def test_purity():
labels = [1, 1, 2, 2]
preds = [1, 1, 2, 2]
purity = general.purity(labels, preds)
assert purity == 1
import numpy as np import opfython.math.general as g # Defining array, labels and predictions array = np.asarray([1.5, 2, 0.5, 1.25, 1.75, 3]) labels = [0, 0, 0, 1, 1, 1, 2] preds = [0, 0, 1, 1, 0, 1, 2] # Normalizing the array norm_array = g.normalize(array) print(norm_array) # Calculating the confusion matrix c_matrix = g.confusion_matrix(labels, preds) print(c_matrix) # Calculating OPF-like accuracy opf_acc = g.opf_accuracy(labels, preds) print(opf_acc) # Calculating OPF-like accuracy per label opf_acc_per_label = g.opf_accuracy_per_label(labels, preds) print(opf_acc_per_label) # Calculating purity measure purity = g.purity(labels, preds) print(purity)