sklearn.cluster.k_means._kmeans_single()
def _kmeans_single(X, n_clusters, x_squared_norms, max_iter=300,
init='k-means++', verbose=False, random_state=None,
tol=1e-4, precompute_distances=True):
"""A single run of k-means, assumes preparation completed prior.
Parameters
----------
X: array-like of floats, shape (n_samples, n_features)
The observations to cluster.
n_clusters: int
The number of clusters to form as well as the number of
centroids to generate.
max_iter: int, optional, default 300
Maximum number of iterations of the k-means algorithm to run.
init: {'k-means++', 'random', or ndarray, or a callable}, optional
Method for initialization, default to 'k-means++':
'k-means++' : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.
'random': generate k centroids from a Gaussian with mean and
variance estimated from the data.
If an ndarray is passed, it should be of shape (k, p) and gives
the initial centers.
If a callable is passed, it should take arguments X, k and
and a random state and return an initialization.
tol: float, optional
The relative increment in the results before declaring convergence.
verbose: boolean, optional
Verbosity mode
x_squared_norms: array
Precomputed x_squared_norms.
precompute_distances : boolean, default: True
Precompute distances (faster but takes more memory).
random_state: integer or numpy.RandomState, optional
The generator used to initialize the centers. If an integer is
given, it fixes the seed. Defaults to the global numpy random
number generator.
Returns
-------
centroid: float ndarray with shape (k, n_features)
Centroids found at the last iteration of k-means.
label: integer ndarray with shape (n_samples,)
label[i] is the code or index of the centroid the
i'th observation is closest to.
inertia: float
The final value of the inertia criterion (sum of squared distances to
the closest centroid for all observations in the training set).
n_iter : int
Number of iterations run.
"""
#将random_state统一转化为np.random.RandomState.详参函数。
random_state = check_random_state(random_state)
#定义变量。
best_labels, best_inertia, best_centers = None, None, None
# init。初始化起始中心点。默认方法为kmeans++。详参函数。
centers = _init_centroids(X, n_clusters, init, random_state=random_state,
x_squared_norms=x_squared_norms)
# I don't know why.
if verbose:
print("Initialization complete")
# Allocate memory to store the distances for each sample to its
# closer center for reallocation in case of ties
#为每个样本点到其最近中心距离分配存储空间。shape(x.shape[0],)
distances = np.zeros(shape=(X.shape[0],), dtype=np.float64)
# iterations.迭代。max_iter.
for i in range(max_iter):
# 保存现有中心点。
centers_old = centers.copy()
# labels assignment is also called the E-step of EM
# 分类。寻找最近中心，将样本点划分多各自的类即分派标签。
labels, inertia = \
_labels_inertia(X, x_squared_norms, centers,
precompute_distances=precompute_distances,
distances=distances)
# computation of the means is also called the M-step of EM
# 计算每个类的均值点作为中心。
if sp.issparse(X):
centers = _k_means._centers_sparse(X, labels, n_clusters,
distances)
else:
centers = _k_means._centers_dense(X, labels, n_clusters, distances)
if verbose:
print("Iteration %2d, inertia %.3f" % (i, inertia))
# 与best_inertia比较并更新best_inertia
if best_inertia is None or inertia < best_inertia:
best_labels = labels.copy()
best_centers = centers.copy()
best_inertia = inertia
# 比较新旧中心点，看其是否达到精度。达到精度break。
if squared_norm(centers_old - centers) <= tol:
if verbose:
print("Converged at iteration %d" % i)
break
# 返回 标签，距离和，中心点，迭代次数。
return best_labels, best_inertia, best_centers, i + 1
函数：_labels_inertia()