text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import pickle
import numpy as np
from PIL import Image
import torch.utils.data
from torchvision import transforms
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.202... | {"hexsha": "3014339a5a38db737e56cb3c14ed8ff5af6ab13a", "size": 3136, "ext": "py", "lang": "Python", "max_stars_repo_path": "classification/generate_dataset.py", "max_stars_repo_name": "Chen-Junbao/NeuralNetwork", "max_stars_repo_head_hexsha": "7d8f764e6d3e8364f5a2e87aa4651ab577553d5d", "max_stars_repo_licenses": ["MIT"... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import random
eps = 1e-7
# CE and LDAM are supported
# If you would like to add other losses, please have a look at:
# Focal Loss: https://github.com/kaidic/LDAM-DRW
# CRD, PKT, and SP Related Part: https://github.com/HobbitLong/R... | {"hexsha": "62543538e2b26018f7cbc8cd1b6a346e66016f54", "size": 27953, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/loss.py", "max_stars_repo_name": "mitming/OpenLT", "max_stars_repo_head_hexsha": "33fa2a91fd04ecd2c2bcc399c985ed276a42a22e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 37, "max_... |
# -*- coding: utf-8 -*-
import numpy as np
def _plot_dep(plt, func):
_max = 1.8
dom = np.linspace(0, _max, 200)
linear = dom.copy()
ours = func(dom)
plt.plot(dom, linear, "--", label="no distortion")
plt.plot(dom, ours, "-", label="distortion")
plt.set_xlim(0, _max)
plt.set_ylim(0, _ma... | {"hexsha": "a5e86f0d51e6df7332206f432ec71dd9ea775a72", "size": 425, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/scripts/common.py", "max_stars_repo_name": "matejak/antibarrel", "max_stars_repo_head_hexsha": "d3c16c6f84388af0d2f9e74cecb644e6e23b2a9b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
using Pkg
if isfile("Project.toml")
Pkg.activate(".")
end | {"hexsha": "768c0a275dfbcaca373a3f6f31368b67e630b927", "size": 61, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia_config/startup.jl", "max_stars_repo_name": "RodrigoZepeda/docker-julia", "max_stars_repo_head_hexsha": "c77a9be4405778fe82a2f729ca1f994674a91275", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Code for "TDN: Temporal Difference Networks for Efficient Action Recognition"
# arXiv: 2012.10071
# Limin Wang, Zhan Tong, Bin Ji, Gangshan Wu
# tongzhan@smail.nju.edu.cn
import argparse
import time
import os
import torch.nn.parallel
import torch.optim
from sklearn.metrics import confusion_matrix
from ops.dataset im... | {"hexsha": "40ba451ddb26af2b805906dd60c5063d8d9624f6", "size": 4412, "ext": "py", "lang": "Python", "max_stars_repo_path": "pkl_to_results.py", "max_stars_repo_name": "hardik01shah/TDN", "max_stars_repo_head_hexsha": "fcae164736271943528876e5f81fd79e38d884b9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
im = Image.open('dataset/jaffe/KA.AN2.40.tiff')
(w, h) = im.size
bags = []
pos_patch = lambda n: [
(0, 0, n//2, n//2), (n//2, 0, n, n//2),
(0, n//2, n//2, n), (n//2, n//2, n, n)
]
sub_image = lambda image, dim: [image.crop(patch) for ... | {"hexsha": "70da953a7ba3e466240ddbf66ef2f90015407142", "size": 1119, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "adadesions/AdaDynamics", "max_stars_repo_head_hexsha": "5251a739fdf457f7371ae8818d90d310c3094fb9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import numpy as np
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from tensorboardX import SummaryWriter
from envs import MGEnv, CleanupEnv, HarvestEnv, GridWorldEnv,GridWorldAdaptiveEnv, MGSingleEnv, MGAdaptiveEnv
from algorithm.ppo import PPO
from algorithm.mod... | {"hexsha": "9e6a1c0b2fbbfa926d0522f401035d5442f53964", "size": 9958, "ext": "py", "lang": "Python", "max_stars_repo_path": "GridWorld/scripts/eval/eval.py", "max_stars_repo_name": "staghuntrpg/rpg", "max_stars_repo_head_hexsha": "78901f0cb2505b08d7d09603d46515ef292a0784", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""
Copyright (C) 2020 Shandong University
This program is licensed under the GNU General Public License 3.0
(https://www.gnu.org/licenses/gpl-3.0.html).
Any derivative work obtained under this license must be licensed
under the GNU General Public License as published by the Free
Software F... | {"hexsha": "40f24078683863615afbd62c36c5a754f5a22db2", "size": 2047, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchhelper/tune/rampup.py", "max_stars_repo_name": "sailist/TorchHelper", "max_stars_repo_head_hexsha": "7a0b18e9a60f24e71d69290cb5712973af7fcc23", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
"""
The classes contained in this module are supposed to be agnostic to recording format
and encapsulate some generic mechanisms for producing things like spike timing
autocorrelograms, power spectrum calculation and so on
"""
import numpy as np
from scipy import signal, spatial, misc, ndimage, stats, io
from scipy.si... | {"hexsha": "7f7319ee1d227f3142df1b53d9222ba762fe46c0", "size": 77192, "ext": "py", "lang": "Python", "max_stars_repo_path": "ephysiopy/common/ephys_generic.py", "max_stars_repo_name": "hwpdalgleish/ephysiopy", "max_stars_repo_head_hexsha": "6dd8ea9fa253e2711d29cf0c50bcded352115897", "max_stars_repo_licenses": ["MIT"], ... |
\subsection{Saving the Measurement Results Automatically}
When you have a time series sequence and you want to measure multiple signals with
multiple parameters in each frame, measurement results in each frame needs to be somehow saved.
Here, we learn how to export measurement results in your hard disk automatically... | {"hexsha": "d98826bde0e81a56e0477687ef5bac527156ad54", "size": 5972, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "assets/experimentalTools/ImageJ/reference/cmci-ij_textbook2-d852848/sections/io/macroIO_resultsSaving.tex", "max_stars_repo_name": "mistltoe/mistltoe.github.io", "max_stars_repo_head_hexsha": "2e465... |
import cv2
from more_itertools import unique_everseen
import numpy as np
##### Right ###########
# 큰 리벳 검출할 영역(관심영역)을 지정 (start point to end point of Rectangle Box).
ROI_list = [
[(5, 90), (500, 950)],
]
# 작은 리벳 검출할 영역(관심영역)을 지정 (start point to end point of Rectangle Box).
ROI_list_small = [
[(550, 90), (72... | {"hexsha": "0e858def968e8ffd86c5d7be722e196adffbdcfa", "size": 7446, "ext": "py", "lang": "Python", "max_stars_repo_path": "AceVision/old/AceVision_GUI_EXE_conversion/version_0.1_Gige_cam_190326/build_exe/test/circle_find.py", "max_stars_repo_name": "lyj911111/OpenCV_Project", "max_stars_repo_head_hexsha": "9acbfbf6661... |
from __future__ import absolute_import
from __future__ import print_function
import autograd.numpy as np
import autograd.numpy.random as npr
from autograd import grad
from autograd.util import quick_grad_check
from six.moves import range
from six.moves import zip
from neural_net_utilities import WeightsParser, make_... | {"hexsha": "59c1dd275b76064662b5d86a17bf7f21b71fc73b", "size": 8445, "ext": "py", "lang": "Python", "max_stars_repo_path": "chord2vec/CBOW.py", "max_stars_repo_name": "czhuang/ChordRipple", "max_stars_repo_head_hexsha": "1d7f1f05cc895983101865665d5df18aeff99d7f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
import numpy as np
import matplotlib.pyplot as plt
'''
Here defines a class that implements a simple discrete distribution
that is used to keep track of the confidence distribution of the interested
parameter.
