text string | source string |
|---|---|
n=int(input())
a=list()
for i in range(n):
a.append('0')
print (a)
def gray(i):
if(i==n-1):
print("".join(a))
a[i]='1' if(a[i]=='0')else('0')
print("".join(a))
return
c=1
while (c<=2):
gray(i+1)
if(c!=2):
a[i]='1' if(a[i]=='0')else('0')
c+=1
gray(0)
| code |
from car import Car
import math
import numpy as np
import random
import shared as g
class BufferBuilder(Car):
def __init__(self, sim, lane, speed, maxspeed, id, carAhead, carUpAhead, carDownAhead, laneidx, size, canvasheight,
lanes, slowdown):
super(BufferBuilder, self).__init__(sim, lane... | code |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
class Search(object):
def __init__(self, vocab_size, pad, unk, eos):
self.pad = pad
self.unk ... | code |
#!/usr/bin/env python
# coding=utf-8
"""
@desc:
@author: Luo.lu
@date: 2019-07-10
"""
class Solution(object):
def wordBreak(self, s, wordDict):
"""
:type s: str
:type wordDict: List[str]
:rtype: List[str]
"""
# 借助139，判断是否存在可分的情况，否则特例可能会超时
if not s:
... | code |
import os
import sys
import json
import matplotlib.pyplot as plt
import pandas as pd
def test_plot():
test_reses = []
for i in range(10):
res_path = './RESULTS/zoom20_rr%d/' % i
test_file = os.path.join(res_path, 'test.json')
with open(test_file, 'r') as f:
test_res = json... | code |
"""
Description
-----------
This module defines the :obj:`ParaMol.Force_field.force_field.ForceField` class which is the ParaMol representation of a force field that contains all the information about the force field terms and correspondent parameters (even relatively to those that will not enter the optimization).
"""... | code |
# search any element in a html page
from selenium import webdriver
browser = webdriver.Firefox()
type(browser)
browser.get('https://gabrielecirulli.github.io/2048/')
try:
elem = browser.find_element_by_class_name('game-explanation')
print('found <%s> element with this class name!' %(elem.tag_name))
except:
prin... | code |
class Solution:
def isPowerOfTwo(self, n: int) -> bool:
#方法2
# 若 x 为 2 的幂，则它的二进制表示中只包含一个 1，则有 x & (-x) = x;
# 若x 不是2 的幂，则它的二进制中不止一个1，则有x &(-x) !=x
#时间复杂度：O(1)
#空间复杂度：O(1)
#if n==0:
# return False
#return n&(-n)==n
#方法1
#去除二进制中最右边的 1... | code |
"""
Metaprogramming
"""
from collections import namedtuple
class ParamsRegistry(type):
def __init__(cls, name, bases, namespace):
super(ParamsRegistry, cls).__init__(name, bases, namespace)
if not hasattr(cls, 'registry'):
cls.registry = set()
cls.registry.add(cls)
