text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
from .registry import register
import tensorflow as tf
import numpy as np
import random
NUM_CLASSES = 15 # i.e. number of sort indices
# These sequence lengths do not include the extra padding we need
# to delay RNN outputs until the entire sequence is seen, which is crucial
# because otherwise our model would have t... | {"hexsha": "db7320d47d3c2509d6b7b13932af8637fa99e73d", "size": 2473, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/sort_reader.py", "max_stars_repo_name": "for-ai/ACT", "max_stars_repo_head_hexsha": "efe259117a11d0583434d09440702fd75ebcdb99", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 18, "max... |
from .mmodel import MetaModel, load_model
import numpy as np
class SimpleTripMetaModel(MetaModel):
def __transform_input__(self, y, params):
network = self.network
cmodel = load_model(network.nodes[0].cmodel)
sets = cmodel.sets
N = len(network.nodes)
matrices = { s... | {"hexsha": "210907963a91eb20db77e1eea5e5a7ff588d300d", "size": 4706, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mmodel/simple_trip.py", "max_stars_repo_name": "Maximiza-Atemoriza/meta-population-network-model", "max_stars_repo_head_hexsha": "7dfde8d92c50935a963c919227058c99fcd0c649", "max_stars_repo_lic... |
# Check if curl is installed
try
print("Checking for curl...")
cmd = `curl --version`
(VERSION >= v"0.5")? readstring(cmd) : readall(`curl --version`)
println("OK!")
catch
error("Curl not found. Please install curl to use BCBData.")
end
"""
A Julia package to read Brazilian Central Bank (BCB) time series data.
"... | {"hexsha": "a3a4cf94ea96b564ab81b747371445c20898ca60", "size": 3173, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/BCBData.jl", "max_stars_repo_name": "lucasprocessi/BCBData.jl", "max_stars_repo_head_hexsha": "b0fe00aa4347ea18853bf6e11bff630cbb0d61f0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
using Documenter, TestThings
makedocs(;
modules=[TestThings],
format=Documenter.HTML(),
pages=[
"Home" => "index.md",
"Foo" => "foo.md"
],
repo="https://github.com/under-Peter/TestThings.jl/blob/{commit}{path}#L{line}",
sitename="TestThings.jl",
authors="Andreas Peter",
)
d... | {"hexsha": "e7cf9a3ede4191ec7165d8b2c238c65ea6c292ca", "size": 383, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "under-Peter/TestThings.jl", "max_stars_repo_head_hexsha": "fec7d90ca332d5e1abd8cdbeb3b94a14d65beb90", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import numpy as np
from sge.parameters import params
import json
import os
def evolution_progress(generation, pop):
fitness_samples = [i['fitness'] for i in pop]
data = '%4d\t%.6e\t%.6e\t%.6e' % (generation, np.min(fitness_samples), np.mean(fitness_samples), np.std(fitness_samples))
if params['VERBOSE']:... | {"hexsha": "3f22785106e4184b1f3c8cf0299c0da8e12b0efd", "size": 1203, "ext": "py", "lang": "Python", "max_stars_repo_path": "sge/sge/logger.py", "max_stars_repo_name": "HiroseTomoyuki/sge3", "max_stars_repo_head_hexsha": "3b88f3ef6cbb6dbef959bc73b4fe862222192d67", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9... |
## Author: Sergio García Prado
## Title: Statistical Inference - Non parametric Tests - Exercise 01
rm(list = ls())
x <- c(518, 174, 613, 2010, 2139, 156, 450, 536)
(n.x <- length(x))
# 8
y <- c(899, 326, 2118, 839, 820, 1423, 1687, 1010, 3011, 1739, 1185, 1320, 646,
505, 4236, 4481, 1433, 1806, 400, 421, 335... | {"hexsha": "72c9df0237a8dde43c5bc794c74ae2f87031da17", "size": 1399, "ext": "r", "lang": "R", "max_stars_repo_path": "statistical-inference/non-parametric/exercise-01.r", "max_stars_repo_name": "garciparedes/r-examples", "max_stars_repo_head_hexsha": "0e0e18439ad859f97eafb27c5e7f77d33da28bc6", "max_stars_repo_licenses"... |
"""
edit_with(y::Dict{Any,Any}; kwargs...)
edit_with(df::DataFrame, editor::T) where T<:Edit
edit_with(df::DataFrame, lst::Array{T}) where T<:Edit
edit_with(df::DataFrame, x::Describe, file::T) where T<:File
edit_with(file::T, y::Dict{Any,Any}; kwargs...)
edit_with(files::Array{T,N} where N, y::... | {"hexsha": "c8f1dcde4db74924eefe4cad082eaea623fd3b45", "size": 26402, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/parse/edit_data.jl", "max_stars_repo_name": "matbesancon/SLiDE", "max_stars_repo_head_hexsha": "e5df044be8733865d432a51fbdc48912e384ec00", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
-- Errors should precede warnings in info buffer
-- Reported by nad 2018-11-27
module Issue3416 where
A : Set
A = A
B : Set
B = Set
| {"hexsha": "45386b4f27083b3c2a68979d3496b48711590969", "size": 134, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/interaction/Issue3416.agda", "max_stars_repo_name": "cruhland/agda", "max_stars_repo_head_hexsha": "7f58030124fa99dfbf8db376659416f3ad8384de", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import logging
import numpy as np
import rlberry.spaces as spaces
from rlberry.envs.finite import GridWorld
from rlberry.rendering import Scene, GeometricPrimitive
logger = logging.getLogger(__name__)
class SixRoom(GridWorld):
"""
GridWorld with six rooms.
Parameters
----------
reward_free : boo... | {"hexsha": "131c3a3ef5f3bfa10589e8e6dc907b39570adf06", "size": 4339, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlberry/envs/benchmarks/grid_exploration/six_room.py", "max_stars_repo_name": "rlberry-py/rlberry", "max_stars_repo_head_hexsha": "b4a8dcf17da69fc49a4d2d8b8d1a93e6cb054078", "max_stars_repo_licens... |
import numpy as np
import time, json, pickle
# activation functions
def sigmoid(x,deriv=False):
if deriv:
return sigmoid(x)*(1-sigmoid(x))
return 1/(1+np.exp(-x))
def relu(x,deriv=False):
if deriv:
return (x>0).astype(int)
else:
return np.maximum(x,0)
def softmax(inputs,deriv=... | {"hexsha": "fff2cb6621ab7ff3d753ba4c999363702bb0c861", "size": 8824, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn.py", "max_stars_repo_name": "ramonus/pyneuro", "max_stars_repo_head_hexsha": "0b89bfac3e41fd8b3d58df85c64fb19cf86cfa0b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_rep... |
Load LFindLoad.
From lfind Require Import LFind.
Unset Printing Notations.
Set Printing Implicit.
Require Import Arith.
Inductive natural : Type := Succ : natural -> natural | Zero : natural.
Inductive lst : Type := Cons : natural -> lst -> lst | Nil : lst.
Inductive tree : Type := Node : natural -> tree -> tree ... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal38_theorem0_56_goal37/goal38.v"} |
!>----------------------------------------------------------
!! Simple convection resolving convective parameterization code
!!
