text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
def normalize(mat):
"""
Normalize a matrix along the axis 1.
:param np.ndarray mat: Matrix to normalize.
:return np.ndarray: Normalized matrix.
"""
if len(mat.shape) == 1:
summed = mat.sum()
if summed == 0:
return np.zeros_like(mat)
return... | {"hexsha": "85533e03c6995d269dccbb6e833ea499d83a4bd9", "size": 3922, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/math_util.py", "max_stars_repo_name": "hiaoxui/D2T_Grounding", "max_stars_repo_head_hexsha": "4c46f8a8d2867712399ac7c0e7f7f34ef911a69a", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from preprocess import *
from pyspark.ml.classification import RandomForestClassifier
from pyspark.sql.functions import col, when, concat_ws
from pyspark.ml.feature import StringIndexer, VectorAssembler
from pyspark.ml.regression import LinearRegression
import pandas as pd
from sklearn.model_selection import train_test... | {"hexsha": "4c323fe45d3f0e2a3a3c961ef775feba1540f38d", "size": 4124, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlPrep.py", "max_stars_repo_name": "JonathanSolvesProblems/Soen-471-Project", "max_stars_repo_head_hexsha": "f9039df455a3a150a0211bd0241f1ae611ac3a28", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) BaseDetection, Inc. and its affiliates. All Rights Reserved
from typing import Dict
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from cvpods.layers import Conv2d, ConvTranspose2d, ShapeSpec
class FCNHead(nn.Modu... | {"hexsha": "c44a46240388ba33d5ec1b76c5575e58de7054af", "size": 5180, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvpods/modeling/meta_arch/fcn.py", "max_stars_repo_name": "reinforcementdriving/cvpods", "max_stars_repo_head_hexsha": "32d98b74745020be035a0e20337ad934201615c4", "max_stars_repo_licenses": ["Apac... |
#!/usr/bin/env python3
"""
Grid features extraction script.
"""
import argparse
import os
import torch
import tqdm
from fvcore.common.file_io import PathManager
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.engine import default_setup
from detectron2.eva... | {"hexsha": "7c62e1ba59e97f238e09a86895f6c890c24d960e", "size": 5819, "ext": "py", "lang": "Python", "max_stars_repo_path": "CLIP-ViL-Direct/vqa/pythia_clip_grid_feature.py", "max_stars_repo_name": "HermannLiang/CLIP-ViL", "max_stars_repo_head_hexsha": "49c28bc5ece1aacfcbfd9c8810db70663ca0516a", "max_stars_repo_licenses... |
# Taylor problem 1.39
last revised: 07-Jan-2019 by Dick Furnstahl [furnstahl.1@osu.edu]
The goal of this notebook is to practice Python while considering some visualizations of problem 1.39 to see how they might help check the results, interpret the behavior, or suggest new ideas on how to extend the problem. Sugges... | {"hexsha": "323c92de84f17124f78bed782413edf9f93d87eb", "size": 605785, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "2020_week_1/Taylor_problem_1.39.ipynb", "max_stars_repo_name": "CLima86/Physics_5300_CDL", "max_stars_repo_head_hexsha": "d9e8ee0861d408a85b4be3adfc97e98afb4a1149", "max_stars_repo_... |
from bootstrapping import bootstrap
import numpy as np
import matplotlib.pyplot as plt
# generate 10,000 standard normal variables
sample = np.random.randn(10000)
# Run the bootstrap algorithm. Do 50,000 random resamplings and then calculate
# the standard deviation for each one.
bootstrap_values = bootstrap(sample,... | {"hexsha": "ad1f3fba828217e8cc54168a934612cf982265c8", "size": 640, "ext": "py", "lang": "Python", "max_stars_repo_path": "bootstrap_test.py", "max_stars_repo_name": "ananswam/bootstrapping", "max_stars_repo_head_hexsha": "3dd412917751b4ea9295311881fe79851a9552b1", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#ifndef _INCLUDED_UBLAS_VECTOR_HPP_
#define _INCLUDED_UBLAS_VECTOR_HPP_
#include <boost/serialization/list.hpp>
#include <boost/serialization/string.hpp>
#include <boost/serialization/version.hpp>
#include <boost/serialization/split_free.hpp>
#include <boost/numeric/ublas/vector.hpp>
namespace boost {
namespace ser... | {"hexsha": "3d083c36a47c071d5d8fbb7e3ff831f89616f1b4", "size": 1282, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/boost_ext/ublas_vector.hpp", "max_stars_repo_name": "ahmadia/hypermesh", "max_stars_repo_head_hexsha": "c694d634a8493c94be39488b85aacc2d1b8884e7", "max_stars_repo_licenses": ["MIT"], "max_st... |
c------------------------------------------------------------------
c Computes double covariance or correlation matrix
c------------------------------------------------------------------
C NCLFORTSTART
subroutine dcovarxy(x,y,xmsg,ymsg,cxy,n,m,lag,ncrit,iopt)
implicit none
c ... | {"hexsha": "0b367a2b93c245f089035ed2108ee24931ff3a79", "size": 1841, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ni/src/lib/nfpfort/covcorm_xy_matrix_ncl.f", "max_stars_repo_name": "tenomoto/ncl", "max_stars_repo_head_hexsha": "a87114a689a1566e9aa03d85bcf6dc7325b47633", "max_stars_repo_licenses": ["Apache-2.... |
"""
pretty(doc)
Pretty print the parsed HTML `doc`.
