text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
SUBROUTINE DUMOD5
C
C MSFC ROUTINE, TO CONVERT NASTRAN TABULAR DATA BLOCKS INTO 2-
C DIMENSIONAL DATA BLOCKS (S.P. REAL ONLY) FOR CONVENIENCE IN
C MANIPULATION AND OUTPUT, SPECIALLY TO BE USED WITH OUTPUT5 AND
C INPUT5.
C
C THIS VERSION WAS MODIFIED BY R. MOORE/MSFC IN JAN. 1989
C ... | {"hexsha": "f0f335098f2c8b0483af8956c663a2d5259f6a3b", "size": 15241, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "mis/dumod5.f", "max_stars_repo_name": "ldallolio/NASTRAN-95", "max_stars_repo_head_hexsha": "6d2c175f5b53ebaec4ba2b5186f7926ef9d0ed47", "max_stars_repo_licenses": ["NASA-1.3"], "max_stars_count":... |
function [Ep,Cp,Eh,F,L,dFdp,dFdpp] = spm_nlsi_GN(M,U,Y)
% Bayesian inversion of nonlinear models - Gauss-Newton/Variational Laplace
% FORMAT [Ep,Cp,Eh,F] = spm_nlsi_GN(M,U,Y)
%
% [Dynamic] MIMO models
%__________________________________________________________________________
%
% M.IS - function name f(P,M,U) - generat... | {"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/spm_nlsi_GN.m"} |
from collections.abc import Iterable
import calendar
import cftime
import numpy as np
import xarray as xr
# units
mwCO2 = 44.
mwC = 12.01
mwAir = 28.966
mon_per_year = 12
g_per_Pg = 1e-15
g_per_kg = 1e3
d_per_yr = 365.25
s_per_d = 86400.
molC_to_PgC = g_per_Pg * mwC
kgCmon_to_PgCyr = mon_per_year * g_per_kg * g_... | {"hexsha": "a7d238df511603134effb2ea687558738ead6c5c", "size": 10096, "ext": "py", "lang": "Python", "max_stars_repo_path": "so-co2-airborne-obs/models/calc.py", "max_stars_repo_name": "mgrover1/so-co2-airborne-obs", "max_stars_repo_head_hexsha": "254945a4fcad7817cc0874e5ff53fb692daabadf", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python3
"""
Copyright (C) 2018-2020 Intel Corporation
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applic... | {"hexsha": "90a506cce2ced12492972d99f8011a25e2eecb07", "size": 11322, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/segmentation_demo/python/segmentation_demo.py", "max_stars_repo_name": "APrigarina/open_model_zoo", "max_stars_repo_head_hexsha": "b1ff98b64a6222cf6b5f3838dc0271422250de95", "max_stars_repo... |
#!/usr/bin/env python
from __future__ import division
from __future__ import print_function
import datetime
import numpy as np
import sys
import tensorflow as tf
import tensorflow.contrib.metrics as tf_metrics
import tensorflow.contrib.layers as tf_layers
import tensorflow.contrib.seq2seq as tf_seq2seq
import morpho... | {"hexsha": "338c591776f18cc40f66c3243e016bf89628a641", "size": 29081, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/posteditor.py", "max_stars_repo_name": "varisd/MLFix", "max_stars_repo_head_hexsha": "383d3c71e57eaa0d0829624f6d0d890f9c720567", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
[STATEMENT]
lemma bind_returns_result_E2:
assumes "h \<turnstile> f \<bind> g \<rightarrow>\<^sub>r y" and "pure f h"
obtains x where "h \<turnstile> f \<rightarrow>\<^sub>r x" and "h \<turnstile> g x \<rightarrow>\<^sub>r y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>x. \<lbrakk>h \<turnstile> f \<ri... | {"llama_tokens": 468, "file": "Core_DOM_common_preliminaries_Heap_Error_Monad", "length": 2} |
import numpy as np
import torch
'''
Mixup code from https://github.com/facebookresearch/mixup-cifar10
'''
def mixup_data(x, y, alpha=1.0, use_cuda=True):
'''Returns mixed inputs, pairs of targets, and lambda'''
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size =... | {"hexsha": "ee0a0be286acf426926735334deecc21ecd0f2f7", "size": 6844, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "DiscoBroccoli/Classification-of-Image-Data", "max_stars_repo_head_hexsha": "c415f4a220b47efc00f618b760ffa9c52754d6c6", "max_stars_repo_licenses": ["MIT"], "max_s... |
from functools import partial
import numpy as np
from scipy.special import gammainc
from sklearn.neighbors import KernelDensity
from sklearn.utils.extmath import row_norms
from sklearn.utils import check_random_state
from sklearn.model_selection import GridSearchCV
from scipy.stats import multivariate_normal
from mel... | {"hexsha": "e1af07a7a7ace65c32a8f0d6d44e444c2e6cc884", "size": 5217, "ext": "py", "lang": "Python", "max_stars_repo_path": "melime/generators/kde_gen.py", "max_stars_repo_name": "elian204/melime", "max_stars_repo_head_hexsha": "aef885fa4b6b02f7bf7294140d78a85fe546b622", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
module pub
implicit none
integer yn,kn,ne,N,enn,hn
real eta,dk,de
parameter(yn = 50,hn = 4,kn = 50, ne = 50,N = yn*4,eta = 0.01,dk = 0.01,de = dk)
real,parameter::pi = 3.1415926535
complex,parameter::im = (0.,1.0)
complex Ham(N,N),one(N,N)
!=================================
real ... | {"hexsha": "451575a7b5f1cf5ae40c3ab57ab6555c97dca52b", "size": 8079, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "assets/data/bhz.f90", "max_stars_repo_name": "hongyi-zhao/yxli8023.github.io", "max_stars_repo_head_hexsha": "4fec5ff3ddbf89e84796148aee86cf93d293a99f", "max_stars_repo_licenses": ["MIT"], "max_... |
module GradDescent
using LinearAlgebra
export
Optimizer,
VanillaGradDescent,
Inversedecay,
Momentum,
Adagrad,
Adadelta,
RMSprop,
Adam,
Adamax,
Nadam,
update,
t
include("AbstractOptimizer.jl")
include("VanillaGradDescent.jl")
include("InverseDecayOptimizer.jl")
include("... | {"hexsha": "3d867d6e6785a8a2fc119040ede69851326c013f", "size": 538, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GradDescent.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/GradDescent.jl-e1397348-e965-55d8-8fb3-3dd9faf6e4f1", "max_stars_repo_head_hexsha": "a3b2c35983a3cf5c88e1f3bb0df4f43bbacb4... |
#include <boost/units/physical_dimensions/inductance.hpp>
| {"hexsha": "35e1c9a88e631aae998a3410af83c70b0095ef8e", "size": 58, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_units_physical_dimensions_inductance.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licen... |
'''Train Basic CIFAR-10 model'''
from __future__ import print_function
import logging
import os
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
from lib.cifar_resnet import *
from lib.dataset_utils import *
def evaluate(net, dataloader, criteri... | {"hexsha": "8a889ca6bf55a9e59d5d4ec2b81e18dc5bee8147", "size": 5093, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_scripts/train_cifar10.py", "max_stars_repo_name": "chawins/entangle-rep", "max_stars_repo_head_hexsha": "3e9e0d6e7536b0de0e35d7f8717f2ccc8e887759", "max_stars_repo_licenses": ["MIT"], "max_s... |
from semeval2020.language_models.bertwrapper import BertWrapper
from semeval2020.data_loader.sentence_loader import SentenceLoader
from semeval2020.util import preprocessing
from semeval2020.factory_hub import config_factory
import numpy as np
import torch
import os.path
import tqdm
#################################... | {"hexsha": "b07cd0204998dbad4f1a01ef81edf3b9adb8f2e4", "size": 2765, "ext": "py", "lang": "Python", "max_stars_repo_path": "semeval2020/main/compute_bert_embeddings.py", "max_stars_repo_name": "DavidRother/semeval2020-task1", "max_stars_repo_head_hexsha": "715f82afb8b282669d59ff610b63714d19db4618", "max_stars_repo_lice... |
//////////////////////////////////////////////////////////////////////////////
//
// Released under MIT License
// Copyright (c) 2020 Hernan Perrone (hernan.perrone@gmail.com)
//////////////////////////////////////////////////////////////////////////////
#include <fstream>
#include <iostream>
#include <iomanip>
#inclu... | {"hexsha": "f3ab9b186739241831c1b7078f4694b97408ee27", "size": 1186, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/ft_utils.cpp", "max_stars_repo_name": "hperrone/file_transfer", "max_stars_repo_head_hexsha": "303e3c139c814685ffc7b13d1a1fc8f03c678fa8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
"""dask kernels for :mod:`xarray_extras.interpolate`
.. codeauthor:: Guido Imperiale
"""
from typing import Iterable, Optional, Tuple, Union
import numpy as np
from scipy.interpolate import BSpline, make_interp_spline
from scipy.interpolate._bsplines import _as_float_array, _augknt, _not_a_knot
def _memoryview_safe... | {"hexsha": "2c957e9917123e3431c8a4d5fb24ff010cd85115", "size": 4660, "ext": "py", "lang": "Python", "max_stars_repo_path": "xarray_extras/kernels/interpolate.py", "max_stars_repo_name": "crusaderky/xarray_extras", "max_stars_repo_head_hexsha": "d82be4dab330a2a00cd7664a8dd77b60d870cee5", "max_stars_repo_licenses": ["Apa... |
The Davis Daytime Toastmasters Community Organizations club is a safe environment for you to become the Public Speaking speaker and leader you want to be. As of September 2013, we are a mix of beginning and intermediate Toastmasters, with about 8 to 10 members in attendance each week.