cls.r... | code |
# test cases:
# cookie("Ryan") --> "Who ate the last cookie? It was Zach!"
# cookie(26) --> "Who ate the last cookie? It was Monica!"
# cookie(2.3) --> "Who ate the last cookie? It was Monica!"
# cookie(true) --> "Who ate the last cookie? It was the dog!"
def cookie(x):
if type(x) is str:
return "Who ate... | code |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 9 10:01:24 2019
@author: ADMIN
"""
from sklearn.cluster import KMeans
#from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
data=pd.read_csv("km1.csv")
df1=pd.DataFrame(data)
print(df1)
f1=df1['Distance_Featur... | code |
#!/user/bin/env python
# coding=utf-8
import traceback
class func(object):
def __enter__(self):
# raise Exception("haha")
pass
def __exit__(self, type, value, trace):
print type
print value
print trace
print traceback.format_exc(trace)
# return True ... | code |
'''
@Author: Shuai Wang
@Github: https://github.com/wsustcid
@Version: 1.0.0
@Date: 2020-03-26 11:45:38
@LastEditTime: 2020-04-02 11:26:00
'''
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
# Read in the image
image = mpimg.imread('lane.jpg')
print('This image is: ',... | code |
import numpy as np
import numpy.linalg as alg
l1 = []
rows = int(input("enter rows:"))
cols = int(input("enter cols:"))
for i in range(rows):
for j in range(cols):
l1.append(int(input()))
print(l1)
m = np.reshape(l1, (rows, cols))
print(m)
Values, Vectors = alg.eig(m)
print(Values)
print(Vectors[:, 0])
prin... | code |
# -*- coding: utf-8 -*-
## 时间: 2015-02-13
## 跟新内容：
## 增加URL请求时间计算
## 时间: 2015-04-01
## 跟新内容：
## 将指定的测试文件名写入配置文件中，同时增加一个获取当前路径的类
##
import urllib.request
import urllib.parse
import urllib.error
from pathlib import Path
import json
import io
import sys
import traceback
import os
import os.path
import time
import ... | code |
number = int(input(" Please Enter any Positive Integer : "))
if((number % 5 == 0) and (number % 11 == 0)):
print("Given Number is Divisible by 5 and 11",number)
else:
print("Given Number is Not Divisible by 5 and 11",number)
| code |
matriz = [[], [], []]
R = list(range(0, 3))
for c in R:
for i in R:
matriz[c].append(int(input(f'Digite um valor para[{c}, {i}]: ')))
print('-' * 30)
for d in R:
print('(', end=' ')
for j in r:
print(f'[{matriz[d][j]:^5}]', end=' ')
print(')') | code |
"""Sources:
dataset: https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29
"""
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.compose i... | code |
v = int(input())
i = 0
while i < v:
n, m = input().split(" ")
n = int(n)
m = int(m)
tam = n ** m
tam = str(tam)
tam = list(tam)
print(len(tam))
i = i + 1 | code |
import maya.cmds as cmds
import random
import math
import imp
v = imp.load_source('v', '/lhome/kristina/Documents/code/maya/boids/vector_class.py') # maya python 2.7 weirdness
width = 100
height = 100
depth = 100 # was 150
class Particle(v.vec3):
'''
Class defining a single particle.
'''
def __init_... | code |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 27 11:18:36 2017
@author: roger
"""
import numpy as np
import itertools as itt
import matplotlib.pyplot as plt
from numpy import random as rand
def proj_basis(d,D): #Projects the basis of a D-dim space to a d-dim space
W=rand.normal(0,1/d,(d,D)) #Generate a rando... | code |
import sublime, sublime_plugin
class keymapperCommand(sublime_plugin.TextCommand,sublime_plugin.WindowCommand):
"""Key Mapper, sadly you still have to define all the keymaps in your
.sublime-keymap just point them all here that you want to be able to map around.
this subclasses both TextCommand and Windo... | code |
# adapted from https://www.kaggle.com/dan3dewey/santa-s-simple-scheduler
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from santaspkg.cost_function import soft_cost_function as cost_function
from santaspkg.refinement import refinement_pass, refine_until_convergence
from santaspkg.dataset impor... | code |
#! python3
# _*_ coding: utf-8 _*_
from colorama import init, Fore
init(autoreset=False)
class Colored:
# 前景色:红色 背景色:默认
def red(self, s):
return Fore.LIGHTRED_EX + s + Fore.RESET
# 前景色:绿色 背景色:默认
def green(self, s):
return Fore.LIGHTGREEN_EX + s + Fore.RESET
# 前景色:黄色 背景色:默... | code |
from textblob import TextBlob
lst=[]
with open('Adverb.txt','r') as f:
for i in f.readlines():
word=i.strip('\n')
text=TextBlob(word)
print(word,text.sentiment.polarity) | code |