"""
function pretty(doc)
io = IOBuffer()
print(io, doc; pretty=true)
return String(take!(io))
end
function map!(f::Function, doc::HTMLDocument)
for elem in PreOrderDFS(doc.root)
if elem isa HTMLElement
# Changing elem directly... | {"hexsha": "3beb1f72d3f705acbc0593824c089cc4e99d1d34", "size": 3347, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/select.jl", "max_stars_repo_name": "rikhuijzer/Skann.jl", "max_stars_repo_head_hexsha": "3e5cb898b5ab360fd7982aeffe794d62b09c575f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "ma... |
import os
import numpy as np
import cv2
from . import find_tools as ft
# noinspection PyArgumentList
hog = cv2.HOGDescriptor((32, 64), (16, 16), (8, 8), (8, 8), 9, 1, 4, 0, 0.2, 0, 64)
def find_right_lung_hog(image):
hog.setSVMDetector(
np.loadtxt(os.path.dirname(__file__) + os.sep + "right_lung_hog.np",... | {"hexsha": "314742ef9a8fe02b44e26dfa8b3383f9be9f1af8", "size": 745, "ext": "py", "lang": "Python", "max_stars_repo_path": "lungs_finder/hog_finder.py", "max_stars_repo_name": "ggalal/lungs-finder", "max_stars_repo_head_hexsha": "d31e20a88f1de3c9b62025dc2875b01f7806f4d9", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
# Copyright (c) 2019 - The Procedural Generation for Gazebo authors
# For information on the respective copyright owner see the NOTICE file
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#... | {"hexsha": "139f048cc9a43f5f55994932c3564dbf92494c5e", "size": 35077, "ext": "py", "lang": "Python", "max_stars_repo_path": "pcg_gazebo/generators/creators.py", "max_stars_repo_name": "TForce1/pcg_gazebo", "max_stars_repo_head_hexsha": "9ff88016b7b6903236484958ca7c6ed9f8ffb346", "max_stars_repo_licenses": ["ECL-2.0", "... |
import itertools
import numpy as np
from game import Grid, Game
from config import *
config = Base()
def get_grid(tiles, directions):
g = Grid(config.SIZE)
g.tiles = tiles.copy()
for direction in directions:
g.run(direction)
g.add_random_tile()
return g.tiles
def printf(tiles):
... | {"hexsha": "7b85e5dd64f8a2f31f942c379355d398b6597db4", "size": 7745, "ext": "py", "lang": "Python", "max_stars_repo_path": "01 VacantHusky-2048GameAutoMovePython/2048python/ai.py", "max_stars_repo_name": "Guleixibian2009/Game-Collection", "max_stars_repo_head_hexsha": "1d8997b3ab0ea38958ed67dab7132bc89d467644", "max_st... |
import numpy as np
import time
from collections import Counter
class Vocabulary(object):
UNK = '<unk>'
def __init__(self, offset=0, unk=True):
self.word_to_ind = {}
self.ind_to_word = {}
self.word_count = Counter()
self.size = 0
self.offset = offset
self.specia... | {"hexsha": "b11d7392633e1034d8a976c1b27f00a171ae588d", "size": 2811, "ext": "py", "lang": "Python", "max_stars_repo_path": "cocoa_folder/cocoa/model/vocab.py", "max_stars_repo_name": "s-akanksha/DialoGraph_ICLR21", "max_stars_repo_head_hexsha": "d5bbc10b2623c9f84d21a99a5e54e7dcfdfb1bcc", "max_stars_repo_licenses": ["Ap... |
/**
* Copyright (C) 2014 MongoDB Inc.
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU Affero General Public License, version 3,
* as published by the Free Software Foundation.
*
* This program is distributed in the hope that it will be usefu... | {"hexsha": "5a5f9798299f445e66894af43d4228e9689e84ac", "size": 19322, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/mongo/db/commands/pubsub_commands.cpp", "max_stars_repo_name": "EshaMaharishi/pubsub-1", "max_stars_repo_head_hexsha": "13cb194078ed39b00ea623db0d87df8e153e7981", "max_stars_repo_licenses": ["A... |
import numpy as np
from numpy.linalg import inv
from basics.base_agent import BaseAgent
class LinUCBAgent(BaseAgent):
def __init__(self):
super().__init__()
self.name = "LinUCB"
def agent_init(self, agent_info=None):
if agent_info is None:
agent_info = {}
self.... | {"hexsha": "39ce44ecbbdd78c4fedb80695d28731a901711de", "size": 7750, "ext": "py", "lang": "Python", "max_stars_repo_path": "CMAB/LinUCB.py", "max_stars_repo_name": "RecoHut-Stanzas/S873634", "max_stars_repo_head_hexsha": "ae67db296ada7ab31d77c51d048254c7c028620e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma is_min2_Empty[simp]: "\<not>is_min2 x {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<not> is_min2 x {}
[PROOF STEP]
by (auto simp: is_min2_def) | {"llama_tokens": 79, "file": "Priority_Search_Trees_PST_General", "length": 1} |
#include "plugins/lasso3d/lasso3d.h"
#include <QDebug>
#include <QEvent>
#include <QKeyEvent>
#include <QAction>
#include <QGLShaderProgram>
#include <QGLBuffer>
#include <QTabWidget>
#include <QApplication>
#include <QToolBar>
#include <QVBoxLayout>
#include <QDoubleSpinBox>
#include <QLabel>
#include <QSpacerItem>
#i... | {"hexsha": "c6e3dc9154f6aba9b0ab679f2a557678287199f3", "size": 7280, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/plugins/lasso3d/lasso3d.cpp", "max_stars_repo_name": "circlingthesun/cloudclean", "max_stars_repo_head_hexsha": "4b9496bc3b52143c35f0ad83ee68bbc5e8aa32d5", "max_stars_repo_licenses": ["MIT"], "m... |
# -*- coding:utf8 -*-
"""
