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using OptEnrichedSetCover
using Test, LinearAlgebra, DataFrames
const OESC = OptEnrichedSetCover
# run the tests from jlfile
macro testfile(jlfile)
quote
@testset "\"$($jlfile)\" tests" begin
include($jlfile)
end
end
end
@testfile "test_set_score.jl"
@testfile "test_sparse_mask_ma... | {"hexsha": "75bde55fc1ef40440b44389d6e73ca0e204373e9", "size": 535, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "alyst/OptEnrichedSetCover.jl", "max_stars_repo_head_hexsha": "2bcabf6cb08d108e2678bb03328d072921eb0fc9", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_... |
"""Apply a function to 1-D slices along the given axis."""
from __future__ import annotations
from functools import wraps
from typing import Any, Callable, List
import numpy
import numpoly
from ..baseclass import ndpoly, PolyLike
from ..dispatch import implements
@implements(numpy.apply_along_axis)
def apply_along_... | {"hexsha": "25144c7cd8110b827cee98db840b9695a51c0bc5", "size": 4043, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpoly/array_function/apply_along_axis.py", "max_stars_repo_name": "jonathf/npoly", "max_stars_repo_head_hexsha": "9df4bd2a3b134e8a196e24389c0ad84c26da9662", "max_stars_repo_licenses": ["BSD-2-Cl... |
using DataFrames
using Random
using LinearAlgebra
using FixedEffectModels
Random.seed!(123)
N = 2
T = 100_000
x = randn(T,N)
β = [1 0; -1 2; 0.5 0.6 ; 0.3 0; 0.2 1]
y = zeros(Float64, T)
for t in 1:T,l in 0:min(4,t-1)
y[t] += dot(β[l+1, :], x[t-l, :]) + 0.01*randn()
end
# Make the dataframe
df = DataFrame(y=y)
df... | {"hexsha": "0329c4a56af44a9958a3a954cb2c70261040022d", "size": 886, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/level_test.jl", "max_stars_repo_name": "joe5saia/LocalProjections.jl", "max_stars_repo_head_hexsha": "0d23925ebf5118b973b42114b4b35a15618058a2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
(* Title: HOL/UNITY/FP.thy
Author: Lawrence C Paulson, Cambridge University Computer Laboratory
Copyright 1998 University of Cambridge
From Misra, "A Logic for Concurrent Programming", 1994
*)
section{*Fixed Point of a Program*}
theory FP imports UNITY begin
definition FP_Orig :: "'a program =>... | {"author": "Josh-Tilles", "repo": "isabelle", "sha": "990accf749b8a6e037d25012258ecae20d59ca62", "save_path": "github-repos/isabelle/Josh-Tilles-isabelle", "path": "github-repos/isabelle/Josh-Tilles-isabelle/isabelle-990accf749b8a6e037d25012258ecae20d59ca62/src/HOL/UNITY/FP.thy"} |
\section{\scshape Proposal}\label{sec:proposal}
\subsection{Research questions}
\begin{frame}{Research questions}
\begin{itemize}
\item How to reliably learn new reusable semantic assembly skills from human demonstrations?
\item How to automatically extract assembly information from CAD / SOP data?
\item How to... | {"hexsha": "946d4861e1eeca3f57d968150bd004285b17cab8", "size": 2687, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/sections/proposal.tex", "max_stars_repo_name": "carlosmccosta/prodei-research-planning-presentation", "max_stars_repo_head_hexsha": "c144ec287e2d4ed934586b031485cdbda5495d1e", "max_stars_repo_li... |
# -*- coding: utf-8 -*-
# Copyright (c) 2019 the HERA Project
# Licensed under the MIT License
"""Tests for metrics_io module."""
import pytest
import yaml
import numpy as np
import os
import h5py
import pyuvdata.tests as uvtest
from hera_qm.data import DATA_PATH
from hera_qm import metrics_io
import hera_qm.tests as ... | {"hexsha": "fbeb276f418deda2567f9b07b9761426703f8f03", "size": 29946, "ext": "py", "lang": "Python", "max_stars_repo_path": "hera_qm/tests/test_metrics_io.py", "max_stars_repo_name": "HERA-Team/hera_qm", "max_stars_repo_head_hexsha": "64734f519e05579fd4fbcdc960af64ccb42acd6b", "max_stars_repo_licenses": ["MIT"], "max_s... |
from IMLearn.learners import UnivariateGaussian, MultivariateGaussian
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
import plotly.express as px
pio.templates.default = "simple_white"
float_formatter = "{:.3f}".format
np.set_printoptions(formatter={'float_kind':float_formatter})
def tes... | {"hexsha": "d562d1a689533f9404b49beb3ba9bcc5368d89a1", "size": 3370, "ext": "py", "lang": "Python", "max_stars_repo_path": "exercises/fit_gaussian_estimators.py", "max_stars_repo_name": "dvirassulin/IML.HUJI", "max_stars_repo_head_hexsha": "52ac69455ba185c15ac491edd1d148edf3383506", "max_stars_repo_licenses": ["MIT"], ... |
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 29 11:36:45 2018
@author: suvod
"""
from __future__ import division
from . import git_access
import json
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import math
import networkx as nx
class git_api_access(object):
def __init__(self,toke... | {"hexsha": "9ec93f22a4ea3997a6a65ed283988351f4a34d95", "size": 9797, "ext": "py", "lang": "Python", "max_stars_repo_path": "github/API_V3/api/api_access.py", "max_stars_repo_name": "ai-se/Data_Miner", "max_stars_repo_head_hexsha": "69a363703c91f95f9f296170bf23b0a01f344088", "max_stars_repo_licenses": ["MIT"], "max_star... |
from numpy.lib.function_base import average
import pandas as pd
def topic_popularity(data):
"""
Compute popularity for each topic and returns for each paper in the dataset the most popular (highest) topic frequency (integer).
Input:
- df['topics']: dataframe (dataset) [pan... | {"hexsha": "b21da701dc924a88e0049d0a29455244531f28da", "size": 1441, "ext": "py", "lang": "Python", "max_stars_repo_path": "CODE/features/topic_popularity.py", "max_stars_repo_name": "SelinZ/machinelearning", "max_stars_repo_head_hexsha": "105273b2cf5907b23a2ee2b4c076d89f215c38ff", "max_stars_repo_licenses": ["MIT"], "... |
###############################
#
# (c) Vlad Zat 2017
# Student No: C14714071
# Course: DT228
# Date: 14-10-2017
#
# Title: Testing Brute Force Matching Algorithms
import numpy as np
import cv2
import easygui
imagesPath = 'images/'
outputPath = 'output/'
fileExtension = '.jpg'
def bfmatcher(window, img1, kp1... | {"hexsha": "eb274f16d24a8607fd86688e41271bba62c42770", "size": 6306, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/bruteForceMatchingTest.py", "max_stars_repo_name": "yusufsarikaya/Image_Comparison", "max_stars_repo_head_hexsha": "4c52ab537cad26177a6130f6ab8707e22171d866", "max_stars_repo_licenses": ["MI... |
[STATEMENT]
lemma enat_less_imp_le:
assumes k: "!!k. n < enat k \<Longrightarrow> m < enat k"
shows "m \<le> n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. m \<le> n
[PROOF STEP]
proof(cases n)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. \<And>nat. n = enat nat \<Longrightarrow> m \<le> n
2. n = \<inf... | {"llama_tokens": 456, "file": "Coinductive_Coinductive_Nat", "length": 6} |
#-*-coding: utf-8-*-
#Image Resize
import numpy as np
import cv2
img = cv2.imread('resimler/python.jpg')
res=cv2.resize(img,None,fx=2,fy=2,interpolation=cv2.INTER_CUBIC) #resmi yeniden boyutlandırdık.
#resmi yeniden boyutlandırmak icin asagıdaki metodlar kullanılabilir.
