text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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# ------------------------------------------------------
# Morphological Operations: Erosion
#
# Created by Khushi Agrawal on 19/09/21.
# Copyright (c) 2021 Khushi Agrawal. All rights reserved.
#
# ------------------------------------------------------
import cv2
import numpy as np
# Image path
# Tried with other im... | {"hexsha": "13f3bb05992aaa9d6f6397c22efc327a06575dc7", "size": 1067, "ext": "py", "lang": "Python", "max_stars_repo_path": "opencv/2021/lab-2/ques2/erosion.py", "max_stars_repo_name": "khushi-411/tutorials", "max_stars_repo_head_hexsha": "e7f627f9242d21f76e49c235edeaa99e5befc24c", "max_stars_repo_licenses": ["Apache-2.... |
function [tf,te,to,sc,I,up] = tsurf_cad(F,V,varargin)
% [tf,te,to,sc,I,up] = tsurf_cad(F,V,varargin)
%
% TSURF_CAD Display a triangle mesh in a CAD-like rendering style: edges,
% shadow, floor.
%
% Inputs:
% F #F by 3 list of face indices
% V #V by 3 list of vertex positions
% Outputs:
% tf ... | {"author": "alecjacobson", "repo": "gptoolbox", "sha": "a0cb37d8edbcfb1e3587f793df8f24c76a2d7305", "save_path": "github-repos/MATLAB/alecjacobson-gptoolbox", "path": "github-repos/MATLAB/alecjacobson-gptoolbox/gptoolbox-a0cb37d8edbcfb1e3587f793df8f24c76a2d7305/mesh/tsurf_cad.m"} |
from typing import TextIO
import numpy as np
class Logger:
"""
Logs the train progress to stdout and log file
"""
def multi_stage_to_text(self, losses):
losses = [f"{l:4.2f}" for l in losses]
return '->'.join(losses)
def log_train_progress(self, epoch: int, losses, num_stages: int... | {"hexsha": "4ad79a2d89c173987f5f6a7a14e6732f5d9ad4ca", "size": 3128, "ext": "py", "lang": "Python", "max_stars_repo_path": "utility/logger.py", "max_stars_repo_name": "davda54/meta-tasnet", "max_stars_repo_head_hexsha": "8f3fee69f5f03e67bc495b55736ffb51eb428a45", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
"""
test_measures
-------------
Collections of measures.
"""
import numpy as np
from ..artificial_data.create_artificial_timeseries import create_random_ts,\
create_random_raster, create_brownian_noise_regular_ts
from ..Measures.hurst_measures import hurst
from ..Measures.hurst_measures import create_RS_scales_s... | {"hexsha": "cddc5f8665c202469377a5f3fef06c098202687a", "size": 4089, "ext": "py", "lang": "Python", "max_stars_repo_path": "TimeSeriesTools/tests/test_measures.py", "max_stars_repo_name": "Psicowired87/TimeSeriesTools", "max_stars_repo_head_hexsha": "de42dbcc5371ee576df6c9521b1c79a47c147dd1", "max_stars_repo_licenses":... |
[STATEMENT]
lemma evaln_Suc: "<c,s> -n-> s' ==> <c,s> -Suc n-> s'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. <c,s> -n-> s' \<Longrightarrow> <c,s> -Suc n-> s'
[PROOF STEP]
apply (erule evaln_nonstrict)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. n \<le> Suc n
[PROOF STEP]
apply auto
[PROOF STATE]
proof (p... | {"llama_tokens": 178, "file": null, "length": 3} |
# --------------------------------------------------------
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import argparse
import datetime
import numpy as np
import itertools
import torch
from core.bc import BC
from core.ddpg import DDPG
from core.... | {"hexsha": "99039f805c6ace195d0e28894cfd6a2a79c2344e", "size": 27530, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/train_online.py", "max_stars_repo_name": "liruiw/HCG", "max_stars_repo_head_hexsha": "a928ce7fb0df022cb2ceaeff32925f13de369519", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "ma... |
import dgl
import tensorflow as tf
from tensorflow.keras.models import Model
from gnn.models.tools import Linear, evaluate
class GATLayer(Model):
def __init__(self, out_feats, batch_norm=False, dropout_rate=None):
super(GATLayer, self).__init__()
# init fully connected linear layer
self.... | {"hexsha": "5ee99937020c207fe9e7c0c2832b601efcb6ba33", "size": 7886, "ext": "py", "lang": "Python", "max_stars_repo_path": "gnn/models/gat_conv.py", "max_stars_repo_name": "cwfparsonson/gnn", "max_stars_repo_head_hexsha": "ed41968b7253382d0a791608e042c64b97168071", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
include("./src/MyPlots.jl")
import .MyPlots as plo
plo.plot()
| {"hexsha": "1b5aad5f06a829f3e63d2f67a587bab3d89a92b5", "size": 64, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "coord-desc/MyPlots/main.jl", "max_stars_repo_name": "applied-math-coding/article-snippets", "max_stars_repo_head_hexsha": "3ac3dc58905a9f6c662e3922dafc9b230a9ffd6e", "max_stars_repo_licenses": ["MIT"... |
/* RevKit: A Toolkit for Reversible Circuit Design (www.revkit.org)
* Copyright (C) 2009-2011 The RevKit Developers <revkit@informatik.uni-bremen.de>
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software ... | {"hexsha": "febace0d9d5bfdbaf2796e75b26ee66ce3e1c455", "size": 34785, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "rkqc/src/core/circuit.hpp", "max_stars_repo_name": "clairechingching/ScaffCC", "max_stars_repo_head_hexsha": "737ae90f85d9fe79819d66219747d27efa4fa5b9", "max_stars_repo_licenses": ["BSD-2-Clause"],... |
"""Unit tests for the main detector code"""
import logging
from pathlib import Path
import numpy as np
import pytest
from copydetect import CodeFingerprint, CopyDetector, compare_files, utils
TESTS_DIR = str(Path(__file__).parent)
class TestTwoFileDetection:
"""Test of the user-facing copydetect code for a si... | {"hexsha": "8d323ea9c0bf6fc6091d4362d8bcd57d4944e23f", "size": 9518, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_detector.py", "max_stars_repo_name": "Apex-Design/copydetect", "max_stars_repo_head_hexsha": "ef9e172988e3605bab22a896dbe694d551fed7c5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from mAP import cal_mAP
def readtxt(path):
label = []
with open(path, 'r') as f:
x = f.readlines()
for name in x:
temp = int(name.strip().split()[1])
label.append(temp)
label = np.array(label)
return label
train_label = readtxt(
"/home/a... | {"hexsha": "db50236b999ca6e5a758d0e3f4e73f633aaf6424", "size": 876, "ext": "py", "lang": "Python", "max_stars_repo_path": "Deep-Hash-learning-for-Remote-Sensing-Image-Retrieval/Metric/test.py", "max_stars_repo_name": "LiuChaoXD/Remote-Sensing-Image-Retrieval-Models", "max_stars_repo_head_hexsha": "c135562263102080716e3... |
from scipy import ndimage
from skimage import measure
import numpy as np
import cv2
def crop_rectangle(image, rect):
# rect has to be upright
num_rows = image.shape[0]
num_cols = image.shape[1]
if not inside_rect(rect = rect, num_cols = num_cols, num_rows = num_rows):
print("Proposed rectang... | {"hexsha": "f73c27bb08a8934e59e20f51fc072681cf6f55ce", "size": 4278, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/DataAugmentations.py", "max_stars_repo_name": "DrJonoG/StomataGSMax", "max_stars_repo_head_hexsha": "18e5f993ed875ae6af07a4c7d1c0e4ff97e2c947", "max_stars_repo_licenses": ["Apache-2.0"], ... |
"""Group together various number density descriptors."""
import numpy as np
import frbpoppy.precalc as pc
class NumberDensity:
"""Class for cosmic number density."""
