text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma Vfrom_rank_eq: "Vfrom A (rank(x)) = Vfrom A x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Vfrom A (rank x) = Vfrom A x
[PROOF STEP]
proof (rule order_antisym)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. Vfrom A (rank x) \<le> Vfrom A x
2. Vfrom A x \<le> Vfrom A (rank x)
[PROOF STEP]
sh... | {"llama_tokens": 2379, "file": "ZFC_in_HOL_ZFC_Cardinals", "length": 23} |
using ITensors
using ITensors.ITensorNetworkMaps
using KrylovKit
using LinearAlgebra
χ = 3
d = 2
l = Index(χ, "l")
s = Index(d, "s")
l0 = addtags(l, "c=0")
l1 = addtags(l, "c=1")
A = randomITensor(l0, l1, s)
A′ = prime(dag(A); inds=(l0, l1))
T = ITensorNetworkMap([A, A′]; input_inds=(l1, l1'), output_inds=(l0, l0'))... | {"hexsha": "051b3d9bad0fc5491c028771c0b707ad6e62bad9", "size": 463, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/ITensorNetworkMaps/imps_transfer_matrix.jl", "max_stars_repo_name": "anupam-mitra/ITensors.jl", "max_stars_repo_head_hexsha": "ed8a4a45099bd55358c1dc0a7521a34a4c49c663", "max_stars_repo_lic... |
import argparse
import matplotlib.pyplot as plt
from matplotlib.ticker import ScalarFormatter
import numpy as np
from astropy.table import QTable
import astropy.units as u
from astropy.modeling.fitting import LevMarLSQFitter
from plot_irv_params import G21mod
def plot_irv_ssamp(
ax, itab, label, color="k", line... | {"hexsha": "938a7d34357a88fd878e06a3b5429a0b5c052d02", "size": 10766, "ext": "py", "lang": "Python", "max_stars_repo_path": "Figs/plot_irv_params_waveregion.py", "max_stars_repo_name": "karllark/fuv_mir_rv_relationship", "max_stars_repo_head_hexsha": "a25ef0500be5b9c3477883b7f568b41e91b59a90", "max_stars_repo_licenses"... |
# Copyright 2018-2019, Carnegie Mellon University
# See LICENSE for details
Class(arctan, AutoFoldExp, rec(
ev := self >> self._ev(self.args).ev(),
computeType := self >> TReal,
));
Class(TArcTan, Tagged_tSPL_Container, rec(
abbrevs := [ () -> []],
dims := self >> [1, 2],
transpose := self >> Co... | {"hexsha": "20a6e90c6d34eda2aba4a19f57df48e47b912e0b", "size": 693, "ext": "gi", "lang": "GAP", "max_stars_repo_path": "arctan.gi", "max_stars_repo_name": "spiral-software/spiral-package-hcol", "max_stars_repo_head_hexsha": "b4a0118382e3bba91ecd82a6c667f2cdb6389ceb", "max_stars_repo_licenses": ["BSD-2-Clause-FreeBSD"],... |
import argparse
import os
from typing import Optional, List, Union
import numpy as np
import pandas as pd
import torch
from pytorch_toolbelt.utils.distributed import all_gather
from xview3 import *
from xview3.centernet.models.inference import get_box_coder_from_model
from xview3.inference import (
model_from_che... | {"hexsha": "35caa3a08bb8566a5cb5f6a4a9f8878d0de08270", "size": 5267, "ext": "py", "lang": "Python", "max_stars_repo_path": "submit_multilabel.py", "max_stars_repo_name": "BloodAxe/xView3-The-First-Place-Solution", "max_stars_repo_head_hexsha": "9a9600e7dfbaa24ff5a72c81061fbbbfed865847", "max_stars_repo_licenses": ["MIT... |
using Test
using SafeTestsets
using Plots
unicodeplots()
include(joinpath(@__DIR__, "generate_example_tests.jl"))
include(joinpath(@__DIR__, "download_dumps.jl"))
# Note: comment outer @testset to stop after first @safetestset failure
@time @testset verbose = true "Krotov.jl Package" begin
print("\n* Example 1... | {"hexsha": "ee0f491dbb9f9c7a551d4d49826de1ee9ccef0d5", "size": 1031, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaQuantumControl/Krotov.jl", "max_stars_repo_head_hexsha": "6eab22b1d2e084210ecaf7fe32fcf28a2183be83", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/////////1/////////2/////////3/////////4/////////5/////////6/////////7/////////8
// Name :
// Author : Avi
// Revision : $Revision: #30 $
//
// Copyright 2009- ECMWF.
// This software is licensed under the terms of the Apache Licence version 2.0
// which can be obtained at http://www.apache.org/license... | {"hexsha": "b82e22d601618041e2473b2bc814b7de61aade57", "size": 10650, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Base/src/cts/LogCmd.cpp", "max_stars_repo_name": "mpartio/ecflow", "max_stars_repo_head_hexsha": "ea4b89399d1e7b897ff48c59b1e885e6d53cc8d6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
const _allowslow = Ref(true)
allowslow(flag = true) = (_allowslow[] = flag)
function assertslow(op = "Operation")
_allowslow[] || error("$op is disabled")
return
end
Base.IndexStyle(::Type{<:GPUArray}) = IndexLinear()
function _getindex(xs::GPUArray{T}, i::Integer) where T
x = Array{T}(1)
copy!(x, 1... | {"hexsha": "40a4fd3c9ff6e19b58d36e32a26c77ff57fe37d2", "size": 1822, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/indexing.jl", "max_stars_repo_name": "americast/GPUArrays.jl", "max_stars_repo_head_hexsha": "fa1f9de3440f72b542945b99184804216d81c133", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import os
from .common_setup import *
import numpy as np
import os
import tensorflow as tf
from bayes_filter import jitter
from bayes_filter.misc import (random_sample, flatten_batch_dims, load_array_file, timer, diagonal_jitter,
log_normal_solve_fwhm,make_example_datapack, maybe_create_posterior_solsets, graph_s... | {"hexsha": "37101c9b5c8d102863a00f5eb5625e0706192b1e", "size": 4304, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayes_filter/tests/test_misc.py", "max_stars_repo_name": "Joshuaalbert/bayes_filter", "max_stars_repo_head_hexsha": "2997d60d8cf07f875e42c0b5f07944e9ab7e9d33", "max_stars_repo_licenses": ["Apache-... |
module Effect.StdIO
import Effects
import Control.IOExcept
data StdIO : Effect where
PutStr : String -> { () } StdIO ()
GetStr : { () } StdIO String
instance Handler StdIO IO where
handle () (PutStr s) k = do putStr s; k () ()
handle () GetStr k = do x <- getLine; k x ()
{-
instance Handler ... | {"hexsha": "abda7f5b6100651c6bfab7a7532b6a0776031b90", "size": 1878, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Effect/StdIO.idr", "max_stars_repo_name": "edwinb/Eff-new", "max_stars_repo_head_hexsha": "e906e2d22a1606de015086bd92acc6f4e0e40ec1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
## This file is meant to replicate the exact same process, P. Schwab has implemented
# for the voice data. The goal is to extract the same train, test and validation
# sets!
# dated 04-01-2019
#%% importing libraries
import pandas as pd
import numpy as np
#%%
# lets load the demographics file here
demo_data = pd.read... | {"hexsha": "9bddc63884bec5346aadcb1ec5f0800fc6cb7b7d", "size": 3636, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/Voice/voiceEDA.py", "max_stars_repo_name": "khizar-anjum/mPowerAnalysis", "max_stars_repo_head_hexsha": "890abda28e692b31640c957b4a01d076025e3fc4", "max_stars_repo_licenses": ["MIT"], "max... |
import os
import sys
import json
import pickle
import argparse
import numpy as np
sys.path.append(
os.path.realpath(os.path.join(os.path.dirname(__file__), '..')))