#cv2.INTER_AREA
#cv2.INTER_CUBIC (slow)... | {"hexsha": "08472bd77614c723b06e919792ce56e47e05e10c", "size": 418, "ext": "py", "lang": "Python", "max_stars_repo_path": "Goruntu Isleme/Beginning/ornk16.py", "max_stars_repo_name": "NevzatBOL/Paket-Kurulumlar-", "max_stars_repo_head_hexsha": "f5ce3b8205b11d072b9dadd305c11c278f184388", "max_stars_repo_licenses": ["MIT... |
"""
This module implements VideoClip (base class for video clips) and its
main subclasses:
- Animated clips: VideofileClip, ImageSequenceClip
- Static image clips: ImageClip, ColorClip, TextClip,
"""
import os
import subprocess as sp
import tempfile
import warnings
import numpy as np
from imageio import imread, im... | {"hexsha": "6ad8de976f9298099dd70185e98e367fc28b53f1", "size": 41390, "ext": "py", "lang": "Python", "max_stars_repo_path": "moviepy/video/VideoClip.py", "max_stars_repo_name": "mrkem598/moviepy", "max_stars_repo_head_hexsha": "a90d4ab4825cd368963e05ad01fd526d51edd9a5", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python3
import os
import typing
import cv2
import numpy as np
import rospy
from cv_bridge import CvBridge
from neural_networks.msg import BoundingBox, BoundingBoxes
from sensor_msgs.msg import Image
import pyzbar.pyzbar
import torch
from model import Model
device = "cuda" if torch.cuda.is_available... | {"hexsha": "334ea3d1aaefef94d13c3f02375df5148ca802d9", "size": 2015, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/health_code_color_detector/health_code_color_detector_node.py", "max_stars_repo_name": "hcngdaniel/neural_networks_ros", "max_stars_repo_head_hexsha": "bf2c46e111093b41656533ef0f30ffb34943cc68... |
#!/usr/bin/env python3
import sys
import os.path
import cv2
import numpy as np
if __name__ == '__main__':
argv = sys.argv
argc = len(argv)
i = 0
for arg in argv:
i = i + 1
if i == 1:
continue
name, ext = os.path.splitext(arg)
if ext != '.jpg':
continue
img = cv2.imread(arg)
gray = cv2.cvtC... | {"hexsha": "3516274d3968ea31336122397504d1ee5f2dede4", "size": 804, "ext": "py", "lang": "Python", "max_stars_repo_path": "face_recognize/face_recognize.py", "max_stars_repo_name": "naonaorange/bing_image_collector", "max_stars_repo_head_hexsha": "99d6deb3c95fabf2adbe0d5408f0f518a47ace02", "max_stars_repo_licenses": ["... |
# Created by William Edwards (wre2@illinois.edu), 2021-01-25
# Standard library includes
import copy
from pdb import set_trace
# Internal library includes
from .utils.cs_utils import *
from .sysid.model import ModelFactory, Model
from .control.controller import Controller, ControllerFactory
from .costs.cost import Co... | {"hexsha": "aee688a48b1e3e689040c16415594a358347243c", "size": 6736, "ext": "py", "lang": "Python", "max_stars_repo_path": "autompc/pipeline.py", "max_stars_repo_name": "StochLab/autompc", "max_stars_repo_head_hexsha": "657cf9c6ae6771b65b20fdcbaaadde31150afdff", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
import pandas as pd
import numpy as np
class MutantDataset(pd.DataFrame):
'''<Subclassed Pandsas DataFrame>
Given the potential of multiple sources for mutant datasets,
this calss serves to improve clarity, debugging, and reusability
Change Mutation(s)_PDB to Mutation(s)_cleaned?
... | {"hexsha": "f51170a77903d799a6404beb165238ed895bdbfd", "size": 2889, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/data_class.py", "max_stars_repo_name": "tcardlab/optimus_bind_sample", "max_stars_repo_head_hexsha": "55ec93b78551b57060d7e441cf299b4d341ff0af", "max_stars_repo_licenses": ["MIT"], "max_s... |
// Copyright (c) 2018 Graphcore Ltd. All rights reserved.
#include <fstream>
#include <boost/filesystem.hpp>
#include <filereader.hpp>
#include <onnxutil.hpp>
#include <popart/dotvisualizer.hpp>
#include <popart/error.hpp>
#include <popart/graph.hpp>
#include <popart/ir.hpp>
#include <popart/logging.hpp>
#include <po... | {"hexsha": "a1e6ceed605aaec9e78ecd33716cb9eb02b1025e", "size": 26316, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "willow/src/session.cpp", "max_stars_repo_name": "graphcore/popart", "max_stars_repo_head_hexsha": "15ce5b098638dc34a4d41ae2a7621003458df798", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import cv2
import numpy as np
from matplotlib import pyplot as plt
from sys import argv
import math
import os
from numpy.lib.function_base import angle
from lib.format_output import format_output
from lib.coord_test import coord_test, check_errors
try:
from PIL import Image
except ImportError:
imp... | {"hexsha": "a5301897041926bd22ee4fd19974abb840890397", "size": 9726, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/app.py", "max_stars_repo_name": "BrunoUemura/production-control", "max_stars_repo_head_hexsha": "8225294f37cba7a8c9d1449f9c0e0c677c884836", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python
import argparse
import sys
import numpy as np
import h5py
import json
import os
import math
import matplotlib.pyplot as plt
import matplotlib
from keras.models import model_from_json
import random
import time
from dask_generator import concatenate
# ***** main loop *****
if __name__ == "__main__... | {"hexsha": "6d8afc7dd7e61cc62f1f3ebe9f07a09879f37135", "size": 3852, "ext": "py", "lang": "Python", "max_stars_repo_path": "erro.py", "max_stars_repo_name": "TulioLima1502/TG", "max_stars_repo_head_hexsha": "8c54c9f0a88511447b279b752f641addd4aba09c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null,... |
import torch
import torch.multiprocessing as mp
import matplotlib.pyplot as plt
import numpy as np
import supervised_gym as sg
def test_runner():
hyps = {
"exp_len": 20,
"n_envs": 3,
"n_eval_steps": 16,
"batch_size": 16,
"n_frame_stack": 1,
"seq_len": 7,
"ran... | {"hexsha": "32c121cce00b57df9bcac08bbb7f0b16161f920c", "size": 3839, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/visual_tests.py", "max_stars_repo_name": "grantsrb/supervised_gym", "max_stars_repo_head_hexsha": "10c5f41d67a6c95a087ed1a83bdef1400ec689c3", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
# Copyright (c) 2018 NVIDIA Corporation. All rights reserved.
# This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
# https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
"""
This file starts a ROS node to run DOPE,
listening to ... | {"hexsha": "9ccde3bc26c4dfc945d84f5435918403cf092f7e", "size": 36035, "ext": "py", "lang": "Python", "max_stars_repo_path": "sbpl_perception/src/scripts/tools/fat_dataset/dope_image.py", "max_stars_repo_name": "Tacha-S/perception", "max_stars_repo_head_hexsha": "aefbb5612c84b46a745c7db4fe860a2456d6e7ef", "max_stars_rep... |
import jax.numpy as np
import numpy as onp
from qtensornetwork.circuit import Circuit
from qtensornetwork.components import Gate, Measurement
from qtensornetwork.util import data_to_qubits
from qtensornetwork.gate import *
from sklearn.datasets import load_iris
from sklearn import preprocessing
from sklearn.model_selec... | {"hexsha": "af8ec4d5d20599c96125abbea4b968e10acc16c7", "size": 2322, "ext": "py", "lang": "Python", "max_stars_repo_path": "example2.py", "max_stars_repo_name": "wotto27oct/QTensorNet", "max_stars_repo_head_hexsha": "35f8d373743ff06e3b13dd8b5c340ff1f0e1e416", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
from __future__ import division
from __future__ import print_function
from __future__ import absolute_import
from __future__ import unicode_literals
import os
import math
import numpy as np
import pandas as pd
import sklearn.linear_model as linear_model
import scipy
import sklearn
import influence.experiments as e... | {"hexsha": "e3c4ede9c6e83781c0bbb5d6f111e77fb03222ab", "size": 2963, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/run_adult_exp1.py", "max_stars_repo_name": "zhuchen03/influence", "max_stars_repo_head_hexsha": "fec7d4759da4843e356976f00e2af95cf0ea3078", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#
# The MIT License
#
# @copyright Copyright (c) 2017 Intel Corporation
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"),
# to deal in the Software without restriction,
# including without limitation the rights to use... | {"hexsha": "cbf5d67bf05272a03f5c011539e6fc9fae71caf0", "size": 8518, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/TestDescriptors.py", "max_stars_repo_name": "vuiseng9/vdms", "max_stars_repo_head_hexsha": "9bc14219c8942a3d686936b3f1105cc02a788a12", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# coding=utf-8
# Copyright 2021 The Balloon Learning Environment 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 require... | {"hexsha": "8c0363d7f3c51b1c4628296c0507a18d87f39675", "size": 6474, "ext": "py", "lang": "Python", "max_stars_repo_path": "balloon_learning_environment/agents/station_seeker_agent.py", "max_stars_repo_name": "taodav/balloon-learning-environment", "max_stars_repo_head_hexsha": "4beb45f52ab7127a4c051df25894738420e8e691"... |
// Warning! This file is autogenerated.