def __init__(self,
model='vol_co',
z_max=2.0,
H_0=67.74,
W_m=0.3089,
... | {"hexsha": "3dee2d03cfb271714cc08f3041fc2321bd4de1ad", "size": 3430, "ext": "py", "lang": "Python", "max_stars_repo_path": "frbpoppy/number_density.py", "max_stars_repo_name": "macrocosme/frbpoppy", "max_stars_repo_head_hexsha": "b23a0c1dbf4e6559f26e79994147ed2a9352ffc7", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from flask import Flask,render_template, Response,request
import json,base64,io,websockets,cv2,asyncio,socket
import numpy as np
from shapeDetectionClass import ShapeDetection
from webSocketsOpencvClient import WebSocketsOpencvClient
from roboSocketCom import RoboSocketCom
from websocket import create_connection
app=F... | {"hexsha": "124b5a1e720210d54a9a2fbc297e0ef4eaa8df85", "size": 7179, "ext": "py", "lang": "Python", "max_stars_repo_path": "Rover/Controller-PC/loopPC.py", "max_stars_repo_name": "harunlakodla/Gallipoli_Rov", "max_stars_repo_head_hexsha": "5dea2c681c35d81a3560ba93f2820cb717550446", "max_stars_repo_licenses": ["MIT"], "... |
# Copyright 2016-2020 Blue Marble Analytics LLC.
#
# 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": "6965b181f48e8d2322355a11e9d543ee80529032", "size": 4287, "ext": "py", "lang": "Python", "max_stars_repo_path": "gridpath/system/reliability/prm/prm_requirement.py", "max_stars_repo_name": "anamileva/gridpath", "max_stars_repo_head_hexsha": "e55eacb88ca5e6c034a90b18819e17cbd6f43854", "max_stars_repo_licenses... |
//
// Copyright (c) 2021 Richard Hodges (hodges.r@gmail.com)
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE or copy at http://www.boost.org/LICENSE_1_0.txt)
//
#ifndef NEW_YEAR_2021_HTTP_CONNECTION_KEY_HPP_1D2C5F73C1CF482A9289DABEEBCBA1C8
#define NEW_YEAR_2021_HTTP_C... | {"hexsha": "669a65dee3b68099c56e2ec705056c529a5400ab", "size": 1055, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/http/connection_key.hpp", "max_stars_repo_name": "madmongo1/blog-new-year-2021", "max_stars_repo_head_hexsha": "b6e247e523f9a65a8e0a6375fd727878494dfac5", "max_stars_repo_licenses": ["BSL-1.0"],... |
f1:=proc() return 1; end proc:
f2:=proc() return 2; end proc:
f3:=proc() return 3; end proc:
f4:=proc() return 4; end proc:
fun:=proc(f1)
global f2,f3,f4;
print(f1(),f2(),f3(),f4());
end proc:
coverArgs:={},{}:
Grid:-Launch(fun,coverArgs,eval(f1),
imports=['f2',"f3",':-f4'=eval(f4)]); | {"hexsha": "74a949cd8a7092b2746800a612a963f38343a74b", "size": 306, "ext": "mpl", "lang": "Maple", "max_stars_repo_path": "Grid/imports/procs.mpl", "max_stars_repo_name": "yu961549745/MapleParallel", "max_stars_repo_head_hexsha": "6fe9ceb7766e0803761ab7368caa9b3f856dcf5b", "max_stars_repo_licenses": ["MIT"], "max_stars... |
// Copyright (C) 2021 Djordje Vukcevic <djordje dot vukcevic at h-brs dot de>
// Version: 1.0
// Author: Djordje Vukcevic <djordje dot vukcevic at h-brs dot de>
// URL: http://www.orocos.org/kdl
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General P... | {"hexsha": "c60b882d41ae1eb765c18c6293b593ff5e918f4c", "size": 6683, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3rdparty/kdl/src/chainexternalwrenchestimator.hpp", "max_stars_repo_name": "rocos-sia/rocos-app", "max_stars_repo_head_hexsha": "83aa8aa31dd303d77693cfc5ad48055d051fa4bc", "max_stars_repo_licenses":... |
#MenuTitle: Myanmar Medial Ra Maker
# -*- coding: utf-8 -*-
import GlyphsApp
import numpy as np
import re
number_of_glyphs = 5
def mean(it):
return sum(it)/len(it)
def roundto(x,y):
return int(x/y)*y
def ssq(j, i, sum_x, sum_x_sq):
if (j > 0):
muji = (sum_x[i] - sum_x[j-1]) / (i - j + 1)
... | {"hexsha": "e1599d982c1a88cdbb92075127e9f1478961a3d9", "size": 6686, "ext": "py", "lang": "Python", "max_stars_repo_path": "Myanmar Medial Ra Maker.py", "max_stars_repo_name": "simoncozens/GlyphsScripts", "max_stars_repo_head_hexsha": "dc474b598a4016b5df8aa088362e72bdaffa8034", "max_stars_repo_licenses": ["MIT"], "max_... |
#===============================================================================
DeconvolutionMatrices.jl
types for K matrices and their inverses
used in deconvolution.jl
Author: Tom Price
Date: June 2019
===============================================================================... | {"hexsha": "f34ea720307f4ca240dd7568296608e55e05bfc9", "size": 3714, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "bioinformatics/QpcrAnalysis/defines/DeconvolutionMatrices.jl", "max_stars_repo_name": "MakerButt/chaipcr", "max_stars_repo_head_hexsha": "a4c0521d1b2ffb2aa1c90ff21f3ca4779b6831d1", "max_stars_repo_... |
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import time
import random
import datetime
from datetime import date
from datetime import timedelta
from dateutil.relativedelta import relativedelta
import pickle
from pyomo.environ import *
from pyomo.opt import SolverFactory
def... | {"hexsha": "2d7c5fd17d4db26834e86eb81f9bed5d3da9d417", "size": 4692, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/risk_methods_pyomo.py", "max_stars_repo_name": "DavimenUC3M/IronIA-RoboAdvisor", "max_stars_repo_head_hexsha": "06d37889d5cb9c40139ceb6a41c959b92fff3291", "max_stars_repo_licenses": ["MIT"], "... |
import os
import numpy as np
import torch
opj = os.path.join
import pickle as pkl
device = 'cuda' if torch.cuda.is_available() else 'cpu'
from awave.transform1d import DWT1d
from awave.transform2d import DWT2d
def warm_start(p, out_dir):
'''load results and initialize model
'''
print('\twarm starting... | {"hexsha": "ad105f5e7bad6b1f3b4053ac47b4778544f6f429", "size": 2289, "ext": "py", "lang": "Python", "max_stars_repo_path": "awave/utils/warmstart.py", "max_stars_repo_name": "Yu-Group/adaptive-wavelets", "max_stars_repo_head_hexsha": "e67f726e741d83c94c3aee3ed97a772db4ce0bb3", "max_stars_repo_licenses": ["MIT"], "max_s... |
import .mod_f
import .hol_bdd
import number_theory.modular
import algebra.big_operators.basic
import .q_expansion
import analysis.complex.unit_disc.basic
import number_theory.modular
--import data.nat.lattice
open complex
open_locale big_operators classical
noncomputable theory
open modular_form modular_group com... | {"author": "ferrandf", "repo": "valenceformula", "sha": "c542edc32e3fc0ef142d69a0c897192f040e4b3e", "save_path": "github-repos/lean/ferrandf-valenceformula", "path": "github-repos/lean/ferrandf-valenceformula/valenceformula-c542edc32e3fc0ef142d69a0c897192f040e4b3e/src/valence_formula.lean"} |
# --------------
# Initialization
# --------------
using LLLplus
using Test
using LinearAlgebra
using Documenter
# --------------
# tests with small matrices
# --------------
println("tests with small matrices...")