#!/usr/bin/env python
import sys
list1=[]
for line in sys.stdin:
line=line.strip()
words=line.split("\n")
list1.append(words[0])
for x in xrange(len(list1)):
print list1[x]
| code |
# Lucas de Jesus Silva - 20731356 - atividade 2 PLP - Estruturado
#=================================================================================================
# método para determinar vencedor do levantamento de pesos
def levantamentoPeso (x,y):
vencedor = ""
if x["peso"] > y["peso"]:
vencedor = x["no... | code |
# Import
import pygame
# Initialize game engine
pygame.init()
# Open window
window_size = (640, 480)
screen = pygame.display.set_mode(window_size)
pygame.display.set_caption("The Quest")
WHITE = (255, 255, 255)
RED = (255, 0, 0)
done = False
clock = pygame.time.Clock()
# MAIN GAME LOOP
whil... | code |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/7/29 18:56
# @Site :
# @File : qianxu_144.py
# @Software: PyCharm
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, x):
# self.val = x
# self.left = None
# self.right = None
#前序
class Solution(o... | code |
import pandas as pd
import spacy
from spacy.kb import KnowledgeBase
def entities_info(path):
entity_info = dict()
with open(path, 'r', encoding='utf8') as infile:
for line in infile:
row = line.split('\t')
entity_info[row[0]] = dict()
entity_info[row[0]]['name'] = r... | code |
def process_text(filename):
"""Makes histogram of text"""
d = dict()
fp = open(filename, 'r')
for line in fp:
for word in line.split():
while not (word == '' or word[0].isalpha() or word[0].isdigit()):
word = word[1:]
while not (word == '' or word[-1].isalpha() or word[-1].isdigit()):
word = word[0... | code |
from math import ceil,floor,factorial,gcd,sqrt,log2,cos,sin,tan,acos,asin,atan,degrees,radians,pi,inf
from itertools import accumulate,groupby,permutations,combinations,product,combinations_with_replacement
from collections import deque,defaultdict,Counter
from bisect import bisect_left,bisect_right
from operator impor... | code |
size = int(input("Enter no. of items you want to add: "))
array = []
for n in range(size):
m = n + 1
array.insert(n, int(input("Enter item no. %d: " % m)))
max_val = 0
pair = []
for n in range(size-1):
for m in range(n+1, size):
val = array[n] * array[m]
if val > max_val:
max_val... | code |
import md5
import base64
import string
b64_str='./0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz'
final_str=""
def b64_from_24bit(a, b ,c ,d):
global final_str
w = (ord(a)<<16)|(ord(b)<<8)|ord(c)
for i in range(0, d):
final_str+=b64_str[w & 0x3f]
w = w >> 6
m=md5.new('chfT... | code |
import os,magic
import re
from genericpath import isdir, isfile
from flask import Flask,send_file,render_template,request
def show_dir(pwd):
files = os.listdir(pwd)
return render_template("index.html",files = files,pwd = pwd)
def send_to_client(pwd):
path = pwd[:-1]
return send_file(path,as_attac... | code |
from pyspark.sql import SparkSession
from pyspark.sql.functions import UserDefinedFunction
from pyspark.sql.functions import collect_set, array_contains, col, max, mean, desc, sum
from pyspark.sql.types import ArrayType
import os
os.environ["PYSPARK_PYTHON"] = "/home/pawel/PycharmProjects/HPC/venv/bin/python3.5"
os.en... | code |
#Number guessing program
from random import randint
a=(randint(0, 50))
print(a)
inp=int(input(("Enter the value")))
while(1==1):
if(inp>a):
print('Your Guess is above the number please inputcorrectly')
inp=int(input(("continue the game")))
elif(inp<a):
print('Your Guess is below the number please... | code |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: 3ngthrust
"""
import os
import gpiozero
import functools
import time
from MCP3002 import MCP3002
def split_into_equal_sublists(a_list, number_of_parts):
""" Splits a list into a list of sublists
Arguments
----------
a_list : list object
... | code |
# ex23
def compare(x, y):
if x > y:
return x
elif y > x:
return y
else:
raise Exception("Numbers are equal")
def three_multiple(x):
if x % 3 == 0:
return True
else:
return False
def power(a, n):
return a ** n
| code |
from random import uniform
from sys import argv
from math import log
from matplotlib import pyplot as plt
counter = 0
Lambda=[]
n=int(argv[1])
for i in range(int(argv[2])):
Lambda.append(float(argv[i+3]))
for l in Lambda:
dist=[]
for i in range(1,n+1):
u = uniform(0,1)
