text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
\section{\module{code} ---
Interpreter base classes}
\declaremodule{standard}{code}
\modulesynopsis{Base classes for interactive Python interpreters.}
The \code{code} module provides facilities to implement
read-eval-print loops in Python. Two classes and convenience
functions are included which can be use... | {"hexsha": "36410b28867d6dd983c97ec0c4b4f5d311002e22", "size": 7406, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/lib/libcode.tex", "max_stars_repo_name": "marcosptf/cpython-2.0.1", "max_stars_repo_head_hexsha": "73c739a764e8b1dc84640e73b880bc66e1916bca", "max_stars_repo_licenses": ["PSF-2.0"], "max_stars_c... |
import gym, xlwt
import numpy as np
from itertools import count
def initial_excel():
global worksheet, workbook
# xlwt 库将数据导入Excel并设置默认字符编码为ascii
workbook = xlwt.Workbook(encoding='ascii')
# 添加一个表 参数为表名
worksheet = workbook.add_sheet('resources usage')
# 生成单元格样式的方法
# 设置列宽, 3为列的数目, 12为列的宽度,... | {"hexsha": "374da368e4d7b8503ff6c3acc8614890d1be3962", "size": 2574, "ext": "py", "lang": "Python", "max_stars_repo_path": "resources_monitor_tetris.py", "max_stars_repo_name": "Livioni/Cloud-Workflow-Scheduling-base-on-Deep-Reinforcement-Learning", "max_stars_repo_head_hexsha": "eb246ebba160567277c9c1aa226e359f48629da... |
r"""
The modified gamma distribution PSD
===================================
The form of the modified gamma distribution (MGD) used here is as follows:
.. math::
\frac{N(X)}{dX} = N \frac{\nu}{\Gamma(1 + \alpha)}\lambda^{\nu(1 + \alpha)}
D^{\nu(1 + \alpha) - 1} \cdot \exp \{-(\lambda D)^\... | {"hexsha": "b79955c038659628794a14f055fbcb460917aea5", "size": 9049, "ext": "py", "lang": "Python", "max_stars_repo_path": "artssat/scattering/psd/modified_gamma.py", "max_stars_repo_name": "simonpf/pARTS", "max_stars_repo_head_hexsha": "b4d9f4c2ceac594273c5589e44fe6a3a4f8d7028", "max_stars_repo_licenses": ["MIT"], "ma... |
function F = exclude(X,Y)
%EXCLUDE Excludes a binary solution
%
% F = exclude(X,value)
%
%EXCLUDE is used to avoid a particular binary solution. This can be used
% to repeatedly solve MILP problems while exluding all past solutions
%
% A = randn(30,15);
% b = 25*rand(30,1);
% c = randn(15,1);
% x = binvar(15,1);
% M... | {"author": "yalmip", "repo": "YALMIP", "sha": "f6d5a6d4222a4d722de30bffb43cae4b3e13b860", "save_path": "github-repos/MATLAB/yalmip-YALMIP", "path": "github-repos/MATLAB/yalmip-YALMIP/YALMIP-f6d5a6d4222a4d722de30bffb43cae4b3e13b860/@sdpvar/exclude.m"} |
[STATEMENT]
lemma list_induct_2_rev[consumes 1, case_names Nil Cons]:
assumes "length x = length y"
assumes "P [] []"
assumes "\<And>x xs y ys. length xs = length ys \<Longrightarrow> P xs ys \<Longrightarrow> P (xs@[x]) (ys@[y])"
shows "P x y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P x y
[PROOF STEP... | {"llama_tokens": 2324, "file": "Equivalence_Relation_Enumeration_Equivalence_Relation_Enumeration", "length": 23} |
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "0730618e31f272cc87e06256c0482f9f9a80db9e", "size": 8763, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/python/training/adagrad_da_test.py", "max_stars_repo_name": "abhaikollara/tensorflow", "max_stars_repo_head_hexsha": "4f96df3659696990cb34d0ad07dc67843c4225a9", "max_stars_repo_licenses... |
theory Post_Visibility_Traceback
imports Traceback_Intro
begin
consts PID :: postID
consts VIS :: vis
subsection \<open>Tracing Back Post Visibility Status\<close>
text \<open>We prove the following traceback property:
If, at some point \<open>t\<close> on a system trace, the visibility of a post \<open>PID\<close... | {"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/CoSMed/Traceb... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Sep 25 19:24:13 2019
@author: george
"""
import numpy as np
import scipy as sc
def generate_Coin(A = None, init = None, p_Coin = None, c_type = "Standard",
N = 5000):
"""
coin experiment for HMM testing
"""
... | {"hexsha": "e2d5cc39927a2380b868ec5bc57b18b98925f27f", "size": 2801, "ext": "py", "lang": "Python", "max_stars_repo_path": "MHMM/Tests/_experiments.py", "max_stars_repo_name": "jorje1908/MHMM", "max_stars_repo_head_hexsha": "e77f6d6dfa65444d7e7bbe4b3c469119306c429c", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
//---------------------------------------------------------------------------//
//!
//! \file MonteCarlo_AnalogElasticElectronScatteringDistribution.hpp
//! \author Luke Kersting
//! \brief The electron analog elastic scattering distribution base class
//!
//----------------------------------------------------------... | {"hexsha": "6dc348212470749ac4c02d0590e6d8e135c2a908", "size": 4631, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "packages/monte_carlo/collision/native/src/MonteCarlo_AnalogElasticElectronScatteringDistribution.hpp", "max_stars_repo_name": "lkersting/SCR-2123", "max_stars_repo_head_hexsha": "06ae3d92998664a520d... |
/*
* Copyright Andrey Semashev 2007 - 2014.
* 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)
*/
/*!
* \file sources/features.hpp
* \author Andrey Semashev
* \date 17.07.2009
*
* The... | {"hexsha": "295cc0bb4f9a86ff2618f843251928ebcb7801e5", "size": 3870, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3party/boost/boost/log/sources/features.hpp", "max_stars_repo_name": "bowlofstew/omim", "max_stars_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_stars_repo_licenses": ["Apache-... |
// Boost.Range library
//
// Copyright Neil Groves 2010. Use, modification and
// distribution is 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)
//
// For more information, see http://www.boost.org/libs/range/
//
#in... | {"hexsha": "dc91d52f2ac8a1eb77d9288dc83b2276bcfa673a", "size": 3069, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/libs/range/test/begin.cpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd49326bffd", "max_stars_repo_licenses"... |
export hausdorff_distance
"""
hausdorff_distance(X::LazySet{N}, Y::LazySet{N}; [p]::N=N(Inf),
[ε]=N(1e-3)) where {N}
Compute the Hausdorff distance between two convex sets up to a given threshold.
