(X, Y, param=0): return X @ M @ M.T @ Y.T -kernel_custom = KCCA(latent_dims=latent_dims, kernel=[my_kernel, my_kernel], - kernel_params=[{'param': 1}, {'param': 1}]).fit((X, Y)) +kernel_custom = KCCA( + latent_dims=latent_dims, + kernel=[my_kernel, my_kernel], + kernel_params=[{"param": 1}, {"param": 1}], +).fit((X, Y))