text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
from __future__ import print_function
import sh
import array
import extract
import numpy as np
import window
import edimax
import sys
import csv
from datetime import datetime
import argparse
class AudioSampleCollector(object):
def __init__(self, device_name, edimax_ip, audio_device="hw:0,0"):
self.featur... | {"hexsha": "ead275c5f366819315b6c30c30e86ff311347099", "size": 2683, "ext": "py", "lang": "Python", "max_stars_repo_path": "energy_models/mike/run_it.py", "max_stars_repo_name": "nglrt/virtual_energy_sensor", "max_stars_repo_head_hexsha": "f3ba1c00baf5be80bb4262395afcd3bdea10cafd", "max_stars_repo_licenses": ["MIT"], "... |
/-
Copyright (c) 2020 Markus Himmel. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Markus Himmel, Scott Morrison
! This file was ported from Lean 3 source module category_theory.preadditive.yoneda.projective
! leanprover-community/mathlib commit f8d8465c3c392a93b9ed2... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/CategoryTheory/Prea... |
import random
import numpy as np
class MockRobot(object):
def __init__(self):
self.cam_intrinsics = 1.0
self.cam_depth_scale = 1.0
self.cam_pose = 1.0
self.use_cam = True
try:
# -- Is the camera connected? if so, use it
# -- Use when want to test ca... | {"hexsha": "556a7cb5799da8167db71111cb6216d0f405ac5c", "size": 1429, "ext": "py", "lang": "Python", "max_stars_repo_path": "mock_robot.py", "max_stars_repo_name": "nouyang/throwdini", "max_stars_repo_head_hexsha": "e8d6e8e1a41222cac3b39391fc9018949ed170fe", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count"... |
# syntax: proto3
using ProtoBuf
import ProtoBuf.meta
mutable struct OpDeprecation <: ProtoType
__protobuf_jl_internal_meta::ProtoMeta
__protobuf_jl_internal_values::Dict{Symbol,Any}
__protobuf_jl_internal_defaultset::Set{Symbol}
function OpDeprecation(; kwargs...)
obj = new(meta(OpDeprecation)... | {"hexsha": "e672ec5d6c1b41717be41be7cb3c550061995faf", "size": 10849, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/protojl/tensorboard/op_def_pb.jl", "max_stars_repo_name": "JJMinton/TensorBoardLogger.jl", "max_stars_repo_head_hexsha": "25d8db22c5082d029ff1ec876512633b2b24dbc8", "max_stars_repo_licenses": ... |
import cv2
#import numpy as np
face_cascade =cv2.CascadeClassifier('./cascades/data/haarcascade_frontalface_alt_tree.xml')
img = cv2.imread('./images/1.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray,scaleFactor=1.3,minNeighbors=2)
for (x,y,w,h) in faces:
cv2.rectangle... | {"hexsha": "1ab46ce6d8ee8504cb926086fc964c746257c63b", "size": 425, "ext": "py", "lang": "Python", "max_stars_repo_path": "devtube/detector.py", "max_stars_repo_name": "FoolMasque/LBPH-FaceNet-Face-Recognition", "max_stars_repo_head_hexsha": "6bf1c9a0b010f1528ce478cf7951b56e50aee429", "max_stars_repo_licenses": ["MIT"]... |
import argparse
import collections
import itertools
import glob
import json
import logging
import os
import shlex
import subprocess
import natsort
import numpy as np
import yaml
logger = logging.getLogger(__name__)
TrainedModel = collections.namedtuple('TrainedModel', [
'base_directory', 'model', 'environment',... | {"hexsha": "7fa78b726d6ad2a7ae44d6117fc79a52b5b93b37", "size": 19996, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/run_experiments.py", "max_stars_repo_name": "llucid-97/rl-generalization", "max_stars_repo_head_hexsha": "5561067d6fe0f8873a1e83b2479a6a6faa820dfa", "max_stars_repo_licenses": ["MIT"], "... |
import os
import glob
import numpy as np
airfoilname = "NACA4408_original"
path = glob.glob(os.path.join(os.getcwd(), "../airfoils/" + airfoilname + "*")) # Find path of airfoil file
airfoil_data = np.genfromtxt(path[0]) # Import airfoil coordinates
idx_ul = np.argwhere(airfoil_data[:, 0] == 0)[0, 0] + 1 # Index ... | {"hexsha": "b4db8c4bc8a2ab0c7af8fda2d39fd83e6482ef54", "size": 781, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/airfoils/other/NACA4408_writer.py", "max_stars_repo_name": "themrdjj/thin-airfoil-dvm", "max_stars_repo_head_hexsha": "e198fc623ad4ff6a0eadc51a53bc7e9962437d78", "max_stars_repo_licenses": ["M... |
const defcolors = ["#1F77B4", "#FF7F0E", "#2CA02C", "#D62728", "#9467BD",
"#8C564B", "#E377C2", "#7F7F7F", "#BCBD22", "#17BECF"]
sv = IScatterSpectrum.ScatterVolume(230e6, 1e-6, 50000e-9, 0.0)
p = IScatterSpectrum.Plasma(1.5e11, 2000., 1000., sv)
f = 5.0:5:5000
freq = [0; f]
tr = GenericTrace[]
... | {"hexsha": "cbd023dc1019cd40df45a1bb6b679a3358449a7c", "size": 4282, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/plottest.jl", "max_stars_repo_name": "stephancb/IScatterSpectrum.jl", "max_stars_repo_head_hexsha": "b4512871b34ba27d852d6a19302115e617640529", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import json, re, io, os, sys, time, math
import numpy as np
## These are the function that I intend to re-use in future projects ##
# encode so that numpy doesnt kill JSON
class Npencoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.integer):
return int(obj)
elif is... | {"hexsha": "3187a412fbc2f4e2938a81109209c1fc2990fe86", "size": 6430, "ext": "py", "lang": "Python", "max_stars_repo_path": "Functions/j_functions.py", "max_stars_repo_name": "jeffrey-clark/gender_in_academia", "max_stars_repo_head_hexsha": "25f76abfccb06ee7d6a630ee1d4cecdbf6dbe21d", "max_stars_repo_licenses": ["MIT"], ... |
include("struct.jl")#
include("input_data/functions.jl")#
include("wind/functions.jl")#
include("wind/wind_module.jl")#
include("eens/functions.jl")#
include("database/functions.jl")
#include("input_data/test_cases.jl")#
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
#NOTE EENS is set to zero for flexlan !!!!!!!!!!!!!!!!!!!!!!!!!!... | {"hexsha": "e717d93d2a51036029661b500af245c7b49ece40", "size": 9017, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/economics/functions.jl", "max_stars_repo_name": "sdwhardy/cordoba.jl", "max_stars_repo_head_hexsha": "49de8a6a5862c6ee9a70f241a498e0a48ef41eed", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
from dekisugi.sequence_model import get_sequence_model
model = get_sequence_model(
7500,
emb_sz=500,
pad_idx=2,
dropoute=0,
rnn_hid=500,
rnn_layers=3,
bidir=False,
dropouth=0.2,
dropouti=0.2,
... | {"hexsha": "3b19b69f492c3ffcd1adfacabb3537084d822de1", "size": 5023, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/douban_segmented_regressor_inference.py", "max_stars_repo_name": "ceshine/modern_chinese_nlp", "max_stars_repo_head_hexsha": "e1d5941f381431ac114f440472d3e0f976437777", "max_stars_repo_l... |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.cluster import KMeans
from cvProcessor import CVProcessor
##################################################
# Opinion Plot Class #
##################################################
class OpinionPlot:
def _... | {"hexsha": "431c291d5ffbafc0a7c75432adbd73e2aa911b22", "size": 5904, "ext": "py", "lang": "Python", "max_stars_repo_path": "opinionPlot.py", "max_stars_repo_name": "Adsey666/opinionPlot", "max_stars_repo_head_hexsha": "4f6eab9e10fc182934f6e80dd89ac5c2f0d96c8a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Jared
"""
import sklearn.kernel_ridge as kr
import numpy as np
import pandas as pd
import plotter
