text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma size_single: "size {#b#} = 1"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. size {#b#} = 1
[PROOF STEP]
by (simp add: size_multiset_overloaded_def size_multiset_single) | {"llama_tokens": 86, "file": null, "length": 1} |
# NOTE:
# To force matplotlib to use inline rendering, insert
# the following line inside the ipython notebook:
# %matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import os
import sys
import random
from cStringIO import StringIO
import numpy as np
from functools import partial
import PIL.Image
from... | {"hexsha": "645eddc8e1b5cbc236fc33a1ed6e1c33eff7a720", "size": 3382, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorlight/visualization.py", "max_stars_repo_name": "bsautermeister/tensorlight", "max_stars_repo_head_hexsha": "3139cf508a4d4d76e30c1591e26933d117883b49", "max_stars_repo_licenses": ["MIT"], "m... |
\section{Variance of OLS estimators}
| {"hexsha": "8f4e1418b582a69f0f337642090d8d77bb0fd92b", "size": 39, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "src/pug/theory/statistics/olsInference/02-00-variance.tex", "max_stars_repo_name": "adamdboult/nodeHomePage", "max_stars_repo_head_hexsha": "266bfc6865bb8f6b1530499dde3aa6206bb09b93", "max_stars_repo_... |
function [struct_irf_record, D_record, gamma_record]=irfsignrespanel(beta_gibbs,sigma_gibbs,It,Bu,IRFperiods,n,p,m,k,signrestable,signresperiods)
% function [struct_irf_record D_record gamma_record]=irfsignrespanel(sigma_gibbs,irf_record,It,Bu,IRFperiods,n,signrestable,signresperiods,checkalgo,checkiter)
% runs the gi... | {"author": "european-central-bank", "repo": "BEAR-toolbox", "sha": "f33aae80c40f7a2e78a54de99b2ce3663f59aa75", "save_path": "github-repos/MATLAB/european-central-bank-BEAR-toolbox", "path": "github-repos/MATLAB/european-central-bank-BEAR-toolbox/BEAR-toolbox-f33aae80c40f7a2e78a54de99b2ce3663f59aa75/tbx/bear/+bear/irfsi... |
################################################################################
# CLASS FOR BCC UNIT CELL MESHES GENERATED USING THE GMSH-PYTHON-API #
################################################################################
# This file provides a class definition for a generation of unit cells with a... | {"hexsha": "0b0fa4a6bb6882f9fbf6f0fa95c8c217b9cb4d14", "size": 11484, "ext": "py", "lang": "Python", "max_stars_repo_path": "gmshModel/Model/BodyCenteredCubicCell.py", "max_stars_repo_name": "gawelk/F3DAS", "max_stars_repo_head_hexsha": "4a4e7233add608820de9ee0fd1c369c2fa1d24c1", "max_stars_repo_licenses": ["BSD-3-Clau... |
import numpy as np
import scipy as sp
from ngboost.scores import LogScore
from ngboost.distns import Normal
from ngboost.manifold import manifold
from ngboost.learners import default_tree_learner, default_linear_learner
from sklearn.utils import check_random_state
from sklearn.base import clone
from sklearn.tree impo... | {"hexsha": "0e54716de8b0ccd514a8c023c5decaf68ba2b2af", "size": 11309, "ext": "py", "lang": "Python", "max_stars_repo_path": "ngboost/ngboost.py", "max_stars_repo_name": "mahat/ngboost", "max_stars_repo_head_hexsha": "0a30225318b25d4c4caace1719be073946fc8401", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
/*
* Copyright (c) 2014, Autonomous Systems Lab
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions... | {"hexsha": "cdcea0a34cdfccec4f5e74644c1868d13a740f17", "size": 13059, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/rovio/LocalizationLandmarkUpdate.hpp", "max_stars_repo_name": "ethz-asl/maplab_rovio", "max_stars_repo_head_hexsha": "58d7b79c912415613b60771f1a0402e48a0ebda6", "max_stars_repo_licenses": [... |
from unittest import TestCase
import numpy as np
import numpy.testing as npt
from . import sparse_permutations as sp
from . import dense_permutations as dp
class TestSparsePermutations(TestCase):
tol = 0.00001
def test_get_sort_permutation(self):
vector = [0.3, 0.2, 0.4, 0.1]
npt.assert_all... | {"hexsha": "421927e88c6377ee20352ca976e1b84b00d0333c", "size": 936, "ext": "py", "lang": "Python", "max_stars_repo_path": "semvecpy/permutations/sparse_permutations_test.py", "max_stars_repo_name": "kearnsw/semvecpy", "max_stars_repo_head_hexsha": "bb3871b16f0bd28563510dfee857264ddfcb4685", "max_stars_repo_licenses": [... |
import os
import numpy as np
path = os.path.abspath(os.path.dirname(__file__))
from scripts.change_pressure import set_pressure
def test_fort4():
from mcflow.file_formatting.reader import read_fort4
from mcflow.file_formatting.writer import write_fort4
data = read_fort4(os.path.join(path, 'test-data', 'fo... | {"hexsha": "ede5e2afae7fe5f9c294db594a4cbbcf995abb25", "size": 1234, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_io.py", "max_stars_repo_name": "dejac001/MCFlow", "max_stars_repo_head_hexsha": "19d1ee21318b49102842d75493a2fb830ec116f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
\chapter{Examples}
The following sections demonstrate some example embedded meta entries in various file types.
If you have an additional file type example that is missing in this section, post a minimum-demonstrating-example as an issue at \url{https://github.com/UCREL/CL-metaheaders/issues} either as a plain request... | {"hexsha": "e419f3ca3c17297ef56226544ec1cb46bfc6b05c", "size": 950, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Active Draft/8_samples.tex", "max_stars_repo_name": "UCREL/CL-metaheaders", "max_stars_repo_head_hexsha": "6ffb4b114b8745ad523abcfac34702d082da18de", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import tensorflow as tf
import numpy as np
import random
import os
def set_seed(seed=200):
"""set global seed to fix random-generated value for reproducible.
available at Functional API, tf.keras.Sequential and tf.keras subclass.
NOTE: operation seed is not fixed.
