操练代码之PCA主成分分析

PCA分析

一，代码

import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from kmeans_pytorch import kmeans
from torch.autograd import Variable
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset
import time
from sklearn.decomposition import PCA

region = pd.read_csv('./主成分分析/region.csv')
region_d = region[['x1', 'x2', 'x3', 'x4']]
region['target'] = region['y']

transfer_1 = PCA(n_components=2)
region_d = transfer_1.fit_transform(region_d)
x = torch.from_numpy(region_d)
y = torch.from_numpy(np.array(region['target']))
x, y = Variable(x), Variable(y)

net = torch.nn.Sequential(
    torch.nn.Linear(2, 10),
    torch.nn.ReLU(),
    torch.nn.Linear(10, 3),
)
print(net)

optimizer = torch.optim.SGD(net.parameters(), lr=1e-4)
loss_func = torch.nn.CrossEntropyLoss()

for t in range(100):
    out = net(x.float())
    loss = loss_func(out, y.long())

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if t % 25 == 0:
        plt.cla()
        prediction = torch.max(out, 1)[1]
        pred_y = prediction.data.numpy()
        target_y = y.data.numpy()

        plt.scatter(x.data.numpy()[:, 0],
                    x.data.numpy()[:, 1],
                    c=pred_y,
                    s=100,
                    lw=5,
                    cmap='coolwarm')
        accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)
        print('Accuracy = %.2f'.format(accuracy))
        plt.pause(0.1)

    plt.show()

二，效果

2024-08-21 11_59_29-ch2 – 333.py.png