python scikit-learn dataset 在Python中,sklearn可以在[-1,1]框中创建一个圆数据集。我想知道是否有可能增加圆的半径,例如在一个[-5,5]或[-10,10]的盒子里 from sklearn import datasets X, l = datasets.make_circles(n_samples=1000, shuffle=True, noise=0.08, random_state=42, factor=0.8) 发布...
sklearn.datasets.make_circles - scikit-learn 0.23.1 documentation包括使用实例 sklearn.datasets.make_circles
X, y = make_circles(n_samples=100, noise=0.05) 完整例子如下所示。 fromsklearn.datasetsimportmake_circles frommatplotlibimportpyplot frompandasimportDataFrame # generate 2d classification dataset X, y = make_circles(n_samples=100, noise=0.05) # scatter plot, dots colored by class value df =Da...
from sklearn.ensemble import GradientBoostingClassifier from sklearn.datasets import make_circles X, y = make_circles(noise=0.25, factor=0.5, random_state=1) # 为了便于说明,我们将两个类别重命名为"blue"和"red" y_named = np.array(["blue", "red"])[y] # 我们可以对任意个数组调用train_tes...
make_circles 函数将自动创建一个复杂的数据分布,类似于我们将应用于 DBSCAN 的两个圆。让我们从创建 100000 个数据点的数据集开始,并在图中可视化: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 X,y=make_circles(n_samples=int(1e5),factor=.35,noise=.05)X[:,0]=3*X[:,0]X[:,1]=3*X[...
1.2.4 用sklearn.datasets.make_circles和make_moons来生成数据 生成环线数据 1 2 sklearn.datasets.make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8) factor:外环和内环的尺度因子<1 1 2 sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, ...
from sklearn.datasets import make_circles, make_moons, make_blobs,make_classification 1. 2. 3. 4. 5. 2. 创建数据集,定义核函数的选择 n_samples = 100 datasets = [ make_moons(n_samples=n_samples, noise=0.2, random_state=0),
方法/步骤 1 先给定数据：import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasetsx1,_=datasets.make_circles(n_samples=5000, factor=.6, noise=0.05)x2,_=datasets.make_blobs(n_samples=1000, n_features=2, centers=[[1.2,1.2]], cluster_std=[[.1]],...
X, y = make_circles(100, factor=0.1, noise=0.1) # 加入径向基函数 clf = SVC(kernel='rbf') clf.fit(X, y) plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn') plot_SVC_decision_function(clf, plot_support=False) ...
from sklearn.datasets import make_moons, make_circles X,_=make_circles(n_samples=1000,factor=0.5,noise=0.1) db = DBSCAN(4, 0.15) db.fit(X) db.plot_dbscan_2D() X,_ = make_moons(n_samples=1000, noise=0.05) db = DBSCAN(10, 0.15) db.fit(X) db.plot_dbscan_2D() 运行结果: 从...