text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
function x = inv_digamma(y,niter)
% INV_DIGAMMA Inverse of the digamma function.
%
% inv_digamma(y) returns x such that digamma(x) = y.
% a different algorithm is provided by Paul Fackler:
% http://www.american.edu/academic.depts/cas/econ/gaussres/pdf/loggamma.src
% Newton iteration to solve digamma(x)-y = 0
x = e... | {"author": "FuzhenZhuang", "repo": "Transfer-Learning-Toolkit", "sha": "24b5323b354aee844b8b7df9fcad17fdfb191dc4", "save_path": "github-repos/MATLAB/FuzhenZhuang-Transfer-Learning-Toolkit", "path": "github-repos/MATLAB/FuzhenZhuang-Transfer-Learning-Toolkit/Transfer-Learning-Toolkit-24b5323b354aee844b8b7df9fcad17fdfb19... |
import mmcv
import os
import sys
import numpy as np
def write_submission(outputs):
import pandas as pd
import numpy as np
from scipy.special import softmax
from mmdet.datasets.kaggle_pku_utils import quaternion_to_euler_angle
submission = 'Nov20-18-24-45-epoch_50.csv'
predictions = {}
PATH... | {"hexsha": "6cab9bd2e0f4b76af21c4b36145c19df52ef072a", "size": 1749, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/pkl2csv.py", "max_stars_repo_name": "tyunist/Kaggle_PKU_Baidu", "max_stars_repo_head_hexsha": "48651d8a0fc8a7beda0822a2db794861feada7c6", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
#' Count Letters, Words, and Lines of a File
#'
#' See title.
#'
#' @details
#' \code{wc_l()} is a shorthand for counting only lines, similar to \code{wc -l}
#' in the terminal. Likewise \code{wc_w()} is analogous to \code{wc -w} for
#' words.
#'
#' @param file
#' Location of the file (as a string) from which the co... | {"hexsha": "2f0efafe17901fd7b73dfeea81f2a8058e738c2f", "size": 2070, "ext": "r", "lang": "R", "max_stars_repo_path": "R/wc.r", "max_stars_repo_name": "wrathematics/lineSampler", "max_stars_repo_head_hexsha": "b3683ea15888b0da6e1f983233c395d23cf9e2b6", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count": 15, ... |
[STATEMENT]
lemma ln_upper_11_neg:
assumes "0 < x" and x1: "x \<le> 1" shows "ln(x) \<le> ln_upper_11 x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ln x \<le> ln_upper_11 x
[PROOF STEP]
apply (rule gen_upper_bound_decreasing [OF x1 d_delta_ln_upper_11])
[PROOF STATE]
proof (prove)
goal (3 subgoals):
1. \<And>... | {"llama_tokens": 489, "file": "Special_Function_Bounds_Log_CF_Bounds", "length": 4} |
/*****************************************************************************
*
* This file is part of Mapnik (c++ mapping toolkit)
*
* Copyright (C) 2015 Artem Pavlenko
*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as p... | {"hexsha": "012288507bf7e4f8efbba2e0d3fa16d5d19381ba", "size": 1938, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "external/mapnik/include/mapnik/text/formatting/text.hpp", "max_stars_repo_name": "baiyicanggou/mapnik_mvt", "max_stars_repo_head_hexsha": "9bde52fa9958d81361c015c816858534ec0931bb", "max_stars_repo_... |
module calc_kine_temp_module
implicit none
private
public :: calc_Ndof, get_Ndof
public :: calc_kine
public :: calc_temp
real(8),parameter :: TOJOUL=4.35975d-18 ! (J/hartree)
real(8),parameter :: kB_J=1.380658d-23 ! (J/K)
real(8),parameter :: kB=kB_J/TOJOUL ! (hartree/K)
integer :: N_degree_o... | {"hexsha": "85494231f481f63c21906e9f5225d00eb6d25a4a", "size": 2014, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/mdsource/calc_kine_temp_module.f90", "max_stars_repo_name": "j-iwata/RSDFT_DEVELOP", "max_stars_repo_head_hexsha": "14e79a4d78a19e5e5c6fd7b3d2f2f0986f2ff6df", "max_stars_repo_licenses": ["Ap... |
function cmatchregret(I::Int64, r::Vector{Vector{Float64}}, gs::GameSet)
ni_stage, na_stage = gs.ni_stage, gs.na_stage
na = numactions(I, ni_stage, na_stage)
σ = Vector{Float64}(undef, na)
for a in 1:na
denom = sum(max(r[I][b], 0.0) for b in 1:na)
if denom > 0.0
σ[a] =... | {"hexsha": "8cd0c6a06c068b98465bace1eb567a70bda57a2f", "size": 7876, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ccfrops_solve.jl", "max_stars_repo_name": "ajkeith/Cyber-Air-Defense", "max_stars_repo_head_hexsha": "856b833e7c6cbfe389b2ec1b21edb5d6e6ee97e3", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import pickle,os
from Common.Module import Module
class FakeRateWeighter(Module):
def analyze(self,data,dataset,cfg):
if dataset.name == "ZX":
cfg.collector.event_weight = np.ones(data["genWeight"].shape) * cfg.collector.selection_weight
idx_3p1f = data["nFailedLeptonsZ2"] == 1
idx_2p2f... | {"hexsha": "d66f0391a36c1505c7a41708cf8e0ef5efb2fe44", "size": 2125, "ext": "py", "lang": "Python", "max_stars_repo_path": "Wto3l/Weighter/FakeRateWeighter.py", "max_stars_repo_name": "Nik-Menendez/PyCudaAnalyzer", "max_stars_repo_head_hexsha": "4b43d2915caac04da9ba688c2743e9c76eacdd5b", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Script de comparação dos resultados gerados.
"""
import numpy as np
import rasterio as rio
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
ds_30062020 = rio.open("sits/30-06-2020/classification-results/Sinop_probs_class_bayesian_2013_9_2014_8_v1.... | {"hexsha": "cfb308e31d0e890ae9dfce4aafe5871b9b1e7ac2", "size": 2118, "ext": "py", "lang": "Python", "max_stars_repo_path": "verification/difference_plot.py", "max_stars_repo_name": "M3nin0/experiment-software-lulc-versions", "max_stars_repo_head_hexsha": "734e8e6acc369d6bdf5dd8d694d3e3d61740ce44", "max_stars_repo_licen... |
[STATEMENT]
lemma lt_plus_distinct_eq_max:
assumes "lt p \<noteq> lt q"
shows "lt (p + q) = ord_term_lin.max (lt p) (lt q)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lt (p + q) = ord_term_lin.max (lt p) (lt q)
[PROOF STEP]
proof (rule ord_term_lin.linorder_cases)
[PROOF STATE]
proof (state)
goal (3 subgoals... | {"llama_tokens": 2869, "file": "Polynomials_MPoly_Type_Class_Ordered", "length": 28} |
import pandas as pd
import numpy as np
from random import sample
import random
import torch
import torch.nn as nn
# # reference from https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
# class QLearningTable:
# def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9):
# ... | {"hexsha": "3cd4382fefb246c6375ea96343f268b2e5a39029", "size": 9552, "ext": "py", "lang": "Python", "max_stars_repo_path": "s09287/racepack/utils.py", "max_stars_repo_name": "parksurk/skcc-drl-sc2-course-2020_1st", "max_stars_repo_head_hexsha": "951d09424b93c76093bab51ed6aaa75eb545152e", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python3
import json
from pathlib import Path
import numpy as np
import tokenizers as tk
import torch
from theissues import training, utils
from theissues.model import TrainArgs, TransformerModel
def main(
path_tokenizer: Path,
dir_model: Path,
):
tokenizer = tk.Tokenizer.from_file(str(pa... | {"hexsha": "12135916bdd686410843e30a9fbcc07d8f155a6c", "size": 1715, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/generate.py", "max_stars_repo_name": "gmcgoldr/theissues", "max_stars_repo_head_hexsha": "4e4c9eb66c543cdbcda4f1b96a7d2b163450368c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import pyplume
import numpy as np
# Mechanism management
cti = 'test.cti'
pyplume.mech.mechFileAdd(cti) #Add mechanism file
pyplume.mech.mechFileDelete(cti) #Delete mechanism file
pyplume.mech.mechFileRestore() #Restore mechanism files
pyplume.mech.mechFileList() #list mechanism files
pyplume.tests.testMechs.runT... | {"hexsha": "3a347070f2528f430522e977d062fa721cba87e7", "size": 557, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/scratch.py", "max_stars_repo_name": "awa1k3r/plume-generation-and-analysis", "max_stars_repo_head_hexsha": "926f2b09fa1011515310167f0d2b34a051539db1", "max_stars_repo_licenses": ["BSD-3-Cl... |
#include <boost_python_exception/util.hpp>
#include <boost/python/import.hpp>
using namespace boost::python;
namespace boost_python_exception {
object builtins()
{
#if PY_MAJOR_VERSION == 2
return import("__builtin__");
#elif PY_MAJOR_VERSION == 3
return import("builtins");
#endif
}
}
| {"hexsha": "9eca11c507a8c5291bb26c6eb51d012d5e9523c0", "size": 299, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/boost_python_exception/util.cpp", "max_stars_repo_name": "abingham/boost_python_exception", "max_stars_repo_head_hexsha": "7882d5e8df051494498a58c06e046cb52421620b", "max_stars_repo_licenses": ["... |
from skimage import data, filters
from skimage.viewer import ImageViewer
from skimage import filters
import scipy
from scipy import ndimage
import matplotlib.pyplot as plt
smooth_mean=[ [1/9,1/9,1/9],
[1/9,1/9,1/9],
[1/9,1/9,1/9]]
############################
edge1 = [[-1, -1, -1],
... | {"hexsha": "081a3048222dfc58e5ab016024feafcb8e910675", "size": 2784, "ext": "py", "lang": "Python", "max_stars_repo_path": "Modules/module3/opdracht2.py", "max_stars_repo_name": "Pink-Shadow/VISN", "max_stars_repo_head_hexsha": "4a484610cd86a170a9612a65c81e082394cc08f0", "max_stars_repo_licenses": ["BSL-1.0"], "max_sta... |
using GitHubActionsUtils
using Test
using Luxor
@testset "GitHubActionsUtils.jl" begin
# move up from the `test` folder to the main repo
cd("..")