'''
class Simple_Dist:
# param = [(x,density)]
# constructed left-to-right on [init[0][0],end... | {"hexsha": "bb0d99577a682fdd5465e58837880e56d59e24b9", "size": 5647, "ext": "py", "lang": "Python", "max_stars_repo_path": "PTA algorithm/simpleDist.py", "max_stars_repo_name": "zrobertson466920/Probabilistic_Metric_Elicitation", "max_stars_repo_head_hexsha": "281a0ac85c8f0f637355f1ee5426e81b2f222fa5", "max_stars_repo_... |
import Basic
import Tangle
open Brick
structure Graph (α : Type) where
V: List α
E: List (α × α)
deriving Repr
namespace Graph
def from_edges {α : Type} [BEq α] (e : List (α × α)) : Graph α :=
Graph.mk
(e.foldr (fun n ns =>
match (ns.elem n.fst, ns.elem n.snd) with
| (true, true) => ns
... | {"author": "shua", "repo": "leanknot", "sha": "5c50fc107c1e98978d2cd966d4c6b22348e1ee4a", "save_path": "github-repos/lean/shua-leanknot", "path": "github-repos/lean/shua-leanknot/leanknot-5c50fc107c1e98978d2cd966d4c6b22348e1ee4a/Graph.lean"} |
using KernelAbstractions
using KernelAbstractions.Extras
using CUDA
using CUDAKernels
using Random123
using WormlikeChain
using BenchmarkTools
using FastClosures
#BAOAB integration
@kernel function simulate!(positions,
velocities,
externalforce!,
... | {"hexsha": "2c131e64fd75e7ec8f02e95881406cad1d4fd0f6", "size": 4294, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "simkernelproto.jl", "max_stars_repo_name": "nhz2/WormlikeChain.jl", "max_stars_repo_head_hexsha": "108c71d29c7015deaa586fe32443e0efd00e5ab1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
export marginalrule
@marginalrule typeof(*)(:A_in) (m_out::NormalDistributionsFamily, m_A::PointMass, m_in::F) where { F <: NormalDistributionsFamily } = begin
A = mean(m_A)
W = A' * precision(m_out) * A
b_in = convert(promote_variate_type(F, NormalWeightedMeanPrecision), A' * weightedmean(m_out), W)
... | {"hexsha": "47c9bcebabccc7acce84bc7a6ee1dac763930708", "size": 400, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rules/multiplication/marginals.jl", "max_stars_repo_name": "albertpod/ReactiveMP.jl", "max_stars_repo_head_hexsha": "71c390e6b41e6890ba808640d0bf3ef2f66efc71", "max_stars_repo_licenses": ["MIT"]... |
import skvideo.io
import sys
import numpy as np
import hashlib
import os
from numpy.testing import assert_equal
def hashfile(afile, hasher, blocksize=65536):
buf = afile.read(blocksize)
while len(buf) > 0:
hasher.update(buf)
buf = afile.read(blocksize)
return hasher.hexdigest()
def _vwrite... | {"hexsha": "18287585f7c0eca771dfda004fe3271f8bbaa1e8", "size": 1261, "ext": "py", "lang": "Python", "max_stars_repo_path": "venv/lib/python3.6/site-packages/skvideo/tests/test_vwrite.py", "max_stars_repo_name": "mesquitadev/grpc", "max_stars_repo_head_hexsha": "747660f2ed4e62e30999741f4359793192158cad", "max_stars_repo... |
# ==============================================================================================
# beg: basic imports and setup
# ==============================================================================================
from datetime import datetime
from loguru import logger
import joblib
import numpy as np
impor... | {"hexsha": "b8a0686ac5dcb30bb13bdcb8a2f5614d16ae7dd4", "size": 7350, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "rakesh4real/stock-prediction", "max_stars_repo_head_hexsha": "ac363cd3dee3bf37aade4edbc9514ab54c9107cb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
"""
.. _l-b-reducesumsquare:
Compares implementations of ReduceSumSquare
===========================================
This example compares the *numpy* for the operator *ReduceSumSquare*
to :epkg:`onnxruntime` implementation.
If available, :epkg:`tensorflow` and :epkg:`pytorch` are included as well.
.. contents::
... | {"hexsha": "132ce39687d67b406da9092611bcd5bbfe58fde7", "size": 7662, "ext": "py", "lang": "Python", "max_stars_repo_path": "_doc/examples/plot_op_reducesumsquare.py", "max_stars_repo_name": "henrywu2019/mlprodict", "max_stars_repo_head_hexsha": "4c09dc39d5ba7a7235fa321d80c81b5bf4f078ad", "max_stars_repo_licenses": ["MI... |
import random
from dataclasses import dataclass
import numpy as np
from dynaparse.parameters.base_parameter import BaseParameter
str_with_none = lambda x: None if x == "None" else str(x)
@dataclass
class StringParameter(BaseParameter):
default: str = None
parameter_type: str = "str"
def get_typefunc(s... | {"hexsha": "db440c9367e68bc5d3d5717c589e520a2e7da5a3", "size": 622, "ext": "py", "lang": "Python", "max_stars_repo_path": "dynaparse/parameters/string_parameter.py", "max_stars_repo_name": "kungfuai/dynaparse", "max_stars_repo_head_hexsha": "6c4001718b80deaf9552ffa95b69f961fee6ff89", "max_stars_repo_licenses": ["MIT"],... |
/-
Copyright (c) 2020 Yury G. Kudryashov. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Yury G. Kudryashov
! This file was ported from Lean 3 source module linear_algebra.affine_space.slope
! leanprover-community/mathlib commit 70fd9563a21e7b963887c9360bd29b2393e6225... | {"author": "leanprover-community", "repo": "mathlib4", "sha": "b9a0a30342ca06e9817e22dbe46e75fc7f435500", "save_path": "github-repos/lean/leanprover-community-mathlib4", "path": "github-repos/lean/leanprover-community-mathlib4/mathlib4-b9a0a30342ca06e9817e22dbe46e75fc7f435500/Mathlib/LinearAlgebra/AffineSpace/Slope.lea... |
import time
import numpy as np
import sklearn.model_selection
import torch
from corai.src.classes.estimator.history.estim_history import Estim_history
from corai.src.classes.training_stopper.early_stopper_vanilla import Early_stopper_vanilla
from corai.src.train.history import translate_history_to_dataframe
from cora... | {"hexsha": "2f74da7c4885bf9f09896e8f358455de46a96f43", "size": 13743, "ext": "py", "lang": "Python", "max_stars_repo_path": "corai/src/train/kfold_training.py", "max_stars_repo_name": "Code-Cornelius/python_libraries", "max_stars_repo_head_hexsha": "71c388da60e2aeb94369c3813faca93bf6a18ebf", "max_stars_repo_licenses": ... |
module Import_Test
using FactCheck, JNeuron
facts() do
myimport=input(string(dirname(Base.source_path()),"/../examples/data/cell2.asc"));
@fact length(myimport.sections) --> 198
blank_neuron=instantiate(myimport);
@fact length(blank_neuron.secs) --> 198
blank_neuron2=instantiate(string(dirname... | {"hexsha": "89195f22190bb0b061b938a7fe75827f50d66c56", "size": 657, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/import_test.jl", "max_stars_repo_name": "paulmthompson/JNeuron", "max_stars_repo_head_hexsha": "d6a389506e27df1955ac59eb08376795d20bb1b6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
"""
The aim of this file is to give a standalone example of how an environment runs.