### Input
- `X` -- convex set
- `Y` -- convex set
- `p` -- (optional, default: `Inf`) norm p... | {"hexsha": "9ce23edce8833322a4dc3d82685d810394d89e5f", "size": 3724, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Approximations/hausdorff_distance.jl", "max_stars_repo_name": "goretkin/LazySets.jl", "max_stars_repo_head_hexsha": "6e829d9179bc25b8d7f6afb190a015e53760c601", "max_stars_repo_licenses": ["MIT"... |
from __future__ import absolute_import, division
from io import StringIO
import os.path as op
import numpy as np
import pandas as pd
from _common import cooler_cmp
from click.testing import CliRunner
import cooler
# import pytest
### EXPORT ###
from cooler.cli.info import info
from cooler.cli.dump import dump
from c... | {"hexsha": "bcaa1e4083db8d8901ef13d743080e61383029f3", "size": 4686, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_cli_export.py", "max_stars_repo_name": "mimakaev/cooler", "max_stars_repo_head_hexsha": "84b0d510dc3baf0b9ef3592f9d27ba795e1802ee", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
/*
Copyright (c) 2020, Ford Motor Company
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this
list of conditions and the fol... | {"hexsha": "c9affa5760a71897c0aa34d527d9261ad7dda2ff", "size": 15873, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/components/application_manager/rpc_plugins/vehicle_info_plugin/src/vehicle_info_pending_resumption_handler.cc", "max_stars_repo_name": "Sohei-Suzuki-Nexty/sdl_core", "max_stars_repo_head_hexsha"... |
# [Super SloMo]
##High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
from comet_ml import Experiment, ExistingExperiment
import argparse
import torch
import torchvision
import torchvision.transforms as transforms
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional a... | {"hexsha": "0ea7768fd185301fdacd4b292ea08a02f037179e", "size": 21031, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_cloudcast.py", "max_stars_repo_name": "tianyu-z/Super-SloMo", "max_stars_repo_head_hexsha": "55a278cc46b6edb731895548b5a5c26e9b3439ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
classdef SettingsLevelSetSmoothRectangleInclusion < SettingsLevelSetCreator
properties (Access = public)
widthH
widthV
pnorm
end
methods (Access = public)
function obj = SettingsLevelSetSmoothRectangleInclusion(varargin)
obj.loadParams('paramsLe... | {"author": "SwanLab", "repo": "Swan", "sha": "f8355f3561bb1a1603f56b3676873147d22a511e", "save_path": "github-repos/MATLAB/SwanLab-Swan", "path": "github-repos/MATLAB/SwanLab-Swan/Swan-f8355f3561bb1a1603f56b3676873147d22a511e/Topology Optimization/DesignVaribleInitializer/LevelSetInitializer/Settings/SettingsLevelSetSm... |
!|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
module hmix_del2
!BOP
! !MODULE: hmix_del2
! !DESCRIPTION:
! This module contains routines for computing Laplacian horizontal
! diffusion of momentum and tracers.
!
! !REVISION HISTORY:
! CVS:$Id: hmix_del2.F90,v 1.20 2003/02/24 20:43:04 pwjon... | {"hexsha": "403b054c5cb65c35ebec57d34d7db34256893daf", "size": 35901, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "bench01opt_cpu_n1t1p1/compile/hmix_del2.f90", "max_stars_repo_name": "app-on-mic/POP-2.0.1-opt", "max_stars_repo_head_hexsha": "c23e290333d50293386f3004f26a355db9da4bcb", "max_stars_repo_licens... |
import torch
from torch import nn
import pandas as pd
import os
from tqdm import tqdm
import torchaudio
import librosa
import numpy as np
import gc
def sample2melspectrogram(samples,sample_rate):
melspectrogram = librosa.feature.melspectrogram(samples,sample_rate,center=False)
melspectrogram = libr... | {"hexsha": "4c3679630c6bdf2543e2fe2cbed0a609028dec8a", "size": 1373, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "Cuda-Chen/Tomofun-Dog-Voice-Recognition-AI-Million-Challenge", "max_stars_repo_head_hexsha": "cdc7f7cf9b1c29e8d1b1d6d19301154a7616d8f4", "max_stars_repo_licens... |
import pandas as pd
import numpy as np
import random
from csv import writer
import csv
import math
def genPastDayInfectNum(totalVisited):
#被调用, 以每一次每一栋楼每一天的total visited number进行对应计算
infectedNum = totalVisited * (random.randint(0,2000)/10000)
infectedNum = math.floor(infectedNum)
return infectedNum
def genPas... | {"hexsha": "e527d7e769fea02f03c976ccceb475f0fc3290fd", "size": 1837, "ext": "py", "lang": "Python", "max_stars_repo_path": "files/genRandomCovidData.py", "max_stars_repo_name": "YuudachiXMMY/ProSeed-Hackthon-2022", "max_stars_repo_head_hexsha": "662973f7f6f338281aed36aa77e0e49d737de31e", "max_stars_repo_licenses": ["MI... |
import sys, os, glob, string
import numpy as np
import matplotlib.pyplot as plt
from pyraf import iraf
from tqdm import tqdm
import odi_config as odi
import pandas as pd
from astropy.wcs import WCS
from astropy.table import Table
from astropy.io import fits
from collections import OrderedDict
def get_sdss_coords(img, ... | {"hexsha": "3cea37412ff4cf6940edc5b15a535901c1058b0b", "size": 35001, "ext": "py", "lang": "Python", "max_stars_repo_path": "odi_coords.py", "max_stars_repo_name": "bjanesh/odi-tools", "max_stars_repo_head_hexsha": "a9cf686762234f118c9a25c43a25c04462d30a80", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
import numpy as np
import itertools
from .draw import _DrawingMixin
from collections import deque
__all__ = [ 'Graph' ]
class Edges():
def __init__( self ):
self.parent_edge = None
self.child_edges = []
class _PolytreeBaseMixin():
def __init__( self ):
""" This is the base class for ... | {"hexsha": "f2211ee702483422b4d88510b914edf1cf6b730e", "size": 9876, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/graph.py", "max_stars_repo_name": "EddieCunningham/CausalInference", "max_stars_repo_head_hexsha": "5938787a41222ae1810d5c649a1f3b93285fbb1e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from PIL import Image
import numpy as np
import time
from dead_end_filler import DeadEndFiller
class Solver:
def __init__(self, path):
maze = Image.open(path)
(self.width, self.height) = maze.size
self.pixels = np.array(maze)
def dead_end_filler(self, time_it=False):
return... | {"hexsha": "06114e2ec272a41b303285ff7b3b622dae101c42", "size": 944, "ext": "py", "lang": "Python", "max_stars_repo_path": "solver.py", "max_stars_repo_name": "SpyrosRoum/Maze-Generatori-and-Solver", "max_stars_repo_head_hexsha": "c6a65efbde12f0623ff2f1ca8d1ad0fbb02de3cc", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\chapter{Signal Processing}
\section*{Introduction}
\newpage
\cex
\inputminted[linenos=true,resetmargins=true]{c}{./c_examples/example18.c}
\newpage
\section*{Fourier Transforms}\addcontentsline{toc}{section}{Fourier Transforms}
\subsection*{Vector FFT}\addcontentsline{toc}{subsection}{Vector FFT}
\subsection*(Vector F... | {"hexsha": "a32d74d8bc107575897dcc3cc024931ebc85b50e", "size": 1785, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/jvsip_book/c6.tex", "max_stars_repo_name": "rrjudd/jvsip", "max_stars_repo_head_hexsha": "56a965fff595b027139ff151d27d434f2480b9e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "m... |
using QMTK
using QMTK.Consts.Pauli
using Compat.Test
@testset "local hamiltonian" begin
mat = σ₁⊗σ₂
h = LocalHamiltonian(mat)
rhs = SubSites(Bit, 1, 0)
rhs_idx = Int(rhs) + 1
itr = LHIterator(h, rhs)
for (val, lhs) in itr
lhs_idx = Int(lhs) + 1
@test val == mat[lhs_idx, rhs_id... | {"hexsha": "c1f891f7641113d1ff4bff9b7e030cfaff8003a1", "size": 346, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Hamiltonian/Core.jl", "max_stars_repo_name": "Roger-luo/QMTK.jl", "max_stars_repo_head_hexsha": "90987261588fc8a4aefa73df2b1fb5d0c5a3f9d5", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# VERSION >= v"0.4.0-dev+6521" && __precompile__(true)
module Script
export _nullFunction
export _debug
export compile
export invoke
global const _nullFunction = function() end
global _debug = true
function compile(file::String)
result = nothing
try
result = evalfile(file)
if _debug == true
println("file co... | {"hexsha": "d33dd809100907438a1961fc9532f6c70cd01f77", "size": 761, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Script.jl", "max_stars_repo_name": "Gilga/JuliaScriptLoader.jl", "max_stars_repo_head_hexsha": "beca946519b921006e90563e3aa33d7a2ff9edc1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 15 14:49:49 2018
@author: shams
"""
import numpy as np
import pandas as pd
import networkx as nx
#from keras.preprocessing import sequence
#from keras.models import load_model
#from keras.layers import Dense, Input, LSTM, GRU
#from keras.models import Model
#import h5p... | {"hexsha": "d406908735e214707e994df7c082e67933fd8c3e", "size": 4362, "ext": "py", "lang": "Python", "max_stars_repo_path": "HPC_code/data_prep.py", "max_stars_repo_name": "nasim-shams/SlackTrack", "max_stars_repo_head_hexsha": "09d9d4522679ac2f95efc2d7653d7d1e432326b6", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from __future__ import annotations
import dataclasses as dcls
import functools
import logging
from dataclasses import dataclass
from numbers import Number
from typing import Any, Callable, Dict, Generic, Tuple, Type, TypeVar, final, overload
import torch
from numpy import ndarray
from rich.logging import RichHandler
... | {"hexsha": "91d86eebb467d3c0c19e53eba41ea5a771569ee3", "size": 3919, "ext": "py", "lang": "Python", "max_stars_repo_path": "koila/tensors/delayed.py", "max_stars_repo_name": "techthiyanes/koila", "max_stars_repo_head_hexsha": "b665482ff99a02bfeeceaa1323589fb89495a30c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
""" Input/output relation task. Every input and output
is explicitly defined. XOR is an example of this task.