@show GitHubActionsUtils.is_github_actions()
@show GitHubActionsUtils.event_name()
@show GitHubActionsUtils.is_push()
@show GitHubActionsUtils.is_... | {"hexsha": "01b1b21a70bc70f296f64a3e9aeda24c58d925fc", "size": 1807, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "jkrumbiegel/GitHubActionsUtils.jl", "max_stars_repo_head_hexsha": "077a54df983b4362715148197427a191c80fe4f7", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import torch
from typing import Iterable, Union, Dict, List, Callable
from data import *
from config import *
from torch import nn
import numpy as np
@dataclass
class ModelOutput:
loss: Union[torch.Tensor, np.array]
@dataclass
class ClassifierOutput(ModelOutput):
predictions: Union[torch.Tensor, np.array, No... | {"hexsha": "d92c40c921280c40f388e63e7af7c42faa30773e", "size": 10079, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules.py", "max_stars_repo_name": "cr1m5onk1ng/semantic-search-api", "max_stars_repo_head_hexsha": "25ecdde4509943bb6420a5a678e4aaa0b1f5a866", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
#!/usr/bin/env python3
import os
import numpy as np
from katsdpsigproc.accel import Operation, IOSlot, create_some_context, build
class SumTemplate:
def __init__(self, context):
self.wgs = 128
self.program = build(context, 'sum.mako', {'wgs': self.wgs},
extra_dirs=[... | {"hexsha": "1f6457f48bbc9a393f54d5c0adea117040679140", "size": 1676, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/user/examples/sum.py", "max_stars_repo_name": "ska-sa/katsdpsigproc", "max_stars_repo_head_hexsha": "d471d05a3c340ff217db4fd85de0599fe9dfad80", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
Require Import HoTT.
Require Import Auxiliary.Family.
Require Import Auxiliary.WellFounded.
Require Import Syntax.ScopeSystem.
Require Import Auxiliary.Coproduct.
Require Import Auxiliary.Closure.
Require Import Syntax.All.
Require Import Typing.Context.
Require Import Typing.Judgement.
Require Import Typing.RawTypeThe... | {"author": "peterlefanulumsdaine", "repo": "general-type-theories", "sha": "596f032e5d59fa017c2f2595136448b24b810f1d", "save_path": "github-repos/coq/peterlefanulumsdaine-general-type-theories", "path": "github-repos/coq/peterlefanulumsdaine-general-type-theories/general-type-theories-596f032e5d59fa017c2f2595136448b24b... |
# Copyright (c) 2018-2021, Carnegie Mellon University
# See LICENSE for details
NewRulesFor(TTensorInd, rec(
# base cases
# I x A
dsA_base_vec_push := rec(
info := "IxA base",
forTransposition := false,
applicable := nt -> IsParPar(nt.params) and nt.isTag(1, AVecReg),
children ... | {"hexsha": "d372251dd158386fef612b9c7eb6db1dbb073609", "size": 1854, "ext": "gi", "lang": "GAP", "max_stars_repo_path": "namespaces/spiral/paradigms/vector/breakdown/ttensorind.gi", "max_stars_repo_name": "sr7cb/spiral-software", "max_stars_repo_head_hexsha": "349d9e0abe75bf4b9a4690f2dbee631700f8361a", "max_stars_repo_... |
from nose import SkipTest
import networkx as nx
from networkx.generators.degree_seq import havel_hakimi_graph
class TestLaplacian(object):
numpy=1 # nosetests attribute, use nosetests -a 'not numpy' to skip test
@classmethod
def setupClass(cls):
global numpy
global assert_equal
glo... | {"hexsha": "7174e924cadb948595015aa9d49d7ee3f6b82980", "size": 2686, "ext": "py", "lang": "Python", "max_stars_repo_path": "networkx/linalg/tests/test_laplaican.py", "max_stars_repo_name": "rafguns/networkx", "max_stars_repo_head_hexsha": "ce5e7394e56c3ee92f3f40a392b7344ce1f7e366", "max_stars_repo_licenses": ["BSD-3-Cl... |
theory Flatten_Iter_Spec
imports
Basic_Assn
"Separation_Logic_Imperative_HOL.Imp_List_Spec"
"HOL-Real_Asymp.Inst_Existentials"
begin
text "This locale takes an iterator that refines a list of elements that themselves
can be iterated and defines an iterator over the flattened list of lower level elements"
loc... | {"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/BTree/Flatten... |
SUBROUTINE ZACAI(ZR, ZI, FNU, KODE, MR, N, YR, YI, NZ, RL, TOL,
* ELIM, ALIM)
C***BEGIN PROLOGUE ZACAI
C***REFER TO ZAIRY
C
C ZACAI APPLIES THE ANALYTIC CONTINUATION FORMULA
C
C K(FNU,ZN*EXP(MP))=K(FNU,ZN)*EXP(-MP*FNU) - MP*I(FNU,ZN)
C MP=PI*MR*CMPLX(0.0,1.0)
C
C TO CONTINUE... | {"hexsha": "aa05a5c7b6c6446198f397fac714d5a02594c4fa", "size": 3719, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mathext/internal/amos/amoslib/zacai.f", "max_stars_repo_name": "blackrez/gonum", "max_stars_repo_head_hexsha": "aad36a059009dc681b68a7d9fbdcadd09c9db798", "max_stars_repo_licenses": ["BSD-3-Clause... |
import sys
# sys.path.remove('/opt/ros/kinetic/lib/python2.7/dist-packages')
# import open3d as o3d
import numpy as np
import random
import paddle.fluid as fluid
import argparse
from shapenet_part_loader import PartDataset
import utils
from utils import distance_squre, PointLoss
import copy
from model_PFNet import PFNe... | {"hexsha": "21c5c21e8b4792ede02634c4d99f512ab8491aa1", "size": 7499, "ext": "py", "lang": "Python", "max_stars_repo_path": "pf-net/Test_PFNet.py", "max_stars_repo_name": "63445538/Contrib", "max_stars_repo_head_hexsha": "8860692e341020bb4332ff9f59b17a0c8cd9c748", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
#include <boost/geometry/algorithms/centroid.hpp>
| {"hexsha": "2c5991633ba677a84d872db945e3ddcb42972b18", "size": 50, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_geometry_algorithms_centroid.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma shrK_notin_image_publicKey [simp]: "shrK x \<notin> publicKey b ` AA"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. shrK x \<notin> publicKey b ` AA
[PROOF STEP]
by auto | {"llama_tokens": 78, "file": "Inductive_Confidentiality_DolevYao_Public", "length": 1} |
## philvals.py
## This is my implementation of phivals.m
## Computation of scaling function and wavelet by recursion
## using Python libraries numpy, scipy
##
## The main reference that I'll use is
## Gilbert Strang, and Kevin Amaratunga. 18.327 Wavelets, Filter Banks and Applications, Spring 2003. (Massachusetts Inst... | {"hexsha": "cb840dd7bc47cda9382799aacf13049eb034becf", "size": 7549, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/phivals.py", "max_stars_repo_name": "ernestyalumni/18-327-wavelets-filter-banks", "max_stars_repo_head_hexsha": "eeb3fd65b42808cf907aa716110417515dbbfd82", "max_stars_repo_licenses": ["MIT"]... |
from copy import deepcopy
from functools import wraps
import numpy as np
from scipy.optimize import OptimizeResult
from scipy.optimize import minimize as sp_minimize
from sklearn.base import is_regressor
from sklearn.ensemble import GradientBoostingRegressor
from joblib import dump as dump_
from joblib import load as ... | {"hexsha": "83d5e27abc0c077adef7a176f39a897b559a22b0", "size": 26526, "ext": "py", "lang": "Python", "max_stars_repo_path": "skopt/utils.py", "max_stars_repo_name": "sqbl/scikit-optimize", "max_stars_repo_head_hexsha": "c1866d5a9ad67efe93ac99736bfc2dc659b561d4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
@testset "EigenAngles.jl" begin
@info "Testing EigenAngles"
@test isbits(EigenAngle(deg2rad(complex(85.0, -1.0))))
@test EigenAngle(deg2rad(80-0.5im)) > EigenAngle(deg2rad(75-0.3im))
@test_logs (:warn, "θ > 2π. Make sure θ is in radians.") EigenAngle(complex(85.0, 0.31))
end
