text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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###################################
# CALIBRATION DETECTION AND CORRECTION #
###################################
# This file includes functionality for identification and correction of calibration events.
# Functions include detection based on edges or persistence restricted by day of week and hour of day, identificati... | {"hexsha": "3207021d72dcf99c31dd48e99a6d4491385bb59f", "size": 11257, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyhydroqc/calibration.py", "max_stars_repo_name": "AmberSJones/PyHydroQC", "max_stars_repo_head_hexsha": "9f8992672ce3163eb048964e85680c526b4fd3f3", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
(*
Author: Norbert Schirmer
Maintainer: Norbert Schirmer, norbert.schirmer at web de
License: LGPL
*)
(* Title: Quicksort.thy
Author: Norbert Schirmer, TU Muenchen
Copyright (C) 2004-2008 Norbert Schirmer
Some rights reserved, TU Muenchen
This library is free software; you can red... | {"author": "seL4", "repo": "l4v", "sha": "9ba34e269008732d4f89fb7a7e32337ffdd09ff9", "save_path": "github-repos/isabelle/seL4-l4v", "path": "github-repos/isabelle/seL4-l4v/l4v-9ba34e269008732d4f89fb7a7e32337ffdd09ff9/tools/c-parser/Simpl/ex/Quicksort.thy"} |
function catalogobj = associate(obj, maxTimeDiff, sites, source)
%ASSOCIATE Associate detections into events
% catalogobj = associate(detectionObj, maxTimeDiff) will scan through an
% Detection object and look
% for times where there are at least 2 detections on
% within maxTimeDiff seconds of each other, and declare ... | {"author": "geoscience-community-codes", "repo": "GISMO", "sha": "a4eafca9d2ac85079253510005ef00aa9998d030", "save_path": "github-repos/MATLAB/geoscience-community-codes-GISMO", "path": "github-repos/MATLAB/geoscience-community-codes-GISMO/GISMO-a4eafca9d2ac85079253510005ef00aa9998d030/core/@Detection/associate.m"} |
import numpy as np
import pytest
from latte.functional.disentanglement.modularity import modularity
from latte.functional.disentanglement.mutual_info import single_mutual_info
class TestModularity:
def test_single_attr(self):
z = np.random.randn(16, 2)
a = np.random.randn(16, 1)
with pyte... | {"hexsha": "367df40bffbf30af9d6d3bf6be3f98e467af41cf", "size": 2239, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/functional/disentanglement/test_modularity.py", "max_stars_repo_name": "SoftwareImpacts/SIMPAC-2021-192", "max_stars_repo_head_hexsha": "92c6eb8bb8b1f45b0b86d222b87b2f1e4e949d04", "max_stars... |
# -*- coding: utf-8 -*-
# @Time : 6/25/2018 4:23 PM
# @Author : sunyonghai
# @File : generator.py
# @Software: ZJ_AI
import itertools
import json
import random
import threading
from data_processing.fusion import fusion_utils
import numpy as np
import os
data_path = '/home/syh/tf-faster-rcnn/data/fusion/mask'
b... | {"hexsha": "cca89ab9eb4af733daaf166385e1fef2b6fa6fb1", "size": 6394, "ext": "py", "lang": "Python", "max_stars_repo_path": "development/server/algorithm/tf_faster_rcnn/data_processing/fusion/generator.py", "max_stars_repo_name": "FMsunyh/re_com", "max_stars_repo_head_hexsha": "1510881bce07a2750c47834b6520d90f2f4ed254",... |
from collections import defaultdict
import numpy as np
import tree # pip install dm_tree
from typing import Dict
from ray.rllib.utils.annotations import DeveloperAPI
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.utils.typing import PolicyID
# Instant metrics (keys for metrics.info).
LEAR... | {"hexsha": "6f96d6956ef640e425e0878198e615b6a5244d4a", "size": 3977, "ext": "py", "lang": "Python", "max_stars_repo_path": "rllib/utils/metrics/learner_info.py", "max_stars_repo_name": "willfrey/ray", "max_stars_repo_head_hexsha": "288a81b42ef0186ab4db33b30191614a7bdb69f6", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
\documentclass{warpdoc}
\newlength\lengthfigure % declare a figure width unit
\setlength\lengthfigure{0.158\textwidth} % make the figure width unit scale with the textwidth
\usepackage{psfrag} % use it to substitute a string in a eps figure
\usepackage{subfigure}
\usepackage{rotating}
\usepacka... | {"hexsha": "4adea5b01f775ea68a3dd13374d45a71bf38d9ac", "size": 27873, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "model/thermo/doc/report.tex", "max_stars_repo_name": "TRPrasanna/CFDWARP", "max_stars_repo_head_hexsha": "505ffeea6c518e462322e2146ffb112d539075d5", "max_stars_repo_licenses": ["BSD-2-Clause"], "ma... |
from keras.layers import UpSampling2D
import numpy as np
import tensorflow as tf
x=np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16])
x=x.reshape(1,4,4,1)
print(x)
x=tf.convert_to_tensor(x)
y=UpSampling2D(size=(2,2))(x)
with tf.Session() as sess:
print(y.eval())
| {"hexsha": "b67838ed4b6df1ddeab365818e79a1fc977b9e3a", "size": 268, "ext": "py", "lang": "Python", "max_stars_repo_path": "practice.py", "max_stars_repo_name": "jiagnhaiyang/YOLO3-", "max_stars_repo_head_hexsha": "b0f86b214b436bb5cc4fcb40557d78a950c8fdb4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
[STATEMENT]
theorem sturm_below:
assumes "poly p b \<noteq> 0"
shows "card {x. poly p x = 0 \<and> x < b} = changes_le_smods b p (pderiv p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. int (card {x. poly p x = 0 \<and> x < b}) = changes_le_smods b p (pderiv p)
[PROOF STEP]
using sturm_tarski_below[OF assms, u... | {"llama_tokens": 275, "file": "Sturm_Tarski_Sturm_Tarski", "length": 2} |
import math
import os
import numpy as np
from tqdm import tqdm
def Spatial_basis_POD(D, PSI_P, Sigma_P, MEMORY_SAVING,
N_T, FOLDER_OUT='./', N_PARTITIONS=1,
SAVE_SPATIAL_POD=False):
"""
Given the temporal basis now the POD spatial ones are computed
-----------... | {"hexsha": "317752eeb1466de2311b95a93354ec494f8aa98d", "size": 6039, "ext": "py", "lang": "Python", "max_stars_repo_path": "modulo/_pod_space.py", "max_stars_repo_name": "lorenzoschena/modulo_vki_testing", "max_stars_repo_head_hexsha": "dc5dfcf8ecadd2a6410744ae0ca5368a9b5110eb", "max_stars_repo_licenses": ["BSD-3-Claus... |
import os
import torch
import numpy as np
import torch.nn.functional as F
from tqdm import tqdm
import torch.utils.data as data
import PIL.Image as Image
from torchvision import transforms
## This random crop is important and make the training data easy to learn
def resizeImg(I, minSize = 256) :
w, h = I.size
... | {"hexsha": "e149b1b5f74024cf58d6fb7003bf5660a5eff2f5", "size": 3008, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/dataloader.py", "max_stars_repo_name": "Pandinosaurus/RANSAC-Flow", "max_stars_repo_head_hexsha": "fd333a1465ec0c537f39bb6f0065bfde4d58a1f5", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
/* Copyright 2008 (C) Nicira, Inc.
*
* This file is part of NOX.