You need to call this before the ope... | {"hexsha": "e985e7161d5fa5b51aad695033a0643cebdc76f3", "size": 1232, "ext": "py", "lang": "Python", "max_stars_repo_path": "tf_keras_random_seed/seed.py", "max_stars_repo_name": "tokusumi/tf-keras-random-seed", "max_stars_repo_head_hexsha": "0dc1a92455acf4a4f80892b117c63be2e471fc2f", "max_stars_repo_licenses": ["MIT"],... |
function copy = spm_cfg_eeg_copy
% configuration file for copying
%__________________________________________________________________________
% Copyright (C) 2009-2012 Wellcome Trust Centre for Neuroimaging
% Vladimir Litvak
% $Id: spm_cfg_eeg_copy.m 5377 2013-04-02 17:07:57Z vladimir $
%-----------------------------... | {"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/config/spm_cfg_eeg_copy.m"} |
# Lecture 8
## Complex Numbers
```python
import numpy as np
import sympy as sp
import scipy.integrate
sp.init_printing()
##################################################
##### Matplotlib boilerplate for consistency #####
##################################################
from ipywidgets import interact
from ipywid... | {"hexsha": "e69cfdd6088817606cd45e486f0a8f28f32b4ebb", "size": 24145, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "lectures/lecture-08-complex-numbers.ipynb", "max_stars_repo_name": "SABS-R3/2020-essential-maths", "max_stars_repo_head_hexsha": "5a53d60f1e8fdc04b7bb097ec15800a89f67a047", "max_star... |
import numpy as np
import pandas as pd
import pickle
import os
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--dimension', type=int, default=1, help='dimension of the normal data')
parser.add_argument('--save_csv', type=bool, default=False, help='whether to save the data in csv format')
FLAGS... | {"hexsha": "edd053721e832cf6f9f0fc921329665536709d4e", "size": 3244, "ext": "py", "lang": "Python", "max_stars_repo_path": "simulate.py", "max_stars_repo_name": "vicissitude1999/multi-level-vae", "max_stars_repo_head_hexsha": "83bc98fbe5046c61941298d4fd49b08fd868ee89", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import tensorflow as tf
import os
import time
from datetime import datetime
from utils import *
from model import *
import numpy as np
import pdb
# ##############################################################################
# SEGMENTATION CLASS
# #####################################################################... | {"hexsha": "5694fcfb0e7e31247ea8c3487970bd9c0f453bee", "size": 29229, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/segmenter.py", "max_stars_repo_name": "Anguse/salsa_fusion", "max_stars_repo_head_hexsha": "fb820b2a6cb16e008e15af466ab438fea164f4a6", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <nlopt.h>
typedef struct {
int N;
double *x, *y; /* length N; */
} lorentzdata;
static double sqr(double x)
{
return x * x;
}
static int count = 0;
static double lorentzerr(int n, const double *p, double *grad, void *data)
{... | {"hexsha": "6566425e3872dcf734410f18d2523e75e0afe8ae", "size": 2791, "ext": "c", "lang": "C", "max_stars_repo_path": "test/lorentzfit.c", "max_stars_repo_name": "bowie7070/nlopt", "max_stars_repo_head_hexsha": "95df031058531d84fe9c0727458129f773d22959", "max_stars_repo_licenses": ["MIT-0", "MIT"], "max_stars_count": 12... |
#Function that performs PSR Bitaper Neff - Waveguide Width Sweep
#General Purpose Libaries
try:
import matplotlib.pyplot as plt
except:
import pip
pip.main(['install', 'matplotlib'])
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import platform
#Import LUMAPI
from lumerical_... | {"hexsha": "03346b2625b2e7bc48bd2dd59a6335cbb531a0ce", "size": 5296, "ext": "py", "lang": "Python", "max_stars_repo_path": "PDK_Generator/design_automation/polarization_splitter_rotator/psr_bitaper/neff_taper_width_sweep.py", "max_stars_repo_name": "seanlam97/PDK_Generator", "max_stars_repo_head_hexsha": "15c1f4f56575f... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sys, os
import pathlib
import os.path as osp | {"hexsha": "b2fbd17746b62bc68bd30efcd5350ae11f97b4df", "size": 122, "ext": "py", "lang": "Python", "max_stars_repo_path": "ipython/init.py", "max_stars_repo_name": "matherm/rootrepo", "max_stars_repo_head_hexsha": "f1b432018f685c3a3d8d28588c064002983c863a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count"... |
import rospy
import tf
import numpy as np
from matplotlib import pyplot as plt
class VSCaleCalibrator(object):
def __init__(self):
rospy.init_node('vscale_calibrator')
self._tfl = tf.TransformListener()
self._data = [] # (timestamp, distance)
self._t0 = rospy.Time.now()
def step(self):
try:
t, q = sel... | {"hexsha": "73d31b73f0697c1375256fae93edb02d86cbf3aa", "size": 1108, "ext": "py", "lang": "Python", "max_stars_repo_path": "pwm_dev/scripts/vscale.py", "max_stars_repo_name": "olinrobotics/Powered-Mobility", "max_stars_repo_head_hexsha": "7294a6ff35dffe130a4c21a7725783515f0de255", "max_stars_repo_licenses": ["MIT"], "m... |
# License: 3-clause BSD
# Copyright (c) 2016-2018, Ml4AAD Group (http://www.ml4aad.org/)
from typing import List, Optional, Tuple, Union
from ConfigSpace import ConfigurationSpace
import numpy as np
import sklearn.gaussian_process.kernels
from openbox.surrogate.base.base_model import AbstractModel
import openbox.sur... | {"hexsha": "12d03c0c05b8a18b00a248b83c10262d389fe617", "size": 5500, "ext": "py", "lang": "Python", "max_stars_repo_path": "openbox/surrogate/base/base_gp.py", "max_stars_repo_name": "Dee-Why/lite-bo", "max_stars_repo_head_hexsha": "804e93b950148fb98b7e52bd56c713edacdb9b6c", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
(*
This file is a part of MMIsar - a translation of Metamath's set.mm to Isabelle 2005 (ZF logic).
Copyright (C) 2006 Slawomir Kolodynski
This program is free software; Redistribution and use in source and binary forms,
with or without modification, are permitted provided that the following conditi... | {"author": "SKolodynski", "repo": "IsarMathLib", "sha": "879c6b779ca00364879aa0232b0aa9f18bafa85a", "save_path": "github-repos/isabelle/SKolodynski-IsarMathLib", "path": "github-repos/isabelle/SKolodynski-IsarMathLib/IsarMathLib-879c6b779ca00364879aa0232b0aa9f18bafa85a/IsarMathLib/MMI_prelude.thy"} |
import torch
import torch.nn as nn
import numpy as np
from torch.jit import Final
from typing import List
class NeuralStateSpaceModel(nn.Module):
n_x: Final[int]
n_u: Final[int]
n_feat: Final[int]
def __init__(self, n_x, n_u, n_feat=64, scale_dx=1.0, init_small=True, activation='relu'):
super... | {"hexsha": "86e03f5e18b20f16b2b23b6656a8c1603d7c1a9d", "size": 9324, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchid/ssmodels_ct.py", "max_stars_repo_name": "forgi86/sysid-neural-continuous", "max_stars_repo_head_hexsha": "d4a4c7a8302977a90e63738265cbcd0bf5836e18", "max_stars_repo_licenses": ["Apache-2.0... |
"""
Copyright 2018 Goldman Sachs.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software di... | {"hexsha": "cf514d79b1c135beda8ddd138fa17ef03cf1191c", "size": 27017, "ext": "py", "lang": "Python", "max_stars_repo_path": "gs_quant/test/timeseries/test_statistics.py", "max_stars_repo_name": "S-Manglik/gs-quant", "max_stars_repo_head_hexsha": "af22aa8574571db45ddc2a9627d25a26bd00e09b", "max_stars_repo_licenses": ["A... |
# Copyright 2021 Medical Imaging Center, Vingroup Big Data Insttitute (VinBigdata), Vietnam
#
# 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
... | {"hexsha": "5537b19f2cc62b589d98a72ea970a7f56e06faec", "size": 7292, "ext": "py", "lang": "Python", "max_stars_repo_path": "spine/classification/dataloader.py", "max_stars_repo_name": "vinbigdata-medical/vindr-spinexr", "max_stars_repo_head_hexsha": "ac9603a10684a4c6469cc480c954504ad127bc20", "max_stars_repo_licenses":... |
module TwoPlayerTest
using Test
import Cribbage: CribbageGame, GameState, UnexpectedPlayerException
import Cribbage.RandomPlay: RandomPlayer
using Cribbage.TwoPlayer
@testset "Test TwoPlayerGame Constructor" begin
p₁ = RandomPlayer("player one")
@test_throws AssertionError TwoPlayerGame(p₁, p₁)
p₂ = Ra... | {"hexsha": "4f8a66abbd46ee225737f6280928675bf92b138a", "size": 2910, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_two_player.jl", "max_stars_repo_name": "KyleSJohnston/Cribbage.jl", "max_stars_repo_head_hexsha": "3eff95ed8fe1bc90973d1542067dcfc4b8072284", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma prefix_refl_conv[simp]: "(prefix\<cdot>xs\<cdot>xs = TT) \<longleftrightarrow> (xs \<noteq> \<bottom>)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (prefix\<cdot>xs\<cdot>xs = TT) = (xs \<noteq> \<bottom>)
[PROOF STEP]
by auto | {"llama_tokens": 103, "file": "BirdKMP_Theory_Of_Lists", "length": 1} |
program envelopef
implicit none
include "sacf.h"
! Define the Maximum size of the data Array
integer MAX
parameter (MAX=4000)
! Define the Data Array of size MAX
real*4 :: ya(MAX), yb(MAX), yc(MAX)
! Declare Variables used in the rsac1() subroutine
real beg, del... | {"hexsha": "5cc2f94b598b4142c79f91e334b82f0f9208fbe2", "size": 1198, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "examples/convolve_saclib/convolvef.f90", "max_stars_repo_name": "savage13/sac", "max_stars_repo_head_hexsha": "f13063ae2e3331e40096037d191563c4ec1ca18b", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding:utf-8 -*-
import json
import math
import os
import pickle
from collections import Counter
from datetime import datetime
import numpy as np
from gensim.models import KeyedVectors
UNK_CHAR = "<UNK>"
PAD_CHAR = "<PAD>"
class NNData(object):
"""
将文本数据转换为适合神经网络的数据格式
"""
def __init__(self, ... | {"hexsha": "fcb5b54429c6a1c803451d6da79d1dcbfa96dff4", "size": 10379, "ext": "py", "lang": "Python", "max_stars_repo_path": "youmin_textclassifier/preprocessing/nn_dataset.py", "max_stars_repo_name": "WENGIF/youmin_textclassifier", "max_stars_repo_head_hexsha": "15410aaba009019ec387a8e64aec4734ae396922", "max_stars_rep... |
Require Import rt.util.all.
