anton-l/python_sample_10k · Datasets at Hugging Face

JulseJiang/DrugKBPrototype

3,032,246,933,707

699b403b2d1b2d7deae4507a01013afe8739cb95

3b9194499503f9e06dbebe113c53afad0b572ab2

/Visualization/functionhelp.py

2c17b58cfc37b4fa09a344204cc4c1932e90d348

[]

no_license

https://github.com/JulseJiang/DrugKBPrototype

0bffa42b2913996d22e40942d24c4da0feb76f33

34567630fe8febb1ea6827541fd3d92f42935ecc

refs/heads/main

2023-03-19T17:57:14.822743

2021-03-18T11:54:22

null

0

null

# -*- encoding: utf-8 -*- #------------------------------------------------------------------------------- # @file: function # @Author: GuoSijia # @Purpose: # @Created: 2019-04-21 # @update: 2019-04-21 9:39 # @Software: PyCharm #------------------------------------------------------------------------------- import pymongo import numpy as np import math class DataStorage: def __init__(self, collection_name="", ip_adress='127.0.0.1', port=27017, user="tanxian123", password="123456", db_name="Denovo"): self.name = collection_name self.path = self.__login(ip_adress, port, user, password, db_name) def __login(self, ip_adress, port, user, password, db_name): client = pymongo.MongoClient("127.0.0.1", 27017) db = client['Denovo'] # db.authenticate("tanxian123", "123456") collection = client['Denovo'][self.name] return collection def Storage(self, dic): return self.path.insert(dic) def FindByID(self, ID): return self.path.find_one({'ENTREZ_ID': ID}) def find_one_by_one_condition(self, db_field, value): return self.path.find_one({db_field: value}) def find_count_by_one_condition(self, db_field, value): result_list = [] for item in self.path.find({db_field: value}): result_list.append(item) return result_list class PPIPointClass(object): def __init__(self): self.ds_gene = DataStorage('Genes') # self.ds_protein_exp = DataStorage('ProteinExpress_Update') # self.ds_gene_express = DataStorage('Gtex_Gene_AllTissue') def get_mutation_count(self, id): ''' 判断目标基因携带新发突变个数 :param id: 基因ENTREZ_ID :return: 该基因携带新发突变个数 ''' gene_inforamtion = self.ds_gene.FindByID(id) if gene_inforamtion == None: return 0 else: return gene_inforamtion.get('MutationCount') def get_protein_brain_exp_flag(self, id): ''' 判断目标基因对应的蛋白在脑组织中是否有表达 :param id: 基因ENTREZ_ID :return: 蛋白层面是否在脑中有表达 ''' gene_inforamtion = self.ds_gene.FindByID(id) if gene_inforamtion == None: return False else: return gene_inforamtion.get('ProteinBrainExpress') def get_gene_brain_exp_flag(self, id): ''' 判断目标基因在脑组织中是否有表达 :param id: 基因ENTREZ_ID :return: 基因层面是否在脑中有表达 ''' gene_inforamtion = self.ds_gene.FindByID(id) if gene_inforamtion == None: return False else: return gene_inforamtion.get('GeneBrainExpress') # if __name__ == '__main__': # node_id='126961' # data_nodes = {'data': {}} # ppipointclass=PPIPointClass() # print(ppipointclass.get_mutation_count(node_id)) # print(ppipointclass.get_protein_brain_exp_flag(node_id)) # print(ppipointclass.get_gene_brain_exp_flag(node_id)) # if ((ppipointclass.get_mutation_count(node_id) != 0) and ( # ppipointclass.get_protein_brain_exp_flag(node_id) == False) and ( # ppipointclass.get_gene_brain_exp_flag(node_id) == False)): # data_nodes['data']['color'] = '#FE0000' # 红色 # data_nodes['data']['type'] = 'circle' # elif ((ppipointclass.get_mutation_count(node_id) == 0) and ( # ppipointclass.get_protein_brain_exp_flag(node_id) == True) and ( # ppipointclass.get_gene_brain_exp_flag(node_id) == False)): # data_nodes['data']['color'] = '#000081' # 蓝色 # data_nodes['data']['type'] = 'circle' # elif ((ppipointclass.get_mutation_count(node_id) == 0) and ( # ppipointclass.get_protein_brain_exp_flag(node_id) == False) and ( # ppipointclass.get_gene_brain_exp_flag(node_id) == True)): # data_nodes['data']['color'] = '#039735' # 绿色 # data_nodes['data']['type'] = 'circle' # elif ((ppipointclass.get_mutation_count(node_id) != 0) and ( # ppipointclass.get_protein_brain_exp_flag(node_id) == True) and ( # ppipointclass.get_gene_brain_exp_flag(node_id) == False)): # data_nodes['data']['color'] = '#D60093' # data_nodes['data']['type'] = 'triangle' # elif ((ppipointclass.get_mutation_count(node_id) != 0) and ( # ppipointclass.get_protein_brain_exp_flag(node_id) == False) and ( # ppipointclass.get_gene_brain_exp_flag(node_id) == True)): # data_nodes['data']['color'] = '#FFFE35' # data_nodes['data']['type'] = 'rectangle' # elif ((ppipointclass.get_mutation_count(node_id) == 0) and ( # ppipointclass.get_gene_brain_exp_flag(node_id) == True) and ( # ppipointclass.get_protein_brain_exp_flag(node_id) == True)): # data_nodes['data']['color'] = '#3497CE' # data_nodes['data']['type'] = 'barrel' # elif ((ppipointclass.get_mutation_count(node_id) != 0) and ( # ppipointclass.get_gene_brain_exp_flag(node_id) == True) and ( # ppipointclass.get_protein_brain_exp_flag(node_id) == True)): # data_nodes['data']['type'] = 'diamond' # data_nodes['data']['color'] = '#000000' # else: # data_nodes['data']['type'] = 'circle' # data_nodes['data']['color'] = '#669999' # print(data_nodes)

UTF-8

Python

false

4,950

py

67

functionhelp.py

54

0.631889

0.608119

0

135

34.222222

112

pranavgarg1506/Machine_Learning

9,156,870,305,944

2f1d912d53450d23d7cfaa7a80ea93f7a7b68c63

9a92c117403e9b8ed6d0ab4d25ece315288fdfe1

/passwordGenerator/passwordgenerate.py

94b3ed859e53fcace9f280b53a8a4da21b93f48b

[]

no_license

https://github.com/pranavgarg1506/Machine_Learning

b459c1eb0fecf91f958ab349a60d653e8dad51a9

865906d6d9edf76657f71cc8ae275ca1e27ae93a

refs/heads/master

2022-04-11T14:49:28.227047

2020-03-31T15:35:12

null

0

null

import numpy as np import datetime Capital = "ABCDEFGIJKLMNOPQRSTUVWXYZ" small = 'abcdefgijklmnopqrstuvwxyz' number = '0123456789' specialCharacters = '!@#$%^&*()' digit = int(input()) print("digit ", digit) password = '' index = 0 # defining probabilities for all the sets # p_Capital = 0 # p_small = 0 # p_number = 0 # p_character = 0 # calculating the currentSum on the basis of current Time def choice1(): currentTimeStamp = datetime.datetime.now() currentMonth = currentTimeStamp.month currentYear = currentTimeStamp.year currentDay = currentTimeStamp.day currentMinute = currentTimeStamp.minute currentHour = currentTimeStamp.hour currentSecond = currentTimeStamp.second currentMicroSecond = currentTimeStamp.microsecond currentSum = currentYear + currentMonth + currentDay + currentHour + currentMinute + currentSecond + currentMicroSecond print("currentSum ", currentSum) return currentSum def checkForDuplicates(currentSum, flag): byMod = 0 if (flag == 0): tempArray = Capital byMod = len(Capital) elif (flag == 1): tempArray = small byMod = len(small) elif (flag == 2): tempArray = number byMod = len(number) elif (flag == 3): tempArray = specialCharacters byMod = len(specialCharacters) tempIndex = currentSum % byMod flag = True while (flag): for i in password: if i == tempArray[tempIndex % byMod]: tempIndex = tempIndex + 1 flag = True break flag = False return tempIndex def choiceCapital(currentSum): # index = currentSum % 26 index = checkForDuplicates(currentSum, 0) print("value of index for Capital", index) print("Capital index ", Capital[index]) return Capital[index] def choiceSmall(currentSum): # index = currentSum % 26 index = checkForDuplicates(currentSum, 1) print("value of index for Small", index) print("small index", small[index]) return small[index] def choicenumber(currentSum): # index = currentSum % 10 index_number = checkForDuplicates(currentSum, 2) print("value of index for number", index_number) print("number index", number[index_number]) return number[index_number] def choiceCharacters(currentSum): # index = currentSum % 10 index_character = checkForDuplicates(currentSum, 3) print("value of index for characters", index_character) print("character index", specialCharacters[index_character]) return specialCharacters[index_character] for index in range(0, digit): currentSum = choice1() if currentSum % 4 == 0: character = choiceCapital(currentSum) password = password + character elif currentSum % 4 == 1: character = choiceSmall(currentSum) password = password + character elif currentSum % 4 == 2: character = choicenumber(currentSum) password = password + character elif currentSum % 4 == 3: character = choiceCharacters(currentSum) password = password + character else: print("Error 404") break print("Generated Password", password)

UTF-8

Python

false

3,210

py

6

passwordgenerate.py

4

0.664174

0.649533

0

116

26.672414

123

Chenjianqi1992/Movie_comment

1,400,159,367,740

6889d13882024bab36cf319628e955794316e501

e17b9b9978ba214294d752d170bb50caff6ec70a

/movie_comment/movie/migrations/0002_remove_movie_info_movie_published.py

cc5629c9c3ed9e2a5b6dde1d8de57e2c94e3c8ee

[]

no_license

https://github.com/Chenjianqi1992/Movie_comment

e0563ab215e456f1da8dd87ed473c988c1f6013f

73900ef887fa38bc2a4fc770c39eef0e4ebcb4c3

refs/heads/master

2021-07-16T13:01:33.512789

2020-05-19T02:31:09

144,814,257

0

null

# Generated by Django 2.0.7 on 2018-08-07 06:08 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('movie', '0001_initial'), ] operations = [ migrations.RemoveField( model_name='movie_info', name='movie_published', ), ]

UTF-8

Python

false

345

py

13

0002_remove_movie_info_movie_published.py

9

0.550725

0.495652

0

17

18.294118

47

buraksmg/e-itim

14,465,449,897,259

7f190558ca228b7bbdb54b6480c79b67fd24754c

d9c7c6da6c55d6f77f9cbbb754ce88863195b871

/egitim_1.py

94953c4d0d8ac4425bc4f5503a046310d5b1fe67

[]

no_license

https://github.com/buraksmg/e-itim

f2f89d3bf2764d3a5c7f3363c2b4daef74e12da7

fc518478ccc2470c96db9a03c52fe2851e3fc0c4

refs/heads/master

2022-11-20T08:43:42.429429

2020-07-20T13:01:52

280,889,960

0

1

null

"""test""" print("Hello world") print("merhaba doğum tarihinizi yazınız") a = int(input()) print("yaşınız:"+str(2020-a))

UTF-8

Python

false

134

py

7

egitim_1.py

7

0.640625

0.609375

0

6

19.333333

41

csm9493/denoising_gan

19,061,064,862,038

f5122de663339e52f2ad2185a9c17746ea863284

71b193a2bb083efd33406fb3b00b07f9f1469abd

/main_noisegan.py

f935b0fd315307a0906506d6e50fbe3aa6a3daa4

[]

no_license

https://github.com/csm9493/denoising_gan

cbbb4d73e29ab8d1a7bc2a26914e6d0d65ed9b2d

0a2dc2329035ae1f0a8fb044b8e6be4c2c306949

refs/heads/master

2020-04-25T20:09:57.178031

2019-03-27T00:31:47

173,045,206

2

0

null

from core.noisegan import NoiseGAN import torch import torch.nn as nn import torch.nn.functional as F class ResidualBlock(nn.Module): def __init__(self, in_features): super(ResidualBlock, self).__init__() conv_block = [ nn.ReflectionPad2d(1), nn.Conv2d(in_features, in_features, 3), nn.InstanceNorm2d(in_features), nn.ReLU(inplace=True), nn.ReflectionPad2d(1), nn.Conv2d(in_features, in_features, 3), nn.InstanceNorm2d(in_features) ] self.conv_block = nn.Sequential(*conv_block) def forward(self, x): return x + self.conv_block(x) class Generator(nn.Module): def __init__(self, input_nc, output_nc, n_residual_blocks=9): super(Generator, self).__init__() # Initial convolution block # model = [ nn.ReflectionPad2d(3), # nn.Conv2d(input_nc, 64, 7), # nn.InstanceNorm2d(64), # nn.ReLU(inplace=True) ] self.pad2d_1 = nn.ReflectionPad2d(3) self.conv2d_1 = nn.Conv2d(input_nc, 64, 7) self.norm2d_1 = nn.InstanceNorm2d(64) self.relu_1 = nn.ReLU(inplace=True) self.rv_embedding = nn.Linear(1, 64) self.tanh = nn.Tanh() model = [] # Downsampling in_features = 64 out_features = in_features*2 for _ in range(2): model += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1), nn.InstanceNorm2d(out_features), nn.ReLU(inplace=True) ] in_features = out_features out_features = in_features*2 # Residual blocks for _ in range(n_residual_blocks): model += [ResidualBlock(in_features)] # Upsampling out_features = in_features//2 for _ in range(2): model += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1), nn.InstanceNorm2d(out_features), nn.ReLU(inplace=True) ] in_features = out_features out_features = in_features//2 # Output layer model += [ nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7), nn.Tanh() ] self.model = nn.Sequential(*model) def forward(self, x, z): out1 = self.pad2d_1(x) out2 = self.conv2d_1(out1) out1_1 = self.rv_embedding(z) out1_2 = self.tanh(out1_1) out1_3 = out1_2.view(-1,64,1,1) out2_1 = out2*out1_3 out3 = self.norm2d_1((out2_1)) out4 = self.relu_1((out3)) return self.model(out4) class Discriminator(torch.nn.Module): def __init__(self, channels): super().__init__() # Filters [256, 512, 1024] # Input_dim = channels (Cx64x64) # Output_dim = 1 self.main_module = nn.Sequential( # Omitting batch normalization in critic because our new penalized training objective (WGAN with gradient penalty) is no longer valid # in this setting, since we penalize the norm of the critic's gradient with respect to each input independently and not the enitre batch. # There is not good & fast implementation of layer normalization --> using per instance normalization nn.InstanceNorm2d() # Image (Cx32x32) nn.Conv2d(in_channels=channels, out_channels=256, kernel_size=4, stride=2, padding=1), nn.InstanceNorm2d(256, affine=True), nn.LeakyReLU(0.2, inplace=True), # State (256x16x16) nn.Conv2d(in_channels=256, out_channels=512, kernel_size=4, stride=2, padding=1), nn.InstanceNorm2d(512, affine=True), nn.LeakyReLU(0.2, inplace=True), # State (512x8x8) nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=4, stride=2, padding=1), nn.InstanceNorm2d(1024, affine=True), nn.LeakyReLU(0.2, inplace=True)) # output of main module --> State (1024x4x4) self.output = nn.Sequential( # The output of D is no longer a probability, we do not apply sigmoid at the output of D. nn.Conv2d(in_channels=1024, out_channels=1, kernel_size=4, stride=1, padding=0)) def forward(self, x): x = self.main_module(x) return self.output(x) def feature_extraction(self, x): # Use discriminator for feature extraction then flatten to vector of 16384 x = self.main_module(x) return x.view(-1, 1024*4*4) def weights_init_normal(m): classname = m.__class__.__name__ if classname.find('Conv') != -1: torch.nn.init.normal(m.weight.data, 0.0, 0.02) elif classname.find('BatchNorm2d') != -1: torch.nn.init.normal(m.weight.data, 1.0, 0.02) torch.nn.init.constant(m.bias.data, 0.0) input_nc = 1 output_nc = 1 lr_g = 2e-4 #learning rate for generator lr_c = 5e-5 #learning rate for critic ep = 20 #training epochs decay_ep = 10 #learning rate decay mbs = 32 #mini batch size im_size = 100 #cropped image size tr_data_name = 'gan_trdata_20500_patch_120x120.hdf5' #training data name critic_iter = 5 #iterations for trainin critic tr_type = 'only_zi' #training type 'only_zi', 'shuffle' experiment_type = '_zi-zi_hat_mbs32_not_equal_loss_x0001_cycleloss_x5' #information about experiment (name for saving result file and weight) sv_name = '190322_cycleGAN_wGAN_experiment_'+ tr_type + experiment_type netG_A2B = Generator(input_nc, output_nc) netG_B2A = Generator(output_nc, input_nc) netD_B = Discriminator(input_nc) netG_A2B.cuda() netG_B2A.cuda() netD_B.cuda() netG_A2B.apply(weights_init_normal) netG_B2A.apply(weights_init_normal) netD_B.apply(weights_init_normal) trainer = NoiseGAN(netG_A2B, netG_B2A, netD_B, save_name=sv_name, lr_g=lr_g, lr_critic=lr_c, epochs=ep, decay_epoch = decay_ep, mini_batch_size=mbs, img_size=im_size, tr_data_name = tr_data_name, critic_iter = critic_iter, input_nc=input_nc, output_nc = output_nc, train_type=tr_type)

UTF-8

Python

false

6,326

py

5

main_noisegan.py

5

0.585836

0.543155

0

169

36.372781

149

N1te0wl1384/inzopr2020

12,773,232,784,532

7c817870f4cf59a571325a419d63e442ee3250b4

c465c76e7bbbcc5c2a74dc2a1fd47d8def839d51

/git_operations/pracownicy.py

87b7b8d27ec520da52970c52b00810c619b15bb5

[]

no_license

https://github.com/N1te0wl1384/inzopr2020

547f796662834be13ee7746b63a09f5959131313

c25aa1bf401f114a9702b663bf288a36ac1eb5b5

refs/heads/master

2022-05-28T23:38:28.849880

2020-04-30T14:37:01

259,095,822

0

null

false

2020-04-30T14:37:02

2020-04-26T17:48:34

2020-04-30T14:24:23

2020-04-30T14:37:01

16

0

Python

false

class Pracownik: def __init__(self, imie, pensja): self.imie = imie self.pensja = pensja def obliczPensje(self): b = self.pensja c = float('%.2f'% (round(b*0.0976,2) + round(b*0.015,2) + round(b*0.0245,2))) d = b - c e = float('%.2f'% round(d*0.09, 2)) f = float('%.2f'%round(d*0.0775, 2)) g = 111.25 h = float('%.2f'%round(b - g - c)) i = float('%.2f'%(round(h*0.18,2) -46.33)) j = round(i - f) k = b - c - e - j return k def skladkiPracodawcy(self): b = self.pensja c = float('%.2f'%(round(b*0.0976,2) + round(b*0.065,2) + round(b*0.0245,2) + round(b*0.0193,2) + round(b*0.001,2))) return c liczba_pracownikow = int(input()) pracownicy = []; for i in range(liczba_pracownikow): pracownik = input() dane=[] for dana in pracownik.split(): dane.append(dana) pracownicy.append(dane) lacznie = 0 for i in pracownicy: pracownik_instancja = Pracownik(i[0], float(i[1])) skladki_pracodawcy = pracownik_instancja.skladkiPracodawcy() pensja = pracownik_instancja.pensja laczny_koszt = skladki_pracodawcy + pensja print(pracownik_instancja.imie, '%.2f' % pracownik_instancja.obliczPensje(), '%.2f' % skladki_pracodawcy, '%.2f' % laczny_koszt) lacznie = lacznie + laczny_koszt print('%.2f' % lacznie)

UTF-8

Python

false

1,383

py

5

pracownicy.py

4

0.573391

0.514823

0

41

32.731707

132

meijida258/mediaSpider

16,269,336,159,180

c3dfbe1bf9a93c64aa15e7b3ab852440462ea30c

54891a2c2ddd56de28c495309a52e98b36290cf0

/crawl/mmjpg.py

3caec44e10e4ad56f26a8056c7d8449ac3d672e8

[]

no_license

https://github.com/meijida258/mediaSpider

61287f5ca58650c6a9578afdbab764fe063c5b39

9a9ef40d1f3dbd46dff25fad4a8b87ee85e5e1fd

refs/heads/master

2021-01-23T08:10:14.211074

2018-06-20T02:19:12

86,479,178

0

null

import asyncio, time, re, os, requests, json, random, datetime from lxml import etree from multiprocessing.dummy import Pool from pymongo import MongoClient from PIL import Image from tool.ImgOut import iop client = MongoClient('localhost', 27017) db = client.MMjpg collection1 = db.Image1 collection2 = db.Image2 collection3 = db.Image3 collection4 = db.Image4 set_path = 'C:/Users/Administrator/Desktop/图/fhx20161004' out_path = 'C:/Users/Administrator/Desktop' def random_output(output_path, source_collection): st = time.time() img_list = source_collection.find() img_random = img_list[random.randint(0, img_list.count())] img_set_name = img_random['set_name'] img_set_list = source_collection.find({'set_name':img_set_name}) if not os.path.exists(output_path + os.path.sep + img_set_name): os.mkdir(output_path + os.path.sep + img_set_name) for img_dict in img_set_list: save_path = output_path + os.path.sep + img_dict['set_name'] + os.path.sep + img_dict['img_num'] + '.' + img_dict['img_type'] with open(save_path, 'wb') as img: img.write(img_dict['img_data']) img.close() print('输出完毕，耗时%s' % str(time.time()-st)) # iop.random_output(out_path, collection4) def insert_pic(i, save_collection = collection4): img_name_list = os.listdir(set_path + os.path.sep + i) for each_img in img_name_list: if save_collection.find({'set_name': i, 'img_num': each_img.split('.')[0]}).count() == 0: start_time = time.time() with open(set_path + os.path.sep + i + os.path.sep + each_img, 'rb') as img: img_data = img.read() img.close() insert_dict = {} insert_dict['img_data'] = img_data insert_dict['set_name'] = i insert_dict['img_type'] = each_img.split('.')[-1] insert_dict['img_num'] = each_img.split('.')[0] save_collection.insert(insert_dict) print('录入一张图片，来自%s的第%s张图片，耗时%s' % (insert_dict['set_name'], insert_dict['img_num'], str(time.time() - start_time))) def up_photos(): collection = [collection1, collection2, collection3, collection4] save_file = out_path + os.sep + 'photos' for i in collection: pic_list = i.find() collection_count = 0 while True: try: save_pic_count = len(os.listdir(save_file)) save_path = save_file + os.sep + 'img_%s.' % str(save_pic_count + 1) + pic_list[collection_count]['img_type'] with open(save_path, 'wb') as pic: pic.write(pic_list[collection_count]['img_data']) pic.close() collection_count += 2 except: break up_photos() # exit() # b = requests.get('http://imgs.aixifan.com/live/1493028073796/1493028073796.jpg').content # # fl = open('a.txt', 'wb') # fl.write(b) # fl.close() # fl = open('a.txt', 'rb') # with open('b.jpg', 'wb') as c: # c.write(fl.read()) # c.close()

UTF-8

Python

false

3,072

py

122

mmjpg.py

93

0.597948

0.577763

0

78

37.75641

133

hi-noikiy/hall0

5,995,774,369,967

c56c8fd31581724bab21bc7911e6e3126f917611

2b5dfacdb7389aefff64c67fac863e3f82d3723e

/source/tyframework/src/tyframework/_private_/dao/userprops_/usermedal.py

95de216065f5bc50b36cdd1ed59226c47e7bc7a5

[]

no_license

https://github.com/hi-noikiy/hall0

54ef76c715f7ac7fec4c9ca175817e12f60fbd6a

21ea94c5b048bc611fb1557ac0b6e3ef4fdbbc09

refs/heads/master

2020-04-08T21:58:55.239106

2018-01-15T14:58:32

null

0

null

# -*- coding=utf-8 -*- from tyframework._private_.dao.userprops_.daobase import DaoBase from tyframework._private_.dao.userprops_.daoconst import DaoConst from tyframework._private_.dao.userprops_.decorator.structdataitem import load_struct_data, \ dump_struct_data class UserMedal(DaoConst, DaoBase): def _init_singleton_(self): pass def get_medal_by_id(self, uid, gameid, medalid, medal_cls): ''' 取得一个medalid对应内容, 将其内容实例化为medal_cls类的新实例 class类必须为使用 @struct_data_item 修饰的类返回: 依据medal_cls的@struct_data_item的标记内容,返回@struct_data_item的实例或实例数组 ''' data = self.__ctx__.RedisGame.execute(uid, 'HGET', 'medal:' + str(gameid) + ':' + str(uid), medalid) return load_struct_data(medal_cls, data) def get_medal_by_id_list(self, uid, gameid, medalid_list, medal_cls): ''' 取得一个medalid_list集合中所有medalid对应的内容, 将所有内容实例化为medal_cls类的新实例 class类必须为使用 @struct_data_item 修饰的类返回: 数组, 每一项对应的medal_cls的实例或数组 ''' datas = self.__ctx__.RedisGame.execute(uid, 'HMGET', 'medal:' + str(gameid) + ':' + str(uid), *medalid_list) values = [] for data in datas: obj = load_struct_data(medal_cls, data) values.append(obj) return values def get_medal_by_id_dict(self, uid, gameid, medalid_cls_dict): ''' medalid_cls_dict为一个集合, key为medalid value为itmeid所对应的class类 class类必须为使用 @struct_data_item 修饰的类返回: dict集合, key为medalid, value为对应的类的实例 ''' medalids = medalid_cls_dict.keys() datas = self.__ctx__.RedisGame.execute(uid, 'HMGET', 'medal:' + str(gameid) + ':' + str(uid), *medalids) values = {} for x in xrange(len(medalids)): data = datas[x] medalid = medalids[x] medal_cls = medalid_cls_dict[medalid] obj = load_struct_data(medal_cls, data) values[medalid] = obj return values def update_medal_by_id(self, uid, gameid, medalid, medal_obj): ''' 更新一个medal的内容, medal_obj必须为使用@struct_data_item修饰过的类实例 ''' data = dump_struct_data(medal_obj) self.__ctx__.RedisGame.execute(uid, 'HSET', 'medal:' + str(gameid) + ':' + str(uid), medalid, data) def update_medal_by_id_list(self, uid, gameid, medalid_list, medal_value_list): ''' 更新一组medal的内容, medalid_list 为medalid的列表集合 medal_value_list为medalid对应的数据集合 medal_value_list中的每一项,可以为单对象数据也可以为同一类的实例的list集合数据项目必须为使用@struct_data_item修饰过的类实例 ''' datas = [] for x in xrange(len(medalid_list)): datas.append(medalid_list[x]) datas.append(medal_value_list[x]) self.__ctx__.RedisGame.execute(uid, 'HMSET', 'medal:' + str(gameid) + ':' + str(uid), *datas) def update_medal_by_id_dict(self, uid, gameid, medalid_ins_dict): ''' 更新一组medal的内容, medalid_ins_dict为一个dict, key为medalid,value为medalid对应的数据值 value可以为单对象数据也可以为同一类的实例的list集合数据项目必须为使用@struct_data_item修饰过的类实例 ''' datas = [] for k, v in medalid_ins_dict.items(): datas.append(k) datas.append(dump_struct_data(v)) self.__ctx__.RedisGame.execute(uid, 'HMSET', 'medal:' + str(gameid) + ':' + str(uid), *datas) def remove_medal_by_id(self, uid, gameid, medalid): ''' 删除一个medal ''' self.__ctx__.RedisGame.execute(uid, 'HDEL', 'medal:' + str(gameid) + ':' + str(uid), medalid) def remove_medal_by_id_list(self, uid, gameid, medalid_list): ''' 删除一组medal ''' self.__ctx__.RedisGame.execute(uid, 'HDEL', 'medal:' + str(gameid) + ':' + str(uid), *medalid_list) def insure_medal_ids(self, uid, gameid, all_medalid_list): ''' 删除所有不再all_medalid_list中的其他的所有ID项目, 通常为整理数据内容时使用 ''' allids = set() for x in all_medalid_list: allids.add(str(x)) delids = [] rids = self.__ctx__.RedisGame.execute(uid, 'HKEYS', 'medal:' + str(gameid) + ':' + str(uid)) for rid in rids: if rid not in allids: delids.append(rid) if delids: self.__ctx__.RedisGame.execute(uid, 'HDEL', 'medal:' + str(gameid) + ':' + str(uid), *delids)

UTF-8

Python

false

4,966

py

1,079

usermedal.py

601

0.584984

0.584754

0

113

37.424779

116

PurpleMyst/porcupine

18,219,251,289,688

f017a806ba28bc62cc281deb3445d569032374c1

3c98132198637077b55d3b6630a7134f747e8e39

/porcupine/plugins/autopep8.py

5d24f6b133af09b5fdd842989959e709202cdeff

[ "MIT" ]

permissive

https://github.com/PurpleMyst/porcupine

ebff59278fb426e8cba862ac6ef6f3ce7d4bd3a9

1ce4e15a5b2fed97efb889e56216c93c042fc7a8

refs/heads/master

2021-01-25T06:56:11.144326

2017-09-07T19:11:12

93,628,416

0

1

null

true

2017-06-07T11:35:06

2017-06-07T11:35:05

2017-05-29T12:16:37

2017-06-07T10:00:48

774

0

null

import platform import subprocess import porcupine from porcupine import tabs, utils def run_autopep8(code): try: import autopep8 # noqa except ImportError: # this command is wrong in some cases, but most of the time # it's ok if platform.system() == 'Windows': command = "py -m pip install autopep8" app = 'command prompt or PowerShell' else: command = "python3 -m pip install --user autopep8" app = 'terminal' utils.errordialog( "Cannot find autopep8", "Looks like autopep8 is not installed.\n" + "You can install it by running this command on a %s:" % app, command) return None # autopep8's main() does some weird signal stuff, so we'll run it in # a subprocess just to make sure that the porcupine process is ok command = [utils.python_executable, '-m', 'autopep8', '-'] process = subprocess.Popen( command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE) (output, errors) = process.communicate(code.encode('utf-8')) if process.returncode != 0: utils.errordialog( "Running autopep8 failed", "autopep8 exited with status code %r." % process.returncode, errors.decode('utf-8', errors='replace')) return None return output.decode('utf-8') def on_click(): widget = porcupine.get_tab_manager().current_tab.textwidget before = widget.get('1.0', 'end - 1 char') after = run_autopep8(before) if after is None: # error return if before != after: widget['autoseparators'] = False widget.delete('1.0', 'end - 1 char') widget.insert('1.0', after) widget.edit_separator() widget['autoseparators'] = True def setup(): porcupine.add_action(on_click, "Tools/autopep8", tabtypes=[tabs.FileTab])

UTF-8

Python

false

1,957

py

57

autopep8.py

44

0.60603

0.593255

0

63

30.063492

77

ahtouw/Projects

9,680,856,317,183

0d3c8d4f4845c46313d5fc3e6f849dc877b5ea4f

23019dc9f099efe5361d387817022130af59334b

/Comp Sci/Python/hw4/hw4-3.py

f7b06e5e5a91ccc4c724207580a34c8d0e47990e

[]

no_license

https://github.com/ahtouw/Projects

950dc23b371fe0997b48c4475ad268aa0342fbca

15d25449e3ddcfe204d2ac6546ccb9272f074928

refs/heads/master

2020-04-14T15:38:02.363349

2019-01-03T07:16:34

163,932,996

0

null

# Homework 4 - Part 3 - Substitution # William Ah Tou # 3/12/17 # Mustafa Al-Lail import sys import re inFile = sys.argv[1] outFile = sys.argv[2] def readFile(fileName): with open(fileName,'r') as f: lines = f.read().splitlines() return lines def writeFile(newLine): appendStr = newLine + '\n' with open(outFile,'a') as i: i.write(appendStr) def modify(testStr): a = re.compile(r"($" r"\d\d\d" r"$" r"\d\d\d" r"\-" r"\d\d\d\d)") b = re.compile(r"($" r"\d\d\d" r"$\-" r"\d\d\d" r"\-" r"\d\d\d\d)") c = re.compile(r"($" r"\d\d\d" r"$\s" r"\d\d\d" r"\-" r"\d\d\d\d)") d = re.compile(r"(" r"\d\d\d" r"\-" r"\d\d\d" r"\-" r"\d\d\d\d)") e = re.compile(r"($" r"\d\d\d" r"$" r"\d\d\d" r"\." r"\d\d\d\d)") f = re.compile(r"($" r"\d\d\d" r"$\." r"\d\d\d" r"\." r"\d\d\d\d)") g = re.compile(r"($" r"\d\d\d" r"$\s" r"\d\d\d" r"\." r"\d\d\d\d)") h = re.compile(r"(" r"\d\d\d" r"\." r"\d\d\d" r"\." r"\d\d\d\d)") substit = testStr if a.search(substit): substit = testStr if b.search(substit): substit = re.sub(r"($\d\d\d$)-(\d\d\d)-(\d\d\d\d)",r"\1\2-\3",substit) if c.search(substit): substit = re.sub(r"($\d\d\d$)\s(\d\d\d)-(\d\d\d\d)",r"\1\2-\3",substit) if d.search(substit): substit = re.sub(r"(\d\d\d)-(\d\d\d)-(\d\d\d\d)", r"(\1)\2-\3", substit) if e.search(substit): substit = re.sub(r"($\d\d\d$)(\d\d\d).(\d\d\d\d)", r"\1\2-\3", substit) if f.search(substit): substit = re.sub(r"($\d\d\d$).(\d\d\d).(\d\d\d\d)", r"\1\2-\3", substit) if g.search(substit): substit = re.sub(r"($\d\d\d$)\s(\d\d\d).(\d\d\d\d)", r"\1\2-\3", substit) if h.search(substit): substit = re.sub(r"(\d\d\d).(\d\d\d).(\d\d\d\d)", r"(\1)\2-\3", substit) print(substit) return(substit) lines = readFile(inFile) modLines = [] for i in lines: modLines.append(modify(i)) for i in modLines: writeFile(i)

UTF-8

Python

false

2,151

py

40

hw4-3.py

16

0.451883

0.437936

0

98

20.959184

77

darionyaphet/python.learning

8,521,215,159,316

fc035da22f55faa92bbe141491c880559f67884d

f319e49b4a4852539fca48fbe830f3871f9f5a2f

/pandas/start.py

77a7d5af338e73b20cf02844f9ccfadc6d009e0d

[]

no_license

https://github.com/darionyaphet/python.learning

75b4d8f7e863286f212f55a6b0478a68b11e48d9

eec24569ef11c7c459a2a10aff9f6522edb1e352

refs/heads/master

2020-05-21T12:23:02.060758

2018-12-12T07:03:10

36,119,713

0

null

''' 10 Minutes to pandas: http://pandas.pydata.org/pandas-docs/stable/10min.html ''' import pandas as pd import numpy as np import matplotlib.pyplot as plt #Creating a Series by passing a list of values, letting pandas create a default integer index s = pd.Series([1,3,5,np.nan,6,8]) ''' 0 1 1 3 2 5 3 NaN 4 6 5 8 dtype: float64 ''' #Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns dates = pd.date_range('20130101', periods=6) ''' DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04', '2013-01-05', '2013-01-06'], dtype='datetime64[ns]', freq='D', tz=None) ''' df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=list('ABCD')) ''' A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 ''' #Creating a DataFrame by passing a dict of objects that can be converted to series-like df2 = pd.DataFrame({ 'A' : 1., 'B' : pd.Timestamp('20130102'), 'C' : pd.Series(1,index=list(range(4)),dtype='float32'), 'D' : np.array([3] * 4,dtype='int32'), 'E' : pd.Categorical(["test","train","test","train"]), 'F' : 'foo' }) ''' A B C D E F 0 1 2013-01-02 1 3 test foo 1 1 2013-01-02 1 3 train foo 2 1 2013-01-02 1 3 test foo 3 1 2013-01-02 1 3 train foo ''' df2.dtypes ''' A float64 B datetime64[ns] C float32 D int32 E category F object dtype: object ''' df.head() ''' A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 ''' df.tail(3) ''' A B C D 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 '''

UTF-8

Python

false

2,443

py

53

start.py

50

0.561195

0.289808

0

84

28.047619

93

MarcoBurgos/ayeWedding

17,093,969,866,911

d18d3d8c1b5acc479b13e121eaffcefebc75b5e1

74315bccd4faef88b2ac33dcb1b26977121a4d0f

/ayeApp/models.py

50d474e7bd40906ad5c44f90540cd1c2f8633429

[]

no_license

https://github.com/MarcoBurgos/ayeWedding

b5aebe825272f8a9a1a32348515a7260b164bf8b

16d231ae5e6bd623c66874ef150d7e2ea3d7eb1f

refs/heads/main

2022-12-30T12:25:33.158855

2020-10-20T02:02:42

305,221,217

0

null

from django.db import models from django.forms import ModelForm # Create your models here. class Invitation(models.Model): invitation_owner_name = models.CharField(max_length=90, verbose_name="Propietario de la invitación") email = models.EmailField(max_length = 128, unique=True) telephone = models.CharField(max_length=16) guest_names = models.CharField(max_length=256, verbose_name="Nombres de invitados") number_of_guests = models.IntegerField(verbose_name="Total de invitados") guests_confirmed = models.CharField(max_length=256, null=True, blank=True, verbose_name="Nombres confirmados") total_guests_confirmed = models.IntegerField(null=True, blank=True, verbose_name="Total de confirmados") is_RSVP = models.BooleanField(null=True, blank=True, verbose_name="¿Confirmó?") date_RSVP =models.DateField(null=True, blank=True, verbose_name="Fecha confirmación") def __str__(self): return str(self.guest_names) class Meta: verbose_name_plural = "Invitaciones"

UTF-8

Python

false

1,029

py

13

models.py

8

0.736585

0.723902

0

20

50.25

114

SaurabhAgarwala/grapp

13,503,377,206,205

3f9d28756c601b20f5534d1fcbc663f99a9e7dc5

c7f47100965c15b4cb120610a4c8af33ab727c3d

/grievances/forms.py

bc2030b50bd712fcbf7d331d05b93d19f30c0210

[]

no_license

https://github.com/SaurabhAgarwala/grapp

a60213f262115e6599b77a03cdd0c5b46678644a

d0d568add196308e6940c94735a1e980d9b23a38

refs/heads/master

2020-03-26T22:30:33.608655

2018-08-20T19:58:37

null

0

null

from django import forms from . import models class ComplainForm(forms.ModelForm): class Meta: model = models.Complain fields = ['title', 'body'] class RespondForm(forms.ModelForm): class Meta: model = models.Complain fields = ['status', 'comments']

