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# Copyright 2019 D-Wave Systems Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in w... | {"hexsha": "94c91d23d5b08c014857c20781ba4b7abe59f204", "size": 3712, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_feature_selection.py", "max_stars_repo_name": "AlexanderNenninger/mutual-information-feature-selection", "max_stars_repo_head_hexsha": "7ed4fa9c2f7928fbeb07fa442f9d9413697a1fe1", "max_s... |
Require Import Basics.
Require Import Types.
Require Import Diagrams.Diagram.
Require Import Diagrams.Graph.
Require Import Diagrams.Cocone.
Require Import Colimits.Colimit.
(** * Colimit of the dependent sum of a family of diagrams *)
(** Given a family diagram [D(y)], and a colimit [Q(y)] of each diagram, one can c... | {"author": "HoTT", "repo": "Coq-HoTT", "sha": "ab70acd360367dbda13d537748f792384fb882a3", "save_path": "github-repos/coq/HoTT-Coq-HoTT", "path": "github-repos/coq/HoTT-Coq-HoTT/Coq-HoTT-ab70acd360367dbda13d537748f792384fb882a3/theories/Colimits/Colimit_Sigma.v"} |
import torch
import numpy as np
import torch.optim as optim
from lib.utils.util import check_path, empty_folder
from lib.utils.meter import AverageMeter
from torch.nn import DataParallel
from torch.backends import cudnn
__all__ = ['NetBase']
class NetBase(object):
def __init__(self, nClass, nCam, model_client, u... | {"hexsha": "98ea667f8cfa6ccc7ccdef44a74cd7ca072caa82", "size": 5664, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/network/model_factory/netbase.py", "max_stars_repo_name": "chenyanghungry/person-reid-lib", "max_stars_repo_head_hexsha": "783e66c9bfedf582e2cf935b9f5be960b543ac3c", "max_stars_repo_licenses":... |
\documentclass[a4paper, 12pt]{report}
\usepackage[T1]{fontenc}
% \usepackage[icelandic]{babel}
\usepackage{latexsym,amssymb,amsmath,amsthm}
\usepackage{graphicx}
\usepackage[colorlinks=true,linkcolor=black,anchorcolor=black,citecolor=black,filecolor=black,menucolor=black,runcolor=black,urlcolor=black]{hyperref}
\usepa... | {"hexsha": "c428006a865ab3029f8e359f69778c11b34896e1", "size": 32753, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "notes/devjournal/development.tex", "max_stars_repo_name": "lvthnn/SimEVO", "max_stars_repo_head_hexsha": "6e4245bf25533f3de8f4393f93e9d0d9430fc162", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from numpy import array, cross, dot, float64
from pynurbs.config import Settings
from pynurbs.geometry.geom import Geometry
from pynurbs.geometry.methods.evaluate import check_param
from pynurbs.geometry.methods.geom_utils import (global_to_local_param,
local_to_global_... | {"hexsha": "f9a26c1a41b49c65af8149d553059ae3250e2457", "size": 13235, "ext": "py", "lang": "Python", "max_stars_repo_path": "pynurbs/geometry/icurve.py", "max_stars_repo_name": "trelau/pyNURBS", "max_stars_repo_head_hexsha": "5dfd082fe368c1140ce485dc64b049b32c267d1f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
'''
Using OpenCV takes a mp4 video and produces a number of images.
Requirements
----
You require OpenCV 3.2 to be installed.
Run
----
Place file in same directory as filenames.
Open the mp4_to_jpg.py and edit student name and filenames. Then run:
$ cd <file_location>
$ python mp4_to_jpg.py
Which will produce a folder ... | {"hexsha": "0a1773cb5a8bd64b2b81fe7d9b7118d1711c446c", "size": 1716, "ext": "py", "lang": "Python", "max_stars_repo_path": "limited_parse.py", "max_stars_repo_name": "PRAkTIKal24/DeepFake_Classification", "max_stars_repo_head_hexsha": "de2c8d191145fc938cedb9de412ff2d949347272", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma subst_bv1_beta:
"subst_bv1 s (length (T#Ts)) x \<rightarrow>\<^sub>\<beta> subst_bv1 t (length (T#Ts)) x
\<Longrightarrow> typ_of1 Ts s = Some ty
\<Longrightarrow> typ_of1 Ts t = Some ty
\<Longrightarrow> s \<rightarrow>\<^sub>\<beta> t"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbr... | {"llama_tokens": 9277, "file": "Metalogic_ProofChecker_BetaNorm", "length": 32} |
#!/usr/bin/env python
"""
Created on Thu Jan 23 10:43:35 2014
Author: Oren Freifeld
Email: freifeld@csail.mit.edu
"""
import numpy as np
#from scipy.linalg import expm
from scipy.sparse.linalg import expm # scipy.linalg.expm is just a wrapper around this one.
#from expm_hacked import expm
#from scipy.sparse.linalg... | {"hexsha": "4874ee6a5b04c01ecb0b86a849226b8e7572ab41", "size": 2518, "ext": "py", "lang": "Python", "max_stars_repo_path": "cpab/cpa2d/calcs/_CpaCalcs.py", "max_stars_repo_name": "freifeld/cpabDiffeo", "max_stars_repo_head_hexsha": "22df6cdbd7111b9ae3e7f1c0e31ff85e92d281a6", "max_stars_repo_licenses": ["MIT"], "max_sta... |
theory Turan
imports
"Girth_Chromatic.Ugraphs"
"Random_Graph_Subgraph_Threshold.Ugraph_Lemmas"
begin
section \<open>Basic facts on graphs\<close>
lemma wellformed_uverts_0 :
assumes "uwellformed G" and "uverts G = {}"
shows "card (uedges G) = 0" using assms
by (metis uwellformed_def card.empty ex_in_c... | {"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/Turans_Graph_... |
"""
scipy.interpolate module
- [Interpolation (scipy.interpolate) Reference Guide](https://docs.scipy.org/doc/scipy/reference/interpolate.html)
# Examples
You can interpolate 1D data:
```julia-repl
julia> x = collect(0:10);
julia> y = exp.(-x/3.0);
julia> f = SciPy.interpolate.interp1d(x, y);
julia> f(0.5)
0-dimen... | {"hexsha": "7086f29ff736260c963104d11613e98050941dd2", "size": 1927, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/interpolate.jl", "max_stars_repo_name": "AtsushiSakai/SciPy.jl", "max_stars_repo_head_hexsha": "073706533f68989ccd761d813cd35593ea7c2a50", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#!/usr/bin/env python
# coding: utf-8
import numpy as np
import os
from astropy.table import Table, vstack
from collections import OrderedDict
## Import some helper functions, you can see their definitions by uncomenting the bash shell command
from desispec.workflow.exptable import default_obstypes_for_exptable
fr... | {"hexsha": "c41d804a676cf7f90456f43eda57f3627ee0a10f", "size": 19179, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/desispec/workflow/proctable.py", "max_stars_repo_name": "Waelthus/desispec", "max_stars_repo_head_hexsha": "8be844ef3734cb831558caf794d7258a4b7017cc", "max_stars_repo_licenses": ["BSD-3-Clause... |
HEROKU = False
if HEROKU:
import os
from random import randint
import flask
dropd_color = 'black'
dropd_back = 'gray'
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_daq as daq
from dash.dependencies import Input, Output, State, ALL, MATCH
import plotly.grap... | {"hexsha": "c5b0b5ca64e6f95a7c9b2f86f8e7da7c13c72d0a", "size": 26447, "ext": "py", "lang": "Python", "max_stars_repo_path": "score/revised_score_analyze.py", "max_stars_repo_name": "SuperShinyEyes/Score-Tool", "max_stars_repo_head_hexsha": "907dd9e695c9950c6f168480e591a239cdf6a826", "max_stars_repo_licenses": ["MIT"], ... |
(*******************************************************************************
Project: Refining Authenticated Key Agreement with Strong Adversaries
Module: Channels.thy (Isabelle/HOL 2016-1)
ID: $Id: Channels.thy 132885 2016-12-23 18:41:32Z csprenge $
Author: Joseph Lallemand, INRIA Nancy <joseph.la... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Key_Agreement_Strong_Adversaries/Channels.thy"} |
class step_by_step_brian_sim(object):
'''
Step-by-Step Brian Simulation (Nov/2014 - ricardo.deazambuja@plymouth.ac.uk)