"""
import os
import numpy as np
from tgym.core import DataGenerator
from tgym.envs.trading_tick import TickTrading
from tgym.gens.deterministic import WavySignal, RandomGenerator
from tgym.gens.csvstream import CSVStreamer
gen_type ... | {"hexsha": "80f65f9822f34ecd1afa47685374539ee8afe90b", "size": 1730, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/trading_tick_env.py", "max_stars_repo_name": "ma-da/CryptoTGYM", "max_stars_repo_head_hexsha": "90218a2296030ef3f8e4925ce79a5264c2aecfb8", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma hcomplex_mult_minus_one: "- 1 * z = - z"
for z :: hcomplex
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. - 1 * z = - z
[PROOF STEP]
by simp | {"llama_tokens": 74, "file": null, "length": 1} |
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
// This program is distributed in the hope that it will be useful,
... | {"hexsha": "10e8a8f56ae6fdcb0820569ade4b91838d21fe6d", "size": 101782, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tyrant_optimize.cpp", "max_stars_repo_name": "APN-Pucky/TestGit", "max_stars_repo_head_hexsha": "7de432b1ba3511d866239a4a0519c4d65c8066d8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import subprocess
from setuptools import setup, Extension
try:
pandoc = subprocess.Popen(['pandoc', 'README.md', '--to', 'rst'],
stdout=subprocess.PIPE)
readme = pandoc.communicate()[0].decode()
except OSError:
with open('README.md') as f:
readme = f.read()
cmdclass... | {"hexsha": "63941acd5ea7c627eccf6daf6a03aa134aea7caf", "size": 1269, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "aaren/pharminv", "max_stars_repo_head_hexsha": "b3d3d11c81bafa40a72583aa98f51b05acec9835", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 12, "m... |
import torch
import numpy as np
import configs.asrf_config as asrf_cfg
import sys
sys.path.append('./backbones/asrf')
from libs.postprocess import PostProcessor
def predict_refiner(model, main_backbone_name, backbones, split_dict, model_dir, result_dir, features_path, vid_list_file, epoch, actions_dict, devic... | {"hexsha": "86610a08d6cbc61b06959c77176b89007f807713", "size": 12544, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/predict.py", "max_stars_repo_name": "cotton-ahn/hasr_iccv2021", "max_stars_repo_head_hexsha": "c8994eb8db1fae78a41023414505297f49950646", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
""" A collection of metrics to evalaute agents with. """
import warnings
import numpy as np
from featureExtractor.drone_feature_extractor import dist_2d, angle_between
def compute_trajectory_smoothness(trajectory):
"""
Returns the total and per step change in the orientation (in degrees)
of the agent duri... | {"hexsha": "2af27d97bb1285b5d0be0e05fbb2402ce4248fdf", "size": 11400, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics/metrics.py", "max_stars_repo_name": "ranok92/deepirl", "max_stars_repo_head_hexsha": "88c7e76986243cf0b988d8d7dc0eef6b58e07864", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, ... |
#!/usr/bin/env python3
#
#
#
#======================================
import os
import argparse
import json
import pdb
import pickle
import datetime
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("tkAgg")
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import seabor... | {"hexsha": "767da1a259a7900420199fbc21dc21db7460e55d", "size": 21151, "ext": "py", "lang": "Python", "max_stars_repo_path": "sleep_scorer/plt-trainmodels.py", "max_stars_repo_name": "focolab/sleep-classifier", "max_stars_repo_head_hexsha": "6a7eb376267c66d697782d49785ade6948a17a85", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma length_ge_Suc_imp_not_empty:"Suc n \<le> length xs \<Longrightarrow> xs \<noteq> []"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Suc n \<le> length xs \<Longrightarrow> xs \<noteq> []
[PROOF STEP]
by fastforce | {"llama_tokens": 90, "file": "List-Infinite_ListInf_List2", "length": 1} |
PROGRAM F006
! Copyright 2021 Melwyn Francis Carlo
IMPLICIT NONE
INTEGER :: N, N_SUM, SQUARE_OF_SUM, SUM_OF_SQUARE
N = 100
N_SUM = (N * (N + 1)) / 2
SQUARE_OF_SUM = N_SUM * N_SUM;
SUM_OF_SQUARE = (N * (N + 1) * ((2 * N) + 1)) / 6;
PRINT ('(I0)'), SQUARE_OF_SUM - SUM_OF_SQUARE
END P... | {"hexsha": "9057bade6bbda757765d56d7aea53cd5c7dc7a80", "size": 332, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "problems/006/006.f90", "max_stars_repo_name": "melwyncarlo/ProjectEuler", "max_stars_repo_head_hexsha": "c4d30ed528ae6de82232f3d2044d608c6e8f1c37", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Larger CNN for the MNIST Dataset
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import Flatten
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
... | {"hexsha": "170753e61acda2b72039e367081c86257a9183fb", "size": 10639, "ext": "py", "lang": "Python", "max_stars_repo_path": "IRNET48_NEW.py", "max_stars_repo_name": "GuptaVishu2002/IRNET", "max_stars_repo_head_hexsha": "a430d17df3ececfe6cfd8ab469fff070e1c262e7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
function loadNeuralNet()
path(s) = normpath("$(@__DIR__)/"*s)
@load path("../models/agz_128_base.bson") bn
@load path("../models/agz_128_value.bson") value
@load path("../models/agz_128_policy.bson") policy
@load path("../models/weights/agz_128_base.bson") bn_weights
@load path("../models/weight... | {"hexsha": "9ff5118cbc452ae9ca534e7d46c08d063c5bf113", "size": 2107, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/helpers.jl", "max_stars_repo_name": "tejank10/AlphaGo.jl", "max_stars_repo_head_hexsha": "8a2651c7f705d7fe6d0818648ce9620fdb2cc704", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
C %W% %G%
real function tzlead (t2, t1, dt, x0, x1, y0, ndivs)
implicit none
real t2, t1, dt, x0, x1, y0
integer ndivs
c - solves a first order lead block:
c sT2
c -------
c 1 + sT1
c
c - Uses sub-time step logic if ndivs > 1
c
c - subscript... | {"hexsha": "dd48ef270b4cbd5df00b710394519567fa19444b", "size": 1607, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "libtsp/tzlead.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14... |
import torch
import sys
import os
# sys.path.append(os.path.dirname(os.path.dirname(__file__)))
sys.path.insert(0,'..') # inorder to run within the folder
import numpy as np
import json
from CarRacing.network import Actor as Actor
from CarRacing.orca_env_function import getNFcollosionreward
import car_racing_simulator... | {"hexsha": "9b21881595174e2d6e1689c6646144b42a8c2983", "size": 6408, "ext": "py", "lang": "Python", "max_stars_repo_path": "CarRacing/results.py", "max_stars_repo_name": "manish-pra/trcopo", "max_stars_repo_head_hexsha": "df8730f07ef554970c7a0aa653cc42d4886948ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import torch as tr
import sys, time
import pickle as pk
sys.path.append('../../envs')
from blocks_world import BlocksWorldEnv, random_thing_below
import block_stacking_problem as bp
import neural_virtual_machine as nv
from abstract_machine import make_abstract_machine, memorize_problem
from nvm import virtualize
from r... | {"hexsha": "3205c3705c629dd436b31f1ed91e68f40a1f2f28", "size": 16633, "ext": "py", "lang": "Python", "max_stars_repo_path": "pybullet/tasks/pick_and_place/nvm_rvm_compare.py", "max_stars_repo_name": "garrettkatz/poppy-muffin", "max_stars_repo_head_hexsha": "43ac00e6a151346ca7df005c009fcb762f16bd35", "max_stars_repo_lic... |
#!/usr/bin/env sage
import os
import sys
from shutil import rmtree
from sage.all import *
from sage.graphs.graph_input import from_graph6
if len(sys.argv) < 2:
raise ValueError(
"Nombre de sommets necessaires a passer en argument 1"
)
n = int(sys.argv[1])
file = os.path.join("input", "graph%d.g6" % n... | {"hexsha": "a242dff57b2e8f8f1c35f15c1ab05472eae01e9b", "size": 1390, "ext": "py", "lang": "Python", "max_stars_repo_path": "filter_graphs.py", "max_stars_repo_name": "vbouquet/graph_tools", "max_stars_repo_head_hexsha": "7d25a8eadd5176b6e35abf47e5ec7ea2fde4be97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#!/usr/bin/env python
from ATK.Core import DoubleInPointerFilter, DoubleOutPointerFilter
from ATK.Adaptive import DoubleBlockLMSFilter
from nose.tools import raises
def filter(input, reference):
import numpy as np
output = np.zeros(input.shape, dtype=np.float64)
infilter = DoubleInPointerFilter(input, False)
... | {"hexsha": "1646ad68e50a0fcb2c0a9fe7fa4627db721ef899", "size": 2111, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/Python/Adaptive/PyATKAdaptive_blocklms_test.py", "max_stars_repo_name": "D-J-Roberts/AudioTK", "max_stars_repo_head_hexsha": "accf009d7238f32702eb1d5ee23c5148fc68e3bd", "max_stars_repo_licen... |
import cv2
import numpy as np
#read the image
image=cv2.imread('cameramannoise.jpg')
#apply the 3*3 mean filter on the image
kernel=np.ones((3, 3), np.float32) / 9
processed_image=cv2.filter2D(image, -1, kernel)
#display image
cv2.imshow('Mean Filter Processing', processed_image)
#save image to disk
cv2.imw... | {"hexsha": "f3e62a8f9b279ef6ac1847d8bf49d4fa85df519e", "size": 455, "ext": "py", "lang": "Python", "max_stars_repo_path": "CompVisionMedian filter.py", "max_stars_repo_name": "AneezaNiamat/ComputerVision", "max_stars_repo_head_hexsha": "35a584a4003dc814ca4db7539da757c0cf9d2a2b", "max_stars_repo_licenses": ["MIT"], "max... |
function client(master, port, log_dir)
jobid = ENV["SLURM_JOB_ID"]
nodeid = parse(Int,ENV["SLURM_NODEID"])
localid = parse(Int,ENV["SLURM_LOCALID"])
open("$(log_dir)/client-$(jobid)-$(nodeid)-$(localid).log", "w+") do log_file
client_logger = SimpleLogger(log_file, Logging.Debug)
@debug... | {"hexsha": "7b4b6c2500b40042bddaae0455073b640862b48f", "size": 1115, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/client.jl", "max_stars_repo_name": "jagot/SlurmSweeps.jl", "max_stars_repo_head_hexsha": "2591ad2a570ad20de5acd2589eafa6aa5020fee9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
module Solvers
export
PoissonSolver, PoissonBCs, solve_poisson_3d!,
BatchedTridiagonalSolver, solve_batched_tridiagonal_system!