| {"hexsha": "11e1bfa3441c6abb2595236399693d97717de3c9", "size": 294, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/EigenAngles.jl", "max_stars_repo_name": "fgasdia/LongwaveModePropagator.jl", "max_stars_repo_head_hexsha": "d99750b7e248f93c36beb9a291e6481da08bb8c9", "max_stars_repo_licenses": ["MIT"], "max_s... |
import os
import pickle
from tune.api.factory import TUNE_OBJECT_FACTORY
from typing import Any, Optional, Tuple
from uuid import uuid4
import numpy as np
import pandas as pd
from sklearn.metrics import get_scorer
from sklearn.model_selection import cross_val_score
from triad import FileSystem
from tune import NonIter... | {"hexsha": "cacf9391b22f36c181d310d851e3c1395daf5f9d", "size": 4703, "ext": "py", "lang": "Python", "max_stars_repo_path": "tune_sklearn/objective.py", "max_stars_repo_name": "fugue-project/tune", "max_stars_repo_head_hexsha": "bf2288ddcb29c8345d996a9b22c0910da9002da1", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import time
import numpy as np
import torch
import torch.optim
import torch.utils.data
import torch.nn.functional as F
import models
import train_img_pairs
from inverse_warp import compensate_pose, pose_vec2mat, inverse_rotate
from logger import AverageMeter
train_img_pairs.parser.add_argument('-d', '--target-mean-de... | {"hexsha": "1f01e68910227dbcfd95c03adc7cadcd550e106a", "size": 5773, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_flexible_shifts.py", "max_stars_repo_name": "ClementPinard/unsupervised-depthnet", "max_stars_repo_head_hexsha": "71bc54afd8a22d5c99e1db88618119c33956b8c4", "max_stars_repo_licenses": ["MIT"... |
module SimplePackage
using Boot
include_folder(SimplePackage, @__FILE__)
end
| {"hexsha": "e18bf887ebf0377b3b8c55da38800a5d97503cd8", "size": 84, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/packages/SimplePackage/src/SimplePackage.jl", "max_stars_repo_name": "djsegal/Boot.jl", "max_stars_repo_head_hexsha": "25aefa8ffc7467ece2951f4df0ae44c1c5897f25", "max_stars_repo_licenses": ["MIT... |
import sys
import re
import pandas as pd
import numpy as np
import linecache
def main():
args = sys.argv
"""args[1] : threshold number of word [2]:cancer type"""
K = 96
threshold = int(args[1])
pre_data_file = 'data/data1/Pre_data1_o' + args[1] + '.txt'
pre_data = pd.read_csv(pre_data_file, d... | {"hexsha": "8d8335a3cf4dce196c355cbdcc26e79dcf8f9b52", "size": 6412, "ext": "py", "lang": "Python", "max_stars_repo_path": "Preprocessing/get_M1.py", "max_stars_repo_name": "qkirikigaku/MS_LDA", "max_stars_repo_head_hexsha": "7eea53759e21c95cd6cb3afd2937388a6b222c5b", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""Methods for drawing a bounding box on an image."""
import cv2
import numpy as np
import selfsupmotion.data.objectron.dataset.box as Box
_LINE_TICKNESS = 10
_POINT_RADIUS = 10
_COLORS = [
(255, 0, 0),
(0, 255, 0),
(0, 0, 255),
(128, 128, 0),
(128, 0, 128),
(0, 128, 128),
(255, 255, 255),... | {"hexsha": "a27197e1f25e079ba12699d3ef5b02cb29b44afe", "size": 2901, "ext": "py", "lang": "Python", "max_stars_repo_path": "selfsupmotion/data/objectron/dataset/graphics.py", "max_stars_repo_name": "sbrodeur/selfsupmotion", "max_stars_repo_head_hexsha": "32ba34a090e7e575b43a6a6f14c52c0a5f363d40", "max_stars_repo_licens... |
# Copyright 2021 The NetKet Authors - All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | {"hexsha": "53453f36e1d8b361f56b89731f4a019a25d1bf11", "size": 10831, "ext": "py", "lang": "Python", "max_stars_repo_path": "netket/variational/mc_mixed_state.py", "max_stars_repo_name": "inailuig/netket", "max_stars_repo_head_hexsha": "ab57a6fb019edb9ac298969950724781f2ae2b22", "max_stars_repo_licenses": ["Apache-2.0"... |
#include <utility>
// You must include this before including boost headers.
#include "resource-types-fwd.h"
#include <boost/python/class.hpp>
#include <boost/python/def.hpp>
#include <kj/io.h>
#include <capnp/any.h>
#include <capnp/dynamic.h>
#include <capnp/message.h>
#include <capnp/schema.h>
#include <capnp/seri... | {"hexsha": "9cc15762bb4f9147b6cc1501e987964993235ac9", "size": 6248, "ext": "cc", "lang": "C++", "max_stars_repo_path": "py/g1/third-party/capnp/src/message.cc", "max_stars_repo_name": "clchiou/garage", "max_stars_repo_head_hexsha": "446ff34f86cdbd114b09b643da44988cf5d027a3", "max_stars_repo_licenses": ["MIT"], "max_st... |
import sys
import pickle as pkl
# import libraries
import nltk
from nltk.corpus import stopwords
nltk.download(['punkt', 'wordnet'])
st = set(stopwords.words('english'))
import re
import time
import pandas as pd
import numpy as np
from sqlalchemy import create_engine
from sklearn.pipeline import Pipeline
from sklear... | {"hexsha": "95cc29f65c69dfba6312cc3768849ee788d6cc91", "size": 6924, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/train_classifier.py", "max_stars_repo_name": "lewi0332/disaster_relief_ml_pipeline", "max_stars_repo_head_hexsha": "774b8459f2d6e337c8003cfb4012adf70461caeb", "max_stars_repo_licenses": ["M... |
from torch.utils.data import DataLoader, Dataset
import torch.nn as nn
import os
import glob
import torch
import numpy as np
from examples.mnist.gendata import get_projection_grid, project_2d_on_sphere_sun360, rand_rotation_matrix, rotate_grid
import cv2
from utils import rotate_map_given_R, calculate_Rmatrix_from_phi_... | {"hexsha": "08d16f4441195a3cfb5129446014a1504eedcd93", "size": 3842, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/sun360/sun360_dataset.py", "max_stars_repo_name": "csm-kr/s2cnn", "max_stars_repo_head_hexsha": "09652af9811357c4bf6f7a6d3e912a06d7826f70", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import argparse
import json
import os
import nltk
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('punkt')
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import numpy as np
import scipy
from gensim.models import TfidfModel
from gensim.corpora import Dictionary
def par... | {"hexsha": "93e232b6688bcc34af781c3319bd121cee2252fe", "size": 2401, "ext": "py", "lang": "Python", "max_stars_repo_path": "etc/compute_related.py", "max_stars_repo_name": "learning2hash/learning2hash.github.io", "max_stars_repo_head_hexsha": "71447a57e0288660ba5fc245e19b2cc748884be6", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env Rscript
library(readr)
library(lmerTest)
library(car)
library(psych)
library(scales)
speed_data <- read_csv('data.csv')
#calculate reading speed in WPM
speed_data$speed <- speed_data$num_words/(speed_data$adjust_rt/60000)
#remove retake participants
speed_data <- subset(speed_data, retake != 1)
#rem... | {"hexsha": "b5d79192eb1bf559f3d35a6c3ae09886d6e0ffaf", "size": 1306, "ext": "r", "lang": "R", "max_stars_repo_path": "example/reading/script.r", "max_stars_repo_name": "uwdata/boba", "max_stars_repo_head_hexsha": "80ff10ffd9a2ae99002bc7e88d173869b86c736c", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
# Copyright (c) 2021 Sony Corporation. 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 applicabl... | {"hexsha": "6e1dd0bff4dd60afe467839a14f37f336d5a1ddf", "size": 1665, "ext": "py", "lang": "Python", "max_stars_repo_path": "responsible_ai/data_cleansing/datasets/utils.py", "max_stars_repo_name": "JonathanLehner/nnabla-examples", "max_stars_repo_head_hexsha": "2971b987484945e12fb171594181908789485a0f", "max_stars_repo... |
\documentclass[english]{../thermomemo/thermomemo}
\usepackage[utf8]{inputenc}
\usepackage{amsmath}
\usepackage{array}% improves tabular environment.