x_i = -1*log(1-u)/l
... | code |
#! /usr/bin/env python3
# Copyright 2020 Desh Raj
# Apache 2.0.
"""This script takes an input RTTM and transforms it in a
particular way: all overlapping segments are re-labeled
as "overlap". This is useful for 2 cases:
1. By retaining just the overlap segments (grep overlap),
the resulting RTTM can be used to t... | code |
# https://www.urionlinejudge.com.br/judge/pt/problems/view/1168
n = int(input())
for i in range(0,n):
v = input()
total = 0
for digito in v:
if digito == '0':
total += 6
elif digito == '1':
total += 2
elif digito == '2':
total += 5
elif digito == '3':
total += 5
elif digito == '4':
total +... | code |
input_int = list(input())
for index in range(int(len(input_int) / 2)):
temp = input_int[index]
input_int[index] = input_int[-index - 1]
input_int[-index - 1] = temp
print(''.join(input_int)) | code |
from time import sleep
from decouple import config
from bot import Near
from bot import Twitter
if __name__ == '__main__':
near = Near()
twitter = Twitter()
CURRENCY = config('CURRENCY_TO_CONVERT')
TEXT_LAST_24_HRS = config('TEXT_LAST_24_HRS')
sleep_time = 86400 / int(config('TWEETS_PER_DAY'))
... | code |
import matplotlib.pyplot as plt
import numpy as np
import random
from matplotlib.backends.backend_pdf import PdfPages
from scipy import stats
import math
from scipy.special import factorial
from scipy.optimize import curve_fit
def poisson(k,lamb):
return (lamb**k/factorial(k)) * np.exp(-lamb)
if __name__ == "__ma... | code |
from __future__ import division, print_function, absolute_import
import numpy as np
from ..util import img_as_ubyte, crop
from ._skeletonize_3d_cy import _compute_thin_image
def skeletonize_3d(img):
"""Compute the skeleton of a binary image.
Thinning is used to reduce each connected component in a binary im... | code |
def format_duration(seconds):
if seconds==0:
return "now"
year = seconds // 31536000
yearDay = seconds % 31536000
day = yearDay // 86400
dayHour = yearDay % 86400
hour = dayHour // 3600
hourMinute = dayHour % 3600
minute = hourMinute // 60
second = hourMinute % 60
res = [... | code |
import math
class TreasureAngleToWorldmapPositionConverter:
def __init__(self, table_calibration_service, treasure_angles, robot):
self.table_calibration_service = table_calibration_service
self.treasure_angles = treasure_angles
self.robot_angle = robot.get_angle()
self.robot_posit... | code |
import sys
# d = {'a': 'Maman', 'b': 'Papa', 'c':'Grand Father', 'd': 'Grand Mother', 'e': 'Son', 'f': 'Daughter'}
# for k in sorted(d.keys()):
# print('Key '+k.upper()+' -> '+d[k])
# print(d.items()[0])
def File(filename):
f = open(filename, 'rU')
for line in f:
a = line.split()
... | code |
import math
# Anterior e Sucessor
num = int(input('Digita um número aí: '))
ant = num - 1
suc = num + 1
print('O número antes de {} é {} e o depois dele é {}'.format(num, ant, suc))
# Dobro, Triplo e Raiz quadrada
n = int(input('Manda um número: '))
d = n * 2
t = n * 3
r = math.sqrt(n)
# print('O dobro de {} é {}'... | code |
# Definition for an interval.
class Interval:
def __init__(self, s=0, e=0):
self.start = s
self.end = e
class Solution:
def merge(self, intervals):
"""
:type intervals: List[Interval]
:rtype: List[Interval]
"""
intervals = intervals[:]
#Always thi... | code |
def check(lista):
return sum(lista) == len(lista)
def convert(string):
return [True if i=='+' else False for i in string]
def invert(lis):
return [not i for i in lis]
def fun(string,n):
lista = convert(string)
count = 0
for i in range(len(lista)-n+1):
if not lista[i]:
... | code |
# -*- coding: utf-8 -*-
# Define here the models for your scraped items
#
# See documentation in:
# https://doc.scrapy.org/en/latest/topics/items.html
import scrapy
from scrapy.loader import ItemLoader
from scrapy.loader.processors import TakeFirst, MapCompose
import re
from w3lib.html import remove_tags # 这个模块专门用来去... | code |
#!/usr/bin/python3
"""Task 6"""
from flask import Flask
from flask import render_template
from models import storage
from models.state import State
from models.city import City
app = Flask(__name__)
@app.route('/states', strict_slashes=False)
def states():
"""Function that return states"""
states = storage.a... | code |
greeting = "Hello"
name = "Michael"
message = f"{greeting}, {name}. Welcome!"
print(message)
| code |
# Importing the required libraries
import pandas as pd
import lightkurve as lk
import matplotlib.pyplot as plt
import os, shutil
import numpy as np
from scipy.stats import skew
from scipy.stats import kurtosis
from tqdm import tqdm
import warnings
import seaborn as sns
os.chdir('..')