from perception.utterance.eval import UtteranceEncoder
from interaction.common.utils import stable_utterance_hash
from interaction.action import action... | {"hexsha": "dfbc5c4e0db6756c2d7f403e8dbfcb965a99b078", "size": 4218, "ext": "py", "lang": "Python", "max_stars_repo_path": "HRI/TFVT_HRI/scripts/collect_act_emb.py", "max_stars_repo_name": "WorldEditors/PaddleRobotics", "max_stars_repo_head_hexsha": "d02efd74662c6f78dfb964e8beb93f1914dcb2f3", "max_stars_repo_licenses":... |
[STATEMENT]
lemma emp_to_emp': "w = \<epsilon> \<Longrightarrow> f w = \<epsilon>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. w = \<epsilon> \<Longrightarrow> f w = \<epsilon>
[PROOF STEP]
using morph_on[of \<epsilon> \<epsilon>] self_append_conv2[of "f \<epsilon>" "f \<epsilon>"]
[PROOF STATE]
proof (prove)
usi... | {"llama_tokens": 248, "file": "Combinatorics_Words_Morphisms", "length": 2} |
using EngEconomics, Roots, Plots
# Given
init = -610000
annual = 200000
N = 10
salvage = -1500000
MARR = 0.1
# Determine the better alternative
NPW(x) = init + annual * seriesPresentAmountFactor(x, N) + salvage * presentWorthFactor(x, N)
xVec = collect(0:0.01:1.0)
zVec = zeros(size(xVec))
# Two irrs that are equal t... | {"hexsha": "7dd99a72c082573d8d95246bd977d8e9231dffc9", "size": 368, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "problems/examples/multiple_irr1.jl", "max_stars_repo_name": "zborffs/EngineeringEconomics.jl", "max_stars_repo_head_hexsha": "f17d84f0ae79453a516a6b7d9e958d6ff08a87a0", "max_stars_repo_licenses": ["... |
# ~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~=~
# MIT License
#
# Copyright (c) 2021 Nathan Juraj Michlo
#
# 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 Softwar... | {"hexsha": "0367833b948e9da0d2f3950f789e6db2ac6c41fd", "size": 7616, "ext": "py", "lang": "Python", "max_stars_repo_path": "disent/schedule/_schedule.py", "max_stars_repo_name": "neonkitchen/disent", "max_stars_repo_head_hexsha": "0f45fefea03473690dfdbf48ef83f6e17ca9b8b3", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Helpers to test runtimes.
"""
import numpy
import pandas
import warnings
from skl2onnx.helpers.onnx_helper import (
select_model_inputs_outputs,
enumerate_model_node_outputs,
enumerate_model_initializers
)
from skl2onnx.algebra.type_helper import _guess_type
from .utils_backend import (
load_data_a... | {"hexsha": "0aa8b62c9cadca44d1da5dce8b225fd566d34598", "size": 21510, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_utils/utils_backend_onnxruntime.py", "max_stars_repo_name": "MaxNoe/sklearn-onnx", "max_stars_repo_head_hexsha": "698c9347e7c70cbb1a2c5bba1657e6548ff5897d", "max_stars_repo_licenses": ... |
import unittest
import numpy as np
from learning_rate import *
def make_flags():
from yacs.config import CfgNode as CN
flags = CN()
flags.max_iter = 160000 # Maximum training iterations
flags.lr_type = 'step' # Learning rate type: step or cos
flags.learning_rate = 0.1 # Initial learning rate... | {"hexsha": "d53426c8497e2ecd1d4fe998b689e9fc27f44772", "size": 5767, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/script/tests/test_learning_rate.py", "max_stars_repo_name": "christinazavou/ANNFASS_Structure", "max_stars_repo_head_hexsha": "f7b6d3e44d2466ed15009a3335e757def62adfa6", "max_stars_repo... |
//
// test-video-coder.cc
//
// Created by Peter Gusev on 15 April 2016.
// Copyright 2013-2016 Regents of the University of California
//
#include <cstdlib>
#include <ctime>
#include <stdlib.h>
#include <boost/asio.hpp>
#include "gtest/gtest.h"
#include "src/video-coder.hpp"
#include "mock-objects/encoder-delegate... | {"hexsha": "4e6bc1c5e249ba497e5b670b03e93f6d6e972e2c", "size": 36514, "ext": "cc", "lang": "C++", "max_stars_repo_path": "cpp/tests/test-video-coder.cc", "max_stars_repo_name": "peurpdapeurp/ndnrtc", "max_stars_repo_head_hexsha": "59552bff9398ee2e49636f32cac020cc8027ae04", "max_stars_repo_licenses": ["BSD-2-Clause"], "... |
[STATEMENT]
lemma fv_fo_fmla_list_exists: "fv_fo_fmla_list (Exists n \<phi>) = filter ((\<noteq>) n) (fv_fo_fmla_list \<phi>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fv_fo_fmla_list (Exists n \<phi>) = filter ((\<noteq>) n) (fv_fo_fmla_list \<phi>)
[PROOF STEP]
by (auto simp: fv_fo_fmla_list_def)
(metis... | {"llama_tokens": 225, "file": "Eval_FO_Ailamazyan", "length": 1} |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
class Visualizer:
def __init__(self, path):
self.data = np.genfromtxt(path, delimiter=',')
print(self.data)
def show(self, cumsum=True):
x = self.data[:, 0]
inside = self.data[:, 1]
... | {"hexsha": "f37b5c19676bf4681b0cfa6dc7fb6588488bd877", "size": 1231, "ext": "py", "lang": "Python", "max_stars_repo_path": "tracking/log_visualizer.py", "max_stars_repo_name": "t9s9/BeeMeter", "max_stars_repo_head_hexsha": "d0dfbf621a9147c047708a18540ba61266324176", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
###
# helpers
###
# make sure a passed distance matrix is a square
function check_square(m, msg)
n = size(m, 1)
if n != size(m, 2)
error(msg)
end
return n
end
"""
legal_circuit(circuit::AbstractArray{<:Integer})
Check that an array of integers is a valid circuit. A valid circuit over `n` ... | {"hexsha": "a0e96539d0f0c461f6d57d2b91e914f3f29e11a5", "size": 6081, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/helpers.jl", "max_stars_repo_name": "evanfields/TravelingSalesmanHeuristics.jl", "max_stars_repo_head_hexsha": "40f33ae2c07c836840674313ce9345a7d755f09b", "max_stars_repo_licenses": ["MIT"], "m... |
# File to compute the number of connections between
# R > G, G < R, G > G and R > R
# TODO: Check the code; there seems to be unnatural trends in the data.
import numpy as np
import glob
import pandas as pd
files = glob.glob("../../output/csv/ADJ/ADJ_A_*.csv")
CWC = []
CNC = []
for i, _file in enumerate(files):
I... | {"hexsha": "ade3c62d9ef666da5bdf59dfb7022cbc6f138bb8", "size": 1304, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/SNA_relations.py", "max_stars_repo_name": "soham1112/spider-networks", "max_stars_repo_head_hexsha": "0607f54044f2b16de8f543df4755c5ce3875e153", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
%% SECTION HEADER /////////////////////////////////////////////////////////////////////////////////////
\section{Subsection}
\label{sec61}
%% SECTION CONTENT ////////////////////////////////////////////////////////////////////////////////////
\lipsum[1]
%% SUBSECTION HEADER //////////////////////////////////////////... | {"hexsha": "46432d3bc0f30d245b889c1476e138a99592d12b", "size": 733, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapters/Chapter6/sect61.tex", "max_stars_repo_name": "SeaShadow/LaTeX-AMC-PhD-Thesis-Template", "max_stars_repo_head_hexsha": "9e8255d5406211b07253fca29788a3557860edc0", "max_stars_repo_licenses": [... |
#include <octomap/GaussionOcTree.h>
#include <octomap/octomap.h>
#include <pcl/common/centroid.h>
#include <pcl/common/transforms.h>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <Eigen/Dense>
#include <unordered_map>
#include <unordered_set>
#define maxdepth 16 // unit: layer
#define resolution ... | {"hexsha": "4a6c9ce4105d42d4b0f54db1091d6317513cd1ef", "size": 8319, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "octomap/src/testing/test_gaussion_tree.cpp", "max_stars_repo_name": "Peiwvy/octomap_gaussion", "max_stars_repo_head_hexsha": "74d46af2046c8f0e95419e17a584501240e24f3c", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2017 Aruul Mozhi Varman S.
