text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import sys
import glob,os,tqdm
import soundfile as sf
import numpy as np
import zipfile
import argparse
def save_single_channel_wav(source_path,tgt_path):
for item in glob.glob(source_path+'/*.wav'):
s,f=sf.read(item)
fname=os.path.basename(item)
sf.write(tgt_path+'/'+fname,s[:,0],f)
def main(... | {"hexsha": "16d1566b22b0bf17a7b424a4fe99cdf9169f39d4", "size": 3269, "ext": "py", "lang": "Python", "max_stars_repo_path": "asr/python/gen_asrinput_raw_utterance.py", "max_stars_repo_name": "chenzhuo1011/libri_css", "max_stars_repo_head_hexsha": "9e3b7b0c9bffd8ef6da19f7056f3a2f2c2484ffa", "max_stars_repo_licenses": ["B... |
/**
* Swaggy Jenkins
* Jenkins API clients generated from Swagger / Open API specification
*
* OpenAPI spec version: 1.1.1
* Contact: blah@cliffano.com
*
* NOTE: This class is auto generated by OpenAPI-Generator 3.2.1-SNAPSHOT.
* https://openapi-generator.tech
* Do not edit the class manually.
*/
#include ... | {"hexsha": "0dee1581b9bb327fc06eb115363ee54fdfa0a83c", "size": 3803, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "clients/cpp-restbed-server/generated/model/HudsonMasterComputermonitorData.cpp", "max_stars_repo_name": "PankTrue/swaggy-jenkins", "max_stars_repo_head_hexsha": "aca35a7cca6e1fcc08bd399e05148942ac2f... |
c Subroutine to get qc_key
c AJ_Kettle, 17Nov2020
SUBROUTINE get_qckey(s_directory_qckey,s_filename_qckey,
+ l_qckey,
+ s_qckey_sourceflag,s_qckey_c3sflag,s_qckey_timescale)
IMPLICIT NONE
c************************************************************************
c Declare variables ... | {"hexsha": "2bc8b9b1fa6c49984e1d0b6c915c92d0662324f1", "size": 2538, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "r202106_gsom_to_cdm/Step1_cdmmake/Subroutine/get_qckey.f", "max_stars_repo_name": "glamod/glamod-nuim", "max_stars_repo_head_hexsha": "eed6f9d7d71b0c456ef39fdea6b58677e13ab50c", "max_stars_repo_li... |
#!/bin/env python
"Simulate seeing based on a model."
import sys
from argparse import ArgumentParser
import configparser
from collections import namedtuple
import csv
import numpy as np
import pandas as pd
import astropy.time
# constants
__author__ = "Eric H. Neilsen, Jr."
__maintainer__ = "Eric H. Neilsen, Jr."
__... | {"hexsha": "cda36d8ad25e2bd2df7ba87bd5e27e0be186671a", "size": 19676, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/simsee/python/simsee.py", "max_stars_repo_name": "galdering/ObsStrat", "max_stars_repo_head_hexsha": "1032e5c66ee9d7e6b8d8ddde443670489d32e7fa", "max_stars_repo_licenses": ["MIT"], "max_star... |
from typing import Callable, Tuple, Union
import numpy as np
from emukit.core.acquisition import Acquisition
from emukit.core.interfaces import IModel, IPriorHyperparameters
class IntegratedHyperParameterAcquisition(Acquisition):
"""
This acquisition class provides functionality for integrating any acquisit... | {"hexsha": "04c89e1baad8b4ce42679f4b5708bfd2e3d51fb6", "size": 3777, "ext": "py", "lang": "Python", "max_stars_repo_path": "emukit/core/acquisition/integrated_acquisition.py", "max_stars_repo_name": "EmuKit/Emukit", "max_stars_repo_head_hexsha": "2df951e42c82400192220eb18af428f3eb764f6c", "max_stars_repo_licenses": ["A... |
# -*- coding: utf-8 -*-
"""
Landscape Evaporative Response Index
This script will download all or update existing LERI files used in the app. It
uses the same FTP server as EDDI.
Production notes:
- LERI does not usually cover the full grid and only dates back to
2000, so maybe there would be ... | {"hexsha": "f84e53117827bf9bf15afbb0e10111f35a5a8a68", "size": 12572, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/Get_LERI.py", "max_stars_repo_name": "WilliamsTravis/Drought-Index-Portal", "max_stars_repo_head_hexsha": "af1c2ce53ec2c04eceba0758f73135c155082dd6", "max_stars_repo_licenses": ["MIT"], "... |
[STATEMENT]
lemma None_in_map_option_set:
"None \<in> map_option_set f x \<longleftrightarrow> None \<in> Set.bind (set_option x) f \<or> x = None"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (None \<in> map_option_set f x) = (None \<in> set_option x \<bind> f \<or> x = None)
[PROOF STEP]
by(cases x) simp_all | {"llama_tokens": 131, "file": "Probabilistic_While_While_SPMF", "length": 1} |
const seaborn_rocket_gradient = [RGBA(0.01060815, 0.01808215, 0.10018654),
RGBA(0.01428972, 0.02048237, 0.10374486),
RGBA(0.01831941, 0.0229766, 0.10738511),
RGBA(0.02275049, 0.02554464, 0.11108639),
... | {"hexsha": "585724a198e952e9116529fc3a25611211fad598", "size": 38485, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/seaborn_color_gradients.jl", "max_stars_repo_name": "isentropic/PlotThemes.jl", "max_stars_repo_head_hexsha": "2d8afccc844584a64d9e14bd02939c3da64c3871", "max_stars_repo_licenses": ["MIT"], "m... |
# Copyright (C) 2020 coneypo
# SPDX-License-Identifier: MIT
# Author: coneypo
# Blog: http://www.cnblogs.com/AdaminXie
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
# Mail: coneypo@foxmail.com
# 利用 OT 对于单张人脸追踪, 实时人脸识别 (Real-time face detection and recognition via Object-tracking f... | {"hexsha": "4a6aaa311646e367c84f5277d668a9920615d084", "size": 15677, "ext": "py", "lang": "Python", "max_stars_repo_path": "face_reco_from_camera_ot_single_person.py", "max_stars_repo_name": "python-faker/Dlib_face_recognition_from_camera", "max_stars_repo_head_hexsha": "ee3a26b3c6669c4f4f08a7cc52af54fe4449aa75", "max... |
import pprint
import sys
import time
import socket
import pickle
import cv2
import numpy
import zmq
def send_array(socket, A, f_num, flags=0, copy=True, track=False):
"""send a numpy array with metadata"""
md = dict(
dtype='uint8',
shape=A.shape,
frame_num=f_num,
)
socket.sen... | {"hexsha": "04ba2c7e5187c4ee61818973fac69fe9b0ffdf72", "size": 1500, "ext": "py", "lang": "Python", "max_stars_repo_path": "collector1.py", "max_stars_repo_name": "naderabdalghani/distributed-systems-video-analyzer", "max_stars_repo_head_hexsha": "df47a655d6d8ca4a9686fb227dba04a8acfc1625", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python
import numpy as np
import h5py
from util import *
# Change dir to caffe root or prototxt database paths won't work wrong
import os
print os.getcwd()
os.chdir('..')