"""
### IMPORTS ###
import random
# Libraries
import numpy as np
# Local
from ..networks.rnn import NeuralNetwork
class MappingTask(object):
# Default XOR input/output pairs
INPUTS = [(-0... | {"hexsha": "1633353fb8f10d60daaab8ae39b6e5e5ac67446d", "size": 1876, "ext": "py", "lang": "Python", "max_stars_repo_path": "peas/tasks/mapping.py", "max_stars_repo_name": "promanev/PDSTEP_SNN_PEAS", "max_stars_repo_head_hexsha": "864cef4a86989b757f7b849b7d0486a83c6a85ca", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from flask import Flask, render_template, request,jsonify
from tensorflow.keras.models import load_model
import cv2
import numpy as np
import base64
from PIL import Image
import io
import re
img_size=100
app = Flask(__name__)
model=load_model('model/model-015.model')
label_dict={0:'Covid19 Negative'... | {"hexsha": "539aeaad4936607a7192bcd9657eef402b3f29fd", "size": 1391, "ext": "py", "lang": "Python", "max_stars_repo_path": "webapp/app.py", "max_stars_repo_name": "amitd307/Covid-19-prediction-using-X-Ray-images", "max_stars_repo_head_hexsha": "2a12f6975b3301466957d41e08899940ebd44840", "max_stars_repo_licenses": ["MIT... |
// Copyright (C) 2009-2012 Lorenzo Caminiti
// Distributed under the Boost Software License, Version 1.0
// (see accompanying file LICENSE_1_0.txt or a copy at
// http://www.boost.org/LICENSE_1_0.txt)
// Home at http://www.boost.org/libs/local_function
#ifndef BOOST_LOCAL_FUNCTION_AUX_PP_DECL_TRAITS_PARAMS_HPP_
#defi... | {"hexsha": "10dd60961bc01f4f77f6a867b4c256ffee7de299", "size": 2429, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/src/boost_1_65_1/boost/local_function/aux_/preprocessor/traits/decl_params.hpp", "max_stars_repo_name": "shreyasvj25/turicreate", "max_stars_repo_head_hexsha": "32e84ca16aef8d04aff3d49ae9984bd4... |
import collections
import numpy as np
from django.test import TestCase
from dptable.variance_reduce import VarianceReduce
class TestDPTable(TestCase):
def setUp(self):
self.domain = collections.OrderedDict([
('A', [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, ... | {"hexsha": "8e752eae7f38b1487d765b82a0d0c12b5d63589e", "size": 2978, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_dptable.py", "max_stars_repo_name": "sylar233/de-identification", "max_stars_repo_head_hexsha": "44731e9c22de647063bd82a19936b4c5a144ea6e", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#include <boost/test/unit_test.hpp>
#include <test_block.hpp>
#include <timer.hpp>
#include <utils.hpp>
#include <crypto/hex.hpp>
BOOST_AUTO_TEST_CASE(hex_test)
{
std::vector<uint8_t> bytes;
auto from_hex_f = [&]() { bytes = std::move(crypto::from_hex<uint8_t>(test_block)); };
std::string hex;
auto ... | {"hexsha": "d8005670f5d7e0eb0c6d1b40eaabbe9e9d65ce31", "size": 647, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/crypto/hex_test.cpp", "max_stars_repo_name": "asuvalov/climb", "max_stars_repo_head_hexsha": "e1349d2deb1d2cfbd8ac01146cf9c1dedc7e51e2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
opt.table<-function(out.file,dirs,labels,first.y=2051,last.y=2051,stochastic=F,test=F){
#####################
for (dir in dirs) {
if ( file.access(file.path(data.path,dir,"op.dat"), mode = 0)!=0) stop(paste('Directory',dir,'does not exist'))
}
Init.function() # get SMS.contol object including sp.n... | {"hexsha": "1266d0ac60d9966277d4e059241dcde5e2f6ee3a", "size": 5040, "ext": "r", "lang": "R", "max_stars_repo_path": "SMS_R_prog/hcr_op_batch_optimize_compare.r", "max_stars_repo_name": "ices-eg/wg_WGSAM", "max_stars_repo_head_hexsha": "d5f93c431d1ec6c2fb1f3929f63cd9e636fc258a", "max_stars_repo_licenses": ["MIT"], "max... |
__id__ = "$Id: GetData.py 635 2009-06-24 01:19:00Z jlconlin $"
__author__ = "$Author: jlconlin $"
__version__ = " $Revision: 635 $"
__date__ = "$Date: 2009-06-23 19:19:00 -0600 (Tue, 23 Jun 2009) $"
"""
This module is used to extract the data from the output files used in this
parameter study.
"""
import ... | {"hexsha": "6afad656003ef7d124ca56ba0f3b6c08924e029b", "size": 2674, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code/trunk/cpp/Research/ParametricStudy/Relaxed/50mfp/Trial2/GetData.py", "max_stars_repo_name": "jlconlin/PhDThesis", "max_stars_repo_head_hexsha": "8e704613721a800ce1c59576e94f40fa6f7cd986", "ma... |
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
import miscClin
import miscMath
import miscMatrix
import miscTCGA
import plotMatrix
import tsvIO
import numpy
import sys
# -#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#-#
NA_VALUE = -999999
# -#-#-#-#-#-... | {"hexsha": "fa7b660e2434ea068f3ca0d258dab9a199e645c5", "size": 4372, "ext": "py", "lang": "Python", "max_stars_repo_path": "commands/feature_matrix_construction/main/filterByGeneList.py", "max_stars_repo_name": "cancerregulome/gidget", "max_stars_repo_head_hexsha": "6c9e9a37f9992267c7505c7a396ff7e2638599ab", "max_stars... |
# updates eddy viscosity (ev/rev)
# append to path so we can access Field class
import sys
sys.path.append("../../../")
# class dependencies
import numpy as np
from bin.Field import Field, max, abs, isfinite
# fortran module
from bin.model_funcs.fortran_versions import turb2_fort
def turb_BL(model,ws,w,ncyc=0):
... | {"hexsha": "a3fb15ec3465ea388356f86709a397af05d30061", "size": 1067, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/model_funcs/fortran_versions/turb2_wrap.py", "max_stars_repo_name": "AlexT-L/RANS", "max_stars_repo_head_hexsha": "f4f477b30429e5028f9a0a53d59787f9f3821a00", "max_stars_repo_licenses": ["MIT"]... |
import warnings
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn.externals import joblib
from sklearn import metrics
from sklearn import linear_model
from CreateVector import WordVector
from sklearn.svm import LinearSVC
from sklearn.datasets import make_classification
import logging
impor... | {"hexsha": "764532cbb09b2ea69b1d656d5d1536c52011034e", "size": 3327, "ext": "py", "lang": "Python", "max_stars_repo_path": "AlgorithmQuestionAnswering/QuestionClassification/CreateModel.py", "max_stars_repo_name": "zointblackbriar/QuestionAnswering", "max_stars_repo_head_hexsha": "319c3623ced22254d75c2918929a875090bd2b... |
\documentclass[a4paper]{article}
%% Language and font encodings
\usepackage[english]{babel}
\usepackage[utf8x]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{caption}
%% Sets page size and margins
\usepackage[a4paper,top=3cm,bottom=2cm,left=3cm,right=3cm,marginparwidth=1.75cm]{geometry}
%% Useful packages
\usepackag... | {"hexsha": "48104dbbf208a0696b4dc630a5d823a124cc25b9", "size": 15461, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Section_06/tex/main.tex", "max_stars_repo_name": "Harvard-CS182-F18/courseware", "max_stars_repo_head_hexsha": "b1c5cc83dd45091c0ab74e0252405bc79ce51718", "max_stars_repo_licenses": ["MIT"], "max_s... |
# coding: utf-8
# In[ ]:
#### Modules for selecting features
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sys
# random forest
from sklearn.ensemble import RandomForestRegressor
# Logistic regression
from sklearn.linear_model import Logis... | {"hexsha": "98a510fc0e35680a31090b55a19aca414465ac38", "size": 4586, "ext": "py", "lang": "Python", "max_stars_repo_path": "MY_select_features.py", "max_stars_repo_name": "igor-yusupov/autorace", "max_stars_repo_head_hexsha": "0294873a62f3dbfdf3564bb2b63e97e917be6de6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
using SpecialFunctions
import Base.Broadcast
const linearity_known_1 = IdDict{Function,Bool}()
const linearity_known_2 = IdDict{Function,Bool}()
const linearity_map_1 = IdDict{Function, Bool}()
const linearity_map_2 = IdDict{Function, Tuple{Bool, Bool, Bool}}()
# 1-arg
const monadic_linear = [deg2rad, +, rad2deg, ... | {"hexsha": "09dde1ec762dc0370d6d2a2244fea80023a97d75", "size": 5630, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/linearity.jl", "max_stars_repo_name": "sharanry/Symbolics.jl", "max_stars_repo_head_hexsha": "eeee4366850459b929b46c438a7d6f63e027b4ca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9... |
import csv
import serial
import time
import numpy
z1baudrate = 115200
z1port = 'COM3' # set the correct port before run it
b = 0.00
z1serial = serial.Serial(port=z1port, baudrate=z1baudrate)
