准确率为0.71的程序

这里我只把时域转为了频域。

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[:,]

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[:,]

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(240,1),nn.Sigmoid())

import torch.optim as optim

optimizer = optim.Adam(network.parameters(), lr=0.004)

for epoch in range(6000):

    optimizer.zero_grad()

    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)

    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 acc>0.84 and test_acc==0.80:

        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)

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)