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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from os import path as osp
import numpy as np
import pytest
import habitat_sim
from habitat_sim.sensors.noise_models i... | {"hexsha": "8f0257d6fe6e6c48357b5fa84b83e6ebf040b6d6", "size": 1555, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_noise_models.py", "max_stars_repo_name": "Jiayuan-Gu/habitat-sim", "max_stars_repo_head_hexsha": "ccbfa32e0af6d2bfb5a3131f28f69b72f184e638", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import numpy as np
arr = np.array([
[[255,255,255], [255,255,255], [0,0,0]],
[[255,255,255], [0,0,0], [0,0,0]],
[[0,0,0], [0,0,0], [0,0,0]]
]);
# arr = np.array([0, 0, 0])
print(arr.flatten())
f = arr.flatten()
if 255 in f:
print("oh no!")
else:
print("oh yeah ;)")
# t = np.where(arr == 255)
# print(t)
| {"hexsha": "769cd1aed205c76454414b427ae777feaae0a20a", "size": 320, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "halesyy/hsc-2019-major-work", "max_stars_repo_head_hexsha": "baeab9db0431b021fd5a19b63be6d5df8c6d0513", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
# GWR kernel function specifications
__author__ = "STWR is XiangQue xiangq@uidaho.edu and GWR,MGWR is Taylor Oshan Tayoshan@gmail.com"
import scipy
from scipy.spatial.kdtree import KDTree
import numpy as np
from scipy.spatial.distance import cdist as cdist_scipy
from math import radians, sin, cos, sqrt, asin,exp,atan... | {"hexsha": "83528d0e2395c62c7b6d5f92b8fda35e6c09ac53", "size": 34112, "ext": "py", "lang": "Python", "max_stars_repo_path": "stwr/kernels.py", "max_stars_repo_name": "quexiang/STWR", "max_stars_repo_head_hexsha": "e06bd4415f85de7f8543dfb7bef446f38b069349", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
using Flux, Flux.Data.MNIST, Statistics, BSON, Random, StatsPlots; pyplot()
using Flux: onehotbatch, onecold, crossentropy, @epochs
epochs = 30
eta = 1e-3
batchSize = 200
trainRange, validateRange = 1:1000, 1001:5000
function minibatch(x, y, idxs)
xBatch = Array{Float32}(undef, size(x[1])..., 1, length(idxs))
... | {"hexsha": "1448c73bbb7b6ec8b68edaa8041ea81d5bb91ba3", "size": 1932, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "9_chapter/dropout.jl", "max_stars_repo_name": "Yoshinobu-Ishizaki/StatsWithJuliaBook", "max_stars_repo_head_hexsha": "4c704e96d87b91e680122a6b6fa2d2083c70ea88", "max_stars_repo_licenses": ["MIT"], ... |
#!/usr/bin/env python3
# coding: utf-8
# -------- #
# GPS Plot #
# -------- #
### Modules
# standard library
import colorsys
import time as tm
import csv
import calendar
import xml.dom.minidom as mnd
import urllib
import urllib.request
import io
from os.path import join
from math import radians, log, tan, cos, pi, a... | {"hexsha": "620f0b50fe5bfd99ee9386e340f4beba8c30869a", "size": 10655, "ext": "py", "lang": "Python", "max_stars_repo_path": "pupil_code/gps_plot.py", "max_stars_repo_name": "pignoniG/cognitive_analysis_tool", "max_stars_repo_head_hexsha": "90568fc83493a10b567c1f957a57b3ef3a1cf69f", "max_stars_repo_licenses": ["MIT"], "... |
# Código desenvolvido pelo Pedro Piassa
# Artigo http://www.uel.br/cce/dc/wp-content/uploads/PRELIMINAR-PEDRO-VITOR-PIASSA.pdf todos os direitos reservados.
import os
from math import *
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import... | {"hexsha": "0ef02d4cfc1faf8314ad3d3f2641e5dc2eb22651", "size": 9230, "ext": "py", "lang": "Python", "max_stars_repo_path": "teste/lstm.py", "max_stars_repo_name": "joandesonandrade/nebulosa", "max_stars_repo_head_hexsha": "5bc157322ed0bdb81f6f00f6ed1ea7f7a5cadfe0", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
% %\documentclass[prb,preprint,showpacs,amsmath,amssymb ]{revtex4}
% \documentclass[prb, showpacs,amsmath,amssymb,twocolumn]{revtex4}
%
% % Some other (several out of many) possibilities
% %\documentclass[preprint,aps]{revtex4}
% %\documentclass[preprint,aps,draft]{revtex4}
% %\documentclass[prb... | {"hexsha": "75a7398d3b8ccd923b7f5120d36445859982e7c0", "size": 34293, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/Anderson_Localization_as_position-dependent_diffusion_in_disordered_waveguides__Phys_Rev_B/quasi1d_dofz_prb.tex", "max_stars_repo_name": "bhpayne/physics_phd_dissertation", "max_stars_repo... |
## circulant_embedding.jl : Gaussian random field generator using fft; only for uniformly spaced GRFs
"""
CirculantEmbedding <: GaussianRandomFieldGenerator
A [`GaussiandRandomFieldGenerator`](@ref) that uses FFT to compute samples of the Gaussian random field. Circulant embedding can only be applied if the points ar... | {"hexsha": "a6e23d080c9a92957a9f0370af961d9d2b14208f", "size": 8214, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/generators/circulant_embedding.jl", "max_stars_repo_name": "UnofficialJuliaMirror/GaussianRandomFields.jl-e4b2fa32-6e09-5554-b718-106ed5adafe9", "max_stars_repo_head_hexsha": "678c847826377e430... |
from collections import namedtuple
from datetime import datetime, timedelta
from xml.etree.ElementTree import ElementTree
import os
import subprocess
from matplotlib.path import Path
import matplotlib.pyplot as plt
import numpy as np
class TTF:
def __init__(self, ttx_path, px_step=200, font_height=4):
wi... | {"hexsha": "ef173678c28b451646d0f5158c241c0c3f4c69c8", "size": 5645, "ext": "py", "lang": "Python", "max_stars_repo_path": "ghht/ghht.py", "max_stars_repo_name": "matiaslindgren/ghht", "max_stars_repo_head_hexsha": "1e310a3573730dd546551fa3003e2403f6fd71ef", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
SUBROUTINE TF01MY( N, M, P, NY, A, LDA, B, LDB, C, LDC, D, LDD,
$ U, LDU, X, Y, LDY, DWORK, LDWORK, INFO )
C
C SLICOT RELEASE 5.5.
C
C Copyright (c) 2002-2012 NICONET e.V.
C
C PURPOSE
C
C To compute the output sequence of a linear time-invariant
C open-loop sys... | {"hexsha": "57b7052a985d587f4276c087c955a009ddcb6540", "size": 12231, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "External/SLICOT/TF01MY.f", "max_stars_repo_name": "bgin/MissileSimulation", "max_stars_repo_head_hexsha": "90adcbf1c049daafb939f3fe9f9dfe792f26d5df", "max_stars_repo_licenses": ["MIT"], "max_star... |
using Pkg
Pkg.activate(".")
