MLCM creates a 2D Multi-Label Confusion Matrix

Please read the following paper for more information:
M. Heydarian, T. Doyle, and R. Samavi, MLCM: Multi-Label Confusion Matrix,
IEEE Access, Feb. 2022, DOI: 10.1109/ACCESS.2022.3151048
For other projects please see https://biomedic.ai/

Please cite the paper if you are using the MLCM.
This work is licensed under a Creative Commons Attribution 4.0 License.
For more information, see https://creativecommons.org/licenses/by/4.0/

An example on how to use MLCM package:

% Importing libraries

from mlcm import mlcm
import numpy as np

% Creating random input (multi-label data)

number_of_samples = 1000
number_of_classes = 5
label_true = np.random.randint(2, size=(number_of_samples, number_of_classes))
label_pred = np.random.randint(2, size=(number_of_samples, number_of_classes))

% Calling mlcm and illustrating the results

conf_mat,normal_conf_mat = mlcm.cm(label_true,label_pred)
print('\nRaw confusion Matrix:')
print(conf_mat)
print('\nNormalized confusion Matrix (%):')
print(normal_conf_mat)

one_vs_rest = mlcm.stats(conf_mat)