code stringlengths 501 5.19M | package stringlengths 2 81 | path stringlengths 9 304 | filename stringlengths 4 145 |
|---|---|---|---|
from __future__ import print_function
from __future__ import absolute_import
import numpy as np
def normalize_word(word):
new_word = ''
for char in word:
if char.isdigit():
new_word += '0'
else:
new_word += char
return new_word
def read_instance(input_file, word_... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_event/utils/functions.py | functions.py |
from __future__ import print_function
from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
from doc_event.model.wordsequence import WordSequence
from doc_event.model.crf import CRF
class SeqLabel(nn.Module):
def __init__(self, data):
super(SeqLabel, s... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_event/model/seqlabel.py | seqlabel.py |
from __future__ import print_function
from __future__ import absolute_import
import torch
import torch.nn as nn
import numpy as np
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from doc_event.model.wordrep import WordRep
seed_num = 42
torch.manual_seed(seed_num)
torch.cuda.manual_seed_all... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_event/model/wordsequence.py | wordsequence.py |
from __future__ import print_function
from __future__ import absolute_import
import os
import torch
import torch.nn as nn
import numpy as np
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM, WordpieceTokenizer
seed_num = 42
torch.manual_seed(seed_num)
np.random.seed(seed_num)
torch.cuda.... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_event/model/wordrep.py | wordrep.py |
from __future__ import print_function
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
START_TAG = -2
STOP_TAG = -1
# Compute log sum exp in a numerically stable way for the forward algorithm
def log_sum_exp(vec, m_size):
"""
calculate log of exp sum
a... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_event/model/crf.py | crf.py |
import os
from doc_similarity.model.cosine_similarity import CosineSimilarity
from doc_similarity.model.jaccard import JaccardSimilarity
from doc_similarity.model.levenshtein import LevenshteinSimilarity
from doc_similarity.model.min_hash import MinHashSimilarity
from doc_similarity.model.sim_hash import SimHashSimil... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_similarity/data/compare.py | compare.py |
import xlrd
import xlsxwriter
def xlsx2list(xlsx_path: str) -> list:
wb = xlrd.open_workbook(xlsx_path)
sh = wb.sheet_by_name('Sheet1')
total_list = list()
for i in range(sh.nrows):
if i < 3:
continue
row = sh.row_values(i)
total_list.append({
'id': int... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_similarity/data/data_process.py | data_process.py |
import re
import math
from simhash import Simhash
from doc_similarity.model.base_similarity import BaseSimilarity
from doc_similarity.utils.tool import Tool
class OldSimHashSimilarity(BaseSimilarity):
def __init__(self):
super(OldSimHashSimilarity, self).__init__()
@staticmethod
def _filter_h... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_similarity/model/sim_hash.py | sim_hash.py |
import numpy as np
import gensim
import jieba
import re
from sklearn.metrics.pairwise import cosine_similarity
class Similarity(object):
def __init__(self, model_path, stopword_path):
self.Word2VecModel = gensim.models.KeyedVectors.load_word2vec_format(model_path, binary=False)
self.vocab_list = ... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_similarity/model/similarity_tx.py | similarity_tx.py |
from sklearn.metrics.pairwise import cosine_similarity
from doc_similarity.model.base_similarity import BaseSimilarity
from doc_similarity.utils.tool import Tool
class CosineSimilarity(BaseSimilarity):
"""
余弦相似度
"""
def __init__(self, stop_words_path):
super(CosineSimilarity, self).__init__... | zzsn-nlp | /zzsn_nlp-0.0.1.tar.gz/zzsn_nlp-0.0.1/doc_similarity/model/cosine_similarity.py | cosine_similarity.py |
import os
import ftplib
from ftplib import FTP
from flask import Flask, request, url_for, send_from_directory
from werkzeug.utils import secure_filename
from julei.kmeans import Kmeans
HOST = '127.0.0.1'
DEBUG = False
PORT = 8010
ALLOWED_EXTENSIONS = set(['xls', 'xlsx'])
app = Flask(__name__)