The Davis Daytime Toastmasters ... | {"hexsha": "ca8eb2a29d8b6e7910737385d17081331eba977d", "size": 1605, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Davis_Daytime_Toastmasters.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "m... |
#script was heavily based on ipython notebook created by Steve Crawford
import glob
import os
import numpy as np
from astropy.io import fits
from astropy import units as u
from ccdproc import CCDData
from pyhrs import create_masterbias
from pyhrs import create_masterflat
from pyhrs import normalize_image
from pyhrs... | {"hexsha": "9a340373685813897753b25b70e22fa35f66624b", "size": 10761, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/make_calibrationframes.py", "max_stars_repo_name": "crawfordsm/pyhrs", "max_stars_repo_head_hexsha": "b1eeca635a41791e17ce0c5529b427245bded341", "max_stars_repo_licenses": ["BSD-3-Clause"... |
module Editor where
open import Agda.Builtin.FromNat
open import BasicIO
open import Data.Bool
open import Data.Char
open import Data.List hiding (_++_)
open import Data.String
open import Data.Unit
open import Function
open import Int
open import Terminal
readTimeout : Int
readTimeout = 0
readMinChars : Int
readMin... | {"hexsha": "f651415abb1a2e7ce114557ade0f969f8f336c9f", "size": 1247, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Editor.agda", "max_stars_repo_name": "cruhland/agda-editor", "max_stars_repo_head_hexsha": "c5ffd117f6d5a98f7c68a2a6b9be54a150c70945", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Oct 13 12:43:45 2021
@author: cadecastro.com
"""
import numpy as np
import matplotlib.pyplot as plt
#Caudal máximo:
Qmax=0.050#[m^3/s] Caudal máximo a simular
N=int(100)#Puntos de caudal a simular
#Geometría ducto:
D=0.1016#[m] Diámetro (hidráulico si ... | {"hexsha": "b899d8cc40f84f5e4bd45fd5b12fd1858e4f0857", "size": 2148, "ext": "py", "lang": "Python", "max_stars_repo_path": "curva_bombeo.py", "max_stars_repo_name": "cadecastro/hidraulica", "max_stars_repo_head_hexsha": "1670e00d27fdf5a36d0b7bab7bf0fa9f7650e0a3", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_count... |
Require Import Category4.
Require Import Category5.
(* given a Category5 we define the data necessary to create a Category4 *)
Definition Obj4_ (C:Category5) : Type := Obj C.
Inductive Mor4_ (C:Category5) : Type :=
mor4_ : forall (a b:Obj C), Hom a b -> Mor4_ C.
Arguments mor4_ {C} _ _ _.
Definition dom4_ (C... | {"author": "possientis", "repo": "Prog", "sha": "0144f74338b9d35a2983e8956f10e615ed26b8cb", "save_path": "github-repos/coq/possientis-Prog", "path": "github-repos/coq/possientis-Prog/Prog-0144f74338b9d35a2983e8956f10e615ed26b8cb/coq/cat/Category5AsCategory4.v"} |
""" Visualization code for MuJoCo Maze.
"""
import itertools as it
from typing import List, Optional, Tuple
from gym.spaces import Box
import numpy as np
import torch
from mujoco_maze.maze_env import MazeEnv
import rainy
from vis_utils import get_n_row_cols, ValueHeatMap, Trajectory
from rainy.lib.hooks import Eval... | {"hexsha": "5b50e492f91ace53fe986ed290c8c1310c5e9f72", "size": 10338, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/vis_mjmaze.py", "max_stars_repo_name": "kngwyu/infomax-option-critic", "max_stars_repo_head_hexsha": "9d907c041c1d0280db9b23eb2fdf9e0033e33bf3", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import os
import unittest
if not os.getcwd().endswith('Tests'):
os.chdir('Tests')
import numpy as np
from PIL import Image
from VSR.Util import imread, rgb_to_yuv
from VSR.Backend import BACKEND
URL = 'data/set5_x2/img_001_SRF_2_LR.png'
class ImageTest(unittest.TestCase):
def psnr(self, x: np.ndarray, y: np.nd... | {"hexsha": "5b306cbe8dded471403700fd4255c8fcb2ff363b", "size": 3343, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tests/image_test.py", "max_stars_repo_name": "Kadantte/VideoSuperResolution", "max_stars_repo_head_hexsha": "4c86e49d81c7a9bea1fe0780d651afc126768df3", "max_stars_repo_licenses": ["MIT"], "max_sta... |
"""
Mask R-CNN Copyright (c) 2017 Matterport, Inc.
Train on the CElegans segmentation dataset
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
Modified for CElegans fitness assessment by Krzysztof Fiok
------------------------------------------------------------
Example use:
python3 c... | {"hexsha": "552289040588bcf8bd8f8524693926bdfd44fbdd", "size": 13624, "ext": "py", "lang": "Python", "max_stars_repo_path": "ce_segmentation.py", "max_stars_repo_name": "krzysztoffiok/c_elegans_fitness", "max_stars_repo_head_hexsha": "d16270f882890aa42df55739d9bb0efc2e2168e6", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
This expirment tests whether or not the magnetic field affects the objects in
the LEO by correlating the resultant intensity of the magnetic field with the
accelertaion and angular velocity of the ISS
"""
""" Importing the libraries and the modules """
from datetime import datetime,timedelta
import magn
import ... | {"hexsha": "fb08a6f9974bf70839b795548f6f1c4e247689f0", "size": 5874, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment-2.py", "max_stars_repo_name": "RoboneClub/Mechabot-Analysis", "max_stars_repo_head_hexsha": "c81791f9b5333bb88cb1378697872d4840b56455", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
from dsbox.template.template import DSBoxTemplate
from d3m.metadata.problem import TaskKeyword
from dsbox.template.template_steps import TemplateSteps
from dsbox.schema import SpecializedProblem
import typing
import numpy as np # type: ignore
class SRIClassificationTemplate(DSBoxTemplate):
def __init__(self... | {"hexsha": "c978b614564b15ad98ff9be9b231eda20bb8f13d", "size": 6405, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/dsbox/template/template_files/loaded/SRIClassificationTemplate.py", "max_stars_repo_name": "usc-isi-i2/dsbox-ta2", "max_stars_repo_head_hexsha": "85e0e8f5bbda052fa77cb98f4eef1f4b50909fd2", ... |
module SeqAdaptiveIS
using StatsBase, Random
# Struct for holding results of inference.
struct MCPosterior
samples::AbstractMatrix
logW::Vector
end
StatsBase.weights(P::MCPosterior) = softmax(P.logW)
# resample(P::MCPosterior, N::Int) = P.samples[rand(Categorical(weights(P)), N),:]
resample(rng::AbstractRNG,... | {"hexsha": "910f812653e294f9cf640eb58b1323bde65524e8", "size": 1023, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/SeqAdaptiveIS.jl", "max_stars_repo_name": "ornithos/SeqAdaptiveIS", "max_stars_repo_head_hexsha": "b01ef2e24e6cab19549382e159f8ddc7a807a9bc", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
// Copyright (C) 2020 T. Zachary Laine
//
// 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)
// Warning! This file is autogenerated.