# Matrix from http://home.ie.cuhk.edu.hk/~wkshum/wordpress/?p=442
H =[1 9 1 2;
1 8 8 3;... | {"hexsha": "b1c8b0285613743ae93fe8c379b1b58438d8a021", "size": 2204, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "GiggleLiu/LLLplus.jl", "max_stars_repo_head_hexsha": "01fbfa2ec47cbee224cf6827e90a1c2f208ba9ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 51... |
#define CATCH_CONFIG_MAIN
#include <catch.hpp>
#define BOOST_THREAD_PROVIDES_FUTURE
#define BOOST_THREAD_PROVIDES_FUTURE_CONTINUATION // Enables future::then
#include <boost/thread.hpp>
#include <boost/asio.hpp>
#include <cstdio>
using namespace boost;
using namespace boost::asio;
TEST_CASE ("future_42Test", "[futu... | {"hexsha": "1e18504bd1d3e0e069ffaf141db3292d93478959", "size": 532, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/boost_ms/future_42Test.cpp", "max_stars_repo_name": "tlanc007/coroutine-ts", "max_stars_repo_head_hexsha": "2da75f32178597a3377a0e8b489b2123d18591bf", "max_stars_repo_licenses": ["MIT"], "max_s... |
import argparse
import os
import os.path as osp
import cc3d
import numpy as np
import torch
import trimesh
import kaolin as kal
from architectures import Generator
from hanging_points_generator.create_mesh import create_urdf
from hanging_points_generator.generator_utils import save_json
parser = argparse.ArgumentPar... | {"hexsha": "48a899d10b6734559febe9b9681515c72e457b2f", "size": 5156, "ext": "py", "lang": "Python", "max_stars_repo_path": "random_mesh/random_mesh_generator.py", "max_stars_repo_name": "kosuke55/hanging_points_generator", "max_stars_repo_head_hexsha": "36738fd348a3d8950769cb8125fc0a731447c204", "max_stars_repo_license... |
"""
=====================================================
Galactic Synchrotron (:mod:`~cora.foreground.galaxy`)
=====================================================
.. currentmodule:: cora.foreground.galaxy
Generate semi-realistic simulations of the full sky synchrotron emission
from the Milky Way.
Classes
=======
... | {"hexsha": "b97c4857d1e2601049ba89d0d2b4d2d5631cf360", "size": 11390, "ext": "py", "lang": "Python", "max_stars_repo_path": "cora/foreground/galaxy.py", "max_stars_repo_name": "sjforeman/cora", "max_stars_repo_head_hexsha": "48d127d9e00b1fb1cf2024004d1d1e7441fd1e1f", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import cv2
import torch
import random
import librosa
import numpy as np
cv2.setNumThreads(0)
def image_crop(image, bbox):
return image[bbox[1]:bbox[3], bbox[0]:bbox[2]]
def gauss_noise(image, sigma_sq):
h, w = image.shape
gauss = np.random.normal(0, sigma_sq, (h, w))
gauss = gauss.reshape(h, w)
... | {"hexsha": "f4c67c18e677ba218ad1c5a297d53014928f4b28", "size": 8729, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/transforms.py", "max_stars_repo_name": "SarthakYadav/GISE-51-pytorch", "max_stars_repo_head_hexsha": "2cf816ed14db27d468b44a88702f0386f7c733c1", "max_stars_repo_licenses": ["MIT"], "max_s... |
library(shiny)
library(dplyr)
library(plotly)
library(readxl)
library(lubridate)
#library(shinyalert)
library(shinyWidgets)
library("httr")
library("jsonlite")
library(rgdal)
library(leaflet)
library(lubridate)
knitr::opts_chunk$set(echo = TRUE)
#library('BBmisc')
#library(viridis) # paletas de colores
set.seed(19990)... | {"hexsha": "e38862b557c41101b5513a5430cec3ed3bb98e49", "size": 7374, "ext": "r", "lang": "R", "max_stars_repo_path": "app/server.r", "max_stars_repo_name": "felinblackcat/Trabajo1TAE2020", "max_stars_repo_head_hexsha": "3e6d9dc66b7771f4eb534b96f2b3f74083107dc9", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
from src.execise_7 import execise_7
from src.execise_8 import execise_8
import matplotlib.pyplot as plt
import numpy as np
from numpy import array
class exercise4:
def __init__(self, arg):
self.arg = arg
self.obs = []
self.goal_ = [-2.0, 0]
self.start_ = [2.0, 0]
self.goal ... | {"hexsha": "a4fac31092f8a6b297720627fc93dd00bc409afe", "size": 6879, "ext": "py", "lang": "Python", "max_stars_repo_path": "HW3_gradient_descent_C_space_links_rotation/src/exercise4.py", "max_stars_repo_name": "CooviM/motion_planning", "max_stars_repo_head_hexsha": "9d94feebcd85f71fe51f0ac62a6472d3086a5955", "max_stars... |
# -*- coding: utf-8 -*-
u"""
:copyright: Copyright (c) 2020 RadiaSoft LLC. All Rights Reserved.
:license: http://www.apache.org/licenses/LICENSE-2.0.html
"""
from __future__ import absolute_import, division, print_function
import time
import os
import yaml
import numpy as np
def record_time(func, time_list, *args, ... | {"hexsha": "373839e70c9fcbb0a725a1ed6ee590e364a0220e", "size": 5186, "ext": "py", "lang": "Python", "max_stars_repo_path": "rswarp/run_files/tec/tec_utilities.py", "max_stars_repo_name": "radiasoft/rswarp", "max_stars_repo_head_hexsha": "867fdcfb0b9b75ad7458b6e5ae065de340ff59bb", "max_stars_repo_licenses": ["Apache-2.0... |
# coding=utf-8
import numpy as np
# 选项
CHOICES = "ABCDE"
# 一行选项+题号列数，例如一行有3题，一题4个选项，所以总共有3*4+3个列
CHOICE_COL_COUNT = 18
# 每题题选项数
CHOICES_PER_QUE = 5
# 每个选项框里面白色点所占比例阈值，小于则说明该选项框可能被填涂
WHITE_RATIO_PER_CHOICE = 0.80
# 受限于环境，光源较差的情况下或腐蚀膨胀参数设置不对，
# 可能会有误判，这个参数这是比较两个都被识别为涂写的选项框是否有误判的阈值
MAYBE_MULTI_CHOICE_THRESHOLD = 0.07... | {"hexsha": "4ba60e89a6c41691a76fac512f0ebde848350384", "size": 1351, "ext": "py", "lang": "Python", "max_stars_repo_path": "settings.py", "max_stars_repo_name": "godinthehell/answer-sheet-scan", "max_stars_repo_head_hexsha": "aaf7d322a212d68edd348b05194df6166bbc2e5c", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# For licensing see accompanying LICENSE.txt file.
# Copyright (C) 2021 Apple Inc. All Rights Reserved.
# HDR Environment Map Estimation for Real-Time Augmented Reality, CVPR 2021.
# Demo application using the reference implementations of the AngularError and FID metric used in the above paper.
import cv2
import nump... | {"hexsha": "999796b76de9235376f9986a70cc42b7e51c3847", "size": 3882, "ext": "py", "lang": "Python", "max_stars_repo_path": "DemoEnvMapNetMetrics.py", "max_stars_repo_name": "LaudateCorpus1/ml-envmapnet", "max_stars_repo_head_hexsha": "326379ed50cd2bf3a277a3d16fc85d6d4b703eb2", "max_stars_repo_licenses": ["AML"], "max_s... |
'''
A custom Keras layer to perform L2-normalization.
Copyright (C) 2017 Pierluigi Ferrari
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any la... | {"hexsha": "5d5f0498bced30b90b5f2a32f172fba897a71c7e", "size": 2179, "ext": "py", "lang": "Python", "max_stars_repo_path": "keras_layer_L2Normalization.py", "max_stars_repo_name": "sa-y-an/Face-detection-with-mobilenet-ssd", "max_stars_repo_head_hexsha": "58fafb6e93d28531797aac1e9a4436730c8cee7c", "max_stars_repo_licen... |
[STATEMENT]
lemma HYH_is_minusY [simp]:
"H * Y * H = - Y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. H * Y * H = - Y
[PROOF STEP]
apply(simp add: Y_def H_def times_mat_def)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. mat 2 2 (\<lambda>(i, j). row (mat 2 2 (\<lambda>(i, j). row (1 / complex_of_real (sqrt ... | {"llama_tokens": 1208, "file": "Isabelle_Marries_Dirac_Quantum", "length": 3} |
From Test Require Import tactic.
Section FOFProblem.
Variable Universe : Set.
Variable UniverseElement : Universe.
Variable p_ : Universe -> Prop.
Variable l_ : Universe -> Prop.
Variable i_ : Universe -> Universe -> Prop.
Variable goal_ : Prop.
Variable e_ : Universe -> Universe -> Prop.