#
# This 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, or (at your option)
# any later version.
#
#... | {"hexsha": "1dc868a1b3b9aa4533c04b4f17cb6b52df75d705", "size": 2045, "ext": "py", "lang": "Python", "max_stars_repo_path": "gr-ConvCodes/python/ConvCode_encoder.py", "max_stars_repo_name": "AruulmozhivarmanS/Convolution-Encoder-SDR-GNU-Radio", "max_stars_repo_head_hexsha": "cf860349d4cad62e28faadb9e1b54cc8c9dfee67", "m... |
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
# code starts here
bank=pd.read_csv(path)
categorical_var=bank.select_dtypes(include='object')
print(categorical_var)
numerical_var=bank.select_dtypes(include='number')
print(numerical_var)
# code ends here... | {"hexsha": "62e88e0e53e902fc19cd512c4d2ebfa27cd4aa98", "size": 1595, "ext": "py", "lang": "Python", "max_stars_repo_path": "Data-Wrangling-With-Pandas/code.py", "max_stars_repo_name": "fakhruddin950/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "388b13867667167514ef8a6cb314daa06e862850", "max_stars_repo_licenses... |
// wait.hpp --------------------------------------------------------------//
// Copyright 2010 Vicente J. Botet Escriba
// Copyright 2015 Andrey Semashev
// Distributed under the Boost Software License, Version 1.0.
// See http://www.boost.org/LICENSE_1_0.txt
#ifndef BOOST_DETAIL_WINAPI_WAIT_HPP
#define BOOST... | {"hexsha": "bd20d46826cc899300465343beb469098d44f4f1", "size": 2371, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/libboost/boost_1_62_0/boost/detail/winapi/wait.hpp", "max_stars_repo_name": "189569400/ClickHouse", "max_stars_repo_head_hexsha": "0b8683c8c9f0e17446bef5498403c39e9cb483b8", "max_stars_repo_... |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import os
import sys
import argparse
import json
import shutil
from collections import defaultdict
import numpy as np
import pandas as pd
from sklearn import linear_mo... | {"hexsha": "2d107c55473157cc507ad07451ae607566299aab", "size": 18707, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_defenses.py", "max_stars_repo_name": "iamgroot42/data-poisoning-release", "max_stars_repo_head_hexsha": "fef371060878b7524af9b31225d3144d268b98b3", "max_stars_repo_licenses": ["MIT"], "max_s... |
abstract type AbstractGPLayer end;
struct SVGPLayer{GPType} <: AbstractGPLayer
dim::Int
gps::Vector{GPType}
function SVGPLayer(dim,μ,Σ,Z,kernel,μ₀)
gps = [SVGP_Base(copy(μ),copy(Σ),copy(Z),deepcopy(kernel),deepcopy(μ₀)) for _ in 1:dim]
new{SVGP_Base}(dim,gps)
end
end
Base.le... | {"hexsha": "dcaf4f461a10a12465fc691dca73dddd4e156461", "size": 2528, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/layers.jl", "max_stars_repo_name": "JuliaTagBot/DeepGP.jl", "max_stars_repo_head_hexsha": "b05fb0be388358bcfe7c31ce2b2dd959b846dae4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import pyuvdata.utils as uvutils
import sys
import numpy as np
import os
from pyuvdata import UVData, UVBeam
import healpy as hp
from astropy.constants import c
#include locally-revised Github code:
sys.path.insert(1, '/home/atj/Github_Repos/local_edits/') # insert at 1, 0 is the script path (or '' in REPL)
c_ms = c.... | {"hexsha": "a15abd579b69cdf5afe884ba20c87a849fe065ee", "size": 33661, "ext": "py", "lang": "Python", "max_stars_repo_path": "CoupledRadioInterferometer/Functions/first_order_coupling_functions.py", "max_stars_repo_name": "alphatangojuliett/coupled-radio-interferometer", "max_stars_repo_head_hexsha": "6b3de76dddb5eaf5fd... |
(*
Copyright 2016 Luxembourg University
Copyright 2017 Luxembourg University
This file is part of Velisarios.
Velisarios 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 Licen... | {"author": "vrahli", "repo": "Velisarios", "sha": "6fb353b18610cd79210755fcc90123536c367aaa", "save_path": "github-repos/coq/vrahli-Velisarios", "path": "github-repos/coq/vrahli-Velisarios/Velisarios-6fb353b18610cd79210755fcc90123536c367aaa/PBFT/PBFTnew_view_learns_or_knows.v"} |
[STATEMENT]
lemma nonneg_incseq_Bseq_subseq_iff:
fixes f :: "nat \<Rightarrow> real"
and g :: "nat \<Rightarrow> nat"
assumes "\<And>x. f x \<ge> 0" "incseq f" "strict_mono g"
shows "Bseq (\<lambda>x. f (g x)) \<longleftrightarrow> Bseq f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Bseq (\<lambda>x. f ... | {"llama_tokens": 245, "file": null, "length": 2} |
import numpy as np
#
#---------function definitions----------
#---------you might want to move along to the main() funtion section. more fun over there---------------
#
#function: assign colour by plate id
def get_colour_by_plateid(plate_id):
from matplotlib import colors
converter = colors.ColorConverter()
... | {"hexsha": "d2bda0aaf4e70898e83ad3c33d72cf6fb3551f51", "size": 2048, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/plate_tectonic_utils.py", "max_stars_repo_name": "datuadiatma/pygplates-tutorials", "max_stars_repo_head_hexsha": "69801b101fae375565b705358ff6e52c5c1a2ddd", "max_stars_repo_licenses": ["CC-B... |
\appendix
\section{Appendix}
We release our source code online under the free MIT license.\footnote{\url{https://github.com/heinrichreimer/modern-talking}}
| {"hexsha": "b0d02a5ef27b7b8af32e8770c92f8e31d5301030", "size": 157, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/05-appendix.tex", "max_stars_repo_name": "maxhenze/modern-talking", "max_stars_repo_head_hexsha": "2a08027a1ee197bc86bb5b3eee75bc7c952dd288", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright (c) 2018-2020, NVIDIA CORPORATION.
# 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... | {"hexsha": "cab139ec4bd7335ae5db32cfd2724e54038d7f70", "size": 13680, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/utils/run_benchmarks.py", "max_stars_repo_name": "BradReesWork/cugraph", "max_stars_repo_head_hexsha": "9ddea03a724e9b32950ed6282120007c76482cbc", "max_stars_repo_licenses": ["Apache-2.0"]... |
# This file includes intregrator construction
function construct_integrator(deproblem, input, righthandside, state, t, modelargs=(), solverargs=();
alg=nothing, stateder=state, modelkwargs=NamedTuple(), solverkwargs=NamedTuple(), numtaps=3)
# If needed, construct interpolant for input.
interpolant = inpu... | {"hexsha": "80c196df3afd1be46331278a8663246bd2fdbc65", "size": 1845, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/components/systems/dynamicalsystems/init.jl", "max_stars_repo_name": "NumHack/Causal.jl", "max_stars_repo_head_hexsha": "6cfc0eaacae742c681bf02f3275e413db4f5e3d9", "max_stars_repo_licenses": ["... |
/*
Copyright 2019 Glen Joseph Fernandes
(glenjofe@gmail.com)
Distributed under the Boost Software License, Version 1.0.
