Python之机器学习-sklearn生成随机数据

sklearn-生成随机数据
import numpy as np import pandas as pd import matplotlib.pyplot as plt from
matplotlib.font_manager import FontProperties from sklearn import datasets
%matplotlib inline font = FontProperties(fname='/Library/Fonts/Heiti.ttc')
多标签分类数据
X1, y1 = datasets.make_multilabel_classification( n_samples=1000, n_classes=4,
n_features=2, random_state=1) datasets.make_multilabel_classification()
plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1) plt.show()


生成分类数据
import matplotlib.pyplot as plt %matplotlib inline plt.figure(figsize=(10,
10)) plt.subplot(221) plt.title("One informative feature, one cluster per
class", fontsize=12) X1, y1 = datasets.make_classification(n_samples=1000,
random_state=1, n_features=2, n_redundant=0, n_informative=1,
n_clusters_per_class=1) plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1)
plt.subplot(222) plt.title("Two informative features, one cluster per class",
fontsize=12) X1, y1 = datasets.make_classification(n_samples=1000,
random_state=1, n_features=2, n_redundant=0, n_informative=2,
n_clusters_per_class=1) plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1)
plt.subplot(223) plt.title("Two informative features, two clusters per class",
fontsize=12) X1, y1 = datasets.make_classification( n_samples=1000,
random_state=1, n_features=2, n_redundant=0, n_informative=2) plt.scatter(X1[:,
0], X1[:, 1], marker='*', c=y1) plt.subplot(224) plt.title("Multi-class, two
informative features, one cluster", fontsize=12) X1, y1 =
datasets.make_classification(n_samples=1000, random_state=1, n_features=2,
n_redundant=0, n_informative=2, n_clusters_per_class=1, n_classes=4)
plt.scatter(X1[:, 0], X1[:, 1], marker='*', c=y1) plt.show()


图像数据集
# 图像数据集 china = datasets.load_sample_image('china.jpg') plt.axis('off')
plt.title('中国颐和园图像', fontproperties=font, fontsize=20) plt.imshow(china)
plt.show()

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