using Oceananigans.Grids
using Oceananigans: @hascuda
@hascuda using CUDAnative, CuArrays
abstract type PoissonBCs end
include("solver_utils.jl")
include("poisson_solver_cpu.jl")... | {"hexsha": "bd4c16fdce391d3f0d260227436943a34c9a347c", "size": 400, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Solvers/Solvers.jl", "max_stars_repo_name": "arcavaliere/Oceananigans.jl", "max_stars_repo_head_hexsha": "588890004e69cfc7db10472b12a9840b8a9ad7b6", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma isContD: "isCont f x \<Longrightarrow> f \<midarrow>x\<rightarrow> f x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. isCont f x \<Longrightarrow> f \<midarrow>x\<rightarrow> f x
[PROOF STEP]
by (simp add: isCont_def) | {"llama_tokens": 91, "file": null, "length": 1} |
[STATEMENT]
lemma subset_union_same1 [backward]: "B \<subseteq> C \<Longrightarrow> A \<union> B \<subseteq> A \<union> C"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. B \<subseteq> C \<Longrightarrow> A \<union> B \<subseteq> A \<union> C
[PROOF STEP]
by auto | {"llama_tokens": 95, "file": "Auto2_HOL_HOL_Set_Thms", "length": 1} |
import numpy as np
import cv2
def imcv2_recolor(im, a=.1):
# t = [np.random.uniform()]
# t += [np.random.uniform()]
# t += [np.random.uniform()]
# t = np.array(t) * 2. - 1.
t = np.random.uniform(-1, 1, 3)
# random amplify each channel
im = im.astype(np.float)
im *= (1 + t * a)
mx ... | {"hexsha": "f9b1eab90c0402599a4bf92ba610eae9b8a1e397", "size": 973, "ext": "py", "lang": "Python", "max_stars_repo_path": "DetJoint/yolo_v2/utils/im_transform.py", "max_stars_repo_name": "Tommy-Ngx/AutoGradingOA", "max_stars_repo_head_hexsha": "5e69bd38abaf01f03d8d837da68701a86bac1bb0", "max_stars_repo_licenses": ["MIT... |
import os
import pandas as pd
import numpy as np
import pickle
import flask
from flask import Flask, request, jsonify
from ensemble import Ensemble
import boto3
BUCKET_NAME = 'ff-inbound-videos' # replace with your bucket name
s3 = boto3.resource('s3')
DETECTOR_WEIGHTS_PATH = 'WIDERFace_DSFD_RES152.fp16.pth'
VIDE... | {"hexsha": "b56914c148dfe06258af1013d5ca2721aefe755c", "size": 1738, "ext": "py", "lang": "Python", "max_stars_repo_path": "detectors/ntech/app.py", "max_stars_repo_name": "zhampel/FakeFinder", "max_stars_repo_head_hexsha": "2891a8649acc1dabdef07554d6acb346dd23dbae", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import torch
import numpy as np
from collections import OrderedDict
from torch import optim
from itertools import chain
#from .swapgan import SwapGAN
from .twogan import TwoGAN
from torch import nn
class ACAIF3(TwoGAN):
"""
Fixed version of ACAI with min() formulation and
the discriminator also on reconstr... | {"hexsha": "4f0cd60fdb8b18269f717a676c9262b98141c235", "size": 6308, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/acai_f3.py", "max_stars_repo_name": "christopher-beckham/amr", "max_stars_repo_head_hexsha": "1bd67b9b4fb2fcf07cc8faba3c863f5ad5d4c4c0", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
#!/usr/bin/env python3
import argparse
import random
import numpy as np
import scipy.stats
import sacrebleu
from tqdm import trange
from comet.models import download_model
def load_file(fh):
sentences = []
for line in fh:
sentences.append(line.strip())
fh.close()
return sentences
def confi... | {"hexsha": "ae415b88e63b7a7120bbf850b48f76d5338b0d00", "size": 1914, "ext": "py", "lang": "Python", "max_stars_repo_path": "char_scripts/eval_with_bootsrap_resampling.py", "max_stars_repo_name": "jlibovicky/char-nmt-fairseq", "max_stars_repo_head_hexsha": "b883b437eef2331ab328978d0ed71bbffbf0aa22", "max_stars_repo_lice... |
import numpy as np
import torch
import heat as ht
from .test_suites.basic_test import TestCase
class TestTypes(TestCase):
def assert_is_heat_type(self, heat_type):
self.assertIsInstance(heat_type, type)
self.assertTrue(issubclass(heat_type, ht.datatype))
def assert_non_instantiable_heat_type... | {"hexsha": "91676650f8d32287ebc8482f126fa4b79b53bd86", "size": 16059, "ext": "py", "lang": "Python", "max_stars_repo_path": "heat/core/tests/test_types.py", "max_stars_repo_name": "Dhruv454000/heat", "max_stars_repo_head_hexsha": "885f686af1193d8b297a643249bb8ae1ca40b897", "max_stars_repo_licenses": ["MIT"], "max_stars... |
PURE FUNCTION func_stderr(arr, dof) RESULT(ans)
! NOTE: See https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sem.html
USE ISO_FORTRAN_ENV
IMPLICIT NONE
! Declare inputs/outputs ...
INTEGER(kind = INT64), INTENT(in), OPTIONAL :: dof
REAL(kind = R... | {"hexsha": "bb655e64fb4ea6b6c2dc7faf1b44840fca2ddc14", "size": 910, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "mod_safe/func_stderr.f90", "max_stars_repo_name": "Guymer/fortranlib", "max_stars_repo_head_hexsha": "30e27b010cf4bc5acf0f3a63d50f11789640e0e3", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# -*- coding: utf-8 -*-
"""
Created on Fri Jul 5 18:48:21 2019
@author: Theodore
"""
# -*- coding: utf-8 -*-
"""
@author: Theodore
"""
import numpy as np
API_KEY = "WWLN1J8UJ2I7Q8ML"
# get data from worldtradingdata
def stock_url(function, symbol, outputsize):
url = (f"https://www.alphavantage.co/query?funct... | {"hexsha": "7d560031031e749b42746bab88ecdc97b611a1c6", "size": 846, "ext": "py", "lang": "Python", "max_stars_repo_path": "misc_function2.py", "max_stars_repo_name": "edotheodore/Thesis-Project", "max_stars_repo_head_hexsha": "5e36aea435fe1a5032e96b44792cec8c888240d2", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma dvd_Lcm_fin:
"a \<in> A \<Longrightarrow> a dvd Lcm\<^sub>f\<^sub>i\<^sub>n A"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a \<in> A \<Longrightarrow> a dvd Lcm\<^sub>f\<^sub>i\<^sub>n A
[PROOF STEP]
by (induct A rule: infinite_finite_induct)
(auto intro: dvd_trans) | {"llama_tokens": 128, "file": null, "length": 1} |
# Math
Some essential math
## Vectors
Represented by a matrix `r` as:
\begin{equation*}
r = \begin{bmatrix} i \\ j \end{bmatrix}
\end{equation*}
The vector above has 2 components `i` and `j`.