This class was created to make it easier to run a Brian simulation step-by-step, passing input spikes without
running out of memory or having to create the input spikes beforehan... | {"hexsha": "c5ecd0c741f35ca419260a44ed3aace85a022ba5", "size": 5572, "ext": "py", "lang": "Python", "max_stars_repo_path": "step_by_step_brian.py", "max_stars_repo_name": "ricardodeazambuja/IJCNN2017", "max_stars_repo_head_hexsha": "817165185de6152041bbaf21cbad6d12fb58f064", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
#学習した場所領域のサンプルをrviz上に可視化するプログラム
#作成者 石伏智
#作成日 2015年12月
#サンプリング点プロット→ガウスの概形描画に変更（磯部、2016卒論）
#編集、更新：谷口彰 更新日：2017/02/10
#mu 2次元、sig 2×2次元版
#自己位置も取得して描画するのは別プログラム
"""
実行前に指定されているフォルダが正しいかをチェックする
file_read.pyも同様に ！
実行方法
python place_draw.py (parameterフォルダの絶対パス) (表示する場所領域を指定したい場... | {"hexsha": "6abcb5edf0cebd034b39b8b5354d3c7961299e97", "size": 12178, "ext": "py", "lang": "Python", "max_stars_repo_path": "learning/new_place_draw_online.py", "max_stars_repo_name": "a-taniguchi/SpCoSLAM_evaluation", "max_stars_repo_head_hexsha": "d24cbcc12a437d831049228ad1b80b22574c9ec0", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma lhd_inf_llist [simp]: "lhd (inf_llist f) = f 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lhd (inf_llist f) = f 0
[PROOF STEP]
by(simp add: inf_llist_def) | {"llama_tokens": 89, "file": "Coinductive_Coinductive_List", "length": 1} |
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import dash_bootstrap_components as dbc
import microdf as mdf
import os
from numerize import numerize
from components im... | {"hexsha": "fbbe871350b8d25bb1ac09c88f585c79d2365bd8", "size": 42681, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "fedderw/us-calc", "max_stars_repo_head_hexsha": "36a4935122ab7688656f2e5dfcd3fe2cb5d88c8c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_r... |
\chapter{Miscellaneous Transformations}
\section{Fresnel Term - Schlick's approximation}
The \emph{Fresnel equations} describes the reflection and transmission of a electromagnetic wave at an interface. The Fresnel equation provides a reflection and transmission coefficients for waves. In Computer Graphics we often u... | {"hexsha": "d4f5fe3e19e39e9ff82e7dcf6ac5adccd8206859", "size": 4280, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "document/Source/Chapters/appendix.tex", "max_stars_repo_name": "simplay/Bachelor-Thesis", "max_stars_repo_head_hexsha": "ef450c5420b768b2a1fd84c9ad768f34db12fc88", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import ast
def newtonInterpolation(x, y):
x = ast.literal_eval(x)
y = ast.literal_eval(y)
n = len(y)
table = np.zeros([n, n]) # Create a square matrix to hold table
table[::, 0] = y # first column is y
results = {"table": [], "coefficient": []}
results["tabl... | {"hexsha": "14b7cc3639476ce7acd8b507dbf62e2164b1947f", "size": 1268, "ext": "py", "lang": "Python", "max_stars_repo_path": "methods/newtonInterpolation.py", "max_stars_repo_name": "eechava6/NumericalAnalysis", "max_stars_repo_head_hexsha": "1b44349fe4c5e24413c3d5faeca7d227272814ec", "max_stars_repo_licenses": ["MIT"], ... |
# Copyright (c) xiaoxuan : https://github.com/shawnau/kaggle-HPA
# modified by sailfish009
import sys
sys.path.append('../')
import os
import math
import operator
from functools import reduce
from collections import Counter
import numpy as np
import pandas as pd
from .ml_stratifiers import MultilabelStratifiedShuffle... | {"hexsha": "a948edbf64c48251f59fc659a9447dd6892630c6", "size": 5930, "ext": "py", "lang": "Python", "max_stars_repo_path": "custom/protein/preprocess.py", "max_stars_repo_name": "sailfish009/mydl", "max_stars_repo_head_hexsha": "ca2dfd1ff1b609dbd76ef966bbe53b7854f6f78f", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import sys
logging.basicConfig(
stream=sys.stdout,
level=logging.DEBUG,
format='%(asctime)s %(name)s-%(levelname)s: %(message)s',
datefmt='%Y-%m-%d %H:%M:%S')
from os.path import... | {"hexsha": "df41555b18281e7b1b5ce196861c57f7bd6e3924", "size": 6471, "ext": "py", "lang": "Python", "max_stars_repo_path": "string-method/src/stringprocessor/processing_utils.py", "max_stars_repo_name": "delemottelab/gpcr-string-method-2019", "max_stars_repo_head_hexsha": "b50786a4a8747d56ad04ede525592eb31f1890fd", "ma... |
/-
Copyright (c) 2020 Kenny Lau. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Kenny Lau
-/
import ring_theory.integrally_closed
import ring_theory.valuation.integers
/-!
# Integral elements over the ring of integers of a valution
The ring of integers is integrally... | {"author": "jjaassoonn", "repo": "projective_space", "sha": "11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce", "save_path": "github-repos/lean/jjaassoonn-projective_space", "path": "github-repos/lean/jjaassoonn-projective_space/projective_space-11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce/src/ring_theory/valuation/integral.lean"} |
# Copyright 2017 Softplan
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, softw... | {"hexsha": "3b6e1c7678354b11c31463fa6993eb5863c91a8e", "size": 12266, "ext": "py", "lang": "Python", "max_stars_repo_path": "service.py", "max_stars_repo_name": "vinigomes/intellead-classification", "max_stars_repo_head_hexsha": "8a5ed549606288f906f07dd30096a50675c1c71c", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 6 20:16:27 2018
@author: Isaac
"""
import numpy
def sort_minimum(numbers):
""" This is the description of the function ~ Loves it1
Parameters
----------
numbers : array
array to sort
Returns
-------
... | {"hexsha": "fed9ef8f001856e2169688de9d6bfcf2d9e1d59e", "size": 1247, "ext": "py", "lang": "Python", "max_stars_repo_path": "week6.py", "max_stars_repo_name": "IsaacW4/Operational-Research", "max_stars_repo_head_hexsha": "2f172a14e9302ea56a4beb8b0e334b84df7b406b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may... | {"hexsha": "67a2d1a7f824b63f7807dc7114448ed8d99819fe", "size": 35331, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/kudu/tools/tool_action_local_replica.cc", "max_stars_repo_name": "toddlipcon/kudu", "max_stars_repo_head_hexsha": "e5ee5e08c68c9c661ce676ad629b4ad3abf57def", "max_stars_repo_licenses": ["Apache-... |
from collections import namedtuple
import os
import re
from astropy import units as u
from astropy.cosmology import FlatLambdaCDM
import h5py
import pandas as pd
import numpy as np
import numpy.ma as ma
from numpy.random import default_rng
from desc.skycatalogs.utils.common_utils import print_dated_msg
__all__ = ['L... | {"hexsha": "dd5a0fe247d15b10c8c84d49eea5f17d5ed99650", "size": 15243, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/desc/skycatalogs/utils/sed_utils.py", "max_stars_repo_name": "LSSTDESC/skyCatalogs", "max_stars_repo_head_hexsha": "39807b6fb510e45d7db79cf903e2eaa59befa81b", "max_stars_repo_licenses": ["... |
# Takes one dimensional time series, performs delay embedding
one_d <-function(data, embed_dim = 4)
{
edata <- embed(data, embed_dim)
return(edata)
}
#Takes time series and scales, either using max values or taking log
pre_process <- function(data, scaling_method)
{
tmp <- data
if (scaling_method ==... | {"hexsha": "57ecd679c85292cef58a7d54ceae8d8321665b6b", "size": 5523, "ext": "r", "lang": "R", "max_stars_repo_path": "Minor Project/TDA_Finance.r", "max_stars_repo_name": "sakshi-vats/tda-for-crypto", "max_stars_repo_head_hexsha": "8ffb690587f985b829ed507d0251715cd86bd265", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