#include <boost/text/collation_table.hpp>
#include <boost/text/collate.hpp>
#include <boost/text/data/all.hpp>
#ifndef LIMIT_TESTING_FOR_CI
#include <boost/text/save_load_table.hpp>
#include <boost/filesystem.hpp>
#endif
#include <gtest/gtest.h>
using namespace boost::text;
... | {"hexsha": "445c1d637e995b4dc455720fc9c006232d0307db", "size": 312429, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/tailoring_rule_test_zh_big5han_012.cpp", "max_stars_repo_name": "jan-moeller/text", "max_stars_repo_head_hexsha": "c61e51c82dfb0ae6e74200c01ce040fa6db730c4", "max_stars_repo_licenses": ["BSL-... |
import torch.nn as nn
import pretrainedmodels
import pytorch_lightning as pl
import torch.nn.functional as F
import torch
from sklearn.metrics import accuracy_score, roc_auc_score
from torch.optim.lr_scheduler import MultiStepLR, ReduceLROnPlateau
import numpy as np
class LabelSmoothLoss(nn.Module):
def __init__... | {"hexsha": "e62e1a38480dbe7198790979529326a820382ba0", "size": 5776, "ext": "py", "lang": "Python", "max_stars_repo_path": "classifier/fracture_detector/model/_seresnet.py", "max_stars_repo_name": "MIPT-Oulu/DeepWrist", "max_stars_repo_head_hexsha": "9c26ee8639d748671f30a7a45487885989c53fa1", "max_stars_repo_licenses":... |
# coding=utf-8
# Copyright 2021 The Google Research 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 applicab... | {"hexsha": "e1865d6eb1fb3c7a40d7205baf4d62a70c0b9fb3", "size": 10594, "ext": "py", "lang": "Python", "max_stars_repo_path": "cold_posterior_bnn/models.py", "max_stars_repo_name": "lorenzonoci/google-research", "max_stars_repo_head_hexsha": "b29ec5559d8e4a3e77ddde0368bb219be8e6911c", "max_stars_repo_licenses": ["Apache-... |
'''MIT License
Copyright (c) 2022 Carlos M.C.G. Fernandes
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge,... | {"hexsha": "462b988daec2574e4a41ed239f981b19dd6b4e7e", "size": 6662, "ext": "py", "lang": "Python", "max_stars_repo_path": "CLASSES/PLOTTING.py", "max_stars_repo_name": "cfernandesFEUP/GEARpie", "max_stars_repo_head_hexsha": "06915490b260970520a36164dc997edc7175054f", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import argparse
import logging
import sys
import time
import os
from tf_pose import common
import cv2
# import math
import numpy as np
from tf_pose.estimator import TfPoseEstimator
from tf_pose.networks import get_graph_path, model_wh
logger = logging.getLogger('TfPoseEstimatorRun')
logger.handlers.clear()
logger.set... | {"hexsha": "82a10f34677718572688f256de7bafa921739d2f", "size": 9448, "ext": "py", "lang": "Python", "max_stars_repo_path": "run.py", "max_stars_repo_name": "rmcanada/pitcher-release-point", "max_stars_repo_head_hexsha": "df9c6a1f8172b35062bcc07506ef6f97c4b6763e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
from enum import Enum
import pandas as pd
import numpy as np
from matplotlib import (
pyplot as plt,
patches as mp,
)
DEFAULT_BOUNDS = (0, 1, 10, 100, 1000, 10000, 100000)
def get_aggregate(data, dimensions, measures=('cnt', 'revenue'), aggregator='sum', relation_field='price', add_x=-1):
result = data.... | {"hexsha": "b4d1831d96d6a08912a5f83b082cf318c1b9c569", "size": 17346, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/eda_pandas.py", "max_stars_repo_name": "kefir/snakee", "max_stars_repo_head_hexsha": "a17734d4b2d7dfd3e6c7b195baa128fbc84d197b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
# Project: Surveyer
# Description: Package of Land and Engineering Surveying utilities
# Creating design matrix
# Authors: Milutin Pejovic, Milan Kilibarda, Branislav Bajat, Aleksandar Sekulic and Petar Bursac
rm(list = ls())
# Packages
library(tidyverse)
library(magrittr)
library(ggplot2)
library(geomnet)
library(gg... | {"hexsha": "585bb418732e5e130e3ca5612fabbdd1b0bae486", "size": 3316, "ext": "r", "lang": "R", "max_stars_repo_path": "R/deprecated/design.r", "max_stars_repo_name": "pejovic/Surveyor", "max_stars_repo_head_hexsha": "40839e3cea8836b2b8e2681ffed591a6567ab173", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
% \documentclass{article}
% \usepackage{graphicx}
% \usepackage[a4paper, margin=0.5in]{geometry}
% \usepackage{subcaption}
% \usepackage{printlen}
% \uselengthunit{cm}
% \newlength\imageheight
% \newlength\imagewidth
% \begin{document}
\section{MSP\_A TX1 MSP\_C RX18 Minipod Loopback}\label{sec:MSPATX1MSPCRX18Minipo... | {"hexsha": "48b3183e114538e61415c151efa0f5774d645b62", "size": 25838, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "out/tex/MSP_A_TX1_MSP_C_RX18_Minipod_Loopback_12.8-optimized.tex", "max_stars_repo_name": "mvsoliveira/IBERTpy", "max_stars_repo_head_hexsha": "7d702ed87f0c8fbe90f4ef0445e2d4f77a79ec02", "max_stars... |
[STATEMENT]
lemma Crypt_imp_keysFor: "Crypt K X \<in> H ==> K \<in> keysFor H"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Crypt K X \<in> H \<Longrightarrow> K \<in> keysFor H
[PROOF STEP]
by (metis Crypt_imp_invKey_keysFor invKey_K) | {"llama_tokens": 102, "file": null, "length": 1} |
import argparse
import chainer
from chainer import functions as F
from chainer import links as L
from chainer import optimizers as O
from chainer import cuda
import numpy
import six
from deepmark_chainer import net
from deepmark_chainer.utils import timer
from deepmark_chainer.utils import cache
parser = argparse.A... | {"hexsha": "47417eaa020306a16c9309199753fe9fe4337cf2", "size": 4570, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluate/train_audio.py", "max_stars_repo_name": "delta2323/chainer-deepmark", "max_stars_repo_head_hexsha": "8147f5169cab06ad8c66a599663f4f0671e5180b", "max_stars_repo_licenses": ["MIT"], "max_st... |
# This code is part of Qiskit.
#
# (C) Copyright IBM 2021.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative wo... | {"hexsha": "1a402cae3c34370937e1b37efd9a6a74e42f4052", "size": 3815, "ext": "py", "lang": "Python", "max_stars_repo_path": "qiskit_machine_learning/utils/loss_functions/loss_functions.py", "max_stars_repo_name": "divshacker/qiskit-machine-learning", "max_stars_repo_head_hexsha": "f01a1217d8bd4a81a0ead48a62bf0f272de2bb2... |
@testset "add two numbers" begin
a = ListNode{Int}([2, 4, 3])
b = ListNode{Int}([5, 6, 4])
expected = ListNode{Int}([7, 0, 8])
@test expected == add_two_numbers(a, b)
@test add_two_numbers(ListNode{Int}([0]), ListNode{Int}([0])) == ListNode{Int}([0])
@test add_two_numbers(
ListNode{Int}(... | {"hexsha": "f97934ed559b418bda65cdaa0b519e479df31378", "size": 434, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/problems/2.add-two-numbers.jl", "max_stars_repo_name": "noob-data-analaysis/LeetCode.jl", "max_stars_repo_head_hexsha": "94d91b295e988948e77e737c10d2f0e3ecb7c2b0", "max_stars_repo_licenses": ["... |
using MultivariateFunctions
using Test
# Run tests
println("Test of Univariate Functions.")