#include <boost/text/bidirectional.hpp>
#include "bidi_tests.hpp"
#include <gte... | {"hexsha": "704b87cc9af448c45732a2bdb5a1b658b9d2f6df", "size": 961049, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/generated/bidi_test_570.cpp", "max_stars_repo_name": "Ryan-rsm-McKenzie/text", "max_stars_repo_head_hexsha": "15aaea4297e00ec4c74295e7913ead79c90e1502", "max_stars_repo_licenses": ["BSL-1.0"]... |
import os
import json
import sys
import numpy as np
from xml.dom.minidom import Document
import xml.dom.minidom
sys.path.append('../../..')
from utils.tools import makedirs
from libs.utils.coordinate_convert import backward_convert, forward_convert
def make_xml(filename, path, box_list, labels, w, h, d):
# dic... | {"hexsha": "c5ca4fe447b2ec794ed11dd40fda602013ade1a8", "size": 4957, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloader/dataset/HRSID/json2xml.py", "max_stars_repo_name": "Artcs1/RotationDetection", "max_stars_repo_head_hexsha": "095be17345ee9984d8de8f24eb6b5a0b2d764a06", "max_stars_repo_licenses": ["Apa... |
"""
Gaussian likelihood module for shear bandpowers.
"""
import glob
import os.path
import time
import warnings
import numpy as np
def mvg_logpdf_fixedcov(x, mean, inv_cov):
"""
Log-pdf of the multivariate Gaussian distribution where the determinant and inverse of the covariance matrix are
precomputed a... | {"hexsha": "b8ba5c723566ddcdc274fdd23c2a658b0917431d", "size": 11429, "ext": "py", "lang": "Python", "max_stars_repo_path": "shear_pcl_cov/likelihood.py", "max_stars_repo_name": "robinupham/shear_pcl_cov", "max_stars_repo_head_hexsha": "6afc8bb48f714b87d4b7143575033b9723ef9df4", "max_stars_repo_licenses": ["MIT"], "max... |
[STATEMENT]
theorem harry_sum_closed_form: "harry_sum n = n * (n + 1) * 2 ^ n div 4"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. harry_sum n = n * (n + 1) * 2 ^ n div 4
[PROOF STEP]
using harry_sum_closed_form_aux[of n]
[PROOF STATE]
proof (prove)
using this:
4 * harry_sum n = n * (n + 1) * 2 ^ n
goal (1 subgoal... | {"llama_tokens": 193, "file": "IMO2019_IMO2019_Q5", "length": 2} |
import random
import numpy as np
import torch
def get_patch(*args, patch_size=48, scale=2):
ih, iw = args[0].shape[:2]
ip = patch_size
tp = ip * scale
ix = random.randrange(0, iw - ip + 1)
iy = random.randrange(0, ih - ip + 1)
tx, ty = scale * ix, scale * iy
ret = [
args[0][iy:i... | {"hexsha": "20465cda10f50dc996580da4a35e6ba6bd13174e", "size": 1311, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/common.py", "max_stars_repo_name": "JintaoLee-Roger/SeismicSuperResolution", "max_stars_repo_head_hexsha": "d89cde2d88f6e523c4133776b40a4987ebbaa880", "max_stars_repo_licenses": ["MIT"], ... |
\chapter{Moves in Detail}
\index{Moves in Detail} \index{Moves} \index{Detail}
| {"hexsha": "0601652ba9bf11a0fb9872417f46eea0f6b6d750", "size": 89, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "tex/dwpocket_038_Moves_in_Detail.tex", "max_stars_repo_name": "maxlambertini/DungeonWorld-ConTeXt", "max_stars_repo_head_hexsha": "3066435d403380cd603436bc0523a04c874c03f1", "max_stars_repo_licenses":... |
import os
import cv2
import numpy as np
from os.path import join, dirname
# Trained XML car classifier that describes some features of cars which we want to detect
cascade_file = join(dirname(__file__), "haarcascade_car.xml")
cars_cascade = cv2.CascadeClassifier(cascade_file)
videos_directory = join(os.getcwd(), "vide... | {"hexsha": "03ef973a9345dd8b96d75ef6f07b72387c9b1dec", "size": 1720, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/car_detection/car_detect.py", "max_stars_repo_name": "DanNduati/Parking-Management-System", "max_stars_repo_head_hexsha": "0bd9c254c49f9685b4442fbec43e36b5fb2b471b", "max_stars_repo_licenses... |
#!/bin/julia
include("./lib/core/eq-core.jl")
include("./lib/core/score-engine.jl")
include("./lib/core/dealer.jl")
using CSV
using DelimitedFiles
chan = Channel(256)
function main(args)
num_players = parse(Int, args[1])
trials = parse(Int, args[2])
player_range = CSV.read(args[3])
output = args[4]
... | {"hexsha": "36ce7766755cc3de16eb9836f8db9b6de158797a", "size": 3299, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "EQPoker.jl", "max_stars_repo_name": "ttowncompiled/EQPoker", "max_stars_repo_head_hexsha": "6a984b2e1d114e8437b3af11392cff68adda32dc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
;; ---------------------------------------------------------------------
;; text_manipulate.jl
;;
;; Mar/02/2011
;;
;; --------------------------------------------------------------------
(require 'tables)
;; --------------------------------------------------------------------
(defun dict_display_proc_single (key u... | {"hexsha": "039a5a6e58b438640daba00af3a5395baa84f7e7", "size": 3567, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "common/rep_common/text_manipulate.jl", "max_stars_repo_name": "ekzemplaro/data_base_language", "max_stars_repo_head_hexsha": "e77030367ffc595f1fac8583f03f9a3ce5eb1611", "max_stars_repo_licenses": [... |
### A Pluto.jl notebook ###
# v0.12.15
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
lo... | {"hexsha": "75f8c088fce4a9a8827a12e3596b2a89e45087da", "size": 7311, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "notebooks/Plotting.jl", "max_stars_repo_name": "MathAero/IndEco", "max_stars_repo_head_hexsha": "b4179ee32fed85f87931b8f12afe6bee24113879", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
#!/usr/bin/python
# Copyright 2020 Makani Technologies 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 applicabl... | {"hexsha": "87eeeefc15220e8943b53c0d3d3db646da08f48a", "size": 8037, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/flight_data/h5_to_google_earth.py", "max_stars_repo_name": "leozz37/makani", "max_stars_repo_head_hexsha": "c94d5c2b600b98002f932e80a313a06b9285cc1b", "max_stars_repo_licenses": ["Apache-... |
import torch
import torch.nn as nn
from timm.models.layers import DropPath, trunc_normal_
from .dgcnn_group import DGCNN_Grouper
import numpy as np
from knn_cuda import KNN
knn = KNN(k=8, transpose_mode=False)
def get_knn_index(coor_q, coor_k=None):
coor_k = coor_k if coor_k is not None else coor_q
# coor: b... | {"hexsha": "55e8aaade17ab386bc9fac7914015780168c6e96", "size": 15397, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/PoinTr/models/Transformer.py", "max_stars_repo_name": "Sakura176/PointRCNN", "max_stars_repo_head_hexsha": "a7fbb25e931609a39c32cb821a7c98a326e8b0c0", "max_stars_repo_licenses": ["MIT"], "max... |
#%%
import pandas as pd
import numpy as np
import re
import yaml
from azure.storage.blob import BlobClient
acceptable_image_types = ["png", "jpg"]
params = yaml.safe_load(open("../blob_keys.yaml"))
def get_blob(name):
blob = BlobClient.from_connection_string(
conn_str=params["conn_str"],
contain... | {"hexsha": "6addd2a24e7650dba717e0c824ba52eb2b95a9ef", "size": 1343, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/upload_pictures.py", "max_stars_repo_name": "BillmanH/opensocial", "max_stars_repo_head_hexsha": "f6a2d7f6792607a33eebd3a09b3f20e846e32fa4", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
