准确率为0.865的程序

我之前猜想振幅大的可能是有用的特征，因此我根据振幅筛选特征，结果获得8个维度的特征。在验证集准确率是0.865，跟36维度的效果类似。

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import torch

import torch.fft as fft

df = pd.read_csv('train.csv')

df=df.drop(['ID'],axis=1)

nmp=df.to_numpy()

feature=nmp[:-20,:-1]

label=nmp[:-20,-1]#（210，240）

feature=torch.fft.fft(torch.Tensor(feature))

feature=torch.abs(feature)/240*2

feature=feature[:,[0, 60, 180, 120, 61, 179, 1, 239]]

test_feature=nmp[-20:,:-1]

test_label=nmp[-20:,-1]#（210，240）

test_feature=torch.fft.fft(torch.Tensor(test_feature))

test_feature=torch.abs(test_feature)/240*2

test_feature=test_feature[:,[0, 60, 180, 120, 61, 179, 1, 239]]

from torch import nn

import torch

loss=nn.MSELoss()

feature=torch.Tensor(feature)

label=torch.Tensor(label)

label=label.reshape(-1,1)

test_feature=torch.Tensor(test_feature)

test_label=torch.Tensor(test_label)

test_label=test_label.reshape(-1,1)

network=nn.Sequential(nn.Linear(8,4),nn.Sigmoid(),nn.Linear(4,2),nn.Sigmoid(),nn.Linear(2,1),nn.Sigmoid())

#network=nn.Sequential(nn.Linear(36,2),nn.Sigmoid(),nn.Linear(2,1),nn.Sigmoid())

#network=nn.Sequential(nn.Linear(36,1),nn.Sigmoid())

#network=nn.Sequential(nn.Linear(36,2),nn.Sigmoid(),nn.Linear(2,2),nn.Sigmoid(),nn.Linear(2,2),nn.Sigmoid(),nn.Linear(2,1),nn.Sigmoid())

#network=nn.Sequential(nn.Linear(36,2),nn.Sigmoid(),nn.Linear(2,1),nn.Sigmoid())

#network=nn.Sequential(nn.Linear(36,4),nn.Sigmoid(),nn.Linear(4,1),nn.Sigmoid())

import torch.optim as optim

optimizer = optim.Adam(network.parameters(), lr=0.05)

for epoch in range(100000):

    optimizer.zero_grad()

    network.train()

    out=network(feature)

    l=loss(out,label)

    l.backward()

    optimizer.step()

    Y = torch.ge(out, 0.5).float()

    acc=Y.eq(label).float().sum()/len(label)

    network.eval()

    out=network(test_feature)

    Y = torch.ge(out, 0.5).float()

    test_acc=Y.eq(test_label).float().sum()/len(test_label)

    print(epoch,l,acc,test_acc)

    #if test_acc==0.50 and acc>0.93:

    if acc>=0.81 and test_acc>=0.85:

        break

df = pd.read_csv('test.csv')

df=df.drop(['ID'],axis=1)

nmp=df.to_numpy()

feature=nmp[:,:]

feature=torch.fft.fft(torch.Tensor(feature))

feature=torch.abs(feature)/240*2

feature=torch.Tensor(feature[:,[0, 60, 180, 120, 61, 179, 1, 239]])

out=network(feature)

out=out.detach().numpy()

out=out>0.5

out=out.astype(np.int)

out=pd.DataFrame(out)

out.columns = ['CLASS']

w=[]

for k in range(out.shape[0]):

    w.append(k+210)

out['ID']=np.reshape(w,(-1,1))

out[['ID','CLASS']].to_csv('out.csv',index=False)