z1serial.timeout = 2 # set read timeout
# print z1serial # debug serial.
print(z1serial.is_open) # True for opened
if z1ser... | {"hexsha": "79451dcde0249e930f89c984e48640fd17eba3af", "size": 1312, "ext": "py", "lang": "Python", "max_stars_repo_path": "Accelerometer_and_circular_store/seri.py", "max_stars_repo_name": "PremSuresh/Udaya-bon", "max_stars_repo_head_hexsha": "27298512e33815a08807896e8743b08ad4e09355", "max_stars_repo_licenses": ["MIT... |
-import pandas as pd
import numpy as np
from PIL import Image
from sklearn.preprocessing import LabelEncoder,StandardScaler
from sklearn.grid_search import GridSearchCV
from skimage.transform import resize
from sklearn.svm import SVC
import pickle
no_of_training_data = 6500
train_data = pd.read_csv('labels... | {"hexsha": "6607311c39ba116f8b481cf24de4f3a6fbc4a723", "size": 1698, "ext": "py", "lang": "Python", "max_stars_repo_path": "prog.py", "max_stars_repo_name": "adibyte95/Dog-Breed-Identification-Kaggle", "max_stars_repo_head_hexsha": "1ac111237bd0c681b5a2127edf783061be601447", "max_stars_repo_licenses": ["MIT"], "max_sta... |
############## CELL CYCLE DISTRIBUTION ESTIMATION FROM DAPI INTENSITIES ################
#
# licensed under the MIT License:
#
# Copyright (c) 2016 Andreas Stengl, David Hoerl, Heinrich Leonhardt and Jonas Helma
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# ... | {"hexsha": "be6155aa27463d1cbe2c160efa021791db7ebe92", "size": 6082, "ext": "r", "lang": "R", "max_stars_repo_path": "DAPI_CellCycle_Fit.r", "max_stars_repo_name": "hoerldavid/CellCycleFit", "max_stars_repo_head_hexsha": "17a55ded5f7aaade8a2fba8a619bf099ee3d03ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import torch
print("PyTorch Version: ",torch.__version__)
import torch.nn as nn
import torch.optim as optim
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os,glob,shutil
import copy
print("PyTorch Version: ",torch.__version_... | {"hexsha": "7d01ac43d153e634b4a5c53ae42068aeb786d0ab", "size": 2103, "ext": "py", "lang": "Python", "max_stars_repo_path": "wpkit/cv/examples/torch/resnet/val.py", "max_stars_repo_name": "Peiiii/wpkit", "max_stars_repo_head_hexsha": "23a07548be766b559b80e3114ecc24e3f2f65ea5", "max_stars_repo_licenses": ["MIT"], "max_st... |
from scipy.stats import pearsonr
def calculate_corr(seq_i, seq_j):
if len(seq_i)>=len(seq_j):
longer_signal=seq_i
shorter_signal=seq_j
else:
longer_signal=seq_j
shorter_signal=seq_i
LD=len(longer_signal)
LK=len(shorter_signal)
corr=[]
for a in ran... | {"hexsha": "09f430e900755408792fc5d662270e50b9c6afad", "size": 856, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/helpers/calculate_corr.py", "max_stars_repo_name": "knalecz/tsp_assembly", "max_stars_repo_head_hexsha": "aa723c2ff6d2859e0aa77976487b8d19302021e9", "max_stars_repo_licenses": ["MIT"], "max_sta... |
using PowerSystems
cost = VariableCost([(1.0, 1.0), (2.0, 1.1), (3.0, 1.2)])
slopes = get_slopes(cost)
res = [1.0, 10.0, 10.0]
for (ix, v) in enumerate(slopes)
@test isapprox(v, res[ix])
end
bps = get_breakpoint_upperbounds(cost)
res = [1.0, 0.1, 0.1]
for (ix, v) in enumerate(bps)
@test isapprox(v, res[ix])
en... | {"hexsha": "6958491cff2b93b6c474975dc455a5d147482fe3", "size": 322, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_cost_functions.jl", "max_stars_repo_name": "Nongchao/PowerSystems.jl", "max_stars_repo_head_hexsha": "0d7e74e71dc8957e3bf5f27846ec22d22ece7172", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
% !TeX root = FoodFile.tex
% Content Begins
\begin{menu}{January}
\begin{recipelist}
{\scriptsize[1-2]} Spiced Chicken\\
{\scriptsize[3-4]} Tagliatelle and Mushroom Sauce\\
{\scriptsize[5-6]} Chick Pea and Tomato Curry\\
{\scriptsize[7]} Curry and Couscous\\... | {"hexsha": "7b153e94c3686d5f87d08343e89ae4cb7d7700ef", "size": 142561, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "data/processed/FoodFileContent.tex", "max_stars_repo_name": "joejcollins/FoodFile", "max_stars_repo_head_hexsha": "eb2369279147f51434a70c44b341560d7a92e9bc", "max_stars_repo_licenses": ["MIT"], "m... |
function Investment_OPF_stage1(optimizer,set_opt_thermalgenerators,set_opt_winds,set_thermalgenerators,set_winds,set_demands,set_nodes,set_nodes_ref,set_nodes_noref,set_scenarios,set_times,P,V,max_demand,R,p_D,D,γ,Τ,wind,wind_opt,Ns_H,links,F_max_dict,B_dict,MapG,MapG_opt,MapD,MapW,MapW_opt,tech_thermal,tech_thermal_op... | {"hexsha": "294de0917e8a243c16a00552c7bdb576d46023ac", "size": 11379, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Investment_OPF_stage1.jl", "max_stars_repo_name": "bdvalqui/DTU_BrayamValqui_SP2021.jl", "max_stars_repo_head_hexsha": "cde096a6d5f2cf03b567056ef0655908e68769e7", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma LIMSEQ_le_const2: "X \<longlonglongrightarrow> x \<Longrightarrow> \<exists>N. \<forall>n\<ge>N. X n \<le> a \<Longrightarrow> x \<le> a"
for a x :: "'a::linorder_topology"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>X \<longlonglongrightarrow> x; \<exists>N. \<forall>n\<ge>N. X n \<l... | {"llama_tokens": 169, "file": null, "length": 1} |
module Neural
using Random
using Plots
include("functions.jl")
mutable struct Layer
W::AbstractArray{Float64} # weights
b::Vector{Float64} # bias
afun::Function # activaion function
dafun::Function # derivative of the activation function
z::Vector{Float6... | {"hexsha": "c183a2b6f2858fa32044b30f535f1e687a4988dd", "size": 4869, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/neural.jl", "max_stars_repo_name": "achjaj/shape-recognition", "max_stars_repo_head_hexsha": "ff83b69f65df3a74d28d5eada027420cac4e364f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
SUBROUTINE DT_DDATAR
IMPLICIT NONE
INTEGER i , ia , iaa , ib , ibb , ip , iv , j , l
DOUBLE PRECISION ONE , TINY10 , ZERO
SAVE
INCLUDE 'inc/dtflka'
PARAMETER (TINY10=1.0D-10,ONE=1.0D0,ZERO=0.0D0)
C quark-content to particle index conversion (DTUNUC 1.x)
INCLUDE ... | {"hexsha": "a55cc9c8e239b2a0588e18b88411b162aa205076", "size": 3214, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/dpmjet/DT_DDATAR.f", "max_stars_repo_name": "pzhristov/DPMJET", "max_stars_repo_head_hexsha": "946e001290ca5ece608d7e5d1bfc7311cda7ebaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt
import math
tf.logging.set_verbosity(tf.logging.INFO)
#-----------------------------------------------
#variables
epoch = 2000
learningRate = 0.1
batch_size = 120