[STATEMENT]
lemma Lindelof_space_perfect_map_image:
"\<lbrakk>Lindelof_space X; perfect_map X Y f\<rbrakk> \<Longrightarrow> Lindelof_space Y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Lindelof_space X; perfect_map X Y f\<rbrakk> \<Longrightarrow> Lindelof_space Y
[PROOF STEP]
using Lindelof_space_q... | {"llama_tokens": 279, "file": null, "length": 2} |
-makelib ies_lib/xil_defaultlib -sv \
"/data/opt/Xilinx/Vivado/2017.4/data/ip/xpm/xpm_cdc/hdl/xpm_cdc.sv" \
"/data/opt/Xilinx/Vivado/2017.4/data/ip/xpm/xpm_fifo/hdl/xpm_fifo.sv" \
"/data/opt/Xilinx/Vivado/2017.4/data/ip/xpm/xpm_memory/hdl/xpm_memory.sv" \
-endlib
-makelib ies_lib/xpm \
"/data/opt/Xilinx/Vivado/... | {"hexsha": "f3b71a72a87f835d9bbb1433532d28c0dab3d41d", "size": 10240, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "riscvonpynq/flute/tcm/flute/flute.ip_user_files/sim_scripts/flute/ies/run.f", "max_stars_repo_name": "indirajhenny/RISC-V-On-PYNQ", "max_stars_repo_head_hexsha": "d27bbd6fb037b63034956b4065e55c8e... |
from __future__ import print_function
import numpy as np
from discretize import utils
from .base import BaseRectangularMesh, BaseTensorMesh
from .View import TensorView
from .DiffOperators import DiffOperators
from .InnerProducts import InnerProducts
from .MeshIO import TensorMeshIO
class TensorMesh(
BaseTensor... | {"hexsha": "5407f1cde1052acaf3bebbb15ef60aa5d3b3bf79", "size": 13464, "ext": "py", "lang": "Python", "max_stars_repo_path": "discretize/TensorMesh.py", "max_stars_repo_name": "bluetyson/discretize", "max_stars_repo_head_hexsha": "a4ead91d6a1f84658ab20946da5fa86dc9ccc831", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -*- coding: utf-8 -*-
"""
Created on Sun Feb 23 2020
@name: Utility Objects
@author: Jack Kirby Cook
"""
import numpy as np
import warnings
from abc import ABC, abstractmethod
__version__ = "1.0.0"
__author__ = "Jack Kirby Cook"
__all__ = ["UtilityIndex", "UtilityFunction"]
__copyright__ = "Copyright 2020, Jack ... | {"hexsha": "9a0defe8419916e587d3fa4883306f3b5bfccd2e", "size": 7639, "ext": "py", "lang": "Python", "max_stars_repo_path": "utility.py", "max_stars_repo_name": "JackKirbyCook82/utilities", "max_stars_repo_head_hexsha": "ce9bac26785652fe90a4a89b6fbcb2a7d2c8078f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
from styx_msgs.msg import TrafficLight
import numpy as np
import cv2
import tensorflow as tf
import rospy
import traceback
import json
import time
# Frozen inference graph files. NOTE: change the path to where you saved the models.
#------------------------------------------------------------------------------------
#... | {"hexsha": "0b9cb29272369da79ea0c2acab3aa068cc86293f", "size": 8523, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros/src/tl_detector/light_classification/tl_classifier.py", "max_stars_repo_name": "frankynavar/ANewTeam_CarND_Integration", "max_stars_repo_head_hexsha": "54f2564ed92be882e3fe1a284210946abce64a9a... |
/*
* MIT License
*
* Copyright (c) 2020 Robert Grupp
*
* 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 without limitation the rights
* to use, copy, mod... | {"hexsha": "1045ea08c3a14f05f0fff883eb579db4ffc21509", "size": 15051, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/ray_cast/xregRayCastSurRenderOCL.cpp", "max_stars_repo_name": "rg2/xreg", "max_stars_repo_head_hexsha": "c06440d7995f8a441420e311bb7b6524452843d3", "max_stars_repo_licenses": ["MIT"], "max_star... |
module TestDoctest
using StaticStorages
using Documenter: doctest
using Test
test_doctest() = doctest(StaticStorages, manual = false)
end # module
| {"hexsha": "d9309358167f9e53287a181ebdd4f1cc3b36958d", "size": 151, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/StaticStoragesTests/src/test_doctest.jl", "max_stars_repo_name": "tkf/StaticStorages.jl", "max_stars_repo_head_hexsha": "9756903be2b3f317f8168463cf55f9513d42dd16", "max_stars_repo_licenses": ["... |
""" Functionality to analyse random telegraph signals
Created on Wed Feb 28 10:20:46 2018
@author: riggelenfv /eendebakpt
"""
import operator
import warnings
from typing import Optional, Tuple, Union
import matplotlib.pyplot as plt
import numpy as np
import qcodes
from qtt.algorithms.fitting import fit_double_gaus... | {"hexsha": "b44a9c235707a304ebeba76df3324135df799ee6", "size": 22503, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/qtt/algorithms/random_telegraph_signal.py", "max_stars_repo_name": "VandersypenQutech/qtt", "max_stars_repo_head_hexsha": "94366c5a4a8fe1c14fc89a8129fca49ea36d16a0", "max_stars_repo_licenses"... |
\section{\module{ossaudiodev} ---
Access to OSS-compatible audio devices}
\declaremodule{builtin}{ossaudiodev}
\platform{Linux, FreeBSD}
\modulesynopsis{Access to OSS-compatible audio devices.}
\versionadded{2.3}
This module allows you to access the OSS (Open Sound System) audio
interface. OSS is available... | {"hexsha": "cecf53c161a1975bd6cf1f897cd3289611db4ff5", "size": 15850, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Doc/lib/libossaudiodev.tex", "max_stars_repo_name": "deadsnakes/python2.4", "max_stars_repo_head_hexsha": "f493d5415b662e99a73d017bcafe2148c5bc8fb5", "max_stars_repo_licenses": ["PSF-2.0"], "max_st... |
# Copyright (c) 2020 Vincent Liu
#
# 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
# without limitation the rights to use, copy, modify, merge, publish,
# distr... | {"hexsha": "db45690289a635817f3b66717290fea184907f36", "size": 5804, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/utils.py", "max_stars_repo_name": "vliu15/tts-gan", "max_stars_repo_head_hexsha": "6246c584a83f67dedaa25155c3b1491b99658319", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "ma... |
import pickle
import csv
import os
import pandas as pd
import numpy as np
from util.params import Params
def filter_glove(word_indices_path, filtered_output):
print("Read files...")
iterator = pd.read_csv('data/glove.42B.300d.txt', header=None, index_col=0,
delim_... | {"hexsha": "7923efd06ef4ddd4a23c3b9e18d9223fc60cce9b", "size": 1628, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/glove_filter.py", "max_stars_repo_name": "isaacsultan/comp-550", "max_stars_repo_head_hexsha": "24e7d22a6f998a94ad6eb020f1df13970da4b150", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#define BOOST_TEST_DYN_LINK
#include <canard/net/ofp/v13/any_oxm_match_field.hpp>
#include <boost/test/unit_test.hpp>
#include <boost/test/data/test_case.hpp>
#include <boost/test/data/monomorphic.hpp>
#include <cstdint>
#include <type_traits>
#include <utility>
#include <vector>
#include <boost/asio/ip/address_v4.hpp... | {"hexsha": "639dd5bad77b9ef869a733a6ac0491658d377abb", "size": 12048, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/v13/utility/any_oxm_match_field_test.cpp", "max_stars_repo_name": "amedama41/bulb", "max_stars_repo_head_hexsha": "2e9fd8a8c35cfc2be2ecf5f747f83cf36ffbbdbb", "max_stars_repo_licenses": ["BSL-1... |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
""" Helper functions and class to calculate Average Precisions for 3D object detection.
