clustering/light-fcm-clustering/visual.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# time: 2023/6/28 16:03
# file: visual.py
# author: David
# company: shenyang JY


import matplotlib.pyplot as plt
from sklearn.decomposition import PCA  # 加载PCA算法包
from sklearn.datasets import load_iris
import numpy as np


def cluster_scatter(x, y):
    #PCA降维
    pca = PCA(n_components=2)  # 加载PCA算法，设置降维后PC数目为2
    reduced_x = pca.fit_transform(x)  # 对样本进行降维
    # reduced_x = np.dot(reduced_x, pca.components_) + pca.mean_  # 还原数据

    # 画图
    red_x, red_y = [], []
    blue_x, blue_y = [], []
    green_x, green_y = [], []
    yellow_x, yellow_y = [], []
    # print(reduced_x)
    for i in range(len(reduced_x)):
      if y[i] == 0:
        red_x.append(reduced_x[i][0])
        red_y.append(reduced_x[i][1])
      elif y[i] == 1:
        blue_x.append(reduced_x[i][0])
        blue_y.append(reduced_x[i][1])
      elif y[i] == 2:
        green_x.append(reduced_x[i][0])
        green_y.append(reduced_x[i][1])
      elif y[i] == 3:
        yellow_x.append(reduced_x[i][0])
        yellow_y.append(reduced_x[i][1])
    plt.scatter(red_x, red_y, c='r', marker='.')
    plt.scatter(blue_x, blue_y, c='b', marker='.')
    plt.scatter(green_x, green_y, c='g', marker='.')
    plt.scatter(yellow_x, yellow_y, c='y', marker='.')
    plt.show()