*
* NOX is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* N... | {"hexsha": "8de9623de6505e5217cf0827ac4a94a375087e6a", "size": 8105, "ext": "cc", "lang": "C++", "max_stars_repo_path": "nox/src/nox/netapps/storage/storage.cc", "max_stars_repo_name": "ayjazz/OESS", "max_stars_repo_head_hexsha": "deadc504d287febc7cbd7251ddb102bb5c8b1f04", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
[STATEMENT]
lemma FG_consitutents_n0:
"of_nat (card G) \<noteq> (0::'f::field)
\<Longrightarrow> 0 \<notin> set (FG_constituents::('f,'g) aezfun set list)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. of_nat (card G) \<noteq> (0::'f) \<Longrightarrow> 0 \<notin> set FG_constituents
[PROOF STEP]
using som... | {"llama_tokens": 818, "file": "Rep_Fin_Groups_Rep_Fin_Groups", "length": 3} |
import numpy as np
from PIL import Image
IMAGE = '/Users/yiws/Desktop/Screen Shot 2018-09-20 at 12.00.46 PM.png'
im = Image.open(IMAGE)
rgba_im = im.convert('RGBA')
data = np.array(rgba_im)
for row in data:
for pixels in row:
if all(pixels == 255):
pixels[0] = 0
pixels[1] = 0
... | {"hexsha": "f85ea7ab8de75eb5d0f7c0e8a57a53f1f9e1385c", "size": 814, "ext": "py", "lang": "Python", "max_stars_repo_path": "img_split2.py", "max_stars_repo_name": "yiwensong/slack-image-splitter", "max_stars_repo_head_hexsha": "2fc9190013986d393372e936a4131883ae0cebb4", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# AUTOGENERATED! DO NOT EDIT! File to edit: notebooks/WIP_OCO2_Map.ipynb (unless otherwise specified).
__all__ = ['inventory_map_only', 'peaks_capture_map']
# Cell
import pandas as pd
import geopandas as gpd
import numpy as np
from numpy import exp, loadtxt, pi, sqrt, log
import math
# import matplotlib
# import matp... | {"hexsha": "431165dc53d6cc4bf53d1aaba3d6881b1f774742", "size": 7027, "ext": "py", "lang": "Python", "max_stars_repo_path": "oco2peak/oco2mapfolium.py", "max_stars_repo_name": "trancept/batch7_satellite_ges", "max_stars_repo_head_hexsha": "31126b398eba4312a245b97bfae35c55fbd5be37", "max_stars_repo_licenses": ["Apache-2.... |
using Plots, SNN
#
# N = 1000
# G = SNN.NoisyIF(N; τm=1, Vt=1, Vr=0, El=0, σ=0.8)
# fill!(G.I, 1.01)
# G.v = -1.5 + rand(N)
# SNN.monitor(G, [:fire, :v])
#
# SNN.sim!([G], []; duration=3)
# # SNN.raster([G])
# # SNN.activity([G])
# SNN.density(G, :v)
#
#
# using Plots, SNN
#
#
N = 1000
G = SNN.NoisyIF(N; τm=1, Vt=1, ... | {"hexsha": "bd4992bd102c481c6e411660b88a69c8a4455d70", "size": 840, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/deprecated/fp_eq.jl", "max_stars_repo_name": "Wenlab/SpikingNeuralNetworks.jl", "max_stars_repo_head_hexsha": "96e8771783db86edc1a6a88f80535c09285a82c6", "max_stars_repo_licenses": ["MIT"],... |
"""
Test class for 'sslh/graphGenerator'
Author: Wolfgang Gatterbauer
"""
import numpy as np
import sys
sys.path.append('./../sslh')
from graphGenerator import (create_distribution_vector,
local_randint,
calculate_nVec_from_Xd,
calcula... | {"hexsha": "86ab026f274236a175f80c630289ca8d647cde23", "size": 23517, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_sslh/test_graphGenerator.py", "max_stars_repo_name": "northeastern-datalab/factorized-graphs", "max_stars_repo_head_hexsha": "167b0d172c3461f9a75861872ed758c51f4a9aa9", "max_stars_repo_licen... |
import dataclasses
from abc import ABC, abstractmethod
from typing import Any, Iterable, List, Optional, Tuple, Union
import nltk
import numpy as np
class BaseInputExample(ABC):
"""Parser input for a single sentence (abstract interface)."""
# Subclasses must define the following attributes or properties.
... | {"hexsha": "5343e841d3afea916acc1452e9571faa8570f32a", "size": 8054, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/benepar/parse_base.py", "max_stars_repo_name": "speedcell4/self-attentive-parser", "max_stars_repo_head_hexsha": "644a27d07316d1441a62425c85f78128b8dee4fe", "max_stars_repo_licenses": ["MIT"],... |
/-
Copyright (c) 2019 Scott Morrison. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Scott Morrison
-/
import category_theory.limits.concrete_category
import group_theory.quotient_group
import category_theory.limits.shapes.kernels
import algebra.category.Module.basic
... | {"author": "Mel-TunaRoll", "repo": "Lean-Mordell-Weil-Mel-Branch", "sha": "4db36f86423976aacd2c2968c4e45787fcd86b97", "save_path": "github-repos/lean/Mel-TunaRoll-Lean-Mordell-Weil-Mel-Branch", "path": "github-repos/lean/Mel-TunaRoll-Lean-Mordell-Weil-Mel-Branch/Lean-Mordell-Weil-Mel-Branch-4db36f86423976aacd2c2968c4e4... |
module Foo
using Preferences
set!(key, value) = @set_preferences!(key=>value)
get(key) = @load_preference(key)
end # module
| {"hexsha": "0cd72d4280a968b32a9bc87f0aba36d7bdbf9c35", "size": 127, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_packages/Sandbox_PreservePreferences/dev/Foo/src/Foo.jl", "max_stars_repo_name": "barucden/Pkg.jl", "max_stars_repo_head_hexsha": "1c84da1a29b35f2ab0b2715a45a3b8644461a45c", "max_stars_rep... |
from matplotlib import pyplot as plt
import matplotlib.patches as mpatches
import pandas as pd
import numpy as np
import os
colors = ['#60A917', 'cornflowerblue', 'orange', '#D62728']
line_styles = ["-", "--"]
def plot_daily(data):
fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(12, 12), )
for i, metric in... | {"hexsha": "0655f6220b63c2e7179ece8aad0b1171218cfe76", "size": 2728, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plotting/daily_plot.py", "max_stars_repo_name": "PlanTL-SANIDAD/covid-predictive-model", "max_stars_repo_head_hexsha": "40b74d638825433203079e55dcc6c5cebff785ce", "max_stars_repo_licenses": ["... |
/*
* Copyright (c) 2011, Mattia Penati <mattia.penati@gmail.com>
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification,
* are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright not... | {"hexsha": "4e25749d848adc597ad670d67ff8fa4e9cc6c494", "size": 4106, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/ama/tensor/iexp/iexp_cwise_binary.hpp", "max_stars_repo_name": "mattiapenati/amanita", "max_stars_repo_head_hexsha": "c5c16d1f17e71151ce1d8e6972ddff6cec3c7305", "max_stars_repo_licenses": ["... |
using Test
using NeuralAttentionlib
using Random
using Flux
using NNlib
using Static
using ChainRulesCore
using ChainRulesTestUtils
const tests = [
"collapseddim",
"matmul",
"mask",
"mha",
]
Random.seed!(0)
include("old_impl/old_impl.jl")
using .Old_Impl
using .Old_Impl: batched_triu!, batched_tril!... | {"hexsha": "bc34b44b006ecc5d6807c26e9a1c447bc1a39b44", "size": 498, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "foldfelis/NeuralAttentionlib.jl", "max_stars_repo_head_hexsha": "52cb258807c9b8d308e14db0f99ec0d3492607c9", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
class DownResBlock(layers.Layer):
def __init__(self, channels, kernel_size, initial_activation=None, normalization=None, downsample_rate=2, regularization=None):
super(DownResBlock, self).__init__()
self.out_channels = c... | {"hexsha": "f4ca65d5934c7f698a92f264ccd34434e076ee0f", "size": 9376, "ext": "py", "lang": "Python", "max_stars_repo_path": "sources/models/Blocks.py", "max_stars_repo_name": "cwi-dis/affect-gan", "max_stars_repo_head_hexsha": "aea0f7dd7dc412f7e3fc44bc2db3526b09aaf131", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
% As a sample LaTeX document, this is an actual assignment
% written in LaTeX with my template for MATH 417,
% Honors Real Variables (Measure Theory) at University of Alberta.