print os.getcwd()
# The caffe module needs to be on the Python path;
# we'll add it here explicitly.
import sys
sys.path.insert(0, ... | {"hexsha": "e74b1c7e797c0bb51173e7aee1d079f6fe9fd60d", "size": 2505, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/extract_feature_whole.py", "max_stars_repo_name": "daqingliu/bottom-up-attention", "max_stars_repo_head_hexsha": "d5f6d57fd015447364e225f3f1a30ac51e4d0a55", "max_stars_repo_licenses": ["MIT"... |
double precision md,mu,ms,mc,mb,mt
common/qmass/md,mu,ms,mc,mb,mt
| {"hexsha": "c4646fdf51c61d22896e79e5b2168332ec011809", "size": 78, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/Inc/qmass.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
[STATEMENT]
lemma f_join_all_conv: "(xs \<Join>\<^sub>f I = xs) = ({..<length xs} \<subseteq> I)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (xs \<Join>\<^sub> I = xs) = ({..<length xs} \<subseteq> I)
[PROOF STEP]
apply (case_tac "length xs = 0", simp add: f_join_Nil)
[PROOF STATE]
proof (prove)
goal (1 subgoal)... | {"llama_tokens": 3052, "file": "AutoFocus-Stream_IL_AF_Stream", "length": 21} |
MODULE global_variables
use bb_module
use atom_module
use rgrid_sol_module
use rgrid_module
use ggrid_module
use bz_module
use fd_module
use strfac_module
use pseudopot_module
use ps_local_module
use ps_pcc_module
use ps_initrho_module
use ps_nloc2_init_module
use ps_nloc2_module
use bc_m... | {"hexsha": "52e053b136e0739f7a55505a030c85819742ba52", "size": 2188, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/global_variables.f90", "max_stars_repo_name": "j-iwata/RSDFT_DEVELOP", "max_stars_repo_head_hexsha": "14e79a4d78a19e5e5c6fd7b3d2f2f0986f2ff6df", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
#!/usr/bin/env python3
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | {"hexsha": "5013f875a152f3948dac6716d5d339b4d0e67c27", "size": 6053, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/camera_cv.py", "max_stars_repo_name": "isabella232/ftc-object-detection", "max_stars_repo_head_hexsha": "e8923e208c449dd457b60d84fc21eb715364f0de", "max_stars_repo_licenses": ["Apache-2.0... |
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
import cv2
import pickle
def camera_calibration(nx, ny, path, show=True):
images = glob.glob(path)
objpoints = [] # 3D points in real world space
imgpoints = [] # 2D points in the image plane
objp = np.zer... | {"hexsha": "5b1a29b521aae11917a8b8d161678c6b21768c2d", "size": 1658, "ext": "py", "lang": "Python", "max_stars_repo_path": "distortion_correction.py", "max_stars_repo_name": "MenglingHettinger/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "049509f2f3fd160d3db5df8caef4985932e8bc5b", "max_stars_repo_licenses"... |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2018.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... | {"hexsha": "6bd62aab580f7cc2d88bc34280a231cd19466a58", "size": 6146, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/python/circuit/test_qiskit_code.py", "max_stars_repo_name": "rfclambert/qiskit-terra", "max_stars_repo_head_hexsha": "9f236c9744fd95c8ff40d874197e683db024d993", "max_stars_repo_licenses": ["A... |
import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np
tfd = tfp.distributions
tfb = tfp.bijectors
class DiomedesModel(tf.Module):
def __init__(self):
# Set up fixed effects and other parameters.
# These are free parameters to be optimized in E-steps
self._intercept = tf.Varia... | {"hexsha": "c0612ac605a85c34af55bf3174c585ef073c04d2", "size": 4529, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model.py", "max_stars_repo_name": "aleccrowell/diomedes", "max_stars_repo_head_hexsha": "69c4dcddd62a1c846337ced59c2b16a81bc6991f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
[STATEMENT]
lemma bounded_inner_imp_bdd_above:
assumes "bounded s"
shows "bdd_above ((\<lambda>x. x \<bullet> a) ` s)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. bdd_above ((\<lambda>x. x \<bullet> a) ` s)
[PROOF STEP]
by (simp add: assms bounded_imp_bdd_above bounded_linear_image bounded_linear_inner_left... | {"llama_tokens": 129, "file": null, "length": 1} |
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from itertools import combinations
from curve import HilbertCurve, ZCurve
from utils import *
from environment import CurveEnvironment
from agent import Agent
CUDA = torch.cuda.... | {"hexsha": "45b7c09a7c77ef8339cc9d167d7549344173965b", "size": 3071, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/driver_policy_gradient.py", "max_stars_repo_name": "zzong2006/space-filling-curve-with-RF-learning", "max_stars_repo_head_hexsha": "30823745dae91240c0977185fb1831c9b4771a40", "max_stars_repo_... |
% Convert an edge list to an adjacency list.
%
% INPUTS: edge list, mx3, m - number of edges
% OUTPUTS: adjacency list
%
% Note: Information about edge weights (if any) is lost.
% GB: last updated, September 25, 2012
function adjL = edgeL2adjL(el)
nodes = unique([el(:,1)' el(:,2)']);
adjL=cell(numel(nodes),1);
for ... | {"author": "aeolianine", "repo": "octave-networks-toolbox", "sha": "e70f79eb62a54ef96934d900830f9177caf732c9", "save_path": "github-repos/MATLAB/aeolianine-octave-networks-toolbox", "path": "github-repos/MATLAB/aeolianine-octave-networks-toolbox/octave-networks-toolbox-e70f79eb62a54ef96934d900830f9177caf732c9/edgeL2adj... |
#include <boost/align/aligned_alloc.hpp>
int main()
{
void* p = boost::alignment::aligned_alloc(16, 100);
if (p)
{
boost::alignment::aligned_free(p);
}
return 0;
}
| {"hexsha": "c01eb23030b40462b7b01135c44f33afad9e149a", "size": 172, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Boost/Algorithms/align_alloc.cpp", "max_stars_repo_name": "zzragida/CppExamples", "max_stars_repo_head_hexsha": "d627b097efc04209aa4012f7b7f9d82858da3f2d", "max_stars_repo_licenses": ["Apache-2.0"], ... |
import CuArrays: cu
import YaoArrayRegister: _measure, measure, measure!, measure_collapseto!, measure_remove!
import YaoBase: batch_normalize!
import Yao: expect
export cpu, cu, GPUReg
cu(reg::ArrayReg{B}) where B = ArrayReg{B}(CuArray(reg.state))
cpu(reg::ArrayReg{B}) where B = ArrayReg{B}(collect(reg.state))
const... | {"hexsha": "01732ad387ca33e1b961cfb674eb3f736c498cc4", "size": 5242, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GPUReg.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/CuYao.jl-b48ca7a8-dd42-11e8-2b8e-1b7706800275", "max_stars_repo_head_hexsha": "edf0be4bb914ac8c022dc5f95ced9db949ea19c5", "max... |
#!/usr/bin/env python
"""
Script to run the BEAST on the PHAT-like data.
Assumes that the datamodel.py file exists in the same directory as this script.
And it must be called datamodel.py
used in make_model.py code in physicsmodel, more recoding needed to remove
this dependency
"""
# system imports
from __... | {"hexsha": "5d82352ef320398602614bdf022996ffa26c64a3", "size": 20415, "ext": "py", "lang": "Python", "max_stars_repo_path": "beast/examples/subgridding/run_beast_subgrids.py", "max_stars_repo_name": "marthaboyer/beast", "max_stars_repo_head_hexsha": "1ca71fb64ab60827e4e4e1937b64f319a98166c3", "max_stars_repo_licenses":... |
function nthroot(n::Integer, r::Real)
r < 0 || n == 0 && throw(DomainError())
n < 0 && return 1 / nthroot(-n, r)
r > 0 || return 0
x = r / n
prevdx = r
while true
y = x ^ (n - 1)
dx = (r - y * x) / (n * y)
abs(dx) ≥ abs(prevdx) && return x
x += dx
prevdx =... | {"hexsha": "a5acccf10f0bef1177ba7addc93ea331b5f38d91", "size": 418, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lang/Julia/nth-root.jl", "max_stars_repo_name": "ethansaxenian/RosettaDecode", "max_stars_repo_head_hexsha": "8ea1a42a5f792280b50193ad47545d14ee371fb7", "max_stars_repo_licenses": ["MIT"], "max_star... |
using ArgMacros
function main()
@beginarguments begin
@argumentdefault Int 1 opt1 "-o" "--opt1"
@argumentdefault Int 2 opt2 "--opt2"
@argumentflag flag "--flag"
@positionaloptional String arg "arg"
end
println(" arg=>", arg)
println(" opt1=>", opt1)
println(" opt2=>... | {"hexsha": "29dadf3402e023253d4d3afc7a90b1d37f47c584", "size": 381, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "example/argmacros.jl", "max_stars_repo_name": "Eggiverse/Comonicon.jl", "max_stars_repo_head_hexsha": "aed96bc93cbf8427a7a2acd5a2297ce367c110b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# -*- coding:utf-8 -*-
# Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved.