\usepackage{dcolumn}% also improves tabular environment, with decimal centring.
\usepackage{booktabs}
\usepackage{todonotes}
\usepackage{subcaption,caption}
\usepackage{xs... | {"hexsha": "e65cadcd3f6e20efc5a4999ec5770ec963e87b93", "size": 21701, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/memo/UNIFAC/unifac.tex", "max_stars_repo_name": "SINTEF/Thermopack", "max_stars_repo_head_hexsha": "63c0dc82fe6f88dd5612c53a35f7fbf405b4f3f6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import unittest
import numpy as np
class TestCase(unittest.TestCase):
def _GetNdArray(self, a):
if not isinstance(a, np.ndarray):
a = np.array(a)
return a
def assertAllEqual(self, a, b):
"""Asserts that two numpy arrays have the same values.
Args:
a: the ex... | {"hexsha": "10f5b9326be2b3bb5b30fcc514c98dc64b1a31a2", "size": 4524, "ext": "py", "lang": "Python", "max_stars_repo_path": "second/framework/test.py", "max_stars_repo_name": "jerry99s/second.pytorch", "max_stars_repo_head_hexsha": "80143908a349b9f3ff1642d21dacaf23455b3cf8", "max_stars_repo_licenses": ["MIT"], "max_star... |
import numpy as np
from data_loading import load_data, store_song
from transitions_creation import fade
def main():
load_path = "../songs/dev_songs_house/"
store_path = "../listening_test/mixes/mix_A.wav"
store_path_transition_times = "../listening_test/mixes/mix_A_transition_times.txt"
# Load data
... | {"hexsha": "94c1706ac282bc2cc74ab3bdd6db85d2fcd95db0", "size": 1749, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/fadeinfadeout_mix.py", "max_stars_repo_name": "erikpiscator/song_mixing", "max_stars_repo_head_hexsha": "8fb430311e46d9e917d75ccdd85be57bac67f262", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Copyright (c) 2018-2020 Manfred Moitzi
# License: MIT License
from typing import Iterable, Tuple, List, Sequence, Union, Any
from itertools import repeat
import math
import reprlib
__all__ = [
'Matrix', 'gauss_vector_solver', 'gauss_matrix_solver', 'gauss_jordan_solver', 'gauss_jordan_inverse',
'LUDecomposit... | {"hexsha": "0c493de70629d63ee4ee9d202c223c6925740a7e", "size": 33565, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ezdxf/math/linalg.py", "max_stars_repo_name": "hh-wu/ezdxf", "max_stars_repo_head_hexsha": "62509ba39b826ee9b36f19c0a5abad7f3518186a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
\chapter{Guidelines on the preparation of theses} \label{ch-1}
The guidelines below set out the organization and formatting requirements of the OIST PhD thesis, in order to assist students in the preparation of theses for submission.
The academic requirements of the thesis are defined in the Academic Program Policie... | {"hexsha": "146c1eb6ba434931e5b9368dab46cd4fa7fc97f3", "size": 8102, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "PhD Thesis/MainText/chapter1.tex", "max_stars_repo_name": "Pradeep20oist/LaTeX-templates", "max_stars_repo_head_hexsha": "658b7f8745cc4d1ae157c1b75bc197fb4fa146b4", "max_stars_repo_licenses": ["MIT"... |
type Configuration{T <: Parameter} <: Parameter
parameters::Dict{String, T}
name::String
value::Dict{String, Any}
function Configuration(parameters::Dict{String, T}, name::String, values::Dict{String, Any})
for key in keys(parameters)
@inbounds values[key] = parameters[key].value
... | {"hexsha": "45e565b27b38d22a7b3db08fc69fb56d0b5b4caf", "size": 1429, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/core/configuration.jl", "max_stars_repo_name": "JuliaPackageMirrors/StochasticSearch.jl", "max_stars_repo_head_hexsha": "58e48c8812fb402e4a46ffff1d5bcb87fca3fd05", "max_stars_repo_licenses": ["... |
import time
import numpy as np
import trajPlot
class TrajectoryController:
def __init__(self,speedMax,accMax,size=3):
self.initPoint = np.zeros((size,1))
self.endPoint = np.zeros((size,1))
self.speedMax = speedMax
self.accMax = accMax
self.speed = 0
self.D = np.zeros((size,... | {"hexsha": "659b711ae9577aee656b3fcc011c788ac72b5587", "size": 1733, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/traj/trajectoryController.py", "max_stars_repo_name": "Fdepraetre/PinokioProject", "max_stars_repo_head_hexsha": "dfea3ee23f10a44d761597d2547db3a1ff196fb1", "max_stars_repo_licenses": ["MIT"],... |
from datetime import datetime
from lib.pyparsing import Word, Keyword, alphas, ParseException, Literal, CaselessLiteral \
, Combine, Optional, nums, Or, Forward, ZeroOrMore, StringEnd, alphanums, oneOf \
, QuotedString, quotedString, removeQuotes, delimitedList, nestedExpr, Suppress, Group, Regex, operatorPrecedence \
... | {"hexsha": "7ead86cd946d8d3a8f75bb1ae4c172e8013d5b15", "size": 12391, "ext": "py", "lang": "Python", "max_stars_repo_path": "expressionParser.py", "max_stars_repo_name": "lagvier/echo-sense", "max_stars_repo_head_hexsha": "fe8ab921e7f61c48b224f0cc2832103a395a6cf7", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
# coding=utf-8
import numpy as np
import cv2
file = open("../build/Record.txt")
cv2.namedWindow("Window")
for line in file.readlines():
background = np.zeros((1024, 1024, 3), dtype=np.uint8)
line = line[:-1]
points = line.split(" ")
oriPt = (-int(100 * float(points[0])), int(10... | {"hexsha": "b54cab0afadf764b7837d920324a2e014809e84a", "size": 644, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/drawCircle.py", "max_stars_repo_name": "srm2021/WMJ2021", "max_stars_repo_head_hexsha": "ce142019ed55ca591a27f5f79abb26cdb98fdb0e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22, "... |
[STATEMENT]
lemma Class_cover_imp_subset_or_disj:
assumes "A = (\<Union> (Class B ` C))" and "x \<in> G" and "C \<subseteq> G"
shows "Class B x \<subseteq> A \<or> Class B x \<inter> A = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Class B x \<subseteq> A \<or> Class B x \<inter> A = {}
[PROOF STEP]
by (si... | {"llama_tokens": 147, "file": "Kneser_Cauchy_Davenport_Kneser_Cauchy_Davenport_preliminaries", "length": 1} |
(==){G<:Grasp, R<:Real}(a::Rvl{G,R}, b::Rvl{G,R}) = ((a.lo == b.lo) & (a.hi == b.hi))
(!=){G<:Grasp, R<:Real}(a::Rvl{G,R}, b::Rvl{G,R}) = ((a.lo != b.lo) | (a.hi != b.hi))
(<=){G<:Grasp, R<:Real}(a::Rvl{G,R}, b::Rvl{G,R}) = ((a.lo <= b.lo) & (a.hi <= b.hi))
(>=){G<:Grasp, R<:Real}(a::Rvl{G,R}, b::Rvl{G,R}) = ((a.lo >=... | {"hexsha": "3032c9655d47b699a8988f76ffa9a7c6bd67f5b8", "size": 606, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/number/compares.jl", "max_stars_repo_name": "J-Sarnoff/InterVal.jl", "max_stars_repo_head_hexsha": "320e6980b596fc89f50b460669481ea0d80645d2", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import Random
struct VariableIndex
value::Int64
end
struct ConstraintIndex
value::Int64
end
const CI = ConstraintIndex
const VI = VariableIndex
function chooseNbVar(L::Vector{Float64})
x::Float64 = rand()
if x < L[1]
return 2
elseif x+L[1] < L[2]
return 3
else
return... | {"hexsha": "45d46921ae5948d8d4b17f0c5e7865a5c5e6715a", "size": 4074, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Dm/SppGenerator.jl", "max_stars_repo_name": "LucasBaussay/AntOptim.jl", "max_stars_repo_head_hexsha": "d97041d2763a66d92fd3a7a205670aa963dabd68", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
from PyQt5.QtCore import pyqtSlot, QThread
from checkers.gui.worker import Worker
from checkers.image.pawn_colour import opposite
from checkers.logic.move import check_move
from checkers.logic.move_status import MoveStatus
first_matrix = np.array(
[[0, 1, 0, 1, 0, 1, 0, 1],