!! Because this scheme assume it is being run at convection resolving
!! scales, it does not modify the microphysical or cloud fraction variables
!! Instead the simple cu scheme modifies the w... | {"hexsha": "0af05f2e30d4f2e882da8917325f289bf4e3cc48", "size": 5155, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/physics/cu_simple.f90", "max_stars_repo_name": "scrasmussen/icar", "max_stars_repo_head_hexsha": "88c59fed7595b176a81127993785fdeb514f28a3", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
% Studies the recovery probability of different algorithms
% at a fixed sparsity level and varying number of signals.
close all;
clear all;
clc;
rng('default');
% Create the directory for storing images
[status_code,message,message_id] = mkdir('bin');
% Signal space
N = 256;
% Number of measurements
M = 48;
% Number ... | {"author": "indigits", "repo": "sparse-plex", "sha": "43cae2978f62938d001baaa03308a2a717ee6c9b", "save_path": "github-repos/MATLAB/indigits-sparse-plex", "path": "github-repos/MATLAB/indigits-sparse-plex/sparse-plex-43cae2978f62938d001baaa03308a2a717ee6c9b/experiments/cosamp_mmv/ex_comparison_with_s.m"} |
import sys
import time
import cv2 as cv2
import numpy as np
from keras.models import load_model
xtest = []
# Read the test image
test_img = sys.argv[1]
test_img = cv2.imread(test_img)
# Reducing the size of image to 64x64x3
rsize = 64
test_img = cv2.resize(test_img,(rsize,rsize), interpolation = cv2.INTER_CUBIC)
xt... | {"hexsha": "4bc72b806f7f9412d398a5c9e78fc27018052f45", "size": 739, "ext": "py", "lang": "Python", "max_stars_repo_path": "scene_recognition.py", "max_stars_repo_name": "shreyagu/Scene_Recognition", "max_stars_repo_head_hexsha": "2f5e64d6b546c2af3bed972263e55d6dce8ba952", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma R_g_ode_law: "(\<forall>s\<in>S. P s \<longrightarrow> (\<forall>t\<in>T. (\<forall>\<tau>\<in>down T t. G (\<phi> \<tau> s)) \<longrightarrow> Q (\<phi> t s))) \<Longrightarrow>
(x\<acute>= (\<lambda>t. f) & G on (\<lambda>s. T) S @ 0) \<le> Ref \<lceil>P\<rceil> \<lceil>Q\<rceil>"
[PROOF STATE]
p... | {"llama_tokens": 402, "file": "Hybrid_Systems_VCs_KleeneAlgebraTests_HS_VC_KAT_ndfun", "length": 2} |
[STATEMENT]
lemma Literal_eq_iff [simp]:
"Literal b0 b1 b2 b3 b4 b5 b6 s = Literal c0 c1 c2 c3 c4 c5 c6 t
\<longleftrightarrow> (b0 \<longleftrightarrow> c0) \<and> (b1 \<longleftrightarrow> c1) \<and> (b2 \<longleftrightarrow> c2) \<and> (b3 \<longleftrightarrow> c3)
\<and> (b4 \<longleftrightarrow> c4... | {"llama_tokens": 299, "file": null, "length": 1} |
!***********************************************************************
! Integrated Water Flow Model (IWFM)
! Copyright (C) 2005-2021
! State of California, Department of Water Resources
!
! This program is free software; you can redistribute it and/or
! modify it under the terms of the GNU General Public Lic... | {"hexsha": "1d7a42ec8b2a764dff1f767fa0636797cac7a083", "size": 108945, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "code/SourceCode/Package_AppStream/VERSION_5.0/Class_AppStream_v50.f90", "max_stars_repo_name": "giorgk/IWFM-SAFE", "max_stars_repo_head_hexsha": "849abb22bd7b29c113d0537d228340abe6672b40", "ma... |
#=
This file is auto-generated. Do not edit.
=#
#! format: off
"""
mutable struct AndersonFouadMachine <: Machine
R::Float64
Xd::Float64
Xq::Float64
Xd_p::Float64
Xq_p::Float64
Xd_pp::Float64
Xq_pp::Float64
Td0_p::Float64
Tq0_p::Float64
... | {"hexsha": "e046a8e599cd9ee2bf6c67acf7d74f169c8e8901", "size": 7190, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/models/generated/AndersonFouadMachine.jl", "max_stars_repo_name": "marenat/PowerSystems.jl", "max_stars_repo_head_hexsha": "c4adfafff95808e75c884e5f9162b1fabdbc9622", "max_stars_repo_licenses":... |
# -*- coding: utf-8 -*-
import numpy as np,sys
import os
import glob
import cv2
import math
import pickle
import datetime, random
import pandas as pd
from utils import *
np.random.seed(1)
img_rows=224
img_cols=224
df = pd.read_csv('driver_imgs_list.csv') # supplied by kaggle
by_drivers = df.groupby('... | {"hexsha": "12d299abcc35044e794a136bd0f008fab59d1dd7", "size": 5130, "ext": "py", "lang": "Python", "max_stars_repo_path": "image_process.py", "max_stars_repo_name": "18461271/state_farm_driver_distraction_detection", "max_stars_repo_head_hexsha": "6032ed7fb5d88deb60bfd22af12ce8046d3fd5d3", "max_stars_repo_licenses": [... |
#coding: utf-8
import os
import sys
import copy
import json
import random
import torch
import pickle
import random
import numpy as np
import pandas as pd
from torch.utils.data import Dataset
from collections import defaultdict, OrderedDict
try:
from .fix_label import fix_general_label_error
except:
from fix_l... | {"hexsha": "63271bbd51f4c30429e382e95c14c777e595dd26", "size": 13432, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/interface.py", "max_stars_repo_name": "cjliux/mdst.c2f", "max_stars_repo_head_hexsha": "5617624b25ddaa11ffbc07401d3fe0276ca220d5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
/*
* Config.cpp
*
* Copyright (c) 2014, Alessandro Pezzato
*/
#include "Config.h"
#include <boost/property_tree/json_parser.hpp>
namespace threescanner {
using namespace boost::property_tree;
using namespace boost::property_tree::json_parser;
Config::Config(const std::string& filename) :
pt_() {
read_json... | {"hexsha": "655995811e6f5fbddf8d03fb704015cc96a9d707", "size": 573, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/lib/common/Config.cpp", "max_stars_repo_name": "alepez/threescanner", "max_stars_repo_head_hexsha": "7fe03ecde0c7f18c4059f42a69c59e56e854c7f2", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
############################################################################
# Copyright ESIEE Paris (2018) #
# #
# Contributor(s) : Benjamin Perret #
# ... | {"hexsha": "94fe38d576e091d00ce3f04ccdcb8bb9c4138b21", "size": 2115, "ext": "py", "lang": "Python", "max_stars_repo_path": "higra/image/image_utils.py", "max_stars_repo_name": "deisemaia/Higra", "max_stars_repo_head_hexsha": "82cb78b606a383f3961faa882457a9a987f802e0", "max_stars_repo_licenses": ["CECILL-B"], "max_stars... |
function [xxx1,xxx2] =dymism(r1,r3,l,r4,r5,choice,rpm,loc)
%-----------------------------------------------------------
format bank;
% clf;
t = 0:1:360;
tg = t*pi/180.0;
cx =[0,0,l,0];
r2 = sqrt(l^2 - r3^2 + r1^2);
jj = 0;
%-----------------------------------------------------------
f... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/8690-linkage-mechanism-mechanical-engineerin... |
[STATEMENT]
lemma cond_disj_distr:"(P \<or> (Q \<triangleleft> b \<triangleright> S)) = ((P \<or> Q) \<triangleleft> b \<triangleright> (P \<or> S))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (P \<or> Q \<triangleleft> b \<triangleright> S) = (P \<or> Q) \<triangleleft> b \<triangleright> (P \<or> S)
[PROOF STE... | {"llama_tokens": 145, "file": "UTP_utp_utp_pred_laws", "length": 1} |
module Diagnostics
using NCDatasets
using Statistics
using LinearAlgebra
using EnsembleKalmanProcesses
using EnsembleKalmanProcesses.ParameterDistributions
import EnsembleKalmanProcesses: construct_sigma_ensemble, construct_mean, construct_cov
import EnsembleKalmanProcesses: construct_successful_mean, construct_succes... | {"hexsha": "cc4eb6b7b8924eaf3f2f7b6f47535c637c20c1ce", "size": 37121, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Diagnostics.jl", "max_stars_repo_name": "CliMA/CalibrateEDMF.jl", "max_stars_repo_head_hexsha": "237e21f9b2228bd0cb008976fc4d61ea3722196c", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import numpy as np
import pytest
from gamegym.games.matrix import (GameOfChicken, MatchingPennies, MatrixGame, MatrixZeroSumGame,
PrisonersDilemma, RockPaperScissors)
from gamegym.strategy import ConstStrategy, UniformStrategy
from gamegym.utils import get_rng
from gamegym.algorithms.... | {"hexsha": "013c8b41ef23a5429f0ef0b27806ea1532aff567", "size": 1722, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/games/test_matrix.py", "max_stars_repo_name": "spirali/gamegym", "max_stars_repo_head_hexsha": "8c2dbb7969cabae9ca86c0dab74c6ddc5fbd21bf", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
ELEMENTAL FUNCTION func_radians(x) RESULT(ans)
! This function returns the passed angle converted from degrees to radians.