# This program is free software; you can redistribute it and/or modify
# it under the terms of the MIT License.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the ... | {"hexsha": "13b66ec5a684e4b020f4413369eae206f8f36e84", "size": 5009, "ext": "py", "lang": "Python", "max_stars_repo_path": "vega/algorithms/nas/modnas/data_provider/dataset/torch/image_cls.py", "max_stars_repo_name": "shaido987/vega", "max_stars_repo_head_hexsha": "14d5d49fb8bdf96bd1f3fcfac201ce6b6712c3b6", "max_stars_... |
This editor can edit this entry and tell us a bit about themselves by clicking the Edit icon.
20101005 08:19:43 nbsp Howdy and welcome to the wiki! My names Evan, pleased to meet you! Thanks for adding Gan Haverim, it sounds like a neat preschool. I reformatted it a bit to match other entries to try to help yo... | {"hexsha": "3ab1e7b91a7e489126c290eb11a64fe611122fbf", "size": 566, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/edubin.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from scipy.sparse.linalg.eigen.arpack import eigsh
import sys
import re
import datetime
def parse_index_file(filename):
"""Parse index file."""
index = []
for line in open(filename):
index.append(in... | {"hexsha": "5c8f334271453a97644e438211d294a0d2ba59e0", "size": 8197, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "Sherrylone/Ensemble-Neural-Network", "max_stars_repo_head_hexsha": "31a8ad63460208c82293126d9e3e5e61796dff96", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
___author__ = 'Asus'
from IClassifier import IClassifier
from Utils.utilities import load_stf
from glove import Glove
from scipy.spatial.distance import cosine
from scipy.spatial.distance import euclidean
import numpy as np
class GloveClassifier(IClassifier):
def __init__(self):
self.GloveInstace = None
self.Cent... | {"hexsha": "b6d174ccf498dc93eceff7ddd2747056196d3ddc", "size": 1662, "ext": "py", "lang": "Python", "max_stars_repo_path": "SimilarityClassification/Classifiers/GloveCenteredESLExtendedClassifier.py", "max_stars_repo_name": "dudenzz/word_embedding", "max_stars_repo_head_hexsha": "7aa7c7619f8a56903384176f3d83e999153c55c... |
import torch
import numpy as np
import pandas as pd
from tqdm import tqdm
import os
import time
import pickle
import jieba
from collections import Counter
from gensim.models import KeyedVectors
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import StratifiedShuffleSplit, train_... | {"hexsha": "200301a2f0feb7c4f3156fc5bc8c34479bdf76a7", "size": 8844, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "kangyangWHU/ML_Project", "max_stars_repo_head_hexsha": "cc4c0406ff9cd1dd6e1fad827100d4ffc9b7f574", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
"""
Module dipole_pruning
================
This module supplies one class, DipolePruning, that is used to find the
minimum number of elements required to resolve the dipole operator, to the
requested tolerance.
"""
import os
import numpy as np
class DipolePruning(object):
r"""This class takes the dipole operator... | {"hexsha": "b8ab7b5bcf3bf81004fddaec17add90e933b61e6", "size": 4969, "ext": "py", "lang": "Python", "max_stars_repo_path": "ultrafastultrafast/dipole_pruning.py", "max_stars_repo_name": "peterarose/UF2", "max_stars_repo_head_hexsha": "cfc2c6625467945e12ac08bd267f79b6741e567f", "max_stars_repo_licenses": ["MIT"], "max_s... |
LOG_GROUP_COST_FUNCTIONS = :CostFunctionsConstructor
LOG_GROUP_OPTIMZATION_CONTAINER = :OptimizationContainer
LOG_GROUP_TYPE_REGISTRATIONS = :TypeRegistrations
LOG_GROUP_FEEDFORWARDS_CONSTRUCTION = :FeedforwardConstructor
LOG_GROUP_BRANCH_CONSTRUCTIONS = :BranchConstructor
LOG_GROUP_SERVICE_CONSTUCTORS = :ServicesCons... | {"hexsha": "140951fb42490bb28549b4c1973ab9ad9a42f1d5", "size": 658, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/logging.jl", "max_stars_repo_name": "NREL/PowerSimulations.jl", "max_stars_repo_head_hexsha": "17891d4a61d071ce98efd96706ec7586e5d833cc", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
from typing import List, Any
from collections import defaultdict
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor as T
from ..utils.dist_utils import gather_and_compare
def calculate_softmax_np(logits: np.ndarray, dim: int):
"""
Numpy-version softmax.
Args:
... | {"hexsha": "e0e91ee6606b808577a14a35e2178a2120c7ef0f", "size": 6166, "ext": "py", "lang": "Python", "max_stars_repo_path": "mmdet/active_learning/al/utils.py", "max_stars_repo_name": "the-linh-ai/mmdetection", "max_stars_repo_head_hexsha": "d7fa62fef08715810fed9afd057b2ec048a67313", "max_stars_repo_licenses": ["Apache-... |
#!/usr/bin/env python
"""
Simple module to deal with unit conversion
"""
__all__ = ['Units']
import numpy as np
class Units(object):
"""Simple class to keep track of unit conversions"""
# Could be replaced by astropy...
# length
cm = 1
m = 1e2 # m to cm
km = m * 1e3 # km to... | {"hexsha": "d54fe28e4c130d5f479049feb82c6cedbabff935", "size": 2982, "ext": "py", "lang": "Python", "max_stars_repo_path": "dmsky/utils/units.py", "max_stars_repo_name": "kadrlica/dmsky", "max_stars_repo_head_hexsha": "a8f0c47c43164851b738b4a59a723addf89054bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
From VyZX Require Export GateDefinitions.
From VyZX Require Export GateRules. | {"author": "inQWIRE", "repo": "VyZX", "sha": "86c6e2b2bdc380173a99c1a6fa777544bdd59cdb", "save_path": "github-repos/coq/inQWIRE-VyZX", "path": "github-repos/coq/inQWIRE-VyZX/VyZX-86c6e2b2bdc380173a99c1a6fa777544bdd59cdb/src/Gates/Gates.v"} |
using Test
function check_readme(repository_root::AbstractString)
readme_path = joinpath(repository_root, "README.md")
example_path = joinpath(repository_root, "example.yml")
readme_contents = read(readme_path, String)
example_contents = string(
"```yaml\n",
read(example_path, String),
... | {"hexsha": "c70af4944911c34776439e6c08d516163431a464", "size": 466, "ext": "jl", "lang": "Julia", "max_stars_repo_path": ".ci/check_readme.jl", "max_stars_repo_name": "JuliaRegistries/compathelper-action", "max_stars_repo_head_hexsha": "3e6a675d956931a21542d8dd74d8e40374f3eda8", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
def PolyDetrend(t,x,Order=1):
'''
Detrends a time series using a polynomial.
