text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
/* The file is part of Snowman decompiler. */
/* See doc/licenses.asciidoc for the licensing information. */
//
// SmartDec decompiler - SmartDec is a native code to C/C++ decompiler
// Copyright (C) 2015 Alexander Chernov, Katerina Troshina, Yegor Derevenets,
// Alexander Fokin, Sergey Levin, Leonid Tsvetkov
//
// Th... | {"hexsha": "7e7b66fd4b9521609cb2f2d3c8f6bbf963176f04", "size": 9048, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/nc/core/irgen/IRGenerator.cpp", "max_stars_repo_name": "treadstoneproject/tracethreat_nrml", "max_stars_repo_head_hexsha": "bcf666b01c20f7da4234fed018dad3b2cf4d3d28", "max_stars_repo_licenses": ... |
(* author:wzh *)
(* then = from this *)
(* hence = from his have *)
(* thus = from this show *)
theory Exercise5
imports Main
begin
(* Exercise 5.1 *)
lemma assumes T: "\<forall> x y. T x y \<or> T y x"
and A: "\<forall> x y. A x y \<and> A y x \<longrightarrow> x = y"
and TA: "\<forall> x y. T x y \<longright... | {"author": "yogurt-shadow", "repo": "Isar_Exercise", "sha": "27658bff434e0845a23aeb310eeb971e4fc20b98", "save_path": "github-repos/isabelle/yogurt-shadow-Isar_Exercise", "path": "github-repos/isabelle/yogurt-shadow-Isar_Exercise/Isar_Exercise-27658bff434e0845a23aeb310eeb971e4fc20b98/Exercise5.thy"} |
from util.data.data import Data, flatten, merge_on_column
# TODO: Test for file that has a type digression happen on a line
# with empty strings in it. This caused a crash [2019-12-17].
# TODO: Test data with no names getting other data added in place,
# should overwrite the names!
# Some tests for ... | {"hexsha": "9d8847af3a842439f65722a5b77489b38c1e6f14", "size": 21002, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/data/test.py", "max_stars_repo_name": "tchlux/util", "max_stars_repo_head_hexsha": "eff37464c7e913377398025adf76b057f9630b35", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_... |
# Course URL:
# https://deeplearningcourses.com/c/natural-language-processing-with-deep-learning-in-python
# https://udemy.com/natural-language-processing-with-deep-learning-in-python
import numpy as np
import matplotlib.pyplot as plt
import os
import sys
sys.path.append(os.path.abspath('..'))
from hmm_class.hmmd_scal... | {"hexsha": "2131811a8f77bbbfb3c25b4cfbfd8dd29cccc2af", "size": 2259, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine_learning_examples/nlp_class2/pos_hmm.py", "max_stars_repo_name": "austinburks/nlp-udemy", "max_stars_repo_head_hexsha": "2e2ddb39b22a057448666c5069b1bdda7a4318c3", "max_stars_repo_licenses... |
#!/usr/bin/env python
import glob
import numpy as np
import instance_occlsegm_lib
import chainer_mask_rcnn as mrcnn
import contrib
def main():
class_names = contrib.core.get_class_names()
bboxes_, labels_, masks_ = None, None, None
json_files = sorted(glob.glob('*.json'))
for json_file in json_f... | {"hexsha": "b522d380fa5189610460ab887c52cb13d7a2bcd4", "size": 1849, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/instance_occlsegm/examples/synthetic2d/arc2017_occlusion_dataset/annotation_view_annotations.py", "max_stars_repo_name": "pazeshun/jsk_apc", "max_stars_repo_head_hexsha": "0ff42000ad5992f8a3... |
import argparse
import logging
import multiprocessing
import os
import pickle
import time
from functools import partial
import h5py
import numpy as np
import pandas as pd
import tensorflow as tf
from tqdm import tqdm
from data_reader import DataReader_mseed_array, DataReader_pred
from model import ModelConfig, UNet
f... | {"hexsha": "e6bbde5d6757e0cc6a8582a46647fa5c9919405f", "size": 7661, "ext": "py", "lang": "Python", "max_stars_repo_path": "phasenet/predict.py", "max_stars_repo_name": "AI4Earth/PhaseNet", "max_stars_repo_head_hexsha": "ca9e29f634fd2b53ab2da67f5a9152a546f77cfb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# -*- coding: utf-8 -*-
import numpy as np
eps = np.finfo(float).eps
def infnorm(x):
return np.linalg.norm(x, np.inf)
def scaled_tol(n):
tol = 5e1*eps if n < 20 else np.log(n)**2.5*eps
return tol
# bespoke test generators
def infNormLessThanTol(a, b, tol):
def asserter(self):
self.assertLes... | {"hexsha": "fe892cee2ccc30f11228f46da09d378347ae470a", "size": 1253, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/utilities.py", "max_stars_repo_name": "tschm/chebpy", "max_stars_repo_head_hexsha": "7248249a75ed65bfc0ea9d711986c3c9c51278a0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
import os
import argparse
import streamlit as st
import time
import numpy as np
from astropy.table import QTable
import astropy.units as u
import numpy as np
import pandas as pd
import altair as alt
# for rendering plots
from vega import VegaLite
from json import loads
from pathlib import Path
from specutils import S... | {"hexsha": "fa32683c6948c3ba36a0a6bfda7e214575d6acc3", "size": 6073, "ext": "py", "lang": "Python", "max_stars_repo_path": "use_cases/Streamlit/plotting_demo.py", "max_stars_repo_name": "einshoe/ssv-examples", "max_stars_repo_head_hexsha": "5bdf430c21ba3048e0dee7bfc0b6790143d6bec5", "max_stars_repo_licenses": ["MIT"], ... |
import torch
import numpy as np
import pdb
class PatchAttacker:
def __init__(self, model, mean, std, kwargs):
std = torch.tensor(std)
mean = torch.tensor(mean)
self.epsilon = kwargs["epsilon"] / std
self.steps = kwargs["steps"]
self.step_size = kwargs["step_size"] / std
... | {"hexsha": "b292278edbe85e5f3045bcff6fe45ff10d394210", "size": 4693, "ext": "py", "lang": "Python", "max_stars_repo_path": "attacks/patch_attacker.py", "max_stars_repo_name": "Ping-C/certifiedpatchdefense", "max_stars_repo_head_hexsha": "f1dbb7e399c320413c17e1412d2fb0ee0d6c812a", "max_stars_repo_licenses": ["BSD-2-Clau... |
(* Title: HOL/Library/Extended_Nonnegative_Real.thy
Author: Johannes Hölzl
*)
subsection \<open>The type of non-negative extended real numbers\<close>
theory Extended_Nonnegative_Real
imports Extended_Real Indicator_Function
begin
lemma ereal_ineq_diff_add:
assumes "b \<noteq> (-\<infinity>::ereal)... | {"author": "SEL4PROJ", "repo": "jormungand", "sha": "bad97f9817b4034cd705cd295a1f86af880a7631", "save_path": "github-repos/isabelle/SEL4PROJ-jormungand", "path": "github-repos/isabelle/SEL4PROJ-jormungand/jormungand-bad97f9817b4034cd705cd295a1f86af880a7631/case_study/isabelle/src/HOL/Library/Extended_Nonnegative_Real.t... |
import requests
import time
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
# Enforce incognito mode
chrome_options = webdriver.ChromeOptions... | {"hexsha": "9e811d8fb6e64d6f2a2c00fb2ec249c9b7159c21", "size": 11752, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/NHL_summary_data_pull_functions.py", "max_stars_repo_name": "justinjoliver/NHL-Analytics", "max_stars_repo_head_hexsha": "b369ec0716a2f32f9cdc6539d894a38455c7ab09", "max_stars_repo_licenses":... |
import json
import itertools
from typing import Optional
from collections import defaultdict
import numpy as np
from _jsonnet import evaluate_file as jsonnet_evaluate_file
from nltk.stem.snowball import SnowballStemmer
from scipy.special import expit
from sklearn.cluster import AgglomerativeClustering, DBSCAN
from skl... | {"hexsha": "a596311b15fd109ef6db837c6ffb196c677e8221", "size": 11521, "ext": "py", "lang": "Python", "max_stars_repo_path": "purano/clusterer/clusterer.py", "max_stars_repo_name": "IlyaGusev/purano", "max_stars_repo_head_hexsha": "07234a55e8c80d1e9d8aeb8197c58e36dd26da54", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.offline as po
import plotly.graph_objs as go
import dash_table
import numpy as np
import pandas as pd