from ml.errors import mean_relative_error as mre
from sklearn.metrics import mean_squared_error as mse
import myConfig
from sklearn.model_selection import cro... | {"hexsha": "315147c3e15b614e7fb44feae4bc9a846ba6a69c", "size": 5941, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml/krr.py", "max_stars_repo_name": "jcamstan3370/MachineLearningPerovskites", "max_stars_repo_head_hexsha": "d7bc433bac349bf53473dc6d636954cae996b8d2", "max_stars_repo_licenses": ["MIT"], "max_sta... |
open import Agda.Primitive
variable
ℓ : Level
A : Set ℓ
P : A → Set ℓ
f : (x : A) → P x
postulate
R : (a : Level) → Set (lsuc a)
r : (a : Level) → R a
Id : (a : Level) (A : Set a) → A → A → Set a
cong₂ : (a b c : Level) (A : Set a) (B : Set b) (C : Set c) (x y : A) (u v : B)
(f : A → B → C)... | {"hexsha": "c5df27b7349587527475155402bc4502b3f177a5", "size": 548, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Succeed/Issue4893.agda", "max_stars_repo_name": "shlevy/agda", "max_stars_repo_head_hexsha": "ed8ac6f4062ea8a20fa0f62d5db82d4e68278338", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import os
import cv2
import numpy as np
import torch
from torch.utils.data import DataLoader
import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
from augmentations.transforms import get_resize_augmentation, MEAN, STD
from utils.utils import write_to_video
from utils.counting import vi... | {"hexsha": "1490e57fcc79fb5cc3d1b5280aa85e091ebfd30b", "size": 5624, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/datasets.py", "max_stars_repo_name": "Rakeshiva/vehicle-counting", "max_stars_repo_head_hexsha": "b178780b4829c4e6f8e1089e57bc56cd57a93d0a", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
setwd("/home/eric/Desktop/MXelsCalendGovt/elecReturns/")
########
# 2008 #
########
d <- read.csv("datosBrutos/nay2008aycasilla.regidDemarcacion.csv", stringsAsFactors = FALSE)
head(d)
d$v01 <- d$pan
d$l01 <- "pan"
d$v02 <- d$pt
d$l02 <- "pt"
d$v03 <- d$asd
d$l03 <- "asd"
d$v04 <- d$prd.pvem
d$l04 <- "prd-pvem"
d$v0... | {"hexsha": "2595e8e40de661bd431120ffaf1d08630b4a6cd0", "size": 7828, "ext": "r", "lang": "R", "max_stars_repo_path": "code/nayDem.r", "max_stars_repo_name": "RicardoTM96/elecRetrns", "max_stars_repo_head_hexsha": "9947602c9f8db1de7947375319dd46bedbcd197e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_... |
'''
sampled_rnn - based on rnn method in tensorflow_backend.py in keras
Main difference is in how to handle dimensions of states.
# think carefully about the distribution of the random sampled variables...
'''
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops
from tensorf... | {"hexsha": "ea4e5e3e4bcc479e810d1d64ccaebe8ea2c0dff2", "size": 10041, "ext": "py", "lang": "Python", "max_stars_repo_path": "chemvae/sampled_rnn_tf_new.py", "max_stars_repo_name": "amirnikooie/chemical_vae", "max_stars_repo_head_hexsha": "54beb07610a34f6d890915f6dae8695b5c3e61a4", "max_stars_repo_licenses": ["Apache-2.... |
-- TODO: Adapt to `HasIdentity`:
-- Add type classes to "upgrade" a meta-relation to a relation,
-- and especially to upgrade instance equivalences to an equality-like recursor
-- (see `IsIdentity` below).
#exit
import UniverseAbstractions.Axioms.Universes
import UniverseAbstractions.Axioms.Universe.Functors
import ... | {"author": "SReichelt", "repo": "universe-abstractions", "sha": "0bf2bae4c1b0f8d96c37e231dd238abda788e843", "save_path": "github-repos/lean/SReichelt-universe-abstractions", "path": "github-repos/lean/SReichelt-universe-abstractions/universe-abstractions-0bf2bae4c1b0f8d96c37e231dd238abda788e843/UniverseAbstractions/Axi... |
[STATEMENT]
lemma confluent_unique_normal_form:
"\<lbrakk> confluent R; R^** a b; R^** a c; \<not> Domainp R b; \<not> Domainp R c \<rbrakk> \<Longrightarrow> b = c"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>confluent R; R\<^sup>*\<^sup>* a b; R\<^sup>*\<^sup>* a c; \<not> Domainp R b; \<not> Domain... | {"llama_tokens": 208, "file": "Free-Groups_Cancelation", "length": 1} |
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 9 20:10:05 2022
@author: PlantPhisiology
"""
### GIT HUB REPOSIORY ####
### https://github.com/plantphysiology/Take-a-peek-of-data-set ###
### Take a peak of Sentinel satellite bands values and binary grape yield five years data sets
import numpy as np
import ... | {"hexsha": "8fbbcbdf6a569c7b7a799f37cdf592940e756838", "size": 7260, "ext": "py", "lang": "Python", "max_stars_repo_path": "satellite and binary yield dataset.py", "max_stars_repo_name": "plantphysiology/Take-a-peek-of-data-set", "max_stars_repo_head_hexsha": "3bfa2514579208bf27d50f426a9956f6ebd00737", "max_stars_repo_... |
import random
import string
from unittest.mock import patch
import anndata
import numpy
from pandas import DataFrame
from scipy.sparse.csr import csr_matrix
from backend.corpora.common.dataset_validator import DatasetValidator
from backend.corpora.common.utils.corpora_constants import CorporaConstants
from .. import ... | {"hexsha": "8191649ba9e682b5099750d4a90ffaafb783e4da", "size": 22171, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/backend/corpora/common/test_dataset_validator.py", "max_stars_repo_name": "isabella232/corpora-data-portal", "max_stars_repo_head_hexsha": "09ed3cad3165f8b0db854b76404e0d5d0ea0b7d9", "... |
"""Missions view."""
import copy
import csv
import io
import json
import logging
import math
import numpy as np
import pyproj
import zipfile
from auvsi_suas.models import distance
from auvsi_suas.models import mission_evaluation
from auvsi_suas.models import units
from auvsi_suas.models.mission_config import MissionCo... | {"hexsha": "4035451a37e6f9a5512a2340c41e25d671c2c887", "size": 23849, "ext": "py", "lang": "Python", "max_stars_repo_path": "server/auvsi_suas/views/missions.py", "max_stars_repo_name": "RMMichael/interop", "max_stars_repo_head_hexsha": "b68a1b0b2324b5a1d9b2683b97299cb6f214cdb9", "max_stars_repo_licenses": ["Apache-2.0... |
from Main import MachineSpecificSettings
from Main.Environments.Connect4 import Utils
from RootDir import ROOT_DIR
from ctypes import *
import numpy as np
from numpy.ctypeslib import ndpointer
from PositionFile import POSITION
'''
class POSITION(Structure):
_fields_ = [
('current_position', c_uint64),
... | {"hexsha": "67ce4f293d3bea7679cd654b690ccb6d41e82403", "size": 4782, "ext": "py", "lang": "Python", "max_stars_repo_path": "Main/Environments/Connect4/Connect4Bitmaps.py", "max_stars_repo_name": "ikaroszhang96/Convex-AlphaZero", "max_stars_repo_head_hexsha": "d96c9790529e48ff4e2ec34649bdc312a0abcc53", "max_stars_repo_l... |
SUBROUTINE GMBDRD
C
IMPLICIT REAL*8(A-H,O-Z)
C
COMMON/CGIMBD/ELIN(3,3),ELAX(3),ELCG(3),ELMS,ZTZT(3,3)
C
COMMON/DMBICS/ELEVI,ELEVID,GMUP(2),GMDN(2)
C
COMMON/DMINTF/GMK1(2),GMK2(2),GMDMP(2),GMSTP(2)
C
COMMON/DMPRPL/ GMBAZ,GMBAZD,GMBEL,GMBELD
C
COMMON/ELKDMP/ OMKDMP(3,10),IOMKDM(1... | {"hexsha": "b9cbad80408f1ea07063d3a3b2bc8b82cb3b4670", "size": 2603, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "gsc-13006/fsd/source/gmbdrd.for", "max_stars_repo_name": "SteveDoyle2/nasa-cosmic", "max_stars_repo_head_hexsha": "c8015a9851a04f0483b978d92c2cbaee31c81fe3", "max_stars_repo_licenses": ["BSD-Sou... |
"""AttentionDecoderCorr takes an array of correlations and outputs attention.