@time @test include("1_test_univariate.jl")
println("Test of MultivariateFunctions taking a date.")
@time @test include("2_test_dates.jl")
println("Test of Multivariate Functions.")
@time @test include("3_test_multivariate.jl"... | {"hexsha": "a292f1dd87b4ae745864fbf31389ba6e74bad343", "size": 1201, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/MultivariateFunctions.jl-54e48022-12d0-513b-b20d-e273c072c6db", "max_stars_repo_head_hexsha": "f9169da3ceed2f3e8a70a2338054... |
# Import libraries
from modules.plots import *
from modules.map import *
import pandas as pd
import numpy as np
from scipy import stats #statstics fn are located in the sub pachage scipy.stats
from sklearn.cluster import KMeans
import folium #Interactive maps
import xgboost as xgb #provides the gradient... | {"hexsha": "59f334cb018ad3c2582bdfb61f5d977530cf0de9", "size": 6478, "ext": "py", "lang": "Python", "max_stars_repo_path": "backend_components/Traffic-Condition-Recognition-Using-The-K-Means-Clustering-Method/Main.py", "max_stars_repo_name": "getsantanupathak/prayan-mutli-mode-transportation", "max_stars_repo_head_hexs... |
"""
Testing Kalman filter for randomly generated data.
"""
import numpy as np
from Kalman import Kalman
from matplotlib import pyplot as plt
if __name__ == '__main__':
# Define some generic values
history = 7
order = 2
totNum = 2000
obsMean = 15.4
obsVar = 2.3
modBias = 2.5
modVar = 0... | {"hexsha": "0a890688e010429eba97e8d31f3806785535be2c", "size": 1524, "ext": "py", "lang": "Python", "max_stars_repo_path": "testScriptKalman.py", "max_stars_repo_name": "aliakatas/Kalman_Bayesian_filter", "max_stars_repo_head_hexsha": "5a512310bf024fd503c4a78ef76f470d37791df8", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
ZipSource(xform::Transducer)
Branch input into two "flows", inject one into `xform` and then merge
(zip) the output of `xform` with the original (source) input.
$_experimental_warning
To illustrate how it works, consider the following usage
```
xf0 |> ZipSource(xf1) |> xf2
```
where `xf0`, `xf1`, and `xf2`... | {"hexsha": "ee30d1d96770bd24a67e64a641822ee57dc71166", "size": 5979, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/teezip.jl", "max_stars_repo_name": "harryscholes/Transducers.jl", "max_stars_repo_head_hexsha": "7c0d95f1a4dc045222184062868359484b34a72d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma rotate_into_pref_sq: assumes "p \<le>f w\<cdot>w" and "\<^bold>|p\<^bold>| \<le> \<^bold>|w\<^bold>|"
obtains w' where "w \<sim> w'" "p \<le>p w'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>w'. \<lbrakk>w \<sim> w'; p \<le>p w'\<rbrakk> \<Longrightarrow> thesis) \<Longrightarrow> thesis... | {"llama_tokens": 373, "file": "Combinatorics_Words_CoWBasic", "length": 2} |
[STATEMENT]
lemma farkas_coefficients_ns_unsat:
assumes "farkas_coefficients_ns ns C"
shows "\<nexists> v. v \<Turnstile>\<^sub>n\<^sub>s\<^sub>s ns"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<nexists>v. v \<Turnstile>\<^sub>n\<^sub>s\<^sub>s ns
[PROOF STEP]
proof
[PROOF STATE]
proof (state)
goal (1 sub... | {"llama_tokens": 3487, "file": "Farkas_Farkas", "length": 32} |
include("layers.jl")
| {"hexsha": "a1d53534455a4db36b5bc604ee4318c7e7702706", "size": 21, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "YingboMa/DiffEqML.jl", "max_stars_repo_head_hexsha": "b6153a3ce79479f840517271531e78c8662148a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
[STATEMENT]
lemma fds_converges_deriv_aux:
assumes conv: "fds_converges f (s0 :: 'a)" and gt: "s \<bullet> 1 > s0 \<bullet> 1"
shows "fds_converges (fds_deriv f) s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fds_converges (fds_deriv f) s
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. ... | {"llama_tokens": 33093, "file": "Dirichlet_Series_Dirichlet_Series_Analysis", "length": 214} |
import csv
import matplotlib.pyplot as plt
import numpy as np
def index_sizes():
fp = open("./index_size.csv")
x = csv.reader(fp, delimiter='\t')
sizes = []
for line in x:
size = float(line[0].strip()[:-1])
sizes.append(size)
temp = sorted(sizes[:-1])
nodes = [i for i in range(1... | {"hexsha": "97b281cd2e09060653c37e8623382835d9e1206e", "size": 3891, "ext": "py", "lang": "Python", "max_stars_repo_path": "2_data_files/plotter.py", "max_stars_repo_name": "Abhipanda4/RQs_in_Regex_Graphs", "max_stars_repo_head_hexsha": "80b86b5b3f92ef28102ac0f5049bb495b5cc07f9", "max_stars_repo_licenses": ["Apache-2.0... |
import taichi as ti
import numpy as np
ti.init(arch=ti.x64)
RES = 1024
K = 2
R = 7
N = K ** R
Broot = ti.root
B = ti.root
for r in range(R):
B = B.bitmasked(ti.ij, (K, K))
qt = ti.var(ti.f32)
B.place(qt)
img = ti.Vector(3, dt=ti.f32, shape=(RES, RES))
@ti.kernel
def action(p: ti.ext_arr()):
a = ti.cast(p[... | {"hexsha": "16aa398fa6218cc4fae7666b4b47c6d5ea369826", "size": 1340, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/quadtree.py", "max_stars_repo_name": "ppwwyyxx/taichi", "max_stars_repo_head_hexsha": "ef0c3367bb06ad78b3457b8f93b5370f14b1d9c4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
\documentclass[paper-main.tex]{subfiles}
\begin{document}
A continuous gravitational-wave signal may wander slowly (and randomly) in frequency over time, due to stochastic internal processes in the superfluid interior of isolated neutron stars, or variable accretion from a stellar companion for neutron stars in bina... | {"hexsha": "257a4968d700cc501939aca711cbc1b8b2641ae7", "size": 15912, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/ifo-wanderingTone.tex", "max_stars_repo_name": "daccordeon/gravexplain", "max_stars_repo_head_hexsha": "4fb188b8bb37ba2c4f2b8eaf2ef478d278bbad09", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
@non_differentiable !(::Any)
@non_differentiable *(::Union{Regex, AbstractChar, AbstractString}...)