USE ISO_FORTRAN_ENV
IMPLICIT NONE
! Declare input variables ...
REAL(kind = REAL64), INTENT(in) :: x ! The input angle in d... | {"hexsha": "c9d9c3e93dc5aa135771b34bf37093f596e33ee1", "size": 622, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "mod_safe/func_radians.f90", "max_stars_repo_name": "Guymer/fortranlib", "max_stars_repo_head_hexsha": "30e27b010cf4bc5acf0f3a63d50f11789640e0e3", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
[STATEMENT]
lemma ofilter_Un[simp]: "\<lbrakk>ofilter A; ofilter B\<rbrakk> \<Longrightarrow> ofilter(A \<union> B)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>local.ofilter A; local.ofilter B\<rbrakk> \<Longrightarrow> local.ofilter (A \<union> B)
[PROOF STEP]
unfolding ofilter_def
[PROOF STATE]
proof ... | {"llama_tokens": 267, "file": null, "length": 2} |
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 10 01:28:34 2019
@author: Titus
"""
import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import core_photo_analyzer as cpa
d1=7448.1
d2=7449.55
folder="../../58_32_Core_CT_Scans/7448.1-7449.55"
n=len(os... | {"hexsha": "3fd745848761346acf43dba180b396d0c8984417", "size": 703, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/core_visualizer.py", "max_stars_repo_name": "titusquah/Final-Project", "max_stars_repo_head_hexsha": "21615f2f524fe1a18765305eca823e54585af121", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
## Automatically adapted for numpy.oldnumeric Apr 14, 2008 by -c
from builtins import range
def writeMeshMatlabFormat(mesh,meshFileBase):
"""
build array data structures for matlab finite element mesh representation
and write to a file to view and play with in matlatb
in matlab can then print mesh wi... | {"hexsha": "aa756acd81e3df8341363e1657f73a5931b7a50d", "size": 3523, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/ppmatlab.py", "max_stars_repo_name": "acatwithacomputer/proteus", "max_stars_repo_head_hexsha": "80dfad95da6ab4d18a88a035f55c26b03540a864", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license. See LICENSE file in the project root for
# full license information.
import numpy as np # we're going to use numpy to process input and output data
import onnxruntime # to inference ONNX models, we use the ONNX Runtime
import onnx
... | {"hexsha": "38bc38a7c1c12533febe02c6045e11036098b1c0", "size": 4765, "ext": "py", "lang": "Python", "max_stars_repo_path": "factory-ai-vision/EdgeSolution/modules/VisionSampleModule/image_classification.py", "max_stars_repo_name": "piyushka17/azure-intelligent-edge-patterns", "max_stars_repo_head_hexsha": "0d088899afb0... |
[STATEMENT]
lemma path_image_rectpath:
assumes "Re a1 \<le> Re a3" "Im a1 \<le> Im a3"
shows "path_image (rectpath a1 a3) =
{z. Re z \<in> {Re a1, Re a3} \<and> Im z \<in> {Im a1..Im a3}} \<union>
{z. Im z \<in> {Im a1, Im a3} \<and> Re z \<in> {Re a1..Re a3}}" (is "?lhs = ?rhs")
[PROOF STATE]... | {"llama_tokens": 2111, "file": null, "length": 12} |
################################################################################
# The Neural Network (NN) based Speech Synthesis System
# https://svn.ecdf.ed.ac.uk/repo/inf/dnn_tts/
#
# Centre for Speech Technology Research
# University of Edinburgh, UK
# ... | {"hexsha": "587a61622845920f47e78c921127e3533d50eb54", "size": 10937, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/dnn.py", "max_stars_repo_name": "shartoo/merlin-tf-slim", "max_stars_repo_head_hexsha": "4c7d48d5f634273dd51d2e29562d3ed1195d9151", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
%!TEX root = ../../main.tex
\chapter{LaTeX Beispielcode}
Dies ist der Text des ersten Kapitels.Nur erwähnte Literaturverweise werden auch im Literaturverzeichnis gedruckt: \cite[S.12 ff]{baumgartner:2002}, \cite[S.1-3]{dreyfus:1980}
Meine erste Fußnote\footnote{Ich bin eine Fußnote} darf auch nicht fehlen. Fußnoten s... | {"hexsha": "9152d63d1c80cbba23f62739ae42868f09d1ed9e", "size": 6601, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "content/chapter/codesnippets.tex", "max_stars_repo_name": "sezuisa/T2000_Sarah_Haegele", "max_stars_repo_head_hexsha": "906b8194849a0cbbb0d002258ac9007ccdf13797", "max_stars_repo_licenses": ["MIT"],... |
!*******************************************************************************
! File thscte_mc.f
! Contains module thscte_mc
! SUBROUTINE thscte_mc_model_compute
!
! Module is part of the V3FIT stellarator equilibrium reconstruction code.
! The postfix _mc in the name indicates Model Compute
! The mod... | {"hexsha": "a7f17dc607ee9b182a7e221deefe0c11a48b8728", "size": 5500, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/V3FITA/Sources/thscte_mc.f", "max_stars_repo_name": "jonathanschilling/VMEC_8_49", "max_stars_repo_head_hexsha": "9f1954d83b2db13f4f4b58676badda4425caeeee", "max_stars_repo_licenses": ["MIT"],... |
using Optim
using BITEModel
const BM=BITEModel
######
# Choose glacier & options
######
@def plotyes = false
# ITMIX glacier with IV data:
glaciers_iv = [:Brewster, :Hellstugubreen, :NorthGlacier,
:SouthGlacier, :Tasman, :Unteraar, :Synthetic1,
:Synthetic2, :Synthetic3]
glacier = glacier... | {"hexsha": "f428931afdf4a17ac0d0f71793a14d4c5557408d", "size": 3147, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/inverse.jl", "max_stars_repo_name": "mauro3/BITEmodel.jl", "max_stars_repo_head_hexsha": "897eca85fc3c3b736ef49e23850b8f4bd6f2806a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
module MultilayerQG
export
fwdtransform!,
invtransform!,
streamfunctionfrompv!,
pvfromstreamfunction!,
updatevars!,
set_q!,
set_ψ!,
energies,
fluxes
using
FFTW,
CUDA,
LinearAlgebra,
StaticArrays,
Reexport
@reexport using FourierFlows
using LinearAlgebra: mul!, ldiv!