This code is based on Matlab code published here:
https://github.com/sinamiran/Real-Time-Tracking-of-Selective-Auditory-Attention
Based on the work:
S. Miran, S. Akram, A. Sheikhattar, J. Z. Simon, T. Zhang, and B. Babadi,
Real-Time Trackin... | {"hexsha": "b11aa07d8ff4682a19e8d74726296d52143e7875", "size": 17663, "ext": "py", "lang": "Python", "max_stars_repo_path": "telluride_decoding/attention_decoder.py", "max_stars_repo_name": "RULCSoft/telluride_decoding", "max_stars_repo_head_hexsha": "ff2a5b421a499370b379e7f4fc3f28033c045e17", "max_stars_repo_licenses"... |
[STATEMENT]
lemma ab_semigroup_mult_sep_conj: "class.ab_semigroup_mult (**)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. class.ab_semigroup_mult (\<and>*)
[PROOF STEP]
by (unfold_locales)
(auto simp: sep_conj_ac) | {"llama_tokens": 98, "file": "Separation_Algebra_Separation_Algebra", "length": 1} |
import textwrap
import numpy
from OpenGL import GL
from OpenGL.GL.shaders import compileShader, compileProgram
from OpenGL.arrays.vbo import VBO
import glfw
class CameraState(object):
def __init__(self):
# Common values for both orthographic and perspective projection
self.focus = [0.... | {"hexsha": "d6f58cc12ec294cd9e87dd270ae2debb366bad89", "size": 6496, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/python/triangle_viewer.py", "max_stars_repo_name": "cmbruns/vr_samples", "max_stars_repo_head_hexsha": "8dee056766bccca1a602c6dd58fd0a641c5033a5", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
class Box:
"""
This class implements functions to manage continuous states and action
spaces. It is similar to the ``Box`` class in ``gym.spaces.box``.
"""
def __init__(self, low, high, shape=None):
"""
Constructor.
Args:
low ([float, np.nda... | {"hexsha": "6a68909a702035bcac2e212604b0146d1463a8a1", "size": 2736, "ext": "py", "lang": "Python", "max_stars_repo_path": "mushroom/utils/spaces.py", "max_stars_repo_name": "doroK/mushroom", "max_stars_repo_head_hexsha": "47e5b1d09b65da585c1b19a6cc7f0366849d7863", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
[STATEMENT]
lemma left_total_rel_converter:
"\<lbrakk> left_unique A; right_total A; left_total B; left_total C; left_unique R; right_total R \<rbrakk>
\<Longrightarrow> left_total (rel_converter A B C R)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>left_unique A; right_total A; left_total B; left_to... | {"llama_tokens": 1097, "file": "Constructive_Cryptography_Converter", "length": 6} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Socker Programming - Multi Connection Server"""
from __future__ import (division, absolute_import, print_function,
unicode_literals)
import numpy as np
import pandas as pd
import matplotlib.transforms as mtransforms
import matplotlib.pyplot as p... | {"hexsha": "fa127030b00076334322309b917a632c84c327d0", "size": 1616, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/plotting/fredgraph.py", "max_stars_repo_name": "imjoseangel/100DaysOfCode", "max_stars_repo_head_hexsha": "bff90569033e2b02a56e893bd45727125962aeb3", "max_stars_repo_licenses": ["MIT"], "ma... |
'''
The MIT License (MIT)
Copyright (c) 2014, 2017 Hometown Software Solutions
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,... | {"hexsha": "c943f43a18482b033de4fbb7b287d60ec2b38f3e", "size": 21201, "ext": "py", "lang": "Python", "max_stars_repo_path": "BouncingBeachBall.py", "max_stars_repo_name": "HometownSoftware/PySolutions", "max_stars_repo_head_hexsha": "374b9f410438181cdb62668e2a4c4402f5b8cd16", "max_stars_repo_licenses": ["MIT"], "max_st... |
@testset "Unconstrained" begin
@testset "Basic" begin
# These basic tests are simple ways of checking that your solver isn't breaking.
nlp = ADNLPModel(
x -> (x[1] - 1)^2 + (x[2] - 2)^2 / 4,
zeros(2)
)
output = with_logger(NullLogger()) do
uncsolver(nlp)
end
@test isapprox(ou... | {"hexsha": "3091fdca6ad6e85a84ef9a7fa51cdceb85033ae6", "size": 1024, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/unc.jl", "max_stars_repo_name": "abelsiqueira/JSOSolverTemplates.jl", "max_stars_repo_head_hexsha": "5d6e4ce9490ec86869bc076d5e98edfec393fc15", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import pandas as pd
import csv
import logging
import os
import io
import sys
#import mygene
path = '/Users/obawany/Desktop/GItHub Repositories/Text-Extraction/'
# my_dict = {}
with open('mart_export_Gene_Name_Id.txt', 'r') as dictonary, open('GeneNameByScoreOrdered.txt', 'r') as namestoLookup, ope... | {"hexsha": "9bd02c4bab7258d1caace99ad231f2e21d8962ae", "size": 2638, "ext": "py", "lang": "Python", "max_stars_repo_path": "listOfGenes.py", "max_stars_repo_name": "obawany/Text-Extraction", "max_stars_repo_head_hexsha": "f680da3eb37b6715b59e3a65031ddca7e608946d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
%% Kruskal tensors
% Kruskal format is a decomposition of a tensor X as the sum of the outer
% products a the columns of matrices. For example, we might write
%
% $${\mathcal X} = \sum_r a_r \circ b_r \circ c_r$$
%
% where a subscript denotes column index and a circle denotes outer
% product. In other words, the tens... | {"author": "andrewssobral", "repo": "mtt", "sha": "0152a77df09f24af4c294f46845931e4e0e63b55", "save_path": "github-repos/MATLAB/andrewssobral-mtt", "path": "github-repos/MATLAB/andrewssobral-mtt/mtt-0152a77df09f24af4c294f46845931e4e0e63b55/libs/tensor_toolbox_2.5/doc/D_ktensor_doc.m"} |
import pickle
import matplotlib.pyplot as plt
import math
import argparse
import scipy
import numpy as np
import matplotlib
x = pickle.load(open("/nfs/projects/humanattn/data/eyesum/dataset.pkl","rb"))
parser = argparse.ArgumentParser(description='')
parser.add_argument('--fid', type=int, default=None)
parser.add_arg... | {"hexsha": "9c88d575a733ccbc7d86e02914d3c287c6c2bbfe", "size": 1692, "ext": "py", "lang": "Python", "max_stars_repo_path": "humantrain/humanplotter.py", "max_stars_repo_name": "humanattn/humanattn2022", "max_stars_repo_head_hexsha": "1ccf8aa03ad42f692bf840925f6e0e20268a4a1c", "max_stars_repo_licenses": ["MIT"], "max_st... |
# ---
# title: 137. Single Number II
# id: problem137
# author: Indigo
# date: 2021-06-03
# difficulty: Medium
# categories: Bit Manipulation
# link: <https://leetcode.com/problems/single-number-ii/description/>
# hidden: true
# ---
#
# Given an integer array `nums` where every element appears **three times**
# except... | {"hexsha": "7f941b379b5edc59df031413fda0e83ceda1f7a2", "size": 1163, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/problems/137.single-number-ii.jl", "max_stars_repo_name": "jmmshn/LeetCode.jl", "max_stars_repo_head_hexsha": "dd2f34af8d253b071e8a36823d390e52ad07ab2e", "max_stars_repo_licenses": ["MIT"], "ma... |
#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# 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 the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | {"hexsha": "bf8cbd8c18db6e5f339dd92fcffdfca699f495c2", "size": 4804, "ext": "py", "lang": "Python", "max_stars_repo_path": "cookbook/05-Plugin/PluginReposity/ReversePlugin/testReversePlugin.py", "max_stars_repo_name": "Jie-Fang/trt-samples-for-hackathon-cn", "max_stars_repo_head_hexsha": "17f6fe006267b703b756944142c2fa... |
Require Import Crypto.Arithmetic.PrimeFieldTheorems.
