def test_fit_predict_n_jobs_all(data_random):
n_clusters = data_random['n_clusters']
kmeans = MultiviewKMeans(n_clusters=n_clusters, n_jobs=-1)
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert(data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_n_clusters():
n_clusters = 3
v1_data = np.array([[0, 0],[1, 0],[0, 1]])
v2_data = np.array([[0, 0],[1, 0],[0, 1]])
data = [v1_data, v2_data]
kmeans = MultiviewKMeans(n_clusters=n_clusters)
cluster_pred = kmeans.fit_predict(data)
cluster_pred = list(set(cluster_pred))
assert(len(cluster_pred) == n_clusters)
def test_fit_predict_init_random(data_random):
n_clusters = data_random['n_clusters']
init = 'random'
kmeans = MultiviewKMeans(n_clusters=n_clusters, init='random')
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert(data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_patience(data_random):
n_clusters = data_random['n_clusters']
patience=10
kmeans = MultiviewKMeans(n_clusters=n_clusters, patience=patience)
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert(data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert(cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_max_iter(data_random):
n_clusters = data_random['n_clusters']
max_iter = 5
kmeans = MultiviewKMeans(n_clusters=n_clusters, max_iter=max_iter)
cluster_pred = kmeans.fit_predict(data_random['test_data'])
assert (data_random['n_test'] == cluster_pred.shape[0])
for cl in cluster_pred:
assert (cl >= 0 and cl < data_random['n_clusters'])
def test_fit_predict_init_predefined():
n_clusters = 2
v1_centroid = np.array([[0, 0],[1, 1]])
v2_centroid = np.array([[0, 0],[1, 1]])
centroids = [v1_centroid, v2_centroid]
v1_data = np.array([[0, 0],[0.3, 0.2],[0.5, 0.5],[0.7, 0.7],[1, 1]])
v2_data = np.array([[0, 0],[0.2, 0.4],[0.5, 0.5],[0.4, 0.7],[1, 1]])
data = [v1_data, v2_data]
kmeans = MultiviewKMeans(n_clusters=n_clusters, init=centroids)
cluster_pred = kmeans.fit_predict(data)