using FFTW: rfft, ir... | {"hexsha": "954c01f3267b3a6cb45898f9bdd708457ab16fe8", "size": 21746, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/multilayerqg.jl", "max_stars_repo_name": "liasiegelman/GeophysicalFlows.jl", "max_stars_repo_head_hexsha": "2bbc137d560aebcde4a90bece4e71c16b313b93f", "max_stars_repo_licenses": ["MIT"], "max_... |
import os
import random
import pickle
import numpy as np
from TrainingData.load_corpus.load_corpus_data import get_cds_words
# seed rng for sampling of contexts in function 'get_data_set'
random.seed(6675)
# helper functions
def get_file_path():
return os.path.dirname(os.path.realpath(__file__))
def scale_to_... | {"hexsha": "7aa084a53bce09bed81810ec7033a9990ee8f201", "size": 7378, "ext": "py", "lang": "Python", "max_stars_repo_path": "TrainingData/data_sets/extract_from_corpus.py", "max_stars_repo_name": "RobGrimm/prediction_based", "max_stars_repo_head_hexsha": "b31e43cd9ca56016d265ee6438dea53554e3efab", "max_stars_repo_licens... |
import os
import cv2
import numpy as np
from keras.models import model_from_json
from keras.preprocessing import image
import time
import csv
import os
import os.path
from collections import Counter
import matplotlib.pyplot as plt
# emotions array
emotions_csv = []
# list of emotions
emotio... | {"hexsha": "62a7455ef19f1efb2cf79a52cb0cf667f7dea895", "size": 4501, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_video.py", "max_stars_repo_name": "Vineethpaul09/Facial-Emotions-Detection-Training", "max_stars_repo_head_hexsha": "23a78dcee3380b34e302d593c0dc9ea28dc1482d", "max_stars_repo_licenses": ["MI... |
from copy import deepcopy
import numpy as np
from astropy import units as u
import pytest
from ctapipe.containers import ImageParametersContainer, HillasParametersContainer
from ctapipe.instrument import SubarrayDescription, TelescopeDescription
from ctapipe.image.cleaning import tailcuts_clean
from ctapipe.image.hill... | {"hexsha": "418d6461f42388cf35cbce0fedac1af478b41e1b", "size": 13562, "ext": "py", "lang": "Python", "max_stars_repo_path": "ctapipe/reco/tests/test_HillasReconstructor.py", "max_stars_repo_name": "watsonjj/ctapipe", "max_stars_repo_head_hexsha": "fc98748d7a38f50040f1fbe3ce5e174ad8c0ba0a", "max_stars_repo_licenses": ["... |
"""
Technical Analysis Factors
--------------------------
"""
from bottleneck import (
nanargmax,
nanmax,
nanmean,
nansum,
)
from numpy import (
abs,
clip,
diff,
fmax,
inf,
isnan,
NINF,
)
from numexpr import evaluate
from zipline.pipeline.data import USEquityPricing
from zip... | {"hexsha": "ba58dd58867f780da86358c7e8b541fb1af4b826", "size": 2698, "ext": "py", "lang": "Python", "max_stars_repo_path": "zipline/pipeline/factors/technical.py", "max_stars_repo_name": "jimgoo/zipline-fork", "max_stars_repo_head_hexsha": "7e898ae36d0cadafe443491e4f3670d587e9716c", "max_stars_repo_licenses": ["Apache-... |
# Rounded random numbers from discrete uniform distribution between [1,n]
unidrnd(n::Int64) = round(Int,rand(Uniform(1,n)))
# Sort rows based on the col reference
function sortrows!(A, col; order=false)
if isa(col, Int)
return A[sortperm(A[:,col], rev=order),:]
else
# Sorted along col[1]
... | {"hexsha": "17b9664d40f0b2c8c5c93d51d8d1c9eccce53c0a", "size": 2359, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/clearing/utils.jl", "max_stars_repo_name": "hradet/Metaheuristics.jl", "max_stars_repo_head_hexsha": "bce6647547fdd9b27f5c40db7e0de129b1196d7f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: © 2021 Massachusetts Institute of Technology.
# SPDX-FileCopyrightText: © 2021 Lee McCuller <mcculler@mit.edu>
# NOTICE: authors should document their contributions in concisely in NOTICE
# with details inline ... | {"hexsha": "0cf0518b115a218c50a2a95705ba97f03c6acca3", "size": 3946, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/wavestate/iirrational/statespace/dense/eig_algorithms.py", "max_stars_repo_name": "wavestate/wavestate-iirrational", "max_stars_repo_head_hexsha": "01d6dba8b2131fa2a099a74f17e6540f30cee606", "... |
## A Quick Tour of DifferentialEquations.jl
DifferentialEquations.jl is a metapackage for solving differential equations in Julia. The basic workflow is:
- Define a problem
- Solve a problem
- Plot the solution
The API between different types of differential equations is unified through multiple dispatch
## Example... | {"hexsha": "4eab33102485b20a82e32bd1a898727ec9fbb4f0", "size": 43278, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Notebooks/DiffEq.ipynb", "max_stars_repo_name": "jngod2011/IntroToJulia", "max_stars_repo_head_hexsha": "7364926e7844390a8b169aca353be7b631050224", "max_stars_repo_licenses": ["MIT"]... |
Require Import Crypto.Arithmetic.PrimeFieldTheorems.
Require Import Crypto.Specific.solinas32_2e127m1_6limbs.Synthesis.
(* TODO : change this to field once field isomorphism happens *)
Definition add :
{ add : feBW_tight -> feBW_tight -> feBW_loose
| forall a b, phiBW_loose (add a b) = F.add (phiBW_tight a) (phiBW... | {"author": "anonymous-code-submission-01", "repo": "sp2019-54-code", "sha": "8867f5bed0821415ec99f593b1d61f715ed4f789", "save_path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code", "path": "github-repos/coq/anonymous-code-submission-01-sp2019-54-code/sp2019-54-code-8867f5bed0821415ec99f593b1d61f715ed4f7... |
import copy
import json
import math
import six
import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers as contrib_layers
class ModelConfig(object):
def __init__(self,
vocab_size,
embedding_size=128,
hidden_size=4096,
num... | {"hexsha": "152433b81eac7350892365af71d1f927992d5c1b", "size": 28385, "ext": "py", "lang": "Python", "max_stars_repo_path": "modeling.py", "max_stars_repo_name": "pwldj/nls", "max_stars_repo_head_hexsha": "c85adafcbb88d0d93df1141a1a04ba0581cc6bfb", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "ma... |
# This Python file uses the following encoding: utf-8
"""
MIT License
Copyright (c) 2020 Nils DEYBACH & Léo OUDART
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including ... | {"hexsha": "317353e1d3d8dee3b484e60a36cd7d8edeeb6d5c", "size": 9054, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/DipSimComputor.py", "max_stars_repo_name": "ndeybach/DipSim", "max_stars_repo_head_hexsha": "091f147f933b000b6ab829ec7d10eef985c260b2", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from carla_utils import carla
import numpy as np
from typing import List, Any
import pickle
import os
from os.path import join
from ..basic import Data, YamlConfig
from ..world_map import Role, get_topology
from ..augment import GlobalPath
from ..agents import AgentListMaster, BaseAgent
from .scenario import Scenari... | {"hexsha": "0cdb6246c29b69f6045ea5866e1eee2ac064b3b2", "size": 6809, "ext": "py", "lang": "Python", "max_stars_repo_path": "carla_utils/rl_template/recorder.py", "max_stars_repo_name": "IamWangYunKai/DG-TrajGen", "max_stars_repo_head_hexsha": "0a8aab7e1c05111a5afe43d53801c55942e9ff56", "max_stars_repo_licenses": ["MIT"... |
#!/usr/local/bin/env Rscript --vanilla
args <- commandArgs(TRUE)
if (!is.null(args[1])) {
df <- read.csv(args[1], sep = "\t", row.names = 1, check.names=F)
if (max(df, na.rm = TRUE) < 50) {
df <- 2^df
}
} else {
stop("No expression matrix provided!")
}
library(matrixStats)
library(EPIC)
if (le... | {"hexsha": "ddfcb90567d071b72e9082b4802fbb67e81303d8", "size": 3457, "ext": "r", "lang": "R", "max_stars_repo_path": "tumorDeconvAlgs/epic/epic.r", "max_stars_repo_name": "PNNL-CompBio/proteomicsTumorDeconv", "max_stars_repo_head_hexsha": "a88c3ee729a0dab267ebd5d802a486ebacbf0c0c", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
import scipy
from scipy.optimize import linprog
import qpsolvers
import json
import matplotlib
from matplotlib import cm
from mpl_toolkits.mplot3d.art3d import Line3DCollection
import matplotlib.pyplot as plt
import typing as t
import click
DEFAULT_RESOLUTION = 100 # Runtime is O(n^2) w... | {"hexsha": "554ee6cf3edae8149cfc5dbfb5eaf4671e23ed5f", "size": 21854, "ext": "py", "lang": "Python", "max_stars_repo_path": "tau.py", "max_stars_repo_name": "NoahMollerstuen/Thruster-Arrangement-Utility", "max_stars_repo_head_hexsha": "71c35b55c43c35a7f3883fc73c0b4c803566e15f", "max_stars_repo_licenses": ["Apache-2.0"]... |
import numpy as np # Make sure that numpy is imported
def makeFeatureVec(words, model, num_features):
# Function to average all of the word vectors in a given
# paragraph
#
# Pre-initialize an empty numpy array (for speed)
featureVec = np.zeros((num_features,),dtype="float32")
#
nwords = 0... | {"hexsha": "0dc761544d0de4aa550560dbc1bf48bc76e9f079", "size": 1799, "ext": "py", "lang": "Python", "max_stars_repo_path": "word2Vec.py", "max_stars_repo_name": "siddharthshah3030/Sentiment_Analysis", "max_stars_repo_head_hexsha": "380b9513461c5bd17db0f8fb8efe959db4469031", "max_stars_repo_licenses": ["MIT"], "max_star... |