(http://www.boost.org/LICENSE_1_0.txt)
*/
#include <boost/core/alloc_construct.hpp>
#include <boost/core/default_allocator.hpp>
#include <boost/core/lightweight_test.hpp>
class type {
public:
ty... | {"hexsha": "0a86fc8f3d3adc64fedf439fb3d499fe75e43274", "size": 873, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/core/test/alloc_construct_throws_test.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max... |
# For compatibility with Python2 #
from __future__ import print_function, division, absolute_import
##################################
import numpy as np
import spekpy.SpekConstants as Const
from scipy import integrate
import spekpy.SpekAniso as aniso
## References (Note: Ref. 1-3 describe "legacy" model i.e. SpekCalc... | {"hexsha": "5d9ff991e7cf0efba8dc7f2dedc17a94c2320750", "size": 19104, "ext": "py", "lang": "Python", "max_stars_repo_path": "spekpy/SpekModel.py", "max_stars_repo_name": "salvol/metalsf2", "max_stars_repo_head_hexsha": "4900173da33114216890ba2e57e12c18ec051416", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
if __name__ == '__main__':
import os
import sys
import shutil
import argparse
import numpy as np
from ray import tune
sys.path.append(os.getcwd())
from predict_utils import predict_rna
parser = argparse.ArgumentParser()
parser.add_argument('--indir', type=str, default=None, he... | {"hexsha": "34769dd35837624a5c7197dc4d6bbd3635e231a6", "size": 2198, "ext": "py", "lang": "Python", "max_stars_repo_path": "3_prediction/predict.py", "max_stars_repo_name": "phiwei/prostate_coexpression", "max_stars_repo_head_hexsha": "2cfbd4d292e52377b881f816aff5eaaa61f32a41", "max_stars_repo_licenses": ["MIT"], "max_... |
// Copyright (C) 2013 Vicente J. Botet Escriba
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
// <boost/thread/synchronized_value.hpp>
// class synchronized_value<T,M>
// strict_lock_ptr<T,M> synch... | {"hexsha": "6044ea200c33787dac84c127025bba34d31f9606", "size": 963, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/thread/test/sync/mutual_exclusion/synchronized_value/synchronize_pass.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo... |
import queue
import threading
import random
import numpy as np
import pandas as pd
import time
class Recognizer(threading.Thread):
def __init__(self, stop_event, select_event, sig_queue, pat_queues, algo, n, interval, pats, model_period, model_delay):
threading.Thread.__init__(self)
self.algo = alg... | {"hexsha": "c9dc56a20597403e189fa21bb73ac511b8914957", "size": 9140, "ext": "py", "lang": "Python", "max_stars_repo_path": "recognizer.py", "max_stars_repo_name": "saintnever/synctap_study_platform", "max_stars_repo_head_hexsha": "8188aa57b1ed2ad4762741ac03bca9ce03f9ffc3", "max_stars_repo_licenses": ["MIT"], "max_stars... |
[STATEMENT]
lemma Rep_Abs_1: "Rep (Abs 1) = 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Rep (Abs 1) = 1
[PROOF STEP]
by (simp add: Abs_inverse size1) | {"llama_tokens": 80, "file": null, "length": 1} |
"""
ccenergy.py: CC T-amplitude Solver
"""
if __name__ == "__main__":
raise Exception("This file cannot be invoked on its own.")
import psi4
import time
import numpy as np
from opt_einsum import contract
from .utils import helper_diis
from .cc_eqs import build_Fae, build_Fmi, build_Fme
from .cc_eqs import build_... | {"hexsha": "15ca125589eac1e1bab96a4dd93d1cb8acc9272c", "size": 6897, "ext": "py", "lang": "Python", "max_stars_repo_path": "pycc/ccenergy.py", "max_stars_repo_name": "RitvikPrabhu/pycc", "max_stars_repo_head_hexsha": "3ec3a0545296e44cd8dfbb4ad3e92553f3b8a9a8", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
'''
Reference:
https://github.com/santi-pdp/segan_pytorch/blob/master/segan/utils.py
https://github.com/svj1991/Adaptive_front_ends/blob/master/sepcosts.py
'''
from subprocess import run, PIPE
from scipy.linalg import toeplitz
from scipy.io import wavfile
# from numba import jit, int32, float32
import soundfile as sf... | {"hexsha": "23ec1863d101fe8c405372b429550b99796d1438", "size": 21143, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics.py", "max_stars_repo_name": "jeffreyjeffreywang/SSE", "max_stars_repo_head_hexsha": "9276f96d3b54bd542e32568bbc5b3a157b55b5e7", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count"... |
%MINENCLOSINGCIRCLE Finds a circle of the minimum area enclosing a 2D point set
%
% [center,radius] = cv.minEnclosingCircle(points)
%
% ## Input
% * __points__ Input vector of 2D points, stored in numeric array
% (Nx2/Nx1x2/1xNx2) or cell array of 2-element vectors (`{[x,y], ...}`).
%
% ## Output
% * __center__ ... | {"author": "kyamagu", "repo": "mexopencv", "sha": "d29007b2a484d0fd92e6e941dc5fd4750014fa6a", "save_path": "github-repos/MATLAB/kyamagu-mexopencv", "path": "github-repos/MATLAB/kyamagu-mexopencv/mexopencv-d29007b2a484d0fd92e6e941dc5fd4750014fa6a/+cv/minEnclosingCircle.m"} |
import numpy as np
import os.path as path
import pickle
from keras.layers.core import Dense
from keras.layers.pooling import GlobalMaxPooling1D
from keras.layers.recurrent import LSTM
from keras import optimizers
from fnc.models.Keras_utils import EarlyStoppingOnF1, convert_data_to_one_hot, calculate_class_weight, spl... | {"hexsha": "ad2f2b3cb39358ee8cb19a98f7bf6f45b83f376d", "size": 21994, "ext": "py", "lang": "Python", "max_stars_repo_path": "fnc/models/single_f_ext_LSTM.py", "max_stars_repo_name": "UKPLab/coling2018-fake-news-challenge-", "max_stars_repo_head_hexsha": "6446c4459b520b7f7713bc66117917e341d899dc", "max_stars_repo_licens... |
from gurobipy import Model, GRB, quicksum
from networkx import Graph, connected_components
edge2var = None
def callback_cycle(model, where):
""" Callback inserts constraints to forbid more than one cycle in solution candidates
:param model: a `gurobipy model <https://www.gurobi.com/documentation/9.1/refman/... | {"hexsha": "d73e74bd5032a2cfcd5307cfd4217cc2db65be36", "size": 6136, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphilp/network/tsp_callbacks.py", "max_stars_repo_name": "VF-DE-CDS/GraphILP-API", "max_stars_repo_head_hexsha": "841b80256f06b5dfc9f3bd4e514f1e24fb82b6ce", "max_stars_repo_licenses": ["MIT"], "... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright (c) 2003-2018 by The University of Queensland
% http://www.uq.edu.au
%
% Primary Business: Queensland, Australia
% Licensed under the Apache License, version 2.0
% http://www.apache.org/licenses/LICENSE-2.0
%
% Development until... | {"hexsha": "166a297a6f1eb74d1833290b6b259478d53cb804", "size": 7060, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/user/levelsetmodel.tex", "max_stars_repo_name": "markendr/esys-escript.github.io", "max_stars_repo_head_hexsha": "0023eab09cd71f830ab098cb3a468e6139191e8d", "max_stars_repo_licenses": ["Apache-2... |
"""GraphWave class implementation."""
import pygsp
import random
import numpy as np
import pandas as pd
from tqdm import tqdm
import networkx as nx
from pydoc import locate
class WaveletMachine:
"""
An implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets".
