text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | {"hexsha": "c4b5a7596ae94a0ba04ff1fc2f3f534cac43fee0", "size": 2587, "ext": "py", "lang": "Python", "max_stars_repo_path": "integration-tests/test_psql_parity.py", "max_stars_repo_name": "urbanlogiq/arrow-datafusion", "max_stars_repo_head_hexsha": "05d5f01fa8ec7bf9baa3aa632ccedb914d0b49a2", "max_stars_repo_licenses": [... |
program alocate_test
use write_in_file, only: write_iteration
use nr_module, only: four1, gasdev, ran1
implicit none
real, dimension(:, :), allocatable :: array
real, dimension(4) :: a = (/0.0, 0.0, 0.0, 0.0/)
real :: random_num
integer :: err, idum_num
allocate(array(0:4, 0:2), stat=err)
if (err /... | {"hexsha": "9c01167ad483e2cd1a14f99898ba8dcc162e5a2d", "size": 633, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "bin/allocate_test.f90", "max_stars_repo_name": "heyfaraday/FORTCMB", "max_stars_repo_head_hexsha": "56366423546ca73e876eda58dc8a01a25e640098", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# Copyright (C) 2004-2018 by
# All rights reserved.
# MIT license.
#
# Author: Vadim Ivlev
# Some functions to show tree graphs.
# Can be used both in standalone programs
# and in `jupyther` nonebooks.
# Preconditions
# -------------
# The folowing libraries should be installed
# `matplotlib, networkx, grap... | {"hexsha": "801db46dafa9ea5dfafec77fbd22aab1393c0de0", "size": 5137, "ext": "py", "lang": "Python", "max_stars_repo_path": "showtree.py", "max_stars_repo_name": "vadim-ivlev/show-tree", "max_stars_repo_head_hexsha": "b2e133f223be903c7d1b0cdfb23af7b3e7925cf4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from tqdm import tqdm
import time
import copy
from dataload import get_cifar,get_test_loader_cifar
from general_utils import test_data_evaluation
from KD_Loss import kd_loss
import numpy as np
import torch
from torch import nn
from DML_Loss import dml_loss_function
import pdb
criterion = nn.CrossEntropyLoss()
def tra... | {"hexsha": "31e80f2ad7e41c73094e2ea98efe6d84d4bddb62", "size": 34074, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_funcs.py", "max_stars_repo_name": "srinivasraodaru/Distilling-Knowledge-via-Intermediate-Classifiers", "max_stars_repo_head_hexsha": "9bddcd90c5d776e5afa78c3c3da4acd46970c70d", "max_stars_r... |
"""
This recipe evaluates an oracle ideal ratio mask on the mix_clean
and min subset in the WHAM dataset using phase sensitive spectrum
approximation. Output of this script for psa:
┌────────────────────┬────────────────────┬────────────────────┐
│ │ OVERALL (N = 6000) │ │
╞════... | {"hexsha": "9c73c8aa436db494b71c5596bc147f9170ea895f", "size": 3223, "ext": "py", "lang": "Python", "max_stars_repo_path": "recipes/wham/ideal_ratio_mask.py", "max_stars_repo_name": "ZhaoJY1/nussl", "max_stars_repo_head_hexsha": "57aabeabca3b2e75849e1659a522e3c2f77e9172", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
Subroutine chkin1(l, i1, i2, i3, t, tmin, nc)
Implicit Double Precision (A-H, O-Z)
Save
itest = 0
Do i = i1 - 1, i1 + 1
Do j = i2 - 1, i2 + 1
Do k = i3 - 1, i3 + 1
If (i>=1 .And. i<=10 .And. j>=1 .And. j<=10 .And. k>=1 .And. k<=10) Then
Call chkcel(l, i, j, k, t, tmin, nc)
... | {"hexsha": "b36386ba41b1a61bb8ff5a0bdb5030986b79698c", "size": 584, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/chkin1.f90", "max_stars_repo_name": "xiaohaijin/AMPT", "max_stars_repo_head_hexsha": "90c7a1ab4dc04a092e64af759d53e22f6fea5b02", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
#include "libheader.hpp"
#include <boost/program_options.hpp>
#include <iostream>
using namespace std;
int main(int argc, char** argv)
{
namespace po = boost::program_options;
po::options_description generalOptions("Genral options");
generalOptions.add_options()
("help,h", "Print help message")
... | {"hexsha": "f699702bafded8b35e52b417372ca7e3bcbef40d", "size": 1314, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "make/simpliest-make/program/main.cpp", "max_stars_repo_name": "DAlexis/example-build-systems-workshop", "max_stars_repo_head_hexsha": "f5b25d33d9059a2e0addc461e86a3b43a5c3806f", "max_stars_repo_lice... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Things that don't belong anywhere else
"""
import hashlib
import os
import shutil
import errno
from datetime import datetime
from collections import Counter
import numpy as np
import torch
import torch.nn as nn
def make_weights_for_balanced... | {"hexsha": "98f6d7f4370e01420147b74926574232369b83f6", "size": 5173, "ext": "py", "lang": "Python", "max_stars_repo_path": "domainbed/lib/misc.py", "max_stars_repo_name": "zhaoxin94/swad", "max_stars_repo_head_hexsha": "df9213cf5946d9b357dbd9363565fe0eabe50951", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 63... |
[STATEMENT]
lemma in_set_vwalk_arcs_append2:
assumes nonempty: "p \<noteq> []" "q \<noteq> []"
assumes disj: "x \<in> set (vwalk_arcs p) \<or> x = (last p, hd q)
\<or> x \<in> set (vwalk_arcs q)"
shows "x \<in> set (vwalk_arcs (p @ q))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> set (vwalk_arcs... | {"llama_tokens": 851, "file": "Graph_Theory_Vertex_Walk", "length": 7} |
"""
Set of scripts intended to link SVs to genes by rules based on gains and losses of regulatory elements caused by TAD disruptions
The idea is that we first make a 'neighborhood' in which all regulatory elements are assigned to genes (regulator set).
These are the regulatory elements within the TAD of the gene, a... | {"hexsha": "91217e12c1d0f0a55094f01878bf7af34b637495", "size": 5076, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/linkSVsGenes/main.py", "max_stars_repo_name": "UMCUGenetics/svMIL", "max_stars_repo_head_hexsha": "b17f9b34702aac976dd5e233cb4e1ce051d19bbf", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
\chapter{Fonts}
\RCSID$Id: ch05.tex,v 1.1 2002/08/23 14:58:46 nwalsh Exp $
\label{chap:fonts}
\ifincludechapter\else\endinput\fi
All of the common \TeX\ macro packages use
the \idx{Computer Modern fonts}\index{fonts}\index{tex@\TeX!fonts}\index{fonts!Computer Modern}
by default. In fact, the Computer Modern fonts... | {"hexsha": "130af6c64b46ba4c6854237d1268574b8cf0c415", "size": 95004, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/ch05.tex", "max_stars_repo_name": "d277/MakingTeXWork", "max_stars_repo_head_hexsha": "1d5bed9ab9f5e078a78d0ed0dbaddc0cf4829777", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 2... |
/**
* Copyright 2012 JDSU
* Purpose: Implementation of the stack & layer manager interface.
