是。无论sklearn.cluster.KMeans
对象是否被腌制( )都不会影响您可以使用该predict
方法对新观察结果进行聚类。
一个例子:
from sklearn.cluster import KMeans
from sklearn.externals import joblib
model = KMeans(n_clusters = 2, random_state = 100)
X = [[0,0,1,0], [1,0,0,1], [0,0,0,1],[1,1,1,0],[0,0,0,0]]
model.fit(X)
出:
KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=2, n_init=10,
n_jobs=1, precompute_distances='auto', random_state=100, tol=0.0001,
verbose=0)
继续:
joblib.dump(model, 'model.pkl')
model_loaded = joblib.load('model.pkl')
model_loaded
出:
KMeans(copy_x=True, init='k-means++', max_iter=300, n_clusters=2, n_init=10,
n_jobs=1, precompute_distances='auto', random_state=100, tol=0.0001,
verbose=0)
如何看n_clusters
和random_state
参数之间的相同model
和model_new
对象?你很好
使用“新”模型进行预测:
model_loaded.predict([0,0,0,0])
Out[64]: array([0])