Inputs
======
t : float
Time array
x : float
Value array to be detrended
Order : int
Order of polynomial to use
Returns
=======
y : float
Detrended x
'''
#get the polyfit first
good = np.isfinit... | {"hexsha": "4339597f567a44c3be38ddb275bc7c7bc3a2894e", "size": 595, "ext": "py", "lang": "Python", "max_stars_repo_path": "wavespec/Tools/PolyDetrend.py", "max_stars_repo_name": "mattkjames7/wavespec", "max_stars_repo_head_hexsha": "2928ac3f4fb921a21caba3663b0d7c22e43692fa", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python3
import unittest, random, sys, copy, argparse, inspect, time
from graderUtil import graded, CourseTestRunner, GradedTestCase
import numpy
# Import student submission
import submission
numpy.random.seed(0)
#############################################
# HELPER FUNCTIONS FOR CREATING TEST INPUTS #... | {"hexsha": "5f47843d830eeb0fed713c363881c25ad2d3df2d", "size": 11402, "ext": "py", "lang": "Python", "max_stars_repo_path": "XCS229ii-PS3-Sandbox/src/grader.py", "max_stars_repo_name": "bearbearyu1223/Stanford-XCS-229-II", "max_stars_repo_head_hexsha": "7e5743fb326352a168400bb96694c54ed476773f", "max_stars_repo_license... |
-- Dominio de discurso
variable A : Type
lemma Example1 (P Q : A → Prop) :
(∀ x, P x → Q x) → (∀ x, P x) → (∀ x, Q x) :=
begin
intros hpq hp,
intro a,
apply hpq, -- lines below can be replaced by apply ((hpq a) (hp a))
apply hp
end
lemma Example2 (P Q : A → Prop) :
(∀ x, P x ∧ Q x) ↔ (∀ x, P... | {"author": "ahevia", "repo": "CC3101_2021", "sha": "178392747409383f39e357b0dde919553d214aeb", "save_path": "github-repos/lean/ahevia-CC3101_2021", "path": "github-repos/lean/ahevia-CC3101_2021/CC3101_2021-178392747409383f39e357b0dde919553d214aeb/Clases/Clase5/demo_Clase_5_Logica_Predicados.lean"} |
@testset "Strings" begin
@testset "Strings: Is Palindrome" begin
s = "AA" # A palindrome
@test is_palindrome(s) == true
s = "ABC" # Not a palindrome
@test is_palindrome(s) == false
s = "Statistics" # Not a palindrome
@test is_palindrome(s) == false
s = "Stats" # A palindrome
... | {"hexsha": "9d7b9bccca34bc1a1bd7436452fee9ec52d322da", "size": 512, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/strings.jl", "max_stars_repo_name": "arubhardwaj/Julia", "max_stars_repo_head_hexsha": "cc8e8e942072f7bfb5479b25482133fd2c7141a6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#!/usr/bin/env python3
import numpy as np
import h5py as h5
import argparse
import os
import subprocess
parser = argparse.ArgumentParser()
parser.add_argument('--npts', type=int, default=101,
help='Number of gridpoints per dimension.')
parser.add_argument('--target', type=float, nargs=3, default=[... | {"hexsha": "6776093698b7f2929c57cf3fe15e0bcd1e6cc9dc", "size": 1239, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/scripts/eikonal3d.py", "max_stars_repo_name": "mcpca/fsm", "max_stars_repo_head_hexsha": "df4081fa0e595284ddbb1f30f20c5fb2063aa41f", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
// Copyright (C) 2013,2014 Vicente J. Botet Escriba
//
// 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)
//
// 2013/11 Vicente J. Botet Escriba
// first implementation of a simple user scheduler.
// 2013/1... | {"hexsha": "ca2cdda6b2afb052da1e45a21d295004655b23f5", "size": 5789, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "deps/boost/include/boost/thread/executors/loop_executor.hpp", "max_stars_repo_name": "kindlychung/mediasoup-sfu-cpp", "max_stars_repo_head_hexsha": "f69d2f48f7edbf4f0c57244280a47bea985f39cf", "max_s... |
# -*- coding: utf-8 -*-
"""
Iridas .cube LUT Format Input / Output Utilities
================================================
Defines *Iridas* *.cube* *LUT* Format related input / output utilities objects.
- :func:`colour.io.read_LUT_IridasCube`
- :func:`colour.io.write_LUT_IridasCube`
References
----------
- ... | {"hexsha": "133fa516185c1d9f6dd77a641bfc7ad9d011c7b5", "size": 7863, "ext": "py", "lang": "Python", "max_stars_repo_path": "colour/io/luts/iridas_cube.py", "max_stars_repo_name": "wenh06/colour", "max_stars_repo_head_hexsha": "445fdad2711ae39c95b4375166905568d24a95f4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
# **CS224W - Colab 4**
In Colab 2 we constructed GNN models by using PyTorch Geometric's built in GCN layer, `GCNConv`. In Colab 3 we implemented the **GraphSAGE** ([Hamilton et al. (2017)](https://arxiv.org/abs/1706.02216)) layer. In this colab you'll use what you've learned and implement a more powerful layer: **GA... | {"hexsha": "5292b73310efa402795071f7b7e345bc4581c8e5", "size": 80448, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Colab 4/CS224W - Colab4_victor.ipynb", "max_stars_repo_name": "victorcroisfelt/aau-cs224w-ml-with-graphs", "max_stars_repo_head_hexsha": "adb38651be8da98cc574f127763c785ed16dfb5a", "... |
import mlflow
import numpy as np
import torch
import torchani
from ael import grad, loaders, models
np.random.seed(42)
torch.manual_seed(42)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Radial coefficients
RcR = 5.2
EtaR = torch.tensor([16.0], device=device)
RsR = torch.tensor([0.9], devi... | {"hexsha": "900f85e8824af3ad2a5cba1010ed75f54ea9d162", "size": 3383, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_grad.py", "max_stars_repo_name": "hengwei-chan/aescore", "max_stars_repo_head_hexsha": "0eba684e757db2994fc6062736eef73ba8365a9b", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_star... |
'''
This script define the agent we would use to train
'''
import numpy as np
import gym_super_mario_bros
from gym_super_mario_bros.actions import RIGHT_ONLY, SIMPLE_MOVEMENT, COMPLEX_MOVEMENT
from nes_py.wrappers.joypad_space import JoypadSpace
from gym import spaces
from gym.spaces.box import Box
from gym import Wra... | {"hexsha": "25c76c8c332b9b3f00255b46cb34950d5b13c8fd", "size": 4361, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/agent.py", "max_stars_repo_name": "yuanyyf/Super-Mario-Bro-version-A3C", "max_stars_repo_head_hexsha": "42660bc3f327a2d8e014634615666c5d68eaee90", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma less_eq_integer_code [code]:
"0 \<le> (0::integer) \<longleftrightarrow> True"
"0 \<le> Pos l \<longleftrightarrow> True"
"0 \<le> Neg l \<longleftrightarrow> False"
"Pos k \<le> 0 \<longleftrightarrow> False"
"Pos k \<le> Pos l \<longleftrightarrow> k \<le> l"
"Pos k \<le> Neg l \<longlef... | {"llama_tokens": 348, "file": null, "length": 1} |
#include <iostream>
#include <boost/asio.hpp>
#include <boost/bind.hpp>
#include <boost/shared_ptr.hpp>
#include <boost/enable_shared_from_this.hpp>
using namespace std;
using boost::asio::ip::tcp;
class Connection : public boost::enable_shared_from_this<Connection>
{
private:
tcp::socket sock;
enum
{
... | {"hexsha": "4bfc7a691d6f39026044e0ae2a82e88c841c147b", "size": 3891, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/TCP/header/server.hpp", "max_stars_repo_name": "chenxull/UDP_Project", "max_stars_repo_head_hexsha": "2490ee11dea3f0a11ff509f59338e510db25d0d9", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
/*
* Morph target animation file
*
* This file is part of the "SoftPixel Engine" (Copyright (c) 2008 by Lukas Hermanns)
* See "SoftPixelEngine.hpp" for license information.