Variable dom_ : Universe ->... | {"author": "janicicpredrag", "repo": "Larus", "sha": "a095ca588fbb0e4a64a26d92946485bbf85e1e08", "save_path": "github-repos/coq/janicicpredrag-Larus", "path": "github-repos/coq/janicicpredrag-Larus/Larus-a095ca588fbb0e4a64a26d92946485bbf85e1e08/benchmarks/coq-problems/coherent-logic-benches/cro_8_2_in.v"} |
import numpy as np
from sklearn.model_selection import train_test_split
from problem_2.parameters import *
# Function to load data from file
def load_data(train_file):
# Matrices to store data
X = np.zeros([TRAIN_SAMPLES, DIMENSIONS])
Y = np.zeros(TRAIN_SAMPLES)
# Read train data file
file = open... | {"hexsha": "9bc47d1c21cbbe99934d09bb12b5aeaa3f1cd76b", "size": 912, "ext": "py", "lang": "Python", "max_stars_repo_path": "problem_2/load_data.py", "max_stars_repo_name": "vineeths96/SVM-and-Neural-Networks", "max_stars_repo_head_hexsha": "84d734542d4f7fc718c49a8d63db07b0597ccbc7", "max_stars_repo_licenses": ["MIT"], "... |
#include <libv/lma/ttt/mpl/naming.hpp>
#include <iostream>
#include <boost/mpl/remove_if.hpp>
#include <boost/mpl/vector.hpp>
#include <boost/mpl/equal.hpp>
#include <boost/mpl/less.hpp>
#include <boost/mpl/count.hpp>
#include <iostream>
template<class ... T> struct Struct
{
};
using namespace boost;
template<clas... | {"hexsha": "552da93067b3e99d84930e17be6e096bccb0411b", "size": 735, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/rename.cpp", "max_stars_repo_name": "bezout/LMA", "max_stars_repo_head_hexsha": "9555e41eed5f44690c5f6e3ea2d22d520ff1a9d2", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": 29.0, "max... |
from gdsctools.anova import ANOVA
from gdsctools.anova_report import ANOVAReport
from gdsctools import ic50_test
import numpy as np
def test_html():
# same as above, could factorise
an = ANOVA(ic50_test)
features = an.features.df
features = features[features.columns[0:30]]
an = ANOVA(ic50_test, fe... | {"hexsha": "485a8942b7d248bf37decc6c0ad11aefd513912e", "size": 778, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/gdsctools/test_anova_html.py", "max_stars_repo_name": "Donnyvdm/gdsctools", "max_stars_repo_head_hexsha": "164ccd284e33202117f505210af96ae44d819203", "max_stars_repo_licenses": ["Python-2.0", ... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
//===========================... | {"hexsha": "9157f17ed4458ebc79f9a1e6a773e58e669fd741", "size": 1832, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/constant/nbmantissabits.hpp", "max_stars_repo_name": "SylvainCorlay/pythran", "max_stars_repo_head_hexsha": "908ec070d837baf77d828d01c3e35e2f4bfa2bfa", "max_stars_repo_license... |
We have used different ideas and methods in this project. In this chapter methods are explained in following
\textit{\hyperref[mth:preproces]{Preprocessing}},
\textit{\hyperref[mth:wordrep]{Word Representation}},
\textit{\hyperref[mth:features]{Features}},
\textit{\hyperref[mth:ml]{Machine Learning}},
\textit{\hyp... | {"hexsha": "e5eff760a3dd4a3901b796b0b9874f76e1480e1a", "size": 16091, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "06-methodology.tex", "max_stars_repo_name": "mahsaghn/FinalProject_Doc", "max_stars_repo_head_hexsha": "06008f61f77ae5257cabbf7216edf9bcc37c9986", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/python3
import cv2
import math
import numpy as np
import matplotlib.pyplot as plt
import pdb
from PIL import Image, ImageDraw
import torch
from typing import List, Mapping, Optional, Tuple
from mseg.utils.conn_comp import scipy_conn_comp
from mseg.utils.colormap import colormap
from mseg.utils.cv2_utils i... | {"hexsha": "0ef31e9ec23092bb9f5908d5b0937f23b6d80ef3", "size": 34550, "ext": "py", "lang": "Python", "max_stars_repo_path": "mseg/utils/mask_utils.py", "max_stars_repo_name": "mintar/mseg-api", "max_stars_repo_head_hexsha": "df7b899b47b33ad82dcbf17c289856a1f1abea22", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars... |
{-# OPTIONS --allow-unsolved-metas #-}
open import Agda.Builtin.Nat
test : (n : Nat) → Nat
test n with zero
... | n = {!n!}
| {"hexsha": "9f59153d554c7dda605b9b7fadbdafaee285624e", "size": 126, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Succeed/Issue1820.agda", "max_stars_repo_name": "cagix/agda", "max_stars_repo_head_hexsha": "cc026a6a97a3e517bb94bafa9d49233b067c7559", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_... |
[STATEMENT]
lemma replicate_stutter': "u \<frown> replicate n (v 0) \<frown> v \<approx> u \<frown> v"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. u \<frown> replicate n (v 0) \<frown> v \<approx> u \<frown> v
[PROOF STEP]
using stutter_extend_concat replicate_stutter
[PROOF STATE]
proof (prove)
using this:
?u \<... | {"llama_tokens": 231, "file": "Partial_Order_Reduction_Basics_Stuttering", "length": 2} |
import pytest
import numpy as np
from deoxys_image import normalize
from deoxys_image import apply_random_brightness, apply_random_contrast
from deoxys_image import apply_random_gaussian_noise
def test_normalize_all_channel():
base_data = np.array([np.arange(30) for _ in range(5)])
normalized_image = np.zero... | {"hexsha": "d368d12644d783da5f54b71588fc91bdee543c3f", "size": 6646, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_point_operation.py", "max_stars_repo_name": "huynhngoc/deoxys-image", "max_stars_repo_head_hexsha": "69faff2e28e062356ddfdc067e482aaae5db014d", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma mono_Increasing_o:
"mono g ==> Increasing f \<subseteq> Increasing (g o f)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. mono g \<Longrightarrow> Increasing f \<subseteq> Increasing (g \<circ> f)
[PROOF STEP]
apply (simp add: Increasing_def Stable_def Constrains_def stable_def
... | {"llama_tokens": 320, "file": null, "length": 3} |
from __future__ import absolute_import, division, print_function
__metaclass__ = type
# see https://wiki.python.org/moin/PortingToPy3k/BilingualQuickRef
from os.path import join, basename, splitext
from astwro.exttools import Runner
from astwro.config import find_opt_file
from .Sextractor import SexResults
class Sca... | {"hexsha": "4d932915b54bbdb5fb98fff1b432e4331d466b71", "size": 3074, "ext": "py", "lang": "Python", "max_stars_repo_path": "astwro/sex/Scamp.py", "max_stars_repo_name": "majkelx/astwro", "max_stars_repo_head_hexsha": "4a9bbe3e4757c4076ad7c0d90cf08e38dab4e794", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "... |
from utils import SAP
import tensorflow as tf
import numpy as np
class ActorCriticAgent:
def __init__(self, env, actor, critic):
self.env = env
self.actor = actor
self.critic = critic # We should initialize critic with small random values
# Callbacks for logging (self -> ...)
... | {"hexsha": "d87e4fbfcda7cde5edb1454f277524c67138977a", "size": 3050, "ext": "py", "lang": "Python", "max_stars_repo_path": "assignment-1/agent.py", "max_stars_repo_name": "patrikkj/it3105", "max_stars_repo_head_hexsha": "2e856c42a8affec841d700b29bb59a426f67b232", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
import torch
import torchvision
from glob import glob
from models import model_transfer, use_cuda
from PIL import Image
import torchvision.transforms as transforms,ToPILImage
# list of class names by index, i.e. a name can be accessed li... | {"hexsha": "86f534399a338c03e9d84460d25a3da7d458f3e0", "size": 2711, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "rajan-blackboxes/dog-breed-classifier", "max_stars_repo_head_hexsha": "d82b83e8bc20aad7110277253f2f5948b933c84a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 9 22:01:28 2018
@author: JeremyJ
"""
import pandas as pd
import matplotlib.pylab as plt
import numpy as np
import itertools
from mpl_toolkits.mplot3d import Axes3D
from collections import Counter
import util
from model import Model
import matplotlib.gridspec as gridsp... | {"hexsha": "77b34fd53b1386ed3347febc1146c2a9233a100d", "size": 47763, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Python/library.py", "max_stars_repo_name": "skyshine102/FitnessLandscape_SD", "max_stars_repo_head_hexsha": "529360e4f3bbb2ed5ed4cbecd624e725f6c245d8", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import cv2
import matplotlib.pyplot as plt
from helper_functions import *
# Define a class to receive the characteristics of each line detection
class Line():
def __init__(self, image_shape, debug = False):
# HYPERPARAMETERS
# Number of sliding windows
self.nwind... | {"hexsha": "51e0a412faf798dece36b3427647a48a52cf43ff", "size": 10149, "ext": "py", "lang": "Python", "max_stars_repo_path": "line.py", "max_stars_repo_name": "JoaoGranja/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "2341a152d07e7681fce7807d2e4d26b39265f8f7", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from collections import defaultdict
from typing import Dict
from typing import List
import numpy as np
class ArrayStore:
"""Storage class for keeping track of arrays."""
