Pushing the docs to dev/ for branch: main, commit ca0862a9dbc5dadab2ccd30828de6de0c6f1f69d

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -41,7 +41,7 @@
 plt.subplot(4, 1, 1)
 plt.xlim(0, 512)
 plt.title("Sparse signal")
-plt.stem(idx, w[idx], use_line_collection=True)
+plt.stem(idx, w[idx])
 
 # plot the noise-free reconstruction
 omp = OrthogonalMatchingPursuit(n_nonzero_coefs=n_nonzero_coefs)
@@ -51,7 +51,7 @@
 plt.subplot(4, 1, 2)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noise-free measurements")
-plt.stem(idx_r, coef[idx_r], use_line_collection=True)
+plt.stem(idx_r, coef[idx_r])
 
 # plot the noisy reconstruction
 omp.fit(X, y_noisy)
@@ -60,7 +60,7 @@
 plt.subplot(4, 1, 3)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noisy measurements")
-plt.stem(idx_r, coef[idx_r], use_line_collection=True)
+plt.stem(idx_r, coef[idx_r])
 
 # plot the noisy reconstruction with number of non-zeros set by CV
 omp_cv = OrthogonalMatchingPursuitCV()
@@ -70,7 +70,7 @@
 plt.subplot(4, 1, 4)
 plt.xlim(0, 512)
 plt.title("Recovered signal from noisy measurements with CV")
-plt.stem(idx_r, coef[idx_r], use_line_collection=True)
+plt.stem(idx_r, coef[idx_r])
 
 plt.subplots_adjust(0.06, 0.04, 0.94, 0.90, 0.20, 0.38)
 plt.suptitle("Sparse signal recovery with Orthogonal Matching Pursuit", fontsize=16)