"""
def __init__(sel... | {"hexsha": "ec920d5fb485ee977d7f5f2b8e3e2b22c56f8385", "size": 4863, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/spectral_machinery.py", "max_stars_repo_name": "cafeal/GraphWaveMachine", "max_stars_repo_head_hexsha": "e6b84a4a60dd1fc660995999d89fea7f54282a5f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
inverter.py
"""
from typing import List
from dataclasses import dataclass
import numpy as np
from pydantic import validator, BaseModel
from scipy.optimize import minimize
import matplotlib.pyplot as plt
from opt_einsum import contract
# from pyscf import scf
from .methods.wuyang import WuYang
from .methods.wuyan... | {"hexsha": "a264791cbfc8b0b83c261259d936fac31da5f638", "size": 7210, "ext": "py", "lang": "Python", "max_stars_repo_path": "partition/inverter/inverter.py", "max_stars_repo_name": "VHchavez/Partition", "max_stars_repo_head_hexsha": "526823ac6497ef36b428ad013df789e97546a4c9", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
using Test, SegmentIntersections
@testset "Test event creation" begin
e1 = Event(1,2)
e2 = Event(0,5)
e3 = Event(1,2)
@test e1.point == Point(1,2)
@test e2 < e1
@test e1 == e3
end
| {"hexsha": "e201392a4cd81d362d4d6b162366ac417361f9f7", "size": 205, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/bentley_ottmann/event_tests.jl", "max_stars_repo_name": "arnauqb/BentleyOttmann.jl", "max_stars_repo_head_hexsha": "638caf8906a431eebbc61f68569ddeb1a8cbc1f8", "max_stars_repo_licenses": ["MIT"]... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from std_msgs.msg import Float32, UInt8
from sensor_msgs.msg import Image, CompressedImage
from mission_processor import MissionProcessor
from intersection_detector import IntersectionDetector
from nav_msgs.msg import Odometry
from cv_bridge import CvBridge
import numpy as ... | {"hexsha": "3e09a41b5738a31e52e8ec334030f769084227ef", "size": 22927, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mission_node/src/intersection_mission_node.py", "max_stars_repo_name": "mommy79/AuDi-GIT-turtlebot3_autorace", "max_stars_repo_head_hexsha": "fd1382246f1ee74ee70857006563184d672a6666", "max_s... |
[STATEMENT]
lemma linear_irreducible_int:
fixes p :: "int poly"
assumes deg: "degree p = 1" and cp: "content p dvd 1"
shows "irreducible p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. irreducible p
[PROOF STEP]
proof (intro irreducibleI)
[PROOF STATE]
proof (state)
goal (3 subgoals):
1. p \<noteq> 0
2. \<... | {"llama_tokens": 1946, "file": "Algebraic_Numbers_Algebraic_Numbers_Prelim", "length": 23} |
from __future__ import print_function # In python 2.7
from flask import Flask, session, render_template, make_response, jsonify, request, send_from_directory, g, url_for
from flask_limiter import Limiter
from flask_limiter.util import get_remote_address
import json
from sklearn.naive_bayes import GaussianNB
import num... | {"hexsha": "1d2eb05ca643c4645167b8fc3de81f0c5dcc20e8", "size": 6148, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "sparrow-platform/sparrow-disease-diagnostics", "max_stars_repo_head_hexsha": "62a5d92202852caf57b872ebf62ffc9613603a96", "max_stars_repo_licenses": ["Apache-2.0"],... |
# FFSSolver: (F)orward (S)ingle (S)hooting (Solver)
# Solves indirect trajectory optimization problem using a forward
# single shooting based approach.
# NOTE: Likely only supports spacecraft trajectory optimization problems
# involving a single spacecraft using a 6 element state representation
# with mass (7 total e... | {"hexsha": "3087f9187738b2919814a5e24fca06e1efbc716a", "size": 9635, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/FSSSolver.jl", "max_stars_repo_name": "GrantHecht/IndirectShooting.jl", "max_stars_repo_head_hexsha": "7a33a1b9af86b08718b871c65436233835cd8b1c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
/*=============================================================================
Copyright (c) 2001-2011 Joel de Guzman
Copyright (c) 2006 Dan Marsden
Distributed under the Boost Software License, Version 1.0. (See accompanying
file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
======... | {"hexsha": "8b0eb1fda42f7a3a7649e15f3ea7f36e42bb6b7e", "size": 3046, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/boost/fusion/view/zip_view/detail/begin_impl.hpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "m... |
from collections import defaultdict
from contextlib import contextmanager
from pathlib import Path
import networkx as nx
import numpy as np
import torch
from plan import Plan, get_sub_plans
from tree import Tree
def to_forest(plan):
forest = []
for root in plan.get_roots():
g = plan.G.subgraph(nx.de... | {"hexsha": "2ec8d245151eb0ad130eb9c926254c20666ac05c", "size": 5008, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/utils.py", "max_stars_repo_name": "simplerick/sqlopt", "max_stars_repo_head_hexsha": "59bc781e752887c9a5e9157d53e81fdd24ac77f1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
/-
Copyright (c) 2018 Robert Y. Lewis. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Robert Y. Lewis
Define the p-adic numbers (rationals) ℚ_p as the completion of ℚ wrt the p-adic norm.
Show that the p-adic norm extends to ℚ_p, that ℚ is embedded in ℚ_p, and that ℚ_... | {"author": "digama0", "repo": "mathlib-ITP2019", "sha": "5cbd0362e04e671ef5db1284870592af6950197c", "save_path": "github-repos/lean/digama0-mathlib-ITP2019", "path": "github-repos/lean/digama0-mathlib-ITP2019/mathlib-ITP2019-5cbd0362e04e671ef5db1284870592af6950197c/src/data/padics/padic_numbers.lean"} |
/* (c) Copyright 2012 Felipe Magno de Almeida
*
* Distributed under the Boost Software License, Version 1.0. (See
* accompanying file LICENSE_1_0.txt or copy at
* http://www.boost.org/LICENSE_1_0.txt)
*/
#ifndef MORBID_IDL_COMPILER_TYPEDEF_GENERATOR_HPP
#define MORBID_IDL_COMPILER_TYPEDEF_GENERATOR_HPP
#include ... | {"hexsha": "3c7968d2ac1392de88de9f6e071d021ce782ad1c", "size": 1448, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "idl_compiler/include/morbid/idl_compiler/generator/typedef_generator.hpp", "max_stars_repo_name": "felipealmeida/mORBid", "max_stars_repo_head_hexsha": "3ebc133f9dbe8af1c5cfb39349a0fbf5c125229b", "m... |
#!/usr/bin/env python3
import re
import sys
import logging
import pysam
import json
import statistics as stats
from subprocess import run
from scipy.stats import mannwhitneyu
from scipy.stats import ttest_ind
from collections import defaultdict
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
from Bio import... | {"hexsha": "a6641da07fe11bb44b73d941347a28249c9c4d83", "size": 22941, "ext": "py", "lang": "Python", "max_stars_repo_path": "bleties/main.py", "max_stars_repo_name": "Swart-lab/bleties", "max_stars_repo_head_hexsha": "92387085db8f6e3eaf6c2b3271b0a1576cb53608", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
"""
OXASL - Structural data module
Copyright (c) 2008-2020 Univerisity of Oxford
"""
import os
import glob
import numpy as np
import fsl.wrappers as fsl
from fsl.data.image import Image
from fsl.utils.path import PathError
from oxasl.options import OptionCategory, OptionGroup
from oxasl.reporting import LightboxIma... | {"hexsha": "db2da67f8574923ccde5fea7f3bd3781b206d785", "size": 7381, "ext": "py", "lang": "Python", "max_stars_repo_path": "oxasl/struc.py", "max_stars_repo_name": "physimals/oxasl", "max_stars_repo_head_hexsha": "e583103f3313aed2890b60190b6ca7b265a46e3c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1... |
/*
* S2E Selective Symbolic Execution Platform
*
* Copyright (c) 2015, Dependable Systems Laboratory, EPFL
* 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... | {"hexsha": "4f3be48990322b6e6cf41e0f68f91d70ad5966b2", "size": 4470, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "klee/lib/Solver/Z3IteBuilder.cpp", "max_stars_repo_name": "sebastianpoeplau/s2e", "max_stars_repo_head_hexsha": "995cac6126e7d80337e8c4a72bfa9a87eea7eb68", "max_stars_repo_licenses": ["MIT"], "max_s... |
from datetime import timezone, timedelta, datetime
from unittest.mock import MagicMock, patch
import re
import geopandas as gpd
import numpy as np
import pandas as pd
import pytest
from pandas import Timestamp
from metloom.pointdata import CDECPointData, PointDataCollection
from metloom.variables import CdecStationVa... | {"hexsha": "59604d0fd71e95a54bd4e9e2afc3c57c6e051d79", "size": 21524, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_cdec.py", "max_stars_repo_name": "M3Works/metloom", "max_stars_repo_head_hexsha": "3e41dbdd6b2889c9917828f4198460a0edc151b4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
import numpy as np
from PIL import Image
def plot(image, filename):
"""Plot the image as a log2 greyscale image"""
image = np.log2(1 + image.astype(np.float64))
image *= 255.0 / np.max(image)
Image.fromarray(image.astype(np.uint8)).save(filename)
def field(scl):
"""Generate a field of complex va... | {"hexsha": "ee2975dc1f11cac359815185d401f394a0d03d41", "size": 647, "ext": "py", "lang": "Python", "max_stars_repo_path": "mandelbrot/__init__.py", "max_stars_repo_name": "graeme-winter/sidewinder", "max_stars_repo_head_hexsha": "c0c7f61dbf3aadad0f9dbf803051f6c81a255317", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
# SPDX-License-Identifier: Apache-2.0
"""
Tests h2o's tree-based methods' converters.
