sklearn-生成隨機數(shù)據(jù)

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')

多標簽分類數(shù)據(jù)

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()

生成分類數(shù)據(jù)

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()

圖像數(shù)據(jù)集

# 圖像數(shù)據(jù)集

china = datasets.load_sample_image('china.jpg')

plt.axis('off')

plt.title('中國頤和園圖像', fontproperties=font, fontsize=20)

plt.imshow(china)

plt.show()

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