From 903d1c178f699f82c33d35b1e1b56d4edec9ed56 Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Wed, 6 Jul 2022 17:51:19 -0700 Subject: [PATCH 1/3] Replace "consequences" with "advantages" The word "consequence" usually means something negative, so it's better to replace it with "advantage" to refer to the positive outcomes. --- examples/cluster/plot_agglomerative_clustering.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/cluster/plot_agglomerative_clustering.py b/examples/cluster/plot_agglomerative_clustering.py index 9d590f572f121..5bb87a9386bf8 100644 --- a/examples/cluster/plot_agglomerative_clustering.py +++ b/examples/cluster/plot_agglomerative_clustering.py @@ -6,7 +6,7 @@ local structure in the data. The graph is simply the graph of 20 nearest neighbors. -Two consequences of imposing a connectivity can be seen. First, clustering +There are two advantages of imposing a connectivity. First, clustering without a connectivity matrix is much faster. Second, when using a connectivity matrix, single, average and complete From e6a230657e6ddb0e251929d5e9adda1558e76d70 Mon Sep 17 00:00:00 2001 From: "Christine P. Chai" Date: Fri, 8 Jul 2022 15:38:18 -0700 Subject: [PATCH 2/3] Added linestyle changes Linestyle changes make the graph easier to read for people with color blindness. --- examples/classification/plot_lda.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/classification/plot_lda.py b/examples/classification/plot_lda.py index 47487fc1f2caf..a93a59be27ed3 100644 --- a/examples/classification/plot_lda.py +++ b/examples/classification/plot_lda.py @@ -71,6 +71,7 @@ def generate_data(n_samples, n_features): linewidth=2, label="Linear Discriminant Analysis with Ledoit Wolf", color="navy", + linestyle="dashed" ) plt.plot( features_samples_ratio, @@ -78,6 +79,7 @@ def generate_data(n_samples, n_features): linewidth=2, label="Linear Discriminant Analysis", color="gold", + linestyle="solid" ) plt.plot( features_samples_ratio, @@ -85,6 +87,7 @@ def generate_data(n_samples, n_features): linewidth=2, label="Linear Discriminant Analysis with OAS", color="red", + linestyle="dotted" ) plt.xlabel("n_features / n_samples") From 85aba369de2a394e42757950aa9029e4d831d8dc Mon Sep 17 00:00:00 2001 From: star1327p Date: Sun, 10 Jul 2022 18:50:08 -0700 Subject: [PATCH 3/3] reformatted using black --- examples/classification/plot_lda.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/classification/plot_lda.py b/examples/classification/plot_lda.py index a93a59be27ed3..4213fc614a31a 100644 --- a/examples/classification/plot_lda.py +++ b/examples/classification/plot_lda.py @@ -71,7 +71,7 @@ def generate_data(n_samples, n_features): linewidth=2, label="Linear Discriminant Analysis with Ledoit Wolf", color="navy", - linestyle="dashed" + linestyle="dashed", ) plt.plot( features_samples_ratio, @@ -79,7 +79,7 @@ def generate_data(n_samples, n_features): linewidth=2, label="Linear Discriminant Analysis", color="gold", - linestyle="solid" + linestyle="solid", ) plt.plot( features_samples_ratio, @@ -87,7 +87,7 @@ def generate_data(n_samples, n_features): linewidth=2, label="Linear Discriminant Analysis with OAS", color="red", - linestyle="dotted" + linestyle="dotted", ) plt.xlabel("n_features / n_samples")