Require Import rt.model.arrival.basic.job rt.model.arrival.basic.task rt.model.priority.
Require Import rt.model.schedule.uni.schedule rt.model.schedule.uni.schedulability.
Require Import rt.model.schedule.uni.susp.suspension_intervals.
Require Import rt.analysis.uni.basic.workload_bound_fp.... | {"author": "cd-public", "repo": "rt-proofs", "sha": "ebef0b65460fe009c51f638fe2b459f16a6d1dd5", "save_path": "github-repos/coq/cd-public-rt-proofs", "path": "github-repos/coq/cd-public-rt-proofs/rt-proofs-ebef0b65460fe009c51f638fe2b459f16a6d1dd5/implementation/uni/susp/dynamic/oblivious/fp_rta_example.v"} |
import argparse
import time
import torchvision
import torch
from torchvision import transforms as T
from PIL import Image
import importlib.util
import tensorflow_datasets as tfds
import tensorflow_hub as hub
import sys
import os
import yaml
import re
import numpy as np
import subprocess
import random
# import tensor... | {"hexsha": "985b60aed32599c1829918b1c0952c9a1910e1ca", "size": 4443, "ext": "py", "lang": "Python", "max_stars_repo_path": "imagenet_pky.py", "max_stars_repo_name": "parkinkon1/simclr", "max_stars_repo_head_hexsha": "2c1a19baf28e91db119ab32df75d3a6e474dc1b1", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count"... |
import sys
sys.path.append('..')
from pronoun_cracker import *
import numpy as np
import pandas as pd
cracker = PronounCracker('pronoun', '../input', '../output')
cracker.load_data()
print(cracker.train.columns)
renaming = {
'p_a' : 'P-A-e2e', 'p_b' : 'P-B-e2e',
'a_p' : 'A-P-e2e', 'b_p' : 'B-P-e2e'}
train... | {"hexsha": "2c14d0c74ede0f48ea2cb890edca7cac2dd20912", "size": 703, "ext": "py", "lang": "Python", "max_stars_repo_path": "frozen_model/e2e_external/e2e_coref/e2e_output.py", "max_stars_repo_name": "Yorko/gender-unbiased_BERT-based_pronoun_resolution", "max_stars_repo_head_hexsha": "67d8c6b3fce94bbeb75bbc644a3111b168e7... |
import time
from garage.misc import logger
from garage.misc import ext
from garage.misc.overrides import overrides
from garage.tf.algos import BatchPolopt
from garage.tf.optimizers.cg_optimizer import CGOptimizer
from garage.tf.misc import tensor_utils
from garage.core.serializable import Serializable
import tensorflow... | {"hexsha": "65d7d3e9ebd5c752e95fa63b1193825df4cdaf7e", "size": 18330, "ext": "py", "lang": "Python", "max_stars_repo_path": "garage/tf/algos/catrpo.py", "max_stars_repo_name": "Mee321/HAPG_exp", "max_stars_repo_head_hexsha": "ccd0d92ad2ffcd8438efbd6bc09123a4c3aafabe", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
/*
* This file is open source software, licensed to you under the terms
* of the Apache License, Version 2.0 (the "License"). See the NOTICE file
* distributed with this work for additional information regarding copyright
* ownership. You may not use this file except in compliance with the License.
*
* You may ... | {"hexsha": "9ceb79ee64e9f3ba86bba0d581ba03998bc938c2", "size": 3978, "ext": "hh", "lang": "C++", "max_stars_repo_path": "include/seastar/util/backtrace.hh", "max_stars_repo_name": "bhalevy/seastar", "max_stars_repo_head_hexsha": "f17d48138d5c159b351c2468de890002e013da7d", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
[STATEMENT]
lemma imp_graph_insert [simp]:
"imp_graph (insert cl cls) = edges_of_clause cl \<union> imp_graph cls"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. imp_graph (insert cl cls) = edges_of_clause cl \<union> imp_graph cls
[PROOF STEP]
by (auto simp: imp_graph_def) | {"llama_tokens": 112, "file": "Containers_Examples_TwoSat_Ex", "length": 1} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Dec 29 09:17:18 2020
@author: sblair
This is a Python implementation of an example problem from Lecture 31 of EM424.
The example is the solution of the Wave Equation in Polar Coordinates.
For this script I have implemented only the "ex2" initial cond... | {"hexsha": "22706bcabbb4ab4803c5a110195a4a890d1bdf33", "size": 4909, "ext": "py", "lang": "Python", "max_stars_repo_path": "lecture_31_python.py", "max_stars_repo_name": "stu314159/pyFourierExp", "max_stars_repo_head_hexsha": "889d824f269403f8c2bd190b5da63a82931d1bcf", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "47c3fc80f561e769b6ba1925f535279215a8227c", "size": 10405, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/cv/ESRGAN/src/util/util.py", "max_stars_repo_name": "leelige/mindspore", "max_stars_repo_head_hexsha": "5199e05ba3888963473f2b07da3f7bca5b9ef6dc", "max_stars_repo_licenses": ["Apache-2.0... |
"""Base map class that defines the rendering process
"""
import matplotlib.pyplot as plt
import numpy as np
from gym.spaces import Box, Dict
from ray.rllib.agents.callbacks import DefaultCallbacks
from ray.rllib.env import MultiAgentEnv
_MAP_ENV_ACTIONS = {
"MOVE_LEFT": [0, -1], # Move left
"MOVE_RIGHT": [0,... | {"hexsha": "6f45b028b2985fc8cf6689f072d755f79c4b7808", "size": 38216, "ext": "py", "lang": "Python", "max_stars_repo_path": "social_dilemmas/envs/map_env.py", "max_stars_repo_name": "Caffa/sequential_social_dilemma_games", "max_stars_repo_head_hexsha": "de9af51f6cad2fbbd1fb28707364f997e7fc14f6", "max_stars_repo_license... |
struct ShaderSpecification
source_file::String
reuse_descriptors::Bool
entry_point::Symbol
stage::Vk.ShaderStageFlag
language::ShaderLanguage
end
function ShaderSpecification(source_file, stage::Vk.ShaderStageFlag; reuse_descriptors = false, entry_point = :main)
ShaderSpecification(source_file,... | {"hexsha": "a67cd3dfc51cd847dbc6344c9a79d2a280928dc6", "size": 1103, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/shaders/specification.jl", "max_stars_repo_name": "serenity4/Lava.jl", "max_stars_repo_head_hexsha": "6dc3b27c660a6b555178bb738b634aaa588dc4b2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright (c) 2020, Xilinx
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the follow... | {"hexsha": "108c97c2e83b7f3ca9dd6ead746b3ef8b4d10af5", "size": 5740, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/brevitas/test_brevitas_mobilenet.py", "max_stars_repo_name": "mmrahorovic/finn", "max_stars_repo_head_hexsha": "d1cc9cf94f1c33354cc169c5a6517314d0e94e3b", "max_stars_repo_licenses": ["BSD-3-... |
from .conftest import base_config
import numpy as np
from numpy.testing import assert_allclose
import openamundsen as oa
from pathlib import Path
import pytest
@pytest.mark.slow
def test_evapotranspiration(tmp_path):
config = base_config()
config.start_date = '2020-07-01'
config.end_date = '2020-07-15'
... | {"hexsha": "f2861d5d40c5699134ec7f9416680061b4419c84", "size": 4291, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_evapotranspiration.py", "max_stars_repo_name": "openamundsen/openamundsen", "max_stars_repo_head_hexsha": "2ac09eb34b0c72c84c421a0dac08d114a05b7b1c", "max_stars_repo_licenses": ["MIT"],... |
abstract type PDXObject end
mutable struct PDFormXObject <: PDXObject
doc::PDDoc
cosXObj::CosIndirectObject{CosStream}
matrix::Matrix{Float32}
bbox::CDRect{Float32}
fonts::Dict{CosName, PDFont}
xobjs::Dict{CosName, PDXObject}
content_objects::PDPageObjectGroup
function PDFormXObject(doc... | {"hexsha": "7e2ac4f1d2304ea0af0d807e34ba8ef4c44eeaae", "size": 3076, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PDXObject.jl", "max_stars_repo_name": "gwierzchowski/PDFIO.jl", "max_stars_repo_head_hexsha": "224834081047f55eb42f1fdc293b32795e433512", "max_stars_repo_licenses": ["Zlib"], "max_stars_count":... |
Extraction Language Scheme.
