From a2a5cb7dd90f28086a66c619b3256fd79b690e63 Mon Sep 17 00:00:00 2001 From: BalzaniEdoardo Date: Wed, 8 Nov 2023 11:11:23 -0500 Subject: [PATCH] fixed spacing --- docs/examples/plot_glm_demo.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/docs/examples/plot_glm_demo.py b/docs/examples/plot_glm_demo.py index 7f267b76..f154ccf7 100644 --- a/docs/examples/plot_glm_demo.py +++ b/docs/examples/plot_glm_demo.py @@ -158,6 +158,7 @@ # back-end. # # Here is an example of how we can perform 5-fold cross-validation via `scikit-learn`. +# # **Ridge** parameter_grid = {"solver__regularizer_strength": np.logspace(-1.5, 1.5, 6)} @@ -253,11 +254,11 @@ n_basis_coupling = coupling_basis.shape[1] fig, axs = plt.subplots(2,2) plt.suptitle("Coupling filters") -for neu_i in range(2): - for neu_j in range(2): - axs[neu_i,neu_j].set_title(f"neu {neu_j} -> neu {neu_i}") - coeff = basis_coeff[neu_i, neu_j*n_basis_coupling: (neu_j+1)*n_basis_coupling] - axs[neu_i, neu_j].plot(np.dot(coupling_basis, coeff)) +for unit_i in range(2): + for unit_j in range(2): + axs[unit_i,unit_j].set_title(f"unit {unit_j} -> unit {unit_i}") + coeff = basis_coeff[unit_i, unit_j * n_basis_coupling: (unit_j + 1) * n_basis_coupling] + axs[unit_i, unit_j].plot(np.dot(coupling_basis, coeff)) plt.tight_layout() fig, axs = plt.subplots(1,1)