# 限定上传文件最大不超过50M
app.c... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/julei/app.py | app.py |
import time
import os
import pickle
import numpy as np
import gensim
from julei.tfidf import Tfidf
from julei.word2vec_train import Word2vec
class Representation:
def __init__(self):
pass
def make_dir(self):
if os.path.isdir('result/representation/') == False:
os.makedirs(r'result/r... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/julei/representation.py | representation.py |
import time
import os
import re
import pickle
import xlrd
import collections
from pyhanlp import JClass
class Segment:
def __init__(self):
pass
def make_dir(self):
if os.path.isdir('result/segment/') == False:
os.makedirs(r'result/segment/') # 为分词结果创建文件夹
# 定义从excel中读取内容的函数 （exce... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/julei/segment.py | segment.py |
import time
import xlrd
import os
import math
import pickle
import numpy as np
from openpyxl import Workbook
from sklearn.cluster import KMeans
from julei.representation import Representation
class Kmeans:
def __init__(self):
pass
def make_dir(self, path):
dir_path = os.path.join(os.getcwd(), pa... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/julei/kmeans.py | kmeans.py |
from urllib import request, error
import sys
import zipfile
import tarfile
import socket
socket.setdefaulttimeout(15)
def progressbar(cur):
percent = '{:.2%}'.format(cur)
sys.stdout.write('\r')
sys.stdout.write('[%-100s] %s' % ('=' * int(cur*100), percent))
sys.stdout.flush()
print(cur)
def schedu... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/download_data/download.py | download.py |
from flask import Flask, g, render_template, flash, redirect, url_for, request, abort, session
from werkzeug.utils import secure_filename
import time
import os, sys
# sys.path.append('./app/SVM/')
from sentiment_analysis.svm_app import predict_one
from sentiment_analysis.SVM.svm import svm
import warnings
warnings.filt... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/app.py | app.py |
from svm import *
import sys
sys.path.append('../../utils')
from augmentation_utils import *
'''
Currently, data augmentation makes result worse. Better augmentation method should be proposed.
'''
connection_string = 'cis/cis_zzsn9988@118.190.174.96:1521/orcl'
from_date = '2017-06-01'
to_date = '2017-08-03'
wordvec... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/SVM/svm_with_virtual_complementary_augmentation.py | svm_with_virtual_complementary_augmentation.py |
import sys
# sys.path.append('../../utils')
from sentiment_analysis.utils.utils import *
from sentiment_analysis.utils.word2vec_utils import *
from sklearn.svm import SVC
from sklearn.externals import joblib
import os
import cx_Oracle
class svm():
def __init__(self, label_dict=None, probability=True, C=5, kernel='rbf'... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/SVM/svm.py | svm.py |
from svm import *
import sys
sys.path.append('../../utils')
from augmentation_utils import *
'''
Currently, data augmentation makes result worse. Better augmentation method should be proposed.
'''
connection_string = 'cis/cis_zzsn9988@118.190.174.96:1521/orcl'
from_date = '2017-06-01'
to_date = '2017-08-03'
wordvec... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/SVM/svm_with_virtual_data_augmentation.py | svm_with_virtual_data_augmentation.py |
import sys
sys.path.append('../../utils')
from utils import *
from word2vec_utils import *
from sklearn.svm import SVC
import os
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
connection_string = 'cis/cis_zzsn9988@118.190.174.96:1521/orcl'
from_date = '2017-06-01'
t... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/SVM/svm_parameter_selection.py | svm_parameter_selection.py |
from tfidf_utils import Vocabulary
import numpy as np
import time
def avgvector_virtue_augmentation(cut_data, label, model, emotion_dict, num_aug=10000, neg_aug_ratio=0.8, \
ratio=[0.3, 0.5, 0.2], min_virtue_sent_len=10, max_virtue_sent_len=500):
'''ratio: [p1, p2, p3], p1: prob of words from related emotion di... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/utils/augmentation_utils.py | augmentation_utils.py |
import pickle, os
from gensim.models import word2vec, KeyedVectors
import numpy as np
from sklearn.decomposition import PCA
from sklearn.externals import joblib
import jieba
import cx_Oracle