#!/usr/bin/julia
# Trizen
# 27 April 2017
# https://github.com/trizen
# Complex transform of an image, by mapping each pixel position to a complex function.
# usage:
# julia complex_transform.jl [image]
using Images
#using SpecialFunctions
function map_val(value, in_min, in_max, out_min, out_max)
(value - in... | {"hexsha": "b33c7f20b8fd92bd67743917172c4a2b7d081e99", "size": 2006, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Image/complex_transform.jl", "max_stars_repo_name": "trizen/julia-scripts", "max_stars_repo_head_hexsha": "26015006f2b37e0fcdb9dc4a96ea37a8b312a7ec", "max_stars_repo_licenses": ["Unlicense"], "max_... |
% Created 2022-01-25 Tue 18:13
% Intended LaTeX compiler: pdflatex
\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{graphicx}
\usepackage{longtable}
\usepackage{wrapfig}
\usepackage{rotating}
\usepackage[normalem]{ulem}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{... | {"hexsha": "27289a73e68af62dd4387c23448a61d4d6fee09d", "size": 6895, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "roam/20210406132914-pim_enclave.tex", "max_stars_repo_name": "kha-dinh/org-notes", "max_stars_repo_head_hexsha": "a8e954aa8af587c065501825e6d85f6ec1a818d0", "max_stars_repo_licenses": ["CC-BY-4.0"],... |
"""
check_MD5( file_path, checksum )
returns a MD5 hash from a file location.
Note: this converts Int8 representations to comma delimitted strings.
"""
get_MD5( file_path ) = join( string.( open(md5, file_path) ), "," )
"""
check_MD5( file_path, checksum )
Checks the result of an MD5 hash vs a stored checksum... | {"hexsha": "330650ed37a20990c9c9488ce8128d5b21afd4a4", "size": 466, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Validation.jl", "max_stars_repo_name": "caseykneale/ChemometricsData.jl", "max_stars_repo_head_hexsha": "22b2c7b298b4abb94a84737a0aba4e9478959086", "max_stars_repo_licenses": ["MIT"], "max_stars... |
include("header.jl")
@testset "iterate" begin
o = (:delta => 0.01,)
function itr1(w)
total = 0.0
for wi in w; total+=sum(wi); end
return total
end
function itr2(w)
total = 0.0
for (i,wi) in enumerate(w); total+=sum(wi); end
return total
end
fun... | {"hexsha": "e04f3caf1e79b25f8f3f29f8527a7914510ad3c9", "size": 1028, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/iterate.jl", "max_stars_repo_name": "UnofficialJuliaMirror/AutoGrad.jl-6710c13c-97f1-543f-91c5-74e8f7d95b35", "max_stars_repo_head_hexsha": "b6d4e6858f4cd2b520a54a74f10345615aa4013e", "max_sta... |
"""
mutable struct SolidStateDetector{T <: SSDFloat, CS} <: AbstractConfig{T}
CS: Coordinate System: -> :cartesian / :cylindrical
"""
mutable struct SolidStateDetector{T <: SSDFloat, CS} <: AbstractConfig{T}
name::String # optional
inputunits::Dict{String, Unitful.Units}
world::World{T, 3}
config... | {"hexsha": "61c27ca2b8c0fa8a31d1b355702a8650d44731db", "size": 6077, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/DetectorGeometries/SolidStateDetector.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/SolidStateDetectors.jl-71e43887-2bd9-5f77-aebd-47f656f0a3f0", "max_stars_repo_head_hexsha": "9c... |
from __future__ import print_function
import numpy as np
from numgrad import eval_numerical_gradient
def tanh_grad(x):
return 1 - np.tanh(x) ** 2
if __name__ == '__main__':
x = np.array([1.0, 2.1, 0.3, 0.7])
print('tanh', np.tanh(x))
print('tanh_grad', tanh_grad(x))
# Note: eval_numerical_grad... | {"hexsha": "5f41a590721078f271ef36d111fac5fc422a6b15", "size": 553, "ext": "py", "lang": "Python", "max_stars_repo_path": "gradients/tanh.py", "max_stars_repo_name": "eliben/deep-learning-samples", "max_stars_repo_head_hexsha": "d5ca86c5db664fabfb302cbbc231c50ec3d6a103", "max_stars_repo_licenses": ["Unlicense"], "max_s... |
'''Helper class for Monte Carlo Studies for (currently) statistical tests
Most of it should also be usable for Bootstrap, and for MC for estimators.
Takes the sample generator, dgb, and the statistical results, statistic,
as functions in the argument.
Author: Josef Perktold (josef-pktd)
'''
import numpy as np
#c... | {"hexsha": "3b7c8be1ae7ee5eacdced5ceb211f8454beb7818", "size": 6133, "ext": "py", "lang": "Python", "max_stars_repo_path": "scikits/statsmodels/sandbox/tools/mctools.py", "max_stars_repo_name": "matthew-brett/statsmodels", "max_stars_repo_head_hexsha": "915c9dc2d762c5592ac17a7cf5f1cc957fcbde1c", "max_stars_repo_license... |
import torch
import torch.nn as nn
import math, random, sys
from optparse import OptionParser
import pickle
import rdkit
import json
import rdkit.Chem as Chem
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import minimum_spanning_tree
from collections import defaultdict
import copy
import tor... | {"hexsha": "361a42e9d69a8921a44ab73c780149ee2738fcf1", "size": 854, "ext": "py", "lang": "Python", "max_stars_repo_path": "genmol/JTVAE/sample.py", "max_stars_repo_name": "bayeslabs/genmol", "max_stars_repo_head_hexsha": "55837c9c3b7d9f835433db7886bce8b27099e544", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
from datetime import datetime, timedelta
import importlib
import itertools
import warnings
import urllib
import iris
import iris_hypothetic
import pandas as pd
import numpy as np
import tempfile
import boto3
from botocore.handlers import disable_signing
from intake.source.base import DataSource, Schema
from . import... | {"hexsha": "1c2974095646c30b10f4961afa734c8d388cf267", "size": 7191, "ext": "py", "lang": "Python", "max_stars_repo_path": "intake_hypothetic/hypothetic.py", "max_stars_repo_name": "informatics-lab/intake-hypothetic", "max_stars_repo_head_hexsha": "6ebc8b800aac4caa37820e2a5d1a01346fb65ecd", "max_stars_repo_licenses": [... |
\documentclass[a4paper,12pt]{article}
\usepackage{graphicx}
\usepackage{anysize}
\usepackage{multicol}
% USE setspace BEFORE hyperref (for footnote link)
\usepackage{setspace}
\usepackage{hyperref}
\usepackage{spverbatim}
\usepackage{enumitem}
\usepackage[top=0.5in, left=0.75in, bottom=0.5in, right=0.5in, includefoot]... | {"hexsha": "1e1107250010d2efa97682477b689eb942a80ef6", "size": 15205, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Lab_Reports/CG_LAB_4_Report/cg_lab_4_report.tex", "max_stars_repo_name": "Brihat9/CG", "max_stars_repo_head_hexsha": "e6af5a39729fe7def3d6af81fe636f23f23e2bdb", "max_stars_repo_licenses": ["MIT"], ... |
import pandas as pd
import numpy as np
import os
import re
import html
import pyprind
import pickle
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.utils import shuffle
stopwords = stopwords.words('english')
... | {"hexsha": "f4e535eeaf65fcb5b9be022da2370d7877b68c00", "size": 2177, "ext": "py", "lang": "Python", "max_stars_repo_path": "classifier.py", "max_stars_repo_name": "Cranxter/Twitter_Sentiment_Analysis", "max_stars_repo_head_hexsha": "82dfe530579849ad7079b48e5b433b3e28923fb8", "max_stars_repo_licenses": ["MIT"], "max_sta... |
/**
* PluginManager.cpp
*
* Note: Some functions in this file are Cocoa dependent
*
* History:
* David Cox on Fri Dec 27 2002 - Created.