"""
import unittest
import os
import sys
import numpy as np
import pandas as pd
from onnx.defs import onnx_opset_version
from onnxconverter_common.onnx_ex import DEFAULT_OPSET_NUMBER
from sklearn.datasets import load_diabetes, load_i... | {"hexsha": "db5d990af8f37cfe17e0890210493cc2498f12ec", "size": 11166, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/h2o/test_h2o_converters.py", "max_stars_repo_name": "xadupre/onnxmltools", "max_stars_repo_head_hexsha": "a1588df9aaa239e4c2bd67acf76d900db22dc58c", "max_stars_repo_licenses": ["Apache-2.0"... |
! .................................................
! ____ _ _ ____ _____ _
! | _ \| | |_| | _ \| ___| |_|
! | |_) | |___ _ | |_) | |___ _
! | _ /| _ | | | | _ /|___ | | |
! | | | | | | | | | | ... | {"hexsha": "2c791e1b88d0f05261a151adbb7ccae16226a647", "size": 4161, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Force_Vector.f", "max_stars_repo_name": "PhiPsi-Software/PhiPsi_XFEM_Fortran_Codes_Early_Version", "max_stars_repo_head_hexsha": "1eae63a8f0dc968b9d5220397879645a83e5d083", "max_stars_repo_license... |
#
# Copyright 2020 Spotify AB
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, s... | {"hexsha": "5231c2ac6a40381b62ca5f742313b72e436cf22b", "size": 2964, "ext": "py", "lang": "Python", "max_stars_repo_path": "rips/policy/base.py", "max_stars_repo_name": "spotify-research/RIPS_KDD202", "max_stars_repo_head_hexsha": "84f9cde9528a176b36cad2417f363a7890e92486", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
from math import ceil
import random
from scipy.io import loadmat
from scipy import signal
import numpy as np
import os
from PIL import Image
from matplotlib.pyplot import get_cmap
import shutil
def butter_highpass_filter(data, cutoff=1, fs=128, order = 5):
''' -> Used to remove the low frequency signals causing b... | {"hexsha": "1ddb01729119e4951f88e3f3fd5b6e14313d9dc2", "size": 7951, "ext": "py", "lang": "Python", "max_stars_repo_path": "createDataset.py", "max_stars_repo_name": "SriniMaiya/ECG-Signal-Classification", "max_stars_repo_head_hexsha": "8ba953ce344f10dc27aca1c9221a3a9272ff8bef", "max_stars_repo_licenses": ["MIT"], "max... |
module Search
include("beam_search.jl")
export beam_search, greedy_search
end # module
| {"hexsha": "82d9d3ca11f287e23d835e34732fbaa8fb708e8a", "size": 89, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/search/Search.jl", "max_stars_repo_name": "heinrichreimer/text-summarization-bert-reproducability", "max_stars_repo_head_hexsha": "728bdde483b53332e25b60f97981df7cc7c5ff9c", "max_stars_repo_licen... |
from functools import lru_cache
from typing import Tuple
import numpy as np
import pandas as pd
from scipy.optimize import minimize
TRADING_DAYS_PER_YEAR = 365
def get_log_returns_over_period(price_history: pd.DataFrame) -> np.array:
"""
Given the price time series, compute the logarithm of the ration betwe... | {"hexsha": "73319907ebe708cb699262e90b93d73283801ea5", "size": 5799, "ext": "py", "lang": "Python", "max_stars_repo_path": "mpt.py", "max_stars_repo_name": "thelearningwolf/mpt_medium", "max_stars_repo_head_hexsha": "461378336080de7da05fb5b1a4a8834c777450c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "m... |
#ifndef XAOS_DETAIL_BACKEND_ALLOC_HPP
#define XAOS_DETAIL_BACKEND_ALLOC_HPP
#include <boost/core/pointer_traits.hpp>
namespace xaos {
namespace detail {
struct alloc_interface {
virtual auto relocate(void* alloc) -> void* = 0;
virtual void delete_this(void* alloc) = 0;
protected:
~alloc_interface() = defau... | {"hexsha": "a8a2d05db4863f037f573fb68d24ec6cb6771051", "size": 1904, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/xaos/detail/backend_alloc.hpp", "max_stars_repo_name": "grisumbras/xaos", "max_stars_repo_head_hexsha": "9d8a93911b9284a826ad43ba133a2febf960481c", "max_stars_repo_licenses": ["BSL-1.0"], "m... |
import torch
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import sys
from os.path import join as pjoin
import scanpy as sc
import squidpy as sq
import anndata
from sklearn.metrics import r2_score, mean_squared_error
from gpsa import VariationalGPSA, rbf_kernel
from gpsa... | {"hexsha": "735c06655c3db8434eec9ddb5eafd5f68b02591d", "size": 11815, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/expression/visium/visium_prediction.py", "max_stars_repo_name": "andrewcharlesjones/spatial-alignment", "max_stars_repo_head_hexsha": "70aecf800c5efea6a92990ccf87a1950752a268b", "max_... |
r"""Shift in linear model"""
import numpy as np
from . import pw_constant
def pw_linear(n_samples=200, n_features=1, n_bkps=3, noise_std=None, seed=None):
"""Return piecewise linear signal and the associated changepoints.
Args:
n_samples (int, optional): signal length
n_features (int, option... | {"hexsha": "a50f52ac91f428e15bc6d448874a07be53f5a729", "size": 1065, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ruptures/datasets/pw_linear.py", "max_stars_repo_name": "Lucas-Prates/ruptures", "max_stars_repo_head_hexsha": "9685818d08ca024c0abb6ecf6121f2f86fb26dba", "max_stars_repo_licenses": ["BSD-2-Cl... |
@testset "Manopt.jl Error Measures" begin
M = Sphere(2)
N = PowerManifold(M, NestedPowerRepresentation(), 2)
using Random: seed!
seed!(42)
d = Manifolds.uniform_distribution(M, [1.0, 0.0, 0.0])
w = rand(d)
x = rand(d)
y = rand(d)
z = rand(d)
a = [w, x]
b = [y, z]
@test me... | {"hexsha": "f891b109932ddbb60c0bcfaaf6e36b68a6c1f1c7", "size": 595, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/helpers/test_error_measures.jl", "max_stars_repo_name": "fkastner/Manopt.jl", "max_stars_repo_head_hexsha": "89c60404c7cf756102bcf45dd58dc443ef2b2d4e", "max_stars_repo_licenses": ["MIT"], "max_... |
Inductive IND2 (A:Type) (T:=fun _ : Type->Type => A) := CONS2 : IND2 A -> IND2 (T IND2).
| {"author": "princeton-vl", "repo": "CoqGym", "sha": "0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e", "save_path": "github-repos/coq/princeton-vl-CoqGym", "path": "github-repos/coq/princeton-vl-CoqGym/CoqGym-0c03a6fba3a3ea7e2aecedc1c624ff3885f7267e/coq/test-suite/coqchk/bug_8655.v"} |
import pandas as pd
from textblob import TextBlob
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation impor... | {"hexsha": "3710df5041c0a85243abff568a6e732ef931bb18", "size": 30710, "ext": "py", "lang": "Python", "max_stars_repo_path": "cooking1.py", "max_stars_repo_name": "Bolaka/kaggle-whats-cooking", "max_stars_repo_head_hexsha": "2e8eac8bdc36188c43eac1979a47a77e246db617", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
using BinaryBuilder, Pkg
name = "MKL"
version = v"2021.1.1"
# Bash recipes for building across all platforms
script = read(joinpath(@__DIR__, "script.sh"), String)
script_macos = read(joinpath(@__DIR__, "script_macos.sh"), String)
non_reg_ARGS = filter(arg -> arg != "--register", ARGS)
platform_sources = [
(
... | {"hexsha": "899af5ed1ec3f1107cc564b0f5abffa27b50137a", "size": 3577, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "M/MKL/build_tarballs.jl", "max_stars_repo_name": "plut/Yggdrasil", "max_stars_repo_head_hexsha": "594ecaf79fb5f7a7e53055b8d125d8d4538ea68e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
# -------------------------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information.
# ----------------------------------------------------------------------... | {"hexsha": "94fa8cd14b3ef0bb752b60e4cfe18e274cd0d5f8", "size": 4161, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyStationB/libraries/ABEX/tests/plotting/test_convergence_plotting.py", "max_stars_repo_name": "BrunoKM/station-b-libraries", "max_stars_repo_head_hexsha": "ea3591837e4a33f0bef789d905467754c27913b... |
subroutine DecisionsRet
!****************************************************************************
!