"""
import os
import sys
import numpy as np
import torc... | {"hexsha": "ed7f23edf3b3a82ebe0c9431c383a5b9c9c7b1f7", "size": 87853, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/ap_helper.py", "max_stars_repo_name": "keremyldrr/votenet_with_mc_dropout", "max_stars_repo_head_hexsha": "9c26774287be01d5b19dc2740259c7606c17ca7b", "max_stars_repo_licenses": ["MIT"], "m... |
#include <boost/mpl/aux_/preprocessed/plain/set.hpp>
| {"hexsha": "7be6c8e24021a563246f51efc7dfc255eb95cde6", "size": 53, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_mpl_aux__preprocessed_plain_set.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses":... |
import pandas as pd
from sklearn.model_selection import ParameterGrid
import numpy as np
from enum import Enum
class DataEnum(Enum):
FISHING = 'FISHING'
HURRICANES = 'HURRICANES'
GEOLIFE = 'GEOLIFE'
class AlgoEnum(Enum):
CBSMoT = 'CB-SMoT'
DBSMoT = 'DB-SMoT'
def get_data(d, algorithm):
ret... | {"hexsha": "252ae012977eccfce8714c5d8bdcb6142e1086a8", "size": 4925, "ext": "py", "lang": "Python", "max_stars_repo_path": "databases/load_datasets.py", "max_stars_repo_name": "JRose6/TrajLib", "max_stars_repo_head_hexsha": "2a5749bf6e9517835801926d6a5e92564ef2c7f0", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
"""
==============================
Interpolating the Sine function.
* Interpolating the sine function using Lagrangian and Newton's methods.
==============================
"""
import matplotlib.pyplot as plt
import numpy as np
from projects.nnum.interpolation.lagrangian import LagrangianInterpolator
print(__doc__)
... | {"hexsha": "f8d5d475d5df8dd08712c8a23de615097f2c9a9c", "size": 1008, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/nnum/examples/interpolation/sine.py", "max_stars_repo_name": "lucasdavid/numerical-analysis", "max_stars_repo_head_hexsha": "461d156769dae65471665ca72ff41e6cd3c8e944", "max_stars_repo_lic... |
#
# BSD 3-Clause License
#
# Copyright (c) 2017-2018, plures
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this... | {"hexsha": "b9aa097e0bfa99f98a44a3249fdaa3f8b09c80e9", "size": 12166, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/test_gumath.py", "max_stars_repo_name": "mrkn/gumath", "max_stars_repo_head_hexsha": "5725aa2bda2d9a9581e7adedda00e9f1dcaa881a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
###############################################################################
#
# File: evaluate_unflow.py
#
# Primitive hacking of things from eval_gui.py in the UnFlow package to
# allow running evaluations
#
# History:
# 07-30-20 - Levi Burner - Created file
#
######################################################... | {"hexsha": "82ff054a644d1a336e0e63b5c35185f35c034fb4", "size": 11056, "ext": "py", "lang": "Python", "max_stars_repo_path": "motion_illusions/evaluate_unflow.py", "max_stars_repo_name": "ysnan/motion_illusions", "max_stars_repo_head_hexsha": "1b7e8901cbd228a6bdfc8762f6d4756f62361b1f", "max_stars_repo_licenses": ["BSD-3... |
using LinQuadOptInterface
using .ClpCInterface
const LQOI = LinQuadOptInterface
const MOI = LQOI.MOI
const SUPPORTED_OBJECTIVES = [
LQOI.Linear,
LQOI.SinVar
]
const SUPPORTED_CONSTRAINTS = [
(LQOI.Linear, LQOI.EQ),
(LQOI.Linear, LQOI.LE),
(LQOI.Linear, LQOI.GE),
(LQOI.SinVar, LQOI.EQ),
... | {"hexsha": "d18b8e07a037234219ec415ec304aa617b755e1a", "size": 11601, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/MOIWrapper.jl", "max_stars_repo_name": "ianfiske/Clp.jl", "max_stars_repo_head_hexsha": "a767718f97be041c9d339c652c41fb98d9a2f2db", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""
transforms的实例。
【输入】*inputs必须是张量或者是图像的路径(包含文件名)！！
如果输入是多张图像路径/多个张量，将他们依次传入driver_transform()即可，注意最后一个参数是mode...
【输出】是最终喂给模型的张量(numpy.ndarray/torch.tensor/tensorflow.placeholder等等)。
【注意】在做分割的时候，彩色图像和分割图像需要做同步的处理！
特别是在含有随机变换的时候！！
对图像的预处理本质上是做矩阵运算。
如：pytorch的to... | {"hexsha": "711d5b032550406f0321f7c41c9dd3886175ff64", "size": 4956, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataLoader/transformation/transform_demo.py", "max_stars_repo_name": "achanger/Deep-Learning-on-Pytorch", "max_stars_repo_head_hexsha": "c38c29c4c19c816fee97514e47fce402ec699b75", "max_stars_repo_... |
# --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick
# --------------------------------------------------------
"""Train a Fast R-CNN network."""
from fast_rcnn.config import cfg
imp... | {"hexsha": "cbbeaa978a77f1e2db070bbb618e90ef0dc6ba4b", "size": 13928, "ext": "py", "lang": "Python", "max_stars_repo_path": "tlib/fast_rcnn/train.py", "max_stars_repo_name": "shallowyuan/cosegmentor-crf", "max_stars_repo_head_hexsha": "c84a9418b70f3f3c7c6a7e998de5835182619f30", "max_stars_repo_licenses": ["BSD-2-Clause... |
[STATEMENT]
lemma add_mset_commute:
"add_mset x (add_mset y M) = add_mset y (add_mset x M)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. add_mset x (add_mset y M) = add_mset y (add_mset x M)
[PROOF STEP]
by (auto simp: multiset_eq_iff) | {"llama_tokens": 117, "file": null, "length": 1} |
[STATEMENT]
lemma null_space_orthogonal_complement_row_space:
fixes A::"'a^'cols::{finite, wellorder}^'rows::{finite, wellorder}"
shows "null_space A = COLS.v.orthogonal_complement (row_space (\<chi> i j. cnj (A $ i $ j)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. null_space A = COLS.v.orthogonal_complemen... | {"llama_tokens": 4065, "file": "QR_Decomposition_Generalizations2", "length": 33} |
[STATEMENT]
lemma reaches_on_run_hd_t:
assumes "reaches_on run_hd init_hd vs e"
shows "\<exists>x. reaches_on run_t t0 (map fst vs) x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>x. reaches_on local.run_t local.t0 (map fst vs) x
[PROOF STEP]
proof (cases vs rule: rev_cases)
[PROOF STATE]
proof (state... | {"llama_tokens": 904, "file": "VYDRA_MDL_Monitor", "length": 9} |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Groups of optimizers for use in benchmarks
"""
import typing as tp
import numpy as np
import nevergrad as ng
from never... | {"hexsha": "a2abf01ac10fba311aefe91da7fba6783aa04a26", "size": 7339, "ext": "py", "lang": "Python", "max_stars_repo_path": "nevergrad/benchmark/optgroups.py", "max_stars_repo_name": "juliendehos/nevergrad", "max_stars_repo_head_hexsha": "b31a66bdc883e29a6c8572e341b4b56cc4157a9d", "max_stars_repo_licenses": ["MIT"], "ma... |
(* Title: POPLmark/POPLmark.thy
Author: Stefan Berghofer, TU Muenchen, 2005
*)
theory POPLmark
imports Basis
begin
section \<open>Formalization of the basic calculus\<close>
text \<open>
\label{sec:basic-calculus}
In this section, we describe the formalization of the basic calculus
without records. As... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/POPLmark-deBruijn/POPLmark.thy"} |
function [h, p, ci, stats] = ttest2(D, varname, wh_keep1, wh_keep2, varargin)
% Two sample ttest for two samples of one subject-level variable
%
% :Usage:
% ::
%
% ttest2(D, varname, wh_keep1, wh_keep2, [optional inputs])
%
% ..