Pkg.instantiate()
using ArgParse
using CGT
using CurrencyAmounts
function parse_commandline()
s = ArgParseSettings(description="Computes the capital gain tax (in Ireland)")
@add_arg_table s begin
"--verbose", "-v"
help = "Show transactions"
action = :store_true
"file"
help ... | {"hexsha": "3878bc702d8113461fb914dbca425825be104a4b", "size": 600, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "main.jl", "max_stars_repo_name": "ppanhoto78/CGT.jl", "max_stars_repo_head_hexsha": "c4c88bb8c665fba549a1353caea4bfa936357dc3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_stars_r... |
"""
Function：测试最后一跳激活函数
Author：lzb
Date：2021.01.31
"""
import numpy as np
from activation.last_hop_activation import LastHopActivation, DichotomyLHA, SoftMaxLHA
def test_last_hop_activation():
lha = DichotomyLHA()
nn_y = np.asarray([[[0.496053], [0.142468], [0.692607]],
[[-0.152569], [... | {"hexsha": "2e0893fddcd5ad86c9388521a0c81ed84ae8401b", "size": 766, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/activation/test_lha.py", "max_stars_repo_name": "1801573781/wind", "max_stars_repo_head_hexsha": "f7eaa84a9d98d081bf170f6ef6bff3c94a999c86", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# %%
## Most part of the code taken from https://github.com/microsoft/onnxruntime/blob/master/onnxruntime/python/tools/transformers/notebooks/PyTorch_Bert-Squad_OnnxRuntime_CPU.ipynb
import os
import requests
from transformers import BertConfig, BertForQuestionAnswering, BertTokenizer
from transformers.data.proc... | {"hexsha": "e301612a559918174491b08498c275178d00627b", "size": 6832, "ext": "py", "lang": "Python", "max_stars_repo_path": "bert_onnx.py", "max_stars_repo_name": "MatRazor/ONNXRuntime_tutorial_collection", "max_stars_repo_head_hexsha": "9fe46311896391f769a51cc4d07814e6bfafd8ee", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
import sys
import os
import time
import numpy as np
import pysal as ps
from fj_vect import fisher_jenks as vFisher
from mpi4py import MPI
#Override sys.execpthook
_excepthook = sys.excepthook
def excepthook(t, v, tb):
_excepthook(t, v, tb)
if (not MPI.Is_finalized() and MPI.Is_initiali... | {"hexsha": "3d510da4a88b536a997df2b3cfa0dc7565644334", "size": 5101, "ext": "py", "lang": "Python", "max_stars_repo_path": "ppysal/geoda_cluster/fisher_jenks/fj_mpi_integrated.py", "max_stars_repo_name": "ElsevierSoftwareX/SOFTX_2018_242", "max_stars_repo_head_hexsha": "9a7d7c02b2f1d7b6bfd1b08fbb2150c30ddd1046", "max_s... |
from typing import Any, Dict, Union
import pytorch_lightning as pl
import torch
import torch.nn as nn
import numpy as np
from nowcasting_utils.models.base import register_model
from nowcasting_utils.models.loss import get_loss
from satflow.models.layers.ConvLSTM import ConvLSTMCell
import torchvision
@register_mod... | {"hexsha": "1c30629c59327e19ebbf9c332c541f043500aef2", "size": 8776, "ext": "py", "lang": "Python", "max_stars_repo_path": "satflow/models/conv_lstm.py", "max_stars_repo_name": "mfrasco/satflow", "max_stars_repo_head_hexsha": "2e56b46dfd81a05670c6d2b1bda8c9eec38301a7", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# @Author: yican, yelanlan
# @Date: 2020-06-16 20:36:19
# @Last Modified by: yican.yc
# @Last Modified time: 2020-06-16 20:36:19
# Standard libraries
import os
import gc
from pathlib import Path
from pydoc import classname
from time import time, sleep
import traceback
from typing import Dict
import numpy as np
import... | {"hexsha": "8ffa850648d4aaa061f1695d9bc1f9f096b3dcf6", "size": 8403, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_multistage.py", "max_stars_repo_name": "LuletterSoul/cvpr2020-plant-pathology", "max_stars_repo_head_hexsha": "e26bcfaa860ea67aec3c6df815d276615cc190c1", "max_stars_repo_licenses": ["MIT"], ... |
from scipy.integrate import odeint
class SIS:
sets = ['S', 'I', 'N']
params = ['beta', 'gamma']
equations = {
'S' : lambda S,I,N,_S,_I,_N,beta,gamma: f' -({beta} * {S} * {_I}) / ({_N}) + {gamma} * {I}',
'I' : lambda S,I,N,_S,_I,_N,beta,gamma: f' ({beta} * {S} * {_I}) / ({_N}) - {gamma} * {I}',
'N' : lambda S,... | {"hexsha": "c015fb6c345e59629024bc5e8570e51d2c74de45", "size": 647, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/cmodel/repo/SIS.py", "max_stars_repo_name": "Maximiza-Atemoriza/meta-population-network-model", "max_stars_repo_head_hexsha": "7dfde8d92c50935a963c919227058c99fcd0c649", "max_stars_repo_license... |
subroutine wrgrid(comfil ,lundia ,error ,mmax ,nmax , &
& kmax ,nmaxus , &
& xcor ,ycor ,guu ,gvv ,guv , &
& gvu ,gsqs ,gsqd ,alfas ,thick , &
& rbuff ,rbuffz ,sferic ... | {"hexsha": "bf0679aff85ea03c12727506c5c5fca1bdadd565", "size": 16580, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/flow2d3d/packages/io/src/output/wrgrid.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad43531d02ac6... |
<div class="alert alert-block alert-info">
<u><h1>Introduction to respy</h1></u>
</div>
### What is **respy**?
**respy** is an open source framework written in Python for the simulation and estimation of some finite-horizon discrete choice dynamic programming models. The group of models which can be currentl... | {"hexsha": "32743538e453fd9cc4b4cd7b3d3ef4d7cac23aaa", "size": 398114, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "respy-showcase-short.ipynb", "max_stars_repo_name": "amageh/respy-tut", "max_stars_repo_head_hexsha": "115a8a3cf0069b03ee18c6eac70b0b9b1cba6857", "max_stars_repo_licenses": ["MIT"],... |
from __future__ import print_function
from datetime import datetime
import numpy as np
from baseline_algorithm import *
from parameters import *
import os
import csv
import json
import wfdb
## Classifying arrhythmia alarms
# Returns true if... | {"hexsha": "8a17b01a9406ef09e552fe00dad0e18eb17b1849", "size": 8816, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyfar/pipeline.py", "max_stars_repo_name": "vishwas1234567/false-alarm-reduction", "max_stars_repo_head_hexsha": "e67357004d26ba858bd55fab4913daa749f966cc", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma box_d_d_same:
"|d(x)]d(x) = 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. | d x ] d x = (1::'a)
[PROOF STEP]
using box_x_d d_complement_zero
[PROOF STATE]
proof (prove)
using this:
| ?x ] d ?y = a (?x * a ?y)
d ?x * a ?x = bot
goal (1 subgoal):
1. | d x ] d x = (1::'a)
[PROOF STEP]
by auto | {"llama_tokens": 168, "file": "Correctness_Algebras_Relative_Modal", "length": 2} |
[STATEMENT]
lemma finprod_mono_neutral_cong:
assumes [simp]: "finite B" "finite A"
and *: "\<And>i. i \<in> B - A \<Longrightarrow> h i = \<one>" "\<And>i. i \<in> A - B \<Longrightarrow> g i = \<one>"
and gh: "\<And>x. x \<in> A \<inter> B \<Longrightarrow> g x = h x"
and g: "g \<in> A \<rightarrow> carr... | {"llama_tokens": 1001, "file": null, "length": 13} |
[STATEMENT]
lemma (in is_cat_pw_lKe) cat_pw_lKe_the_pw_cat_lKe_colimit_is_cat_colimit:
assumes "\<KK> : \<BB> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>" and "\<TT> : \<BB> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<AA>" and "c \<in>\<^sub>\<circ> \<CC>\<lparr>Obj\<rparr>"
shows "the_pw_cat_... | {"llama_tokens": 5707, "file": "CZH_Universal_Constructions_czh_ucategories_CZH_UCAT_PWKan", "length": 22} |
import open3d as o3d
import numpy as np
from open3d.pybind.core import Dtype
from open3d.pybind.core import Device
from open3d.pybind.core import DtypeUtil
from open3d.pybind.core import cuda
from open3d.pybind.core import NoneType
from open3d.pybind.core import TensorList
none = NoneType()
def _numpy_dtype_to_dtyp... | {"hexsha": "c465d4d9a27ffa20f2ad03afcdd15436d6a59e10", "size": 22403, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/open3d/core.py", "max_stars_repo_name": "Matnay/Open3D", "max_stars_repo_head_hexsha": "781b735d8391353a0fd63524ca7ec460bf065a3a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
/*
* 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"); ... | {"hexsha": "4b363048bdc2ef5c6f1f84cb0628209a7c7d7367", "size": 2048, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/cpp/test/TPipedTransportTest.cpp", "max_stars_repo_name": "shivam00/thrift", "max_stars_repo_head_hexsha": "d81e9e3d22c130ef5ddba7b06fb9802267d9d1d7", "max_stars_repo_licenses": ["Apache-2.0"], ... |
%!TEX root = ../dissertation.tex
\chapter{Background}
\label{ch:background}
In this chapter, we first introduce visual-textual grounding problem
in Sec.~\ref{sec:visual-grounding}, distinguishing between visual
grounding and referring expression grounding. In
Sec.~\ref{sec:two-stage-vs-one-stage} we describe the two ... | {"hexsha": "e941a94c856ea7d82711228d5938d6033f8e6f6b", "size": 49562, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/background.tex", "max_stars_repo_name": "lparolari/master-thesis", "max_stars_repo_head_hexsha": "81c5cc5daec63426e51bdc470d4f9b532b06286b", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
// Copyright (c) 2012, Thomas Goyne <plorkyeran@aegisub.org>
//
// Permission to use, copy, modify, and distribute this software for any
// purpose with or without fee is hereby granted, provided that the above
// copyright notice and this permission notice appear in all copies.