! PROGRAM: DecisionsRet
!
! PURPOSE: Compute Decision Rules of Retired
!
! VERSION:
! 0.1, 11-June-2012
! 1.0, 27-May-2013
! 1.1, 10-June-2014
!
! LAST EDITED BY: Kurt, 13-June-201... | {"hexsha": "3793df05826b6ff94fa1818e2e40f3e6613ffeb7", "size": 50956, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "FortranCode/benchmark-code/DecisionsRet.f90", "max_stars_repo_name": "lnsongxf/housing-boom-bust", "max_stars_repo_head_hexsha": "bb75a2fd0646802dcdf4d5e56d1392bae7090e1c", "max_stars_repo_lice... |
/*
* GridTools
*
* Copyright (c) 2014-2021, ETH Zurich
* All rights reserved.
*
* Please, refer to the LICENSE file in the root directory.
* SPDX-License-Identifier: BSD-3-Clause
*/
#pragma once
#include <ostream>
#include <string>
#include <typeinfo>
#include <boost/core/demangle.hpp>
#include <nlohmann/json... | {"hexsha": "5d8c9d6e7b690b8d2604b694eb45204379e0617f", "size": 6116, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/gridtools/stencil/dump.hpp", "max_stars_repo_name": "afanfa/gridtools", "max_stars_repo_head_hexsha": "6b1fd1f916d291f9a1cab1d27b48aa0282097d68", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
# -*- coding: utf-8 -*-
"""
Class for "reading" fake data from an imaginary file.
For the user, it generates a :class:`Segment` or a :class:`Block` with a
sinusoidal :class:`AnalogSignal`, a :class:`SpikeTrain` and an
:class:`EventArray`.
For a developer, it is just an example showing guidelines for someone who wants... | {"hexsha": "c79f7e8eba26857a59e577634aea58f52f706d46", "size": 11859, "ext": "py", "lang": "Python", "max_stars_repo_path": "neo/io/exampleio.py", "max_stars_repo_name": "tclose/python-neo", "max_stars_repo_head_hexsha": "338d381b735a019f6be68ab7196366eed33815fe", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
[STATEMENT]
lemma norm_\<P>\<^sub>L_le_one: "norm (\<P>\<^sub>L d) \<le> 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. norm (\<P>\<^sub>L d) \<le> 1
[PROOF STEP]
using norm_\<P>\<^sub>L_le norm_\<P>\<^sub>1
[PROOF STATE]
proof (prove)
using this:
norm (\<P>\<^sub>L ?d) \<le> norm (\<P>\<^sub>1 (mk_dec_det ?d))
n... | {"llama_tokens": 199, "file": "MDP-Algorithms_Splitting_Methods", "length": 2} |
"""Zernike polynomials."""
from collections import defaultdict
import numpy as truenp
from .jacobi import jacobi, jacobi_sequence
from prysm.mathops import np, kronecker, sign, is_odd
from prysm.util import sort_xy
from prysm.plotting import share_fig_ax
def zernike_norm(n, m):
"""Norm of a Zernike polynomial... | {"hexsha": "16adf1ae7b5caa70b9478190d844f451bb853432", "size": 14644, "ext": "py", "lang": "Python", "max_stars_repo_path": "prysm/polynomials/zernike.py", "max_stars_repo_name": "deisenroth/prysm", "max_stars_repo_head_hexsha": "53a400ef89697041f67192e879e61ad28c451318", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
module clblas
use cl
implicit none
private
public :: &
clblasGetVersion, &
clblasSetup, &
clblasTeardown, &
clblasDtrsmEx, &
clblasZtrsmEx, &
clblasDgemmEx, &
clblasZgemmEx, &
clblasDsyrkEx, &
clblasZhe... | {"hexsha": "fd44eab9df158ab6d36104ed375715a23d2d852d", "size": 13856, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "external_libs/fortrancl/clblas.f90", "max_stars_repo_name": "gimunu/octopus-metric", "max_stars_repo_head_hexsha": "baabccd5402922a2f62f5cf6030d15e7ea76dc9b", "max_stars_repo_licenses": ["Apach... |
import category_theory.base
import category_theory.replete
open category_theory
open category_theory.category
local notation f ` ∘ `:80 g:80 := g ≫ f
universes v u
namespace homotopy_theory.weak_equivalences
class has_weak_equivalences (C : Type u) [category C] :=
(is_weq : Π ⦃a b : C⦄, (a ⟶ b) → Prop)
def is_weq ... | {"author": "rwbarton", "repo": "lean-homotopy-theory", "sha": "39e1b4ea1ed1b0eca2f68bc64162dde6a6396dee", "save_path": "github-repos/lean/rwbarton-lean-homotopy-theory", "path": "github-repos/lean/rwbarton-lean-homotopy-theory/lean-homotopy-theory-39e1b4ea1ed1b0eca2f68bc64162dde6a6396dee/src/homotopy_theory/formal/weak... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import processing_cxx
from utils.detection_input import DetectionAugmentation
from rangedet.core.util_func import jit_class_aware_expand, sample_data
EPS = 1e-3
class LoadRecord(Detection... | {"hexsha": "4790402dad54af7d6ab01704aa18dab9a9a93cdb", "size": 24363, "ext": "py", "lang": "Python", "max_stars_repo_path": "rangedet/core/input.py", "max_stars_repo_name": "jie311/RangeDet", "max_stars_repo_head_hexsha": "5078ce339c6d27a009aed1ca2790911ce4d10bc7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import numpy as np
from keras.models import load_model
data = np.load('face_test.npz')
# print(data['arr_0'])
trainX, trainy, testX, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
print('Loaded: ', trainX.shape, trainy.shape, testX.shape, testy.shape)
model = load_model('facenet_keras.h5')
print(... | {"hexsha": "46fb5f90665c67086a815fd1f61fe1e8101e2e58", "size": 1039, "ext": "py", "lang": "Python", "max_stars_repo_path": "Face Attendance/detect.py", "max_stars_repo_name": "dipesh-commits/ML_Materials", "max_stars_repo_head_hexsha": "e5168b5e7287ce99c0e875169a2e11fba2a5462e", "max_stars_repo_licenses": ["MIT"], "max... |
# Copyright 2021 Fedlearn authors.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writi... | {"hexsha": "3a8382b37ed6e3cc7aa4af996a753d5b54dac9b9", "size": 9105, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/kernelmethod/server_kernelmethod.py", "max_stars_repo_name": "monadyn/fedlearn-algo", "max_stars_repo_head_hexsha": "c4459d421139b0bb765527d636fff123bf17bda4", "max_stars_repo_licenses": ["A... |
import matplotlib.pyplot as plt
import numpy as np
lines = np.loadtxt("episode_reward_3.txt", comments="#", delimiter="\n", unpack=False)
lines_2 = np.loadtxt("episode_reward_2.txt", comments="#", delimiter="\n", unpack=False)
plt.plot(lines)
plt.plot(lines_2)
plt.show()
| {"hexsha": "6e464cbc4fae401b193233f84c36c4a882904777", "size": 273, "ext": "py", "lang": "Python", "max_stars_repo_path": "plot.py", "max_stars_repo_name": "anirudh9119/credit_assignment", "max_stars_repo_head_hexsha": "b3135f82ca40a93a0490ae103b007cf1b837531a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
import unittest
import numpy as np
from ffthompy.matvecs import DFT, VecTri
class Test_matvec(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_matrix_versions(self):
print('\nChecking Matrices...')