Require Import NArith.
Require Import Arith.
Require Import Bool.
Require Import List.
Require Import Bag.
Require Import Dict.
Require Import CpdtTactics.
Require Import JamesTactics.
Require Import KonneTactics.
Require Import Coq.Program.Basics.
Require Import EqDec.
Require Import Enume... | {"author": "konne88", "repo": "bagpipe", "sha": "9338220fe1fec2e7196e1143c92065ce5d5a7b46", "save_path": "github-repos/coq/konne88-bagpipe", "path": "github-repos/coq/konne88-bagpipe/bagpipe-9338220fe1fec2e7196e1143c92065ce5d5a7b46/src/bagpipe/coq/Test/BagpipeExtract.v"} |
#!/usr/bin/python
import sys
from numpy import *
import random
import numpy.random as nrd
from optparse import OptionParser
parser = OptionParser(usage="-r REF expressionFile1.exp [expressionFiles2.exp]\n\n Program generates reads from fasta file based on read counts provided in the expression files fi... | {"hexsha": "2c939e375894f0e9e4bdf29ee1ca555e05315adb", "size": 4758, "ext": "py", "lang": "Python", "max_stars_repo_path": "codes/matlab/sim/getReads.py", "max_stars_repo_name": "PROBIC/diffsplicing", "max_stars_repo_head_hexsha": "09b5c846de8834696c15459816e0a1916efa8b44", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Unit tests for judiRHS and judiWavefield (without PDE solves)
# Philipp Witte (pwitte.slim@gmail.com)
# May 2018
#
# Mathias Louboutin, mlouboutin3@gatech.edu
# Updated July 2020
########################################################### judiRHS ####################################################
@testset "judiRH... | {"hexsha": "37012fc0e392942f84c5183f0c42dc7454456703", "size": 2924, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_abstract_vectors.jl", "max_stars_repo_name": "nogueirapeterson/JUDI", "max_stars_repo_head_hexsha": "cc76e950929f0b7a3cf29c2dff71e432e8ea26f8", "max_stars_repo_licenses": ["MIT"], "max_st... |
\documentclass[article,oneside]{memoir}
%%% custom style file with standard settings for xelatex and biblatex. Note that when [minion] is present, we assume you have minion pro installed for use with pdflatex.
%\usepackage[minion]{org-preamble-pdflatex}
%%% alternatively, use xelatex instead
\usepackage{org-preamble-... | {"hexsha": "5a40a3927b2cc0706a243c21d1d1edcdde8863df", "size": 5983, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "NJCU/Resources/Templates/EthicsTemp.tex", "max_stars_repo_name": "scoconno/scoconno.github.io", "max_stars_repo_head_hexsha": "b62e9848878a57ca28cc9cacecc6c1ef05096b49", "max_stars_repo_licenses": [... |
from embeddings import sentence_embedding
import numpy as np
from training import mlpc_model_for_s2v
from hw_helpers import create_csv_submission
def run():
neg_embeddings, pos_embeddings, test_embeddings = sentence_embedding("train_pos_full.txt", "train_neg_full.txt", "test_data.txt")
train_data = np.vstack((... | {"hexsha": "946fe5872590fe4d7d36ab1d618cbb861aca84c6", "size": 1056, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_s2v.py", "max_stars_repo_name": "lggoch/Proj_2", "max_stars_repo_head_hexsha": "5c893359d8f456664ceb2366ec0d946ea230600b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": null,... |
# -*- mode: python; coding: utf-8 -*-
# Copyright 2017 the HERA Collaboration
# Licensed under the 2-clause BSD license.
"""Testing for `hera_mc.roach`.
"""
from __future__ import absolute_import, division, print_function
import unittest
import nose.tools as nt
from math import floor
from astropy.time import Time, T... | {"hexsha": "77caed20b85d26c0338cfad30b4ae0f6ea9eb7be", "size": 11646, "ext": "py", "lang": "Python", "max_stars_repo_path": "hera_mc/tests/test_roach.py", "max_stars_repo_name": "pkgw/hera_mc", "max_stars_repo_head_hexsha": "d2769a716a0e68fe709d3834362b94f547136836", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
# coding: utf-8
# %load jupyter_default.py
import pandas as pd
import numpy as np
import os
import re
import datetime
import time
import glob
from tqdm import tqdm_notebook
from colorama import Fore, Style
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
import matplotlib.colors
i... | {"hexsha": "608a28999bb14cc0cb54083d3a88f5a652c7c16d", "size": 39933, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/py/4_bayes_poisson.py", "max_stars_repo_name": "agalea91/nhl-goalie-pull-optimization", "max_stars_repo_head_hexsha": "7e57d50163c5f96a22dd5afd96c6e1ba5487c600", "max_stars_repo_license... |
# utils
import numpy as np
import copy
SELECT_COL = 'SELECT_COL'
SELECT_AGG = 'SELECT_AGG'
WHERE_COL = 'WHERE_COL'
WHERE_OP = 'WHERE_OP'
WHERE_VAL = 'WHERE_VAL' # for models with value prediction
# spider
WHERE_ROOT_TERM = 'WHERE_ROOT_TERM'
ANDOR = 'ANDOR'
GROUP_COL = 'GROUP_COL'
GROUP_NHAV = 'GROUP_NHAV'
HAV_COL = '... | {"hexsha": "db7401fa6217c1c0a76585a90a19d5defd4833a2", "size": 5229, "ext": "py", "lang": "Python", "max_stars_repo_path": "MISP_SQL/utils.py", "max_stars_repo_name": "Deliangus/MISP", "max_stars_repo_head_hexsha": "8632b5ea120f8385825a08eb930232d3ea74c426", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 54, "m... |
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelBinarizer
import gc
import time
####
# load the data
####
print('reading in data')
all_train = pd.read_csv('./data/train_cleaned.csv')
#all_train.head()
final_test = pd.read_csv('./data/test_cleaned.csv')
#final_test.head()
#raw_test =... | {"hexsha": "ce3e08b61eb38e5cbf427c06787ded0c18f8e783", "size": 7080, "ext": "py", "lang": "Python", "max_stars_repo_path": "google_analytics/clean_to_np_matrix.py", "max_stars_repo_name": "mathxyz/stock2", "max_stars_repo_head_hexsha": "1e07156dea37f987efbc03025693b9ca2acf3f96", "max_stars_repo_licenses": ["MIT"], "max... |
# Maze generator -- Randomized Prim Algorithm
## Imports
import random
import numpy as np
import time
from colorama import init
from colorama import Fore, Back, Style
## Functions
def printMaze(maze, height, width):
for i in range(0, height):
for j in range(0, width):
if (maze[i][j] == 'u'):
... | {"hexsha": "1a06791e3e1846f083de359bab1e710488600367", "size": 10904, "ext": "py", "lang": "Python", "max_stars_repo_path": "envs/d4rl/d4rl_content/pointmaze/generate_new_maze.py", "max_stars_repo_name": "AliengirlLiv/babyai", "max_stars_repo_head_hexsha": "51421ee11538bf110c5b2d0c84a15f783d854e7d", "max_stars_repo_lic... |
import sys
sys.path.insert(0, './python/')
import caffe
import numpy as np
import pdb
#weights='./models/lenet300100/caffe_lenet300100_original.caffemodel'
weights='./models/lenet300100/compressed_lenet300100.caffemodel'
#weights='/home/gitProject/Dynamic-Network-Surgery/models/lenet300100/caffe_lenet300100_sparse.caff... | {"hexsha": "734cf6ef4f3dd16ca774d70d3c36b169fd1b5f6a", "size": 1583, "ext": "py", "lang": "Python", "max_stars_repo_path": "CS303_Artifical-Intelligence/NCS/OLMP/sparsity_lenet300100.py", "max_stars_repo_name": "Eveneko/SUSTech-Courses", "max_stars_repo_head_hexsha": "0420873110e91e8d13e6e85a974f1856e01d28d6", "max_sta... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import argparse
import random as rand
import time
import numpy as np
import pickle
from PIL import Image, ImageDraw, ImageFilter, ImageEnhance, ImageOps, ImageFile
from delaunay import delaunay
from voronoi import createVoronoiFromDelaunay
#
# Add a prefix to a... | {"hexsha": "a1e804af7b60717342cdc1d4c76a0a9f0a5bfb4a", "size": 11365, "ext": "py", "lang": "Python", "max_stars_repo_path": "drawTriangles.py", "max_stars_repo_name": "hoojaoh/Delaunay_Triangulation", "max_stars_repo_head_hexsha": "17e65fa8793ca4d7d6d7e25b4899a08beb6499d5", "max_stars_repo_licenses": ["0BSD"], "max_sta... |
import os
import shutil
import numpy as np
import tensorflow as tf
from utils import conv, fc, plot
"""
Run file for testing modularity-inducing regularization term in the toy example of MNIST.