@non_differentiable -(::AbstractChar, ::AbstractChar)
@non_differentiable <=(::Any)
@non_differentiable <=(::Any, ::Any)
@non_differentiable <(::Any)
@non_differentiable <(::Any, ::Any)
@non_differentiable >=(::Any)
@non... | {"hexsha": "2aadcac37b95bc52dca6bf5b88525f658ce67b3d", "size": 18331, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rulesets/Base/nondiff.jl", "max_stars_repo_name": "DhairyaLGandhi/ChainRules.jl", "max_stars_repo_head_hexsha": "76ef95c326e773c6c7140fb56eb2fd16a2af468b", "max_stars_repo_licenses": ["MIT"], ... |
#!/usr/bin/python3
# Copyright 2020 Josh Pieper, jjp@pobox.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
#
# Unless required by appl... | {"hexsha": "eca67a97c66146a860ef621f3007b2bba6ad9c44", "size": 6761, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/dyno_static_torque_ripple.py", "max_stars_repo_name": "fxd0h/moteus", "max_stars_repo_head_hexsha": "e66ba9fb54ad0482a0bdf9a32420f5bf18677216", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
from . import PynbodyPropertyCalculation
from .. import LivePropertyCalculation
import numpy as np
import functools
import contextlib
class CentreAndRadius(PynbodyPropertyCalculation):
names = "shrink_center", "max_radius"
def calculate(self, halo, existing_properties):
# this does not appear at modul... | {"hexsha": "9dff8cd4e3175e937a1fb0f8b0ce9ab205fc9b26", "size": 2652, "ext": "py", "lang": "Python", "max_stars_repo_path": "tangos/properties/pynbody/centring.py", "max_stars_repo_name": "TobiBu/tangos", "max_stars_repo_head_hexsha": "decab8c892c5937fd68474a375089abef198dba2", "max_stars_repo_licenses": ["BSD-3-Clause"... |
export DepthwiseConvDims
"""
DepthwiseConvDims
Concrete subclass of `ConvDims` for a depthwise convolution. Differs primarily due to
characterization by C_in, C_mult, rather than C_in, C_out. Useful to be separate from
DenseConvDims primarily for channel calculation differences.
"""
struct DepthwiseConvDims{N,S... | {"hexsha": "a0555ff58cd719cada9d12c0e3ca55af27c3c1df", "size": 3760, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/dim_helpers/DepthwiseConvDims.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/NNlib.jl-872c559c-99b0-510c-b3b7-b6c96a88d5cd", "max_stars_repo_head_hexsha": "889c46705097019afb63565d... |
[STATEMENT]
lemma rtrancl_mono_proof[mono]:
"(\<And>a b. x a b \<longrightarrow> y a b) \<Longrightarrow> rtranclp x a b \<longrightarrow> rtranclp y a b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>a b. x a b \<longrightarrow> y a b) \<Longrightarrow> x\<^sup>*\<^sup>* a b \<longrightarrow> y\<^sup>*\<... | {"llama_tokens": 483, "file": "Complx_SmallStep", "length": 4} |
# Author:
# Stanislav Khrapov
# mailto:khrapovs@gmail.com
# http://sites.google.com/site/khrapovs/
import numpy as np
from numpy.random import multivariate_normal
import matplotlib.pylab as plt
class Heston(object):
def __init__(self):
self.eps = None
self.names = ['mu_r','kappa','mu_v','eta'... | {"hexsha": "7d51e9491bf4863021f4a6ee660f499d02ca3a09", "size": 4585, "ext": "py", "lang": "Python", "max_stars_repo_path": "Heston.py", "max_stars_repo_name": "khrapovs/finmetrix-code", "max_stars_repo_head_hexsha": "f278df1c15a225385846c2f0d7a6700c5737e901", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "m... |
"""
2D–3D Geometric Fusion network using Multi-Neighbourhood Graph Convolution for RGB-D indoor scene classification
2021 Albert Mosella-Montoro <albert.mosella@upc.edu>
"""
import numpy as np
import h5py
from skimage import io
import glob
from tqdm import tqdm
from Fusion2D3DMUNEGC.utilities import utils
im... | {"hexsha": "77109b0137113f643c66b05021e7d9d038abc67d", "size": 1995, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/datagen/texture2d/img2d_sunrgbd.py", "max_stars_repo_name": "imatge-upc/munegc", "max_stars_repo_head_hexsha": "92a820c1665e760bc7736595dd5dced19df448c1", "max_stars_repo_licenses": ["MIT"... |
word()::String=rand(data["faker"]["lorem"]["words"])
words(;number_words::Int=3)::Array{String, 1} = map( x -> word(), 1:number_words)
function sentence(;number_words::Int=6, variable_nb_words::Bool=true)::String
number_words <= 0 && (return " ");
variable_nb_words && (number_words = rand... | {"hexsha": "7d4a7566e3babb4b01bf5e891345875a2d907098", "size": 1667, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/lorem.jl", "max_stars_repo_name": "vtjnash/Faker.jl", "max_stars_repo_head_hexsha": "f367e13186f4f9c340cad457ffa1ac21ba861a00", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 32, "max_s... |
/*
* This file belongs to the Galois project, a C++ library for exploiting parallelism.
* The code is being released under the terms of the 3-Clause BSD License (a
* copy is located in LICENSE.txt at the top-level directory).
*
* Copyright (C) 2018, The University of Texas at Austin. All rights reserved.
* UNIVER... | {"hexsha": "f559a485d3c177139f0782df2d522080d8705dca", "size": 27746, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lonestar/preflowpush/Preflowpush.cpp", "max_stars_repo_name": "bowu/Galois", "max_stars_repo_head_hexsha": "81f619a2bb1bdc95899729f2d96a7da38dd0c0a3", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
!**************************************************************
!* AceGen 6.702 Windows (4 May 16) *
!* Co. J. Korelc 2013 10 Apr 20 13:09:36 *
!**************************************************************
! User : Full professional version
! Notebook : MainFile
! E... | {"hexsha": "57026b77dc2f66b6337b2faa85af01ddbb1574ea", "size": 317264, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "models/Shi2014/acegen/jacobian.f90", "max_stars_repo_name": "kengwit/MaterialModels", "max_stars_repo_head_hexsha": "97de48a2f3ba8f2d605b892a6e0603c9b3feb42c", "max_stars_repo_licenses": ["MIT... |
# Inverse: given observed data of u(t, x) -> model/pde parameters λ
import time, sys, os, json
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.mplot3d import Axes3D
# from plotting import newfig, savefig
# from mpl_toolkits.axes_grid1 ... | {"hexsha": "ebceb0f81056f10ab08e2502bd43d8adbbd87985", "size": 18330, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/1d/inverse_1d.py", "max_stars_repo_name": "QiuhongAnnaWei/PINNs", "max_stars_repo_head_hexsha": "5276b625f75ff613cdcc0133d9737a5f55b8a5eb", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
%results
\acresetall
\part{Results}
\label{sec:results}
\chapter[Golden Dual Fullerenes]{Golden Dual Fullerenes\footnote{This chapter is
composed of sections previously published in the article
\citetitle*{Trombach_HollowGoldCages_2016}\autocite{Trombach_HollowGoldCages_2016}
and is reprinted by permissi... | {"hexsha": "0deeaef2952a4d660cc7b994cd5edfa9ce9e4c58", "size": 168279, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "results.tex", "max_stars_repo_name": "Trombach/thesis", "max_stars_repo_head_hexsha": "0eb1d2a109a0f91271fbdd6d85b4706b2e281d93", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null,... |
[STATEMENT]
lemma prv_\<phi>L_eqv:
"\<phi> \<in> fmla \<Longrightarrow> Fvars \<phi> = {} \<Longrightarrow> prv (eqv (\<phi>L \<phi>) (imp (PP \<langle>\<phi>L \<phi>\<rangle>) \<phi>))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<phi> \<in> fmla; Fvars \<phi> = {}\<rbrakk> \<Longrightarrow> prv (eqv ... | {"llama_tokens": 448, "file": "Goedel_Incompleteness_Loeb_Formula", "length": 2} |
