PCA降维+Python matplotlib绘制动态散点图

        代码

import matplotlib.pyplot as plt
from sklearn.decomposition import PCA


def main():
    # 打开交互模式
    plt.ion()
    # 设置plt参数
    plt.rcParams['figure.figsize'] = (9 * 1920 / 1080, 9)
    plt.rcParams['font.sans-serif'] = ['Times New Roman']
    plt.subplots_adjust(left=0.06, right=0.94, top=0.92, bottom=0.08)
    # 根据循环动态显示散点的变化
    for i in range(1000):
        # 清空当前的图
        plt.clf()
        # 设置plt参数
        plt.rcParams['figure.figsize'] = (9 * 1920 / 1080, 9)
        plt.rcParams['font.sans-serif'] = ['Times New Roman']
        plt.subplots_adjust(left=0.06, right=0.94, top=0.92, bottom=0.08)
        # 加载散点数据
        xs, ys, ls, cs = get_data(i)
        # 绘制散点
        for index in range(len(xs)):
            x = xs[index]
            y = ys[index]
            label = ls[index]
            color = cs[index]
            plt.scatter(x, y, c=color, s=90)
            plt.text(x, y, label, fontsize=15, verticalalignment="top", horizontalalignment="right")
        plt.draw()
        # 暂停0.1秒
        plt.pause(0.1)


# 切片：切成两边，一边是xs，另一边是ys
def row2col(data_list):
    xs = []     # x list
    ys = []     # y list
    n = len(data_list)
    for i in range(n):
        xs.append(data_list[i][0])
        ys.append(data_list[i][1])
    return xs, ys


# 根据step动态更新数据
def get_data(step):
    # 散点可取的颜色
    color_1 = "#0CECDD"  # teal
    color_2 = "#FFF338"  # yellow
    color_3 = "#FF67E7"  # pink
    color_4 = "#C400FF"  # purple
    color_5 = "#170055"  # navy
    color_6 = "#3E00FF"  # blue
    color_7 = "#B5FFD9"  # green

    # 数据的增量(用于更新数据)
    increment = [[0.1, 0.1, 0.1, 0.1], [-0.1, -0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1], [-0.1, 0.1, -0.1, 0.1], [0.1, 0.1, 0.1, 0.1], [-0.1, 0.1, 0.1, 0.1]]
    data_list = [[0, 1, 2, -1], [20, 20, 20, -21], [999, 800, 798, 901], [900, 808, 805, 903], [-505, -606, -790, -1000], [-580, -616, -800, -1024]]
    # 更新数据
    for i in range(len(data_list)):
        row = data_list[i]
        for j in range(len(row)):
            data_list[i][j] = data_list[i][j] + step * increment[i][j]
    # 使用PCA对高维数据进行降维(得到2维数据)
    pca_tool = PCA(n_components=2)
    reduced_data_list = pca_tool.fit_transform(data_list)
    # 对降维后的数据进行切片
    xs, ys = row2col(reduced_data_list)
    # 获取数据的标签、颜色(用于可视化)
    clas_list = [0, 0, 1, 1, 2, 2]
    colr_list = [color_1, color_1, color_2, color_2, color_3, color_3]
    # 返回数据
    return xs, ys, clas_list, colr_list


# 程序入口
if __name__ == '__main__':
    main()

        效果