*/
#include "SceneGraph/Animation/spMorphTargetAnimation.hpp"
#include <boost/foreach.hpp>
namespace sp
{
namespace scene
{
MorphTarget... | {"hexsha": "0695a8b6dda107d227135195d1387fc824bcf9fb", "size": 3255, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "sources/SceneGraph/Animation/spMorphTargetAnimation.cpp", "max_stars_repo_name": "rontrek/softpixel", "max_stars_repo_head_hexsha": "73a13a67e044c93f5c3da9066eedbaf3805d6807", "max_stars_repo_licens... |
# The simple
struct Newton{T1, Tlin, Tage, TFact} <: QuasiNewton{T1}
approx::T1
linsolve::Tlin
reset_age::Tage
factorizer::TFact
end
hasprecon(::Newton) = NoPrecon()
# struct DefaultSequence end
DefaultNewtonLinsolve(B::Number, g) = B\g
function DefaultNewtonLinsolve(B, g)
B\g
end
function DefaultNewtonLi... | {"hexsha": "b4fe3f25dc93f1089429850a291b60738bf026c5", "size": 659, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/quasinewton/approximations/newton.jl", "max_stars_repo_name": "mohamed82008/NLSolvers.jl", "max_stars_repo_head_hexsha": "1c0252243975bfd3e45f210caf60947b46785611", "max_stars_repo_licenses": ["... |
#redirect Reserve Officers Training Corps
| {"hexsha": "070b1915c8c7ce563607f6122baeeaa74ba34c2c", "size": 42, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/ROTC.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
function cnn_resnet_finetune(varargin)
if ~isempty(gcp('nocreate')),
delete(gcp)
end
addpath('resnet');
opts = cnn_setup_environment();
opts.train.gpus = [1] ;
% opts.train.gpus = [ 1 : 3 ]
opts.dataSet = 'Remat';
% opts.dataSet = 'hmdb51';
opts.aug = 'stretch';
opts.border = 0;
opts.pad = 0... | {"author": "zhangqianqianQQ", "repo": "MachineVisionAlgorithm", "sha": "683338f6c3b1aab9fa2b80026915fe936aebf0ee", "save_path": "github-repos/MATLAB/zhangqianqianQQ-MachineVisionAlgorithm", "path": "github-repos/MATLAB/zhangqianqianQQ-MachineVisionAlgorithm/MachineVisionAlgorithm-683338f6c3b1aab9fa2b80026915fe936aebf0e... |
\documentclass[a4paper,Times New Roman 11pt]{article}
\usepackage{graphicx}
\usepackage{pdfpages}
\usepackage{booktabs}
\usepackage{csquotes}
\usepackage{hyperref}
\usepackage{ragged2e}
\usepackage{multirow}
\usepackage[style=authoryear-ibid,backend=biber]{biblatex}
\usepackage{fancyhdr}
\pagestyle{fancy}
\fancyhf... | {"hexsha": "2b4b7f2ebbba75998b1359b0a38ca6eefdf24671", "size": 136083, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "ProjectReport&Documentation/Scheduler Project Report.tex", "max_stars_repo_name": "johnjogrady/scheduler_symfony", "max_stars_repo_head_hexsha": "4ad07739fa72200c2b053ce110e93bb42e6008a4", "max_st... |
[STATEMENT]
lemma (in weak_lower_semilattice) finite_inf_closed [simp]:
"[| finite A; A \<subseteq> carrier L; A \<noteq> {} |] ==> \<Sqinter>A \<in> carrier L"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>finite A; A \<subseteq> carrier L; A \<noteq> {}\<rbrakk> \<Longrightarrow> \<Sqinter>A \<in> carr... | {"llama_tokens": 1453, "file": null, "length": 11} |
#ifndef INCLUDE_OKVIS_KEYFRAME_FOR_LOOP_DETECTION_HPP_
#define INCLUDE_OKVIS_KEYFRAME_FOR_LOOP_DETECTION_HPP_
#include <vector>
#include <Eigen/Core>
#include <Eigen/StdVector>
#include <okvis/MultiFrame.hpp>
#include <okvis/assert_macros.hpp>
#include <okvis/class_macros.hpp>
#include <loop_closure/InverseTransform... | {"hexsha": "73d801d4becbe47fd95780384198909e6a7e673f", "size": 13873, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "loop_closure/include/loop_closure/KeyframeForLoopDetection.hpp", "max_stars_repo_name": "wbl1997/okvis", "max_stars_repo_head_hexsha": "65e30d6ab25380d65c96c665485148e2ab55e93e", "max_stars_repo_li... |
"""Test the write database.
"""
import json
import pytest
import os
import time
import numpy as np
from vectorai.models.deployed import ViText2Vec
from vectorai.write import ViWriteClient
from vectorai.errors import APIError, MissingFieldError, MissingFieldWarning, CollectionNameError
from vectorai.client import ViClie... | {"hexsha": "45aa721778cfda9edfd0b07d2e4b98d441119931", "size": 6103, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_write_multiprocessing.py", "max_stars_repo_name": "Tiamat-Tech/vectorai", "max_stars_repo_head_hexsha": "79e088a70ff79fc6bf18c6a6c0a4f367c1113648", "max_stars_repo_licenses": ["Apache-2... |
from __future__ import absolute_import, division, unicode_literals
import pytest
try:
import plotly
import plotly.graph_objs as go
import plotly.io as pio
pio.templates.default = None
except:
plotly = None
plotly_available = pytest.mark.skipif(plotly is None, reason="requires plotly")
import nump... | {"hexsha": "07fae2c3261f43ea2834bb028a1ea97f9ebb3050", "size": 3744, "ext": "py", "lang": "Python", "max_stars_repo_path": "panel/tests/pane/test_plotly.py", "max_stars_repo_name": "rupakgoyal/panel-", "max_stars_repo_head_hexsha": "4e1e01e1766ebfc2fc1efb409734fd51efc60c01", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
import gloro
def add_extra_column(y):
if isinstance(y, np.ndarray):
return add_extra_column_np(y)
else:
return add_extra_column_tf(y)
def add_extra_column_tf(y):
return tf.concat((y, tf.zeros((tf.shap... | {"hexsha": "b5b32daca1e296f85888a0b443566294392d9ac0", "size": 996, "ext": "py", "lang": "Python", "max_stars_repo_path": "gloro/utils.py", "max_stars_repo_name": "klasleino/gloro", "max_stars_repo_head_hexsha": "5ebfe0f3850bca20e4ee4414fa2ee8a4af303023", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "max_... |
"""
@file core.py
@version 0.1.0
@author Lait-au-Cafe
@date 10/05/2019
"""
# -*- coding: utf-8 -*-
import sys
import os
import typing
import dataclasses
import numpy as np
import cv2
import OpenGL.GL as gl
import glfw
import glm
import ctypes
@dataclasses.dataclass
class CameraProperty:
transform_matrix: glm.mat4
... | {"hexsha": "059c558f243ea82e3e67519a00401ba246ac22b6", "size": 28387, "ext": "py", "lang": "Python", "max_stars_repo_path": "py3dviewer/core.py", "max_stars_repo_name": "Lait-au-Cafe/py3dviewer", "max_stars_repo_head_hexsha": "29b4e09371f1308e37579be9a3e29a3ad3bcd39f", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import pytest
from unittest.mock import Mock
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from figpptx.slide_editor import SlideEditor, SlideTransformer
@pytest.mark.parametrize(
"instance, expected",
[
((1, 2), (11, 58)),
([3, 5], [13, 55]),
([1, 2, 3, 5]... | {"hexsha": "ba4a51a90b4d7c4c3407e00b8f5a24705da9d4f6", "size": 853, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_slide_editor.py", "max_stars_repo_name": "Sillte/figpptx", "max_stars_repo_head_hexsha": "bf5539b09eeef4e6a17bb4483f62f29d286138b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import ndhist
a = ndhist.constant_bin_width_axis(np.array([0.,1.,2.,3.]))
