text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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!> \file test_phizero.f90
!! \BRIEF Fortran 90 program to test phizero routine in gasx.f90
PROGRAM test_kprime
USE msingledouble
USE gasx
IMPLICIT NONE
INTEGER, PARAMETER :: n = 1
! Output variables:
REAL(kind=r8), DIMENSION(1) :: kp_cfc11, kp_cfc12, kp_sf6
! Input variables
REAL(kind=rx)... | {"hexsha": "a78276a1fbfa66d5d632aaa8de0f3d74d7bdc90a", "size": 1339, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "examples/test_kprime.f90", "max_stars_repo_name": "tomaslovato/mocsy", "max_stars_repo_head_hexsha": "ede19cde4be5cd37ed192a3f3394e81302d11616", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
---
pandoctools:
profile: Kiwi
out: "*.ipynb"
# out: "*.pdf"
input: False
eval: True
echo: False
error: raise
...
"""
# %% Markdown cell that doesn't affect PyCharm code inspection and Atom+Hydrogen 'Run All':
"""
# Markdown to Jupyter notebook example
Here is a SugarTeX example with @eq:max and @fig:img.
... | {"hexsha": "e9d3d55cb37d7a2cc0baaac5c2eab73aa7375e65", "size": 1805, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/notebook.py", "max_stars_repo_name": "contactzen/pandoctools", "max_stars_repo_head_hexsha": "d3a9906ea00643de9354c58404785a623209609f", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# encoding: utf-8
import copy
import itertools
import numpy as np
import torch
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch import nn, optim
from .resnet import ResNet
def weights_init_kaiming(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
... | {"hexsha": "b4d7710eaf3e98a228d796af98ffd59a89294d80", "size": 2739, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/SoftmaxTriplet.py", "max_stars_repo_name": "anonymous1computervision/REID", "max_stars_repo_head_hexsha": "6364d7d0e75ebb81fafc765be41b9b3fd434eeae", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import scipy.interpolate
import typing
# TODO: Could define the orientation of zeros to be the sign of the [symmetric] discrete
# derivative, which is a reasonable thing to do if the interpolation scheme is cubic Bezier.
def oriented_zeros (
f_v:np.ndarray,
*,
t_v:typing.Optional[np.nda... | {"hexsha": "271da0f542529f3720e143e05eaf489b0bcf31a5", "size": 20784, "ext": "py", "lang": "Python", "max_stars_repo_path": "vorpy/realfunction/piecewiselinear.py", "max_stars_repo_name": "vdods/vorpy", "max_stars_repo_head_hexsha": "68b6525ae43d99f451cf85ce254ffb0311521320", "max_stars_repo_licenses": ["MIT"], "max_st... |
#ifndef RPTOGETHER_SERVER_SAFEBEASTWEBSOCKETBACKEND_HPP
#define RPTOGETHER_SERVER_SAFEBEASTWEBSOCKETBACKEND_HPP
#include <boost/beast/ssl.hpp>
#include <RpT-Network/BeastWebsocketBackendBase.inl>
/**
* @file SafeBeastWebsocketBackend.hpp
*/
namespace RpT::Network {
/**
* @brief Implementation for secure HTTPS ... | {"hexsha": "9514bfc8410736ef5dbd81914dd689ca9a8d7188", "size": 2044, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "rpt-network/include/RpT-Network/SafeBeastWebsocketBackend.hpp", "max_stars_repo_name": "ThisALV/RpTogether-Server", "max_stars_repo_head_hexsha": "7487844cd340632ff95df80405539a344b137151", "max_sta... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
import random
delta = 0.1
minXY = -5.0
maxXY = 5.0
nContour = 50
alpha = 0.01
def Jacob(state):
u"""
jacobi matrix of Himmelblau's function
"""
x = state[0, 0]
y = state[0, 1]
dx = 4 * x ** 3 + 4 * x ... | {"hexsha": "ad394636102ddc767882d94ebc9e768bf94f0ee4", "size": 1758, "ext": "py", "lang": "Python", "max_stars_repo_path": "NonlinearOptimization/SteepestDescentMethod/SteepestDescentMethod.py", "max_stars_repo_name": "AtsushiSakai/PyOptSamples", "max_stars_repo_head_hexsha": "c498e8978c64496be2dfe3c6a3b328b33da4b2c1",... |
ifPolindrome <- function(x) {
x <- toString(x)
newStr <- strsplit(x, "")[[1]]
newStr <- rev(newStr)
newStr <- paste(newStr, collapse="")
if (newStr == x) {
return (TRUE)
}
return (FALSE)
}
ifSquare <- function(x) {
cnt <- 999
while (cnt >= 100) {
if (x/cnt >= 100 && ... | {"hexsha": "65df6eee023d10daf381e29f4d0da7bd30e089e6", "size": 663, "ext": "r", "lang": "R", "max_stars_repo_path": "R/Problem4.r", "max_stars_repo_name": "nuhfurkan/projectEuler", "max_stars_repo_head_hexsha": "f29ca583154b15bdbe527152a2df3909ed0d3332", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
{"mathlib_filename": "Mathlib.GroupTheory.Subgroup.MulOpposite", "llama_tokens": 0} | |
"""Three points are chosen uniformly at random from the
perimeter of a unit circle. Use Monte Carlo simulation
to compute the probability of the points forming an acute
triangle, what is the probability?
"""
import numpy as np
def point():
theta = 2*np.pi*np.random.rand()
x = np.cos(theta)
y = np.sin(theta)
retur... | {"hexsha": "58c9d3c6b9a075db3a6ed166ad3ccce2e6bb0333", "size": 952, "ext": "py", "lang": "Python", "max_stars_repo_path": "monte_carlo_triangle.py", "max_stars_repo_name": "JoaoAreias/Brilliant", "max_stars_repo_head_hexsha": "f914581012c816ffe4b2023dc0bc1be4df2dae17", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
program t
implicit none
! io-control-spec read stmt err label (with error)
character*4::out
open (95, file='infile', status='old', access='direct', recl=3)
read (95, rec=1, err=100) out
print *,'i wish this'
100 print *,'test was successful'
endprogram t
| {"hexsha": "99a566fd195f053a122a5f15d9a895c9d65663af", "size": 270, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "tests/t0294r/t.f90", "max_stars_repo_name": "maddenp/ppp", "max_stars_repo_head_hexsha": "81956c0fc66ff742531817ac9028c4df940cc13e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
#include <iostream>
#include <iomanip>
#include <string>
#define CGAL_SLS_TEST_SPEED_THINGS_UP_FOR_THE_TESTSUITE
//#define CGAL_STRAIGHT_SKELETON_ENABLE_TRACE 100
//#define CGAL_STRAIGHT_SKELETON_TRAITS_ENABLE_TRACE 10000000
//#define CGAL_POLYGON_OFFSET_ENABLE_TRACE 10000000
void Straight_skeleton_external_trace(st... | {"hexsha": "240bc06ab49676c9d700b7d0b6e5610928dede90", "size": 46234, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Straight_skeleton_2/test/Straight_skeleton_2/test_sls_offset.cpp", "max_stars_repo_name": "gaschler/cgal", "max_stars_repo_head_hexsha": "d1fe2afa18da5524db6d4946f42ca4b8d00e0bda", "max_stars_repo_... |
#coding:utf-8
import numpy as np
import tensorflow as tf
def _write(cfg,mem,ctrl_state,adj):
"""
adj:a tensor of shape [batch_size,]
"""
with tf.variable_scope('write'):
mem_old = mem
mem = tf.reshape(tf.contrib.layers.fully_connected(mem,cfg.NETWORK.CELL_SIZE),[-1, cfg.NETWORK.MEM_SIZE, cfg.NETWORK.C... | {"hexsha": "d62d53365a6a3e5c0cb8f46593412bfbcd406128", "size": 4670, "ext": "py", "lang": "Python", "max_stars_repo_path": "nets/memorynet.py", "max_stars_repo_name": "Blssel/Graph-Memory-Networks", "max_stars_repo_head_hexsha": "094845accae21e386a875a765d41f079143218d0", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
#!/usr/bin/env python
'''log_analyzer.py parses event log generated by AWE and generates performance
results (in tables or figures)
This script is a rather dynamic. And some of the existing functions may be
used for some specific analysis only. Users may use this as a template to write
new analysis functions for their... | {"hexsha": "0991a987387dffde2ef1d05e83d709051b49092e", "size": 26562, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/log_analyzer.py", "max_stars_repo_name": "folker/AWE", "max_stars_repo_head_hexsha": "baa9780d8ff8ba3b07d9405f29f618d2543bb92d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_coun... |
{-# OPTIONS --cubical --no-import-sorts --safe #-}
