python-科研绘图系列（5）-用TSNE降维并可视化

最新推荐文章于 2025-02-28 00:15:00 发布

好人就是拉风

最新推荐文章于 2025-02-28 00:15:00 发布

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文章标签：可视化 python

本文链接：https://blog.youkuaiyun.com/qq_41645987/article/details/109148310

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1. 用TSNE 降维


import numpy as np
import matplotlib.pyplot as plt
from sklearn import manifold
import  pickle
import time
import pandas as pd

def visual(X):
    tsne = manifold.TSNE(n_components=2,init='pca', random_state=501)
    X_tsne = tsne.fit_transform(X)

    print("Org data dimension is {}. Embedded data dimension is {}".format(X.shape[-1], X_tsne.shape[-1]))

    #'''嵌入空间可视化'''
    x_min, x_max = X_tsne.min(0), X_tsne.max(0)
    X_norm = (X_tsne - x_min) / (x_max - x_min)

    return  X_norm

n_components=2 控制降维后的维度
init=‘pca’ 用PCA 方法降维
random_state=501 代表随机种子
输入：需要降维的矩阵 X shape=(m,n) m:代表样本数量，n:代表样本长度
返回：降维后的矩阵X_norm shape=(m,2)

2.画图

import numpy as np
import matplotlib.pyplot as plt
from sklearn import manifold
import pandas as pd


S_X1=np.random.random((200,200))#原始特征

class_num=10