% This source has been released with permission with the instructor,
% Professor John C. Bowman as the solutions are available at
% https://ww... | {"hexsha": "de96321b76d09e842e3534646afd8805eb6b345c", "size": 4000, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sample/math417hw3/m417hw3.tex", "max_stars_repo_name": "supakorn-ras/latex-templates", "max_stars_repo_head_hexsha": "94ac5cbb3addc4135db8e33395d4e9ce21716cbc", "max_stars_repo_licenses": ["MIT"], "... |
from __future__ import with_statement
from collections import OrderedDict
import warnings
import numpy
from StringIO import StringIO
from sqlalchemy import (types as satypes, Column, Table, Index,
create_engine, MetaData)
import string, random
#from http://stackoverflow.com/questions/2257441/python-random-string-g... | {"hexsha": "b6bc13df7651027d227d2cee66572f261235e865", "size": 5439, "ext": "py", "lang": "Python", "max_stars_repo_path": "new_showMaf/lsst/sims/maf/db/dblib/utils.py", "max_stars_repo_name": "nanchenchen/lsst-new-showMAF", "max_stars_repo_head_hexsha": "6b30e7c06662ae1970837cba5bc46591acd6d7fe", "max_stars_repo_licen... |
# import of the required libraries
import numpy as np
import timeit
from pyGPGO.covfunc import squaredExponential
from pyGPGO.surrogates.GaussianProcess import GaussianProcess
from pyGPGO.surrogates.RandomForest import RandomForest
from pyGPGO.GPGO import GPGO
from pyGPGO.acquisition import Acquisition
from pyGPGO.co... | {"hexsha": "7941372f855aa71c2579b434b917618f320ed3eb", "size": 4620, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment.py", "max_stars_repo_name": "JeyDi/BayesianMLOptimization", "max_stars_repo_head_hexsha": "ba3ddf5bb9919a5043b4e982dea46425631696d3", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
! ##################################################################################################################################
! Begin MIT license text.
! ___________________________________________________________________________... | {"hexsha": "d2fc4a09a0a6ca19935c1bad6dfe3ad2078ec58d", "size": 8580, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/EMG/EMG6/BBMIN3.f90", "max_stars_repo_name": "JohnDN90/MYSTRAN", "max_stars_repo_head_hexsha": "1dd7dc19e54c1bb2b5235244af65115950e21488", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# This file was generated, do not modify it. # hide
r = range(model, :(linear_regressor.lambda), lower=1e-2, upper=100_000, scale=:log10)
tm = TunedModel(model=model, ranges=r, tuning=Grid(resolution=50),
resampling=CV(nfolds=3, rng=4141), measure=rms)
mtm = machine(tm, Xc, y)
fit!(mtm, rows=train)
be... | {"hexsha": "11b4156ccdc2b71c11780631092f01b6b2e7d284", "size": 411, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "__site/assets/isl/lab-6b/code/ex14.jl", "max_stars_repo_name": "giordano/DataScienceTutorials.jl", "max_stars_repo_head_hexsha": "8284298842e0d77061cf8ee767d0899fb7d051ff", "max_stars_repo_licenses"... |
# Copyright (c) 2016 Shunya Sato
# Author: Shunya Sato
#
# 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, mod... | {"hexsha": "d215e707550893c373fc0d9eedcf9a20bb05a2ac", "size": 14141, "ext": "pyw", "lang": "Python", "max_stars_repo_path": "serialLogger.pyw", "max_stars_repo_name": "aerialist/serial_logger", "max_stars_repo_head_hexsha": "feb371de25eb95bc7a924791c4573aa25ac51ab9", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright (c) 2018-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from itertools import zip_longest
import numpy as np
from common.arguments.basic_args import parse_args
from common.transforma... | {"hexsha": "f8f7e1e594713abf19af416b5db9bf9355231bb1", "size": 16402, "ext": "py", "lang": "Python", "max_stars_repo_path": "common/dataset/data_generators.py", "max_stars_repo_name": "ailingzengzzz/Split-and-Recombine-Net", "max_stars_repo_head_hexsha": "1b6285c9f5b46140832e7e4d24e8e5a7dbb24234", "max_stars_repo_licen... |
import os
import json
import random
import numpy as np
import pandas as pd
from string import punctuation
from nltk import word_tokenize
from joblib import dump, load
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.feature_extraction.text import TfidfVect... | {"hexsha": "927551cb67641fd9f432b08bdcfcd7e92555bc61", "size": 9185, "ext": "py", "lang": "Python", "max_stars_repo_path": "botmodel.py", "max_stars_repo_name": "kr-prince/VICCI-Python-Chatbot", "max_stars_repo_head_hexsha": "e9ed637a029c314cf30d72172629a9a681c24b39", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
/*-----------------------------------------------------------------------------+
Copyright (c) 2011-2011: Joachim Faulhaber
+------------------------------------------------------------------------------+
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENCE.txt or copy at
... | {"hexsha": "d18a56726d3135282c277e8743a964a16e0536c3", "size": 8051, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/external/boost/boost_1_68_0/libs/icl/test/fix_tickets_/fix_tickets.cpp", "max_stars_repo_name": "Bpowers4/turicreate", "max_stars_repo_head_hexsha": "73dad213cc1c4f74337b905baea2b3a1e5a0266c", "... |
#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | {"hexsha": "f47c8c496647422e0dacc7e3b357851ef615cd38", "size": 4947, "ext": "py", "lang": "Python", "max_stars_repo_path": "trt_util/trt_lite.py", "max_stars_repo_name": "yihui8776/TensorRT-DETR", "max_stars_repo_head_hexsha": "1f32e9a2f98e26ec5b2376f9a2695193887430fb", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
program test_free_energy_fft
! nuclear charge: 1 Gaussian
! electronic charge: 1 Gaussian
! calculation: single free energy evaluation
! This test uses FFT and produces the same result as test_free_energy3
use types, only: dp
use constants, only: i_
use ofdft, only: read_pseudo
use ofdft_fft, only: free_energy, radi... | {"hexsha": "2a94e9159a6cbc8fcfcfa7d507ad1f2712afbe13", "size": 2746, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/tests/fem/test_free_energy_fft.f90", "max_stars_repo_name": "certik/hfsolver", "max_stars_repo_head_hexsha": "b4c50c1979fb7e468b1852b144ba756f5a51788d", "max_stars_repo_licenses": ["BSD-2-Cl... |
# **CS224W - Colab 3**
In Colab 2 we constructed GNN models by using PyTorch Geometric's built in GCN layer, `GCNConv`. In this Colab we will go a step deeper and implement the **GraphSAGE** ([Hamilton et al. (2017)](https://arxiv.org/abs/1706.02216)) layer directly. Then we will run our models on the CORA dataset, wh... | {"hexsha": "5b6f726735785883d1fe17210a8e31b239be092f", "size": 1003009, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Colab 3/CS224W - Colab3_victor.ipynb", "max_stars_repo_name": "victorcroisfelt/aau-cs224w-ml-with-graphs", "max_stars_repo_head_hexsha": "adb38651be8da98cc574f127763c785ed16dfb5a",... |
# ************
# File: NeuralNetwork.py
# Top contributors (to current version):
# Panagiotis Kouvaros (panagiotis.kouvaros@gmail.com)
# This file is part of the Venus project.
# Copyright: 2019-2021 by the authors listed in the AUTHORS file in the
# top-level directory.
# License: BSD 2-Clause (see the file LICENSE ... | {"hexsha": "acf187c204cdadafa46ecbd4bd74047f442263aa", "size": 6369, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/NeuralNetwork.py", "max_stars_repo_name": "pkouvaros/venus2_vnncomp21", "max_stars_repo_head_hexsha": "57e9608041d230b5d78c4f2afb890b81035436a1", "max_stars_repo_licenses": ["BSD-2-Clause"], "... |
module vegetables_assert_equals_integer_tensor_m
use iso_varying_string, only: varying_string, operator(//), var_str
use strff, only: join
use vegetables_messages_m, only: &
make_equals_failure_message, &
make_equals_success_message, &
with_user_message
use vegetables... | {"hexsha": "bf0dc7c747976d2009953084844fa5c84b17d6ae", "size": 5094, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/vegetables/assert/equals_integer_tensor_m.f90", "max_stars_repo_name": "everythingfunctional/vegetables", "max_stars_repo_head_hexsha": "5625f1f3e318fb301d654e7875e254fa3e0cc4a1", "max_stars... |
import warnings
import numbers
import collections.abc
import numpy
import numpy.random
from logging import getLogger
_log = getLogger(__name__)
__all__ = ["sample_inputs"]
def sample_points(rng, N=None, conc=None, lower=None, upper=None, start=0, ndim=3):
"""Generate points distributed uniformly.