### Vector Operations
#### Scalar Multiplication
\begin{equation*}
A \times r = A \begin{bmatrix} r_i \\ r_j \end{bma... | {"hexsha": "2757340a0361fa489572eda0ed95c8d581093ffd", "size": 30338, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "machine-learning/math/LinearAlgebra.ipynb", "max_stars_repo_name": "sthaha/notes", "max_stars_repo_head_hexsha": "1866ba8e8d916d23d3d4f19413e27136421d6e39", "max_stars_repo_licenses"... |
import dgl
import torch as to
import torch.nn as nn
import matplotlib.pyplot as plt
def get_device():
device = to.device('cuda' if to.cuda.is_available() else 'cpu')
print('running on', device)
return device
def build_graph():
# edges: 0->1, 0->2, 1-2
src = [0, 0, 1]
tar = [1, 2, 2]
gra... | {"hexsha": "64834ea1ee93b7f26c52543fcf7a988e302a73bc", "size": 1596, "ext": "py", "lang": "Python", "max_stars_repo_path": "gcn_minimal.py", "max_stars_repo_name": "maet3608/ex-dl-graph-conv-1", "max_stars_repo_head_hexsha": "95801f1b216fa78b7baa4eb4add411fa6612b500", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import openmdao.api as om
import numpy as np
class NormalForceODE(om.ExplicitComponent):
def initialize(self):
self.options.declare('num_nodes', types=int)
def setup(self):
nn = self.options['num_nodes']
#constants
self.add_input('M', val=0.0, desc='mass', units='kg')
... | {"hexsha": "0becc5639aafe9765cef2f80d9dff2ff9e130419", "size": 4654, "ext": "py", "lang": "Python", "max_stars_repo_path": "dymos/examples/racecar/normalForceODE.py", "max_stars_repo_name": "pwmdebuck/dymos-1", "max_stars_repo_head_hexsha": "0fb3e91e8c32b34fca41e8c1ec9bec66f31af341", "max_stars_repo_licenses": ["Apache... |
abstract type AbstractAttenOp end
abstract type AbstractAttenScoreOp end
abstract type AbstractMixingOp end
struct DotProductScore <: AbstractAttenScoreOp end
(::DotProductScore)(args...) = dot_product_score(args...)
struct ScaledDotProductScore <: AbstractAttenScoreOp end
(::ScaledDotProductScore)(args...) = scaled_... | {"hexsha": "3e85408f25059e8d2b98bf598decec2ffaf05fce", "size": 1099, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/types.jl", "max_stars_repo_name": "foldfelis/NeuralAttentionlib.jl", "max_stars_repo_head_hexsha": "52cb258807c9b8d308e14db0f99ec0d3492607c9", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma (in group) lift_closed[simp]:
assumes cl: "f \<in> gens \<rightarrow> carrier G"
and "x \<in> lists (UNIV \<times> gens)"
shows "lift f x \<in> carrier G"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lift f x \<in> carrier G
[PROOF STEP]
proof-
[PROOF STATE]
proof (state)
goal (1 subgoa... | {"llama_tokens": 531, "file": "Free-Groups_FreeGroups", "length": 7} |
#!/usr/bin/python
import matplotlib.pyplot as plt
from math import *
from scipy import special
from optparse import OptionParser
def psi(n, x):
a = sqrt(2.0 * n + 1.0)
A = sqrt(2.0 / pi)
g = x/a
p = sqrt(fabs(2.0 * n + 1.0 - x**2))
if (x == a) or (x == -a):
return 0
if (x < -a):
... | {"hexsha": "082b33b234e6549d1799aa6229ffd028429ad9ca", "size": 2947, "ext": "py", "lang": "Python", "max_stars_repo_path": "Teorphys/graph.py", "max_stars_repo_name": "ncos/hometasks", "max_stars_repo_head_hexsha": "9504ef7ed8fe30b5bc78ca1e423a2b85e46734a1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "ma... |
function [out1,out2,out3] = ginput4(arg1)
[out1,out2,out3] = ginput(arg1);
return;
%GINPUT Graphical input from mouse.
% [X,Y] = GINPUT(N) gets N points from the current axes and returns
% the X- and Y-coordinates in length N vectors X and Y. The cursor
% can be positioned using a mouse (or by using the Ar... | {"author": "JzHuai0108", "repo": "ekfmonoslam", "sha": "443f6be744732453cdb90679abcaf5c962a6295e", "save_path": "github-repos/MATLAB/JzHuai0108-ekfmonoslam", "path": "github-repos/MATLAB/JzHuai0108-ekfmonoslam/ekfmonoslam-443f6be744732453cdb90679abcaf5c962a6295e/EKF_monoSLAM_1pRANSAC/matlab_code/matlabcalibration2ourca... |
! { dg-do run }
! { dg-options "-fcray-pointer" }
!
use iso_c_binding
real target(10)
real pointee(10)
pointer (ipt, pointee)
integer(c_intptr_t) :: int_cptr
real :: x
if (c_sizeof(ipt) /= c_sizeof(int_cptr)) call abort()
if (c_sizeof(pointee) /= c_sizeof(x)*10) call abort()
end
| {"hexsha": "127a24ab6a4ffd6ba5535ddfaac80b73dff08430", "size": 280, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "gcc-gcc-7_3_0-release/gcc/testsuite/gfortran.dg/c_sizeof_5.f90", "max_stars_repo_name": "best08618/asylo", "max_stars_repo_head_hexsha": "5a520a9f5c461ede0f32acc284017b737a43898c", "max_stars_rep... |
# -*- coding: utf-8 -*-
from igakit.nurbs import NURBS
from numpy import sqrt, zeros
class opNURBS(object):
"""
this class implements a generic differential operator applied to a NURBS
object.
"""
def __init__(self, nrb):
"""
creates a gradiant map from a NURBS object
"""
... | {"hexsha": "9b9bbc6d6b7522edbe8f611f03722bc9e52f5786", "size": 24779, "ext": "py", "lang": "Python", "max_stars_repo_path": "caid/op_nurbs.py", "max_stars_repo_name": "ratnania/caid", "max_stars_repo_head_hexsha": "5bc5428007c8642412762f80c36e8531e56cd15e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max... |
import unittest
import numpy as np
from blmath.numerics import vector_shortcuts as vx
class TestVector(unittest.TestCase):
def test_normalize(self):
import math
v = np.array([1, 1, 0])
expected = np.array([math.sqrt(2) / 2., math.sqrt(2) / 2., 0])
np.testing.assert_array_almost_eq... | {"hexsha": "18ff7bdc45a3ca07e777124956c2256a9d43b1a8", "size": 7847, "ext": "py", "lang": "Python", "max_stars_repo_path": "blmath/numerics/test_vector_shortcuts.py", "max_stars_repo_name": "metabolize/blmath", "max_stars_repo_head_hexsha": "8ea8d7be60349a60ffeb08a3e34fca20ef9eb0da", "max_stars_repo_licenses": ["BSD-2-... |
from Source.HDFGroup import HDFGroup
import collections
import sys
import warnings
import numpy as np
from numpy import matlib as mb
import scipy as sp
import datetime as datetime
import copy
from PyQt5 import QtWidgets
from tqdm import tqdm
import HDFRoot
from MainConfig import MainConfig
from AncillaryReader impor... | {"hexsha": "d29091fa828d7338b65e4de9e101d20a27d40da1", "size": 106136, "ext": "py", "lang": "Python", "max_stars_repo_path": "Source/ProcessL2.py", "max_stars_repo_name": "jackassruiz/T_Nasa", "max_stars_repo_head_hexsha": "f396c6be6014eb5cbe4a69ee27201f25c1284a1f", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# basic library
import os
import shutil
import math
import time
import menpo.io as mio
import menpo3d.io as m3io
import numpy as np
import h5py
import pandas as pd
from menpo.shape import ColouredTriMesh, PointCloud
from menpo.image import Image
from menpo.transform import Homogeneous
from menpo3d.rasterize import ras... | {"hexsha": "ab86adeeb6ec43314ea3eb8d156e8168a8fa91d9", "size": 10073, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepmachine/contrib/training/keras_3DMM.py", "max_stars_repo_name": "yuxiang-zhou/deepmachine", "max_stars_repo_head_hexsha": "b8a64354f7d37664172ef79a66b1fc0a9fa0f493", "max_stars_repo_licenses"... |
from typing import Optional
from gym import Env
import numpy as np
from agents import AbstractAgent
class SarsaAgent(AbstractAgent):
def __init__(self, env: Env, epsilon: float = 1.0, epsilon_min: float = 0,
epsilon_reduction: float = 0.0, alpha: float = 0.01, alpha_min: float = 0,
... | {"hexsha": "e60b3b6d8b598d303ef08362bd757edb034ddd82", "size": 2312, "ext": "py", "lang": "Python", "max_stars_repo_path": "agents/sarsa_agent.py", "max_stars_repo_name": "SeJV/ComparisonRLapproaches", "max_stars_repo_head_hexsha": "e1988a97ed5fab10c847350d607e9feafeced61e", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