os.environ['NLS_LANG'] = 'SIMPLIFIED CHINESE_CHINA.UTF8'
_backend = 'jieba'
try:
from jpype import *
startJVM(getDefaultJVMPa... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/utils/utils.py | utils.py |
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
class Vocabulary(object):
def __init__(self, signature, min_word_len=2, name='voc'):
self.signature = signature
self.min_word_len = min_word_len
self.name = name
self.voc = dict()
self.f... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/sentiment_analysis/utils/tfidf_utils.py | tfidf_utils.py |
from flask import Flask, g, render_template, flash, redirect, url_for, request, abort, session
from ner.extract import Extract
import pandas as pd
import traceback
import json
import os
DEBUG = False
PORT = 8018
HOST = '0.0.0.0'
# HOST = '127.0.0.1'
app = Flask(__name__)
file_path = os.path.dirname(os.path.realpath(__... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/ner/app.py | app.py |
import pandas as pd
import re
import jieba.posseg as pseg
import emoji
import time as time_time
import os
import difflib
from pyhanlp import *
class Extract:
def __init__(self, country):
self.country = country
def read_txt(self, filenames):
# r'./feature_dict.txt'
lines = []
f =... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/ner/extract.py | extract.py |
import re
import pandas as pd
import json
import emoji
a = r'<h1>青岛双星控股股东双星集团</h1><p>响了青岛市属国有企业混改第一枪……10月9日，青岛双星<span style="font-size: 24px;">股价应声涨停，显示了市场对于这一举动的期待。</span></p><p><span style="font-size: 24px;">作为国资大省,山东省国有企业三年混改计划和青岛市国有企业改革正<span style="font-family: 隶书, SimLi; font-size: 24px;">步入深水区,双星集</span></span><... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/de_duplication/process.py | process.py |
import jieba
import jieba.posseg as pseg
from relativeness_analysis.vocabulary import Vocabulary
from relativeness_analysis.classifier2 import xgboost
import xlrd, xlwt
import os, sys
import argparse
import numpy as np
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text imp... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relativeness_analysis/relevant_analysis.py | relevant_analysis.py |
import pickle
import numpy as np
class Vocabulary(object):
def __init__(self, signature, min_word_len=2, name='voc'):
self.signature = signature
self.min_word_len = min_word_len
self.name = name
self.voc = dict()
self.freq = dict()
self.doc_freq = dict()
self.oov = None
self.size = 0
self._fixed_v... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relativeness_analysis/vocabulary.py | vocabulary.py |
# -*- coding: utf-8 -*-
from flask import Flask, g, render_template, flash, redirect, url_for, request, abort, session
import os
from relativeness_analysis.relevant_analysis import main, test
from relativeness_analysis.manager import test as train_test
import warnings
warnings.filterwarnings('ignore')
DEBUG = False
PO... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relativeness_analysis/app.py | app.py |
import numpy as np
import jieba
import xlrd
import sys, time
import pickle
from relativeness_analysis.vocabulary import Vocabulary
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_selection import SelectFpr, mutual_info_classif... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relativeness_analysis/utils.py | utils.py |
from __future__ import print_function
import xlrd
import numpy as np
import scipy.sparse.csr
import scipy.sparse.csc
import pickle
# from gensim import models
import sys, os
from relativeness_analysis.utils import *
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relativeness_analysis/classifier2.py | classifier2.py |
from relativeness_analysis.vocabulary import Vocabulary
from relativeness_analysis.classifier2 import xgboost
from relativeness_analysis.utils import data_processor
import time, os
import numpy as np
# import pandas as pd
import cx_Oracle
import pickle
os.environ['NLS_LANG'] = 'SIMPLIFIED CHINESE_CHINA.UTF8'
class... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relativeness_analysis/manager.py | manager.py |
#please refer to https://hub.tensorflow.google.cn/google/bert_chinese_L-12_H-768_A-12/1
import sys
sys.path.insert(0, 'D:/peking_code/code_python/Bert201912/bert-master')
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import bert
from bert import run_classifier