This module contains visualization tools for uesgraphs
"""
import networkx as nx
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.pylab import mpl
from matplotlib.collections import LineCollection
from matplotlib import gridspec
from mpl_toolkits.axes_grid1 import make_axes_l... | {"hexsha": "9735fd9e83b2812107e4a8aa0406c98c02dcf095", "size": 63342, "ext": "py", "lang": "Python", "max_stars_repo_path": "uesgraphs/visuals.py", "max_stars_repo_name": "RWTH-EBC/uesgraphs", "max_stars_repo_head_hexsha": "498fc57cd251dc93f51279275a965d77775e68b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/python3
# https://matplotlib.org/examples/pylab_examples/contourf_demo.html
import numpy as np
from matplotlib import gridspec
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from matplotlib.colors import LogNorm
from skimage.measure import block_reduce
from astropy.io import fits
# Eve... | {"hexsha": "c2a303cf1965fe7cc009f60143d217c0cd799314", "size": 12030, "ext": "py", "lang": "Python", "max_stars_repo_path": "Na_im.py", "max_stars_repo_name": "jpmorgen/IoIO", "max_stars_repo_head_hexsha": "c22523d77bc38162cabf3ddb6d7446e4005864e8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_r... |
"""
File: pfb_peek.py
USAGE:
python pfb_peek.py <pfb_file>
DESCRIPTION:
Peeks into a parflow .pfb file and display a summary of the file.
This prints to stdout a summary of the .pfb file. It prints the file header from the first 64 bytes.
Then prints the subgrid headers of ... | {"hexsha": "c9a032f13373fdf2a1956d31ec997bbd5aefda12", "size": 5901, "ext": "py", "lang": "Python", "max_stars_repo_path": "pf_xarray/tests/pfb_peek.py", "max_stars_repo_name": "wh3248/pf-xarray", "max_stars_repo_head_hexsha": "f971e0c3e9962958fcf807e45d2623a0784cba8c", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import sys
sys.path.append('../../')
import unittest
import numpy as np
from qwopt.compiler.parser import GraphParser
class GraphParserTest(unittest.TestCase):
def test_dim(self):
test_mat = [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]]
gparser = GraphParser(test_mat)
gdim = gp... | {"hexsha": "345402cd4eaf1f6b4774927650a2338be5593007", "size": 2260, "ext": "py", "lang": "Python", "max_stars_repo_path": "qwopt/test/parser_test.py", "max_stars_repo_name": "Chibikuri/qwopt", "max_stars_repo_head_hexsha": "e65549db83142af4c6b63cce9f55050ee87fb27a", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import matplotlib.pyplot as plt
import numpy as np
# add noise to y axis to avoid overlapping
def rand_jitter(arr):
# "Range" = (max(arr) - min(arr))
nosie = .01 * (max(arr) - min(arr))
return arr + np.random.randn(len(arr))
# https://stackoverflow.com/questions/4383571/importing-files-from-different-fo... | {"hexsha": "c9371075add54ebd67285a385122202297f68547", "size": 4360, "ext": "py", "lang": "Python", "max_stars_repo_path": "jb/rand_jitter.py", "max_stars_repo_name": "james-w-balcomb/kaggle--home-credit-default-risk", "max_stars_repo_head_hexsha": "b8346ba1640c42daf8457588f7fadd81ded74b8f", "max_stars_repo_licenses": ... |
# vs_circuit_solver.py
# версия 0.1
# язык Python
#
# программа подбора значений R,C для вариантов электронной схемы
# исходя из моделирования подобной схемы в ngspice
# поставляется без всякой оптимизации, ибо имеет целью установление методики
# расчета таких вещей и определения границ применимости этой методики
#
# ... | {"hexsha": "3dadbecacffb0a124c3eb501a4f9e43915d34fb7", "size": 37787, "ext": "py", "lang": "Python", "max_stars_repo_path": "vs_circuit_solver/vs_circuit_solver.py", "max_stars_repo_name": "EPC-MSU/VS-circuit-solver", "max_stars_repo_head_hexsha": "7d877dcc6b5aed5919c8f0833fc003a7fa651bc5", "max_stars_repo_licenses": [... |
import argparse
import os, sys
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
import torchvision
from torchvision import transforms
from PIL import Image
import numpy as np
from tqdm import tqdm
from sklearn.cluster import KMeans
from scipy.stats import ortho_group
... | {"hexsha": "43d1043317898b4642b6c414573dc3fda193df8f", "size": 8685, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/trainMyPCL.py", "max_stars_repo_name": "Crazy-Jack/SpatialExpGeneCluster", "max_stars_repo_head_hexsha": "9e57c308d1c577a936a2358d0641c65b8130034f", "max_stars_repo_licenses": ["MIT"], "max_st... |
# /usr/bin/env python3.5
# -*- mode: python -*-
# =============================================================================
# @@-COPYRIGHT-START-@@
#
# Copyright (c) 2019, Qualcomm Innovation Center, Inc. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification,... | {"hexsha": "d16d855fd4f368cdc0850dd99146f277d3de54b0", "size": 34558, "ext": "py", "lang": "Python", "max_stars_repo_path": "TrainingExtensions/tensorflow/src/python/aimet_tensorflow/cross_layer_equalization.py", "max_stars_repo_name": "quic-bharathr/aimet", "max_stars_repo_head_hexsha": "363308217dca3fc52644bdda31e69e... |
{- Byzantine Fault Tolerant Consensus Verification in Agda, version 0.9.