mnis... | {"hexsha": "2c64b445a4ed72414e3064ab95e1fc38c9197f5c", "size": 2689, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/gradient-vanishing.py", "max_stars_repo_name": "GangababuManam/tensorflow-101", "max_stars_repo_head_hexsha": "f5ba6b298ecdf0ca77ffe871c678f6699ab59a21", "max_stars_repo_licenses": ["MIT"],... |
function compare(c1::Channel,
c2::Channel;
skip::Vector{Symbol}=[] )
#
TP = true
TP = TP && c1.state == c2.state
TP = TP && c1.sz_max == c2.sz_max
TP = TP && c1.data |> length == c2.data |> length
# exit early if tests already failed
!TP && (return false)
# now check contents of data
for i in 1:length(c1.data)
TP... | {"hexsha": "fb6f48a062bc72a9589012727b9bccb80e57a31a", "size": 475, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/NeedsResolution.jl", "max_stars_repo_name": "akhand9999/IncrementalInference.jl", "max_stars_repo_head_hexsha": "8f847d0e32c42d06f5cc6e4652afb1f5fb95ba63", "max_stars_repo_licenses": ["MIT"], "m... |
# Copyright (c) 2020, Huawei Technologies.All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# Unless required by applicable law... | {"hexsha": "d3fb5afc10a18dfd9fce1efdfe0a0a55e755ed85", "size": 3976, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_npu/test_network_ops/test_logspace.py", "max_stars_repo_name": "Ascend/pytorch", "max_stars_repo_head_hexsha": "39849cf72dafe8d2fb68bd1679d8fd54ad60fcfc", "max_stars_repo_licenses": ["BS... |
#!/usr/bin/env python3
import numpy as np
from computeCost import computeCost
def gradientDescent(X, y, theta, alpha, num_iters):
#GRADIENTDESCENT Performs gradient descent to learn theta
# theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
# taking num_iters gradient steps with ... | {"hexsha": "b4fcd9d456b62a74db26b0d4cd0d03c0998bd200", "size": 1172, "ext": "py", "lang": "Python", "max_stars_repo_path": "machine-learning-ex1/ex1/gradientDescent.py", "max_stars_repo_name": "altermarkive/machine-learning-course", "max_stars_repo_head_hexsha": "2f0a2c2269dab2bd61d34d96a75ccdd8b87683c7", "max_stars_re... |
(*
* Copyright 2014, General Dynamics C4 Systems
*
* SPDX-License-Identifier: GPL-2.0-only
*)
theory Bits_R
imports Corres
begin
crunch_ignore (add:
bind return "when" get gets fail assert put modify unless select alternative assert_opt gets_the
returnOk throwError lift bindE liftE whenE unlessE throw_opt ass... | {"author": "seL4", "repo": "l4v", "sha": "9ba34e269008732d4f89fb7a7e32337ffdd09ff9", "save_path": "github-repos/isabelle/seL4-l4v", "path": "github-repos/isabelle/seL4-l4v/l4v-9ba34e269008732d4f89fb7a7e32337ffdd09ff9/proof/refine/ARM/Bits_R.thy"} |
import numpy as np # type: ignore
from typing import List, Optional
def _is_multi_dimensional(series) -> bool:
try:
series[0][0]
return True
except:
return False
class MultiSeries:
def __init__(self, ys, xs=None):
# Init types
self.xs: List[np.array] = []
... | {"hexsha": "84308143555048f5feaf49413079693ffd351777", "size": 1625, "ext": "py", "lang": "Python", "max_stars_repo_path": "uniplot/multi_series.py", "max_stars_repo_name": "olavolav/textplot", "max_stars_repo_head_hexsha": "f665a0d8cf1822b46db7c3ffe1766888ff1de3bf", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# coding: utf-8
# 2021/3/28 @ liujiayu
import random
import numpy as np
import pytest
@pytest.fixture(scope="package")
def conf():
user_num = 5
item_num = 2
know_num = 3
return user_num, item_num, know_num
@pytest.fixture(scope="package")
def data(conf):
user_num, item_num, know_num = conf
... | {"hexsha": "f8ef61392a645baf343c7ea9a810160c30a748d3", "size": 920, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/fuzzycdf/conftest.py", "max_stars_repo_name": "zelo2/EduCDM", "max_stars_repo_head_hexsha": "d725dc50ec677dfe409d88a3ffea6dce8effad62", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
!
! Parallel Sparse BLAS version 3.5
! (C) Copyright 2006-2018
! Salvatore Filippone
! Alfredo Buttari
!
! Redistribution and use in source and binary forms, with or without
! modification, are permitted provided that the following conditions
! are met:
! ... | {"hexsha": "aaed4bb18a680ef63f8fadc26e93b17dadfd2349", "size": 12688, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "base/modules/tools/psb_s_tools_mod.f90", "max_stars_repo_name": "fccf/psblas3", "max_stars_repo_head_hexsha": "b6cfcf93ac2f08e7b1a1970ee638af9890502291", "max_stars_repo_licenses": ["BSD-3-Clau... |
import pyaos
import cv2
import os
import unittest
import sys
import glm
import numpy as np
import numpy.testing
from pathlib import Path
class TestAOSRenderTwice(unittest.TestCase):
_window = None
_aos1 = None
_aos2 = None
_fovDegrees = 50
def setUp(self):
self._window = pyaos.PyGlfwWind... | {"hexsha": "988827ed8553de150ecf4f22d9f82a60c22b0deb", "size": 6745, "ext": "py", "lang": "Python", "max_stars_repo_path": "LFR/python/pyaos_test.py", "max_stars_repo_name": "zhouheping239/AOS", "max_stars_repo_head_hexsha": "2346ab523dacffe7612da2e45173b98c4433fc5a", "max_stars_repo_licenses": ["Intel"], "max_stars_co... |
"""
@author: DeepCaT_Z
"""
#%% PRE-PROCESSING (mandatory):
# Resizing all frames to pre-defined pixel's resolution
# OBS: augmentation operations will be carried out while training the model.
#%% ############################################
######### IMPORTS: DO NOT TOUCH ##############
##############... | {"hexsha": "ca2c753ca39dae435c46c8a9cb139577b8c6b7eb", "size": 2913, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/TRAIN_MODELS/CLASSIFICATION/preprocess_Classification.py", "max_stars_repo_name": "CaT-zTools/Deep-CaT-z-software", "max_stars_repo_head_hexsha": "9b4b48b62b6621f124fbce3e87160a7b2a2d626c", "m... |
# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
from yolo_v4 import _conv2d_fixed_padding, _fixed_padding, _get_size, \
_detection_layer, _upsample
slim = tf.contrib.slim
_BATCH_NORM_DECAY = 0.9
_BATCH_NORM_EPSILON = 1e-05
_LEAKY_RELU = 0.1
_ANCHORSTINY = [(10, 14), (23, 27), (... | {"hexsha": "312f4c266306e5f3aef0edb0b7c604a9d91129c5", "size": 3203, "ext": "py", "lang": "Python", "max_stars_repo_path": "yolov4tiny/yolo_v4_tiny.py", "max_stars_repo_name": "TNTWEN/OpenVINO-YOLO-Automatic-Generation", "max_stars_repo_head_hexsha": "bc052c9e6bc054a451ac28bbbab33a5088eb02de", "max_stars_repo_licenses"... |
r"""Note.
when H, W \le 10^5 on grid problem
and it's impossible to create an actual graph
because it's O(HW) space,
consider y-axis and x-axis seperately.