import base64
import io
plot_layout=html.Div(className='nav-div',children=[
html.Nav(className='nav',
... | {"hexsha": "9178eed8a77c55dbb031c1a6cf5e1b93e172315f", "size": 2430, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot_data.py", "max_stars_repo_name": "lgkartik/graphvidz", "max_stars_repo_head_hexsha": "f3e5febef641017d0fd5697695ce4d6f30ae78bd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import torch
from torch.autograd import Variable
import numpy as np
from org.archive.eval.metric import rele_gain
from org.archive.utils.pytorch.pt_extensions import Power
""" Extended torch functions """
power = Power.apply
def tor_stable_softmax_bp(histogram, base=None):
max_v, _ = torch.max(histogram, dim=... | {"hexsha": "83a2baa350635ddbbe8c4dd203476a5f1e00cb83", "size": 9441, "ext": "py", "lang": "Python", "max_stars_repo_path": "org/archive/ranking/listwise/wassrank/wasserstein_cost_mat.py", "max_stars_repo_name": "arita37/ptl2r.github.io", "max_stars_repo_head_hexsha": "6bd716c189e54df985062e22926c2d680cac8a8b", "max_sta... |
import png
import numpy as np
import sys
import time
import os
import struct
import socket
import select
import random
import tensorflow as tf
import time
import datetime
"""
encapsulate network communication, client side
"""
def recvall(sock, n):
# Helper function to recv n bytes or return None if EOF is hit
... | {"hexsha": "674ca4857bf7e6a84b936a8a344a9529e105c9a4", "size": 11996, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyutil/envClient.py", "max_stars_repo_name": "alex-krull/DeepSPM-mirror", "max_stars_repo_head_hexsha": "6bdd8593f47f1cc0b7811f3a0163ac9196275d7f", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
// Copyright 2017 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#include "redundancygroupdistribution.h"
#include <vespa/vespalib/util/exceptions.h>
#include <vespa/vespalib/text/stringtokenizer.h>
#include <boost/lexical_cast.hpp>
#include <algorithm>
namespace ... | {"hexsha": "6ac521a0f0128020ddd2202cff00fad3348428eb", "size": 5138, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "vdslib/src/vespa/vdslib/distribution/redundancygroupdistribution.cpp", "max_stars_repo_name": "gsmcwhirter/vespa", "max_stars_repo_head_hexsha": "afe876252b56b5a30735865047d7392958835f6a", "max_star... |
(*
* Copyright (C) 2014 NICTA
* All rights reserved.
*)
(* Author: David Cock - David.Cock@nicta.com.au *)
header "Loops"
theory LoopExamples imports "../pGCL" begin
text {* Reasoning about loops in pGCL is mostly familiar, in particular in the use of invariants.
Proving termination for truly probabilistic loops... | {"author": "Josh-Tilles", "repo": "AFP", "sha": "f4bf1d502bde2a3469d482b62c531f1c3af3e881", "save_path": "github-repos/isabelle/Josh-Tilles-AFP", "path": "github-repos/isabelle/Josh-Tilles-AFP/AFP-f4bf1d502bde2a3469d482b62c531f1c3af3e881/thys/pGCL/Tutorial/LoopExamples.thy"} |
#=================================================================
# Load Libraries
#=================================================================
import pandas as pd
import numpy as np
#==================================================================================
# Helpers
#=================================... | {"hexsha": "6f1cef5a42eee6eab219e00214ee78618892aa7e", "size": 2072, "ext": "py", "lang": "Python", "max_stars_repo_path": "AWS/stylometry_tokenPL/metrics.py", "max_stars_repo_name": "wcex1994/EssAI", "max_stars_repo_head_hexsha": "968f07d06f4d49a1538fb56fe505f13ff5f6fa8e", "max_stars_repo_licenses": ["MIT"], "max_star... |
im_path = 'screen-test-rgb.jpg'
import cv2
# pip install ?
im_cv = cv2.imread(im_path)
im_rgb = cv2.cvtColor(im_cv, cv2.COLOR_BGR2RGB)
print(type(im_cv), im_cv.shape)
from matplotlib.image import imread
#
im_matplotlib = imread(im_path)
print(type(im_matplotlib), im_matplotlib.shape)
import skimage
print(skimage.__... | {"hexsha": "4fb8760dee749a2a5851f5d7afa91762ab8fa80c", "size": 737, "ext": "py", "lang": "Python", "max_stars_repo_path": "image/test_imread.py", "max_stars_repo_name": "miroslavradojevic/python-snippets", "max_stars_repo_head_hexsha": "753e1c15dc077d3bcf5de4fd5d3a675daf0da27c", "max_stars_repo_licenses": ["MIT"], "max... |
#################################################################
# This file defines the abstract distribution type
# AbstractDistribution: the abstract super type for the transition and observation distributions
# DiscreteDistribution: discrete distributions support state indexing and length functions
###############... | {"hexsha": "22bfc20ebb558e7174e125b9ab0b8ff6091a7ce6", "size": 921, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "modules/POMDPs/src/distribution.jl", "max_stars_repo_name": "sisl/ExprSearch", "max_stars_repo_head_hexsha": "f143bbb84e1e11a70170085df81eb45ca5cecfa1", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import sys
import gzip
import numpy as np
import scipy.sparse as sparse
from os import listdir
import json
data_dir = sys.argv[1]
pass_from_gen = False
pass_all = False
pass_from_qual = False
if len(sys.argv)>2 and sys.argv[2] == '--pass_from_gen':
pass_from_gen = True
ped_file = sys.argv[3]
print('Genera... | {"hexsha": "8967e7289152ec6ef0ab7610208b9b57804bbc41", "size": 4295, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing/pull_pass.py", "max_stars_repo_name": "kpaskov/FamilySeqError", "max_stars_repo_head_hexsha": "d85767dea5d23c6f6908cb696dc0b6ef561d1fc7", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import os
import sys
import struct
import numpy as np
import pickle
from automon.common_messages import messages_header_format
from test_utils.stats_analysis_utils import get_period_approximation_error
from test_utils.test_utils import read_config_file
from experiments.visualization.plot_dimensions_stats import get_num... | {"hexsha": "a90df00e7c6c434492614938eec39d3dcfeac4fe", "size": 46584, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/visualization/plot_aws_stats.py", "max_stars_repo_name": "hsivan/automon", "max_stars_repo_head_hexsha": "222b17651533bdb2abce7de36a80156ab7b9cc21", "max_stars_repo_licenses": ["BSD-3... |
import numpy as np
import os
import dtdata as dt
import matplotlib.pyplot as plt
import math
import random
import pprint as pp
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn import preprocessing
from sklearn.decomposition import PCA
from... | {"hexsha": "4b4d50a4382b40a240d860e4d262634d68c6c334", "size": 2668, "ext": "py", "lang": "Python", "max_stars_repo_path": "lstm.py", "max_stars_repo_name": "coreyauger/daytrader-utils", "max_stars_repo_head_hexsha": "cdb5b4b7f32e75814486a8e186a8fd9b35dfd8a7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import gin
import ray
import time
import numpy as np
from torch.utils.data import DataLoader
from .. import datasets
@gin.configurable('dataloader', blacklist=['dataset'])
def initialize_dataloader(dataset, batch_size=8, shuffle=True, num_workers=0):
return DataLoader(dataset, batch_size=batch_size,
... | {"hexsha": "8018e188f6157791fedf6cfd7036e6b0d3a968e0", "size": 1871, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/data.py", "max_stars_repo_name": "princeton-vl/selfstudy", "max_stars_repo_head_hexsha": "d18ab26c11843557ee75aaccf6ccb63de85b397c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
# -*- coding: utf-8 -*-
import unittest
import numpy as np
from nelson_siegel_svensson import NelsonSiegelSvenssonCurve
class TestNelsonSiegelSvenssonCurveImplementation(unittest.TestCase):
'''Tests for Nelson-Siegel-Svensson curve implementation.'''