% Author and copyright information:
%
% Copyright (C) 2013 Tor Wager
%
% Thi... | {"author": "canlab", "repo": "CanlabCore", "sha": "af242e120f0480c4feaeea90471c015a14f1f60e", "save_path": "github-repos/MATLAB/canlab-CanlabCore", "path": "github-repos/MATLAB/canlab-CanlabCore/CanlabCore-af242e120f0480c4feaeea90471c015a14f1f60e/CanlabCore/@canlab_dataset/ttest2.m"} |
!(LICENSE:PD)
! @(#) draw a simple menu using ncurses(3c) from Fortran
! differences between Fortran and C usage
! printw(3c) is not implimented; do internal WRITE(3f) into a character variable and call addstr(3c)
! add C_NULL_CHAR to the end of strings when calling addstr(3c)
! C arrays start at 0, Fortran at 1 by def... | {"hexsha": "77b84a463eccf083675a98118245b5b5b6da5a06", "size": 9276, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "example/nc_simple_key.f90", "max_stars_repo_name": "freevryheid/M_ncurses", "max_stars_repo_head_hexsha": "b4b1e2ee10928fac3843f5b8310fddf935d3b30a", "max_stars_repo_licenses": ["Unlicense"], "m... |
# Mel-cepstrum analysis
# re-coded from SPTK
!isdefined(Base, :FFTW) && using FFTW
function fill_al!{T<:AbstractFloat}(al::Vector{T}, α::AbstractFloat)
al[1] = one(T)
for i=2:length(al)
@inbounds al[i] = -α*al[i-1]
end
al
end
function fill_toeplitz!{T}(A::AbstractMatrix{T}, t::AbstractVector{... | {"hexsha": "81fb554668ab2103205345b186e53814c9b54ad7", "size": 5309, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/mcep.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/MelGeneralizedCepstrums.jl-89d8d30b-2485-52b0-94e9-e2a8fdf814ee", "max_stars_repo_head_hexsha": "987ac2b16e32d206c934b139d33f7f8... |
using aoc2019.computer: load_program, io, run
using Match
input = joinpath(@__DIR__, "input")
p = load_program(input)
function camera!(channel, view)
i, j = 0, 0
for msg in channel
if msg == 10
i = 0
j += 1
else
view[Pair(i, j)] = msg
i += 1
... | {"hexsha": "7fcf0c1d01ad055984c607db4a422166328948ac", "size": 5331, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "bin/seventeen/run.jl", "max_stars_repo_name": "talentdeficit/aoc2019", "max_stars_repo_head_hexsha": "70692f4fd61c3b640ce601bed3afdd74f93a5d73", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#!/usr/bin/env python
"""
Create new copy of MEF fits file with ImageHDU order permuted
according to command line options.
"""
import os
import sys
import argparse
from random import shuffle
from astropy.io import fits
# put parent directory into sys.path
bp = os.path.dirname(os.path.realpath(__file__)).split(os.sep... | {"hexsha": "8a7a4461c36308160435394aa5cdc3805c8bda69", "size": 3515, "ext": "py", "lang": "Python", "max_stars_repo_path": "imutils/hduorder.py", "max_stars_repo_name": "lsst-camera-dh/mutils", "max_stars_repo_head_hexsha": "80edb76c16bb3f00f22f77cf6aa2b2a1d02d73fe", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"], "m... |
from PIL import Image
import numpy as np
import os
class Operations :
def __init__(self, img1, img2 = None):
os.system("clear")
print("Processing image...")
self.Lmax = 255
if(img2 is None) :
self.img = self.openImage(img1)
else :
self.img = self.openImage(img1)
self.imgAux = self.openImage(img2)
... | {"hexsha": "3fd929b345b5a8d9e435fc615c30666fb5cc66d6", "size": 2738, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/entities/operations.py", "max_stars_repo_name": "ThiagoPereiraUFV/Image-Editor", "max_stars_repo_head_hexsha": "d542509e72b0196097935976d7ddce829049eb74", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""This module contains the class Animater to animate ddos simulations"""
__Lisence__ = "BSD"
__maintainer__ = "Justin Furuness"
__email__ = "jfuruness@gmail.com, agorbenko97@gmail.com"
__status__ = "Development"
from copy import deepcopy
from enum import Enum
import os... | {"hexsha": "50b6d27bf5f1b76cd572e670b24348e219b5bff7", "size": 12015, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib_ddos_simulator/animations/animater.py", "max_stars_repo_name": "jfuruness/lib_ddos_simulator", "max_stars_repo_head_hexsha": "2d860fd3f35f4c25262f5269251eed89975f95e8", "max_stars_repo_licens... |
import numpy as np
def k_fold(n_samples, n_folds, randomize = False):
"""
计算K-fold交叉验证产生的样本索引值
Input:
n_samples, 样本的数量
n_folds, 分包的数量, 如果n_folds=n_samples,对应留一法交叉验证
if randomize = True(default,False), 首先对所有样本的索引值打乱，这对于那些样本按照一定
顺序排列的情况特别适用，比如所有的正样本都在前，负样本都在后。
例子：
k_fold(100, 3)
... | {"hexsha": "4b0c5ad3486ff4c6153acdc15fc9363c6e2af96f", "size": 1596, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlapp/MLAPP_CODE/MLAPP-C7-Code/Kfold.py", "max_stars_repo_name": "xishansnow/MLAPP", "max_stars_repo_head_hexsha": "2f30cd94fd852a3f66fe92a124f65722bd2af509", "max_stars_repo_licenses": ["MIT"], "... |
#=##############################################################################
# # DESCRIPTION
# Examples of VTK formatting and usage of VTKtools
# # AUTHORSHIP
# * Author : Eduardo J Alvarez
# * Email : Edo.AlvarezR@gmail.com
# * Created : Nov 2017
# * License : MIT License
# * Modified : K... | {"hexsha": "4acbef55b4d631a456eb4827054ebb249af0097d", "size": 18783, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/save_vtk.jl", "max_stars_repo_name": "byuflowlab/moore2019multipropopt", "max_stars_repo_head_hexsha": "116e2ec2ec7885285b8865ef571864874dfa4574", "max_stars_repo_licenses": ["MIT"], "max_star... |
from os.path import dirname, join
from pliers.stimuli import ImageStim
from pliers.extractors.base import Extractor, ExtractorResult
import numpy as np
from copy import deepcopy
def get_test_data_path():
"""Returns the path to test datasets """
return join(dirname(__file__), 'data')
class DummyExtractor(Ext... | {"hexsha": "0e17c4250704b7d253e3aca6d15f159d0b6525e6", "size": 1091, "ext": "py", "lang": "Python", "max_stars_repo_path": "pliers/tests/utils.py", "max_stars_repo_name": "rafiahmed40/media-workflow", "max_stars_repo_head_hexsha": "32411d1214302176b0a3d15e6f68a3071a5e3762", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
import numpy as np
import sys
from timeit import default_timer as timer
sys.path.append("../../")
from core import wnn
from encoding import thermometer
from encoding import util
#Load Diabetes data
base_path = "../../dataset/diabetes/"
#2/3 Test
bits_encoding = 20
train_data, train_label, test_data, test_label, data... | {"hexsha": "6ad3007b95e5d17415b05151d343ee3326e45e1d", "size": 2157, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment/diabetes/accuracy_info.py", "max_stars_repo_name": "leandro-santiago/bloomwisard", "max_stars_repo_head_hexsha": "4c02610c4ef2d2cf8424797c8a815da182ca2383", "max_stars_repo_licenses": [... |
import csv
import os
import numpy as np
working_dir = os.getcwd()
clf1_out = os.path.join(working_dir, "classifier1.tsv")
clf2_out = os.path.join(working_dir, "classifier2.tsv")
clf3_out = os.path.join(working_dir, "classifier3.tsv")
weights = [1.0,1.0,1.0]
def return_prob(classifier_path):
all_prob = []
with open... | {"hexsha": "fc551479c4c1dea7c9e41306ffb93cc6e3b23cf4", "size": 1130, "ext": "py", "lang": "Python", "max_stars_repo_path": "terra/ensemble.py", "max_stars_repo_name": "siddharthvaria/GI-DL", "max_stars_repo_head_hexsha": "715b5fe4426d737ed1b23ffbb812058a90433682", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Shared Memory (part of B.2)
export @cuStaticSharedMem, @cuDynamicSharedMem, CuStaticSharedArray, CuDynamicSharedArray
"""
CuStaticSharedArray(T::Type, dims) -> CuDeviceArray{T,AS.Shared}
Get an array of type `T` and dimensions `dims` (either an integer length or tuple shape)
pointing to a statically-allocated ... | {"hexsha": "68faab27d2241959de561f11b013b31cfe19326f", "size": 4335, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/device/intrinsics/memory_shared.jl", "max_stars_repo_name": "ovanvincq/CUDA.jl", "max_stars_repo_head_hexsha": "33aa453f30d6f70bd318fa480522a2a993220d7f", "max_stars_repo_licenses": ["MIT"], "m... |
import cv2
import torch
import numpy as np
from PIL import Image
from torchvision import transforms
from .commons import sample_frames_metafunc, sample_clips_metafunc, preprocess_frame_metafunc, preprocess_clip_metafunc
class VideoDataset(object):
def __init__(self, stride, mean, std, resize_to, crop_to, type='f... | {"hexsha": "1eb453b98d54f80451ef5e934d3d9c8309f17c23", "size": 1684, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/video_loader.py", "max_stars_repo_name": "Tramac/awesome-video-feature-extractor", "max_stars_repo_head_hexsha": "d160f71cfe4405592442902b867633f2cdccdad6", "max_stars_repo_licenses": ["Apac... |
using Markdown
using Pkg
using Blink
# Blink.AtomShell.install()
using Interact
#%%
text = @md_str """# Title
This is a markdown test
## Subsection
and this is a subsection
"""
text2=display(text)
loadbutton = filepicker()
hellobutton = button("Hello!")
