make_blobs方法: sklearn.datasets.make_blobs(n_samples=100,n_features=2,centers=3, cluster_std=1.0,center_box=(-10.0,10.0),shuffle=True,random_state=None) make_blobs 函数是为聚类或分类产生数据集,产生一个数据集和相应的标签 n_samples: 表示数据样本点个数,默认值100 n_features: 是每个样本的...
import matplotlib.pyplot as plt from sklearn import datasets from sklearn.cluster import DBSCAN #matplotlib inline X1, y1=datasets.make_circles(n_samples=5000, factor=.6, noise=.05) X2, y2 = datasets.make_blobs(n_samples=1000, n_features=2, centers=[[1.2,1.2]], cluster_std=[[.1]]...
from sklearn import datasets import matplotlib.pyplot as plt X,y = datasets.make_blobs(n_samples=1000, n_features=2, centers=4, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None) plt.scatter(X[:,0],X[:,1],c=y,s=8) 2.2 产生同心圆样本点 datasets.make_...
hdbscan 是scikit-learn 中用于层次密度聚类的模块。它提供了一个 HDBSCAN 类,用于执行基于密度的聚类算法。下面是一个简单的代码示例,展示了如何使用 HDBSCAN 进行聚类: python from sklearn.cluster import HDBSCAN from sklearn.datasets import make_blobs import matplotlib.pyplot as plt import numpy as np # ...
# Import necessary modulesfromsklearn.neighborsimportKNeighborsClassifierfromsklearn.model_selectionimporttrain_test_splitfromsklearn.datasetsimportload_irisimportnumpyasnpimportmatplotlib.pyplotaspltirisData=load_iris()# Create feature and target arraysX=irisData.datay=irisData.target# Split into training ...
importnumpyasnpimportmatplotlib.pyplotaspltimportmatplotlib.colorsimportsklearn.linear_modelfromsklearn.model_selectionimporttrain_test_splitfromsklearn.datasetsimportmake_classification, make_blobsfrommatplotlib.colorsimportListedColormapimportsklearnimportsklearn.datasets ...
# Imports import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn import datasets def accuracy(y_true, y_pred): accuracy = np.sum(y_true == y_pred) / len(y_true) return accuracy X, y = datasets.make_blobs( n_samples=150, n_features=2, ce...
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