//
// THE SOFTWARE IS PROVIDED "AS IS" A... | {"hexsha": "a68ca4359144675da13e81e745d9d5e612a0caa6", "size": 4917, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "aegisub/src/dialog_autosave.cpp", "max_stars_repo_name": "rcombs/Aegisub", "max_stars_repo_head_hexsha": "58f35cd31c7f0f5728e0a28e6a7a9fd6fce70c50", "max_stars_repo_licenses": ["ISC"], "max_stars_co... |
import numpy as np
from PIL import Image
from heatmap_generator.abstract_heatmap_generator import AbstractHeatmapGenerator
class AnisotropicLaplaceHeatmapGenerator(AbstractHeatmapGenerator):
def __init__(self):
super().__init__()
def get_heatmap_image(self, landmark_point):
x_axis_mtx, y_axi... | {"hexsha": "98091e76e039bfc88f0b4f9ab9956e8a2800168c", "size": 707, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/heatmap_generator/isotropic_laplace_heatmap_generator.py", "max_stars_repo_name": "k1101jh-univ/ISBI_2015_landmark_localization", "max_stars_repo_head_hexsha": "339cab46b4ea49b2322315d35435d1... |
MODULE init
IMPLICIT NONE
PRIVATE
PUBLIC :: line, lineorig, new_phreeqc_id, readinput, replacestring, cx, cy
INTEGER(KIND=4) :: ID_IPHREEQC(2),thisid1, thisid2
CHARACTER(LEN=160), DIMENSION(120) :: line = ""
CHARACTER(LEN=160), DIMENSION(120) :: lineorig = ""
CHARACTER(LEN=32) :: cx, cy
contains
!*... | {"hexsha": "de5e735ef1904a142f8acadfb5418a0eb8b64aba", "size": 2904, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "IPhreeqcMMS/IPhreeqc/memory_leak_f/init.f90", "max_stars_repo_name": "usgs-coupled/webmod", "max_stars_repo_head_hexsha": "66419e3714f20a357a7db0abd84246d61c002b88", "max_stars_repo_licenses": [... |
% Copyright (c) 2014 Adobe Systems Incorporated. All rights reserved.
% Licensed under the Apache License, Version 2.0 (the "License");
% you may not use this file except in compliance with the License.
% You may obtain a copy of the License at
% http://www.apache.org/licenses/LICENSE-2.0
% Unless required by applic... | {"hexsha": "ed3b34d9c54f7f12b21219cfed15ec41bec06190", "size": 3615, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "as4wp/same.tex", "max_stars_repo_name": "adobe-research/ActionScript4", "max_stars_repo_head_hexsha": "7817de971559b37b47a110f7a5a4e62840b3d7b5", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import sys
sys.path.insert(1, "./lib")
import cv2
import torch
import argparse
import numpy as np
from pathlib import Path
from tqdm import tqdm
from torch.nn import functional as F
from lib.models.ddrnet_23_slim import DualResNet, BasicBlock
color_palette = [
(0, 0, 0),
(150, 100, 100),
(220, 20, 60),
... | {"hexsha": "69afaf5b5341c607f6bb871fb389a80d78db11e1", "size": 4176, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/predict_video.py", "max_stars_repo_name": "BuiKhoi/DDRNet.pytorch", "max_stars_repo_head_hexsha": "8460b0c5b6deb7637f5755230d22ec6e1dcf0619", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\section{Power Reactor Terminology}
\begin{labeling}
\item [\underline{Coolant}:] Material used to remove heat from core, to
heat water, to push a turbine, etc.
\item [\underline{Steam or Coolant Loops}:] Number of heat transfer mechanisms.
Must be at least 1.
\item [\underline{Moderator}:] ... | {"hexsha": "285a536b09bcb88057093c823a8e69c650034dbd", "size": 3226, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "notes/n03.tex", "max_stars_repo_name": "scopatz/rxps", "max_stars_repo_head_hexsha": "bef0045f12215ebe0ac15f9e944047eaefb0ec36", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, "m... |
import cv2
import time
import numpy as np
import HandTrackingModule as htm
import math
from cvzone.SerialModule import SerialObject
from time import sleep
# arduino = SerialObject()
arduino = SerialObject("COM7")
#########################################
wCam, hCam = 640, 480
###########################... | {"hexsha": "e602191c3c95506654c1cb0b6f3787221cec2a51", "size": 1983, "ext": "py", "lang": "Python", "max_stars_repo_path": "BrightnessHandControl.py", "max_stars_repo_name": "zubairatha/LED-control-via-Hand-Tracking", "max_stars_repo_head_hexsha": "6e0cf5503cee1b68f41b2c5757f82517d14dbea9", "max_stars_repo_licenses": [... |
#! /usr/bin/python
# -*- coding: utf-8 -*-
"""
Set of utility function in order to retrieve datas (news and
stock data), build the newsletter template and send the email.
"""
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
import datetime
import... | {"hexsha": "14715a9a0f6c851e268565cc8e446130c2394633", "size": 8403, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/newsletter.py", "max_stars_repo_name": "coupetmaxence/newsletter-dashboard", "max_stars_repo_head_hexsha": "14b83926734c3ebca476744ff0cfcce83a92b9db", "max_stars_repo_licenses": ["MIT"], "m... |
import io
import os
import pathlib
import warnings
from collections import OrderedDict
from copy import deepcopy
import gym
import numpy as np
import pytest
import torch as th
from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
from stable_baselines3.common.base_class import BaseAlgorithm
from stable_baseline... | {"hexsha": "bcc85b6570b3597e10f55205bb64a67913bf4d99", "size": 20517, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_save_load.py", "max_stars_repo_name": "lorepieri8/stable-baselines3", "max_stars_repo_head_hexsha": "f3e1dae4a8484b66e59daf092dc590add31f3152", "max_stars_repo_licenses": ["MIT"], "max... |
"""
Created on 2020. 9. 16.
@author: Inwoo Chung (gutomitai@gmail.com)
License: BSD 3 clause.
"""
import numpy as np
import json
from datetime import datetime
import tensorflow as tf
from tensorflow.keras.layers import Flatten, Dense
from tensorflow.keras.models import Model
from tensorflow.keras import optimizers
f... | {"hexsha": "7de615c33693a25db131053ac33458bd89c5771d", "size": 4500, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mnist_digit_classfication/nobody_convnet2d_mnist.py", "max_stars_repo_name": "tonandr/keras_unsupervised", "max_stars_repo_head_hexsha": "fd2a2494bca2eb745027178e220b42b5e5882f94", "max_s... |
//////////////////////////////////////////////////////////////////////////////
//
// (C) Copyright Ion Gaztanaga 2009-2011. Distributed under the Boost
// Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
//
// See http://www.boost.org/libs/interpr... | {"hexsha": "7ebfb3081c9601d6c45f2cdd21750c0f16181f68", "size": 2285, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Qt-GL-Simple-Scene-master/include/boost_1_5/include/boost/interprocess/detail/windows_intermodule_singleton.hpp", "max_stars_repo_name": "nacsa/Retopology", "max_stars_repo_head_hexsha": "03c009462d... |
#pragma once
#include <boost/array.hpp>
#include <boost/asio.hpp>
#include <flowmq/message.hpp>
#include <flowmq/session.hpp>
#include <functional>
#include <iostream>
#include <map>
using boost::asio::ip::tcp;
namespace flowmq {
// Manages connections with other nodes in the cluster.