from bert import optimizatio... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relation_extraction/hub_TextEmbedding.py | hub_TextEmbedding.py |
from flask import Flask, g, render_template, flash, redirect, url_for, request, abort, session
from werkzeug.utils import secure_filename
import time
import os
#import sys
#sys.path.append("./src")
#os.chdir(os.path.join(os.getcwd(),'src'))
#import rel_prediction
import traceback
from relation_extraction.preprocessi... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relation_extraction/app.py | app.py |
import jieba
import re
import os
import xlwt
# 使用停用词
filepath = os.path.dirname(os.path.realpath(__file__))
stop = open(os.path.join(filepath, './user_data/stop.txt'), 'r+', encoding='utf-8')
stopword = stop.read().split("\n")
# 最长句子长度
word_len = 600
# 判断汉字个数
def han_number(char):
number = 0
for item in char:... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relation_extraction/preprocessing_xls.py | preprocessing_xls.py |
from collections import Counter
import os
from relation_extraction import rel_ext
import pandas as pd
def simple_bag_of_words_featurizer(kbt, corpus, feature_counter):
for ex in corpus.get_examples_for_entities(kbt.sbj, kbt.obj):
#print(ex.middle)
for word in ex.middle.split(' '):
featu... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relation_extraction/rel_train.py | rel_train.py |
from relation_extraction import rel_ext
import os
import pandas as pd
import xlrd, xlwt
from sklearn.metrics import precision_recall_fscore_support
import collections
from collections import namedtuple
def simple_bag_of_words_featurizer(kbt, corpus, feature_counter):
for ex in corpus.get_examples_for_entities... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relation_extraction/rel_prediction.py | rel_prediction.py |
from collections import Counter, defaultdict, namedtuple
import gzip
import numpy as np
import os
import random
from sklearn.feature_extraction import DictVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import precision_recall_fscore_support
from sklearn.model_selection import train_... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/relation_extraction/rel_ext.py | rel_ext.py |
import os
import pickle
from catl.utilities import preprocess_train
from catl.model import ensemble
from openpyxl import Workbook
name = input('Please input the name of company: ')
current_path = os.getcwd()
if os.path.isdir('data/'+name+'/preprocess') == False:
os.makedirs(r'data/'+name+'/preprocess')
if os.pa... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/catl/train.py | train.py |
import os
import pickle
import xlrd
import re
import jieba
from openpyxl import Workbook
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.preprocessing import normalize
from sklearn import metrics
from sklearn.externals import joblib
... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/catl/draft.py | draft.py |
from __future__ import unicode_literals
import array
from collections import Mapping, defaultdict
import numbers
from operator import itemgetter
import re
import unicodedata
import numpy as np
import scipy.sparse as sp
from ..base import BaseEstimator, TransformerMixin
from ..externals import six
from ..externals.si... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/catl/text.py | text.py |
import os
import pickle
import xlrd
import re
import jieba
from openpyxl import Workbook
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.preprocessing import normalize
from sklearn import metrics
from openpyxl import Workbook
from sk... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/catl/utilities.py | utilities.py |
import numpy as np
import heapq
from sklearn import linear_model
from sklearn.externals import joblib
import matplotlib.pyplot as plt
from sklearn import metrics
class ensemble(object):
def __init__(self,name,r,data,labels,model_save_path,results_save_path):
self.Name = name
self.Data = data
... | zzsnML | /zzsnML-1.0.1-py3-none-any.whl/catl/model.py | model.py |
from zzstocklib_pkg import zzlogger
from urllib import request,parse
import time,datetime
import json
import re
import pandas as pd
import numpy as np
logger = zzlogger.logger
def get_sinacodelist(stock_list):
"""根据股票代码转译为sina所需要的代码，香港hk，沪sh，深sz""" #https://www.cnblogs.com/xuliangxing/p/8492705.html
