def test_gaussian_mixture_fit_predict_n_init():
# Check that fit_predict is equivalent to fit.predict, when n_init > 1
X = np.random.RandomState(0).randn(1000, 5)
gm = GaussianMixture(n_components=5, n_init=5, random_state=0)
y_pred1 = gm.fit_predict(X)
y_pred2 = gm.predict(X)
assert_array_equal(y_pred1, y_pred2)
def test_gaussian_mixture_fit_predict_n_init():
# Check that fit_predict is equivalent to fit.predict, when n_init > 1
X = np.random.RandomState(0).randn(1000, 5)
gm = GaussianMixture(n_components=5, n_init=5, random_state=0)
y_pred1 = gm.fit_predict(X)
y_pred2 = gm.predict(X)
assert_array_equal(y_pred1, y_pred2)
def fuse(self, agent1, agent2):
gmm = GaussianMixture(n_components=self.num_pattern).fit(np.concatenate((agent1.patterns[0], agent2.patterns[0])))
s1 = agent1.S_label_pattern
s2 = agent2.S_label_pattern
idx1 = gmm.predict(agent1.patterns[0])
idx2 = gmm.predict(agent2.patterns[0])
s = np.ones((self.num_pattern, 10))
for j in range(self.num_pattern):
i1 = np.argwhere(idx1 == j)[:, 0]
for i in i1:
s[j, :] *= s1[i, :]
i2 = np.argwhere(idx2 == j)[:, 0]
for i in i2:
s[j, :] *= s2[i, :]
normalization_const = np.sum(s, axis=1, keepdims=True)
# normalization_const = np.reshape(normalization_const, (self.num_pattern, 10))
normalization_const = np.tile(normalization_const, (1, 10))
s /= normalization_const
return Agent(agent1.sess, (gmm.means_, gmm.covariances_), s)
def test_gaussian_mixture_predict_predict_proba():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
Y = rand_data.Y
g = GaussianMixture(n_components=rand_data.n_components,
random_state=rng, weights_init=rand_data.weights,
means_init=rand_data.means,
precisions_init=rand_data.precisions[covar_type],
covariance_type=covar_type)
# Check a warning message arrive if we don't do fit
assert_raise_message(NotFittedError,
"This GaussianMixture instance is not fitted "
"yet. Call 'fit' with appropriate arguments "
"before using this method.", g.predict, X)
g.fit(X)
Y_pred = g.predict(X)
Y_pred_proba = g.predict_proba(X).argmax(axis=1)
assert_array_equal(Y_pred, Y_pred_proba)
assert_greater(adjusted_rand_score(Y, Y_pred), .95)
def test_gaussian_mixture_predict_predict_proba():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
Y = rand_data.Y
g = GaussianMixture(n_components=rand_data.n_components,
random_state=rng, weights_init=rand_data.weights,
means_init=rand_data.means,
precisions_init=rand_data.precisions[covar_type],
covariance_type=covar_type)
# Check a warning message arrive if we don't do fit
assert_raise_message(NotFittedError,
"This GaussianMixture instance is not fitted "
"yet. Call 'fit' with appropriate arguments "
"before using this method.", g.predict, X)
g.fit(X)
Y_pred = g.predict(X)
Y_pred_proba = g.predict_proba(X).argmax(axis=1)
assert_array_equal(Y_pred, Y_pred_proba)
assert_greater(adjusted_rand_score(Y, Y_pred), .95)
g = GaussianMixture(n_components=N, n_init=N_INIT, init_params=INIT_METHOD)
for j, side in enumerate(SIDES.keys()):
data_ = data[side_index == j]
labels_ = labels[side_index == j]
final_labels = labels_.copy()
# reencode the labels to take into account the added clusters
final_labels[labels_ == 2] = 4
# select central cluster
data_ = data_[labels_ == 1]
g.fit(data_)
w = g.weights_
m = g.means_
c = g.covariances_
pred_labels = g.predict(data_)
# directly sort the labels according to the precentral coordinates of the cluster
t = np.argsort(m, axis=0)[:, 0]
sorted_means = m[t]
sorted_cov = c[t]
sorted_weigth = w[t]
sorted_pred_labels = pred_labels.copy()
for z, s in enumerate(t):
# s+1 to account for the existence of ventral cluster
sorted_pred_labels[pred_labels == s] = z + 1
final_labels[labels_ == 1] = sorted_pred_labels
np.save(CLUSTERING_LABELS[side], final_labels)
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
estimator.means_init = np.array([[0, 1], [0, -1]])
# Train the other parameters using the EM algorithm.
estimator.fit(X_train)