// Mainly used in the ClusterN... | {"hexsha": "a6e0e880dcbf4d5e9d6029c510420b7108c51bc8", "size": 2160, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/flowmq/cluster_manager.hpp", "max_stars_repo_name": "rmh2009/flowmq", "max_stars_repo_head_hexsha": "33f59c6d14249426a28f36a857206b18f2ae9655", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
from typing import Tuple, Union, Sequence, Callable, List
import pandas as pd
import numpy as np
import tensorflow as tf
Adjacency = Union[pd.DataFrame, np.ndarray]
Aggregate = Callable[[List[tf.Tensor]], tf.Tensor]
StaticShape = Tuple[int, ...]
RankStaticShape = Tuple[Union[int, tf.Tensor], ...]
OptLayerNames = Uni... | {"hexsha": "a0a71712cb94de0cf0bb10a242f0d02efc087157", "size": 812, "ext": "py", "lang": "Python", "max_stars_repo_path": "degraph/types.py", "max_stars_repo_name": "aljabr0/degraph", "max_stars_repo_head_hexsha": "ac44a1c979da7605ed424853e7fef8ffa8790b27", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
/* LICENSE:
Copyright (c) Members of the EGEE Collaboration. 2010.
See http://www.eu-egee.org/partners/ for details on the copyright
holders.
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
... | {"hexsha": "fc46e6683b499b5199ab6721e8a918d1eb73641d", "size": 5078, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "ice/src/glite-wms-ice-safe.cpp", "max_stars_repo_name": "italiangrid/wms", "max_stars_repo_head_hexsha": "5b2adda72ba13cf2a85ec488894c2024e155a4b5", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
/*=============================================================================
Copyright (c) 2006 Eric Niebler
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)
================================================... | {"hexsha": "815f2768c6d64a5d31a5134859eace3a753480fe", "size": 1320, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/boost_1_72_0/boost/fusion/support/is_segmented.hpp", "max_stars_repo_name": "henrywarhurst/matrix", "max_stars_repo_head_hexsha": "317a2a7c35c1c7e3730986668ad2270dc19809ef", "max_stars_repo_lic... |
function [RSS, XYproj] = Residuals_ellipse(XY,ParG)
%
% Projecting a given set of points onto an ellipse
% and computing the distances from the points to the ellipse
%
% This is a modified version of an iterative algorithm published by D. Eberly
% Internet publication: "Distance from a point to an ellipse in ... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/27708-distance-from-points-to-an-ellipse/Res... |
# calculate the stoichiometry coefficients
function stoich_coeff(species::Vector{T}, reaction::DiffEqBiological.ReactionStruct, e_order::Vector{Int}) where T <: AlgebraSet
coeff = 1
for ind in eachindex(e_order)
coeff *= DiffEqBiological.get_stoch_diff(reaction, Symbol(species[ind]))^e_order[ind]
en... | {"hexsha": "466a915009866f5c0588819fd9249c4868c15652", "size": 3952, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "J-Revell/MomentExpansions.jl", "max_stars_repo_head_hexsha": "adbcac62399742ab402783da433077f1856c394c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
using Compat, FastTransforms, LowRankApprox
using Compat.Test
import FastTransforms: Cnλ, Λ, lambertw, Cnαβ, Anαβ, pochhammer
import FastTransforms: clenshawcurtisnodes, clenshawcurtisweights, fejernodes1, fejerweights1, fejernodes2, fejerweights2
import FastTransforms: chebyshevmoments1, chebyshevmoments2, chebyshevja... | {"hexsha": "c15d2ea7bbe9a37b00e790676265a243dfe4aa59", "size": 4279, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/basictests.jl", "max_stars_repo_name": "dlfivefifty/FastTransforms.jl", "max_stars_repo_head_hexsha": "e49710ae578dc14f25dcb49e4edec06373e37819", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Tests for miscellaneous (non-magic) ``np.ndarray``/``np.generic`` methods.
More extensive tests are performed for the methods'
function-based counterpart in `../from_numeric.py`.
"""
from __future__ import annotations
import operator
from typing import cast, Any
import numpy as np
class SubClass(np.ndarray): ... | {"hexsha": "62024603c9492ea2e99f598e962eea6160818d85", "size": 2716, "ext": "py", "lang": "Python", "max_stars_repo_path": "crabageprediction/venv/Lib/site-packages/numpy/typing/tests/data/pass/ndarray_misc.py", "max_stars_repo_name": "13rianlucero/CrabAgePrediction", "max_stars_repo_head_hexsha": "92bc7fbe1040f49e8204... |
%------------------------------------------------------------------%
% Cannabis Data Science Presentation 3/17/2021
%
% FIXME: Bibliography
% https://tex.stackexchange.com/questions/148893/package-biblatex-error-incompatible-package-ucs-begindocument?noredirect=1&lq=1
% https://tex.stackexchange.com/questions/261595/ho... | {"hexsha": "3092239da69cf4de18c6ce2003ab99c7e2a6e351", "size": 6482, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "2021-03-17/presentation/presentation.tex", "max_stars_repo_name": "cannlytics/cannabis-data-science", "max_stars_repo_head_hexsha": "8ff11330692d3c5a39e3a9f9843af917caf1a720", "max_stars_repo_licens... |
[STATEMENT]
lemma \<gamma>_inf_rep: "\<gamma>_rep(inf_rep p1 p2) = \<gamma>_rep p1 \<inter> \<gamma>_rep p2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<gamma>_rep (inf_rep p1 p2) = \<gamma>_rep p1 \<inter> \<gamma>_rep p2
[PROOF STEP]
by(auto simp:inf_rep_def \<gamma>_rep_cases split: prod.splits extended.spli... | {"llama_tokens": 140, "file": null, "length": 1} |
import json
import math
import networkx as nx
from ccxt import async as ccxt
class ExchangeNotInCollectionsError(Exception):
def __init__(self, market_ticker):
super(ExchangeNotInCollectionsError, self).__init__("{} is either an invalid exchange or has a broken API."
... | {"hexsha": "cac60174cc5b8fdaca3d3e1e53424e5d68f7daec", "size": 3491, "ext": "py", "lang": "Python", "max_stars_repo_path": "peregrinearb/utils/general.py", "max_stars_repo_name": "lyn716/peregrine", "max_stars_repo_head_hexsha": "5b1f6a839bf4a86198ad85f527b04b9a34ea7ab9", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
%% Copyright (C) 2010, 2011, Gostai S.A.S.
%%
%% This software is provided "as is" without warranty of any kind,
%% either expressed or implied, including but not limited to the
%% implied warranties of fitness for a particular purpose.
%%
%% See the LICENSE file for more information.
\section{UValueSerializable}
Thi... | {"hexsha": "2c5446861faabf1931f8ebe17292dfd80370f55c", "size": 1733, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/specs/uvalue-serializable.tex", "max_stars_repo_name": "jcbaillie/urbi", "max_stars_repo_head_hexsha": "fb17359b2838cdf8d3c0858abb141e167a9d4bdb", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
/**
* Copyright (C) 2016 Turi
* All rights reserved.
*
* This software may be modified and distributed under the terms
* of the BSD license. See the LICENSE file for details.
