def _fit(self, X: np.ndarray) -> np.ndarray:
pred = self._cluster_and_decide(X)
self.children: Tuple["DivisiveCluster"] = cast(
Tuple["DivisiveCluster"], tuple())
uni_labels = np.unique(pred)
labels = pred.reshape((-1, 1)).copy()
if len(uni_labels) > 1:
for ul in uni_labels:
inds = pred == ul
new_X = X[inds]
dc = DivisiveCluster(
cluster_method=self.cluster_method,
max_components=self.max_components,
min_split=self.min_split,
max_level=self.max_level,
cluster_kws=self.cluster_kws,
delta_criter=self.delta_criter,
)
dc.parent = self
if (len(new_X) > self.max_components
and len(new_X) >= self.min_split
and self.depth + 1 < self.max_level):
child_labels = dc._fit(new_X)
while labels.shape[1] <= child_labels.shape[1]:
labels = np.column_stack(
(labels, np.zeros((len(X), 1), dtype=int)))
labels[inds, 1:child_labels.shape[1] + 1] = child_labels
else:
# make a "GaussianMixture" model for clusters
# that were not fitted
if self.cluster_method == "gmm":
cluster_idx = len(dc.parent.children) - 1
parent_model = dc.parent.model_
model = GaussianMixture()
model.weights_ = np.array([1])
model.means_ = parent_model.means_[
cluster_idx].reshape(1, -1)
model.covariance_type = parent_model.covariance_type
if model.covariance_type == "tied":
model.covariances_ = parent_model.covariances_
model.precisions_ = parent_model.precisions_
model.precisions_cholesky_ = (
parent_model.precisions_cholesky_)
else:
cov_types = ["spherical", "diag", "full"]
n_features = model.means_.shape[-1]
cov_shapes = [
(1, ),
(1, n_features),
(1, n_features, n_features),
]
cov_shape_idx = cov_types.index(
model.covariance_type)
model.covariances_ = parent_model.covariances_[
cluster_idx].reshape(cov_shapes[cov_shape_idx])
model.precisions_ = parent_model.precisions_[
cluster_idx].reshape(cov_shapes[cov_shape_idx])
model.precisions_cholesky_ = (
parent_model.precisions_cholesky_[cluster_idx].
reshape(cov_shapes[cov_shape_idx]))
dc.model_ = model
return labels
x1 = x1.dot(m)
x2 = multivariate_normal(mean=(-1, 10), cov=cov1, size=N2)
x = np.vstack((x1, x2))
y = np.array([True] * 500 + [False] * 300)
return x, y
if __name__ == '__main__':
x, y = get_data()
types = ('spherical', 'diag', 'tied', 'full')
err = np.empty(len(types))
bic = np.empty(len(types))
for i, type in enumerate(types):
model = GaussianMixture(n_components=2, random_state=0)
model.covariance_type = type
model.fit(x)
y_hat = model.predict(x)
accuracy = accuracy_score(y_hat.ravel(), y.ravel())
if accuracy > 0.5:
err[i] = 1 - accuracy
else:
err[i] = accuracy
bic[i] = model.bic(x)
print('错误率:', err.ravel())
print('BIC:', bic.ravel())
xpos = np.arange(4)
plt.figure(facecolor='w')
ax = plt.axes()
b1 = ax.bar(xpos - 0.3, err, width=0.3, color='#77E0A0')
b2 = ax.twinx().bar(xpos, bic, width=0.3, color='#FF8080')