[1, 0, 1, 0, 1,... | {"hexsha": "a1b5f9798a60312e498ea2d620a0ad4ab099bf6c", "size": 3170, "ext": "py", "lang": "Python", "max_stars_repo_path": "checkers/gui/worker_no_cam.py", "max_stars_repo_name": "mnajborowski/pt-projekt", "max_stars_repo_head_hexsha": "fa02580464579dbe3eb13b6b07f4f8f3cb4d44ce", "max_stars_repo_licenses": ["MIT"], "max... |
-- Integration over the complex closed disk
import measure_theory.function.jacobian
import measure
import prod
import simple
import tactics
open complex (abs arg exp I)
open linear_map (to_matrix_apply)
open measure_theory
open metric (ball closed_ball sphere)
open real (cos sin)
open set (Icc Ioc)
open_locale real
... | {"author": "girving", "repo": "ray", "sha": "e0c501756e067711e2d3667d4b1d18045d83a313", "save_path": "github-repos/lean/girving-ray", "path": "github-repos/lean/girving-ray/ray-e0c501756e067711e2d3667d4b1d18045d83a313/src/fubini_ball.lean"} |
# -*- coding: utf-8 -*-
from numpy import linspace, zeros
from ....Classes.Segment import Segment
from ....Classes.Arc1 import Arc1
from ....Classes.SurfLine import SurfLine
def get_surface_active(self, alpha=0, delta=0):
"""Return the full active surface
Parameters
----------
self : SlotM13
... | {"hexsha": "461aff09744eef9c4242fb9f308e7cadb4eb6425", "size": 1416, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyleecan/Methods/Slot/SlotM13/get_surface_active.py", "max_stars_repo_name": "IrakozeFD/pyleecan", "max_stars_repo_head_hexsha": "5a93bd98755d880176c1ce8ac90f36ca1b907055", "max_stars_repo_license... |
import torch
import numpy as np
from PIL import Image
import random
from ..model.vocab import Vocab
from ..tool.translate import process_image
import os
from collections import defaultdict
import math
from prefetch_generator import background
class BucketData(object):
def __init__(self, device):
self.max_l... | {"hexsha": "e021618745358585932b4a057a1a0790207879bb", "size": 5009, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/ocr/loader/dataloader_v1.py", "max_stars_repo_name": "martinhoang11/vietnamese-ocr-toolbox", "max_stars_repo_head_hexsha": "524b4908bedceb0c87b2c7cd7b5e3f6e1126ace5", "max_stars_repo_licen... |
//#include <QtGui/QApplication>
#include <QApplication>
#include <QtDebug>
#include <QFile>
#include <QTextStream>
#include <QDateTime>
#include <QDir>
#include <QDesktopServices>
#include <boost/program_options.hpp>
namespace po = boost::program_options;
#include <iostream>
#include <algorithm>
#include <iterator>
... | {"hexsha": "4987ca14bc46f54244fad87ba20aecc711c530f8", "size": 4816, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/main.cpp", "max_stars_repo_name": "jychoi-hpc/pviz3", "max_stars_repo_head_hexsha": "d55c84a45df0a5bf30ecb832b370e03f0c7ab4c1", "max_stars_repo_licenses": ["xpp"], "max_stars_count": null, "max_... |
"""
Helper routines to perform bias subtraction and overscan trimming of LRIS data.
"""
import scipy
def oneamp(data):
"""
Subtracts bias from data and returns the overscan region-subtracted
image.
"""
bias = (data[:,2:21].mean(axis=1)*18+data[:,2069:2148].mean(axis=1)*80)/98.
out_data = data[:,21:2069]-bias... | {"hexsha": "0a70da429f7935c17f2e70b5bb068a76a0895e83", "size": 2377, "ext": "py", "lang": "Python", "max_stars_repo_path": "keckcode/lris_redux/lris/lris_biastrim.py", "max_stars_repo_name": "cdfassnacht/keck_code", "max_stars_repo_head_hexsha": "a952b3806b3e64eef70deec2b2d1352e6ef6dfa0", "max_stars_repo_licenses": ["M... |
from tpot import TPOTRegressor
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from deap import creator
from sklearn.model_selection import cross_val_score, cross_val_predict
import numpy as np
from tempfile import mkdtemp
from shutil import rmtree
random_seed = 42
housing... | {"hexsha": "d74dcddc44fa6a8d5664ba158fea073671809d7f", "size": 3065, "ext": "py", "lang": "Python", "max_stars_repo_path": "BayOptPy/tpot/debug/tpot_boston.py", "max_stars_repo_name": "Mind-the-Pineapple/tpot-age", "max_stars_repo_head_hexsha": "2969bfa6dc5c652d5b4f00f59e9b0b23869f6bef", "max_stars_repo_licenses": ["MI... |
from Pipeline.main.PullData.Misc.PullCoinMarketCap import PullCoinMarketCap
import numpy as np
class MarketDetails:
"""
Period:
short: 1h, mid: 24h, long: 1w
"""
def __init__(self):
self.pull = PullCoinMarketCap()
def multiTicks(self, tickSizeList):
coinPage = se... | {"hexsha": "9d86a8c3dde79b322b53513d36394d8329a20965", "size": 1350, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pipeline/main/Monitor/MarketDetails.py", "max_stars_repo_name": "simonydbutt/b2a", "max_stars_repo_head_hexsha": "0bf4a6de8547d73ace22967780442deeaff2d5c6", "max_stars_repo_licenses": ["MIT"], "ma... |
"""RDF datasets
Datasets from "A Collection of Benchmark Datasets for
Systematic Evaluations of Machine Learning on
the Semantic Web"
"""
import os
from collections import OrderedDict
import itertools
import rdflib as rdf
import abc
import re
import networkx as nx
import numpy as np
import dgl
import dgl.backend as ... | {"hexsha": "ce223ba004fe589837c77572cfc6b4e184964e57", "size": 23627, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/dgl/data/rdf.py", "max_stars_repo_name": "vipermu/dgl", "max_stars_repo_head_hexsha": "c9ac6c9889423019977e431c8b74a7b6c70cdc01", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
from newspaper import Article
import random
import string
import nltk
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import warnings
warnings.filterwarnings('ignore')
#Download the punkt package
nltk.download('punkt', quiet=True)
a... | {"hexsha": "4714973d1b1dbb64d7f2e87f385f2bd08aa52f6c", "size": 2826, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/Chat_bot.py", "max_stars_repo_name": "Deborah-code/Chatbot", "max_stars_repo_head_hexsha": "db211bdd7032018c69e1c34fd933f3b81a47e208", "max_stars_repo_licenses": ["0BSD"], "max_stars_count... |
import numpy as np
import math
scale = 255.0/32768.0
scale_1 = 32768.0/255.0
def ulaw2lin(u):
u = u - 128
s = np.sign(u)
u = np.abs(u)
return s*scale_1*(np.exp(u/128.*math.log(256))-1)
def lin2ulaw(x):
s = np.sign(x)
x = np.abs(x)
u = (s*(128*np.log(1+scale*x)/math.log(256)))
u = np.... | {"hexsha": "b79d4315bf1fa7cfc1236c71e79762e0511713fb", "size": 381, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ulaw.py", "max_stars_repo_name": "Mozilla-GitHub-Standards/ee089678ec78c1555fc3f1eff2962a95ae31dcf042f14e37b019b4fbb4b13288", "max_stars_repo_head_hexsha": "5d4d89070dc8da54a716bb3d0db7f394334b... |
"""
Estimate a linear model with high dimensional categorical variables / instrumental variables
### Arguments
* `df::AbstractDataFrame`
* `model::Model`: A model created using [`@model`](@ref)
* `save::Union{Bool, Symbol} = false`: Should residuals and eventual estimated fixed effects saved in a dataframe? Use `save ... | {"hexsha": "a90f70a4cbd3bb380924d54ca7e6f94b3c9d2426", "size": 16564, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/reg.jl", "max_stars_repo_name": "maxnorton/FixedEffectModels.jl", "max_stars_repo_head_hexsha": "b97a25dfbe2cc5c325df6133ead55b2d0e5609fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os
import numpy as np
import matplotlib.pyplot as plt
RESULTS_FOLDER = './results/'
NUM_BINS = 100
BITS_IN_BYTE = 8.0
MILLISEC_IN_SEC = 1000.0
M_IN_B = 1000000.0
VIDEO_LEN = 64
VIDEO_BIT_RATE = [1500, 4900, 8200, 11700, 32800, 152400]
COLOR_MAP = plt.cm.jet #nipy_spectral, Set1,Paired
SIM_DP = 'sim_dp'
SCHEME... | {"hexsha": "fc1577804d65b36b5888b7aedb81e894a07b5b95", "size": 5382, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_exp/plot_results.py", "max_stars_repo_name": "ahmadhassan997/pensieve", "max_stars_repo_head_hexsha": "d54f16bc398d2f24c7b0525dad90df002b31506a", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