import os
import numpy as np
from osgeo import ogr
from osgeo import osr
from osgeo import gdal
import pygeonet_prepare as Parameters
# Writing drainage network node (head/junction) shapefiles
def write_drainage_nodes(xx,yy, node_type, fileName, shapeName):
print "Writing", node_type, "shapefile"
#... | {"hexsha": "ccf6dd232fd0949eab0b7dfd801405d8e9f75162", "size": 9398, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygeonet_vectorio.py", "max_stars_repo_name": "uva-hydroinformatics/wetland_identification", "max_stars_repo_head_hexsha": "21b797eec1f4babe5c4fb53441bc256385dc2094", "max_stars_repo_licenses": ["... |
import numpy as np
from scipy.sparse import dia_matrix
import matplotlib.pyplot as plt
from scipy.sparse.linalg import spsolve
from scipy.sparse import csc_matrix
from numpy.linalg import matrix_rank
from scipy.sparse import diags
import timeit
import matplotlib.pyplot as plt
def overland_setup(eta_vector):
wd =... | {"hexsha": "6d05adc8c49f84ea12e0e785ff874c10d6f588ce", "size": 13898, "ext": "py", "lang": "Python", "max_stars_repo_path": "module_st/F_overland_Q.py", "max_stars_repo_name": "Qinayan/Soil-thickness", "max_stars_repo_head_hexsha": "23f110d663dcd9f25593b2d10dec47ff78b19643", "max_stars_repo_licenses": ["Apache-2.0"], "... |
'''creating a connect 4 game inspired by Keith Galli'''
from numpy import zeros as array_zero
from numpy import flip as flip_that
##=========================================== PLAYER
class LOGIC():
def __init__(self):
self.OVER = False
self.YOUR_TURN = 0
self.change_turn = True
sel... | {"hexsha": "2a79f1a63b4faf9ea80b106051a520116061edeb", "size": 15987, "ext": "py", "lang": "Python", "max_stars_repo_path": "Connect4Game-x64/_connect4_logic.py", "max_stars_repo_name": "xenz25/Connect4GameMysteryEdition-Python", "max_stars_repo_head_hexsha": "00c7e9a08ef4eb59fd2d04e9b9f4fc8829c9acce", "max_stars_repo_... |
# File path of cleaned_loan_data.csv is stored in path
import pandas as pd
from sklearn.model_selection import train_test_split
data = pd.read_csv(path)
y = data['paid.back.loan']
X = data.iloc[:, 1:-1]
print(X.shape)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3, random_state=0)
import matplotli... | {"hexsha": "d17eee2a61e5dec229d5057413ab98d90d2e4dfa", "size": 2571, "ext": "py", "lang": "Python", "max_stars_repo_path": "loan_defaulters/loan_defaulter.py", "max_stars_repo_name": "SahaanaIyer/dsci-greyatom", "max_stars_repo_head_hexsha": "b6d1ff4ac716954faafdd31f0293132d9a01f618", "max_stars_repo_licenses": ["MIT"]... |
from __future__ import print_function
import sys, time
import numpy as np
import matplotlib.pyplot as plt
sys.path.append('../build/pybind11/Release')
import ChebTools as CT
N = 100000
c = range(50)
c1 = CT.ChebyshevExpansion(c)
tic = time.clock()
CT.mult_by(c1, 1.0001, N)
toc = time.clock()
print((toc-tic)/N*1e6,'... | {"hexsha": "2733fe2fcece59c91cb0af841ff6bfb5ff84ec7a", "size": 1188, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/speed_tests.py", "max_stars_repo_name": "usnistgov/chebby", "max_stars_repo_head_hexsha": "75dbccfd9a029e91cbfdfd263befc51b893822ea", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#include "ImageBatch.h"
#define STB_IMAGE_IMPLEMENTATION
#include <stb_image.h>
#include <boost/filesystem.hpp>
namespace filesystem = boost::filesystem;
namespace Tristeon
{
namespace Data
{
//Static
std::map<std::string, Image> ImageBatch::cachedImages;
Image ImageBatch::getImage(std::string path)
{
... | {"hexsha": "edaa446b869ed53a54a4f37d445aec6db705bb88", "size": 1524, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Data/ImageBatch.cpp", "max_stars_repo_name": "HyperionDH/Tristeon", "max_stars_repo_head_hexsha": "8475df94b9dbd4e3b4cc82b89c6d4bab45acef29", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
! { dg-do compile }
!
! PR 45507: [4.6 Regression] Bogus Error: Can't convert TYPE(c_ptr) to INTEGER(4)
!
! Contributed by Andrew Benson <abenson@its.caltech.edu>
use, intrinsic :: iso_c_binding
type :: cType
type(c_ptr) :: accelPtr = c_null_ptr
end type cType
type(cType), allocatable, dimension(:) :: filters... | {"hexsha": "2af069293536a8f76c2f5d4b99c8d3a3b018743b", "size": 410, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/allocate_alloc_opt_12.f90", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars... |
# test the how the data shows
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import readsav
from scipy.interpolate import griddata,interp2d
import datetime
import matplotlib.dates as mdates
from skimage import measure
import matplotlib as mpl
# try to use the precise epoch
mpl.rcParams['date.epoch'... | {"hexsha": "62a66359df27c3b96c0f2c284d806793ba91594c", "size": 2553, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/BF/sav_LOFAR_read.py", "max_stars_repo_name": "peijin94/LOFAR-Sun-tools", "max_stars_repo_head_hexsha": "23ace5a5e8c0bdaa0cbb5ab6e37f6527716d16f3", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
from pandas import DataFrame
class procrustes_test(object):
"""
Docstring for function ecopy.procrustes_test
====================
Conducts permutation procrustes test of relationship
between two non-diagonal (raw) matrices
Use
----
procrustes_test(mat1, mat2, npe... | {"hexsha": "df3357b2ae7bd36c286430c2de5441509871bff6", "size": 2356, "ext": "py", "lang": "Python", "max_stars_repo_path": "ecopy/matrix_comp/procrust_test.py", "max_stars_repo_name": "MatthewRalston/ecopy", "max_stars_repo_head_hexsha": "b936c91d3de0c610a7439a593a49af5070083992", "max_stars_repo_licenses": ["MIT"], "m... |
import tactic
import category_theory.limits.shapes.pullbacks
namespace category_theory
open category_theory.limits
variables {C D : Type*} [category C] [category D] (e : C ≌ D)
{X Y B : D} (f : X ⟶ B) (g : Y ⟶ B) [has_pullback (e.inverse.map f) (e.inverse.map g)]
lemma equivalence.hom_eq_map {X Y : C} (f : e.funct... | {"author": "leanprover-community", "repo": "lean-liquid", "sha": "92f188bd17f34dbfefc92a83069577f708851aec", "save_path": "github-repos/lean/leanprover-community-lean-liquid", "path": "github-repos/lean/leanprover-community-lean-liquid/lean-liquid-92f188bd17f34dbfefc92a83069577f708851aec/src/for_mathlib/pullbacks.lean"... |
# Copyright (c) 2012-2018, University of Strathclyde
# Authors: Lawrence T. Campbell
# License: BSD-3-Clause
"""
@reduceField.py Exemplar script to create a reduced field mesh file
from the full dump. This example just forms a 1D field from the
central transverse node in an example dump. It should be easy to
extend t... | {"hexsha": "aaa7e22582b1b242658fce2e012e8f9d9f512a32", "size": 5692, "ext": "py", "lang": "Python", "max_stars_repo_path": "utilities/post/reduceField.py", "max_stars_repo_name": "mightylorenzo/Puffin", "max_stars_repo_head_hexsha": "6631eb91a5d98d8bc3d40fe9f09b90932f20776a", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
"""Utilities used in the Kadenze Academy Course on Deep Learning w/ Tensorflow.
Creative Applications of Deep Learning w/ Tensorflow.
Kadenze, Inc.
Parag K. Mital
Copyright Parag K. Mital, June 2016.