#define BOOST_TEST_DYN_LINK
#include <canard/net/ofp/v13/common/oxm_match_field.hpp>
#include <boost/test/unit_test.hpp>
#include <boost/test/data/test_case.hpp>
#include <boost/test/data/monomorphic.hpp>
#include <stdexcept>
#include <boost/optional/optional.hpp>
#include <boost/optional/optional_io.hpp>
namespace o... | {"hexsha": "3388f17e6aa719dc1bcad044ae9fa7ebe5152911", "size": 2443, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/v13/oxm_match/ipv6_flabel_test.cpp", "max_stars_repo_name": "amedama41/bulb", "max_stars_repo_head_hexsha": "2e9fd8a8c35cfc2be2ecf5f747f83cf36ffbbdbb", "max_stars_repo_licenses": ["BSL-1.0"], "... |
macro symbol_func(cur_expr::Expr)
@assert cur_expr.head == :function
cur_call = cur_expr.args[1]
cur_func_name = cur_call.args[1]
cur_main_var = cur_call.args[2].args[1]
cur_param = Expr(
:kw,
Expr(
:(::),
:is_direct_call,
:Bool
),
false
)
push!(cur_call.args, cur_pa... | {"hexsha": "78f26342f45b09dfb6f9e84335392001c6527f4b", "size": 520, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/symbol_func.jl", "max_stars_repo_name": "djsegal/Fusion.jl", "max_stars_repo_head_hexsha": "a0540fbf3345a778965fa092e9e56907a44c6521", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
Global Land Cover Facility GLCF MCD12Q1
http://glcf.umd.edu/data/lc/
"""
import numpy as np
import numpy.ma as ma
import matplotlib.patches as mpatches
CLASSES_NAMES = {
0: 'Water',
1: 'Evergreen needleleaf forest',
2: 'Evergreen broadleaf forest',
3: 'Deciduous needleleaf forest',
4: 'Deciduou... | {"hexsha": "aadde694a60dc5a2db07731f445d41be3b046a43", "size": 2011, "ext": "py", "lang": "Python", "max_stars_repo_path": "rastercube/datasources/glcf.py", "max_stars_repo_name": "terrai/rastercube", "max_stars_repo_head_hexsha": "c8c6214fd682f72e94df4979f5d737cea4778617", "max_stars_repo_licenses": ["MIT"], "max_star... |
\graphicspath{ {img/BR/} }
\chapter[Bandwidth Reservation as a Coexistence Strategy in Opportunistic Spectrum Access Environments][Bandwidth Reservation in OSA]{Bandwidth Reservation as a Coexistence Strategy in Opportunistic Spectrum Access Environments}\label{BR_chap}
\section{Introduction}\label{sec:Introduction}
... | {"hexsha": "c56fa48405903aa4816320d6c3a17332261f5560", "size": 63731, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/BR.tex", "max_stars_repo_name": "Mario-LopezTelecom/phdTesis", "max_stars_repo_head_hexsha": "f71367a04c6fccbd7b804ce8fe9e15ee873ae28b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
!!### MODULE: PRINTING subroutine Grid2
MODULE PRN_Grid2
!!#### PURPOSE
!! This subroutine prints a block of discrete function values
!! F(1:Nx,1:Ny) provided at points on a regular grid, or just with indices
!! if the values of <x(1:Nx)> and <y(1:Ny)> are not provided.
!!#### FORTRAN STANDARDS
USE ISO_varying_string ... | {"hexsha": "80d46635292664526614a0d9d7828c6228233cda", "size": 2453, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/09-B-PRN_Grid2.f90", "max_stars_repo_name": "wawiesel/tapack", "max_stars_repo_head_hexsha": "ac3e492bc7203a0e4167b37ba0278daa5d40d6ef", "max_stars_repo_licenses": ["Unlicense"], "max_stars_... |
import pandas as pd
import numpy as np
from tracking_grants import (
articles_f,
references_f,
wos_f,
altmetric_f,
trials_f,
awards_f,
)
def load_references():
return pd.read_csv(references_f)
def load_awards():
def research_topic(s):
if pd.notna(s):
primary_topi... | {"hexsha": "95faefc61a99f004eb28e86bc62191bc4f454600", "size": 6157, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tracking_grants/utils/helpers.py", "max_stars_repo_name": "ScholCommLab/tracking-grants", "max_stars_repo_head_hexsha": "6590b877c3be8c057412c3fe6f3d0a1ea1b4119e", "max_stars_repo_licenses": [... |
using StructIO
using Test
# First, exercise the `@io` macro a bit, to ensure it can handle different
# kinds of type declarations
@io struct TwoUInts
x::UInt
y::UInt
end
abstract type AbstractType end
@io struct ConcreteType <: AbstractType
A::UInt32
B::UInt16
C::UInt128
D::UInt8
end align_pac... | {"hexsha": "29b61a3de27dc753417b11f0e1453708c0ae22be", "size": 4997, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "UnofficialJuliaMirror/StructIO.jl-53d494c1-5632-5724-8f4c-31dff12d585f", "max_stars_repo_head_hexsha": "455543ce2f240402fca7fe65622734ff8a2eda8f", "max_st... |
import unittest
class NumpyTest(unittest.TestCase):
def test_numpy_is_importable(self):
import numpy
self.assertIsNotNone(numpy.nan)
| {"hexsha": "5c77c1b01006b584008cc98099b9cae968591f89", "size": 156, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/python_rules/numpy_test.py", "max_stars_repo_name": "samwestmoreland/please", "max_stars_repo_head_hexsha": "1616742eeefca3dd0b3194e4c1ec9a8542ec13c7", "max_stars_repo_licenses": ["Apache-2.0"... |
SUBROUTINE RCOVSL (NAME,ITEM,IN,AMAT,SCR2,SCR3,OUT,Z,IZ,LCORE,
1 FIRST,RFNO)
C
C RCOVSL CALCULATES THE STATIC LOAD VECTORS FOR THE SUBSTRUCTURING
C PHASE 2 AND PHASE 3 OPERATIONS FROM THE SUBSTRUCTURE SOLN ITEM
C
LOGICAL FIRST
INTEGER NAME(2),AMAT,... | {"hexsha": "587f6983097030cbc6cd2c1caba00662b47ba4ad", "size": 4405, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/rcovsl.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count": ... |
import pandas as pd
import torch
import torch.optim as optim
from torch.utils.data import Subset, DataLoader, TensorDataset
from torchvision.transforms import ToTensor
from typing import Union
import numpy as np
from torchvision.datasets.mnist import MNIST
from src.AutoMLpy.optimizers.optimizer import HpOptimizer
fro... | {"hexsha": "a79d46700fb890f7f6eb23f737f73fec11374039", "size": 5910, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pytorch_items/pytorch_hp_optimizers.py", "max_stars_repo_name": "JeremieGince/AutoMLpy", "max_stars_repo_head_hexsha": "59c2214da0eb6e767446cc2157395c348fddff4e", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma ct_prefixE [elim?]:
assumes "ct_prefix xs ys"
obtains as zs where "ys = as @ zs" "ct_list_eq as xs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>as zs. \<lbrakk>ys = as @ zs; ct_list_eq as xs\<rbrakk> \<Longrightarrow> thesis) \<Longrightarrow> thesis
[PROOF STEP]
using assms
[PROOF ST... | {"llama_tokens": 336, "file": "WebAssembly_Wasm_Checker_Types", "length": 3} |
"""
# F-1 Score for Multi-Class Classification
## Exercise problems
Exercise 1. Prediction robots' performance comparison (Minsuk Heo 허민석, 2017))
robot1 = [[100, 80, 10, 10],
[0, 9, 0, 1],
[0, 1, 8, 1],
[0, 1, 0, 9]]
robot2 = [[198, 2, 0, 0],
[7, 1, 0, 2],
[0, 8, 1,... | {"hexsha": "e35822edb2320ec2581aabde31949bcc7e9134ed", "size": 1869, "ext": "py", "lang": "Python", "max_stars_repo_path": "Accuracy, precision, recall & f1/f1_score_exercise.py", "max_stars_repo_name": "CodingWillow/MachineLearning", "max_stars_repo_head_hexsha": "340c9d91d4178a2ab56921502bdcee73864a1a59", "max_stars_... |
[STATEMENT]
lemma nsqn_quality_increases_dhops [elim]:
assumes "i\<in>kD(rt \<xi>)"
and "quality_increases \<xi> \<xi>'"
and "nsqn (rt \<xi>) i = nsqn (rt \<xi>') i"
shows "the (dhops (rt \<xi>) i) \<ge> the (dhops (rt \<xi>') i)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. the (dhops (rt \<xi>'... | {"llama_tokens": 504, "file": "AODV_variants_c_gtobcast_C_Quality_Increases", "length": 3} |
\section{Introduction}
\subsection{System Purpose}