UTF-8

Python

false

291

py

16

forms.py

8

0.649485

0

12

23.333333

39

xinsec/python-framework

14,654,428,435,711

d1338348b197822003edf7738f8c79af7532e8d5

4ebf8067f596707dc5336914bf0ea0a97d9fbcb7

/tornadoapp/tornadoapp/websocket/client.py

ed4bc7ae825713f550c9436098dfec7305bcaf03

[]

no_license

https://github.com/xinsec/python-framework

0ade696c5467635a6fd38125f1b2467d58047184

2da2258896145456312342f471d91cec95ddfb24

refs/heads/master

2018-02-06T19:13:18.152713

2017-06-28T03:54:21

null

0

null

""" pip install websocket-client """ from websocket import create_connection if __name__ == '__main__': ws_server = "ws://localhost:30001/websocket/" ws = create_connection(ws_server) # ws.send('{"name":"admin"}') ws.send('{"message":{"reload":1},"destination":"admin"}') print ws.recv() ws.close()

UTF-8

Python

false

324

py

35

client.py

27

0.611111

0.592593

0

13

24

61

woobinda/mysite_v2

764,504,206,826

bf4a14bc18cc7d923fcdcf6b3732ec332a1c7c94

cad8d97ff00189c5bcc5f9d8278cb809ff044772

/musicians/migrations/0027_auto_20160412_1443.py

869dfa91f853e9d54658f82642d8f5cb68aa9120

[]

no_license

https://github.com/woobinda/mysite_v2

d1a405148ffd1bb87b254c9449f46dc50c73085f

9a105e042c4ae1c204d031a4a2a9cd712ce0e831

refs/heads/master

2016-06-03T08:11:04.791096

2016-04-15T17:56:08

51,135,993

1

0

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.1 on 2016-04-12 11:43 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('musicians', '0026_auto_20160412_1407'), ] operations = [ migrations.CreateModel( name='MusicianInstrument', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=100)), ('slug', models.SlugField(max_length=100, unique=True, verbose_name='slug')), ], ), migrations.RemoveField( model_name='musician', name='instrument', ), migrations.RemoveField( model_name='musician', name='instrument_slug', ), migrations.AlterField( model_name='musician', name='playing_style', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='musicians.MusicStyle', verbose_name='Playing style'), ), ]

UTF-8

Python

false

1,203

py

81

0027_auto_20160412_1443.py

46

0.58271

0.551122

0

37

31.513514

149

cyriltasse/FindCluster

8,315,056,690,198

1e00c4aa44c79a38d75e139eb7c778e19dcbae5c

593930a095ace43d083e850c6a155eea3824dfcf

/ClassLogDiffLikelihoodMachine.py

d938c2eb8cc3d2cfb3a80bd245f372c5d26fb48c

[]

no_license

https://github.com/cyriltasse/FindCluster

edc96280e0e5824ed2d607a5b4a4d62686c99559

f8010c04b6802751669a81bfc63bb5a84777ca31

refs/heads/master

2023-01-28T12:26:54.549341

2020-05-27T14:34:03

186,429,403

0

null

import numpy as np import pylab import ClassMassFunction import ClassCatalogMachine import ClassDisplayRGB from DDFacet.Other import ClassTimeIt from DDFacet.ToolsDir.ModToolBox import EstimateNpix np.random.seed(1) from DDFacet.Other import logger log = logger.getLogger("ClassLikelihoodMachine") from DDFacet.Other.AsyncProcessPool import APP, WorkerProcessError import ClassInitGammaCube from ClassAndersonDarling import * from ClassShapiroWilk import * from ClassEigenShapiroWilk import * class ClassLikelihoodMachine(): def __init__(self,CM): self.CM=CM self.zParms=self.CM.zg_Pars self.logMParms=self.CM.logM_Pars self.logM_g=np.linspace(*self.logMParms) self.MassFunction=ClassMassFunction.ClassMassFunction() self.MassFunction.setSelectionFunction(self.CM) self.NSlice=self.zParms[-1]-1 self.MAP=1 # APP.registerJobHandlers(self) def ComputeIndexCube(self,NPix): self.NPix=NPix self.IndexCube=np.array([i*np.int64(NPix**2)+np.int64(self.CM.Cat_s.xCube*NPix)+np.int64(self.CM.Cat_s.yCube) for i in range(self.NSlice)]).flatten() X,Y=self.CM.Cat_s.xCube,self.CM.Cat_s.yCube # # X=np.int64(np.random.rand(X.size)*NPix) # # Y=np.int64(np.random.rand(X.size)*NPix) # # X.fill(NPix//2) # # Y.fill(NPix//2) self.MassFunction.GammaMachine.ThisMask.fill(0) self.IndexCube_xy_Slice=(np.int64(X*NPix)+np.int64(Y)).flatten() indy,indx=np.where(self.MassFunction.GammaMachine.ThisMask==0) #self.IndexCube_Mask=np.array([i*(np.int64(indy).flatten()*NPix+np.int64(indx).flatten()) for i in range(self.NSlice)]).flatten() self.IndexCube_Mask_Slice=(np.int64(indy).flatten()*NPix+np.int64(indx).flatten()) self.IndexCube_Mask=np.array([i*NPix**2+(np.int64(indy).flatten()*NPix+np.int64(indx).flatten()) for i in range(self.NSlice)]).flatten() if self.MAP: GM=self.MassFunction.GammaMachine self.LCAD=[] self.LCSW=[] self.PowerMAP=1.#4.#2.5 # for iSlice in range(self.NSlice): # CAD=ClassAndersonDarlingMachine() # CAD.generatePA2(GM.L_NParms[iSlice],NTry=2000) # self.LCAD.append(CAD) # self.CSWFull=ClassShapiroWilk() # self.CSWFull.Init(GM.NParms,NTry=2000) self.CSW=ClassEigenShapiroWilk(GM.L_NParms) # for iSlice in range(self.NSlice): # log.print("======================================="%iSlice) # log.print("Init ShapiroWilk for slice %i"%iSlice) # CAD=ClassShapiroWilk() # CAD.Init(GM.L_NParms[iSlice],NTry=2000) # self.LCSW.append(CAD) def measure_dlogPdg(self,g0,DoPlot=0): g=g0.copy() L0=self.logL(g,DoPlot=DoPlot) dL_dg0=self.dlogLdg(g) NN=g.size dg=.001 NTest=100 # Parm_id=np.arange(NN)[::-1][0:NTest] Parm_id=np.int64(np.random.rand(NTest)*g.size) dL_dg1=np.zeros((Parm_id.size,),np.float64) for i in range(Parm_id.size): g1=g.copy() g1[Parm_id[i]]+=dg L1=self.logL(g1,DoPlot=DoPlot) dL_dg1[i]=(L0-L1)/((g-g1)[Parm_id[i]]) if (L0-L1)==0: print(" 0 diff for iParm = %i"%i) pylab.figure("Jacob") pylab.clf() # pylab.plot(dL_dg0[Parm_id]/dL_dg1) pylab.plot(dL_dg0[Parm_id],label="Computed") pylab.plot(dL_dg1,ls="--",label="Measured") pylab.legend() pylab.draw() pylab.show(block=False) pylab.pause(0.1) # ############################################# def measure_d2logPdg2(self,g0,DoPlot=0): g=g0.copy() NN=g.size dg=1e-2 NTest=1 Parm_id=np.int64(np.random.rand(NTest)*g.size) Parm_id=np.arange(NN) dJdg0=self.d2logPdg2(g,Diag=True) dJdg0_Full=self.d2logPdg2(g, Diag=False, ParmId=Parm_id) #dJdg2=self.buildFulldJdg(g,ParmId=Parm_id) pylab.figure("Hessian") pylab.clf() pylab.plot(dJdg0[Parm_id],label="CalcDiag",color="black") pylab.plot(np.diag(dJdg0_Full)[Parm_id],label="np.diag(CalcFull)",ls="--",lw=2) # #pylab.plot(np.diag(dJdg2)[Parm_id]) # pylab.draw() # pylab.show(block=False) # pylab.pause(0.1) dLdg_0=self.dlogPdg(g) dJdg1_row=np.zeros((Parm_id.size,dLdg_0.size),np.float64) dJdg1=np.zeros((Parm_id.size,),np.float64) for i in range(Parm_id.size): print("%i/%i"%(i,Parm_id.size)) g1=g.copy() g1[Parm_id[i]]+=dg dLdg_1=self.dlogPdg(g1) dJdg1[i]=(dLdg_0[Parm_id[i]]-dLdg_1[Parm_id[i]]) / ((g-g1)[Parm_id[i]]) dJdg1_row[i,:]=(dLdg_1-dLdg_0) / dg # pylab.plot(dL_dg0[Parm_id]/dL_dg1) pylab.plot(dJdg1,label="measure of diag",ls="-.",lw=2) pylab.legend() pylab.draw() pylab.show(block=False) pylab.pause(0.1) #I0=np.log10(np.abs(dJdg0_Full)) #I1=np.log10(np.abs(dJdg1_row)) I0=(np.abs(dJdg0_Full)) I1=(np.abs(dJdg1_row)) I0[I0<=0]=1e-10 I1[I1<=0]=1e-10 I1=np.log10(I1) I0=np.log10(I0) v0=np.min([I0.min(),I1.min()]) v1=np.max([I0.max(),I1.max()]) #m=np.median(I0[I0>0]) #v0,v1=0,100*m pylab.figure("Full Hessian") pylab.clf() ax=pylab.subplot(2,2,1) pylab.imshow(I1,interpolation="nearest",vmin=v0,vmax=v1) pylab.title("measured") pylab.subplot(2,2,2,sharex=ax,sharey=ax) pylab.imshow(I0,interpolation="nearest",vmin=v0,vmax=v1) pylab.title("computed") pylab.subplot(2,2,3,sharex=ax,sharey=ax) pylab.imshow(I1-I0,interpolation="nearest",vmin=-1,vmax=1) pylab.title("resid") pylab.colorbar() # ind=np.int64(np.random.rand(1000)*dJdg1_row.size) # x=dJdg1_row.T.flatten()[ind] # y=dJdg0_Full[Parm_id,:].T.flatten()[ind] # #x=np.log10(np.abs(x)) # #y=np.log10(np.abs(y)) # #x=np.log10(np.abs(x)) # #y=np.log10(np.abs(y)) # pylab.plot(x,label="row measured",color="black") # pylab.plot(y,ls="--",label="row computed") # # pylab.plot(dJdg1_row.T,label="row measured",color="black") # # pylab.plot(dJdg0_Full[Parm_id,:].T,ls="--",label="row computed") # pylab.legend() pylab.draw() pylab.show(False) pylab.pause(0.1) # ################################################ def logP(self,g): logL=self.logL(g) #logL=0. if self.MAP: logL+=self.CSW.logP_x(g.flatten()) return logL def dlogPdg(self,g): NParms=self.MassFunction.GammaMachine.NParms J=np.zeros((NParms,),np.float32) J+=self.dlogLdg(g) #J.fill(0) if self.MAP: J+=self.CSW.dlogPdx(g.flatten()) return J def d2logPdg2(self,g0,Diag=True,ParmId=None): if Diag: H=self.d2logLdg2(g0) #H.fill(0) if self.MAP: H[:]+=self.CSW.d2logPdx2_Diag(g0.flatten()) else: # H=np.zeros((g0.size,g0.size),np.float64) # dg=1e-3 # dLdg0=self.dlogLdg(g0) # if ParmId is None: # ParmId=np.arange(g0.size) # for ii,i in enumerate(ParmId): # print("%i/%i"%(ii,ParmId.size)) # g1=g0.copy() # g1[i]+=dg # dLdg1=self.dlogLdg(g1) # H[i]=(dLdg1-dLdg0) / dg H=self.d2logLdg2_Full(g0) #H.fill(0) if self.MAP: H+=self.CSW.d2logPdx2_Full(g0) return H # ################################################ def logprob(self,g): return np.float64(self.logP(g)),np.float64(self.dlogPdg(g)) def logL(self,g0,DoPlot=0): GM=self.MassFunction.GammaMachine g0=np.float64(g0) GM.computeGammaCube(g0) self.CellRad_0=self.MassFunction.GammaMachine.CellRad self.CellRad_1=(.00001/3600)*np.pi/180 g=g0.reshape((-1,1)) L_SqrtCov=GM.L_SqrtCov L_NParms=GM.L_NParms Ns=self.CM.Cat_s.shape[0] n_z=self.CM.DicoDATA["DicoSelFunc"]["n_z"] n_zt=self.CM.Cat_s.n_zt TypeSum=np.float64 #TypeSum=np.float32 # self.funcNormLog=np.log10 # self.fNormLog=np.log(10) self.funcNormLog=np.log self.fNormLog=1. if DoPlot: GM.PlotGammaCube(Cube=GM.GammaCube,FigName="JacobCube") Nx_1=np.zeros((Ns,),np.float64) # Ax_1=np.zeros((Ns,),np.float32) L_Ax_1_z=[] for iSlice in range(self.NSlice): Gamma_i=GM.GammaCube[iSlice].flat[self.IndexCube_xy_Slice] Nx_1[:]+=n_z[iSlice]*Gamma_i*self.CellRad_1**2 Ax_1_z=n_zt[:,iSlice]*Gamma_i*self.CellRad_1**2 #Ax_1[:]+=Ax_1_z L_Ax_1_z.append(Ax_1_z) L=np.float64([0.]) # ####################### # SumNx_0=np.sum(TypeSum((n_z.reshape(-1,1,1)*self.CellRad_0**2)*GM.GammaCube)) SumNx_0=np.sum(TypeSum(n_z.reshape(-1,1,1)*GM.GammaCube).flat[self.IndexCube_Mask])*self.CellRad_0**2/self.fNormLog L+=-SumNx_0 # ####################### SumNx_1=np.sum(TypeSum(Nx_1)) L+= SumNx_1 # ####################### self.Ax_1_z=np.array(L_Ax_1_z) self.Ax_1=np.sum(TypeSum(self.Ax_1_z),axis=0) SumAx_1=np.sum(self.funcNormLog(TypeSum(self.Ax_1))) L+= SumAx_1 self.gCurrentL=g0.copy() return L[0] def dlogLdg(self,g0,Slice=None): T=ClassTimeIt.ClassTimeIt() T.disable() GM=self.MassFunction.GammaMachine GM.computeGammaCube(g0) g=g0.reshape((-1,1)) T.timeit("Compute Gamma") L_SqrtCov=GM.L_SqrtCov L_NParms=GM.L_NParms NParms=GM.NParms Ns=self.CM.Cat_s.shape[0] n_z=self.CM.DicoDATA["DicoSelFunc"]["n_z"] n_zt=self.CM.Cat_s.n_zt # Sum_z_Ax_1_z=np.sum(self.Ax_1_z,axis=0) L_Ax_1_z=[] for iSlice in range(self.NSlice): Gamma_i=GM.GammaCube[iSlice].flat[self.IndexCube_xy_Slice] Ax_1_z=n_zt[:,iSlice]*Gamma_i*self.CellRad_1**2 L_Ax_1_z.append(Ax_1_z) Ax_1_z=np.array(L_Ax_1_z) Sum_z_Ax_1_z=np.sum(Ax_1_z,axis=0) # print(GM.GammaCube.flat[0]) ii=0 J=np.zeros((NParms,),np.float64) if Slice is None: Sl=slice(None) else: Sl=slice(Slice,Slice+1) for iSlice in range(self.NSlice)[Sl]: ThisNParms=L_NParms[iSlice] iPar=ii jPar=iPar+ThisNParms GammaSlice=GM.GammaCube[iSlice] SqrtCov=L_SqrtCov[iSlice] SqrtCov_xy=SqrtCov[self.IndexCube_xy_Slice,:] # ####################### dNx_0_dg=n_z[iSlice]*SqrtCov[:,:]*GammaSlice.reshape((-1,1))*self.CellRad_0**2 Sum_dNx_0_dg=np.sum(dNx_0_dg[self.IndexCube_Mask_Slice,:],axis=0) J[iPar:jPar]+= -Sum_dNx_0_dg # ######################### dNx_1_dg=n_z[iSlice]*SqrtCov_xy[:,:]*GammaSlice.flat[self.IndexCube_xy_Slice].reshape((-1,1))*self.CellRad_1**2 Sum_dNx_1_dg=np.sum(dNx_1_dg,axis=0) J[iPar:jPar]+= Sum_dNx_1_dg # ######################### dAx_dg_0 = n_zt[:,iSlice].reshape((-1,1))*SqrtCov_xy[:,:]*GammaSlice.flat[self.IndexCube_xy_Slice].reshape((-1,1))#*np.log(10) dAx_dg_1 = Sum_z_Ax_1_z dAx_dg = dAx_dg_0/dAx_dg_1.reshape((-1,1))*self.CellRad_1**2 Sum_dAx_dg=np.sum(dAx_dg,axis=0) J[iPar:jPar]+= + Sum_dAx_dg ii+=ThisNParms return J # ################################# # HESSIANs def d2logLdg2(self,g0): T=ClassTimeIt.ClassTimeIt() T.disable() GM=self.MassFunction.GammaMachine GM.computeGammaCube(g0) g=g0.reshape((-1,1)) T.timeit("Compute Gamma") L_SqrtCov=GM.L_SqrtCov L_NParms=GM.L_NParms NParms=GM.NParms Ns=self.CM.Cat_s.shape[0] n_z=self.CM.DicoDATA["DicoSelFunc"]["n_z"] n_zt=self.CM.Cat_s.n_zt # Sum_z_Ax_1_z=np.sum(self.Ax_1_z,axis=0) L_Ax_1_z=[] for iSlice in range(self.NSlice): Gamma_i=GM.GammaCube[iSlice].flat[self.IndexCube_xy_Slice] Ax_1_z=n_zt[:,iSlice]*Gamma_i*self.CellRad_1**2 L_Ax_1_z.append(Ax_1_z) Ax_1_z=np.array(L_Ax_1_z) Sum_z_Ax_1_z=np.sum(Ax_1_z,axis=0) ii=0 H=np.zeros((NParms,),np.float32) for iSlice in range(self.NSlice): ThisNParms=L_NParms[iSlice] iPar=ii jPar=iPar+ThisNParms GammaSlice=GM.GammaCube[iSlice] SqrtCov=L_SqrtCov[iSlice] SqrtCov_xy=SqrtCov[self.IndexCube_xy_Slice,:] # ##################################" dNx_0_dg=n_z[iSlice] * (SqrtCov[:,:])**2 * GammaSlice.reshape((-1,1))*self.CellRad_0**2*self.fNormLog Sum_dNx_0_dg=np.sum(dNx_0_dg[self.IndexCube_Mask_Slice,:],axis=0) H[iPar:jPar]+= -Sum_dNx_0_dg # ##################################" dNx_1_dg=n_z[iSlice]*(SqrtCov_xy[:,:])**2 * GammaSlice.flat[self.IndexCube_xy_Slice].reshape((-1,1))*self.CellRad_1**2 Sum_dNx_1_dg=np.sum(dNx_1_dg,axis=0) H[iPar:jPar]+= + Sum_dNx_1_dg # ##################################" dAx_dg_0 = n_zt[:,iSlice].reshape((-1,1))* (SqrtCov_xy[:,:]) \ * GammaSlice.flat[self.IndexCube_xy_Slice].reshape((-1,1))#*np.log(10) dAx_dg_1 = Sum_z_Ax_1_z dAx_dg_A = SqrtCov_xy[:,:]*dAx_dg_0 / dAx_dg_1.reshape((-1,1)) * self.CellRad_1**2 dAx_dg_B = - ( (dAx_dg_0 * dAx_dg_0) * self.CellRad_1**4 / (dAx_dg_1.reshape((-1,1)))**2 ) dAx_dg = dAx_dg_A + dAx_dg_B Sum_dAx_dg=np.sum(dAx_dg,axis=0) H[iPar:jPar]+= + Sum_dAx_dg ii+=ThisNParms return H # ############################# # FULLLLL def d2logLdg2_Full(self,g0): T=ClassTimeIt.ClassTimeIt() T.disable() GM=self.MassFunction.GammaMachine GM.computeGammaCube(g0) g=g0.reshape((-1,1)) T.timeit("Compute Gamma") L_SqrtCov=GM.L_SqrtCov L_NParms=GM.L_NParms NParms=GM.NParms Ns=self.CM.Cat_s.shape[0] n_z=self.CM.DicoDATA["DicoSelFunc"]["n_z"] n_zt=self.CM.Cat_s.n_zt # Sum_z_Ax_1_z=np.sum(self.Ax_1_z,axis=0) L_Ax_1_z=[] for iSlice in range(self.NSlice): Gamma_i=GM.GammaCube[iSlice].flat[self.IndexCube_xy_Slice] Ax_1_z=n_zt[:,iSlice]*Gamma_i*self.CellRad_1**2 L_Ax_1_z.append(Ax_1_z) Ax_1_z=np.array(L_Ax_1_z) Sum_z_Ax_1_z=np.sum(Ax_1_z,axis=0) DicoIndex={} ii=0 for iSlice in range(self.NSlice): ThisNParms=L_NParms[iSlice] iPar=ii jPar=iPar+ThisNParms DicoIndex[iSlice]=(iPar,jPar) ii+=ThisNParms ii=0 H=np.zeros((NParms,NParms),np.float32) dAidg=np.zeros((Ns,NParms),np.float32) for iSlice in range(self.NSlice): GammaSlice=GM.GammaCube[iSlice] i0,i1=DicoIndex[iSlice] ThisNParms=i1-i0 SqrtCov=L_SqrtCov[iSlice] SqrtCov_xy=SqrtCov[self.IndexCube_xy_Slice,:] iPar,jPar=i0,i1 ng=n_zt[:,iSlice].reshape((-1,1))* GammaSlice.flat[self.IndexCube_xy_Slice].reshape((-1,1)) dAidg[:,i0:i1] = ng * SqrtCov_xy.reshape((Ns,ThisNParms)) for iSlice in range(self.NSlice): GammaSlice=GM.GammaCube[iSlice] i0,i1=DicoIndex[iSlice] ThisNParms=i1-i0 SqrtCov=L_SqrtCov[iSlice] SqrtCov_xy=SqrtCov[self.IndexCube_xy_Slice,:] iPar,jPar=i0,i1 # ################################## dNx_0_dg=n_z[iSlice]\ * SqrtCov.reshape((self.NPix**2,ThisNParms,1))\ * SqrtCov.reshape((self.NPix**2,1,ThisNParms))\ * GammaSlice.reshape((-1,1,1))*self.CellRad_0**2*self.fNormLog Sum_dNx_0_dg=np.sum(dNx_0_dg[self.IndexCube_Mask_Slice,...],axis=0) H[iPar:jPar,iPar:jPar]+= - Sum_dNx_0_dg # ################################## dNx_1_dg=n_z[iSlice].reshape(-1,1,1)\ * SqrtCov_xy.reshape((Ns,ThisNParms,1))\ * SqrtCov_xy.reshape((Ns,1,ThisNParms))\ * GammaSlice.flat[self.IndexCube_xy_Slice].reshape((-1,1,1))*self.CellRad_1**2 Sum_dNx_1_dg=np.sum(dNx_1_dg,axis=0) H[iPar:jPar,iPar:jPar]+= Sum_dNx_1_dg ng=n_zt[:,iSlice].reshape((-1,1))* GammaSlice.flat[self.IndexCube_xy_Slice].reshape((-1,1)) dAx_dg_0 = ng.reshape(-1,1,1) * SqrtCov_xy.reshape((Ns,ThisNParms,1)) * SqrtCov_xy.reshape((Ns,1,ThisNParms)) H[iPar:jPar,iPar:jPar]+= np.sum(dAx_dg_0 * (Sum_z_Ax_1_z.reshape((-1,1,1)))**(-1),axis=0)*self.CellRad_1**2 for jSlice in range(self.NSlice): GammaSlice_j=GM.GammaCube[jSlice] j0,j1=DicoIndex[jSlice] ThisNParms_j=j1-j0 SqrtCov_j=L_SqrtCov[jSlice] SqrtCov_xy_j=SqrtCov_j[self.IndexCube_xy_Slice,:] # ################################## Js=dAidg[:,i0:i1] Js_j=dAidg[:,j0:j1] H[i0:i1,j0:j1]+= - np.sum(Js.reshape((Ns,ThisNParms,1))*Js_j.reshape((Ns,1,ThisNParms_j)) \ * (Sum_z_Ax_1_z.reshape((-1,1,1)))**(-2),axis=0)*self.CellRad_1**4 return H def recenterNorm(self,X): return X return self.CSW.recenterNorm(X)

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import albumentations as albu import numpy as np import torch # from model_training.detection.retinanet_v2 import RetinaNet from model_training.detection.retinanet import RetinaNet from .ssd import SSD from .detector_postprocessing import DetectorPostProcessing class Detector(object): def __init__(self, config): super(Detector, self).__init__() self._init_model(config) self.img_size = config['img_size'] self.multiclass_suppression = config.get('multiclass_suppression', True) self.transform = self._get_transform() self.post_processing = DetectorPostProcessing(config) def __call__(self, img): with torch.no_grad(): x = self._process_image(img) if torch.cuda.is_available(): x = x.cuda() loc, conf, priors = self.net(x) priors = priors[0] return self.post_processing(loc, conf, priors, img.shape, multiclass_suppression=self.multiclass_suppression) def _process_image(self, img): return torch.from_numpy(np.transpose(self.transform(img), (2, 0, 1))).unsqueeze(0) def _init_model(self, config): if config['name'] == 'ssd': self.net = SSD(config) else: self.net = RetinaNet(config, pretrained=config.get('pretrained', False)) model_dict = torch.load(config['filepath'], map_location=None if torch.cuda.is_available() else 'cpu') self.net.load_state_dict(model_dict['model']) self.net = self.net.eval() if torch.cuda.is_available(): self.net = self.net.cuda() def _get_transform(self): pipeline = albu.Compose([ albu.Resize(self.img_size, self.img_size), albu.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) def process(a): r = pipeline(image=a) return r['image'] return process

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from vetor import * class Matrix(object): def __init__(self,matrix,w,h): # 矩阵的初始化 self.__matrix = matrix self.__width = w self.__height = h def __str__(self): # 返回矩阵的字符表达形式 ans = "" for i in range(self.__height): ans += "|" for j in range(self.__width): if j < self.__width -1: ans += str(self.__matrix[i][j]) + "," else: ans += str(self.__matrix[i][j]) + "|\n" return ans def changeComponent(self,x,y, value): # 改变矩阵特定位置的元素 if x >= 0 and x < self.__height and y >= 0 and y < self.__width: self.__matrix[x][y] = value else: raise Exception ("changeComponent: indices out of bounds") def component(self,x,y): # 返回矩阵特定位置的元素 if x >= 0 and x < self.__height and y >= 0 and y < self.__width: return self.__matrix[x][y] else: raise Exception ("changeComponent: indices out of bounds") def width(self): # 返回矩阵的宽度 return self.__width def height(self): # 返回矩阵的高度 return self.__height def __mul__(self,other): # 矩阵的乘积 if isinstance(other, Vector): # 判断与矩阵乘积的对象是向量 if (len(other) == self.__width): ans = zeroVector(self.__height) for i in range(self.__height): summe = 0 for j in range(self.__width): summe += other.component(j) * self.__matrix[i][j] ans.changeComponent(i,summe) summe = 0 return ans else: raise Exception("vector must have the same size as the " + "number of columns of the matrix!") elif isinstance(other,int) or isinstance(other,float): #判断与矩阵乘积的是标量，即数乘 matrix = [[self.__matrix[i][j] * other for j in range(self.__width)] for i in range(self.__height)] return Matrix(matrix,self.__width,self.__height) def __add__(self,other): # 矩阵的加法 if (self.__width == other.width() and self.__height == other.height()): matrix = [] for i in range(self.__height): row = [] for j in range(self.__width): row.append(self.__matrix[i][j] + other.component(i,j)) matrix.append(row) return Matrix(matrix,self.__width,self.__height) else: raise Exception("matrix must have the same dimension!") def __sub__(self,other): # 矩阵的减法 if (self.__width == other.width() and self.__height == other.height()): matrix = [] for i in range(self.__height): row = [] for j in range(self.__width): row.append(self.__matrix[i][j] - other.component(i,j)) matrix.append(row) return Matrix(matrix,self.__width,self.__height) else: raise Exception("matrix must have the same dimension!") if __name__ == "__main__": m=Matrix([[1,2,3],[4,5,6],[7,8,9]],3,3) v=Vector([1,2,3]) print(m*v)

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matrix.py

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parkus/FLAIIL

16,149,077,071,889

b978cfc4f3020848f337a6704d53e2b6db1e35d6

aed2049a6cc9cf2434221632b1b0d60da7d10d60

/ranges.py

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refs/heads/master

2020-03-16T17:18:35.783996

2020-02-21T18:15:26

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""" Utilities for handling numerical ranges. """ from __future__ import division, print_function, absolute_import import numpy as np def rangeset_union(ranges0, ranges1): """ Return the union of two sets of *sorted* ranges. Parameters ---------- ranges0 : Nx2 array ranges1 : Nx2 array Returns ------- union_ranges : Nx2 array """ invrng0, invrng1 = list(map(rangeset_invert, [ranges0, ranges1])) xinv = rangeset_intersect(invrng0, invrng1) return rangeset_invert(xinv) def rangeset_intersect(ranges0, ranges1, presorted=False): """ Return the intersection of two sets of ranges. Parameters ---------- ranges0 : Nx2 array ranges1 : Nx2 array presorted : bool If True, ranges are assumed to be sorted. Returns ------- intersecting_ranges : Nx2 array """ if len(ranges0) == 0 or len(ranges1) == 0: return np.empty([0, 2]) rng0, rng1 = list(map(np.asarray, [ranges0, ranges1])) rng0, rng1 = [np.reshape(a, [-1, 2]) for a in [rng0, rng1]] if not presorted: rng0, rng1 = [r[np.argsort(r[:,0])] for r in [rng0, rng1]] for rng in [rng0, rng1]: assert np.all(rng[1:] > rng[:-1]) l0, r0 = rng0.T l1, r1 = rng1.T f0, f1 = [rng.flatten() for rng in [rng0, rng1]] lin0 = inranges(l0, f1, [1, 0]) rin0 = inranges(r0, f1, [0, 1]) lin1 = inranges(l1, f0, [0, 0]) rin1 = inranges(r1, f0, [0, 0]) #keep only those edges that are within a good area of the other range l = weave(l0[lin0], l1[lin1]) r = weave(r0[rin0], r1[rin1]) return np.array([l, r]).T def rangeset_invert(ranges): """ Return the inverse of the provided ranges. Parameters ---------- ranges : Nx2 array Returns ------- inverted_ranges : Nx2 array """ if len(ranges) == 0: return np.array([[-np.inf, np.inf]]) edges = ranges.ravel() rnglist = [edges[1:-1].reshape([-1, 2])] if edges[0] != -np.inf: firstrng = [[-np.inf, edges[0]]] rnglist.insert(0, firstrng) if edges[-1] != np.inf: lastrng = [[edges[-1], np.inf]] rnglist.append(lastrng) return np.vstack(rnglist) def inranges(values, ranges, inclusive=[False, True]): """Determines whether values are in the supplied list of sorted ranges. Parameters ---------- values : 1-D array-like The values to be checked. ranges : 1-D or 2-D array-like The ranges used to check whether values are in or out. If 2-D, ranges should have dimensions Nx2, where N is the number of ranges. If 1-D, it should have length 2N. A 2xN array may also be used, but note that it will be assumed to be Nx2 if N == 2. inclusive : length 2 list of booleans Whether to treat bounds as inclusive. Because it is the default behavior of numpy.searchsorted, [False, True] is the default here as well. Using [False, False] or [True, True] will require roughly triple computation time. Returns ------ inside : array a boolean array indexing the values that are in the ranges. """ ranges = np.asarray(ranges) if ranges.ndim == 2: if ranges.shape[1] != 2: ranges = ranges.T ranges = ranges.ravel() if inclusive == [0, 1]: return (np.searchsorted(ranges, values) % 2 == 1) if inclusive == [1, 0]: return (np.searchsorted(ranges, values, side='right') % 2 == 1) if inclusive == [1, 1]: a = (np.searchsorted(ranges, values) % 2 == 1) b = (np.searchsorted(ranges, values, side='right') % 2 == 1) return (a | b) if inclusive == [0, 0]: a = (np.searchsorted(ranges, values) % 2 == 1) b = (np.searchsorted(ranges, values, side='right') % 2 == 1) return (a & b) def weave(a, b): """ Insert values from b into a in a way that maintains their order. Both must be sorted. """ mapba = np.searchsorted(a, b) return np.insert(a, mapba, b)

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urmithakkar/testrepo

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[]

no_license

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#creating a file in brach1 print("Inside branch 1")

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fd-facu/termodinamica

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/grafico_temp_calor.py

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[]

no_license

https://github.com/fd-facu/termodinamica

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2017-12-12T20:56:40

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2017-06-25T02:29:31

2017-12-06T17:41:23

2022-01-19T23:29:56

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import matplotlib.pyplot as plt plt.ylabel('Temperatura (K)') plt.xlabel('Tiempo (seg)') plt.title('Grafico PV') def generar_grafico(lista_etapas): instancia_x = [] instancia_y = [] for tupla in lista_etapas: instancia_x.append(tupla[0]) instancia_y.append(tupla[1]) plt.plot(instancia_x, instancia_y) plt.show() # Test #generar_grafico()

UTF-8

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py

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grafico_temp_calor.py

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Bihan-tifr/Exam

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5bf5da35ccd39c6e99cf6f904f250ed117bc019c

/p9.py

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[]

no_license

https://github.com/Bihan-tifr/Exam

52615027df4a86bbf980b303335094b1c50f4e59

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refs/heads/master

2022-10-08T21:40:56.950279

2020-06-05T15:49:10

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null

import numpy as np A=np.array([[2,1],[1,0],[0,1]]) print("The Matrix:\n{}".format(A)) print("Singular value decomposition:") u,s,v=(np.linalg.svd(A)) print("u:\n{}".format(u)) print("s:\n{}".format(s)) print("v:\n{}".format(v)) input("press any key to evaluate svd of second matrix") B=np.array([[1,1,0],[1,0,1],[0,1,1]]) print("The Matrix:\n{}".format(B)) print("Singular value decomposition") u,s,v=(np.linalg.svd(B)) print("u:\n{}".format(u)) print("s:\n{}".format(s)) print("v:\n{}".format(v))

UTF-8

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p9.py

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1017-MJ/1017_MJ

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/27.py

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[]

no_license

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2021-06-11T11:46:15

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x = [3,2,4,1] for n in x: print(n) x = [3,2,4,1] y = ["Hello", "There"] for c in y: print(c) x = [3,2,4,1] #0,1,2,3 y = ["Hello", "There"] print(x.index(4)) print(y.index("Hello")) x = [3,2,4,1] #0,1,2,3 y = ["Hello", "There"] print("bye" in y) print("Hello" in y) if "Hello" in y: print("Hello가 있어요.") if "bye" in y: print("bye") print("bye는 없어요.")