*/
#include <boost/foreach.hpp>
#include "loader/stack_layer_manager.h"
#include "loader/stack_parser.h"
#include "loader/protocol_parser.h"
#include "loader/protocol_cfg.h"
#include "loader/loader_logger.h"
StackLayerManag... | {"hexsha": "92bc8a3335e7b3917af170b9dab262045322701d", "size": 5399, "ext": "cc", "lang": "C++", "max_stars_repo_path": "project/c++/mri/src/decode/loader/src/stack_layer_manager.cc", "max_stars_repo_name": "jia57196/code41", "max_stars_repo_head_hexsha": "df611f84592afd453ccb2d22a7ad999ddb68d028", "max_stars_repo_lice... |
import tomviz.operators
class PeronaMalikAnisotropicDiffusion(tomviz.operators.CancelableOperator):
def transform(self, dataset, conductance=1.0, iterations=100,
timestep=0.0625):
"""This filter performs anisotropic diffusion on an image using
the classic Perona-Malik, gradient ... | {"hexsha": "4206b97418d21682cdb20241e7ec5dca72c571fe", "size": 2567, "ext": "py", "lang": "Python", "max_stars_repo_path": "tomviz/python/PeronaMalikAnisotropicDiffusion.py", "max_stars_repo_name": "sankhesh/tomviz", "max_stars_repo_head_hexsha": "7116f4eb75b30534a24462f4ddfb1694fe41c308", "max_stars_repo_licenses": ["... |
import os
import pytest
import numpy as np
import numpy.testing as nt
import xgeo
from xgeo.crs import XCRS
import xarray as xr
here = os.path.dirname(__file__)
datapath = os.path.join(here, "data")
zones_shp = os.path.join(datapath, "zones.shp")
zones_geojson = os.path.join(datapath, "zones.geojson")
@pytest.fixt... | {"hexsha": "b5bd2c81d59ec36488e1911777405b61ba51fd09", "size": 4874, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_raster.py", "max_stars_repo_name": "Geosynopsis/xgeo", "max_stars_repo_head_hexsha": "e0818a11abeadc6035dad93f838d6d49cf4bfe95", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import numpy as np
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
import pickle
data = np.load("data/syna_embeddings.npz")
X_train, y_train, X_val, y_val = data["arr_0"], data["arr_1"], data["arr_2"], d... | {"hexsha": "3bb8aae4843384950226127a99b5510928237491", "size": 1009, "ext": "py", "lang": "Python", "max_stars_repo_path": "svm_classifier.py", "max_stars_repo_name": "countrymarmot/testFacenet", "max_stars_repo_head_hexsha": "92e966930d931d41ab3bb49928b7de2034c3ac62", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
using Test
using QuantumLattices.Prerequisites.VectorSpaces
using QuantumLattices.Interfaces: dimension, rank
import QuantumLattices.Prerequisites.VectorSpaces: shape, ndimshape
import QuantumLattices.Prerequisites.Traits: contentnames, getcontent
struct SimpleVectorSpace{B, N} <: EnumerativeVectorSpace{B}
sorted... | {"hexsha": "6da42662d20b8270af920073d86f57c139f58974", "size": 4429, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Prerequisites/VectorSpaces.jl", "max_stars_repo_name": "Quantum-Many-Body/QuantumLattices.jl", "max_stars_repo_head_hexsha": "203034b5281887811028484e24ce5a5ea9556185", "max_stars_repo_license... |
# --------------------------------------------------------
# SiamMask
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
import numpy as np
import math
from masking.utils.bbox_helper import center2corner, corner2center
class ... | {"hexsha": "9c63369a1c93caf1ecb32953a00cefbd0cd2461e", "size": 3228, "ext": "py", "lang": "Python", "max_stars_repo_path": "masking/utils/anchors.py", "max_stars_repo_name": "Ayonveig/Video-Object-Removal", "max_stars_repo_head_hexsha": "c05f7036f5e51a03b9a0f5d1e0b669545f00bd06", "max_stars_repo_licenses": ["MIT"], "ma... |
from qutip.solver.options import SolverOdeOptions
from qutip.solver.sesolve import SeSolver
from qutip.solver.mesolve import MeSolver
from qutip.solver.solver_base import Solver
import qutip
import numpy as np
from numpy.testing import assert_allclose
import pytest
class TestIntegratorCte():
_analytical_se = lamb... | {"hexsha": "80af242a7bb7f87aedcf5d078c7a6f8fb7815999", "size": 3986, "ext": "py", "lang": "Python", "max_stars_repo_path": "qutip/tests/solver/test_integrator.py", "max_stars_repo_name": "jakelishman/qutip", "max_stars_repo_head_hexsha": "fbb7fad5bc205910228db622d90601c82db45e4b", "max_stars_repo_licenses": ["BSD-3-Cla... |
# Copyright 2017-2021 QuantRocket LLC - 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 applicabl... | {"hexsha": "f25f71f3dd682d83274bf23262a8737ab35a0299", "size": 20397, "ext": "py", "lang": "Python", "max_stars_repo_path": "moonshot/strategies/ml.py", "max_stars_repo_name": "windblood/moonshot", "max_stars_repo_head_hexsha": "d79cf26e7fb5ce3fcb34060771ea4992e19dc46a", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
using Graphs
function create_graph(start_node, end_node)
@assert length(start_node)==length(end_node)
no_node = max(maximum(start_node), maximum(end_node))
no_arc = length(start_node)
graph = simple_inclist(no_node)
for i=1:no_arc
add_edge!(graph, start_node[i], end_node[i])
end
r... | {"hexsha": "9fe1979487199c4fb8d7c72bb7b72ae2e2b6d53f", "size": 3556, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Julia_files/tap_MSA_multi_class.jl", "max_stars_repo_name": "jingzbu/InverseVITraffic", "max_stars_repo_head_hexsha": "c0d33d91bdd3c014147d58866c1a2b99fb8a9608", "max_stars_repo_licenses": ["MIT"],... |
(* (c) Copyright 2006-2016 Microsoft Corporation and Inria. *)
(* Distributed under the terms of CeCILL-B. *)
From mathcomp Require Import ssreflect ssrfun ssrbool eqtype ssrnat div seq.
From mathcomp Require Import choice fintype finfun bigop prime binomial.
(********... | {"author": "math-comp", "repo": "math-comp", "sha": "e39f9173b484f2e8e7f69f746a619dcc8f3abc1b", "save_path": "github-repos/coq/math-comp-math-comp", "path": "github-repos/coq/math-comp-math-comp/math-comp-e39f9173b484f2e8e7f69f746a619dcc8f3abc1b/mathcomp/algebra/ssralg.v"} |
(**
This file is part of the Coquelicot formalization of real
analysis in Coq: http://coquelicot.saclay.inria.fr/
Copyright (C) 2011-2015 Sylvie Boldo
#<br />#
Copyright (C) 2011-2015 Catherine Lelay
#<br />#
Copyright (C) 2011-2017 Guillaume Melquiond
This library is free software; you can redistribute it and/or
mod... | {"author": "jtassarotti", "repo": "coquelicot-ext", "sha": "1c11715037fdd4e15de6491bfdb9f923209ddb63", "save_path": "github-repos/coq/jtassarotti-coquelicot-ext", "path": "github-repos/coq/jtassarotti-coquelicot-ext/coquelicot-ext-1c11715037fdd4e15de6491bfdb9f923209ddb63/theories/Hierarchy.v"} |
#######################################
# Input Example ::
# python hotspot_predict.py -lat 11.05 -long 76.1 -rad 0.2 -hpts 5
#######################################
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
import numpy as np
import math
from tensorflow.keras.models import Sequential
from ten... | {"hexsha": "61bd7f10067cd1d2a3d137f34c266a6cb0594cf5", "size": 4961, "ext": "py", "lang": "Python", "max_stars_repo_path": "Hotspot_Prediction(MAP)/hotspot_predict.py", "max_stars_repo_name": "chandran-jr/Reubus", "max_stars_repo_head_hexsha": "eca2cc5926b567524355b4b2b3c1e0d69785e6ff", "max_stars_repo_licenses": ["MIT... |
import chainer.functions as F
import chainer.links as L
from chainer import Variable, Chain
import numpy as np
__all__ = [
'AffineTransform',
]
class AffineTransform(Chain):
def __init__(self, *in_sizes: int, out_size: int,
nonlinear=F.tanh, nobias: bool = False, initialW=None, initial_bias=... | {"hexsha": "2924f8c5c2bfffa9cf86f04239c65439bb167693", "size": 1080, "ext": "py", "lang": "Python", "max_stars_repo_path": "vapour/links/connections/affine_transform.py", "max_stars_repo_name": "speedcell4/vapour", "max_stars_repo_head_hexsha": "c00b9b8fffddf0b134bec3ebb26d961e0468194a", "max_stars_repo_licenses": ["MI... |