Much code adopted from Tensorflow's Tensorboard tutorial, available at:
https://github.com/tensorflow/tensorflow/blob/r1.8... | {"hexsha": "db804935b8be7a18fe710452f3ca49d4bf0d27f2", "size": 7315, "ext": "py", "lang": "Python", "max_stars_repo_path": "modular/run.py", "max_stars_repo_name": "AI-RG/modular", "max_stars_repo_head_hexsha": "71760680297b6c346e67fb7e077a7a34e7488a7b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
import copy
import datetime
import os
import random
import shutil
from dataclasses import dataclass
from typing import Optional
import numpy as np
import pandas as pd
import pytest
import scipy
import psykoda.detection
import psykoda.utils
from psykoda.cli import internal
from psykoda.feature_extraction import Featur... | {"hexsha": "b0d9229b0d324efa185316964ac6aa0a1f4e0761", "size": 21822, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_internal.py", "max_stars_repo_name": "FujitsuResearch/psykoda", "max_stars_repo_head_hexsha": "4268b04064350f0d45a6be9e2f91ace06745d7d6", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""Unit test(s) for ordering.py"""
import pytest
import os
import shutil
from collections import OrderedDict
import copy
import pickle
import numpy as np
from riddle import ordering
PRECISION = 4
class TestSummary:
@pytest.fixture(scope='module')
def summary(self):
list_feat = ['John', 'James', ... | {"hexsha": "ad90118e4c61852e5ff870f6779ee4cf4e76159c", "size": 7360, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/riddle/test_ordering.py", "max_stars_repo_name": "LaudateCorpus1/RIDDLE-1", "max_stars_repo_head_hexsha": "c8d6ad5ed1f2c94b947cc30ff9e63fe4a8ff32bd", "max_stars_repo_licenses": ["Apache-2.0"... |
import numpy as np
import pandas as pd
import re
### preformatting
class text_clean():
def __init__(self,sentence):
self.my_sentence = sentence
def clean_words(self):
my_sentence = self.my_sentence
my_sentence=my_sentence.lower()
rep = {
"fell down": "loss_of... | {"hexsha": "29aa3a13b7a77ed74d8c6ecea3bbbb38aac81586", "size": 5412, "ext": "py", "lang": "Python", "max_stars_repo_path": "webdev/audio/python_codes/clean_up.py", "max_stars_repo_name": "adikr24/Django_web_development", "max_stars_repo_head_hexsha": "16b10b3547c2e40cc4039b754afe4f9addd9136c", "max_stars_repo_licenses"... |
using LichessBot
using Test
@time begin
include("eval.jl")
include("netcode.jl")
# Write your tests here.
end
| {"hexsha": "aff26d4997495d055693743cc8f9262ed3c9dea5", "size": 123, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "dave7895/LichessBot", "max_stars_repo_head_hexsha": "72820b0f1ddc1407abce52ded27b104cd367a32e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
# --------------
import pandas as pd
from sklearn.model_selection import train_test_split
#path - Path of file
df=pd.read_csv(path)
# Code starts here
X=df.drop(['customerID','Churn'],axis=1)
y=df.Churn
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3,random_state = 0)
# --------------
import n... | {"hexsha": "8871c4229e06e5cc36eace9d1f668d789870f703", "size": 3102, "ext": "py", "lang": "Python", "max_stars_repo_path": "boosting-project/code.py", "max_stars_repo_name": "rkkirpane/Best-Projects-ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "b9d52a29eff734696ae269cafc8407d2121b40b0", "max_stars_repo_licenses... |
//
// Copyright 2007-2012 Christian Henning, Andreas Pokorny, Lubomir Bourdev
//
// 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
//
#ifndef BOOST_GIL_IO_READ_AND_CONVERT_VIEW_HPP
#define BOOST_GIL_IO_READ_AND_CONVER... | {"hexsha": "f0ce942a5ac557ecdc6a048ef85e562cc3629dd1", "size": 9838, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost/gil/io/read_and_convert_view.hpp", "max_stars_repo_name": "sdebionne/gil-reformated", "max_stars_repo_head_hexsha": "7065d600d7f84d9ef2ed4df9862c596ff7e8a8c2", "max_stars_repo_licenses... |
/*
This program is free software; you can redistribute it and/or modify it under
the terms of the European Union Public Licence - EUPL v.1.1 as published by
the European Commission.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCH... | {"hexsha": "0fa1c0d6898ab923ca888740d2d9e5693218642a", "size": 2823, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sta-src/Services/serviceAngleRateUnit.cpp", "max_stars_repo_name": "hoehnp/SpaceDesignTool", "max_stars_repo_head_hexsha": "9abd34048274b2ce9dbbb685124177b02d6a34ca", "max_stars_repo_licenses": ["IJ... |
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from rpyopencl import RPyOpenCLCluster
import json
import numpy as np
from decorators import timer
# Globals to simplify sample tuning
object_type = np.float32
size = 50000
kernel_name = "sum_mul"
a_np = np.random.... | {"hexsha": "1f864e0f9c65d1477fe77fc5656a5d99752bbd79", "size": 3037, "ext": "py", "lang": "Python", "max_stars_repo_path": "samples/app_sync_node.py", "max_stars_repo_name": "shazz/DistributedOpenCL", "max_stars_repo_head_hexsha": "ddfac3ea1be84b13539e7ac07f3ef7811bbd81b6", "max_stars_repo_licenses": ["MIT"], "max_star... |
import data.nat.basic
import data.int.parity
import tactic
open int
/-lifted from tutorial project. I think there's potential to explain and
develop these lemmas and parity in detail, but it could make the tutorial pretty long-/
def odd (n : ℤ) : Prop := ∃ k, n = 2*k + 1
#check int.not_even_iff
theorem not_even_i... | {"author": "iceplant", "repo": "mathcamp-tutorials", "sha": "481db142430e47f892e8f984aa08eecfb3167bb5", "save_path": "github-repos/lean/iceplant-mathcamp-tutorials", "path": "github-repos/lean/iceplant-mathcamp-tutorials/mathcamp-tutorials-481db142430e47f892e8f984aa08eecfb3167bb5/root_2_irrational.lean"} |
import numpy as np
from tinyml import LinearRegression as lr
from tinyml import LogisticRegression as lo
from sklearn import datasets
# Linear Regression
X,y = datasets.make_regression(n_features=1,n_informative=1, noise=20, random_state=1)
table=np.column_stack((X,y))
p = lr.LinearRegression(table,reg=True,lamda=10)... | {"hexsha": "115eca7593d7f11e50d97382263a7ef95062fb40", "size": 680, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples.py", "max_stars_repo_name": "parasdahal/tinyml", "max_stars_repo_head_hexsha": "cf2fcc021ae65df19d420e3142e4a38d20ca87e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11, "max_s... |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
# ==============================================================================
#
# Functions to parse the table cells in text back-end.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
"... | {"hexsha": "129dabcee8fbfdc7c8e3c6152d8c607fc501c61b", "size": 5361, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/backends/text/cell_parse.jl", "max_stars_repo_name": "aminnj/PrettyTables.jl", "max_stars_repo_head_hexsha": "904265922ec6ef34600027120d6cefe18f10ba30", "max_stars_repo_licenses": ["MIT"], "max... |
import os
import cv2
import numpy as np
from flask import Flask, render_template, request, jsonify, redirect, url_for
from werkzeug.utils import secure_filename
from pyimagesearch.colordescriptor import ColorDescriptor
from pyimagesearch.searcher import Searcher
# create flask instance
app = Flask(__name__)
IN... | {"hexsha": "b43ff9d86c9dfb76dc8b0ec6b1865c71f546e3ad", "size": 2221, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/app.py", "max_stars_repo_name": "SaraLatif99/image-search-engine", "max_stars_repo_head_hexsha": "50e9fd106d4f56d49afd5367a15b9810117dc510", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# BioCore.jl
