a069ccc782d18b7cd930995cc20ae90703a0df20,examples/iris_dataset/uncertainty_sampling.py,,,#,15

Before Change

n_queries = 10
for idx in range(n_queries):
    query_idx, query_instance = learner.query(pool_data)
    print(query_idx, query_instance)
    learner.teach(
        X=pool_data[query_idx].reshape(1, -1),
        y=pool_labels[query_idx].reshape(1, )
    )

After Change

    // remove queried instance from pool
    pool_data = np.delete(pool_data, query_idx, axis=0)
    pool_labels = np.delete(pool_labels, query_idx)
    print("Accuracy after query no. %d: %f" % (idx+1, learner.score(iris["data"], iris["target"])))

with plt.style.context("seaborn-white"):
    prediction = learner.predict(iris["data"])
    plt.scatter(x=pca[:, 0], y=pca[:, 1], c=prediction, cmap="viridis")

Instances