module Cubical.Algebra.RingSolver.CommRingHornerForms where
open import Cubical.Foundations.Prelude
open import Cubical.Data.Nat using (ℕ)
open import Cubical.Data.FinData
open import Cubical.Data.Vec
open import Cubical.Data.Bool using (Bool; true; false; if_then_el... | {"hexsha": "18a36a364d7407f549da703df573d1a574d353af", "size": 4244, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/Algebra/RingSolver/CommRingHornerForms.agda", "max_stars_repo_name": "dan-iel-lee/cubical", "max_stars_repo_head_hexsha": "fd8059ec3eed03f8280b4233753d00ad123ffce8", "max_stars_repo_licens... |
LOSS = -RATE(10)*Y(11)*D-RATE(11)*Y(3)*D-RATE(12)*Y(35)&
&*D-RATE(13)*Y(100)*D-RATE(14)*Y(32)*D-RATE(109)*Y(117)*D-RATE(192)&
&*Y(52)*D-RATE(193)*Y(65)*D-RATE(194)*Y(4)*D-RATE(195)*Y(57)&
&*D-RATE(196)*Y(7)*D-RATE(197)*Y(27)*D-RATE(363)-RATE(407)-RATE(1098)&
&*Y(181)*D-RATE(1099)*Y(12)*D-RATE(1100... | {"hexsha": "8d82b2b3ff79f2f85cc6c681bb27743174b68494", "size": 221405, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/odes.f90", "max_stars_repo_name": "Bamash/UCLCHEM", "max_stars_repo_head_hexsha": "95fa2a7537d73a73594e36cca0e02d25cfd8f276", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
import matplotlib.pyplot as plt
import math
import numpy as np
degrees = list(map(int, open('data/degrees.txt').readlines()))
simd_width = 8
degrees_simd = []
for i in range(min(degrees), max(degrees) + 1):
counter = len(list(filter(lambda x: x == i, degrees)))
actual = math.ceil(i / simd_width) * simd_width... | {"hexsha": "d5cd107c6f0645c868e2a8b02626767acaed44b0", "size": 814, "ext": "py", "lang": "Python", "max_stars_repo_path": "lang/scripts/plot_degrees.py", "max_stars_repo_name": "AnimatedRNG/taichi", "max_stars_repo_head_hexsha": "f1f403042dadf8b58887431dbf7a9a661c005bb2", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Copyright 2019 The TensorFlow Authors. 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 applica... | {"hexsha": "910f4b398e15dbe053a96fc6deff9dc129f9b6ce", "size": 2690, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/lite/testing/op_tests/constant.py", "max_stars_repo_name": "KosingZhu/tensorflow", "max_stars_repo_head_hexsha": "7ac2521a4e609ddef0f0ea3ffc2e76102da934d7", "max_stars_repo_licenses": [... |
# HPD - Highest Posterior Density (HPD) Interval
#-----8<-----
# Using boa package
ans <- boa.hpd(resultado.final[,i], alpha = 0.05)
write.csv2( ans , paste("HPD ",i,".csv", sep=""))
print( ans )
#-----8<-----
# [1] "Computing HPD Interval for NR.LGA"
# Lower Bound Upper Bound
# 0.08768426 0.09717933
# [1] "Compu... | {"hexsha": "13440caa4ff088554360522ade3fcf65a53c760d", "size": 1077, "ext": "r", "lang": "R", "max_stars_repo_path": "Simulation Gestational Diabetes WHO/HPD_NR_WHO_IADPSG.r", "max_stars_repo_name": "isix/Othprojects", "max_stars_repo_head_hexsha": "f53c443e3314a0097f2abd9b872dd45d55e8e275", "max_stars_repo_licenses": ... |
module CUDNN
using CUDAapi
using CUDAapi: libraryPropertyType
using CUDAdrv
using CUDAdrv: CUstream
import CUDAnative
using CEnum
using ..CuArrays
using ..CuArrays: active_context, @argout, @workspace
import ..CuArrays.unsafe_free!
import NNlib
const libcudnn = Ref("libcudnn")
# core library
include("libcudnn_c... | {"hexsha": "79f80290400c6ac6f75ece45c312edb9587e5663", "size": 1154, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/dnn/CUDNN.jl", "max_stars_repo_name": "PallHaraldsson/CuArrays.jl", "max_stars_repo_head_hexsha": "977d9aa913ce0936e6cd4f4ea2dcc992ce7881c0", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma greedy_conforming_path_properties:
assumes "v0 \<in> V" "strategy p \<sigma>" "strategy p** \<sigma>'"
shows
greedy_path_not_null: "\<not>lnull (greedy_conforming_path p \<sigma> \<sigma>' v0)"
and greedy_path_v0: "greedy_conforming_path p \<sigma> \<sigma>' v0 $ 0 = v0"
an... | {"llama_tokens": 29022, "file": "Parity_Game_Strategy", "length": 100} |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
# @Time : 2019/3/4 15:11
# @Email : Zhuangshui@qiyi.com
# @Desc :
import random
import logging
import numpy as np
from PIL import Image
from keras.callbacks import EarlyStopping, ModelCheckpoint
from model.keras_model import get_ssd_model
from config import *
loggi... | {"hexsha": "c59ba5ead4bbf645c94af53f80b54ddf96c8939e", "size": 2234, "ext": "py", "lang": "Python", "max_stars_repo_path": "KerasCaptcha/model/train.py", "max_stars_repo_name": "xiaoshicae/Others", "max_stars_repo_head_hexsha": "a5df75f1da527f94c1c79870a8f5ac7c9a7353c2", "max_stars_repo_licenses": ["Apache-1.1"], "max_... |
from LESO.plotly_extension import lighten_color
import pandas as pd
import numpy as np
import plotly.graph_objects as go
## NP REL ATB
# storage cost
ref_2020_s = 277 # $/kWh
storage_projection = np.array([
[1.0, 1.0, 1.0],
[0.61, 0.69, 0.85],
[0.41, 0.53, 0.7],
[0.25, 0.37, 0.7]
]) # kWh from ATB ... | {"hexsha": "9f9e181d5e2afbccf21877e201869e6a98aa21d1", "size": 2945, "ext": "py", "lang": "Python", "max_stars_repo_path": "thesis scripts/learning_rates/lithium_NPREL.py", "max_stars_repo_name": "thesethtruth/WB-thesis-model", "max_stars_repo_head_hexsha": "f786d15fa446e8c089431853a82aa4d577e13f20", "max_stars_repo_li... |
[STATEMENT]
lemma faceCountMax_bound:
"\<lbrakk> tame g; v \<in> \<V> g \<rbrakk> \<Longrightarrow> tri g v + quad g v \<le> 7"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>tame g; v \<in> \<V> g\<rbrakk> \<Longrightarrow> tri g v + quad g v \<le> 7
[PROOF STEP]
using tri_quad_le_degree[of g v]
[PROOF ST... | {"llama_tokens": 248, "file": "Flyspeck-Tame_TameProps", "length": 2} |
import torch
from core_train import core_train
from fc_classification import FCNet
from torch import optim
from dataset import Refuge2, Resize2_640, RandomRotation, RandomFlip
from torchvision.transforms import Compose
from tqdm import tqdm
from torch.autograd import Variable
import torch.nn as nn
from dedicated_Resnet... | {"hexsha": "db43bd581984e0d1d0c34892718bac815cb1c7b2", "size": 4925, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "SuperbTUM/Glaucoma-Screening-with-DENet", "max_stars_repo_head_hexsha": "c96cd7ed71d5fef12904722db1513e1137289d28", "max_stars_repo_licenses": ["MIT"], "max_star... |
!**********************************************************************************************************************************
MODULE AeroGenSubs
USE NWTC_LIBRARY
USE AeroDyn14_Types
IMPLICIT NONE
! SUBROUTINE AllocArrays( Arg )
! SUBROUTINE ElemOpen( ElemFile )
! SUBROUTINE ElemOut( )
INTEGE... | {"hexsha": "e02fd5cbc4df5b65aef2f09573892ccb51e5b3d3", "size": 31114, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "modules/aerodyn14/src/GenSubs.f90", "max_stars_repo_name": "ndevelder/openfast", "max_stars_repo_head_hexsha": "3456a645581456883e44d441eb285ed688e98797", "max_stars_repo_licenses": ["Apache-2.... |
from datetime import datetime
from math import log1p, sqrt
import numpy as np
from sqlalchemy import select as select, func, and_, distinct
import string
# utils
def _check_already_calculated(feature_name, feature_values):
for fv in feature_values:
if feature_name not in fv:
return False
... | {"hexsha": "67296364b2b67e4e5bf3846d11c7f7837049f639", "size": 28489, "ext": "py", "lang": "Python", "max_stars_repo_path": "eddytools/events/activity_identifier_feature_functions.py", "max_stars_repo_name": "fyndalf/eddytools", "max_stars_repo_head_hexsha": "004dc484d303723307b39cd7f7569ddfd762c800", "max_stars_repo_l... |
import cv2
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
import random
from urllib.request import Request, urlopen
def main_help(functions, author):
"""
Prints the !help message to the chat.