# ==========
#
# Core types and methods common to many packages in the BioJulia ecosystem.
#
# This file is a part of BioJulia.
# License is MIT: https://github.com/BioJulia/BioCore.jl/blob/master/LICENSE.md
__precompile__()
module BioCore
include("declare.jl")
include("Exceptions.jl")
include("IO.jl")
... | {"hexsha": "535896078c021012dd23f8866583f09aedf1a7fc", "size": 483, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/BioCore.jl", "max_stars_repo_name": "BenJWard/BioCore.jl", "max_stars_repo_head_hexsha": "23e7669aa854cd59e7e37ae04526d4a079d0c053", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
from pydec.testing import *
from scipy import fabs, random, rand, array, sqrt
from pydec.math.volume import unsigned_volume, signed_volume
def test_unsigned_volume():
cases = []
cases.append((array([[1]]), 1))
cases.append((array([[1],[10]]), 9))
cases.append((array([[0,0],[1,1]]), sqrt(2)))
ca... | {"hexsha": "3beebd630dcf63f4916f2e25f392eab87110d88a", "size": 1417, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydec/math/tests/test_volume.py", "max_stars_repo_name": "michaels10/pydec", "max_stars_repo_head_hexsha": "738c3d9cf1cedc95a61be63fae36073e038d08bc", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2020 MBI-Division-B
# MIT License, refer to LICENSE file
# Author: Luca Barbera / Email: barbera@mbi-berlin.de
from tango import AttrWriteType, DevState, DebugIt
from tango.server import Device, attribute, command, device_property
from random import ran... | {"hexsha": "9ba73ae66a82534d532c19913b89431c92056372", "size": 2806, "ext": "py", "lang": "Python", "max_stars_repo_path": "DummyTDS.py", "max_stars_repo_name": "lucabar/taurusGUI-motor_control", "max_stars_repo_head_hexsha": "95fd384fa8ad42e1be14fb193396bf28d69e0a22", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Test the subcommand scripts
"""
import os
from os import path
import unittest
import logging
import csv
import sys
import json
import copy
from numpy import std, average,ceil
from operator import itemgetter
from itertools import groupby
from msings.subcommands import analyzer
from msings.subcommands import count... | {"hexsha": "530ac009a5b02c551ff395ca1e1223df87ae4426", "size": 9934, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_subcommands.py", "max_stars_repo_name": "sheenamt/msings", "max_stars_repo_head_hexsha": "7510b3f0e5a72a6774b5e81d6e3305d299320e74", "max_stars_repo_licenses": ["AFL-1.1"], "max_stars_c... |
from scipy.spatial import distance
import numpy as np
class VBM:
def __init__(self, actual_high, actual_low):
self.actual_high = actual_high
self.actual_low = actual_low
def scipy_distance(self, vector1, vector2, dist='euclidean'):
if dist == 'euclidean':
return di... | {"hexsha": "c127b9ba37e36fb2c962bb742fb69c82471fa61e", "size": 2661, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/api_v1/estimate/vbm.py", "max_stars_repo_name": "yasirabd/api-diagnostic", "max_stars_repo_head_hexsha": "2a08b1bd7d01c5922a6438cbf99b512e865653a8", "max_stars_repo_licenses": ["MIT"], "max_st... |
*
* $Id$
*
subroutine integrate_kbppv3e_ray(version,rlocal,
> nrho,drho,lmax,locp,nmax,
> n_extra,n_expansion,zv,
> vp,wp,rho,f,cs,sn,
> nray,G_ray,vl_ray,vnl_ray,
> ... | {"hexsha": "e93087e9ffe3e852c3e7d374cd13f8a17630aad8", "size": 8222, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/nwpw/pspw/kbpp/integrate_kbppv3e_ray.f", "max_stars_repo_name": "dinisAbranches/nwchem", "max_stars_repo_head_hexsha": "21cb07ff634475600ab687882652b823cad8c0cd", "max_stars_repo_licenses": ["... |
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "f4545d406901cec30fb30162ccfdd4182e7c97dc", "size": 5468, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/paddle/fluid/tests/unittests/test_index_select_op.py", "max_stars_repo_name": "zhusonghe/Paddle", "max_stars_repo_head_hexsha": "9147da08e136104a7eb48c724a40732c1cda449d", "max_stars_repo_l... |
"""
Sep 21 -- Used to figure out how to best fit data using NRG (i.e. what fitting method of lmfit to use and to try and
figure out a way to have the "zero" of NRG data line up somewhere close to an occupation of 0.5 for convenience when
fitting.