Copyright (c) 2021, Oracle and/or its affiliates.
Licensed under the Universal Permissive License v 1.0 as shown at https://opensource.oracle.com/licenses/upl
-}
open import LibraBFT.Concrete.Records
open import LibraBFT.Concrete.System
op... | {"hexsha": "8e071ba4b8532447d7491e3c8f57d7223e714738", "size": 33911, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/LibraBFT/Impl/Properties/VotesOnce.agda", "max_stars_repo_name": "LaudateCorpus1/bft-consensus-agda", "max_stars_repo_head_hexsha": "a4674fc473f2457fd3fe5123af48253cfb2404ef", "max_stars_repo... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Copyright © 2017 Jinsong Liu <jinsongliu@utexas.edu>
#
# Distributed under terms of the GNU-License license.
"""
"""
import sys
sys.path.append('/Users/jinsongliu/Box Sync/Dissertation_UT/OMAE2018/UQ_FOWT')
import os
import csv
import numpy as np
i... | {"hexsha": "e3986cbd3e554174173aebd1f7b070c8e465d657", "size": 7445, "ext": "py", "lang": "Python", "max_stars_repo_path": "uqra/uqplot/MUSEPlot.py", "max_stars_repo_name": "Jinsongl/UQRA", "max_stars_repo_head_hexsha": "09c7042f8c35a262a942224e2367540b5fd2b077", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_... |
using Documenter
push!(LOAD_PATH, "../../src")
using Stipple, Stipple.Elements, Stipple.Layout, Stipple.Typography
makedocs(
sitename = "Stipple - data dashboards and reactive UIs for Julia",
format = Documenter.HTML(prettyurls = false),
pages = [
"Home" => "index.md",
"Stipple API" => [... | {"hexsha": "a5ab07072505141a1a3a0d2c94acfe2f7d21e264", "size": 627, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "jeremiedb/Stipple.jl", "max_stars_repo_head_hexsha": "4dafaa54219a9b837db7e040f3121acd64c94351", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max... |
//
// Copyright (c) 2013-2017 Vinnie Falco (vinnie dot falco at gmail dot com)
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
//
#ifndef BEAST_UNIT_TEST_SUITE_LIST_HPP
#define BEAST_UNIT_TEST_SUITE_LIST_HPP
#i... | {"hexsha": "41526a2d052b21144b3ac98a4ea9a772a2eb866c", "size": 1801, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/beast/extras/beast/unit_test/suite_list.hpp", "max_stars_repo_name": "sneh19337/rippled", "max_stars_repo_head_hexsha": "442205bdf270d26f0936f7ece5f03bcc952a6a8b", "max_stars_repo_licenses": ["B... |
import os
import collections
import logging
import yaml
import torch
import torchvision
import numpy as np
from skimage import io
from mathtools import utils, torchutils, metrics
logger = logging.getLogger(__name__)
class ImageClassifier(torch.nn.Module):
def __init__(
self, out_dim,
f... | {"hexsha": "cf3c54d80000970785cbe2afd0152ec47f67db98", "size": 11183, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/predict_video_pytorch.py", "max_stars_repo_name": "jd-jones/kinemparse", "max_stars_repo_head_hexsha": "279a9989981aa2a0eef1e46e5d02833f51ae352d", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
'''
@package prototype.coverage.record_set
@file prototype/coverage/record_set.py
@author David Stuebe
@author Tim Giguere
@brief https://confluence.oceanobservatories.org/display/CIDev/R2+Construction+Data+Model
'''
import numpy
class IterableExpression(dict):
"""
This class should i... | {"hexsha": "b6edb32c18309b46b1810fb3975458e41115c34e", "size": 864, "ext": "py", "lang": "Python", "max_stars_repo_path": "prototype/coverage/iterable_expression.py", "max_stars_repo_name": "ooici/pyon", "max_stars_repo_head_hexsha": "122c629290d27f32f2f41dafd5c12469295e8acf", "max_stars_repo_licenses": ["BSD-2-Clause"... |
# -*- coding: utf-8 -*-
''' Smooth Component (1)
This module contains the class for the convex heuristic for a piecewise linear
function. A piecewise constant function has a sparse second-order difference;
many changes in slope are exactly zero and a small number of them can be large.
A convex approximation of this p... | {"hexsha": "e9ffa5f5816e1e31dd03642d2627e6c0bfba438a", "size": 7717, "ext": "py", "lang": "Python", "max_stars_repo_path": "osd/classes/norm1_second.py", "max_stars_repo_name": "bmeyers/optimal-signal-decomposition", "max_stars_repo_head_hexsha": "14376d38e3b2965e0ccdaf4a8a1c3683697c146c", "max_stars_repo_licenses": ["... |
# -*- coding: utf-8 -*-
"""
Author: @gabvaztor
StartDate: 04/03/2017
This file contains the next information:
- Libraries to import with installation comment and reason.
- Data Mining Algorithm.
- Sets (train,validation and test) information.
- ANN Arquitectures.