author: muzexlxl
email: muzexlxl@foxmail.com
time series factors
bias: -1 0 1
neut: 1, 0
"""
import pandas as pd
import numpy as np
from datetime import datetime
import collections
import math
# import src.data.clickhouse_control as cc
class FactorX:
def __init__(self, id: list, timeframe: str, data_source... | {"hexsha": "3d98bf2982ee336f49382e01bed153699d261da1", "size": 4278, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/factor/factor.py", "max_stars_repo_name": "jiangtiantu/JAB", "max_stars_repo_head_hexsha": "39d91043619c337c07ade87a86f3f876b05ad3e3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3,... |
import logging
from functools import reduce
from operator import mul
from torch import optim
import math
import numpy as np
import random
import torch
from torch import nn
import torch.nn.functional as F
class CdnnClassifier():
def __init__(self, vec_len, cnn_params=[(21, 12), (9, 6)], dnn_params=[(0.5, 0.2)], ... | {"hexsha": "f625902306d993549f4c98b7aa34c4dae0eddc83", "size": 5738, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/attacks/CdnnClassifier.py", "max_stars_repo_name": "tahleen-rahman/linkability_stepcount", "max_stars_repo_head_hexsha": "ed873782453d391865ad15e7c2d538058f5db88a", "max_stars_repo_licenses": ... |
# Rough outline
from typing import Union, Tuple, Callable
import numpy as np
import scipy.linalg
from general.Environment import Environment
from general.Exceptions import ConvergenceFailure
from utils.MutableFloat import MutableFloat
class Circuit:
def __init__(self, environment: Environment):
self.mat... | {"hexsha": "22424386e25fa6c6d434d391c46335a994b8df1b", "size": 4640, "ext": "py", "lang": "Python", "max_stars_repo_path": "general/Circuit.py", "max_stars_repo_name": "MrAttoAttoAtto/CircuitSimulatorC2", "max_stars_repo_head_hexsha": "4d821c86404fe3271363fd8c1438e4ca29c17a13", "max_stars_repo_licenses": ["MIT"], "max_... |
From iris.program_logic Require Export weakestpre.
From iris.heap_lang Require Export notation lang.
From iris.proofmode Require Export tactics.
From iris.heap_lang Require Import proofmode.
From iris.base_logic.lib Require Export invariants.
Set Default Proof Using "Type".
Section IncRA.
Inductive incRAT : Type :=
... | {"author": "ocecaco", "repo": "iris-iterators", "sha": "ce5c1bf34178e0cd7592dc08884956f0fad2403a", "save_path": "github-repos/coq/ocecaco-iris-iterators", "path": "github-repos/coq/ocecaco-iris-iterators/iris-iterators-ce5c1bf34178e0cd7592dc08884956f0fad2403a/experiments/IncRA.v"} |
% corrected VD 98
\subsection{Requirements}
% Describe here all the properties that characterize the deliverables you
% produced. It should describe, for each main deliverable, what are the expected
% functional and non functional properties of the deliverables, who are the actors
% exploiting the deliverables. It is ... | {"hexsha": "6dd00b77b8618641676737275b275aefe6e20d86", "size": 1269, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "sections/scientific/requirements.tex", "max_stars_repo_name": "Lemswasabi/bsps3-report", "max_stars_repo_head_hexsha": "ca3f7bee2d4740c5c7ad9f586766ab04a0e5f58b", "max_stars_repo_licenses": ["MIT"],... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
#Import Cancer data from the Sklearn library
# Dataset can also be found here (http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28diagnostic%29)
from sklearn.datasets import load_breast_cancer
canc... | {"hexsha": "6ac99f8c6a97ce7553603aba0211837dfb3b5635", "size": 842, "ext": "py", "lang": "Python", "max_stars_repo_path": "cancerbreast.py", "max_stars_repo_name": "axyroxxx/Breast-Cancer", "max_stars_repo_head_hexsha": "f7bbc00b43ddee0a810191e1fc1ee667f01586ac", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 16 15:44:19 2018
@author: tmthydvnprt
This function is adapted from the discussion at:
https://stackoverflow.com/questions/6620471/fitting-empirical-distribution-to-theoretical-ones-with-scipy-python
Though I've made it easy to use, I did NOT write this awesome code - mr... | {"hexsha": "be5325b00d534f4d1b6de895e5173f2d8ac6392a", "size": 812, "ext": "py", "lang": "Python", "max_stars_repo_path": "Example/fineDataAnalysis.py", "max_stars_repo_name": "richmr/QuantitativeRiskSim", "max_stars_repo_head_hexsha": "f98d416d075dc6232fdc573844847f8c4843e7f8", "max_stars_repo_licenses": ["MIT"], "max... |
using BinaryBuilder, Pkg
name = "MKL"
version = v"2021.1.1"
sources = [
ArchiveSource("https://anaconda.org/intel/mkl/2021.1.1/download/linux-64/mkl-2021.1.1-intel_52.tar.bz2",
"bfb0fd056576cad99ae1d9c69ada2745420da9f9cf052551d5b91f797538bda2"; unpack_target = "mkl-x86_64-linux-gnu"),
Archiv... | {"hexsha": "1d84a86bfbb28d1c6197e3080603aa79f4cce59f", "size": 3120, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "M/MKL/build_tarballs.jl", "max_stars_repo_name": "waralex/Yggdrasil", "max_stars_repo_head_hexsha": "bba5443f75b221c6973d479e2c6727cf0ae3a0b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#' Dates of different days within isoweekyears
#'
#' @format
#' \describe{
#' \item{yrwk}{Isoweek-isoyear.}
#' \item{mon}{Date of Monday.}
#' \item{tue}{Date of Tuesday.}
#' \item{wed}{Date of Wednesday.}
#' \item{thu}{Date of Thursday.}
#' \item{fri}{Date of Friday.}
#' \item{sat}{Date of Saturday.}
#' \item{sun}{Date... | {"hexsha": "84b5f108c1a67237ffa6becb8fa0149c8292fa2f", "size": 966, "ext": "r", "lang": "R", "max_stars_repo_path": "R/days.r", "max_stars_repo_name": "folkehelseinstituttet/municipdata", "max_stars_repo_head_hexsha": "eae72bd8eb130adb6397b9d5f3f8c00a02982b8c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import os
from functools import partial
import tensorflow as tf
import numpy as np
def _parse(filename, channels):
image_string = tf.io.read_file(filename)
image_decoded = tf.image.decode_png(image_string, channels=channels)
return tf.cast(image_decoded, tf.float32)
def _flip(x):
x = tf.image.rando... | {"hexsha": "53efdd40403c43787dbcb7c6d85ccc659750060d", "size": 1403, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/datasets/from_images.py", "max_stars_repo_name": "cyprienruffino/CycleGAN-TensorFlow", "max_stars_repo_head_hexsha": "5eaa864e406d4ff0a1b86a85cf43a9096d0d0395", "max_stars_repo_licenses": ["MI... |
///////////////////////////////////////////////////////////////
// Copyright 2018 John Maddock. Distributed under the Boost
// Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at https://www.boost.org/LICENSE_1_0.txt
//[eigen_eg
#include <iostream>
#include <boost/multiprecision/cpp_... | {"hexsha": "a70e3fbcf527f14e5a5c5261351e7e3ee173affe", "size": 1487, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "3rdParty/boost/1.71.0/libs/multiprecision/example/eigen_example.cpp", "max_stars_repo_name": "rajeev02101987/arangodb", "max_stars_repo_head_hexsha": "817e6c04cb82777d266f3b444494140676da98e2", "max... |
import numpy as np
import torch
def filter_samples(Y_hat: torch.Tensor, Y: torch.Tensor, weights):
if weights is None:
return Y_hat, Y
if isinstance(weights, torch.Tensor):
idx = torch.nonzero(weights).view(-1)
else:
idx = torch.tensor(np.nonzero(weights)[0])
if Y.dim() > 1:... | {"hexsha": "bd484694a685b35de9acbbd6ffcd1e8141a53461", "size": 1627, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "JonnyTran/LATTE", "max_stars_repo_head_hexsha": "613c976c1361560d1b5b78f1d8131002cbeabfc5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
(*
This is the definition of formal syntax for Dan Grossman's Thesis,
"SAFE PROGRAMMING AT THE C LEVEL OF ABSTRACTION".