new_codel... | zzstocklib-pkg-pubbyzz | /zzstocklib_pkg_pubbyzz-0.0.2-py3-none-any.whl/zzstocklib_pkg/sinaFinanceUtility.py | sinaFinanceUtility.py |
import re
import os
import tempfile
class Properties:
def __init__(self, file_name):
self.file_name = file_name
self.properties = {}
if os.path.exists(file_name):
with open(file_name) as f:
for line in f:
tline = line.strip()
... | zzstocklib-pkg-pubbyzz | /zzstocklib_pkg_pubbyzz-0.0.2-py3-none-any.whl/zzstocklib_pkg/propertiesUtility.py | propertiesUtility.py |
import pandas as pd
import requests
from lxml import etree
import time
import json
from pandas.io.json import json_normalize
import os
import re
def get_KZZInfo_from_JSL():
now_time = time.time()
url = 'https://www.jisilu.cn/data/cbnew/cb_list/?___jsl=LST___t=' + str(now_time)
# 发送请求并解析HTML对象
response ... | zzstocklib-pkg-pubbyzz | /zzstocklib_pkg_pubbyzz-0.0.2-py3-none-any.whl/zzstocklib_pkg/KZZUtility.py | KZZUtility.py |
import pandas as pd
import struct
import datetime
import os
#only deal with tongxinda shanghai&shenzhen stock lday data
def stock_csv(code):
file_object_path = 'C:/workspace/stock/stockdata/lday/' + code +'.csv'
filepath ='C:/new_jyplug/vipdoc/sz/lday/sz' + code +'.day'
if not os.path.exists(filepath)... | zzstocklib-pkg-pubbyzz | /zzstocklib_pkg_pubbyzz-0.0.2-py3-none-any.whl/zzstocklib_pkg/genKZZreport.py | genKZZreport.py |
import os
import smtplib
import time
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
class EmailConf:
EmailQQ = {"host": "smtp.qq.com", "port": 465}
Email163 = {"host": "smtp.163.com", "port": 465}
class SendEmail:
""... | zzsukitest | /zzsukitest-1.0.6.tar.gz/zzsukitest-1.0.6/zzsuki_test/core/send_email.py | send_email.py |
import re
import sys
import inspect
import warnings
from functools import wraps
from types import MethodType as MethodType
from collections import namedtuple
try:
from collections import OrderedDict as MaybeOrderedDict
except ImportError:
MaybeOrderedDict = dict
from unittest import TestCase
try:
from un... | zzsukitest | /zzsukitest-1.0.6.tar.gz/zzsukitest-1.0.6/zzsuki_test/core/parameterized.py | parameterized.py |
import hmac
import hashlib
import base64
import urllib.parse
import requests
import os
import smtplib
import time
from email.mime.text import MIMEText
from email.mime.application import MIMEApplication
from email.mime.multipart import MIMEMultipart
class SendEmail:
"""Send mail"""
def __init__(self, host, us... | zzsukitest | /zzsukitest-1.0.6.tar.gz/zzsukitest-1.0.6/zzsuki_test/core/result_push.py | result_push.py |
from functools import wraps
import json
import yaml
def _create_test_name(index, name):
if index + 1 < 10:
test_name = name + "_00" + str(index + 1)
elif index + 1 < 100:
test_name = name + "_0" + str(index + 1)
else:
test_name = name + "_" + str(index + 1)
return test_name
d... | zzsukitest | /zzsukitest-1.0.6.tar.gz/zzsukitest-1.0.6/zzsuki_test/core/data_driver.py | data_driver.py |
import keyword
# 实现了所以关键字的列出
"""
num = 10
# print(num, id(num))
# num = 30
del num
print(num, id(num))
"""
"""result = input('请输入bool型的参数: ')
print('输入的参数: ', result, type(0))
if result:
print('你好,沐言科技')
"""
"""score = 10.0
if 90<=score<=100:
print("优等生")
elif 60<=score<90:
print("良等生")
else:
print("差... | zzt-message | /zzt_message-0.1-py3-none-any.whl/com/zzt/info/demo02.py | demo02.py |
import pandas as pd
import tensorflow as tf
import numpy as np
from tensorflow.keras import layers,activations
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.callbacks import ModelCheckpoint
#SE模块
#如需使用，得加到下采样层里
class Squeeze_excitation_layer(tf.keras.Model):
def __init__(self, filter_s... | zzx-deep-genome | /zzx_deep_genome-0.1.5-py3-none-any.whl/zzx_deep_genome/tf_model.py | tf_model.py |
import time #内置模块
import pysam
import pyBigWig
import numpy as np
import pandas as pd
from pysam import FastaFile
from scipy.ndimage import gaussian_filter1d
#辅助函数，用于one-hot编码
#用此函数对100万条1000长度的序列编码需要约700秒（GPU02节点）
def one_hot_dna(dna):
dna_dict={'A':[1.0,0.,0.,0.],'C':[0.,1.0,0.,0.],'G':[0.,0.,1.0,0.],'T':[0.,0.... | zzx-deep-genome | /zzx_deep_genome-0.1.5-py3-none-any.whl/zzx_deep_genome/get_dataset.py | get_dataset.py |
import os
import re
import numpy as np
import pandas as pd
import torch
import time #内置模块
import pysam
import pyBigWig
from pysam import FastaFile