print(a.nbins)
h = ndhist.ndhist((a,))
print(h.nbins)
| {"hexsha": "813c1013df11317d8a23970bb8501d0e1acfa28b", "size": 148, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/python/axis_test.py", "max_stars_repo_name": "martwo/ndhist", "max_stars_repo_head_hexsha": "193cef3585b5d0277f0721bb9c3a1e78cc67cf1f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
[STATEMENT]
lemma success_newI [success_intros]:
"success (new n x) h"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. success (new n x) h
[PROOF STEP]
by (auto intro: success_intros simp add: new_def) | {"llama_tokens": 83, "file": null, "length": 1} |
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize as opt
import scipy.signal as sig
# matplotlib.use('Agg') # For remote use
font = {'size': 14}
matplotlib.rc('font', **font)
root = '/home/george/Desktop/lith_bulk_002/'
pk_wl = np.genfromtxt(root+'peak_wls.txt', usecols=(2), u... | {"hexsha": "0c9493488e88a2f1a821257fdbf0b3f8364a37db", "size": 2120, "ext": "py", "lang": "Python", "max_stars_repo_path": "noise_analysis.py", "max_stars_repo_name": "g-duff/processing_absorption_spectra", "max_stars_repo_head_hexsha": "813eede469ae4bce5ad9af9e9135aa2eb49e5ab5", "max_stars_repo_licenses": ["MIT"], "ma... |
# -*- coding: utf-8 -*-
'''
GUIDemo.py
PyQt5 and matplotlib based
See my GitHub uwallgren, ujgwallgren@gmail.com
'''
import sys, os
import numpy as np
import pickle
from PyQt5 import QtCore, QtGui, Qt
from PyQt5.QtGui import QIcon
from PyQt5.QtWidgets import QApplication, QWidget, QAction, QVBoxLayout, ... | {"hexsha": "56784ce06d8f2ac80e96d15f41455903655a9a52", "size": 7810, "ext": "py", "lang": "Python", "max_stars_repo_path": "MPLDemo.py", "max_stars_repo_name": "uwallgren/PyQt-Matplotlib", "max_stars_repo_head_hexsha": "f559f6e17096052249832e069afc5914c7cf0307", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import sklearn.datasets as datasets
import sklearn.manifold as manifold
if os.path.exists("../image") is False:
os.mkdir("../image")
if __name__ == "__main__":
# Data Loading from sklearn.datasets
data ... | {"hexsha": "7688456ca2e7d1220d09992aed7d24d4969b9fe2", "size": 1449, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/main.py", "max_stars_repo_name": "PeppeSaccardi/mnist-visualization-via-tsne", "max_stars_repo_head_hexsha": "0e30a697da4a54e7f88c14a8c2787c198ff2811d", "max_stars_repo_licenses": ["MIT"], "ma... |
(* autogenerated from github.com/mit-pdos/gokv/dmvcc/txncoordinator *)
From Perennial.goose_lang Require Import prelude.
From Goose Require github_com.mit_pdos.gokv.dmvcc.index.
Section code.
Context `{ext_ty: ext_types}.
Local Coercion Var' s: expr := Var s.
(* 0_server.go *)
Definition Server := struct.decl [
"i... | {"author": "mit-pdos", "repo": "perennial", "sha": "76dafee3cd47e1c5e5a6d5436f87738a06f13ee0", "save_path": "github-repos/coq/mit-pdos-perennial", "path": "github-repos/coq/mit-pdos-perennial/perennial-76dafee3cd47e1c5e5a6d5436f87738a06f13ee0/external/Goose/github_com/mit_pdos/gokv/dmvcc/txncoordinator.v"} |
import json
import pandas as pd
import numpy as np
from scipy import signal
from scipy.interpolate import interp1d
from astropy.timeseries import LombScargle
import plotly.graph_objects as go
from plotly.utils import PlotlyJSONEncoder
from datasets.constants import domain_types
from datasets.processing_methods.method_... | {"hexsha": "5e179b624e45480a7930ce80c0d2110188404530", "size": 9622, "ext": "py", "lang": "Python", "max_stars_repo_path": "backend/datasets/processing_methods/frequency_domain.py", "max_stars_repo_name": "hpi-dhc/alps", "max_stars_repo_head_hexsha": "6f6ab641f5486fc9de6ab42d74fbb0b90b702cdf", "max_stars_repo_licenses"... |
program testhbhstokesgreenid
ccccccccccccccccccccccccccccccccccccccccccccccccccccccccc
c
c This program tests the Green's identity for the
c Helmholtz Stokes single and double layer kernels
c
implicit real *8 (a-h,o-z)
parameter (nmax = 100000)
integer adjs(2,nmax)
real *8... | {"hexsha": "3516ae40be28ef6436aad8054e3aed8c8fc73aee", "size": 6246, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ye-olde-fortran-test/testhbhstokesgreenid.f", "max_stars_repo_name": "askhamwhat/biharm-evals", "max_stars_repo_head_hexsha": "d836302f544670b3d899bd91ea4cb49e9afb6a75", "max_stars_repo_licenses":... |
# -*- coding: utf-8 -*-
"""
Defines methods for configuration.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
__all__ = ["INPUT_TRAIN", "INPUT_VAL", "INPUT_TEST", "load_config",
"configure_logger", "configure_input",
"configure_e... | {"hexsha": "38213a340e53938cd01ed4423cbfbd6ca7d46763", "size": 7316, "ext": "py", "lang": "Python", "max_stars_repo_path": "nnetmaker/config.py", "max_stars_repo_name": "0xsx/nnmaker", "max_stars_repo_head_hexsha": "d7d78e99dc3f738c733895fd937a0bdd092457fe", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
# Project 3: Competition and Collaboration
# Udacity Deep Reinforcement Learning nanodegree
#
#
# Deep Neural Network architectures for function approximators
# of MADDPG agent and critic functions
#
# Architectures' code based on that described in the DDPG paper and modify accordingly to fit
# with the MADDPG algorith... | {"hexsha": "aae8ce216d28b2f69f2205b2a95061834586f64c", "size": 5347, "ext": "py", "lang": "Python", "max_stars_repo_path": "P3-Competition_and_Collaboration/actor_critic_nets.py", "max_stars_repo_name": "felixrlopezm/Udacity_Deep_Reinforcement_Learning", "max_stars_repo_head_hexsha": "78a08da0b40371cc1a2c8ecc06cf845b44... |
#include "teca_tc_classify.h"
#include "teca_table.h"
#include "teca_array_collection.h"
#include "teca_variant_array.h"
#include "teca_metadata.h"
#include "teca_distance_function.h"
#include "teca_saffir_simpson.h"
#include "teca_geometry.h"
#include "teca_geography.h"
#include <iostream>
#include <string>
#include... | {"hexsha": "9c82ab80f10a783ac4aa38c49603ff74a46c90ed", "size": 25019, "ext": "cxx", "lang": "C++", "max_stars_repo_path": "alg/teca_tc_classify.cxx", "max_stars_repo_name": "LBL-EESA/TECA", "max_stars_repo_head_hexsha": "63923b8a12914f3758dc9525239bc48cd8864b39", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], "max_s... |
// Copyright 2012 Cloudera Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in wr... | {"hexsha": "11e40b84d01a7ce4cb742885ec8d9b1cac161601", "size": 29203, "ext": "cc", "lang": "C++", "max_stars_repo_path": "be/src/util/runtime-profile.cc", "max_stars_repo_name": "wangxnhit/impala", "max_stars_repo_head_hexsha": "d7a37f00a515d6942ca28bd8cd84380bc8c93c5a", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
import os
import sys
import operator
import math
import pickle
import numpy as np
import pandas as pd
'''
P(class|words) --> posterior
P(words) --> evidence
P(words|class) --> likelihood
P(class) --> prior
'''
class bayes_classifier(object):
def __init__(self, labels, eps = 0.01, sigma = 0.00001):
# requir... | {"hexsha": "93e303d7554ef6ef3f11454ef3bdcdfaa624ac27", "size": 3115, "ext": "py", "lang": "Python", "max_stars_repo_path": "classifier/Bayes_Classifier.py", "max_stars_repo_name": "geneti/cse881-data-mining", "max_stars_repo_head_hexsha": "05ffc3c8a9c31338687c596629501f43348eaa39", "max_stars_repo_licenses": ["MIT"], "... |
# Copyright 2021 AI Singapore
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing... | {"hexsha": "25a1cf38e32be7042bbcbafe6b1e3afdf8908c84", "size": 2695, "ext": "py", "lang": "Python", "max_stars_repo_path": "peekingduck/pipeline/nodes/model/yolov4/yolo_model.py", "max_stars_repo_name": "leeping-ng/PeekingDuck", "max_stars_repo_head_hexsha": "16784b4c35f30c463fcc0c7caccdda6141797a6b", "max_stars_repo_l... |
module NVML
using ..APIUtils
using ..CUDA
using CEnum
using Memoize
using Libdl
@memoize function libnvml()
if Sys.iswindows()
# the NVSMI dir isn't added to PATH by the installer
nvsmi = joinpath(ENV["ProgramFiles"], "NVIDIA Corporation", "NVSMI")
if isdir(nvsmi)
joinpath... | {"hexsha": "183b77ae183e4f82be1585107bb960641d1a654c", "size": 733, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lib/nvml/NVML.jl", "max_stars_repo_name": "glwagner/CUDA.jl", "max_stars_repo_head_hexsha": "7788985167bda2951bdde370e636c1c238c29d70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# Author: Jin Xu
# Data: 2019-12-24
# Function: Pre-Processing the dataset
#-------------------------- import package --------------------------#
import os
import re
import random
import codecs
import numpy as np
import networkx as nx
import scipy as sp
#-------------------------- global variable ... | {"hexsha": "ec69855738619690ec78b4391cbef2b4ad4cb5a4", "size": 19605, "ext": "py", "lang": "Python", "max_stars_repo_path": "Pre_ProcessDataset.py", "max_stars_repo_name": "KampfWut/MORE", "max_stars_repo_head_hexsha": "2b42764812becfa29e0f32f033427e76d5dfe9a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
include("src/SSPS.jl")
using .SSPS
julia_main()
| {"hexsha": "361e794eed0315780e23f56344e5230198883101", "size": 51, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "SSPS/ssps_wrapper.jl", "max_stars_repo_name": "gitter-lab/ssps", "max_stars_repo_head_hexsha": "8557cb1961bcd951c5f78102070925e945567600", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 14, "... |
#!/usr/bin/env python
# coding: utf-8
# # Residual Networks
#
# Welcome to the first assignment of this week! You'll be building a very deep convolutional network, using Residual Networks (ResNets). In theory, very deep networks can represent very complex functions; but in practice, they are hard to train. Residual N... | {"hexsha": "4c62bdd805802b6fc332936b33fe4d2b04208d62", "size": 34144, "ext": "py", "lang": "Python", "max_stars_repo_path": "4 - Convolutional Neural Networks/Residual_Networks.py", "max_stars_repo_name": "pouyalj/DeepLearningCoursera", "max_stars_repo_head_hexsha": "4c0d79a53bbdd24fbb77503fed35e73d24949be2", "max_star... |
!----------------------------------*-F90-*----------------------------------
!
