make_blobs()是 sklearn.datasets中的一个函数。 主要是产生聚类数据集,产生一个数据集和相应的标签。 函数的源代码如下: defmake_blobs(n_samples =100, n_features =2, centers =3, cluster_std =1.0, center_box = (-10.0,10.0), shuffle =True, random_state =None):"""Generate isotropic Gaussian...
frommpl_toolkits.mplot3dimportAxes3D fromsklearn.datasets.samples_generatorimportmake_blobs # X为样本特征,Y为样本簇类别, 共1000个样本,每个样本3个特征,共4个簇 X,y=make_blobs(n_samples=10000,n_features=3,centers=[[3,3,3], [0,0,0], [1,1,1], [2,2,2]],cluster_std=[0.2,0.1,0.2...
1.make_bolbs() 函数 1 2 3 fromsklearn.datasets.samples_generatorimportmake_blobs importnumpy as np importmatplotlib.pyplot as plt 1 X , y=make_blobs(n_samples=1000, n_features=2,centers=[[-1,-1],[0,0],[1,1],[2,2]],cluster_std=[0.4,0.3,0.3,0.4],random_state=1) 其中: n_...
例子: >>>fromsklearn.datasetsimportmake_blobs>>>X, y =make_blobs(n_samples=10, centers=3, n_features=2,...random_state=0)>>>print(X.shape) (10,2)>>>y array([0,0,1,0,2,2,2,1,1,0])>>>X, y =make_blobs(n_samples=[3,3,4], centers=None, n_features=2,...random_...
sklearn中的make_blobs函数主要是为了生成数据集的,具体如下: 调用make_blobs make_blobs的用法 data, label = make_blobs(n_features=2, n_samples=100, centers=3, random_state=3, cluster_std=[0.8, 2, 5]) n_features表示每一个样本有多少特征值 ...
首先,你需要安装scikit-learn包。你可以使用pip来安装它: bash pip install scikit-learn 2. 导入必要的库和模块 接下来,在你的Python脚本或Jupyter Notebook中导入必要的库和模块: python import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.cluster impo...
sklearn中的make_blobs函数主要是为了生成数据集的,具体如下: 调用make_blobs make_blobs的用法 data, label = make_blobs(n_features=2, n_samples=100, centers=3, random_state=3, cluster_std=[0.8, 2, 5]) n_features表示每一个样本有多少特征值 ...
sklearn.datasets 获取小数据集(本地加载):datasets.load_xxx( ) 获取大数据集(在线下载):datasets.fetch_xxx( ) 本地生成数据集(本地构造):datasets.make_xxx( ) 数据集读取的部分代码: from sklearn import datasets import matplotlib.pyplot as plt ...
以下是一个使用Scikit-learn计算轮廓系数的示例: 代码语言:txt 复制 from sklearn.cluster import KMeans from sklearn.datasets import make_blobs from sklearn.metrics import silhouette_score # 生成随机数据 X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0) # 应用K-me...
from sklearn import datasets from sklearn.model_selection import train_test_split iris=datasets.load_iris() 2.将特征与标签分开 x,y=datasets.load_iris(return_X_y=True) x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3) ...