Require Import Crypto.Specific.solinas32_2e213m3_9limbs.Synthesis.
(* TODO : change this to field once field isomorphism happens *)
Definition freeze :
{ freeze : feBW_tight -> feBW_limbwidths
| forall a, phiBW_limbwidths (freeze a) = phiBW_tight a }.
Proof.
S... | {"author": "anonymous-code-submission-01", "repo": "sp2019-54-code", "sha": "8867f5bed0821415ec99f593b1d61f715ed4f789", "save_path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code", "path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code/sp2019-54-code-8867f5bed0821415ec99f593b1d61f715ed4f7... |
"""The selection module provides methods to run the statistical inference for a
set of models on given data (model selection and parameter estimation). Use the
top-level function `select_models`; `select_models` then calls required helper
functions (such as `net_estimation`) automatically."""
# TODO: user input vali... | {"hexsha": "007c81546191bbbda5e45deef3ce96ffc0448480", "size": 28786, "ext": "py", "lang": "Python", "max_stars_repo_path": "memocell/selection.py", "max_stars_repo_name": "hoefer-lab/memocell", "max_stars_repo_head_hexsha": "5dc08d121e64fbde1ccdce86f0f1390e6918d255", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
using SymbolicUtils: Sym, FnType, Term, symtype
using SymbolicUtils
using Test
@testset "@syms" begin
let
@syms a b::Float64 f(::Real) g(p, h(q::Real))::Int
@test a isa Sym{Number}
@test a.name === :a
@test b isa Sym{Float64}
@test b.name === :b
@test f isa Sym{Fn... | {"hexsha": "d6bc058141b194ea0c60f843cc9a175a32792e12", "size": 1317, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/basics.jl", "max_stars_repo_name": "soraros/SymbolicUtils.jl", "max_stars_repo_head_hexsha": "c8af011ada3ebfe01f6f63143d77bd0d756526d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import os.path as op
import argparse
import numpy as np
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
def read_cfg(fc):
(orgs, fis) = ([], [])
fhc = open(fc, "r")
for line in fhc:
line = line.strip("\n")
... | {"hexsha": "232caaa4e656039b89d11e1258b04c574e278531", "size": 3504, "ext": "py", "lang": "Python", "max_stars_repo_path": "formats/fastortho.py", "max_stars_repo_name": "orionzhou/biolib", "max_stars_repo_head_hexsha": "940fb66f1b2608d34a2d00ebdf41dc84c6381f42", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_... |
# Copyright (c) 2019 The Felicia Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
import sys
import numpy as np
import import_order_resolver # Should be before import felicia_py
import felicia_py as fel
import felicia_py.command_line... | {"hexsha": "1c0b558acc29e41b5386774fe673f6e54bd1dcd5", "size": 4195, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/deep_learning/object_detection/object_detection_from_camera.py", "max_stars_repo_name": "chokobole/felicia-examples", "max_stars_repo_head_hexsha": "83303cf0f4bf688e9794ee574394d98619a160... |
""" test the scalar Timestamp """
import calendar
from datetime import datetime, timedelta
import locale
import unicodedata
from dateutil.tz import tzutc
import numpy as np
import pytest
import pytz
from pytz import timezone, utc
from pandas._libs.tslibs.timezones import dateutil_gettz as gettz, get_tim... | {"hexsha": "0ae0ee61070505ce890dc24594a8c1b62ac7648d", "size": 21055, "ext": "py", "lang": "Python", "max_stars_repo_path": "mypython/Lib/site-packages/pandas/tests/scalar/timestamp/test_timestamp.py", "max_stars_repo_name": "lilianatang/data-modelling-with-postgresql", "max_stars_repo_head_hexsha": "4b5d057d23c346cc36... |
import numpy as np
import unittest
import sys
sys.path.append('../')
from nelder_mead.nelder_mead import NelderMead
class TestNelderMead(NelderMead):
def buildSimplexPoints(self):
self.simplex = np.vstack([np.eye(len(self.f_variables), dtype = float), self.f_variables])
for index, value in enumera... | {"hexsha": "a93cd0d469240553f6521793c20fe6ebfa7fe6d1", "size": 9383, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/nelder_test.py", "max_stars_repo_name": "bmartins95/NelderMead", "max_stars_repo_head_hexsha": "b296e4e70230b7f7efd4c9b80139b997809ba977", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# general imports
import yaml
import numpy as np
from pathlib import Path
from math import floor
# AHA imports
import magma as m
import fault
# FPGA-specific imports
from svreal import get_svreal_header
from msdsl import get_msdsl_header
# DragonPHY imports
from dragonphy import get_file
BUILD_DIR = Path(__file__).... | {"hexsha": "a9b282011c0d58d9c895fb3ac1ec1fb6e91096d1", "size": 2832, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/fpga_block_tests/adc_model/test_adc_model.py", "max_stars_repo_name": "StanfordVLSI/dragonphy2", "max_stars_repo_head_hexsha": "996cc14f800b01c5ec0534e79dd2340f4de5e704", "max_stars_repo_lic... |
# Code
## Standard imports
```python
# Data manipulation
import pandas as pd
import numpy as np
# Options for pandas
pd.options.display.max_columns = 50
pd.options.display.max_rows = 30
from IPython import get_ipython
ipython = get_ipython()
# autoreload extension
if 'autoreload' not in ipython.extension_manager.... | {"hexsha": "c17888ddcb3c2e033616a4db1fc7a5f5a176c9a5", "size": 639045, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Connection steady state of IBM and steady state of gLV.ipynb", "max_stars_repo_name": "lanadescheemaeker/rank_abundance", "max_stars_repo_head_hexsha": "906e4adb1405f468efd7908c4721... |
[STATEMENT]
lemma necessitation_averse_axiom_instance[axiom]: "[\<phi>] \<Longrightarrow> [\<phi> in dw]"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. [\<phi>] \<Longrightarrow> [\<phi> in dw]
[PROOF STEP]
by axiom_meta_solver | {"llama_tokens": 91, "file": "PLM_TAO_7_Axioms", "length": 1} |
subroutine eval_dU_gauss(qin,dU,f_in,g_in,irr,mitot,mjtot,lwidth,
& dtn,dtnewn,lstgrd,dx,dy,flag,iorder,xlow,ylow,mptr,
& vtime,steady,qx,qy,level,difmax,lastout,
& meqn,time, ffluxlen, gfluxlen, istage)
implicit double precision (a-h,o-z)
... | {"hexsha": "d6aaa3f614e40bc25a0f27e103a2b70ee8c2ac4a", "size": 15698, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/2d/ag_ho/eval_dU_gauss.f", "max_stars_repo_name": "mjberger/ho_amrclaw_amrcart", "max_stars_repo_head_hexsha": "0e0d37dda52b8c813f7fc4bd7e61c5fdb33b0ada", "max_stars_repo_licenses": ["BSD-3-C... |
import unittest
from itertools import cycle
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
from divik.cluster import _kmeans as km
from divik.cluster._kmeans import _core as cc
from divik.cluster._kmeans._core import redefine_centroids
from test.cluster.kmeans import data
class Labelin... | {"hexsha": "75d97ce6ff419238098c5c67ab36f6f8bece7492", "size": 5475, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/cluster/kmeans/test_core.py", "max_stars_repo_name": "Hirni-Meshram/divik", "max_stars_repo_head_hexsha": "0f542ec2669428458a4ecf6bb450dc90c33b0653", "max_stars_repo_licenses": ["Apache-2.0"]... |
from CoolProp.CoolProp import PropsSI, PhaseSI, HAPropsSI
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys
import datetime
def find_path():
# Return DATA Folder Path
data_path = sys.path[0].split('CODE')[0]
data_path = f'{data_path}\\Fluid_Selection\\Results\\'
retur... | {"hexsha": "c1f9f03a6278100211ca118e8f0cb04575d57ef6", "size": 2750, "ext": "py", "lang": "Python", "max_stars_repo_path": "Thermal_Solutions/Fluid_Data_Reader.py", "max_stars_repo_name": "perkier/Perkier.Energy", "max_stars_repo_head_hexsha": "c5dd58c5f842a415dc86ba46baeba1e96b316ff3", "max_stars_repo_licenses": ["MIT... |
import pymysql
import matplotlib.pyplot as plt
plt.rcdefaults()
import matplotlib.pyplot as plt
import numpy as np
connection = pymysql.connect(host='localhost',
user='root',
password=' ',
db='sys',
cha... | {"hexsha": "1bbb9ebcd0e1dc804a052dfdc852dd8bd98eecc6", "size": 1788, "ext": "py", "lang": "Python", "max_stars_repo_path": "visual_4.py", "max_stars_repo_name": "so3500/crawling", "max_stars_repo_head_hexsha": "76b95738cd18d6568497cbd6e060c88ad834d172", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_sta... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 13 21:59:41 2021.