# import system module
import sys
# import some PyQt5 modules
from PyQt5.QtWidgets import QApplication, QWidget
from PyQt5.QtGui import QImage, QPixmap, QColor
from PyQt5.QtCore import QTimer
# import Opencv modules
import cv2
import numpy as np
import math
from handGUI import *
class MainWindow(QWidget):
# cla... | {"hexsha": "81b53ffcc0f325abf309cf7c19d6b6eae82b04ef", "size": 7953, "ext": "py", "lang": "Python", "max_stars_repo_path": "gui/handHandler.py", "max_stars_repo_name": "Johan809/P3-ML", "max_stars_repo_head_hexsha": "ffdc92a452eb9b8ac203bf51d79324eca3d6e0cf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
import numpy as np
import time, os, math, operator, statistics, sys
import tensorflow as tf
from random import Random
class Sample(object):
def __init__(self, id, image, true_label):
# image id
self.id = id
# image pixels
self.image = image
# image true label
... | {"hexsha": "6f4614028a3262b6d05f93ba6d56d202035ad2a9", "size": 675, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/structure/sample.py", "max_stars_repo_name": "songhwanjun/Coteaching", "max_stars_repo_head_hexsha": "3fc8fee7f44b0cc8e61bbfb650284e9c9462e6ee", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
'''
this is EMU^r (recursive computation of expected marginal utility) algorithm of Bhattacharjee et.al
REFERENCES:
Bhattacharjee, K.S., Singh, H.K., Ryan, M., Ray, T.: Bridging the gap: Manyobjective optimization and informed decision-making. IEEE Trans. Evolutionary
Computation 21(5), 813{820 (2017)
'''
import ... | {"hexsha": "26673e93752c6735e06f54085b6e2480270a4c8c", "size": 10056, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/algorithms/EMUr.py", "max_stars_repo_name": "COLA-Laboratory/kpi", "max_stars_repo_head_hexsha": "c4bb23e6302e17c37d1c4419c849a1cf24b0fb84", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
[STATEMENT]
lemma snapshot_produces_marker:
assumes
"trace init t final" and
"~ has_snapshotted (S t i) p" and
"has_snapshotted (S t (Suc i)) p" and
"channel cid = Some (p, q)"
shows
"Marker : set (msgs (S t (Suc i)) cid) \<or> has_snapshotted (S t i) q"
[PROOF STATE]
proof (prove)
goal (1 subgo... | {"llama_tokens": 10334, "file": "Chandy_Lamport_Snapshot", "length": 51} |
import os
import tensorflow as tf
import numpy as np
from math import sqrt, pow
def plot_sgd(x,y, name = "SGD"):
import plotly.plotly as py
import plotly.graph_objs as go
#data = []
data = [go.Scatter(x = x, y = y, name = name)]
layout = go.Layout(xaxis = dict(type = 'log', autorange = True),yaxis = dict(autorang... | {"hexsha": "6e385f97bfc12f95edbcf2033b7c59fb313f1bb0", "size": 3212, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/SGD_LR_oneMachine.py", "max_stars_repo_name": "yingcuhk/Distributed-and-Asynchronos-SGD", "max_stars_repo_head_hexsha": "f45758082d1d8e0908c9f1978a7ee607a861c21e", "max_stars_repo_licenses": [... |
import os
import pyfits
import scipy
from scipy import ndimage,optimize
# Function poststamp - cuts out a postage stamp from a larger image
#
# Inputs:
# data - full image data array
# cx - x value of central pixel
# cy - y value of central pixel
# csize - length of one side of the postage stamp
# Outpu... | {"hexsha": "4a4131535636e755ef05fb7f821183b36176c4c7", "size": 3769, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/subtract_psf.py", "max_stars_repo_name": "cdfassnacht/CodeCDF", "max_stars_repo_head_hexsha": "f4a3ed7c3a88c06de2c520c6ed858a3c8de5d703", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import os
import numpy as np
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import gzip
import csv
import time
import argparse
from utils import *
from sharenet import *
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--data_dir... | {"hexsha": "3b92b8c2f127818de8b5435acb94087e76aa1c93", "size": 3417, "ext": "py", "lang": "Python", "max_stars_repo_path": "sharenet_example2.py", "max_stars_repo_name": "alexw16/sharenet", "max_stars_repo_head_hexsha": "122ce05e1adb05bce8e8f5f5c3aeceae2e56b303", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
\documentclass[twoside]{homework}
\usepackage{dsfont}
\usepackage{graphicx}
\usepackage{listings}
\usepackage{amsmath}
\usepackage{bm}
\usepackage{xcolor}
\lstset{
rulesepcolor= \color{gray}, %代码块边框颜色
breaklines=true, %代码过长则换行
numbers=left, %行号在左侧显示
numberstyle= \small,%行号字体
%keywordstyle= \colo... | {"hexsha": "7dd92a621e28e000e0a117f98e283b4cdf84d1bd", "size": 43121, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Homework/HW1/StaLearning-HW1/UNI_HW#.tex", "max_stars_repo_name": "Zhuoyue-Xing/STAT-LRNING-BIOL-INFO-SYST", "max_stars_repo_head_hexsha": "dfc1fe0cb36c71d5a2ffb44961f2abbe9fe3e7b2", "max_stars_rep... |
import math
import time
import numpy as np
import pandas as pd
import torch
from tqdm.contrib import tenumerate
from millipede import CountLikelihoodSampler, NormalLikelihoodSampler
from .containers import SimpleSampleContainer, StreamingSampleContainer
from .util import namespace_to_numpy
def populate_alpha_beta_... | {"hexsha": "c26af06b18a37ffbac81f32fd9311e22d44c3a4f", "size": 42519, "ext": "py", "lang": "Python", "max_stars_repo_path": "millipede/selection.py", "max_stars_repo_name": "broadinstitute/millipede", "max_stars_repo_head_hexsha": "4b6a61027e559a6953fabee138b074afc4164489", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
#ifndef IRODS_AUTHENTICATION_PLUGIN_FRAMEWORK_HPP
#define IRODS_AUTHENTICATION_PLUGIN_FRAMEWORK_HPP
#include "irods/authCheck.h"
#include "irods/authPluginRequest.h"
#include "irods/authRequest.h"
#include "irods/authResponse.h"
#include "irods/authenticate.h"
#include "irods/irods_auth_constants.hpp"
#include "irods/... | {"hexsha": "67f35d366efb8860d1c0da4a88e722be3fa9328f", "size": 4968, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/core/include/irods/authentication_plugin_framework.hpp", "max_stars_repo_name": "aghsmith/irods", "max_stars_repo_head_hexsha": "31d48a47a4942df688da94b30aa8a5b5210261bb", "max_stars_repo_licens... |
import numpy as np
import pandas as pd
from .constants import *
from .functions import *
def calculate_daily_aqi(data, column_names):
''' calculate_daily_aqi funcion
data: a pandas dataframe
column_names: names of factor columns insequence of ['SO2', 'NO2', 'PM10', 'CO', 'O3', 'O3_8H', 'PM_25']
... | {"hexsha": "ff6495d27973749f768df2eb118c4953f31a228b", "size": 3297, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnemc_calculator/calculate_aqi.py", "max_stars_repo_name": "coeusite/cnemc_calculator", "max_stars_repo_head_hexsha": "7874940adb38bcdf684e9032f55de10adfa10345", "max_stars_repo_licenses": ["MIT"]... |
from dnc.envs.base import KMeansEnv
import numpy as np
from rllab.core.serializable import Serializable
from rllab.envs.base import Step
from rllab.misc.overrides import overrides
from rllab.misc import logger
import os.path as osp
raise NotImplementedError('This is taken from DNC repo and needs to be made to work ... | {"hexsha": "7c0d04fda1fd15210ef29409725f042f177f9073", "size": 5284, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlkit/envs/lob.py", "max_stars_repo_name": "yifan-you-37/rl_swiss", "max_stars_repo_head_hexsha": "8b0ee7caa5c1fa93860916004cf4fd970667764f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
//
// Created by michel on 11-02-21.
