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.detach().numpy()
sum=1
li=[]
for i in range(feature.shape[0]):
index=feature[i,:]>=0.3
index=index.astype(np.int)
index=np.nonzero(index)
for j in index:
for j1 in j:
if j1 not in li:
li.append(j1)
print(li)
print(len(li))
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[:,li]
feature=feature.detach().numpy()
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[:,li]
from torch import nn
import torch
label=label.reshape(-1,1)
test_label=test_label.reshape(-1,1)
from sklearn import svm
import matplotlib.pyplot as plt
from sklearn import tree
clf=tree.DecisionTreeClassifier(criterion='entropy',random_state=0,max_depth=7) # .SVC（）就是 SVM 的方程，参数 kernel 为线性核函数
# 训练分类器
import sklearn
from sklearn.metrics import accuracy_score
clf.fit(feature, label)
w=clf.predict(feature)
pr=accuracy_score(label, w)
print(pr)
w=clf.predict(test_feature)
pr=accuracy_score(test_label, w)
print(pr)
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[:,li])
feature=feature.detach().numpy()
out=clf.predict(feature)
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)