def test_agglomerative_clustering_with_distance_threshold_edge_case(
linkage, threshold, y_true):
# test boundary case of distance_threshold matching the distance
X = [[0], [1]]
clusterer = AgglomerativeClustering(n_clusters=None,
distance_threshold=threshold,
linkage=linkage)
y_pred = clusterer.fit_predict(X)
assert adjusted_rand_score(y_true, y_pred) == 1
X[:, 1],
c=color,
alpha=alpha,
edgecolor='k')
# Generate some training data from clustering
X, y = make_blobs(n_samples=N_SAMPLES,
cluster_std=[1.0, 1.0, 0.5],
centers=[(-5, -5), (0, 0), (5, 5)],
random_state=RANDOM_STATE)
# Train a clustering algorithm on the training data and get the cluster labels
clusterer = AgglomerativeClustering(n_clusters=3)
cluster_labels = clusterer.fit_predict(X)
plt.figure(figsize=(12, 4))
plt.subplot(131)
plot_scatter(X, cluster_labels)
plt.title("Ward Linkage")
# Generate new samples and plot them along with the original dataset
X_new, y_new = make_blobs(n_samples=10,
centers=[(-7, -1), (-2, 4), (3, 6)],
random_state=RANDOM_STATE)
plt.subplot(132)
plot_scatter(X, cluster_labels)