goodbyebutton = button("Good bye!")
ui = vbox(... | {"hexsha": "2b129a7354ab36eccb732481704d78828f9313bd", "size": 590, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lezione15/test_interact.jl", "max_stars_repo_name": "aurelio-amerio/corso-cpp", "max_stars_repo_head_hexsha": "53d19aa83679a7be59f5518ee5c9256269ea7d1a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
//==================================================================================================
/*!
@file
Defines the type hierarchies for IEEE-754 like types
@copyright 2016 NumScale SAS
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boo... | {"hexsha": "9abdae760bd6264d2fa25de86d306102861ec110", "size": 4557, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "third_party/boost/simd/detail/dispatch/hierarchy/integer_types.hpp", "max_stars_repo_name": "SylvainCorlay/pythran", "max_stars_repo_head_hexsha": "908ec070d837baf77d828d01c3e35e2f4bfa2bfa", "max_st... |
module Invincy.Parsing
import Data.Vect
import Invincy.Core
%access public export
mutual
data Result : (i, r : Type) -> Type where
Done : Stream t s => s -> r -> Result s r
Partial : Stream t s => Inf (Parser s r) -> Result s r
Failure : Stream t s => String -> Result s r
data Parser : (i, r : Type)... | {"hexsha": "f904ad3c53373055f895229826ea131bb7d4f88b", "size": 4856, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Invincy/Parsing.idr", "max_stars_repo_name": "defanor/invincy", "max_stars_repo_head_hexsha": "a8f47b1f249ecbaeb1eac31e3873e85e9e1c634b", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count... |
from __future__ import annotations
import numpy as np
import pandas as pd
from sklearn import datasets
from IMLearn.metrics import mean_square_error
from IMLearn.model_selection import cross_validate
from IMLearn.learners.regressors import PolynomialFitting, LinearRegression, \
RidgeRegression
from sklearn.linear_mode... | {"hexsha": "4f73d9b9cdbd9dc97c2a7deadaa71a9277b111fa", "size": 5650, "ext": "py", "lang": "Python", "max_stars_repo_path": "exercises/perform_model_selection.py", "max_stars_repo_name": "ereldebel/IML.HUJI", "max_stars_repo_head_hexsha": "1c3d7042071a74ed60f92c013ef6051e2341304c", "max_stars_repo_licenses": ["MIT"], "m... |
""" An example of a wide resnet network with Nestorov-momentum back-prop on CIFAR10 data. (details in Zagoruyko and Komodakis, 2017)
"""
import deepnodal as dn
from time import time
import datetime
import numpy as np
# PARAMETERS
n_epochs = 200
batch_size = 128
test_split = 50
max_lr = 0.1
learning_rates = {0:max_l... | {"hexsha": "130a86e0cbedefed1f212db6a2237e8022b1d310", "size": 5114, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/cifar10_wide_resnet.py", "max_stars_repo_name": "Bhumbra/DeepNodal", "max_stars_repo_head_hexsha": "33afb2efa5e78ae6558ce60a36bb87c186c1f448", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
from __future__ import print_function, division
import scipy
from keras.datasets import mnist
from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, Concatenate
from keras.layers import BatchNormalization, Activation,... | {"hexsha": "6cfc93338e0162cf6e5a5b8626c27e083c789def", "size": 9243, "ext": "py", "lang": "Python", "max_stars_repo_path": "ccyclegan/classifier.py", "max_stars_repo_name": "gtesei/Keras-GAN", "max_stars_repo_head_hexsha": "8f57901fb637d8d179e780a191683da347af30b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
/**
* @file start.cpp
* @author Noah Witt <nawitt18@ole.augie.edu>
* @brief tests for the start system
* @version 0.1
* @date 2021-10-18
*
* @copyright Copyright (c) 2021
*
*/
#include <boost/test/unit_test.hpp>
#include <boost/log/trivial.hpp>
#include "../start.hpp"
BOOST_AUTO_TEST_SUITE(start_test)
BOOS... | {"hexsha": "4a9bb5af40b716d302c2466e7d2cd8d27b9832e1", "size": 708, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/start.cpp", "max_stars_repo_name": "noah-witt/riscv-engine", "max_stars_repo_head_hexsha": "4caf55da2d8e5d473ac7e0634bef6a1d5ddc728b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
import os
import re
import numpy as np
from monty.io import zopen
from monty.serialization import loadfn, dumpfn
hf = re.compile(r"\s+SCF energy\s+=\s+([\-\.0-9]+)")
mp2 = re.compile(r"\s+MP2 energy\s+=\s+([\-\.0-9]+)")
ccsd_corr = re.compile(r"\s+CCSD correlation energy\s+=\s+([\-\.0-9]+)")
ccsd_total = re.compile(... | {"hexsha": "cbe6ab9014dfa56fd379b30cf03fbad26de7d3d3", "size": 4498, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/calc/parsing_cc.py", "max_stars_repo_name": "espottesmith/hydrobench", "max_stars_repo_head_hexsha": "e117774c94cff11debd764d231757174ec211e99", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# _*_ coding: utf-8 _*_
from __future__ import absolute_import, division, print_function
import os
import sys
module_path = os.path.abspath(os.path.join('..'))
sys.path.append(module_path)
import numpy as np
import pandas as pd
import lightgbm as lgb
from sklearn.model_selection import StratifiedKFold
from sklearn.m... | {"hexsha": "ed5b007c8fcbc77a80c7b56bd6089f1a3ba09d0e", "size": 5876, "ext": "py", "lang": "Python", "max_stars_repo_path": "model/model_roof/lq_lightgbm_roof.py", "max_stars_repo_name": "SunnyMarkLiu/Datacastle_Travel_Services_Predict", "max_stars_repo_head_hexsha": "0823a8aaab4e42a7ee5067171901c6f597bc5d7e", "max_star... |
Require Import Crypto.Specific.Framework.RawCurveParameters.