def __init__(self) -> None:
self._container = defaultdict() # type: Dict[str, LiFoStack]
def __repr__(self) -> str:
... | {"hexsha": "99f523648cbaf8ad84b2d4b236cb0db021668ec9", "size": 1453, "ext": "py", "lang": "Python", "max_stars_repo_path": "glacier_flow_model/utils/store.py", "max_stars_repo_name": "munterfi/glacier-flow-model", "max_stars_repo_head_hexsha": "fd79b4dde841c7b49a2d9da57c203bb943873d49", "max_stars_repo_licenses": ["MIT... |
from flask import Flask, jsonify, request, abort
from flask_cors import CORS
import os
import sys
import numpy as np
from keras.models import load_model
from keras import backend as K
import tensorflow as tf
import json
models = None
graph = None
"""
Function responsible for loading our models from a given path... | {"hexsha": "e891acbc3a9a781a67b2a446b18dc5eac0662f19", "size": 5695, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/web/api.py", "max_stars_repo_name": "icmc-data/bot-tweet", "max_stars_repo_head_hexsha": "eae84e5a5d54a642ae30825130122465996d1470", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import unittest
import numpy as np
from flutter_utilities import *
class TestComplex2RealNotation(unittest.TestCase):
@classmethod
def setUpClass(cls):
pass
@classmethod
def tearDownClass(cls):
pass
def setUp(self):
pass
def tearDown(self):
pass
def tes... | {"hexsha": "ebe8ffea7791af6a7f79be0b707030c57724c91f", "size": 1371, "ext": "py", "lang": "Python", "max_stars_repo_path": "5_flutter_analysis_tools/test_flutter_utilities.py", "max_stars_repo_name": "mpentek/SWEToolbox", "max_stars_repo_head_hexsha": "dbebf6ea2b35dd7ed2c459e2ed8377e93b49fbaa", "max_stars_repo_licenses... |
//=======================================================================
// Copyright 1997, 1998, 1999, 2000 University of Notre Dame.
// Authors: Andrew Lumsdaine, Lie-Quan Lee, Jeremy G. Siek
//
// Distributed under the Boost Software License, Version 1.0. (See
// accompanying file LICENSE_1_0.txt or copy at
// http... | {"hexsha": "5e33894f5e3f3539d8adfdcce7b1a4f5af008ee5", "size": 4125, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "game24/aoc171202.cpp", "max_stars_repo_name": "jiayuehua/adventOfCode", "max_stars_repo_head_hexsha": "fd47ddefd286fe94db204a9850110f8d1d74d15b", "max_stars_repo_licenses": ["Unlicense"], "max_stars... |
from thinc.api import strings2arrays, NumpyOps, Ragged, registry
import numpy
import pytest
from ..util import get_data_checker
@pytest.fixture(params=[[], [(10, 2)], [(5, 3), (1, 3)], [(2, 3), (0, 3), (1, 3)]])
def shapes(request):
return request.param
@pytest.fixture
def ops():
return NumpyOps()
@pytes... | {"hexsha": "8de5341d785cca4ec0db2d11eada7bb5f4d41818", "size": 1975, "ext": "py", "lang": "Python", "max_stars_repo_path": "thinc/tests/layers/test_transforms.py", "max_stars_repo_name": "TheVinhLuong102/thinc", "max_stars_repo_head_hexsha": "7b54f728ddec7765a1d8a5e553d4b4b90b9edaec", "max_stars_repo_licenses": ["MIT"]... |
"""
StringCollision
string collision between contact points
origin_parent: position of contact on parent body relative to center of mass
origin_child: position of contact on parent body relative to center of mass
length: maximum distance between contact point
"""
mutable struct StringCollision{T,O... | {"hexsha": "af55b26ad6864bdb55e17ea11e574880bfa2286e", "size": 8247, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/contacts/collisions/string.jl", "max_stars_repo_name": "dojo-sim/Dojo.jl", "max_stars_repo_head_hexsha": "33ccdde8d7f74c4ea3c3bffdebcc6ed65959a5be", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Tweet Analysis -- objective, predict the retweet level of a tweet
import string
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
from sklearn.model_selection import train_test_split
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyser = S... | {"hexsha": "bcb846731afdde2b470ca97e61006fc8ad15313d", "size": 9457, "ext": "py", "lang": "Python", "max_stars_repo_path": "Clinton-Logistic-Regression.py", "max_stars_repo_name": "jcg122562/JCG-CEBD-1160-Project", "max_stars_repo_head_hexsha": "54a73d3cb6d0c6ca157e9b0916b16091882c8261", "max_stars_repo_licenses": ["MI... |
# Load libraries
from scipy.stats import uniform
from sklearn import linear_model, datasets
from sklearn.model_selection import RandomizedSearchCV
# Load data
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Create logistic regression
logistic = linear_model.LogisticRegression()
# Create regularization p... | {"hexsha": "c92c27b45a399b3060268bbda95988f8aceafa95", "size": 987, "ext": "py", "lang": "Python", "max_stars_repo_path": "Model Selection/tuning-using-random-search.py", "max_stars_repo_name": "WyckliffeAluga/data-chronicles", "max_stars_repo_head_hexsha": "5219fe9cdbafb9fd7be88727483952c4c13f2790", "max_stars_repo_li... |
# -*- coding: utf-8 -*-
"""
"""
import numpy as np
from skimage import io, util
# Loading a RAW image
img_raw = util.img_as_float(io.imread("./../lighthouse_RAW_noisy_sigma0.01.png"))
# Size of RAW image
(ydim, xdim) = img_raw.shape
# Creating array for each channel RGB
cr = np.zeros((ydim, xdim))
cg = np.zeros((ydi... | {"hexsha": "aaf3e27866e9f849824a0d5c15329a51210beb10", "size": 5703, "ext": "py", "lang": "Python", "max_stars_repo_path": "task02/task02.py", "max_stars_repo_name": "supremacey/ComphotLab01", "max_stars_repo_head_hexsha": "94f16665083ba2742a3dc5507396f601bf381a98", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
class GeneralizedSuttonChenEAM:
def __init__(self,StructureContainer=[],A=None,B=None,C=None):
"""
Equation is E_i = (sum 1/(r_ij^B))^C + sum 1/(r_ij^A)
Attributes
-----------
hyperparameters : dictionary(hyperparamters)
dicitonary wit... | {"hexsha": "aaebaa7ed3563eda3a1f563ef85a5537c078bdfe", "size": 7801, "ext": "py", "lang": "Python", "max_stars_repo_path": "ipm/Potentials/ManyBodyPotentials.py", "max_stars_repo_name": "wilsonnater/IPM", "max_stars_repo_head_hexsha": "36f579c79a9b2d8c97df5c3636b4d0d606b0c0dc", "max_stars_repo_licenses": ["MIT"], "max_... |
# %%
import geopandas as gpd
import pandas as pd
from numpy import array_split
from itertools import islice
from sqlalchemy import create_engine
from tqdm import tqdm
from shapely.geometry import LineString
import numpy as np
from valhalla import Actor, get_config, get_help
from valhalla.utils import decode_polyline
e... | {"hexsha": "057bc4520ab006d2783a0e036f0b2a8b985571d8", "size": 3043, "ext": "py", "lang": "Python", "max_stars_repo_path": "cabi/valhalla_routing.py", "max_stars_repo_name": "mlinds/cabi-data", "max_stars_repo_head_hexsha": "2fa93fe5e0f87bdd13daff09e7224aafeeccbc10", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
@testset "Statistics" begin
fname = joinpath(dirname(@__FILE__), "..", "data", "test_Hz19.5-testing.bdf")
s = read_SSR(fname)
@testset "Global Field Power" begin
~ = gfp(s.data) # TODO: Check result
end
end
