2.X, y = make_circles(n_samples=100, noise=0.05) 完整例子如下所示。 1.from sklearn.datasets import make_circles 2.from matplotlib import pyplot 3.from pandas import DataFrame 4.# generate 2d classification dataset 5.X, y = make_circles(n_samples=100, noise=0.05) 6.# scatter plot, d...
sklearn.datasets.make_circles - scikit-learn 0.23.1 documentation包括使用实例 sklearn.datasets.make_circles
make_blobs( ) 生成多类单标签数据集 make_biclusters( ) 生成双聚类数据集 make_checkerboard( ) 生成棋盘结构数组,进行双聚类 make_circles( ) 生成二维二元分类数据集 make_classification( ) 生成多类单标签数据集 make_friedman1( ) 生成采用了多项式和正弦变换的数据集 make_gaussian_quantiles( ) 生成高斯...
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, rand...
from sklearn.datasetsimportmake_circles from sklearn.model_selectionimporttrain_test_split np.random.seed(123)%matplotlib inline 数据集 In [4]: 代码语言:javascript 复制 X,y=make_circles(n_samples=1000,factor=0.5,noise=.1)fig=plt.figure(figsize=(8,6))plt.scatter(X[:,0],X[:,1],c=y)...
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 复制 X,y=make_circles(n_samples=int(1e5),factor=.35,noise=.05)X[:,0]=3*X[:,0]X[:,1]=3*X[:,1]plt.scatter(X[:,0]...
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),
make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable ]figure = plt.figure(figsize=(6, 9)) i = 1 for ds_index, ds in enumerate(datasets): X, y = ds X = StandardScaler().fit_transform(X) X_train, X_test, y_train, y_test = train_test_split(X, y, test_...
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 zqscore import ZQ_scorefrom sklearn.datasets import make_circlesnoisy_circles = make_circles(n_samples=1000, factor=.5, noise=.05, random_state=15)X = noisy_circles[0]plt.axes(aspect='equal')plt.scatter(X[:, 0], X[:, 1], marker='o', c=noisy_circles[1])plt.show()print(...