@author: peterp
"""
# %%%%%%%%%%%%%%%%%%%%% Anonymous functions %%%%%%%%%%%
# mncn = @(x) (x-mean(x)); % column-wise mean center
# auto = @(x) ((x-mean(x))./std(x)); % column-wise mean center and scale to unit variance
# %%%%%%%%%... | {"hexsha": "7a2f658c44c571b27ed348bc2b0f279e426436e4", "size": 2809, "ext": "py", "lang": "Python", "max_stars_repo_path": "carl-har-pls/data/generate_data.py", "max_stars_repo_name": "ppiont/carl-har-pls", "max_stars_repo_head_hexsha": "6d744ea8ca329307b045ec7a532bb8c55b15acda", "max_stars_repo_licenses": ["Unlicense"... |
from numpy import *
from numpy.testing import dec,assert_,assert_raises,assert_almost_equal,assert_allclose
from matplotlib.pyplot import *
import sys,pdb,time
from os import path
sys.path.insert(0,'../')
from tba.hgen import SpinSpaceConfig,sx,sy,sz
from toymodel import *
from rbm import *
from linop import *
from gr... | {"hexsha": "24660ff352ba5a9a7d16bc2ff2c343c5b4898079", "size": 2726, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_linop.py", "max_stars_repo_name": "GiggleLiu/QuRBM", "max_stars_repo_head_hexsha": "2cb16e534ccbf875b88c164837bb8ffada5a2b03", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
#! /usr/bin/env python
# -*- coding:utf-8 -*-
#
# Capture calibration chessboard
#
# External dependencies
import time
import cv2
import numpy as np
# Calibration pattern size
pattern_size = ( 9, 6 )
# Get the camera
camera = cv2.VideoCapture( 0 )
# Acquisition loop
while( True ) :
# Capture image-by-image
_... | {"hexsha": "0f294d341dcc8575dd2734f10a70612b798918f6", "size": 1230, "ext": "py", "lang": "Python", "max_stars_repo_path": "capture_chessboard.py", "max_stars_repo_name": "microy/RobotVision", "max_stars_repo_head_hexsha": "89349fbf73b3377c73bcd5c6c44e24c3a4f62809", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
clim_data = pd.read_csv('daily_clim_data.csv')
formind_file = pd.read_csv('Projects/Project_Beech/formind_parameters/Climate/weatherGermany_100_ori.txt', delimiter=' ')
num_days = 39*365
new_formind_arr = np.zeros((num_days, 6))
new_formind_arr[:,... | {"hexsha": "00a6debfd1c456544611d804e1f3bb07c90b6999", "size": 1001, "ext": "py", "lang": "Python", "max_stars_repo_path": "script_4_gen_clim.py", "max_stars_repo_name": "melioristic/FANPY", "max_stars_repo_head_hexsha": "2d68d222de4f1e6d6d802268253ce446cd924914", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
from sklearn import svm, tree, linear_model, neighbors, naive_bayes, ensemble, discriminant_analysis, gaussian_process
from xgboost import XGBClassifier
from sklearn.model_sele... | {"hexsha": "bb7590d12784c9699712061bf7de6e6fa437a8d8", "size": 27460, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/afl_model_build.py", "max_stars_repo_name": "Sage-of-Sparta/probabilistic-afl-tipping", "max_stars_repo_head_hexsha": "a58e0ca5b224c6658db1904815609c13eec0919f", "max_stars_repo_license... |
#!/bin/env python
"""
Probability forecasts
=====================
This example script shows how to forecast the probability of exceeding an
intensity threshold.
The method is based on the local Lagrangian approach described in Germann and
Zawadzki (2004).
"""
import matplotlib.pyplot as plt
import numpy as np
from ... | {"hexsha": "ee996c9ea189de67feb9a0760d96a1080ad391ac", "size": 4653, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/probability_forecast.py", "max_stars_repo_name": "leabeusch/pysteps", "max_stars_repo_head_hexsha": "5f162d4b1155e4cfd894c9635eed3f0e823adedd", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
'''
MIT License
Copyright (c) [2018] [Ji Zhang]
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, publish, ... | {"hexsha": "6d28787294db8b1245604d73618c3f2ad87a9698", "size": 14750, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/BandB/MissingValues.py", "max_stars_repo_name": "pywash/pywash", "max_stars_repo_head_hexsha": "f105752f67ad5c4648117a2bebd875f8c88caeb2", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
import os
import re
import csv
import time
import pickle
import logging
import torch
from torchvision import datasets, transforms
import torchvision.utils
from torch.utils import data
import torch.nn.functional as F
from options import HiDDenConfiguration, TrainingOptions
from model.hidden import H... | {"hexsha": "f0ce8ae61c6519a51ee576e138e9ac0decb2ee3f", "size": 8057, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "Litchichu/Deniable-Steganography", "max_stars_repo_head_hexsha": "e39dc71b049b0051e6e16eee9a6eea55a526459b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
__all__ = ['dump']
def dump(mesh, f):
from baiji.serialization.util.openlib import ensure_file_open_and_call
return ensure_file_open_and_call(f, _dump, 'w', mesh)
def _dump(f, mesh):
'''
Writes a mesh to collada file format.