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zvonicek/IT3708

10,660,108,846,122

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/task5_q_learning/q_flatland/reinforcement_flatland/flatland.py

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[]

no_license

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d4e4653cb1579c0765bd4837677d798f696f57af

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refs/heads/master

2020-05-20T06:01:23.754233

2015-05-19T10:59:32

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null

import random class Cell: Food, Poison, Empty, Agent = range(0, 4) class Turn: Left, Right, Up, Down = range(0, 4) class Flatland(): def __init__(self, world, agent_coord, food_num, poison_num): self.agent_init = agent_coord self.agent_coord = agent_coord self.grid = world self.food_num = food_num self.poison_num = poison_num # copy the grid so as it can be restored for next generation self.start_grid = [x[:] for x in self.grid] @classmethod def random_world(cls, length, fpd, agent_coord): grid = [[Cell.Empty for _ in range(length)] for _ in range(length)] grid[agent_coord[0]][agent_coord[1]] = Cell.Agent food_positions = [(x, y) for x in range(length) for y in range(length) if x != y] food_num = round(fpd[0] * length ** 2) for pos in random.sample(food_positions, food_num): grid[pos[0]][pos[1]] = Cell.Food poison_positions = [(x, y) for x in range(length) for y in range(length) if x != y and grid[x][y] == Cell.Empty] poison_num = round(fpd[1] * (length ** 2 - food_num)) for pos in random.sample(poison_positions, poison_num): grid[pos[0]][pos[1]] = Cell.Poison return cls(grid, agent_coord, food_num, poison_num) @classmethod def from_file(cls, filename): grid = [] food_num = 0 poison_num = 0 agent_coord = None with open(filename, 'r') as f: header = f.readline().split() agent_coord = int(header[3]), int(header[2]) line = f.readline().split() while line: row = [] for cell in line: if cell == "-2": row.append(Cell.Agent) elif cell == "-1": row.append(Cell.Poison) poison_num += 1 elif cell == "0": row.append(Cell.Empty) else: row.append(Cell.Food) food_num += 1 grid.append(row) line = f.readline().split() return cls(grid, agent_coord, food_num, poison_num) def reset(self): """resets the world to initial state""" self.grid = [x[:] for x in self.start_grid] self.agent_coord = self.agent_init def move(self, turn): curr_row, curr_col = self.get_agent() new_row, new_col = curr_row, curr_col if turn == Turn.Up: new_row = (new_row - 1) % len(self.grid) elif turn == Turn.Right: new_col = (new_col + 1) % len(self.grid[0]) elif turn == Turn.Down: new_row = (new_row + 1) % len(self.grid) elif turn == Turn.Left: new_col = (new_col - 1) % len(self.grid[0]) cell_state = self.grid[new_row][new_col] self.grid[curr_row][curr_col] = Cell.Empty self.grid[new_row][new_col] = Cell.Agent self.agent_coord = (new_row, new_col) return cell_state def get_agent(self): return self.agent_coord def print_stats(self): food = 0 poison = 0 for row in range(len(self.grid)): for col in range(len(self.grid)): if self.grid[row][col] == Cell.Food: food += 1 elif self.grid[row][col] == Cell.Poison: poison += 1 print("food eaten:", self.food_num - food, "poison eaten:", self.poison_num - poison)

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hypnguyen1209/infosec-algorithm

154,618,845,393

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no_license

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af4552da0e937135e41e54538fa35807a989edb7

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refs/heads/main

2023-07-02T12:41:26.696442

2021-08-16T09:49:17

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null

import math a = 123456 W = 8 p = 2147483647 def solve(a, W, p): result = [] m = round(math.log2(p)) t = round(m/W) n = [pow(2, i*W) for i in range(t)] for i in n[::-1]: result.append(math.floor(a/i)) a = a%i return result if __name__ == "__main__": print(solve(a, W, p))

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Ironman778/WebShell-C2

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refs/heads/main

2023-08-24T18:33:53.822209

2021-11-03T13:57:18

null

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null

import time import glob session = input("Session ID :") if session not in glob.glob("./data/*.*"): with open(f"./data/{session}",'a') as file: file.write('') previous = "" while True: with open(f"./data/{session}",'r') as file: data = file.read() if data!=previous: previous = data print(previous) time.sleep(5)

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pulumi/pulumi-aws-native

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/sdk/python/pulumi_aws_native/configuration/get_configuration_recorder.py

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refs/heads/master

2023-08-16T04:46:09.356539

2023-08-10T06:13:07

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2023-09-14T12:47:08

2019-11-04T19:00:32

2023-09-08T08:20:52

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# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import copy import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from .. import _utilities from . import outputs __all__ = [ 'GetConfigurationRecorderResult', 'AwaitableGetConfigurationRecorderResult', 'get_configuration_recorder', 'get_configuration_recorder_output', ] @pulumi.output_type class GetConfigurationRecorderResult: def __init__(__self__, id=None, recording_group=None, role_arn=None): if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if recording_group and not isinstance(recording_group, dict): raise TypeError("Expected argument 'recording_group' to be a dict") pulumi.set(__self__, "recording_group", recording_group) if role_arn and not isinstance(role_arn, str): raise TypeError("Expected argument 'role_arn' to be a str") pulumi.set(__self__, "role_arn", role_arn) @property @pulumi.getter def id(self) -> Optional[str]: return pulumi.get(self, "id") @property @pulumi.getter(name="recordingGroup") def recording_group(self) -> Optional['outputs.ConfigurationRecorderRecordingGroup']: return pulumi.get(self, "recording_group") @property @pulumi.getter(name="roleArn") def role_arn(self) -> Optional[str]: return pulumi.get(self, "role_arn") class AwaitableGetConfigurationRecorderResult(GetConfigurationRecorderResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetConfigurationRecorderResult( id=self.id, recording_group=self.recording_group, role_arn=self.role_arn) def get_configuration_recorder(id: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetConfigurationRecorderResult: """ Resource Type definition for AWS::Config::ConfigurationRecorder """ __args__ = dict() __args__['id'] = id opts = pulumi.InvokeOptions.merge(_utilities.get_invoke_opts_defaults(), opts) __ret__ = pulumi.runtime.invoke('aws-native:configuration:getConfigurationRecorder', __args__, opts=opts, typ=GetConfigurationRecorderResult).value return AwaitableGetConfigurationRecorderResult( id=pulumi.get(__ret__, 'id'), recording_group=pulumi.get(__ret__, 'recording_group'), role_arn=pulumi.get(__ret__, 'role_arn')) @_utilities.lift_output_func(get_configuration_recorder) def get_configuration_recorder_output(id: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetConfigurationRecorderResult]: """ Resource Type definition for AWS::Config::ConfigurationRecorder """ ...

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AlexG31/EcgDiagnosis

11,836,929,879,787

06fc69d38fed30deb9f9b476d857d3b8bd302ac7

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/mcmc/hermit_model.py

2f1c53a3876b5e8d00c3a00fb702b0ecab49676f

[]

no_license

https://github.com/AlexG31/EcgDiagnosis

65cb4a98aa1da1db916744e867e28bedf55bdf19

16b1c81351957393f27675e5eaea4920be787e28

refs/heads/master

2021-01-12T02:46:26.899927

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#encoding:utf8 # MCMC Model import os import sys import pymc import matplotlib.pyplot as plt import scipy.signal as signal from pymc import DiscreteUniform, Exponential, deterministic, Poisson, Uniform import numpy as np import json # Load ECG segment array with open('./segment.json', 'r') as fin: raw_sig = json.load(fin) raw_sig = np.array(raw_sig) raw_sig = raw_sig[30:] # Normalize min_val = np.min(raw_sig) max_val = np.max(raw_sig) raw_sig -= min_val raw_sig /= (max_val - min_val) # Hermit functions def HermitFunction(level, size): '''Return hermit function.''' if size < 0: raise Exception('Size must be greater or equal to zero!') def He0(x): return 1.0 def He1(x): return x def He2(x): return x * x - 1 def He3(x): return x ** 3.0 - 3.0 * x def He4(x): return x ** 4.0 - 6.0 * x ** 2.0 + 3.0 def He5(x): return x ** 5.0 - 10.0 * x ** 3.0 + 15.0 * x # Mapping wave_width to range [-3,3] wave_width = 50 x_ratio = 6.0 / wave_width if level == 0: hermit = He0 elif level == 1: hermit = He1 elif level == 2: hermit = He2 elif level == 3: hermit = He3 elif level == 4: hermit = He4 elif level == 5: hermit = He5 data = [hermit((x - size / 2) * x_ratio) / 20.0 for x in xrange(0, size)] return np.array(data) # plt.plot(raw_sig) # plt.plot(HermitFunction(5, len(raw_sig))) # plt.show() # Length of the ECG segment len_sig = raw_sig.size # wave_center = DiscreteUniform('wave_center', lower=0, upper=len_sig, doc='WaveCetner[index]') hc0 = pymc.Normal('hc0', 1, 0.25) hc1 = pymc.Normal('hc1', 1, 0.25) hc2 = pymc.Normal('hc2', 1, 0.25) hc3 = pymc.Normal('hc3', 1, 0.25) hc4 = pymc.Normal('hc4', 1, 0.25) hc5 = pymc.Normal('hc5', 1, 0.25) @deterministic(plot=False) def wave_shell(hc0=hc0, hc1=hc1, hc2=hc2, hc3=hc3, hc4=hc4, hc5=hc5, ): ''' Concatenate wave.''' coefs = [hc0, hc1, hc2, hc3, hc4, hc5,] out = np.zeros(len_sig,) for level, coef in zip(xrange(0,6), coefs): out += HermitFunction(level, len_sig) * coef return out ecg = pymc.Normal('ecg', mu=wave_shell, tau = 4e4, value=raw_sig, observed=True) def test(): '''Compare Gaussian Function.''' import scipy.signal as signal xlist = signal.gaussian(100, 7) # plt.hist(xlist) plt.plot(xlist) plt.title('ECG Segment') plt.show() # test()

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PantherPrg/Rollercoaster_Height

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xieqiumei/data_analysis

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/feature_analysis/model_analysis.py

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https://github.com/xieqiumei/data_analysis

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refs/heads/master

2023-05-30T21:20:44.033699

2021-06-28T00:32:15

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#!D:/Code/python # -*- coding: utf-8 -*- # @File:model_analysis.py # @Software:PyCharm import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.svm import SVR from sklearn.metrics import r2_score # 要注意预测评估函数，有回归和分类之分 from sklearn.model_selection import GridSearchCV from sklearn.linear_model import LinearRegression from sklearn.feature_selection import SelectKBest from sklearn.feature_selection import mutual_info_regression, f_regression from feature_analysis.train_config import svr_params_dict from sklearn.ensemble import RandomForestRegressor from utils.metrics import get_adjusted_r_squared, get_p_values from utils.feature_select import backward_regression, select_by_recursive_feature_elimination, select_by_lasso, \ column_index pd.set_option('display.max_rows', None) pd.set_option('display.max_columns', None) def select_features(x_train, y_train, x_test, x_cloumns, y_column, select_type, k=None): """ feature selection :param x_train: :param y_train: :param x_test: :param x_cloumns: :param y_column: :param select_type: :param k: :return: """ if select_type == 'backward': features, feature_ids = backward_regression(x_train, y_train, x_cloumns, y_column) return x_train[:, feature_ids], x_test[:, feature_ids], features if select_type == 'rfe': features, feature_ids = select_by_recursive_feature_elimination(x_train, y_train, x_cloumns) return x_train[:, feature_ids], x_test[:, feature_ids], features if select_type == 'lasso': features, feature_ids = select_by_lasso(x_train, y_train, x_cloumns) return x_train[:, feature_ids], x_test[:, feature_ids], features if select_type in ['mi', 'f']: if k is None: return None, None, None # configure to select a subset of features if select_type == 'mi': fs = SelectKBest(score_func=mutual_info_regression, k=k) else: fs = SelectKBest(score_func=f_regression, k=k) # learn relationship from training dataf_regression fs.fit(x_train, y_train) # transform train input data # print(fs.get_support()) x_train_fs = fs.transform(x_train) # transform test input data x_test_fs = fs.transform(x_test) # 特征的重要性 support = fs.get_support() index = np.where(support == 1)[0] return x_train_fs, x_test_fs, np.array(x_cloumns)[index] return None, None, None def get_scale_data(x_train, x_test, y_train, y_test): """ 对数据进行缩放，缩放后的数据用scaler.inverse_transform(test_S)还原 :return: """ scale_x = StandardScaler().fit(x_train) x_train = scale_x.transform(x_train) x_test = scale_x.transform(x_test) scale_y = StandardScaler().fit(y_train) y_train = scale_y.transform(y_train).ravel() y_test = scale_y.transform(y_test).ravel() return x_train, x_test, y_train, y_test, scale_y def get_svr_best_params(x_train, y_train): """ 网格搜索寻找最优的svr参数 :param x_train: :param y_train: :return: """ # 创建SVR实例 svr = SVR() gscv = GridSearchCV( estimator=svr, param_grid=svr_params_dict, n_jobs=2, scoring='r2', cv=6) gscv.fit(x_train, y_train) # 寻找最优参数 best_params = gscv.best_params_ return best_params def create_model(x_train, y_train, model_type): if model_type == 'svr': # 网格搜索寻找最优的svr参数 best_params = get_svr_best_params(x_train, y_train) print(f"Best svr params are {best_params}.") kernel = best_params['kernel'] return SVR(C=best_params['C'], kernel=kernel, gamma=best_params['gamma'], epsilon=best_params['epsilon']) if model_type == 'linear': return LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False) elif model_type == 'rf': return RandomForestRegressor(n_estimators=50, oob_score=True, random_state=100) else: return None def find(arr, min, max): """ 用于查找预测数据落在±50mm范围的数据 :param arr: :param min: :param max: :return: """ pos_min = arr >= min pos_max = arr <= max pos_rst = pos_min & pos_max return np.where(pos_rst == True) # where的返回值刚好可以用[]来进行元素提取 def model_fit_and_score(x_train_fs, y_train, x_test_fs, y_test, model_type, selected_features, scale_y): model = create_model(x_train_fs, y_train, model_type) if model is None: print(f"Currently can't predicate by this model type.") return model.fit(x_train_fs, y_train) # evaluate the model y_predicate = model.predict(x_test_fs) # importance = model.coef_ y_test = scale_y.inverse_transform(y_test) y_predicate = scale_y.inverse_transform(y_predicate) tmp = y_predicate - y_test print("误差±50mm的数量和百分比：", len(find(tmp, -50.0, 50.0)[0]), len(tmp), len(find(tmp, -50.0, 50.0)[0]) * 100 / len(tmp)) # evaluate predictions:explained_variance_score, r2_score,mean_absolute_error r2score = r2_score(y_test, y_predicate) adjusted_r2squared = get_adjusted_r_squared(x_test_fs, y_test, r2score) print( f'Test R^2 of selected features {selected_features} is {r2score}, \nand adjusted_R^2 is {adjusted_r2squared}.') def svr_predicate_with_full_feature(x_array, y_value, y_name, feature_names, model_type, test_size=0.2): """ svr回归预测 :param x_array: :param y_value: :param y_name: :param feature_names: :param model_type: :param test_size: :return: """ # 按照比例分成测试语料和训练语料 x_train, x_test, y_train, y_test = train_test_split(x_array, y_value, test_size=test_size, random_state=999) y_train = np.array(y_train).reshape(-1, 1) y_test = np.array(y_test).reshape(-1, 1) # 数据缩放 x_train, x_test, y_train, y_test, scale_y = get_scale_data(x_train, x_test, y_train, y_test) model = create_model(x_train, y_train, model_type) if model is None: print(f"Currently can't predicate by this model type.") return model.fit(x_train, y_train) y_predicate = model.predict(x_test) # evaluate predictions score = r2_score(y_test, y_predicate) print(f'Test R^2 of {y_name} by features {feature_names[:-1]} is {score}') def svr_predicate_with_feature_select(x_array, y_array, x_cloumns, y_column, model_type, select_type, test_size=0.2, selected_feature=None): """ :param x_array: :param y_value: :param feature_names: :param model_type: :param k_num: :param test_size: :param select_type: :return: """ # x_train, x_test, y_train, y_test = train_test_split(x_array, y_value, test_size=test_size) n_train = int(x_array.shape[0] * (1 - test_size)) x_train = x_array[:n_train, :] x_test = x_array[n_train:, :] y_train = y_array[:n_train] y_test = y_array[n_train:] # 分隔输入X和输出y # print("测试前：", y_test) y_train = np.array(y_train).reshape(-1, 1) y_test = np.array(y_test).reshape(-1, 1) # 数据缩放 x_train, x_test, y_train, y_test, scale_y = get_scale_data(x_train, x_test, y_train, y_test) if selected_feature is not None: X_df = pd.DataFrame(data=x_array, columns=x_cloumns) feature_ids = column_index(X_df, selected_feature) model_fit_and_score(x_train[:, feature_ids], y_train, x_test[:, feature_ids], y_test, model_type, selected_feature, scale_y) return if select_type is None or select_type not in ['f', 'mi', 'backward', 'rfe', 'lasso']: print("The wrong feature select type!") return # 特征选择 if select_type in ['f', 'mi']: k_num = [i for i in range(1, x_array.shape[1] + 1)] # k_num = [i for i in range(1, 6)] # k_num = [x_array.shape[1]] for e in k_num: x_train_fs, x_test_fs, features = select_features(x_train, y_train, x_test, x_cloumns, y_column, select_type, k=e) model_fit_and_score(x_train_fs, y_train, x_test_fs, y_test, model_type, features, scale_y) print("\n") else: x_train_fs, x_test_fs, features = select_features(x_train, y_train, x_test, x_cloumns, y_column, select_type) model_fit_and_score(x_train_fs, y_train, x_test_fs, y_test, model_type, features, scale_y) def outlier_treatment_by_median(df_data): for column in df_data.columns.tolist(): if np.abs(df_data[column].skew()) > 1: # median = df_data[column].quantile(0.5) # _95percentile = df_data[column].quantile(0.95) # # print(median, _95percentile) # df[column] = np.where(df[column] >= _95percentile, median, df[column]) flooring = df_data[column].quantile(0.1) capping = df_data[column].quantile(0.9) df_data[column] = np.where(df_data[column] < flooring, flooring, df_data[column]) df_data[column] = np.where(df_data[column] > capping, capping, df_data[column]) return df_data # df = outlier_treatment_by_median(df) # print(df.corr()['P'].sort_values()) if __name__ == '__main__': df = pd.read_excel("D:/工作项目/鸿玖/中泰数据 min级汇总/按分钟汇总数据/1min采样/C电极的参数汇总_1min__增加炉底温度.xlsx", sheet_name='Sheet1') # 剔除电流异常数据 df.drop([894, 895, 896, 897, 898, 899, 900, 901, 903], inplace=True) X = df[['焦炭实际配料值', '兰炭实际配料值', '石灰二实际配料值', '石灰一实际配料值', '电流_电极电流 KA_IA', '电流_电极电流 KA_IB', '电流_电极电流 KA_IC', '电压_电极电压 V_UA', '电压_电极电压 V_UB', '电压_电极电压 V_UC', '功率_电极功率_Pa', '功率_电极功率_Pb', '功率_电极功率_Pc', '功率_无功功率', '功率_有功功率', '温度_炉底 ℃_3', '加热元件温度C']].values Y = df['C电极长度mm(计算)'].values svr_predicate_with_feature_select(X, Y, ['焦炭实际配料值', '兰炭实际配料值', '石灰二实际配料值', '石灰一实际配料值', '电流_电极电流 KA_IA', '电流_电极电流 KA_IB', '电流_电极电流 KA_IC', '电压_电极电压 V_UA', '电压_电极电压 V_UB', '电压_电极电压 V_UC', '功率_电极功率_Pa', '功率_电极功率_Pb', '功率_电极功率_Pc', '功率_无功功率', '功率_有功功率', '温度_炉底 ℃_3', '加热元件温度C'], ['C电极长度mm(计算)'], 'rf', 'mi', test_size=0.3, selected_feature=None) """" linear预测明显效果优于svr """

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zodiac/competitive-programming

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#!/usr/bin/env pypy3 T = int(input()) for _ in range(T): x, y = input().split() x = int(x) y = int(y) print(x-1, y)

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janion/CARMA

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/CARMA/src/backend/event/events/CheckSignalEvent.py

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[]

no_license

https://github.com/janion/CARMA

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refs/heads/master

2021-01-10T23:16:27.224636

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''' Created on 27 Sep 2016 @author: Janion ''' class CheckSignalEvent(): ''' Event which relates to a train needing to check a signal. ''' CODE = "C" def __init__(self, sectionName): ''' Constructor ''' self.entityName = sectionName ################################################################################ def getEntityName(self): return self.entityName ################################################################################ @staticmethod def isToHardware(): return False; ################################################################################ @staticmethod def isEvent(message): return message.index(CheckSignalEvent.CODE) != -1

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CheckSignalEvent.py

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SLLittrell/GameRater-server

1,133,871,392,209

719636f364a112f4890d7746305d3ed0af289d9e

15ae6080ac1ba504ec2c19dd02edc6ac585e13cb

/raterprojectapi/views/game.py

729dae9aecf94f0625a3d43e312e7cdacb2e9709

[]

no_license

https://github.com/SLLittrell/GameRater-server

1d824df4d662567a944c2697c43e1a890af6ad1d

dbd55ab096408b738ade7c3407ad71bdcbd7a541

refs/heads/main

2023-05-09T00:24:43.183349

2021-05-26T19:05:03

364,363,110

0

null

from django.core.exceptions import ValidationError from rest_framework import status from django.http import HttpResponseServerError from rest_framework.viewsets import ViewSet from rest_framework.response import Response from rest_framework import serializers from rest_framework import status from raterprojectapi.models import Game, Player, Category, Review class GameViewSet(ViewSet): def create(self, request): creator = Player.objects.get(user=request.auth.user) game = Game() game.title = request.data['title'] game.description = request.data['description'] game.release_year = request.data['releaseYear'] game.number_players = request.data['numberPlayers'] game.time_to_play = request.data['timeToPlay'] game.age = request.data['age'] game.creator = creator try: game.save() categories = Category.objects.in_bulk(request.data['categories']) game.categories.set(categories) serializer = GameSerializer(game, context={'request': request}) return Response(serializer.data, status=status.HTTP_201_CREATED) except ValidationError as ex: return Response({"reason": ex.message}, status=status.HTTP_400_BAD_REQUEST) def retrieve(self, request, pk=None): try: game = Game.objects.get(pk=pk) serializer = GameSerializer(game, context={'request': request}) return Response(serializer.data) except Exception as ex: return HttpResponseServerError(ex) def update(self, request, pk=None): game = Game() creator = Player.objects.get(user=request.auth.user) if creator is not game.creator: return Response({}, status=status.HTTP_403_FORBIDDEN) game.title = request.data['title'] game.description = request.data['description'] game.release_year = request.data['releaseYear'] game.number_players = request.data['numberPlayers'] game.time_to_play = request.data['timeToPlay'] game.age = request.data['age'] game.creator = creator categories = Category.objects.in_bulk(request.data['categories']) game.categories.set(categories) game.save() return Response({}, status=status.HTTP_204_NO_CONTENT) def destroy(self, request, pk=None): try: game = Game.objects.get(pk=pk) game.delete() return Response({}, status=status.HTTP_204_NO_CONTENT) except Game.DoesNotExist as ex: return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND) except Exception as ex: return Response({'message': ex.args[0]}, status=status.HTTP_500_INTERNAL_SERVER_ERROR) def list(self, request): games = Game.objects.all() serializer = GameSerializer( games, many=True, context={'request': request}) return Response(serializer.data) class GameSerializer(serializers.ModelSerializer): class Meta: model = Game fields = ('id', 'title', 'description', 'release_year', 'number_players', 'time_to_play', 'age', 'creator', 'categories', 'average_rating') depth = 1

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darkoob12/POSTagger

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08c7d5fadbfce44166f29d23fe8734e7e87c6094

/program.py

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[]

no_license

https://github.com/darkoob12/POSTagger

0ae08cf0fcd3ecbe6c1ad42e31a56b4c7158e933

8de77008f28661a1e7d600dae9ab2e859da1d424

refs/heads/master

2021-01-16T17:36:53.901293

2017-08-11T08:10:53

100,008,605

0

null

from POSTagger import * import time import random def read_data(path): """ str -> list loads data from a csv file removes redundant info :param path: :return: list of sentences """ data = [] with open(path, 'r') as f: sentence = [] for line in f: a = line.split(' ') if len(a) < 3: # end of sentence data.append(sentence) sentence.insert(0, ['', '##']) if sentence[-1][0] != '.': sentence.append(['.', '.']) sentence = [] else: sentence.append([a[0].strip(), a[1].strip()]) return data def concat_sentences(corpus): seq = [''] for sent in corpus: words = [p[0] for p in sent] seq.extend(words[1:-1]) seq.append('.') return seq def get_words(data): ret = [] for s in data: ret.append([p[0] for p in s]) return ret def read_tags(path): t = [] with open(path, 'r') as f: for line in f: tokens = line.strip().split(',') t.append(tokens[0]) return t def check_eol(data): dot_count = 0 anomaly_count = 0 for sentence in data: if sentence[-1][0] == '.': dot_count += 1 else: anomaly_count += 1 print("{0} out of {1} has '.' as their terminal state.".format(dot_count, len(data))) def concat_chunks(record): """ list -> str convert words of a sentence to its values :param record: list of words and their pos tag :return: a sentence """ words = [x[0] for x in record] return ' '.join(words) def test_model(mod, data): total = 0 # total number of words correct = 0 # correctly tagged s = np.zeros(len(data)) i = 0 for sentence in data: ws = [] ts = [] for chunk in sentence: ws.append(chunk[0]) ts.append(chunk[1]) predicted_tags = mod.decode(ws) c = np.sum(np.array(ts[1:]) == np.array(predicted_tags[1:])) correct += c l = len(sentence) - 1 total += l s[i] = c / l print("{0} : [length => {1}, corrects => {2}] | Accuracy => {3:.2%}".format(i, l, c, s[i])) i += 1 word_acc = correct / total print("per word accuracy on test data is : {:.2%}".format(word_acc)) sentence_acc = np.mean(s) print("average sentence accuracy on test data is : {:.2%}".format(sentence_acc)) return word_acc, sentence_acc def experiment_p1(): train = read_data('train.txt') test = read_data('test.txt') dic = TextDictionary() dic.add(train) dic.add(test) model = HMM(dic.get_tags(), dic.get_words()) tic = time.time() model.estimate(train) # estimate the probabilities toc = time.time() print('estimation time: {0:.2f}'.format(toc - tic)) model.save() test_model(model, test) def experiment_p2(): train = read_data('train.txt') test = read_data('test.txt') dic = TextDictionary() dic.add(train) dic.add(test) model = HMM(dic.get_tags(), dic.get_words()) model.rand_init() random.shuffle(train) model.estimate(train[:-10]) tic = time.time() model.train(concat_sentences(train[-10:]), max_iter=50) toc = time.time() print('learning time: {0:.2f}'.format(toc - tic)) model.save() test_model(model, test) if __name__ == '__main__': experiment_p2() print('hello, world!')

UTF-8

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false

3,498

py

3

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2

0.534019

0.520011

0

139

24.165468

99

Ashyaa/aoc2020

6,657,199,332,578

fdeda4671d22fb88224d67b201b1c1c7918d9a36

e03c4147acf427ef26b73ad2c26e7a0fcb5f8de4

/2021/day20/solution.py

40e4764a267f69d5662e8871adaafa61be9ac517

[]

no_license

https://github.com/Ashyaa/aoc2020

22b5051d3e77155b93e54df553c852d60f3f4767

844548cd99050f5227bd17ef13b88286cdce60a4

refs/heads/main

2023-02-21T04:30:05.618050

2022-12-25T09:21:41

318,306,558

0

null

#!/usr/bin/env python3 import contextlib from pathlib import Path from typing import * import numpy as np from AoC.util import show CWD = Path(__file__).parent def read_input(filename: str = "input.txt") -> Tuple[str, np.ndarray]: input_file = CWD.joinpath(filename) with open(input_file, "r") as reader: s = reader.readline().strip().replace('.', '0').replace('#', '1') reader.readline() res = [] for l in reader.readlines(): ll = l.strip().replace('.', '0').replace('#', '1') res.append(np.array([c for c in ll], dtype=str)) res = np.array(res) return s, res def step(s: str, arr: np.ndarray, i: int) -> np.ndarray: inverted = s[0] == '1' and s[-1] == '0' cons = i%2 if inverted else 0 arr = np.pad(arr, ((2, 2), (2, 2)), 'constant', constant_values=cons) if inverted and not(i%2): res = np.ones(arr.shape, dtype=int).astype(str) else: res = np.zeros(arr.shape, dtype=int).astype(str) for i in range(1, arr.shape[0]-1): for j in range(1, arr.shape[1]-1): idx = int("".join(arr[i-1:i+2, j-1:j+2].flatten()), 2) res[i,j] = s[idx] return res @show def first(s: str, inp: np.ndarray, steps: int) -> None: arr = inp.copy() for i in range(steps): arr = step(s, arr, i) return arr.astype(int).sum() def test_example() -> None: with contextlib.redirect_stdout(None): s, inp = read_input("example.txt") assert first(s, inp, 2) == 35 assert first(s, inp, 50) == 3351 if __name__ == "__main__": test_example() s, inp = read_input() first(s, inp, 2) # 5065 first(s, inp, 50) # 14790

UTF-8

Python

false

1,708

py

126

solution.py

123

0.556206

0.528103

0

60

27.466667

73

YANH216/myweb

16,475,494,591,582

84d9db9b1020fc3b752906969d549354267a6703

95913618293ab052c7f75fe0411adae8379c4c3a

/SitesApp/migrations/0003_auto_20180605_1307.py

9bac1c31b74b0342ca7c195acde009b4d94d1477

[]

no_license

https://github.com/YANH216/myweb

6553f9028c8e23fe57c9988fb94e2c88f5b44886

c24600a99e0e6c3c1266251c2f6f18caed17f83c

refs/heads/master

2023-06-27T03:18:13.718437

2021-07-29T09:19:28

390,667,214

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# -*- coding: utf-8 -*- # Generated by Django 1.11.7 on 2018-06-05 05:07 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('SitesApp', '0002_auto_20180605_1128'), ] operations = [ migrations.AlterField( model_name='candidate', name='cEmail', field=models.CharField(blank=True, default=None, max_length=20, null=True), ), migrations.AlterField( model_name='user', name='uEmail', field=models.CharField(blank=True, default=None, max_length=20, null=True), ), ]

UTF-8

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false

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py

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0003_auto_20180605_1307.py

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0.591176

0.536765

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larsbak/fletch

8,624,294,346,937

ce4922248d60fbd850cf516c699aa8d31fd3100b

4fffc216d36563b5db4a5ef230d95b6bda3be0e4

/src/vm/vm.gyp

6262c463f1339822da528907eb156cf599a81a51

[ "BSD-3-Clause" ]

permissive

https://github.com/larsbak/fletch

e18ec6cdfe306f75553638433109a2549b589c7a

6ca18fb7c55f052ef5537c650025811434b9a323

refs/heads/master

2018-05-19T05:37:25.789701

2015-01-29T11:19:50

null

0

null

# Copyright (c) 2015, the Fletch project authors. Please see the AUTHORS file # for details. All rights reserved. Use of this source code is governed by a # BSD-style license that can be found in the LICENSE.md file. { 'target_defaults': { 'include_dirs': [ '../../', ], 'actions': [ { # TODO(ahe): Move to .gypi file that is included by the other .gyp # files. 'action_name': 'lint_>(_target_name)', 'inputs': [ '../../third_party/cpplint/cpplint.py', '>@(_sources)', ], 'outputs': [ '>(INTERMEDIATE_DIR)/lint_>(_target_name).log' ], 'action': [ "bash", "-c", "python >(_inputs) && " "LANG=POSIX date '+Lint checked on %+' > <(_outputs)", ], }, ] }, 'targets': [ { 'target_name': 'fletch_vm_base', 'type': 'static_library', 'dependencies': [ '../shared/shared.gyp:fletch_shared', '../double_conversion.gyp:double_conversion', ], 'sources': [ # TODO(ahe): Add header (.h) files. 'assembler_x86.cc', 'assembler_x86_macos.cc', 'assembler_x64.cc', 'assembler_x64_macos.cc', 'event_handler.cc', 'event_handler_macos.cc', 'ffi.cc', 'fletch.cc', 'fletch_api_impl.cc', 'heap.cc', 'interpreter.cc', 'intrinsics.cc', 'lookup_cache.cc', 'natives.cc', 'object.cc', 'object_list.cc', 'object_map.cc', 'object_memory.cc', 'platform_posix.cc', 'port.cc', 'process.cc', 'program.cc', 'scheduler.cc', 'service_api_impl.cc', 'session.cc', 'snapshot.cc', 'stack_walker.cc', 'thread_pool.cc', 'thread_posix.cc', 'weak_pointer.cc', ], }, { 'target_name': 'fletch_vm', 'type': 'static_library', 'dependencies': [ 'fletch_vm_base', '../shared/shared.gyp:fletch_shared', '../double_conversion.gyp:double_conversion', ], 'sources': [ '<(INTERMEDIATE_DIR)/generated.S', ], 'actions': [ { 'action_name': 'generate_generated_S', 'inputs': [ '<(PRODUCT_DIR)/<(EXECUTABLE_PREFIX)' 'fletch_vm_generator' '<(EXECUTABLE_SUFFIX)', ], 'outputs': [ '<(INTERMEDIATE_DIR)/generated.S', ], 'action': [ # TODO(ahe): Change generator to accept command line argument for # output file. Using file redirection may not work well on Windows. 'bash', '-c', '<(_inputs) > <(_outputs)', ], }, ], }, { 'target_name': 'fletch_vm_generator', 'type': 'executable', 'dependencies': [ 'fletch_vm_base', '../shared/shared.gyp:fletch_shared', '../double_conversion.gyp:double_conversion', ], 'sources': [ # TODO(ahe): Add header (.h) files. 'generator.cc', ], }, { 'target_name': 'fletch', 'type': 'executable', 'dependencies': [ 'fletch_vm', ], 'sources': [ # TODO(ahe): Add header (.h) files. 'main.cc', ], }, { 'target_name': 'vm_run_tests', 'type': 'executable', 'dependencies': [ 'fletch_vm', ], 'defines': [ 'TESTING', # TODO(ahe): Remove this when GYP is the default. 'GYP', ], 'sources': [ # TODO(ahe): Add header (.h) files. 'foreign_ports_test.cc', 'object_map_test.cc', 'object_memory_test.cc', 'object_test.cc', 'platform_test.cc', '../shared/test_main.cc', ], }, ], }

UTF-8

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false

3,846

gyp

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vm.gyp

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0.471659

0.468539

0

147

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79

LuisHVieira/valorant_games

11,269,994,186,642

1fbde5dfabbc5a903b62e336d4dcf1a817572e06

d054c44279a959f03fc76147f4dde75b20dd5f6a

/datasValorant.py

0710f807c0d508a259d9b0c3fbbadd6e800b4c9f

[]

no_license

https://github.com/LuisHVieira/valorant_games

03f5991083a3d0394f1acd072be3cfb13d634172

8e8af388a607ff4c76aede1c7d52b7aed549097e

refs/heads/master

2023-08-15T03:23:25.527484

2021-09-30T23:54:14

411,813,213

0

null

import requests import json import datetime import embeds as e import discord def get_games_today(): #Get current date and convert from string today = datetime.datetime.now() year = str(today.year) month = str(today.month) if today.month >= 10 else '0' + str(today.month) day = str(today.day) if today.day >= 10 else '0' + str(today.day) #String complete and only date formartBR dateBr = day + '/' + month + '/' + year stringRangeToday = year + '-' + month + '-' + day + 'T' #Request linkRequest = 'https://api.pandascore.co/valorant/matches/upcoming?&range[begin_at]=' + stringRangeToday + '00:00:00Z,' + stringRangeToday + '23:59:59Z&token=CZ2gKXBwL8LYIKbj_VzKC8cjujXZcVF-vlwPf8P3qC-GcHnSoRQ' #rTest = 'https://api.pandascore.co/valorant/matches/upcoming?&range[begin_at]=2021-09-13T00:00:00Z,2021-09-13T23:59:59Z&token=CZ2gKXBwL8LYIKbj_VzKC8cjujXZcVF-vlwPf8P3qC-GcHnSoRQ' r = requests.get(linkRequest) responseJson = r.json() #list and count a = [] count = 0 count2 = 0 for i in responseJson: idC = count dateTime = i['begin_at'] serieName = i['serie']['name'] thumb = i['league']['image_url'] idSerie = i['serie']['id'] hour = int(dateTime[11:13]) - 3 minSec = dateTime[13:16] dateTimeFormated = dateBr + ' - ' + str(hour) + minSec datas = dict(id=idC, serieName=serieName, dateTime=dateTimeFormated, thumb=thumb, serieID=idSerie) for j in i['opponents']: teamName = j['opponent']['name'] idTeam = j['opponent']['id'] if count % 2 == 0: teams = dict(teamName0=teamName, id0=idTeam) else: teams = dict(teamName1=teamName, id1=idTeam) datas.update(teams) count += 1 count2 += 1 a.append(datas) return a

UTF-8

Python

false

1,701

py

7

datasValorant.py

3

0.673721

0.628454

0

65

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chaitualuru/algorithms

10,196,252,397,575

aa80d07c26f450f42b4189a9755446298101607c

19241f750f9bef7f2d8529bb6d3096eea46d83b5

/eip/sudoku_checker.py

095fbd59f128f3ccad973e90e379edb20854d63e

[]

no_license

https://github.com/chaitualuru/algorithms

ad372146dc9a286963131a59889ee5c83445db17

af58921e23f9c97650f87bfadbdeac51f433a249

refs/heads/master

2022-07-12T03:58:25.129841

2015-08-31T06:00:33

null

0

null

"""Sudoku Checker""" def sudoku_checker(A): for i in range(len(A)): if not valid_line(A[i]): return False unzipped_A = list(zip(*A)) for j in range(len(unzipped_A)): if not valid_line(unzipped_A[j]): return False for k in range(0, len(A), 3): for l in range(0, len(A), 3): grid = [] for x in A[k:k+3]: for y in x[l:l+3]: grid.append(y) if not valid_line(grid): return False return True def valid_line(line): return (len(line) == 9 and sum(line) == sum(set(line))) def main(): """Entry point of the program.""" # valid if sudoku_checker([ [5,3,0,0,7,0,0,0,0], [6,0,0,1,9,5,0,0,0], [0,9,8,0,0,0,0,6,0], [8,0,0,0,6,0,0,0,3], [4,0,0,8,0,3,0,0,1], [7,0,0,0,2,0,0,0,6], [0,6,0,0,0,0,2,8,0], [0,0,0,4,1,9,0,0,5], [0,0,0,0,8,0,0,7,9]]): print "Valid" else: print "Not Valid" if __name__ == '__main__': main()

UTF-8

Python

false

875

py

57

sudoku_checker.py

57

0.544

0.443429

0

45

18.444444

56

moskalikbogdan/CDV

5,257,039,992,144

1241c1a272387bd5034d0044af2b2351952cd4b6

a9d4ba71e1c847e0f4a7f26d76ee1fb6c08f8c76

/Python/Warsztaty/Week02/W02.Z04.2.py

ddfae2e158abb6e57bcba39f1739d0a711780b19

[]

no_license

https://github.com/moskalikbogdan/CDV

569c257b779e14c42fe9e1ff19cfd105bb624afb

90d14a597e66f9e5902ecf2b103baf737ed0e4ea

refs/heads/master

2020-12-15T01:42:18.842169

2020-07-13T00:40:20

234,949,055

0

1

null

false

2020-02-09T08:30:12

2020-01-19T18:55:48

2020-01-21T15:38:26

2020-02-09T08:29:32

17

0

1

Python

false

""" @author: Bogdan Moskalik, DataScience, Niestacjonarne, Grupa 2 """ import numpy as np print('''Policzę srednią i odchylenie standardowe z listy którą wprowadzisz Z ilu elementów ma się składać lista?: ''') a = int(input()) print('''Wprowadź pojedyńczo elementy twojej listy:''') x = [] for i in range(0,a): x.append(int(input('Wprowadź element %i:'%(i+1)))) print () print('Średnia twojej listy to:') print(np.mean(x)) print('Odchylenie standardowe twojej listy to:') print(round(np.std(x),2))