RAVEN is a flexible and multi-purpose uncertainty quantification (UQ), regression analysis, probabilistic risk assessment
(PRA), data analysis and model optimization software. Depending on the tasks to be accomplished and on the
probabilistic
characterization of t... | {"hexsha": "0ede679642ba6c8de8841a283b6d4f376858e5c9", "size": 5504, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/sqa/sdd/ravenIntro.tex", "max_stars_repo_name": "rinelson456/raven", "max_stars_repo_head_hexsha": "1114246136a2f72969e75b5e99a11b35500d4eef", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import os
import keras
import numpy as np
import tensorflow as tf
from keras.models import load_model
from keras.preprocessing import image
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layer... | {"hexsha": "3d9372ff9785c26fc6184ce34d971ac573b90444", "size": 4971, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/train_model.py", "max_stars_repo_name": "Parakconcepts/mlclassification", "max_stars_repo_head_hexsha": "e364f3ce50d4e1199a5b6233c0b44b3d674b25d3", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma ord_option_Some1_iff: "ord_option R (Some a) y \<longleftrightarrow> (\<exists>b. y = Some b \<and> R a b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ord_option R (Some a) y = (\<exists>b. y = Some b \<and> R a b)
[PROOF STEP]
by (cases y; auto) | {"llama_tokens": 112, "file": "Markov_Models_ex_MDP_RP", "length": 1} |
import itertools as it
import os
import tempfile
import xml.etree.ElementTree as ET
from typing import Any, List, Optional, Tuple, Type
from collections import OrderedDict
import gym
from gym.spaces import Dict, Box
import numpy as np
from mujoco_maze import maze_env_utils, maze_task
from mujoco_maze.agent_model impor... | {"hexsha": "dff853883d82dd33b74a60da2f0b0190a7f3825b", "size": 3956, "ext": "py", "lang": "Python", "max_stars_repo_path": "mujoco_maze/goal_maze_env.py", "max_stars_repo_name": "jypark0/mujoco-maze", "max_stars_repo_head_hexsha": "da477ecbf3451fd7cf907b83f03664a6e5358bcc", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
# This README was generated directly from
# [this source file](https://github.com/fredrikekre/Literate.jl/blob/master/examples/README.jl)
# running these commands from the package root of Literate.jl:
| {"hexsha": "376faa4235df87347d9dd8a78067db9172667115", "size": 202, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/readme.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/FocusedBlindDecon.jl-3b47aaff-9524-5d9d-a292-eeb6a187c032", "max_stars_repo_head_hexsha": "071e0f915cb96768f34f359d448804faaef... |
"""Mean covariance estimation."""
from copy import deepcopy
import numpy as np
from .base import sqrtm, invsqrtm, logm, expm
from .ajd import ajd_pham
from .distance import distance_riemann
from .geodesic import geodesic_riemann
def _get_sample_weight(sample_weight, data):
"""Get the sample weights.
If none... | {"hexsha": "bbfba00ada95ca4b323dab1489addc7b7c3e9bf4", "size": 13774, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyriemann/utils/mean.py", "max_stars_repo_name": "qbarthelemy/pyRiemann", "max_stars_repo_head_hexsha": "b35873b0a6cf9d81a1db09bbedb72a2fefe7d0c3", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
[STATEMENT]
lemma finfun_snd_comp_conv: "finfun_snd (f \<circ>$ g) = (snd \<circ> f) \<circ>$ g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finfun_snd (f \<circ>$ g) = (snd \<circ> f) \<circ>$ g
[PROOF STEP]
by(simp add: finfun_snd_def) | {"llama_tokens": 118, "file": "FinFun_FinFun", "length": 1} |
/-
Copyright (c) 2021 Eric Rodriguez. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Eric Rodriguez
-/
import ring_theory.polynomial.cyclotomic.basic
import tactic.by_contra
import topology.algebra.polynomial
import number_theory.padics.padic_norm
/-!
# Evaluating cy... | {"author": "Mel-TunaRoll", "repo": "Lean-Mordell-Weil-Mel-Branch", "sha": "4db36f86423976aacd2c2968c4e45787fcd86b97", "save_path": "github-repos/lean/Mel-TunaRoll-Lean-Mordell-Weil-Mel-Branch", "path": "github-repos/lean/Mel-TunaRoll-Lean-Mordell-Weil-Mel-Branch/Lean-Mordell-Weil-Mel-Branch-4db36f86423976aacd2c2968c4e4... |
import numpy as np
import jax
import jax.numpy as jnp
import jax.scipy as jsp
import jaxtorch
import math
def alpha_sigma_to_t(alpha, sigma):
return jnp.arctan2(sigma, alpha) * 2 / math.pi
def get_cosine_alphas_sigmas(t):
return jnp.cos(t * math.pi/2), jnp.sin(t * math.pi/2)
def get_ddpm_alphas_sigmas(t, in... | {"hexsha": "5b704328fc842b579ff0350df5841f51cd3ef233", "size": 3040, "ext": "py", "lang": "Python", "max_stars_repo_path": "jax-diffusion/jax-guided-diffusion/diffusion_models/schedules.py", "max_stars_repo_name": "Baughn/nixgan", "max_stars_repo_head_hexsha": "20639e37f8263187ef3928fa91974e9d9d0848d8", "max_stars_repo... |
# -*- coding: utf-8 -*-
# ----------------------------------------------------------------------------
# Created By : Francisco Miras García <francisco.mirasg@gmail.com>
# version ='1.0'
# ---------------------------------------------------------------------------
"""
# Codigo para el ejercicio ACTIVIDAD
1.- Construye... | {"hexsha": "3c7a8a22a65bafe8f876f646fe66ee15cc2de4fc", "size": 7593, "ext": "py", "lang": "Python", "max_stars_repo_path": "2021-2022/Entregas/ACTIVIDAD.py", "max_stars_repo_name": "franciscomirasg/umucv", "max_stars_repo_head_hexsha": "703629d5152d55d00821aee02d30fbb3cca1b73e", "max_stars_repo_licenses": ["BSD-3-Claus... |
#include <iostream>
#include <string>
#include <vector>
#include <boost/lockfree/queue.hpp>
#include "test/timed-throughput-fixture.h"
#include "test/timed-throughput.h"
#include "util/parse-cmd-line.h"
#include "util/util.h"
using boost::lockfree::queue;
using std::string;
using std::vector;
using test::timed_throu... | {"hexsha": "d0beac22f1299a3fdf9188207a8178dd399c61c1", "size": 1060, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/boost-lockfree-queue-timed-test.cc", "max_stars_repo_name": "cookyt/parallel-multi-queue", "max_stars_repo_head_hexsha": "1543cc66815c7fbb4cd8e896ce2a9ce56e6213db", "max_stars_repo_licenses": ["U... |
# coding: utf-8
# # 卷积神经网络示例与各层可视化
# In[1]:
import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
get_ipython().magic(u'matplotlib inline')
print ("当前TensorFlow版本为 [%s]" % (tf.__version__))
print ("所有包载入完毕")
# ## 载入 MNIST
#... | {"hexsha": "49a609df234971809a85e6b79debab66bf53284e", "size": 6151, "ext": "py", "lang": "Python", "max_stars_repo_path": "04_CNN_advances/cnn_mnist_simple.py", "max_stars_repo_name": "jastarex/DL_Notes", "max_stars_repo_head_hexsha": "4da8c5c90283d25655abde95263e44432aad343a", "max_stars_repo_licenses": ["Apache-2.0"... |
// Copyright (c) 2001-2011 Hartmut Kaiser
//
// 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)
#if !defined(BOOST_SPIRIT_KARMA_REAL_POLICIES_MAR_02_2007_0936AM)
#define BOOST_SPIRIT_KARMA_REAL_POLICIES_MAR... | {"hexsha": "139856dbdbad2412a729355a61fd3bcbd67c8a97", "size": 16610, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "master/core/third/boost/spirit/home/karma/numeric/real_policies.hpp", "max_stars_repo_name": "importlib/klib", "max_stars_repo_head_hexsha": "a59837857689d0e60d3df6d2ebd12c3160efa794", "max_stars_r... |
function [newnode,newelem,newelem0]=surfboolean(node,elem,varargin)
%
% [newnode,newelem,newelem0]=surfboolean(node1,elem1,op2,node2,elem2,op3,node3,elem3,...)