Args:
... | {"hexsha": "c5682377efaa37f86c96cff342d44d9952888eee", "size": 10768, "ext": "py", "lang": "Python", "max_stars_repo_path": "scopyon/sampling.py", "max_stars_repo_name": "ecell/scopyon", "max_stars_repo_head_hexsha": "99436fbfd34bb684966846eba75b206c2806f69c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
import gym
import numpy as np
class OneHotEncoding(gym.Space):
"""
{0,...,1,...,0}
Example usage:
self.observation_space = OneHotEncoding(size=4)
Credit: https://stackoverflow.com/questions/54022606/openai-gym-how-to-create-one-hot-observation-space
"""
def __init__(self, size=None):
... | {"hexsha": "738d23093ae1539b33b19f9a6d0ad71401c43626", "size": 1035, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym-pokemon/gym_pokemon/envs/one_hot.py", "max_stars_repo_name": "Miyooki/Soft-Boiled", "max_stars_repo_head_hexsha": "a1522502a1a665dee2317f03638f1c3b964c57f1", "max_stars_repo_licenses": ["MIT"]... |
import csv
import base64
import numpy as np
from numpy import array
dbig = np.dtype('>f8')
def decode_float_list(base64_string):
bytes = base64.b64decode(base64_string)
return np.frombuffer(bytes, dtype=dbig).tolist()
def encode_array(arr):
base64_str = base64.b64encode(np.array(arr).astype(dbig)).decode... | {"hexsha": "cbbc695119f34620a09aa9452eaada7694a41150", "size": 984, "ext": "py", "lang": "Python", "max_stars_repo_path": "vec2base64.py", "max_stars_repo_name": "harika-24/Image-Processing-and-Machine-Learning-using-Parallel-Computing", "max_stars_repo_head_hexsha": "b13b8f20551a9d5960b146713182b167e35d65e7", "max_sta... |
import numpy as np
# pip install gym
import gym
# Using the example
# https://gym.openai.com/envs/NChain-v0/
env = gym.make('NChain-v0')
def naive_sum_reward_agent(env, num_episodes=500):
# this is the table that will hold our summated rewards for
# each action in each state
r_table = np.zeros((5, 2))
... | {"hexsha": "e7caaba72e60dc385b8e3a90cb2cd22bf6fdef2a", "size": 3549, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym-nvchain.py", "max_stars_repo_name": "Umberto1988/PythonCourse", "max_stars_repo_head_hexsha": "5adf29111d2d1940af7760d1ba67d7a38f95584f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
[STATEMENT]
lemma amult_eq_eq_r:"\<lbrakk>z \<noteq> 0; a * ant z = b * ant z\<rbrakk> \<Longrightarrow> a = b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>z \<noteq> 0; a * ant z = b * ant z\<rbrakk> \<Longrightarrow> a = b
[PROOF STEP]
apply (cut_tac less_linear[of "z" "0"], simp,
cut_tac mem_a... | {"llama_tokens": 874, "file": "Group-Ring-Module_Algebra1", "length": 4} |
// Copyright (c) 2015-2021 Daniel Cooke
// Use of this source code is governed by the MIT license that can be found in the LICENSE file.
#ifndef individual_caller_hpp
#define individual_caller_hpp
#include <vector>
#include <string>
#include <memory>
#include <boost/optional.hpp>
#include "config/common.hpp"
#inclu... | {"hexsha": "f6c9fc51a7cc7d75df5fbaac45e9e41a6184e9fc", "size": 5406, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/core/callers/individual_caller.hpp", "max_stars_repo_name": "iamh2o/octopus", "max_stars_repo_head_hexsha": "09ebd28945026556e77d73f1dcd8f0212265183c", "max_stars_repo_licenses": ["MIT"], "max_s... |
# -*- coding: utf-8 -*-
from __future__ import division
from __future__ import absolute_import
from ..data.constants import *
from ..data.particles import *
from ..api.channel import ProductionChannel
from . import hadronic_common as h
import numpy as np
import scipy.integrate
from warnings import warn
def normali... | {"hexsha": "c40cf08db2073c790efc54de9abd6ae7b035a8c5", "size": 2801, "ext": "py", "lang": "Python", "max_stars_repo_path": "production/three_body_quartic.py", "max_stars_repo_name": "JLTastet/scalar_portal", "max_stars_repo_head_hexsha": "8d444d72e4c5d31b237a59621935757790c5a0e7", "max_stars_repo_licenses": ["MIT"], "m... |
import copy
from dataclasses import dataclass
import dataclasses
import functools
import traceback
from typing import Any, Dict, List, Optional, Tuple, Union
from async_timeout import enum
from attr import field
from concurrent.futures import ProcessPoolExecutor
from transformers import AutoConfig, T5ForConditionalGen... | {"hexsha": "a0e4e396ece136f891ed0a6a34aaf0b40b1bbe19", "size": 27206, "ext": "py", "lang": "Python", "max_stars_repo_path": "rayserve/t5_sst2_composed.py", "max_stars_repo_name": "drunkcoding/model-inference", "max_stars_repo_head_hexsha": "02d2240bc7052fa32223a80fa63625fe681db102", "max_stars_repo_licenses": ["MIT"], ... |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .jl
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.3.4
# kernelspec:
# display_name: Julia 1.3.1
# language: julia
# name: julia-1.3
# ---
# + [markdown] toc=true
# <... | {"hexsha": "df6ddff5f417ad5bd849830e22335faa8a1b81c1", "size": 17588, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "experiments/notebook/tangent_vector.jl", "max_stars_repo_name": "yasutak/MyWorkflow.jl", "max_stars_repo_head_hexsha": "99deb349edbbaf6b7f797916fb7afbd1b0d33884", "max_stars_repo_licenses": ["MIT"... |
import tensorflow as tf
from tensorflow.keras.layers import Dense, LayerNormalization, Reshape, Permute, Dropout, GlobalAveragePooling1D, Embedding
from tensorflow.keras.activations import softmax, linear
import tensorflow.keras.backend as K
import numpy as np
def gelu(x):
return 0.5*x*(1+tf.tanh(np.sqrt(2/np.pi)*... | {"hexsha": "e7cb0c928c378eb6c46f243f7b6ad0bbb91dd1de", "size": 6524, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/layers.py", "max_stars_repo_name": "kjm1559/vit", "max_stars_repo_head_hexsha": "7bb0c994672cdd22c014dc2b8f65cc341c3f165c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import pyarrow as pa
import pandas as pd
import numpy as np
from ray.experimental.data.deltacat.storage.model.types import DeltaType
from typing import Any, Dict, Union
def of(
stream_position: int,
file_index: int,
delta_type: DeltaType,
table: Union[pa.Table, pd.DataFrame, np.ndarray... | {"hexsha": "5237205622f0c61bde56e33b70a5a4e5305b6885", "size": 1334, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/ray/experimental/data/deltacat/compute/compactor/model/delta_file_envelope.py", "max_stars_repo_name": "goswamig/amazon-ray", "max_stars_repo_head_hexsha": "9984ebcdc9d0da0de65363074021e9af... |
import numpy as np
from hypothesis import given
import hypothesis.strategies as st
from caffe2.python import core
from caffe2.python import workspace
import caffe2.python.hypothesis_test_util as hu
class TestWeightedSample(hu.HypothesisTestCase):
@given(
batch=st.integers(min_value... | {"hexsha": "7c6cfbc9d0db4da7f1b502c66ddb7ea37e65138f", "size": 2819, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/Lib/site-packages/caffe2/python/operator_test/weighted_sample_test.py", "max_stars_repo_name": "Westlanderz/AI-Plat1", "max_stars_repo_head_hexsha": "1187c22819e5135e8e8189c99b86a93a0d66b8d8"... |
"""Plot the quantiles of an output variable against an input variable."""