UTF-8

Python

false

531

py

43

W02.Z04.2.py

36

0.684008

0.676301

0

25

19.8

75

m-nosrati/springboard-course

7,610,682,086,991

be8e030dbfe6707ba2b450fc7a4d679a8f959f68

ff986f9509dbdbe843c223f630a82e80e9dbfebc

/machine-learning/naive_bayes/src/naive_bayes.py

125630359e284d4f2b44167a2543df0d39046f5d

[]

no_license

https://github.com/m-nosrati/springboard-course

65ce414411525ac49c3bd36e0da00164af75b153

bd7da238f32bcf6d5716984dab6e887c90ac94ad

refs/heads/master

2022-02-27T05:55:31.642308

2019-08-13T02:06:51

null

0

null

#%% [markdown] # # Basic Text Classification with Naive Bayes submitted by Ahrim Han (2019/3/25) # *** # In the mini-project, you'll learn the basics of text analysis using a subset of movie reviews from the rotten tomatoes database. You'll also use a fundamental technique in Bayesian inference, called Naive Bayes. This mini-project is based on [Lab 10 of Harvard's CS109](https://github.com/cs109/2015lab10) class. Please free to go to the original lab for additional exercises and solutions. #%% get_ipython().run_line_magic('matplotlib', 'inline') import numpy as np import scipy as sp import matplotlib as mpl import matplotlib.cm as cm import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from six.moves import range # Setup Pandas pd.set_option('display.width', 500) pd.set_option('display.max_columns', 100) pd.set_option('display.notebook_repr_html', True) # Setup Seaborn sns.set_style("whitegrid") sns.set_context("poster") #%% [markdown] # # Table of Contents # # * [Rotten Tomatoes Dataset](#Rotten-Tomatoes-Dataset) # * "fresh" or "rotton": The critics upload their reviews to the movie page on the website, and need to mark their review "fresh" if it's generally favorable or "rotten" otherwise. It is necessary for the critic to do so as some reviews are qualitative and do not grant a numeric score, making it impossible for the system to be automatic. # * Movies and TV shows are Certified Fresh with a steady Tomatometer of 75% or higher after a set amount of reviews (80 for wide-release movies, 40 for limited-release movies, 20 for TV shows), including 5 reviews from Top Critics. # * [Explore](#Explore) # * [The Vector Space Model and a Search Engine](#The-Vector-Space-Model-and-a-Search-Engine) # * [In Code](#In-Code) # * [Naive Bayes](#Naive-Bayes) # * [Multinomial Naive Bayes and Other Likelihood Functions](#Multinomial-Naive-Bayes-and-Other-Likelihood-Functions) # * [Picking Hyperparameters for Naive Bayes and Text Maintenance](#Picking-Hyperparameters-for-Naive-Bayes-and-Text-Maintenance) # * [Interpretation](#Interpretation) # #%% [markdown] # ## Rotten Tomatoes Dataset #%% critics = pd.read_csv('./critics.csv') #let's drop rows with missing quotes critics = critics[~critics.quote.isnull()] critics.head() #%% [markdown] # ### Explore #%% n_reviews = len(critics) n_movies = critics.rtid.unique().size n_critics = critics.critic.unique().size print("Number of reviews: {:d}".format(n_reviews)) print("Number of critics: {:d}".format(n_critics)) print("Number of movies: {:d}".format(n_movies)) #%% critics.fresh.value_counts() #%% df = critics.copy() df['fresh'] = df.fresh == 'fresh' grp = df.groupby('critic') counts = grp.critic.count() # number of reviews by each critic means = grp.fresh.mean() # average freshness for each critic means[counts > 100].hist(bins=10, edgecolor='w', lw=1) plt.xlabel("Average Rating per critic") plt.ylabel("Number of Critics") plt.yticks([0, 2, 4, 6, 8, 10]); #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set I</h3> # <br/> # <b>Exercise:</b> Look at the histogram above. Tell a story about the average ratings per critic. What shape does the distribution look like? What is interesting about the distribution? What might explain these interesting things? # </div> #%% [markdown] # The distribution looks roughly normal with a strange gap around 0.55. Except the gap, the distribution definitely look normal. There appear to be two distinct groups of raters: harsh raters and generous raters. #%% [markdown] # ## The Vector Space Model and a Search Engine #%% [markdown] # All the diagrams here are snipped from [*Introduction to Information Retrieval* by Manning et. al.]( http://nlp.stanford.edu/IR-book/) which is a great resource on text processing. For additional information on text mining and natural language processing, see [*Foundations of Statistical Natural Language Processing* by Manning and Schutze](http://nlp.stanford.edu/fsnlp/). # # Also check out Python packages [`nltk`](http://www.nltk.org/), [`spaCy`](https://spacy.io/), [`pattern`](http://www.clips.ua.ac.be/pattern), and their associated resources. Also see [`word2vec`](https://en.wikipedia.org/wiki/Word2vec). # # Let us define the vector derived from document $d$ by $\bar V(d)$. What does this mean? Each document is treated as a vector containing information about the words contained in it. Each vector has the same length and each entry "slot" in the vector contains some kind of data about the words that appear in the document such as presence/absence (1/0), count (an integer) or some other statistic. Each vector has the same length because each document shared the same vocabulary across the full collection of documents -- this collection is called a *corpus*. # # To define the vocabulary, we take a union of all words we have seen in all documents. We then just associate an array index with them. So "hello" may be at index 5 and "world" at index 99. # # Suppose we have the following corpus: # # `A Fox one day spied a beautiful bunch of ripe grapes hanging from a vine trained along the branches of a tree. The grapes seemed ready to burst with juice, and the Fox's mouth watered as he gazed longingly at them.` # # Suppose we treat each sentence as a document $d$. The vocabulary (often called the *lexicon*) is the following: # # $V = \left\{\right.$ `a, along, and, as, at, beautiful, branches, bunch, burst, day, fox, fox's, from, gazed, grapes, hanging, he, juice, longingly, mouth, of, one, ready, ripe, seemed, spied, the, them, to, trained, tree, vine, watered, with`$\left.\right\}$ # # Then the document # # `A Fox one day spied a beautiful bunch of ripe grapes hanging from a vine trained along the branches of a tree` # # may be represented as the following sparse vector of word counts: # # $$\bar V(d) = \left( 4,1,0,0,0,1,1,1,0,1,1,0,1,0,1,1,0,0,0,0,2,1,0,1,0,0,1,0,0,1,1,1,0,0 \right)$$ # # or more succinctly as # # `[(0, 4), (1, 1), (5, 1), (6, 1), (7, 1), (9, 1), (10, 1), (12, 1), (14, 1), (15, 1), (20, 2), (21, 1), (23, 1),` # `(26, 1), (29,1), (30, 1), (31, 1)]` # # along with a dictionary # # `` # { # 0: a, 1: along, 5: beautiful, 6: branches, 7: bunch, 9: day, 10: fox, 12: from, 14: grapes, 15: hanging, 19: mouth, 20: of, 21: one, 23: ripe, 24: seemed, 25: spied, 26: the, 29:trained, 30: tree, 31: vine, # } # `` # # Then, a set of documents becomes, in the usual `sklearn` style, a sparse matrix with rows being sparse arrays representing documents and columns representing the features/words in the vocabulary. # # Notice that this representation loses the relative ordering of the terms in the document. That is "cat ate rat" and "rat ate cat" are the same. Thus, this representation is also known as the Bag-Of-Words representation. # # Here is another example, from the book quoted above, although the matrix is transposed here so that documents are columns: # # ![novel terms](terms.png) # # Such a matrix is also catted a Term-Document Matrix. Here, the terms being indexed could be stemmed before indexing; for instance, `jealous` and `jealousy` after stemming are the same feature. One could also make use of other "Natural Language Processing" transformations in constructing the vocabulary. We could use Lemmatization, which reduces words to lemmas: work, working, worked would all reduce to work. We could remove "stopwords" from our vocabulary, such as common words like "the". We could look for particular parts of speech, such as adjectives. This is often done in Sentiment Analysis. And so on. It all depends on our application. # # From the book: # >The standard way of quantifying the similarity between two documents $d_1$ and $d_2$ is to compute the cosine similarity of their vector representations $\bar V(d_1)$ and $\bar V(d_2)$: # # $$S_{12} = \frac{\bar V(d_1) \cdot \bar V(d_2)}{|\bar V(d_1)| \times |\bar V(d_2)|}$$ # # ![Vector Space Model](vsm.png) # # # >There is a far more compelling reason to represent documents as vectors: we can also view a query as a vector. Consider the query q = jealous gossip. This query turns into the unit vector $\bar V(q)$ = (0, 0.707, 0.707) on the three coordinates below. # # ![novel terms](terms2.png) # # >The key idea now: to assign to each document d a score equal to the dot product: # # $$\bar V(q) \cdot \bar V(d)$$ # # Then we can use this simple Vector Model as a Search engine. #%% [markdown] # ### In Code #%% from sklearn.feature_extraction.text import CountVectorizer text = ['Hop on pop', 'Hop off pop', 'Hop Hop hop'] print("Original text is\n{}".format('\n'.join(text))) vectorizer = CountVectorizer(min_df=0) # call `fit` to build the vocabulary vectorizer.fit(text) # call `transform` to convert text to a bag of words x = vectorizer.transform(text) # CountVectorizer uses a sparse array to save memory, but it's easier in this assignment to # convert back to a "normal" numpy array x = x.toarray() print("") print("Transformed text vector is \n{}".format(x)) # `get_feature_names` tracks which word is associated with each column of the transformed x print("") print("Words for each feature:") print(vectorizer.get_feature_names()) # Notice that the bag of words treatment doesn't preserve information about the *order* of words, # just their frequency #%% [markdown] # [Coordinate Format (COO)](https://scipy-lectures.org/advanced/scipy_sparse/coo_matrix.html) #%% def make_xy(critics, vectorizer=None): #Your code here if vectorizer is None: vectorizer = CountVectorizer() X = vectorizer.fit_transform(critics.quote) X = X.tocsc() # some versions of sklearn return COO format y = (critics.fresh == 'fresh').values.astype(np.int) return X, y X, y = make_xy(critics) #%% [markdown] # ## Naive Bayes #%% [markdown] # From Bayes' Theorem, we have that # # $$P(c \vert f) = \frac{P(c \cap f)}{P(f)}$$ # # where $c$ represents a *class* or category, and $f$ represents a feature vector, such as $\bar V(d)$ as above. **We are computing the probability that a document (or whatever we are classifying) belongs to category *c* given the features in the document.** $P(f)$ is really just a normalization constant, so the literature usually writes Bayes' Theorem in context of Naive Bayes as # # $$P(c \vert f) \propto P(f \vert c) P(c) $$ # # $P(c)$ is called the *prior* and is simply the probability of seeing class $c$. But what is $P(f \vert c)$? This is the probability that we see feature set $f$ given that this document is actually in class $c$. This is called the *likelihood* and comes from the data. One of the major assumptions of the Naive Bayes model is that the features are *conditionally independent* given the class. While the presence of a particular discriminative word may uniquely identify the document as being part of class $c$ and thus violate general feature independence, conditional independence means that the presence of that term is independent of all the other words that appear *within that class*. This is a very important distinction. Recall that if two events are independent, then: # # $$P(A \cap B) = P(A) \cdot P(B)$$ # # Thus, conditional independence implies # # $$P(f \vert c) = \prod_i P(f_i | c) $$ # # where $f_i$ is an individual feature (a word in this example). # # To make a classification, we then choose the class $c$ such that $P(c \vert f)$ is maximal. # # There is a small caveat when computing these probabilities. For [floating point underflow](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html) we change the product into a sum by going into log space. This is called the LogSumExp trick. So: # # $$\log P(f \vert c) = \sum_i \log P(f_i \vert c) $$ # # There is another caveat. What if we see a term that didn't exist in the training data? This means that $P(f_i \vert c) = 0$ for that term, and thus $P(f \vert c) = \prod_i P(f_i | c) = 0$, which doesn't help us at all. Instead of using zeros, we add a small negligible value called $\alpha$ to each count. This is called Laplace Smoothing. # # $$P(f_i \vert c) = \frac{N_{ic}+\alpha}{N_c + \alpha N_i}$$ # # where $N_{ic}$ is the number of times feature $i$ was seen in class $c$, $N_c$ is the number of times class $c$ was seen and $N_i$ is the number of times feature $i$ was seen globally. $\alpha$ is sometimes called a regularization parameter. #%% [markdown] # ### Multinomial Naive Bayes and Other Likelihood Functions # # Since we are modeling word counts, we are using variation of Naive Bayes called Multinomial Naive Bayes. This is because the likelihood function actually takes the form of the multinomial distribution. # # $$P(f \vert c) = \frac{\left( \sum_i f_i \right)!}{\prod_i f_i!} \prod_{f_i} P(f_i \vert c)^{f_i} \propto \prod_{i} P(f_i \vert c)$$ # # where the nasty term out front is absorbed as a normalization constant such that probabilities sum to 1. # # There are many other variations of Naive Bayes, all which depend on what type of value $f_i$ takes. If $f_i$ is continuous, we may be able to use *Gaussian Naive Bayes*. First compute the mean and variance for each class $c$. Then the likelihood, $P(f \vert c)$ is given as follows # # $$P(f_i = v \vert c) = \frac{1}{\sqrt{2\pi \sigma^2_c}} e^{- \frac{\left( v - \mu_c \right)^2}{2 \sigma^2_c}}$$ #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set II</h3> # # <p><b>Exercise:</b> Implement a simple Naive Bayes classifier:</p> # # <ol> # <li> split the data set into a training and test set # <li> Use `scikit-learn`'s `MultinomialNB()` classifier with default parameters. # <li> train the classifier over the training set and test on the test set # <li> print the accuracy scores for both the training and the test sets # </ol> # # What do you notice? Is this a good classifier? If not, why not? # </div> #%% from sklearn.naive_bayes import MultinomialNB from sklearn.model_selection import train_test_split def xy_train_test(critics, vectorizer) #%% [markdown] # ### Picking Hyperparameters for Naive Bayes and Text Maintenance #%% [markdown] # We need to know what value to use for $\alpha$, and we also need to know which words to include in the vocabulary. As mentioned earlier, some words are obvious stopwords. Other words appear so infrequently that they serve as noise, and other words in addition to stopwords appear so frequently that they may also serve as noise. #%% [markdown] # First, let's find an appropriate value for `min_df` for the `CountVectorizer`. `min_df` can be either an integer or a float/decimal. If it is an integer, `min_df` represents the minimum number of documents a word must appear in for it to be included in the vocabulary. If it is a float, it represents the minimum *percentage* of documents a word must appear in to be included in the vocabulary. From the documentation: #%% [markdown] # >min_df: When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float, the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None. #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set III</h3> # # <p><b>Exercise:</b> Construct the cumulative distribution of document frequencies (df). The $x$-axis is a document count $x_i$ and the $y$-axis is the percentage of words that appear less than $x_i$ times. For example, at $x=5$, plot a point representing the percentage or number of words that appear in 5 or fewer documents.</p> # # <p><b>Exercise:</b> Look for the point at which the curve begins climbing steeply. This may be a good value for `min_df`. If we were interested in also picking `max_df`, we would likely pick the value where the curve starts to plateau. What value did you choose?</p> # </div> #%% # Your turn. #%% [markdown] # The parameter $\alpha$ is chosen to be a small value that simply avoids having zeros in the probability computations. This value can sometimes be chosen arbitrarily with domain expertise, but we will use K-fold cross validation. In K-fold cross-validation, we divide the data into $K$ non-overlapping parts. We train on $K-1$ of the folds and test on the remaining fold. We then iterate, so that each fold serves as the test fold exactly once. The function `cv_score` performs the K-fold cross-validation algorithm for us, but we need to pass a function that measures the performance of the algorithm on each fold. #%% from sklearn.model_selection import KFold def cv_score(clf, X, y, scorefunc): result = 0. nfold = 5 for train, test in KFold(nfold).split(X): # split data into train/test groups, 5 times clf.fit(X[train], y[train]) # fit the classifier, passed is as clf. result += scorefunc(clf, X[test], y[test]) # evaluate score function on held-out data return result / nfold # average #%% [markdown] # We use the log-likelihood as the score here in `scorefunc`. The higher the log-likelihood, the better. Indeed, what we do in `cv_score` above is to implement the cross-validation part of `GridSearchCV`. # # The custom scoring function `scorefunc` allows us to use different metrics depending on the decision risk we care about (precision, accuracy, profit etc.) directly on the validation set. You will often find people using `roc_auc`, precision, recall, or `F1-score` as the scoring function. #%% def log_likelihood(clf, x, y): prob = clf.predict_log_proba(x) rotten = y == 0 fresh = ~rotten return prob[rotten, 0].sum() + prob[fresh, 1].sum() #%% [markdown] # We'll cross-validate over the regularization parameter $\alpha$. #%% [markdown] # Let's set up the train and test masks first, and then we can run the cross-validation procedure. #%% from sklearn.model_selection import train_test_split _, itest = train_test_split(range(critics.shape[0]), train_size=0.7) mask = np.zeros(critics.shape[0], dtype=np.bool) mask[itest] = True #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set IV</h3> # # <p><b>Exercise:</b> What does using the function `log_likelihood` as the score mean? What are we trying to optimize for?</p> # # <p><b>Exercise:</b> Without writing any code, what do you think would happen if you choose a value of $\alpha$ that is too high?</p> # # <p><b>Exercise:</b> Using the skeleton code below, find the best values of the parameter `alpha`, and use the value of `min_df` you chose in the previous exercise set. Use the `cv_score` function above with the `log_likelihood` function for scoring.</p> # </div> #%% from sklearn.naive_bayes import MultinomialNB #the grid of parameters to search over alphas = [.1, 1, 5, 10, 50] best_min_df = None # YOUR TURN: put your value of min_df here. #Find the best value for alpha and min_df, and the best classifier best_alpha = None maxscore=-np.inf for alpha in alphas: vectorizer = CountVectorizer(min_df=best_min_df) Xthis, ythis = make_xy(critics, vectorizer) Xtrainthis = Xthis[mask] ytrainthis = ythis[mask] # your turn #%% print("alpha: {}".format(best_alpha)) #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set V: Working with the Best Parameters</h3> # # <p><b>Exercise:</b> Using the best value of `alpha` you just found, calculate the accuracy on the training and test sets. Is this classifier better? Why (not)?</p> # # </div> #%% vectorizer = CountVectorizer(min_df=best_min_df) X, y = make_xy(critics, vectorizer) xtrain=X[mask] ytrain=y[mask] xtest=X[~mask] ytest=y[~mask] clf = MultinomialNB(alpha=best_alpha).fit(xtrain, ytrain) #your turn. Print the accuracy on the test and training dataset training_accuracy = clf.score(xtrain, ytrain) test_accuracy = clf.score(xtest, ytest) print("Accuracy on training data: {:2f}".format(training_accuracy)) print("Accuracy on test data: {:2f}".format(test_accuracy)) #%% from sklearn.metrics import confusion_matrix print(confusion_matrix(ytest, clf.predict(xtest))) #%% [markdown] # ## Interpretation #%% [markdown] # ### What are the strongly predictive features? # # We use a neat trick to identify strongly predictive features (i.e. words). # # * first, create a data set such that each row has exactly one feature. This is represented by the identity matrix. # * use the trained classifier to make predictions on this matrix # * sort the rows by predicted probabilities, and pick the top and bottom $K$ rows #%% words = np.array(vectorizer.get_feature_names()) x = np.eye(xtest.shape[1]) probs = clf.predict_log_proba(x)[:, 0] ind = np.argsort(probs) good_words = words[ind[:10]] bad_words = words[ind[-10:]] good_prob = probs[ind[:10]] bad_prob = probs[ind[-10:]] print("Good words\t P(fresh | word)") for w, p in zip(good_words, good_prob): print("{:>20}".format(w), "{:.2f}".format(1 - np.exp(p))) print("Bad words\t P(fresh | word)") for w, p in zip(bad_words, bad_prob): print("{:>20}".format(w), "{:.2f}".format(1 - np.exp(p))) #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set VI</h3> # # <p><b>Exercise:</b> Why does this method work? What does the probability for each row in the identity matrix represent</p> # # </div> #%% [markdown] # The above exercise is an example of *feature selection*. There are many other feature selection methods. A list of feature selection methods available in `sklearn` is [here](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection). The most common feature selection technique for text mining is the chi-squared $\left( \chi^2 \right)$ [method](http://nlp.stanford.edu/IR-book/html/htmledition/feature-selectionchi2-feature-selection-1.html). #%% [markdown] # ### Prediction Errors # # We can see mis-predictions as well. #%% x, y = make_xy(critics, vectorizer) prob = clf.predict_proba(x)[:, 0] predict = clf.predict(x) bad_rotten = np.argsort(prob[y == 0])[:5] bad_fresh = np.argsort(prob[y == 1])[-5:] print("Mis-predicted Rotten quotes") print('---------------------------') for row in bad_rotten: print(critics[y == 0].quote.iloc[row]) print("") print("Mis-predicted Fresh quotes") print('--------------------------') for row in bad_fresh: print(critics[y == 1].quote.iloc[row]) print("") #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set VII: Predicting the Freshness for a New Review</h3> # <br/> # <div> # <b>Exercise:</b> # <ul> # <li> Using your best trained classifier, predict the freshness of the following sentence: *'This movie is not remarkable, touching, or superb in any way'* # <li> Is the result what you'd expect? Why (not)? # </ul> # </div> # </div> #%% #your turn #%% [markdown] # ### Aside: TF-IDF Weighting for Term Importance # # TF-IDF stands for # # `Term-Frequency X Inverse Document Frequency`. # # In the standard `CountVectorizer` model above, we used just the term frequency in a document of words in our vocabulary. In TF-IDF, we weight this term frequency by the inverse of its popularity in all documents. For example, if the word "movie" showed up in all the documents, it would not have much predictive value. It could actually be considered a stopword. By weighing its counts by 1 divided by its overall frequency, we downweight it. We can then use this TF-IDF weighted features as inputs to any classifier. **TF-IDF is essentially a measure of term importance, and of how discriminative a word is in a corpus.** There are a variety of nuances involved in computing TF-IDF, mainly involving where to add the smoothing term to avoid division by 0, or log of 0 errors. The formula for TF-IDF in `scikit-learn` differs from that of most textbooks: # # $$\mbox{TF-IDF}(t, d) = \mbox{TF}(t, d)\times \mbox{IDF}(t) = n_{td} \log{\left( \frac{\vert D \vert}{\vert d : t \in d \vert} + 1 \right)}$$ # # where $n_{td}$ is the number of times term $t$ occurs in document $d$, $\vert D \vert$ is the number of documents, and $\vert d : t \in d \vert$ is the number of documents that contain $t$ #%% # http://scikit-learn.org/dev/modules/feature_extraction.html#text-feature-extraction # http://scikit-learn.org/dev/modules/classes.html#text-feature-extraction-ref from sklearn.feature_extraction.text import TfidfVectorizer tfidfvectorizer = TfidfVectorizer(min_df=1, stop_words='english') Xtfidf=tfidfvectorizer.fit_transform(critics.quote) #%% [markdown] # <div class="span5 alert alert-info"> # <h3>Exercise Set VIII: Enrichment <b>(Optional)</b></h3> # # <p> # There are several additional things we could try. Try some of these as exercises: # <ol> # <li> Build a Naive Bayes model where the features are n-grams instead of words. N-grams are phrases containing n words next to each other: a bigram contains 2 words, a trigram contains 3 words, and 6-gram contains 6 words. This is useful because "not good" and "so good" mean very different things. On the other hand, as n increases, the model does not scale well since the feature set becomes more sparse. # <li> Try a model besides Naive Bayes, one that would allow for interactions between words -- for example, a Random Forest classifier. # <li> Try adding supplemental features -- information about genre, director, cast, etc. # <li> Use word2vec or [Latent Dirichlet Allocation](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) to group words into topics and use those topics for prediction. # <li> Use TF-IDF weighting instead of word counts. # </ol> # </p> # # <b>Exercise:</b> Try at least one of these ideas to improve the model (or any other ideas of your own). Implement here and report on the result. # </div> #%% # Your turn

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from flask import Flask app = Flask(__name__) import urllib.request # imports internet textfile reader import random # psuedo-random number generator module import sys # module allows program to access terminal parameters import re # the powerful "regular expression" for decompartimentalizing strings def dictionary_main(): # opens primary dictionary on the machine. ''' This shouldn't vary too much machine to machine, but it may. It is the primary dictionary on your machine, so it holds many words. It isn't originally in list format. ''' file = open("/usr/share/dict/words", "r") text = file.read() file.close() return text def publication_random(): book = round((random.random()*49544)+1) link = f"http://www.gutenberg.org/cache/epub/{book}/pg{book}.txt" with urllib.request.urlopen(link) as response: text = response.read() print("200 SUCCESS") text = str(text) text = text.replace("\\r\\n", "") return text def publication_example(): # opens primary dictionary on the machine. ''' It is the primary dictionary on your machine, so it holds many words. It isn't originally in list format. ''' file = open("./example.txt", "r") text = file.read() file.close() return text def user_number(feed): print(feed) while True: try: number = int(input("enter integer:\n>> ")) assert isinstance(number, int) # throws an error if its not an integer assert number != None assert number != "" return number except: print("INVALID INPUT.\nplease try again.\n") def user_string(feed): print(feed) while True: try: string = str(input("enter string:\n>> ")) assert isinstance(string, str) # throws an error if its not an string return string except: print("INVALID INPUT.\nplease try again.\n") def listify_data(input_data): # takes data and returns its list version. ''' if its already a list, nothing happens. if its a string, it uses REGULAR EXPRESSIONS to transform it into a list. if its an unexpected data type, it throws an assertion error ''' input_data = str(input_data) if (type(input_data) is str): input_data = re.findall(r"[\w']+", input_data) # I still must configure the thing to remove /n /r /xc3 assert type(input_data) is list return input_data def undupli_list(input_list): # removes duplicates from a list. output_list = [] for word in input_list: if word not in output_list: output_list += [word] return output_list def randify_list(input_list, iterations): # randomly shuffles a list. ''' picks a random digit, min 0 max current length of input list pops current digit into output list the `iterations` variable determines the number of words to output ''' output_list = [] while len(input_list) > 0 and (len(output_list) < iterations or iterations == 0): digit_rand = random.randint(0, len(input_list) - 1) input_rand = input_list.pop(digit_rand) output_list += [input_rand] return output_list def textify_list(input_list): # turns a list into a space-deliminated string. ''' output_text is the final return product. each item is added with a space to output_text. the final output_text string is returned ''' output_text = '' for input_item in input_list: if output_text != '': output_text += ' ' output_text += input_item input_list = output_text return input_list def textify_dict(input_dict): output_text = '' for w in sorted(input_dict, key=input_dict.get, reverse=False): if input_dict[w] > 0: output_text += str(input_dict[w]) + ' ' + str(w) + "\n" return output_text def lowerfy_list(input_list): # takes a list of strings and runs lowercase on each item. ''' output_text is the final return product. each item is added with a space to output_text. the final output_text string is returned ''' output_list = [] for string in input_list: string = string.lower() output_list += [string] return output_list def reverse_text(old_word): # simply reverses a text. new_word = "" i = 1 for _ in old_word: new_word += old_word[len(old_word)-i] i+=1 return new_word # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ''' Now setting up specific functions. These will each return complete jobs. These functions will use the previously declared helpers to make code readable. ''' # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # def rearrange(iterations, input_data = dictionary_main()): data = input_data data = listify_data(data) data = lowerfy_list(data) # data = undupli_list(data) ## This function takes too long! data = randify_list(data, iterations) data = textify_list(data) return(data) ### ANAGRAMS def anagrams (input_word, input_data = dictionary_main()): data = input_data data = listify_data(data) data = lowerfy_list(data) new_data = [] i_word = input_word for v_word in data: if verify_anagram(i_word,v_word): new_data += [v_word] new_data = textify_list(new_data) return new_data def verify_anagram(input_word, verify_word): input_list = list(input_word) verify_list = list(verify_word) input_list.sort() verify_list.sort() if input_list == verify_list: return True else: return False ### PALINDROMES def palindromes(input_data = dictionary_main()): data = input_data data = listify_data(data) data = lowerfy_list(data) new_data = [] for word in data: if verify_palindrome(word): new_data += [word] new_data = undupli_list(new_data) new_data = textify_list(new_data) return new_data def verify_palindrome(input_word): i = 1 while (i <= len(input_word)/2): if input_word[i-1] != input_word[-i]: return False i += 1 return True # HISTOGRAM def histogram(input_data = dictionary_main()): data = listify_data(input_data) data = lowerfy_list(data) # data = undupli_list(data) ## This removes duplicates. If this is correct, there will be no outputs when this is uncommented. hist = {} for word in data: if word in hist: hist[word] += 1 else: hist[word] = 1 return hist def histogram_arrays(input_data = dictionary_main()): data = listify_data(input_data) data = lowerfy_list(data) hist = [] for word in data: found = False for item in hist: if word == item[0]: found = True if found: item[1] += 1 else: item[0] = word item[1] = 1 return hist def display_dict(input_data = dictionary_main()): hist = histogram(input_data) output = textify_dict(hist) return output def display_weight(input_data): hist = histogram(input_data) hist = calculate_weight(hist) # testing calculate weight string = prettify(hist) return string def prettify(input_data): string = '' for item in input_data: string += item string += ': ' string += str(input_data[item] * 1000) string += '‰\n' return string # WEIGHTED FUNCTION def calculate_weight(input_dict): weight_dict = {} dict_total = 0 for word in input_dict: dict_total += input_dict[word] for word in input_dict: weight_dict[word] = input_dict[word] / dict_total return weight_dict def random_choice(weight_dict): cumulative = 0 rand_select = random.random() for word in weight_dict: if (cumulative <= rand_select < weight_dict[word] + cumulative): print("SELECTED: " + word) return word cumulative += weight_dict[word] print(rand_select) # for word in weight_dict: # if weight_dict ### STARTER KIT # MUST DO RANDOM WEIGHTING @app.route('/') def hello_world(): return display_weight(publication_random()) if __name__ == '__main__': # print("\nIt looks like you used a parameter when you ran this file.\nI'm going to assume that this is a file, and I'm going to run") entry = user_number("\nI generate several words.\nHow many should I create?") print("\nRandom Words:\n" + rearrange(entry)) entry = user_string("\nI make anagrams, so I need a word.\nWhat base word should I use today?") print("\nAnagrams:\n" + anagrams(entry)) print("\nI do palindromes too, but I\ndon't need any inputs for that!\nI'll go ahead and get started.") print("\nPalindromes:\n" + palindromes()) print("\nI'm going to spit out a large histogram of words, \nsorted by the PERMILLE (‰) of the text that it is used in.") input("press enter to continue: ") my_dict = publication_random() print("\nDisplay Weight:\n" + display_weight(my_dict))

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#!/usr/bin/env python3 # -*- coding: utf-8 -*- # State Representation Learning with Task-Irrelevant Factors of Variation in Robotics # Anonymous Authors 2021 import numpy as np import matplotlib matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab! import matplotlib.pyplot as plt import torch import torch.optim as optim from scipy.stats import norm import sys sys.path.insert(0,'..') import importlib import algorithms.EarlyStopping as ES import torch.utils.data as data # --- # ====================== Training functions ====================== # # --- class AE_Algorithm(): def __init__(self, opt): # Save the whole config self.opt = opt # Training parameters self.batch_size = opt['batch_size'] self.epochs = opt['epochs'] self.current_epoch = None self.loss_fn = opt['loss_fn'] self.snapshot = opt['snapshot'] self.console_print = opt['console_print'] self.lr_schedule = opt['lr_schedule'] self.init_lr_schedule = opt['lr_schedule'] self.model = None self.vae_optimiser = None # Beta scheduling self.beta = opt['beta_min'] self.beta_range = opt['beta_max'] - opt['beta_min'] + 1 self.beta_steps = opt['beta_steps'] - 1 self.beta_idx = 0 # Gamma scheduling self.gamma_warmup = opt['gamma_warmup'] self.gamma = 0 if self.gamma_warmup > 0 else opt['gamma_min'] self.gamma_min = opt['gamma_min'] self.gamma_idx = 0 self.gamma_update_step = (opt['gamma_max'] - opt['gamma_min']) / opt['gamma_steps'] self.gamma_update_epoch_step = (self.epochs - self.gamma_warmup - 1) / opt['gamma_steps'] # Action loss parameters self.min_dist = opt['min_dist_samples'] self.weight_dist_loss = opt['weight_dist_loss'] self.distance_type = opt['distance_type'] if 'distance_type' in opt.keys() else '2' self.batch_dist_dict = {} self.epoch_dist_dict = {} self.min_epochs = opt['min_epochs'] if 'min_epochs' in opt.keys() else 499 self.max_epochs = opt['max_epochs'] if 'max_epochs' in opt.keys() else 499 # Other parameters self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') self.opt['device'] = self.device print(' *- Chosen device: ', self.device) torch.manual_seed(opt['random_seed']) np.random.seed(opt['random_seed']) print(' *- Chosen random seed: ', self.device) if self.device == 'cuda': torch.cuda.manual_seed(opt['random_seed']) self.save_path = self.opt['exp_dir'] + '/' + self.opt['filename'] self.model_path = self.save_path + '_model.pt' def count_parameters(self): """ Counts the total number of trainable parameters in the model. """ return sum(p.numel() for p in self.model.parameters() if p.requires_grad) def plot_grid(self, images, n=5,name="dec"): """ Plots an nxn grid of images of size digit_size. Used to monitor the reconstruction of decoded images. """ digit_size = int(np.sqrt(self.opt['input_dim']/self.opt['input_channels'])) filename = self.save_path +name + '_checkpointRecon_{0}'.format(self.current_epoch) figure = np.zeros((digit_size * n, digit_size * n, self.opt['input_channels'])) # Construct grid of latent variable values grid_x = norm.ppf(np.linspace(0.05, 0.95, n)) grid_y = norm.ppf(np.linspace(0.05, 0.95, n)) # decode for each square in the grid counter = 0 for i, yi in enumerate(grid_x): for j, xi in enumerate(grid_y): digit = images[counter].permute(1,2,0).detach().cpu().numpy() figure[i * digit_size: (i + 1) * digit_size, j * digit_size: (j + 1) * digit_size] = digit counter += 1 plt.figure(figsize=(10, 10)) plt.imshow(figure, cmap='bone') plt.savefig(filename) plt.clf() plt.close() def latent_mean_dist(self, mean1, mean2, action, distance_type='1'): """ Computed the average d distance between the action and no action pairs in the given batch. """ sample1 = mean1 sample2 = mean2 dist = torch.norm(sample1 - sample2, p=float(self.distance_type), dim=1) # Batch size dist=dist**2 # Distances between pairs with an action dist_action = torch.mul(dist, action) # Batch size dist_action = dist_action[dist_action.nonzero()] # num_action, 1 dist_action_mean = torch.mean(dist_action) dist_action_std = torch.std(dist_action) # Distances between pairs without an action dist_no_action = torch.mul(dist, (1-action)) dist_no_action = dist_no_action[dist_no_action.nonzero()] dist_no_action_mean = torch.mean(dist_no_action) dist_no_action_std = torch.std(dist_no_action) # Compute the action loss zeros = torch.zeros(dist.size()).to(self.device) batch_dist = (1 - action) * dist + action * torch.max(zeros, self.min_dist - dist) dist_loss = torch.mean(batch_dist) # Weight the action loss batch_loss = self.weight_dist_loss*self.gamma * batch_dist avg_batch_loss = torch.mean(batch_loss) # Save the result in order to compute average epoch distance # If training, save to training distances if self.model.training: self.epoch_action_dist.append(dist_action.cpu().detach().numpy()) self.epoch_noaction_dist.append(dist_no_action.cpu().detach().numpy()) # If evaluation, save to test distances else: self.test_action_dist.append(dist_action.cpu().detach().numpy()) self.test_noaction_dist.append(dist_no_action.cpu().detach().numpy()) return (avg_batch_loss, dist_loss, dist_action_mean, dist_action_std, dist_no_action_mean, dist_no_action_std) def compute_loss(self, x, dec_mu, enc_mu): """ Computes the usual VAE loss on the training batch given the criterion. """ # Reconstruction loss loss = torch.nn.MSELoss(reduction = 'sum') batch_rec=loss(x,dec_mu) return batch_rec def format_loss(self, losses_list): """Rounds the loss and returns an np array for logging.""" reformatted = list(map(lambda x: round(x.item(), 2), losses_list)) reformatted.append(int(self.current_epoch)) return np.array(reformatted) def init_model(self): """Initialises the VAE model.""" vae = importlib.import_module("architectures.{0}".format(self.opt['model'])) print(' *- Imported module: ', vae) try: class_ = getattr(vae, self.opt['model']) instance = class_(self.opt).to(self.device) return instance except: raise NotImplementedError( 'Model {0} not recognized'.format(self.opt['model'])) def init_optimiser(self): """Initialises the optimiser.""" print(self.model.parameters()) if self.opt['optim_type'] == 'Adam': print(' *- Initialised Adam optimiser.') vae_optim = optim.Adam(self.model.parameters(), lr=self.lr) return vae_optim else: raise NotImplementedError( 'Optimiser {0} not recognized'.format(self.opt['optim_type'])) def update_learning_rate(self, optimiser): """Annealing schedule for the learning rate.""" if self.current_epoch == self.lr_update_epoch: for param_group in optimiser.param_groups: self.lr = self.new_lr param_group['lr'] = self.lr print(' *- Learning rate updated - new value:', self.lr) try: self.lr_update_epoch, self.new_lr = self.lr_schedule.pop(0) except: print(' *- Reached the end of the update schedule.') print(' *- Remaning lr schedule:', self.lr_schedule) def update_gamma(self): """Annealing schedule for the distance term.""" epoch_to_update = self.gamma_idx * self.gamma_update_epoch_step + self.gamma_warmup if (self.current_epoch + 1) > epoch_to_update: self.gamma = self.gamma_min + self.gamma_idx * self.gamma_update_step self.gamma_idx += 1 print (' *- Gamma updated - new value:', self.gamma) def train(self, train_dataset, test_dataset, num_workers=0, chpnt_path=''): """Trains a model with given hyperparameters.""" dataloader = torch.utils.data.DataLoader( train_dataset, batch_size=self.batch_size, shuffle=True, num_workers=num_workers, drop_last=True)#, sampler=train_sampler) n_data = len(train_dataset) assert(train_dataset.dataset_name == test_dataset.dataset_name) print(('\nPrinting model specifications...\n' + ' *- Path to the model: {0}\n' + ' *- Training dataset: {1}\n' + ' *- Number of training samples: {2}\n' + ' *- Number of epochs: {3}\n' + ' *- Loss criterion: {4}\n' + ' *- Batch size: {5}\n' ).format(self.model_path, train_dataset.dataset_name, n_data, self.epochs, self.loss_fn, self.batch_size)) if chpnt_path: # Pick up the last epochs specs self.load_checkpoint(chpnt_path) else: # Initialise the model self.model = self.init_model() self.start_epoch, self.lr = self.lr_schedule.pop(0) try: self.lr_update_epoch, self.new_lr = self.lr_schedule.pop(0) except: self.lr_update_epoch, self.new_lr = self.start_epoch - 1, self.lr self.vae_optimiser = self.init_optimiser() self.valid_losses = [] self.epoch_losses = [] # To track the average epoch action loss self.epoch_action_dist_list = [] self.epoch_noaction_dist_list = [] self.test_action_dist_list = [] self.test_noaction_dist_list = [] print((' *- Learning rate: {0}\n' + ' *- Next lr update at {1} to the value {2}\n' + ' *- Remaining lr schedule: {3}' ).format(self.lr, self.lr_update_epoch, self.new_lr, self.lr_schedule)) es = ES.EarlyStopping(patience=300) num_parameters = self.count_parameters() self.opt['num_parameters'] = num_parameters print(' *- Model parameter/training samples: {0}'.format( num_parameters/len(train_dataset))) print(' *- Model parameters: {0}'.format(num_parameters)) for name, param in self.model.named_parameters(): if param.requires_grad: spacing = 1 print('{0:>2}{1}\n\t of dimension {2}'.format('', name, spacing), list(param.shape)) print('\nStarting to train the model...\n' ) for self.current_epoch in range(self.start_epoch, self.epochs): # Restart the epoch distances self.epoch_action_dist = [] self.epoch_noaction_dist = [] # Update hyperparameters self.model.train() #self.update_beta() self.update_gamma() self.update_learning_rate(self.vae_optimiser) epoch_loss = np.zeros(11) for batch_idx, (img1, img2, action) in enumerate(dataloader): img1 = img1.to(self.device) img2 = img2.to(self.device) action = action.to(self.device) # VAE loss on img1 dec_mean1, enc_mean1 = self.model(img1) loss1= self.compute_loss( img1, dec_mean1, enc_mean1) # VAE loss on img2 dec_mean2, enc_mean2 = self.model(img2) loss2 = self.compute_loss( img2, dec_mean2, enc_mean2) # Average VAE loss for the pair of image batches loss = (loss1 + loss2) / 2 # Action loss between the latent samples (w_dist_loss, pure_dist_loss, dist_action_mean, dist_action_std, dist_no_action_mean, dist_no_action_std) = self.latent_mean_dist( enc_mean1, enc_mean2, action) # Optimise the VAE for the complete loss the_loss = loss + w_dist_loss self.vae_optimiser.zero_grad() the_loss.backward() self.vae_optimiser.step() valid_loss = np.zeros(10) # Check that the at least 350 epochs are done if (es.step(valid_loss[0]) and self.current_epoch > self.min_epochs) \ or (self.current_epoch > self.min_epochs and self.epoch_gap > 0) \ or self.current_epoch > self.max_epochs: self.plot_dist_hists( self.epoch_action_dist, self.epoch_noaction_dist, 'training') break # Update the checkpoint only if no early stopping was done self.save_checkpoint(epoch_loss[0]) # Print current loss values every epoch if (self.current_epoch + 1) % self.console_print == 0: print('Epoch {0}:'.format(self.current_epoch)) print(' Train loss: {0:.3f} recon loss: {1:.3f} KL loss: {2:.3f} dist: {3:.3f}'.format( epoch_loss[0], epoch_loss[1], epoch_loss[2], epoch_loss[4])) print(' Valid loss: {0:.3f} recon loss: {1:.3f} KL loss: {2:.3f} dist: {3:.3f}'.format( valid_loss[0], valid_loss[1], valid_loss[2], valid_loss[4])) print(' Beta: {0:.6e}'.format(self.beta)) print(' Gamma: {0:.6e}'.format(self.gamma)) print(' LR: {0:.6e}'.format(self.lr)) print(' MD: {0:.6e}'.format(self.min_dist)) #print(' Gap: {0:.6e}\n'.format(self.epoch_gap)) # Print validation results when specified if (self.current_epoch + 1) % self.snapshot == 0: # Plot reconstructions self.plot_grid(dec_mean1) self.plot_grid(img1, name="input") self.model.eval() print('Training completed.') #self.plot_model_loss() self.model.eval() # Measure other d distances at the end of training for comparisson print('Calculating other distances...') original_distance_type = self.distance_type all_distance_types = ['1', '2', 'inf'] self.batch_dist_dict = {} self.epoch_dist_dict = {} for dist_type in all_distance_types: self.distance_type = dist_type self.batch_dist_dict[dist_type] = {} self.epoch_dist_dict[dist_type] = {} print(' *- Distance type set to ', self.distance_type) after_training_train = self.compute_test_loss(train_dataset) self.batch_dist_dict[dist_type]['train'] = list(map(lambda x: round(x, 3), after_training_train)) #self.epoch_dist_dict[dist_type]['train_action'] = round(self.test_action_dist / self.test_action_pairs, 2) self.epoch_dist_dict[dist_type]['train_noaction'] = round(self.test_noaction_dist / self.test_noaction_pairs, 2) after_training_test = self.compute_test_loss(test_dataset) self.batch_dist_dict[dist_type]['test'] = list(map(lambda x: round(x, 3), after_training_test)) #self.epoch_dist_dict[dist_type]['test_action'] = round(self.test_action_dist / self.test_action_pairs, 2) self.epoch_dist_dict[dist_type]['test_noaction'] = round(self.test_noaction_dist / self.test_noaction_pairs, 2) self.distance_type = original_distance_type # Save the model torch.save(self.model.state_dict(), self.model_path) def save_checkpoint(self, epoch_ml, keep=False): """ Saves a checkpoint during the training. """ if keep: path = self.save_path + '_checkpoint{0}.pth'.format(self.current_epoch) checkpoint_type = 'epoch' else: path = self.save_path + '_lastCheckpoint.pth' checkpoint_type = 'last' training_dict = { 'last_epoch': self.current_epoch, 'model_state_dict': self.model.state_dict(), 'vae_optimiser_state_dict': self.vae_optimiser.state_dict(), 'last_epoch_loss': epoch_ml, 'valid_losses': self.valid_losses, 'epoch_losses': self.epoch_losses, 'epoch_action_dist_list': self.epoch_action_dist_list, 'epoch_noaction_dist_list': self.epoch_noaction_dist_list, 'test_action_dist_list': self.test_action_dist_list, 'test_noaction_dist_list': self.test_noaction_dist_list, 'beta': self.beta, 'beta_range': self.beta_range, 'beta_steps': self.beta_steps, 'beta_idx': self.beta_idx, 'gamma_warmup': self.gamma_warmup, 'gamma': self.gamma, 'gamma_min': self.gamma_min, 'gamma_idx': self.gamma_idx, 'gamma_update_step': self.gamma_update_step, 'gamma_update_epoch_step': self.gamma_update_epoch_step, 'snapshot': self.snapshot, 'console_print': self.console_print, 'current_lr': self.lr, 'lr_update_epoch': self.lr_update_epoch, 'new_lr': self.new_lr, 'lr_schedule': self.lr_schedule } torch.save({**training_dict, **self.opt}, path) print(' *- Saved {1} checkpoint {0}.'.format(self.current_epoch, checkpoint_type)) def load_checkpoint(self, path, eval=False): """ Loads a checkpoint and initialises the models to continue training. """ checkpoint = torch.load(path, map_location=self.device) self.model = self.init_model() self.model.load_state_dict(checkpoint['model_state_dict']) self.lr = checkpoint['current_lr'] self.lr_update_epoch = checkpoint['lr_update_epoch'] self.new_lr = checkpoint['new_lr'] self.lr_schedule = checkpoint['lr_schedule'] self.vae_optimiser= self.init_optimiser() self.vae_optimiser.load_state_dict(checkpoint['vae_optimiser_state_dict']) self.start_epoch = checkpoint['last_epoch'] + 1 self.snapshot = checkpoint['snapshot'] self.valid_losses = checkpoint['valid_losses'] self.epoch_losses = checkpoint['epoch_losses'] if 'epoch_action_dist_list' in checkpoint.keys(): self.epoch_action_dist_list = checkpoint['epoch_action_dist_list'] self.epoch_noaction_dist_list = checkpoint['epoch_noaction_dist_list'] self.test_action_dist_list = checkpoint['test_action_dist_list'] self.test_noaction_dist_list = checkpoint['test_noaction_dist_list'] self.beta = checkpoint['beta'] self.beta_range = checkpoint['beta_range'] self.beta_steps = checkpoint['beta_steps'] self.beta_idx = checkpoint['beta_idx'] self.gamma_warmup = checkpoint['gamma_warmup'] self.gamma = checkpoint['gamma'] self.gamma_min = checkpoint['gamma_min'] self.gamma_idx = checkpoint['gamma_idx'] self.gamma_update_step = checkpoint['gamma_update_step'] self.gamma_update_epoch_step = checkpoint['gamma_update_epoch_step'] self.snapshot = checkpoint['snapshot'] self.console_print = checkpoint['console_print'] print(('\nCheckpoint loaded.\n' + ' *- Last epoch {0} with loss {1}.\n' ).format(checkpoint['last_epoch'], checkpoint['last_epoch_loss'])) print(' *- Current lr {0}, next update on epoch {1} to the value {2}'.format( self.lr, self.lr_update_epoch, self.new_lr) ) if eval == False: self.model.train() else: self.model.eval()