for dim in [2, 3]:
for n in [4, 5]... | {"hexsha": "f40f1891729caa750b617c381f79d13329b7fd52", "size": 1545, "ext": "py", "lang": "Python", "max_stars_repo_path": "ffthompy/matvecs/unittest_matvec.py", "max_stars_repo_name": "TruongQuocChien/FFTHomPy", "max_stars_repo_head_hexsha": "2c23c80dd2cab46f1090103e613b4f886b3daac7", "max_stars_repo_licenses": ["MIT"... |
from typing import List
import numpy as np
import gunpowder as gp
class MergeMasks(gp.BatchFilter):
def __init__(
self,
arrays: List[gp.ArrayKey],
output_array: gp.ArrayKey):
"""Merge multiple binary masks with a logical and
Args:
arrays: list of bi... | {"hexsha": "33297b55761feecf298920a8ed3463af51b869b3", "size": 1151, "ext": "py", "lang": "Python", "max_stars_repo_path": "incasem/gunpowder/merge_masks.py", "max_stars_repo_name": "kirchhausenlab/incasem", "max_stars_repo_head_hexsha": "ee9e007c5c04571e547e2fb5af5e800bd2d2b435", "max_stars_repo_licenses": ["BSD-3-Cla... |
# Copyright 2016 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 applicable ... | {"hexsha": "938401f80cefa249a9692cd9bff36b5f624da758", "size": 3110, "ext": "py", "lang": "Python", "max_stars_repo_path": "Fine-tuning/datasets/dataset_factory.py", "max_stars_repo_name": "ivclab/NeuralMerger", "max_stars_repo_head_hexsha": "9802f79ee2bfdb24aca5c5710bc68e045ee7563a", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import requests
def hello_world():
print('hello world!')
def get_numpy():
matrix_a = np.random.randn(3, 3)
return matrix_a
def get_requests():
r = requests.get('https://www.google.com')
return r.content
if __name__ == '__main__':
hello_world()
print(get_numpy())
... | {"hexsha": "cc5732d6b6f24ba206d04a02965e5fe39ea0deb7", "size": 344, "ext": "py", "lang": "Python", "max_stars_repo_path": "sayhello/say_hello.py", "max_stars_repo_name": "frzmohammadali/my_pypi_package", "max_stars_repo_head_hexsha": "96d12da422cd1d4a644f4742aeed6aa15960e3f5", "max_stars_repo_licenses": ["MIT"], "max_s... |
import .love01_definitions_and_statements_demo
/-! # LoVe Exercise 1: Definitions and Statements
Replace the placeholders (e.g., `:= sorry`) with your solutions. -/
set_option pp.beta true
set_option pp.generalized_field_notation false
namespace LoVe
/-! ## Question 1: Truncated Subtraction
1.1. Define the fun... | {"author": "jappaaa", "repo": "Bachelor-project", "sha": "56d13d7ad5136ac2142d0d7cccb859c1a96a81e5", "save_path": "github-repos/lean/jappaaa-Bachelor-project", "path": "github-repos/lean/jappaaa-Bachelor-project/Bachelor-project-56d13d7ad5136ac2142d0d7cccb859c1a96a81e5/master course files/logical_verification_2021-main... |
from recon.wall import WallReader, WallWriter
from recon.meld import MeldReader, MeldWriter
def wall2meld(wfp, mfp):
"""
This function reads a wall file in and converts it to a
meld file.
"""
wall = WallReader(wfp)
meld = MeldWriter(mfp, metadata=wall.metadata)
objects = {}
tables = {}... | {"hexsha": "d2b34eb02fe714f241d5c55c69ad802ff71e11a4", "size": 5483, "ext": "py", "lang": "Python", "max_stars_repo_path": "recon/trans.py", "max_stars_repo_name": "xogeny/recon", "max_stars_repo_head_hexsha": "72618d6f6b063bc37bd80a364d6f95934b0f3f79", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_sta... |
%\documentstyle[times,titlepage,twoside,verbatimfiles]{article}
\documentstyle[titlepage,twoside,verbatimfiles]{article}
% margins from ~/tex/opengl/*.tex documents
\topmargin -0.3in
\headheight 10pt
\headsep 15pt
\oddsidemargin -.20in
\evensidemargin -.35in
\textwidth 7.1in
\textheight 9in
\makeindex
... | {"hexsha": "4babd5b22203cd88aa56beac5c274c3238af9ae7", "size": 27058, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "glut/doc/spec/spec2.tex", "max_stars_repo_name": "huidian200803/osg3RD", "max_stars_repo_head_hexsha": "5adb8824e49d339c73587582858ab6896898f9ad", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
"""
Python 3.9 программа класса доски и класса игры между двумя игроками
программа на Python по изучению обучения с подкреплением - Reinforcement Learning
Название файла connect4_game.py
Version: 0.1
Author: Andrej Marinchenko
Date: 2021-12-22
"""
import numpy as np # базовые методы для манипуляции с большими массива... | {"hexsha": "b74c5cdf244fbbe8ebfe01f581abe8025ebf155d", "size": 12725, "ext": "py", "lang": "Python", "max_stars_repo_path": "Game_AI_and_Reinforcement_Learning/ConnectX/v2/connect4_game.py", "max_stars_repo_name": "BEPb/Python-100-days", "max_stars_repo_head_hexsha": "163a68b42d1933d82599774a198eeef1624607bb", "max_sta... |
"""Create 4x4 transformation matrices."""
import numpy
from numpy import linalg
from numpy.linalg import inv
import math
identity = numpy.asfortranarray(numpy.eye(4, dtype=numpy.float32))
def stretching(sx, sy, sz):
"""Create a transformation matrix that represents a stretching along x, y and z direction."""
... | {"hexsha": "456617e5caefc35af400d0cc630d86d8cf0de75b", "size": 2764, "ext": "py", "lang": "Python", "max_stars_repo_path": "rpigl/transforms.py", "max_stars_repo_name": "stephanh42/rpigl", "max_stars_repo_head_hexsha": "6e546d961bb189b8b4c65317c1e9aa2b5cbd160e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 26... |
import networkx as nx
import matplotlib.pyplot as plt
import numpy as np
#graph:
np.random.seed(42)
G = nx.Graph()
fig = plt.figure(figsize=(3,3),dpi=200)
G.add_nodes_from([1,2,3,4,5,6])
G.add_edges_from([(1,2),(2,3),(4,5),(6,5),(3,4),(4,6),(3,1)])
nx.draw(G, node_color='#e3427d')
G2 = nx.Graph()
G2.add_nodes_from([1,... | {"hexsha": "2d3540413d47617dd6b1b9f56eb83e367c051d9f", "size": 1380, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/experiments/figure_link_pred_method.py", "max_stars_repo_name": "ChristianDjurhuus/RAA", "max_stars_repo_head_hexsha": "b2eb1db527bcb09f35598c2bbf8dff2689ad599b", "max_stars_repo_licenses": ["... |
import numpy as np
import torch
import random
class PointcloudRotate(object):
def __call__(self, pc):
bsize = pc.size()[0]
for i in range(bsize):
rotation_angle = np.random.uniform() * 2 * np.pi
cosval = np.cos(rotation_angle)
sinval = np.sin(rotation_angle)
... | {"hexsha": "edb06083d45226b5795b8a46dc9a370069d3a4ef", "size": 4190, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/data_transforms.py", "max_stars_repo_name": "Pang-Yatian/Point-MAE", "max_stars_repo_head_hexsha": "61727f76e9d0c28babf422505073bd43c2f517bc", "max_stars_repo_licenses": ["MIT"], "max_sta... |
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