-----
:param <functions>: <class 'dict'> ; dictionary of available botjack co... | {"hexsha": "3facb97233dfaec0cd821bf44d6d4d3588cfd742", "size": 3646, "ext": "py", "lang": "Python", "max_stars_repo_path": "local_commands/commands.py", "max_stars_repo_name": "TRIVIAHUB72/botjack_discord_bot", "max_stars_repo_head_hexsha": "454f38041d79498eed3c37a12c27684859c26bed", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/env python
"""
Code to load an expert policy and generate roll-out data for behavioral cloning.
Example usage:
python run_expert.py experts/Humanoid-v2.pkl Humanoid-v2 --render --num_rollouts 100
Author of this script and included expert policies: Jonathan Ho (hoj@openai.com)
"""
import os
import pick... | {"hexsha": "976bd3762408f0c497c1695a0efea9bfacddccfe", "size": 3293, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw1/run_expert.py", "max_stars_repo_name": "zhaoheri/homework", "max_stars_repo_head_hexsha": "fcae59f37a947d8409a9e4d14885a8df07124aac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
C This should parse. This is basically the only acceptable form of this statement.
C
GOTO X, (5,10,15)
C This shouldn't parse - the syntax is wrong.
GOTO 5, (X,Y,Z)
C This shouldn't parse - the first value should be a variable
GOTO X (5, 10, 15)
C This shouldn't parse - only three val... | {"hexsha": "47a43c47e0918bb5ba7bd17a311ab56c2f9e83a4", "size": 822, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "test_fortran/Tokenization Tests/ASSIGNED_GOTO_tests.f", "max_stars_repo_name": "ElliotAlexander/EDSAC-FORTRAN-Compiler", "max_stars_repo_head_hexsha": "f5a14d0f4c42fcba5eb1d277cab4b8d700eab699", "m... |
import pandas as pd
from scipy import stats
def mann_whitney(all_classifiers, first, second):
if second >= len(all_classifiers):
return all_classifiers
u, pvalue = stats.mannwhitneyu(all_classifiers[first], all_classifiers[second], alternative='two-sided')
if pvalue < 0.05:
if u > 50:
... | {"hexsha": "db1de36b4d76721725e34ea51305987542dac8ac", "size": 5175, "ext": "py", "lang": "Python", "max_stars_repo_path": "statistical_main.py", "max_stars_repo_name": "GaMJun/MNIST-Recognizer", "max_stars_repo_head_hexsha": "fab5fad1b55fe82b78e551d0eeae2ca1de749e0c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
from transforms3d.euler import euler2mat
class StanceController:
def __init__(self, config):
self.config = config
def position_delta(self, leg_index, state, command):
"""Calculate the difference between the next desired body location and the current body location
P... | {"hexsha": "828354e06eabe6270bc8435447580418bc93e2e3", "size": 1623, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/StanceController.py", "max_stars_repo_name": "wupanhao/LepiPupper", "max_stars_repo_head_hexsha": "cd5463ccf4ea3c038234df56d8270acd7fe65b26", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
## Functions related to unstable nets (some vertices have the same equilibrium placement)
"""
CollisionNode
Store the structure of a collision node through the subgraph `g` extracted with only the
edges bond to the vertices in the node.
The `num` field corresponds to the number of vertices in `g` that collide in... | {"hexsha": "d9131f34cb98fd9cb45cba40f1761f0978553c4a", "size": 17983, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/stability.jl", "max_stars_repo_name": "coudertlab/CrystalNet.jl", "max_stars_repo_head_hexsha": "5d38d226bb492985e748f785c082dda0e3b3f2de", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
-- Andreas, 2020-03-18, issue #4520, reported by Dylan Ede.
--
-- Make the error message concerning ambiguous names
-- in BUILTIN declarations more precise.
open import Agda.Primitive
open import Agda.Builtin.FromNat
open import Agda.Builtin.Nat renaming (Nat to ℕ)
private
variable
ℓ ℓ' : Level
record FromNat ... | {"hexsha": "860852fa3a795a5654677a92056dd5ffa10dcc3f", "size": 796, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Fail/Issue4520.agda", "max_stars_repo_name": "cruhland/agda", "max_stars_repo_head_hexsha": "7f58030124fa99dfbf8db376659416f3ad8384de", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import ast
import numpy as np
import torch as torch
import torch.nn as nn
import torch.nn.functional as F
def get_descendants(node, ls):
for child in node.children:
ls.append(child)
get_descendants(child, ls)
return ls
class Node():
'''
For each node we store its parent and children n... | {"hexsha": "f747e2e645156b9f9428d5f3d88bfe04cf50a0a6", "size": 3044, "ext": "py", "lang": "Python", "max_stars_repo_path": "first_neural_network/node.py", "max_stars_repo_name": "ADCenterNetwork/discern-fmk", "max_stars_repo_head_hexsha": "4781f1a986f7b24f298b2729b87ddee4227cb1d0", "max_stars_repo_licenses": ["MIT"], "... |
import argparse
import cooler
import os
import pandas as pd
import numpy as np
import warnings
import time
import sys
import cv2 as cv
import matplotlib.pyplot as plt
'''
def argumentParser():
parser = argparse.ArgumentParser(description='Stripenn')
parser.add_argument("cool", help="Balanced cool file.")