import numpy as np
import matplotlib.pyplot as plt
import wquantiles
from scipy import signal
def quantile_plot(x, y, quantiles=(0.1, 0.9), ax=None, scatter=True, smooth=True, **kwarg):
""" Plot the quantiles of an output variable against ... | {"hexsha": "11ad4ad5485fd057e5a7a85a4bebe31448d120c8", "size": 6167, "ext": "py", "lang": "Python", "max_stars_repo_path": "lvreuse/utils/quantile_plot.py", "max_stars_repo_name": "mvernacc/lvreuse", "max_stars_repo_head_hexsha": "e2ac6aca334b49b0d4f5f881861cb42ce86dd130", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import os
import torch
import numpy as np
from .data_ner import BertNERDataBunch
from torch import nn
from seqeval.metrics import f1_score, precision_score, recall_score
from typing import Dict, List, Optional, Tuple
from .learner_util import Learner
from transformers import (
AutoConfig,
AutoModelForTokenClas... | {"hexsha": "8a6c12df35b4d63b670578265fcd29ed955871d9", "size": 10658, "ext": "py", "lang": "Python", "max_stars_repo_path": "fast_bert/learner_ner.py", "max_stars_repo_name": "clairett/fast-bert", "max_stars_repo_head_hexsha": "506771b930aa70e7ca2852e5e8ebb14656d97bfa", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
[STATEMENT]
lemma sinh_0 [simp]: "sinh 0 = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sinh (0::'a) = (0::'a)
[PROOF STEP]
by (simp add: sinh_def) | {"llama_tokens": 80, "file": null, "length": 1} |
C LAST UPDATE 16/03/89
C+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
C
SUBROUTINE PLOTON
IMPLICIT NONE
C
C Purpose: Switch graphics on first time only.
C
COMMON /MYGRAF/ OPENGR
LOGICAL OPENGR
C
C--------------------------------------------------------------... | {"hexsha": "ede294b3a56ca7e6e1ac46e5ae3950de38775e0f", "size": 506, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "otoko/src/ploton.f", "max_stars_repo_name": "scattering-central/CCP13", "max_stars_repo_head_hexsha": "e78440d34d0ac80d2294b131ca17dddcf7505b01", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
[STATEMENT]
lemma quasi_isometry_on_perturb:
assumes "lambda C-quasi_isometry_on X f"
"D \<ge> 0"
"\<And>x. x \<in> X \<Longrightarrow> dist (f x) (g x) \<le> D"
shows "lambda (C + 2 * D)-quasi_isometry_on X g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lambda (C + 2 * D) -quasi_isometry_... | {"llama_tokens": 2794, "file": "Gromov_Hyperbolicity_Isometries", "length": 21} |
import numpy as np
import cv2
import matplotlib.pyplot as plt
import glob
import os
import scipy.io
from math import sqrt
import torch
import torch.nn as nn
import torchvision
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch.autograd import Va... | {"hexsha": "a37249cde0ff591c390a0a630458c051cea15801", "size": 5165, "ext": "py", "lang": "Python", "max_stars_repo_path": "vacancy_inference.py", "max_stars_repo_name": "ThomasArtin/VPS-NET", "max_stars_repo_head_hexsha": "e37cafe3698d7a96cfe86b7190aac3a717799d21", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/-
Copyright (c) 2015 Jeremy Avigad. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Jeremy Avigad, Robert Y. Lewis
Ported by: Joël Riou
! This file was ported from Lean 3 source module algebra.group_power.ring
! leanprover-community/mathlib commit fc2ed6f838ce7c9b7c71... | {"author": "leanprover-community", "repo": "mathlib4", "sha": "b9a0a30342ca06e9817e22dbe46e75fc7f435500", "save_path": "github-repos/lean/leanprover-community-mathlib4", "path": "github-repos/lean/leanprover-community-mathlib4/mathlib4-b9a0a30342ca06e9817e22dbe46e75fc7f435500/Mathlib/Algebra/GroupPower/Ring.lean"} |
#!/usr/bin/python
#-*- coding: utf-8 -*-
# >.>.>.>.>.>.>.>.>.>.>.>.>.>.>.>.
# Licensed under the Apache License, Version 2.0 (the "License")
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# --- File Name: shapes3d.py
# --- Creation Date: 16-01-2021
# --- Last Modified: Tue 13 A... | {"hexsha": "1bcf7bc1a1dc49cb3796cf30ba83ab69627ad026", "size": 6158, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/shapes3d.py", "max_stars_repo_name": "zhuxinqimac/CommutativeLieGroupVAE-Pytorch", "max_stars_repo_head_hexsha": "06020834b1ea4abff305d8fb300c3d8fba5b0f27", "max_stars_repo_licenses": ["A... |
### A Pluto.jl notebook ###
# v0.19.0
using Markdown
using InteractiveUtils
# ╔═╡ 9ec6149e-6acd-414c-8902-798771573672
function is_notebook(p)
startswith(read(p, String), "### A Pluto")
end;
# ╔═╡ bdf53020-635d-4cc9-8a0e-a612ca470e85
hook_link(p) = replace(p, ".jl" => ".html");
# ╔═╡ 7c852ed0-36b8-43ea-8ee8-c4bd45... | {"hexsha": "402bea1c18fb7d9ff62c9862220f086d457d9889", "size": 1978, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/index.jl", "max_stars_repo_name": "JuliaPluto/PlutoLinks.jl", "max_stars_repo_head_hexsha": "37f61b16c218c88cc8eb85d81413de00dafcfbb9", "max_stars_repo_licenses": ["Unlicense"], "max_stars_cou... |
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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... | {"hexsha": "3ba3eb64ed2291a03f1060e405b8d82ef2526ce0", "size": 1283, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/ut/python/pipeline/parse/test_if_function.py", "max_stars_repo_name": "GuoSuiming/mindspore", "max_stars_repo_head_hexsha": "48afc4cfa53d970c0b20eedfb46e039db2a133d5", "max_stars_repo_licens... |
/***********************************************************************************
* Copyright (c) 2016, UT-Battelle
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions o... | {"hexsha": "ac0994e177757e349424274dccea61a2a33b604a", "size": 13177, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ir/tests/PauliOperatorTester.cpp", "max_stars_repo_name": "czhao39/xacc-vqe", "max_stars_repo_head_hexsha": "4ad1d9308794e28c37772b7ea29cd3923388168a", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
[STATEMENT]
lemma LIMSEQ_offset: "(\<lambda>n. f (n + k)) \<longlonglongrightarrow> a \<Longrightarrow> f \<longlonglongrightarrow> a"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>n. f (n + k)) \<longlonglongrightarrow> a \<Longrightarrow> f \<longlonglongrightarrow> a
[PROOF STEP]
unfolding tendsto_def
... | {"llama_tokens": 239, "file": null, "length": 2} |
from scipy.spatial import distance
def getRelationDistance(entityData, x=650, y=740):
location = entityData.info.location
loc = location.split()
x1 = float(loc[0])
y1 = float(loc[1])
p = (x1, y1)
xFixed = getDistanceDictionaryX(p[0], x, y)
yFixed = getDistanceDictionaryY(p[1], x, y)
pr... | {"hexsha": "19f894675f92d875c1e9a0c281f6ab1ecb46d221", "size": 847, "ext": "py", "lang": "Python", "max_stars_repo_path": "gradiantDescent/EntityDistance.py", "max_stars_repo_name": "perazim-io/layout-bot", "max_stars_repo_head_hexsha": "b01c440aa4ecd266e65596a1bd4cc7fcb722f715", "max_stars_repo_licenses": ["MIT"], "ma... |
using jInv.Mesh
using jInv.LinearSolvers
using Test
using KrylovMethods
using Multigrid
using LinearAlgebra
using SparseArrays
println("=== Example 2D DivSigGrad ====");
domain = [0.0, 1.0, 0.0, 1.0];
n = [50,50];
Mr = getRegularMesh(domain,n)
G = getNodalGradientMatrix(Mr);
Ar = G'*G;
Ar = Ar ... | {"hexsha": "9f0f1d609d8a92feff1dcce2f7c9eee7d4161964", "size": 995, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Multigrid/testLinSolveMGWrapper.jl", "max_stars_repo_name": "JuliaInv/Multigrid.jl", "max_stars_repo_head_hexsha": "4b759b92eb609d59115e3537b7eb88ba05699fdc", "max_stars_repo_licenses": ["MIT"]... |
@testset "construct" begin
@test Double64(one(Double64)) === one(Double64)
@test Double64(one(Double32)) === one(Double64)
@test Double64(one(Double16)) === one(Double64)
@test Double32(one(Double64)) === one(Double32)
@test Double32(one(Double32)) === one(Double32)
@test Double32(one(Double16)) === o... | {"hexsha": "aaa8e8b31ea97e02dc9fdd8ebeaf4d9a85909c09", "size": 3062, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/construct.jl", "max_stars_repo_name": "UnofficialJuliaMirror/DoubleFloats.jl-497a8b3b-efae-58df-a0af-a86822472b78", "max_stars_repo_head_hexsha": "ccf0c6a690f81eec84caf080c99d58d11e72432d", "m... |
Require Export Arith.