//
#include "test-helper.h"
#include <boost/mpl/list.hpp>
#include <org-simple/util/LockfreeRingBuffer.h>
using namespace boost::unit_test;
using namespace org::simple::util;
enum class WriteMethod { WRITE, RESET, RESET_COUNT };
struct AbstractRingBufferTester {
virtual size_... | {"hexsha": "1bed16304af0449b71e472f4bf70379700fa8a2b", "size": 8542, "ext": "cc", "lang": "C++", "max_stars_repo_path": "test/util/LockFreeRingBufferTests.cc", "max_stars_repo_name": "emmef/org-simple-util", "max_stars_repo_head_hexsha": "80c7ad1c1241ce37c8a312ba1e990ffd5a2db619", "max_stars_repo_licenses": ["Apache-2.... |
(* Title: HOL/Isar_Examples/Basic_Logic.thy
Author: Makarius
Basic propositional and quantifier reasoning.
*)
section \<open>Basic logical reasoning\<close>
theory Basic_Logic
imports Main
begin
subsection \<open>Pure backward reasoning\<close>
text \<open>
In order to get a first idea of how Is... | {"author": "zchn", "repo": "isabelle-practice", "sha": "1c6de196ca011593faeed229808e65c9bfeb659e", "save_path": "github-repos/isabelle/zchn-isabelle-practice", "path": "github-repos/isabelle/zchn-isabelle-practice/isabelle-practice-1c6de196ca011593faeed229808e65c9bfeb659e/examples/src/HOL/Isar_Examples/Basic_Logic.thy"... |
// This file is part of PoseEstimation.
// This file is a modified version of p3p.m <http://rpg.ifi.uzh.ch/software_datasets.html>,
// see 3-Clause BSD license below.
// Copyright (c) 2021, Eijiro Shibusawa <phd_kimberlite@yahoo.co.jp>
// All rights reserved.
//
// Redistribution and use in source and binary forms, wit... | {"hexsha": "3a48c4abd77e6592ce83164eef649840966c1748", "size": 11512, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/P3P.hpp", "max_stars_repo_name": "eshibusawa/PoseEstimation", "max_stars_repo_head_hexsha": "7eca2b21673b2cd42f40c1f05d4f67ee89bdc279", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
function [ a, ipvt, info ] = dchdc ( a, lda, p, ipvt, job )
%*****************************************************************************80
%
%% DCHDC computes the Cholesky decomposition of a positive definite matrix.
%
% Discussion:
%
% A pivoting option allows the user to estimate the condition of a
% positi... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/linpack_d/dchdc.m"} |
import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
#to read a csv
df = pd.read_csv('./asset/lecture_data.txt', sep='\t')
print(df.head())
| {"hexsha": "42dd0c1505a579ab5b255972112f8bf1400b692c", "size": 186, "ext": "py", "lang": "Python", "max_stars_repo_path": "Learning/learnPandas.py", "max_stars_repo_name": "suryaavala/17s1-cs9318", "max_stars_repo_head_hexsha": "fdfa84a5f3330d189af213d670479c65d6c60a28", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import integrator
import gravity
from ephemerides import ephemerides
import csv
import time
from plot import plotter, anim_plotter
import math
class body_differentials(integrator.differential_equation):
def __init__(self, masses, *args, **kwargs):
super().__init__()
if len(masses... | {"hexsha": "a91a65550bbd2a24be1e5798294e66dfec5df06a", "size": 10807, "ext": "py", "lang": "Python", "max_stars_repo_path": "nBody.py", "max_stars_repo_name": "ForrestHurley/nBodySystem", "max_stars_repo_head_hexsha": "72e77665f5e181811a111872debd50eb9b263fa2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
module MSC
export removeMSC, gapFillMSC, getMSC, getMedSC
using ..Cubes
using ..DAT
using ..Proc
import Statistics: quantile!
function removeMSC(aout,ain,NpY::Integer,tmsc,tnmsc)
#Start loop through all other variables
fillmsc(1,tmsc,tnmsc,ain,NpY)
subtractMSC(tmsc,ain,aout,NpY)
nothing
end
"""
re... | {"hexsha": "52631693c0c7f04c7aae8e8e31c9a442fabaede7", "size": 3850, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Proc/MSC.jl", "max_stars_repo_name": "Balinus/ESDL.jl", "max_stars_repo_head_hexsha": "7262fb0b9e8e0f18ba13a6ba65077bc652bccb29", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
# Copyright (c) 2020 PaddlePaddle 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 app... | {"hexsha": "36bffcc7f2ad371b5f7d1f80f214a67c995af16f", "size": 17123, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/dataloader/dataset.py", "max_stars_repo_name": "DevilCarp/Paddle", "max_stars_repo_head_hexsha": "04325d2cbefb029a4478bdc069d3279cd566ac6a", "max_stars_repo_licenses": ["Apach... |
! { dg-do compile }
! { dg-options "-std=legacy" }
!
! Tests the fix for PR28600 in which the declaration for the
! character length n, would be given the DECL_CONTEXT of 'gee'
! thus causing an ICE.
!
! Contributed by Francois-Xavier Coudert <fxcoudert@gcc.gnu.org>
!
subroutine bar(s, n)
integer n
character s*(n)
... | {"hexsha": "c8f804465b7c5da676e08be1a49fb29205ade0d8", "size": 450, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/assumed_charlen_function_4.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_... |
MODULE empRel
!1. Johnson (1966): B-V logTeff relation
!2. Jordi+ (2010): G_BP-G_RP logTeff relation
DOUBLE PRECISION, DIMENSION(4,2), PARAMETER :: cJ=reshape((/ 0.D0, 0.D0, -0.234D0, 3.908D0, &
& -0.316D0, 0.709D0, -0.654D0, 3.999D0 /), &
& (/4,2/))
DOUBLE PRECISION :: DTeJ=0.02 !error ... | {"hexsha": "33c518cc3e16aad4ea4b0d5fb7d150a2e03ea906", "size": 3043, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "empRel.f90", "max_stars_repo_name": "Bonfanti88/MCMCI", "max_stars_repo_head_hexsha": "9dcc1745896ed2e1fc023ad19475d09cce2ee518", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_s... |
#
# Copyright The NOMAD Authors.
#
# This file is part of NOMAD.
# See https://nomad-lab.eu for further info.
#
# 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/lic... | {"hexsha": "69fc2c7fb89f9957f133f53b1d25f61449a2a3b6", "size": 124369, "ext": "py", "lang": "Python", "max_stars_repo_path": "vaspparser/metainfo/vasp_incars.py", "max_stars_repo_name": "nomad-coe/nomad-parser-vasp", "max_stars_repo_head_hexsha": "080e74e84c645aa018527da7d14d903893b279c5", "max_stars_repo_licenses": ["... |
C @(#)ipf_intfce.f 20.4 11/11/97
subroutine ipf_intfce (string, value)
character string *(*)
integer value
C This subroutine provides an single but limited variable
c interface to IPF without recourse to IPF common data blocks.
include 'ipfinc/parametr.inc'
include '... | {"hexsha": "56de05fda8734a93153618323f7f7990a34fef4e", "size": 1049, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/ipf_intfce.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
import random, math
A=np.array(([2, 4, 0, 0], [3, 5, 1, 0], [0, 6, 2, 0],
[5, 7, 0, 1], [6, 8, 4, 2], [0, 9, 5, 3],
[8, 0, 0, 4], [9, 0, 7, 5], [0, 0, 8, 6]))
L=3
N=L**2
E=-2*N
def Nbr(n, k): return A.T[n-1, k-1]
sigma=[random.choice([1, -1]) for i in rang... | {"hexsha": "6f2daaae54ab4f6016e8f2ff693c94f7a02acba3", "size": 939, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter 5/markov-spin-glass.py", "max_stars_repo_name": "indrag49/Computational-Stat-Mech", "max_stars_repo_head_hexsha": "0877f54a0245fce815f03478f4fb219fd6314951", "max_stars_repo_licenses": ["MI... |
using AccelerationBenchmark, DataFrames, CSV, Optim, LineSearches, CUTEst, JLD
const run_tests_more = true # Run the Moré et al. tests from the O-ACCEL paper
const run_tests_cutest = true # Run CUTEst tests
const savejld = true
const savecsv = true
function lstests()
lsm = MoreThuente()
lsh = HagerZhang()
... | {"hexsha": "b21b13b2d1ffc3cb63a1f4b6e526ef12fb979d4b", "size": 2523, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmark/lbfgsscale/runtests.jl", "max_stars_repo_name": "anriseth/AccelerationBenchmark.jl", "max_stars_repo_head_hexsha": "3f01e013b7635a37dd99ea144b22e23db7716c9f", "max_stars_repo_licenses": [... |
import pandas as pd
import numpy as np
from typing import List, Optional
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings("ignore")
class Indices:
"""
Price Technical Indicators
"""
def __init__(
self, df: pd.DataFrame, date_col: str = "date", price_col: str = "price"
... | {"hexsha": "76c87daecb5131b4444cae117e3946dd71c61b60", "size": 14399, "ext": "py", "lang": "Python", "max_stars_repo_path": "PriceIndices/price_indicators.py", "max_stars_repo_name": "dc-aichara/Price-Indices", "max_stars_repo_head_hexsha": "31159417b5ee79d051dea0f68600f1f2f3745eda", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma darcs_mset_elem:
"x \<in># darcs_mset (Node r xs) \<Longrightarrow> \<exists>(t,e) \<in> fset xs. x \<in># darcs_mset t \<or> x = e"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in># darcs_mset (Node r xs) \<Longrightarrow> \<exists>(t, e)\<in>fset xs. x \<in># darcs_mset t \<or> x = e
[PRO... | {"llama_tokens": 286, "file": "Query_Optimization_Dtree", "length": 2} |
SUBROUTINE formtb(pb,km,g)
!