'''
dae = mesh_to_collada(mesh)
dae.write(f.name)
def dumps(mesh... | {"hexsha": "26975eb57266db0398f099bb2127a7200e6b9072", "size": 2690, "ext": "py", "lang": "Python", "max_stars_repo_path": "lace/serialization/dae.py", "max_stars_repo_name": "metabolize/lace", "max_stars_repo_head_hexsha": "75cee6a118932cd027692d6cfe36b3726b3a4a5c", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content/blob/master/tutorials/W2D3_RealNeurons/W2D3_Tutorial3.ipynb" target="_parent"></a>
# Tutorial 3: Synaptic transmission - Models of static and dynamic synapses
**Week 2, Day 3: Real Neurons**
**By Neuromatch Academy**
__Content creator... | {"hexsha": "0ae1a7f43dc9b046f70f4a6170751b682794ef19", "size": 65387, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "tutorials/W2D3_RealNeurons/W2D3_Tutorial3.ipynb", "max_stars_repo_name": "vasudev-sharma/course-content", "max_stars_repo_head_hexsha": "46fb9be49da52acb5df252dda43f11b6d1fe827f", "m... |
[STATEMENT]
lemma truncate_down_nonneg_mono:
assumes "0 \<le> x" "x \<le> y"
shows "truncate_down prec x \<le> truncate_down prec y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. truncate_down prec x \<le> truncate_down prec y
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. truncate_down ... | {"llama_tokens": 9102, "file": null, "length": 69} |
"""
GCC Processor Part
"""
import numpy as np
import wave
import os
class GccGenerator:
def __init__(self):
self.gcc_width_half = 30
self.gcc_width_half_bias = 50
def gcc_phat(self, sig, refsig, fs=1, max_tau=None, interp=1):
if isinstance(sig, list):
sig = np.array(s... | {"hexsha": "d351be67e3a7dfdbe168eab37e89c41ca6b497fd", "size": 3753, "ext": "py", "lang": "Python", "max_stars_repo_path": "main_ssl/ssl_gcc_generator.py", "max_stars_repo_name": "JoeyYoung/sound_localization", "max_stars_repo_head_hexsha": "1ee171e01b51a8f91f506d9ca2662b068b738961", "max_stars_repo_licenses": ["MIT"],... |
import numpy as np
import pandas as pd
import time
import selenium
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
first_link = "https://scholar.google.com/scholar?start=0&q=flame+retardant&hl=en&as_sdt=0,48&as_ylo=2015"
our_html_links = []
pg_counter = 1
def first_page(link):
drive... | {"hexsha": "7adf60918b516552500fe53261a9654558659ce7", "size": 3063, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/selenium_extractor_1.py", "max_stars_repo_name": "p-amyjiang/BETO2020", "max_stars_repo_head_hexsha": "57e899042e50f30819b5d670ee2dd353bd9739a6", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
import matplotlib.pyplot as plt
n = np.array( [ 0, 1, 2, 3, 4, 5 ] ) # 定義n陣列
x = np.array( [ 1, 2, 4, 3, 2, 1 ] ) # 定義x陣列
plt.stem( n, x ) # 繪圖
plt.xlabel( 'n' )
plt.ylabel( 'x[n]' )
plt.show( ) | {"hexsha": "b41a03f01f91494aab8b8f3ce45dc524041edb24", "size": 220, "ext": "py", "lang": "Python", "max_stars_repo_path": "dsp_python_imp/Ch03/digital_signal.py", "max_stars_repo_name": "xrick/Lcj-DSP-in-Python", "max_stars_repo_head_hexsha": "f27ee7036dc0df41b96e0b06ed13bb8fd874a714", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python
# coding: utf-8
# ## DEEPER MULTILAYER PERCEPTRON WITH DROPOUT
# In[1]:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
get_ipython().run_line_magic('matplotlib', 'inline')
# # LOAD MNIST
# In[2]:
mnist ... | {"hexsha": "0526c84b47a0b7a4367d004ea775267344403741", "size": 3528, "ext": "py", "lang": "Python", "max_stars_repo_path": "etc/tf_tutorial/Tensorflow-101-master/mlp_mnist_deeper.py", "max_stars_repo_name": "zhangbo2008/facenet", "max_stars_repo_head_hexsha": "4dfabcb5cf14f99622dbe5f9f12f0539821c169c", "max_stars_repo_... |
import torch
from torch.distributions import constraints
import pyro
import pyro.distributions as dist
from pyro.contrib.autoname import scope
from pyro import poutine
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from pathlib import Path
from sys import path
import os
from os.path import dirna... | {"hexsha": "bdcbc17164f2da278c8878dd2c7c02cd2b1273d0", "size": 22203, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/multiRobotPlanning/uniCycleRobotPlanning.py", "max_stars_repo_name": "damgaardmr/probMind", "max_stars_repo_head_hexsha": "52cbb29a2f1f57f9d880a2bb93d02cfbff80c97d", "max_stars_repo_lice... |
from __future__ import print_function, division
import sys, numpy as np
from copy import copy
from pyscf.nao.m_pack2den import pack2den_u, pack2den_l
from pyscf.nao.m_rf0_den import rf0_den, rf0_den_numba, rf0_cmplx_ref_blk, rf0_cmplx_ref, rf0_cmplx_vertex_dp
from pyscf.nao.m_rf0_den import rf0_cmplx_vertex_ac, si_corr... | {"hexsha": "40f5fafb9e95cc5a564634da5531fd902e3bc8cc", "size": 20886, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyscf/nao/gw.py", "max_stars_repo_name": "mfkasim1/pyscf", "max_stars_repo_head_hexsha": "7be5e015b2b40181755c71d888449db936604660", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
import json
import logging
import os
import numpy as np
import pytest
from lsanomaly import LSAnomaly
log_fmt = "[%(asctime)s %(levelname)-8s], [%(filename)s:%(lineno)s - %(funcName)s()], %(message)s" # noqa
logging.basicConfig(level=logging.DEBUG, format=log_fmt)
here = os.path.dirname(os.path.realpath(__file__))... | {"hexsha": "e6b86c78ea63cf3b2f4e9a80703c87fd2fcdf058", "size": 3504, "ext": "py", "lang": "Python", "max_stars_repo_path": "lsanomaly/tests/conftest.py", "max_stars_repo_name": "lsanomaly/lsanomaly", "max_stars_repo_head_hexsha": "333027ad13a6906450bf147373d648fc9f5f50c2", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def init_lecun_uniform(tensor, scale=1.0):
"""Initializes the tensor with LeCunUniform."""
fan_in = torch.nn.init._calculate_correct_fan(tensor, "fan_in")
s = scale * np.sqrt(3.0 / fan_in)
with torch.no_grad():
... | {"hexsha": "7fa9aa0d460e73e6b98e08955d2a6c052af6f5a8", "size": 2532, "ext": "py", "lang": "Python", "max_stars_repo_path": "pfrl/nn/noisy_linear.py", "max_stars_repo_name": "ummavi/pfrl-1", "max_stars_repo_head_hexsha": "e856a7cca30fcc3871024cdf7522d066006a5f0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8... |
import os.path as osp
from math import ceil, sin, cos
import numpy as np
import random
import torch
import torch.nn.functional as F
from torch_geometric.nn import GINConv, EdgeConv, DynamicEdgeConv, GatedGraphConv
from torch_geometric.nn import GraphConv, TopKPooling, PointConv
from torch_geometric.utils import to_den... | {"hexsha": "0d835122f3a2a3fadc0877f888860fb6852a5f58", "size": 5862, "ext": "py", "lang": "Python", "max_stars_repo_path": "SN-Graph Network/networks.py", "max_stars_repo_name": "cscvlab/SN-Graph", "max_stars_repo_head_hexsha": "e461e9b1f126c4c25b51a5460449a16030c555fa", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
function ScatterMatrixPlot(olddf::DataFrame;colorido=[],filepath::AbstractString="scattermatrix",mime::AbstractString="svg",xwidth=0cm,ywidth=0cm,legenda::Bool=false)
pl1=ScatterMatrix1(olddf, colorido, legenda)
pl2=ScatterMatrix2(olddf, colorido, legenda)
filepath1=string(filepath,"_1")
filepath2=string(fil... | {"hexsha": "b68346f1338666e91b5d88cd2d3cf36a41f46441", "size": 1498, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ScatterMatrixPlot.jl", "max_stars_repo_name": "wakakusa/ScatterMatrixPlots", "max_stars_repo_head_hexsha": "bc8af9cf06896b60035aef0f71f6c7ebd6d32369", "max_stars_repo_licenses": ["MIT"], "max_s... |
module module_kernels_par_sum
contains
subroutine reduce_result_out_8(input_array,tsize,global_result_out_array)
integer :: chunk_size
integer :: local_id
integer :: local_id_fortran
integer :: group_id
integer :: group_id_fortran
integer :: global_id
integer :: r_iter
integer :: ... | {"hexsha": "a08848c54594ae5584cc792645970f4add7ffaaa", "size": 1768, "ext": "f95", "lang": "FORTRAN", "max_stars_repo_path": "evaluation/performance/module_kernels_par_sum.f95", "max_stars_repo_name": "wimvanderbauwhede/AutoParallel-Fortran", "max_stars_repo_head_hexsha": "87a322d1936e6cc92d83235b9523ef86b6d4b26b", "ma... |