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data_one = [] # fill in values from first dataset data_two = [] # fill in this too # if means are different, data was entered incorrectly from statistics import mean assert mean(data_one) == mean(data_two), "Means should be the same" print("The mean for each dataset is FIXME") # print out the mean of the datasets deviations_one = [ abs( value - mean(data_one) ) # measure distance of value from the mean for value in data_one # do this for each value of data ] print(deviations_one) deviations_two = [] # TODO print(deviations_two) # prints [5, 4, 3, 2, 1, 0, 0, 0, 0, 0, 1, 1, 3, 4, 6]

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import pandas as pd from pandas import DataFrame import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D df = pd.read_csv('~/a', parse_dates=True) #print(df.head()) #df['H-L'] = df.High - df.Low #df['100MA'] = pd.rolling_mean(df['Close'], 100) threedee = plt.figure().gca(projection='3d') threedee.scatter(df['x'], df['y'], df['z']) threedee.set_xlabel('x') threedee.set_ylabel('y') threedee.set_zlabel('z') plt.show()

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48321cf4235dfcc0194fed90ff43a57367592bf7

cf5adf0c646474f0259d123fad60ca5ed38ec891

refs/heads/master

2023-09-01T05:58:50.807952

2023-03-17T20:27:37

2023-08-31T11:52:41

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Apache-2.0

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2023-09-14T15:47:23

2014-05-14T15:08:16

2023-09-14T09:59:12

2023-09-14T15:47:22

40,706

4,875

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Python

false

#!/usr/bin/env python3 import sys from pathlib import Path assert(Path(sys.argv[1]).read_text() == 'stage2\n') Path(sys.argv[2]).write_text('int main(void){}\n')

UTF-8

Python

false

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py

3,350

stage2.py

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0.687117

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ulucsahin/Thesis

16,879,221,485,506

c1450d0d58662a515250e9b45b338041218f17c4

00e256c739659169972fca251a3611d137c913a4

/annotation_manager.py

d0fd267cd8abb24265a4e0e222a1cd8ede71ca4f

[]

no_license

https://github.com/ulucsahin/Thesis

3a5378a0f5b5e526c199dae7c4d72ef3330bd573

0f9b85b3ac7284174a011e619fdd6f6e3971360c

refs/heads/master

2023-03-14T20:58:43.282707

2021-03-14T12:41:41

243,502,559

0

null

from random import randint # Annotation indexes bald = 4 bangs = 5 black_hair = 8 blond_hair = 9 brown_hair = 11 gray_hair = 17 receding_hairline = 28 straight_hair = 32 wavy_hair = 33 # gender and beard goatee = 16 male = 20 mustache = 22 no_beard = 24 sideburns = 34 # face chubby = 13 high_cheekbones = 19 oval_face = 25 rosy_cheeks = 29 double_chin = 14 # nose big_nose = 7 pointy_nose = 27 # mouth mouth_open = 21 smiling = 31 # eye area arched_eyebrows = 1 bags_under_eyes = 3 bushy_eyebrows = 12 narrow_eyes = 23 # attractive and makeup + young attractive = 2 make_up = 18 young = 39 # skin pale = 26 # not added: 6,15,30,35,36,37,38 def get_introduction_info(annotations): intro = "" skin = "" age = "" gender = "" if annotations[39] == "1\n": age = " young" else: rand = randint(0, 1) ages = ["n old", " middle aged"] age = ages[rand] if annotations[male] == "1": gender = "male" else: gender = "female" if annotations[pale] == "1": rand = randint(0, 2) skin1 = " with pale skin and" skin2 = " who has a pale skin and" skin3 = " that has pale skin" skins = [skin1, skin2, skin3] skin = skins[rand] intro = f"A{age} {gender} {skin}" return intro def get_gender_auxiliary(annotations): if annotations[male] == "1": return "he" else: return "she" def get_gender_auxiliary2(annotations): if annotations[male] == "1": return "his" else: return "her" def get_hair(annotations): hair_color = get_hair_color(annotations) hair_type = get_hair_type(annotations) gender = "woman" if annotations[20] == "1": gender = "man" result = f" with {hair_color} {hair_type} hair " if annotations[receding_hairline] == "1": rand = randint(0, 1) x = [" and a receding hairline", f" , the {gender} has a receding hairline"] result += x[rand] if annotations[bangs] == "1": result += " with bangs" if annotations[20] == "1" and annotations[sideburns] == "1": result += " and sideburns" return result + " ." def get_hair_color(annotations): if annotations[bald] == "1": return "balding" elif annotations[black_hair] == "1": return "black" elif annotations[blond_hair] == "1": return "blond" elif annotations[brown_hair] == "1": return "brown" elif annotations[gray_hair] == "1": return "gray" else: return "" def get_hair_type(annotations): if annotations[straight_hair] == "1": return "straight" elif annotations[wavy_hair] == "1": return "wavy" else: return "" def get_face(annotations): face = "" face_middle = "a face with " face_end = "" if annotations[chubby] == "1": face = " chubby" face_middle = " face with" face_end = " face" if annotations[oval_face] == "1": face += " oval" face_middle = " face with" face_end = " face" # Almost all of them has this so I removed it. Bad annotations # if annotations[high_cheekbones] == "1": # face += f" {face_middle} high cheekbones" # face_middle = "and" # face_end = "" if annotations[rosy_cheeks] == "1": face += f" {face_middle} rosy cheeks " face_middle = " and" face_end = "" if annotations[double_chin] == "1": face += f" {face_middle} double chin " face_end = "" if face != "": face = face + face_end + " ." return face def get_nose(annotations): nose = "" nose_middle = "" if annotations[pointy_nose] == "1": nose = " pointy" nose_middle = " and" if annotations[big_nose] == "1": nose += f"{nose_middle} big" if nose != "": nose += " nose" return nose def get_beard(annotations): beard = "" if annotations[goatee] == "1": beard = " a goatee" if annotations[mustache] == "1": beard = " a mustache" if annotations[no_beard] == "1": beard = " no beard" return beard def get_eye_information(annotations, he_she, his_her): bags = False narrow = False eyes = "" eyes_middle = "" eyes_end = "" bushy = "" if annotations[narrow_eyes] == "1": eyes = f" {he_she} has narrow eyes" narrow = True if annotations[bags_under_eyes] == "1": if not narrow: eyes = f" {he_she} has bags under {his_her} eyes" else: eyes += f" with bags under {his_her} eyes" bags = True if annotations[bushy_eyebrows] == "1": bushy = " , bushy" if annotations[arched_eyebrows] == "1": if bags: eyes += f" and {he_she} has arched {bushy} eyebrows" else: eyes = f" {he_she} has arched {bushy} eyebrows" return eyes + " ." def get_makeup(annotations): makeup_ = "" young_ = "" gender_ = " woman" attractive_ = "" is_none = True if annotations[young] == "1": _ = " young" if annotations[20] == "1": gender_ = " man" if annotations[attractive] == "1": attractive_ = " attractive" is_none = False if annotations[make_up] == "1": makeup_ = f" The {young_} {attractive_} {gender_} is wearing heavy make up ." is_none = False else: makeup_ = f" The {young_} {gender_} is {attractive_} ." if is_none: return "" else: return makeup_ def get_mouth_area(annotations, he_she): mouth_middle = "" gender = " woman" is_none = True mouth_open = False if annotations[20] == "1": gender = " man" mouth = f" The {gender}" if annotations[mouth_open] == "1": mouth += f" has a slightly open mouth" mouth_middle = " and" is_none = False mouth_open = True if annotations[smiling] == "1": if not mouth_open: he_she = "" mouth += f" {mouth_middle} {he_she} is smiling ." is_none = False else: x = randint(0,9) # add serious with 10% chance because annotations are bad and not all not smiling people are serious looking if x==1: if not mouth_open: he_she = "" mouth += f" {mouth_middle} {he_she} looks serious ." is_none = False if is_none: return "" else: return mouth

UTF-8

Python

false

6,550

py

15

annotation_manager.py

12

0.540153

0.519847

0

308

20.266234

116

pardusnimr/adelscrapper

2,723,009,266,699

7585f0f8451324cdbc6807bb71226fcd7d5f6a5f

fcb5b41c20330ae17093d5449f209d113b2a1775

/wed2.py

51e5d2ab8e7649f4ced88765488a0c60c6ed03ba

[]

no_license

https://github.com/pardusnimr/adelscrapper

0c520650881b2dd05d1cd79265065dd708a769bd

83c66ba82cf6c058cd4e6febc33a70012fb39dc4

refs/heads/master

2021-01-10T05:25:07.229791

2015-12-09T20:33:30

47,716,780

0

null

# coding: utf-8 import lxml.html import pymongo client = pymongo.MongoClient("mongodb://nimr:d7hero@ds041871.mongolab.com:41871/adel") db = client['adel'] f = open('allrabea2.html') ff = f.read() doc = lxml.html.document_fromstring(ff) region = '' city = '' district = '' scheme = '' land = '' deal = '' deal_price = '' land_area = '' price_per_sqr_meter = '' records = db.records for row in doc.xpath("//tr"): chiled = row.getchildren() if chiled[0].attrib['style'] == 'padding-left: 5px': region = chiled[0].text elif chiled[0].attrib['style'] == 'padding-left: 17px': city = chiled[0].text elif chiled[0].attrib['style'] == 'padding-left: 29px': district = chiled[0].text elif chiled[0].attrib['style'] == 'padding-left: 41px': scheme = chiled[0].text elif chiled[0].attrib['style'] == 'padding-left: 53px': land = chiled[0].text elif chiled[0].attrib['style'] == 'padding-left: 65px': deal = chiled[0].text deal_price = chiled[1].text land_area = chiled[2].text price_per_sqr_meter = chiled[3].text record = {"region" : region, "city" : city, "distric" : district, "scheme" : scheme, "land" : land, "deal" : deal, "deal_price" : deal_price, "land_area" : land_area, "price_per_sqr_meter" : price_per_sqr_meter} records.insert(record)

UTF-8

Python

false

1,526

py

78

wed2.py

5

0.543906

0.517693

0

56

26.089286

86

cho-jae-seong/Algorithm

798,863,933,104

99dbc7e94cb5a714ff1c19d5d9bc3e240e4b05d1

6feda26060cfc9f79a98acdedc8ef4bd82bf0587

/BOJ17140.py

288582525c2c43ac445712f9f9b4d16524acda4d

[]

no_license

https://github.com/cho-jae-seong/Algorithm

1ff188ec1dbba370af6cfbf0186728ab536930b6

436dadb8be5c308c276562de53a7f4bf25d03bfa

refs/heads/master

2023-07-04T04:27:33.741983

2021-08-10T10:12:04

260,405,007

1

0

null

import sys from collections import Counter from functools import reduce def R(array): mx=0 for i in range(len(array)): X=Counter(array[i]) del X[0] X=list(X.items()) X.sort(key=lambda x: (x[1],x[0])) if len(X)>50: X=X[:50] array[i]=reduce(lambda x, y: list(x)+list(y), X[1:], list(X[0])) mx=max(mx,len(array[i])) for i in range(len(array)): if len(array[i])<mx: array[i].extend([0]*(mx-len(array[i]))) r,c,k=map(int, sys.stdin.readline().split()) r,c=r-1,c-1 a=[list(map(int, sys.stdin.readline().split()))for _ in range(3)] if r<len(a) and c<len(a[0]) and a[r][c]==k: print(0) exit(0) time=0 while True: row_len=len(a) col_len=len(a[0]) if row_len>=col_len: R(a) else: a=list(map(list, zip(*a))) R(a) a=list(map(list, zip(*a))) time+=1 if time>100: print(-1) exit(0) if r<len(a) and c<len(a[0]) and a[r][c]==k: print(time) exit(0)

UTF-8

Python

false

1,086

py

123

BOJ17140.py

123

0.486188

0.461326

0

43

23.255814

72

fattredd/fattredd.github.io

16,621,523,470,920

0b2ad160523e9b476f4f943353278d81f4b705fb

f1fed01b75e8968a1bf1ab194a12b2c3ad7ccb95

/py/textOffset.py

9f36744b36ed7d5cb9ad4d9953f0838d932cb2da

[]

no_license

https://github.com/fattredd/fattredd.github.io

c0553f938df005b822b3640cdc56d6c0bb453c8c

d45da47ae3a2f5f40848b89f47b97e6127996d48

refs/heads/master

2021-01-19T21:28:11.700195

2019-05-13T14:11:28

21,010,252

0

null

import msvcrt as m import time alpha = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ' def wait(): key = m.getch() def offsetStr(string, num=1): out = '' #23 for x in string: if x in alpha: newNum = ord(x)-65+num if newNum > 25: newNum = newNum%26 elif newNum < 0: newNum += 26 out += chr(newNum+65) else: out += x return out def fileR(name='gf.txt'): f = open(name,'r') out = f.read().split('\n') f.close() return out def fileW(textL, name='gf2.txt'): f = open(name, 'w+') f.write('\n'.join(textL)) f.close() def tryRange(string, num, inum=0): for i in range(inum,num): print i, offsetStr(string, i) wait() def doAll(L,off): out = [] for x in L: out.append(offsetStr(x,off)) fileW(out)

UTF-8

Python

false

721

py

71

textOffset.py

60

0.613037

0.590846

0

42

16.190476

36

raulastu/Algorithm-Competitions-EWS

19,224,273,620,446

9e21c959008307071eb15529af013301085bc6d9

fc4897c00ad5a6a641e12e4affe374d686ae7714

/coderperu/Round13/D.py

c2565b3875920ac9f34256e178908044246de2fa

[]

no_license

https://github.com/raulastu/Algorithm-Competitions-EWS

b651c0af684b7e4a1afc7cfabdc6fd9d1dc8e45d

07801fe774778dc5c81682f6a893f97a79ed4022

refs/heads/master

2020-04-10T12:18:50.923422

2015-08-31T01:45:15

7,485,600

0

null

import sys sys.stdin = open("D.in", "r") def expo(a, b, MOD): if (b==1): return a%MOD; if (b==2): return (a*a)%MOD; if (b%2==0): return expo(expo(a%MOD,b/2,MOD),2,MOD)%MOD else: return a*expo(expo(a%MOD,(b-1)/2,MOD),2,MOD)%MOD; def solve(): A = raw_input().split(" ") a = int(A[0]); b = int(A[1]); k = int(A[2]); r = pow(a,b) # print(r) if(r<10000 and k >= 4): print(-1) return if(r<1000 and k >= 3): print(-1) return if(r<100 and k >= 2): print(-1) return if(r<10 and k >= 1): print(-1) return s=str(r%10000) if(k>=len(s)): print(0) else: print(s[len(s)-1-k]) N = int(raw_input()) while(N>0): A = raw_input().split(" ") a = int(A[0]); b = int(A[1]); k = int(A[2]); r = pow(a,b) # print(r) if(r<10000 and k >= 4): print(-1) continue if(r<1000 and k >= 3): print(-1) continue if(r<100 and k >= 2): print(-1) continue if(r<10 and k >= 1): print(-1) continue s=str(r%10000) if(k>=len(s)): print(0) else: print(s[len(s)-1-k]) # print(r) N -= 1

UTF-8

Python

false

1,276

py

324

D.py

286

0.418495

0.359718

0

69

17.507246

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eqiihuu/EE512

9,577,777,106,043

1b09bd7c3831b1f01408ec4f9ce8124e76bd73f2

a8f778fed3e42cad4554c57ad607c19d10fbc697

/EE512-graphical-model-master/test.py

a0e42ae50f71d4dfe58d3c29c225fd88b799a41a

[]

no_license

https://github.com/eqiihuu/EE512

75b38842979aaae2b90e218a57eaa0c97a7b7183

0ce92f60f6e2a7e212ced7fc30bf988d81faf223

refs/heads/master

2021-08-28T03:34:42.352695

2017-12-05T11:45:11

113,174,681

0

null

import exact_inference as EI from read_data import read_uai #from node_clique import Node, Clique, Graph, Junction_tree_cloud graph = read_uai("../data/test.uai") for clique in graph.cliques: print("-------------------") for node in clique.nodes: print(node.index) EI.min_fill(graph) for node in graph.nodes: print(node.get_neighbor_index()) max_cliques = EI.MSC(graph) for clique in max_cliques: print("------------------") for node in clique: print(node.index) clouds = EI.generate_junction_tree(max_cliques) EI.rotate_axis(max_cliques[0], graph.cliques[9])

UTF-8

Python

false

602

py

5

test.py

4

0.652824

0.649502

0

24

24.083333

65

beverast/LS-DS-Unit-CS

4,947,802,351,867

9ae6cda83b6790e6395ad57070494ffb32de8775

623fa21c3e24373d3e90f7f40a2cef983a32a6b5

/Whiteboard-Lectures/skip_list.py

8df9b745930cc074f51c86e20fb22f18b3678bd1

[]

no_license

https://github.com/beverast/LS-DS-Unit-CS

4c138ba427c87e9ef753512362d8814acc6aba16

e0fb759da5ffa49aa5a04e267bb150d00db119e2

refs/heads/master

2023-05-29T22:47:10.151353

2020-05-16T19:00:58

264,509,235

0

null

# Skip list: probabilistic data structure based on the Linked list # Description of a skip list: https://brilliant.org/wiki/skip-lists/ class SkipNode(object): def __init__(self, elem=None): self.elem = elem # 'Square' representation (not list, station-like representation) self.prev = None self.next = None self.below = None self.above = None class SkipList(object): def __init__(self, max_height): # Should have pointers to every single station in first node self.max_height = max_height self.head = [SkipNode() for i in range(max_height)] if len(self.head) > 1: # Connect all nodes, loop through all nodes in head for i in range(max_height): # For the ith node, connect it's 'below' to the i-1 node if i == 0: self.head[i].above = self.head[i + 1] elif i == len(self.head) - 1: self.head[i].below = self.head[i - 1] else: self.head[i].above = self.head[i + 1] self.head[i].below = self.head[i - 1] def search(self, target): ''' Starts at the top node of the head If the next node along this line > target: Move down a layer, check next node along line again If the next node along line <= target: Move right to next node Stop criteria: target is found ''' # Start at the last node in self.head start = self.head[-1] # If below exists go there while start.below: current = start.below # Go right if next < target while target >= current.next: current = current.next return current def insert(self, val): # Search the skip list for the largest value < val # Assuming the value doesn't exist in the list # Create a new node with the val and connect it to the found node # Add layers to this new node by flipping coins to determine # of layers # Connect layers to nodes in the same layers pass

UTF-8

Python

false

2,195

py

20

skip_list.py

17

0.560364

0.556264

0

61

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abhusnurmath/rando

15,470,472,227,060

32000b9ec633b8ae10abff2854ce83813fb4b0ac

a6df3ca677cb301ae67220d1a2bec37f5045d270

/cit590Examples/fancySort.py

501c66aca4a36a1659d160958626deaf33bbe827

[]

no_license

https://github.com/abhusnurmath/rando

53c6fdf5ef8ecbe8440c09acb79636db7f0bbd42

84260a35399d983ed0154a5e004cadd970d2be6a

refs/heads/master

2020-05-19T11:07:09.372441

2015-02-21T01:35:25

2,403,652

7

10

null

false

2015-01-25T02:02:12

2011-09-17T05:34:24

2015-01-24T02:43:10

651

14

1

Java

null

def compareBasedOnLastComponent(x,y): if x[-1] < y[-1]: return -1 elif x[-1] == y[-1]: return 0 return 1 data = [['arv','Penn', 33], ['fed', 'West Point', 45], ['Ben', 'Cornell', 25], ['Steven', 'UVA', 24]]

UTF-8

Python

false

233

py

153

fancySort.py

153

0.502146

0.437768

0

7

31.571429

54

santiago-dc/MyLearningPath

18,030,272,713,282

7f502f1af7de2e13253bdc93805f3e428b45e9dd

acc7d1568e286afeae2841b218761943ae9e21ba

/skin_cancer/.ipynb_checkpoints/CNN_dataset2-checkpoint.py

cca594931a63a4d3befe3df9b594070cc259510c

[]

no_license

https://github.com/santiago-dc/MyLearningPath

b0b826397b185855492fdbf793cc26232dfb06d8

414809a1770cc58d66c132bafe4eb2c75e83e99d

refs/heads/master

2021-09-11T12:05:17.503457

2021-09-07T17:03:26

241,696,158

1

0

null

import numpy as np import pandas as pd from PIL import Image import os import glob import matplotlib.pyplot as plt from keras.utils.np_utils import to_categorical import tensorflow as tf np.random.seed(5) benign_train_folder = 'data/melanoma/DermMel/train/NotMelanoma' malignant_train_folder = 'data/melanoma/DermMel/train/Melanoma' benign_test_folder = 'data/melanoma/DermMel/test/NotMelanoma' malignant_test_folder = 'data/melanoma/DermMel/test/Melanoma' def read(folder_path): data_path = os.path.join(folder_path,'*jpg') folder = glob.glob(data_path) matrix = [] for f in folder: img = np.asarray(Image.open(f).convert("RGB")) matrix.append(img) matrix = np.asarray(matrix) return matrix #Create data X_benign_train = read(benign_train_folder) X_malignant_train = read(malignant_train_folder) X_benign_test = read(benign_test_folder) X_malignant_test = read(malignant_test_folder) #Create labels Y_benign_train = np.zeros(X_benign_train.shape[0]) Y_malignant_train = np.ones(X_malignant_train.shape[0]) Y_benign_test = np.zeros(X_benign_test.shape[0]) Y_malignant_test = np.ones(X_malignant_test.shape[0]) #Merge and Shuffle data X_train = np.concatenate((X_benign_train, X_malignant_train), axis = 0) Y_train = np.concatenate((Y_benign_train, Y_malignant_train), axis = 0) # s = np.arange(X_train.shape[0]) # np.random.shuffle(s) # X_train = X_train[s] # Y_train = Y_train[s] # train_dataset = tf.data.Dataset.from_tensor_slices((X_train, Y_train)) # train_dataset = train_dataset.shuffle(buffer_size=6000).batch(10) X_test = np.concatenate((X_benign_test, X_malignant_test), axis = 0) Y_test = np.concatenate((Y_benign_test, Y_malignant_test), axis = 0) # s = np.arange(X_test.shape[0]) # np.random.shuffle(s) # X_test = X_test[s] # Y_test = Y_test[s] # test_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test)) # test_dataset = test_dataset.batch(10) # X =np.concatenate((X_train, X_test), axis = 0) # Y =np.concatenate((Y_train, Y_test), axis = 0) # s = np.arange(X.shape[0]) # np.random.shuffle(s) # X = X[s] # Y = Y[s] # X_train = X[0:3500]#2637 # X_test = X[3500:] # Y_train = Y[0:3500] # Y_test = Y[3500:] # Display first 15 images of moles, and how they are classified # w=40 # h=30 # fig=plt.figure(figsize=(12, 8)) # columns = 5 # rows = 3 # for i in range(1, columns*rows +1): # ax = fig.add_subplot(rows, columns, i) # if Y_train[i] == 0: # ax.title.set_text('Benign') # else: # ax.title.set_text('Malignant') # plt.imshow(X_train[i],interpolation='nearest') # plt.show() #Turn labels into one hot encoding (ya veremos si esto hace falta o si lo dejo) Y_train = to_categorical(Y_train, num_classes= 2) Y_test = to_categorical(Y_test, num_classes= 2) # Normalization # X_train = X_train/255. # X_test = X_test/255. #Model from keras.models import Sequential from keras import regularizers from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D, AveragePooling2D, BatchNormalization, Dropout model = Sequential() model.add(BatchNormalization()) model.add(Conv2D(3, kernel_size=10 ,padding='same', strides=2, activation='relu', input_shape=(450,600,3))) model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding='same', data_format=None)) #model.add(BatchNormalization()) #model.add(Dropout(0.25)) # model.add(Conv2D(3, kernel_size=3,padding='same', strides=2, activation='relu')) # model.add(MaxPooling2D(pool_size=(2, 2), strides=2, padding='same', data_format=None)) #model.add(BatchNormalization()) #model.add(Dropout(0.25)) # model.add(Flatten()) # #model.add(Dropout(0.25)) # model.add(Dense(450, activation='relu',kernel_regularizer=regularizers.l2(0.01))) # #model.add(Dropout(0.25)) # model.add(Dense(450, activation='relu',kernel_regularizer=regularizers.l2(0.01))) # # model.add(Dropout(0.25)) # model.add(Dense(200, activation='relu',kernel_regularizer=regularizers.l2(0.01))) # #model.add(Dropout(0.25)) # model.add(Dense(100, activation='relu',kernel_regularizer=regularizers.l2(0.01))) # #model.add(Dropout(0.25)) # model.add(Dense(30, activation='relu',kernel_regularizer=regularizers.l2(0.01))) # model.add(Dense(2, activation='softmax')) model.add(Flatten()) # model.add(Dense(450, activation='relu')) # model.add(Dropout(0.25)) model.add(Dense(450, activation='relu')) #model.add(Dropout(0.25)) model.add(Dense(200, activation='relu')) #model.add(Dropout(0.25)) model.add(Dense(100, activation='relu')) #model.add(Dropout(0.25)) model.add(Dense(30, activation='relu')) model.add(Dense(2, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(train_dataset, validation_data=test_dataset, batch_size=10, epochs=10) test_loss, test_acc = model.evaluate(test_dataset, batch_size=10) print('Test Acc: ', test_acc) print(':)')

UTF-8

Python

false

4,839

py

46

CNN_dataset2-checkpoint.py

6

0.704691

0.66708

0

145

32.372414

108

yamini94/imdb

6,975,026,891,233

793e3b215c83227c4228f998ce76ffb8a07721c4

f7ea2b73e87508ae9a145d79752d0251c362c6ab

/movie/documents.py

c9780389bc7a1494611c42797a2e9cd99ae1e506

[]

no_license

https://github.com/yamini94/imdb

ec730d4a04c266e71ca8d9e01edffe99506daef4

66e341675f5c1976d9f44378be179751e9169d98

refs/heads/master

2022-12-10T10:17:11.509859

2020-08-28T03:11:20

290,918,417

0

null

from django_elasticsearch_dsl import Document from django_elasticsearch_dsl.registries import registry from .models import Imdb,Genre from elasticsearch_dsl import Index from django_elasticsearch_dsl import Document, fields # @registry.register_document imdb_index = Index('imdb') @registry.register_document @imdb_index.document class ImdbDocument(Document): genre = fields.NestedField(properties={ 'name': fields.TextField() }) class Django: model = Imdb related_models = [Genre] fields = [ 'id', 'name', 'popularity', 'director', 'imdb_score', 'created_date', 'modified_date' ] def get_instances_from_related(self, related_instance): return related_instance.imdb_set.all() # class Index: # # Index name # name = 'imdb' # settings = {'number_of_shards': 1, # 'number_of_replicas': 0} # class Django: # model = Imdb # django model name # # Model fields to display # fields = [ # 'name', # 'popularity', # 'director', # 'imdb_score', # 'genre', # 'created_date', # 'modified_date' # ]

UTF-8

Python

false

1,148

py

15

documents.py

13

0.621951

0.620209

0

51

21.470588

56

ChinaChenp/Knowledge

4,956,392,282,476

7adb242787d6d0c807ff3953c1a387a2b2def07f

5e3fb85de7caa7e9529114e97a992f1f88067d6c

/interview/interview_python/jianzhioffer2/16.py

02935955e68e34bcc942e9fdd00ea01bbb4d38d5

[]

no_license

https://github.com/ChinaChenp/Knowledge

ad3f0b290fadf18b1a4be11246041efa81736df7

64bb5286856ad5c01e682fc5e93477c6aa7299b9

refs/heads/master

2021-06-05T03:13:32.071326

2019-12-25T10:45:51

45,509,734

0

null

# 数的N次方 def Power(base, exp): # 0的n次方没有意义，0的-n求倒数会出错 if base >= -1e-9 and base <= 1e-9 and exp < 0: return 0.0 # 次方为负数要用正数n次方求倒数 abs_exp = abs(exp) # 计算次方 re = 1.0 for _ in range(0, abs_exp): re *= base # 次方为负数要求倒数 if exp < 0: return 1.0 / re return re print(Power(2, 3)) print(Power(1, 2)) print(Power(2, -3)) print(Power(-2, -3)) print(Power(0, -3))

UTF-8

Python

false

522

py

368

16.py

300

0.518605

0.460465

0

26

15.538462

50

tomaxh/todo

10,161,892,646,172

2a3a550063bb199e8a875e22dcb7ec4a8277ba4c

be00691fd152502981c6a221d4636db0a0b93e7e

/backend/src/articles/admin.py

b563cb1b766d3b4bde410aa1420b0957a2f97342

[]

no_license

https://github.com/tomaxh/todo

8ca34e9200e114753a671ea391c3d7ee0dfdb71a

2c04aa272be6c3cd56bcd7edd8be287a01c31c12

refs/heads/master

2020-05-18T11:05:47.890825

2019-05-15T22:09:57

184,369,447

0

null

from django.contrib import admin from .models import Todo # Register your models he admin.site.register(Todo)

