2、 make_circles() sklearn.datasets.make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8) 重要参数:n_samples:设置样本数量、noise:设置噪声、factor:0 < double < 1 默认值0.8,内外圆之间的比例因子、random_state:设置随机参数(嘿嘿,无所谓,随便设),我们主要讲参数noi...
random_state:生成随机种子,给定一个int型数据,能够保证每次生成数据相同。 sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None) for example: X, y = datasets.make_moons(500, noise=0.5) 参考文献: 【1】https://scikit-learn.org/stable/modules/generated/sklearn....
X,y=make_moons(n_samples=100, shuffle=True, noise=None,random_state=None) 1. 2. 实例: from sklearn.datasets import make_circles X,y=make_circles(n_samples=100, shuffle=True, noise=None,random_state=None, factor=0.8) print(X[:10]) print(y[:10]) 1. 2. 3. 4. from sklearn.da...
datasets.make_moons(n_samples,shuffle,noise,random_state) from sklearn import datasets import matplotlib.pyplot as plt X,y = datasets.make_moons(n_samples=1000, shuffle=True, noise=0.05, random_state=None) plt.scatter(X[:,0],X[:,1],c=y,s=8) 参考:sklearn.datasets常用功能详解_不二的...
make_moons = datasets.make_moons() m_x = make_moons[0] m_y = make_moons[1] poly_svm_clf = Pipeline([ ('poly_features', PolynomialFeatures(degree=3)), ('scaler', StandardScaler()), ('svm_clf', LinearSVC(C=10, loss='hinge')) ...
x, y = datasets.make_circles(n_samples=5000, noise=0.04, factor=0.7) noise:噪声 factor:内圆与外圆的距离 为1的时候最小 (三) 月牙 x, y = datasets.make_moons(n_samples=3000, noise=0.05) (四) 分类 x, y =datasets.make_classification(n_classes=4, n_samples=1000, n_features=2, n...
前面我们介绍了几种datasets自带的经典数据集,但有些时候我们需要自定义生成服从某些分布或者某些形状的数据集,而datasets中就提供了这样的一些方法: 2.1 产生服从正态分布的聚类用数据 datasets.make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True,...
首先，让我们导入必要的库：import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets import make_classification, make_regression, make_blobs, make_moons, make_circles, make_s_curve, make_swiss_roll, make_checkerboard1. 生成分类数据集要生成分类数据集，可以使用 make_classification函数。...
#用make_moons创建月亮型数据,make_circles创建环形数据,并将三组数据打包起来放在列表datasets中datasets = [make_moons(noise=0.3, random_state=0),make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable] 3. 画出三种数据集和三棵决策树的分类效应图像 #创建画布,宽高比为6*9figure =...
datasets.make_hastie_10_2 datasets.make_low_rank_matrix datasets.make_moons datasets.make_multilabel_classification datasets.make_regression datasets.make_s_curve datasets.make_sparse_coded_signal datasets.make_sparse_spd_matrix datasets.make_sparse_uncorrelated ...