import numpy as np
# from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from ....utils.box_utils import boxes_to_corners_3d
from ....ops.rangeview import point_to_range
class SphereProjection(object):
def __init__(self):
'''
Cartesian: x-front, y-lef... | {"hexsha": "b7b80efc709147724ced91e178ce9fcb412d34da", "size": 12774, "ext": "py", "lang": "Python", "max_stars_repo_path": "pcdet/models/backbones_2d/map_to_rv/range_projection.py", "max_stars_repo_name": "StarsMyDestination/OpenPCDet", "max_stars_repo_head_hexsha": "a9bfdffb2c23f6fe7d4c19085b47ec35728d5884", "max_sta... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# @Author: wwwins
# @Date: 2017-08-09 11:08:28
# @Last Modified by: wwwins
# @Last Modified time: 2017-08-09 18:20:26
import cv2
import math
import numpy as np
from PIL import Image, ImageDraw, ImageFont
def get_gradient_image(image):
imgsize = image.size
innerCo... | {"hexsha": "9701edf67b5589aca45160366681d8c41a9c9083", "size": 1960, "ext": "py", "lang": "Python", "max_stars_repo_path": "ImageText.py", "max_stars_repo_name": "wwwins/OpenCV-Samples", "max_stars_repo_head_hexsha": "6a88c411064d5a8d012fbc2299a6d85b4526785e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import multiprocessing
from dataclasses import dataclass, field
from functools import reduce
from logging import Logger, getLogger
from time import perf_counter
from typing import Dict, List, Union
import numpy as np
import pandas as pd
from fseval.pipeline.estimator import Estimator
from fseval.types import AbstractE... | {"hexsha": "f63a1e8ad864f4bc729a5c31199dcf0dc817eee2", "size": 6162, "ext": "py", "lang": "Python", "max_stars_repo_path": "fseval/pipelines/_experiment.py", "max_stars_repo_name": "dunnkers/fseval", "max_stars_repo_head_hexsha": "49a11a63e09e65b1f14389b6ba3a9ae3aeae086d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from sympy.core.numbers import Rational
from sympy.core.singleton import S
from sympy.core.symbol import symbols
from sympy.parsing.ast_parser import parse_expr
from sympy.testing.pytest import raises
from sympy.core.sympify import SympifyError
import warnings
def test_parse_expr():
a, b = symbols('a, b')
# te... | {"hexsha": "e7922ccbe2b1ab6c648d8f3424f7c6c501b82526", "size": 1251, "ext": "py", "lang": "Python", "max_stars_repo_path": "sympy/parsing/tests/test_ast_parser.py", "max_stars_repo_name": "yupbank/sympy", "max_stars_repo_head_hexsha": "66d7aef9dc1b26055af22e27ba42004c40b95d7c", "max_stars_repo_licenses": ["BSD-3-Clause... |
// Copyright (c) 2016, 2017 Matt Corallo
// Unlike the rest of Bitcoin Core, this file is
// distributed under the Affero General Public License (AGPL v3)
#include "udprelay.h"
#include "chainparams.h"
#include "consensus/consensus.h" // for MAX_BLOCK_SERIALIZED_SIZE
#include "consensus/validation.h" // for CValidati... | {"hexsha": "ea73ac71d517bdb068a483d3b41c14c126a01b99", "size": 55809, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/udprelay.cpp", "max_stars_repo_name": "renovate-bot/bitcoinfibre", "max_stars_repo_head_hexsha": "73daeaceddde31253cb56edc97ebdf3427f02695", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import threading as th
import json
import urllib.request
from ast import literal_eval
# import tensorflow as tf
# import pathlib
# import matplotlib.pyplot as plt
# import pandas as pd
# import numpy as np
#np.set_printoptions(precision=4)
# eur/czk 74450
contents = urllib.request.ur... | {"hexsha": "8ff6ec4df57b3cd6ac2383ee2614eb15f6e51ffe", "size": 1331, "ext": "py", "lang": "Python", "max_stars_repo_path": "Main.py", "max_stars_repo_name": "Fast-Byte22/fuzzy-palm-tree", "max_stars_repo_head_hexsha": "feb1672daf4bb4410a0aa7c0efff97d73f0c3316", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
(** Use some very basic facilities of mathcomp library *)
From mathcomp Require Import ssreflect ssrnat ssrbool eqtype seq div prime ssrfun.
Unset Printing All.
Print rel.
Definition id A (a : A) : A := a.
Check (id nat 3).
Check (id _ 3).
Arguments id {A} a.
Compute @id nat 3.
Check (fun x => @id nat x).
Lemma pri... | {"author": "vyorkin", "repo": "coq-fv", "sha": "d65348888fc51722585d81f189fd1b71da7b8c3b", "save_path": "github-repos/coq/vyorkin-coq-fv", "path": "github-repos/coq/vyorkin-coq-fv/coq-fv-d65348888fc51722585d81f189fd1b71da7b8c3b/playgrounds/playground6.v"} |
import numpy as np
import torch as th
import os
from dgl.data.utils import *
import spacy
from tqdm import tqdm
nlp = spacy.load('en')
_urls = {
'wmt': 'https://s3.us-east-2.amazonaws.com/dgl.ai/dataset/wmt14bpe_de_en.zip',
'scripts': 'https://s3.us-east-2.amazonaws.com/dgl.ai/dataset/transformer_scripts.zip'... | {"hexsha": "f789bffb0e985a12f5bb5c1421960b0affe0eaa1", "size": 5505, "ext": "py", "lang": "Python", "max_stars_repo_path": "transformer/dataset/utils.py", "max_stars_repo_name": "tmpaul06/dgl", "max_stars_repo_head_hexsha": "8f458464b0e14c78978db4b91590e8ca718c5ec6", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
#include "ayla/serialization/glm_serializer.hpp"
#include "ayla/serialization/boost/explicit_instantiation_macros.hpp"
#include "ayla/config.hpp"
#include <boost/serialization/nvp.hpp>
namespace boost {
namespace serialization {
template<class Archive>
void serialize(Archive &ar, glm::vec2& vector, const unsigned i... | {"hexsha": "9f5b5684d6f1c45d47f8c0859aad04202ea7e2fd", "size": 1773, "ext": "cc", "lang": "C++", "max_stars_repo_path": "epoch/ayla/src/ayla/serialization/glm_serializer.cc", "max_stars_repo_name": "oprogramadorreal/vize", "max_stars_repo_head_hexsha": "042c16f96d8790303563be6787200558e1ec00b2", "max_stars_repo_license... |
\documentclass[numbers=enddot,12pt,final,onecolumn,notitlepage]{scrartcl}%
\usepackage[headsepline,footsepline,manualmark]{scrlayer-scrpage}
\usepackage[all,cmtip]{xy}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{framed}
\usepackage{comment}
\usepackage{color}
\usepackage{hyperref}... | {"hexsha": "7e5d5df4471c0e2f536d7038601fed2e8c7e11ef", "size": 183761, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "hw5s.tex", "max_stars_repo_name": "darijgr/nogra", "max_stars_repo_head_hexsha": "74092e9f18aab49b2550da4633a005844b15dfbe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "max_stars_re... |
import pytest
import numpy as np
import pandas as pd
import os
import sys
import get_common_indices
sys.path.append("..")
DATA_PATH = os.path.join("..", "data" )
class TestX_common:
# TODO: Verify that common_indices_C.csv and common_indices_H.csv
# have the same number of elements before being combined.
... | {"hexsha": "68aa249310b06e9ffe97ab3720ecd9ff6f491e0b", "size": 705, "ext": "py", "lang": "Python", "max_stars_repo_path": "genomics_gans/prepare_data/test_X_common.py", "max_stars_repo_name": "Unique-Divine/GANs-for-Genomics", "max_stars_repo_head_hexsha": "e023455ae7c18d5e624bb618184c41e91261a0e4", "max_stars_repo_lic... |
program test
integer, parameter :: a = 1999
print *, a
print *, real(a)
end program test
| {"hexsha": "460e45a1da9ca95a37c4f17e51d9559986c50db8", "size": 98, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/output_tests/real_out1.f90", "max_stars_repo_name": "clementval/fc", "max_stars_repo_head_hexsha": "a5b444963c1b46e4eb34d938d992836d718010f7", "max_stars_repo_licenses": ["BSD-2-Clause"], "ma... |
[STATEMENT]
lemma homeomorphic_contractible:
fixes S :: "'a::real_normed_vector set" and T :: "'b::real_normed_vector set"
shows "\<lbrakk>contractible S; S homeomorphic T\<rbrakk> \<Longrightarrow> contractible T"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>contractible S; S homeomorphic T\<rbrakk> ... | {"llama_tokens": 143, "file": null, "length": 1} |