def setUp(self):
self.y = NelsonSiegelSvenssonC... | {"hexsha": "f2bd204d0d96aa385d0f19927942565c164b2954", "size": 4057, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_nelson_siegel_svensson_curve_implementation.py", "max_stars_repo_name": "luphord/nelson_siegel_svensson", "max_stars_repo_head_hexsha": "e2437a9bf924d6cd54181de018ed8af8214a6055", "max_... |
"""
> Training pipeline for UGAN and UGAN-P models
* Original paper: https://arxiv.org/pdf/1801.04011.pdf
(see github.com/cameronfabbri/Underwater-Color-Correction)
> Maintainer: https://github.com/xahidbuffon
"""
# py libs
import os
import sys
import yaml
import argparse
import numpy as np
from PIL import Im... | {"hexsha": "be839bf7b545745c18a35ce7cf1ddd07269e767e", "size": 6795, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyTorch/train_ugan.py", "max_stars_repo_name": "edgecm/FUnIE-GAN-1", "max_stars_repo_head_hexsha": "b8d0808bc05126294d39c1355688746e177e0dde", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
export ParticleCollisions
struct ParticleCollisions
dt
function ParticleCollisions( p :: Particles)
dt = zeros(p.n, p.n)
fill!(dt, Inf)
for k in 1:p.n
for l in (k+1):p.n
dt[k, l] = compute_dt(p, l, k)
end
end
new( dt )
end
... | {"hexsha": "b8c479108af88245ff14fd58abe3d3329565762c", "size": 1327, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/collisions.jl", "max_stars_repo_name": "pnavaro/Uchiyama.jl", "max_stars_repo_head_hexsha": "450756b9d41e8641906cc6553a8478830d4fa0ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
"""
hinet_downloader.py: download the hinet data based on HinetPy.
"""
from datetime import timedelta
from glob import glob
from os.path import basename, dirname, join
import click
import numpy as np
import obspy
import sh
from HinetPy import Client, win32
from loguru import logger
def init_client(username, password... | {"hexsha": "2857a88fd09e4c8bd00221b307bec76666b39f6b", "size": 3292, "ext": "py", "lang": "Python", "max_stars_repo_path": "seisflow/scripts/download/hinet/hinet_downloader.py", "max_stars_repo_name": "ziyixi/seisflow", "max_stars_repo_head_hexsha": "722c2445f4a5316f42bfbc8b9010d31caad4c76e", "max_stars_repo_licenses":... |
# Copyright (C) 2017-2019 New York University,
# University at Buffalo,
# Illinois Institute of Technology.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of th... | {"hexsha": "94c774f08f61b5dd11782a5e74ef7b2e1c48f673", "size": 19370, "ext": "py", "lang": "Python", "max_stars_repo_path": "vizier/engine/packages/pycell/client/dataset.py", "max_stars_repo_name": "sanchitcop19/web-api-async", "max_stars_repo_head_hexsha": "a3fff70c3b62678f3c8ef8cc07f7c9b4fe155c69", "max_stars_repo_li... |
[STATEMENT]
lemma subst_comp[simp]: "t \<lhd> (r \<lozenge> s) = t \<lhd> r \<lhd> s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. t \<lhd> r \<lozenge> s = t \<lhd> r \<lhd> s
[PROOF STEP]
proof (induct t)
[PROOF STATE]
proof (state)
goal (3 subgoals):
1. \<And>x. Var x \<lhd> r \<lozenge> s = Var x \<lhd> r \<l... | {"llama_tokens": 821, "file": null, "length": 5} |
# Autogenerated wrapper script for Fontconfig_jll for i686-w64-mingw32
export fc_cache, fc_cat, fc_conflist, fc_list, fc_match, fc_pattern, fc_query, fc_scan, fc_validate, fonts_conf, libfontconfig
using FreeType2_jll
using Bzip2_jll
using Zlib_jll
using Libuuid_jll
using Expat_jll
JLLWrappers.@generate_wrapper_header... | {"hexsha": "9df8c4f487fd27b5a69070b1dd8062716e33e96d", "size": 2355, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/i686-w64-mingw32.jl", "max_stars_repo_name": "JuliaBinaryWrappers/Fontconfig_jll.jl", "max_stars_repo_head_hexsha": "4a774dc6b40531890eb0cc37b09976802ac1bbf1", "max_stars_repo_licenses... |
from astropy.table import Table
from astropy import units as u
from ..query import BaseQuery
from ..utils import async_to_sync, prepend_docstr_nosections
from . import conf
from .utils import parse_readme
__all__ = ['Hitran', 'HitranClass']
@async_to_sync
class HitranClass(BaseQuery):
QUERY_URL = conf.query_ur... | {"hexsha": "31b08e0e4e3ae659482c599475203c9ad133e8f3", "size": 11868, "ext": "py", "lang": "Python", "max_stars_repo_path": "astroquery/hitran/core.py", "max_stars_repo_name": "hdevillepoix/astroquery", "max_stars_repo_head_hexsha": "ce8c500c28424fe841e04741d4230b8f695ee194", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
theory Ex7_1 imports "~~/src/HOL/IMP/Big_Step" "~~/src/HOL/IMP/Small_Step" begin
section "Chapter 7 exercises"
subsection "7.1"
fun assigned :: "com \<Rightarrow> vname set" where
"assigned (SKIP) = {}" |
"assigned (Assign vname _) = {vname}" |
"assigned (Seq c0 c1) = assigned c0 \<union> assigned c1" |
... | {"author": "gittywithexcitement", "repo": "isabelle", "sha": "42c53b2797e1b14c741c316f2585449b818a8f07", "save_path": "github-repos/isabelle/gittywithexcitement-isabelle", "path": "github-repos/isabelle/gittywithexcitement-isabelle/isabelle-42c53b2797e1b14c741c316f2585449b818a8f07/Chapter 7/Ex7_1.thy"} |
function levendist1(s::AbstractString, t::AbstractString)
ls, lt = length(s), length(t)
if ls > lt
s, t = t, s
ls, lt = lt, ls
end
dist = collect(0:ls)
for (ind2, chr2) in enumerate(t)
newdist = Vector{Int}(ls+1)
newdist[1] = ind2
for (ind1, chr1) in enumerate... | {"hexsha": "6460f73a495c6bf1100cb6d3db0eb71e8e03d7a3", "size": 580, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/levenshtein-distance-2.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MI... |
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "a3c232d87e5d26d1b537c8844463203e0b84e168", "size": 18707, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/unittest/test_transform_layout.py", "max_stars_repo_name": "XiaoSong9905/tvm", "max_stars_repo_head_hexsha": "48940f697e15d5b50fa1f032003e6c700ae1e423", "max_stars_repo_licenses": ["... |
/**
Boost Logging library
Author: John Torjo, www.torjo.com
Copyright (C) 2007 John Torjo (see www.torjo.com for email)
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE_1_0.txt or copy at
http://www.boost.org/LICENSE_1_0.txt)
See http://www.boos... | {"hexsha": "31d6848d48d792f07e033ea9e78e8ce4cc68137a", "size": 4289, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/sandbox/libs/logging/samples/scenarios/no_levels_with_route.cpp", "max_stars_repo_name": "gbucknell/fsc-sdk", "max_stars_repo_head_hexsha": "11b7cda4eea35ec53effbe37382f4b28020cd59... |
import os, sys
import numpy as np
import pybullet as pb
import math
def get_tip_targets(p, q, d):
m = q
t1 = p[0]-d*m[0], p[1]-d*m[3], p[2]-d*m[6]
t2 = p[0]+d*m[0], p[1]+d*m[3], p[2]+d*m[6]
return (t2, t1)
def get_tip_targets2(p, q, d):
m = q
t1 = p[0]-d*m[1], p[1]-d*m[4], p[2]-d*m[7]
t2 = ... | {"hexsha": "10524b68abcf0b2e55b9940c0edac4afcc01c0b7", "size": 4538, "ext": "py", "lang": "Python", "max_stars_repo_path": "ergo/pybullet/tasks/PicknPlace/PickNPlace.py", "max_stars_repo_name": "garrettkatz/poppy-simulations", "max_stars_repo_head_hexsha": "cd4d132ab6f8b4e69f2edd89662980d252a27966", "max_stars_repo_lic... |