import math
import scipy.ndimage
def frequest(im, orientim, kernel_size, minWaveLength, maxWaveLength):
"""
Based on https://pdfs.semanticscholar.org/ca0d/a7c552877e30e1c5d87dfcfb8b5972b0acd9.pdf pg.14
Function to estimate the fingerprint ridge frequency within a small block
of a fi... | {"hexsha": "ec4e9680b01c8c4515e5813cd1418903e96f66e1", "size": 3461, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/frequency.py", "max_stars_repo_name": "bglezseoane/fingerprint_recognition", "max_stars_repo_head_hexsha": "f670d2fd42ab7b5d7e471a9a16a5490ddd1f9b2d", "max_stars_repo_licenses": ["MIT"], "ma... |
import tkinter as tk
from tkinter import *
from tkinter import filedialog, Text
import os
from PIL import Image, ImageTk
import numpy as np
from tkinter.font import Font
from tkinter.messagebox import *
import time
import first_face_dataset, registeruser, third_face_recognition
image1=''
main = Tk()
dir_path = os.path... | {"hexsha": "aedace9527d762a9f0d40376c2843bdd7fdee22f", "size": 1600, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "Nipun4338/mynotebook", "max_stars_repo_head_hexsha": "58f949b2986bee8b541eb026d3bbd906a1cd6d6e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
using AcuteML
using Dates: Time, Date
export MRDHeader
@enm PatientGender M F O
@aml mutable struct SubjectInformation "~"
patientName::UN{String}=nothing, "~"
patientWeight_kg::UN{Float32}=nothing, "~"
patientID::UN{String}=nothing, "~"
patientBirthdate::UN{Date}=nothing, "~"
patientGender::U... | {"hexsha": "ebc2d887482e60be0e8261564b50ce8fd3de2cc5", "size": 5996, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MRDHeader.jl", "max_stars_repo_name": "dchansen/Gadgetron.jl-1", "max_stars_repo_head_hexsha": "cc811e6e3a5638802d8c77d5f563a327b0143b5b", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
[STATEMENT]
lemma one_dim_iso_adjoint[simp]: \<open>cadjoint one_dim_iso = one_dim_iso\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. one_dim_iso\<^sup>\<dagger> = one_dim_iso
[PROOF STEP]
apply (rule cadjoint_eqI)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>x y. one_dim_iso x \<bullet>\<^sub>C y... | {"llama_tokens": 179, "file": "Complex_Bounded_Operators_One_Dimensional_Spaces", "length": 2} |
function fahrenheit_to_celsius(temp_f) result(temp_c)
implicit none
real temp_f
real temp_c
temp_c = (temp_f - 32.0) * (5.0/9.0)
end function fahrenheit_to_celsius
temp_c = fahrenheit_to_celsius(100.0)
write(*,*) temp_c
| {"hexsha": "e5974a6013b98c7ea51d3738214ed916725833be", "size": 221, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "content/code/fortran/fahrenheit_to_celsius_pure.f90", "max_stars_repo_name": "transferorbit/coderefinery_testing", "max_stars_repo_head_hexsha": "b1011345cd6ed614e702b372bd2d987521bba130", "max_s... |
"""
Copyright MIT and Harvey Mudd College
MIT License
Summer 2020
Lab 6 - Sensor Fusion
"""
########################################################################################
# Imports
########################################################################################
import sys
import cv2 as cv
import nu... | {"hexsha": "5bb9c746b4b114dc85003df992e8d5ce60918ed4", "size": 3817, "ext": "py", "lang": "Python", "max_stars_repo_path": "labs/lab6/lab6.py", "max_stars_repo_name": "MITLLRacecar/racecar-anhad", "max_stars_repo_head_hexsha": "c4f1fc727acf67d2bf1b0ad6ab7ea68b3c24c6fc", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
function [ vX, mX ] = SolveLsL1ComplexCd( mA, vB, lambdaFctr, numIterations )
% ----------------------------------------------------------------------------------------------- %
%[ vX, mX ] = SolveLsL1ComplexPgm( mA, vB, lambdaFctr, numIterations )
% Solves the 0.5 * || A x - b ||_2 + \lambda || x ||_1 problem using
% ... | {"author": "RoyiAvital", "repo": "StackExchangeCodes", "sha": "d2a934616995fa8a9f4df1ca29029402435b9e6f", "save_path": "github-repos/MATLAB/RoyiAvital-StackExchangeCodes", "path": "github-repos/MATLAB/RoyiAvital-StackExchangeCodes/StackExchangeCodes-d2a934616995fa8a9f4df1ca29029402435b9e6f/Mathematics/Q1344369/SolveLsL... |
import hypothesis_utils
import numpy as np
from hypothesis import assume, given, settings
from autorad.feature_selection.selector import AnovaSelector
class TestAnovaSelection:
def setup_method(self):
self.selector = AnovaSelector(n_features=5)
@given(df=hypothesis_utils.medium_df())
@settings(m... | {"hexsha": "ba35546f27733494fbdfab50a4ec713da4a5b7a8", "size": 1044, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/autorad/test_feature_selector.py", "max_stars_repo_name": "pwoznicki/ClassyRadiomics", "max_stars_repo_head_hexsha": "07b6c39a8ea41d9bb18b1fbfe2a817736a26b5ed", "max_stars_repo_licenses": ["... |
#include <gtest/gtest.h>
#include <Eigen/Dense>
#include <vector>
#include "matrix.pb.h"
#include "src/collectors/file_collector.h"
#include "src/collectors/memory_collector.h"
#include "src/utils/proto_utils.h"
TEST(collectors, memory) {
MemoryCollector coll;
coll.start_collecting();
std::vector<Eigen::Vecto... | {"hexsha": "38da70caacfca1a4278a70a959ad2cc097e01964", "size": 1827, "ext": "cc", "lang": "C++", "max_stars_repo_path": "test/collectors.cc", "max_stars_repo_name": "mberaha/bayesmix", "max_stars_repo_head_hexsha": "4448f0e9f69ac71f3aacc11a239e3114790c1aaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
import torch
import torch.nn as nn
from torch.distributions.normal import Normal
import numpy as np
from config import cfg
import time
import os
#from fast_weights import fast_weights_model
from generate import Graph4D
from torch.distributions.kl import kl_divergence
import scores
from tensorboardX import SummaryWrit... | {"hexsha": "92b11b9154520bd35a34f72bbeb14c826a3e57b4", "size": 9856, "ext": "py", "lang": "Python", "max_stars_repo_path": "TEM_simple_rnn.py", "max_stars_repo_name": "Victorwz/Generative-Hippocampal-entorhinal-System", "max_stars_repo_head_hexsha": "5f38b0fea364c1974ebaf25f16576777a35295e3", "max_stars_repo_licenses":... |
# -*- coding: utf-8 -*-
"""
This module contains code to read and write FlyMovieFormat
files, which end with extension .fmf.
Users may like to use these classes:
- :class:`~motmot.FlyMovieFormat.FlyMovieFormat.FlyMovie` : read .fmf files
- :class:`~motmot.FlyMovieFormat.FlyMovieFormat.FlyMovieSaver` : write .fmf fi... | {"hexsha": "a9f85dd62c7355b5027d46f0bb3cbab72878aa9d", "size": 21528, "ext": "py", "lang": "Python", "max_stars_repo_path": "motmot/FlyMovieFormat/FlyMovieFormat.py", "max_stars_repo_name": "motmot/flymovieformat", "max_stars_repo_head_hexsha": "f6ce6043a46e4070dff8d76eba92e0e2a433ec8f", "max_stars_repo_licenses": ["BS... |
import numpy as np
import pymol
import chempy
import sys
from pymol.cgo import *
from pymol import cmd
from random import randint
#############################################################################
#
# drawBoundingBox.py -- Draws a box surrounding a selection
#
#
# AUTHOR: Jason Vertrees
# DATE : 2/20/200... | {"hexsha": "95310d716e8f171d29e6b2d1d9f5d7051cf7cdb1", "size": 9936, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/py2/show_cavbox.py", "max_stars_repo_name": "akors/cavitylearn", "max_stars_repo_head_hexsha": "a03d159cbefce83d4c4c731a9c2573e7261faf91", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
# Author: Aris Tritas <aris.tritas@u-psud.fr>
# License: BSD 3-clause
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
def singleplot(data, horizon, title, filepath, xy_labels=None):
"""
Simple plot of a data array.
:param data: Data array
:param horizo... | {"hexsha": "00607a3c8ed11e01627185d0a0b4d0725b35c41d", "size": 2906, "ext": "py", "lang": "Python", "max_stars_repo_path": "bitl/utils/plot.py", "max_stars_repo_name": "tritas/bitl", "max_stars_repo_head_hexsha": "f394e633e38f983fed10c3e672af7be2883cbdbb", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
using ArtificialBeeColony
# initializer for bees' position
function init()
rand(1)*20 .- 10 # [-10, 10]
end
# target function
function target(x::Vector{Float64})
x[1]^2+10*sin(2*x[1])
end
N = 50 # the number of bees
epoch = 100 # the number of iteration
flag = true # time invariant flag
abc = ABC... | {"hexsha": "1105ebe618c39420b46b50199b63175a323d0106", "size": 456, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "example/ex.jl", "max_stars_repo_name": "peakbook/ArtificialBeeColony.jl", "max_stars_repo_head_hexsha": "5185bb0ccfb9a3b9f24ed1407df60c7e398e0cf0", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import os
import numpy as np
from PIL import Image
from .seg_dataset import SegDataset
from .voc_seg_dataset import VOCMetaInfo
class CityscapesSegDataset(SegDataset):
"""
Cityscapes semantic segmentation dataset.
Parameters:
----------
root : str
Path to a folder with `leftImg8bit` and `... | {"hexsha": "e02b7c26bc6f209453c89e9af38e03aeda151f7b", "size": 5066, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytorch/datasets/cityscapes_seg_dataset.py", "max_stars_repo_name": "naviocean/imgclsmob", "max_stars_repo_head_hexsha": "f2993d3ce73a2f7ddba05da3891defb08547d504", "max_stars_repo_licenses": ["MI... |
struct ScaledDiagonallyDominantVariableBridge{T} <: AbstractVariableBridge
side_dimension::Int
variables::Vector{NTuple{3, MOI.VariableIndex}}
psd2x2::Vector{MOI.ConstraintIndex{MOI.VectorOfVariables,
PositiveSemidefinite2x2ConeTriangle}}
end
function add_variable_bri... | {"hexsha": "94eb7cb9976a0960104ea61efa455a511bc601f1", "size": 3390, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/scaled_diagonally_dominant_variable_bridge.jl", "max_stars_repo_name": "barpit20/SumOfSquares.jl", "max_stars_repo_head_hexsha": "8f64fca496ad29f2dc04241c864038ad2e2b78da", "max_stars_repo_lice... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
def type_to_color(info, i):
if(info['TYPE'][i] == 'Minority'):
return 'o'
elif(info['TYPE'][i] == 'Implanted'):
return 'd'
else:
return '^'
data = pd.read_csv("AP_SCORES.csv... | {"hexsha": "511e8aaf957b8a72b14e954ff64f946e2826a480", "size": 3301, "ext": "py", "lang": "Python", "max_stars_repo_path": "Analysis Scripts/efficiency_plot.py", "max_stars_repo_name": "droubo/meta-level-analysis-of-anomaly-detectors", "max_stars_repo_head_hexsha": "a64671365b6c98ad14fc82f3430d3082b0455a6c", "max_stars... |
\chapter*{Acknowledgments}
\paragraph*{} It is a pleasure to thank all those who made this thesis possible. First of all, I would like to deeply thank my thesis advisor Dr. Yuan Xu for his continued support throughout the months that i worked on this Master Thesis at DAI Labor.
\paragraph*{} The biggest thanks hav... | {"hexsha": "0454081bf0350a8d5da3e5cf45cc1fc6ff117d4a", "size": 1165, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "document/thesis/chapter/preface/acknowledgment.tex", "max_stars_repo_name": "AravinthPanch/gesture-recognition-for-human-robot-interaction", "max_stars_repo_head_hexsha": "42effa14c0f7a03f460fba5cd8... |
import argparse
import copy
import logging
import os
import pickle
import sys
import time
import gym
from gym import logger as gym_logger
import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
gym_logger.setLevel(logging.CRITICAL)
parser = argparse.ArgumentParser()
parser.add_argume... | {"hexsha": "b15f04138efdb78aba30159b76b8a3f6e8c69be3", "size": 1697, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/lander/play_pytorch.py", "max_stars_repo_name": "felix0901/Pytorch-NeuroEvolution", "max_stars_repo_head_hexsha": "e74fd90dc03343137fbcc47d6a41d9dfc5237c9b", "max_stars_repo_licenses": ["... |
import numpy as np
from rubin_sim.utils import (haversine, _raDecFromAltAz, _altAzPaFromRaDec, Site,
ObservationMetaData, _approx_altAz2RaDec, _approx_RaDec2AltAz)
import warnings
from .utils import wrapRA
from .interpComponents import (ScatteredStar, Airglow, LowerAtm, UpperAtm, MergedSpec... | {"hexsha": "2b25bf1f248cff42f0ca55a0ca10091eb3b74a6f", "size": 24339, "ext": "py", "lang": "Python", "max_stars_repo_path": "rubin_sim/skybrightness/skyModel.py", "max_stars_repo_name": "RileyWClarke/flarubin", "max_stars_repo_head_hexsha": "eb7b1ee21c828523f8a5374fe4510fe6e5ec2a2a", "max_stars_repo_licenses": ["MIT"],... |
#pragma once
#include <set>
#include <atomic>
#include <memory>
#include <Qt>
#include <QtGui>
#include <QtWidgets>
#include <boost/filesystem.hpp>
#include <rai/common/errors.hpp>
#include <rai/common/numbers.hpp>
#include <rai/common/alarm.hpp>
#include <rai/wallet/wallet.hpp>
#include <rai/rai_wallet/config.hpp>
n... | {"hexsha": "822dc6580019eb451288153642ddc7b7e56879e6", "size": 11274, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "rai/rai_wallet/qt.hpp", "max_stars_repo_name": "gokoo/Raicoin", "max_stars_repo_head_hexsha": "494be83a1e29106d268f71e613fac1e4033a82f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 94.0... |
import os
import shutil
import numpy as np
class SplitDatasetManager(object):
def __init__(self,
root_dir,
classes_dir,
include_hidden_files=False):
self.root_dir = root_dir
self.classes_dir = classes_dir
self.include_hidden_files = incl... | {"hexsha": "3cf6fd68695fa3f4a4bc2b9537e3179324c80fa4", "size": 2631, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/split_data_into_datasets_manager.py", "max_stars_repo_name": "iglaweb/HippoYD", "max_stars_repo_head_hexsha": "da2c40be8017c43a7b7b6c029e2df30cf7d54932", "max_stars_repo_licenses": ["Apache-2.... |
using GenerativeAD
using FileIO, BSON
using ValueHistories, DistributionsAD
using Flux
using ConditionalDists
using GenerativeModels
using EvalMetrics
using Plots
using Statistics
using DrWatson
dataset = "arrhythmia"
dataset = "wall-following-robot"
dataset = "yeast"
dataset = "letter-recognition"
dataset = "kdd99_sm... | {"hexsha": "e9dad7699600005183d100188e36e37f0d5e9327", "size": 2146, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/clean_val/compute_tst_auc_vs_val_score.jl", "max_stars_repo_name": "aicenter/GMAD.jl", "max_stars_repo_head_hexsha": "fb7ba28c61703df713953ed2b28728f768562ad9", "max_stars_repo_licenses": [... |
import cv2
import numpy as np
import os
import pandas as pd
import Localization
import Recognize
import matplotlib.pyplot as plt
def majority_vote(recognized_plates):
N = len(recognized_plates)
label_count = 0
labels = np.zeros((N, 1))
for i in range(N-1):
ref_number = recognized_plates[i][0]
t... | {"hexsha": "62ff88f1dc3b6166f2413e2b1e3437fc6ae8f645", "size": 9062, "ext": "py", "lang": "Python", "max_stars_repo_path": "CaptureFrame_Process.py", "max_stars_repo_name": "amaury-charlot/LicensePlateRecognition", "max_stars_repo_head_hexsha": "74a79c7911b85b205b18aee76b5aea03b34817d4", "max_stars_repo_licenses": ["FT... |
theory flash109Rev imports flashPub
begin
section{*Main defintions*}
lemma NI_FAckVsInv109:
(*Rule0VsPInv0*)
shows "invHoldForRule' s (inv109 ) (NI_FAck ) (invariants N)" (is " ?P1 s\<or>?P2 s\<or>?P3 s")
by( auto)
lemma NI_InvVsInv109:
(*Rule1VsPInv0*)
assumes a1:"iRule1 \<le> ... | {"author": "lyj238Gmail", "repo": "IsabelleCourse", "sha": "cd49d944d3504328ad8210fbd987abebdf192ed8", "save_path": "github-repos/isabelle/lyj238Gmail-IsabelleCourse", "path": "github-repos/isabelle/lyj238Gmail-IsabelleCourse/IsabelleCourse-cd49d944d3504328ad8210fbd987abebdf192ed8/flash/flash109Rev.thy"} |
from skimage import draw, io
import numpy as np
ratio = 2
imgSize = 64*ratio
img = np.zeros((imgSize, imgSize), dtype=np.uint8)
x = np.array([0, 0, 18, 18])*ratio
y = np.array([0, 32, 50, 0])*ratio
x += (imgSize - (x.max()-x.min()))//2
y += (imgSize - (y.max()-y.min()))//2
# rr, cc = draw.polygon(x, y)
rr, ... | {"hexsha": "395f134057462458d3d54577a8cca8fcce1d7c5f", "size": 524, "ext": "py", "lang": "Python", "max_stars_repo_path": "clump/diagramIn2d/drawAnEllipse.py", "max_stars_repo_name": "chua-n/particle", "max_stars_repo_head_hexsha": "faf966a381bef0b7206498c7c5419432348215b3", "max_stars_repo_licenses": ["Apache-2.0"], "... |
def correlating_feature_filter(df_regprops,threshold):
import numpy as np
import pandas as pd
import PySimpleGUI as sg
# Actually finding the correlating features with pandas
correlation_df = df_regprops.corr().abs()
correlation_matrix = correlation_df.to_numpy()
# using numpy to get the c... | {"hexsha": "bab11b5e968eefe02a8ec3c5ae58f2fe6abd6a9d", "size": 2955, "ext": "py", "lang": "Python", "max_stars_repo_path": "tribolium_clustering/feature_processing/_correlation_filter.py", "max_stars_repo_name": "Cryaaa/tribolium-clustering", "max_stars_repo_head_hexsha": "f5751ec8c007e95e8a9688d2d8e34508b04f0822", "ma... |
"""
Functions for working with shapefiles.
"""
from distutils.version import LooseVersion
import warnings
from pathlib import Path
import os
import collections
import shutil
import fiona
from shapely.geometry import shape, mapping
import numpy as np
import pandas as pd
import pyproj
from pyproj.enums import WktVersion
... | {"hexsha": "13c6b424766269ed3564e45f2b69d02249c43490", "size": 16260, "ext": "py", "lang": "Python", "max_stars_repo_path": "python_packages_static/gisutils/shapefile.py", "max_stars_repo_name": "usgs/neversink_workflow", "max_stars_repo_head_hexsha": "acd61435b8553e38d4a903c8cd7a3afc612446f9", "max_stars_repo_licenses... |
# coding:utf-8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless req... | {"hexsha": "f857f1fd0e68b7e2a766e35eabf4130d54355a1f", "size": 2297, "ext": "py", "lang": "Python", "max_stars_repo_path": "paddlehub/datasets/minicoco.py", "max_stars_repo_name": "chunzhang-hub/PaddleHub", "max_stars_repo_head_hexsha": "c5cfd021f77fd59340fb26e223e09a592e6a345f", "max_stars_repo_licenses": ["Apache-2.0... |
import pandas as pd
import numpy as np
from scipy.stats import entropy
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import seaborn as sn
import mdscaling as mds
# Loading Directed Bipartite Twitter graphs
DG={}
for country in ['chile','france']:
DG[country] = mds.DiBipartite('datasets/twit... | {"hexsha": "67d119c951b2a7b4f687093b354ad2d93718ab9b", "size": 1675, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/twitter_example.py", "max_stars_repo_name": "jphcoi/MDScaling", "max_stars_repo_head_hexsha": "1ace349a195d1af2371d7db9e5ffb5467b06f117", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
from . import augmentation_pool
from . import utils
class RandAugment:
"""
RandAugment class
Parameters
--------
nops: int
number of operations per image
magnitude: int
maximmum magnitude
alg: str
algorithm name
"""
def __init__(self, nop... | {"hexsha": "2ae8f3f1f674b89c7b1eee4b835ec49b55f52201", "size": 1351, "ext": "py", "lang": "Python", "max_stars_repo_path": "cords/utils/data/datasets/SSL/augmentation/rand_augment.py", "max_stars_repo_name": "krishnatejakk/AUTOMATA", "max_stars_repo_head_hexsha": "fd0cf58058e39660f88d9d6b4101e30a497f6ce2", "max_stars_r... |
using WoodburyMatrices
using Test
seed!(123)
n = 5
for elty in (Float32, Float64, ComplexF32, ComplexF64, Int), AMat in (x -> Matrix(Diagonal(x)),)
elty = Float64
a = rand(n); B = rand(n,2); D = rand(2,2); v = rand(n)
if elty == Int
v = rand(1:100, n)
a = rand(1:100, n)
B = rand... | {"hexsha": "5c38ef111cfae690fc33e823353b0dfe57a5f85a", "size": 4417, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests_sym.jl", "max_stars_repo_name": "UnofficialJuliaMirror/WoodburyMatrices.jl-efce3f68-66dc-5838-9240-27a6d6f5f9b6", "max_stars_repo_head_hexsha": "79fd736f5a0e4f380de661b85eed5ccd6e3d73... |
'''
@author: Kai Londenberg
@TODO:
* NoisyMax: Implement an efficient Noisy-Max PotentialTable with sparse parametrization
as described in https://web.archive.org/web/20130622092203/http://www.ia.uned.es/~seve/publications/MAX.pdf
* Particle List Message Passing: Create an efficient particle list... | {"hexsha": "97aa00317a06a1bb6b7c033af8de9137a8906f0b", "size": 15099, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/pypgmc/potential_tables.py", "max_stars_repo_name": "kadeng/pypgmc", "max_stars_repo_head_hexsha": "909445fa3a426b07b39b65d2cb8979b1db8cdfca", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
The goal of this chapter is to provide a comprehensive treatment of the most relevant
gradient and subgradient methods for convex optimization.
We intend to cover
the most robust methods with the fewest assumptions first, and then later move to
methods which require more assumptions but have much better convergence ra... | {"hexsha": "d4c12aed9a42b8e5e5fc4fa0dd15ba68a98fddba", "size": 61635, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "6DL/SubgradientMethods.tex", "max_stars_repo_name": "liuzhengqi1996/math452", "max_stars_repo_head_hexsha": "635b6ce53cb792e316abf4f47396f2e4f0686815", "max_stars_repo_licenses": ["MIT"], "max_star... |
#include <iostream>
#include <boost/signals2.hpp>
#include <boost/log/core.hpp>
#include <boost/log/trivial.hpp>
#include <boost/log/expressions.hpp>
#include "copper/Engine.h"
#include "copper/Operator/OpNode.h"
#include "NodeItem.h"
#include "NodeConnectionItem.h"
#include "NodeFlowScene.h"
namespace copper { n... | {"hexsha": "45b5ef40aab07b3971914edd8e05e6b33f8546b0", "size": 3270, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/copperfx/panels/NodeFlowViewPanel/NodeFlowScene.cpp", "max_stars_repo_name": "all-in-one-of/CopperFX", "max_stars_repo_head_hexsha": "9a50b69a57ebd6aa578d12456e34d792a7c51916", "max_stars_repo_l... |
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