! This subroutine assembles an unsymmetrical band matrix pb from
! element constituent matrices km.
!
IMPLICIT NONE
INTEGER,PARAMETER::iwp=SELECTED_REAL_KIND(15)
REAL(iwp),INTENT(IN)::km(:,:)
INTEGER,INTENT(IN)::g(:)
REAL(iwp),INTENT(OUT)::pb(:,:)
INTEGER::i,j,idof,icd,iw
idof=SIZE(k... | {"hexsha": "1f2f214b4a14a4df528dc9c6f5b0640038a1fd73", "size": 562, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "main/formtb.f90", "max_stars_repo_name": "cunyizju/Programming-FEM-5th-Fortran", "max_stars_repo_head_hexsha": "7740d381c0871c7e860aee8a19c467bd97a4cf45", "max_stars_repo_licenses": ["MIT"], "max... |
import healpy as hp
from scipy.integrate import trapz
from scipy.integrate import simps
from astropy import constants as const
import numpy as np
from astropy import units as u
def kappa_prefactor(H0, om0, length_unit='Mpc'):
"""
Gives prefactor (3 H_0^2 Om0)/2
:param H0: Hubble parameter with astropy un... | {"hexsha": "001f1d1b7d2c6265e1e9b2bba6d502b4b95dc1f8", "size": 8395, "ext": "py", "lang": "Python", "max_stars_repo_path": "bornraytrace/lensing.py", "max_stars_repo_name": "NiallJeffrey/BornRaytrace", "max_stars_repo_head_hexsha": "cb07ed78d206563243ace6e9015804e87c6513e5", "max_stars_repo_licenses": ["MIT"], "max_sta... |
export UserKNN
"""
UserKNN(
data::DataAccessor,
k::Int,
normalize::Bool=false
)
[User-based CF using the Pearson correlation](https://dl.acm.org/citation.cfm?id=312682). `k` represents number of neighbors, and `normalize` specifies if weighted sum of neighbors' rating is normalized.
T... | {"hexsha": "46023817735e9aedbf4648cbf0b842f8d4d28025", "size": 3911, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/model/user_knn.jl", "max_stars_repo_name": "takuti/Recommendation.jl", "max_stars_repo_head_hexsha": "551019ea415efd609edc137f72bf15f0cb436ce8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Base model struct
Base.@kwdef mutable struct guessing{T <: AbstractFloat} <: IRTmodel
a::AbstractVector{T} = [0.0]
d::AbstractVector{T} = [0.0]
c::AbstractVector{T} = [0.0]
group::Vector{Int64} = [1]
fixed::Bool = false
estc::Bool = true
end
function _distribute!(new, old::guessing)
old.a... | {"hexsha": "51615d0014c3bc803d46b109a6a07d8477d3c451", "size": 2333, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/IRTmodels/guessing.jl", "max_stars_repo_name": "takuizum/irtfun.jl", "max_stars_repo_head_hexsha": "326555ca78fcfee5d0894e3b5dd0dc261059d4c0", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import torch
from util.reservoir_w_cur_replay_buffer import Reservoir_with_Cur_Replay_Memory
import random
import matplotlib.pyplot as plt
s_c = torch.load("forward_curiosity")
a_c = torch.load("inverse_curiosity")
mul = 1000
change_var_at = [0, 100, 150, 350]
change_var_at = [change_var_at[i]*mul ... | {"hexsha": "5cb38e57b09a44c99f52b454c81d7b94029d8abc", "size": 1944, "ext": "py", "lang": "Python", "max_stars_repo_path": "additional_test/Test_Filters/test_time_fun.py", "max_stars_repo_name": "punk95/Continual-Learning-With-Curiosity", "max_stars_repo_head_hexsha": "af0c507040e1352beb8740b6b3a7849417fc879a", "max_st... |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
#setup the plotting area
fig, ax = plt.subplots()
plt.subplots_adjust(left=0.15, bottom=0.35)
# Here we start with initial parameters hard coded
x0 = 5
v0 = 0
F0 = 1
m = 1
w0 = 1
#Then we start initial dafault values fo... | {"hexsha": "3d843ae0b7e7166c7e7df2989ed4ca981efbf92f", "size": 2316, "ext": "py", "lang": "Python", "max_stars_repo_path": "slider_force.py", "max_stars_repo_name": "drjenncash/MechanicsInteractives", "max_stars_repo_head_hexsha": "6d2756de6d24beaa6c93396a4a975c055b103121", "max_stars_repo_licenses": ["MIT"], "max_star... |
#include <ros/package.h>
#include <pcl/io/pcd_io.h>
#include <boost/filesystem.hpp>
#include <camera_calibration_parsers/parse_ini.h>
#include <Eigen/Core>
#include <pcl/common/common.h>
#include <pcl/common/transforms.h>
#include <tf_conversions/tf_eigen.h>
#include <boost/algorithm/string/split.hpp>
#include <string>... | {"hexsha": "f84ad8a6ec46ae028495cc14482b85f8ce24b73f", "size": 9660, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "apc_object_perception/apc_capture/src/tote_features_exe.cpp", "max_stars_repo_name": "warehouse-picking-automation-challenges/nimbro_picking", "max_stars_repo_head_hexsha": "857eee602beea9eebee45bbb... |
[STATEMENT]
lemma prime_gauss_int_norm_squareD:
fixes z :: gauss_int
assumes "prime z" "gauss_int_norm z = p ^ 2"
shows "prime p \<and> z = of_nat p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. prime p \<and> z = of_nat p
[PROOF STEP]
using assms(1)
[PROOF STATE]
proof (prove)
using this:
prime z
goal (1... | {"llama_tokens": 2944, "file": "Gaussian_Integers_Gaussian_Integers", "length": 24} |
Require Import bf_stack bf bf_semantics.
Require Import Lists.Streams.
Inductive ae : Set :=
| Int : nat -> ae
| Plus : ae -> ae -> ae
| Minus : ae -> ae -> ae
| Mult : ae -> ae -> ae.
Coercion Int : nat >-> ae.
Notation "a + b" := (Plus a b) : ae_scope.
Notation "a - b" := (Minus a b) : ae_scope.
Notation "a * b" :=... | {"author": "reynir", "repo": "Brainfuck", "sha": "13c1ea7bf376b36f94a542deaadbb7f5d2e2f0db", "save_path": "github-repos/coq/reynir-Brainfuck", "path": "github-repos/coq/reynir-Brainfuck/Brainfuck-13c1ea7bf376b36f94a542deaadbb7f5d2e2f0db/ae_compiler.v"} |
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import scipy.optimize
import multiprocessing as mp
import copy
import pickle
import gasdynamics as gd
from heat_flux import heat_flux
from plug_nozzle_angelino import plug_nozzle
import MOC
import MOC_its
## NASA CEA CONSTAN... | {"hexsha": "ed27c67dec7a2a63d6ae2070743c91b9a3444102", "size": 12128, "ext": "py", "lang": "Python", "max_stars_repo_path": "animations_py/animation_1_trunc.py", "max_stars_repo_name": "coursekevin/AerospikeDesign", "max_stars_repo_head_hexsha": "2385e53fa6af51fb09b8f1280cbb052e7a5c7aea", "max_stars_repo_licenses": ["M... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Inverse kinematics with the Kuka robot where the goal is to follow a moving sphere.
