b4db36d337a4ff83f1bcb37c5a8c615d3134d372,examples/covariance/plot_mahalanobis_distances.py,,,#,55
Before Change
subfig3.axes.set_xticklabels(("inliers", "outliers"), size=15)
subfig3.set_ylabel(r"$\sqrt[3]{\rm{(Mahal. dist.)}}$", size=16)
subfig3.set_title("2. from robust estimates\n(Minimum Covariance Determinant)")
plt.yticks(())
plt.show()
After Change
// whereas the MLE based distances are more influenced by the outlier
// red points.
fig , ax = plt.subplots(figsize=(10, 5))
// Plot data set
inlier_plot = ax.scatter(X[:, 0], X[:, 1],
color="black", label="inliers")In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 3
Instances Project Name: scikit-learn/scikit-learn
Commit Name: b4db36d337a4ff83f1bcb37c5a8c615d3134d372
Time: 2020-05-20
Author: jliu176@gmail.com
File Name: examples/covariance/plot_mahalanobis_distances.py
Class Name:
Method Name:
Project Name: nilearn/nilearn
Commit Name: f25d4df0127537d57d4d7f7cd3fa52ca31ffa3ff
Time: 2017-07-28
Author: moritz.boos@uni-oldenburg.de
File Name: examples/03_connectivity/plot_signal_extraction.py
Class Name:
Method Name:
Project Name: nilearn/nilearn
Commit Name: f25d4df0127537d57d4d7f7cd3fa52ca31ffa3ff
Time: 2017-07-28
Author: moritz.boos@uni-oldenburg.de
File Name: examples/03_connectivity/plot_inverse_covariance_connectome.py
Class Name:
Method Name:
