text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import pybullet as p
import math
import pybullet_data
import time
import random
import numpy as np
import serial
def radToPwm(angle):
return ((2000 * angle) / math.pi) + 1500
# t in ms; the closer t is to 0, more accuracy but less smooth motion
def updateRealServos(ser, t):
# right legs
ser.write(
... | {"hexsha": "15fd16e9d3467135adb284e509e5af639c37cb94", "size": 19815, "ext": "py", "lang": "Python", "max_stars_repo_path": "hexapodengine4.py", "max_stars_repo_name": "jonathan-sung/Hexapod-GA-Gait", "max_stars_repo_head_hexsha": "5e82c2f141f6bd88d8b6c0a7b658c8ce0c5be8f4", "max_stars_repo_licenses": ["MIT"], "max_star... |
from __future__ import absolute_import
import numpy as np
from math import ceil, floor
from keras import backend as K
from keras.optimizers import Optimizer
from keras.legacy import interfaces
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.ops impo... | {"hexsha": "0e46f177a1cef7b1bbb0ed10f60da33965746121", "size": 32434, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/utilities/optimizers.py", "max_stars_repo_name": "Neves4/colab-rl", "max_stars_repo_head_hexsha": "ab2fd9126dcf19030ca241a79906f2362a26dd78", "max_stars_repo_licenses": ["MIT"], "max_stars... |
//=======================================================================
// Copyright 2014-2015 David Simmons-Duffin.
// Distributed under the MIT License.
// (See accompanying file LICENSE or copy at
// http://opensource.org/licenses/MIT)
//=======================================================================
#in... | {"hexsha": "c01de2fbaf20da3ad75dd3745de1aac7431149a9", "size": 1905, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "src/sdpb/solve.cxx", "max_stars_repo_name": "suning1985/sdpb", "max_stars_repo_head_hexsha": "9263b89496d1c356f11d08f995825626b60f5b89", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
from __future__ import division, print_function
import sys
import os
import importlib
import numpy as np
import scipy
from scipy import signal
from matplotlib.colors import ListedColormap
import sys
sys.path.insert(0, '../')
from mars import config, DeterministicFunction, GridWorld
from mars.utils import dict2func
imp... | {"hexsha": "cfe42e85c1fdbc76be8fca0df84efa9b22ee7c4b", "size": 29463, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/example_utils.py", "max_stars_repo_name": "amehrjou/neural_lyapunov_redesign", "max_stars_repo_head_hexsha": "2a4dbb876d897b2bd29bf94e39882869794c4333", "max_stars_repo_licenses": ["MIT"... |
// Copyright (c) 2015 John Maddock
// 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)
//
#ifndef BOOST_MATH_ELLINT_JZ_HPP
#define BOOST_MATH_ELLINT_JZ_HPP
#ifdef _MSC_VER
#pragm... | {"hexsha": "a3fa54746e42a13aa0b0146159583993de37f97b", "size": 2273, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contrib/libboost/boost_1_62_0/boost/math/special_functions/jacobi_zeta.hpp", "max_stars_repo_name": "189569400/ClickHouse", "max_stars_repo_head_hexsha": "0b8683c8c9f0e17446bef5498403c39e9cb483b8", ... |
\documentclass{article}
\usepackage{enumerate}
\usepackage{amsmath, amsthm, amssymb}
\usepackage[margin=1in]{geometry}
\usepackage[parfill]{parskip}
\DeclareMathOperator*{\argmax}{arg\,max}
\title{Econ C103 Problem Set 9}
\author{Sahil Chinoy}
\date{April 18, 2017}
\begin{document}
\maketitle{}
\subsection*{Exercise... | {"hexsha": "a23c1bfdd7e6dd2ccc4f995dede843f9f26d2fa6", "size": 2499, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "hw9/hw9.tex", "max_stars_repo_name": "sahilchinoy/econ103", "max_stars_repo_head_hexsha": "ab2ecbb759eb811e953157e7f04f5a003066a62c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
module Extraction
using Logging
using LightGraphs, MetaGraphs
import JSON
# TODO: Create functions for each
# Cause - is concept label, Effect - definition label
# within a RelationMention with labels "Definition, Entity"
""" definitiongraph(dir::String, namefunc)
read a directory of json files and ingest all t... | {"hexsha": "abc1bb835272cbb07e87be797663d8b1c20a42e0", "size": 3802, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/definitions.jl", "max_stars_repo_name": "mehalter/SemanticModels.jl", "max_stars_repo_head_hexsha": "e19dd6bcdbc889dfad54daaab7ca54a7c6080a72", "max_stars_repo_licenses": ["MIT"], "max_sta... |
from smp_manifold_learning.differentiable_models.ecmnn import EqualityConstraintManifoldNeuralNetwork
from smp_manifold_learning.differentiable_models.utils import convert_into_at_least_2d_pytorch_tensor
import pickle
import numpy as np
import matplotlib.pyplot as plt
from smp_manifold_learning.motion_planner.feature i... | {"hexsha": "957c2ceec50d4e158c90179e182828fff493bb3e", "size": 5758, "ext": "py", "lang": "Python", "max_stars_repo_path": "smp_manifold_learning/scripts/evaluate_manifold_projection.py", "max_stars_repo_name": "gsutanto/smp_manifold_learning", "max_stars_repo_head_hexsha": "60ef8278942c784c8d3bcd0a09031475f80d96fb", "... |
import numpy as np
def read_calib_file(path):
# taken from https://github.com/hunse/kitti
float_chars = set("0123456789.e+- ")
data = {}
with open(path, 'r') as f:
for line in f.readlines():
key, value = line.split(':', 1)
value = value.strip()
data[key] = v... | {"hexsha": "146f78ae47019b82cde5e15c09f05cbf3bca8c5b", "size": 907, "ext": "py", "lang": "Python", "max_stars_repo_path": "read_calib.py", "max_stars_repo_name": "sandeep-kota/sfmlearner", "max_stars_repo_head_hexsha": "418a2a48f033c124dfbc8d558244a75ec48254f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
function r = logdet(M)
% Compute log(det(A)) without the usual numerical inaccuracies.
% Copyright (C) Christian Kothe, SCCN, 2011, christian@sccn.ucsd.edu
%
% 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 F... | {"author": "goodshawn12", "repo": "REST", "sha": "e34ce521fcb36e7813357a9720072dd111edf797", "save_path": "github-repos/MATLAB/goodshawn12-REST", "path": "github-repos/MATLAB/goodshawn12-REST/REST-e34ce521fcb36e7813357a9720072dd111edf797/dependencies/BCILAB/code/misc/logdet.m"} |
from os import environ
from pprint import pprint
import pickle
import numpy as np
import numpy as np
import torch
import pandas as pd
import seaborn as sns
from torch import optim
import time
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import copy
import random
... | {"hexsha": "43f2d7275d9498f7a86543ac04f7aed7004faf16", "size": 28962, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/CostModels/Recursive_LSTM_v2_MAPE/utils.py", "max_stars_repo_name": "reality95/tiramisu", "max_stars_repo_head_hexsha": "8d6f1053b0086f7e50ee56bf36c86d7cbf334e92", "max_stars_repo_licenses"... |
#ifndef BOOST_MPL_AUX_CONFIG_TTP_HPP_INCLUDED
#define BOOST_MPL_AUX_CONFIG_TTP_HPP_INCLUDED
// Copyright Aleksey Gurtovoy 2000-2004
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
//
// See http:... | {"hexsha": "4382b0057f61222e1c296c58a607b76a05c23b34", "size": 1308, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/mpl/aux_/config/ttp.hpp", "max_stars_repo_name": "diersma/sick_visionary_cpp_shared", "max_stars_repo_head_hexsha": "f86e647886fb1199a2ed063fcf128ed78c6651db", "max_stars_repo_licenses... |
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import ode
from mpl_toolkits.mplot3d.axes3d import Axes3D
plt.style.use('dark_background')
def plot(r):
# 3D plot
fig = plt.figure(figsize=(10,10))
ax = fig.add_subplot(111, projection='3d')
# plot trajectory and starting point
ax.plot(... | {"hexsha": "e8b80253bb2503889c03f32a13f36aee347ad0fd", "size": 2473, "ext": "py", "lang": "Python", "max_stars_repo_path": "two_body_problem.py", "max_stars_repo_name": "jasonody/orbital-mechanics", "max_stars_repo_head_hexsha": "abe50da66c1598d1c31f6f3a4faf313e6cdebc7c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
from .Distances import gower_distances
import math
from scipy.spatial import distance
from sklearn.manifold import Isomap
from sklearn.utils import validation
from sklearn.metrics import pairwise, pairwise_distances
from scipy.spatial.distance import pdist, wminkowski
unsquareform = lambda a: a[np.n... | {"hexsha": "f261c3f6d9f5d53261694c68279097b9474d8399", "size": 10205, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/GAPS_Explanation.py", "max_stars_repo_name": "aindrila-ghosh/LAPS_and_GAPS", "max_stars_repo_head_hexsha": "1d8ca1ccd301400dbd9ee107b0a232c260c5ce59", "max_stars_repo_licenses": ["MIT"], "max... |
SUBROUTINE FDVDLD (IENTRY,IIX,IIY)
C
C
C +-----------------------------------------------------------------+
C | |
C | Copyright (C) 1... | {"hexsha": "2fe251853e4817bade513c2f13a1947ce590d918", "size": 33765, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "iraf.v2161/sys/gio/ncarutil/dashsmth.f", "max_stars_repo_name": "ysBach/irafdocgen", "max_stars_repo_head_hexsha": "b11fcd75cc44b01ae69c9c399e650ec100167a54", "max_stars_repo_licenses": ["MIT"], ... |
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("whitegrid", {'axes.grid': False})
import pandas as pd
import matplotlib.patches as mpatches
from matplotlib.collections import PatchCollection
from matplotlib import cm
from kvae.utils.movie... | {"hexsha": "ff4ab258150a6eea0d18d6dbc88aa5ee972658cc", "size": 13152, "ext": "py", "lang": "Python", "max_stars_repo_path": "kvae/utils/plotting.py", "max_stars_repo_name": "abeken0713/kvae", "max_stars_repo_head_hexsha": "51262f4311795476b0adfbc29d15e74d3aedb25e", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#include <set>
#include <ace/Signal.h>
#include <boost/algorithm/string_regex.hpp>
#include "application/Application.h"
#include "application/ApplicationInitialize.h"
#include "application/ApplicationUnInitia.h"
#include "application/ApplicationPeriodRun.h"
#include "Configuration.h"
#include "rest/ConsulConnection.h"... | {"hexsha": "069d872341a65b7c65aa9ae111088f82a775cd70", "size": 30264, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/daemon/Configuration.cpp", "max_stars_repo_name": "FrederickHou/app-mesh", "max_stars_repo_head_hexsha": "8f23a1555d3a8da1481d91e1e60464fc197bd6e5", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# match citation data with aggregated firm data (to be run before firm_merge.py)
import argparse
import numpy as np
import pandas as pd
from tools.tables import read_csv
# parse input arguments
parser = argparse.ArgumentParser(description='Merge patent citation data.')
parser.add_argument('--output', type=str, defaul... | {"hexsha": "66f9ad0ac0ee6e28f51fba575bc642c76be2bce5", "size": 1692, "ext": "py", "lang": "Python", "max_stars_repo_path": "firm_cites.py", "max_stars_repo_name": "iamlemec/patents", "max_stars_repo_head_hexsha": "776b51464dbcdd07aee6554d61434c4d668a2d16", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 100, "ma... |
import numpy as np
import criteria
from datasets.ActivityNet import ActivityNetGCN
from datasets.TACOS import TACOSGCN
from datasets.Charades import CharadesGCN
from datasets.Didemo import DidemoGCN
from utils import load_json, generate_anchors
def get_dataset(dataset, feature_path, data_path, word2vec, max_num_frame... | {"hexsha": "353e09a87ecf2dd9fa20aba4ee889be5e3b269b9", "size": 19194, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/dataloaders/clip_loader.py", "max_stars_repo_name": "liudaizong/IA-Net", "max_stars_repo_head_hexsha": "f19295d13d1468eb582521131cde3de83dfd18f6", "max_stars_repo_licenses": ["MIT"], "max_st... |
from sympy import (Rational, Symbol, Real, I, sqrt, oo, nan, pi, E, Integer,
S, factorial, Catalan, EulerGamma, GoldenRatio, cos, exp,
Number, zoo, log, Mul)
from sympy.core.power import integer_nthroot
from sympy.core.numbers import igcd, ilcm, igcdex, ifactorial, seterr, _intcac... | {"hexsha": "b4800c71b6ef5aebe7e8d39fbe0fd4c7679081ff", "size": 25919, "ext": "py", "lang": "Python", "max_stars_repo_path": "sympy/core/tests/test_numbers.py", "max_stars_repo_name": "pernici/sympy", "max_stars_repo_head_hexsha": "5e6e3b71da777f5b85b8ca2d16f33ed020cf8a41", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
"""
Simple GUIs using the interactive capabilities of :mod:`matplotlib`
**Interactive gravimetric modeling**
* :class:`~fatiando.gui.simple.Moulder`
* :class:`~fatiando.gui.simple.BasinTrap`
* :class:`~fatiando.gui.simple.BasinTri`
**Interactive modeling of layered media**
* :class:`~fatiando.gui.simple.Lasagne`
-... | {"hexsha": "8273bb00b69b00957c87cb5ee0ea2e2453199a4e", "size": 21646, "ext": "py", "lang": "Python", "max_stars_repo_path": "fatiando/gui/simple.py", "max_stars_repo_name": "silky/fatiando", "max_stars_repo_head_hexsha": "5041c6b29758a5e73e9d7b2b906fa5e493fd9aba", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import torch
import math
import matplotlib.pyplot as plt
from scipy import integrate
from scipy.sparse.linalg import LinearOperator
from scipy.sparse.linalg import eigs
import numpy as np
import h5py
def beta_function():
file = h5py.File(".\\data\\TNR beta_function 1.hdf5", "r")
beta_list = file[("beta_list")]... | {"hexsha": "725d800c9a0ddf03d3c22cb4b01f8040fe424016", "size": 5084, "ext": "py", "lang": "Python", "max_stars_repo_path": "test tnr.py", "max_stars_repo_name": "xwkgch/IsoTensor", "max_stars_repo_head_hexsha": "b08e9753d50f082023d4f516361bc666ee359223", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 17, "max_s... |
#include <boost/test/unit_test.hpp>
#include "Copy.hpp"
#include <unordered_map>
#include <vector>
using namespace json;
BOOST_AUTO_TEST_SUITE(TestCopy)
std::string do_copy(const std::string &src)
{
Writer writer;
Parser parser(src.data(), src.data() + src.size());
copy(writer, parser);
return std::m... | {"hexsha": "aa2337a417ca0633fed8b84007bdf111d83c8a0e", "size": 1751, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/Copy.cpp", "max_stars_repo_name": "wnewbery/cpp-json", "max_stars_repo_head_hexsha": "3f4edf99ca67847c6a32ce09cc9ae48c9477d550", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import os
from os import listdir
from os.path import isdir, isfile, join
from typing import List
import numpy as np
def np_softmax(x):
exp_x = np.exp(x)
return exp_x / np.sum(exp_x)
def basename(path: str) -> str:
"""
get '17asdfasdf2d_0_0.jpg' from 'train_folder/train/o/17asdfasdf2d_0_0... | {"hexsha": "715ba813dc40c31c11d9adce446e4679d0a42120", "size": 2197, "ext": "py", "lang": "Python", "max_stars_repo_path": "bert/wtfml/utils/utils.py", "max_stars_repo_name": "jphacks/C_2111", "max_stars_repo_head_hexsha": "df87580614d7e5c225ea30746e5f2cd0576bbc98", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""PyTorch Dataset implementation for CoDraw dataset"""
import h5py
import numpy as np
import torch
import torch.nn as nn
from ..utils.config import keys
class CoDrawDataset(nn.Module):
def __init__(self, path, cfg, img... | {"hexsha": "bb73106e5c02967f3d33cc5f939309b095c3bde5", "size": 5390, "ext": "py", "lang": "Python", "max_stars_repo_path": "irgan/data/codraw_dataset.py", "max_stars_repo_name": "Victarry/IR-GAN-Code", "max_stars_repo_head_hexsha": "51a217029e9dc9b0507525c2fabf51f85424fc68", "max_stars_repo_licenses": ["MIT"], "max_sta... |
macro memoize(cache_var)
filename = String(cache_var) * ".jld2"
quote
if !isfile( $filename )
@save $filename $cache_var
else
@load $filename $cache_var
end
end
end
macro memoize(cache_var, fn)
filename = String(cache_var) * ".jld2"
quote
... | {"hexsha": "dbd4f64728744e466a97bd2165e22a2c5779b525", "size": 475, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Memoize.jl", "max_stars_repo_name": "caseykneale/DeltaSugar.jl", "max_stars_repo_head_hexsha": "bf492abddbf3d3e484d5c0a635762375a3bff05f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Copyright 2019 The ROBEL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wr... | {"hexsha": "728b83a6ab26af05b2220133a5a155f29fd995f1", "size": 8890, "ext": "py", "lang": "Python", "max_stars_repo_path": "robel/scripts/rollout.py", "max_stars_repo_name": "Del9fina/robel", "max_stars_repo_head_hexsha": "63dfac65932757134e5766f1e20a339efe281bc7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
import os
import time
import datetime
import tensorflow as tf
import numpy as np
import data_utils as utils
from tensorflow.contrib import learn
from text_cnn import TextCNN
from data_utils import IMDBDataset
import argparse
import pandas as pd
import pickle
from ekphrasis.classes.preprocessor import TextPreProcesso... | {"hexsha": "1a44f1371411ec4639654335bb1ed7a422da6562", "size": 6692, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "manueltonneau/glove-text-cnn", "max_stars_repo_head_hexsha": "dbf9a14c4a6c03fee1b2b065d6587323079301d8", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
# -*- coding: utf-8 -*-
"""GEModelClass
Solves an Aiygari model
"""
##############
# 1. imports #
##############
import time
import numpy as np
from numba import njit, prange
# consav
from consav import ModelClass, jit # baseline model class and jit
from consav import linear_interp # linear interpolation
from cons... | {"hexsha": "cc1439701268d0540d4cde22a0dd89faa66ff725", "size": 16431, "ext": "py", "lang": "Python", "max_stars_repo_path": "00. DynamicProgramming/GEModel.py", "max_stars_repo_name": "alanlujan91/ConsumptionSavingNotebooks", "max_stars_repo_head_hexsha": "4455500d17fed4dd1f3f4844aeb5dd5d3b89903f", "max_stars_repo_lice... |
import numpy as np
import pandas as pd
pd.set_option('display.height', 1000)
pd.set_option('display.width', 1000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 500)
def xorseq(L,R):
result = 0
for n in xrange(L, R+1):
if (n-2)% 4 == 0:
result = result ^ (n+1)
... | {"hexsha": "e88396fb297aaf5753b1395450628a7f96723ffa", "size": 933, "ext": "py", "lang": "Python", "max_stars_repo_path": "hourrank-5/xor-se/xor-sequence-slow.py", "max_stars_repo_name": "codedsk/challenges", "max_stars_repo_head_hexsha": "bea2e2daef02ca512f67e47bc88519c0940e4be8", "max_stars_repo_licenses": ["MIT"], "... |
import tensorflow as tf
import kerastuner as kt
from utils.generic_utils import print_log
import utils.config as config
import os
import datetime
import time
from utils.analyzing_data import multiclass_analysis
import pickle
import cv2
import numpy as np
def get_label(file_path):
# convert the path to a list of p... | {"hexsha": "5393b41cf1ec6980469dc395139bd4ab0ba7ca09", "size": 18214, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/handle_modes.py", "max_stars_repo_name": "1Stohk1/tami", "max_stars_repo_head_hexsha": "e0aa902bb767631dd2435ed0eac05209b9bd64ed", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import numpy as onp
from optimism.JaxConfig import *
from optimism import FunctionSpace
from optimism import Mesh
from optimism import QuadratureRule
from optimism.TensorMath import tensor_2D_to_3D
PhaseFieldFunctions = namedtuple('PhaseFieldFunctions',
['compute_internal_energy',
... | {"hexsha": "171c7151b9025d800f0256a192b0cbe6acf27bb2", "size": 8162, "ext": "py", "lang": "Python", "max_stars_repo_path": "optimism/phasefield/PhaseField.py", "max_stars_repo_name": "btalami/optimism", "max_stars_repo_head_hexsha": "6bda77ce33348e9a90bcf5fec135d6f27a4e46ba", "max_stars_repo_licenses": ["MIT"], "max_st... |
using MacroTools
function Base.convert(::Type{Expression}, ex::Expr)
ex.head === :if && (ex = Expr(:call, ifelse, ex.args...))
ex.head === :call || throw(ArgumentError("internal representation does not support non-call Expr"))
op = eval(ex.args[1]) # HACK
args = convert.(Expression, ex.args[2:end])
... | {"hexsha": "7776ad8cc1ca5f7d1376c82c1d7f543cad9064fb", "size": 3320, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/ModelingToolkit.jl-961ee093-0014-501f-94e3-6117800e7a78", "max_stars_repo_head_hexsha": "b55981646739886ba6e8ad26243873b9ac94dc... |
import deepchem as dc
smiles = ["C", "O=C=C=C"]
featurizer=dc.feat.ConvMolFeaturizer(per_atom_fragmentation=False)
featurizer1 = dc.feat.MolGraphConvFeaturizer(use_edges=True)
f = featurizer.featurize(smiles)
f1 = featurizer1.featurize(smiles)
print(f[1].canon_adj_list)
print(f1[1].edge_index)
from torch_geom... | {"hexsha": "e0dd2f15b1a671dd4c9fe3a07c84e604393d7eb3", "size": 7592, "ext": "py", "lang": "Python", "max_stars_repo_path": "prog/edge.py", "max_stars_repo_name": "kimmo1019/DeepCDR_LCQ", "max_stars_repo_head_hexsha": "633dc5d6925ba086faa63227160b5421c4cffa3a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import pandas as pd
df = pd.read_csv('balanced_reviews.csv')
df.shape
df.columns
df.sample(10) #sample will pick random data
df['reviewText'][0]
df['overall'].value_counts()
df.isnull().any(axis = 0)
df.dropna(inplace = True)
df = df [df['overall'] != 3]
import numpy as np
df['Positivity'] = np.where(df['... | {"hexsha": "18016158f9b38592fe0950aa38b402a0616b6592", "size": 354, "ext": "py", "lang": "Python", "max_stars_repo_path": "day-48 data preprocessing-making dataset for NLP/Day48.py", "max_stars_repo_name": "itsjaysuthar/DSintern", "max_stars_repo_head_hexsha": "985eb1d13d52d817148fea931597072f9a23fc33", "max_stars_repo... |
from fuzzy_asteroids.util import Scenario
import numpy as np
# "Simple" Scenarios --------------------------------------------------------------------------------------------------#
# Threat priority tests
threat_test_1 = Scenario(
name="threat_test_1",
asteroid_states=[{"position": (0, 300), "angle": -90.0, ... | {"hexsha": "b31df60e00166612398f3b8eb16174c35e86d989", "size": 42680, "ext": "py", "lang": "Python", "max_stars_repo_path": "competition/scenarios.py", "max_stars_repo_name": "xfuzzycomp/FuzzyChallenge2021", "max_stars_repo_head_hexsha": "5876450fdb913c6707352bfe9fcc25748f041f52", "max_stars_repo_licenses": ["MIT"], "m... |
from typing import Dict, Callable, List, Union
from numpy import random
from numpy.lib.function_base import append, select
import torch
import gym
import copy
from torch.optim import optimizer
from torch.serialization import save
from tqdm import tqdm
import numpy as np
import torch.optim as optim
import os
from collec... | {"hexsha": "5dbdd72dc1e7bd7afad912796dcfcccddb30bf3a", "size": 21816, "ext": "py", "lang": "Python", "max_stars_repo_path": "m3ddpg.py", "max_stars_repo_name": "Sebastian-Griesbach/Minimax-Multi-Agent-Deep-Deterministic-Policy-Gradient", "max_stars_repo_head_hexsha": "c54d2bef7860da03d6be8e23f75648f98deb6141", "max_sta... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
from numpy import pi, cos, sin, arccos, arange
#import mpl_toolkits.mplot3d
#import matplotlib.pyplot as pp
import numpy as np
import copy
import open3d as o3d
from biopandas.pdb import PandasPdb
def dot_sphere(x0,y0,z0,occupancy=0.5, num_pts=1000):
num_pts = 1000... | {"hexsha": "38363005f6f189f63c8dd9915ddbbc95d586eedc", "size": 1174, "ext": "py", "lang": "Python", "max_stars_repo_path": "Source Code/pdb_sphere.py", "max_stars_repo_name": "sfernando-BAEN/ELIXIR-A", "max_stars_repo_head_hexsha": "568cba6623d4e953afa18551fcf589d56f092729", "max_stars_repo_licenses": ["Apache-2.0"], "... |
from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4
from datetime import timedelta as delta
from argparse import ArgumentParser
import numpy as np
import dask as da
import dask.array as daArray
from glob import glob
import time as ostime
import matplotlib.pyplot as plt
import os
import parcels
import p... | {"hexsha": "abf9d995edd53549d6c8eaf38d1dc35be6cca921", "size": 11668, "ext": "py", "lang": "Python", "max_stars_repo_path": "performance/example_performanceProfiling.py", "max_stars_repo_name": "noemieplanat/Copy-parcels-master", "max_stars_repo_head_hexsha": "21f053b81a9ccdaa5d8ee4f7efd6f01639b83bfc", "max_stars_repo_... |
"""
Use Approximate Bayesian Computation (ABC) to parametrize the rate function
given a hypothetical experiment timeline.
"""
import csv
from timeit import default_timer as timer
import click
import numpy as np
from pyabc import (ABCSMC, Distribution, RV)
from pyabc.populationstrategy import AdaptivePopulationSize
f... | {"hexsha": "b29d7f06d63f509a43e585f763d5803b302f2b3b", "size": 10354, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/abc.py", "max_stars_repo_name": "Sandalmoth/dual-adaptation", "max_stars_repo_head_hexsha": "1052b47dbd3c473c406bb72d9ecd0693ca0c1f80", "max_stars_repo_licenses": ["Zlib"], "max_stars_count"... |
import tensorflow as tf
from libspn.tests.test import argsprod
import libspn as spn
from libspn.graph.op.conv_sums import ConvSums
import numpy as np
import random
class TestBaseSum(tf.test.TestCase):
@argsprod([False, True], [spn.InferenceType.MARGINAL, spn.InferenceType.MPE])
def test_compare_manual_conv(s... | {"hexsha": "ef1f716f216d41162673d3c526f31f4a22c090ee", "size": 9131, "ext": "py", "lang": "Python", "max_stars_repo_path": "libspn/tests/test_graph_convsum.py", "max_stars_repo_name": "pronobis/libspn", "max_stars_repo_head_hexsha": "b98141ea5a609a02706433220758e58f46bd3f5e", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Simon Scheidegger, 01/19
# edited by Patrick O'Callaghan, with Cameron Gordon and Josh Aberdeen, 11/2021
# ======================================================================
import solver as solver
from parameters import * # parameters of model
from variables import *
from equations import *
import pos... | {"hexsha": "15e82474415a6f04dd08520aef4d48f58bd804f2", "size": 5301, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/_current-vec/main.py", "max_stars_repo_name": "uq-aibe/spir-oz", "max_stars_repo_head_hexsha": "4ae3ff6f230679f21b9c4072529df94187f9e098", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import time
import numpy as np
import numpy.random as rnd
from pymanopt.solvers.solver import Solver
from pymanopt.tools import printer
class ParticleSwarm(Solver):
"""Particle swarm optimization (PSO) method.
Perform optimization using the derivative-free particle swarm optimization
algorithm.
Ar... | {"hexsha": "9bc07d833166a9fe52b039bad3001a7fcb61efe8", "size": 6573, "ext": "py", "lang": "Python", "max_stars_repo_path": "solvers/particle_swarm.py", "max_stars_repo_name": "cjyaras/pymanopt", "max_stars_repo_head_hexsha": "447545fd9a6f33f3060a083fde1a2ac643ed340e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import lightgbm as lgb
import re
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import Labe... | {"hexsha": "dd4c12396eb61eb49ddd4db3e1e0ed99abf9c1d6", "size": 5430, "ext": "py", "lang": "Python", "max_stars_repo_path": "pred.py", "max_stars_repo_name": "debunagoya/GasolineCharge", "max_stars_repo_head_hexsha": "20b4f9476c4f340a62b9ec74e9dc6149327acdb9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from datetime import date
from enum import Enum
from typing import List, Tuple
import numpy as np
from dateutil.relativedelta import relativedelta
from sqlalchemy import MetaData, Table, and_, create_engine
engine = create_engine(
'sqlite:///query/project.db?check_same_thread=False')
metadata = MetaData(engine)
c... | {"hexsha": "e5b7e10179d69c653f76725a2e185019ae798d5e", "size": 4365, "ext": "py", "lang": "Python", "max_stars_repo_path": "query/query.py", "max_stars_repo_name": "TheLurkingCat/Database-term-project", "max_stars_repo_head_hexsha": "c9fb99151c70fb5c3547051df7b4e478e2681bc7", "max_stars_repo_licenses": ["MIT"], "max_st... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os, matplotlib.pyplot as plt, numpy as np, pandas as pd
import seaborn as sns
sns.set_theme()
sns.set_style("white")
from glob import glob
from common_functions import load_data
from scipy import stats
import time
from trace_extract_funcs import (
get_nn,
... | {"hexsha": "f953ab2c589caa50b615e4129c6dbd60482bfdc0", "size": 15481, "ext": "py", "lang": "Python", "max_stars_repo_path": "CMC_compare.py", "max_stars_repo_name": "geno-verse/indoC", "max_stars_repo_head_hexsha": "d3541cbae9579a138fdca1f4ecb62c490dec4f9b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
import utils
import ANN as ann
import numpy as np
from keras import losses as klosses
from functools import reduce
import matplotlib.pyplot as plt
num_epochs = 5
ensemble_size = 15
mlp_structure = '''
{
"input_shape" : [784],
"layers" : [
{
"type" : "Dense",
"units" : 64,
... | {"hexsha": "cc20667a229da23642305b09edbb018482883a06", "size": 6946, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/digits_entropy2.py", "max_stars_repo_name": "Xiaoming94/TIFX05-MScThesis-HenryYang", "max_stars_repo_head_hexsha": "9ab19b8d8bbf328fd3165f0833f4c66a0cfc12b7", "max_stars_repo_licenses": ["MIT"... |
{-
Groupoid quotients:
-}
{-# OPTIONS --cubical --no-import-sorts --safe #-}
module Cubical.HITs.GroupoidQuotients.Properties where
open import Cubical.HITs.GroupoidQuotients.Base
open import Cubical.Core.Everything
open import Cubical.Foundations.Prelude
open import Cubical.Foundations.Isomorphism
open import Cu... | {"hexsha": "591b1e66b2f511ff75e7dd9a687de04c5b9b00f3", "size": 4730, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/HITs/GroupoidQuotients/Properties.agda", "max_stars_repo_name": "Schippmunk/cubical", "max_stars_repo_head_hexsha": "c345dc0c49d3950dc57f53ca5f7099bb53a4dc3a", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma AbstrLevels_A9_A92:
assumes "sA9 \<in> AbstrLevel i"
shows "sA92 \<notin> AbstrLevel i"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sA92 \<notin> AbstrLevel i
[PROOF STEP]
(*<*)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sA92 \<notin> AbstrLevel i
[PROOF STEP]
using assms
[PROOF STATE... | {"llama_tokens": 245, "file": "ComponentDependencies_DataDependenciesCaseStudy", "length": 3} |
*** Start of NAG Library implementation details ***
Implementation title: Linux, 64-bit, Intel C/C++ or I... | {"hexsha": "fd4bc95b716df6d267639fca8679517cd9c49387", "size": 1330, "ext": "r", "lang": "R", "max_stars_repo_path": "simple_examples/baseresults/outputexample.r", "max_stars_repo_name": "numericalalgorithmsgroup/NAGJavaExamples", "max_stars_repo_head_hexsha": "f625b3f043c5c14a88d7ecbc04374acf75d63c82", "max_stars_repo... |
import os
import csv
import cv2
import datetime
import transform
import argparse
import numpy as np
from network import model
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint
class Pipelin... | {"hexsha": "abce1803926fd740d60cc4e5307859d62776f84a", "size": 5330, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "zexihan/CarND-Behavioral-Cloning-P3", "max_stars_repo_head_hexsha": "83af606605d66ba9a95b7cc9c911beaa8bd9f59b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
Fundamental computation for CTMC
"""
"""
unifstep!(tr, P, poi, range, weight, x, y)
Compute the probability vector using the uniformized CTMC.
y = exp(tr(Q)*t) * x
where Q is unifomed by P = I - Q/qv. In the computation, Poisson p.m.f. with mean qv*t is used.
Parameters:
- tr: transpose operator
- P: The unifo... | {"hexsha": "2dfc23a4e0aeb8b70d6f9963e8f6f444cfb76daa", "size": 3742, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/_forward_backward.jl", "max_stars_repo_name": "JuliaReliab/NMarkov.jl", "max_stars_repo_head_hexsha": "cdaacdfe9801af84ea5f6a9cbb4108d77e2a8a0c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import tensorflow as tf
from simulation import simulation
import pickle
import os
simulation_name = 'NLO_HIG'
batch_size = 128
mode = 'IG'
learning_rate = 1.0
truncation = 10**-6
inversion = 'HIG'
opt = tf.keras.optimizers.SGD
seed = 22
Nx = 2 # Number of oscillators
Nt = 96 ... | {"hexsha": "5a18ba6725fed72c3a9e849c1ffb56847c112b6b", "size": 1086, "ext": "py", "lang": "Python", "max_stars_repo_path": "Nonlinear_oscillators/submit_HIG.py", "max_stars_repo_name": "tum-pbs/half-inverse-gradients", "max_stars_repo_head_hexsha": "08d38820054569db869c5f5c0e4b20bd2a044e42", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma top_sorted_abs_mem:
assumes "(top_sorted_abs R (h # l))" "(ListMem x l)"
shows "(\<not> R x h)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> R x h
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
top_sorted_abs R (h # l)
ListMem x l
goal (1 subgoal):
1. \<not> R x h
... | {"llama_tokens": 168, "file": "Factored_Transition_System_Bounding_Acyclicity", "length": 2} |
#BSD 3-Clause License
#
#Copyright (c) 2019, The Regents of the University of Minnesota
#
#All rights reserved.
#
#Redistribution and use in source and binary forms, with or without
#modification, are permitted provided that the following conditions are met:
#
#* Redistributions of source code must retain the above cop... | {"hexsha": "3192b69e038a66fd12ea97bfc5f599b0c7f84ea7", "size": 11204, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/thermalModel.py", "max_stars_repo_name": "VidyaChhabria/TherMOS", "max_stars_repo_head_hexsha": "b2af79d1ecd236699d5304f9af3334d83fe6e78e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
@testset "Composition" begin
encodings = (ProjectiveTransforms((5, 5)), ImagePreprocessing(), OneHot())
blocks = (Image{2}(), Label(1:10))
obs = (rand(RGB{N0f8}, 10, 10), 7)
testencoding(encodings, blocks, obs)
end
| {"hexsha": "c1e56e4c625ccf4e58833cac4eafd728f0312012", "size": 232, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Vision/tests.jl", "max_stars_repo_name": "Chandu-4444/FastAI.jl", "max_stars_repo_head_hexsha": "290ae32e7b1ca502fc93e1a86aa250658cf1f72a", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
C23456789012345678901234567890123456789012345678901234567890123456789012
C
c Program to compute spline fits to fermi integrals
cc Must provide data file 94
subroutine initferm(FilePath)
! use parallel_module
! use mpi
implicit double precision (a-h,o-z)
parameter (n=201)
charact... | {"hexsha": "bd2cb4b9e56c56c6f4a912d2f61109f14cd68bf5", "size": 1355, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "TableCreator/Old/LS/initferm.f", "max_stars_repo_name": "srichers/weaklib", "max_stars_repo_head_hexsha": "4a26ff17d3224d8fe90b922cb55ea9d865200154", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
import pandas as pd
import numpy as np
from .utils.util import filter_entity_type
from .uwb_motion_filters import TrayMotionButterFiltFiltFilter, TrayMotionSavGolFilter
from process_cuwb_data.utils.log import logger
class FeatureExtraction:
def __init__(self, frequency="100ms",
position_filter=T... | {"hexsha": "28025a7495c0b24dec1d3acdc8f07b6069275c5d", "size": 31231, "ext": "py", "lang": "Python", "max_stars_repo_path": "process_cuwb_data/uwb_motion_features.py", "max_stars_repo_name": "WildflowerSchools/wf-process-cuwb-data", "max_stars_repo_head_hexsha": "8d94eeec82401f0f62ce1e0b7fdefd49e0328921", "max_stars_re... |
# *****************************************************************************
# Copyright (c) 2019, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of sou... | {"hexsha": "4a363f27962275522fc763e832a3fe2f236fdbcb", "size": 3049, "ext": "py", "lang": "Python", "max_stars_repo_path": "sdc/datatypes/hpat_pandas_getitem_types.py", "max_stars_repo_name": "samir-nasibli/sdc", "max_stars_repo_head_hexsha": "b9144c8799d6454dec3e5c550e305963b24c1570", "max_stars_repo_licenses": ["BSD-... |
#!/usr/bin/env python
import numpy as np
import cv2
fps = 20
capSize = (1028, 720)
fourcc = cv2.VideoWriter_fourcc('m', 'j', 'p', 'g')
out = cv2.VideoWriter()
success = out.open('output.mov', fourcc, fps, capSize, True)
capture = cv2.VideoCapture(0)
while (capture.isOpened()):
ret, frame = capture.read()
if ret... | {"hexsha": "584fcd9b87ab1dff12c18bb1ce7fdf8761b4cf96", "size": 580, "ext": "py", "lang": "Python", "max_stars_repo_path": "video-write.py", "max_stars_repo_name": "ultranaut/fuzzy", "max_stars_repo_head_hexsha": "4ad2c4d6516c7540fc42cbeb44f26913e62d501d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
library(randomForest)
library(caret)
library(doMC)
library(mmadsenr)
library(futile.logger)
library(dplyr)
library(ggthemes)
# Train and tune random forest classifiers for each of the three data sets coming out of the experiment
# "equifinality-4", for binary analysis.
#
# Assumes that data-preparation.r has previo... | {"hexsha": "76cf01a2421b238b6102b7f8cc265a9c04f2d373", "size": 6096, "ext": "r", "lang": "R", "max_stars_repo_path": "analysis/equifinality-4/modelfitting/population-classification.r", "max_stars_repo_name": "mmadsen/experiment-ctmixtures", "max_stars_repo_head_hexsha": "460fd9f97977a2c6c5d43f6b0f9ca3c0639177f4", "max_... |
Require Import Utf8.
(* Set definition *)
(* In this file, a set is represented by its
characteristic function. *)
Definition Ens {E : Type} := E -> Prop.
Definition In {E : Type} (A :@Ens E) (x:E) := A x.
Notation "x ∈ A" := (In A x) (at level 60).
Local Hint Unfold In.
(* Inclusion relation *)
Definition incl {E: ... | {"author": "jnarboux", "repo": "PA_a_priori_analysis", "sha": "c18d834186695ad09f266d7eb069bda780248e48", "save_path": "github-repos/coq/jnarboux-PA_a_priori_analysis", "path": "github-repos/coq/jnarboux-PA_a_priori_analysis/PA_a_priori_analysis-c18d834186695ad09f266d7eb069bda780248e48/Coq/case_study.v"} |
import io
import os
import sys
from flask import Flask, request, send_file, jsonify
from flask_cors import CORS
from PIL import Image
import numpy as np
import time
import logging
import u2net_test
import json
import base64
import shutil
import subprocess
import re
import pytesseract
from deskew import determine_skew
... | {"hexsha": "a7d4ff4cf3bcf53a06161267ffb82c4d3bf29c25", "size": 5156, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/main.py", "max_stars_repo_name": "amtam0/u2netscan", "max_stars_repo_head_hexsha": "21b3f1b3fd1f8d462822cd6591b238b0011a80ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
import pennylane as qml
from pennylane import numpy as np
from friqml.utils import eps, sz, sx
# EXERCISE 1 (Solution taken from https://pennylane.ai/qml/demos/tutorial_qaoa_maxcut.html)
# unitary operator U_B with parameters beta and n
def U_B(beta, n):
for wire in range(n):
qml.RX(2 * beta, wires=wire)
... | {"hexsha": "b907693266b69d674934e975a6f47cb911741be7", "size": 2617, "ext": "py", "lang": "Python", "max_stars_repo_path": "friqml/solutions/qaoa.py", "max_stars_repo_name": "znajob/fri_qml", "max_stars_repo_head_hexsha": "85b448f3907bcc2e7bf5d1cb79c5a562ed2c60ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 6 10:59:32 2019
@author: harish
"""
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.linear_model import LogisticRegression
import numpy as np
import requests
input_file = "datafiles/castmembers_with_an... | {"hexsha": "5862c83146def2006fb7abe1c33f7bb603316349", "size": 1676, "ext": "py", "lang": "Python", "max_stars_repo_path": "word_embeddings_analysis/fb_data_classifier.py", "max_stars_repo_name": "usc-isi-i2/dig-wikifier", "max_stars_repo_head_hexsha": "50357fdd1c796f7666f691288d228b5980d97187", "max_stars_repo_license... |
import numpy as np
from scipy.optimize import curve_fit
from scipy.optimize import fsolve, brentq
from scipy.interpolate import interp1d
import scipy.integrate
import sys
import os
import writeproperties.velociraptor_python_tools as vpt
from scipy.spatial import cKDTree
import h5py
import re
from constants import *
fro... | {"hexsha": "290d699b5fd0fe25bbc42d5742e4acd52e85d351", "size": 29919, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/writeproperties/writeproperties_old.py", "max_stars_repo_name": "luciebakels/gadgetanalyse", "max_stars_repo_head_hexsha": "63b80b5306ebc4a1fbfd9cfbe3608ca10b8a19a3", "max_stars_repo_licen... |
{-# OPTIONS --universe-polymorphism #-}
module Categories.Adjunction.Composition where
open import Level
open import Categories.Category
open import Categories.Functor hiding (equiv; assoc; identityˡ; identityʳ; ∘-resp-≡) renaming (id to idF; _≡_ to _≡F_; _∘_ to _∘F_)
open import Categories.NaturalTransformation hidi... | {"hexsha": "4f1262fb46f1239f1127db4e408697cc242ebc2a", "size": 5185, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Categories/Adjunction/Composition.agda", "max_stars_repo_name": "copumpkin/categories", "max_stars_repo_head_hexsha": "36f4181d751e2ecb54db219911d8c69afe8ba892", "max_stars_repo_licenses": ["BSD-3... |
[STATEMENT]
lemma optimize_matches_option_generic:
assumes "\<forall> r \<in> set rs. P (get_match r)"
and "(\<And>m m'. P m \<Longrightarrow> f m = Some m' \<Longrightarrow> matches \<gamma> m' p = matches \<gamma> m p)"
and "(\<And>m. P m \<Longrightarrow> f m = None \<Longrightarrow> \<not> matches \<g... | {"llama_tokens": 10096, "file": "Iptables_Semantics_Matching", "length": 23} |
#!/usr/bin/env python3
from pathlib import Path
import sys
import cv2
import depthai as dai
import numpy as np
import time
import argparse
sys.path.append('../../../')
from DodgeTheWrench.Avoidance import DodgeWrench
from DodgeTheWrench.MoveMotor import MoveMotor
import RPi.GPIO as GPIO
# Set up LEDs
greenLED = 27
re... | {"hexsha": "d8192f79629de7ea97706168f498be30e4e39754", "size": 11391, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/SpatialDetection/dtw-main.py", "max_stars_repo_name": "DodgeTheWrenchTeam/depthai-python", "max_stars_repo_head_hexsha": "57a529cae929a564443721ef8e1e6546bd931542", "max_stars_repo_licen... |
# -*- coding: utf-8 -*-
# Copyright 2018-2022 the orix developers
#
# This file is part of orix.
#
# orix 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) ... | {"hexsha": "1ae20ab39134ff02af4f6143240500df6a95230f", "size": 4595, "ext": "py", "lang": "Python", "max_stars_repo_path": "orix/plot/orientation_color_keys/euler_color_key.py", "max_stars_repo_name": "bm424/texpy", "max_stars_repo_head_hexsha": "8d78b568209a6da36fc831c6bc9e2b0cb4c740c8", "max_stars_repo_licenses": ["M... |
\subsubsection{Threshold Value Determination}
\label{subsubsec:threshold}
Ideally, the mean absolute difference between two consecutive unchanged frames is zero.
This is not the case, however, due to image noise and other environmental influences such as \SI{50}{Hz} flickering of the ambient light intensity.
A suitabl... | {"hexsha": "17295c9cdeae92ebff291f15739854401fefd06a", "size": 878, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/report/sections/sw/throw_detection_mechanism/threshold.tex", "max_stars_repo_name": "MuellerDominik/P5-AIonFPGA", "max_stars_repo_head_hexsha": "13fc60fb973a4a87a4af1b49c17d5dd1fd239ed5", "max_st... |
section \<open>The Instantiation\<close>
(*<*)
theory Instance
imports Goedel_Incompleteness.Abstract_Second_Goedel
Incompleteness.Quote Incompleteness.Goedel_I
begin
(*>*)
definition "Fvars t = {a :: name. \<not> atom a \<sharp> t}"
lemma Fvars_tm_simps[simp]:
"Fvars Zero = {}"
"Fvars (Var a) = {a}"
"Fv... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/Goedel_HFSet_... |
using CayleyMengerDeterminant
import InverseFunctions
import Static: StaticInt
import StaticArrays: Dynamic
using Test
@testset "CayleyMengerDeterminant.jl" begin
InverseFunctions.test_inverse.(
binomial2,
[1, 2, 3, 4, 5, 6, Dynamic(), StaticInt(2), StaticInt(3)],
compare = isequal,
)
... | {"hexsha": "d4889eef1077ddcc7280c7427ecff851150c2184", "size": 2686, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "pthariensflame/CayleyMengerDeterminant.jl", "max_stars_repo_head_hexsha": "f7876369fce4a4c9a46867cfcfc6deca5a00479a", "max_stars_repo_licenses": ["MIT"], ... |
/-
Copyright (c) 2021 James Arthur, Benjamin Davidson, Andrew Souther. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: James Arthur, Benjamin Davidson, Andrew Souther
-/
import measure_theory.integral.interval_integral
import analysis.special_functions.sqrt
import analy... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/archive/100-theorems-list/9_area_of_a_circle.le... |
!=======================================================================
!
! Determine the list of particles whose abundances need to be updated
! on the current iteration and load their properties into dummy arrays
! to be passed to the chemistry routine. Reset their abundances to the
! appropriate starting values... | {"hexsha": "01005bec0c098a2a402ee0528c1fe848ee163c62", "size": 4911, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/update_abundances.f90", "max_stars_repo_name": "uclchem/uclpdr", "max_stars_repo_head_hexsha": "a1c5ece6f21852af040ddf0af463cff26757d208", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import urllib.request
import cv2
import numpy as np
import os
def store_raw_images():
neg_images_link = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n04096066"
#neg_images_link = "http://www.image-net.org/api/text/imagenet.synset.geturls?wnid=n03244388"
neg_image_urls = urllib.request.ur... | {"hexsha": "b8552ac7b78a8e981faea8f4d91567556574270f", "size": 900, "ext": "py", "lang": "Python", "max_stars_repo_path": "downloadDatasetNeg.py", "max_stars_repo_name": "Dexter2389/Stop-sign-Haar-cascade", "max_stars_repo_head_hexsha": "109938791f8005b602a4d2a802cfcfbcd4107ffc", "max_stars_repo_licenses": ["MIT"], "ma... |
from sage.all import *
import numpy as np
import momentproblems.moment_functionals
from momentproblems.plotting import _eval_Q, _eval_P
def generate_plots(ds=(4,8), filename=None):
from matplotlib import rc
rc('text', usetex=True)
rc('font', **{'family':'serif','serif':['Computer Modern']})
points = [... | {"hexsha": "e75caaba834cae907f9b7bf51dccb1f53a169055", "size": 2133, "ext": "py", "lang": "Python", "max_stars_repo_path": "momentproblems/examples/example3.py", "max_stars_repo_name": "mwageringel/momentproblems", "max_stars_repo_head_hexsha": "7b5207d9555be894a6450114c70ed78a04419716", "max_stars_repo_licenses": ["MI... |
function r8vec_sort_heap_a_test ( )
%*****************************************************************************80
%
%% R8VEC_SORT_HEAP_A_TEST tests R8VEC_SORT_HEAP_A.
%
% Licensing:
%
% This code is distributed under the GNU LGPL license.
%
% Modified:
%
% 14 April 2009
%
% Author:
%
% John Burkardt
%
... | {"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/r8lib/r8vec_sort_heap_a_test.m"} |
/-
Copyright (c) 2020 Anne Baanen. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Anne Baanen
-/
import field_theory.minpoly
/-!
# Power basis
This file defines a structure `power_basis R S`, giving a basis of the
`R`-algebra `S` as a finite list of powers `1, x, ...... | {"author": "JLimperg", "repo": "aesop3", "sha": "a4a116f650cc7403428e72bd2e2c4cda300fe03f", "save_path": "github-repos/lean/JLimperg-aesop3", "path": "github-repos/lean/JLimperg-aesop3/aesop3-a4a116f650cc7403428e72bd2e2c4cda300fe03f/src/ring_theory/power_basis.lean"} |
########################################
# MIT License
#
# Copyright (c) 2020 Miguel Ramos Pernas
########################################
'''
Definition of functions for GPUs.
NOTE: All functions in this module accept a single type of value.
'''
from . import gpu_cache
from .gpu_core import GPU_SRC
from ..base import... | {"hexsha": "61337beca602bcc0421fce9376a1d0ae920e61cd", "size": 6730, "ext": "py", "lang": "Python", "max_stars_repo_path": "minkit/backends/gpu_functions.py", "max_stars_repo_name": "mramospe/minkit", "max_stars_repo_head_hexsha": "fa6808a6ca8063751da92f683f2b810a0690a462", "max_stars_repo_licenses": ["MIT-0"], "max_st... |
import torch
from generative_playground.utils.gpu_utils import to_gpu
import numpy as np
from collections import OrderedDict
from frozendict import frozendict
class MixedLoader:
def __init__(self, main_loader, valid_ds, invalid_ds):
self.main_loader = main_loader
self.valid_ds = valid_ds
s... | {"hexsha": "509191ffcb8d6a25cee324e84fb4283ef57c7eb1", "size": 4721, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/generative_playground/data_utils/mixed_loader.py", "max_stars_repo_name": "ZmeiGorynych/generative_playground", "max_stars_repo_head_hexsha": "5c336dfbd14235e4fd97b21778842a650e733275", "max_s... |
""" Some validation functions. """
from __future__ import division
import numpy as np
from scipy.stats import norm
def smse(y_true, y_pred):
"""
Standardised mean squared error.
Parameters
----------
y_true: ndarray
vector of true targets
y_pred: ndarray
vector of predicted ... | {"hexsha": "22e6ab530577a297ac4c7c0e3a9a775095502751", "size": 3239, "ext": "py", "lang": "Python", "max_stars_repo_path": "revrand/metrics.py", "max_stars_repo_name": "basaks/revrand", "max_stars_repo_head_hexsha": "4c1881b6c1772d2b988518e49dde954f165acfb6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
import matplotlib.pyplot as plt
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import StandardScaler
import scikitplot as skplt
from sklearn.metrics import mean_squared_error
n = 1000
maxdegree = 6
# Make data s... | {"hexsha": "d6c1601c7b9f1d1f9b7e18fe9794a4addceaac6f", "size": 1619, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/src/SupportVMachines/Programs/gdreg.py", "max_stars_repo_name": "esleon97/MachineLearningECT", "max_stars_repo_head_hexsha": "97a218c9742b43a53e033a888f8a0b1074a2c48b", "max_stars_repo_license... |
import unittest
import numpy as np
from openaerostruct.geometry.monotonic_constraint import MonotonicConstraint
from openaerostruct.utils.testing import run_test
class Test(unittest.TestCase):
def test_sym1(self):
surface = {"symmetry": True, "mesh": np.zeros((1, 5, 3))}
comp = Mo... | {"hexsha": "eaa8a84e2007f37d83a1f301a52df3ee3ff78ceb", "size": 1039, "ext": "py", "lang": "Python", "max_stars_repo_path": "openaerostruct/geometry/tests/test_monotonic_constraint.py", "max_stars_repo_name": "lamkina/OpenAeroStruct", "max_stars_repo_head_hexsha": "d30e2626fc1272e7fe3a27386c4c663157e958ec", "max_stars_r... |
from abc import ABCMeta
import numpy as np
from time import time
class DotProduct(metaclass = ABCMeta):
def naive_dotproduct(self, matrix, kernel):
"""
A naive approach which uses brute force loops. Very slow.
:param matrix: a 3d numpy array of size [width][height][channel]
:param ... | {"hexsha": "2089addd9ce255cc41e2f556cc3468791886ea60", "size": 1970, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/filters/dotproduct.py", "max_stars_repo_name": "DennisPing/image-processor-mvc", "max_stars_repo_head_hexsha": "b687185500404a84f21e16b6c56937be4afc5a1d", "max_stars_repo_licenses": ["MIT"],... |
theory TreeStream = Main:
use tactics
method_setup circular_coinduction = "build_tactic (circular_coinduction_fun)" "all"
method_setup coinduction = "build_tactic (coinduction_fun)" "rule_tac R=?Rzero in ga_cogenerated, instantiate_tac Rzero %s1.?R,step"
method_setup coinduction_test = "build_tactic (coinduction_test... | {"author": "spechub", "repo": "Hets-lib", "sha": "7bed416952e7000e2fa37f0b6071b5291b299b77", "save_path": "github-repos/isabelle/spechub-Hets-lib", "path": "github-repos/isabelle/spechub-Hets-lib/Hets-lib-7bed416952e7000e2fa37f0b6071b5291b299b77/CoCASL/Proof-Support-Examples/TreeStream.thy"} |
"""
This module covers some tests from chapter 6: Frequentist Methods.
"""
import numpy as np
class MajorityClassifier:
""" Selects the majority label from the training data.
This classifier only works on random data, with a binary label it's not meant
to be used for actually problems.
Attributes:
... | {"hexsha": "760d18bc8b0a8a71b454ecfd3dd82dd701825c49", "size": 3799, "ext": "py", "lang": "Python", "max_stars_repo_path": "chapter/six.py", "max_stars_repo_name": "sloscal1/ML-Prob-Perspective", "max_stars_repo_head_hexsha": "3474faf8559cc2229426ab773460000c6c40fbb3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import matplotlib.pyplot as plt
import re
# fit = {3:-1, 4:-1, 5:-1, 7:5, 8:5, 9:6, 10:7, 11:7}
fit = {12:6, 13:6, 14:9, 15:7, 16:9}
for i in [12, 13, 14, 15, 16]:
f = open('podaci/n'+str(i)+'.log')
lines = f.readlines()
f.close()
f = open('fitovi/n'+str(i)+'.log', 'w')
line = lines[7]
line =... | {"hexsha": "5528567ca630af14f8dcba25739f951a7be71674", "size": 1840, "ext": "py", "lang": "Python", "max_stars_repo_path": "'rezultati'/fminsearch_n/plot.py", "max_stars_repo_name": "BogdanRajkov/Petnica2019", "max_stars_repo_head_hexsha": "b0a8abee951ca0e70ee99864145f48b93e2db24a", "max_stars_repo_licenses": ["MIT"], ... |
## script to develop neural cell type deconvolution algorithm
## uses houseman method with new ref data for purified brain cell populations
## editted original code from minfi to take a matrix rather than RGset - this means data is not preprocessed together.
source("FunctionsForBrainCellProportionsPrediction.r")... | {"hexsha": "e106a8929c66a2a460d3421e8cda0f9215884d82", "size": 1945, "ext": "r", "lang": "R", "max_stars_repo_path": "DNAm/CellularCompositionEstimation/createRefData.r", "max_stars_repo_name": "ejh243/BrainFANS", "max_stars_repo_head_hexsha": "903b30516ec395e0543d217c492eeac541515197", "max_stars_repo_licenses": ["Art... |
# Introduction
The goal of this tutorial is to develop a memory based model of addition that predicts choices and reaction times. The model illustrates the use of the Lognormal Race model to describe memory retrieval in ACT-R and the use of marginalization to characterize the many-to-one mapping between retrieved chun... | {"hexsha": "f2920938c57dfb815162fc1ed56f09f2ba89cf0e", "size": 595542, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "models/Siegler/Siegler_Model.ipynb", "max_stars_repo_name": "itsdfish/ACTRFundamentalTools.jl", "max_stars_repo_head_hexsha": "0314b4a79c71712d36052a549f577dd4b4bfd065", "max_stars_... |
import numpy as np
from training_plots import upscale, generated_images_plot, plot_training_loss
from training_plots import plot_generated_images_combined
from keras.optimizers import Adam
from keras import backend as k
import matplotlib.pyplot as plt
from tqdm import tqdm
from GAN import img_generator, img_discrimina... | {"hexsha": "dbf82b9937fe1cc69675f35ee345a2e1093c01ec", "size": 6341, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter13/train_gan.py", "max_stars_repo_name": "Nitin-Mane/Python-Deep-Learning-Projects", "max_stars_repo_head_hexsha": "f4ff04e611ff029bbbd9665fcc4139df935bcad1", "max_stars_repo_licenses": ["M... |
import torch
import numpy as np
import matplotlib.pyplot as plt
import os
import argparse
from tslearn.clustering import TimeSeriesKMeans
from sklearn.manifold import TSNE
from pytorch3d.transforms import quaternion_apply, quaternion_multiply, quaternion_invert
from util.util import dict_map, dict_stack, dict_cat
""... | {"hexsha": "51d249dabf1e813839e1457bab195c10ca496f63", "size": 5302, "ext": "py", "lang": "Python", "max_stars_repo_path": "visualization/tsne_cluster.py", "max_stars_repo_name": "castacks/tartan_drive", "max_stars_repo_head_hexsha": "c731ca65381f4a169a7ce7bcc02e8b1e68c407f4", "max_stars_repo_licenses": ["MIT"], "max_s... |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Make numpy printouts easier to read.
np.set_printoptions(precision=3, suppress=True)
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflo... | {"hexsha": "14540d1e2d5f6be674b90c54dd4db531c84b71c3", "size": 6882, "ext": "py", "lang": "Python", "max_stars_repo_path": "1_Beginner_4_BasicRegression.py", "max_stars_repo_name": "BrunoDatoMeneses/TensorFlowTutorials", "max_stars_repo_head_hexsha": "6430996ed331cf845c2e6eacb0be2f159c41a1d6", "max_stars_repo_licenses"... |
SUBROUTINE read_namelist(iunit, io_stat, lc_name)
USE vmec_input, ONLY: read_indata_namelist,
1 read_mse_namelist
USE vmec_seq, ONLY: vseq
IMPLICIT NONE
!-----------------------------------------------
! D u m m y A r g u m e n t s
!-----------------------------------------------
... | {"hexsha": "d5987ffb0a20b275659255488e56c60063b81ade", "size": 1188, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "Sources/LIBSTELL_minimal/read_namelist.f", "max_stars_repo_name": "mbkumar/VMEC2000", "max_stars_repo_head_hexsha": "334e3bd478f2432b6fe8cbb321f0d81d9a952152", "max_stars_repo_licenses": ["MIT"], ... |
import pandas as pd
import numpy as np
import sklearn
from sklearn import linear_model
from sklearn.utils import shuffle
import matplotlib.pyplot as pyplot
import pickle
from matplotlib import style
data = pd.read_csv("student-mat.csv", sep = ";")
data = data[["G1", "G2", "G3", "studytime", "failures", "ab... | {"hexsha": "2c9784bfd3218599116a0b0dd41358ad380cd254", "size": 1364, "ext": "py", "lang": "Python", "max_stars_repo_path": "Student Grades/Regression Working File.py", "max_stars_repo_name": "anishs37/ML", "max_stars_repo_head_hexsha": "afb6bcd46b19b682b7fd1afa3dc04587a8a50913", "max_stars_repo_licenses": ["MIT"], "max... |
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import os
from numpy import prod
from datetime import datetime
from model.model import CapsuleNetwork, CapsNet
from model.loss import CapsuleLoss
from time import time
class CapsNetTrainer:
"""
Wrapper object for handl... | {"hexsha": "ea7bdcbe589c1229bf3d78f111875520422c1c69", "size": 5045, "ext": "py", "lang": "Python", "max_stars_repo_path": "trainer/trainer.py", "max_stars_repo_name": "lidq92/pytorch-dynamic-routing-between-capsules", "max_stars_repo_head_hexsha": "4388cd36193348cbb10035008360330e67acdd41", "max_stars_repo_licenses": ... |
import numpy as np
import torch
import pybullet as p
from _utils import *
from _pybullet import start_bullet_env
from _controller import *
from _compute import *
from _plot import *
class Node:
def __init__(self, n: list):
self.x = n[0]
self.y = n[1]
self.parent = None
def torch2numpy(x):
... | {"hexsha": "941caec462d56855885352c7a8976fa0a53e33ec", "size": 5137, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/cascade_control_dx.py", "max_stars_repo_name": "hjw-1014/Multi-Objective-Reactive-Motion-Planning-in-Mobile-Manipulators", "max_stars_repo_head_hexsha": "9a8801e9c663174b753c4852b2313c5a3f... |
#=
This file is auto-generated. Do not edit.
=#
mutable struct LoadZones <: Topology
number::Int64
name::String
buses::Vector{Bus}
maxactivepower::Float64
maxreactivepower::Float64
_forecasts::InfrastructureSystems.Forecasts
internal::InfrastructureSystemsInternal
end
function LoadZones(n... | {"hexsha": "6be8139095e186c45f9719fda24005083a1964c2", "size": 1608, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/models/generated/LoadZones.jl", "max_stars_repo_name": "Lilyhanig/PowerSystems.jl", "max_stars_repo_head_hexsha": "bea9e464b3cf7ca0c9f950e9325bcb935ac20fa4", "max_stars_repo_licenses": ["BSD-3-... |
import numpy as np
import matplotlib.pyplot as plt
import datetime as dt
import matplotlib.dates as md
data= np.loadtxt('vmstat_7days_without_header.csv', delimiter=',',
dtype={'names': ['time', 'mon','tue','wed','thrs','fri','sat','sun'],
'formats': ['S8','i4','i4','i4','i4','i4','i4','i4']} )
... | {"hexsha": "8ff24e59bfb450a72d52d1bcd883b72b326dfc98", "size": 1431, "ext": "py", "lang": "Python", "max_stars_repo_path": "cpu_stas_plot.py", "max_stars_repo_name": "kingslair/MatPlotLib", "max_stars_repo_head_hexsha": "66d1accf1a049b901dece69d18edadafbf4b687f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
section \<open>Approximation with Affine Forms\<close>
theory Affine_Approximation
imports
"HOL-Decision_Procs.Approximation"
"HOL-Library.Monad_Syntax"
"HOL-Library.Mapping"
Executable_Euclidean_Space
Affine_Form
Straight_Line_Program
begin
text \<open>\label{sec:approxaffine}\<close>
lemma convex_on_imp... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Affine_Arithmetic/Affine_Approximation.thy"} |
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