p... | {"hexsha": "0288d2d35725f9410bf5fddc5b12630f1a36f173", "size": 2309, "ext": "py", "lang": "Python", "max_stars_repo_path": "build/lib/src/seeimage.py", "max_stars_repo_name": "VahediLab/stripenn", "max_stars_repo_head_hexsha": "f72b14e0506596fe28730289a717e05b92393288", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
\documentclass[11pt, twoside, pdftex]{article}
% This includes all the settings that we should use for the document
\newcommand{\PDFTitle}{Using Intel OpenCL\textsuperscript{\texttrademark} on DE-Series Boards}
\newcommand{\commonPath}{../../Common}
\input{\commonPath/Docs/defaulttext.tex}
\input{\commonPath/Docs/prea... | {"hexsha": "b726140e9892f063a98793be216f50e5870a50b0", "size": 31046, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "High_Level_Design/OpenCL_Introduction/OpenCL.tex", "max_stars_repo_name": "fpgacademy/Tutorials", "max_stars_repo_head_hexsha": "d2c352472bc3dfab88a3497efd259f5fabbf3952", "max_stars_repo_licenses"... |
# Autogenerated wrapper script for KAShim_jll for armv7l-linux-gnueabihf
export libkashim
using LibUV_jll
## Global variables
PATH = ""
LIBPATH = ""
LIBPATH_env = "LD_LIBRARY_PATH"
# Relative path to `libkashim`
const libkashim_splitpath = ["lib", "libkashim.so"]
# This will be filled out by __init__() for all produ... | {"hexsha": "d0ead18271252bb9820d274727686f6116f0573a", "size": 2014, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/armv7l-linux-gnueabihf.jl", "max_stars_repo_name": "JuliaBinaryWrappers/KAShim_jll.jl", "max_stars_repo_head_hexsha": "015be1aa7b914b3ef5b930a49a5935173d3dc556", "max_stars_repo_licens... |
import itertools
import torch
import torch.nn as nn
import numpy as np
import pycocotools.mask as mask_utils
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class BBoxTransform(nn.Module):
def forward(self, anchors, regression):
"""
decode_box_outputs adapted from https://github.com/google/au... | {"hexsha": "110cd587dcfe770ab108f56ae29f8d7aa563b2de", "size": 10894, "ext": "py", "lang": "Python", "max_stars_repo_path": "efficientdet/utils.py", "max_stars_repo_name": "awesome-amy/efficientmask", "max_stars_repo_head_hexsha": "2456d52af92f765de771fbb6bd27fe2b9f19533b", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/env python
import cv2
import matplotlib.pyplot as plt
import numpy as np
EDGE_KERNELS = {
'rectangle': cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)),
'ellipse': cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)),
'cross': cv2.getStructuringElement(cv2.MORPH_CROSS, (5, 5))
}
def display... | {"hexsha": "e5db94737d861f73c8e0e3c03b19bde2992f4ee1", "size": 6345, "ext": "py", "lang": "Python", "max_stars_repo_path": "computer-vision/image_proc.py", "max_stars_repo_name": "nathanielng/python-snippets", "max_stars_repo_head_hexsha": "d310f074acc1ea7fdb41b2db3ab69406b96a18ca", "max_stars_repo_licenses": ["MIT"], ... |
# Copyright 2020 The Flax Authors.
#
# 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 applicable law or agreed to in wri... | {"hexsha": "e6fa0b37a760425a842b2d90e1535a510c2b964e", "size": 3569, "ext": "py", "lang": "Python", "max_stars_repo_path": "flax/optim/radam.py", "max_stars_repo_name": "rwbfd/flax", "max_stars_repo_head_hexsha": "6e6e8324da489369cd9ce05c438ddfb3b3ad82a2", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": n... |
\subsection{Unknown MDP transition matrix}
| {"hexsha": "21af065426b62b225caa61d1a49612cad5eade60", "size": 45, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/ai/reinforcement/01-01-Unknown_MDP.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo_lice... |
import numpy as np
"""
aP&Y attributes extraction.
"""
classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
"car", "cat", "chair", "cow", "diningtable", "dog",
"horse", "motorbike", "person", "pottedplant", "sheep",
"sofa", "train", "tvmonitor"]
num_class = len(classes... | {"hexsha": "ad3530e5275dacd1344bcb4158928e80bb3e7e84", "size": 851, "ext": "py", "lang": "Python", "max_stars_repo_path": "ext_att.py", "max_stars_repo_name": "STARrapier/zsd_dataset", "max_stars_repo_head_hexsha": "2db26462d7eb0227233c1d13b1d11034e0e11a7c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "m... |
# import sys
# sys.path.append("/home/anna/dlbirhoui/")
# sys.path.append("/home/anna/dlbirhoui/fadern/")
import os
import math
import numpy as np
import torch
from options import parse_args
# from torch.utils.tensorboard import SummaryWriter
from dataLoader import UnpairedDataset, UnpairedDatasetImages
from datetime ... | {"hexsha": "1d358824942f480427e5a84dad4d72857038b069", "size": 4561, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "klanita/sigoat", "max_stars_repo_head_hexsha": "941552227639edf38e9b5b745848160ae83f451a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_re... |
from __future__ import division
import numpy as np
import pandas as pd
import math
import cea.config
import cea.globalvar
import cea.inputlocator
import cea.technologies.chiller_vapor_compression as chiller_vapor_compression
import cea.technologies.chiller_absorption as chiller_absorption
import cea.technologies.storag... | {"hexsha": "24fac85a7bb582451116b928fa90c3dbe10cccd5", "size": 15304, "ext": "py", "lang": "Python", "max_stars_repo_path": "cea/optimization/slave/cooling_resource_activation.py", "max_stars_repo_name": "VMarty/CityEnergyAnalyst", "max_stars_repo_head_hexsha": "5ab4385fc008f3b23f5bd5f9ba683d401cfcef38", "max_stars_rep... |
import numpy as np
import typing
from trident.backend.common import get_backend
__all__=[
"int8", "byte",
"int16", "short",
"int32", "intc",
"int64", "intp",
"uint8", "ubyte",
"float16", "half",
"float32", "single",
"float64", "double","long","float",
"bool"]
if get_backend() ==... | {"hexsha": "7a009132d91a3b4caa91c83817e4a0ef07aa5813", "size": 2852, "ext": "py", "lang": "Python", "max_stars_repo_path": "trident/backend/dtype.py", "max_stars_repo_name": "cronin4392/trident", "max_stars_repo_head_hexsha": "1c1eb01bcde861496ce83e265ff071fc9bcb9db2", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
\section{Example implementation}
\label{app-example-implementation}
As an example of how the sequence functions might be implemented using
the functions and macros in \refApp{app-protocol}, we show our
implementation of \texttt{find-list} which is called from
\texttt{find} when the sequence is known to be a list:
{\s... | {"hexsha": "2a0b3573e6e5f4dfe3e929f8cb5b583af53d62ef", "size": 994, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Papers/Sequence-functions/app-implementation.tex", "max_stars_repo_name": "gwerbin/SICL", "max_stars_repo_head_hexsha": "ec5cc25de783ecce373081ab72d2a04359155ad6", "max_stars_repo_licenses": ["BSD-2-... |
# -*- coding: utf-8 -*-
"""
Created on Fri May 20 14:03:20 2016
@author: 19514733
"""
from calc_mcc_interp import calc_mcc_interp
from sklearn import tree#.DecisionTreeClassifier()
#from pmtree import PMDecisionTreeClassifier #pmtree_preISORFreview pmtree
from isoensemble import IsoDecisionTreeClassifier
from expset... | {"hexsha": "d90ed0e12e6e89dcb1c63ef9fdbcc1e449cfa437", "size": 16093, "ext": "py", "lang": "Python", "max_stars_repo_path": "isoensemble/tests/test_isotree_OLD.py", "max_stars_repo_name": "chriswbartley/isoensemble", "max_stars_repo_head_hexsha": "f9492faf27cb4c685b6e53630fa6a30e114fc63d", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma biexp01_well_formedE:
assumes "biexp01_well_formed a"
shows "(\<forall>n. a n \<in> {0,1}) \<and> (\<forall>n. \<exists>m\<ge>n. a m = 0)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<forall>n. a n \<in> {0, 1}) \<and> (\<forall>n. \<exists>m\<ge>n. a m = 0)
[PROOF STEP]
using assms
[PROOF... | {"llama_tokens": 253, "file": "Quasi_Borel_Spaces_StandardBorel", "length": 2} |
#!/usr/bin/env python
"""
TODO
"""
import sys,os,csv,time
import numpy as np
from Codon import Codon
__author__ = "Adam Richards"
class Simulator(Codon):
"""
constructor
"""
def __init__(self,logFile="simulation.log"):
Codon.__init__(self)
## set a non-zero probability of all codo... | {"hexsha": "a52585bec3fa6955d09a223571ef305c641954fa", "size": 7915, "ext": "py", "lang": "Python", "max_stars_repo_path": "library/Simulations.py", "max_stars_repo_name": "ajrichards/phylogenetic-models", "max_stars_repo_head_hexsha": "783f9a6b6cea816d255fa23f2e62423d98059ad9", "max_stars_repo_licenses": ["BSD-3-Claus... |
"""
$(TYPEDEF)
Generalized power cone parametrized by powers `α` in the unit simplex and
dimension `d` of the normed variables.