# MIT License
# Copyright (c) 2019 Vincent SAMY
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, p... | {"hexsha": "3eb111000b8c7f16350a34d92c93cd258658d968", "size": 22116, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pyTests.py", "max_stars_repo_name": "vsamy/pygen-converter", "max_stars_repo_head_hexsha": "13a4f09e93d0b0c72175453451a5c3cd1e5a55b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
% ---------------------------------------------------------------------------- %
% Pre
% Document
\documentclass[10pt]{sigplanconf}
% Standard
\usepackage[utf8]{inputenc}
% \usepackage{hyperref}
\usepackage[pass,letterpaper]{geometry}
\usepackage{xspace}
% Custom
\usepackage{config/toggles}
\usepackage{config/term... | {"hexsha": "293b40e1ddd16d51e7f05d2189f5531e23be22d1", "size": 3154, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper.tex", "max_stars_repo_name": "richard-roberts/AOVMTA", "max_stars_repo_head_hexsha": "782abebda87d0e4e4f9a4e22333c33de7b3fd146", "max_stars_repo_licenses": ["CC-BY-3.0"], "max_stars_count": nu... |
struct FastReadBuffer{V<:AbstractVector{UInt8}} <: IO
data::V
position::Base.RefValue{Int} # last read position
end
FastReadBuffer(data::AbstractVector{UInt8}) = FastReadBuffer(data, Ref(0))
FastReadBuffer() = FastReadBuffer(Vector{UInt8}())
"""
setdata!(buf::FastReadBuffer, data::AbstractVector{UInt8))
... | {"hexsha": "60507896c2076902db5119f57f7935e298bc2011", "size": 3395, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/fastreadbuffer.jl", "max_stars_repo_name": "JeffBezanson/FastIOBuffers.jl", "max_stars_repo_head_hexsha": "7924db96eb99a58058946d816e1a334cee8f61f8", "max_stars_repo_licenses": ["MIT"], "max_st... |
/*
* Sift.cpp
*
* Created on: Jan 25, 2012
* Author: lbossard
*/
#include "root_sift.hpp"
#include <cmath>
#include <glog/logging.h>
#include <opencv2/imgproc/imgproc.hpp>
#include <boost/shared_ptr.hpp>
#include "cpp/third_party/vlfeat/vl/dsift.h"
namespace vision
{
namespace features
{
struct VlFree ... | {"hexsha": "a3013479051b02d20ed2c71d1887ccb2a67ea0a5", "size": 3063, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "MEX/src/cpp/vision/features/low_level/root_sift.cpp", "max_stars_repo_name": "umariqb/3D_Pose_Estimation_CVPR2016", "max_stars_repo_head_hexsha": "83f6bf36aa68366ea8fa078eea6d91427e28503b", "max_sta... |
########################################################################
# Author(s): Shubh Gupta, Ashwin Kanhere
# Date: 21 September 2021
# Desc: Utility code for GNSS computations/simulations
########################################################################
import numpy as np
import math
im... | {"hexsha": "2c268222462c19b82b7e6d2cfa0dfef61992de17", "size": 2247, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gnss_lib/utils.py", "max_stars_repo_name": "Stanford-NavLab/deep_gnss", "max_stars_repo_head_hexsha": "4120337f87444c64d28e0b1ff8c76c9290ba1958", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from collections.abc import Callable
from moai.utils.arguments import (
ensure_path,
ensure_choices,
)
import moai.utils.color.colorize as mic
import os
import torch
import torchvision
import visdom
import numpy
import functools
import typing
import logging
import cv2
log = logging.getLogger(__name__)
__all... | {"hexsha": "658f431478971368f5afe45a973df2a6eaf94106", "size": 4571, "ext": "py", "lang": "Python", "max_stars_repo_path": "moai/export/local/blend2d.py", "max_stars_repo_name": "tzole1155/moai", "max_stars_repo_head_hexsha": "d1afb3aaf8ddcd7a1c98b84d6365afb846ae3180", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import random
from pathlib import Path
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader, Dataset, Subset
from torchvision import transforms
from .misc import RandomSampler, grouped_features_indexes, set_transform
clas... | {"hexsha": "4d04b3e4a9400e861d0cf5a97fd620a7d4372a85", "size": 7702, "ext": "py", "lang": "Python", "max_stars_repo_path": "dlfairness/original_code/nifr/nifr/data/dataset_wrappers.py", "max_stars_repo_name": "lin-tan/fairness-variance", "max_stars_repo_head_hexsha": "7f6aee23160707ffe78f429e5d960022ea1c9fe4", "max_sta... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('stock_sentiment.csv')
import string
string.punctuation
def remove_punc(message):
Test_punc_removed = [char for char in message if char not in string.punctuation]
Test_punc_removed_join = ''.join(Test_punc_removed)... | {"hexsha": "85e184f93fab0b90c9bc19675db480ccef26a94e", "size": 1586, "ext": "py", "lang": "Python", "max_stars_repo_path": "stocks sentiment analysis using SVM.py", "max_stars_repo_name": "krishnaaxo/Stock-Market-Sentiment-Analysis-Using-AI", "max_stars_repo_head_hexsha": "9fddb3c8ab0d8bf8ee14df16daef4a94546f1ef1", "ma... |
import os
from typing import List, Optional, Dict
from copy import copy
import numpy as np
import pandas as pd
from cascade_at.core.log import get_loggers
from cascade_at.dismod.api import DismodAPIError
from cascade_at.dismod.api.dismod_io import DismodIO
from cascade_at.dismod.integrand_mappings import PRIMARY_INTE... | {"hexsha": "51101d404acc437c28efec19744611d234475b68", "size": 11493, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cascade_at/dismod/api/dismod_extractor.py", "max_stars_repo_name": "ihmeuw/cascade-at", "max_stars_repo_head_hexsha": "a5b1b5da1698163fd3bbafc6288968dd9c398096", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python
import spartan
import numpy as np
import test_common
from spartan.util import Assert
class BuiltinTest(test_common.ClusterTest):
def test_arange_shape(self):
# Arange with no parameters.
Assert.raises_exception(ValueError, spartan.arange)
# Arange with shape and stop
Assert.ra... | {"hexsha": "964f41b665ebf7af8f34a522f380a6eb6a1ea548", "size": 4255, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_builtins.py", "max_stars_repo_name": "MaggieQi/spartan", "max_stars_repo_head_hexsha": "24b9f977d0a9ae99e672bf90d80a0f22ac41d133", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
!include 'mkl_vsl.f90'
MODULE korc_random
USE, INTRINSIC :: iso_c_binding
USE korc_types
! use mkl_vsl_type
! use mkl_vsl
IMPLICIT NONE
TYPE(C_PTR), DIMENSION(:), ALLOCATABLE , PRIVATE :: states
TYPE(C_PTR), PRIVATE :: state
! TYPE(VSL_STREAM_STATE), PRIVATE :: stream
INTERFACE
TYP... | {"hexsha": "aaf0965aad286df319652b105826ce4460a1e1e2", "size": 5528, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docs/src/korc_random.f90", "max_stars_repo_name": "ORNL-Fusion/KORC", "max_stars_repo_head_hexsha": "975fc01cdac1e922fe537e739a6b67f01c6a6431", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
Require Export P09.
Theorem distr_rev : forall l1 l2 : natlist,
rev (l1 ++ l2) = (rev l2) ++ (rev l1).
Proof.
intros l1 l2. induction l1.
- simpl. rewrite -> app_nil_end. reflexivity.
- simpl. rewrite -> snoc_append. rewrite -> snoc_append. rewrite -> IHl1. rewrite <- app_assoc. reflexivity.
Qed.
| {"author": "tinkerrobot", "repo": "Software_Foundations_Solutions2", "sha": "c88b2445a3c06bba27fb97f939a8070b0d2713e6", "save_path": "github-repos/coq/tinkerrobot-Software_Foundations_Solutions2", "path": "github-repos/coq/tinkerrobot-Software_Foundations_Solutions2/Software_Foundations_Solutions2-c88b2445a3c06bba27fb9... |
//
// Copyright (c) 2015-2017, Deutsches Forschungszentrum für Künstliche Intelligenz GmbH.