The inverse kinematics is performed using priority tasks and constraints, which are optimized using Quadratic
Programming (QP).
"""
import numpy as np
import time
import pyrobolearn as ... | {"hexsha": "e516b7ad70fdaf88cf20bd20932775fc3087516d", "size": 2566, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyrobolearn/priorities/ik.py", "max_stars_repo_name": "Pandinosaurus/pyrobolearn", "max_stars_repo_head_hexsha": "9cd7c060723fda7d2779fa255ac998c2c82b8436", "max_stars_repo_licenses": ["Apache-2.0... |
import itertools
import numpy as np
from scipy import stats
from pycircstat import var
def convolve_dirac_gauss(t, trial, sigma=1.):
"""
Convolves event series represented as time points of Dirac deltas with
the pdf of a Gaussian
:param t: time points at which the convolution will be computed
:pa... | {"hexsha": "7d0488e93ffbbf6cea44317ca380d7c4b2e04e71", "size": 2662, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycircstat/event_series.py", "max_stars_repo_name": "hoechenberger/pycircstat", "max_stars_repo_head_hexsha": "53d2efdf54c394fd9ecff8d47eaae165c1458fb0", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma head_\<omega>_of_nat[simp]: "head_\<omega> (of_nat n) = (if n = 0 then 0 else 1)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. head_\<omega> (of_nat n) = (if n = 0 then 0 else 1)
[PROOF STEP]
unfolding head_\<omega>_def one_hmultiset_def of_nat_hmset
[PROOF STATE]
proof (prove)
goal (1 subgoal):
... | {"llama_tokens": 230, "file": "Nested_Multisets_Ordinals_Syntactic_Ordinal", "length": 2} |
import sys
from itertools import count
import cv2
import gym_super_mario_bros
from gym.wrappers import Monitor
from gym_super_mario_bros.actions import SIMPLE_MOVEMENT, COMPLEX_MOVEMENT, RIGHT_ONLY
from nes_py.wrappers import JoypadSpace
import matplotlib.pyplot as plt
import numpy as np
import torch
from Policy_Gra... | {"hexsha": "24b470541ada044ebb3828958e1ec443ac17ecef", "size": 5143, "ext": "py", "lang": "Python", "max_stars_repo_path": "Policy_Gradient/policy_main.py", "max_stars_repo_name": "KailashDN/Deep_Reinforcement_Learning_Gym", "max_stars_repo_head_hexsha": "1ecbb54baad93e41c16af7505e2d407a40326da7", "max_stars_repo_licen... |
# File: base.py
# File Created: Thursday, 14th March 2019 4:08:16 pm
# Author: Steven Atkinson (212726320@ge.com)
import abc
from time import time
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import torch
from torch.utils.data import Ten... | {"hexsha": "db6d95044b3fee4acd91b21d1af38862f5f8262e", "size": 4449, "ext": "py", "lang": "Python", "max_stars_repo_path": "pirate/models/base.py", "max_stars_repo_name": "212726320/PIRATE-1", "max_stars_repo_head_hexsha": "eac2d090286e0a5c13be4829259ea12cbda2f75c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import rospy
from representation import Env
import tf
import time
from simulation.srv import EnvGen, EnvGenResponse
from simulation.srv import GoalInfo, GoalInfoResponse
from simulation.srv import StairInfo, StairInfoResponse
import render
import numpy as np
class EnvGene... | {"hexsha": "2d143d269595f02afd6a139939f24be003493292", "size": 6484, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulation/scripts/env_generation/env_gen_services.py", "max_stars_repo_name": "gwaxG/robot_ws", "max_stars_repo_head_hexsha": "ec8c85cc3b5e7262bba881bf96f4213f5403b8b5", "max_stars_repo_licenses"... |
# Copyright 2020 Paul Melis (paul.melis@surf.nl)
#
# 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 a... | {"hexsha": "bad3863fb792b4e004b31e5edb80e99be229ed8c", "size": 7167, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "catmull-clark.jl", "max_stars_repo_name": "paulmelis/blender-julia-test", "max_stars_repo_head_hexsha": "1abd241182eb82f79a196b4f158daab9a8905af0", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
using Gadfly
set_default_plot_size(6inch, 6inch)
z = rand(1:8,100)
plot(x=rand(100), y=rand(100), shape=z, Geom.point,
Scale.shape_discrete(levels=sort(unique(z))))
| {"hexsha": "3eeceacb77ade483751ef7f83123f436a0a204ce", "size": 171, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testscripts/point_shape_numerical.jl", "max_stars_repo_name": "MrVPlusOne/Gadfly.jl", "max_stars_repo_head_hexsha": "a565b732ab8cd90b9f4fe83cd7d146856bb136da", "max_stars_repo_licenses": ["MIT"... |
[STATEMENT]
lemma span_induct' [consumes 1, case_names base step]:
assumes "p \<in> span B" and "P 0"
and "\<And>a q p. a \<in> span B \<Longrightarrow> P a \<Longrightarrow> p \<in> B \<Longrightarrow> q \<noteq> 0 \<Longrightarrow> P (a + q *s p)"
shows "P p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ... | {"llama_tokens": 2597, "file": "Polynomials_More_Modules", "length": 33} |
[STATEMENT]
lemma nodes_\<alpha>g_aux: "invar g \<Longrightarrow> nodes (\<alpha>g g) = \<alpha>nodes_aux g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. invar g \<Longrightarrow> nodes (\<alpha>g g) = \<alpha>nodes_aux g
[PROOF STEP]
unfolding \<alpha>g_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. invar ... | {"llama_tokens": 297, "file": "Prim_Dijkstra_Simple_Undirected_Graph_Impl", "length": 3} |
'''
Classes
-------
LearnAlg
Defines some generic routines for
* saving global parameters
* assessing convergence
* printing progress updates to stdout
* recording run-time
'''
import numpy as np
import time
import logging
import os
import sys
import scipy.io
import learnalg.ElapsedT... | {"hexsha": "4f90d0f142711257c9e17c18e1d3813e5b2ef992", "size": 19925, "ext": "py", "lang": "Python", "max_stars_repo_path": "bnpy/learnalg/LearnAlg.py", "max_stars_repo_name": "zhaottcrystal/bnpy", "max_stars_repo_head_hexsha": "0195a0228e9e698799e52a6dfa1d051e82b43fd0", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
"""
Stores all of the terms used inside the VPT2 representations
"""
import itertools
import numpy as np, functools as fp, itertools as ip, time, enum
from McUtils.Numputils import SparseArray, levi_cevita3, vec_tensordot, vec_outer
from McUtils.Data import UnitsData
from McUtils.Scaffolding import Logger, NullLogger... | {"hexsha": "13700928886a30b7890d8a1e854c5be7a7ef025c", "size": 119889, "ext": "py", "lang": "Python", "max_stars_repo_path": "Psience/VPT2/Terms.py", "max_stars_repo_name": "McCoyGroup/Coordinerds", "max_stars_repo_head_hexsha": "058a4f5b29f157e499cec3c8f2da8b216f0210ef", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
module linear_solve
implicit none
public :: lu_decomposition
public :: lu_back_substitution
public :: lu_inverse
public :: imaxloc
interface lu_decomposition
module procedure lu_decomposition_real
module procedure lu_decomposition_complex
end interface
interface lu_back_substitution
m... | {"hexsha": "48ab212fc4aa3c65e26dac3a024cde2465f3d615", "size": 6429, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/utils/linear_solve.f90", "max_stars_repo_name": "SStroteich/stella-1", "max_stars_repo_head_hexsha": "104556a07b9736e7c28e6f1bf2f799384732f38b", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma subst_typ'_rename_tvar_bind_fv:
assumes "y \<notin> fst ` fv t"
assumes "(b, S) \<notin> tvs t"
assumes "(b, S) \<notin> tvsT \<tau>"
shows "bind_fv (y, subst_typ [((a,S), Tv b S)] \<tau>)
(subst_typ' [((a,S), Tv b S)] (subst_term [((x, \<tau>), Fv y \<tau>)] t))
= subst_typ' [((a,S), ... | {"llama_tokens": 678, "file": "Metalogic_ProofChecker_Logic", "length": 2} |
#! /usr/bin/env python
import numpy as np
from scipy.sparse.linalg import LinearOperator
from dimredu.lib.randomized_svd import randomized_svd
def sparseSVDUpdate(X, U, E, VT):
"""Compute a fast SVD decomposition.