$(FUNCTIONNAME){T}(α::Vector{T}, d::Int, use_dual::Bool = false)
"""
mutable struct GeneralizedPower{T <: Real} <: Cone{T}
use_dual_barrier::Bool
dim::Int
α::Vector{T}
n:... | {"hexsha": "517156556e11c490f7a430c85a610bf70b2b2791", "size": 8147, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Cones/generalizedpower.jl", "max_stars_repo_name": "matbesancon/Hypatia.jl", "max_stars_repo_head_hexsha": "9e1487850b5db87ea23545bd07348e5a36ac8bf3", "max_stars_repo_licenses": ["MIT"], "max_s... |
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import *
import numpy as np
# Attribute encoder in the paper
class EmbeddingSemvec(keras.Model):
def __init__(self):
super(EmbeddingSemvec, self).__init__()
initializer = tf.keras.initializers.TruncatedNormal(stddev=... | {"hexsha": "776761f22b39f621c186b3cbc74344ca0e312be9", "size": 17639, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "tezignlab/LayoutNet-TF2", "max_stars_repo_head_hexsha": "8b54f353cd6f5e70395b7f927c3871ca36674e9d", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_coun... |
'''
Created on Aug 26, 2017
@author: Raz
'''
import mido
import numpy as np
def midifile_to_dict(mid):
tracks = []
for track in mid.tracks:
tracks.append([vars(msg).copy() for msg in track])
return {
'ticks_per_beat': mid.ticks_per_beat,
'tracks': tracks,
}
def test():
... | {"hexsha": "51ed3afc85ee3a5e235455da52ef4be7c50b5b30", "size": 5807, "ext": "py", "lang": "Python", "max_stars_repo_path": "midi/midi_editting.py", "max_stars_repo_name": "razco/music_similarity", "max_stars_repo_head_hexsha": "ba8a6f238382f075cd6db08bb56f2c8d5606a684", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from collections import defaultdict
import json
from math import modf, floor
import time
import numpy as np
import warnings
from subprocess import CalledProcessError
import numbers
import sys
import cv2
from skimage import transform as tf
from skimage import registration
from skimage import filters
from skimage.u... | {"hexsha": "b1155c522c5809a97094dfb6e57db070b589e7fe", "size": 107673, "ext": "py", "lang": "Python", "max_stars_repo_path": "autocnet/matcher/subpixel.py", "max_stars_repo_name": "gsn9/autocnet", "max_stars_repo_head_hexsha": "ddcca3ce3a6b59f720804bb3da03857efa4ff534", "max_stars_repo_licenses": ["CC0-1.0"], "max_star... |
#include <iostream>
#include <boost/asio.hpp>
#include <boost/date_time/posix_time/posix_time.hpp>
#include <fstream>
#include <sstream>
#include <string>
#include <vector>
#include <sys/types.h>
#include <signal.h>
#include <unistd.h>
#include <sstream>
#define PIPE_READ 0
#define PIPE_WRITE 1
#define WRITE_LOGFILE... | {"hexsha": "deb7b39050b046ada2015508d5832f015309f013", "size": 4709, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/cmdScheduler.cpp", "max_stars_repo_name": "theuerse/emulation-cmdscheduler", "max_stars_repo_head_hexsha": "2f7b5e3050f49db44e465600df438a7e73c9b08d", "max_stars_repo_licenses": ["MIT"], "max_st... |
module nonlocal_module
use pseudopot_module, only: pselect, ps_type
implicit none
private
public :: op_nonlocal
public :: calc_force_nonlocal
public :: update_k_dependence_nonlocal
interface op_nonlocal
module procedure d_op_nonlocal, z_op_nonlocal
end interface
contains
subroutine d_op_nonl... | {"hexsha": "a8e524ea2688f0352afe972de39cf3b13623e246", "size": 3682, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/nonlocal_module.f90", "max_stars_repo_name": "j-iwata/RSDFT_DEVELOP", "max_stars_repo_head_hexsha": "14e79a4d78a19e5e5c6fd7b3d2f2f0986f2ff6df", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/env python
# Copyright (c) CTU -- All Rights Reserved
# Created on: 2020-06-5
# Author: Vladimir Petrik <vladimir.petrik@cvut.cz>
# !/usr/bin/env python
# Copyright (c) CTU -- All Rights Reserved
# Created on: 2020-06-4
# Author: Vladimir Petrik <vladimir.petrik@cvut.cz>
from rlpyt_utils.dmp impo... | {"hexsha": "b28c7c6e6339720645f1aff9dc22b459e727ebaf", "size": 2536, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlpyt_utils/tests/plot_dmp.py", "max_stars_repo_name": "petrikvladimir/rlpyt_utils", "max_stars_repo_head_hexsha": "7dbcf514287dcc369a08ca273169ecdc378eb011", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 1 10:20:24 2021
@author: christian
"""
import numpy as np
import matplotlib.pyplot as plt
import
class Aerospike:
def __init__(self):
self.mu = 3.9860e5 # Geocentric gravitational constant, km^3/s^2
self.R_e = 6371 # km
... | {"hexsha": "56e02c26057313e1dae48f5db672db67b00dc96f", "size": 1080, "ext": "py", "lang": "Python", "max_stars_repo_path": "Delta II Aerospike Analysis/aerospike.py", "max_stars_repo_name": "bolanderc/mae-6530", "max_stars_repo_head_hexsha": "2052f4d776046eff37062264f2b9125de3e0c521", "max_stars_repo_licenses": ["MIT"]... |
import logging
import os
import pickle
import numpy as np
from training.xgboost_train import xgboost_data_preparation_to_predict
logger = logging.getLogger(__name__)
formatting = (
"%(asctime)s: %(levelname)s: File:%(filename)s Function:%(funcName)s Line:%(lineno)d "
"message:%(message)s"
)
logging.basicConf... | {"hexsha": "1b2ab25258bc0530baff618a1e16b4ad818dceb6", "size": 3613, "ext": "py", "lang": "Python", "max_stars_repo_path": "prediction/model_predictor.py", "max_stars_repo_name": "KI-labs/ML-Navigator", "max_stars_repo_head_hexsha": "c5371da7411e1522a5f423757ae6bd99135e9dbf", "max_stars_repo_licenses": ["Apache-2.0"], ... |
#!/usr/bin/python
#
# The program was written by
# The program is based on the sample program used for illustrating how to write a program in Keras for Vanilla GRU
#
from keras.models import Sequential
from keras.layers import GRU
from keras.layers import Dense
from keras.models import model_from_json
import numpy
... | {"hexsha": "e4f83a881f1f114cab13a9827e799dd88198876d", "size": 6262, "ext": "py", "lang": "Python", "max_stars_repo_path": "Phase6/Python-Vanilla-GRU.py", "max_stars_repo_name": "MrKLawrence/Course-Registration-Data-Analytics", "max_stars_repo_head_hexsha": "b1a88ab9336a545965752d175d8c4796209e487c", "max_stars_repo_li... |
[STATEMENT]
lemma ordered_map_distinct:
assumes "finite S" "is_map S"
shows "distinct (map fst (ordered_map S))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. distinct (map fst (ordered_map S))
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. distinct (map fst (ordered_map S))
[PROOF STEP]... | {"llama_tokens": 3085, "file": "CakeML_Codegen_Utils_Compiler_Utils", "length": 24} |
/*
* Options.cpp
*
* Copyright (C) 2019-20 by RStudio, PBC
*
* 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,... | {"hexsha": "e2e9f36266dfe61d72a6a908c3643899cc751922", "size": 18473, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sdk/src/options/Options.cpp", "max_stars_repo_name": "andrie/rstudio-launcher-plugin-sdk", "max_stars_repo_head_hexsha": "0b13a149fe263b578c90c98c8ab2d739b830ca60", "max_stars_repo_licenses": ["MIT... |
\tocless\chapter{Appendix A}\label{appendix:a}
| {"hexsha": "deedad8adf819d782b0cc3addeec4333d1a5c0fe", "size": 47, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/appendix.tex", "max_stars_repo_name": "PhilippMatthes/better-tudscr", "max_stars_repo_head_hexsha": "b89e3e1ca96f27eb30ca0048af3fa3d3f8311a3e", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import sys, os
import warnings
from dsbox.datapreprocessing.cleaner.wikifier import WikifierHyperparams ,Wikifier
from d3m.metadata.base import Metadata, DataMetadata, ALL_ELEMENTS
from d3m.container import List
from d3m.base import utils as d3m_utils
import numpy as np
import logging
import pandas as pd
import copy
im... | {"hexsha": "b52494733ff89e7109491b4b2b43d89e03bb3882", "size": 8850, "ext": "py", "lang": "Python", "max_stars_repo_path": "wikifier/utils.py", "max_stars_repo_name": "xkgoodbest/wikidata-wikifier", "max_stars_repo_head_hexsha": "50b118166f4f3443831ed2337edb98a1eac71bb2", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -*- coding: utf-8 -*-
# pylife
#
# Copyright (c) 2018 Cristian Gratie
#
# 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 ... | {"hexsha": "163acebdc2959d2e47d4112c1e69443f0bc1fc5f", "size": 2103, "ext": "py", "lang": "Python", "max_stars_repo_path": "pylife/config.py", "max_stars_repo_name": "cgratie/pylife", "max_stars_repo_head_hexsha": "1cc4058d6f8834dd6b4bd2a3f5b9ba50acd1db05", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
[STATEMENT]
lemma stake_append: "stake n (u @- s) = take (min (length u) n) u @ stake (n - length u) s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. stake n (u @- s) = take (min (length u) n) u @ stake (n - length u) s
[PROOF STEP]
proof (induct n arbitrary: u)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. \<... | {"llama_tokens": 712, "file": null, "length": 5} |
import os
import shutil
from collections import Counter
import numpy as np
import torch
from ppnet.helpers import makedir
import ppnet.find_nearest as find_nearest
def prune_prototypes(dataloader,
prototype_network_parallel,
k,
prune_threshold,
... | {"hexsha": "d4ebeba9320dfbfa97644f5a1a860670027c48ca", "size": 5300, "ext": "py", "lang": "Python", "max_stars_repo_path": "ppnet/prune.py", "max_stars_repo_name": "tshead2/ProtoPNet", "max_stars_repo_head_hexsha": "b4d0ecbd6dcc4847d5415d9840549ec1b33b6be1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
"""
K-means Clustering
============================
CR-Sparse includes a K-means implementation as
part of its sparse subspace clustering module.