- A lot of utils methods which you'... | {"hexsha": "a2ac16b7fcb5012ddf3099913435e1b50c79b433", "size": 6557, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/projects/German_Signal/TFBooster_.py", "max_stars_repo_name": "Gabvaztor/TFBoost__", "max_stars_repo_head_hexsha": "a37b906f5cb47becc3275def8282ff395d06ef45", "max_stars_repo_licenses": ["Apac... |
function conv(path::String)
data = []
lines = open(readlines, path)
lines = map(chomp, lines)
for i = 1:length(lines)
line = lines[i]
if isempty(line)
push!(data, "")
continue
end
items = split(line, " ")
word = String(items[1])
tag... | {"hexsha": "e503653a62fd26612543194267244c7c15dc4256", "size": 1848, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ner/preprocess/convert.jl", "max_stars_repo_name": "hshindo/Merlin-Examples", "max_stars_repo_head_hexsha": "a12fd471d5271b99f6d9680d8c768661dca1ea31", "max_stars_repo_licenses": ["MIT"], "max_star... |
From mathcomp Require Import
all_ssreflect.
From AUChain
Require Import
BlockTree
Blocks
Messages
Parameters
LocalState
StateMonad.
Set Implicit Arguments.
Unset Strict Implicit.
Unset Printing Implicit Defensive.
(** * Protocol
Execution plan for each party pr. round:
... | {"author": "AU-COBRA", "repo": "PoS-NSB", "sha": "8cb62e382f17626150a4b75e44af4d270474d3e7", "save_path": "github-repos/coq/AU-COBRA-PoS-NSB", "path": "github-repos/coq/AU-COBRA-PoS-NSB/PoS-NSB-8cb62e382f17626150a4b75e44af4d270474d3e7/Protocol/Protocol.v"} |
import numpy as np
PARADIGM = np.array([['A','B','C','D','E','F'],
['G','H','I','J','K','L'],
['M','N','O','P','Q','R'],
['S','T','U','V','W','X'],
['Y','Z','1','2','3','4'],
['5','6','7','8','9','_']])
... | {"hexsha": "9fc891d646f710bf2f8261d773a08d5d48c208cb", "size": 324, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/paradigm.py", "max_stars_repo_name": "Yuchen-Wang-SH/Electroencephalography-EEG-Signal-Classification-using-Deep-Learning", "max_stars_repo_head_hexsha": "55a3100182b7b5340ada375d46dd9ca0a... |
"""GLUT replacement for the original checker.py demonstration code
Note:
Has no navigation code ATM.
"""
# This is statement is required by the build system to query build info
if __name__ == '__build__':
raise Exception
__version__='$Revision: 1.1.1.1 $'[11:-2]
__date__ = '$Date: 2007/02/15 19:25:11 $'[6:-2]
imp... | {"hexsha": "4d0bbd301381536d05fe0c47cbb92376ca2b8f79", "size": 2898, "ext": "py", "lang": "Python", "max_stars_repo_path": "002-pyopengl/PyOpenGL-Demo-3.0.1b1/PyOpenGL-Demo/GLUT/tom/checker.py", "max_stars_repo_name": "lhl/vrdev", "max_stars_repo_head_hexsha": "fc1a9af2b51d159c99c8779349ef3392a70ed9ed", "max_stars_repo... |
"""
Molecular depiction features.
"""
__author__ = "Steven Kearnes"
__copyright__ = "Copyright 2014, Stanford University"
__license__ = "BSD 3-clause"
import io
import numpy as np
from PIL import Image
def load(string):
"""
Load an image from a file or binary string.
Parameters
----------
strin... | {"hexsha": "cd41912b76e50ca23b14445c3e70b7206375b683", "size": 2243, "ext": "py", "lang": "Python", "max_stars_repo_path": "vs_utils/utils/image_utils.py", "max_stars_repo_name": "rbharath/pande-gas", "max_stars_repo_head_hexsha": "7a947d087ba2dd77c4bbbb89b604cf83acdff5f3", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
[STATEMENT]
lemma gc_W_empty_invL[intro]:
notes fun_upd_apply[simp]
shows
"\<lbrace> handshake_invL \<^bold>\<and> obj_fields_marked_invL \<^bold>\<and> gc_W_empty_invL \<^bold>\<and> LSTP valid_W_inv \<rbrace>
gc
\<lbrace> gc_W_empty_invL \<rbrace>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbr... | {"llama_tokens": 4532, "file": "ConcurrentGC_Noninterference", "length": 12} |
[STATEMENT]
lemma Standard_hnorm [simp]: "x \<in> Standard \<Longrightarrow> hnorm x \<in> Standard"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> Standard \<Longrightarrow> hnorm x \<in> Standard
[PROOF STEP]
by (simp add: hnorm_def) | {"llama_tokens": 88, "file": null, "length": 1} |
/*
* Copyright (c) 2015, The Regents of the University of California (Regents).