Found that the "powell" method was the only reliable method of fitting to... | {"hexsha": "362f2401d8a26d5db1df393e10104f1c9fceb454", "size": 5591, "ext": "py", "lang": "Python", "max_stars_repo_path": "Analysis/Feb2021/NRG_comparison.py", "max_stars_repo_name": "TimChild/dat_analysis", "max_stars_repo_head_hexsha": "2902e5cb2f2823a1c7a26faf6b3b6dfeb7633c73", "max_stars_repo_licenses": ["MIT"], "... |
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from dask.dataframe.utils import assert_eq, PANDAS_VERSION
# Fixtures
# ========
@pytest.fixture
def df_left():
# Create frame with 10 partitions
# Frame has 11 distinct idx values
partition_sizes = np.array([3, 4, 2, 5, 3, ... | {"hexsha": "e6afc3158453e63ad9944e4ebdb8602bef05a0b0", "size": 5481, "ext": "py", "lang": "Python", "max_stars_repo_path": "dask/dataframe/tests/test_merge_column_and_index.py", "max_stars_repo_name": "srijan-deepsource/dask", "max_stars_repo_head_hexsha": "0673d9084e02f985f3fdf5ba6ede80e8de5ac15c", "max_stars_repo_lic... |
using Quiqbox
using Quiqbox.Molden
mols = [
["H", "H"],
["N", "H", "H", "H"]
]
molNames = [
"H2",
"NH3"
]
br = 0.529177210903
# Data from CCCBDB: https://cccbdb.nist.gov
molCoords = [
[[0.3705,0.0,0.0], [-0.3705,0.0,0.0]],
... | {"hexsha": "bcd5f2359197ff4c1b6144197e307a014b05c7df", "size": 1327, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/Jmol.jl", "max_stars_repo_name": "frankwswang/Quiqbox.jl", "max_stars_repo_head_hexsha": "e3c137d1017235c68db6389ff4a902e789cfa376", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from metaflow import conda_base, FlowSpec, IncludeFile, Parameter, step, S3
def plot_prc(precisions, recalls, thresholds):
import matplotlib.pyplot as plt
plt.plot(thresholds, precisions[:-1], "b--", label="Precision")
plt.plot(thresholds, recalls[:-1], "g-", label="Recall")
plt.xlabel("Thresholds")
... | {"hexsha": "6dcbd56d2986b335943afd82a697d196998896e3", "size": 6104, "ext": "py", "lang": "Python", "max_stars_repo_path": "others/weather_flow.py", "max_stars_repo_name": "rodrigobaron/mlelab", "max_stars_repo_head_hexsha": "9fab643430be1ec4706ba72769a179a6e9d192ff", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import os
import sys
import random
import math
import re
import time
import numpy as np
import cv2
import matplotlib
import matplotlib.pyplot as plt
# Root directory of the project
ROOT_DIR = os.path.curdir
# Import Mask RCNN
sys.path.append(ROOT_DIR) # To find local version of the library
from mrcnn.config import C... | {"hexsha": "fd1029d30356832bd5f1942c3a7004b6c9208475", "size": 6272, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_buildings.py", "max_stars_repo_name": "chenzhaiyu/Mask_RCNN", "max_stars_repo_head_hexsha": "ed1e6c41772cbf9d6b8f6c20f10ed66cd659ce9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import torch
import os.path as osp
import os
from torch.utils.data import Dataset
## This claas loads the feature vector for the videos and the correspoding label.
import numpy as np
from torch.autograd import Variable
import pdb
import csv
class UCF101(Dataset):
def __init__(self, dataset_name, opts):
se... | {"hexsha": "202b8ad212ee9ca4e4484cceb2ecb22c7ef5a1aa", "size": 10576, "ext": "py", "lang": "Python", "max_stars_repo_path": "weakly-supvervized-temp/baseline/data/ucf101.py", "max_stars_repo_name": "nileshkulkarni/vlr-project", "max_stars_repo_head_hexsha": "9393aeb5c7134662caf2951318e310692f5dfc51", "max_stars_repo_li... |
# Plots a chirp signal, it's discrete fourier transform, and it's spectrogram.
import numpy as np
from scipy.signal import chirp
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
time = np.linspace(0.0, 0.01, 2000)
chirp = chirp(time, f0=65.0e3, f1=35.0e3, t1=0.01, method='linear')
samples = len(chirp... | {"hexsha": "a7ad266b4a48bd111654291fc243ad6ad5b6c76c", "size": 1108, "ext": "py", "lang": "Python", "max_stars_repo_path": "printplots/chirpsignalplot.py", "max_stars_repo_name": "leewujung/soundrae", "max_stars_repo_head_hexsha": "34bf858e330a53930b1296ec0c4c36ee71784adf", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import numpy as np
import contextlib
from collections import deque
from spirl.utils.general_utils import listdict2dictlist, AttrDict, ParamDict, obj2np
from spirl.modules.variational_inference import MultivariateGaussian
from spirl.rl.utils.reward_fcns import sparse_threshold
class Sampler:
"""Collects rollouts ... | {"hexsha": "7c5bb86893977616bf99379a741a3ca7f6af3003", "size": 12981, "ext": "py", "lang": "Python", "max_stars_repo_path": "spirl/rl/components/sampler.py", "max_stars_repo_name": "kouroshHakha/fist", "max_stars_repo_head_hexsha": "328c098789239fd892e17edefd799fc1957ab637", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
[STATEMENT]
lemma list_encode_eq: "list_encode x = list_encode y \<longleftrightarrow> x = y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (list_encode x = list_encode y) = (x = y)
[PROOF STEP]
by (rule inj_list_encode [THEN inj_eq]) | {"llama_tokens": 96, "file": null, "length": 1} |
import pickle
import os
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from sklearn.model_selection import StratifiedKFold
from torch.utils.data import Dataset, DataLoader
from Functions import *
import matplotlib.pyplot as plt
tokens='ACGU().BEHIMSX'
#eterna,'nupack','rnastructu... | {"hexsha": "d5bd0f5857100052cdbc3ba6b49699b883be4a1e", "size": 3254, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/OpenVaccine/Dataset.py", "max_stars_repo_name": "Shujun-He/Nucleic-Transformer", "max_stars_repo_head_hexsha": "c6527132cd4c04489b28617beb0694605f320ed9", "max_stars_repo_licenses": ["MIT"], "... |
import cv2
import numpy as np
import pyautogui
from pynput.keyboard import Key, Controller
import time
SCREEN_SIZE = (1920, 1200)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
keyboard = Controller()
keyboard.press(Key.up)
keyboard.release(Key.up)
while True:
img = pyautogui.screenshot(region=(815,7... | {"hexsha": "234a46ffc3f03102021cd5bcdd1d2231951c5238", "size": 2087, "ext": "py", "lang": "Python", "max_stars_repo_path": "Dino.py", "max_stars_repo_name": "CaydendW/Dinogameplayer", "max_stars_repo_head_hexsha": "7382c157b9d4eb665e1279eba58786b2e50316cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
// Copyright (c) 2014-2017 The Dash Core developers
// Copyright (c) 2017-2018 The NIX Core developers
// Distributed under the MIT/X11 software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include "activeghostnode.h"
#include "darksend.h"
#include "ghostnode-pa... | {"hexsha": "0e2568426c5256c0115aa4c4dafe3186025a6b57", "size": 33081, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ghostnode/ghostnode-payments.cpp", "max_stars_repo_name": "nzsquirrell/NixCore", "max_stars_repo_head_hexsha": "0cdc4455b4660f712abe6dc9f2777c77b371461f", "max_stars_repo_licenses": ["MIT"], "m... |
// Copyright (c) 2016 Samsung Electronics Co., Ltd All Rights Reserved
// Use of this source code is governed by a apache 2.0 license that can be
// found in the LICENSE file.
#include "common/plugins/plugin_list_parser.h"
#include <boost/algorithm/string/classification.hpp>
#include <boost/algorithm/string/split.hpp... | {"hexsha": "b0b05c28202cb889d31f839a4048edcc5fb9816f", "size": 6025, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/common/plugins/plugin_list_parser.cc", "max_stars_repo_name": "tizenorg/platform.core.appfw.app-installers", "max_stars_repo_head_hexsha": "54b7b4972c3ab9775856756a5d97220ef344f7e5", "max_stars_r... |
subroutine z_turclo(j ,nmmaxj ,nmmax ,kmax ,ltur , &
& icx ,icy ,tkemod , &
& kcs ,kfu ,kfv ,kfs ,kfuz1 , &
& kfvz1 ,kfsz1 ,kfumin ,kfumax ,kfvmin , &
& kfvmax ... | {"hexsha": "1ff1cc5d5e6595e55b4c8fe31976b58f14c21a4d", "size": 30508, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "docker/water/delft3d/tags/v6686/src/engines_gpl/flow2d3d/packages/kernel/src/compute/z_turclo.f90", "max_stars_repo_name": "liujiamingustc/phd", "max_stars_repo_head_hexsha": "4f815a738abad4353... |
from PIL import Image
from numpy import asarray
from mtcnn.mtcnn import MTCNN
def extract_single_face_facenet(file, size=(160,160)):
# extract single face from given image
image = Image.open(file)
# convert to RGB if required
image = image.convert('RGB')
# convert to numpp array
pixel_array = a... | {"hexsha": "b2197ae017a372cbd60c668b226cebb0153c60f9", "size": 780, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/extract_faces.py", "max_stars_repo_name": "Lekose/FaceNet_Veneto", "max_stars_repo_head_hexsha": "f10ea417104b50a2b14140ec35fb3e7b22129100", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
C @(#)kmpvltdif.f 20.3 2/13/96
C****************************************************************
C
C File: kmpvltdif.f
C
C Purpose: Routine to compares kdiff(p) with kdiff(q)
c
c "key" denotes the interpretation of kdiff(*,*)
c 1 = interpret as bus indices.