"""
# %%
# Configure JAX to work with 64-bit floating point precision.
from jax.config import config
config.update("jax_enable_x64", True)
# %%
# Let's import necess... | {"hexsha": "ffd9ec27ebd42acce47e329c7e9be30af435f069", "size": 2342, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/cluster/kmeans.py", "max_stars_repo_name": "carnot-shailesh/cr-sparse", "max_stars_repo_head_hexsha": "989ebead8a8ac37ade643093e1caa31ae2a3eda1", "max_stars_repo_licenses": ["Apache-2.0"]... |
import LibGit2
import PkgBenchmark
import Statistics
# include("./utils/github/_httpjson_github_api_unauthenticated.jl")
# include("./utils/github/_httpjson_github_api_authenticated.jl")
struct AllowedToIgnoreThisError end
_single_line_travis_ignore_errors(x::AbstractString) = _single_line_travis_ignore_errors(conve... | {"hexsha": "7728b2b90939525aff4126769621d950886305c4", "size": 12900, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/run.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ModelSanitizer.jl-913cd07a-ab48-11e9-38cf-61416429a70b", "max_stars_repo_head_hexsha": "6bb9c0d12aaeeeaf7fd18214ca96d40fdb... |
#include "common/common.hpp"
#include "geodb/filesystem.hpp"
#include "geodb/trajectory.hpp"
#include "geodb/irwi/bulk_load_hilbert.hpp"
#include "geodb/irwi/bulk_load_str.hpp"
#include "geodb/irwi/bulk_load_quickload.hpp"
#include "geodb/irwi/tree.hpp"
#include "geodb/irwi/tree_external.hpp"
#include <boost/program_o... | {"hexsha": "e02a80ed5bc863455eb079f3cfb98fba24d5aa45", "size": 8689, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "code/cmd/loader/main.cpp", "max_stars_repo_name": "mbeckem/msc", "max_stars_repo_head_hexsha": "93e71ba163a7ffef4eec3e83934fa793f3f50ff6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#include <mpi.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <gsl/gsl_math.h>
#include "allvars.h"
#include "proto.h"
#ifdef COSMIC_RAYS
#include "cosmic_rays.h"
#endif
#ifdef MACH_NUM
#include "machfinder.h"
#endif
#ifdef CS_MODEL
#include "cs_metals.h"
#endif
#ifndef DEBUG... | {"hexsha": "63280c231d9a17c1ed2854bc34cb8b1a4cce6a7d", "size": 49227, "ext": "c", "lang": "C", "max_stars_repo_path": "testing/icgen/random_verschillende_resoluties_N-GenIC/gadget3_64/hydra.c", "max_stars_repo_name": "egpbos/egp", "max_stars_repo_head_hexsha": "5e82c2de9e6884795b4ee89f2b15ed5dde70388f", "max_stars_repo... |
[STATEMENT]
lemma Left_Coset_memI [intro]:
"h \<in> H \<Longrightarrow> x \<cdot> h \<in> x \<cdot>| H"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. h \<in> H \<Longrightarrow> x \<cdot> h \<in> x \<cdot>| H
[PROOF STEP]
unfolding Left_Coset_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. h \<in> H \<Longr... | {"llama_tokens": 175, "file": "Jacobson_Basic_Algebra_Group_Theory", "length": 2} |
import pathlib
import argparse
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
from qcore.nhm import load_nhm
class ScalarFormatterClass(ScalarFormatter):
def _set_format(self):
self.format = "%1.4f"
def plot_mw_frequency(f... | {"hexsha": "713cf02f7c7b0f619016cc1557c5e2adfc3e41e0", "size": 4252, "ext": "py", "lang": "Python", "max_stars_repo_path": "calculation/gmhazard_calc/gmhazard_calc/verification/ERF/mag_freq_distributions.py", "max_stars_repo_name": "ucgmsim/gmhazard", "max_stars_repo_head_hexsha": "d3d90b4c94b3d9605597a3efeccc8523a1e50... |
! { dg-additional-options "-cpp" }
! TODO: Have to disable the acc_on_device builtin for we want to test
! the libgomp library function? The command line option
! '-fno-builtin-acc_on_device' is valid for C/C++/ObjC/ObjC++ but not
! for Fortran.
USE OPENACC
IMPLICIT NONE
!Host.
IF (.NOT. ACC_ON_DE... | {"hexsha": "a19045b05638baef04de686f35aa875b19e61a27", "size": 1274, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gcc-gcc-7_3_0-release/libgomp/testsuite/libgomp.oacc-fortran/acc_on_device-1-2.f", "max_stars_repo_name": "best08618/asylo", "max_stars_repo_head_hexsha": "5a520a9f5c461ede0f32acc284017b737a43898c... |
/-
Copyright (c) 2017 Johannes Hölzl. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johannes Hölzl
-/
import data.set.finite
import data.countable.basic
import logic.equiv.list
/-!
# Countable sets
> THIS FILE IS SYNCHRONIZED WITH MATHLIB4.
> Any changes to this fil... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/data/set/countable.lean"} |
```python
# %load /Users/facai/Study/book_notes/preconfig.py
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import SVG
```
逻辑回归在scikit-learn中的实现简介
==============================
分析用的代码版本信息：
```bash
~/W/g/scikit-learn ❯❯❯ git log -n 1
commit d161bfaa1a42da75f4940464f7f... | {"hexsha": "4ad5bf557c87478c0b82d1dcceec088f69a76a79", "size": 42443, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "machine_learning/logistic_regression/sklearn_lr.ipynb", "max_stars_repo_name": "ningchi/book_notes", "max_stars_repo_head_hexsha": "c6f8001f7d5f873896c4b3a8b1409b21ef33c328", "max_st... |
import numpy as np
########################################################################################################################
class IndexMapper:
"""
This class maps the pose detection framework indices.