"""
import typing
import sys
import numpy as np
import numba as nb
@nb.njit((nb.i8, nb.i8, nb.i8[:, :]), cache=True)
def solve(h: int, w: int, rca: np.nda... | {"hexsha": "eab3cee079060daf72a87537943d65b3a2bdbd88", "size": 1174, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/atcoder/abc224/e/sol_0.py", "max_stars_repo_name": "kagemeka/competitive-programming", "max_stars_repo_head_hexsha": "c70fe481bcd518f507b885fc9234691d8ce63171", "max_stars_repo_licenses": ["MI... |
"""
Neural Network from scratch.
A simple Neural Network calss.
License MIT, all rights reserved jerry liu @twairball
"""
import numpy as np
# sigmoid and sigmoid derivative functions
def sigmoid(x):
x = np.clip(x, -500, 500) # avoid overflow
return 1 / ( 1 + np.exp(-x))
def sigmoid_deriv(x):
return x *... | {"hexsha": "fbe7dcc35c33f5ae50882b176e6dbc514e3c8ad4", "size": 3518, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn_model.py", "max_stars_repo_name": "twairball/nn_from_scratch", "max_stars_repo_head_hexsha": "8fcfa54e6041e59e917a789e537ee599733e5db5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
subsection\<open>Peirce\<close>
theory Peirce
imports Types
begin
text\<open>As an example of our $\lambda\mu$ formalisation, we show show a
$\lambda\mu$-term inhabiting Peirce's Law. The example is due to
Parigot~\<^cite>\<open>"DBLP:conf/lpar/Parigot92"\<close>.\<close>
text\<open>Peirce's law:... | {"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/LambdaMu/Peir... |
import ..lectures.love03_forward_proofs_demo
/-! # LoVe Exercise 4: Functional Programming -/
set_option pp.beta true
set_option pp.generalized_field_notation false
namespace LoVe
/-! ## Question 1: Reverse of a List
We define a new accumulator-based version of `reverse`. The first argument,
`as`, serves as the... | {"author": "BrownCS1951x", "repo": "fpv2021", "sha": "10bdbd92e64fb34115b68794b8ff480468f4dcaa", "save_path": "github-repos/lean/BrownCS1951x-fpv2021", "path": "github-repos/lean/BrownCS1951x-fpv2021/fpv2021-10bdbd92e64fb34115b68794b8ff480468f4dcaa/src/exercises/love04_functional_programming_exercise_sheet.lean"} |
# See "Writing benchmarks" in the asv docs for more information.
# https://asv.readthedocs.io/en/latest/writing_benchmarks.html
# or the napari documentation on benchmarking
# https://napari.org/developers/benchmarks.html
import numpy as np
from napari.layers.utils.text_manager import TextManager
class TextManagerSu... | {"hexsha": "0ff830ea5920f1855c509f4c85e93bd748f9ca79", "size": 1577, "ext": "py", "lang": "Python", "max_stars_repo_path": "napari/benchmarks/benchmark_text_manager.py", "max_stars_repo_name": "MaksHess/napari", "max_stars_repo_head_hexsha": "64a144607342c02177fc62fa83a3442ace0a98e7", "max_stars_repo_licenses": ["BSD-3... |
__author__ = 'IVMIT KFU: Gataullin Ravil & Veselovkiy Sergei'
import cv2
import numpy as np
def add_gaussian_noise(bounding_box, mean, sigma):
if bounding_box is not None:
return bounding_box + np.random.normal(mean, sigma, bounding_box.shape)
else:
return None
class LearningComponent:
d... | {"hexsha": "cc12d1c362957a7d15f903db401ec6cccb20c694", "size": 7618, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tracking/learning.py", "max_stars_repo_name": "SAVeselovskiy/KFU_Visual_Tracking", "max_stars_repo_head_hexsha": "af45fd6a93d9f0369fc8bab97af4abecef444943", "max_stars_repo_licenses": ["MIT"], "ma... |
import os
import unittest
import numpy as np
from gnes.indexer.chunk.bindexer import BIndexer
@unittest.SkipTest
class TestBIndexer(unittest.TestCase):
def setUp(self):
self.toy_data = np.array([[1, 2, 1, 2],
[2, 1, 3, 4],
[1, 2, 1, 2],... | {"hexsha": "57d781f042d6a3b4167ea3a94e83ee543505fd34", "size": 2742, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_bindexer.py", "max_stars_repo_name": "micro-pixel/gnes", "max_stars_repo_head_hexsha": "388d1ba718ec04eedaaff3ce34da43689c197ee7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
```python
import numpy as np
from scipy.integrate import simps
#my things
from FermatPrincipleCartesian import *
from Geometry import *
from Symbolic import *
from sympy import Matrix
from RealData import PrepareData
from ForwardEquation import *
def LMSolContinous(dataDict,mu = 0.5):
'''
``rays`` origin a... | {"hexsha": "36ace200b0d254d4f9651cf5c759a00a275f18c9", "size": 318316, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "src/ionotomo/notebooks/ContinuousInversion.ipynb", "max_stars_repo_name": "Joshuaalbert/IonoTomo", "max_stars_repo_head_hexsha": "9f50fbac698d43a824dd098d76dce93504c7b879", "max_sta... |
from sklearn.manifold import TSNE
from sklearn.manifold import MDS
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import OPTICS
from sklearn.cluster import DBSCAN
from sklearn.cluster import AgglomerativeClustering
from sklearn.cluster import AffinityPropagation
from sklearn... | {"hexsha": "ae894773e1eeb322152c298d9f8b774e25f49c7c", "size": 5902, "ext": "py", "lang": "Python", "max_stars_repo_path": "visualize_clusters.py", "max_stars_repo_name": "akonoroshi/cluster_visualization", "max_stars_repo_head_hexsha": "92e38c34d2764afbc00b9b3d9a42f133e7d11a4c", "max_stars_repo_licenses": ["MIT"], "ma... |
#!/usr/bin/env python3
# author: github.com/olehermanse
# import libraries used for plotting and mathematical operations:
import numpy as np
import matplotlib.pyplot as plt
import random
# Define a mathematical expression as a function:
def f(x):
return -x**4 + 2 * x**3 + 2 * x**2 - x
def df(x):
return -4 * ... | {"hexsha": "ef7f6cb46e73b5bf4a1487897827b61ededccdc4", "size": 1347, "ext": "py", "lang": "Python", "max_stars_repo_path": "group_lectures/02_search/02_gradient.py", "max_stars_repo_name": "mpambasange/MachineLearning", "max_stars_repo_head_hexsha": "8b813345264513a57934317b01e1311628dc5b01", "max_stars_repo_licenses":... |
import cv2
import numpy as np
import os
import pyk4a
from pyk4a import Config, PyK4A
# NFOV_2X2BINNED = 1
# NFOV_UNBINNED = 2
# WFOV_2X2BINNED = 3
# WFOV_UNBINNED = 4
# PASSIVE_IR = 5
def main():
config = Config(
color_resolution=pyk4a.ColorResolution.RES_720P,
depth_m... | {"hexsha": "e36d631037ceb27bbd245880d14231b1a899e485", "size": 592, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/test.py", "max_stars_repo_name": "greeknerd1/stereo-rectify", "max_stars_repo_head_hexsha": "98a23c3ff96dd4344ecad13d4ff145060c8fb992", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
theorem improving_att_imp_det_opt:
assumes "\<And>v. \<exists>d. \<nu>_improving v (mk_dec_det d)"
shows "\<nu>\<^sub>b_opt s = (\<Squnion>d \<in> D\<^sub>D. \<nu>\<^sub>b (mk_stationary_det d) s)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. apply_bfun \<nu>\<^sub>b_opt s = (\<Squnion>d\<in>D\<^su... | {"llama_tokens": 1448, "file": "MDP-Rewards_MDP_reward", "length": 11} |
/*
Copyright (c) 2014, Project OSRM, Dennis Luxen, others
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list
of conditions ... | {"hexsha": "0eb65553dfad69352b5fe105d112bee01e8414dd", "size": 3431, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "contractor/processing_chain.hpp", "max_stars_repo_name": "aaronbenz/osrm-backend", "max_stars_repo_head_hexsha": "758d4023050d1f49971f919cea872a2276dafe14", "max_stars_repo_licenses": ["BSD-2-Clause... |
# -*- coding: utf-8 -*-
# Copyright 2020 The PsiZ 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 r... | {"hexsha": "4c07a997159c9c10ea0f900b9dd488771696ab55", "size": 4213, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/keras/models/test_rate.py", "max_stars_repo_name": "greenfieldvision/psiz", "max_stars_repo_head_hexsha": "37068530a78e08792e827ee55cf55e627add115e", "max_stars_repo_licenses": ["Apache-2.0"... |