! file FortranCheck/f90sub/drel.f90
! author Kelly Thompson
! date Tuesday, Jun 12, 2012, 16:03 pm
! brief Test F90 main linking against C++ library and calling a C++ function.
! note Copyright (c) 2016-2019 Triad National Security, L... | {"hexsha": "0d81635e28a91a49573fcba573e450a49bd0ca2d", "size": 4871, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/FortranChecks/f90sub/drel.f90", "max_stars_repo_name": "rspavel/Draco", "max_stars_repo_head_hexsha": "b279b1afbfbb39f2d521579697172394c5efd81d", "max_stars_repo_licenses": ["BSD-3-Clause-Op... |
from collections import defaultdict
import json
import numpy as np
from typing import Set, Iterable, Union, Dict, Any
class DataSet:
"""
A DataSet represents a series of observations (input and output). DataSets are generally constructed via Java
using the Apollo library. Implementations should implement ... | {"hexsha": "bb85ebaa5c568822b6c973f3939983a87106b7e0", "size": 5468, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/apollo/data.py", "max_stars_repo_name": "gengoai/mono-repo", "max_stars_repo_head_hexsha": "50e95c16579aaa8a4ee0776582964868b5625415", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
# check overall performance using randomly chosen points
using Polylogarithms
using DataFrames, CSV
using PyPlot
using PyCall
using Printf
using StatsBase
fs = (4,4)
fs2 = (5,4)
PyCall.PyDict(matplotlib["rcParams"])["legend.markerscale"] = 3.0
# https://matplotlib.org/3.1.0/api/legend_api.html
L = Symbol("Li_s(z)")
... | {"hexsha": "becf5294ed17da14767a2ce2e520509fce67d47c", "size": 5156, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "extended_tests/polylog_bench_rand.jl", "max_stars_repo_name": "AshtonSBradley/Polylogarithms.jl", "max_stars_repo_head_hexsha": "f52e3075cd1d87b0d67d26c72cd1a79a365eddcd", "max_stars_repo_licenses"... |
import re
from functools import lru_cache
from typing import Union
import numpy as np
import pandas as pd
import sidekick as sk
from .types import Region
db = sk.import_later(".db:db", package=__package__)
ISO2 = re.compile(r"[A-Z]{2}")
ISO3 = re.compile(r"[A-Z]{3}")
MUNDI_CODE = re.compile(r"[A-Z]{2}-\w+(:\w+)*")
T... | {"hexsha": "66c684e1c7404f541e95e868872315b151a83268", "size": 3808, "ext": "py", "lang": "Python", "max_stars_repo_path": "mundi/functions.py", "max_stars_repo_name": "pydemic/mundi", "max_stars_repo_head_hexsha": "7c88037bd5aaff8bbed4c63b52f05bebc989b0e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
import numpy as np
import random
import copy
from collections import namedtuple
MoveReport = namedtuple('MoveReport', 'move node')
class Move(object):
def __init__(self, cost_function, depends_on):
self.cost_function = cost_function
self.depends_on = depends_on
def cost(self):
return ... | {"hexsha": "e30c53de9cccb9adc99a949c65ca2e73da4a8084", "size": 3931, "ext": "py", "lang": "Python", "max_stars_repo_path": "parallel_RETIS_game.py", "max_stars_repo_name": "dwhswenson/parallelRETISgame", "max_stars_repo_head_hexsha": "05ba9cd437dd3f7892e26b6226fdfe342c704f31", "max_stars_repo_licenses": ["MIT"], "max_s... |
from jax import random
import jax.numpy as np
from jaxsde import make_brownian_motion
from jaxsde import time_reflect_ito, time_reflect_stratonovich, \
ito_to_stratonovich, stratonovich_to_ito
D = 10
rng = random.PRNGKey(0)
delta_t = 0.0001
def make_example_sde():
t0 = 0.1
t1 = 2.2
y0 ... | {"hexsha": "40394d4d61cf16df3a163915870269ddc768b38b", "size": 2688, "ext": "py", "lang": "Python", "max_stars_repo_path": "jaxsde/tests/test_utils.py", "max_stars_repo_name": "wilsonify/bayesian-sde", "max_stars_repo_head_hexsha": "528f41ac10e65cb8f1ea047a48e3057bb6a777fc", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python3
import pybullet as p
import pybullet_data
import os
import time
import numpy as np
import cv2
import tempfile
#BASEDIR = os.path.dirname(__file__)
from lib.pyb.urdf_printer import URDFPrinter
# W, H, FOVX
#CAMERA_SPECS = (1920, 1080, 64) # https://www.chiefdelphi.com/t/horizontal-fov-of-mic... | {"hexsha": "32cce8c4b65dfed48a3fc7a3575438fef507368d", "size": 8802, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/pyb/pybullet_robot.py", "max_stars_repo_name": "abbbe/eye-on-stick", "max_stars_repo_head_hexsha": "005da329e3565706ea7067cc7ea9e4cd35ca3afc", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
using Combinatorics: combinations
using StatsBase: sample
@doc raw"""
get_contours(fit::AbstractFit, χsq, parameters_combination::Vector{Int};
npts::Int=20, limits=true, sigma = 1.0)
For a given fit `fit` and the ``\chi^2`` function `χsq`, gives an array of parameter arrays,
with each array corr... | {"hexsha": "9c430ed58a536a833a3acfceff48b206606b0b5c", "size": 18374, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/contour.jl", "max_stars_repo_name": "zhanglm79/IMinuit.jl", "max_stars_repo_head_hexsha": "3d5a685d6b955b32aa88dd3c5aa7bbfc83605aca", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 11,... |
library(caret)
# Data splitting with createDataPartition()
inTrain <- createDataPartition(y=iris$Species, p=0.75, list=FALSE)
training <- iris[inTrain, ]
testing <- iris[-inTrain, ]
dim(training)
dim(testing)
| {"hexsha": "44a12f656cf12e9af8d82c001e337a9d313905b5", "size": 215, "ext": "r", "lang": "R", "max_stars_repo_path": "machinelearning/PartitioningData.r", "max_stars_repo_name": "KOBForest/KOBForest.github.io", "max_stars_repo_head_hexsha": "48c5ee6641c591ed1d4d6d95e06381c9df092d59", "max_stars_repo_licenses": ["Apache-... |
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
from lanedetection_depends import LaneDetection
ld = LaneDetection()
images_format = './test_images/straight_lines'
output_format = './output_images/straight_warped'
for idx in range(1, 3):
img = cv2.imread(images_format + str(idx) + '.jpg... | {"hexsha": "9a865f75529ec76943e81c6b6cd1127615d4d21d", "size": 1117, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_perspective.py", "max_stars_repo_name": "yeongseok94/CarND-Advanced-Lane-Lines", "max_stars_repo_head_hexsha": "59367410e6fdecde72bc63073dfb93bd65470fbd", "max_stars_repo_licenses": ["MIT"], ... |
from typing import Optional, List, Sequence
import pandas as pd
import numpy as np
from scipy import stats
import sha_calc as sha_calc
from gmhazard_calc.im import IM, IMType, to_im_list, to_string_list