* 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 a... | {"hexsha": "4e0dda356be6e1642fed0d731292b8eae84367bf", "size": 3059, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/test_rotation.cpp", "max_stars_repo_name": "jamesdsmith/berkeley_sfm", "max_stars_repo_head_hexsha": "de3ae6b104602c006d939b1f3da8c497b86d39ff", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
using DEBTrait, Test
el = "C4H9NO2"
chemFormBiom = [1, 1.8, 0.2, 0.5, 0, 0, 0]
chemical_composition = DEBTrait.ThermoStoichWizard.extract_composition(el) # CHNOSP
@test chemical_composition == [4,9,1,2,0,0]
stoich_electron_donor = DEBTrait.ThermoStoichWizard.get_stoich_electron_donor(el)
@test stoich_electron_donor ... | {"hexsha": "12b781547ff7bc3c7c90a15dd990d2ab2623c8c5", "size": 1080, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_ThermoStoichWizard.jl", "max_stars_repo_name": "giannamars/DEBTrait.jl", "max_stars_repo_head_hexsha": "3ce3dae8224f8226f727d43c5f2a05bd94c9a93f", "max_stars_repo_licenses": ["MIT"], "max... |
\documentclass{article}
\title{AATOM - An Agent-based Airport Terminal Operations Model Simulator}
\author{Stef Janssen
\\\href{mailto:s.a.m.janssen@tudelft.nl}{\textit{s.a.m.janssen@tudelft.nl}}
\\\textit{Delft University of Technology}
}
\date{September 2017}
\usepackage{natbib}
\usepackage{graphicx}
\usepackage{l... | {"hexsha": "0491d8ff766c3ff67c16777228c170a29d2ad77b", "size": 22969, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Tutorials/Tutorial.tex", "max_stars_repo_name": "StefJanssen/SeRiMa-ABM", "max_stars_repo_head_hexsha": "3fdf9f64037b1684b23a4c91323ca38589d86524", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import numpy as np
from .base import BaseOptimizer
class GradientDescent(BaseOptimizer):
def __init__(self, trainable_layers):
super(GradientDescent, self).__init__(trainable_layers)
def initialize(self):
pass
def update(self, learning_rate, w_grads, b_grads, step):
for layer in... | {"hexsha": "6a4b81d58886a1237147a5f7ebf04637a8c627d3", "size": 4237, "ext": "py", "lang": "Python", "max_stars_repo_path": "mini_keras/optimizer.py", "max_stars_repo_name": "Deep-Alchemy/Mini-Keras", "max_stars_repo_head_hexsha": "07aeb129c2530d39dc4dcea139c6fba72268d535", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
A Stage to load data from a CSV datarelease format file into a PISA pi ContainerSet
"""
from __future__ import absolute_import, print_function, division
import numpy as np
import pandas as pd
from pisa import FTYPE
from pisa.core.pi_stage import PiStage
from pisa.utils.profiler import profile
from pisa.core.cont... | {"hexsha": "5d3f670055807c9067dd675aed247aa7a90448dd", "size": 2473, "ext": "py", "lang": "Python", "max_stars_repo_path": "pisa/stages/data/csv_data_hist.py", "max_stars_repo_name": "wym109/pisa", "max_stars_repo_head_hexsha": "696803320f577d241651df900726b76a770d072a", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
module remesh_smoothing_examples
using FinEtools
using FinEtools.MeshExportModule
using FinEtools.TetRemeshingModule
function remesh1()
L= 0.3;
W = 0.3;
a = 0.15;
nL=46; nW=46; na=36;
fens,fes = T4block(a,L,W,nL,nW,na,:a);
t = deepcopy(connasarray(fes));
v = deepcopy(fens.xyz);
tm... | {"hexsha": "22838fb3207beba5336e7a0d690ccc80c8b7237f", "size": 2554, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/meshing/remesh_smoothing_examples.jl", "max_stars_repo_name": "KristofferC/FinEtools.jl", "max_stars_repo_head_hexsha": "b0ce27e6a8aa63d1057307bcf36919239b1f3279", "max_stars_repo_licenses... |
classdef Residuals < Exportable
%--- * --. --- --. .--. ... * ---------------------------------------------
% ___ ___ ___
% __ _ ___ / __| _ | __|
% / _` / _ \ (_ | _|__ \
% \__, \___/\___|_| |___/
% |___/ v 1.0RC1
%
%------------------... | {"author": "goGPS-Project", "repo": "goGPS_MATLAB", "sha": "30644df61d2459e3347ac5f3e31b71d9f69f4b01", "save_path": "github-repos/MATLAB/goGPS-Project-goGPS_MATLAB", "path": "github-repos/MATLAB/goGPS-Project-goGPS_MATLAB/goGPS_MATLAB-30644df61d2459e3347ac5f3e31b71d9f69f4b01/source/obj/Residuals.m"} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 25 08:41:10 2017
@author: Yann Roussel and Tuan Bui
Edited by: Emine Topcu on Sep 2021
"""
from collections import Counter
import Const
import json
import matplotlib.image as mpimg
import numpy as np
# Import pandas for data saving
import pandas as... | {"hexsha": "5b083aa8f51809567ad97b0924bb9bbe878a002d", "size": 14699, "ext": "py", "lang": "Python", "max_stars_repo_path": "Zebrafish spinal locomotor circuit/Version 2/Util.py", "max_stars_repo_name": "Bui-lab/Code", "max_stars_repo_head_hexsha": "6ce5972a4bd0c059ab167522ab1d945f3b0f5707", "max_stars_repo_licenses": ... |
#!/usr/bin/env python
#import numpy as np
import boards
import rules
def print_initial_state(grid):
print("-"*50)
print("initial board:")
grid.print_found()
grid.print_statistics()
print("-"*50)
def print_end_state(grid):
print("-"*50)
print("remaining candidates:")
grid.print_candida... | {"hexsha": "50cd08a3524cf56cee70dec23c3e0437ed3a5574", "size": 1518, "ext": "py", "lang": "Python", "max_stars_repo_path": "solver.py", "max_stars_repo_name": "christiana/sudoku_solver", "max_stars_repo_head_hexsha": "5066cbba736dc07f465f20e6509542ac729651ad", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_cou... |
from pathlib import Path
from functools import partial
import joblib
import torch
import numpy as np
import pandas as pd
import sentencepiece as spm
from tqdm import tqdm
from sklearn.model_selection import StratifiedShuffleSplit
from helperbot import setup_differential_learning_rates, freeze_layers
from helperbot.lr_... | {"hexsha": "7ccdc14dfcfb7feb20e9841253e85275da2a3e81", "size": 8503, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/sentiment_analysis/douban_sentiment.py", "max_stars_repo_name": "ceshine/modern_chinese_nlp", "max_stars_repo_head_hexsha": "e1d5941f381431ac114f440472d3e0f976437777", "max_stars_repo_lice... |
[STATEMENT]
lemma hequiv_names: \<open>hequiv H i j \<Longrightarrow> i \<in> names H\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. hequiv H i j \<Longrightarrow> i \<in> names H
[PROOF STEP]
unfolding hequiv_def names_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Nom j at i in' H \<Longrightarrow> ... | {"llama_tokens": 170, "file": "Hybrid_Logic_Hybrid_Logic", "length": 2} |
import os.path as osp
PATH_TO_ROOT = osp.join(osp.dirname(osp.realpath(__file__)), '..', '..', '..')