// Copyright (c) 2015-2017, University of Bremen
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
... | {"hexsha": "d2f03952d82b228d62fa48d0d3682f9a0b0b7595", "size": 11243, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/grid/VectorGrid.hpp", "max_stars_repo_name": "JanWehrmann/slam-maps", "max_stars_repo_head_hexsha": "c03117e9d66ec312723ad700baabc0af04f36d70", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
! { dg-do compile }
! Some CSHIFT, EOSHIFT and UNPACK conformance tests
!
program main
implicit none
real, dimension(1) :: a1, b1, c1
real, dimension(1,1) :: a2, b2, c2
real, dimension(1,0) :: a, b, c
real :: tempn(1), tempv(5)
real,allocatable :: foo(:)
allocate(foo(0))
tempn = 2.0
a1 = 0
a2 = 0... | {"hexsha": "423fb131516515a066f4ae42174d8afa4dc32750", "size": 1739, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/intrinsic_argument_conformance_2.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517"... |
#### DONNES QUALI : ON VA REPRESENTER LA PROPORTION DE 1.
#Création d'un vecteur avec toutes les commbinaisons dans l'ordre)
name= c("DD","with himself","with DC","with Silur" ,"DC","with himself","with DD","with Silur" ,"Silur","with himself","with DD","with DC" )
average= sample(seq(1,10) , 12 , replace=T)
number= ... | {"hexsha": "d30f56372ce8db8956a9c182560bb772749c8a80", "size": 1155, "ext": "r", "lang": "R", "max_stars_repo_path": "OLD_GALLERY_RSCRIPT/#37_number_of_observation_on_barplot.r", "max_stars_repo_name": "JedStephens/R-graph-gallery", "max_stars_repo_head_hexsha": "a7a65d2f66372ea3724cf6e930d3c4b209f44dad", "max_stars_re... |
import json
import random
import EoN
import networkx as nx
import numpy as np
import math
from typing import Set
from collections import namedtuple
from .utils import find_excluded_contours_edges_PQ2, edge_transmission, allocate_budget
from . import PROJECT_ROOT
SIR_Tuple = namedtuple("SIR_Tuple", ["S... | {"hexsha": "354dc6706384af0cebde1b4436d43e033473744c", "size": 4974, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctrace/simulation.py", "max_stars_repo_name": "gzli929/ContactTracing", "max_stars_repo_head_hexsha": "7be34c9e2ebbd305bef9d4c2ab91beb7b23bed91", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
import config
import datapane as dp
import logging
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objs as go
import streamlit as st
import utils
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=l... | {"hexsha": "399ddadf5cedf528a1100665edb18646d61aa5c6", "size": 25113, "ext": "py", "lang": "Python", "max_stars_repo_path": "public_transport_hourly.py", "max_stars_repo_name": "oguzhanyediel/imm_dataviz", "max_stars_repo_head_hexsha": "10da3d9f66ddae7075ced51a662e9ab4038c583f", "max_stars_repo_licenses": ["MIT"], "max... |
import json
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from .net_utils import run_lstm, col_name_encode
class AggPredictor(nn.Module):
def __init__(self, N_word, N_h, N_depth, use_ca):
super(AggPredictor, self).__init__()
... | {"hexsha": "73af11be05bbc51484ef4d223dd0039730b1ede3", "size": 2285, "ext": "py", "lang": "Python", "max_stars_repo_path": "sqlnet/model/modules/aggregator_predict.py", "max_stars_repo_name": "LucienCheng/sqlnet", "max_stars_repo_head_hexsha": "fa0aad37e4e27d668be0db9869c93274191dbbb8", "max_stars_repo_licenses": ["BSD... |
#import dependencies
from flask import Flask, jsonify
import numpy as np
import sqlalchemy
import datetime as dt
from sqlalchemy import inspect, create_engine, func
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
#Setting up Database
engine = create_engine("sqlite:///Resources/hawai... | {"hexsha": "50256b80cf44b42f4e7a97092504f217b1f47f8f", "size": 4216, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "clairewkh/SQLAlchemy-Challenge", "max_stars_repo_head_hexsha": "c8ea843ea921ee738a8390050dc9cde32286405e", "max_stars_repo_licenses": ["ADSL"], "max_stars_count": ... |
import time
import json
from pathlib import Path
import argparse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from torch import nn, optim
import torch.nn.functional as F
from torchvision import transforms, datasets, models
from PIL import Image
from workspace_utils import active... | {"hexsha": "fcffa448906172030153218926544adbf104078e", "size": 3115, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "usmanzaheer1995/udacity-ai-programming-nanodegree", "max_stars_repo_head_hexsha": "50c3dcd59e5a215089c51960d269b8878690ec5c", "max_stars_repo_licenses": ["MIT"... |
\chapter{The Reorder Buffer (ROB) and the Dispatch Stage}\label{chapter:rob}
The ROB tracks the state of all inflight instructions in the pipeline. The role of the ROB is to provide the illusion to the programmer that his program executes in-order.
After instructions are decoded and renamed, they are then dispatched... | {"hexsha": "299f0f5967e5ca3c92a4ea599fcc3783efd2350e", "size": 8610, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/rob.tex", "max_stars_repo_name": "ccelio/riscv-boom-doc", "max_stars_repo_head_hexsha": "c3b752d82a08b31448b1a957ed57985db86f2c83", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_coun... |
import numpy as np
def checkFaces(da, isBnd, coords_local):
# Loops over all faces of the boundary of a domain - marking dirichlet, neumann and processor to processor boundarie
ranges = da.getGhostRanges()
dim = da.getDim()
sizes = np.empty(dim, dtype=np.int32)
for ir, r in enumerate(ranges):
... | {"hexsha": "9c1c244fda9f743e96d1a06fea5567d36aec550c", "size": 1363, "ext": "py", "lang": "Python", "max_stars_repo_path": "PETScGMsFEM/checkFaces.py", "max_stars_repo_name": "tjdodwell/PETSc-GMsFEM", "max_stars_repo_head_hexsha": "43cbc123092bf4589510f5aca0b1c0b79272226d", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma Seq_NoFaultStuckD1:
assumes noabort: "\<Gamma>\<turnstile>\<langle>Seq c1 c2,s\<rangle> \<Rightarrow>\<notin>({Stuck} \<union> Fault ` F)"
shows "\<Gamma>\<turnstile>\<langle>c1,s\<rangle> \<Rightarrow>\<notin>({Stuck} \<union> Fault ` F)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Gamm... | {"llama_tokens": 2950, "file": "Simpl_Semantic", "length": 38} |
#!/usr/bin/env python
import sys, csv
from pandas import *
from numpy import *
# simple list of the scores of all positions
position_scores = []
# map from player-game to blunderrate
meanerror = {}
q_error_one = {}
q_error_two = {}
meanecho = {}
perfectrate = {}
blunderrate = {}
gameoutcome = {}
... | {"hexsha": "fb9b5a378b7afceb2044716d4adfddbd2a798b27", "size": 3408, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/external/repositories_2to3/137656/blundercheck-master/combine/contest_20150219a/data_prep/show_one_game.py", "max_stars_repo_name": "Keesiu/meta-kaggle", "max_stars_repo_head_hexsha": "87de73... |
(* EXTRACT from HOL/ex/Primes.thy*)
(*Euclid's algorithm
This material now appears AFTER that of Forward.thy *)
theory TPrimes imports Main begin
fun gcd :: "nat \<Rightarrow> nat \<Rightarrow> nat" where
"gcd m n = (if n=0 then m else gcd n (m mod n))"
text {*Now in Basic.thy!