*/
#include <string>
#include <regex>
#include <vector>
#include <map>
#include <set>
#include <parallel/mutex.hpp>
#include <boost/algorit... | {"hexsha": "8b467f52a6bec2d0aa155e68f583bc63dac7aafa", "size": 42710, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "oss_src/sframe/parallel_csv_parser.cpp", "max_stars_repo_name": "venkattgg/venkey", "max_stars_repo_head_hexsha": "796b9bdfb2fa1b881d82080754643c7e68629cd2", "max_stars_repo_licenses": ["BSD-3-Clau... |
#!/usr/bin/env python
# coding: utf-8
# In[7]:
import pandas as pd
import urllib
import numpy as np
import json
from tqdm.autonotebook import tqdm
import re
# %matplotlib inline
tqdm.pandas()
import jellyfish#88942
import dask.dataframe as dd
from dask.multiprocessing import get
from dask.diagnostics impor... | {"hexsha": "f2406621b8075a602febc1b06f421eb3dda9cbd0", "size": 50712, "ext": "py", "lang": "Python", "max_stars_repo_path": "AddressCleanserUtils.py", "max_stars_repo_name": "AlexanderV/NominatimWrapper", "max_stars_repo_head_hexsha": "dcb536dcff3919058d09883bd4d9c551daf5ac2d", "max_stars_repo_licenses": ["MIT"], "max_... |
import math
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from utils import *
def deconv2d(input_, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d", with_w=False):
"""Helper function to construct a deconv "layer" with tf.nn.conv2d_trans... | {"hexsha": "426abce1d4a9eeee4a3a0ab36af637839a523b00", "size": 1811, "ext": "py", "lang": "Python", "max_stars_repo_path": "ops.py", "max_stars_repo_name": "briancylui/ALOCC_Keras", "max_stars_repo_head_hexsha": "2eaee9ed608f7ee4651e8088d83191eb9deca6c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 40, "max_... |
//==================================================================================================
/*!
@file
@copyright 2016 NumScale SAS
@copyright 2016 J.T. Lapreste
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": "6aa64bb5aa00a79b76de0e249a2959138e2b389c", "size": 1233, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/simd/function/compare_neq.hpp", "max_stars_repo_name": "yaeldarmon/boost.simd", "max_stars_repo_head_hexsha": "561316cc54bdc6353ca78f3b6d7e9120acd11144", "max_stars_repo_licenses": ["B... |
from sklearn import metrics
import numpy as np
import time
from scipy import stats
from mlxtend.evaluate import permutation_test
class DataSanitization():
def __init__(self, data):
self.data = data
def is_complete(self, column):
return self.data[column].isnull().sum() == 0
def ha... | {"hexsha": "bc96ecf331ffa2b21b664a02176c2179ee9f18d2", "size": 7155, "ext": "py", "lang": "Python", "max_stars_repo_path": "drifter_ml/columnar_tests/columnar_tests.py", "max_stars_repo_name": "mc-robinson/drifter_ml", "max_stars_repo_head_hexsha": "fe9d0d71b57b9bfba7ad67968bb583dab7dc6212", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma measure_count_space[simp]:
"B \<subseteq> A \<Longrightarrow> finite B \<Longrightarrow> measure (count_space A) B = card B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>B \<subseteq> A; finite B\<rbrakk> \<Longrightarrow> Sigma_Algebra.measure (count_space A) B = real (card B)
[PROOF ... | {"llama_tokens": 209, "file": null, "length": 2} |
# Load dependencies
using NeuralQuantum, QuantumLattices
using Logging, Printf, ValueHistories
# Select the numerical precision
T = Float64
# Select how many sites you want
sites = [3, 3]
Nsites = prod(sites)
# Create the lattice as [Nx, Ny, Nz]
lattice = SquareLattice(sites, PBC=true)
# Create the hamiltonian ... | {"hexsha": "b93d73112473276d013a37cb4e69e1e0ffd91d9a", "size": 2978, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/spins_1d.jl", "max_stars_repo_name": "TheorieMPQ/NeuralQuantum.jl", "max_stars_repo_head_hexsha": "e78e2f44f83da2217965e6f4404eb29f1b87d321", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#include <boost/typeof/message.hpp>
| {"hexsha": "1d508c830f0b07045b6c2045783519cd2f55f7b0", "size": 36, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_typeof_message.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
SUBROUTINE read_reference_spectra ( pge_idx, n_max_rspec, pge_error_status )
USE OMSAO_precision_module
USE OMSAO_indices_module, ONLY: &
max_rs_idx, wvl_idx, spc_idx, pge_static_input_luns, &
pge_o3_idx, o3_t1_idx, o3_t2_idx, o3_t3_idx, comm_idx
USE OMSAO_parameters_module, ONLY: maxchlen, max_... | {"hexsha": "71a8fbb282933e5747d30ec37933793e26fbc7af", "size": 24839, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/read_reference_spectra.f90", "max_stars_repo_name": "ggonzalezabad/OMI_SAO_Shared_VOCs", "max_stars_repo_head_hexsha": "e6b894243d004dc45564dd23feef759d2e4fc3ec", "max_stars_repo_licenses":... |
\begin{ManPage}{\label{man-condor-submit}\Condor{submit}}{1}
{Queue jobs for execution under HTCondor}
\index{HTCondor commands!condor\_submit}
\index{condor\_submit command}
\Synopsis \SynProg{\Condor{submit}}
\oOpt{-verbose}
\oOpt{-unused}
\oOptArg{-name}{schedd\_name}
\oOptArg{-remote}{schedd\_name}
\oOptArg{-addr}... | {"hexsha": "668d6875bb2bc680452e24478c59fe8edf075fd9", "size": 17994, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/man-pages/condor_submit.tex", "max_stars_repo_name": "neurodebian/htcondor", "max_stars_repo_head_hexsha": "113a5c9921a4fce8a21e3ab96b2c1ba47441bf39", "max_stars_repo_licenses": ["Apache-2.0"],... |
(***********************************************************************)
(** * Connecting nominal and LN semantics *)
(***********************************************************************)
(** Our final goal is to show that the abstract nominal machine implements the
same semantics as the LN substitution-base... | {"author": "plclub", "repo": "metalib", "sha": "4ea92d82286cf66e54b4119b2bb2b039827204ab", "save_path": "github-repos/coq/plclub-metalib", "path": "github-repos/coq/plclub-metalib/metalib-4ea92d82286cf66e54b4119b2bb2b039827204ab/Stlc/Connect.v"} |
# -*- coding: utf-8 -*-
import torch
from torch import optim
import numpy as np
import logging
import os
import json
from convlab2.policy.policy import Policy
from convlab2.policy.rlmodule import MultiDiscretePolicy, Value
from convlab2.util.train_util import init_logging_handler
from convlab2.policy.vector.vector_mult... | {"hexsha": "76fa1619c9ab0efb3e08562520887a047274c2d6", "size": 12592, "ext": "py", "lang": "Python", "max_stars_repo_path": "convlab2/policy/gdpl/gdpl.py", "max_stars_repo_name": "sherlock1987/ConvLab-2", "max_stars_repo_head_hexsha": "9547cb09bfd7e297e2c609637c9e38f6c94fdbfb", "max_stars_repo_licenses": ["Apache-2.0"]... |
import numpy as np
from sklearn import metrics as sk_metrics
def compute_moreful_scores(model, dataset, history_name, check_nan = False):
prediction = (np.asarray(model.predict(dataset['test_img'])))[:,:,:,1].round().flatten()
target = dataset['test_label'][:,:,:,1].flatten()
if check_nan:
if np.isnan(np.sum(pr... | {"hexsha": "8816e339740ea9d0b18fb7f54a62b0822a2e871b", "size": 1334, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "lottopotato/railroad_surface_defect_segmentation", "max_stars_repo_head_hexsha": "3c09192bb81825787e50184e416f93bdff7626ad", "max_stars_repo_licenses": ["Apache-... |
import sys
from mpmath import *
from mpmath.calculus.quadrature import GaussLegendre
dps = 300
mp.dps = dps
prec = int(dps * 3.33333)
mp.pretty = False
print("""
inline
std::vector<std::pair<double,double> >
gauss_legendre_nodes(int num_nodes) {
""")
#Note: mpmath gives wrong results for degree==1!
for degree in [... | {"hexsha": "0d66199ca703512017d2f13f09974d24dafa702c", "size": 847, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/gauss_legendre.py", "max_stars_repo_name": "SpM-lab/irbasis", "max_stars_repo_head_hexsha": "5beb5cbe3c0ba0fb42c32e262f04d1f3359d6045", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from __future__ import absolute_import, division, print_function
import collections
import warnings
import networkx
from torch.autograd import Variable
from pyro.distributions.util import scale_tensor
from pyro.util import is_nan, is_inf
def _warn_if_nan(name, value):
if isinstance(value, Variable):
va... | {"hexsha": "2571936efce9589c5fcb58d94f41d2bcf900cfe4", "size": 8417, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyro/poutine/trace.py", "max_stars_repo_name": "cnheider/pyro", "max_stars_repo_head_hexsha": "60bcab73ada30c2b3f05d525690c9664ff6fc22e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
//==============================================================================
// Copyright 2003 - 2011 LASMEA UMR 6602 CNRS/Univ. Clermont II
// Copyright 2009 - 2011 LRI UMR 8623 CNRS/Univ Paris Sud XI
//
// Distributed under the Boost Software License, Version 1.0.