from scipy.ndimage import gaussian_filter1d
#辅助函数，用于one-hot编码
#用此函数对100万条1000长度的序列编码需要约700秒（GPU02节点）
def one_hot_dna(dna):
dna_dict={'A':[1.0,0.,0.,0.],'C':[0.,1.0,... | zzx-deep-genome | /zzx_deep_genome-0.1.5-py3-none-any.whl/zzx_deep_genome/cnn_torch_motif_1d.py | cnn_torch_motif_1d.py |
import numpy as np
import wget
import pandas as pd
from random import randint, sample
#获取pfm
def get_pfm(taxonomic_groups=str('plants'),data_local = None):
if data_local == None:
if taxonomic_groups=='plants':
DATA_URL = 'http://jaspar.genereg.net/download/CORE/JASPAR2020_CORE_plants_non-redund... | zzx-deep-genome | /zzx_deep_genome-0.1.5-py3-none-any.whl/zzx_deep_genome/filter_initialization.py | filter_initialization.py |
import os
import re
import numpy as np
import pandas as pd
import tensorflow as tf
import time #内置模块
import pysam
import pyBigWig
from pysam import FastaFile
from scipy.ndimage import gaussian_filter1d
#辅助函数，用于one-hot编码
#用此函数对100万条1000长度的序列编码需要约700秒（GPU02节点）
def one_hot_dna(dna):
dna_dict={'A':[1.0,0.,0.,0.],'C... | zzx-deep-genome | /zzx_deep_genome-0.1.5-py3-none-any.whl/zzx_deep_genome/cnn_tf_motif_1d.py | cnn_tf_motif_1d.py |
import torch
import numpy as np
import torch
import torch.nn as nn
#最后一层pool改为1的DeeperDeepSEA
class DeeperDeepSEA_pool(nn.Module):
def __init__(self, sequence_length, n_targets):
super(DeeperDeepSEA_pool, self).__init__()
conv_kernel_size = 8
pool_kernel_size = 4
self.conv_net ... | zzx-deep-genome | /zzx_deep_genome-0.1.5-py3-none-any.whl/zzx_deep_genome/torch_model.py | torch_model.py |
=====
zzyzx
=====
Do you believe in the cloud? It's in fact only somebody else's computer.
Those might fail or get hacked.
Do you believe in bug-free software? Nah, it's more likely every now and
then a crash, a bug, a race condition or some other back luck will lead
to data corruption of the things that you work on.... | zzyzx | /zzyzx-2017.1.0.tar.gz/zzyzx-2017.1.0/README.rst | README.rst |
# zzz
Python library that waits until something happens.
## Benefit
You will no longer have to write annoying `while`/`time.sleep()` checks
to wait until a variable is euqal to a certain value.
## Usage
It's real simple.
All you gotta do is just have an import statement:
```
from zzz import z
```
After that, yo... | zzz | /zzz-0.0.2.tar.gz/zzz-0.0.2/README.md | README.md |
import sys
import os
import re
import time
import copy
from threading import Thread
from collections import OrderedDict
from sgtn_properties import Properties
from sgtn_util import FileUtil, NetUtil, SysUtil
from sgtn_util import LOG_TYPE_INFO, KEY_RESULT, KEY_HEADERS
from sgtn_bykey import SingletonByKey
from sgtn_l... | zzz001 | /zzz001-0.0.4.tar.gz/zzz001-0.0.4/sgtnclient/sgtn_client.py | sgtn_client.py |
from collections import OrderedDict
from sgtn_py_base import pybase
MAX_LINE_BUFFER = 1024
class LineReader:
def __init__(self, inCharBuf):
self.lineBuf = [None] * MAX_LINE_BUFFER
self.inLimit = 0
self.inOff = 0
self.inCharBuf = inCharBuf
if self.inCharBuf:
... | zzz001 | /zzz001-0.0.4.tar.gz/zzz001-0.0.4/sgtnclient/sgtn_properties.py | sgtn_properties.py |
import os
import sys
import json
import re
import logging
from collections import OrderedDict
from sgtn_py_base import pybase, SgtnException
from sgtn_debug import SgtnDebug
import ssl
if hasattr(ssl, '_create_unverified_context'): # for python 2.7
ssl._create_default_https_context = ssl._create_unverified_cont... | zzz001 | /zzz001-0.0.4.tar.gz/zzz001-0.0.4/sgtnclient/sgtn_util.py | sgtn_util.py |
from collections import OrderedDict
import threading
lock = threading.Lock()
from sgtn_locale import SingletonLocaleUtil
_indexLocaleItem = 0
class SingletonByKeyItem(object):
def __init__(self, componentIndex, itemIndex):
self._componentIndex = componentIndex
self._pageIndex = itemIndex // S... | zzz001 | /zzz001-0.0.4.tar.gz/zzz001-0.0.4/sgtnclient/sgtn_bykey.py | sgtn_bykey.py |
import re
class SingletonLocale(object):
def __init__(self, locale):
self._localeList = [locale]
def get_near_locale_list(self):
return self._localeList
def add_near_locale(self, locale):
if locale in self._localeList:
return False
self._localeList.append(l... | zzz001 | /zzz001-0.0.4.tar.gz/zzz001-0.0.4/sgtnclient/sgtn_locale.py | sgtn_locale.py |
=============
zzzeeksphinx
=============
This is zzzeek's own Sphinx layout, used by SQLAlchemy.
