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March 28, 2023 11:17
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| from sklearn.model_selection import train_test_split | |
| import numpy as np | |
| a = ["1", "3", "4", "1", "3", "4"] | |
| b = [[2, 4, 3],[0, 4, 5], [8, 9, 5], [5, 4, 5],[8, 4, 5], [2, 9, 5]] | |
| y_train = np.array(a) | |
| x_train = np.array(b) | |
| # Step 2: Select 2 random classes for training | |
| random_words = np.random.choice(np.unique(y_train), size=2, replace=False) | |
| train_indices = np.isin(y_train, random_words) | |
| x_train_random = x_train[train_indices] | |
| y_train_random = y_train[train_indices] | |
| #print(len(train_indices)) | |
| #print(train_indices) | |
| #print(x_train_random) | |
| #print(len(x_train_random)) | |
| #print(y_train_random) | |
| # Step 3: Select the remaining 1 class for the gallery set | |
| remaining_words = np.setdiff1d(np.unique(y_train), random_words) | |
| gallery_indices = np.isin(y_train, remaining_words) | |
| x_gallery_probe = x_train[gallery_indices] | |
| y_gallery_probe = y_train[gallery_indices] | |
| #print(x_gallery_probe) | |
| #print(len(x_gallery_probe)) | |
| #print(y_gallery_probe) | |
| #print(len(y_gallery_probe)) | |
| # Create a list containing both arrays | |
| data1 = [x_train_random, y_train_random] | |
| data2 = [x_gallery_probe, y_gallery_probe] | |
| gallery, probe = train_test_split( | |
| x_gallery_probe, test_size=0.50, random_state=42, stratify=y_gallery_probe) | |
| print(gallery) | |
| print(probe) | |
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