import sys
sys.path.append(PATH_TO_ROOT)
import pickle
import time
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import StepLR
from models.pointnet.src.u... | {"hexsha": "cbbecb922401274e31912c3ad07cb8e5c2af2c9e", "size": 9507, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/segmentation/PointNet/run_pointnet_segmentation.py", "max_stars_repo_name": "devskroy1/ForkedBrainSurfaceTK", "max_stars_repo_head_hexsha": "774035ab5eae6c0a40eb96eab43d489d3f722eaa", "max... |
import csv
import re
import math
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append("..") # adds higher directory to python modules path
from LoaderPACK.Loader import testload_5min
import torch
val_load_file = testload_5min(path = "/home/tyson/data_cutoff/val_model_data",
... | {"hexsha": "0eb4a6950491efd4f36f490786fae22677822ca1", "size": 775, "ext": "py", "lang": "Python", "max_stars_repo_path": "Testing loader/.ipynb_checkpoints/test-loader-checkpoint.py", "max_stars_repo_name": "marctimjen/Artefact-Rejection", "max_stars_repo_head_hexsha": "4e850d172fa8c08ba1776c46e760484673d7e7ad", "max_... |
import os
from abc import ABC, abstractmethod
import numpy as np
import torch
import torch.optim as opt
from sklearn import metrics
from torch import nn
from classifiers import Net
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Classifier(nn.Module):
def __init__(self, model=Net, n... | {"hexsha": "30c67cedabba0c6d6f2623f024540297443b81e2", "size": 6369, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/base_models.py", "max_stars_repo_name": "jbr-ai-labs/PU-OC", "max_stars_repo_head_hexsha": "4030a67353594d864a2a9482dd3f5d206cbd28ae", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
// $Id$
/***********************************************************************
Moses - factored phrase-based, hierarchical and syntactic language decoder
Copyright (C) 2009 Hieu Hoang
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License... | {"hexsha": "c232c9bc37d5eba61e2fd51944c13fbc7441710a", "size": 10654, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "model/mosesdecoder/OnDiskPt/TargetPhrase.cpp", "max_stars_repo_name": "saeedesm/UNMT_AH", "max_stars_repo_head_hexsha": "cc171bf66933b5c0ad8a0ab87e57f7364312a7df", "max_stars_repo_licenses": ["Apac... |
import numpy as np
import cv2
import csv
# Hyper Parameters
L = 2
# Read the csv file.
csv_reader = csv.reader(open('./data/passingevents.csv'))
# The first match.(First match and self passing only.)
passing_list = [row for row in csv_reader if row[1] == 'Huskies']
passing_cnt = len(passing_list)
# Analyzing the da... | {"hexsha": "4aa7761e7d91bac1af65faad25f65dca49a5a970", "size": 2024, "ext": "py", "lang": "Python", "max_stars_repo_path": "problem1_solve1.py", "max_stars_repo_name": "ligongzzz/MCM2020_Code", "max_stars_repo_head_hexsha": "7e5e6f9a6b09b3eb7e21774535c977ba6e974d79", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import requests
from clint.textui import progress
import urllib2
from bs4 import BeautifulSoup
import threading
import pafy
import time
import sys
from pathlib import Path
import os
import vlc
import nltk
from nltk.stem.lancaster import LancasterStemmer
import json
import numpy as np
from pydub import AudioSegment
from... | {"hexsha": "b70fde4beb3000907f788d149128b36275952240", "size": 15382, "ext": "py", "lang": "Python", "max_stars_repo_path": "tv_download.py", "max_stars_repo_name": "pmitra96/multi-threaded-downloader", "max_stars_repo_head_hexsha": "0b879cd05cf588ec497e3762456bdfd5678d00d9", "max_stars_repo_licenses": ["MIT"], "max_st... |
! Copyright 2021 Ivan Pribec
! SPDX-License-Identifier: Apache-2.0
!> Semi-implicit Runge-Kutta method of third order
!>
!> This is a modernized version of the code originally given in
!>
!> Villadsen, J., & Michelsen, M. L. (1978). Solution of differential
!> equation models by polynomial approximation. Prentice-... | {"hexsha": "c95e7bf3659e5ddf66f78fb3e6d46c82ee0ec7ee", "size": 8095, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/stiff3_solver.f90", "max_stars_repo_name": "awvwgk/stiff3", "max_stars_repo_head_hexsha": "7ed7379e1a20d229848fddec62453602216c2074", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
'''
multilabel_confusion_matrix.py
Run MATCH with PeTaL data.
Last modified on 23 July 2021.