tqdm.pandas(desc="Progress: ")
war... | code |
import sys
import cProfile
def brute(arg):
return reduce(lambda x, y: x + int(y), str(2**arg), 0)
if __name__ == "__main__":
arg = int(sys.argv[1])
def main():
print brute(arg)
cProfile.run('main()')
| code |
_employeeName = input()
fixedSalary = float(input())
salesBonus = float(input())
print(f"TOTAL = R$ {fixedSalary + (salesBonus * 0.15):.2f}")
| code |
import os
import torch
import torch.utils.data as data
from datasets.datahelpers import default_loader
class DigitsDataset(data.Dataset):
"""Digits dataset."""
def __init__(self, mode, data_root, transform=None, loader=default_loader):
if not (mode == 'train' or mode == 'dev'):
raise(Runt... | code |
#
# @lc app=leetcode.cn id=884 lang=python3
#
# [884] 两句话中的不常见单词
#
# @lc code=start
class Solution:
def uncommonFromSentences(self, A: str, B: str) -> List[str]:
from collections import Counter
ac = Counter(A.split())
bc = Counter(B.split())
answer = []
for k in ac.... | code |
End of preview. Expand in Data Studio
pretrain-mix-150b
A high-quality, 150-billion-token pre-training dataset meticulously curated for large language model research and development.
This dataset is a strategic mix of high-quality educational web text, comprehensive mathematical documents, and a diverse collection of source code, designed to foster strong reasoning and multi-domain capabilities in pre-trained models.
Dataset Overview
The pretrain-mix-150b dataset was created to provide a robust and balanced foundation for training novel language model architectures. The total dataset comprises approximately 130 million documents, amounting to ~150 billion tokens.
The curation process involved sourcing from three best-in-class, publicly available datasets and mixing them according to a specific ratio to ensure a balanced diet of general knowledge, logical reasoning, and programming syntax.
The composition was programmatically verified after creation:
| Source | Document Count | Percentage | Description |
|---|---|---|---|
| Web (FineWeb-Edu) | 87,570,000 | 67.3% | High-quality educational web content. |
| Code (Stack-Edu) | 23,560,000 | 18.1% | Curated source code from GitHub. |
| Math (FineMath) | 18,900,000 | 14.5% | Mathematical reasoning & problem-solving. |
| Total | 130,030,000 | 100.0% |
Why This Dataset?
While many large-scale datasets exist, pretrain-mix-150b was created with a specific philosophy in mind:
- Balanced Diet for Models: Avoids over-indexing on generic web text by including substantial, high-quality code and math corpora.
- Reproducibility: The entire creation process was scripted, and the composition is fully transparent.
- Efficiency: The data is provided in the highly-efficient Parquet format, ready for large-scale training pipelines.
This dataset is ideal for researchers and engineers looking to pre-train foundation models from scratch, especially those with novel architectures (like Mixture-of-Experts) that can benefit from specialized data sources.
How to Use
The dataset is structured with a data/ directory containing 2,601 Parquet files. You can easily load it using the 🤗 datasets library.
It is highly recommended to use streaming=True to avoid downloading the entire dataset at once.
from datasets import load_dataset
# Load the dataset in streaming mode
# The 'data_files' argument points to all Parquet files in the 'data' directory
repo_id = "meryyllebr543/pretrain-mix-150b"
dataset = load_dataset(repo_id, data_files="data/*.parquet", split="train", streaming=True)
# You can then iterate over the dataset
print("First example from the dataset:")
example = next(iter(dataset))
print(example)
# {'text': '...', 'source': 'web'}
Dataset Schema
Each row in the dataset has a simple, uniform schema:
text(string): The main content of the document.source(string): The origin of the document. Can be one ofweb,math, orcode. This is useful for analyzing model performance on different domains.
Data Sources
This dataset is a mix of the following excellent open-source projects. Please refer to their original dataset cards for more information on their respective curation processes.
- FineWeb-Edu: Sourced from
HuggingFaceFW/fineweb-edu. Thesample-100BTconfiguration was used. - FineMath: Sourced from
HuggingFaceTB/finemath. Thefinemath-3plusconfiguration was used. - Stack-Edu: Sourced from
meryyllebr543/stack-edu-huggingface(a direct-access version ofHuggingFaceTB/stack-edu). The following languages were included:python,rust,markdown,cpp, andcsharp.
Author
This dataset was curated and processed by Francisco Antonio.
- GitHub: MeryylleA
- LinkedIn: Francisco Antonio
This project is part of ongoing independent research into novel AI architectures.
License
The dataset is released under the Apache 2.0 License. Please be aware that the underlying data sources may have their own licenses and terms of use. It is the user's responsibility to adhere to them. ```
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