# Copyright 2022 DeepMind Technologies Limited.
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed... | {"hexsha": "df18a8dd59c11be68a4b5597c2b95b2eb4d72868", "size": 1681, "ext": "py", "lang": "Python", "max_stars_repo_path": "game/dummy_game.py", "max_stars_repo_name": "deepmind/emergent_communication_at_scale", "max_stars_repo_head_hexsha": "1d17ca7ca021c0473f344f44c876decc84980f35", "max_stars_repo_licenses": ["Apach... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
import time
import matplotlib.pyplot as plt
import numpy as np
import rospy
from std_msgs.msg import Float32MultiArray, Int32, String
from geometry_msgs.msg import Pose, PoseStamped
from vanttec_uuv.msg import GuidanceWaypoints
from usv_perception.msg import o... | {"hexsha": "890edd7be825562cf1cb899122b67926d75c695d", "size": 15344, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/choose_side/scripts/auto_nav_position.py", "max_stars_repo_name": "vanttec/vanttec_uuv", "max_stars_repo_head_hexsha": "95a0db636f7b99ac9ad9756e0d962fa1acc71e5e", "max_stars_repo_licenses": [... |
r"""Module defining halo bias models.
The halo bias is defined as the ratio of the power spectrum of halo (centres) for halos
of a given mass, to the linear matter power spectrum. In particular, it is assumed for
the models defined here that the power spectrum of halo centres is merely a scalar multiple
of the linear ... | {"hexsha": "5303f68c95b92d1d4626ba79b89fdf1fb269334a", "size": 26553, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/halomod/bias.py", "max_stars_repo_name": "sjforeman/halomod", "max_stars_repo_head_hexsha": "587db6bc71a77ea60a541b306fc3601eeb424bc9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
import sys
import os
import pytest
from knnFeat import _get_feat
sys.path.append(os.getcwd())
# Case 1: class_index == 0 and k_index == 0
@pytest.mark.success
def test_get_feat_c0k0():
data = np.array([0, 0])
X_train = np.reshape(np.array([0, 1, 3, 4, 5, 6, 1, 1, 0, 3]), (5, 2))
y_train... | {"hexsha": "d6d8163ab93cc76a1e04dbb8ecc53d2d2466c00f", "size": 1852, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_get_feat.py", "max_stars_repo_name": "krishna-patel98/knnFeat", "max_stars_repo_head_hexsha": "257cd43c28ed4c933ef28b41492d263e19cc27db", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
program omi
USE m3utilio
USE ENV_VARS
USE utilities_module
implicit none
character(18) :: rowheader
character(256), allocatable :: OMI_filename( : )
character(256) :: file_name
character(256) :: file_line
character(16) :: OMI_FILE_NCF = 'OMI_FULL_... | {"hexsha": "8179450b2eec1fb9e155175f624e0fa5d627cff3", "size": 30047, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "PREP/create_omi/src/driver.f", "max_stars_repo_name": "Simeng-unique/CMAQ-changed", "max_stars_repo_head_hexsha": "cb83401728ed7ea1bb19a6986c0acc84dabe11a4", "max_stars_repo_licenses": ["CC0-1.0"... |
import tinyflow as tf
import numpy as np
def test_add_grad():
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
ax = np.ones((2, 3))
ay = np.ones((2, 3)) * 4
z = x + y
gx, gy = tf.gradients(z, [x, y])
sess = tf.Session()
agx = sess.run(gx, feed_dict={x:ax, y:ay})
np.test... | {"hexsha": "e577768b39d97a7b93c5b772187c1062fc178666", "size": 1748, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/test_gradients.py", "max_stars_repo_name": "irvingzhang0512/tinyflow", "max_stars_repo_head_hexsha": "92abe0cd43ad8649f306bdfd2a4e870dedfb810a", "max_stars_repo_licenses": ["Apache-2.... |
module NLmodel
using JuMP
#using AmplNLWriter,
using Ipopt
#using CoinOptServices
function runModel(nodes, measuredNodeStateFull, LB, UB, expression, verbose)
model = Model(with_optimizer(Ipopt.Optimizer, print_level=0))
weightRoot = 500
weightMeasured = 10000000
weightHard = 10000
nodesList = c... | {"hexsha": "d3d55a1df075059807a1b461b64fd69cf7877ab8", "size": 5910, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/nlmodel.jl", "max_stars_repo_name": "OICR/mp-biopath", "max_stars_repo_head_hexsha": "3da9fc6e4ce7b3dd0ca184e61d58fab2f63940b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_s... |
import numpy as np
class LogisticRegression(object):
"""Single Class Multivariate logistic regression model using gradient descent"""
def __init__(self):
pass
def train(self, x, y, epochs=10, learning_rate=0.0001):
self.theta_array = np.zeros(np.array(x.ndim)+1)
x = self._add_b... | {"hexsha": "9b7ced15387d3a452f2af7a453ebf4217ece594c", "size": 1845, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlscratch/logistic_regression.py", "max_stars_repo_name": "BoPengGit/Machine-Learning-from-Scratch", "max_stars_repo_head_hexsha": "339c74f4e5e0dfb49cf355e9ca013fca1fd5b024", "max_stars_repo_licen... |
from __future__ import absolute_import
from __future__ import print_function
import os
import itertools
import numpy as np
np.random.seed(1337) # for reproducibility
from kerosene.datasets import cifar100
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.... | {"hexsha": "70561478757068ab145ee6a4d89e2c963a1353b3", "size": 4023, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/cifar100.py", "max_stars_repo_name": "dribnet/kerosene", "max_stars_repo_head_hexsha": "f641710071c603ce46abb0f66a7a176fc832f612", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3... |
[STATEMENT]
lemma upper_mult_float_interval:
"upper (mult_float_interval p x y) = snd (bnds_mult p (lower x) (upper x) (lower y) (upper y))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. upper (mult_float_interval p x y) = snd (bnds_mult p (lower x) (upper x) (lower y) (upper y))
[PROOF STEP]
by transfer auto | {"llama_tokens": 128, "file": null, "length": 1} |
import unittest
import os
import pandas as pd
from pyStarDB import sp_pystardb as pystar
import numpy as np
#print to just check
class MyTestCase(unittest.TestCase):
def test_file_is_written_loop_notag(self):
try:
os.remove("name.star")
except FileNotFoundError:
pass
... | {"hexsha": "b226722a7335b900b07ca6b40831b75e9e312afe", "size": 9876, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_pystardb.py", "max_stars_repo_name": "MPI-Dortmund/pyStarDB", "max_stars_repo_head_hexsha": "0cfe9010fc8673792f061b85483221e413b80a61", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
\documentclass[a4paper]{article}
\usepackage{graphicx}
\usepackage[english]{babel}
\usepackage[utf8x]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{sectsty}
\usepackage{pdfpages}
\usepackage[section]{placeins}
\usepackage{float}% If comment this, figure moves to Page 2
\usepackage{listings}
\usepackage{caption}
\usep... | {"hexsha": "6f9b69bb5d23e47e263db8c2cfe8ade0781787e0", "size": 33751, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "lecture_summary/summary.tex", "max_stars_repo_name": "aseemnarang/sapmnotes", "max_stars_repo_head_hexsha": "8b0c6a2181456a3ba7e6020586687e2c8f64a3f2", "max_stars_repo_licenses": ["MIT"], "max_star... |
import os
import sys
import random
from collections import OrderedDict
