def test_check_means():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
n_components, n_features = rand_data.n_components, rand_data.n_features
X = rand_data.X['full']
g = GaussianMixture(n_components=n_components)
# Check means bad shape
means_bad_shape = rng.rand(n_components + 1, n_features)
g.means_init = means_bad_shape
assert_raise_message(ValueError,
"The parameter 'means' should have the shape of ",
g.fit, X)
# Check good means matrix
means = rand_data.means
g.means_init = means
g.fit(X)
assert_array_equal(means, g.means_init)
def test_check_means():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
n_components, n_features = rand_data.n_components, rand_data.n_features
X = rand_data.X['full']
g = GaussianMixture(n_components=n_components)
# Check means bad shape
means_bad_shape = rng.rand(n_components + 1, n_features)
g.means_init = means_bad_shape
assert_raise_message(ValueError,
"The parameter 'means' should have the shape of ",
g.fit, X)
# Check good means matrix
means = rand_data.means
g.means_init = means
g.fit(X)
assert_array_equal(means, g.means_init)
X_train = np.array([[-4, 2], [-2, 1], [-2, 3], [0, 2], [2, -1], [3, -1], [2, -2], [3, -2]])
estimator = GaussianMixture(n_components=2, max_iter=1000, random_state=0, init_params='random')
plt.figure()
plt.plot(X_train[:, 0], X_train[:, 1], 'k.', markersize=25)
plt.savefig('Figure_6-datapoints.png')
plt.close()
colors = ['r', 'b']
# initial means
estimator.means_init = np.array([[0, 0.5], [0.5, 0]])
# Train the other parameters using the EM algorithm.
estimator.fit(X_train)
classes = estimator.predict(X_train)
plt.figure()
for i in range(2):
plt.plot(X_train[classes == i, 0], X_train[classes == i, 1], colors[i]+'.', markersize=25, label='class'+str(i+1))
plt.plot(estimator.means_init[i, 0], estimator.means_init[i, 1], colors[i]+'*', markersize=20, label='mean_init')
plt.plot(np.mean(X_train[classes == i, 0]), np.mean(X_train[classes == i, 1]), colors[i] + 'P', markersize=15, label='mean_init')
plt.title('mean_init='+str(estimator.means_init))
plt.legend()
plt.savefig('Figure_6-EM1.png')
plt.close()
# initial means