An attempt at a variable module in a context.
*)
Require Import List.
Export ListNotations.
Require Import ZArith.
Require Import Init.Datatypes.
Require Import Coq.Init.Logic.
Require Ex... | {"author": "briangmilnes", "repo": "CycloneCoqSemantics", "sha": "190c0fc57d5aebfde244efb06a119f108de7a150", "save_path": "github-repos/coq/briangmilnes-CycloneCoqSemantics", "path": "github-repos/coq/briangmilnes-CycloneCoqSemantics/CycloneCoqSemantics-190c0fc57d5aebfde244efb06a119f108de7a150/3/TypingInfoProofsSigDef.... |
"""
This library contains metrics to quantify the shape of a waveform
1. threshold_amplitude - only look at a metric while oscillatory amplitude is above a set percentile threshold
2. rdratio - Ratio of rise time and decay time
3. pt_duration - Peak and trough durations and their ratio
3. symPT - symmetry between peak ... | {"hexsha": "6ca3a110e510a0faaca648ad6254d3c19a732baa", "size": 32116, "ext": "py", "lang": "Python", "max_stars_repo_path": "shape.py", "max_stars_repo_name": "voytekresearch/misshapen", "max_stars_repo_head_hexsha": "8ee2afa2da3449789e52bcce63ecd852c191e6fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, ... |
import argparse
import os, sys
import numpy as np
import tensorflow as tf
from emd import tf_auctionmatch
from cd import tf_nndistance
import time
def f_score(label, predict, dist_label, dist_pred, threshold):
num_label = label.shape[0]
num_predict = predict.shape[0]
f_scores = []
for i in range(len(t... | {"hexsha": "723b84239cff71301a57f06567c8d45c5b405827", "size": 4651, "ext": "py", "lang": "Python", "max_stars_repo_path": "pix3d/eval/eval_shapenet_object_centered.py", "max_stars_repo_name": "zouchuhang/Silhouette-Guided-3D", "max_stars_repo_head_hexsha": "884504982f16567f6c9152baf7a676dbf50711e9", "max_stars_repo_li... |
/*
* Legal Notice
*
* This document and associated source code (the "Work") is a preliminary
* version of a benchmark specification being developed by the TPC. The
* Work is being made available to the public for review and comment only.
* The TPC reserves all right, title, and interest to the Work as provided
*... | {"hexsha": "3274847bce6c35530710788d1f9af7192cddc93f", "size": 10627, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "egen/unittest/tc_securityfile.cpp", "max_stars_repo_name": "dotweiba/dbt5", "max_stars_repo_head_hexsha": "39e23b0a0bfd4dfcb80cb2231270324f6bbf4b42", "max_stars_repo_licenses": ["Artistic-1.0"], "m... |
# -*- coding: utf-8 -*-
import gym
import numpy as np
# 目標とする報酬
goal_average_steps = 195
# エピソードのタイムステップの最大の長さ
max_number_of_steps = 200
# エピソード数
num_episodes = 5000
# 保存しておく連続したエピソードの数
num_consecutive_iterations = 100
# 最後のエピソードの報酬
last_time_steps = np.zeros(num_consecutive_iterations)
def bins(clip_min, clip_max, n... | {"hexsha": "1329295127eaa15a8e0415c3165e75eb1d26686e", "size": 3393, "ext": "py", "lang": "Python", "max_stars_repo_path": "example/q-leaning/cartpole.py", "max_stars_repo_name": "Silver-birder/reinforcement-learning-fx", "max_stars_repo_head_hexsha": "043e54015387b105669c7d047ca7f43c43dcc72b", "max_stars_repo_licenses... |
# Copyright (C) 2016-2021 Alibaba Group Holding 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable l... | {"hexsha": "03a917b75964cf8320e60e812530cbaea5ab2803", "size": 4758, "ext": "py", "lang": "Python", "max_stars_repo_path": "efls-train/python/efl/privacy/fixedpoint_tensor.py", "max_stars_repo_name": "finalljx/Elastic-Federated-Learning-Solution", "max_stars_repo_head_hexsha": "fb588fdc03a2c1598b40b36712b27bdffdd24258"... |
from ..plugins.state_init import *
import pytest
import numpy as np
State_initialiser
def test_index_based():
method ='index'
input_list = np.zeros(4)
my_S = State_initialiser(method,input_list)
assert my_S.logic == index_based
def test_energy_based():
method ='energy'
input_list = np... | {"hexsha": "3bca2de37718830fc49cc570ad7527c7e000ea10", "size": 3843, "ext": "py", "lang": "Python", "max_stars_repo_path": "rydprop/hohi/adiabatic_solver/tests/test_state_init.py", "max_stars_repo_name": "jdrtommey/rydprops", "max_stars_repo_head_hexsha": "cdc7e14d61ff33929844ee5d779a18fd64f89f4f", "max_stars_repo_lice... |
#
# Copyright (c) 2015-2016,2018 CNRS
#
import numpy as np
from pinocchio.robot_wrapper import RobotWrapper
from . import libpinocchio_pywrap as pin
from . import utils
from .explog import exp, log
from .libpinocchio_pywrap import *
from .deprecated import *
from .shortcuts import *
pin.AngleAxis.__repr__ = lambda s... | {"hexsha": "cc8ebdf341dc9adbc9f048e54b245f630bb19fa2", "size": 352, "ext": "py", "lang": "Python", "max_stars_repo_path": "bindings/python/scripts/__init__.py", "max_stars_repo_name": "matthieuvigne/pinocchio", "max_stars_repo_head_hexsha": "01f211eceda3ac2e5edc8cf101690afb6f3184d3", "max_stars_repo_licenses": ["BSD-2-... |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
@doc raw"""
ObjsChannel(;
accepted_user=nothing,
created=nothing,
creator=nothing,
id=nothing,
is_archived=nothing,
is_channel... | {"hexsha": "b2f7bb24e8e0a6c8f931e84cc557f08e738f59a8", "size": 13528, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/web/model_ObjsChannel.jl", "max_stars_repo_name": "aviks/SlackSDK.jl", "max_stars_repo_head_hexsha": "5035e0d3c53c6812e364a84e81304b36f00f4340", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
'''
This project is written by Anqi Ni(anqini4@gmail.com)
according the algorithm on the paper:
'The Split Bregman Method for L1-Regularized Problems'(2009)
by Tom Goldstein and Stanley Osher published
on SIAM J. IMAGING SCIENCES Vol2, No. 2, pp323-343.