UTF-8

Python

false

111

py

16

admin.py

12

0.801802

0

5

21.4

32

codingEnzo/YunfangWork

171,798,741,664

bd47edb4791d1e62727386f35abfe4ff2434c2d0

d7ade211dd818f9653c5a1de6edba343dff1514c

/developer_data_push/start_task.py

288489272d9ec9b801cbf338d851d32e8e420703

[]

no_license

https://github.com/codingEnzo/YunfangWork

ee6382feedd663d1906ff54f41988df814395444

da6c3b622ea10e28802bcdead01d422a90784cb5

refs/heads/master

2017-09-10T02:45:10.013828

2017-08-25T03:36:44

81,432,516

0

1

null

false

2017-03-23T12:29:25

2017-02-09T09:22:26

2017-02-09T09:49:47

2017-03-23T12:29:25

458

0

1

0

HTML

null

#coding=utf-8 from __future__ import absolute_import import sqlalchemy import __builtin__ from db_orm import data_handle_instance,db_instance from celery import Celery from settings import * from sqlalchemy import * from sqlalchemy.orm import * from sqlalchemy.ext.declarative import declarative_base from task_env import app def _init(): celery = Celery(broker="redis://192.168.6.34/2",backend="redis://192.168.6.34/3") celery.conf.broker_transport_options = {'visibility_timeout': 240000} celery.conf.worker_prefetch_multiplier = 1 try: _mysql_format = "mysql+pymysql://{db_user_name}:{db_passwd}@{db_address}:{db_port}/{db_name}?connect_timeout=1800&charset=utf8" _mysql_source_path = _mysql_format.format(db_user_name=db_user_name, db_passwd=db_passwd, db_address=db_source, db_port=db_port, db_name=db_source_name) source_engine = create_engine(_mysql_source_path,echo=False) source_conn = source_engine.connect() source_metadata = MetaData(source_engine) source_Session = sessionmaker(bind=source_engine) source_session = source_Session(bind=source_conn) try: source_Base = declarative_base(metadata=source_metadata) source_Base.metadata.reflect(source_engine, only=[db_source_schema,]) schema_instance = source_Base.metadata.tables[db_source_schema] source_schema = __builtin__.type('source_schema',(source_Base,),{'__table__':schema_instance,'__mapper_args__':{'primary_key':[schema_instance.c.TID,]}}) return source_session,source_schema,celery except Exception as e: print Exception,":",e if isinstance(e,sqlalchemy.exc.NoSuchTableError): print "Cannot find the table." return else: return except Exception as e: print Exception,":",e return @app.task(bind=True) def begin_task(self): source_session,source_schema,celery = _init() source_max_id = _get_source_max_id() source_min_id = _get_source_min_id() _reset_table() source_begin_id = source_min_id while True: if source_begin_id+db_source_step >= source_max_id: celery.send_task("start_task.create_new_task", args=[source_begin_id,source_max_id],queue="develop_task") break else: celery.send_task("start_task.create_new_task", args=[source_begin_id,source_begin_id+db_source_step],queue="develop_task") source_begin_id += db_source_step @app.task(bind=True) def create_new_task(self,begin_num,end_num): source_session,source_schema,celery = _init() pdi = data_handle_instance.pandas_instance(dbUserName=db_user_name, dbPasswd=db_passwd, dbAddress=db_source, dbPort=db_port, schema_list=[db_source_schema,], dbName=db_source_name) query_sql_for_new = 'select * from %s where TID >= %s and TID< %s '%(db_source_schema,begin_num,end_num) pdi.create_dataframe('df_for_new', query_sql_for_new.decode('utf-8')) pdi.create_task_new(pdi.df_for_new) def _reset_table(table_name="house_count"): reset_db = db_instance.mysql_DB_instance(dbUserName='root', dbPasswd='', dbAddress='192.168.6.8', dbName='develop_data') reset_db.conn.execute('truncate {table}'.format(table=table_name)) def _get_source_max_id(): source_session,source_schema,celery = _init() res = source_session.query(func.max(source_schema.TID).label('max_id')).all()[0].max_id print "max id is : ",res return res def _get_source_min_id(): source_session,source_schema,celery = _init() res = source_session.query(func.min(source_schema.TID).label('min_id')).all()[0].min_id print "min id is : ",res return res

UTF-8

Python

false

4,317

py

233

start_task.py

209

0.576789

0.566597

0

103

40.912621

165

HPCD/room-number-ocr

3,272,765,127,997

918b26caaf578aead2bee87ced834a314b678b92

039370bec9ba63a49bc48d3f7e11463dc10da032

/testpost.py

468fb715578abaea06f6067cbe13989d741e23b9

[]

no_license

https://github.com/HPCD/room-number-ocr

96660b72e2a83a773de198de25d41ea0393c6fca

d7548c52a83351f8b056ae43e8459a70bf32b962

refs/heads/master

2023-03-24T22:43:20.236234

2021-03-19T03:08:26

349,283,348

0

null

#!/usr/bin/env python #-*- coding:utf-8 -*- """ @author:abner @file:testpost.py @ datetime:2020/8/26 16:45 @software: PyCharm """ import cv2 import base64 import requests import unittest from datetime import datetime import json import os #内部测试地址 in_url_ip = "http://10.74.150.75:32666" # 测试环境外部访问地址 # out_url_ip = "http://ai.gdii-yueyun.com:30067/" # out_url_ip ="http://10.16.153.31:32488/" out_url_ip = "http://ai.gdii-yueyun.com:30092" docker_ip = "http://10.16.153.36:32088/" ip = "http://10.200.117.108:32088/" class DriverLicenseRequestUnitTest(unittest.TestCase): def test_roomnum_ocr(self): """ 车牌号码识别测试样例 """ car_img_path = 'D:\\abner\\project\\dataset\\room\\43.jpg' img = cv2.imread(car_img_path) img_encode = cv2.imencode('.jpg', img)[1] byte_image = str(base64.b64encode(img_encode))[2:-1] # print(byte_image) #post请求 req = {"user_name": "yueyun", "device_id": "A1010", "image": byte_image} print(req) start_time = datetime.now() in_url = docker_ip+"/ai/room/ocr" in_url = "https://open-api.gdii-yueyun.com/ai/room/ocr" print("dfff") TOKEN = requests.post('https://open-api.gdii-yueyun.com/login', json={"username": "AI", "password": "123456"},verify=True).json().get('data') print("请求TOKEN！！") headers = {"AG-X-Access-Token": TOKEN, "Content-Type": 'application/json'} print(TOKEN) res = requests.post(in_url,headers=headers, json=req) result = json.loads(res.text) print(result) if __name__ == "__main__": unittest.main()

UTF-8

Python

false

1,737

py

22

testpost.py

14

0.591751

0.529588

0

69

23.246377

107

Ysnsn/python

7,404,523,624,478

373ff21a836a36261f802c2e6c6e9419db7b8e7e

f89a96461b24dd448007ef221926dd83f5c54562

/基本.py

82af16231743c1b7f42a53d88404c9883d7e0bea

[]

no_license

https://github.com/Ysnsn/python

4b34e96edbd21dfc255f3fcb0256cc23048dc994

ccf6d4f3510d54724c7571ab22bd423d1e7c2d27

refs/heads/master

2023-01-04T13:28:24.013264

2020-08-04T02:59:47

280,341,353

1

0

null

ls=[7,4,5,2,3,5,6] print("列表为",end="") print(ls) print("列表长度为{}".format(len(ls))) print("列表中有{}个5".format(ls.count(5))) print(ls[1:5:2]) new=eval(input("请输入一个新的元素:")) ls.append(new) ls[3:5]=[11,12,13] print("列表为",end="") print(ls) print("列表中的最大值为:{}".format(max(ls))) print("列表中的最小值为:{}".format(min(ls))) n=eval(input("请输入要删除的元素符号:")) del ls[n] print("列表为",end="") print(ls) ls=sorted(ls,reverse=True) print(ls) ls=sorted(ls,reverse=False) print(ls)

UTF-8

Python

false

568

py

36

基本.py

33

0.639738

0.59607

0

29

14.793103

37

damc/TextEditor

13,520,557,082,282

3bed9bc228d353edbf82aad2eb18ac1655f4615f

bfdf5da40942676f17300f48ece3348cf50a67dd

/text_editor/__init__.py

f903ce9bdaf80bda6f5eb89ebc0b7188a416e065

[]

no_license

https://github.com/damc/TextEditor

c81209163a09900e14ab33065cc03391a4cf3ab3

5801b09f490a2845f9ffcb67beb8fc2a39d19e94

refs/heads/master

2023-07-03T17:58:07.829734

2021-02-19T13:58:29

395,630,579

0

null

from .text_editor import TextEditor

UTF-8

Python

false

35

py

6

__init__.py

5

0.857143

0

1

35

OzupeSir/credit_card

5,944,234,746,840

3ac2be34527b4b5a79671f5f93fcf151ce32b512

74ddde6c4d75e82761551190b3347bca38f9a90f

/credit_card/auto_bin.py

9c4fe036c94e346563f99ed96a5a758b55034ea2

[]

no_license

https://github.com/OzupeSir/credit_card

551b5d1e84ce5ae9f7d89ac192a2024705f1bbcd

c7ff733629164d8050c286f3ba95cbfc031fe171

refs/heads/master

2020-07-26T20:56:23.394379

2019-09-25T12:08:56

208,763,691

1

0

null

# utf8 # Hsiaofei Tsien import numpy as np import pandas as pd import scipy class AutoBins: def __init__(self, frame, y): self._frame = frame.copy() self._y = y def _column_qcut(self, column,q): # 进行初始化分箱，先分成 20 个箱体 _drop, bins = pd.qcut(self._frame[column], q=q, retbins=True, duplicates="drop") # 排除因为开闭区间导致的临界值取值问题 bins = list(bins) bins.insert(0, -float("inf")) bins.append(float("inf")) # 按照添加了最大最小值后的箱体重新分箱 self._frame[column+"_qcut"] = pd.cut(self._frame[column], bins=bins) init_counts = list(self._frame[column+"_qcut"].value_counts().sort_index()) # 查看首尾箱体是否占比超过 2%，如果没有那么将之与相邻的箱体进行合并，用于处理添加 inf 后导致的空字段的问题 if init_counts[0] < (len(self._frame)/50): bins.pop(1) if init_counts[-1] < (len(self._frame)/50): bins.pop(-2) # 使用这个箱体的数据进行分箱并做后续的最优化合并 self._frame[column+"_qcut"] = pd.cut(self._frame[column], bins=bins) # 统计每个分段 0，1的数量 inf_init_bins = self._frame.groupby([column+"_qcut", self._y])[self._y].count().unstack() # num_bins值分别为每个区间的上界，下界，0的频次，1的频次 num_bins = [*zip(bins, bins[1:], inf_init_bins[0], inf_init_bins[1])] return num_bins def _merge_zero_bins(self,num_bins): # 用于确保所有的分组均包含两种分类 while 0 in num_bins[0][2:]: # 如果是第一个组某个分类为 0，向后合并 num_bins =self._merger_bins(num_bins,0) idx=1 while idx<len(num_bins): # 如果后面的组某个分类为 0 ，向前合并，合并后 num_bins 变短 # 所以需要继续查看当前的 idx 的位置 if 0 in num_bins[idx][2:]: num_bins =self._merger_bins(num_bins, idx-1) else: # 如果没有出现某个分类统计为 0 ，查看下一个 idx 的位置 idx += 1 return num_bins def _merger_bins(self, num_bins, x): # 合并 num_bins x 索引和 x+1 索引的分组数据 num_bins[x: x+2] = [( num_bins[x][0], num_bins[x+1][1], num_bins[x][2]+num_bins[x+1][2], num_bins[x][3]+num_bins[x+1][3] )] return num_bins # 创建计算 iv 值函数 def _get_iv(self, woe_df): rate = ((woe_df.count_0/woe_df.count_0.sum()) - (woe_df.count_1/woe_df.count_1.sum())) iv = np.sum(rate * woe_df.woe) return iv # 定义计算 woe 的函数 def _get_woe(self, num_bins): # 通过 num_bins 数据计算 woe columns = ["min", "max", "count_0", "count_1"] df = pd.DataFrame(num_bins, columns=columns) df["total"] = df.count_0 + df.count_1 df["percentage"] = df.total / df.total.sum() df["bad_rate"] = df.count_1 / df.total df["woe"] = np.log( (df.count_0 / df.count_0.sum()) / (df.count_1 / df.count_1.sum()) ) return df def _chi2_merge(self, num_bins): p_values = [] # 获取 num_bins 两两之间的卡方检验的置信度（或卡方值） for i in range(len(num_bins)-1): x1 = num_bins[i][2:] x2 = num_bins[i+1][2:] # 0 返回 chi2 值，1 返回 p 值。 pv = scipy.stats.chi2_contingency([x1, x2])[1] # chi2 = scipy.stats.chi2_contingency([x1, x2])[0] p_values.append(pv) # 通过 p 值进行处理。合并 p 值最大的两组 idx = p_values.index(max(p_values)) num_bins = self._merger_bins(num_bins, idx) return num_bins def auto_bins(self, column, n=2, show_iv=True,q=20): print(f"对 {column} 列进行分箱: ") # 初始化分箱 num_bins = self._column_qcut(column,q) # 合并没有包含两类的分箱 num_bins = self._merge_zero_bins(num_bins) # 通过 chi2_merge 不断合并最相似的相邻箱体 while len(num_bins) > n: num_bins = self._chi2_merge(num_bins) woe_df = self._get_woe(num_bins) iv = self._get_iv(woe_df) if all(woe_df['percentage']>=(1/q)): if show_iv: print(f"分组个数: {len(num_bins):02d} \tiv值: {iv}") print(f"woe情况:\n",woe_df) return num_bins, woe_df,iv # if __name__ == "__main__": # model_data = pd.read_csv("ChiMergeData.csv", encoding="utf8") # bins_data = AutoBins(model_data, "SeriousDlqin2yrs") # bins_data.auto_bins("age", n=4)

UTF-8

Python

false

4,951

py

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auto_bin.py

1

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nayamama/asycio-programming

18,038,862,644,295

09d3e2131b1f4a97e4b8ec8e54b48859755f2a8a

1ab21f43ee50952693c06711df296ebf41ec9699

/thread_programming/os_processes.py

dd6534155f6b05a2aaae41b1903c269c4ea8f53a

[]

no_license

https://github.com/nayamama/asycio-programming

17cc478fda383a7d68212b1095e2cc79d789df03

fdc52a7bab97c95e1b50018ab16f4fc99c263eed

refs/heads/master

2020-05-27T06:27:07.759152

2019-05-25T04:46:54

188,520,641

0

null

from multiprocessing import Process from syc_programming import io_bound, cpu_bound, timeit @timeit() def multiprocessed(n_thread, func, *args): pros = [] for _ in range(n_thread): p = Process(target=func, args=args) pros.append(p) # start the process for p in pros: p.start() # ensure all processes have finished for p in pros: p.join() if __name__ == '__main__': a = 7777 b = 200000 urls = [ "http://google.com", "http://yahoo.com", "http://linkedin.com", "http://facebook.com" ] multiprocessed(10, cpu_bound, a, b) multiprocessed(10, io_bound, urls)

UTF-8

Python

false

669

py

7

os_processes.py

6

0.572496

0.55157

0

30

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mobilase/python

12,678,743,494,967

f4a671d94d5442c60f91271df937eb9f2e0476a4

9c6382dec3012f29019d44619233293fbfc2125c

/08/01.py

69798c23e721d25dc36959a71baab1a9f22f4805

[]

no_license

https://github.com/mobilase/python

7a56dd0d5fc63503aa4bad34d7ec677c5685eed7

5690d924c3ef0d9f0df25d184b1752cdc03399ca

refs/heads/master

2022-04-25T05:09:12.692031

2020-04-28T17:55:30

null

0

null

import threading import time import random def worker(number): sleep = random.randrange(1, 10) time.sleep(sleep) print("Worker {} slept {} seconds\n".format(number, sleep)) for i in range(8): t = threading.Thread(target=worker, args=(i,)) t.start() print("All Treads are in queries\n")

UTF-8

Python

false

311

py

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01.py

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nj7/SecureVault

4,990,752,008,265

36ae039c7bc9a5b1887a6221b2fffdfa4e959282

1abb3698e84c61dfe0d8bac3dfbd29d26e3f1036

/secure.py

876be179ae4417b6e3479407e1168e505ab116b8

[]

no_license

https://github.com/nj7/SecureVault

784f14d8d5bbe81cdfe629decbbf132776ee9a0d

d1d5c6c9567a97c0754eadfc7ea29c7f7ea43be9

refs/heads/master

2021-01-23T00:14:46.576516

2017-03-30T01:12:14

85,706,448

0

null

import shutil import os import batchExecute def main(): """taking user input of view a file or adding a file to vault""" while True: choice = takeInput() if choice == 1: hideFile() elif choice == 2: findFile() else: print "Please Select Correct Output" break def takeInput(): while (True): choice = int(raw_input("Select an option \n1.Hide a file \n2.Unhide a file\n")) if choice == 1 or choice == 2: return choice def hideFile(): while(True): file_path=raw_input("Enter File Path\n") file_name=raw_input("Enter File Name\n") src_of_file = file_path+"\\" src_of_file += file_name print "The Source of File you want to Secure :: "+ src_of_file if raw_input("Do you want to Continue (Y/N)") == 'Y' or 'y': break #secure vault source vault = "nj$SeCureVaUlt@ThIsIsTeStIngPhAse4ME" dierectory = "C:\Secure_nj_project" #creating secure vault for first run if not os.path.exists(dierectory): os.makedirs(dierectory) dierectory += "\\" dierectory += vault if not os.path.exists(dierectory): os.makedirs(dierectory) print "true" # copy file file from location to secure vault shutil.copy2(src_of_file, dierectory) #remove file from location os.remove(src_of_file) """Hiding vault having file""" #creating batch file to hide vault dierectory += "\\batch.bat" batch_file = open(dierectory,"w+") batch_file.write("C:\ncd C:\Secure_nj_project\nattrib +h +r +s "+vault+"\necho y|cacls "+vault+" /p everyone:n") batch_file.close() #executing Batch File batchExecute.open_batch(dierectory) """Hiding Project Folder having vault""" #hiding Vault folder batch_file = open("C:\\System.bat","w+") batch_file.write("C:\nattrib +h +r +s Secure_nj_project\necho y|cacls Secure_nj_project /p everyone:n") batch_file.close() #executing Batch File batchExecute.open_batch("C:\\System.bat") def findFile(): while(True): file_name=raw_input("Enter File Name\n") print "The Source of File you want to find :: "+ file_name if raw_input("Do you want to Continue (Y/N)") == 'Y' or 'y': break #secure vault source vault = "nj$SeCureVaUlt@ThIsIsTeStIngPhAse4ME" dierectory = "C:\Secure_nj_project" #creating secure vault for first run if not os.path.exists(dierectory): os.makedirs(dierectory) if not os.path.exists(dierectory + "\\"+ vault): os.makedirs(dierectory) """UnHiding Project Folder having vault #hiding Vault folder batch_file = open("C:\\System.bat","w+") batch_file.write("C:\necho y|cacls Secure_nj_project /p everyone:f\nattrib -h -r -s Secure_nj_project") batch_file.close() #executing Batch File batchExecute.open_batch("C:\\System.bat")""" """UnHiding vault having file""" #creating batch file to hide vault batch_file = open(dierectory + "\\batch.bat","w+") batch_file.write("C:\ncd C:\Secure_nj_project\necho y|cacls "+vault+" /p everyone:f\nattrib -h -r -s "+vault+"\n") batch_file.close() #executing Batch File batchExecute.open_batch(dierectory + "\\batch.bat") for file in os.listdir(dierectory +"\\"+vault): if file == file_name: shutil.copy2(dierectory +"\\"+vault + "\\" + file, "C:\\Users\\nirmit\\Desktop") if __name__ =='__main__': main()

UTF-8

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py

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secure.py

2

0.574828

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0

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118

cassie01/APItesting

14,113,262,579,406

a6e79ea33c785ee83636fe603f5d08e249ca252c

1ae32e324de7e8712b4cde4aaa0a6901fa293301

/before/010.py

64279bab200e84ab76c150ef18e8debca027f161

[]

no_license

https://github.com/cassie01/APItesting

bfd78422aeebcf5a1031ce4357309df3b99e4979

812cff983bc59df3d34e0fd5d2f4229c82cf90ab

refs/heads/master

2020-04-18T07:33:48.901876

2019-01-24T13:47:34

167,364,372

0

null

from selenium import webdriver import unittest import time from selenium.webdriver.common.action_chains import ActionChains class Cnode(unittest.TestCase): def setUp(self): self.Url = 'http://39.107.96.138:3000' self.driver = webdriver.Chrome() self.driver.get(self.Url) #登陆用户 self.driver.find_element_by_css_selector('a[href = "/signin"]').click() self.driver.find_element_by_id('name').send_keys('user1') self.driver.find_element_by_id('pass').send_keys('123456') self.driver.find_element_by_css_selector('input[type = "submit"]').click() def test_post_topic(self): driver = self.driver driver.get('http://39.107.96.138:3000/topic/create') driver.find_element_by_name('tab').click() driver.find_element_by_css_selector('[value="share"]').click() driver.find_element_by_id('title').send_keys('金丝熊000001新熊发帖') content_area = driver.find_element_by_class_name('CodeMirror-scroll') content_area.click() ActionChains(driver).move_to_element(content_area).send_keys('可乐饲养员测试').perform() driver.find_element_by_css_selector('input[type="submit"]').click() def tearDown(self): self.driver.save_screenshot('./01.png') self.driver.quit() if __name__ == "__main__": unittest.main()

UTF-8

Python

false

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py

24

010.py

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0.643755

0.611973

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89

lbgbox/DenoiSeg

15,607,911,190,206

4e8740ace94520da2010da21043c2db6a84e6fa8

1c6763051234c7e00acc0b209d79f9b3a718605b

/denoiseg/utils/seg_utils.py

e9d7ea553b42c6290dbce8efe7dc309987005c82

[ "BSD-3-Clause" ]

permissive

https://github.com/lbgbox/DenoiSeg

07151d513391bb60e0c21131a244d4ac701e99c0

9803cbcf31c0510d28a9ff43be92b29cd47120a0

refs/heads/master

2023-08-30T18:40:53.591717

2021-11-16T13:53:07

null

0

null

import numpy as np from skimage.segmentation import find_boundaries def convert_to_oneHot(data, eps=1e-8): """ Converts labelled images (`data`) to one-hot encoding. Parameters ---------- data : array(int) Array of lablelled images. Returns ------- data_oneHot : array(int) Array of one-hot encoded images. """ data_oneHot = np.zeros((*data.shape, 3), dtype=np.float32) for i in range(data.shape[0]): data_oneHot[i] = onehot_encoding(add_boundary_label(data[i].astype(np.int32))) if ( np.abs(np.max(data[i])) <= eps ): data_oneHot[i][...,0] *= 0 return data_oneHot def add_boundary_label(lbl, dtype=np.uint16): """ Find boundary labels for a labelled image. Parameters ---------- lbl : array(int) lbl is an integer label image (not binarized). Returns ------- res : array(int) res is an integer label image with boundary encoded as 2. """ b = find_boundaries(lbl, mode='outer') res = (lbl > 0).astype(dtype) res[b] = 2 return res def onehot_encoding(lbl, n_classes=3, dtype=np.uint32): """ n_classes will be determined by max lbl value if its value is None """ onehot = np.zeros((*lbl.shape, n_classes), dtype=dtype) for i in range(n_classes): onehot[lbl == i, ..., i] = 1 return onehot def normalize(img, mean, std): """ Mean-Std Normalization. Parameters ---------- img : array(float) Array of source images. mean : float mean intensity of images. std: float standard deviation of intensity of images. Returns ------- (img - mean)/std: array(float) Normalized images """ return (img - mean) / std def denormalize(img, mean, std): """ Mean-Std De-Normalization. Parameters ---------- img : array(float) Array of source images. mean : float mean intensity of images. std: float standard deviation of intensity of images. Returns ------- img * std + mean: array(float) De-normalized images """ return (img * std) + mean def zero_out_train_data(X_train, Y_train, fraction): """ Fractionates training data according to the specified `fraction`. Parameters ---------- X_train : array(float) Array of source images. Y_train : float Array of label images. fraction: float (between 0 and 100) fraction of training images. Returns ------- X_train : array(float) Fractionated array of source images. Y_train : float Fractionated array of label images. """ train_frac = int(np.round((fraction / 100) * X_train.shape[0])) Y_train[train_frac:] *= 0 return X_train, Y_train

UTF-8

Python

false

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py

54

seg_utils.py

12

0.583864

0.573956

0

118

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huseyinbiyik/repository.boogie

10,874,857,197,365

40d46e33c0091f373300cb8f5ceb5875aa120a26

7c8bfb281103e5b59054ff5a957926cf62a7a896

/service.subtitles.turkcealtyazi/service.py

be80bf22a1cb7dbdf5c1810ed2c14cf3c4f96f06

[]

no_license

https://github.com/huseyinbiyik/repository.boogie

06deaee6b4e641330dc6070bb53529f119c0395a

d73d8baa99f2757e9ad13730c51d9b96b17b6512

refs/heads/master

2021-03-12T19:22:09.903700

2021-02-17T00:07:30

39,784,700

0

null

# -*- coding: utf-8 -*- ''' Author : Huseyin BIYIK <husenbiyik at hotmail> Year : 2016 License : GPL This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. ''' import sublib import htmlement import urlparse import re import os domain = "https://turkcealtyazi.org" quals = { "1": 5, # good quality "2": 4, # enough quality "3": 0, # bad quality "4": 2, # not rated yet "5": 1, # waiting for source "6": 3, # archived } def norm(txt): txt = txt.replace(" ", "") txt = txt.lower() return txt def striphtml(txt): txt = re.sub("<.*?>", "", txt) txt = re.sub("\t", "", txt) txt = re.sub("\n", "", txt) txt = txt.replace(" ", " ") return txt def elementsrc(element, exclude=[]): if element is None: return "" if element in exclude: return "" text = element.text or '' for subelement in element: text += elementsrc(subelement, exclude) text += element.tail or '' return striphtml(text) class turkcealtyazi(sublib.service): def checkrecaptcha(self): resp = self.request(domain, method="HEAD", text=False) if not resp.status_code == 200: self.request(domain) def search(self): self.checkrecaptcha() self.found = False self.ignoreyear = False if self.item.imdb: self.find(self.item.imdb) if not self.num() and not self.item.show and self.item.year: self.find("%s %s" % (self.item.title, self.item.year)) if not self.num(): self.ignoreyear = True self.find(self.item.title) def checkpriority(self, txt): # this is a very complicated and fuzzy string work txt = txt.lower().replace(" ", "") cd = re.search("([0-9])cd", txt) # less the number of cds higher the priority if cd: return False, - int(cd.group(1)) # rest is for episodes, if movie then return lowest prio. if self.item.episode < 0 or not self.item.show: return False, -100 ispack = 0 packmatch = 0 epmatch = 0 skip = False se = re.search("s(.+?)\|e(.+)", txt) if not se: se = re.search("s(.+?)(paket)", txt) if se: e = se.group(2) s = se.group(1) # verify season match first if s.isdigit() and self.item.season > 0 and \ not self.item.season == int(s): return True, 0 ismultiple = False # e: 1,2,3,4 ... for m in e.split(","): if m.strip().isdigit(): ismultiple = True else: ismultiple = False break if ismultiple: # check if in range multiples = [int(x) for x in e.split(",")] if self.item.episode in multiples: packmatch = 2 else: skip = True # e: 1~4 if "~" in e: startend = e.split("~") # check if in range if len(startend) == 2 and \ startend[0].strip().isdigit() and \ startend[1].strip().isdigit(): if int(startend[0]) < self.item.episode and \ int(startend[1]) > self.item.episode: packmatch = 2 else: skip = True else: ispack = 1 # e: Paket meaning a package if e == "paket": ispack = 1 # e:1 or e:01 if e.isdigit(): if int(e) == self.item.episode: epmatch = 3 else: skip = True return skip, ispack + epmatch + packmatch def scraperesults(self, page, tree, query=None): for row in tree.findall(".//div[@class='nblock']/div/div[2]"): a = row.find(".//a") if a is None: continue link = a.get("href") name = a.get("title") years = row.findall(".//span") if len(years) > 1: ryear = re.search("([0-9]{4})", years[1].text) if ryear: year = int(ryear.group(1)) if len(years) <= 1 or not ryear: year = "-1" if norm(name) == norm(self.item.title) and \ (self.item.show or (self.ignoreyear or self.item.year is None or self.item.year == year)): self.found = True p = self.request(domain + link, referer=domain) e = htmlement.fromstring(p) self.scrapepage(p, e) break if query and not self.found: pages = tree.findall(".//div[@class='pagin']/a") for page in pages: if "sonra" in page.text.lower(): if self.found: break query = dict(urlparse.parse_qsl(urlparse.urlparse(page.get("href")).query)) self.scraperesults(self.request(domain + "/find.php", query, referer=domain)) def scrapepage(self, page, tree): subs = tree.findall(".//div[@id='altyazilar']/div/div") for s in subs: desc = s.find(".//div[@class='ripdiv']") xname = s.find(".//div[@class='fl']/a") alcd = s.find(".//div[@class='alcd']") if xname is None: continue if alcd is None: continue if desc is None: continue alcd = elementsrc(alcd) name = xname.get("title") link = xname.get("href") desc = elementsrc(desc) skip, priority = self.checkpriority(alcd) if skip: continue tran = elementsrc(s.find(".//div[@class='alcevirmen']/a")) iso = "tr" qualrate = "4" aldil = s.find(".//div[@class='aldil']/span") if aldil is not None: cls = aldil.get("class") riso = re.search('flag([a-z]{2})', cls) if riso is not None: iso = riso.group(1) qual = s.find(".//div[@class='fl']/span") if qual is not None: qual = qual.get("class") if isinstance(qual, (str, unicode)): qual = qual.replace("kal", "") if qual.isdigit(): qualrate = qual namestr = "%s, %s, %s, %s" % (name, alcd, desc, tran) sub = self.sub(namestr, iso) sub.download(domain + link) sub.priority = priority if qual: sub.rating = quals[qualrate] self.addsub(sub) def find(self, query): q = {"cat": "sub", "find": query} page = self.request(domain + "/find.php", q, referer=domain) tree = htmlement.fromstring(page) title = tree.find(".//title") if "arama" in title.text.lower(): self.scraperesults(page, tree, q) else: self.scrapepage(page, tree) def download(self, link): page = self.request(link, referer=domain) tree = htmlement.fromstring(page) idid = tree.find(".//input[@name='idid']").get("value") alid = tree.find(".//input[@name='altid']").get("value") sdid = tree.find(".//input[@name='sidid']").get("value") data = { "idid": idid, "altid": alid, "sidid": sdid } remfile = self.request(domain + "/ind", data=data, referer=link, method="POST", text=False) fname = remfile.headers["Content-Disposition"] fname = re.search('filename=(.*)', fname) fname = fname.group(1) fname = os.path.join(self.path, fname) with open(fname, "wb") as f: f.write(remfile.content) self.addfile(fname)

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Python

false

9,071

py

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service.py

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0.474589

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0

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nehaiyer/nlu-assignment1

5,162,550,735,791

54b955514797e444271e1aa95135d658d5a4270a

89bc5fa4d267fbd796c88c6e60550d9e4c68c0e5

/S1_bigram.py

8e6198437565e82f98f181bb06e76de0177940b1

[]

no_license

https://github.com/nehaiyer/nlu-assignment1

8e373deee32f16880141268cbe55633017d8dcc4

d95e79eb705037a67e8d127e7d27a97d46ab1a68

refs/heads/master

2021-01-24T13:01:12.102595

2018-03-04T09:14:41

123,161,929

0

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Feb 27 17:14:30 2018 @author: neha """ import nltk from nltk.corpus import gutenberg import numpy as np import string from nltk.corpus import brown import random def load(): train=[] test=[] for c in brown.categories(): sent=brown.sents(categories=c) s=[] for str1 in sent: s.append(str1) str2=[] for i in s: str2.append(' '.join(i)) str3='' for i in str2: str3= str3+ ' <s> '+ i punctuation={'`','\''} for c in punctuation: str3= str3.replace(c,"") str3=' '.join(str3.split()) # str3 = '<s> The Fulton County Grand Jury said Friday an investigation of Atlantas recent primary election produced no evidence that any irregularities took place . <s> The jury further said in term-end presentments that the City Executive Committee , which had over-all charge of the election , deserves the praise and thanks of the City of Atlanta for the manner in which the election was conducted . <s> The September-October term jury had been charged by Fulton Superior Court Judge Durwood Pye to investigate reports of possible irregularities in the hard-fought primary which was won by Mayor-nominate Ivan Allen Jr. .' words = str3.split(' ') train.append(words[:round(len(words)*0.8)]) test.append(words[-round(len(words)*0.2):]) train = [item for sublist in train for item in sublist] test = [item for sublist in test for item in sublist] return train,test def cal_ngram(train,n): ngrams = {} #n=2 for index, word in enumerate(train): if index < len(train)-(n-1): w=[] for i in range(n): w.append(train[index+i]) ngram = tuple(w) # print(ngram) if ngram in ngrams: ngrams[ ngram ] = ngrams[ ngram ] + 1 else: ngrams[ ngram ] = 1 # sorted_ngrams = sorted(ngrams.items(), key = lambda pair:pair[1], reverse = True) return ngrams def cal_ngram_list(ngrams): ngrams_list=[] for key,value in ngrams.items(): ngrams_list.append(key) return ngrams_list def unknown(unigrams,train): unknown_list=[] for key, value in unigrams.items(): if value < 2: unknown_list.append(key[0]) for index, word in enumerate(unigrams): if train[index] == key[0]: train[index] = '<UKN>' if len(unknown_list)==500: break return train,unknown_list def cal_probab(ngrams,n_1grams,n): prob = {} for key, value in ngrams.items(): n_1key=[] for k in range(0,n-1): n_1key.append(key[k]) prob[key] = value/(n_1grams[tuple(n_1key)]) return prob def cal_unigram_probab(ngrams,N): prob = {} for key, value in ngrams.items(): prob[key] = value/N return prob def check_existence(key,ngram_list,train_prob,n): found=0 nfound=0 alpha=1; t_prob=-1 for i in reversed(range(len(ngram_list))): # print(i) # print(key) # k=[] # k.append(key) if key in ngram_list[i]: prob = train_prob[i] t_prob = alpha*prob[key] # print('break') found=found+1 break else: key=key[i:n] # print(key) alpha=alpha*0.4 if t_prob==-1: # print('unknown') ukn=tuple(['<UKN>']) nfound=nfound+1 prob = train_prob[0] t_prob=alpha*prob[ukn]/0.4 return t_prob def cal_probab_test(tngram,ngram_list,train_prob,n): t_prob=0 for key, value in tngram.items(): # print(key) prob = check_existence(key,ngram_list,train_prob,n) # print(prob) t_prob = t_prob + np.log2(prob) return t_prob def cal_perplexity(test,ngram_list,train_prob,n): tngram={} tngram=cal_ngram(test,n) tN=len(test) tprob=cal_probab_test(tngram,ngram_list,train_prob,n) perplexity=2 ** (tprob*(-1/tN)) return perplexity def init(train,n): N=len(train) unigrams=cal_ngram(train,1) #replace some vocab with <UKN> train,unknown_list = unknown(unigrams,train) #get all ngrams and their counts ngram=[] for i in range(n): # print(i) ngram.append(cal_ngram(train,i+1)) #calculate 1 to n gram's probabilities train_prob=[] train_prob.append(cal_unigram_probab(ngram[0],N)) for i in range(1,n): # print(i) train_prob.append(cal_probab(ngram[i],ngram[i-1],i+1)) #calculate ngram lists ngram_list=[] for i in range(n): ngram_list.append(cal_ngram_list(ngram[i])) return N,n,train,unknown_list,ngram,train_prob,ngram_list train,test=load() n=2 N,n,train,unknown_list,ngram,train_prob,ngram_list=init(train,n) perplexity = cal_perplexity(test,ngram_list,train_prob,n) print('Perplexity: ',perplexity)

UTF-8

Python

false

5,242

py

5

S1_bigram.py

3

0.566387

0.55227

0

194

26.020619

633

bi3mer/qd_dda_level_generation

1,520,418,452,079

d337bcc89aaee4ce804b5d820265f4b2a1c3f9d2

2bb5ec0ff4676d24e9a2fe000afa3a8ecd843c46

/Utility/Icarus/Fitness.py

4200dc6de0b79b3f86a69960a27f0a695e3e71ce

[]

no_license

https://github.com/bi3mer/qd_dda_level_generation

85f2f6030688b19a7c4c40220019009087df0ddc

463ca6335868bd3424ecdb47c79e3120a4f41cee

refs/heads/main

2023-09-06T04:46:40.150802

2021-11-01T17:32:27

327,395,831

0

null

false

2021-05-04T15:35:39

2021-01-06T18:22:08

2021-05-04T14:43:09

2021-05-04T15:35:39

638

0

Python

false

from .SummervilleAgent import percent_completable def build_slow_fitness_function(grammar): def slow_fitness(slices): # at this point, Icarus slices should be rows bad_transitions = grammar.count_bad_n_grams(slices) play_slices = list(slices) # add an area for the player to start at the bottom play_slices.insert(0, '----------------') play_slices.insert(0, '################') # extend the top by copying the blocks # should ensure the player can jump up above the top but not by landing on what was therex play_slices.append(play_slices[-1]) play_slices.append(play_slices[-1]) levelStr = list(reversed(play_slices)) return percent_completable((1, len(play_slices) - 2, -1), levelStr) return slow_fitness

UTF-8

Python

false

818

py

66

Fitness.py

64

0.632029

0.623472

0

22

36.181818

98

hector-han/pythonweb

14,233,521,650,689

d9ead1b89c605b2aa3d65e3553d5383ecfddb877

1330be11e79f58c7d0a8b427546b258a14f0e2d4

/wechat/urls.py

da87f7425470fe10dde0705e3493ec3a08d7d74a

[]

no_license

https://github.com/hector-han/pythonweb

68cca4861e1c911fe7b27193ca7b31f4475aeafb

baf1af5a1207b90e24e7370f4ae27e2b33aaaed5

refs/heads/master

2022-08-08T03:22:53.225781

2022-07-31T09:13:38

156,961,628

0

null

from django.urls import path from . import views urlpatterns = [ path('main', views.wechat_main, name='wechat_main'), ]

