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get_data_feat_norm.py
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get_data_feat_norm.py
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import scipy.io
import numpy
import os
#import random
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
root_eegmat="./feature/eegmat/"
root_stew="./feature/stew/"
root_nback="./feature/nback/"
def openfile(file_name):
#读取
read_dict=scipy.io.loadmat(file_name)
raw_data=read_dict['data']
#print(raw_data.shape)
#raw_data=numpy.transpose(raw_data,axes=(0,2,1))
#print(raw_data.shape)
return raw_data
def normalization(data):
std=StandardScaler()
data=std.fit_transform(data)
return data
###########################################
#总样本5，训练5，验证1，测试4
class Datas:
def __init__(self,root,tag="train"):
self.datas=[]
self.labels=[]
self.classes=[]
list_dirs=os.listdir(root)
sumclass=int(len(list_dirs)/2)
if tag=="train":
nums=5
idxs=0
elif tag=="validate":
nums=1
idxs=0
elif tag=="test":
nums=4
idxs=1
for sub_path in list_dirs:
samples=openfile(root+sub_path)
if (sub_path[-5]=='0'):
self.labels.append(numpy.zeros(nums,dtype=numpy.int64))
elif (sub_path[-5]=='1'):
self.labels.append(numpy.ones(nums,dtype=numpy.int64))
self.classes.append(numpy.ones(nums,dtype=numpy.int64)*int(sub_path[:-6]))
self.datas.append(samples[idxs:idxs+nums])
self.datas=numpy.stack(self.datas)
self.datas=self.datas.reshape(-1,320)
self.datas=normalization(self.datas)
self.datas=self.datas.reshape(-1,10,32)
#self.datas=self.datas.reshape(-1,self.datas.shape[-2],self.datas.shape[-1])
self.labels=numpy.stack(self.labels)
self.labels=self.labels.reshape(-1)
self.classes=numpy.stack(self.classes)
self.classes=self.classes.reshape(-1)
#standard
#self.datas=self.datas.reshape(self.datas.shape[2])
#self.datas=min_max(self.datas)
#self.datas=self.datas.reshape(-1,14*9)
#self.datas=normalization(self.datas)
#self.datas=self.datas.reshape(sumclass,2,-1,14,9)
self.shape=self.get_shape()
#print(self.shape)
def __getitem__(self,idx):
return self.datas[idx],self.labels[idx],self.classes[idx]
def __len__(self):
return len(self.datas)
def get_shape(self):
return {"data":self.datas.shape,"label":self.labels.shape,"class":self.classes.shape}
def get_data(self):
return list(zip(self.datas,self.labels,self.classes))
eegmat=Datas(root_eegmat,"train")
stew=Datas(root_stew,"train")
nback=Datas(root_nback,"train")
eegmat_test=Datas(root_eegmat,"test")
stew_test=Datas(root_stew,"test")
nback_test=Datas(root_nback,"test")
eegmat_val=Datas(root_eegmat,"validate")
stew_val=Datas(root_stew,"validate")
nback_val=Datas(root_nback,"validate")
if __name__=='__main__':
None
breakpoint()
print(eegmat[0])
print(len(eegmat))
print(eegmat.shape)
a,b,c=zip(*eegmat.get_data())
print(len(a),len(b))