%
% merge two or more triangular meshes and resolve intersecting elements
%
% author: Qianqian Fang <fangq at nmr.mgh.harvard.edu>
%
% input:
% node: node... | {"author": "vigente", "repo": "gerardus", "sha": "4d7c5195b826967781f1bb967872410e66b7cd3d", "save_path": "github-repos/MATLAB/vigente-gerardus", "path": "github-repos/MATLAB/vigente-gerardus/gerardus-4d7c5195b826967781f1bb967872410e66b7cd3d/matlab/ThirdPartyToolbox/Iso2meshToolbox/surfboolean.m"} |
[STATEMENT]
lemma conjugate_char_1:
"conjugate f g \<longleftrightarrow> (\<forall>x y . f(x \<sqinter> -(g y)) \<le> f x \<sqinter> -y \<and> g(y \<sqinter> -(f x)) \<le> g y \<sqinter> -x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. conjugate f g = (\<forall>x y. f (x \<sqinter> - g y) \<le> f x \<sqinter> -... | {"llama_tokens": 187, "file": "Stone_Algebras_P_Algebras", "length": 1} |
import numpy as np
import pandas as pd
import torch
import torchvision
from am_utils.utils import walk_dir
from torch.utils.data import DataLoader
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from tqdm import tqdm
from ..dataset.dataset_object_inference import DatasetObjectInference, DatasetO... | {"hexsha": "29fee7debf540ea734dd7914a8a88c2d382b7fc6", "size": 9089, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml_utils/predict/predict_bbox.py", "max_stars_repo_name": "amedyukhina/ml_utils", "max_stars_repo_head_hexsha": "00176a015ff3b38f28637e66d4c89ec111247806", "max_stars_repo_licenses": ["Apache-2.0"... |
function [normalized_speed, actual_speed] = normalize_speed(speed, failures, skipping, tracker, sequence)
% normalize_speed Normalizes tracker speed estimate
%
% This function normalizes speed estimates based on performance profile and some information about
% the way the measurement was obtained (sequence, number of ... | {"author": "votchallenge", "repo": "toolkit-legacy", "sha": "2fb78d5301dadc102fb329b3a3f1bb02c670e8ee", "save_path": "github-repos/MATLAB/votchallenge-toolkit-legacy", "path": "github-repos/MATLAB/votchallenge-toolkit-legacy/toolkit-legacy-2fb78d5301dadc102fb329b3a3f1bb02c670e8ee/analysis/normalize_speed.m"} |
"""Errand OpenAcc backend module
"""
import os
import numpy
from errand.backend import CppBackendBase, cpp_varclass_template
from errand.compiler import Compilers
from errand.system import select_system
from errand.util import which
struct_template = """
typedef struct arguments {{
{args}
}} ARGSTYPE;
typede... | {"hexsha": "5fe6dec109aa859da5f214f20499ae74367bcfd4", "size": 8986, "ext": "py", "lang": "Python", "max_stars_repo_path": "errand/openacc_cpp.py", "max_stars_repo_name": "grnydawn/errand", "max_stars_repo_head_hexsha": "19c4fa4bb8c6698d56f2d671c1cba3ee070529ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
"""
A basic cost function, where the computed cost is the size
(number of children) of the current expression.
"""
function astsize(n::ENode, g::EGraph, an::Type{<:AbstractAnalysis})
cost = 1 + arity(n)
for id ∈ n.args
eclass = geteclass(g, id)
!hasdata(eclass, an) && (cost += Inf; break)
... | {"hexsha": "ef189e8d9b2fa96203b41f64285a3f639661adb2", "size": 3239, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/EGraphs/extraction.jl", "max_stars_repo_name": "gpeairs/Metatheory.jl", "max_stars_repo_head_hexsha": "782469676fb01db5eb3dc5f385539830b9116bea", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
/*
Copyright (c) 2014-15 Ableton AG, Berlin
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distr... | {"hexsha": "7f83bb443cdbb05f69696e9902a8dc02f98ea7cb", "size": 16211, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Convert.cpp", "max_stars_repo_name": "FMeinicke/aqt-stylesheets", "max_stars_repo_head_hexsha": "83a26ea9acfef80b98f126f2b706ee7f6175b42a", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_co... |
/*
* Copyright (C) 2014-2016 Open Source Robotics Foundation
*
* 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... | {"hexsha": "a3fd8a7b5e8a649f11007f3301e45705644dcb98", "size": 2016, "ext": "cc", "lang": "C++", "max_stars_repo_path": "gazebo/gui/BuildingEditor_TEST.cc", "max_stars_repo_name": "otamachan/ros-indigo-gazebo7-deb", "max_stars_repo_head_hexsha": "abc6b40247cdce14d9912096a0ad5135d420ce04", "max_stars_repo_licenses": ["E... |
# TODO
struct GEFile <: MRIFile
filename::String
end
function MRIBase.RawAcquisitionData(f::GEFile)
error("Not yet implemented!")
end | {"hexsha": "45eb5b312e1bc8556ac4478b2fec96c653dad72c", "size": 139, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "MRIFiles/src/GE/GE.jl", "max_stars_repo_name": "aTrotier/MRIReco.jl", "max_stars_repo_head_hexsha": "7437e5b41a5fdd0f4dff73a7d1913b78c0285493", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#
# Copyright 2020 Logical Clocks AB
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | {"hexsha": "30f7fcc58cc1f7dcb3e216ef8069bcfed7a01029", "size": 28209, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/hsfs/engine/python.py", "max_stars_repo_name": "gibchikafa/feature-store-api", "max_stars_repo_head_hexsha": "314a4d9a390bc371f4495f58c317797302f828ba", "max_stars_repo_licenses": ["Apache... |
"""
This file defines a class MicrogridEnv that wraps the Simulator in this package, so that it follows the
OpenAI gym (https://github.com/openai/gym) format.
