Modifed the example

examples/plot_fmri_data_example.py

@@ -54,8 +54,6 @@
 from sklearn.linear_model import Ridge
 from sklearn.svm import LinearSVR
 from sklearn.utils import Bunch
-from sklearn.model_selection import GridSearchCV
-from sklearn.svm import LinearSVR
 
 from hidimstat.ada_svr import ada_svr
 from hidimstat.clustered_inference import clustered_inference
@@ -141,13 +139,10 @@ def preprocess_haxby(subject=2, memory=None):
 # First, we try to recover the discriminative partern by computing
 # p-values from SVR decoder weights and a parametric approximation
 # of the distribution of these weights.
-
-# We precomputed the regularization parameter by CV (C = 0.1) to reduce the
-# computation time of the example.
 beta_hat, scale = empirical_thresholding(
     X,
     y,
-    linear_estimator=GridSearchCV(LinearSVR(), param_grid={"C": [0.1]}, n_jobs=None),
+    linear_estimator=LinearSVR(),
 )
 pval_std_svr, _, one_minus_pval_std_svr, _ = pval_from_scale(beta_hat, scale)
 
@@ -323,4 +318,4 @@ def plot_map(
 # (EnCluDL) seems realistic as we recover the visual cortex and do not make
 # spurious discoveries.
 
-# show()
+show()