上一篇《sklearn之樣本生成（1）》主要講make_blobs的使用方法。本文重點講make_classification，make_gaussian_quantiles、make_hastie_10_2、make_circles和make_moons

1）make_classification

sklearn.datasets.make_classification(n_samples=100, n_features=20, n_informative=2, n_redundant=2,
					n_repeated=0, n_classes=2, n_clusters_per_class=2, weights=None,
 					flip_y=0.01, class_sep=1.0, hypercube=True,shift=0.0, scale=1.0, 
					shuffle=True, random_state=None)
 

通常用於分類演算法。
n_features :特徵個數= n_informative（） + n_redundant + n_repeated
n_informative：多資訊特徵的個數
n_redundant：冗餘資訊，informative特徵的隨機線性組合
n_repeated ：重複資訊，隨機提取n_informative和n_redundant 特徵
n_classes：分類類別
n_clusters_per_class ：某一個類別是由幾個cluster構成的

2）make_gaussian_quantiles 和make_hastie_10_2

sklearn.datasets.make_gaussian_quantiles(mean=None, cov=1.0, n_samples=100, n_features=2, n_classes=3,
					shuffle=True, random_state=None)
 

利用高斯分位點區分不同資料

sklearn.datasets.make_hastie_10_2(n_samples=12000, random_state=None)

利用Hastie演算法，生成2分類資料

程式碼

import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.datasets import make_blobs
from sklearn.datasets import make_gaussian_quantiles
from sklearn.datasets import make_hastie_10_2

plt.figure(figsize=(8, 8))
plt.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)

plt.subplot(421)
plt.title("One informative feature, one cluster per class", fontsize='small')
X1, Y1 = make_classification(n_samples=1000,n_features=2, n_redundant=0, n_informative=1,
                             n_clusters_per_class=1)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)

plt.subplot(422)
plt.title("Two informative features, one cluster per class", fontsize='small')
X1, Y1 = make_classification(n_samples=1000,n_features=2, n_redundant=0, n_informative=2,
                             n_clusters_per_class=1)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)

plt.subplot(423)
plt.title("Two informative features, two clusters per class", fontsize='small')
X2, Y2 = make_classification(n_samples=1000,n_features=2, n_redundant=0, n_informative=2)
plt.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2)


plt.subplot(424)
plt.title("Multi-class, two informative features, one cluster",
          fontsize='small')
X1, Y1 = make_classification(n_samples=1000,n_features=2, n_redundant=0, n_informative=2,
                             n_clusters_per_class=1, n_classes=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)

plt.subplot(425)
plt.title("Three blobs", fontsize='small')
X1, Y1 = make_blobs(n_samples=1000,n_features=2, centers=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)

plt.subplot(426)
plt.title("Gaussian divided into four quantiles", fontsize='small')
X1, Y1 = make_gaussian_quantiles(n_samples=1000,n_features=2, n_classes=4)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)

plt.subplot(427)
plt.title("hastie data ", fontsize='small')
X1, Y1 = make_hastie_10_2(n_samples=1000)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1)
plt.show()
 

3）make_circles and make_moons

sklearn.datasets.make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8)

生成環形資料
factor ：外圈與內圈的尺度因子<1

sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)

生成半環形圖

from sklearn.datasets import make_circles
from sklearn.datasets import make_moons
import matplotlib.pyplot as plt
import numpy as np

fig=plt.figure(1)
x1,y1=make_circles(n_samples=1000,factor=0.5,noise=0.1)
plt.subplot(121)
plt.title('make_circles function example')
plt.scatter(x1[:,0],x1[:,1],marker='o',c=y1)

plt.subplot(122)
x1,y1=make_moons(n_samples=1000,noise=0.1)
plt.title('make_moons function example')
plt.scatter(x1[:,0],x1[:,1],marker='o',c=y1)
plt.show()