"""
import gym
import numpy as np
from gym import spaces
from gym.utils import seeding
from microgridRLsimulator.simulate.simulator import Simulator
from micr... | {"hexsha": "df38c6599db4a7c8ae41d56f62db2d6a9dcd0e04", "size": 2651, "ext": "py", "lang": "Python", "max_stars_repo_path": "microgridRLsimulator/gym_wrapper/microgrid_env.py", "max_stars_repo_name": "d3sm0/microgridRLsimulator", "max_stars_repo_head_hexsha": "2721dd56430ff81a5ebd86fef6a94ed4acd1f26d", "max_stars_repo_l... |
from models.spacy_based_ir import SpacyIR
from models.bert_sts import BertSTSIR
from models.bert_nli import BertNLIIR
from models.bert_cnn import BertCNNIR
from tqdm import tqdm
import argparse
import pickle
import numpy as np
import os
def choose_model(topk=50):
irmodel = SpacyIR(topk=50)
return irmodel
de... | {"hexsha": "b0f8357f2dc6568d23f6223fd33eb1ab5b227d19", "size": 3979, "ext": "py", "lang": "Python", "max_stars_repo_path": "ir/run_ir.py", "max_stars_repo_name": "tbmihailov/OBQA", "max_stars_repo_head_hexsha": "653c5c64ae7eb164bde0b381813afe5f664dcf67", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
# referring to https://zhuanlan.zhihu.com/p/387853124
import os.path
import tensorrt as trt
import pycuda.driver as cuda
from util import GiB, HostDeviceMem
class TensorrtBase:
"""
Parent Class
"""
trt_logger = trt.Logger(trt.Logger.ERROR)
# make order consistency via onnx.
input_... | {"hexsha": "377f34aa89f64a52d1aeccfab6757e9414eb00d0", "size": 6918, "ext": "py", "lang": "Python", "max_stars_repo_path": "libs/dynamic_base.py", "max_stars_repo_name": "MichaelWU0726/x2trt", "max_stars_repo_head_hexsha": "75f34a8574315178589502ab14f64289e5c49061", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import numpy as np
from natasy.neural_network import Initialization, initializations
def test_initialization():
assert Initialization()
def test__zeros_initialization():
W, b = initializations._zeros_initialization(4, 6)
assert W.shape == (4, 6)
assert b.shape == (4, 1)
assert W.all() == 0 and... | {"hexsha": "9ec55123e6b5631bdeeea1af50318a6aa99c16d3", "size": 477, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_initializations.py", "max_stars_repo_name": "disooqi/DNN", "max_stars_repo_head_hexsha": "f87a10afba0810778ab3669f30e20128779f9da0", "max_stars_repo_licenses": ["AFL-3.0"], "max_stars_co... |
#!/usr/bin/env python3
##@package openzgy.impl.histogram
import numpy as np
class HistogramData:
def __init__(self, range_hint=None, dtype=np.float32):
self._hmin, self._hmax = self._suggestHistogramRange(range_hint, dtype)
self._dtype = dtype
self._size = 256
# Uncomment the next... | {"hexsha": "a4bf305269c174e2956fd9e48ae8a98d37e61c99", "size": 7926, "ext": "py", "lang": "Python", "max_stars_repo_path": "openzgy/impl/histogram.py", "max_stars_repo_name": "equinor/pyzgy", "max_stars_repo_head_hexsha": "94cd3d9050c3027d042a83b98779da9182041137", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
"""
:Author(s) Adam Camer-Pesci, Ryan Forster:
This file contains the methods used to perform calculations on scuba diving profiles.
"""
from rpy2.robjects.vectors import IntVector
import rpy2.robjects as robjects
import numpy as np
import math
import DiveConstants as dc
class Calculations:
def initialise_dive(... | {"hexsha": "8115a0309904eb3cc9f6f540095a7eb7c05409c0", "size": 19780, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/py/main/Calculations.py", "max_stars_repo_name": "AdamPesci/diveR", "max_stars_repo_head_hexsha": "ae7fe415bbdbb008fadd2b96a0a3a5092b04fdc7", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
"""
This file tests the ability for serde.py to convert complex types into
simple python types which are serializable by standard serialization tools.
For more on how/why this works, see serde.py directly.
"""
from syft.serde import native_serde
from syft.serde import serde
from syft.serde import torch_serde
import sy... | {"hexsha": "e8362f2c34f04f495f7df297b8f3c8d8f84bf5e5", "size": 24589, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_serde.py", "max_stars_repo_name": "1000ping/PySyft", "max_stars_repo_head_hexsha": "4d8cb0de436d7335bba6eb0a4a18402698ad3964", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import numpy as np
import statistics as stats
#use numpy libraray to import csv file as an ndarray and assign variable for each vector
data = np.genfromtxt('data/iris.csv', delimiter=',')
sepl = data[:,0]
sepw = data[:,1]
petl = data[:,2]
petw = data[:,3]
#using numpy and stats libraries print and format results
p... | {"hexsha": "c56c6ece91365331cd15a632e505506efe8426a0", "size": 2241, "ext": "py", "lang": "Python", "max_stars_repo_path": "06_iris_numpy_stats.py", "max_stars_repo_name": "tommirrington/52167-Programming-and-Scripting-Final-Project", "max_stars_repo_head_hexsha": "c78fa312eb9f8db4d43fa472b8ab536934a5d55c", "max_stars_... |
import numpy as np
import networkx as nx
import math
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns
# load GraphRicciCuravture package
from GraphRicciCurvature.OllivierRicci import OllivierRicci
from GraphRicciCurvature.FormanRicci import FormanRicci
from collections import default... | {"hexsha": "307fddc765b304f7557ca704a4adc91080600f73", "size": 20160, "ext": "py", "lang": "Python", "max_stars_repo_path": "Ion Aggregation/ollivier_HBN.py", "max_stars_repo_name": "ExpectozJJ/Persistent-Ollivier-Ricci-Curvature", "max_stars_repo_head_hexsha": "508f49e9aaa9f88552b49d01b6d585df9e75d220", "max_stars_rep... |
import enum
import logging
import numpy as np
import pytest
import arim.helpers
from arim.exceptions import InvalidShape, InvalidDimension, NotAnArray
def test_get_name():
metadata = dict(long_name="Nicolas", short_name="Nic")
assert arim.helpers.get_name(metadata) == "Nicolas"
del metadata["long_name"... | {"hexsha": "dbcf55198c58c82af1da0a2a7eab2ae54e7b70fe", "size": 5906, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_helpers.py", "max_stars_repo_name": "will-jj/arim", "max_stars_repo_head_hexsha": "fc15efe171a41355090123fcea10406ee75efe31", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, ... |
[STATEMENT]
lemma ifex_ite_opt_eq: "
ro_ifex i \<Longrightarrow> ro_ifex t \<Longrightarrow> ro_ifex e \<Longrightarrow> ifex_ite_opt i t e = ifex_ite i t e"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>ro_ifex i; ro_ifex t; ro_ifex e\<rbrakk> \<Longrightarrow> ifex_ite_opt i t e = ifex_ite i t e
[PROOF... | {"llama_tokens": 20397, "file": "ROBDD_BDT", "length": 30} |
import os
import pickle
import numpy as np
import pandas as pd
import yaml
import pyclass
#from online_reduction.pipeline import PandasClass
from sicparse import OptionParser
import logging
from jinja2 import Environment, PackageLoader
import subprocess
def jinja_raise(msg):
raise Exception(msg)
def debug(text)... | {"hexsha": "fa5d01d1b1a9f03971510c018815e9b7fb56ae88", "size": 24919, "ext": "py", "lang": "Python", "max_stars_repo_path": "kosma_py_lib/build/lib/kosma_py_lib/pandas_index.py", "max_stars_repo_name": "KOSMAsubmm/kosma_gildas_dlc", "max_stars_repo_head_hexsha": "cfa61dff10713717858a90eea52af76ca95e9fb3", "max_stars_re... |
import json
import nltk
import os
import math
import numpy as np
from tqdm import tqdm
import torch
import argparse
import random
import ast
import itertools
import csv
from Levenshtein import ratio