* Paul Jankunas on Wed Mar 23 2005 - Fixed spacing. Fixxed readPlugin
* function so that it doesn't start its search from
* the package dire... | {"hexsha": "1211b2b55e41b590804cd16c459d5a5bafd50b2e", "size": 3814, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "core/Core/PluginServices/PluginServices.cpp", "max_stars_repo_name": "esayui/mworks", "max_stars_repo_head_hexsha": "0522e5afc1e30fdbf1e67cedd196ee50f7924499", "max_stars_repo_licenses": ["MIT"], "m... |
function to_ast(::Type{T}) where {T}
io = IOBuffer()
print(io, T)
str = String(take!(io))
ast = Meta.parse(str)
return ast
end
function to_pascal_case(s::Symbol)
str = String(s)
new_str = ""
next_upper = true
for letter in str
if next_upper
new_str *= uppercase(l... | {"hexsha": "5f0109b83e00f05bf84b884beb96866374af32ca", "size": 2398, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/write_type.jl", "max_stars_repo_name": "quinnj/JSONTypeProvider.jl", "max_stars_repo_head_hexsha": "783a08f79bb4a4dacc929a4003972d4d505234e3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
Subroutine hf3mkr(Axyz,Bxyz,Cxyz,alpha,Gxyz,
& RS,GC,ff,R,R0,IJK,Nabc,Lg,Lg3)
c
c $Id$
c
Implicit none
c::passed
integer Nabc, Lg, Lg3
c--> Cartesian Coordinates for centers a, b, c
Double Precision Axyz(3),Bxyz(3),Cxyz(3) ! [input]
c--> Exponents (1:3,*) and ES prefactors... | {"hexsha": "d3f7b6006830c525d3f27481899735a31e7ebcd5", "size": 4167, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/NWints/int/hf3mkr.f", "max_stars_repo_name": "dinisAbranches/nwchem", "max_stars_repo_head_hexsha": "21cb07ff634475600ab687882652b823cad8c0cd", "max_stars_repo_licenses": ["ECL-2.0"], "max_sta... |
import argparse
import logging
import sys
import os
import yaml
import numpy as np
from dtld_parsing.calibration import CalibrationData
import cv2
from dtld_parsing.driveu_dataset import DriveuDatabase
import matplotlib.pyplot as plt
from PIL import Image
np.set_printoptions(suppress=True)
# Logging
logging.basicC... | {"hexsha": "73ac9d411d113341dabd534a6c7750a7b495ebaf", "size": 3381, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/real_tl_images/SDCN/create_annotations_from_dtld.py", "max_stars_repo_name": "CaoFM/CarND-Capstone", "max_stars_repo_head_hexsha": "fc4d5a14e63d68f8a9946c37438bafe7dda11c70", "max_stars_rep... |
import numpy as np
import pandas as pd
from sklearn.base import TransformerMixin
def getNWindowColNames(col,N):
return [(col+str(i)) for i in range(0,N)]
class SlidingWindowTransformer(TransformerMixin):
def __init__(self,NwindowSize=10):
super().__init__()
assert NwindowSize >= 1, "Window Siz... | {"hexsha": "69d17675e3f90092f92b0b2fd3b5c0dd314a4b4b", "size": 1752, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scikit_Extensions_For_Stocks/sliding_window_tranformer/_sliding_window_tfrmer.py", "max_stars_repo_name": "uNRealCoder/Scikit-Extensions-Stock", "max_stars_repo_head_hexsha": "0968f2319373b9121858... |
from .utils import PyKEArgumentHelpFormatter
import time, urllib
import sys
import numpy as np
from astropy.io import fits as pyfits
from matplotlib import pyplot as plt
from . import kepio
from . import kepmsg
from . import kepkey
__all__ = ['keptrim']
def keptrim(infile, column, row, imsize, outfile=None, kepid=N... | {"hexsha": "af0c43e33edce50edaa4662296dc07823720be7a", "size": 15381, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyke/keptrim.py", "max_stars_repo_name": "ecalifornica/pyke", "max_stars_repo_head_hexsha": "6a3fcc0513cf012044e4420cc4d17064e582d142", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
# Various chirp preprocessing functions to highlight certain features or
# provide the input that Tensorflow expects.
import numpy as np
def raw_normalize(data): # Minmax normalization
normalized_data = (data - data.min(0)) / data.ptp(0)
return normalized_data
def normalize(data): # z-score normalization
... | {"hexsha": "a27efaf20a44f2276599b3c5f9bd8298f2d22b19", "size": 3259, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing.py", "max_stars_repo_name": "rpp0/lora-phy-fingerprinting", "max_stars_repo_head_hexsha": "e53cab65472b60ae66d79a7ce38f30b0929d6178", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
/*
@copyright Louis Dionne 2015
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
#include <boost/hana/assert.hpp>
#include <boost/hana/bool.hpp>
#include <boost/hana/config.hpp>
#include <boost/hana/ext/std/integral_constant.h... | {"hexsha": "24519a3156065440f0041eb88874730c44d58a82", "size": 3506, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "example/logical.cpp", "max_stars_repo_name": "josephwinston/hana", "max_stars_repo_head_hexsha": "a8586ec1812e14e43dfd6867209412aa1d254e1a", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count"... |
# cython: language_level=3
# -*- coding: utf-8 -*-
# *****************************************************************************
# Copyright (c) 2016-2020, Intel Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided th... | {"hexsha": "61f46f95ba417fc44d53e97950ea0250fb4bc3ab", "size": 3286, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/pytest_benchmark/test_random.py", "max_stars_repo_name": "LukichevaPolina/dpnp", "max_stars_repo_head_hexsha": "5f5a679905d237ac7be1cc9ad1075877a9f77e39", "max_stars_repo_licenses": ["B... |
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import tensorflow.compat.v1 as tf
import cvmodel
import math
import os
import utils
tf.disable_v2_behavior()
class classifier:
def __init__(self):
self.img_path = './data/images'
self.anno_... | {"hexsha": "d3649eaa74f278f9a8c05979b76481abdab7d104", "size": 13238, "ext": "py", "lang": "Python", "max_stars_repo_path": "pruning.py", "max_stars_repo_name": "cjuliani/tf-cnn-pruning", "max_stars_repo_head_hexsha": "50514d1575d3121a9548b435c3e911783508234c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, ... |
# adapted from https://stackoverflow.com/a/3753428/11126567
import sys
from PIL import Image
import numpy as np
im = Image.open(sys.argv[1])
im = im.convert('RGBA')
colors = [
[29,43,83],
[126,37,83],
[0,135,81],
[171,82,54],
[95,87,79],
[194,195,199],
[255,241,232],
[255,0,77],
[... | {"hexsha": "d579831cfdf26b22f5d9d30ecc4b414c961973aa", "size": 675, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/maketex.py", "max_stars_repo_name": "DPS2004/picolove-hd", "max_stars_repo_head_hexsha": "a8b5d927e2cb1d9d7cb62b6abf801614c8187fc5", "max_stars_repo_licenses": ["Zlib"], "max_stars_count": ... |
import numpy
from bfio.bfio import BioReader
from bfio.bfio import BioWriter
from skimage import restoration
from skimage import util
# The number of pixels to be saved at a time must be a multiple of 1024.
TILE_SIZE = 1024
def _rolling_ball(tile, ball_radius: int, light_background: bool):
""" Applies the rollin... | {"hexsha": "e1789d38bd2da4f1d4c95814acf22913c4f8879f", "size": 3239, "ext": "py", "lang": "Python", "max_stars_repo_path": "transforms/images/polus-rolling-ball-plugin/src/rolling_ball.py", "max_stars_repo_name": "mmvih/polus-plugins", "max_stars_repo_head_hexsha": "c424938e3f35900758f7d74f3dfec2adfb3228fc", "max_stars... |
import inspect
import uuid
from typing import Optional, Generic, TypeVar, List, Iterator, Callable, Tuple
from collections import deque
T = TypeVar('T')
class Node(Generic[T]):
value: T
parent: Optional['NodeType'] = None
children: Optional[List['NodeType']] = None
def __init__(self, value: T = None,... | {"hexsha": "60c183e5a4a1593c3e07a677f1a256701afc3d8a", "size": 13199, "ext": "py", "lang": "Python", "max_stars_repo_path": "dqo/tree/__init__.py", "max_stars_repo_name": "danield137/deep_query_optimzation", "max_stars_repo_head_hexsha": "01a25c966338007f15d14dea1b37e388e47bcfe3", "max_stars_repo_licenses": ["MIT"], "m... |
// (C) Copyright Gennadiy Rozental 2005-2008.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
// See http://www.boost.org/libs/test for the library home page.