UTF-8

Python

false

126

py

7

urls.py

5

0.690476

0

6

19.833333

56

Y0URFR13ND/Hot-Board

16,140,487,140,931

9b39cf0aadef2cd330d39682b7aec030fc57079d

965e75eb296f97ccb0e1dd27bdf323a559b60b11

/Software/pyApplication/vcp_handler.py

7bace7818ffa3bedc2282d0d2b18a0d4bf60574b

[]

no_license

https://github.com/Y0URFR13ND/Hot-Board

8851c038a7921e2c1c35143b368ad1136dd87c36

a943e9d2141f623d358388ce7535276c50303101

refs/heads/master

2023-02-13T03:09:19.257778

2020-12-02T07:27:46

292,227,353

0

null

# ****************************************** # file: vcp_handler.py # project: Hot-Board # author: Nils Jäggi # description: communication with the Hardware itself # ****************************************** import glob import sys import serial # python -m pip install pyserial class VirtualComPort(): def __init__(self,switch_Identifier: str ,number_of_digits: int): # protocoll constants self.numberOfDigits = number_of_digits self.switchIdentifier = switch_Identifier # create vcp object self.ser = serial.Serial() def start_hot_board(self, com: str, baud: int = 115200) -> bool: """ opens the comport :returns: flag if it worked or not """ self.ser.port = com self.ser.baudrate = baud self.ser.timeout = 0 # try opening vcp try: self.ser.open() return True except serial.SerialException: return False def check_switch_state(self) -> int: # read serial port buffer serData = self.ser.read( len(self.switchIdentifier)+self.numberOfDigits ).decode('ascii') # if data is valid if self.switchIdentifier in serData: # get the switch number. can be 1 or more digits retVal = 0 for i in range(self.numberOfDigits): retVal += int( serData[(i+1)*(-1)] ) * pow(10,i) return retVal else: return -1 def get_buffer_value(self): """Returns serial buffer convertet to Ascii""" serData = self.ser.read(256).decode('ascii') return serData def get_available_serial_ports(self) -> list: """ Lists serial port names :raises EnvironmentError: On unsupported or unknown platforms :returns: A list of the serial ports available on the system """ if sys.platform.startswith('win'): ports = ['COM%s' % (i + 1) for i in range(256)] elif sys.platform.startswith('linux') or sys.platform.startswith('cygwin'): # this excludes your current terminal "/dev/tty" ports = glob.glob('/dev/tty[A-Za-z]*') elif sys.platform.startswith('darwin'): ports = glob.glob('/dev/tty.*') else: raise EnvironmentError('Unsupported platform') result = [] for port in ports: try: s = serial.Serial(port) s.close() result.append(port) except (OSError, serial.SerialException): pass return result

UTF-8

Python

false

2,682

py

40

vcp_handler.py

3

0.541589

0.533756

0

79

32.924051

97

techkids-c4e/c4e5

13,262,859,038,959

55bf4c6e128558675ab21db1631f0340875590ee

b9a8164b946f1967e3c2cde0d8c0060ee6cc7366

/Phuong Anh/Lesson 5/L5 - Dictionary Ex.py

33bae5994fcaf48e9e99a90376af9d52314530d1

[]

no_license

https://github.com/techkids-c4e/c4e5

d91bb2b4f791ddd978fc486028373a655a3accd2

83df2b5718192f9340c6b9edeb0b2a7ff0b9e148

refs/heads/master

2020-12-25T10:41:19.485635

2016-12-09T07:09:57

61,489,855

0

4

null

#Wave 1: print('Wave 1:') teacher_list = [{"Name":"Tran Quang Hiep","class":"C4E3","session number":"10","rate":"100","month salary":"1,000"}, {"Name":"Nguyen Quang Huy","class":"C4E1","session number":"10","rate":"200","month salary":"2,000"}, {"Name":"Tran Thu Ha","class":"C4E2","session number":"8","rate":"100","month salary":"800"}, {"Name":"Nguyen Ha San","class":"Android","session number":"5","rate":"200","month salary":"1,000"}, {"Name":"Nguyen Ha San","class":"iOS","session number":"8","rate":"100","month salary":"800"}] for i in teacher_list: print(i["Name"], 'teached:', i["session number"], "salary:", i["month salary"]) #in gia tri trong list theo dang dict. #Wave 2: print('Wave 2:') string = input("Please enter your information (Name, session number, rate): ") a = string.split() b = {"Name":a[0], "session number":a[1], "rate":a[2]} teacher_list.append(b) def data_extract(name, session, salary): print(b["Name"], 'teached:', b["session number"], "salary:", b["rate"]) x = data_extract(b["Name"],b["session number"],b["rate"]) #Wave 5: print("Wave 5") input_name = input("Please enter teacher name: ") def payroll(name): pay_roll = 'name not found' for i in teacher_list: if input_name == i["Name"]: pay_roll = int(i["session number"]) * int(i["rate"]) break return pay_roll print(payroll(input_name))

UTF-8

Python

false

1,498

py

412

L5 - Dictionary Ex.py

336

0.570093

0.533378

0

35

40.914286

122

lithium/django18-template

2,319,282,380,986

46ef9dc0595452f9eb34d3080722f45102fb9d9b

d1ddda48764785f903f1d5883651210091c1cf82

/apps/mainsite/settings/fragments/defaults/email.py

5ab0cc1370a39a1c61336eb87b8eae02283b03e5

[]

no_license

https://github.com/lithium/django18-template

2144d38e310ce4428cb1594c52a1e77ea8156a29

501f2feb2d7d7a233aaa260c95aa8f0f86fd035c

refs/heads/master

2021-01-10T08:10:51.551031

2016-02-12T03:21:48

43,374,235

0

null

### # # Email # ### DEFAULT_FROM_EMAIL = 'webmaster@localhost' EMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'

UTF-8

Python

false

125

py

11

email.py

8

0.712

0

8

14.75

61

Jusan-zyh/zqh_riscv

17,368,847,763,319

a2154f39108f246034898d5cc114a5a96c043f72

b627da650f75bdcf7e0dc0ef5c4419cf53a1d690

/src/zqh_amba/zqh_tilelink_2_axi4_parameters.py

f77b294d9d599fad774644911f57cb5d2f7f40c7

[]

no_license

https://github.com/Jusan-zyh/zqh_riscv

4aa8a4c51e19fb786ba0c2a120722f1382994a52

bccde2f81b42ac258b92c21bb450ec6ff848387a

refs/heads/main

2023-08-06T12:56:52.420302

2021-09-21T01:25:41

null

0

null

import sys import os from phgl_imp import * from zqh_tilelink.zqh_tilelink_atomic_transform_parameters import zqh_tilelink_atomic_transform_parameter class zqh_tilelink_2_axi4_parameter(parameter): def set_par(self): super(zqh_tilelink_2_axi4_parameter, self).set_par() self.par('aw_id_max_num', 1) self.par('ar_id_max_num', 1) self.par('atomic_en', 0) self.par('atomic', zqh_tilelink_atomic_transform_parameter())

UTF-8

Python

false

460

py

295

zqh_tilelink_2_axi4_parameters.py

224

0.691304

0.676087

0

12

37.333333

105

y-oksaku/Competitive-Programming

9,277,129,408,746

040c69e54a888d290388b9e7ace73765d16a77bb

bf99b1b14e9ca1ad40645a7423f23ef32f4a62e6

/AtCoder/abc/007b.py

7e3215a086df3151fe76cfda94be1a3ff43a1176

[]

no_license

https://github.com/y-oksaku/Competitive-Programming

3f9c1953956d1d1dfbf46d5a87b56550ff3ab3db

a3ff52f538329bed034d3008e051f30442aaadae

refs/heads/master

2021-06-11T16:14:12.635947

2021-05-04T08:18:35

188,639,647

0

null

A = input() if len(A) == 1 and A[0] == 'a' : print('-1') else : print('a')

UTF-8

Python

false

83

py

1,459

007b.py

1,456

0.421687

0.385542

0

6

13

32

824zzy/Leetcode

13,769,665,176,327

16663bf9c1b774d4a5853b6f10e9070f8f39bfed

a34ec07c3464369a88e68c9006fa1115f5b61e5f

/K_DynamicProgramming/Knapsack/01-Knapsack/L1_740_Delete_and_Earn.py

1b7edc7382be775a9534f95225eae08cc190fceb

[]

no_license

https://github.com/824zzy/Leetcode

9220f2fb13e03d601d2b471b5cfa0c2364dbdf41

93b7f4448a366a709214c271a570c3399f5fc4d3

refs/heads/master

2023-06-27T02:53:51.812177

2023-06-16T16:25:39

69,733,624

14

3

null

false

2022-05-25T06:48:38

2016-10-01T10:56:07

2022-05-10T23:28:02

2022-05-25T06:48:37

2,707

4

1

0

Python

false

""" https://leetcode.com/problems/delete-and-earn/ 1. use hash table for calculating score and sorted keys as A 2. At i-th number, we can skip it or select it, if we select it then check if next number needs to be deleted. """ class Solution: def deleteAndEarn(self, A: List[int]) -> int: cnt = Counter(A) A = [k for k, v in sorted(cnt.items())] @cache def dp(i): if i==len(A): return 0 ans = dp(i+1) # skip if i<len(A)-1 and A[i]+1==A[i+1]: # select and delete return max(ans, cnt[A[i]]*A[i]+dp(i+2)) else: # select and not delete return max(ans, cnt[A[i]]*A[i]+dp(i+1)) return dp(0)

UTF-8

Python

false

726

py

1,842

L1_740_Delete_and_Earn.py

1,795

0.53719

0.523416

0

19

37.263158

110

rtloftin/HAL

11,510,512,358,163

017f5f7f59c7dd6198c83d5e91aec812cb5ecb67

fe3f88bf0344b3408b808a3d9aa3d30b9233a8a6

/navigation/expert.py

021a6352ba663f0f4dd6170f393322433ada88f5

[]

no_license

https://github.com/rtloftin/HAL

45c500877911144b539a11c9f582b864752da0d3

381c019a3c930d943672a65ae651e5a4f52686f8

refs/heads/master

2020-03-20T02:41:09.954395

2019-01-16T00:16:45

137,120,524

0

null

""" Defines an agent that acts optimally in a given navigation environment. """ import numpy as np import tensorflow as tf from .environment import Action class Expert: """ An agent that generates optimal actions for navigation tasks in a specific environment. """ def __init__(self, env): """ Initializes the agent by computing the optimal policies for each task in the environment. :param env: the environment the expert needs to act in """ # Capture the height of the environment, for computing state indices self._height = env.height # Define the transition model transitions = np.empty([env.width * env.height, len(Action)], dtype=np.int32) def index(x, y): return (x * env.height) + y def valid(x, y): if x < 0 or x >= env.width: return False if y < 0 or y >= env.height: return False if env.occupied[x, y]: return False return True for x in range(env.width): for y in range(env.height): cell = index(x, y) transitions[cell, Action.STAY] = cell transitions[cell, Action.UP] = index(x, y + 1) if valid(x, y + 1) else cell transitions[cell, Action.DOWN] = index(x, y - 1) if valid(x, y - 1) else cell transitions[cell, Action.LEFT] = index(x - 1, y) if valid(x - 1, y) else cell transitions[cell, Action.RIGHT] = index(x + 1, y) if valid(x + 1, y) else cell # Define value iteration graph graph = tf.Graph() with graph.as_default(): reward_input = tf.placeholder(dtype=tf.float32, shape=[env.width * env.height]) def update(q, t): v = tf.reduce_max(q, axis=1) n = tf.gather(v, transitions) return n + tf.expand_dims(reward_input, axis=1), t + 1 def limit(q, t): return t < 4 * (env.width + env.height) value_output, _ = tf.while_loop(limit, update, [tf.zeros_like(transitions, dtype=tf.float32), 0]) # Compute the optimal policies self._policies = dict() self._policy = None gpu_options = tf.GPUOptions(allow_growth=True) config = tf.ConfigProto(gpu_options=gpu_options) with tf.Session(graph=graph, config=config) as sess: reward = np.empty(env.width * env.height, dtype=np.float32) for name, task in env.tasks: # Initialize reward function for x in range(env.width): for y in range(env.height): reward[index(x, y)] = 1.0 if task.complete(x, y) else 0.0 # Compute value function values = sess.run(value_output, feed_dict={reward_input: reward}) # Construct policy max = np.max(values, axis=1) policy = [] for s in range(env.width * env.height): actions = [] for a in range(len(Action)): if values[s, a] == max[s]: actions.append(a) policy.append(actions) self._policies[name] = policy def task(self, name): """ Sets the task the expert is currently performing :param name: the name of the task """ self._policy = self._policies[name] def act(self, x, y): """ Samples an expert action for the current state and task. :return: the sampled action """ return np.random.choice(self._policy[(self._height * x) + y])

UTF-8

Python

false

3,773

py

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expert.py

74

0.532202

0.524781

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30.705882

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fvelotti/myScripts

4,767,413,716,782

bb8eae85a6e9a6b59cbd0b8609a0f72d0d7f09de

002f72085a6a1ea6beb0f05d959b093de7478e2c

/LHC_injection/plot_lhc_beams.py

3ef92d67cee591558c43da1094eca6de8a6f6b4c

[]

no_license

https://github.com/fvelotti/myScripts

02ba44ac76194024d2f1e6e3180cfe8368f0dbdf

ceb869a1a80d01b9aed44fe0fd24c4998c899ac2

refs/heads/master

2017-05-06T00:45:50.402990

2017-05-04T08:51:53

12,155,006

0

1

null

import numpy as np import matplotlib.pyplot as plt import dump_library as dp path = '/afs/cern.ch/work/f/fvelotti/private/lhc/' twiss_old_b1 = 'twiss_lhc_old_b1.tfs' twiss_old_b2 = 'twiss_lhc_old_b2.tfs' variables = ['name', 's', 'x', 'y', 'betx', 'bety', 'dx', 'dy', 'mux', 'muy'] name_b1o, s_b1o, x_b1o, y_b1o, betx_b1o, bety_b1o, dx_b1o, dy_b1o, mux_b1o, muy_b1o = dp.get_twiss_var(twiss_old_b1, path, variables) name_b2o, s_b2o, x_b2o, y_b2o, betx_b2o, bety_b2o, dx_b2o, dy_b2o, mux_b2o, muy_b2o = dp.get_twiss_var(twiss_old_b2, path, variables) mom_off_ti2 = 1.6e-3 mom_off_ti8 = -2.2e-3 disp_orbit_b1 = mom_off_ti2 * np.array(dx_b1o) / np.sqrt(7.3e-9 * np.array(betx_b1o)) disp_orbit_b2 = mom_off_ti8 * np.array(dx_b2o) / np.sqrt(7.3e-9 * np.array(betx_b2o)) plt.figure(1) plt.plot(np.array(s_b1o) * 1e-3, disp_orbit_b1) plt.plot(np.array(s_b1o) * 1e-3, -1 * disp_orbit_b1) plt.xlabel('s (km)') plt.ylabel(r'$\delta_p$D$_x/\sigma_{\beta}$') plt.minorticks_on() plt.title(r'Beam 1 (TI2), $\delta_p=$%1.1e' % mom_off_ti2) plt.figure(2) plt.plot(np.array(s_b2o) * 1e-3, disp_orbit_b2) plt.plot(np.array(s_b2o) * 1e-3, -1 * disp_orbit_b2) plt.xlabel('s (km)') plt.ylabel(r'$\delta_p$D$_x/\sigma_{\beta}$') plt.minorticks_on() plt.title('Beam 2 (TI8), $\delta_p=$%1.1e' % (-1 * mom_off_ti8)) plt.show()

UTF-8

Python

false

1,319

py

915

plot_lhc_beams.py

348

0.636846

0.576952

0

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Hryts-hub/eventmaster

4,277,787,433,672

11e590108409b7c019fc147f5a2f6445498bb56c

696bca4311caddbef8b55a3466417d28b81c7bfd

/comrades/management/commands/country.py

2becb009710e1806e187b8270c30fa12ba8a6404

[]

no_license

https://github.com/Hryts-hub/eventmaster

98d40e708f9ff3a0b92c90b743d99be3bf66b0e9

34cbde942d28ddde12272d67493a1ba81d0b4cd3

refs/heads/master

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from django.core.management.base import BaseCommand from events.services import get_countries from comrades.models import Country class Command(BaseCommand): def handle(self, *args, **options): slugs, list_country = get_countries() country_fields = [ Country(slug=slugs[i], country_name=list_country[i]) for i in range(0, len(slugs)) ] Country.objects.bulk_create(country_fields)

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py

27

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25

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zwen000/CSc44500-HW4

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415649637be1cad7d41ede1ab0f3a9796501140f

136d228e6b6193b457b8f46601b350b1349492b3

/BDM_HW4_Wen2.py

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[]

no_license

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540515aea6c59a4fb930007525d5fb6300d4624d

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refs/heads/main

2023-01-23T12:44:02.899568

2020-11-17T05:12:03

312,475,182

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from pyspark import SparkContext import sys import csv if __name__=='__main__': def parseCSV(idx, part): if idx == 0: next(part) for p in csv.reader(part): yield ("\""+p[1]+"\"", p[0].split("-")[0], p[7]) path_in = sys.argv[1] if len(sys.argv)>1 else "complaints_small.csv" path_out = sys.argv[2] if len(sys.argv)>2 else "hw4_output" sc = SparkContext.getOrCreate() rows= sc.textFile(path_in, use_unicode=False).mapPartitionsWithIndex(parseCSV)\ .map(lambda x: ((x[0], x[1], x[2]), 1))\ .reduceByKey(lambda x,y: x+y)\ .map(lambda x:(x[0][:2],x[1]))\ .groupByKey()\ .mapValues(lambda x: (str(sum(x)), str(len(x)), str(int(float(max(x))/sum(x)*100))))\ .sortByKey()\ .map(lambda x:(x[0]+x[1]))\ .map(lambda x: (", ".join(x)))\ .collect() sc.parallelize(rows).saveAsTextFile(path_out)

UTF-8

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py

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BDM_HW4_Wen2.py

2

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ShazwanX9/Hamming_Numbers

2,894,807,972,771

e6c07b6e78ee30fb582439abef0b0fe4bf948a76

f3708130135e9c7e6fe2bd12ad6487a5b0ae14e6

/hamming_numbers.py

a4bd2674eb4fd5ba6f00aefd237fbff1c3298b8b

[]

no_license

https://github.com/ShazwanX9/Hamming_Numbers

e0db06e2bf5f04cd8bdaec0bc87e0cf0843923ee

37b1b8de919d5d384cd37d312049d9fd4913e912

refs/heads/main

2023-07-16T07:47:57.520799

2021-08-31T16:09:45

401,765,105

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null

""" In computer science, regular numbers are often called Hamming numbers, after Richard Hamming, who proposed the problem of finding computer algorithms for generating these numbers in ascending order. In number theory, these numbers are called 5-smooth, because they can be characterized as having only 2, 3, or 5 as prime factors. """ class Hamming: __doc__ = __doc__ @staticmethod def get_nth(n): """ :param n: int nth position :return: int nth Hamming Nummber """ if n <= 0: raise ValueError("n should be more than 0 (positive integer)") table = [1] * n i, j, k = 0, 0, 0 ri, rj, rk = 2, 3, 5 for ref in range(1, n): table[ref] = min(ri, min(rj, rk)) if table[ref] == ri: i += 1 ri = 2 * table[i] if table[ref] == rj: j+= 1 rj = 3 * table[j] if table[ref] == rk: k += 1 rk = 5 * table[k] return table[-1] @staticmethod def is_hamming_number(n): """ :param n: int positive integer to test :return: bool the number is hamming number or not """ if n <= 0: raise ValueError("n should be more than 0 (positive integer)") ref = n while ref>1: if ref%2==0: ref//=2 elif ref%3==0: ref//=3 elif ref%5==0: ref//=5 else: return False return True if __name__ == "__main__": print("Testing for " + __file__) ############################################################################### RES = ( -1, 1, 2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24, 25, 27, 30, 32, 36, 40, 45, 48, 50, 54 ) ############################################################################### print("\nTesting get_nth: ") for n in range(1, len(RES)): check = Hamming.get_nth(n) if n%5==1: print("\n\t", end='') print(check, "==", RES[n], check==RES[n], end = " : ") print() ############################################################################### print("\nTesting is_hamming_number:\n") ############# print("\tShould Return True") for n in range(1, len(RES)): if Hamming.is_hamming_number(RES[n]): print("\t\t==>", True) break else: print("\t\t==>", False) ############# print("\tShould Return False") for n in range(11, 20): if n not in RES: if Hamming.is_hamming_number(n): print("\t\t==>", True) break else: print("\t\t==>", False) ############# print("\tShould Return True") for i in range(20, 50): if Hamming.is_hamming_number(Hamming.get_nth(n)): print("\t\t==>", True) break else: print("\t\t==>", False) ###############################################################################

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hamming_numbers.py

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foru120/PythonRepository

4,956,392,264,768

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/Books/DeepLearningLearningFromTheFounderOfKeras/chapter4/chapter4_2.py

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[]

no_license

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refs/heads/master

2021-01-01T06:53:11.728109

2019-04-25T13:52:50

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#todo K-겹 교차 검증 #todo - 데이터를 동일한 크기를 가진 K 개 분할로 나누고, 각 분할 i에 대해 남은 K - 1개의 분할로 모델을 훈련하고 분할 i에서 모델을 평가한다. #todo 최종 점수는 이렇게 얻은 K개의 점수를 평균한다. #todo 이 방법은 모델의 성능이 데이터 분할에 따라 편차가 클 때 도움이 되고, 홀드아웃 검증처럼 모델의 튜닝에 별개의 검증 세트를 사용한다. import numpy as np k = 4 data = [] test_data = data[int(len(data) * 0.7):] data = data[:int(len(data) * 0.7)] num_validation_samples = len(data) // k class get_model: def train(self, x): pass def evaluation(self, x): pass validation_scores = [] for fold in range(k): validation_data = data[num_validation_samples * fold: num_validation_samples * (fold + 1)] training_data = data[:num_validation_samples * fold] + data[num_validation_samples * (fold + 1):] model = get_model() # 훈련되지 않은 새로운 모델 생성 model.train(training_data) validation_score = model.evaluation(validation_data) validation_scores.append(validation_score) validation_score = np.average(validation_scores) model = get_model() model.train(data) # 테스트 데이터를 제외한 전체 데이터로 최종 모델을 훈련 test_score = model.evaluation(test_data) #todo 셔플링을 사용한 반복 K-겹 교차 검증 #todo - 이 방법은 비교적 가용 데이터가 적고 가능한 정확하게 모델을 평가하고자 할 때 사용한다. #todo 캐글 경연에서는 이 방법이 아주 크게 도움이 된다. #todo 이 방법은 K-겹 교차 검증을 여러 번 적용하되 K 개의 분할로 나누기 전에 매번 데이터를 무작위로 섞는다. #todo 최종 점수는 모든 K-겹 교차 검증을 실행해서 얻은 점수의 평균이 된다. #todo 결국 PxK(P는 반복 횟수)의 모델을 훈련하고 평가하므로 비용이 매우 많이 든다.

UTF-8

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py

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chapter4_2.py

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fclintj/bash_setup

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/.autokey/unicode/rarrow.py

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[]

no_license

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refs/heads/master

2021-01-02T08:38:00.518288

2018-09-28T06:10:54

99,036,013

0

null

# → U+2192 keyboard.send_keys("<ctrl>+<shift>+u+" + "2192") keyboard.send_keys("<ctrl>"+" ")

UTF-8

Python

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py

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rarrow.py

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hw233/lolita_son

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/firefly/utils/interfaces.py

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permissive

https://github.com/hw233/lolita_son

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refs/heads/master

2021-02-13T10:44:11.393406

2019-04-23T15:19:35

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MIT

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2020-03-03T16:42:02

2019-04-23T15:19:42

2019-04-23T15:19:40

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#coding:utf8 ''' Created on 2013-10-17 @author: lan (www.9miao.com) ''' from __future__ import division, absolute_import from zope.interface import Interface class IDataPackProtoc(Interface): def getHeadlength(): """获取数据包的长度 """ pass def unpack(): '''解包 ''' def pack(): '''打包数据包 '''

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interfaces.py

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Alex1820209/AprendiendoPython

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/GuiaPython/compara.py

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[]

no_license

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refs/heads/master

2020-08-01T09:06:35.852829

2019-09-25T21:33:48

210,944,026

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null

numero1 =input("Numero 1: ") numero2=input("Numero 2: ") #se muestran los valores salida="Numeros proporcionados: {} y {}. {}." if (numero1==numero2): #se mete a esta funcion si los capturados son iguales print(salida.format(numero1, numero2,"Los numeros son iguales")) else: #se usa un if dentro de otro (condiciones anidadas) #si los numeros son iguales. if numero1>numero2: #se hace si el primer valor es mayor al segundo print(salida.format(numero1, numero2, "el mayor es el primero")) else: #si no es asi el segundo es mayor print(salida.format(numero1, numero2, "el mayor es el segundo"))

UTF-8

Python

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py

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compara.py

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0

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39.6875

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xingxingdegit/learnpython

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/2.7/threadling/test2.py

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[]

no_license

https://github.com/xingxingdegit/learnpython

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refs/heads/master

2020-02-29T15:05:57.991568

2017-03-30T03:24:08

65,520,341

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null

#!/usr/bin/env python # # --*-- cofing: utf-8 --*-- # import threading,time i = 100 lock = threading.Lock() def push(tid): global i check = True while check: # lock.acquire() if i > 0: i -= 1 print 'i is:',i,'the tid is:',tid else: print 'nothing','the tid is:',tid check = False # lock.release() time.sleep(0.5) for x in range(10): new_thread = threading.Thread(target=push,args=(x,)) new_thread.start()

UTF-8

Python

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py

47

test2.py

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0.495292

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0

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fmhr/procon

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/atcoder/ABC/012/abc12c.py

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[]

no_license

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refs/heads/master

2019-08-27T10:53:57.594751

2019-01-28T09:05:06

59,471,219

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null

#!/usr/bin/env python # -*- coding: utf-8 -*- def main(): n = 2025-int(input()) x = [] for i in range(1, 10, 1): if n%i==0 and n//i<10: print(i, 'x', n//i) if __name__=='__main__': main()

UTF-8

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py

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abc12c.py

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OMalenfantThuot/scripts_raman

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/utils/functions/gramschmidt.py

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[]

no_license

https://github.com/OMalenfantThuot/scripts_raman

ac65fec1c5ac97a13b8c222e02241f02393f7709

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refs/heads/master

2023-07-19T20:11:10.457974

2023-07-18T18:15:26

193,724,148

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null

false

2023-08-28T20:00:38

2019-06-25T14:29:42

2021-12-14T20:08:26

2023-08-28T20:00:37

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1

0

Python

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import numpy as np def gramschmidt(basis, normalize=True): basis = [np.array(vec, dtype=np.float64) for vec in basis] nvectors = len(basis) ndim = len(basis[0]) for vec in basis: assert len(vec) == ndim new_basis = [] for i in range(nvectors): new_vec = basis[i].copy() for vec in new_basis: new_vec -= proj(basis[i], vec) if normalize: new_vec /= np.linalg.norm(new_vec) new_basis.append(new_vec) return np.array(new_basis) def proj(v, u): return np.dot(v, u) / np.dot(u,u) * u

UTF-8

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py

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gramschmidt.py

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smutch/gpu_tests

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/test.py

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[]

no_license

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f9df91357d89310b1679e3a7c855e9b23ea32839

refs/heads/master

2020-04-21T07:02:07.926383

2019-02-06T09:36:22

169,382,095

0

null

import sys sys.path.insert(0, "build/lib") from mpi4py import MPI import pygpu_tests if MPI.COMM_WORLD.rank == 0: print(f"Running test with {MPI.COMM_WORLD.size} ranks...") pygpu_tests.init_cuda()

UTF-8

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vedantshr/python-learning

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/learning/armstrongno.py

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[]

no_license

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refs/heads/main

2023-06-28T04:24:25.215630

2021-07-28T15:13:11

281,611,580

0

null

def armstrong(ip): n = 0 for i in range(len(ip)): n = n + int(ip[i])**len(ip) if n == int(ip): return True else: return False if __name__ == "__main__": ip = input() ip2 = input() print (armstrong(ip)) print (armstrong(ip2))

UTF-8

Python

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283

py

136

armstrongno.py

86

0.480565

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0

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17.933333

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CiiMAV/python-cdr

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79c2a896e856771bb462f8aae1f643156213e53e

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/test/sensor_accel_cdr.py

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[]

no_license

https://github.com/CiiMAV/python-cdr

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refs/heads/master

2020-03-13T06:28:32.340915

2018-06-25T10:20:18

131,005,536

0

null

import ctypes import sys lib = ctypes.cdll.LoadLibrary('./sensor_accel_cdr.so') class sensor_accel(object): """docstring for sensor_accel""" def __init__(self): lib.sensor_accel_new.restype = ctypes.c_void_p lib.sensor_accel_deserialize.argtypes = [ctypes.c_void_p, ctypes.c_char_p, ctypes.c_int] lib.sensor_accel_deserialize.restype = ctypes.c_void_p lib.get_m_x.argtype = ctypes.c_void_p lib.get_m_x.restype = ctypes.c_float self.obj = lib.sensor_accel_new() self.m_x = 0 def get_m_x(self): self.m_x = lib.get_m_x(self.obj) return self.m_x; def deserialize(self,data,length): lib.sensor_accel_deserialize(self.obj,data,length ) self.get_m_x()

UTF-8

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694

py

18

sensor_accel_cdr.py

17

0.681556

0.680115

0

20

32.6

90

askemottelson/minigit

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/gui/clonedialog.py

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[]

no_license

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refs/heads/master

2016-08-04T04:29:48.543996

2014-12-07T14:37:31

null

0

null

from PyQt4 import QtCore, QtGui from clone import Ui_CloneDialog class CloneDialog(): url = "" path = "" def __init__(self, parent): self.parent = parent def urlSelected(self): root = str(QtGui.QFileDialog.getExistingDirectory(self.parent, "Select Directory")) self.clonedialog_ui.path.setText(root) def setup(self): self.clonedialog = QtGui.QDialog(self.parent) self.clonedialog_ui = Ui_CloneDialog() self.clonedialog_ui.setupUi(self.clonedialog) self.clonedialog_ui.browseButton.clicked.connect(self.urlSelected) self.clonedialog.exec_() self.url = self.clonedialog_ui.url.text() self.path = self.clonedialog_ui.path.text() def result(self): return (self.url, self.path)

UTF-8

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py

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clonedialog.py

8

0.700269

0.698925

0

32

22.28125

87

anitabaral/Exercism-exercises

18,098,992,209,019

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/robot_name.py

2dbc69ab107f223f6c2b27494c73bf00e36a9e5a

[]

no_license

https://github.com/anitabaral/Exercism-exercises

983d6b6ce1c0ee8f0d70d5dbe8ab59e8e8b3e878

81034e5174ef6933e07bc595c9dd708265faeaaf

refs/heads/master

2023-05-30T17:22:38.143483

2021-06-14T08:33:47

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0

null

import random import string class Robot: def __init__(self): random.seed() self.reset() def reset(self): self.get_name() def get_name(self): text = [] for _ in range(2): text.append(random.choice(string.ascii_uppercase)) for _ in range(3): text.append(random.choice(string.digits)) self.name = "".join(text)

UTF-8

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py

39

robot_name.py

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0.543424

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bibongbong/pythonCookBook

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#################################################################### # File: getUrl.py # Function: Learning to how to get the specific URL, and parse the HTML page to get the content you want # #################################################################### import urllib.request import operator from bs4 import BeautifulSoup import asyncio # testNum = [3,5,11,12,26,29,15] #testNum = [11, 16, 19, 22, 25, 30, 8] testNum = [8, 10, 17, 20, 27, 30, 1] startUrl = 'http://kaijiang.zhcw.com/zhcw/inc/ssq/ssq_wqhg.jsp' serverUrl = 'http://kaijiang.zhcw.com/' isNeedProxy = True def getWinNum(tr): win_num = [] all_td = tr.find_all("td") for em in all_td[2]: if em.string.isdigit(): win_num.append(int(em.string)) return win_num def getWinDate(tr): all_td = tr.find_all("td") winDate = all_td[0].string return winDate def getnextPageUrl(i): return "http://kaijiang.zhcw.com/zhcw/inc/ssq/ssq_wqhg.jsp?pageNum="+str(i) def getLastPageNum(all_tr): nextPageTr = all_tr[-1] all_strong = nextPageTr.find_all("strong") nextPageItem = all_strong[5].a return nextPageItem['href'][35:] def getResponse(currentUrl): print("current url is %s " % (currentUrl)) if isNeedProxy: # set the http proxy proxy_handler = urllib.request.ProxyHandler({"http": 'http://135.245.248.89:8000'}) opener = urllib.request.build_opener(proxy_handler) urllib.request.install_opener(opener) print("getResponse ",currentUrl) # open the zhcw ShuangSeQiu page response = urllib.request.urlopen(currentUrl) html = response.read() soup = BeautifulSoup(html, "html.parser") return soup def loopAllWinNumInCurrentPage(all_tr,myNum): #loop all winNum in current page for tr in all_tr[2:-1]: winNum = getWinNum(tr) if winNum == myNum: winDate = getWinDate(tr) isFound = True print("Found: %s" % winNum) print(winDate) raise StopIteration def searchFirstPage(currentUrl, myNum): soup = getResponse(currentUrl) all_tr = soup.find_all("tr") isFound = False loopAllWinNumInCurrentPage(all_tr,myNum) lastPageNum = getLastPageNum(all_tr) return lastPageNum def searchPage(i, myNum): print(i) currentUrl = getnextPageUrl(i) soup = yield from getResponse(currentUrl) all_tr = soup.find_all("tr") isFound = False loopAllWinNumInCurrentPage(all_tr,myNum) lastPage = searchFirstPage(startUrl, testNum) print("lastPage ",lastPage) loop = asyncio.get_event_loop() tasks = [searchPage(i, testNum) for i in range(int(lastPage))] loop.run_until_complete(asyncio.wait(tasks)) loop.close()