def convert_l(l):
if type(l) == list:
return l
else:
return ast.literal_eval(l)
def check_dist... | {"hexsha": "949f4d1b390862a42bec87246aca9f8a319a2106", "size": 4086, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/eval/eval_novelty.py", "max_stars_repo_name": "skgabriel/paracomet", "max_stars_repo_head_hexsha": "58dcc88c48f14103c3b890bbda5bb1346a0cdc26", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# -*- coding: utf-8 -*-
import numpy as np
from .Qt import QtGui, QtCore
from .functions import mkColor, eq, colorDistance, clip_scalar, clip_array
from os import path, listdir
from collections.abc import Callable, Sequence
import warnings
__all__ = ['ColorMap']
_mapCache = {}
def listMaps(source=None):
"""
... | {"hexsha": "5d985ad42f0f166797c16c014a1d9f044806641d", "size": 34031, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyqtgraph/colormap.py", "max_stars_repo_name": "leo603222/fix-displace-between-selection-area-and-mouse-pos", "max_stars_repo_head_hexsha": "1f9031884a980432795b69487bd659f5e4ef91aa", "max_stars_... |
[STATEMENT]
lemma less_eq_multiset_total:
fixes M N :: "'a :: linorder multiset"
shows "\<not> M \<le> N \<Longrightarrow> N \<le> M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> M \<le> N \<Longrightarrow> N \<le> M
[PROOF STEP]
by simp | {"llama_tokens": 102, "file": null, "length": 1} |
#!/usr/bin/env python
import numpy as np
from scipy import stats
from icecube.phys_services import I3MT19937
N=1000
def kstest(rs,i3name,i3var,spname,spvar):
sample = [getattr(rs,i3name)(*i3var) for x in range(N)]
return stats.kstest(sample, spname, args=spvar)[1]
def chisqtest(rs,i3name,i3var,spname,spvar,... | {"hexsha": "a3f01ca498875719cac83bc26ae5e08417fc469f", "size": 1489, "ext": "py", "lang": "Python", "max_stars_repo_path": "phys-services/resources/test/test_MT19937_stats.py", "max_stars_repo_name": "hschwane/offline_production", "max_stars_repo_head_hexsha": "e14a6493782f613b8bbe64217559765d5213dc1e", "max_stars_repo... |
import logging
import json
from onnxruntime import InferenceSession
import numpy as np
from pathlib import Path
from transformers import AutoTokenizer
import azure.functions as func
dir = Path.cwd()
model_path_list = [str(x) for x in dir.glob("*") if str(x).endswith("model")]
print(model_path_list)
if len... | {"hexsha": "2fb6daf511e332da2cbf36af7c13248a893f8425", "size": 2242, "ext": "py", "lang": "Python", "max_stars_repo_path": "GermanBert/predict/__init__.py", "max_stars_repo_name": "NeuroCode-io/model-deployments", "max_stars_repo_head_hexsha": "1641c766fbcc6c03647b31eb996fe57c09c173c3", "max_stars_repo_licenses": ["MIT... |
from time import time
import numpy as np
from math import pi
from .txtmark import lib
def count_ns(vts, fs):
dv1 = vts[fs[:,1]] - vts[fs[:,2]]
dv2 = vts[fs[:,1]] - vts[fs[:,0]]
ns = np.cross(dv1, dv2)
ass = np.linalg.norm(ns, axis=1)
ns /= np.linalg.norm(ns, axis=1).reshape((-1,1))
buf = np.zeros_like(vts)
for ... | {"hexsha": "0c6409b8faccf8cde37b0c53f685c0529fd9de05", "size": 5377, "ext": "py", "lang": "Python", "max_stars_repo_path": "imagepy/core/myvi/util.py", "max_stars_repo_name": "siyemuxu888/imagepy", "max_stars_repo_head_hexsha": "a933526483a15da282bacac54608d44d2173beb4", "max_stars_repo_licenses": ["BSD-4-Clause"], "ma... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
This implements a simple routine for writing/reading tables of arrays/simarrays
in a human readable ascii form in a way that preserves dtypes and units.
See write_table() and read_table()
Created on Thu Sep 14 11:45:35 2017
@author: ibackus
"""
import numpy as np
i... | {"hexsha": "4ea80558f1036d2083cc686dad65bba4c4e3ca57", "size": 8440, "ext": "py", "lang": "Python", "max_stars_repo_path": "diskpy/utils/_simarraywriter.py", "max_stars_repo_name": "langfzac/diskpy", "max_stars_repo_head_hexsha": "3b0f4fdc7f1fea21efdd3ab55bbf362181c7a3c4", "max_stars_repo_licenses": ["MIT"], "max_stars... |
from time import perf_counter
import sys
import numpy as np
import pandas as pd
from hurry.filesize import size
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier as RFC
i... | {"hexsha": "fa4cd0db4ba884f238f9438ac705163d01aad996", "size": 3248, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/scripts/weak_scaling/weak_scaling_gpu.py", "max_stars_repo_name": "upmem/scikit-dpu", "max_stars_repo_head_hexsha": "1ddeb5d195b9b119e379eb473b28c82a12a2b5fd", "max_stars_repo_licenses"... |
# Copyright 2019 The TensorNetwork Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | {"hexsha": "be7d59a106c561c623d0f7726db7d056a9bce162", "size": 17148, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/MPS/matrixproductoperators.py", "max_stars_repo_name": "priyansh19/TensorNetwork", "max_stars_repo_head_hexsha": "f83406c3749ed900573b9f80987738feea098df8", "max_stars_repo_licenses":... |
import os
import random
import numpy as np
from utils import read_data
import torch
from torch.utils.data import Dataset, DataLoader
import pdb
class Federated_Dataset(Dataset):
def __init__(self, X, Y, A):
self.X = X
self.Y = Y
self.A = A
def __getitem__(self, index):
X = sel... | {"hexsha": "b5ed61ff90fd136a8d0cda4734453d953fece182", "size": 6740, "ext": "py", "lang": "Python", "max_stars_repo_path": "FUEL/dataload.py", "max_stars_repo_name": "cuis15/FCFL", "max_stars_repo_head_hexsha": "59302004f9cfc20e305222ebb512235c6679cca8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_st... |
import unittest
import ifm_contrib as ifm
from ifm import Enum
import numpy as np
class TestPlot(unittest.TestCase):
def test_fringes(self):
ifm.forceLicense("Viewer")
doc = ifm.loadDocument(r".\models\example_2D.dac")
doc.loadTimeStep(doc.getNumberOfTimeSteps() - 1)
gdf = doc.c.pl... | {"hexsha": "4e30775fbf859dfa9545a95f9166981209e19610", "size": 2475, "ext": "py", "lang": "Python", "max_stars_repo_path": "unittests/test_plot_geopandas.py", "max_stars_repo_name": "DHI/ifm_contrib", "max_stars_repo_head_hexsha": "443c3a86960990115887855a2f4adac07797fc35", "max_stars_repo_licenses": ["MIT"], "max_star... |
import torch
from torch import nn
import numpy as np
import earthnet as en
def get_loss_from_name(loss_name):
if loss_name == "l2":
return Cube_loss(nn.MSELoss())
elif loss_name == "l1":
return Cube_loss(nn.L1Loss())
elif loss_name == "Huber":
return Cube_loss(nn.HuberLoss())
el... | {"hexsha": "90ae4b910923d16af719c4c61167ab96e124ddbe", "size": 5866, "ext": "py", "lang": "Python", "max_stars_repo_path": "drought_impact_forecasting/losses.py", "max_stars_repo_name": "rudolfwilliam/satellite_image_forecasting", "max_stars_repo_head_hexsha": "164ee7e533e1a8d730a0ee9c0062fd9b32e0bcdc", "max_stars_repo... |
"""
Copyright 2020 William Rochira at York Structural Biology Laboratory
"""
import os
import gzip
import pickle
import zipfile
import requests
import numpy as np
from common import setup
from _defs import ROTAMER_OUTPUT_DIR
REFERENCE_DATA_URL = 'https://github.com/rlabduke/reference_data/archive/master.zip'
REFER... | {"hexsha": "5a9a34fb553bed0c0f7b50cc8499ef8b44c74ee6", "size": 6824, "ext": "py", "lang": "Python", "max_stars_repo_path": "iris_tools/rotamer_generate_library.py", "max_stars_repo_name": "FilomenoSanchez/iris-validation", "max_stars_repo_head_hexsha": "a7bbb28dfe239527c32914229e69e007a519e0dd", "max_stars_repo_license... |
