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- def generate_data(n_samples, n_features):
- X, y = make_blobs(n_samples=n_samples, n_features=1, centers=[[-2], [2]])
- if n_features > 1:
- X = np.hstack([X, np.random.randn(n_samples, n_features - 1)])
- return X, y
- acc_clf1, acc_clf2 = [], []
- n_features_range = range(1, n_features_max + 1, step)
- for n_features in n_features_range:
- score_clf1, score_clf2 = 0, 0
- for _ in range(n_averages):
- X, y = generate_data(n_train, n_features)
- clf1 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage='auto').fit(X, y)
- clf2 = LinearDiscriminantAnalysis(solver='lsqr', shrinkage=None).fit(X, y)
- X, y = generate_data(n_test, n_features)
- score_clf1 += clf1.score(X, y)
- score_clf2 += clf2.score(X, y)
- acc_clf1.append(score_clf1 / n_averages)
- acc_clf2.append(score_clf2 / n_averages)
- features_samples_ratio = np.array(n_features_range) / n_train
- plt.plot(features_samples_ratio, acc_clf1, linewidth=2,
- label="Linear Discriminant Analysis with shrinkage", color='navy')
- plt.plot(features_samples_ratio, acc_clf2, linewidth=2,
- label="Linear Discriminant Analysis", color='gold')
- plt.xlabel('n_features / n_samples')
- plt.ylabel('Classification accuracy')
- plt.legend(loc=1, prop={'size': 12})
- plt.suptitle('Linear Discriminant Analysis vs. \
- shrinkage Linear Discriminant Analysis (1 discriminative feature)')
- plt.show()
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