@{thm[display]"dvd_def"}
\rulena... | {"author": "SEL4PROJ", "repo": "jormungand", "sha": "bad97f9817b4034cd705cd295a1f86af880a7631", "save_path": "github-repos/isabelle/SEL4PROJ-jormungand", "path": "github-repos/isabelle/SEL4PROJ-jormungand/jormungand-bad97f9817b4034cd705cd295a1f86af880a7631/case_study/isabelle/src/Doc/Tutorial/Rules/TPrimes.thy"} |
import cv2
import jax.numpy as jnp
import numpy as np
import onnx
from onnx_jax.backend import run_model
from onnx_jax.logger import logger
def _cosin_sim(a, b):
a = a.flatten()
b = b.flatten()
cos_sim = jnp.dot(a, b) / (jnp.linalg.norm(a) * jnp.linalg.norm(b))
return cos_sim
# https://github.com/o... | {"hexsha": "8693b8ff853ffaa0a458363a547c37dc60a31795", "size": 1717, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/model/demo.py", "max_stars_repo_name": "gglin001/onnx_jax", "max_stars_repo_head_hexsha": "08e2a1181250db48f4436f6430903fc895a3a1d6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import os
import pytest
import sys
import numpy as np
from numpy.testing import assert_allclose
from pytest import raises as assert_raises
from scipy.sparse.linalg._svdp import _svdp
from scipy.sparse import csr_matrix, csc_matrix, coo_matrix
TOLS = {
np.float32: 1e-4,
np.float64: 1e-8,
np.complex64: 1e-4... | {"hexsha": "55ffa5bc9cc70141407b28d9a8c4f68a985ede35", "size": 6210, "ext": "py", "lang": "Python", "max_stars_repo_path": "scipy/sparse/linalg/tests/test_propack.py", "max_stars_repo_name": "jake-is-ESD-protected/scipy", "max_stars_repo_head_hexsha": "d7283ff75c218c300f372b5fdd960b987c1709a1", "max_stars_repo_licenses... |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
##===-----------------------------------------------------------------------------*- Python -*-===##
##
## S E R I A L B O X
##
## This file is distributed under terms of BSD license.
## See LICENSE.txt for more information.
##
##===----------... | {"hexsha": "7e193f2c4d688b4ba79aab3c143926eff739613a", "size": 1234, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/serialbox-python/sdb/sdbcore/errorlist.py", "max_stars_repo_name": "elsagermann/serialbox", "max_stars_repo_head_hexsha": "c590561d0876f3ce9a07878e4862a46003a37879", "max_stars_repo_licenses":... |
#!/usr/bin/env python3
import numpy
# code from StackExchange.
def step(world):
(wd, ht) = world.shape
print("Doing a step with world shape:",wd,ht)
print(repr(world))
neighbors = numpy.zeros((wd, ht), dtype='uint8')
neighbors[1:] += world[:-1]
neighbors[:-1] += world[1:]
neighbors[:,1:] +... | {"hexsha": "e857c680ce542d367ece62e35b3802667849b457", "size": 606, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpy_game_of_life.py", "max_stars_repo_name": "btolva/mh19-work", "max_stars_repo_head_hexsha": "bc9d9cd54b0eb004290efbc730baf5cb49eaa190", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
module mpimod
!! NOTES:
!! * Need to consider overloading routines as send_ghost and send_noghost so that
!! it is more clear what the structure of the input arrays should be.
use, intrinsic:: iso_fortran_env, only: stderr=>error_unit
use phys_consts, only : lsp, wp
use autogrid, only : grid_auto
use mpi, only: mpi_... | {"hexsha": "a466398569a77db270163d1a46b81b9d2c8d5c4e", "size": 17632, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/mpimod/mpimod.in.f90", "max_stars_repo_name": "ForestClaw/gemini3d", "max_stars_repo_head_hexsha": "0487c8fad8ade7b5c9d1c2d53cdcc18a7cb8db72", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
[STATEMENT]
lemma real_minus_mult_self_le [simp]: "- (u * u) \<le> x * x"
for u x :: real
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. - (u * u) \<le> x * x
[PROOF STEP]
by (rule order_trans [where y = 0]) auto | {"llama_tokens": 97, "file": null, "length": 1} |
import cv2
import numpy as np
import RLpDefinitions as definitions
import RDetectPlates
from RPossibleChar import RPossibleChar as PossibleChar
import imutils
from imutils import perspective
import matplotlib.pyplot as plt
GAUSSIAN_SMOOTH_FILTER_SIZE = (7, 7)
ADAPTIVE_THRESH_BLOCK_SIZE = 39
ADAPTIVE_THRESH_WEIGHT =... | {"hexsha": "9018176bbb50374181189e2d7571022b94c0cec5", "size": 18382, "ext": "py", "lang": "Python", "max_stars_repo_path": "ImageProcessingProject/AllCodes_img_processing/RLpPreprocess.py", "max_stars_repo_name": "schliffen/Localazing-with-image-processing-techniques", "max_stars_repo_head_hexsha": "9f36bf2f38d15985a8... |
[STATEMENT]
lemma while_true_trel:
assumes "\<sigma> \<dagger> b = true"
shows "(\<sigma>, while b do P od) \<rightarrow>\<^sub>u (\<sigma>, P ;; while b do P od)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<sigma>, while\<^sup>\<top> b do P od) \<rightarrow>\<^sub>u (\<sigma>, P ;; while\<^sup>\<top> b do... | {"llama_tokens": 144, "file": "UTP_utp_utp_rel_opsem", "length": 1} |
[STATEMENT]
lemma congruence_eye [simp]:
shows "congruence eye H = H"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. congruence eye H = H
[PROOF STEP]
by (cases H) (simp add: mat_adj_def mat_cnj_def) | {"llama_tokens": 89, "file": "Complex_Geometry_Matrices", "length": 1} |
#-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: Hector Ta
#
# Created: 24/07/2019
# Copyright: (c) Hector Ta 2019
# Licence: <your licence>
#-------------------------------------------------------------------------------
impor... | {"hexsha": "99debdf03029324376fb278b7ec01c762bdc471b", "size": 2301, "ext": "py", "lang": "Python", "max_stars_repo_path": "Arduino test/Python/plotted_csv - Arduino_oneChannel.py", "max_stars_repo_name": "HectorTa1989/Reflow-soldering-oven-DAQ", "max_stars_repo_head_hexsha": "84e64a97228ef19431e38df180f803dc564290a8",... |
import cv2
import numpy as np
def detect(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150, apertureSize=3)
lines = cv2.HoughLines(edges, 1, np.pi / 180, 200)
for rho, theta in lines[0]:
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
... | {"hexsha": "9c5ce6f08366cab7591b1d42fafd64963217fd87", "size": 549, "ext": "py", "lang": "Python", "max_stars_repo_path": "je_open_cv/modules/hough_line.py", "max_stars_repo_name": "JE-Chen/Python-OPENCV-JE", "max_stars_repo_head_hexsha": "d5dd3823f0a1cfc195da66bdcbe738c9bbdfc59b", "max_stars_repo_licenses": ["MIT"], "... |
theory SemanticsSnippets
imports
Optimizations.CanonicalizationProofs
begin
(*notation (latex)
NoNode ("\<epsilon>")
*)
notation (latex)
kind ("_\<llangle>_\<rrangle>")
syntax (spaced_type_def output)