"""
import matplotlib.pyplot as plt
import tensorflow as tf
import urllib
import numpy as np
import zipfile
import os
... | {"hexsha": "dca27d1f015f9d2474fe9bbab3000f3faaa918e6", "size": 6936, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/utils.py", "max_stars_repo_name": "dulex123/npainter", "max_stars_repo_head_hexsha": "7206b61d40bb3f34bf89a8b6066d42e4323569f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
[STATEMENT]
lemma istate_\<Delta>1:
assumes B: "B vl vl1"
shows "\<Delta>1 istate vl istate vl1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<Delta>1 istate vl istate vl1
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
B vl vl1
goal (1 subgoal):
1. \<Delta>1 istate vl istate vl1
[PROOF STEP]
u... | {"llama_tokens": 688, "file": "CoCon_Paper_Confidentiality_Paper_Aut", "length": 3} |
# Copyright 2022 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": "108226ed062fb56613da595d912a3333c53dd9ed", "size": 9528, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/st/pynative/hook/test_pynative_backward_hook.py", "max_stars_repo_name": "zhz44/mindspore", "max_stars_repo_head_hexsha": "6044d34074c8505dd4b02c0a05419cbc32a43f86", "max_stars_repo_licenses... |
from gui.ui_win import Ui_Form
from gui.ui_draw import *
from PIL import Image, ImageQt
import numpy as np
import random, io, os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from util import task, util
from dataloader.image_folder import make_dataset
from dataloader.data_load... | {"hexsha": "5a258d5f5ea04693aa5e4a497ae97a5f016dbbaa", "size": 12580, "ext": "py", "lang": "Python", "max_stars_repo_path": "gui/ui_model.py", "max_stars_repo_name": "lyndonzheng/TFill", "max_stars_repo_head_hexsha": "04014653368285c54aa0accd4cc540c81a0f3f9b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 48, ... |
import numpy as np
import timeit
from sqlalchemy import MetaData, Table, Column, DateTime, Boolean, Integer, DECIMAL, insert
from datetime import datetime
from settings import engine, metadata
# metadata = MetaData()
conn = engine.connect()
numbers = Table('sort', metadata,
Column('id', Integer(), ... | {"hexsha": "3f69316ef817bf762e88b865fb58a56e47e62c04", "size": 1559, "ext": "py", "lang": "Python", "max_stars_repo_path": "sort.py", "max_stars_repo_name": "KenMwaura1/python-sort", "max_stars_repo_head_hexsha": "3cf16794bf4315987513028c032b037ad1399d40", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import gc
import os
from argparse import ArgumentParser
from datetime import datetime
import numpy as np
import torch
import yaml
from coolname import generate_slug
from pytorch_lightning.callbacks import (
EarlyStopping,
LearningRateMonitor,
ModelCheckpoint,
Callback,
)
from pytorch_lightning.loggers ... | {"hexsha": "132ddc98d29ea93613547564f4106769ecb54298", "size": 9243, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "Anjum48/kaggle_template", "max_stars_repo_head_hexsha": "bac7edc7639a7477602a9c0d9dcf112e8f0afc9b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
{-# OPTIONS --cubical --no-import-sorts --safe #-}
module Cubical.Relation.Nullary.Decidable where
open import Cubical.Core.Everything
open import Cubical.Data.Empty using (⊥)
private
variable
ℓ : Level
-- Negation
infix 3 ¬_
¬_ : Type ℓ → Type ℓ
¬ A = A → ⊥
-- Decidable types (inspired by standard library)
... | {"hexsha": "ea4ae71fae164356fc80821ccae6101ec7489227", "size": 557, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/Relation/Nullary/Decidable.agda", "max_stars_repo_name": "bijan2005/univalent-foundations", "max_stars_repo_head_hexsha": "737f922d925da0cd9a875cb0c97786179f1f4f61", "max_stars_repo_license... |
import numpy as np
def B_mat_RM(x: list, y: list, xg:float, yg:float):
"""Computes strain-displacement matrix (B matrix) for bending moments
and shear forces.
Arg:
x: X coordinates of the element [list]
y: Y coordinates of the element [list]
xg: Gauss ... | {"hexsha": "283c9c2ac44a913e643dd00ab579658de7b0d2a4", "size": 3871, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/functions.py", "max_stars_repo_name": "Medvedku/2D_Triangular_RS", "max_stars_repo_head_hexsha": "4da8ee95ac89aa2621c61ba28004dac8dc56db4b", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import pytest
from astropy.time import Time
@pytest.fixture
def earth_perihelion():
return Time("2020-01-05 07:47:00", scale="tdb")
| {"hexsha": "62872189f445bcf68ba7a203b9dc611c92c1d560", "size": 138, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/conftest.py", "max_stars_repo_name": "niharsalunke/poliastro", "max_stars_repo_head_hexsha": "75a11922f00df3a06c64af265a182dfdbbc4f5c3", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from typing import Callable
import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_equal
import mygrad as mg
from mygrad import Tensor
def test_manual_multiply_no_broadcast():
x = Tensor([1.0, 2.0, 3.0])
y = -x.copy()
mask = np.array([True, False, True])
out = mg.mu... | {"hexsha": "90b20fda0e1301efb477f03ffc7901c86435b3fc", "size": 3081, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_where_mask.py", "max_stars_repo_name": "kw-0/MyGrad", "max_stars_repo_head_hexsha": "307f1bb5f2391e7f4df49fe43a7acf9d1e8ea141", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14... |
REAL*4 FUNCTION
. TSAMPLESIZE(XITYPE,XALPHA,POWER,XDIFF,SIGMA,XM)
USE MSIMSL
C
C-Description-----------------------------------------------------------
IMPLICIT NONE
C
C Function:
C Main routine of program to compute sample size calculations
C involving t tests.
C
C Input prompted for from user:
... | {"hexsha": "6cabba910421264537b499bee54c088da1c60141", "size": 6084, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "misc/old_ps/ps-development-version/psDll/tsamples.for", "max_stars_repo_name": "vubiostat/ps", "max_stars_repo_head_hexsha": "0ac136d449256b8bf4ebfc6311654db5d7a01321", "max_stars_repo_licenses"... |
"""Generate plot(s) for FitzHugh-Nagumo model (noisless observations) experiments."""
import argparse
import json
import os
import numpy as np
import matplotlib.pyplot as plt
from utils import (
add_experiment_grid_args,
add_plot_args,
check_experiment_dir_and_create_output_dir,
set_matplotlib_style,
... | {"hexsha": "267a4223020e3487d88bbc45ce56abfcf1a8bec6", "size": 11373, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/fhn_model_noiseless_obs_generate_plots.py", "max_stars_repo_name": "thiery-lab/manifold-mcmc-for-diffusions", "max_stars_repo_head_hexsha": "a96a76907642e9465e583c7ca87c8c7c4f5f01dc", "ma... |
function tno_sparse_grid_write ( header, l, m, n, x, w )
%*****************************************************************************80
%
%% TNO_SPARSE_GRID_WRITE writes a Truncated Normal Odd Sparse Grid to X and W files.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
%... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/truncated_normal_sparse_grid/tno_sparse_grid_wri... |
import matplotlib.pyplot as plt
import numpy as np
def export_figure(colormap):
x = np.linspace(-np.pi, np.pi, 100)
y = np.linspace(-np.pi, np.pi, 100)
xv, yv = np.meshgrid(x, y)
z = np.sin(xv) * np.sin(yv)
plt.contourf(x, y, z, 200)
plt.set_cmap(colormap)
plt.savefig('test_image_' + color... | {"hexsha": "4135699fe5d69bf5f11c6de20b8eac55a4dc409b", "size": 442, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_data/generate_test_images.py", "max_stars_repo_name": "Arnaud-D/jetkiller-python", "max_stars_repo_head_hexsha": "7f574deace0918e4fefd5dc1bbf152c33e025799", "max_stars_repo_licenses": ["... |
import os
import sys
import h5py
import argparse
import numpy as np
from scipy import io
from PIL import Image
def color_map(N=256, normalized=False):
'''
Get the color maps for the segmentation task.
This is the PYTHON implementation of the VOC cmap extractor.