from gmhazard_calc import gm_data
from gmhazard_calc import site
from gmhazard_calc import constants
from gmhazard_... | {"hexsha": "fa1c57c182f71a8ee822c407642062143b27c4b8", "size": 22751, "ext": "py", "lang": "Python", "max_stars_repo_path": "calculation/gmhazard_calc/gmhazard_calc/gms/gms.py", "max_stars_repo_name": "ucgmsim/gmhazard", "max_stars_repo_head_hexsha": "d3d90b4c94b3d9605597a3efeccc8523a1e50c0e", "max_stars_repo_licenses"... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.model_selection import GroupShuffleSplit, GridSearchCV
from sklearn.metrics import r2_s... | {"hexsha": "0a7f7bd5d1b94ea6f4989841a9d94573aa549f21", "size": 13851, "ext": "py", "lang": "Python", "max_stars_repo_path": "metabolic_homology/__init__.py", "max_stars_repo_name": "Emergent-Behaviors-in-Biology/metabolic-homology", "max_stars_repo_head_hexsha": "a0cb8030dbc507e5314a25a8c6ced3c50b2d7b29", "max_stars_re... |
// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements. See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership. The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may... | {"hexsha": "78e9930338aa4367043054e0d278c156c7e0bb4c", "size": 13499, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/kudu/master/ts_manager.cc", "max_stars_repo_name": "NagithaAbeywickrema/kudu", "max_stars_repo_head_hexsha": "24f85cedcd47e585ea9c308c317da963c5ab8fa9", "max_stars_repo_licenses": ["Apache-2.0"]... |
// Software License for MTL
//
// Copyright (c) 2007 The Trustees of Indiana University.
// 2008 Dresden University of Technology and the Trustees of Indiana University.
// 2010 SimuNova UG (haftungsbeschränkt), www.simunova.com.
// All rights reserved.
// Authors: Peter Gottschling and A... | {"hexsha": "8c4668f75dae1e0ac90ff37fba8c31819e56b6bb", "size": 4418, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "lib/mtl4/boost/numeric/mtl/utility/eval.hpp", "max_stars_repo_name": "spraetor/amdis2", "max_stars_repo_head_hexsha": "53c45c81a65752a8fafbb54f9ae6724a86639dcd", "max_stars_repo_licenses": ["MIT"], ... |
[STATEMENT]
lemma inv_check_left_moving_Suc_nonempty[simp]:
"inv_check_left_moving (as, abc_lm_s lm n (Suc (abc_lm_v lm n))) (s, b, Oc # list) ires \<Longrightarrow> b \<noteq> []"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. inv_check_left_moving (as, abc_lm_s lm n (Suc (abc_lm_v lm n))) (s, b, Oc # list) ires ... | {"llama_tokens": 236, "file": "Universal_Turing_Machine_Abacus", "length": 2} |
import time
from humpday.optimizers.dlibcube import dlib_default_cube
from humpday.optimizers.nevergradcube import nevergrad_ngopt8_cube
from humpday.optimizers.scipycube import scipy_slsqp_cube
from humpday.optimizers.ultraoptcube import ultraopt_gbrt_cube
from humpday.optimizers.nloptcube import nlopt_direct_cube, nl... | {"hexsha": "7f8cc013af499b20a3f03fa56fa5cedef6fc742f", "size": 4161, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/markowitz.py", "max_stars_repo_name": "MDCHAMP/humpday", "max_stars_repo_head_hexsha": "45e2cea95ae951d991ebc6c1e98314cc8c726f25", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5... |
import numpy as np
from brew.metrics.diversity import paired
from brew.metrics.diversity import non_paired
class Diversity(object):
"""Ensemble Diversity Calculator.
The class calculates the diversity of ensemble of classifiers.
Attributes
----------
`metric` : function, receives the oracle out... | {"hexsha": "b9ee799f724e237e8b9db559de8a90de4a5520d2", "size": 3440, "ext": "py", "lang": "Python", "max_stars_repo_path": "brew/metrics/diversity/base.py", "max_stars_repo_name": "va26/brew", "max_stars_repo_head_hexsha": "3531560df785fa44b39094f3ffad83d3b795b15b", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from typing import Mapping, Union, Tuple
from interface.MetricBase import MetricBase
from numpy.linalg import norm
from numpy import ravel, inf, ndarray
class AbsValueNorm(MetricBase):
def __call__(
self,
image: ndarray,
adversarial_image: ndarray,
model_metadata: Mapping[str, U... | {"hexsha": "180be07fa3359122fae7bbf7dea6c4ca022f503c", "size": 1105, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics/PNorm.py", "max_stars_repo_name": "Brauntt/Gicaf_new", "max_stars_repo_head_hexsha": "b3617aeb3c3569ca49ac53bb079e95b1ad03f590", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import numpy
from .. import PulsevizException
from .sampler import Sampler
from ..pulseaudio.simple_client import SampleFormat
def calculate_frequency_weighting(frequency, weighting):
"""
Calculates the weighting in [dB] for a given frequency.
"""
if weighting == 'A':
a = numpy.power(12194.0,... | {"hexsha": "afff9850a7e702fad7a495b9b6ed5615f1a834e5", "size": 4689, "ext": "py", "lang": "Python", "max_stars_repo_path": "pulseviz/dsp/fft.py", "max_stars_repo_name": "drzraf/pulseviz.py", "max_stars_repo_head_hexsha": "25f7b4ab3466e98bca8b8e3cecd533c44837a76c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
\documentclass[12pt]{report}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{amsmath,amssymb,amsfonts} % Required for some math elements, equation environment and inline $ (math) $
\usepackage{makeidx}
\usepackage{graphicx} % Required for the inclusion of images
\usepackage{float}
\usepackage{txfonts}
... | {"hexsha": "ad27042c7cf0445faf9e6b245a05298096ee498e", "size": 69360, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/mw_engineering_thesis.tex", "max_stars_repo_name": "mikkelist/legendary-telegram", "max_stars_repo_head_hexsha": "19c1d0b308bcc9a6871d4b16a6e716bf901f0bd3", "max_stars_repo_licenses": ["MIT"],... |
#!/usr/bin/env python
# Copyright 2016 Vijayaditya Peddinti
# 2016 Vimal Manohar
# Apache 2.0.
from __future__ import division
import argparse
import errno
import logging
import os
import re
import sys
import warnings
sys.path.insert(0, "steps")
import libs.nnet3.report.log_parse as log_parse
import ... | {"hexsha": "bc8fc53fbd3bc68995bf2fbdd4e682fbec8e7546", "size": 40766, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/recipes/librispeech/steps/nnet3/report/generate_plots.py", "max_stars_repo_name": "mlcommons/peoples-speech", "max_stars_repo_head_hexsha": "1bfaa7d843e0f664e16bbdbc308f7fa40ac7e10c", "ma... |
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