And it is For Educational Purposes Only.
... | {"hexsha": "70167a9e8258b68fe2d45e2534bc71084ac9c1a7", "size": 1643, "ext": "py", "lang": "Python", "max_stars_repo_path": "itv.py", "max_stars_repo_name": "ucas010/Split-Bregman-for-TV-Image-Recovery", "max_stars_repo_head_hexsha": "3baf24775f94ac491bc614ce032a74b36731a303", "max_stars_repo_licenses": ["MIT"], "max_st... |
# This file is a part of SimilaritySearch.jl
# License is Apache 2.0: https://www.apache.org/licenses/LICENSE-2.0.txt
export l1_distance, l2_distance, squared_l2_distance, linf_distance, lp_distance
"""
l1_distance(a, b)::Float64
Computes the Manhattan's distance between `a` and `b`
"""
function l1_distance(a, b... | {"hexsha": "69864c38233c7bf36725ea3c718be82d194df96e", "size": 1783, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/distances/vectors.jl", "max_stars_repo_name": "UnofficialJuliaMirror/SimilaritySearch.jl-053f045d-5466-53fd-b400-a066f88fe02a", "max_stars_repo_head_hexsha": "f6815ebd4f018ee3536f5b3be4e39640b3... |
[STATEMENT]
lemma image_mset_ordering_eq:
assumes "M1 = {# (f1 u). u \<in># L #}"
assumes "M2 = {# (f2 u). u \<in># L #}"
assumes "\<forall>u. (u \<in># L \<longrightarrow> (((f1 u), (f2 u)) \<in> r \<or> (f1 u) = (f2 u)))"
shows "(M1 = M2) \<or> ( (M1,M2) \<in> (mult r) )"
[PROOF STATE]
proof (prove)
goal (1 s... | {"llama_tokens": 6653, "file": "SuperCalc_multisets_continued", "length": 54} |
[STATEMENT]
lemma sd_1r_correct:
assumes "s\<^sub>o - s\<^sub>e > safe_distance_1r"
shows "no_collision_react {0..}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. no_collision_react {0..}
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. no_collision_react {0..}
[PROOF STEP]
from assms
[PRO... | {"llama_tokens": 544, "file": "Safe_Distance_Safe_Distance_Reaction", "length": 8} |
#!/usr/bin/env python
### Up to date as of 10/2019 ###
'''Section 0: Import python libraries
This code has a number of dependencies, listed below.
They can be installed using the virtual environment "slab23"
that is setup using script 'library/setup3env.sh'.
Additional functions are housed in file ... | {"hexsha": "49fa5b9ce9b469153fc198ba4a2f0fc2f5e253b8", "size": 82778, "ext": "py", "lang": "Python", "max_stars_repo_path": "slab2code/slab2.py", "max_stars_repo_name": "ftbernales/slab2", "max_stars_repo_head_hexsha": "0070903421eb2ede8cb86bd06609389b0ecf52dd", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count"... |
module DBdatatype # Note: correspondence here is complicated by platform issues.
# See http://julia.readthedocs.org/en/release-0.3/manual/calling-c-and-fortran-code/
using Compat
const DB_INT = 16
const DB_SHORT = 17
const DB_LONG = 18
const DB_FLOAT = 19
const DB_DOUBLE = 20
const DB_CHAR = 21
const DB_LONG_LONG = 22... | {"hexsha": "68d681896852c12e7d01ff46653d70d1f3c745f7", "size": 976, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DBdatatype.jl", "max_stars_repo_name": "UnofficialJuliaMirror/Silo.jl-1d21c727-5350-5715-a0f1-d07632c10ec8", "max_stars_repo_head_hexsha": "f33c1166064914ab67eb9acf8398c70551bcdb15", "max_stars_... |
[STATEMENT]
theorem (in graph) init_root [simp]:
"DataRefinement ({.Init.} o Q2_a) R1_a R2_a Q2'_a"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. DataRefinement ({. Init .} \<circ> Q2_a) R1_a R2_a Q2'_a
[PROOF STEP]
by (simp add: data_refinement_hoare hoare_demonic Q2'_a_def Init_def
Loop'_def R1_a_def R2_a... | {"llama_tokens": 168, "file": "GraphMarkingIBP_StackMark", "length": 1} |
/*
* The MIT License - see LICENSE file for details
*/
#include "DebugPanel.h"
#include "DebugThread.h"
#include "FileUtils.h"
#include <boost/foreach.hpp>
#include <boost/format.hpp>
DEFINE_EVENT_TYPE(wxEVT_MY_EVENT_PRINT_LINE)
DEFINE_EVENT_TYPE(wxEVT_MY_EVENT_NOTIFY_FILE_AND_LINE)
DEFINE_EVENT_TYPE(wxEVT_MY_EVENT_... | {"hexsha": "a6da309bc52d64972aaa7bf6432e20445a33773a", "size": 4261, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "gui/DebugPanel.cpp", "max_stars_repo_name": "hagish/lua-debugger", "max_stars_repo_head_hexsha": "ea74561dec68e09896f42ad49b65cc721227d781", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.... |
import matplotlib.pyplot as plt
import numpy as np
import cv2
SIZE_RATIO = 5
def is_black_square(square, threshold = 0.8):
N = square.shape[0]
total_area = N*N
black_area = np.sum(square == 0)
print("ratio", black_area/total_area)
if (black_area/total_area) > threshold:
return True
... | {"hexsha": "37348b3c0535ab53af8482c787d2b34657224708", "size": 4547, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "tomasr8/qr", "max_stars_repo_head_hexsha": "40eda9a040139b2e800abc798c6d67c6e864fa32", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_rep... |
!***********************************************************************************************************************************
!** S U B R O U T I N E G A T E F L O W **
!****************************************************... | {"hexsha": "7fcd15aa4a862305381e85643078be3666d65f48", "size": 30489, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "gate-spill-pipe.f90", "max_stars_repo_name": "WQDSS/CE-QUAL-W2-Linux", "max_stars_repo_head_hexsha": "62479d6c1ae8a2dcb632327d96e5084b52d6f9b5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#####################################################################
# Task 2 : identify an image region by hue
#####################################################################
import cv2
import numpy as np
#####################################################################
# define video capture with acce... | {"hexsha": "b3814c3f98426c7a6236dcb75e444fa2e197da2d", "size": 1811, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/hsv_colour.py", "max_stars_repo_name": "tobybreckon/colour-filtering", "max_stars_repo_head_hexsha": "1679db2c075036f68dcc8a75c575c8f362e9ec94", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import os
import numpy as np
import pandas as pd
from models.predictor import predict
from evaluation.metrics import evaluate
from plots.rec_plots import pandas_bar_plot
def getGroup(user_counts):
sorted_user_counts = np.sort(user_counts)
full_length = len(user_counts)
first_quater = sorted_user_counts[fu... | {"hexsha": "cb9141f713e83dad1e1f7ad534bed70cf376b14e", "size": 2685, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment/usercategory.py", "max_stars_repo_name": "wuga214/MultiModesPreferenceEstimation", "max_stars_repo_head_hexsha": "f80c2feb196cb498a8b417f2037aadad151cceb3", "max_stars_repo_licenses": [... |
# import os
# import numpy as np
# from PIL import Image
# from .. import utils
# import logging
# logger = logging.getLogger()
# # ----- parsers
# # These objects are mux, they consume and streamline the output
# # Don't know what mux are? Study electronics.
# class BaseParser:
# """This is the base parser cla... | {"hexsha": "cb54c9458b037f16c03d89b29f04291304b49605", "size": 11001, "ext": "py", "lang": "Python", "max_stars_repo_path": "nbox/framework/parsers.py", "max_stars_repo_name": "cshubhamrao/nbox", "max_stars_repo_head_hexsha": "df32552e94c436b3d55b197263e5834bdbb8b724", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
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