'''
def bitget(byteval, idx):
return ((byteval & (... | {"hexsha": "8f74e812ce0867f1fafdf91b0df6b3a7b9e56d3f", "size": 5097, "ext": "py", "lang": "Python", "max_stars_repo_path": "Consistent_Semantic_Segmentation/extract_dataset.py", "max_stars_repo_name": "snlakshm/tensorflow-deeplab-v3", "max_stars_repo_head_hexsha": "357a050c7c697f8d6e1bb7269ec3e03c7e032035", "max_stars_... |
#include "RestartSimulation.h"
#include "Graphics/GraphicsManager.h"
#include "Graphics/CudaLbm.h"
#include "Flow.h"
#include <boost/any.hpp>
using namespace Shizuku::Flow::Command;
RestartSimulation::RestartSimulation(Flow& p_flow) : Command(p_flow)
{
}
void RestartSimulation::Start(boost::any const p_param)
{
... | {"hexsha": "10a80a019d2e8f0bce8de128cced98cf532c740f", "size": 457, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Shizuku.Flow/Command/RestartSimulation.cpp", "max_stars_repo_name": "blackoffee/Shizuku", "max_stars_repo_head_hexsha": "dde4c4f437ca271181d59a78da2815dee01800eb", "max_stars_repo_licenses": ["MIT"],... |
function frst=show_progress(cnt,ttl,frst);
persistent hit
%Initialise hit only when it is first declared as an empty array
if frst==0
hit=zeros(20,1);
fprintf('10%% 20%% 30%% 40%% 50%% 60%% 70%% 80%% 90%% 100%%\n');
end
prp=cnt/ttl;
if prp>=0.05 & hit(1)==0
fprintf('||');
hit(1)=1;
elseif prp>=0.1 & ... | {"author": "yetianmed", "repo": "subcortex", "sha": "76179cf552b773e79b06a54568eae1fdd13722f4", "save_path": "github-repos/MATLAB/yetianmed-subcortex", "path": "github-repos/MATLAB/yetianmed-subcortex/subcortex-76179cf552b773e79b06a54568eae1fdd13722f4/functions/show_progress.m"} |
#include "Common/Common.h"
#include "Demos/Visualization/MiniGL.h"
#include "Demos/Visualization/Selection.h"
#include "GL/glut.h"
#include "Simulation/TimeManager.h"
#include <Eigen/Dense>
#include "Simulation/SimulationModel.h"
#include "Simulation/TimeStepController.h"
#include <iostream>
#include "Utils/OBJLoader.h... | {"hexsha": "a1a391c506a50ccc484c142133ccc7a884522f51", "size": 10191, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Demos/RigidBodyDemos/JointDemo.cpp", "max_stars_repo_name": "schnitzeltony/PositionBasedDynamics", "max_stars_repo_head_hexsha": "5988cdaaf33f6c4c7437509e9dc9943503b1cbee", "max_stars_repo_licenses... |
import numpy as np
from matplotlib import pyplot as plt
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']
data = np.loadtxt('olympic100m.txt', delimiter=',')
year = data[:,0]
time = data[:,1]
# 画出数据方便观察
plt.plot(year,time,'ro')
plt.xlabel('奥运会届数')
plt.ylabel('夺冠耗时(秒)')
plt.show() | {"hexsha": "e6343fde08e7042aac3fc2452ed1ddfb2b51bbf5", "size": 287, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/1-1.py", "max_stars_repo_name": "archwalker/PyMatches", "max_stars_repo_head_hexsha": "42e619cf8272d0a31c88e2d9538f1cb722ce0f20", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
#define BOOST_TEST_DYN_LINK
#define BOOST_TEST_MODULE parse_literal
#include <boost/test/unit_test.hpp>
#include "../src/parse_literal.hpp"
#include "../src/parse_literal.hpp"
#include "../src/error/compile_exception.hpp"
#include "state_utils.hpp"
using std::string;
BOOST_AUTO_TEST_CASE(standard)
{
state s = ... | {"hexsha": "6c224c9f0d11d86d33a64a24f9b910888102e865", "size": 1278, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/parse_literal.cpp", "max_stars_repo_name": "mbmaier/asm-lisp", "max_stars_repo_head_hexsha": "a09a1d53d324c6a2b177a02a6233cb71124fb768", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import pickle
import matplotlib.pyplot as plt
import numpy as np
import Levenshtein
import math
import pandas as pd
from sklearn.metrics import silhouette_score ,calinski_harabasz_score,davies_bouldin_score
from scipy.spatial import distance
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
im... | {"hexsha": "f778555ca73e29e71b68050b3827ab6d518dcde9", "size": 63844, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment/evaluate.py", "max_stars_repo_name": "Trustworthy-Software/BATS", "max_stars_repo_head_hexsha": "eb122150dff61543bd8c88ac7e08987a0a3e47e0", "max_stars_repo_licenses": ["MIT"], "max_sta... |
// (C) Copyright Edward Diener 2011
// Use, modification and distribution are subject to 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).
#if !defined(TTI_MF_TEMPLATE_PARAMS_HPP)
#define TTI_MF_TEMPLATE_PARAMS_HPP
#include <bo... | {"hexsha": "7162ae484f6b67e7b7e980e825c8299802b280b7", "size": 2476, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/tti/mf/mf_has_template_check_params.hpp", "max_stars_repo_name": "juslee/boost-svn", "max_stars_repo_head_hexsha": "6d5a03c1f5ed3e2b23bd0f3ad98d13ff33d4dcbb", "max_stars_repo_licenses": ["BSL-... |
#ifndef CONFIG_FILE_PARSER_CLASS_HPP
#define CONFIG_FILE_PARSER_CLASS_HPP
#include <boost/property_tree/ptree.hpp>
#include <boost/property_tree/ini_parser.hpp>
#include <boost/foreach.hpp>
#include <fstream>
#include <string>
#include <iomanip>
#include "nlohmann/json.hpp"
// <boost/property_tree/json... | {"hexsha": "b03cb106ce46dafcd47a8c65c57307bf67bbacf0", "size": 787, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/config_file_parser_class.hpp", "max_stars_repo_name": "hhaoao/QuickOpenProject", "max_stars_repo_head_hexsha": "e0f2512903dc1c2fe2f743db1f9d3ef078063068", "max_stars_repo_licenses": ["MIT"], ... |
function switch_pkgbutler_channel(package_name::AbstractString, channel::Symbol)
ctx = Pkg.Types.Context()
haskey(ctx.env.project.deps, package_name) || error("Unkonwn package $package_name.")
pkg_uuid = ctx.env.project.deps[package_name]
pkg_path = ctx.env.manifest[pkg_uuid].path
pkg_path===nothing... | {"hexsha": "dfd5cf3ccdf42117053f63c80d7f397d1f8dae1d", "size": 5207, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/pkgbutler.jl", "max_stars_repo_name": "StefanKarpinski/PkgDev.jl", "max_stars_repo_head_hexsha": "fb962187f28e65156079cba7f8ad48ed5bd3d2f4", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import ast
import os
from lcc.entities.exceptions import InvalidFilesPath
import numpy as np
from lcc.utils.helpers import sub_dict_in_dict
from lcc.utils.helpers import check_depth
class StatusResolver(object):
'''
This class is responsible for status files generated thru systematic searches
into databa... | {"hexsha": "c75de0529140761864c11da0a0ff82a4ee2dacbe", "size": 9127, "ext": "py", "lang": "Python", "max_stars_repo_path": "lcc/data_manager/status_resolver.py", "max_stars_repo_name": "mavrix93/LightCurvesClassifier", "max_stars_repo_head_hexsha": "a0a51f033cb8adf45296913f0de0aa2568e0530c", "max_stars_repo_licenses": ... |
import pandas as pd
import numpy as np
from flask import Flask, request, jsonify, render_template
import pickle
app = Flask(__name__)
@app.route('/')
def welcome():
return render_template('index2.html')
@app.route('/predict', methods =["GET", "POST"])
def predict():
if request.method == "POST":... | {"hexsha": "056204904574585b3d8bf39d8d441d041a2451eb", "size": 1024, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "shreyasvmane/Diabetes-Prediction-System", "max_stars_repo_head_hexsha": "41ac710066c4a10fed529de08a7818a339cffa2c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
(* *********************************************************************)
(* *)
(* The Compcert verified compiler *)
(* *)
(* Xavier Leroy... | {"author": "robbertkrebbers", "repo": "compcert", "sha": "524c26591e884a3676a5fbef77d8c79193955b82", "save_path": "github-repos/coq/robbertkrebbers-compcert", "path": "github-repos/coq/robbertkrebbers-compcert/compcert-524c26591e884a3676a5fbef77d8c79193955b82/backend/Tailcall.v"} |
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