Require Import Crypto.Util.LetIn.
(***
Modulus : 2^489 - 21
Base: 48.9
***)
Definition curve : CurveParameters :=
{|
sz := 10%nat;
base := 48 + 9/10;
bitwidth := 64;
s := 2^489;
c := [(1, 21)];
carry_chains := Some [seq 0 (pred 10)... | {"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... |
Set Implicit Arguments.
(*** preliminary Meta Theory assumptions ***)
Require Export Prelim.
Module Type TG.
Parameter set : Type.
(** * Axioms of set theory **)
(** Finally, we add the primitive operators and axioms of Tarski-Grothendieck Set Theory (Zermelo-Fraenkel with Grothendieck Universes). ***)
(** In is... | {"author": "verimath", "repo": "set", "sha": "aa4aea3a9f6c3b2ad9d8a7834d5e447c408754f4", "save_path": "github-repos/coq/verimath-set", "path": "github-repos/coq/verimath-set/set-aa4aea3a9f6c3b2ad9d8a7834d5e447c408754f4/src/TG0.v"} |
#!/usr/bin/env python3
# Requires python 3.5+
import importlib.util
import logging
import os
import numpy as np
import pylab as plt
import torch
from omegaconf import OmegaConf
import swyft
DEVICE = "cuda:0"
logging.basicConfig(level=logging.DEBUG, format="%(message)s")
def main():
# Pretty hacky way to impor... | {"hexsha": "39426c7795414ea9dfceef9b8df2deb868b73307", "size": 1871, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/run_swyft.py", "max_stars_repo_name": "adam-coogan/swyft", "max_stars_repo_head_hexsha": "c54bdd9f77ddf02fda857e26640df012cbe545fc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
Git(;
ignore=String[],
name=nothing,
email=nothing,
branch=nothing,
ssh=false,
jl=true,
manifest=false,
gpgsign=false,
)
Creates a Git repository and a `.gitignore` file.
## Keyword Arguments
- `ignore::Vector{<:AbstractString}`: Patterns to ... | {"hexsha": "5b235d569ca31b10c24413e8b502802c76bf69a9", "size": 4778, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/plugins/git.jl", "max_stars_repo_name": "sethaxen/PkgTemplates.jl", "max_stars_repo_head_hexsha": "9eea9eb8404da6e337fd13536999db2a6a7fbc80", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[GOAL]
α : Type u_1
inst✝¹ : Lattice α
inst✝ : OrderBot α
a : α
P : Finpartition a
b : α
hb : b ∈ P.parts
⊢ b ≠ ⊥
[PROOFSTEP]
intro h
[GOAL]
α : Type u_1
inst✝¹ : Lattice α
inst✝ : OrderBot α
a : α
P : Finpartition a
b : α
hb : b ∈ P.parts
h : b = ⊥
⊢ False
[PROOFSTEP]
refine' P.not_bot_mem (_)
[GOAL]
α : Type u_1
inst... | {"mathlib_filename": "Mathlib.Order.Partition.Finpartition", "llama_tokens": 28454} |
STATIC_PROPERTIES = [:n, :num_steps, :dt, :seed, :randomize, :nmac]
function initialize(log::Dict{String,Any}, sim::Simulator)
sim.logging || return
for p in STATIC_PROPERTIES
log["$p"] = getproperty(sim, p)
end
log["t"] = [sim.step * sim.dt]
for i in 1:sim.n
log["ac_$i"] = [vec(si... | {"hexsha": "6b1ec967958a4d2eb883ad29b3b8b0505d7883a1", "size": 752, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/cas/src/logging.jl", "max_stars_repo_name": "NASA-SW-VnV/AdaStress.jl", "max_stars_repo_head_hexsha": "a8802eeb2c7890a100ff87470853b7d1acda03fb", "max_stars_repo_licenses": ["NASA-1.3"], "m... |
import numpy as np
import tensorflow as tf
from .bbox import *
class BBoxesLayer(object):
def __init__(self, mod_tra=True, box_cls_num=None, img_shp=None):
self.img_shp = img_shp
self.mod_tra = mod_tra
self.box_cls_num = box_cls_num
self.box_pol_num = 300
... | {"hexsha": "d4d83416541bb5a9cd9551941b1fa1fbe012dd7f", "size": 18091, "ext": "py", "lang": "Python", "max_stars_repo_path": "Mybase/mask_rcnn_utils/bboxes_layer1.py", "max_stars_repo_name": "czyczyyzc/MyMaskRCNN", "max_stars_repo_head_hexsha": "e5a451fd05c593ae05d6e596813fc63aad7af2de", "max_stars_repo_licenses": ["MIT... |
# 0712.py
import cv2
import numpy as np
#1
src = cv2.imread('./data/lena.jpg')
down2 = cv2.pyrDown(src)
down4 = cv2.pyrDown(down2)
print('down2.shape=', down2.shape)
print('down2.shape=', down2.shape)
#2
up2 = cv2.pyrUp(src)
up4 = cv2.pyrUp(up2)
print('up2.shape=', up2.shape)
print('up4.shape=', up4.sh... | {"hexsha": "28c8260bcdade8e99d8570dbadd094e6bd9c1078", "size": 472, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter07/0712.py", "max_stars_repo_name": "0201shj/Python-OpenCV", "max_stars_repo_head_hexsha": "249f8cc9404e547da0f5c68000f29f2e598562a5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : Sklearn_lego.py
@Time : 2019/07/22 20:16:06
@Author : xiao ming
@Version : 1.0
@Contact : xiaoming3526@gmail.com
@Desc : 用sklearn实现下岭回归
@github : https://github.com/aimi-cn/AILearners
'''
# here put the import lib
# -*-coding:utf-8 -... | {"hexsha": "189b81e7ecf1c5d489e5821596e15ef387829f00", "size": 3737, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/py2.x/ml/jqxxsz/8.Regression/lego/Sklearn_lego.py", "max_stars_repo_name": "BinLeeBit/AILearners", "max_stars_repo_head_hexsha": "39e96337b89470cd75e0653eb94ee069b7409724", "max_stars_repo_lic... |
import os
import sys
import subprocess
import numpy as np
from openvino.inference_engine import IECore
class OpenVINOModel:
"""Class providing OpenVINO backend for TensorFlow models.
Class performs conversion to OpenVINO IR format using Model Optimizer tool.
"""
def __init__(self, base_model):
... | {"hexsha": "61834cc625085bc2cbf0cdcc9c79b021d1332c71", "size": 2255, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml3d/tf/models/openvino_model.py", "max_stars_repo_name": "krshrimali/Open3D-ML", "max_stars_repo_head_hexsha": "e6352ee84d38a4b90c71dd7f376f5570fe849537", "max_stars_repo_licenses": ["MIT"], "max... |
"""
This module acts as an eye. An input processing module responsible for processing visual raw data and activating the
ganglion cells that act as the gateway to the visual cortical pathways.
"""
import os
import struct
import numpy as np
from math import floor
from datetime import datetime
from inf import runtime_dat... | {"hexsha": "0f13531f69a0d7fcdc4cb5f71578dfac9cf0e852", "size": 19487, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ipu/processor/image.py", "max_stars_repo_name": "feagi/feagi-core", "max_stars_repo_head_hexsha": "d83c51480fcbe153fa14b2360b4d61f6ae4e2811", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
!-----------------------------------------------------------------------
subroutine tridiagonal(N,au,bu,cu,du,fi,lt,value)
!
! !DESCRIPTION:
! A linear equation with tridiagonal matrix structure is solved here. The main
! diagonal is stored on {\tt bu}, the upper diagonal on {\tt au}, and the
! lower diagonal on {\tt c... | {"hexsha": "de8ae4c2c1589fdad8410b22936dc13d73bf3299", "size": 1371, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/tridiagonal.f90", "max_stars_repo_name": "BingzhangChen/HOT", "max_stars_repo_head_hexsha": "c166ac038cd67a0e4dd5c8512bf5d678eb07b6cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
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