// Se... | {"hexsha": "c4a024eacddf9927969a41d98108e6c5ff7f1cbf", "size": 1638, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "modules/boost/simd/bitwise/include/boost/simd/toolbox/bitwise/functions/simd/common/bitwise_notor.hpp", "max_stars_repo_name": "timblechmann/nt2", "max_stars_repo_head_hexsha": "6c71f7063ca4e5975c9c... |
import os
import sys
import numpy as np
import healpy as hp
if len(sys.argv) == 3:
mcstart = int(sys.argv[1])
mcstop = int(sys.argv[2])
else:
mcstart = 800
mcstop = 900
mapdir_in = "/global/cscratch1/sd/keskital/npipe_maps/npipe6v20"
mapdir_out = "fixmaps"
fn_mask = "/global/cscratch1/sd/keskital/hf... | {"hexsha": "a5a47d4c60fe5e6f131ad8c154e69649ec8267d2", "size": 3311, "ext": "py", "lang": "Python", "max_stars_repo_path": "pipelines/make_fixmaps.py", "max_stars_repo_name": "planck-npipe/toast-npipe", "max_stars_repo_head_hexsha": "ca3e92ea3a81a6146e246ec1d0c5bdcaea3b49f2", "max_stars_repo_licenses": ["BSD-2-Clause"]... |
import pytest
import toolz
import datetime
import numpy as np
import pandas as pd
from pandahouse.http import execute
from pandahouse.core import to_clickhouse, read_clickhouse
from pandas.testing import assert_frame_equal
@pytest.fixture(scope="module")
def df():
df = pd.DataFrame(np.random.randint(0, 100, si... | {"hexsha": "21186cc5ed281b06b85e367a32a49c1453af6b6d", "size": 5415, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandahouse/tests/test_core.py", "max_stars_repo_name": "Intelecy/pandahouse", "max_stars_repo_head_hexsha": "62c495a87c5880b7bdbfa3ab4d09d4f507ab7ad0", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
# AUTOGENERATED! DO NOT EDIT! File to edit: 03_downsampler.ipynb (unless otherwise specified).
__all__ = ['Downsampler', 'get_kernel']
# Cell
import torch
import torch.nn as nn
import numpy as np
# Cell
class Downsampler(nn.Module):
'''
http://www.realitypixels.com/turk/computergraphics/ResamplingFilters... | {"hexsha": "0a19dd93c777784fc123978aa8f8284768e7f1fb", "size": 4504, "ext": "py", "lang": "Python", "max_stars_repo_path": "inpaint_melanoma/downsampler.py", "max_stars_repo_name": "octaviomtz/inpaint_melanoma", "max_stars_repo_head_hexsha": "19cf85a0d51f04ad3e1e3ef68ddf1cc5e27a0b84", "max_stars_repo_licenses": ["Apach... |
#!/usr/bin/env python3
"""
uvotexpmap2.py: Script to create exposure maps, using the updated attitude file.
"""
import os
import subprocess
from argparse import ArgumentParser
from typing import Optional, Sequence
import numpy as np
from astropy.io import fits
from dresscode.utils import load_config
try:
impor... | {"hexsha": "2088ebef2e3c829cffe2694b21cde9998200d5c4", "size": 4996, "ext": "py", "lang": "Python", "max_stars_repo_path": "dresscode/uvotexpmap.py", "max_stars_repo_name": "spacetelescope/DRESSCode", "max_stars_repo_head_hexsha": "29e432363072560335583b2819cf8106d41f5b9a", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import os
import tempfile
from typing import Callable, Tuple
import numpy as np
import tensorflow as tf
from absl.testing import absltest, parameterized
from psutil import virtual_memory
from test_efficientnet_v2.test_model import TEST_PARAMS
from test_efficientnet_v2.utils import get_inference_function
# Some conve... | {"hexsha": "aa3761692e5493d4f2d2dc8ffa037965e858a710", "size": 3460, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_efficientnet_v2/test_tflite_conversion.py", "max_stars_repo_name": "sebastian-sz/efficientnet-v2-keras", "max_stars_repo_head_hexsha": "b1d76b90a2a066ac5608aacb271af8ba67a09bca", "max_stars_r... |
import copy
import json
import logging
import math
import os
import pickle
import random
import numpy as np
import nni
import torch
import torch.nn as nn
import torch.optim as optim
from scipy import stats
from nni.nas.pytorch.utils import AverageMeterGroup
from torch.utils.tensorboard import SummaryWriter
from confi... | {"hexsha": "c3578d415fd2225dc1f14cbf057e232bfd63c233", "size": 3649, "ext": "py", "lang": "Python", "max_stars_repo_path": "tricks/nb101/train_split.py", "max_stars_repo_name": "matluster/anonymous-2102", "max_stars_repo_head_hexsha": "3825f0077cdbebc7c3b34a68e43a3affa22a3e60", "max_stars_repo_licenses": ["Apache-2.0"]... |
# --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file
# Code starts here
df = pd.read_csv(path)
X = df.drop(['customerID', 'Churn'], axis = 1)
y = df.iloc[:, -1]
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state=0)
# ------... | {"hexsha": "9c9b0ee21cb038a170f39c42e817761d4cbe108d", "size": 2564, "ext": "py", "lang": "Python", "max_stars_repo_path": "Telecom-Churn-Prediction-with-Boosting-/code.py", "max_stars_repo_name": "siddhirane/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "d78f28c886a7b34040527aa2e08a656d15f80dbe", "max_stars_rep... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
from io import BytesIO
from itertools import cycle
from unittest import mock
import numpy as np
import numpy.testing as npt
import torch
from parameterized.parameterized import parameterized
from pytorch_lig... | {"hexsha": "60bd82cde50f3ec69e6c9339dc1f559f1d875511", "size": 15582, "ext": "py", "lang": "Python", "max_stars_repo_path": "reagent/test/mab/test_mab.py", "max_stars_repo_name": "dmitryvinn/ReAgent", "max_stars_repo_head_hexsha": "f98825b9d021ec353a1f9087840a05fea259bf42", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import numpy as np
import pandas as pd
from scattertext.distancemeasures.EuclideanDistance import EuclideanDistance
from scattertext.semioticsquare.SemioticSquare import SemioticSquareBase
class SemioticSquareFromAxes(SemioticSquareBase):
def __init__(self,
term_doc_matrix,
axes... | {"hexsha": "d406feb8b815be63630e64fa5d1af166fb98fec8", "size": 3086, "ext": "py", "lang": "Python", "max_stars_repo_path": "scattertext/semioticsquare/SemioticSquareFromAxes.py", "max_stars_repo_name": "shettyprithvi/scattertext", "max_stars_repo_head_hexsha": "a15613b6feef3ddc56c03aadb8e1e629d28a427d", "max_stars_repo... |
import numpy as np
from numpy.core.defchararray import _center_dispatcher
from numpy.lib.twodim_base import triu_indices_from
import pytest
from sdia_python.lab2.ball_window import BallWindow, UnitBallWindow
from sdia_python.lab2.box_window import BoxWindow, UnitBoxWindow
def test_raise_assertion_error_when_center_i... | {"hexsha": "97b07794838a70a0a7f4027046899a57489169fe", "size": 6588, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/lab2/test_ball_window.py", "max_stars_repo_name": "aurelienO/sdia-python-1", "max_stars_repo_head_hexsha": "ac51505ca3656c9fab111f7088b69ba53bd4579d", "max_stars_repo_licenses": ["MIT"], "ma... |
Set Implicit Arguments.