| {"hexsha": "06d2823c8ec3a5deaef4233fc56d4a171720fa8b", "size": 236, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/statistics/gfp.jl", "max_stars_repo_name": "behinger/Neuroimaging.jl", "max_stars_repo_head_hexsha": "0bed495b40cced7f1945931f26305b809cf0b501", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
#!/usr/bin/python
import numpy as np
import pytest
from cvxopt import matrix, solvers
from homeworks.hw01.perceptron import (calculate_p_f_neq_g,
perceptron_learning, label_points, random_line, random_points)
NUM_RUNS = 1000
MONTE_CARLO_NUM_POINTS = 1000
solvers.options['show_progress'] = False # Disable c... | {"hexsha": "f936d70c8ec309b3029ea9586b2da2a0f6c11df1", "size": 3155, "ext": "py", "lang": "Python", "max_stars_repo_path": "homeworks/hw07/svm.py", "max_stars_repo_name": "danieljl/Caltech-LFD-Solution", "max_stars_repo_head_hexsha": "c50b25f4540124879bca8288fe7f0b3d56573ca8", "max_stars_repo_licenses": ["MIT"], "max_s... |
from __future__ import division
import numpy as np
import tensorflow as tf
import os
import pickle
import math
from math import exp, expm1
# FUN = performs uniform quantization
def uniform_quant(x,x_max,x_min,nbits,type=0):
L = 2**nbits
range = x_max-x_min
q = range/L # step size
if (type):
... | {"hexsha": "c1b86908c183da668acd7af97a37a0cf3f1c58df", "size": 1968, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/examples/classifier_compression/sinreq_v2_svhn_runcode/mu_law_quantize.py", "max_stars_repo_name": "he-actlab/waveq.code", "max_stars_repo_head_hexsha": "024d55af6d989d4074d3e555d03b76a2f7eac... |
\section{Reference frame of the grids}
\subsection{Vertical alignment}
There are several non-intuitive hits about definition of coordinete system in
the code:
\begin{itemize}
\item The code works in coordinate system where the origin of sampling grid
is always in point (x,y,z)=(0,0,0). The geometry of sampl... | {"hexsha": "0d66721f50f38f70e8ba69f9a57f6c7be84d8fad", "size": 1940, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/technical_details/technical_details.tex", "max_stars_repo_name": "eimrek/ProbeParticleModel", "max_stars_repo_head_hexsha": "31494db595cdc1763de5fb73b2f63dfe8280e7fe", "max_stars_repo_licenses":... |
import os
import time
import numpy as np
import cv2
import depthai as dai
import contextlib
import glob2 as glob
# first do: pip install future
# tkinter should work after this
import tkinter as tk
# Enable / Disable debug statements
verbose = True
currExp = 1
currISO = 100
fInit = 0
expInit = 0
isoInit = 0
wbInit ... | {"hexsha": "47022d91d3c9649b6f2363465f9cdcfcd8e4f0c6", "size": 6874, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/calibrationRM.py", "max_stars_repo_name": "ScripteJunkie/T3", "max_stars_repo_head_hexsha": "f0f205f39bf3dc23c2dc13d0037fbae6ac296874", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# @title Zurich Instruments HDAWG instrument driver
# @author Christian Križan
# @contrib Andreas Bengtsson, Simon Gustavsson, Christopher Warren
# @date 2020-09-14
# @version v0.835.1
# @other The author of this driver takes no responsibility for
# any and all ... | {"hexsha": "b00ce76050094ca07434fddf61f6f114b06ed89e", "size": 142593, "ext": "py", "lang": "Python", "max_stars_repo_path": "Zurich Instruments HDAWG.py", "max_stars_repo_name": "christiankrizan/Labber-drivers-for-Zurich-Instruments-HDAWG-and-UHFQA", "max_stars_repo_head_hexsha": "df399b257d61b9056a709793f4510045bc725... |
[STATEMENT]
lemma (in ring) indexed_eval_in_carrier:
assumes "list_all carrier_coeff Ps" shows "carrier_coeff (indexed_eval Ps i)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. carrier_coeff (indexed_eval Ps i)
[PROOF STEP]
using assms indexed_eval_aux_in_carrier[of "rev Ps"]
[PROOF STATE]
proof (prove)
using thi... | {"llama_tokens": 211, "file": null, "length": 2} |
/*
==============================================================================
KratosStructuralApplication
A library based on:
Kratos
A General Purpose Software for Multi-Physics Finite Element Analysis
Version 1.0 (Released on march 05, 2007).
Copyright 2007
Pooyan Dadvand, Riccardo Rossi, Janosch Staschei... | {"hexsha": "2d8294df9e60ed3da192165c63acaaf0bce84a77", "size": 15834, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "custom_elements/kinematic_linear_nurbs.cpp", "max_stars_repo_name": "rwilliams01/isogeometric_structural_application", "max_stars_repo_head_hexsha": "5f0468c35ae0b1f8e16861b3568d755222b8a967", "max... |
import logging
import os
import networkx as nx
import copy
import re
from enum import Enum
from IPython import embed
from fnmatch import fnmatch
from android.dac import Cred, AID_MAP, AID_MAP_INV
from android.sepolicy import SELinuxContext
from android.capabilities import Capabilities
log = logging.getLogger(__name__... | {"hexsha": "f79deb089741027feddcec2c5ba7400fb8ce1d67", "size": 82680, "ext": "py", "lang": "Python", "max_stars_repo_path": "overlay.py", "max_stars_repo_name": "7homasSutter/BigMAC", "max_stars_repo_head_hexsha": "4c434796decf8d6119813e6bd30ee822193ee046", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
\documentclass[a4paper,11pt]{article}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{underscore}
\newcommand{\code}[1]{\texttt{#1}}
\newtheorem{theorem}{Theorem}
\title{Drawing thermal ellipsoids with OpenGL}
\author{Luc J. Bourhis}
\begin{document}
\maketitle
\section{From anisotropic displacements to elli... | {"hexsha": "85d9f41d38566f8c2d1ae75cad7c2e2c9b47cf36", "size": 6435, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "gltbx/adp-display.tex", "max_stars_repo_name": "rimmartin/cctbx_project", "max_stars_repo_head_hexsha": "644090f9432d9afc22cfb542fc3ab78ca8e15e5d", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], ... |
"""
Bowls and Oranges problem in cpmpy.
From BitTorrent Developer Challenge
http://www.bittorrent.com/company/about/developer_challenge
'''
You have 40 bowls, all placed in a line at exact intervals of
1 meter. You also have 9 oranges. You wish to place all the oranges
in the bowls, no more than one orange in each b... | {"hexsha": "32d803b6c43dc89c8f5bebd9798f359db2200d3f", "size": 1555, "ext": "py", "lang": "Python", "max_stars_repo_path": "cpmpy/bowls_and_oranges.py", "max_stars_repo_name": "hakank/hakank", "max_stars_repo_head_hexsha": "313e5c0552569863047f6ce9ae48ea0f6ec0c32b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/******************************************************************************
* NOTICE *
* *
* This software (or technical data) was produced for the U.S. Government *
... | {"hexsha": "ed0dc6fdb78bf9a8a92edc82e6940b801181660b", "size": 23334, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cpp/KeywordTagging/KeywordTagging.cpp", "max_stars_repo_name": "openmpf/openmpf-components", "max_stars_repo_head_hexsha": "acf012aeda0bac902e4678a97338b0aa5ffe38bf", "max_stars_repo_licenses": ["A... |
"""
Notes: Many of .dat files are written using Matlab.