"""
def __init__(self):
"""
Basic index mapper
"""
... | {"hexsha": "6732780891e78b297c53c08b1ee882fc4a83fd84", "size": 4506, "ext": "py", "lang": "Python", "max_stars_repo_path": "handtracking/src/app/handler/wrapper.py", "max_stars_repo_name": "rafaeltoyo/unity-vr-server", "max_stars_repo_head_hexsha": "f07edb64a15673cd79287668280742267eb38418", "max_stars_repo_licenses": ... |
Require Import Essentials.Notations.
Require Import Essentials.Types.
Require Import Essentials.Facts_Tactics.
Require Import Category.Main.
Require Import Basic_Cons.Equalizer.
Require Import Coq_Cats.Type_Cat.Type_Cat.
Local Obligation Tactic := idtac.
(** Just like in category of sets, in category of types, the eq... | {"author": "amintimany", "repo": "Categories-HoTT", "sha": "fd6018c7abd496b44fd31b1119f6b31795c251d8", "save_path": "github-repos/coq/amintimany-Categories-HoTT", "path": "github-repos/coq/amintimany-Categories-HoTT/Categories-HoTT-fd6018c7abd496b44fd31b1119f6b31795c251d8/Coq_Cats/Type_Cat/Equalizer.v"} |
# Lint as: python3
# Copyright 2021 The TensorFlow Authors. 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 ... | {"hexsha": "36d6f6651bdad299f0c82e308b877728f283f5e8", "size": 13503, "ext": "py", "lang": "Python", "max_stars_repo_path": "lingvo/jax/layers/pipeline.py", "max_stars_repo_name": "ruomingp/lingvo", "max_stars_repo_head_hexsha": "ba59e8c46471be77d5d3c48177f0f0dd8d5d44e9", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
from sympy import ( symbols, lambdify, diff, exp, solve, pprint )
import matplotlib.pyplot as plt
import numpy as np
g_xlim = [ -5, 40 ]
g_ylim = [ -5, 70 ]
def plot_fun( fun, name, col ):
x_vals = np.linspace( g_xlim[0], g_xlim[1], 1000, endpoint=True )
y_vals = fun( x_vals )
plt.plot( x_vals, y_vals, label = nam... | {"hexsha": "9758776a32c93151cf94b6f799d214bd74f29165", "size": 912, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Classes/MSDS400/PFinal/Q_09.py", "max_stars_repo_name": "bmoretz/Python-Playground", "max_stars_repo_head_hexsha": "a367ec7659b85c24363c21b5c0ac25db08ffa1f6", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import matplotlib.pyplot as plt
# fig = plt.figure()
fig = plt.figure(figsize=(3, 6)) # 指定画图区大小（长，宽）
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
ax1.plot(np.random.randint(1, 5, 5), np.arange(5)) # 第一个子图画图
ax2.plot(np.arange(10) * 3, np.arange(10)) # 第二个子图画图
plt.show()
| {"hexsha": "3da588ee3db5790b5753ad082d11278ebd217ab2", "size": 312, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpytest/matplotlib/matplotlib_tjh_01.py", "max_stars_repo_name": "tjhlp/tests", "max_stars_repo_head_hexsha": "7407df7dbfdf12a5f69ba7bc8bf8d14131534ac2", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
import pandas as pd
import multiprocessing as mp
from sklearn.metrics.pairwise import euclidean_distances, manhattan_distances
def _cluster_calculation(params):
'''
Run a KVariable calculation
Parameters
----------
df : pandas dataframe
dataframe of data
n_c... | {"hexsha": "39d3666cfb41ab8e1af6f6103ba66fa6b9402b66", "size": 10982, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/_util_kcluster.py", "max_stars_repo_name": "Chrisebell24/kcluster", "max_stars_repo_head_hexsha": "be5ed069b3b47143cf1e98535f1f978f5d155dc6", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
//==================================================================================================
/**
EVE - Expressive Vector Engine
Copyright : EVE Contributors & Maintainers
SPDX-License-Identifier: MIT
**/
//==================================================================================================
#... | {"hexsha": "023d3a45980aefacdcddafe4fc7569621793a1c1", "size": 1742, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "test/unit/module/real/core/legendre/diff/legendre.hpp", "max_stars_repo_name": "orao/eve", "max_stars_repo_head_hexsha": "a8bdc6a9cab06d905e8749354cde63776ab76846", "max_stars_repo_licenses": ["MIT"... |
'''
input1: genotype matrix with biallelic SNP or indel
'''
import sys,os,argparse
import pandas as pd
import numpy as np
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
def main():
parser = argparse.ArgumentParser(description='This code is for converting the genotype matrix to the fastPHASE fo... | {"hexsha": "1bf410b11876f348bbace6c83192ad208b0e3438", "size": 1692, "ext": "py", "lang": "Python", "max_stars_repo_path": "05_convert_genotype_matrix_to_fastPHASE_format.py", "max_stars_repo_name": "peipeiwang6/Genomic_prediction_in_Switchgrass", "max_stars_repo_head_hexsha": "1fba3508c0d81d16e0629e3cf94ff4d174a85b13"... |
"""
Stacked(bs)
Stacked(bs, ranges)
stack(bs::Bijector{0}...) # where `0` means 0-dim `Bijector`
A `Bijector` which stacks bijectors together which can then be applied to a vector
where `bs[i]::Bijector` is applied to `x[ranges[i]]::UnitRange{Int}`.
# Arguments
- `bs` can be either a `Tuple` or an `Abstra... | {"hexsha": "5c3fdb6b69943ac3372c38f08bd5bb05da6038f8", "size": 5192, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/bijectors/stacked.jl", "max_stars_repo_name": "oschulz/Bijectors.jl", "max_stars_repo_head_hexsha": "31b1c387ed6a243e02fd906cb615b61de47b935f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
// ---------------------------------------------------------------------
// pion: a Boost C++ framework for building lightweight HTTP interfaces
// ---------------------------------------------------------------------
// Copyright (C) 2007-2012 Cloudmeter, Inc. (http://www.cloudmeter.com)
//
// Distributed under the ... | {"hexsha": "9c87a87e34a7ab77e858901b3387dbb94e827149", "size": 14196, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/pion/http/writer.hpp", "max_stars_repo_name": "paolovolpi/pion", "max_stars_repo_head_hexsha": "d33971164080b3b5a99264ddc3f90a2c208f1b5c", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars... |
#设置镜像，这部分代码复制粘贴运行就可以
local({
r <- getOption("repos")
r["CRAN"] <- "http://mirrors.tuna.tsinghua.edu.cn/CRAN/"
options(repos=r)
})
options(BioC_mirror="https://mirrors.tuna.tsinghua.edu.cn/bioconductor")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocMa... | {"hexsha": "be13d22afc6b8c924b2142ef7ae0040e32afb564", "size": 382, "ext": "r", "lang": "R", "max_stars_repo_path": "docker/package.r", "max_stars_repo_name": "pandora414/dna", "max_stars_repo_head_hexsha": "42c4fc1686744a3b4595c4d68663b0a43932c5f1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "... |
# -*- coding: utf-8 -*-
r"""
Word paths
This module implements word paths, which is an application of Combinatorics
on Words to Discrete Geometry. A word path is the representation of a word
as a discrete path in a vector space using a one-to-one correspondence
between the alphabet and a set of vectors called steps. M... | {"hexsha": "b4028340a29d7e00298387b030b349417082b685", "size": 90657, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/combinat/words/paths.py", "max_stars_repo_name": "RexWzh/sage-1", "max_stars_repo_head_hexsha": "1e8ba0aac4cd2975141950daa5c9e0d294b070e4", "max_stars_repo_licenses": ["BSL-1.0"], "max_s... |
mutable struct AFArray{T,N} <: AbstractArray{T,N}
arr::af_array
function AFArray{T,N}(arr::af_array) where {T,N}
@assert get_type(arr) == T
a = new{T,N}(arr)
finalizer(release_array, a)
end
end
AFVector{T} = AFArray{T,1}
AFMatrix{T} = AFArray{T,2}
AFVolume{T} = AFArray{T,3}
AFTensor... | {"hexsha": "273c538b59087b1cc01c0f4bc9a3ed953139eddd", "size": 14817, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/array.jl", "max_stars_repo_name": "JuliaComputing/ArrayFire.jl", "max_stars_repo_head_hexsha": "fa7a2de4516c13b48dbb2aa5fec0f4a24d38f7f3", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# New concepts and differences from Theano:
# - stride is the interval at which to apply the convolution
# - unlike previous course, we use constant-size input to the network
# since not doing that caused us to start swapping
# - the output after convpool is a different size (8,8) here, (5,5) in Theano
# https://dee... | {"hexsha": "92395972d1b07127cbbd6c710464cd8f42b2b568", "size": 6414, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine_learning_examples/cnn_class/cnn_tf.py", "max_stars_repo_name": "austinburks/nlp-udemy", "max_stars_repo_head_hexsha": "2e2ddb39b22a057448666c5069b1bdda7a4318c3", "max_stars_repo_licenses":... |
import os
import du
trial_name = os.path.basename(__file__)[:-3]
with du.trial.run_trial(trial_name=trial_name) as trial:
import numpy as np
import scipy.stats
import tensorflow as tf
import tfu
import tfu.sandbox.batch_normalization as bn
import sklearn.metrics
import sklearn.linear_model... | {"hexsha": "fcc08d12073b52d97560f728a64280730804a9bf", "size": 8636, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/20170220_memorization/mnist_mlp_probe2.py", "max_stars_repo_name": "diogo149/tfu", "max_stars_repo_head_hexsha": "8928c85c9f8050dc25061e2882757dd64927b59d", "max_stars_repo_licenses": ... |
@with_kw mutable struct SequenceMDP{S, A} <: MDP{S,A}
mdps::Array{MDP{S,A}, 1} # Sequence of mdps to play
Ns::Array{Int} # Number of experience samples for each mdp before switching to the next one
count::Int = 0 # current count of the gen function
logging = false
end
function get_index(Ns, count)
... | {"hexsha": "995aba630d0c8d2e27c387c988b88c47c53a85d3", "size": 2015, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/extra/sequence_mdp.jl", "max_stars_repo_name": "ancorso/POMDPGym.jl", "max_stars_repo_head_hexsha": "044d909f89f696a1a9fb4e1d7dca04c451b8a1b7", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
'''
* @license
* Copyright Bruno Henrique Meyer. All Rights Reserved.
