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Code.py
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Code.py
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#-*-coding:utf-8-*-
# 测试ball_tree和kd_tree的用法
# from sklearn.neighbors import KDTree, BallTree
#
# import numpy as np
# import pickle
#
# # rng = np.random.RandomState(0)
# # X = rng.random_sample((10, 3)) # 10 points in 3 dimensions
# X = np.random.rand(10, 3)
# tree = BallTree(X, leaf_size=2)
# s = pickle.dumps(tree)
# tree_copy = pickle.loads(s)
# dist, ind = tree_copy.query(X[:1], k=3)
# print(ind) # indices of 3 closest neighbors
# print(dist)
# a = [ind[j][q] for q in range(ind.shape[1]) for j in range(ind.shape[0])]
# print(a)
# b = list(ind[0, :])
# print(b)
# 获取路径
import os
import time
# 当前目录
# current_path = os.getcwd()
# print(current_path)
#
# # 上一级目录
# father_path = os.path.abspath(os.path.dirname(os.getcwd()))
# print(father_path)
#
# # 上上一级目录
# grandfather_path = os.path.abspath(os.path.join(os.getcwd(), '../..'))
# print(grandfather_path)
#
# a = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
# print(a)
#========================================$
# 读取tensor
# import torch
#
#
# a = [torch.LongTensor([i]).cuda()
# if torch.cuda.is_available() else torch.LongTensor([i])
# for i in range(10)]
# a.append(2)
# print(a)
# print(type(a[0]), type(a[-1]))
#
# b = [int(i.cpu().numpy()) if i != 2 else i for i in a]
# print(b)
#========================================#
# set集合的使用
# a = [[3, 4, 5, 6, 7, 7],
# [3, 5, 6, 7, 7, 8],
# [5, 6, 7, 7, 8, 2],
# [3, 4, 4, 6, 7, 7],
# [3, 4, 4, 6, 7, 7],
# [3, 4, 4, 5, 6, 7],
# [3, 4, 5, 5, 7, 7],
# [3, 4, 5, 6, 7, 7]]
#
# b = []
# for i in a:
# if 2 in i: # 如果i中有2的话,去除2
# i.remove(2)
# i = sorted(set(i), reverse=False) # 先去重,然后升序排列
# b.append(i)
#
# print(b)
#=================================#
# 保存数据到Excel中
import os
import csv
import random
# lab_num = 6
# ins_num = 10
#
# real_labs = [[random.randint(0, lab_num) for _ in range(lab_num)] for _ in range(ins_num)]
# predict_labs = [[random.randint(0, lab_num) for _ in range(lab_num)] for _ in range(ins_num)]
#
# with open((os.getcwd() + '//MLL_Save_Csv//real_labs.csv'), 'w', newline='') as csv_file:
# csv_writer = csv.writer(csv_file)
# for list in real_labs:
# print(list)
# csv_writer.writerow(list)
#
# with open((os.getcwd() + '//MLL_Save_Csv//predict_labs.csv'), 'w', newline='') as csv_file:
# csv_writer = csv.writer(csv_file)
# for list in predict_labs:
# print(list)
# csv_writer.writerow(list)
import numpy as np
a = np.array([[0.3, 0.1, 0.4],
[0.5, 0.2, 0.7]])
print(a.shape)
b = 1 - a
print(b)