def test_three_blobs_multi():
km = Kmeans(k=3,
max_iter=50,
random_seed=1,
print_progress=0)
y_pred = km.fit(X).predict(X)
assert (y_pred == y).all()
def test_three_blobs_centroids():
km = Kmeans(k=3, max_iter=50, random_seed=1, print_progress=0)
centroids = np.array([[-1.5947298, 2.92236966], [2.06521743, 0.96137409],
[0.9329651, 4.35420713]])
km.fit(X)
np.testing.assert_almost_equal(centroids, km.centroids_, decimal=2)
def test_three_blobs_1sample():
km = Kmeans(k=3,
max_iter=50,
random_seed=1,
print_progress=0)
sample = X[1, :].reshape(1, 2)
y_pred = km.fit(X).predict(sample)
assert y_pred[0] == y[1]
def test_nonfitted():
km = Kmeans(k=3,
max_iter=50,
random_seed=1,
print_progress=0)
km._is_fitted = False
assert_raises(AttributeError,
'Model is not fitted, yet.',
km.predict,
X)
def test_three_blobs_centroids():
km = Kmeans(k=3,
max_iter=50,
random_seed=1,
print_progress=0)
centroids = np.array([[-1.5947298, 2.92236966],
[2.06521743, 0.96137409],
[0.9329651, 4.35420713]])
km.fit(X)
np.testing.assert_almost_equal(centroids, km.centroids_, decimal=2)
def test_continue_training():
km = Kmeans(k=3,
max_iter=1,
random_seed=1,
print_progress=0)
first_iter = np.array([[-1.33, 3.26],
[1.95, 0.99],
[1.09, 4.26]])
second_iter = np.array([[-1.5947298, 2.92236966],
[2.06521743, 0.96137409],
[0.9329651, 4.35420713]])
km.fit(X)
np.testing.assert_almost_equal(first_iter, km.centroids_, decimal=2)
assert km.iterations_ == 1, km.iterations_
km.fit(X, init_params=False)
np.testing.assert_almost_equal(second_iter, km.centroids_, decimal=2)
assert km.iterations_ == 2, km.iterations_
def test_continue_training():
km = Kmeans(k=3, max_iter=1, random_seed=1, print_progress=0)
first_iter = np.array([[-1.33, 3.26], [1.95, 0.99], [1.09, 4.26]])
second_iter = np.array([[-1.5947298, 2.92236966], [2.06521743, 0.96137409],
[0.9329651, 4.35420713]])
km.fit(X)
np.testing.assert_almost_equal(first_iter, km.centroids_, decimal=2)
assert km.iterations_ == 1, km.iterations_
km.fit(X, init_params=False)
np.testing.assert_almost_equal(second_iter, km.centroids_, decimal=2)
assert km.iterations_ == 2, km.iterations_
def test_print_progress_3():
km = Kmeans(k=3,
max_iter=50,
random_seed=1,
print_progress=3)
km.fit(X)
def test_clone():
km = Kmeans(k=2)
clone(km)
# # Kmeans
# In[106]:
import matplotlib.pyplot as plt
from mlxtend.data import three_blobs_data
X, y = three_blobs_data()
plt.scatter(X[:, 0], X[:, 1], c='b')
plt.show()
# In[107]:
from mlxtend.cluster import Kmeans
km = Kmeans(k=3, max_iter=50, random_seed=1, print_progress=3)
km.fit(X)
print('Iterations until convergence:', km.iterations_)
print('Final centroids:\n', km.centroids_)
# In[108]:
y_clust = km.predict(X)
plt.scatter(X[y_clust == 0, 0],
X[y_clust == 0, 1],
s=50,
c='lightgreen',
marker='s',