*
* Use of this source code is governed by an MIT-style license that can be
* found in the LICENSE file at
* https://github.com/BrunoMeyer/gene-selection-to-classification/blob/master/LICENSE
'''
from __future__ import print_function
from __fut... | {"hexsha": "b9ec201322a9b9a31e2a9f04b11062e1dfce90f1", "size": 13097, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "BrunoMeyer/gene-selection-to-classification", "max_stars_repo_head_hexsha": "bc88288ae4289395650dd01cb4a7cb670d6f7d80", "max_stars_repo_licenses": ["MIT"], "max_... |
%% dbDivision.m
% This script randomly divides database into 3 parts - training (TRN),
% validating (VAL) and testing (TST).
%
% 16-07-10 Michal Uricar
% 21-03-12 Michal Uricar, LFW annotation in one file
clearvars; close all;
%% Timestamp
fprintf(1,'Started on %s\n\n', datestr(now));
%% Load pruned database
addp... | {"author": "uricamic", "repo": "flandmark", "sha": "ecf122f93f73504fe7d8faccca525c6b1e98fdcd", "save_path": "github-repos/MATLAB/uricamic-flandmark", "path": "github-repos/MATLAB/uricamic-flandmark/flandmark-ecf122f93f73504fe7d8faccca525c6b1e98fdcd/learning/code/dbDivision.m"} |
from LSvehicleOneDimLookup_2 import vehicleOneDimLookup_2 as vehicle_ODL_2
from LSvehicleTwoDimLookup_2 import vehicleTwoDimLookup_2 as vehicle_TDL_2
from TwoDimLookup_motor import TwoDimLookup_motor as motor_TDL
from scipy import interpolate
import numpy as np
class Acceleration:
def __init__(self, Vehicle):
... | {"hexsha": "6ac1aad26bdf1ecd6bf1481721c10afb287546a7", "size": 1678, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Acceleration.py", "max_stars_repo_name": "CB1204/LapSimulation", "max_stars_repo_head_hexsha": "7d7f7c43a6bc3db3dbf02050d939da3f17647c2c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
%%
%
function show_2dresults(m,n,varargin)
k = nargin;
vars = varargin;
if(k < 3)
error('Not supported operation!');
end
for i = 1:size(vars{1},2)
for j = 1:k-2
T = vars{j};
T = reshape(T(:,i),m,n);
switch(k)
case 1
imshow(T,[],'InitialMagnification','fit');
... | {"author": "andrewssobral", "repo": "lrslibrary", "sha": "06d457349cb5f1fc56a583cd61af9f1d5150e3a1", "save_path": "github-repos/MATLAB/andrewssobral-lrslibrary", "path": "github-repos/MATLAB/andrewssobral-lrslibrary/lrslibrary-06d457349cb5f1fc56a583cd61af9f1d5150e3a1/utils/show_2dresults.m"} |
/* Command.hpp (exercise 3.5.2)
Description:
* Class used to execute commands in priority queue.
*/
#include <boost/thread/thread.hpp>
#include <functional>
#include <iostream>
using FunctionType = std::function<double(double)>;
class Command
{
private:
long ID;
FunctionType algo;
public:
///////////////////////... | {"hexsha": "470c71ba9924f7978d1a6cb6d4e0f62fa337d26b", "size": 1227, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Advanced C++ Course/Benjamin Rutan HW 3 Submission/3.5/3.5.2/3.5.2/Command.hpp", "max_stars_repo_name": "BRutan/Cpp", "max_stars_repo_head_hexsha": "8acbc6c341f49d6d83168ccd5ba49bd6824214f9", "max_s... |
! { dg-do compile }
!
! PR 54190: TYPE(*)/assumed-rank: Type/rank check too relaxed for dummy procedure
!
! Contributed by Tobias Burnus <burnus@gcc.gnu.org>
implicit none
call sub(f) ! { dg-error "Type mismatch in argument" }
contains
subroutine f(x)
type(*) :: x
end subroutine
subroutine sub(g)
in... | {"hexsha": "48cb43e7f8de42ff4b92c91bc66d0011e412b0cf", "size": 433, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/assumed_type_7.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_l... |
"""
Project to a Plane
~~~~~~~~~~~~~~~~~~
:class:`pyvista.PolyData` surfaces and pointsets can easily be projected to a
plane defined by a normal and origin
"""
# sphinx_gallery_thumbnail_number = 2
import numpy as np
import pyvista as pv
from pyvista import examples
poly = examples.load_random_hills()
poly.plot()
... | {"hexsha": "99b01b2e95fb0f58a2524b82f5e726b9070e1536", "size": 655, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/01-filter/project-plane.py", "max_stars_repo_name": "imsodin/pyvista", "max_stars_repo_head_hexsha": "af3bd7dc7b5b8551b732f6e6fa74f6675027469c", "max_stars_repo_licenses": ["MIT"], "max_st... |
[STATEMENT]
lemma of_drop_to_bl:
"of_bl (drop n (to_bl x)) = (x AND mask (size x - n))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. of_bl (drop n (to_bl x)) = x AND mask (size x - n)
[PROOF STEP]
by (simp add: of_bl_drop word_size_bl) | {"llama_tokens": 112, "file": "Word_Lib_Reversed_Bit_Lists", "length": 1} |
\documentclass[a4paper]{article}
\usepackage{graphicx}
\usepackage{caption}
\title{Report: BOTNET vs CLOUD INFRASTRUCTURE}
\author{Nicolò Vinci \\
\and
Marcello Meschini \\
}
\date{}
% \date{June 7, 2021}
\begin{document}
\maketitle
\section{Infrastructure as a Service}
\label{iaas}
The OpenStack machine is u... | {"hexsha": "6b63bc20e586869934d2610bb3b5e11e3816a5af", "size": 6139, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "projectReport/11-report-botnet-vs-cloud-infr/11-report-botnet-vs-cloud-infr.tex", "max_stars_repo_name": "nico989/FogAndCloudComputing", "max_stars_repo_head_hexsha": "e3f3361adce95a20b850ed1f5217da... |
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