也可以,每一次迭代中查看。 importnumpyasnpfromsklearn.clusterimportKMeansfromsklearn.datasetsimportload_iris# Load the iris datasetiris=load_iris()X=iris.data# Set the number of clustersk=3# Initialize the centroidscentroids=[0,0,0,0]# Iterate until convergenceforiinrange(5):# Fit the model...
In addition to those k-means refinements, several algorithms have been proposed anew to cluster the points around centroids while avoiding the problems of k-means. Hierarchical clustering algorithms are of relevance for CLUBS+, and come in two flavors: divisive and agglomerative. Divisive clustering ...
deftest_fit_sample_object():ratio ='auto'cluster = KMeans(random_state=RND_SEED) cc =ClusterCentroids( ratio=ratio, random_state=RND_SEED, estimator=cluster) X_resampled, y_resampled = cc.fit_sample(X, Y) X_gt = np.array([[0.92923648,0.76103773], [0.47104475,0.44386323], [0.13347175,0...
# 需要导入模块: from imblearn.under_sampling import ClusterCentroids [as 别名]# 或者: from imblearn.under_sampling.ClusterCentroids importfit_resample[as 别名]print(__doc__)# Generate the datasetX, y = make_classification(n_classes=2, class_sep=2, weights=[0.1,0.9], n_informative=3, n...