Hence, there are "-1" subtraction to Python 0-based indexing
"""
from __future__ import division
import math
import numpy as np
from config import _3DMM_DEFINITION_DIR
VERTEX_NUM = 53215
TRI_NUM = 105840
def load_3DMM_tri():
# Triangle definition (i.e. fro... | {"hexsha": "d7799b59d5001aa0815f5af0f4c79f62f47ac9dd", "size": 2307, "ext": "py", "lang": "Python", "max_stars_repo_path": "_3dmm_utils.py", "max_stars_repo_name": "3D-Face/Nonlinear_Face_3DMM", "max_stars_repo_head_hexsha": "f0f0fafa7c1453cad80b73079f391858e9fa5af6", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
theory trinhlibs
imports Main
begin
(*----supporting functions-------------------------------------------------------*)
fun lookup_1 :: "nat \<Rightarrow> (nat * nat list) list \<Rightarrow> nat list" where
"lookup_1 k [] = []" |
"lookup_1 k (x#xs) = (if fst x = k then (snd x) else lookup_1 k xs)"
fun lookup_2 :: "... | {"author": "TrinhLK", "repo": "Isabelle-Code", "sha": "6eb41d730967df6b4dca606376a6825d16968c20", "save_path": "github-repos/isabelle/TrinhLK-Isabelle-Code", "path": "github-repos/isabelle/TrinhLK-Isabelle-Code/Isabelle-Code-6eb41d730967df6b4dca606376a6825d16968c20/Jan21-Isab/trinhlibs.thy"} |
# This module contains implementation of the primal-dual algorithm and itc coordinate extensions for the basis pursuit problem.
import numpy as np
import scipy.linalg as LA
from time import process_time, time
from numba import jit, vectorize
from prox_numba import prox_l1
from utils import subdif_gap
def pd_basis_p... | {"hexsha": "4c4a6147125c8df46b7a768ee1c85eb44c9f27ee", "size": 11961, "ext": "py", "lang": "Python", "max_stars_repo_path": "basis_pursuit/algorithms.py", "max_stars_repo_name": "ymalitsky/coo-pda", "max_stars_repo_head_hexsha": "8b604c1b2927d3f0f9adb49f2d09f88481e5d734", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
import fidimag
# PdFe on Ir(111) [PRL, 114(17):1-5, 2015]
Ms = 1.1e6
D = -3.9e-3
XL = 15e-9
YL = XL
ZL = XL
nx = 2
ny = 1
nz = 1
def set_D(pos):
x, y, z = pos
if x < XL/2:
return 0
else:
return -D
mesh = fidimag.common.CuboidMesh(nx=nx,ny=ny,nz=nz, dx=XL/nx, dy=YL/n... | {"hexsha": "c1f9106a167e9698e3da81e8ebbf20863de6e4ce", "size": 525, "ext": "py", "lang": "Python", "max_stars_repo_path": "sandbox/more-dmi/dmi-test.py", "max_stars_repo_name": "computationalmodelling/fidimag", "max_stars_repo_head_hexsha": "07a275c897a44ad1e0d7e8ef563f10345fdc2a6e", "max_stars_repo_licenses": ["BSD-2-... |
[STATEMENT]
lemma radical_sqrt_circle_circle_intersection:
assumes absA: "(abscissa A) \<in> radical_sqrt" and ordA: "(ordinate A) \<in> radical_sqrt"
and absB: "(abscissa B) \<in> radical_sqrt" and ordB: "(ordinate B) \<in> radical_sqrt"
and absC: "(abscissa C) \<in> radical_sqrt" and ordC: "(ordinate C... | {"llama_tokens": 5161, "file": "Impossible_Geometry_Impossible_Geometry", "length": 37} |
import typing
import json
import os
import warnings
import pandas as pd
import numpy as np
from .base import AbstractAdapter
import dalmatian
class FirecloudAdapter(AbstractAdapter):
"""
Job input adapter
Parses inputs as firecloud expression bois
if enabled, job outputs will be written back to workspa... | {"hexsha": "79c4a24c377e85a8367c033d378dee0293c8e67a", "size": 7396, "ext": "py", "lang": "Python", "max_stars_repo_path": "canine/adapters/firecloud.py", "max_stars_repo_name": "Marlin-Na/canine", "max_stars_repo_head_hexsha": "5a56360231d381239934e4a44143f9c9aed7ef22", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
[STATEMENT]
lemma vertices_restrict[simp]:
"vertices (restrict G) = vertices G"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. vertices (restrict G) = vertices G
[PROOF STEP]
by(cases G,auto simp:restrict_def) | {"llama_tokens": 82, "file": "Graph_Saturation_LabeledGraphs", "length": 1} |
import numpy as np
from scipy.stats import pearsonr
from sklearn.metrics import mean_absolute_error, mean_squared_error
# Mean absolute error
def mae(y_pred, y, mask=None, device=None):
try:
y_pred, y = y_pred.numpy().squeeze(), y.numpy().squeeze()
except TypeError:
y_pred, y, mask = y_pred.cpu().n... | {"hexsha": "725167bdfa7d05b35d539b95301cc56d4e15a65e", "size": 1633, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepsphere_model/deepsphere/utils/metrics.py", "max_stars_repo_name": "metinsuloglu/ca-flow-from-shape", "max_stars_repo_head_hexsha": "b4839a726cfe65267d51253228a37f8fd68b7f27", "max_stars_repo_l... |
\documentclass[letterpaper,final,12pt,reqno]{amsart}
\usepackage[total={6.3in,9.2in},top=1.1in,left=1.1in]{geometry}
\usepackage{times,bm,bbm,empheq,fancyvrb,graphicx}
\usepackage[dvipsnames]{xcolor}
\usepackage{tikz}
\usetikzlibrary{decorations.pathreplacing}
% hyperref should be the last package we load
\usepackag... | {"hexsha": "4f81ae0383500d0565395cfb572c1d6df70fc4c1", "size": 76492, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/simp.tex", "max_stars_repo_name": "bueler/stokes-implicit", "max_stars_repo_head_hexsha": "e6f40798c9d441316d1b88a93b9103b8e6cb5e89", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
"""
Trading-Technical-Indicators (tti) python library
File name: test_utils_trading_simulation.py
tti.utils package, trading_simulation.py module unit tests.
"""
import unittest
import pandas as pd
import numpy as np
from tti.utils.trading_simulation import TradingSimulation
from tti.utils.exceptions import Wron... | {"hexsha": "3da6ea931ba75aeceeae3b059c7b9e4cb7100a32", "size": 32952, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_utils_trading_simulation.py", "max_stars_repo_name": "Bill-Software-Engineer/trading-technical-indicators", "max_stars_repo_head_hexsha": "fc00008a41da54f160609343e866c72306f4962c", "m... |
import cmath
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import scipy.signal as signal
sns.set()
class ModifiedCovarianceMethod():
def __init__(self):
self.f = None
self.default_f = np.linspace(0, 0.5, 500)
self.x = None
self.N = None
self.p = ... | {"hexsha": "78b283e3f93567c0beeae6d1c8dba3861312c06c", "size": 3176, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/source/parametric/ModifiedCovarianceMethod.py", "max_stars_repo_name": "e71828/spectral-estimation", "max_stars_repo_head_hexsha": "9e4c3f4ed867e97ec764f85d577a7fcaac6a8a7c", "max_stars_rep... |
"""
Create feature files.
Before this script is run, the `download.py` should get executed to generate
a handwriting_datasets.pickle with exactly those symbols that should also
be present in the feature files and only raw_data that might get used for the
test-, validation- and training set.
"""
# Core Library module... | {"hexsha": "6092274fdf05ebda6998f922ffcfe63742e14ea8", "size": 13400, "ext": "py", "lang": "Python", "max_stars_repo_path": "hwrt/create_ffiles.py", "max_stars_repo_name": "MartinThoma/hwrt", "max_stars_repo_head_hexsha": "7b274fa3022292bb1215eaec99f1826f64f98a07", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from stock_data_analysis_module.reinforcement_learning.deep_q.memory.agent_transitional_memory_abc import AgentTransitionMemoryABC
from typing import Tuple, List
import numpy as np
class CircularTransitionalMemory(AgentTransitionMemoryABC):
def __init__(self, input_shape: Tuple[int, int, int], memory_size... | {"hexsha": "4494f91fcb736bb4dfa9901374a30bdb3568f625", "size": 2696, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/stock_data_analysis_module/reinforcement_learning/deep_q/memory/circular_transitional_memory.py", "max_stars_repo_name": "Freitacr/ML-StockAnalysisProject", "max_stars_repo_head_hexsha": "3741... |
# 4. faza: Analiza podatkov
logisticna <- function(x) { 100 / (1 + exp(-x)) }
logisticna_inv <- function(y) { log(y / (100 - y)) }
logisticna_breaks <- . %>% logisticna() %>% extended_breaks()() %>% logisticna_inv()
logisticna_trans <- trans_new("logistična funkcija", logisticna, logisticna_inv)
primerjava_rasti_l... | {"hexsha": "ea93dfe90827d93dae9e1d7719b04089fa04262a", "size": 7497, "ext": "r", "lang": "R", "max_stars_repo_path": "analiza/analiza.r", "max_stars_repo_name": "M4rble/APPR-2019-20", "max_stars_repo_head_hexsha": "f247539508025cb8c0ccfb0d9c9d2d60118f51ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
setwd('/home/pc-752828/Dev/results-search-master/outputs-normal')
library(reshape2)
library(nortest)
data_norm <- read.csv(file = 'output-n-price.csv', sep = ',', header = T)
get_df <- function(seed, data) {
my_ite <- 10
bo1 <- list()
bo2 <- list()
bo3 <- list()
bo4 <- list()
rs <- list()
app <- list()... | {"hexsha": "2a61484c199ed56d86bcf61f70a3dd9b8ff921cd", "size": 4776, "ext": "r", "lang": "R", "max_stars_repo_path": "results-dissertation/test-cost.r", "max_stars_repo_name": "lmcad-unicamp/PB3Opt", "max_stars_repo_head_hexsha": "21759ca06c36e8a05f310d43a08e43063b1efd76", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""Metrics.