The is computes the SVD update of a matrix formed from the sum
of a sparse matrix :math:`X... | {"hexsha": "e7190db0e21077e46db5a405e2486fe3c4cbeca7", "size": 2950, "ext": "py", "lang": "Python", "max_stars_repo_path": "dimredu/lib/sparseSVDUpdate.py", "max_stars_repo_name": "Marissa4/RPyCA", "max_stars_repo_head_hexsha": "e3c229361a4cd9ddd53accc5541b7c8b5f8939e0", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from numpy import sqrt
import scipy.constants as cs
import datproc.print as dpr
## General
output = __name__ == '__main__'
## Data
lda_mfr = 532.0 * cs.nano
d_lda_mfr = 1.0 * cs.nano
s1 = np.array([0.0000, 0.3010, 0.6000, 0.9010, 1.2010]) * cs.milli
d_s1 = np.array([0.0010, 0.0010, 0.0010, 0.0010... | {"hexsha": "1086a515130ba835eb0d882cec1928050939dd90", "size": 1171, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/interferometer/wavelength.py", "max_stars_repo_name": "jackerschott/AP21", "max_stars_repo_head_hexsha": "8f49b3c4901e50770b1df05fa6470d45e469258a", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma idempotent_dual:
"idempotent x \<longleftrightarrow> idempotent (x\<^sup>d)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. idempotent x = idempotent (x\<^sup>d)
[PROOF STEP]
using dual_involutive idempotent_transitive_dense transitive_iff_dense_dual
[PROOF STATE]
proof (prove)
using this:
?x\<^s... | {"llama_tokens": 223, "file": "Correctness_Algebras_Lattice_Ordered_Semirings", "length": 2} |
#include "hg_intersect.h"
#include <vector>
#ifndef HAVE_OLD_CPP
# include <unordered_map>
#else
# include <tr1/unordered_map>
namespace std { using std::tr1::unordered_map; }
#endif
#include "fast_lexical_cast.hpp"
#include <boost/functional/hash.hpp>
#include "verbose.h"
#include "tdict.h"
#include "hg.h"
#include ... | {"hexsha": "02f5a401f0f456ad3964cea333c4c466b1a9346f", "size": 5183, "ext": "cc", "lang": "C++", "max_stars_repo_path": "decoder/hg_intersect.cc", "max_stars_repo_name": "kho/cdec", "max_stars_repo_head_hexsha": "d88186af251ecae60974b20395ce75807bfdda35", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL", "Apache-2.0"], ... |
"""
Tensor Transforms
~~~~~~~~~~~~~~~~~
"""
from skimage import transform as _sktransform
import numpy as np
import torch as _torch
class Resize:
""" Resize transform """
def __init__(self, output_size: int):
assert isinstance(output_size, (int, tuple))
self.output_size = output_size
def ... | {"hexsha": "cfca4b232c2234b2ae3cf6ad3e9fa4b1ac3aef7c", "size": 1342, "ext": "py", "lang": "Python", "max_stars_repo_path": "transforms.py", "max_stars_repo_name": "vidakDK/U-2-Net", "max_stars_repo_head_hexsha": "bd1f2d11b45ab5514418c25c0a763faf370667e0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nu... |
using Lava: IFeature
mutable struct TestFeature <: IFeature
mOnInstanceCreatedCalled
mBeforeInstanceDestruction
mOnLogicalDeviceCreated
mOnPhysicalDeviceSelected
mBeforeDeviceDestructionCalled
function TestFeature()
this = new()
this.mOnInstanceCreatedCalled = false
thi... | {"hexsha": "4521f14565573987e5c4f79177ab124223e23ff0", "size": 1472, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/TestFeature.jl", "max_stars_repo_name": "gcmiao/Lava.jl", "max_stars_repo_head_hexsha": "fb9656944c63a9612927867cef9b0d0b33fe5f24", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
import numpy as np
from os import path
import pytest
from autoconf import conf
import autofit as af
import autolens as al
from autolens import exc
directory = path.dirname(path.realpath(__file__))
class TestAnalysisAbstract:
pass
class TestAnalysisDataset:
def test__use_border__determin... | {"hexsha": "9f8051d238a0d46f04978b5bb205106ab26a5aef", "size": 3912, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_autolens/lens/model/test_analysis.py", "max_stars_repo_name": "Jammy2211/PyAutoLens", "max_stars_repo_head_hexsha": "728100a3bf13f89f35030724aa08593ab44e65eb", "max_stars_repo_licenses": ["MI... |
import os, sys
sys.path.append(os.getcwd())
import time
import tflib as lib
import tflib.save_images
import tflib.mnist
import tflib.cifar10
import tflib.plot
import tflib.inception_score
import os
import numpy as np
import torch
import torchvision
from torch import nn
from torch import autograd
from torch import o... | {"hexsha": "f79b90a01e11bdbd12695d80862f24f45a0c970e", "size": 10960, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch/examples/wgan-gp/gandag_cifar10.py", "max_stars_repo_name": "sutd-visual-computing-group/dag-gans", "max_stars_repo_head_hexsha": "68a76153650df6de2a6919a93a2d3b98ca6407e6", "max_stars_re... |
[STATEMENT]
lemma subst_lconsts_empty_subst[simp]: "subst_lconsts empty_subst = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. subst_lconsts empty_subst = {}
[PROOF STEP]
by (metis empty_subst_spec) | {"llama_tokens": 83, "file": "Incredible_Proof_Machine_Abstract_Formula", "length": 1} |
class IntPair:
"""
A pair of unordered hashable integers. Use this class for dictionary keys.
```python
import numpy as np
from tdw.int_pair import IntPair
id_0 = 0
pos_0 = np.array([0, 1, 0])
id_1 = 1
pos_1 = np.array([-2, 2.5, 0.8])
# Start a dictionary of distances between o... | {"hexsha": "c74ef696ac92427dc016f7dfb3c6c764293472c1", "size": 1209, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/tdw/int_pair.py", "max_stars_repo_name": "felixbinder/tdw", "max_stars_repo_head_hexsha": "eb2b00b74b9fcf8ef2dcba1baa62424640c520b1", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars... |
import sys
sys.path.insert(0,'../../../deeplab-public-ver2/python')
import caffe
import leveldb
import numpy as np
from caffe.proto import caffe_pb2
import csv
import cv2
# Wei Yang 2015-08-19
# Source
# Read LevelDB/LMDB
# ==================
# http://research.beenfrog.com/code/2015/03/28/read-leveldb-lmdb-f... | {"hexsha": "f71076ba0db1bec326551e5faa965b228c3a02be", "size": 1296, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/resnet_model/read_lmdb.py", "max_stars_repo_name": "Granular-data/cloudless", "max_stars_repo_head_hexsha": "e45d93b48b8e668a8a6cea6fab51d59f389591a8", "max_stars_repo_licenses": ["Apache-2.0"... |
"""
The purpose of this file is to create a usable class out of the lane detection
scripts that Juan Carlos wrote, and integrate it with the DRIVR system.