# Collection of functions used in the artemis pipeline.
# simetra = artemis backwards
import sys
from astropy.io import fits
from astropy.time import Time, TimeDelta
import numpy as np
import mfilter
import matplotlib.pyplot as pp
import logging
########################################################################... | {"hexsha": "5c4c8dbf14ec176f908fdfb14e803d3035f6528d", "size": 14393, "ext": "py", "lang": "Python", "max_stars_repo_path": "simetra/simetra.py", "max_stars_repo_name": "lufeng5001/simetra", "max_stars_repo_head_hexsha": "4f046b57386255ffd3c9e70b63d1ef36e7139aea", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
Tests for ttflow.utils.py
"""
############
# Standard #
############
import inspect
import logging
from pathlib import Path
from collections.abc import Iterable
###############
# Third Party #
###############
import pytest
import numpy as np
##########
# Module #
##########
from ttflow import utils
logger = logg... | {"hexsha": "fc2b0d4c4feb6bdaf8765ec8798870ca66c223f3", "size": 1596, "ext": "py", "lang": "Python", "max_stars_repo_path": "ttflow/tests/test_utils.py", "max_stars_repo_name": "slaclab/TimeToolFlow", "max_stars_repo_head_hexsha": "dd87fa9dfe632422f5005295aeec95c507849537", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
#################################################################################
# Copyright (c) 2018-2021, Texas Instruments Incorporated - http://www.ti.com
# All Rights Reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditio... | {"hexsha": "d36fa0d214b592cfe9b45f1b5396254ae160b343", "size": 4671, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchvision/edgeailite/xnn/utils/image_utils.py", "max_stars_repo_name": "TexasInstruments/vision", "max_stars_repo_head_hexsha": "abaf29de0798e8e8d3f996dc272cd3c515562695", "max_stars_repo_licens... |
# This file is part of the pyMOR project (http://www.pymor.org).
# Copyright 2013-2016 pyMOR developers and contributors. All rights reserved.
# License: BSD 2-Clause License (http://opensource.org/licenses/BSD-2-Clause)
import numpy as np
from pymor.core.interfaces import ImmutableInterface
from pymor.core.logger im... | {"hexsha": "4d56ddb615e06e9e1805753500a2cd6d9b81e048", "size": 8014, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pymor/reductors/parabolic.py", "max_stars_repo_name": "JuliaBru/pymor", "max_stars_repo_head_hexsha": "46343b527267213f4279ea36f208b542ab291c4e", "max_stars_repo_licenses": ["Unlicense"], "max... |
import os
import torch
import seaborn as sns
import pandas as pd
import numpy as np
from src.datasets.datasets import MNIST_offline
from src.models.WitnessComplexAE.wc_ae import WitnessComplexAutoencoder
from src.models.autoencoder.autoencoders import DeepAE_MNIST, ConvAE_MNIST_3D, DeepAE_MNIST_3D
from src.utils.plot... | {"hexsha": "4d81d1b7b70d7f0cb2f218850cd1e783c00433b3", "size": 3171, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/ssc/plots_forthesis/mnist_latents_pretty.py", "max_stars_repo_name": "MrBellamonte/MT-VAEs-TDA", "max_stars_repo_head_hexsha": "8881b5db607c673fb558f7b74ece27f244b16b77", "max_stars_repo_l... |
/*=============================================================================
Copyright (c) 2017 Paul Fultz II
implicit.cpp
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)
==========================... | {"hexsha": "2138f1cacf178aaa21aa040524dffa4947ba0196", "size": 1382, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/hof/test/implicit.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e188fe871f47c2ef9cdef36d8a... |
import random
import numpy as np
import torch
# Add graussian noise with zero mean and standard deviation 0.01 to 0.04
class AddGaussianNoise(object):
def __call__(self, x):
var = random.random() * 0.04 + 0.01
noise = np.random.normal(0, var, (1000))
x += noise
x = np.clip(x, 0, 1)
... | {"hexsha": "9cc5a638165eb0d20b6437dd5822270e13ae238a", "size": 1130, "ext": "py", "lang": "Python", "max_stars_repo_path": "transform.py", "max_stars_repo_name": "DenglinGo/bacteria-SANet", "max_stars_repo_head_hexsha": "fb1d2e9990f79cd7ef2067da381ebc8d98f16604", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
import gif
# Pendulum Lenghts and masses
L1, L2 = 1, 1
m1, m2 = 1, 1
# Gravity
g = 9.81
def deriv(y, t):
theta1, z1, theta2, z2 = y
c, s = np.cos(theta1 - theta2), np.sin(theta1 - theta2)
z1dot = (m2*g*np.sin(t... | {"hexsha": "af1387553617f3b3a4074a62f4e44293c58e74b0", "size": 1518, "ext": "py", "lang": "Python", "max_stars_repo_path": "DoublePendulum.py", "max_stars_repo_name": "damuopel/DoublePendulum", "max_stars_repo_head_hexsha": "4794f68952a4486b9a6cea648adfd0d6d2fca2d1", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python
""" Read protobufs sent from HDFS and converts them to JPG images for further processing. """
import os
import sys
import image_pb2
from google.protobuf.internal import encoder
import varint
import cv2
from cv2 import cv
import numpy as np
import shutil
import argparse
import ConfigParser
import... | {"hexsha": "c068069412c6d96523b4da5f914d9bd9fe115760", "size": 7068, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/convert_protobuf_to_images.py", "max_stars_repo_name": "oakfr/img-search", "max_stars_repo_head_hexsha": "474a775b3f65001b3d2f1b60138dac1422ff86b2", "max_stars_repo_licenses": ["MIT"], "max... |
function net = gtminit(net, options, data, samp_type, varargin)
%GTMINIT Initialise the weights and latent sample in a GTM.
%
% Description
% NET = GTMINIT(NET, OPTIONS, DATA, SAMPTYPE) takes a GTM NET and
% generates a sample of latent data points and sets the centres (and
% widths if appropriate) of NET.RBFNET.
%
% I... | {"author": "ilarinieminen", "repo": "SOM-Toolbox", "sha": "f2597abc1ae33c2060e0443d49e854011ff21831", "save_path": "github-repos/MATLAB/ilarinieminen-SOM-Toolbox", "path": "github-repos/MATLAB/ilarinieminen-SOM-Toolbox/SOM-Toolbox-f2597abc1ae33c2060e0443d49e854011ff21831/gtm/gtminit.m"} |
using LittleManComputer
using Test
@testset "All Tests" begin
include("assem_tests.jl")
include("disassem_tests.jl")
include("simulator_tests.jl")
end | {"hexsha": "b9c4fde607aa9f5142d3c4a63fc19ebdad555205", "size": 153, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "FourMInfo/LittleManComputer.jl", "max_stars_repo_head_hexsha": "602f21bcfb7cc8bda6f3868d4603672ceab59afd", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# @Author: Ivan
# @LastEdit: 2020/8/13
import os
import time
import numpy as np # install
import keras # install
from keras.regularizers import l1, l2
from keras import backend as K
from keras.utils import np_utils
from keras.utils import plot_model
from keras.optimizers import SGD
from keras.models import Sequential... | {"hexsha": "78c96439298b16c4a558739143039ea165d42c2d", "size": 7746, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_model.py", "max_stars_repo_name": "ivanwhaf/gta5-auto-driver", "max_stars_repo_head_hexsha": "ff9b466e33c81ee95abb82c7c50cbff8aea432d0", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from __future__ import absolute_import
from numbers import Integral
from numpy import cumprod
def leading_args(args, shape):
if not args:
return args, shape, 0
stride = cumprod((1,) + shape[::-1])[-2::-1]
offset = 0
args, shape = list(args), list(shape)
# First trim any fixed integer val... | {"hexsha": "15279e22b3c934cedcadfa908a8d3124b92c3337", "size": 3306, "ext": "py", "lang": "Python", "max_stars_repo_path": "qnd/utils.py", "max_stars_repo_name": "jdsalmonson/qnd", "max_stars_repo_head_hexsha": "00febfaf18c0948f48c758e9d2fb84a70fc5e838", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 4... |
#include <boost/utility/addressof.hpp>
#include <string>
#include <iostream>
struct animal
{
std::string name;
int legs;
int operator&() const { return legs; }
};
int main()
{
animal a{"cat", 4};
std::cout << &a << '\n';
std::cout << boost::addressof(a) << '\n';
} | {"hexsha": "f9be14f240ba83a0092e0ebdc080b0477369eef5", "size": 279, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Example/utility_05/main.cpp", "max_stars_repo_name": "KwangjoJeong/Boost", "max_stars_repo_head_hexsha": "29c4e2422feded66a689e3aef73086c5cf95b6fe", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
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