import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('train.csv')
df=df.drop(['ID'],axis=1)
heat=df.corr()
fe=heat.index[abs(heat["CLASS"])>0.3]
nmp=df.to_numpy()
label=nmp[:,-1]
feature=nmp[:,:-1]
feature=np.abs(np.fft.fft(feature))/240*2
df = pd.read_csv('test.csv')
df=df.drop(['ID'],axis=1)
w=[4,8,10,12,14,16,18,20,22,24,26,28,30,32,36,40,44,48,76,84,88,92,132,140
,144,148,152,160,164,168,192,196,200,204,206,208,210
,212,216,224,226,232,236]
label=np.reshape(label,(-1,1))
ds=np.concatenate((feature[:,w],label),axis=1)
ds=pd.DataFrame(ds)
heat=ds.corr()
fe=heat.index[abs(heat[43])>0.1]
q=[]
for i in fe:
if i!=43:
q.append(w[i])
print(q)
sns.heatmap(heat[fe].corr(),square=True,annot=True)
plt.show()