I) IMAGE READING
II) LSD ALGORITHM
III) SCANNING OF THE IMAGE AND DETECTION OF ROAD MARK SIGNATURE
IV) ELIMINATION OF WHITE ROAD MARK DUPLICATES
V) MERGING ALL WHIT... | {"hexsha": "6c747a3599549bd9a13ac1d5615073717a269148", "size": 13086, "ext": "py", "lang": "Python", "max_stars_repo_path": "lane_detector.py", "max_stars_repo_name": "syeda27/MonoRARP", "max_stars_repo_head_hexsha": "71415d9fc71bc636ac1f5de1a90f033b4e519538", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "... |
import pytorch_lightning as pl
import torch
import tqdm
import json
import os
import re
import numpy as np
import pickle
from typing import Sequence, Union
from torchvision import transforms
import glob
from PIL import Image
class PixivDataset(torch.utils.data.Dataset):
def __init__(self, dataset_path: str, size:... | {"hexsha": "e2fe9e2eccc66da51acd58072e0a3f481be6f5b7", "size": 1928, "ext": "py", "lang": "Python", "max_stars_repo_path": "lemon/datasets/pixiv_dataset.py", "max_stars_repo_name": "lemon-chat/LemonAvatar", "max_stars_repo_head_hexsha": "d8c5bc10c04af49bd12217f5de11a6f9ceaeb55f", "max_stars_repo_licenses": ["MIT"], "ma... |
using JLD2, Attitude, MATLAB, SatelliteDynamics, Dierckx
# include ksfunctions.jl
include(joinpath(dirname(dirname(@__FILE__)),"ks_functions.jl"))
@load "rate_control_transfers/112_day_transfer.jld2" X U
dscale = 1e7 # m
tscale = 20000 # s
uscale = 10000.0
dt = 4e-2
t_days,t_hist = get_time_transfer(X,dt,tscale)
r... | {"hexsha": "5b2d7f3dc93e3aafc05e975d81ae48051a2e1952", "size": 7224, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "plotting_scripts/100_day_plot.jl", "max_stars_repo_name": "RoboticExplorationLab/KSLowThrust", "max_stars_repo_head_hexsha": "ba14ab1c9990e50caf41382a3b313e284111cffb", "max_stars_repo_licenses": [... |
from datetime import timedelta
from time import time
import warnings
from gdbn.dbn import buildDBN
from gdbn import activationFunctions
import numpy as np
from sklearn.base import BaseEstimator
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
warnings.warn("""\
The nolear... | {"hexsha": "0eee38462a6960daf6396b6176669d3f717049e9", "size": 16726, "ext": "py", "lang": "Python", "max_stars_repo_path": "nolearn/dbn.py", "max_stars_repo_name": "KEVINYZY/nolearn", "max_stars_repo_head_hexsha": "342915012081f31bb88f69daa8857fd2f4e15a1d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 968, "... |
import numpy as np
import heapq
class Node:
def __init__(self, data, dim, left=None, right=None):
self.data = data
self.dim = dim
self.left = left
self.right = right
def __str__(self):
return str(self.data)
__repr__ = __str__
class KNN:
def __init__(self, da... | {"hexsha": "528c4491d8b025bc7c91299ec883e172133b6922", "size": 2116, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine_learning/algorithms/knn.py", "max_stars_repo_name": "z-yin/Leetcode-learning", "max_stars_repo_head_hexsha": "e84c2fb067b767ed5f24d8736274c7ebce5dc00e", "max_stars_repo_licenses": ["MIT"],... |
using FEASTSolver
using LinearAlgebra
using DelimitedFiles
using SparseArrays
N = 100
# A = spdiagm(-1 => fill(-1.0, 99), 0 => fill(2.0, 100), 1 => fill(-1.0, 99))
A = diagm(-1 => fill(-1.0, N-1), 0 => fill(1.0, N), 1 => fill(1.0, N-1), 2 => fill(1.0, N-2), 3 => fill(1.0, N-3))
# B = diagm(-1 => fill(-1.0, N-1), 0 => ... | {"hexsha": "0e3ba354a30d63fc69ca1d9927fdab806124e8e6", "size": 1296, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/non_hermitian.jl", "max_stars_repo_name": "rleegates/FEASTSolver.jl", "max_stars_repo_head_hexsha": "1f226df0ea72ae3cb2eca782439ae672e8488a85", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
# coding=utf-8
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision
from torchvision import datasets, models, transforms
from torch.autograd import Variable
import numpy as np
import time
import os
im... | {"hexsha": "c8a20fa57f0741fde54a0dcc8aefb1c47aeb90ba", "size": 3838, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils_incremental/compute_confusion_matrix.py", "max_stars_repo_name": "chrysts/geodesic_continual_learning", "max_stars_repo_head_hexsha": "8a5ba6310541f2643660ebc8b66c304ffd392fd9", "max_stars_r... |
# IMPORT LIBRARIES
import warnings
warnings.filterwarnings("ignore")
import datetime as dt
import pandas as pd
import numpy as np
pd.options.mode.chained_assignment = None
pd.set_option('chained_assignment', None)
import plotly.express as px
import plotly.graph_objects as go
import dash_auth, dash
from dash import d... | {"hexsha": "b6e0ce3a506cdca5b25e347da4c51ecee01aa8b6", "size": 17275, "ext": "py", "lang": "Python", "max_stars_repo_path": "Dashboard/app.py", "max_stars_repo_name": "briangodwinlim/Data-Science", "max_stars_repo_head_hexsha": "7e8992eb6121c4a2f3e2fefb43a4d2a2a3dbee60", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
data Vect : Nat -> Type -> Type where
Nil : Vect Z a
(::) : a -> Vect k a -> Vect (S k) a
Show a => Show (Vect n a) where
show xs = "[" ++ showV xs ++ "]"
where
showV : forall n . Vect n a -> String
showV [] = ""
showV [x] = show x
showV (x :: xs) = show x ++ ", " ++ showV xs
f... | {"hexsha": "0d116fca82b1deb9392ce33e36df7ffba6db5eff", "size": 739, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "tests/chez/chez005/Filter.idr", "max_stars_repo_name": "boxfire/rapid", "max_stars_repo_head_hexsha": "91f012caad854dc454cef0375b49bcf9e1af20c7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
[STATEMENT]
lemma semilat_le_err_plus_Err [simp]:
"\<lbrakk> x \<in> err A; semilat(err A, le r, f) \<rbrakk> \<Longrightarrow> x +_f Err = Err"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>x \<in> err A; semilat (err A, le r, f)\<rbrakk> \<Longrightarrow> x \<squnion>\<^bsub>f\<^esub> Err = Err
[PROOF ... | {"llama_tokens": 205, "file": null, "length": 1} |
#=
This file is part of the replication code for: Hasenzagl, T., Pellegrino, F., Reichlin, L., & Ricco, G. (2020). A Model of the Fed's View on Inflation.
Please cite the paper if you are using any part of the code for academic work (including, but not limited to, conference and peer-reviewed papers).
=#
function ex_b... | {"hexsha": "7f0d49b8ab3a89009cfab18f0373c1e6f4da5816", "size": 1025, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "code/Metropolis-Within-Gibbs/subroutines/extra/ex_blkdiag.jl", "max_stars_repo_name": "thasenzagl/replication-hasenzagl-et-al-2020", "max_stars_repo_head_hexsha": "86a873a77cc63f1f14309254997421b99... |
\chapter{Introduction}%
\label{chap:intro}
\section{Motivation}%
\label{sec:intro:motivation}
Computer technology is one of the most influential inventions in human history and a central driving force behind innovation in the \nth{20} and early \nth{21} centuries. It was indispensable for several scientific and techn... | {"hexsha": "9f2a5cd40e2d9955a2f1bebad27e8078978b2e80", "size": 32423, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapter1-introduction.tex", "max_stars_repo_name": "abusse/phd-thesis", "max_stars_repo_head_hexsha": "b693b1d001925311151fc53fdcd687ace0b20cae", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_star... |
\chapter{Introduction}
\label{Introduction}
Differential equations, ordinary or partial, allow modeling phenomena that evolve with respect to space and time. They are commonly used to describe the propagation of sound or heat and appear frequently in models related to electrostatics, electrodynamics, fluid dynamics, ... | {"hexsha": "39fd63679e32e9ca2493e79e869e2cce462fbe5c", "size": 6533, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/introduction/Introduction.tex", "max_stars_repo_name": "alanmatzumiya/Maestria", "max_stars_repo_head_hexsha": "c5e2a019312fb8f9bc193b04b07b7815e6ed4032", "max_stars_repo_licenses": ["MIT"], "m... |
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