【机器学习】3-4-2 流行学习

article/2025/4/27 11:20:12

#3-4-2流行学习
import mglearn
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.datasets import make_moons
from sklearn.datasets import make_blobs
from sklearn.datasets import make_circles
from sklearn.datasets import load_iris
from sklearn.datasets import fetch_lfw_people
from sklearn.datasets import load_digits
from sklearn.decomposition import NMF
from sklearn.decomposition import PCA
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.svm import LinearSVC
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.manifold import TSNE
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from numpy.core.umath_tests import inner1d
from mpl_toolkits.mplot3d import Axes3D,axes3d

digits = load_digits()
fig,axes = plt.subplots(2,5,figsize=(10,5),subplot_kw={'xticks':(),'yticks':()})
for ax,img in zip(axes.ravel(),digits.images):ax.imshow(img)

在这里插入图片描述

pca = PCA(n_components=2)
pca.fit(digits.data)
digits_pca = pca.transform(digits.data)
colors = ['#476A2A','#7851B8','#BD3430','#4A2D4E','#875525','#A83683','#4E655E','#853541','#3A3120','#535D8E']
plt.figure(figsize=(10,10))
plt.xlim(digits_pca[:,0].min(),digits_pca[:,0].max())
plt.ylim(digits_pca[:,1].min(),digits_pca[:,1].max())
for i in range(len(digits.data)):plt.text(digits_pca[i,0],digits_pca[i,1],str(digits.target[i]),color=colors[digits.target[i]],fontdict={'weight':'bold','size':9})
plt.xlabel('first principal component')
plt.ylabel('second principal component')

在这里插入图片描述

tsne = TSNE(random_state=42)
digits_tsne = tsne.fit_transform(digits.data)
plt.figure(figsize=(10,10))
plt.xlim(digits_tsne[:,0].min(),digits_tsne[:,0].max())
plt.ylim(digits_tsne[:,1].min(),digits_tsne[:,1].max())
for i in range(len(digits.data)):plt.text(digits_tsne[i,0],digits_tsne[i,1],str(digits.target[i]),color=colors[digits.target[i]],fontdict={'weight':'bold','size':9})
plt.xlabel('t-sne feature 0')
plt.ylabel('t-sne feature 1')