# Copyright (C) 2019 Harvard University. All Rights Reserved. Unauthorized
# copying of this file, via any medium is strictly prohibited Proprietary and
# confidential
# Developed by Mohammad Haft-Javaherian <mhaft-javaherian@mgh.harvard.edu>,
# <7javaherian@gmail.com>.
# =========... | {"hexsha": "423c52985996366f7dcf4e6fe24aca4ba2496bb3", "size": 5956, "ext": "py", "lang": "Python", "max_stars_repo_path": "util/plot_log_file.py", "max_stars_repo_name": "mvWellman/OCTseg", "max_stars_repo_head_hexsha": "c3fe1098d031f74422956e2335dd4bae16dde7b6", "max_stars_repo_licenses": ["FSFAP"], "max_stars_count"... |
{-# LANGUAGE ForeignFunctionInterface #-}
module Haskstat where
import Data.List
import Data.Maybe
import Data.Complex
foreign import ccall "erf" c_erf :: Double -> Double
floatLength :: Fractional a => [b] -> a
floatLength xs = fromIntegral $ length xs
mean :: Fractional a => [a] -> a
mean xs = (foldl' (+) 0 xs) ... | {"hexsha": "a727ff20f91704df805f2a3edaf194c9a667e3cf", "size": 7187, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "Haskstat.hs", "max_stars_repo_name": "kevintyloo/haskstat", "max_stars_repo_head_hexsha": "1df809220ff904161719e5ec5272bda239c352c9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
# -*- coding: utf-8 -*-
import itertools
import numpy as np
import shapely.geometry
MESH_LEVEL_ALIAS = {
"80km": 1, "10km": 2, "1km": 3,
"500m": 4, "1/2": 4, "half": 4,
"250m": 5, "1/4": 5, "quarter": 5,
"125m": 6, "1/8": 6, "oneeighth": 6
}
# lat*120 = km, lon*80 = km
# i.e. km/120 = lat, km/80 = l... | {"hexsha": "67c3b5061384ff40a420ee7e52e661a2e3d5c85b", "size": 7843, "ext": "py", "lang": "Python", "max_stars_repo_path": "meshjp/meshjp.py", "max_stars_repo_name": "kotamori4/meshjp", "max_stars_repo_head_hexsha": "598100298bd46d05ef6dd90a49c305db3118d721", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import numpy as np
BATCH_SIZE = 128
NUM_CLASSES = 10
EPOCHS = 20
def get_dataset():
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train... | {"hexsha": "865244eaeb21537c364a18e6e892b0dff7d307cd", "size": 1595, "ext": "py", "lang": "Python", "max_stars_repo_path": "deeplearning/keras/mnist/mnist_mlp.py", "max_stars_repo_name": "terasakisatoshi/pythonCodes", "max_stars_repo_head_hexsha": "baee095ecee96f6b5ec6431267cdc6c40512a542", "max_stars_repo_licenses": [... |
# <Copyright 2019, Argo AI, LLC. Released under the MIT license.>
from typing import List, Optional, Sequence
import numpy as np
class LaneSegment:
def __init__(
self,
id: int,
has_traffic_control: bool,
turn_direction: str,
is_intersection: bool,
l_neighbor_id: Op... | {"hexsha": "07616f99062f8374c8e18c81de70e96bdacb138a", "size": 1515, "ext": "py", "lang": "Python", "max_stars_repo_path": "argoverse/map_representation/lane_segment.py", "max_stars_repo_name": "ajinkyakhoche/argoverse-api", "max_stars_repo_head_hexsha": "b1730f9e4377325436f3364abb4c1fe54ec71b0a", "max_stars_repo_licen... |
import os
# import tensorflow as tf
import tensorrt as trt
from tensorrt.parsers import uffparser
import pycuda.driver as cuda
# import uff
import cv2
import numpy as np
from tqdm import tqdm
TEST_PATH = "/media/andy/Data/DevWorkSpace/Projects/imageClassifier/data/test/"
# TEST_PATH = "/home/andy/caffe/examples/myda... | {"hexsha": "86ebee0ee11fc6591c5267c871d8c85eb398d558", "size": 5685, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tensorrt/tools/caffe_engine/call_engine_to_infer_all_print_predict_on_image_6classes.py", "max_stars_repo_name": "aimuch/AIEnvConfig", "max_stars_repo_head_hexsha": "4ccd54e9c601e8c91efebcec1a... |
import sys
sys.path.append('..')
from common.core import *
from common.gfxutil import *
from common.audio import *
from common.mixer import *
from common.note import *
from common.wavegen import *
from common.wavesrc import *
from common.writer import *
from Enemy import *
from Background import *
from Foreground impo... | {"hexsha": "51f3223b910e62715e229f32a2aea19878e09cfc", "size": 12024, "ext": "py", "lang": "Python", "max_stars_repo_path": "project/Handler.py", "max_stars_repo_name": "osmidy/Dischord", "max_stars_repo_head_hexsha": "3c3802eb4917adb9384256d8a0c7ba4f123fd166", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
SUBROUTINE RU_PLVL ( field, above, level, pres, iret )
C************************************************************************
C* RU_PLVL *
C* *
C* This subroutine gets the level number and pressure from a group *
C* which is in the form LLPPP. LL must be the same integer, repeated; *
C* for example... | {"hexsha": "4675a9b128e136c42783e6f39f4a1656605aef36", "size": 1936, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/bridge/ru/ruplvl.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
# David R Thompson
import argparse, sys, os
import numpy as np
import pylab as plt
from copy import deepcopy
from glob import glob
from spectral.io import envi
from scipy.stats import norm
from scipy.linalg import solve, inv
from astropy import modeling
from sklearn.linear_model import RANSACRegressor
from scipy.optimi... | {"hexsha": "6b8a84177bc57b204d6cb3209ed04b287754ed46", "size": 6859, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/optimizeghost.py", "max_stars_repo_name": "emit-sds/emit-sds-l1b", "max_stars_repo_head_hexsha": "be5307fe6821a043971becdd33609b4cf89b1974", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
c { dg-do run }
c { dg-options "-std=legacy" }
c
c Produced a link error through not eliminating the unused statement
c function after 1998-05-15 change to gcc/toplev.c. It's in
c `execute' since it needs to link.
c Fixed by 1998-05-23 change to f/com.c.
values(i,j) = val((i-1)*n+j)
end
| {"hexsha": "855b9a442d70c1f826771565fffbcb9755063eb6", "size": 317, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/g77/980520-1.f", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_repo_license... |
# -*- coding: utf-8 -*-
"""
computeKey
computes the musical key of an input audio file
Args:
afAudioData: array with floating point audio data.
f_s: sample rate
afWindow: FFT window of length iBlockLength (default: hann)
iBlockLength: internal block length (default: 4096 samples)
iHopLe... | {"hexsha": "230e161d8822436d9c22ff4a9faef18a5b6893b8", "size": 3311, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyACA/computeKey.py", "max_stars_repo_name": "ruohoruotsi/pyACA", "max_stars_repo_head_hexsha": "339e9395b65a217aa5965638af941b32d5c95454", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8... |
import cv2
import numpy as np
import sys
from libs.real.get_map import rotateImage
def write_files(lower, upper, color_name):
np.save('color_values/' + 'lower_' + color_name, lower)
np.save('color_values/' + 'upper_' + color_name, upper)
def load_map_setup():
map_img = cv2.imread('map_setup/map.png')
... | {"hexsha": "ecdf522f2c78f7b2afd8ae84e9afbdba396e45db", "size": 2454, "ext": "py", "lang": "Python", "max_stars_repo_path": "real_setup_colors.py", "max_stars_repo_name": "skkywalker/tcc", "max_stars_repo_head_hexsha": "5c0faf6dd6c4a66fb7774aae7caf33c5af8f7721", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
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