//
// File : $RCSfile$
//
// Version ... | {"hexsha": "4476251d4084b04d1d79b25f1d4cb70694b07750", "size": 14334, "ext": "ipp", "lang": "C++", "max_stars_repo_path": "boost/test/impl/framework.ipp", "max_stars_repo_name": "jonstewart/boost-svn", "max_stars_repo_head_hexsha": "7f6dc0c0cb807b28072c7bdd3d77bb01ab290c59", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
import logging
import neo4j
from numpy.lib.utils import source
import pandas as pd
from neo4j import GraphDatabase
from dku_neo4j.query_templates import (
LOAD_FROM_CSV_PREFIX,
UNWIND_PREFIX,
BATCH_DELETE_NODES,
DELETE_NODES,
SOURCE_MERGE_STATEMENT,
PROPERTIES_STATEMENT,
CREATE_CONSTRAINT_IF... | {"hexsha": "2d9a9a30cd7970f33d7c238cc28ff2aa7221fd02", "size": 25332, "ext": "py", "lang": "Python", "max_stars_repo_path": "python-lib/dku_neo4j/neo4j_handle.py", "max_stars_repo_name": "dataiku/dss-plugin-neo4j", "max_stars_repo_head_hexsha": "f3de1780dfb121eee7dd27518da93cd6b59f1ba7", "max_stars_repo_licenses": ["Ap... |
Although the body of literature on the area of proposed research is not
expansive, there have been a number of relevant studies on the prediction of
forensically relevant categories or quantities of nuclear materials using
statistical methods.
\subsection{Special Nuclear Materials Studied}
With regards to broader fo... | {"hexsha": "165b9e4c13e04a341689b6dfef5a6b5e6e5742d8", "size": 4994, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "document/chapters/litrev/stats4nf.tex", "max_stars_repo_name": "opotowsky/prelim", "max_stars_repo_head_hexsha": "100a27fb533beee1c985ad72ae70bdb646b04bab", "max_stars_repo_licenses": ["CC0-1.0"], "... |
import pandas as pd
import numpy as np
from pathlib import Path
from data_params import Data
import matplotlib.pyplot as plt
import matplotlib
matplotlib.font_manager.findSystemFonts(fontpaths=None, fontext='ttf')
import seaborn as sns
import pycountry
def coef_bar_plot(data, y, filename, fig_size, fig_dpi):
plt.f... | {"hexsha": "0a7c59f9bea01886aee184f7e5bc1face490eccb", "size": 4501, "ext": "py", "lang": "Python", "max_stars_repo_path": "top_features.py", "max_stars_repo_name": "far-from-normal/Kiva-Insight", "max_stars_repo_head_hexsha": "dfcebd4d766b007aeaa3ff6a54f4a28141006f2d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import numpy as np
relationsDic = np.load(os.getcwd() + "../../data/relationDic.npy")
entityDic = dict()
for line in open(os.getcwd() + "../../data/entity.csv"):
triple = line.strip().split(",")
entityDic[triple[0]] = triple[1]
relationsSet = set()
for item in relationsDic:
if int(item[1]) > 10:
... | {"hexsha": "1aa531b30080489a97325926edc52ff0aa78caf9", "size": 854, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dbprocess/cleanRelation.py", "max_stars_repo_name": "AutoKnowledge/AutoKnowledge", "max_stars_repo_head_hexsha": "1a9fce1449d9605dc0289ab13736d073453ed102", "max_stars_repo_licenses": ["Apache-... |
"""Docs"""
import imageio
import numpy as np
from scipy.stats import entropy
import cv2
from _cpbd import compute
class Image:
"""Docs"""
def __init__(self, path: str = "") -> None:
self.path = path
self.image = cv2.imread(self.path)
self.image_gray = cv2.imread(self.path, 0)
... | {"hexsha": "671c6f5199dad4e33d5012305eec215bb9cced77", "size": 2353, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_extraction.py", "max_stars_repo_name": "danrimr/Avance", "max_stars_repo_head_hexsha": "05683f4b9897210331bfb75f207f1c6d195b63d7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
# %%
import sys
sys.path.append("../../src")
sys.path.append("../")
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
from __init__ import *
import datetime
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from lorenz import Lorenz
from traini... | {"hexsha": "0dca4c00b16a8a43c4ac482259bd3a1e77577e02", "size": 5273, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/testcases/h2z_evolution.py", "max_stars_repo_name": "josephbakarji/deep-delay-autoencoder", "max_stars_repo_head_hexsha": "eed23447f930aac140b0f37b888ccab688aa6294", "max_stars_repo_licen... |
!===============================================================================
! One of Andy Nowacki's Fortran utility modules for dealing with seismic
! anisotropy and other problems.
!
! Andy Nowacki <andy.nowacki@bristol.ac.uk>
!
! See the file LICENCE for licence details.
!========================================... | {"hexsha": "34d2cb61822fc9fb754008d53bd70908782f3ab4", "size": 47181, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "spherical_geometry.f90", "max_stars_repo_name": "JackWalpole/seismo-fortran-fork", "max_stars_repo_head_hexsha": "18ba57302ba2dbd39028e6ff55deb448d02bd919", "max_stars_repo_licenses": ["MIT"], ... |
(* Title: HOL/Auth/n_g2kAbsAfter_lemma_inv__13_on_rules.thy
Author: Yongjian Li and Kaiqiang Duan, State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
Copyright 2016 State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
*)
header{*T... | {"author": "lyj238Gmail", "repo": "newParaVerifier", "sha": "5c2d49bf8e6c46c60efa53c98b0ba5c577d59618", "save_path": "github-repos/isabelle/lyj238Gmail-newParaVerifier", "path": "github-repos/isabelle/lyj238Gmail-newParaVerifier/newParaVerifier-5c2d49bf8e6c46c60efa53c98b0ba5c577d59618/examples/n_g2kAbsAfter/n_g2kAbsAft... |
function X = redo_scaling(X_scal,param)
% redo scaling (from scaled data to original data)
%
% INPUT
% X_scal: pretreated data matrix (samples x variables)
% param: output data structure from data_pretreatment routine
%
% OUTPUT
% X: data matrix (samples x variables)
%
% version 1.0 - september 2009
% Davi... | {"author": "kmansouri", "repo": "OPERA", "sha": "fcbe8024c01f49cd9498187c0ff8c5c45d6dc833", "save_path": "github-repos/MATLAB/kmansouri-OPERA", "path": "github-repos/MATLAB/kmansouri-OPERA/OPERA-fcbe8024c01f49cd9498187c0ff8c5c45d6dc833/OPERA_Source_code/redo_scaling.m"} |
struct Pnmp1toPlm{T} <: AbstractRotation{T}
rotations::Vector{Givens{T}}
end
function Pnmp1toPlm(::Type{T}, n::Int, m::Int, α::T, β::T, γ::T) where T
G = Vector{Givens{T}}(n)
@inbounds for ℓ = 1:n
c = sqrt((2m+β+γ+2)/(ℓ+2m+β+γ+2)*(2ℓ+2m+α+β+γ+2)/(ℓ+2m+α+β+γ+2))
s = sqrt(ℓ/(ℓ+2m+β+γ+2)*(ℓ+α)... | {"hexsha": "dc647630f5efd7312e10d845d6880a913f6e18f5", "size": 4885, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TriangularHarmonics/slowplan.jl", "max_stars_repo_name": "putianyi889/FastTransforms.jl", "max_stars_repo_head_hexsha": "491716260d0b8de4aa3b9bab15c98a8f8ce59970", "max_stars_repo_licenses": ["... |
import os
import tqdm
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from src.data.dataset import ImageDataset
from src.losses.loss import PerceptualLoss, ColorLoss
class Trainer():
def __init__(self,device,d... | {"hexsha": "472864f24173b897505a0a5bcd4a445cc89d8456", "size": 4919, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/training/trainer.py", "max_stars_repo_name": "shregar1/SKETCH_TO_ANIME", "max_stars_repo_head_hexsha": "7a1eae54c93ea26201e8bc6d6580834d223c15af", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: utf-8 -*-
"""
DENSITY MATRIX PROPAGATOR
"""
import numpy
from .dmevolution import DensityMatrixEvolution
class DMPropagator:
def __init__(self, timeaxis, ham):
self.timeaxis = timeaxis
self.ham = ham
self.Odt = self.timeaxis.data[1]-self.timeaxis.d... | {"hexsha": "520688a3d2297886ce04c2cbae3d906b10a6605d", "size": 1583, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantarhei/qm/propagators/dmpropagator.py", "max_stars_repo_name": "slamavl/quantarhei", "max_stars_repo_head_hexsha": "d822bc2db86152c418e330a9152e7866869776f7", "max_stars_repo_licenses": ["MIT"... |
"""
Scatterplot with marginal ticks
===============================
_thumb: .68, .32
"""
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", color_codes=True)
# Generate a random bivariate dataset
rs = np.random.RandomState(9)
mean = [0, 0]
cov = [(1, 0), (0, 2)]
x, y = rs.... | {"hexsha": "e8389ac2a21a682ceca31bd3e3e3798aace87f6f", "size": 556, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/marginal_ticks.py", "max_stars_repo_name": "berkeleyapplied/seaborn", "max_stars_repo_head_hexsha": "1b087a3a574922220d77b6219af99280905718f8", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
Require Import
CoRN.stdlib_omissions.List Coq.Numbers.Natural.Peano.NPeano
Coq.QArith.QArith Coq.QArith.Qabs
CoRN.model.totalorder.QposMinMax
CoRN.model.metric2.Qmetric
Coq.Program.Program
CoRN.stdlib_omissions.N
CoRN.stdlib_omissions.Z
CoRN.stdlib_omissions.Q.