This layout is first and foremost pulled in for the SQLAlchemy documentation
builds (and possibly other related projects).
.. note:: The stability of zzzeeksphinx is **not** guaranteed and APIs and
behaviors can chang... | zzzeeksphinx | /zzzeeksphinx-1.4.0.tar.gz/zzzeeksphinx-1.4.0/README.rst | README.rst |
ZzzFS: dataset management à la ZFS
ZzzFS ("snooze FS") brings a set of ZFS management commands to non-ZFS volumes,
turning any directory on a traditional filesystem into a zpool-like object.
Using only the Python standard library, ZzzFS can be useful to, for example,
test tools that use ZFS functionality on a system ... | zzzfs | /zzzfs-0.1.2.tar.gz/zzzfs-0.1.2/README | README |
# Copyright (c) 2015 Daniel W. Steinbrook. All rights reserved.
def validate_component_name(component_name, allow_slashes=False):
'''Check that component name starts with an alphanumeric character, and
disalllow all non-alphanumeric characters except underscore, hyphen, colon,
and period in component nam... | zzzfs | /zzzfs-0.1.2.tar.gz/zzzfs-0.1.2/libzzzfs/util.py | util.py |
# Copyright (c) 2015 Daniel W. Steinbrook. All rights reserved.
#
# ZzzFS strucutre:
#
# <ZZZFS_ROOT>/
# <pool_name>/
# data -> <disk>
# properties/
# filesystems/
# <fs_name>/
# data -> ../data/<fs_name>/
# properties/
# snapshots/
# <snapshot... | zzzfs | /zzzfs-0.1.2.tar.gz/zzzfs-0.1.2/libzzzfs/dataset.py | dataset.py |
# Copyright (c) 2015 Daniel W. Steinbrook. All rights reserved.
import os
import sys
import shutil
import filecmp
from libzzzfs.dataset import (
get_all_datasets, get_dataset_by, Filesystem, Pool, Snapshot)
from libzzzfs.util import tabulated, validate_component_name, ZzzFSException
# Each method returns a str... | zzzfs | /zzzfs-0.1.2.tar.gz/zzzfs-0.1.2/libzzzfs/zfs.py | zfs.py |
# Copyright (c) 2015 Daniel W. Steinbrook. All rights reserved.
import argparse
from libzzzfs.util import PropertyAssignment, PropertyList
class CommandInterpreter(object):
'''Base class for ZzzfsCommandInterpreter/ZzzpoolCommandInterpreter'''
def __init__(self, argv):
self.parser = argparse.Argume... | zzzfs | /zzzfs-0.1.2.tar.gz/zzzfs-0.1.2/libzzzfs/interpreter.py | interpreter.py |
Requests: Python utils - Time
=========================
Get demand time from all kinds of original time format after delay without specified type.
Usage
---------------
def get_time(ts=None, delay=0, fmt=19)
Arguments: [original_time] [delay] [output_fmt:{0,6,8,10,16,17,19}]
output_fmt:
19: '%Y-%m-%d %H:%M:%S'... | zzzutils | /zzzutils-0.1.7.tar.gz/zzzutils-0.1.7/README.rst | README.rst |
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