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# -*- coding: utf-8 -*- """ Created on Sun Jul 7 17:58:18 2019 @author: w3321 """ import pyrealsense2 as rs import numpy as np import cv2 from . import rs1_cv as rs1 import math import time import os from . import square_distance_angle as da import pandas as pd from .. import output2 as op from .. import find pc = rs.pointcloud() # We want the points object to be persistent so we can display the last cloud when a frame drops points = rs.points() imgk=np.ones([550,550,3],np.uint8)*255 pipeline = rs.pipeline() config = rs.config() config.enable_stream(rs.stream.depth,640,480,rs.format.z16,30) config.enable_stream(rs.stream.color,640,480,rs.format.bgr8,30) # Start streaming # Create an align object # rs.align allows us to perform alignment of depth frames to others frames # The "align_to" is the stream type to which we plan to align depth frames. align_to = rs.stream.color align = rs.align(align_to) global vtx def GetLineLength(p1,p2): '''计算边长''' length = math.pow((p2[0]-p1[0]),2) + math.pow((p2[1]-p1[1]),2)+ math.pow((p2[2]-p1[2]),2) length = math.sqrt(length) return length depth_pixel = [320, 240] def draw_circle(event,x,y,flags,param): global depth_pixel if event == cv2.EVENT_MOUSEMOVE: depth_pixel = [x, y] def get_deep(): while True: frames = pipeline.wait_for_frames() aligned_frames = align.process(frames) depth_frame = aligned_frames.get_depth_frame() color_frame = aligned_frames.get_color_frame() if not depth_frame or not color_frame: continue break return depth_frame,color_frame def get_xyz(p): global vtx i=p[1]*640+p[0] p1=[np.float(vtx[i][0]),np.float(vtx[i][1]),np.float(vtx[i][2])] return p1 def get_one(path1): global vtx #time.sleep(5) start=time.time() pipe_profile=pipeline.start(config) depth_sensor = pipe_profile.get_device().first_depth_sensor() depth_sensor.set_option(rs.option.visual_preset,1) depth_sensor = pipe_profile.get_device().first_depth_sensor() depth_sensor.set_option(rs.option.visual_preset,1) sensor = pipe_profile.get_device().query_sensors()[1] sensor.set_option(rs.option.enable_auto_exposure,1) sensor.set_option(rs.option.backlight_compensation,0.000) sensor.set_option(rs.option.brightness,0.000) sensor.set_option(rs.option.contrast,50.000) sensor.set_option(rs.option.gain,61.000) sensor.set_option(rs.option.gamma,220.000) sensor.set_option(rs.option.hue,0.000) sensor.set_option(rs.option.saturation,68.000) sensor.set_option(rs.option.sharpness,50.000) cv2.namedWindow('depth_frame') cv2.setMouseCallback('depth_frame',draw_circle) while True: depth_frame1,color_frame1=get_deep() depth_frame2,color_frame2=get_deep() depth_frame,color_frame=get_deep() img_color = np.asanyarray(color_frame.get_data()) img_test =img_color.copy() img_depth1 = np.asanyarray(depth_frame1.get_data()) img_depth2 = np.asanyarray(depth_frame2.get_data()) img_depth = np.asanyarray(depth_frame.get_data()) img_depthx=(img_depth1+img_depth2+img_depth)//3 depth_colormap1 = cv2.applyColorMap(cv2.convertScaleAbs(img_depth1, alpha=0.03), cv2.COLORMAP_BONE) depth_colormap2 = cv2.applyColorMap(cv2.convertScaleAbs(img_depth2, alpha=0.03), cv2.COLORMAP_BONE) depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(img_depth, alpha=0.03), cv2.COLORMAP_BONE) p,imgf=rs1.rs1_cv(depth_colormap1,depth_colormap2,depth_colormap) # Intrinsics & Extrinsics depth_intrin = depth_frame.profile.as_video_stream_profile().intrinsics color_intrin = color_frame.profile.as_video_stream_profile().intrinsics depth_to_color_extrin = depth_frame.profile.get_extrinsics_to(color_frame.profile) # Depth scale - units of the values inside a depth frame, i.e how to convert the value to units of 1 meter depth_sensor = pipe_profile.get_device().first_depth_sensor() depth_scale = depth_sensor.get_depth_scale() # Map depth to color depth_point = rs.rs2_deproject_pixel_to_point(depth_intrin, depth_pixel, depth_scale) color_point = rs.rs2_transform_point_to_point(depth_to_color_extrin, depth_point) color_pixel = rs.rs2_project_point_to_pixel(color_intrin, color_point) #print ('depth: ',color_point) #print ('depth: ',color_pixel) x1,y1=depth_pixel pc.map_to(color_frame) points = pc.calculate(depth_frame) v, t = points.get_vertices(), points.get_texture_coordinates() vtx = np.asanyarray(v).view(np.float32).reshape(-1, 3) # xyz tex = np.asanyarray(t).view(np.float32).reshape(-1, 2) # uv i = y1*640+x1 ''' i0=p[0][1]*640+p[0][0] i1=p[1][1]*640+p[1][0] i2=p[2][1]*640+p[2][0] i3=p[3][1]*640+p[3][0] p0=[np.float(vtx[i0][0]),np.float(vtx[i0][1]),np.float(vtx[i0][2])]#左下 p1=[np.float(vtx[i1][0]),np.float(vtx[i1][1]),np.float(vtx[i1][2])]#右下 p2=[np.float(vtx[i2][0]),np.float(vtx[i2][1]),np.float(vtx[i2][2])]#左上 p3=[np.float(vtx[i3][0]),np.float(vtx[i3][1]),np.float(vtx[i3][2])]#右上 ''' p0=get_xyz(p[0]) p1=get_xyz(p[1]) p2=get_xyz(p[2]) p3=get_xyz(p[3]) l0=GetLineLength(p0,p1) l1=GetLineLength(p0,p2) l2=GetLineLength(p1,p3) if l0==0: continue r=550/l0#mm/xlength l1_1=int(r*l1)#左边像素长度 l2_1=int(r*l2)#右边像素长度 #print(r,p0,p1,p2,p3) pts1 = np.float32([[0,550],[550,550],[0,550-l1_1],[550,550-l2_1]]) pts2 = np.float32([[p[0][0],p[0][1]],[p[1][0],p[1][1]],[p[2][0],p[2][1]],[p[3][0],p[3][1]]]) M = cv2.getPerspectiveTransform(pts1,pts2) dst = cv2.warpPerspective(imgk,M,(640,480)) dst=cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY) imgn, cnt, h = cv2.findContours(dst, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if cnt: cnt=cnt[0] cv2.drawContours(img_color, [cnt], 0, (0,255,0), 1) approx = cv2.approxPolyDP(cnt,8,True) cv2.drawContours(img_color, [approx], 0, (0,0,255), 3) a=np.asarray(approx) a=a.reshape(-1,2) #print(tuple(a[0])) ''' cv2.circle(img_color,tuple(a[0]),5,(255,0,0),-1) cv2.circle(img_color,tuple(a[1]),5,(255,0,0),-1) cv2.circle(img_color,tuple(a[2]),5,(255,0,0),-1) cv2.circle(img_color,tuple(a[3]),5,(255,0,0),-1) ''' #print ('depth: ',[np.float(vtx[i][0]),np.float(vtx[i][1]),np.float(vtx[i][2])]) cv2.circle(img_color,(x1,y1), 8, [255,0,255], thickness=-1) cv2.putText(img_color,"Dis:"+str(img_depth[y1,x1]), (40,40), cv2.FONT_HERSHEY_SIMPLEX, 1.2,[255,0,255]) cv2.putText(img_color,"X:"+str(np.float(vtx[i][0])), (80,80), cv2.FONT_HERSHEY_SIMPLEX, 1.2,[255,0,255]) cv2.putText(img_color,"Y:"+str(np.float(vtx[i][1])), (80,120), cv2.FONT_HERSHEY_SIMPLEX, 1.2,[255,0,255]) cv2.putText(img_color,"Z:"+str(np.float(vtx[i][2])), (80,160), cv2.FONT_HERSHEY_SIMPLEX, 1.2,[255,0,255]) cv2.imshow('depth_frame',img_color) #cv2.imshow('3',depth_colormap) cv2.imshow('4',imgf) #cv2.imshow('5',dst) key = cv2.waitKey(1) end=time.time() if (end-start)>5: #data = pd.DataFrame(img_depth) #data.to_csv('./1.csv',index = False) #if key & 0xFF == ord('q'): break cv2.destroyAllWindows() pipeline.stop() cv2.imwrite(path1+'/test_image/img_test.jpg',img_test) return r,p0,p1,p2,p3,img_depth,img_test def find_p(imgs): m=find.find(imgs) n=[] b=0 for i in m: if imgs[int(i[0,0]),int(i[0,1])] !=0: #print([int(i[0,0]),int(i[0,1])]) n+=[int(i[0,0]),int(i[0,1])] if n==[]: b=1 n=np.asarray(n).reshape(-1,2) return n,b def fake_det(img,pm0): p=0 if (img[pm0[1]+30,pm0[0]]!=0): if abs(int(img[pm0[1]+30,pm0[0]])-int(img[pm0[1],pm0[0]]))<700: if abs(int(img[pm0[1],pm0[0]])-int(img[pm0[1]+30,pm0[0]]))>350: #print(abs(int(img[pm0[1],pm0[0]])-int(img[pm0[1]+30,pm0[0]]))) print('error') p=1 return p def out(list,p0,p1,p2,p3,img): z=[['ZA001',0,0,0,0], ['ZA002',0,0,0,0], ['ZA003',0,0,0,0], ['ZA004',0,0,0,0], ['ZA005',0,0,0,0], ['ZB001',0,0,0,0], ['ZB002',0,0,0,0], ['ZB003',0,0,0,0], ['ZB004',0,0,0,0], ['ZB005',0,0,0,0], ['ZB006',0,0,0,0], ['ZB007',0,0,0,0], ['ZB008',0,0,0,0], ['ZB009',0,0,0,0], ['ZB010',0,0,0,0], ['ZC001',0,0,0,0], ['ZC002',0,0,0,0], ['ZC003',0,0,0,0], ['ZC004',0,0,0,0], ['ZC005',0,0,0,0], ['ZC006',0,0,0,0], ['ZC007',0,0,0,0], ['ZC008',0,0,0,0], ['ZC009',0,0,0,0], ['ZC010',0,0,0,0], ['ZC011',0,0,0,0], ['ZC012',0,0,0,0], ['ZC013',0,0,0,0], ['ZC014',0,0,0,0], ['ZC015',0,0,0,0], ['ZC016',0,0,0,0], ['ZC017',0,0,0,0], ['ZC018',0,0,0,0], ['ZC019',0,0,0,0], ['ZC020',0,0,0,0], ['ZC021',0,0,0,0], ['ZC022',0,0,0,0], ['ZC023',0,0,0,0], ['CA001',0,0,0,0], ['CA002',0,0,0,0], ['CA003',0,0,0,0], ['CA004',0,0,0,0], ['CD001',0,0,0,0], ['CD002',0,0,0,0], ['CD003',0,0,0,0], ['CD004',0,0,0,0], ['CD005',0,0,0,0], ['CD006',0,0,0,0]] b=[['ZA001',1,0,15], ['ZA002',0,0,42], ['ZA003',1,1,20], ['ZA004',0,1,25], ['ZA005',1,0,35], ['ZB001',0,0,32], ['ZB002',0,0,32], ['ZB003',0,0,20], ['ZB004',1,0,15], ['ZB005',0,0,25], ['ZB006',0,0,40], ['ZB007',1,0,20], ['ZB008',0,0,32], ['ZB009',0,1,30], ['ZB010',1,0,20], ['ZC001',0,1,15], ['ZC002',0,1,15], ['ZC003',0,1,15], ['ZC004',0,1,15], ['ZC005',0,0,15], ['ZC006',0,1,15], ['ZC007',0,1,15], ['ZC008',0,1,15], ['ZC009',0,1,15], ['ZC010',0,1,15], ['ZC011',1,1,15], ['ZC012',1,1,15], ['ZC013',0,1,15], ['ZC014',0,0,15], ['ZC015',0,0,32], ['ZC016',0,0,32], ['ZC017',0,0,32], ['ZC018',0,0,32], ['ZC019',0,0,32], ['ZC020',0,0,32], ['ZC021',0,0,32], ['ZC022',0,0,32], ['ZC023',0,0,32], ['CA001',0,0,32], ['CA002',0,0,32], ['CA003',0,0,32], ['CA004',0,0,32], ['CD001',0,0,32], ['CD002',0,0,32], ['CD003',0,0,32], ['CD004',0,0,32], ['CD005',0,0,32], ['CD006',0,0,32]] for i in list[1:]: zr=0. y1=int(i[1]) x1=int(i[2]) y2=int(i[3]) x2=int(i[4]) x=(x1+x2)//2 y=(y1+y2)//2 p=[x,y] p10=[x-5,y] p11=[x+5,y] if b[int(i[6]-1)][2]==1: imgs=img[y1:y2,x1:x2] m,c=find_p(imgs) if c==0: #print(m) j=m.shape[0]//2 pm0=[m[j][1]+x1,m[j][0]+y1] ''' depth_colormap = cv2.applyColorMap(cv2.convertScaleAbs(img, alpha=0.03), cv2.COLORMAP_BONE) for l in m: cv2.circle(depth_colormap,(int(l[1])+x1,int(l[0])+y1), 1, [0,0,255], thickness=-1) #img_color=cv2.imread('../../vision_output/output/result/img_test.jpg') #cv2.circle(img_color,(x,y), 8, [255,0,255], thickness=-1) cv2.circle(depth_colormap,(pm0[0],pm0[1]), 2, [255,0,255], thickness=-1) cv2.circle(depth_colormap,(pm0[0],pm0[1]+10), 2, [255,0,255], thickness=-1) cv2.imshow('1',depth_colormap) cv2.waitKey() cv2.destroyAllWindows() ''' else: print('s') pm0=p else: pm0=p fd=fake_det(img,pm0) if fd ==1: continue pm=get_xyz(pm0) p21=get_xyz(p10) p22=get_xyz(p11) zb=da.finddistan(p0,p2,p1,pm) if b[int(i[6]-1)][1]==1: zr=da.findangle(p0,p2,p1,p21,p22) if (b[int(i[6]-1)][0]=='ZC011') or (b[int(i[6]-1)][0]=='ZC011'): pass elif img[p10[1],p10[0]]<img[p11[1],p11[0]]: zr=180-zr z[int(i[6]-1)][4]+=1 z[int(i[6]-1)][1]+=zb[0] z[int(i[6]-1)][2]+=zb[1]-b[int(i[6]-1)][3] z[int(i[6]-1)][3]+=zr return z ''' path=os.path.dirname(os.path.abspath(__file__)) path1=os.path.normpath(path+'../../../vision_output/output') start1=time.time() r,p0,p1,p2,p3,img_d,img_c=get_one(path1) op.tf_dete(pth=path1) #pipeline.stop() #print(r,p0,p1,p2,p3) #print(path1) #os.system('python '+path1) data=pd.read_csv(path1+'/result/img_test.jpg.csv',header=None) list=data.values.tolist() z=out(list,p0,p1,p2,p3,img_d) zc=np.asanyarray(z).reshape(-1,4) print(zc) end1=time.time() print(end1-start1) '''

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# lat31.py class Orang: pass org = Orang() print(org)

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""" This script is mainly written for tanglegram which connect leafs from two dendrograms. Further, it also implement some useful transferring function from iTOL. for adding color to the leaves of the dendrogram. you could use the iTOL file.. or something simpler like removing the description """ from collections import defaultdict import click import os import plotly import plotly.graph_objs as go from tqdm import tqdm from Tree_for.api_IO import read_tree from Tree_for.plotly_draw_newick_tree import main as get_plotly_data_from_newick def get_leafs(newick): t = read_tree(newick, format='auto') return t.get_leaf_names() def get_preferred_scale(newick1, newick2): t1 = read_tree(newick1, format='auto') t2 = read_tree(newick2, format='auto') num_1 = len(t1.get_leaf_names()) num_2 = len(t2.get_leaf_names()) yscale = num_1 / num_2 return 1 / yscale def parse_color_scheme_files(file, extra_set=False,get_raw_name=False): """ :param file: :param extra_set: :param get_raw_name: :return: """ lines = open(file).read().split('\n') lines = [_ for _ in lines if _] field_labels = [_ for _ in lines if _.startswith("FIELD_LABELS")] field_colors = [_ for _ in lines if _.startswith("FIELD_COLORS")] name2color = {} sep_indicator = [_ for _ in lines if _.startswith("SEPARATOR")][0] sep_indicator = sep_indicator.split(' ')[-1] s2s = {"TAB": '\t', "COMMA": ',', "SPACE": ' '} sep = s2s.get(sep_indicator, ',') if field_labels and field_colors: colors = field_colors[0].split('\t')[1:] anno_names = field_labels[0].split('\t')[1:] _name2data = [_ for _ in lines[lines.index("DATA") + 1:] if _ and not _.startswith('#')] for line in _name2data: line.strip('\n') vs = line.split('\t') name = vs[0] color = [c for c, _ in zip(colors, vs[1:]) if str(_) != '0'] if color: name2color[name] = color[0] # would overlap.. be careful.. else: lines = [_ for _ in lines[lines.index("DATA") + 1:] if _ and not _.startswith('#')] c2name = {} for line in lines: values = line.split(sep) name = values[0] if "range" in line: color = values[2] c2name[color] = values[-1] else: color = values[1] name2color[name] = color if get_raw_name: return c2name if str(extra_set) == 'rename': new_name2color = {k.split('_')[-1].replace('.', 'v'): v for k, v in name2color.items()} name2color = new_name2color.copy() return name2color def main(newick1, newick2, color_file1, color_file2, l_legnth='max', sep='_', extra_set=False, identical=False): left_leaves = get_leafs(newick1) right_leaves = get_leafs(newick2) yscale = get_preferred_scale(newick1, newick2) fig = plotly.subplots.make_subplots(rows=1, cols=3, shared_yaxes=True, horizontal_spacing=0.05/3) # get dendrogram parts tqdm.write('drawing dendrograms') datas, labels, _, labels_draw_text, labels_x, labels_y = get_plotly_data_from_newick(newick1, fixed_length=l_legnth, yscale=yscale) datas2, labels2, _, labels_draw_text2, labels_x2, labels_y2 = get_plotly_data_from_newick(newick2, fixed_length=l_legnth) # add colors or something else into above datas l2color = {_: '#000000' for _ in labels_draw_text} r2color = {_: '#000000' for _ in labels_draw_text2} l2color.update(parse_color_scheme_files(color_file1, extra_set=False)) r2color.update(parse_color_scheme_files(color_file2, extra_set=extra_set)) # add color into generated trace tqdm.write('adding color') name2trace = {_['name']: _ for _ in datas if _['name'] is not None} for l, color in l2color.items(): if color != '#00000' and l in labels_draw_text: trace = name2trace[l] trace['line']['color'] = color name2trace = {_['name']: _ for _ in datas2 if _['name'] is not None} for r, color in r2color.items(): if color != '#00000' and r in labels_draw_text2: trace = name2trace[r] trace['line']['color'] = color # add dendrogram parts into figure. # data1/newick1 would be the left, data2/newick2 would be the right and the leaves of it will point to left # for _ in datas: fig.add_traces(datas, rows=[1] * len(datas), cols=[1] * len(datas)) # for _ in datas2: fig.add_traces(datas2, rows=[1] * len(datas2), cols=[3] * len(datas2)) # init data of middle part # get the y-coordinate information from below. put them into two dict. left_data = {k:v for k,v in dict(zip(labels_draw_text, labels_y)).items() if k in left_leaves} right_data = {k:v for k,v in dict(zip(labels_draw_text2, labels_y2)).items() if k in right_leaves} # get mapping relationship, default is from left to the right.. one to multi # so, the leaf names from the left tree should be the part of right, separate with `underline` or `space` l2r = defaultdict(list) for leaf1 in left_data.keys(): if identical: leaf2s = [r for r in right_data.keys() if leaf1 == r] else: leaf2s = [r for r in right_data.keys() if leaf1.split(sep)[0] == r] l2r[leaf1] = leaf2s # init the data from above mapping dict c2data = defaultdict(lambda: ([], [])) for l, color in l2color.items(): _xs = c2data[color][0] _ys = c2data[color][1] rs = l2r.get(l, []) l_y = left_data.get(l, 0) for r in rs: r_y = right_data[r] _xs += [0, 1, None] _ys += [l_y, r_y, None] for color, (_xs, _ys) in c2data.items(): trace = go.Scatter(x=_xs, y=_ys, mode='lines', line=dict(color=color, ), hoverinfo='none', showlegend=True) fig.add_traces([trace], rows=[1], cols=[2]) fig.layout.xaxis3.autorange = 'reversed' fig.layout.xaxis.showticklabels = False fig.layout.xaxis2.showticklabels = False fig.layout.xaxis3.showticklabels = False fig.layout.xaxis.zeroline = False fig.layout.xaxis2.zeroline = False fig.layout.xaxis3.zeroline = False fig.layout.yaxis.zeroline = False fig.layout.yaxis.showticklabels = False return fig @click.command() @click.option("-newick1", help="first tree file, would be placed on the left") @click.option("-newick2", help="second tree file, would be placed on the right") @click.option("-output", "output_file", help="the path of html output ") @click.option("-cf1", help="the file for color annotation to first tree and the links") @click.option("-cf2", help="the file for color annotation to second tree ") @click.option("-length", default="max", help="the length of leaves you want to extend to. normally all leaves will extend to identical length which similar to the longest one") @click.option("-sep", default="_", help="the separator which used to . ") @click.option("-extra_set", "extra_set", is_flag=True, default=False) @click.option("-identical", "identical", is_flag=True, default=False,help='if the leaf names are identical. you should pass this parameter or redundant lines will show up ') def cli(newick1, newick2, output_file, cf1, cf2, length, sep, extra_set,identical): fig = main(newick1=newick1, newick2=newick2, color_file1=cf1, color_file2=cf2, l_legnth=length, sep=sep, extra_set=extra_set, identical=identical) fig.layout.width = 1400 fig.layout.height = 3000 if not exists(dirname(output_file)): os.makedirs(dirname(output_file)) fig.write_html(output_file) if __name__ == '__main__': from os.path import dirname, join, exists example_dir = join(dirname(dirname(dirname(__file__))), 'example', 'tanglegram') newick1 = join(example_dir, 'nxrA.newick') newick2 = join(example_dir, 'species.txt') color_file1 = join(example_dir, 'gene_annotation.txt') color_file2 = join(example_dir, 'phylum_annotate.txt') fig = main(newick1, newick2, color_file1, color_file2, l_legnth='max', sep='_', extra_set='rename') fig.layout.width = 1400 fig.layout.height = 3000 fig.write_html(join(example_dir, 'p.html'))

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udemirezen/rational_activations

6,012,954,218,353

ed9e090bd0b27670529672ad6d04bcd92f4118a1

22ad9abeb6c9bcddb6ee127309cd1afce9e1cf3c

/rational/torch/rationals.py

2551ab2c8038a17dd89087ea537af0a9196e9539

[ "MIT" ]

permissive

https://github.com/udemirezen/rational_activations

8e9abf3475d78251ddb8e9f0b37bd6bbf8135dbb

5bc4ade862eb8967e940afb15d33e0d6653668cc

refs/heads/master

2023-08-11T15:38:01.254359

2021-08-10T13:23:22

null

0

null

""" Rational Activation Functions for Pytorch ========================================= This module allows you to create Rational Neural Networks using Learnable Rational activation functions with Pytorch networks. """ import torch from torch._C import device import torch.nn as nn from torch.cuda import is_available as torch_cuda_available from rational.utils.get_weights import get_parameters from rational.utils.warnings import RationalWarning from rational._base.rational_base import Rational_base from rational.torch.rational_pytorch_functions import Rational_PYTORCH_A_F, \ Rational_PYTORCH_B_F, Rational_PYTORCH_C_F, Rational_PYTORCH_D_F, \ Rational_NONSAFE_F, Rational_CUDA_NONSAFE_F, _get_xps if torch_cuda_available(): try: from rational.torch.rational_cuda_functions import Rational_CUDA_A_F, \ Rational_CUDA_B_F, Rational_CUDA_C_F, Rational_CUDA_D_F except ImportError: pass class Rational(Rational_base, nn.Module): """ Rational activation function inherited from ``torch.nn.Module``. Arguments: approx_func (str): The name of the approximated function for initialisation. \ The different initialable functions are available in \ `rational.rationals_config.json`. \n Default ``leaky_relu``. degrees (tuple of int): The degrees of the numerator (P) and denominator (Q).\n Default ``(5, 4)`` cuda (bool): Use GPU CUDA version. \n If ``None``, use cuda if available on the machine\n Default ``None`` version (str): Version of Rational to use. Rational(x) = P(x)/Q(x)\n `A`: Q(x) = 1 + \|b_1.x\| + \|b_2.x\| + ... + \|b_n.x\|\n `B`: Q(x) = 1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `C`: Q(x) = 0.1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `D`: like `B` with noise\n Default ``A`` trainable (bool): If the weights are trainable, i.e, if they are updated during \ backward pass\n Default ``True`` Returns: Module: Rational module """ def __init__(self, approx_func="leaky_relu", degrees=(5, 4), cuda=None, version="A", trainable=True, train_numerator=True, train_denominator=True, name=None): if name is None: name = approx_func super().__init__(name) if cuda is None: cuda = torch_cuda_available() if cuda is True: device = "cuda" elif cuda is False: device = "cpu" else: device = cuda w_numerator, w_denominator = get_parameters(version, degrees, approx_func) self.numerator = nn.Parameter(torch.FloatTensor(w_numerator).to(device), requires_grad=trainable and train_numerator) self.denominator = nn.Parameter(torch.FloatTensor(w_denominator).to(device), requires_grad=trainable and train_denominator) self.register_parameter("numerator", self.numerator) self.register_parameter("denominator", self.denominator) self.device = device self.degrees = degrees self.version = version self.training = trainable self.init_approximation = approx_func self._saving_input = False if "cuda" in str(device): if version == "A": rational_func = Rational_CUDA_A_F elif version == "B": rational_func = Rational_CUDA_B_F elif version == "C": rational_func = Rational_CUDA_C_F elif version == "D": rational_func = Rational_CUDA_D_F elif version == "N": rational_func = Rational_CUDA_NONSAFE_F else: raise NotImplementedError(f"version {version} not implemented") if 'apply' in dir(rational_func): self.activation_function = rational_func.apply else: self.activation_function = rational_func else: if version == "A": rational_func = Rational_PYTORCH_A_F elif version == "B": rational_func = Rational_PYTORCH_B_F elif version == "C": rational_func = Rational_PYTORCH_C_F elif version == "D": rational_func = Rational_PYTORCH_D_F elif version == "N": rational_func = Rational_NONSAFE_F else: raise NotImplementedError(f"version {version} not implemented") self.activation_function = rational_func def forward(self, x): return self.activation_function(x, self.numerator, self.denominator, self.training) def _cpu(self): if self.version == "A": rational_func = Rational_PYTORCH_A_F elif self.version == "B": rational_func = Rational_PYTORCH_B_F elif self.version == "C": rational_func = Rational_PYTORCH_C_F elif self.version == "D": rational_func = Rational_PYTORCH_D_F elif self.version == "N": rational_func = Rational_NONSAFE_F else: raise ValueError("version %s not implemented" % self.version) self.activation_function = rational_func self.device = "cpu" def _cuda(self, device): if self.version == "A": rational_func = Rational_CUDA_A_F elif self.version == "B": rational_func = Rational_CUDA_B_F elif self.version == "C": rational_func = Rational_CUDA_C_F elif self.version == "D": rational_func = Rational_CUDA_D_F elif self.version == "N": rational_func = Rational_CUDA_NONSAFE_F else: raise ValueError("version %s not implemented" % self.version) if "cuda" in str(device): self.device = f"{device}" else: self.device = f"cuda:{device}" if 'apply' in dir(rational_func): self.activation_function = rational_func.apply else: self.activation_function = rational_func def _to(self, device): """ Moves the rational function to its specific device. \n Arguments: device (torch device): The device for the rational """ if "cpu" in str(device): self.cpu() elif "cuda" in str(device): self.cuda(device) def _apply(self, fn): if "Module.cpu" in str(fn): self._cpu() elif "Module.cuda" in str(fn): device = fn.__closure__[0].cell_contents self._cuda(device) elif "Module.to" in str(fn): for clos in fn.__closure__: if type(clos.cell_contents) is torch.device: device = clos.cell_contents self.device = str(device) self._to(device) break return super()._apply(fn) def numpy(self): """ Returns a numpy version of this activation function. """ from rational.numpy import Rational as Rational_numpy rational_n = Rational_numpy(self.init_approximation, self.degrees, self.version) rational_n.numerator = self.numerator.tolist() rational_n.denominator = self.denominator.tolist() return rational_n def _from_old(self, old_rational_func): self.version = old_rational_func.version self.degrees = old_rational_func.degrees self.numerator = old_rational_func.numerator self.denominator = old_rational_func.denominator if "center" in dir(old_rational_func) and old_rational_func.center != 0: print("Found a non zero center, please adapt the bias of the", "previous layer to have an equivalent neural network") self.training = old_rational_func.training if "init_approximation" not in dir("init_approximation"): self.init_approximation = "leaky_relu" else: self.init_approximation = old_rational_func.init_approximation if "cuda" in str(self.device): if self.version == "A": rational_func = Rational_CUDA_A_F elif self.version == "B": self.rational_func = Rational_CUDA_B_F elif self.version == "C": rational_func = Rational_CUDA_C_F elif self.version == "D": rational_func = Rational_CUDA_D_F elif self.version == "N": rational_func = Rational_CUDA_NONSAFE_F else: raise ValueError("version %s not implemented" % self.version) if 'apply' in dir(rational_func): self.activation_function = rational_func.apply else: self.activation_function = rational_func else: if self.version == "A": rational_func = Rational_PYTORCH_A_F elif self.version == "B": rational_func = Rational_PYTORCH_B_F elif self.version == "C": rational_func = Rational_PYTORCH_C_F elif self.version == "D": rational_func = Rational_PYTORCH_D_F elif self.version == "N": rational_func = Rational_NONSAFE_F else: raise ValueError("version %s not implemented" % self.version) self.activation_function = rational_func self._handle_retrieve_mode = None self.distribution = None return self def change_version(self, version): assert version in ["A", "B", "C", "D"] if version == self.version: print(f"This Rational function has already the correct type {self.version}") return if "cuda" in str(self.device): if version == "A": rational_func = Rational_CUDA_A_F elif version == "B": rational_func = Rational_CUDA_B_F elif version == "C": rational_func = Rational_CUDA_C_F elif version == "D": rational_func = Rational_CUDA_D_F elif self.version == "N": rational_func = Rational_CUDA_NONSAFE_F else: raise ValueError("version %s not implemented" % version) if 'apply' in dir(rational_func): self.activation_function = rational_func.apply else: self.activation_function = rational_func self.version = version else: if version == "A": rational_func = Rational_PYTORCH_A_F elif version == "B": rational_func = Rational_PYTORCH_B_F elif version == "C": rational_func = Rational_PYTORCH_C_F elif version == "D": rational_func = Rational_PYTORCH_D_F elif self.version == "N": rational_func = Rational_NONSAFE_F else: raise ValueError("version %s not implemented" % self.version) self.activation_function = rational_func self.version = version def input_retrieve_mode(self, auto_stop=False, max_saves=1000, bin_width=0.1): """ Will retrieve the distribution of the input in self.distribution. \n This will slow down the function, as it has to retrieve the input \ dist.\n Arguments: auto_stop (bool): If True, the retrieving will stop after `max_saves` \ calls to forward.\n Else, use :meth:`torch.Rational.training_mode`.\n Default ``False`` max_saves (int): The range on which the curves of the functions are fitted \ together.\n Default ``1000`` """ if self._handle_retrieve_mode is not None: # print("Already in retrieve mode") return if "cuda" in self.device: from rational.utils.histograms_cupy import Histogram else: from rational.utils.histograms_numpy import Histogram self.distribution = Histogram(bin_width) # print("Retrieving input from now on.") if auto_stop: self.inputs_saved = 0 self._handle_retrieve_mode = self.register_forward_hook(_save_input_auto_stop) self._max_saves = max_saves else: self._handle_retrieve_mode = self.register_forward_hook(_save_input) def clear_hist(self): self.inputs_saved = 0 bin_width = self.distribution.bin_size if "cuda" in self.device: from rational.utils.histograms_cupy import Histogram else: from rational.utils.histograms_numpy import Histogram self.distribution = Histogram(bin_width) def training_mode(self): """ Stops retrieving the distribution of the input in `self.distribution`. """ # print("Training mode, no longer retrieving the input.") if self._handle_retrieve_mode is not None: self._handle_retrieve_mode.remove() self._handle_retrieve_mode = None @classmethod def save_all_inputs(self, save, auto_stop=False, max_saves=10000, bin_width="auto"): """ Have every rational save every input. Arguments: save (bool): If True, every instanciated rational function will \ retrieve its input, else, it won't. auto_stop (bool): If True, the retrieving will stop after `max_saves` \ calls to forward.\n Else, use :meth:`torch.Rational.training_mode`.\n Default ``True`` max_saves (int): The range on which the curves of the functions are fitted \ together.\n Default ``10000`` bin_width (float or "auto"): The size of the histogram's bin width to store the input \ in.\n If `"auto"`, then automatically determines the bin width \ to have ~100 bins.\n Default ``"auto"`` """ if save: for rat in self.list: rat._saving_input = True rat.input_retrieve_mode(auto_stop, max_saves, bin_width=bin_width) else: for rat in self.list: rat._saving_input = False rat.training_mode() @property def saving_input(self): return self._saving_input @saving_input.setter def saving_input(self, new_value): if new_value is True: self._saving_input = True self.input_retrieve_mode() elif new_value is False: self._saving_input = False self.training_mode() else: print("saving_input of rationals should be set with booleans") class AugmentedRational(nn.Module): """ Augmented Rational activation function inherited from ``Rational`` Arguments: approx_func (str): The name of the approximated function for initialisation. \ The different initialable functions are available in `rational.rationals_config.json`. \n Default ``leaky_relu``. degrees (tuple of int): The degrees of the numerator (P) and denominator (Q).\n Default ``(5, 4)`` cuda (bool): Use GPU CUDA version. If None, use cuda if available on the machine\n Default ``None`` version (str): Version of Rational to use. Rational(x) = P(x)/Q(x)\n `A`: Q(x) = 1 + \|b_1.x\| + \|b_2.x\| + ... + \|b_n.x\|\n `B`: Q(x) = 1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `C`: Q(x) = 0.1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `D`: like `B` with noise\n Default ``A`` trainable (bool): If the weights are trainable, i.e, if they are updated during backward pass\n Default ``True`` Returns: Module: Augmented Rational module """ def __init__(self, approx_func="leaky_relu", degrees=(5, 4), cuda=None, version="A", trainable=True, train_numerator=True, train_denominator=True): super(AugmentedRational, self).__init__() self.in_bias = nn.Parameter(torch.FloatTensor([0.0])) self.out_bias = nn.Parameter(torch.FloatTensor([0.0])) self.vertical_scale = nn.Parameter(torch.FloatTensor([1.0])) self.horizontal_scale = nn.Parameter(torch.FloatTensor([1.0])) def forward(self, x): x = self.horizontal_scale * x + self.in_bias out = self.activation_function(x, self.numerator, self.denominator, self.training) return self.vertical_scale * out + self.out_bias class RationalNonSafe(Rational_base, nn.Module): """ Rational activation function inherited from ``torch.nn.Module`` Arguments: approx_func (str): The name of the approximated function for initialisation. \ The different initialable functions are available in \ `rational.rationals_config.json`. \n Default ``leaky_relu``. degrees (tuple of int): The degrees of the numerator (P) and denominator (Q).\n Default ``(5, 4)`` cuda (bool): Use GPU CUDA version. \n If ``None``, use cuda if available on the machine\n Default ``None`` version (str): Version of Rational to use. Rational(x) = P(x)/Q(x)\n `A`: Q(x) = 1 + \|b_1.x\| + \|b_2.x\| + ... + \|b_n.x\|\n `B`: Q(x) = 1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `C`: Q(x) = 0.1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `D`: like `B` with noise\n Default ``A`` trainable (bool): If the weights are trainable, i.e, if they are updated during \ backward pass\n Default ``True`` Returns: Module: Rational module """ def __init__(self, degrees=(5, 4), cuda=None, trainable=True, train_numerator=True, train_denominator=True): super().__init__() if cuda is None: cuda = torch_cuda_available() if cuda is True: device = "cuda" elif cuda is False: device = "cpu" else: device = cuda self.numerator = nn.Parameter(torch.tensor([ 0., 1.01130152, -0.25022214, -0.10285302, 0.02551535]).to(device), requires_grad=True) self.denominator = nn.Parameter(torch.tensor([-0.24248419, 0.07964891, -0.02110156]).to(device), requires_grad=True) # self.numerator = nn.Parameter(torch.ones(degrees[0]+1).to(device), # requires_grad=True) # self.denominator = nn.Parameter(torch.ones(degrees[1]).to(device), # requires_grad=True) self.register_parameter("numerator", self.numerator) self.register_parameter("denominator", self.denominator) self.device = device self.degrees = degrees self.training = trainable self.version = "NonSafe" # # def forward(self, x, y): # z = x.view(-1) # len_num, len_deno = len(self.numerator), len(self.denominator) # # xps = torch.vander(z, max(len_num, len_deno), increasing=True) # xps = _get_xps(z, len_num, len_deno).to(self.numerator.device) # numerator = xps.mul(self.numerator).sum(1) # denominator = xps[:, 1:len_deno+1].mul(self.denominator).sum(1) * y.to(self.numerator.device) # return (numerator - denominator).view(x.shape) def forward(self, x): z = x.view(-1) len_num, len_deno = len(self.numerator), len(self.denominator) # xps = torch.vander(z, max(len_num, len_deno), increasing=True) xps = _get_xps(z, len_num, len_deno).to(self.numerator.device) numerator = xps.mul(self.numerator).sum(1) denominator = xps[:, 1:len_deno+1].mul(self.denominator).sum(1) return numerator.div(1 + denominator).view(x.shape) def fit(self, x, y): """ Linear regression trick to calculate the numerator and denominator \ based on x and y """ from sklearn import linear_model clf = linear_model.LinearRegression(fit_intercept=False) [np.ones_like(x), x, x**2, x**3, x**4, -y*x, -y*x**2, -y*x**3].T clf.fit(np.array(), y) class EmbeddedRational(Rational, nn.Module): nb_rats = 2 list = [] def __init__(self, approx_func="leaky_relu", degrees=(3, 2), cuda=None, version="A", *args, **kwargs): super().__init__(approx_func, degrees) if approx_func == "leaky_relu": approx_func += "_0.1" RationalWarning.warn("Using a leaky_relu_0.1 to make " \ "EmbeddedRational approx leaky_relu") self.init_approximation = approx_func self.degrees=degrees self.cuda = cuda self.version = version self.successive_rats = [] for i in range(self.nb_rats): rat = Rational(approx_func, degrees, cuda, version, *args, **kwargs) self.add_module(f"rational_{i}", rat) self.successive_rats.append(rat) self.list.append(self) del self.numerator del self.denominator self.numerators = [rat.numerator for rat in self.successive_rats] self.denominators = [rat.denominator for rat in self.successive_rats] def forward(self, x): for rat in self.successive_rats: x = rat(x) return x def _apply(self, fn): for rat in self.successive_rats: for clos in fn.__closure__: if type(clos.cell_contents) is torch.device: device = clos.cell_contents rat.device = device break return super()._apply(fn) def numpy(self): from rational.numpy import EmbeddedRational as ERational_numpy ERational_numpy.nb_rats = self.nb_rats erational_n = ERational_numpy(self.init_approximation, self.degrees, self.version) for trat, nrat in zip(self.successive_rats, erational_n.successive_rats): nrat.numerator = trat.numerator.tolist() nrat.denominator = trat.denominator.tolist() return erational_n def __repr__(self): return (f"Embedded Rational Activation Function (PYTORCH version " f"{self.version}) of degrees {self.degrees} running on " f"{self.device}") # @property() # def list(self): class RecurrentRational(): """ Recurrent rational activation function - wrapper for Rational Arguments: approx_func (str): The name of the approximated function for initialisation. \ The different initialable functions are available in \ `rational.rationals_config.json`. \n Default ``leaky_relu`` degrees (tuple of int): The degrees of the numerator (P) and denominator (Q).\n Default ``(5, 4)`` cuda (bool): Use GPU CUDA version. \n If ``None``, use cuda if available on the machine\n Default ``None`` version (str): Version of Rational to use. Rational(x) = P(x)/Q(x)\n `A`: Q(x) = 1 + \|b_1.x\| + \|b_2.x\| + ... + \|b_n.x\|\n `B`: Q(x) = 1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `C`: Q(x) = 0.1 + \|b_1.x + b_2.x + ... + b_n.x\|\n `D`: like `B` with noise\n Default ``A`` trainable (bool): If the weights are trainable, i.e, if they are updated during \ backward pass\n Default ``True`` Returns: Module: Rational module """ def __init__(self, approx_func="leaky_relu", degrees=(5, 4), cuda=None, version="A", trainable=True, train_numerator=True, train_denominator=True): self.rational = Rational(approx_func=approx_func, degrees=degrees, cuda=cuda, version=version, trainable=trainable, train_numerator=train_numerator, train_denominator=train_denominator) def __call__(self, *args, **kwargs): return RecurrentRationalModule(self.rational) class RecurrentRationalModule(nn.Module): def __init__(self, rational): super(RecurrentRationalModule, self).__init__() self.rational = rational self._handle_retrieve_mode = None self.distribution = None def forward(self, x): return self.rational(x) def __repr__(self): return (f"Recurrent Rational Activation Function (PYTORCH version " f"{self.rational.version}) of degrees {self.rational.degrees} running on " f"{self.rational.device}") def cpu(self): return self.rational.cpu() def cuda(self): return self.rational.cuda() def numpy(self): return self.rational.numpy() def fit(self, function, x=None, show=False): return self.rational.fit(function=function, x=x, show=show) def input_retrieve_mode(self, auto_stop=True, max_saves=10000, bin_width=0.01): """ Will retrieve the distribution of the input in self.distribution. \n This will slow down the function, as it has to retrieve the input \ dist.\n Arguments: auto_stop (bool): If True, the retrieving will stop after `max_saves` \ calls to forward.\n Else, use :meth:`torch.Rational.training_mode`.\n Default ``True`` max_saves (int): The range on which the curves of the functions are fitted \ together.\n Default ``10000`` """ if self._handle_retrieve_mode is not None: # print("Already in retrieve mode") return from rational.utils.histograms_cupy import Histogram as hist1 self.distribution = hist1(bin_width) # print("Retrieving input from now on.") if auto_stop: self.inputs_saved = 0 self._handle_retrieve_mode = self.register_forward_hook(_save_input_auto_stop) self._max_saves = max_saves else: self._handle_retrieve_mode = self.register_forward_hook(_save_input) def training_mode(self): """ Stops retrieving the distribution of the input in `self.distribution`. """ print("Training mode, no longer retrieving the input.") self._handle_retrieve_mode.remove() self._handle_retrieve_mode = None def show(self, input_range=None, display=True): return self.rational.show(input_range=input_range, display=display) def _save_input(self, input, output): self.distribution.fill_n(input[0]) def _save_input_auto_stop(self, input, output): self.inputs_saved += 1 self.distribution.fill_n(input[0]) if self.inputs_saved > self._max_saves: self.training_mode()

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