Evaluation metrics
* :func:`.log_prob`
* :func:`.acc`
* :func:`.accuracy`
* :func:`.mse`
* :func:`.sse`
* :func:`.mae`
----------
"""
__all__ = [
"accuracy",
"mean_squared_error",
"sum_squared_error",
"mean_absolute_error",
"r_squared",
"true_positive_rate",
"true_negative_... | {"hexsha": "6f7d9a69e421eaba6fcf3f607083c4d2127d8817", "size": 4549, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/probflow/utils/metrics.py", "max_stars_repo_name": "chiragnagpal/probflow", "max_stars_repo_head_hexsha": "1ba0619cd4f482a015cd25633d2f113d5d0f3476", "max_stars_repo_licenses": ["MIT"], "max_s... |
/************************************************************************/
/* */
/* Copyright 2009 by Ullrich Koethe */
/* */
/* This file is p... | {"hexsha": "2819bc80e25ec3831613e4421a31a893eaf5a883", "size": 35172, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "vigranumpy/src/core/convolution.cxx", "max_stars_repo_name": "BSeppke/vigra", "max_stars_repo_head_hexsha": "490213d8954a03bdb985b52cfaafd6389431efd8", "max_stars_repo_licenses": ["MIT"], "max_star... |
impts=zeros(yw*exf,xw*exf);
n_rendered=0;
weight=str2double(get(handles.weight,'String'));
size_fac=str2double(get(handles.size_fac_edit,'String'));
for i=nstart:nend
if xc(i)>=1 && yc(i)>=1 && xc(i)<xw*exf && yc(i)<yw*exf && N(i)>0
wide=ceil(size_fac*lppix(i)*1.5+1);
% if wide>20
% wide=20;... | {"author": "aludnam", "repo": "MATLAB", "sha": "020b5cb02cc843e09a0ed689589382f18cce5e6d", "save_path": "github-repos/MATLAB/aludnam-MATLAB", "path": "github-repos/MATLAB/aludnam-MATLAB/MATLAB-020b5cb02cc843e09a0ed689589382f18cce5e6d/TGgui070708/render_func.m"} |
#!/bin/python
import numpy
import unittest
from src import ciphertext
from src import homomorphic_arithmetic
class HomomorphicArithmeticTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.dimension = 3
cls.odd_modulus = 5
cls.ciphertexts = [
ciphertext.Ciphertex... | {"hexsha": "76398d5d90b2722b845b39beedd0ff07eb7afffa", "size": 4829, "ext": "py", "lang": "Python", "max_stars_repo_path": "tst/homomorphic_arithmetic_test.py", "max_stars_repo_name": "UMComp4140ATeam/Raymond", "max_stars_repo_head_hexsha": "2ccff4d15835ab386d3e25b2bb3a7ed03feffe81", "max_stars_repo_licenses": ["MIT"],... |
function generate_execute(domain::Domain, state::State,
domain_type::Symbol, state_type::Symbol)
execute_def = quote
function execute(domain::$domain_type, state::$state_type, term::Term)
execute(domain, state, get_action(domain, term.name), term.args)
end
... | {"hexsha": "3768cd92b7a4dbcaf7084b92d7974f94179e994f", "size": 2489, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/compiler/execute.jl", "max_stars_repo_name": "JuliaPlanners/PDDL.jl", "max_stars_repo_head_hexsha": "8bbb7a6a3f87ea9d18ace8c4356dc5658cc75fad", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
"""Compute the mean number of labels."""
import os
import numpy as np
def _get_nb_labels_apply(row, column_label, column_alt):
all_labels = []
nb_labels = 0
label = row[column_label]
alt = row[column_alt]
if label != '' and label is not None:
nb_labels = 1
all_labels += [label]
... | {"hexsha": "538d9107726227a0ea6514dad0c9c07619827af8", "size": 2366, "ext": "py", "lang": "Python", "max_stars_repo_path": "orphanet_translation/metrics/synonyms.py", "max_stars_repo_name": "euranova/orphanet_translation", "max_stars_repo_head_hexsha": "87a761ed273928d4b606db43e813f936682ee0e3", "max_stars_repo_license... |
import copy
import numpy as np
import distance
from jellyfish._jellyfish import damerau_levenshtein_distance
import unicodecsv
def DL_Distance(str1, str2):
print(str1, str2)
print("distance 1: ", distance.nlevenshtein(str1, str2))
print("distance 2: ", damerau_levenshtein_distance(str1, str2))
dls = (... | {"hexsha": "98f6dd0852a3d514daa216499c6405d0400126e3", "size": 575, "ext": "py", "lang": "Python", "max_stars_repo_path": "Evaluate/StringDistance.py", "max_stars_repo_name": "DinhLamPham/PredictingESN", "max_stars_repo_head_hexsha": "f8e6b8f9c0a2d4c052c6178f4b4fe793055050ff", "max_stars_repo_licenses": ["Apache-2.0"],... |
[STATEMENT]
lemma adj_iso2: "f \<stileturn> g \<Longrightarrow> mono g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. f \<stileturn> g \<Longrightarrow> mono g
[PROOF STEP]
unfolding adj_def mono_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>x y. (f x \<le> y) = (x \<le> g y) \<Longrightarrow> \<fo... | {"llama_tokens": 178, "file": "Order_Lattice_Props_Galois_Connections", "length": 2} |
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, so... | {"hexsha": "81453c3bf0995be0bd3bcc0ed64d6e9976316e50", "size": 6172, "ext": "py", "lang": "Python", "max_stars_repo_path": "bigbench/models/query_logging_model.py", "max_stars_repo_name": "sarahadlerrous/BIG-bench", "max_stars_repo_head_hexsha": "e71e63598538674cece60f8457475f8ddbdf785a", "max_stars_repo_licenses": ["A... |
import numpy as np
import collections
import copy
from generic.data_provider.batchifier import AbstractBatchifier
from generic.data_provider.image_preprocessors import get_spatial_feat
from generic.data_provider.nlp_utils import padder, padder_3d
class GuesserBatchifier_RAH(AbstractBatchifier):
def __init__(sel... | {"hexsha": "086b59de4402f2fbb556f42914f9adb75dba4574", "size": 4108, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/guesswhat/data_provider/guesser_qa_batchifier.py", "max_stars_repo_name": "zipengxu-c/ADVSE-GuessWhat", "max_stars_repo_head_hexsha": "44ca8b1e2368b4afd90a74a8c22a6553e8fc351e", "max_stars_rep... |
##### file path
# input
path_df_D = "tianchi_fresh_comp_train_user.csv"
path_df_part_1 = "df_part_1.csv"
path_df_part_2 = "df_part_2.csv"
path_df_part_3 = "df_part_3.csv"
path_df_part_1_tar = "df_part_1_tar.csv"
path_df_part_2_tar = "df_part_2_tar.csv"
path_df_part_1_uic_label = "df_part_1_uic_label.csv"
... | {"hexsha": "98ad20fd81768c65669eabd9c601f2a81b23bd53", "size": 74288, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_lr_and_gdbt_and_xgboost/feature_construct/feature_construct_part_3.py", "max_stars_repo_name": "Goooaaal/ali_freshman_compatiton", "max_stars_repo_head_hexsha": "8d3dd71f0757cb08d915f89daf1... |
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