Require Export ArithRing.
Require Export Omega.
Require Export Wf_nat.
Fixpoint div2 (n : nat) : nat :=
match n with S (S p) => S (div2 p) | _ => 0 end.
Theorem div2_ind:
forall (P : nat -> Prop),
P 0 -> P 1 -> (forall n, P n -> P (S (S n))) -> forall n, P n.
Proof.
intros P H0 H1 Hstep ... | {"author": "kalfazed", "repo": "Coq---Programming-Language", "sha": "829948eab329a9781b8681249e1f1343f226c5c6", "save_path": "github-repos/coq/kalfazed-Coq---Programming-Language", "path": "github-repos/coq/kalfazed-Coq---Programming-Language/Coq---Programming-Language-829948eab329a9781b8681249e1f1343f226c5c6/Tsinghua ... |
"""
Copyright (C) 2020, Marek Gagolewski, https://www.gagolewski.com
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, mod... | {"hexsha": "d7c9ae6f99031a0af8a3f76fe289ea5be1d5e23f", "size": 2693, "ext": "py", "lang": "Python", "max_stars_repo_path": "do_benchmark_fastcluster.py", "max_stars_repo_name": "gagolews/clustering_results_v1", "max_stars_repo_head_hexsha": "f3007018a195124433a4bbb5b15259cf8e838334", "max_stars_repo_licenses": ["BSD-3-... |
# http://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/
# https://www.datacamp.com/community/tutorials/machine-learning-python#explore
# Data Manipulation Library pandas
#
# digits = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/optdigits/optdigits.tra", header=None)
# training ... | {"hexsha": "719b1524b6f6d52c4c2aeec558c1ea01953be5e1", "size": 1763, "ext": "py", "lang": "Python", "max_stars_repo_path": "Studies/Study02/Study02.py", "max_stars_repo_name": "yazici/Pamux.MachineLearning", "max_stars_repo_head_hexsha": "25816a9868eff90f849c2ec373e77f567e4a3f5e", "max_stars_repo_licenses": ["MIT"], "m... |
# Copyright 2019 the ProGraML authors.
#
# Contact Chris Cummins <chrisc.101@gmail.com>.
#
# 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
#
# ... | {"hexsha": "0f230900dbc96e561bf6bb22bc8de615b1648c17", "size": 3023, "ext": "py", "lang": "Python", "max_stars_repo_path": "deeplearning/ml4pl/graphs/labelled/dataflow/reachability/reachability.py", "max_stars_repo_name": "Zacharias030/ProGraML", "max_stars_repo_head_hexsha": "cd99d2c5362acd0b24ee224492bb3e8c4d4736fb",... |
import os
import numpy as np
import torch
from torch.utils.data import DataLoader
from generate_data import generate_vrp_data
from utils import load_model
from problems import CVRP
model, _ = load_model('pretrained/cvrp_50/')
torch.manual_seed(1234)
dataset = CVRP.make_dataset(size=50, num_samples=10)
# Need a data... | {"hexsha": "e2185f921a321a9c1afb0563efd8353e810b94c0", "size": 601, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_vrp.py", "max_stars_repo_name": "asakidaisuke/attention-learn-to-route", "max_stars_repo_head_hexsha": "cb207916a23a56fc2bf28365c9936092c100f1b9", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma singleton_in_conc:
"[x] : A @@ B \<longleftrightarrow> [x] : A \<and> [] : B \<or> [] : A \<and> [x] : B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ([x] \<in> A @@ B) = ([x] \<in> A \<and> [] \<in> B \<or> [] \<in> A \<and> [x] \<in> B)
[PROOF STEP]
by (fastforce simp: Cons_eq_append_conv ap... | {"llama_tokens": 172, "file": "Regular-Sets_Regular_Set", "length": 1} |
#include <crow/app.h>
#include <ast_jpeg_decoder.hpp>
#include <ast_video_puller.hpp>
#include <boost/endian/arithmetic.hpp>
#include <string>
namespace crow
{
namespace kvm
{
static const std::string rfb33VersionString = "RFB 003.003\n";
static const std::string rfb37VersionString = "RFB 003.007\n";
static const st... | {"hexsha": "747a137b8728e2768de9c70bda1136f7c3f70888", "size": 15034, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/web_kvm.hpp", "max_stars_repo_name": "hyche/bmcweb", "max_stars_repo_head_hexsha": "ebc692c9d14b59ffea43f6a83d3fc1467fe09aff", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
Inductive natural : Type := Succ : natural -> natural | Zero : natural.
Inductive lst : Type := Cons : natural -> lst -> lst | Nil : lst.
Inductive tree : Type := Node : natural -> tree -> tree -> tree | Leaf : tree.
Inductive Pair : Type := mkpair : natural -> natural -> Pair
with Zlst : Type := zcons : Pair -... | {"author": "artifactanon", "repo": "lfind_benchmarks_pldi22", "sha": "7bf78a4e51fede5a63911e82a38f86e61cef2aec", "save_path": "github-repos/coq/artifactanon-lfind_benchmarks_pldi22", "path": "github-repos/coq/artifactanon-lfind_benchmarks_pldi22/lfind_benchmarks_pldi22-7bf78a4e51fede5a63911e82a38f86e61cef2aec/clam/goal... |
from __future__ import division
import numpy as np
import sys
sys.path.append('/gpfs/projects/bsc28/tiramisu_semantic_transfer/tiramisu_source/')
from tiramisu.tensorflow.core.backend import read_embeddings
import os
from collections import Counter, OrderedDict
import matplotlib
matplotlib.use('pdf')
import matplotlib.... | {"hexsha": "58a55b088c340a084f4f5dcaec14f26aa812c5ad", "size": 8537, "ext": "py", "lang": "Python", "max_stars_repo_path": "plots_and_analysis/on_step3/synset_feat_layer_distrib_thresholded/synset_feat_layer_distrib_fixed/plot_synset_layer_distribution_fixed.py", "max_stars_repo_name": "HPAI-BSC/neural_patterns_abstrac... |
#!/usr/bin/env python
from control.matlab import *
from matplotlib import pyplot as plt
from scipy import arange
def main():
k=1.0
m=0.1
c=0.1
num = [0, 0,1]
den = [m, c, k]
sys1 = tf(num, den)
print sys1
(y1a, T1a) = initial(sys1,X0 = [0, 1],T = arange(0, 10, 0.01))
plt.axhline(... | {"hexsha": "97376308491892ebcbfb88a230d06d74a38e4be1", "size": 431, "ext": "py", "lang": "Python", "max_stars_repo_path": "responce_initial.py", "max_stars_repo_name": "nnn112358/python-control_test", "max_stars_repo_head_hexsha": "58e1b5e6feec0477fd4bad3683fb8af470faed4f", "max_stars_repo_licenses": ["MIT"], "max_star... |
function [VIn,MIn] = partition_distance(Cx,Cy)
%PARTITION_DISTANCE Distance between community partitions
%
% This function quantifies the distance between pairs of community
% partitions with information theoretic measures.