Require Import Bedrock.Platform.Cito.ADT.
Module Make (Import E : ADT).
Require Import Bedrock.Platform.Cito.Semantics.
Module Import SemanticsMake := Make E.
Section TopSection.
Require Import Bedrock.Platform.Cito.GoodModule.
Require Import Bedrock.Platform.Cito.GLabelMap.
... | {"author": "mit-plv", "repo": "bedrock", "sha": "e3ff3c2cba9976ac4351caaabb4bf7278bb0dcbd", "save_path": "github-repos/coq/mit-plv-bedrock", "path": "github-repos/coq/mit-plv-bedrock/bedrock-e3ff3c2cba9976ac4351caaabb4bf7278bb0dcbd/Bedrock/Platform/Cito/LinkFacts.v"} |
import struct
import matplotlib.pyplot as plt
import numpy as np
import serial
import sys
# use ggplot style for more sophisticated visuals
plt.style.use('ggplot')
def live_plotter(x_vec, y_data, lines, identifier='', pause_time=0.001):
if lines[0] == []:
# this is the call to matplotlib that allows dyn... | {"hexsha": "99f8f353792c4540eee657059aa3d6213ccc8642", "size": 3061, "ext": "py", "lang": "Python", "max_stars_repo_path": "SensorTest/python/live-plotter.py", "max_stars_repo_name": "bstarynk/Recovid-Controller", "max_stars_repo_head_hexsha": "4f292d693d949e43e84540e58c7e4fcf5a384eda", "max_stars_repo_licenses": ["MIT... |
% Define document class & import showyourwork
\documentclass[twocolumn]{aastex631}
% Begin!
\begin{document}
% Title
\title{An open source scientific article}
% Author list
\author{@rodluger}
% Abstract
\begin{abstract}
This is a sample open source scientific article automatically generated using the \texttt{sh... | {"hexsha": "13c647f28db6f26728682bbe4691c5946cea9cd4", "size": 3210, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/ms.tex", "max_stars_repo_name": "gusbeane/fdbk_eos-temp", "max_stars_repo_head_hexsha": "0a5565f65ded922ca2ed67e2801f6f83d2ca942d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
% \chapter{Reproduction of GCN}
\section{Graph Convolution Layer and GCN model}
A neutral network based on graph convolution consists of layers of graph convolution and non-linear activation function. To reproduct the work of Kipf et al.\cite{DBLP:journals/corr/KipfW16}, a neutral network is modeled by the forward fu... | {"hexsha": "1919069f6d4b0472ab8531dff99bd773524de1d1", "size": 2609, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/chapters/chapter-2-reproduction-of-gcn.tex", "max_stars_repo_name": "primus2019/BDTA-Course-Project", "max_stars_repo_head_hexsha": "e455bb554d22319b4dbbcc2430970b890a67faaa", "max_stars_repo_li... |
from itertools import chain
from math import ceil, floor
from nose.plugins.skip import SkipTest
from nose_extra_tools import assert_almost_equal, assert_equal, assert_less_equal, assert_is, assert_raises #@UnresolvedImport
import shutil
from sympy import Abs
import tempfile
from beam_integrals import a, mu_m
fr... | {"hexsha": "c7631add7d90f96fa62e355300779dd986c74180", "size": 8124, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/integrals/test_integration.py", "max_stars_repo_name": "petarmaric/beam_integrals", "max_stars_repo_head_hexsha": "211d55f365da0475d96d173ddc040096965eeeec", "max_stars_repo_licenses": ["BSD... |
"""
main_model.py: We define the class 'model_writer' for our main ILP model, which essentially is a collection of
the main Pyomo model object and functions to create additional Constraint objects within the main model object
if needed during the solution process.
"""
from pyomo.environ import *
class model_writer():
... | {"hexsha": "879ebed889ff6efe06981f509ad6d4e4f0548934", "size": 4535, "ext": "py", "lang": "Python", "max_stars_repo_path": "bayene/ilp_model/cussens/main_model.py", "max_stars_repo_name": "bwseoh/Bayene", "max_stars_repo_head_hexsha": "96763df02736ee2798d66d84cf99c217cd05ebbc", "max_stars_repo_licenses": ["BSD-3-Clause... |
#!/usr/bin/env python3
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
import megengine.data.transform as T
import numpy as np
import pytest
from basecls.data.rand_erase import RandomErasing
@pytest.mark.parametrize("prob", [0.25])
@pytest.mark.parametrize("ratio", [0.4, (0.4, 1.5)])
@pytest.mark.parametr... | {"hexsha": "c359c79f462b3a5332646ece9f62cf6b3a01709e", "size": 1181, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/unit/data/test_rand_erase.py", "max_stars_repo_name": "megvii-research/basecls", "max_stars_repo_head_hexsha": "6b395a0a888370b4523764afb78a5a7634a3f6cd", "max_stars_repo_licenses": ["Apache-... |
## types for diffusionmap calculation
struct Diffusionmap
data::Matrix
kernel::AbstractKernel
laplace_type::AbstractLaplacian
threshold::Int64
end
Diffusionmap(data; kernel::AbstractKernel=InverseDistanceKernel(), laplace_type::AbstractLaplacian=RowNormalizedLaplacian(), threshold::Int64=size(data,1... | {"hexsha": "b53f3cff458d93472cbe47aa61226586342fb9af", "size": 823, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/diffusionTypes.jl", "max_stars_repo_name": "bastikusch/DiffusionMap.jl", "max_stars_repo_head_hexsha": "32935471b97d159dde3ec959ae5e098e6982948f", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import lasagne
import numpy as np
from theano.tensor.signal.downsample import max_pool_2d
WIDTH_INDEX = 3
HEIGHT_INDEX = 2
LAYER_INDEX = 1
class SpatialPoolingLayer(lasagne.layers.Layer):
# I assume that all bins has square shape for simplicity
# Maybe later I change this behaviour
def __init__(self, inc... | {"hexsha": "131e6e55cc52bafa2c0e8247c95b2932a3468b0a", "size": 1047, "ext": "py", "lang": "Python", "max_stars_repo_path": "layers.py", "max_stars_repo_name": "dimmddr/roadSignsNN", "max_stars_repo_head_hexsha": "b45669b3967714a73c79f39e9c5c49b20c0234f4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import LSTM
from keras.preprocessing import sequence
max_features = 1024
len_train = np.random.randint(20, size=(1000, 1))
x_train = np.array([np.random.randint(10, size=... | {"hexsha": "5bc2cb025a4c2c64a167ec34c13c051031cbb60e", "size": 1213, "ext": "py", "lang": "Python", "max_stars_repo_path": "turorials/KerasDoc/projects/01_01_04_LSTM/01_01_04_main.py", "max_stars_repo_name": "Ubpa/LearnTF", "max_stars_repo_head_hexsha": "2c9f5d790a9911a860da1e0db4c7bb56a9eee5cb", "max_stars_repo_licens... |
import cv2
import numpy as np
import face_recognition as fr
import serial, time
import os,fnmatch
from datetime import date
from gpiozero import LED
from numpy import savetxt
from numpy import loadtxt
from datetime import datetime
class SaGe:
# 0 : null, 1 : ard, 2 : rPI
def __init__(self, mode=0, port = "COM... | {"hexsha": "d3cf8999f57f3c82bbc70c3be8ae13835272a772", "size": 4153, "ext": "py", "lang": "Python", "max_stars_repo_path": "sage.py", "max_stars_repo_name": "MachLucid/SaGe", "max_stars_repo_head_hexsha": "25062988e0f19c79c40dc1009734202de6b646b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_re... |
import numpy as np
from numpy import random
import math
from math import sqrt, log, exp
from matplotlib import pyplot as plt
from scipy import spatial
from .utils import random_unit_vector, set_random_cells, set_cell_sheet, generate_positions_array, \
random_forces, generate_axes
class Monolayer:
"""Monolayer... | {"hexsha": "597b816622ccbcefe585d37c5b82f30f195087ba", "size": 20403, "ext": "py", "lang": "Python", "max_stars_repo_path": "OS_model/monolayer.py", "max_stars_repo_name": "codiewood/BSP_Project", "max_stars_repo_head_hexsha": "56eb3302c6940b89aca130412502fb9656fda0b2", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
@marginalrule Transition(:out_in) (m_out::Categorical, m_in::Categorical, q_a::MatrixDirichlet) = begin