"_constrain" :: "logic => type => logic" ("_ :: _" [4, 0] 3)
text_raw \<open>\Snip{isbinary}
\begin{cent... | {"author": "uqcyber", "repo": "veriopt-releases", "sha": "4ffab3c91bbd699772889dbf263bb6d2582256d7", "save_path": "github-repos/isabelle/uqcyber-veriopt-releases", "path": "github-repos/isabelle/uqcyber-veriopt-releases/veriopt-releases-4ffab3c91bbd699772889dbf263bb6d2582256d7/Papers/Semantics/SemanticsSnippets.thy"} |
module KrylovMethods
import Base.BLAS
include("cg.jl")
include("blockCG.jl")
include("cgls.jl")
include("bicgstb.jl")
include("blockBiCGSTB.jl")
include("gmres.jl")
include("lanczosBidiag.jl")
include("ssor.jl")
include("lsqr.jl")
include("lanczosTridiag.jl")
include("lanczos.jl")
include("minres.jl... | {"hexsha": "6e1653b0d6074b99985593fbcafff2f210f8a317", "size": 344, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/KrylovMethods.jl", "max_stars_repo_name": "JuliaPackageMirrors/KrylovMethods.jl", "max_stars_repo_head_hexsha": "964bca6c9ab389b7c5bfa33f5a9c943dc54901b8", "max_stars_repo_licenses": ["MIT"], "m... |
import torch
from torch import nn
import scipy.sparse as sp
import torch.nn.functional as F
from shaDow.utils import adj_norm_sym, adj_norm_rw, coo_scipy2torch, get_deg_torch_sparse
from torch_scatter import scatter
from torch_geometric.nn import global_sort_pool
import numpy as np
import torch.nn.functional as F
from... | {"hexsha": "f477dd99a471af5d5da18b05d486a2a9b415730f", "size": 30530, "ext": "py", "lang": "Python", "max_stars_repo_path": "shaDow/layers.py", "max_stars_repo_name": "yxia-fb/shaDow-GNN", "max_stars_repo_head_hexsha": "2b867011c7084d4ed1b407e29f3ee09632fcc3dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import argparse
import gym
import os
import sys
import numpy as np
from itertools import count
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.distributions import Categorical
import matplotlib.pyplot as plt
plt.switch_backe... | {"hexsha": "054e87e1b20a3cde12425e02bb8e66083f00efa2", "size": 14952, "ext": "py", "lang": "Python", "max_stars_repo_path": "imitation_work/a2c.py", "max_stars_repo_name": "09jvilla/CS234_gym", "max_stars_repo_head_hexsha": "ef77567eda4932b181965fa3081b00e7f57d37c8", "max_stars_repo_licenses": ["Python-2.0", "OLDAP-2.7... |
@doc raw"""
HeteroscedasticLikelihood(λ::T=1.0)->HeteroscedasticGaussianLikelihood
## Arguments
- `λ::Real` : The maximum precision possible (this is optimized during training)
---
Gaussian with heteroscedastic noise given by another gp:
```math
p(y|f,g) = \mathcal{N}(y|f,(\lambda \sigma(g))^{-1})
```
Where ... | {"hexsha": "91581927217fcd69cd476fa2f116a9c45479adf0", "size": 6105, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/likelihood/heteroscedastic.jl", "max_stars_repo_name": "theogf/AugmentedGaussianProcesses.jl", "max_stars_repo_head_hexsha": "eb5eb7f886c209a80e596ea6e0f678dd2e5f0a7b", "max_stars_repo_licenses... |
#!/usr/bin/env python
"""
Utility functions for quickdraw project:
For data preprocessing:
- npy_to_df: load .npy file(s) with preprocessed drawings and turn into df
- prepare_data: preprocess images and labels, turn into arrays
normalise images and binarize labels and split test train
For model output:
... | {"hexsha": "b5f107d30fe42f001b89e136a2d02224d3bd212e", "size": 6274, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/quickdraw_utils.py", "max_stars_repo_name": "nicole-dwenger/cdsviusal-quickdraw", "max_stars_repo_head_hexsha": "183ed7c5be2afce6baa3dbbd0d996a41bc39dff2", "max_stars_repo_licenses": ["MIT"]... |
# Field class (relativistic) for OLIVE
#
# Class is initialized with an array of modes and amplitudes as well as corresponding metadata
#
#
# Units
# -Assume CGS units for now
#
import numpy as np
from scipy.constants import c as c_mks
c = c_mks*1.e2
class Field(object):
def __init__(self, cavity):
""... | {"hexsha": "3565762b040a85aa964f56ad3863b5b0232f77cf", "size": 9783, "ext": "py", "lang": "Python", "max_stars_repo_path": "olive/fields/field.py", "max_stars_repo_name": "radiasoft/olive", "max_stars_repo_head_hexsha": "f7b80112f0fc8be0c50b6eedceccd422997081b5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import numpy as np
import itertools
np.random.seed(0)
def main():
# 4.14.1
print('4.14.1')
# Generator matrix for Hamming code
G = np.array([
[1, 0, 1, 1],
[1, 1, 0, 1],
[0, 0, 0, 1],
[1, 1, 1, 0],
[0, 0, 1, 0],
[0, 1, 0, 0],
... | {"hexsha": "5d23e50981fe17a0963731cd01d7128ee185f653", "size": 4864, "ext": "py", "lang": "Python", "max_stars_repo_path": "coding-the-matrix/error_correction_code.py", "max_stars_repo_name": "hiromakimaki/study-memo", "max_stars_repo_head_hexsha": "71700551d3d14867c0276541764ac6a3e1f11944", "max_stars_repo_licenses": ... |
%!TEX root = ../main.tex
\chapter{About the Author}\lipsum | {"hexsha": "401ee8411dae5b96371780f09954258fbd52a447", "size": 58, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "dirac/additional/aboutauthor.tex", "max_stars_repo_name": "rmathsphys/latex-templates", "max_stars_repo_head_hexsha": "7009f28fb1f7eafb51209182a875751d199dbe11", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma option_assn_simps[simp]:
"option_assn P None v' = \<up>(v'=None)"
"option_assn P v None = \<up>(v=None)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. option_assn P None v' = \<up> (v' = None) &&& option_assn P v None = \<up> (v = None)
[PROOF STEP]
apply (cases v', simp_all)
[PROOF STATE]
pro... | {"llama_tokens": 222, "file": "Refine_Imperative_HOL_Sepref_HOL_Bindings", "length": 3} |
#include <iostream>
#include <vector>
#include <math.h>
#include <sys/utsname.h>
using namespace std;
#include "dae.h"
#include "dom/domCOLLADA.h"
#ifdef __dom150COLLADA_h__
using namespace ColladaDOM150;
#endif
#include <fstream>
#include "yaml-cpp/yaml.h"
#include <boost/foreach.hpp>
#include <boost/filesystem/pat... | {"hexsha": "b5b0142561ada0946d9e5a7855b022a509f6547f", "size": 85017, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "euscollada/src/collada2eus.cpp", "max_stars_repo_name": "k-okada/jsk_model_tools", "max_stars_repo_head_hexsha": "7c191fd7b4f7c53f30662e476d2d4299ff3c3fcf", "max_stars_repo_licenses": ["BSD-3-Claus... |
!! Copyright (C) Stichting Deltares, 2012-2016.
!!
!! This program is free software: you can redistribute it and/or modify
!! it under the terms of the GNU General Public License version 3,
!! as published by the Free Software Foundation.
!!