%
% VIn = partition_distance(Cx,Cy)
% [VIn MIn] = partition_distance(Cx,Cy)
%
... | {"author": "fieldtrip", "repo": "fieldtrip", "sha": "c2039be598a02d86b39aae76bfa7aaa720f9801c", "save_path": "github-repos/MATLAB/fieldtrip-fieldtrip", "path": "github-repos/MATLAB/fieldtrip-fieldtrip/fieldtrip-c2039be598a02d86b39aae76bfa7aaa720f9801c/external/bct/partition_distance.m"} |
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{ctex}
\usepackage{amsmath} %% This is package for typesetting derivations.
\title{test for \LaTeX\ document}
\author{thwzjx}
\date{\today}
\begin{document}
\maketitle
\tableofcontents
\section{chap1}
\subsection{Introduction for Real Analysis}
\end{documen... | {"hexsha": "dab7ccc6e4323a87cd30c02f01f76cd50e91336c", "size": 322, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/chap1.tex", "max_stars_repo_name": "thwzjx/real-analysis", "max_stars_repo_head_hexsha": "030d44b8b85fb3e6be280dbe8d6f37c4d19bb19a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
"""ToupCam Camera API. Adjustments have been made to fit this project,
but the original source is referenced below"""
# ===============================================================================
# Copyright 2015 Jake Ross
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this f... | {"hexsha": "f8e6b39eb0132536cdcf3d4c95508d7c81b7a39e", "size": 10970, "ext": "py", "lang": "Python", "max_stars_repo_path": "camera.py", "max_stars_repo_name": "dakota0064/Fluorescent_Robotic_Imager", "max_stars_repo_head_hexsha": "423e6df956269fb2d6c438dd5fce1a6cbc947b3d", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# The Leginon software is Copyright 2004
# The Scripps Research Institute, La Jolla, CA
# For terms of the license agreement
# see http://ami.scripps.edu/software/leginon-license
#
import leginon.gui.wx.Dialog
import leginon.version
import sys
import wx
import numpy
import _mysql
from PIL import Image
class Dialog(l... | {"hexsha": "6134c88aebeae8cf3cec6bbffb69066e3488f49c", "size": 3179, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/python2.7/site-packages/leginon/gui/wx/About.py", "max_stars_repo_name": "leschzinerlab/myami-3.2-freeHand", "max_stars_repo_head_hexsha": "974b8a48245222de0d9cfb0f433533487ecce60d", "max_star... |
from __future__ import division, print_function
import requests, json
requests.packages.urllib3.disable_warnings()
from nsepy import get_history
from nsetools import Nse
import MySQLdb as mysqldb
import pandas as pd
from StockNest.celery import app
from models import company, stockData, companyTweets, predictionData,\... | {"hexsha": "b18e2d60f13ad4a6a6c2af10a010fd6ada29f244", "size": 13963, "ext": "py", "lang": "Python", "max_stars_repo_path": "StockNest/stock_backend/apis.py", "max_stars_repo_name": "vaibhavantil2/Stock-Price-Forecasting-Using-Artificial-Intelligence", "max_stars_repo_head_hexsha": "69192454542432c7120cbf95ea443b567a24... |
Require Import lib.
Class Size (A : Type) := size : A -> nat.
Ltac gen_Size :=
hnf; match goal with [ |- ?A -> nat] =>
fix size' 1; intros s;
assert(size_inst : Size A);[exact size' | idtac];
destruct s eqn:E;
let term := type of s in
match goal with
[E : s = ?s' |- _] =>
let rec map s :=
(match s w... | {"author": "tebbi", "repo": "semantics", "sha": "1fa96bb90694f762b176cdbe15f5be9ed8aecc8c", "save_path": "github-repos/coq/tebbi-semantics", "path": "github-repos/coq/tebbi-semantics/semantics-1fa96bb90694f762b176cdbe15f5be9ed8aecc8c/gen-syntax/Size.v"} |
[STATEMENT]
lemma USUP_image_eq [simp]: "USUP (\<lambda>i. \<guillemotleft>i\<guillemotright> \<in>\<^sub>u \<guillemotleft>f ` A\<guillemotright>) g = (\<Squnion> i\<in>A \<bullet> g(f(i)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<Squnion> i \<in> f ` A \<bullet> g i) = (\<Squnion> i \<in> A \<bullet> g (... | {"llama_tokens": 170, "file": "UTP_utp_utp_pred_laws", "length": 1} |
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
class GCELoss(nn.Module):
def __init__(self, q=0.7, k=0.5, trainset_size=50000, num_classes=2):
super(GCELoss, self).__init__()
self.q = q
self.k = k
self.weight = torch.nn.Parameter(d... | {"hexsha": "ff746569ffebaecdfbe84a97e4175016870c22a7", "size": 1573, "ext": "py", "lang": "Python", "max_stars_repo_path": "GCE/GCEloss.py", "max_stars_repo_name": "yjbang/math6380", "max_stars_repo_head_hexsha": "045bf9dd877b4b387580459fd747a4e428cbe8ff", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_... |
import torch
import torch.nn as nn
import torch.nn.functional as f
import torch.optim as optim
import torch.utils.data as d
import numpy as np
from summariser.cnn.datasets import Predictionset
from summariser.cnn.warp import WARPLoss
from summariser.cnn.relative_margin_loss import RelativeMargin
from summariser.utils.a... | {"hexsha": "a8f77ffa705bf733ff4a74bdbb5140505168857d", "size": 5611, "ext": "py", "lang": "Python", "max_stars_repo_path": "summariser/cnn/test_cnn.py", "max_stars_repo_name": "UKPLab/ijcai2019-relis", "max_stars_repo_head_hexsha": "8a40762dcfa90c075a4f6591cbdceb468026ef17", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
Test various information theory inequalities.
"""
from hypothesis import given, settings, unlimited, HealthCheck
import pytest
import numpy as np
from dit.utils.testing import distributions, markov_chains
from dit import ScalarDistribution as SD
from dit.divergences import (chernoff_information,
... | {"hexsha": "c4c4d197153108e3d3caa98d9c9f20ef56435214", "size": 6690, "ext": "py", "lang": "Python", "max_stars_repo_path": "dit/tests/test_inequalities.py", "max_stars_repo_name": "marwahaha/dit", "max_stars_repo_head_hexsha": "feaa7dfa87b4f6067039be4ac05c7e645fdcec3c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
import argparse
import sys
from rvg import NumPyRVG
parser = argparse.ArgumentParser(
description='rvg - Random Values Generator',
epilog='''NOTE: rvg can be run with no flags, which is equivalent to running `rvg --numpy float32 -limits 0 1`
(i.e. sampling of the uniform(0, 1) distribution)
'''
)
pars... | {"hexsha": "e5fe5144cf0f2d6c8629d0736e1d7f473b082b94", "size": 1979, "ext": "py", "lang": "Python", "max_stars_repo_path": "rvg/cli.py", "max_stars_repo_name": "zehanort/rvg", "max_stars_repo_head_hexsha": "a7aff2a5f00248e3dac45b45456a2cdb9d7de309", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_stars_r... |
"""Emmental learner."""
import collections
import copy
import logging
import math
import time
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Union
import numpy as np
import pickle
import torch
from numpy import ndarray
from torch import optim as optim
from to... | {"hexsha": "a3ec331501b97b5daa4e25a4c76aa540687b9a71", "size": 31535, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/emmental/learner.py", "max_stars_repo_name": "mleszczy/emmental", "max_stars_repo_head_hexsha": "879902626ed9e97f43fa42fe471275cbfad52f90", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import numpy as np
import torch
from tqdm import tqdm
import matplotlib.pyplot as plt
import seaborn as sns
from neural_clf.controllers.clf_qp_net import CLF_QP_Net
from models.pvtol import (
control_affine_dynamics,
u_nominal,
n_controls,
n_dims,
low_m,
high_m,
low_I,
high_I,
)
# Bea... | {"hexsha": "d53dfc553e26ab2e40aca8230894c3a5c8a5a042", "size": 3020, "ext": "py", "lang": "Python", "max_stars_repo_path": "neural_clf/plotting/pvtol_robust_clf_qp_V.py", "max_stars_repo_name": "dawsonc/neural_clf_cbf_optimal_control", "max_stars_repo_head_hexsha": "a8fe042504efbe3370e6e7783833986ff20bbb44", "max_stars... |
# Copyright 2020 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": "e69970c21b38e4e4086daa7aa3664d6234206023", "size": 5872, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/object_detection/models/keras_models/hourglass_network_tf2_test.py", "max_stars_repo_name": "zhaowt96/models", "max_stars_repo_head_hexsha": "03182253673b0e2666ad9a33839759834c0acebd", "m... |
!