import math
import copy
import logging
import pickle
import glob
import numpy as np
import pandas as pd
from PIL import Image
import xml.etree.ElementTree as ElementTree
import torch
import torch.utils.data as data
import torchvision.transforms as ... | {"hexsha": "d1c99bea3c6b745c86eced1570ee0cda18ada1b9", "size": 35253, "ext": "py", "lang": "Python", "max_stars_repo_path": "os2d/data/dataset.py", "max_stars_repo_name": "MenshovSergey/DetectChess", "max_stars_repo_head_hexsha": "1baea0d688723b2624d83be001b00870cf1ae634", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import torch
import numpy as np
from gym import spaces
from stable_baselines3.dqn.policies import QNetwork
from sb3_contrib.qrdqn.policies import QuantileNetwork
class OnlyObsSingleActionModel(torch.nn.Module):
def __init__(self, model, num_classes, scaler, batch_size=50):
super().__init__()
sel... | {"hexsha": "752ef74daf873c1233e184e20a462b1abb7efff8", "size": 2256, "ext": "py", "lang": "Python", "max_stars_repo_path": "randsm/model.py", "max_stars_repo_name": "anvinhnguyendinh/DiscreteRSonRL", "max_stars_repo_head_hexsha": "af9433f56c6b72f17e0fcc97c0e4ebddeecf96b9", "max_stars_repo_licenses": ["MIT"], "max_stars... |
The chapter addresses the problem of optimally controlling an industrial micro-grid featuring a large share of renewable energy and a high volatility of electricity prices. We consider a micro-grid as a localized group of energy sources, loads and storage components that can operate in two distinct modes: grid-connecte... | {"hexsha": "3fe7f1fc597c7034b2931aae08a6e4ef5afda2bf", "size": 15629, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "author/system_model.tex", "max_stars_repo_name": "jupiterbak/Artificial-Intelligence-in-Industry-4.0", "max_stars_repo_head_hexsha": "7ddeb55de44c4e50b195edf7a75aa4afb99fcd9e", "max_stars_repo_lice... |
const LOCATIONS = Dict( k => i-1 for (i, k) in enumerate((
"none", "upper right", "upper left", "lower left", "lower right",
"right", "center left", "center right", "lower center", "upper center", "center",
"outer upper right", "outer center right", "outer lower right"
)))
# Legend
function legend!(p::Plot... | {"hexsha": "aabbf2e8fabf028bb624d90761462b10a97eefb4", "size": 25801, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/attributes.jl", "max_stars_repo_name": "jheinen/GRUtils.jl", "max_stars_repo_head_hexsha": "e5437225b8847bf6c29c8db41987285939aeee2c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
include("Misfits.jl")
| {"hexsha": "bab9a301e2679f7e7ea8ce6f9129d6b9070960e8", "size": 24, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "pawbz/Misfits.jl", "max_stars_repo_head_hexsha": "bee8937544d19ffc6b47213f10e3312fcb92f36f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
#!/usr/bin/env python
from __future__ import absolute_import, division, print_function, unicode_literals, with_statement
from mpi4py import MPI
import sys
import os
import numpy as np
import pympit as pt
world = MPI.COMM_WORLD
rank = world.rank
procs = world.size
startup = pt.work.since_start(MPI.COMM_WORLD)
if w... | {"hexsha": "c3248fba47ddc82f806025c13aa5b0d98f8efa32", "size": 1717, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/pympit_collective.py", "max_stars_repo_name": "tskisner/pympit", "max_stars_repo_head_hexsha": "b522d0db0747c958186ee8a094a0f50d68a9a0cb", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_sta... |
import numpy as np
from keras.models import Sequential, model_from_json
from keras.layers import Dense
from keras.layers import LSTM, Convolution1D, Flatten, Dropout, Activation, Input, Bidirectional
from keras.layers.embeddings import Embedding
from keras.layers.pooling import MaxPooling1D
from keras.preprocessing imp... | {"hexsha": "4658d0b5a2cfc10302e8eafb27685c885752bb57", "size": 32022, "ext": "py", "lang": "Python", "max_stars_repo_path": "NeuralNetwork.py", "max_stars_repo_name": "xabarass/cil-tweeter", "max_stars_repo_head_hexsha": "cf6c09879ef4cd431a61b6573a5b0f9e03ea3309", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import json
import collections
import numpy as np
from scipy.stats import multivariate_normal
# import matplotlib.pyplot as plt
def parse_json_parameters(func):
def inner(*args, **kwargs):
print(args, kwargs)
args = [json.loads(value) if type(value) is str else value
... | {"hexsha": "4f25539a7c0c30ccf2e316ab7f7df71a729fa981", "size": 2747, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/gauss.py", "max_stars_repo_name": "tangym/autoapi", "max_stars_repo_head_hexsha": "adc3ce02a803dd989be787ff21568231103d8625", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": n... |
# -*- coding: utf-8 -*-
"""Tests for sparkdatachallenge package."""
from re import I
import numpy as np
import pytest
import sparkdatachallenge
incheck_pass = [
(np.array([1]), np.array([2]), True),
(np.array([1]), np.array([1, 2]), False),
(np.array([1002]), np.array([1, 2]), False),
(np.array([-1]... | {"hexsha": "62053bff46d7be15c8950669e42168cd774afebe", "size": 3281, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_sparkdatachallenge.py", "max_stars_repo_name": "tomerten/sparkdatachallenge", "max_stars_repo_head_hexsha": "d20dbf5008a4dc5909b886486bb7f5658edd0e73", "max_stars_repo_licenses": ["MIT"... |
[STATEMENT]
lemma cbiovi:"b^-1 O ov^-1 \<subseteq> b^-1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. b\<inverse> O ov\<inverse> \<subseteq> b\<inverse>
[PROOF STEP]
using covb
[PROOF STATE]
proof (prove)
using this:
ov O b \<subseteq> b
goal (1 subgoal):
1. b\<inverse> O ov\<inverse> \<subseteq> b\<inverse>
[PR... | {"llama_tokens": 146, "file": "Allen_Calculus_allen", "length": 2} |
"""
Utils functions for LSTM network.
"""
from keras.models import Sequential, load_model
from keras.layers import Dense, Activation, Dropout
from keras.layers import LSTM
from keras.optimizers import RMSprop
import io
import numpy as np
def create_sequences(text, sequence_length, step):
sequences = []
... | {"hexsha": "8965bef5e90fbf0b52e49c3f3265a1de7d03c3a1", "size": 1917, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras/lyrics/helper.py", "max_stars_repo_name": "PipelineAI/models", "max_stars_repo_head_hexsha": "d8df07877aa8b10ce9b84983bb440af75e84dca7", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
"""
@author: Jun Wang
@date: 20210308
@contact: jun21wangustc@gmail.com
"""
# based on:
# https://github.com/deepinsight/insightface/tree/master/evaluation/IJB
import numpy as np
from numpy import matlib
from prettytable import PrettyTable
from sklearn.metrics import roc_curve
class IJBCEvaluator(object):
"""Imp... | {"hexsha": "af27fbcacc3be371d6d848dddf63fd10b5db9723", "size": 6087, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_protocol/ijbc/ijbc_evaluator.py", "max_stars_repo_name": "weihaoxie/FaceX-Zoo", "max_stars_repo_head_hexsha": "db0b087e4f4d28152e172d6c8d3767a8870733b4", "max_stars_repo_licenses": ["Apache-2... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.