!! This program is distributed in the hope that it will be useful,
!! b... | {"hexsha": "23992d3a84ceb2b77cb86229531a37c246d6821e", "size": 2753, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/waq/packages/waq_kernel/src/bloom/grazin.f", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac66f5... |
# -*- coding: utf-8 -*-
"""
Created on Mon Aug 1 14:44:28 2016
@author: poyu
"""
import os
# SET THE GPU DEVICE
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import numpy as np
import theano
import sys
import lasagne
import lasagne.layers.dnn
from theano.tensor import TensorType
def get_CNNparameters():
number_filter ... | {"hexsha": "0d40b6c1e32719fe07f2e7e8893cce4c45075b2a", "size": 13428, "ext": "py", "lang": "Python", "max_stars_repo_path": "mTOP2016_CNNFeatureExtraction.py", "max_stars_repo_name": "pykao/mTOP2016", "max_stars_repo_head_hexsha": "71e65a0de812160968195be51ee15b5fbdff904d", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
import pandas as pd
import csv
import matplotlib.pyplot as plt
import random as rnd
# Read data file
headers = ['x', 'y']
df = pd.read_csv('data_2.csv', names=headers)
# Extracting x and y columns
x = df['x'].values
y = df['y'].values
# Converting string to float
for dt in range(0, len(x)):
x[... | {"hexsha": "a773776155c3d3ab845b6333f01fd3437b0b1f4c", "size": 1676, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/Data_2.py", "max_stars_repo_name": "namangupta98/ENPM673_HW1", "max_stars_repo_head_hexsha": "c6a6304221d8601e82bfdc33d492db5477f519d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
using Hecke, DelimitedFiles
boundcond = ARGS[1]
gtype = ARGS[2]
_gtype = replace(replace(replace(replace(replace(gtype, '[' => '_'), ']' => '_'), ' ' => '_'), ',' => '_'), "__" => "_")
file = "conductors_" * boundcond * "_" * _gtype;
bound = fmpz(Meta.eval(Meta.parse(boundcond)))
gtype = convert(Vector{Int}, Meta.e... | {"hexsha": "5ed1ab89fe934c3f36221b7dedba8beba5354a87", "size": 2078, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/FieldEnumeration/conductors_from_structure_absolute_abelian.jl", "max_stars_repo_name": "StevellM/Hecke.jl", "max_stars_repo_head_hexsha": "64938b616109da05d1e294e87b2ca3e0ad41fb8e", "max_... |
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
# File: DepthwiseSep2D.py
# Author: Julian Faraone <julian.faraone@sydney.edu.au>
import numpy as np
import tensorflow as tf
import math
from ._common import layer_register
from ..utils import logger
#slim allows us to combine convolutions, batch norm and relu into one f... | {"hexsha": "8b491984a8a559c5f19f32cfaf3a10bebd975ffb", "size": 2488, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorpack/models/DepthwiseSep2D.py", "max_stars_repo_name": "arassadin/SYQ", "max_stars_repo_head_hexsha": "d30e6f0053ada3ad504038698a8756425594aa22", "max_stars_repo_licenses": ["Apache-2.0"], "... |
# "1HBV:A:ARG8;1HBV:B:ARG8;"
#python27 -i mkLigandXML.py 1HBV_1HBV_Lig/1HBV_Lig.pdbqt -center 2.659 7.004 -7.047 -boxDim 8.0 8.0 14.0 -o test.xml
## FIXME use getopt to handl command line parameters
import sys
from MolKit import Read
from AutoDockFR.utils import pdbqt2XML
import numpy
def usage():
print "*******... | {"hexsha": "7f391cd1e685428baaef456c7f40cc7d66f56d79", "size": 3503, "ext": "py", "lang": "Python", "max_stars_repo_path": "MetaScreener/external_sw/mgltools/MGLToolsPckgs/AutoDockFR/bin/mkLigandXML.py", "max_stars_repo_name": "bio-hpc/metascreener", "max_stars_repo_head_hexsha": "6900497629f601c4b6c0c37da26de58ffa2219... |
# Based off https://github.com/pytorch/examples/blob/master/dcgan/main.py
import argparse
import os
import random
import numpy as np
import torch
import torchvision
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm, trange
from data.dataloaders import get_dataloader
from model.dcgan import Discr... | {"hexsha": "6543271cd39912a940b8853fa2a41f4640f4aa8f", "size": 4847, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_dcgan.py", "max_stars_repo_name": "nik-sm/generator-surgery", "max_stars_repo_head_hexsha": "b4a2213a86b8faae88efce18cb129eeaf4161252", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#include <boost/multi_array/index_gen.hpp>
| {"hexsha": "1c33152011c2c10f2a1d6ce9df7d91b2af84e7ed", "size": 43, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_multi_array_index_gen.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0... |
[STATEMENT]
lemma R_star: "(Ref i i)\<^sup>\<star> \<le> Ref i i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Ref i i\<^sup>\<star> \<le> Ref i i
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. Ref i i\<^sup>\<star> \<le> Ref i i
[PROOF STEP]
have "H i (Ref i i) i"
[PROOF STATE]
proof (prov... | {"llama_tokens": 668, "file": "Hybrid_Systems_VCs_KleeneAlgebraTests_HS_VC_KAT", "length": 10} |
# =============================================================================
# PC2015Masoli_model.py
#
# created 01 August 2017 Lungsi
#
# This py-file contains the class of the model.
# The template of the model in the directory PC2015Masoli/
# is based on http://dx.doi.org/10.3389/fncel.2015.00047
# available in
... | {"hexsha": "1df761b07553b30e76c17441654f519c1a068eee", "size": 14017, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/cells/PC2015Masoli_model.py", "max_stars_repo_name": "appukuttan-shailesh/hbp-cerebellum-models", "max_stars_repo_head_hexsha": "b3dd9726f8473a805f2d83daf9e5239374e60eb6", "max_stars_repo_... |
import sys
import os
sys.path = [os.path.join(os.path.dirname(__file__), "..")] + sys.path
# from physt.histogram1d import Histogram1D
from physt import histogram
import numpy as np
import pytest
class TestNumpyBins:
def test_nbin(self):
arr = np.random.rand(100)
hist = histogram(arr, bins=15)
... | {"hexsha": "91ddbb423f5cfd6696dde6b5a5414d82df0df08c", "size": 1618, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_histogram.py", "max_stars_repo_name": "marinang/physt", "max_stars_repo_head_hexsha": "6536d559dff6d70d98e66bf0711e81099aa5699c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.optimizers import RMSprop, Adam, SGD
from tensorflow.keras.layers import LeakyReLU
from tensorflow.keras.callback... | {"hexsha": "493682091ef27075d94da83188370e911d834b9c", "size": 5577, "ext": "py", "lang": "Python", "max_stars_repo_path": "tradingBot_DQN_v1/Magician.py", "max_stars_repo_name": "Skinok/AI_Framework", "max_stars_repo_head_hexsha": "3889d69e4aa68067f29285b6cb6a07f4f3886636", "max_stars_repo_licenses": ["MIT"], "max_sta... |
module TimeStepping
using DataStructures
using JuMP
using Mosek
using MosekTools
using Ipopt
using LinearAlgebra
using Revise
import MathOptInterface
include("moreau.jl")
include("integrator.jl")
export Moreau, step, set_state
export Integrator, integrate
end
| {"hexsha": "784b4f91d581bb38555d95810456b8a00eacfe3e", "size": 264, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TimeStepping.jl", "max_stars_repo_name": "Symplectomorphism/TimeStepping.jl", "max_stars_repo_head_hexsha": "065d3bcb8d3eb01eed76567c2affc506fb372467", "max_stars_repo_licenses": ["MIT"], "max_s... |
#### All this code needs to be modified. We need to modify for LiTS.
##### Neeed to probably do some kind of
from promise2012.Vnet.model_vnet3d import Vnet3dModule
from promise2012.Vnet.util import convertMetaModelToPbModel
import numpy as np
import pandas as pd
import cv2
def train():
'''
P... | {"hexsha": "f44c0b64c493788350bfc6e9346de598f409759b", "size": 2072, "ext": "py", "lang": "Python", "max_stars_repo_path": "LiTS/vnet3d_train_predict.py", "max_stars_repo_name": "Sempronius/LiTS---Liver-Tumor-Segmentation-Challenge", "max_stars_repo_head_hexsha": "1d9ccdce52d30314fd5cc9f0a351a361101bc8db", "max_stars_r... |
{-# OPTIONS --cubical --no-import-sorts #-}
module Number.Consequences where
open import Agda.Primitive renaming (_⊔_ to ℓ-max; lsuc to ℓ-suc; lzero to ℓ-zero)
open import Cubical.Foundations.Everything renaming (_⁻¹ to _⁻¹ᵖ; assoc to ∙-assoc)
open import Cubical.Foundations.Logic renaming (inr to inrᵖ; inl to inlᵖ)
... | {"hexsha": "d6377335b961517cdee89c6951a94ec0ffcf443e", "size": 53961, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "agda/Number/Consequences.agda", "max_stars_repo_name": "mchristianl/synthetic-reals", "max_stars_repo_head_hexsha": "10206b5c3eaef99ece5d18bf703c9e8b2371bde4", "max_stars_repo_licenses": ["MIT"],... |
module TestDBCollection
using SimString
using Test
@testset "Check single updates of DictDB using CharacterNGrams" begin
db = DictDB(CharacterNGrams(3, " "))
push!(db, "foo")
push!(db, "bar")
push!(db, "fooo")
@test db.string_collection == ["foo", "bar", "fooo"]
@test db.string_size_map[5] ==... | {"hexsha": "a12c6a6c5e5d5ee60939068bffe4d82703dc75b7", "size": 4649, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test01_dictdb.jl", "max_stars_repo_name": "PyDataBlog/SimString.jl", "max_stars_repo_head_hexsha": "a76423ac68650f7f238d88be7c1f09a40863c1bc", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.