! This file is released under terms of BSD license
! See LICENSE file for more information
!
MODULE mo_column
IMPLICIT NONE
CONTAINS
SUBROUTINE compute(nz, q, t, s)
IMPLICIT NONE
INTEGER, INTENT(IN) :: nz ! Size of the array field
REAL, INTENT(INOUT) :: t(:) ! Field declared as one column only
... | {"hexsha": "57b6834237651b0bed03a783b036e3a60649167f", "size": 1672, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/claw/sca/sca32/mo_column.f90", "max_stars_repo_name": "FrostyMike/claw-compiler", "max_stars_repo_head_hexsha": "e9fe6dbd291454ce34dd58f21d102f7f1bdff874", "max_stars_repo_licenses": ["BSD-... |
## Gráfica de T de rocío
$f(\psi)=\sum_{i=1}^{c}\frac{z_i[1-k_i]}{1+\psi[k_i-1]}$
Fracciones del problema realizado en clase:
$z_{n-Butano}=0.2$
$z_{n-Pentano}=0.25$
$z_{n-Hexano}=0.25$
$z_{n-Heptano}=0.3$
Constantes del problema realizado en clase:
$K_{n-Butano}=4$
$K_{n-Pentano}=1.9$
$K_{n-Hexano}=1$
$K_... | {"hexsha": "64c3bce9c6ca66226028ea9bae2e14ab21fdae70", "size": 452216, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Playground Procesos_II.ipynb", "max_stars_repo_name": "RamiroFuentes/Procesos-de-separacion-II", "max_stars_repo_head_hexsha": "d68873f8ee3e9eb081f1040f9510335746b1a0b4", "max_stars... |
# Ikeda for many ships
```python
# %load imports.py
"""
These is the standard setup for the notebooks.
"""
%matplotlib inline
%load_ext autoreload
%autoreload 2
from jupyterthemes import jtplot
jtplot.style(theme='onedork', context='notebook', ticks=True, grid=False)
import pandas as pd
pd.options.display.max_rows... | {"hexsha": "33ab124bfc147aa4cb6c1b7df2811b036b431cd4", "size": 507294, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/10.2_ikeda_many.ipynb", "max_stars_repo_name": "rddaz2013/Prediction-of-roll-motion-using-fully-nonlinear-potential-flow-and-Ikedas-method", "max_stars_repo_head_hexsha": ... |
# Copyright 2020 University of Groningen
#
# 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 i... | {"hexsha": "7193f4010ab8bb67b46ae45356d39cc6293131e2", "size": 11412, "ext": "py", "lang": "Python", "max_stars_repo_path": "polyply/src/generate_templates.py", "max_stars_repo_name": "marrink-lab/polyply_1.0", "max_stars_repo_head_hexsha": "4e48f86fb309b38391c73d8f9bcc1f7c6090d2cf", "max_stars_repo_licenses": ["Apache... |
from typing import Dict, Optional, Tuple, Union
import cv2
import numpy as np
from . import compute, cv2ext, pages, split, unskew
from .angle import Angle
from .crop import (
crop_around_data,
crop_around_page,
CropAroundDataInPageParameters,
)
from .debug_image import DebugImage, inc_debug
from .exceptex... | {"hexsha": "b5f9f4c4a8f1bc9f3c71c41762fd5ee424fb9c0a", "size": 10819, "ext": "py", "lang": "Python", "max_stars_repo_path": "diptych/script.py", "max_stars_repo_name": "bansan85/diptych", "max_stars_repo_head_hexsha": "297e6b291893a6e7abaab16025dc04d7d397a493", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 16 14:45:45 2020
@author: Administrator
"""
from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import random
import tkinter
import os
import cv2
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from PIL import I... | {"hexsha": "3471f71ebd893ee94ee038b2b46d52f9ced8120a", "size": 7992, "ext": "py", "lang": "Python", "max_stars_repo_path": "cocogen.py", "max_stars_repo_name": "ahirsharan/SegNet", "max_stars_repo_head_hexsha": "3044f5fbc78c98685ba2f1b7d45be6b60d7aaaef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_s... |
[STATEMENT]
lemma option_of_exception_\<I> [simp]: "map_\<I> id option_of_exception (exception_\<I> \<I>) = stop_\<I> \<I>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. map_\<I> id option_of_exception (exception_\<I> \<I>) = stop_\<I> \<I>
[PROOF STEP]
by(simp add: exception_\<I>_def o_def id_def[symmetric]) | {"llama_tokens": 132, "file": "Constructive_Cryptography_CM_More_CC", "length": 1} |
//==============================================================================
// Copyright 2003 - 2013 LASMEA UMR 6602 CNRS/Univ. Clermont II
// Copyright 2009 - 2013 LRI UMR 8623 CNRS/Univ Paris Sud XI
//
// Distributed under the Boost Software License, Version 1.0.
// ... | {"hexsha": "33cdd2217f5c346ad967b5f1be6f99d163a53b8c", "size": 3643, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "modules/type/complex/base/unit/arithmetic/simd/min.cpp", "max_stars_repo_name": "psiha/nt2", "max_stars_repo_head_hexsha": "5e829807f6b57b339ca1be918a6b60a2507c54d0", "max_stars_repo_licenses": ["BS... |
import numpy as np
from dipy.viz import fos
from dipy.core import track_performance as pf
tracks=[np.array([[0,0,0],[1,0,0,],[2,0,0]]),
np.array([[3,0,0],[3.5,1,0],[4,2,0]]),
np.array([[3.2,0,0],[3.7,1,0],[4.4,2,0]]),
np.array([[3.4,0,0],[3.9,1,0],[4.6,2,0]]),
np.arr... | {"hexsha": "359813ac2b1e4630f28656fde274b820e984ef01", "size": 1528, "ext": "py", "lang": "Python", "max_stars_repo_path": "scratch/very_scratch/check_flipping.py", "max_stars_repo_name": "JohnGriffiths/dipy", "max_stars_repo_head_hexsha": "5fb38e9b77547cdaf5eb140730444535733ae01d", "max_stars_repo_licenses": ["BSD-3-C... |
[STATEMENT]
lemma Levellist_unique_ex_conj_simp [simp]:
"Levellist hds next ll \<Longrightarrow> (\<exists>ll. Levellist hds next ll \<and> P ll) = P ll"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Levellist hds next ll \<Longrightarrow> (\<exists>ll. Levellist hds next ll \<and> P ll) = P ll
[PROOF STEP]
by (aut... | {"llama_tokens": 131, "file": "BDD_LevellistProof", "length": 1} |
[STATEMENT]
lemma inR4': "\<Gamma> \<Rightarrow> F, G, H, I, \<Delta> \<down> n \<Longrightarrow> \<Gamma> \<Rightarrow> H, I, F, G, \<Delta> \<down> n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Gamma> \<Rightarrow> F, G, H, I, \<Delta> \<down> n \<Longrightarrow> \<Gamma> \<Rightarrow> H, I, F, G, \<Delta> \... | {"llama_tokens": 147, "file": "Propositional_Proof_Systems_SC_Depth", "length": 1} |
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