Set Automatic Introduction.
Set Automati... | {"author": "coq-community", "repo": "corn", "sha": "cfbf6b297643935f0fe7e22d2b14b462bf7e3095", "save_path": "github-repos/coq/coq-community-corn", "path": "github-repos/coq/coq-community-corn/corn-cfbf6b297643935f0fe7e22d2b14b462bf7e3095/util/Qsums.v"} |
The Andrew Donnell Tree honors the memory of Andrew Douglas Donnell. It sits near the fire circle on the western end of the arboretum.
| {"hexsha": "d9d1fd722774da53c6b5bfb9efe355186a9ff7c0", "size": 137, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Andrew_Donnell_Tree.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
submodule (io:plasma_output) plasma_output_nc
use timeutils, only : date_filename
use nc4fortran, only: netcdf_file
implicit none (type, external)
contains
module procedure output_root_stream_mpi_nc4
!! COLLECT OUTPUT FROM WORKERS AND WRITE TO A FILE USING STREAM I/O.
!! STATE VARS ARE EXPECTED INCLUDE GHOST CELLS... | {"hexsha": "4450b4d156ee870b30cee6aa1187c167c21b32ff", "size": 6367, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/io/plasma_output_ncdf.f90", "max_stars_repo_name": "jklenzing/gemini", "max_stars_repo_head_hexsha": "aed028fbb6f6187ca951e4cd243fdec929514b2a", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
Load LFindLoad.
Load LFindLoad.
From adtind Require Import goal8.
From lfind Require Import LFind.
Require Import Extraction.
Extract Inductive nat => nat [ "(O)" "S" ].
Extract Inductive list => list [ "Nil" "Cons" ].
Extraction "/home/yousef/lemmafinder/benchmark/_lfind_clam_lf_goal8_drop_Cons_assoc_34_drop_Cons/goa... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal8_drop_Cons_assoc_34_drop_Cons/lfin... |
import numpy as np
from objects.misc.default_functions import DefaultFunctions
class Problem(DefaultFunctions):
'''
Parametes object. Effectively represents each problem that we want to submit to the solver.
Refer to objects/default_functions.py for the meaning of each parameter.
'''
def __init_... | {"hexsha": "950fd85d186081cd6c21211e8f9b654aad5a8aec", "size": 1772, "ext": "py", "lang": "Python", "max_stars_repo_path": "problems/problem.py", "max_stars_repo_name": "alessandrobalata/pyrlmc", "max_stars_repo_head_hexsha": "493d1ad5378823a9bbb032077bea2838db76602c", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import pytest
from tridesclous import *
import numpy as np
import scipy.signal
import time
import os
import shutil
from tridesclous.dataio import DataIO
from tridesclous.catalogueconstructor import CatalogueConstructor
from tridesclous import Peeler
from tridesclous.peeler_cl import Peeler_OpenCl
from tridesclous.p... | {"hexsha": "54fd38f372713089032e6340558cd94f3e585799", "size": 12805, "ext": "py", "lang": "Python", "max_stars_repo_path": "tridesclous/tests/test_peeler.py", "max_stars_repo_name": "caniko/tridesclous", "max_stars_repo_head_hexsha": "9f412a42697561e3c7d8e3a35249cd13240239a0", "max_stars_repo_licenses": ["MIT"], "max_... |
import os
import numpy as np
from tqdm import tqdm
sys.path.insert(0, 'classes')
from DomainSegmentor import *
# TODO make parameters commandline accessible.
eval_dir = 'nhlrc3_set2'
target_class_idx = [8, 9, 10, 11]
include_max = True # Include results using the max of the selected classes.
# Generate path list.
pa... | {"hexsha": "ef0c28b471094cd1952442412943c819c1ccdbeb", "size": 2132, "ext": "py", "lang": "Python", "max_stars_repo_path": "custom/evaluate_design_set.py", "max_stars_repo_name": "egurapha/prot_domain_segmentor", "max_stars_repo_head_hexsha": "407ae9f5ff37ae20a32f07dd46b85ef8201659e1", "max_stars_repo_licenses": ["MIT"... |
import io
import logging
import numpy as np
import pandas as pd
import core.artificial_signal_generators as casgen
import core.config as cconfig
import core.dataflow.nodes.test.helpers as cdnth
import core.dataflow.nodes.transformers as cdnt
import helpers.unit_test as hut
_LOG = logging.getLogger(__name__)
class ... | {"hexsha": "4312a7bddb78b2bf8d3059b806c9e5d522ed1af6", "size": 7848, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/dataflow/nodes/test/test_transformers.py", "max_stars_repo_name": "ajmal017/amp", "max_stars_repo_head_hexsha": "8de7e3b88be87605ec3bad03c139ac64eb460e5c", "max_stars_repo_licenses": ["BSD-3-... |
// Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: Apache-2.0
// GameKit
#include <aws/gamekit/core/model/account_info.h>
// Boost
#include <boost/algorithm/string/case_conv.hpp>
std::string GameKit::TruncateAndLower(const std::string& str, const std::regex& pattern)
{
... | {"hexsha": "5ad3fa60d5962a5164aad65f24f6ca30f6373c54", "size": 2154, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "aws-gamekit-core/source/aws/gamekit/core/model/account_info.cpp", "max_stars_repo_name": "aws/aws-gamekit", "max_stars_repo_head_hexsha": "7ea5a9067c53a70ed279e1870008cf3a6d8d3b2e", "max_stars_repo_... |
module SBML
using SBML_jll, Libdl
using SparseArrays
using Symbolics
using IfElse
using Unitful
include("types.jl")
include("structs.jl")
include("version.jl")
include("converters.jl")
include("math.jl")
include("readsbml.jl")
include("symbolics.jl")
include("utils.jl")
sbml(sym::Symbol) = dlsym(SBML_jll.libsbml_ha... | {"hexsha": "def10757deaa7a07ab85bda257b7aa5d5626d5dd", "size": 461, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SBML.jl", "max_stars_repo_name": "paulflang/SBML.jl", "max_stars_repo_head_hexsha": "5ca3e1988bff7009e410fb315f0bc360c7476098", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null... |
#!/usr/bin/env python
from __future__ import division, absolute_import, print_function
import numpy as np
import scipy.stats as stats
__all__ = ['lhs']
def lhs(dist, param, nsample):
"""
Latin Hypercube Sampling of any distribution without correlations
after Stein (1987).
Definition
----------... | {"hexsha": "ab0d5e4d438943d941fa9ec0d088fde3e55d0ce2", "size": 5134, "ext": "py", "lang": "Python", "max_stars_repo_path": "jams/lhs.py", "max_stars_repo_name": "mcuntz/jams_python", "max_stars_repo_head_hexsha": "41b4504d2f55a77a7876fc6d146e4eb91dd8b2b9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_... |
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