将时序序列转为频域

因为240维运动特征类似声波，因为人的运动不同步用时刻作为特征是不对的应转为频域。

模型在验证集最准是0.8

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

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

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(600):

    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 test_acc==0.50 and acc>0.93:

    if acc==1:

        break

ax1 = plt.subplot(2,1,1)

ax2 = plt.subplot(2,1,2)

out=network(feature)

out=out.detach().numpy()

plt.sca(ax1)

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

    if label[i]==1:

        plt.scatter(out[i],0,color='red')

    if label[i]==0:

        plt.scatter(out[i],0,color='blue')        

out=network(test_feature)

out=out.detach().numpy()

plt.sca(ax2)

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

    if test_label[i]==1:

        plt.scatter(out[i],0,color='red')

        #if out[i]>0.9:

            #print(i)

    if test_label[i]==0:

        plt.scatter(out[i],0,color='blue')  

        #if out[i]>0.9:

            #print(i)

plt.show()

print('执行成功')