B = Diagonal(probvec(m_out)) * exp.(mean(log, q_a)) * Diagonal(probvec(m_in))
return Contingency(B ./ sum(B))
end | {"hexsha": "bcef6eacf38a4602588a5f22371e9be8b71178fd", "size": 226, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/rules/transition/marginals.jl", "max_stars_repo_name": "HoangMHNguyen/ReactiveMP.jl", "max_stars_repo_head_hexsha": "f3e848ab171e0786e3d8eb6a0843dbf6dacc7415", "max_stars_repo_licenses": ["MIT"]... |
# -*- coding:utf-8 -*-
import os
import pickle
import sys
import time
from collections import deque
import numpy as np
import torch
import matplotlib.pyplot as plt
import zlib
current_path = os.path.dirname(os.path.realpath(__file__))
PROJECT_HOME = os.path.abspath(os.path.join(current_path, os.pardir, os.pardir))
if... | {"hexsha": "0f2b985c85473c68634f54c46ea01e42aebd3c87", "size": 14635, "ext": "py", "lang": "Python", "max_stars_repo_path": "temp/old/worker_fast_rl_rip_double_agents.py", "max_stars_repo_name": "linklab/link_rl", "max_stars_repo_head_hexsha": "e3d3196dcd49fd71b45941e07fc0d8a27d1d8c99", "max_stars_repo_licenses": ["MIT... |
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 25 12:14:03 2015
@author: ktritz
"""
from __future__ import print_function
from builtins import str, range
import inspect
import types
import numpy as np
from collections import MutableMapping
from .container import containerClassFactory
class Shot(MutableMapping):
# ... | {"hexsha": "73bfe0bcb965a6023ebfa6cd6c82c388a9d1ca1f", "size": 4623, "ext": "py", "lang": "Python", "max_stars_repo_path": "fdp/lib/shot.py", "max_stars_repo_name": "Fusion-Data-Platform/fdp", "max_stars_repo_head_hexsha": "d87a52207238f168ed69b9f96dc8f20f4481366d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import logging
import sys
import os
import uuid
import matplotlib.pyplot as plt
from util import get_request, get_data_size
import time
class CDF(object):
def __init__(self, proxyDict, urls):
self.proxyDict = proxyDict
self.urls = urls
def run(self, numPoints):
for url in self.urls:
... | {"hexsha": "d0621864f4c3cfb1dd60986c0e5f75eb888ae601", "size": 1840, "ext": "py", "lang": "Python", "max_stars_repo_path": "test-harness/cdf.py", "max_stars_repo_name": "jeguiguren/http-proxy", "max_stars_repo_head_hexsha": "760a29a02097128e1acd70552aba464cbcf39713", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
/*==============================================================================
Copyright (c) 2017, 2018 Matt Calabrese
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)
=============================================... | {"hexsha": "fe76c317962fcaa78cc1663efdde6c742b59488d", "size": 7154, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/argot/gen/concept_body/detail/node_kind.hpp", "max_stars_repo_name": "mattcalabrese/argot", "max_stars_repo_head_hexsha": "97349baaf27659c9dc4d67cf8963b2e871eaedae", "max_stars_repo_licenses... |
"""
Interpolation method based on Tables in NPSS.
This was added to bridge the gap between some of the slower scipy implementations.
"""
from __future__ import division, print_function, absolute_import
from six.moves import range
import numpy as np
from openmdao.components.structured_metamodel_util.grid_interp_base ... | {"hexsha": "f9eb327c853806480d32a3b5ab05d7379fd6e35b", "size": 35892, "ext": "py", "lang": "Python", "max_stars_repo_path": "openmdao/components/structured_metamodel_util/python_interp.py", "max_stars_repo_name": "toddrme2178/OpenMDAO", "max_stars_repo_head_hexsha": "379cc6216d13d380e11cb3a46f03960981de4660", "max_star... |
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# import common packages
import sys
import shutil
import numpy as np
# Fromt the Qiskit base package
from qiskit import Aer
from qiskit import QuantumRegister, QuantumCircuit
# lib from Qiskit Aqua
from qiskit.aqua import Operator, QuantumInstance
from qiskit.aqua.alg... | {"hexsha": "6b5cc6a3e92e5947f5887a9750b5b3c1636e4138", "size": 9367, "ext": "py", "lang": "Python", "max_stars_repo_path": "ground_state_energy.py", "max_stars_repo_name": "MartenSkogh/QuantCompQuantChem", "max_stars_repo_head_hexsha": "9b6e3393c43630b5a9ddd4b466cca9213099d3f7", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
#import os
import pkg_resources
import numpy as np
import torch
import warnings
import time
# define ANN architecture
class Net(torch.nn.Module):
def __init__(self, NUM_LAYER, NUM_UNIT):
super(Net, self).__init__()
self.input_layer = torch.nn.Sequential(
torch.n... | {"hexsha": "804ee6ae82cee88a0a948295d694edf9fafa824c", "size": 3528, "ext": "py", "lang": "Python", "max_stars_repo_path": "micsann/main.py", "max_stars_repo_name": "yutaka-shoji/micsann", "max_stars_repo_head_hexsha": "1fb832e16d3667588ed23e90c2dceed2dc6806f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#include <boost/log/trivial.hpp>
#include <boost/assert.hpp>
#include "main.h"
using namespace std;
using namespace boost::program_options;
extern int h264_demo(const variables_map& vm);
const char* name_usage = "please specify codec name: h264 or opus";
const char* input_usage = "please specify input file";
E... | {"hexsha": "80cc67e90956c5e7cbd3d09f127351bebaa471eb", "size": 2176, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "video/main.cpp", "max_stars_repo_name": "walterfan/webrtc_snippets", "max_stars_repo_head_hexsha": "6964560a91ad3338fe8c50a9a250d0a0978eb42a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
!==========================================================================
! BTBMEPNLIB2.f90
!
! second set of routines for handling pn, ppn, and pnn
! intended specifically for parallel MPI applications
! when Lanczos vectors are fragmented
!
! initialized July 2014 by CWJ @ SDSU
!
! Basic idea: between fragments, id... | {"hexsha": "d1a94a983b68ff076e21337195893bc64243c4b1", "size": 2034, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/btbmepnlib2.f90", "max_stars_repo_name": "cwjsdsu/BigstickPublick", "max_stars_repo_head_hexsha": "b195fea5bd35438c9dd17858ce9d0a9726cda7ff", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/*
* Copyright (c) 2019 Opticks Team. All Rights Reserved.
*
* This file is part of Opticks
* (see https://bitbucket.org/simoncblyth/opticks).
*
* 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 ... | {"hexsha": "208c8be7b5ece1763a14f44112c67aff1f3c01aa", "size": 62892, "ext": "cc", "lang": "C++", "max_stars_repo_path": "ggeo/GMesh.cc", "max_stars_repo_name": "hanswenzel/opticks", "max_stars_repo_head_hexsha": "b75b5929b6cf36a5eedeffb3031af2920f75f9f0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 1... |
import chainer
import chainer.functions as F
import math
import numpy as np
from chainer import Chain
class PositionalEncoding(Chain):
"""
Positional encoding, based on sin and cos functions as proposed in section 3.5
of the paper "Attention is all you need"
"""
def __init__(self, size, ... | {"hexsha": "dd89edc0fd85676272a72af5736a72dc78d121a4", "size": 1323, "ext": "py", "lang": "Python", "max_stars_repo_path": "transformer/transformer/positional_encoding.py", "max_stars_repo_name": "chainer/models", "max_stars_repo_head_hexsha": "33fd51dfef2ae50fd615bfa28a3d7e62e0b56c22", "max_stars_repo_licenses": ["MIT... |
\input ../6001mac.tex
\begin{document}
\psetheader{Sample Programming Assignment}{The Game of Twenty-one}
Louis Reasoner took a course on game theory and became interested in
the card game Twenty-One (also called Blackjack). Louis was also
treasurer of his living group. By the end of the semester, he had
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