c 2 = interpret ... | {"hexsha": "52fbcbfed8ca9d487c0473a7a7ab4d3a75f00cbc", "size": 5286, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ipf/kmpvltdif.f", "max_stars_repo_name": "mbheinen/bpa-ipf-tsp", "max_stars_repo_head_hexsha": "bf07dd456bb7d40046c37f06bcd36b7207fa6d90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14... |
# pass_args.py
import numpy as np
import _scalar_args
print _scalar_args.scalar_args.__doc__
# these are simple python scalars.
int_in = 1.0
real_in = 10.0
# since these are intent(inout) variables, these must be arrays
int_inout = np.zeros((1,), dtype = np.int32)
real_inout = np.zeros((1,), dtype = np.float32)
# a... | {"hexsha": "bee2c4a8faf69adba93155e647aa317dd3a13d71", "size": 605, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/f2py/pass_args.py", "max_stars_repo_name": "kbroman/UW-Madison-swc-boot-camps", "max_stars_repo_head_hexsha": "a1c4b98c74afc06dfc34d64b066c4e5ffebb5aba", "max_stars_repo_licenses": ["CC-BY-3... |
[STATEMENT]
lemma zero_less_Limit: "Limit \<beta> \<Longrightarrow> 0 < \<beta>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Limit \<beta> \<Longrightarrow> 0 < \<beta>
[PROOF STEP]
by (simp add: Limit_def OrdmemD) | {"llama_tokens": 86, "file": "ZFC_in_HOL_ZFC_in_HOL", "length": 1} |
-- Inverso_del_inverso_en_grupos.lean
-- Inverso del inverso en grupos
-- José A. Alonso Jiménez
-- Sevilla, 7 de julio de 2021
-- ---------------------------------------------------------------------
-- ---------------------------------------------------------------------
-- Sea G un grupo y a ∈ G. Demostrar que
-- ... | {"author": "jaalonso", "repo": "Calculemus", "sha": "0fb664ab298c0e90b4b8034729a2cdad20503e18", "save_path": "github-repos/lean/jaalonso-Calculemus", "path": "github-repos/lean/jaalonso-Calculemus/Calculemus-0fb664ab298c0e90b4b8034729a2cdad20503e18/src/Inverso_del_inverso_en_grupos.lean"} |
This editor can edit this entry and tell us a bit about themselves by clicking the Edit icon.
20080905 14:48:13 nbsp Welcome to the Wiki Howdy, Ms. or Mr. 139, and Welcome to the Wiki! You might want to check out the importance of using your RealName, just so we can get to know you (or not: its your choice, but peo... | {"hexsha": "c5d65f81d0147f1cf5b5eb47d067536f9df3389c", "size": 738, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/ces139.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
// test_thread_clock.cpp ----------------------------------------------------------//
// Copyright 2009 Vicente J. Botet Escriba
// Distributed under the Boost Software License, Version 1.0.
// See http://www.boost.org/LICENSE_1_0.txt
#include <boost/chrono/thread_clock.hpp>
#include <boost/type_trait... | {"hexsha": "822723853bc010d1461eeb97a54a7e0c5e126b07", "size": 1191, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "REDSI_1160929_1161573/boost_1_67_0/libs/chrono/example/test_thread_clock.cpp", "max_stars_repo_name": "Wultyc/ISEP_1718_2A2S_REDSI_TrabalhoGrupo", "max_stars_repo_head_hexsha": "eb0f7ef64e188fe871f4... |
Require Import Coq.Strings.String Coq.Lists.List.
Require Export Fiat.Common.Coq__8_4__8_5__Compat.
Set Implicit Arguments.
Local Open Scope list_scope.
Local Open Scope string_scope.
Fixpoint list_of_string (s : string) : list Ascii.ascii
:= match s with
| "" => nil
| String ch s' => ch :: list_of_s... | {"author": "mit-plv", "repo": "fiat", "sha": "4c78284c3a88db32051bdba79202f40c645ffb7f", "save_path": "github-repos/coq/mit-plv-fiat", "path": "github-repos/coq/mit-plv-fiat/fiat-4c78284c3a88db32051bdba79202f40c645ffb7f/src/Common/StringOperations.v"} |
# -*- coding: utf-8 -*-
"""TimeDelayingRidge class."""
# Authors: Eric Larson <larson.eric.d@gmail.com>
# Ross Maddox <ross.maddox@rochester.edu>
#
# License: BSD (3-clause)
import numpy as np
from .base import BaseEstimator
from ..cuda import _setup_cuda_fft_multiply_repeated
from ..filter import next_fast_... | {"hexsha": "16af02c6a2f8324ca8da8855c3d58efd16d45223", "size": 13497, "ext": "py", "lang": "Python", "max_stars_repo_path": "mne/decoding/time_delaying_ridge.py", "max_stars_repo_name": "LukeTheHecker/mne-python", "max_stars_repo_head_hexsha": "7d508e4fded73b5beb73564e4a01169530e058a8", "max_stars_repo_licenses": ["BSD... |
//---------------------------------------------------------------------------//
// Copyright (c) 2018-2021 Mikhail Komarov <nemo@nil.foundation>
// Copyright (c) 2020-2021 Nikita Kaskov <nbering@nil.foundation>
//
// MIT License
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of th... | {"hexsha": "d1ca81c72d803644b4ed42c6ad9abfc3a7aa8e96", "size": 144822, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/marshalling/core/test/types.cpp", "max_stars_repo_name": "Curryrasul/knapsack-snark", "max_stars_repo_head_hexsha": "633515a13906407338a81b9874d964869ddec624", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
# coding: utf-8
# Supplementary codes for:
# #Potential severity and control of Omicron waves depending on pre-existing immunity and immune evasion
#
# Ferenc A. Bartha, Péter Boldog, Tamás Tekeli, Zsolt Vizi, Attila Dénes and Gergely Röst
#
#
#
# ---
# In[ ]:
use_colab = False
if use_colab... | {"hexsha": "17544ab55562f67792f2a3e2a6b457fbac0dc0f3", "size": 36434, "ext": "py", "lang": "Python", "max_stars_repo_path": "omicron_waves.py", "max_stars_repo_name": "epidelay/covid-19-omicron-waves", "max_stars_repo_head_hexsha": "e4d2d4dd4a4089d1cc808b3e5723a773d8ccf4d3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# -*- coding: utf-8 -*-
"""
Created on Mon Dec 28 18:41:27 2020
@author: Administrator
"""
import cv2
import paddlehub as hub
import os
import CVTools
import time
import numpy as np
from tqdm import tqdm
from tqdm._tqdm import trange
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def filesInFolder(roo... | {"hexsha": "1706f4089ab4a42a671decbb2fc51d3f7bfe4b84", "size": 4016, "ext": "py", "lang": "Python", "max_stars_repo_path": "cartonModule.py", "max_stars_repo_name": "kevinfu1717/multimediaChatbot", "max_stars_repo_head_hexsha": "2fb8a38b99c04f1e26104d6ae7784b6f655f5a26", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import numpy as np
class GeneticOperations:
"""
GeneticOperations implements crossover and two types of mutation
"""
@staticmethod
def simpleCrossover(pro1, pro2):
""" Two point crossover """
fracStart1 = np.random.randint(len(pro1.seq))
fracEnd1 = fracStart1 + np.random.r... | {"hexsha": "f2205b921c627a4c3c4911b5bb38f754f095b244", "size": 5825, "ext": "py", "lang": "Python", "max_stars_repo_path": "linear_genetic_programming/_genetic_operations.py", "max_stars_repo_name": "ChengyuanSha/linear_genetic_programming", "max_stars_repo_head_hexsha": "0185cc51ad0e7d732a6dc6b40d35674d03cd086c", "max... |
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