DESCRIPTION
multilabel_confusion_matrix.py plots multilabel confusion matrices
based on the data in MATCH/PeTaL/results.
In a multilabel confusion matrix, the rows correspond to
... | {"hexsha": "52115071b8b8ca4d0375c7a75165c8a3646d5b38", "size": 8978, "ext": "py", "lang": "Python", "max_stars_repo_path": "auto-labeler/MATCH/analysis/multilabel_confusion_matrix.py", "max_stars_repo_name": "nasa-petal/PeTaL-labeller", "max_stars_repo_head_hexsha": "b68d534c8c9f026860ce7fe869eef4c16fe35505", "max_star... |
(* Some results about real numbers *)
From intuitionism Require Import lib set seq spr fan func classic choice.
From intuitionism Require Import bcp bar.
(*
Describing intervals of real numbers as binary sequences
--------------------------------------------------------
We can describe the real numbers in [a_0, b_0] ... | {"author": "bergwerf", "repo": "intuitionism", "sha": "581ac55e8a5382d3f35cf8f9b09accb9b5f89ae8", "save_path": "github-repos/coq/bergwerf-intuitionism", "path": "github-repos/coq/bergwerf-intuitionism/intuitionism-581ac55e8a5382d3f35cf8f9b09accb9b5f89ae8/reals.v"} |
import keras
from keras.preprocessing.image import ImageDataGenerator
from keras.datasets import cifar100,mnist,cifar10,fashion_mnist
from scipy.io import loadmat
import numpy as onp #original numpy
import jax.numpy as jnp #jax numpy
import itertools
#import custom_datasets
# TODO: Setup this function to take in a s... | {"hexsha": "48ae7516a75cb185f899718420d9eae5cc7c8cce", "size": 8739, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_data.py", "max_stars_repo_name": "anonymous-code-submission/ICML2021_anon_code_submission", "max_stars_repo_head_hexsha": "0c6b57c6170dd763e400e32392ce946ff6a86dfd", "max_stars_repo_licen... |
"""
@file
@brief Shape object.
"""
import numpy
class BaseDimensionShape:
"""
Base class to @see cl DimensionObject,
@see cl ShapeOperator, @see cl ShapeObject.
"""
def to_string(self, use_x=True):
"""
Converts the object into a string.
"""
raise NotImplementedErro... | {"hexsha": "d223d40bfc40a1d73ae541b43f2d86c830d70e77", "size": 29965, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlprodict/onnxrt/shape_object.py", "max_stars_repo_name": "xadupre/mlprodict", "max_stars_repo_head_hexsha": "f82c8a26a60104948c67849b1c4af95ca812c153", "max_stars_repo_licenses": ["MIT"], "max_s... |
\section{Efficient Implementation on ARM8}
\label{sec:arm}
We show that our semantics compiles efficiently to \armeight{}
\cite{deacon-git,DBLP:journals/pacmpl/PulteFDFSS18}. With one exception, we use the translation
strategy of \citet{DBLP:journals/pacmpl/PodkopaevLV19}, which was extended to
SC access by \citet[\t... | {"hexsha": "52fc31ffdc222e480786eb6a26f666bfe524b9c7", "size": 21155, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "corrigendum/short.tex", "max_stars_repo_name": "chicago-relaxed-memory/memory-model", "max_stars_repo_head_hexsha": "fd606fdb6a04685d9bb0bee61a5641e4623b10be", "max_stars_repo_licenses": ["CC-BY-4.... |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.rcParams['axes.unicode_minus'] = False
def make_a_figure():
data = np.arange(10)
p = plt.figure(figsize=(8, 6))
plt.title('line')
plt.xlabel('X')
plt.xlabel('Y')
plt.xlim(0, 5)
... | {"hexsha": "7b6bf9498441da99c2443512ac8f8a33f5c41cdd", "size": 3091, "ext": "py", "lang": "Python", "max_stars_repo_path": "11_data_science/matplotlib/test_pyplot.py", "max_stars_repo_name": "edgardeng/python-advance-interview", "max_stars_repo_head_hexsha": "59fd7bee8e871acdc7fdfecf2a110db840c47ebb", "max_stars_repo_l... |
//
// Created by David Oberacker on 2019-07-31.
//
#include <string>
#include <map>
#include <queue>
#include <boost/dynamic_bitset.hpp>
#include "common/common.hpp"
struct Node
{
uint8_t Symbol;
bool IsLeaf;
int64_t Left;
int64_t Right;
};
uint8_t decodeByte(boost::dynamic_bitset<> data, int64_t* o... | {"hexsha": "68a7f4cdf45bf66a465c7d4210dde3b9320bea23", "size": 2302, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/common/decoder.cpp", "max_stars_repo_name": "Oberacda/BorderlandsSaveEditor", "max_stars_repo_head_hexsha": "b959dc2c872f2a2ed4cc516c644b58f1f4425925", "max_stars_repo_licenses": ["MIT"], "max_s... |
import tensorflow as tf
import numpy as np
import json
from layers import Dense
class SimpleRNN(object):
def __init__(self,rnn_cell,overstructure,seq_len=5,feature_len=28,learning_rate=0.001,use_rnn_cell=True):
self._rnn_cell = rnn_cell
self._overstructure = overstructure
self.learning_rate = learning_rate
s... | {"hexsha": "2be597f5336bbc2877204bd1284387f0792203fa", "size": 3508, "ext": "py", "lang": "Python", "max_stars_repo_path": "rnn_net.py", "max_stars_repo_name": "Rufaim/Filtering-Clouds", "max_stars_repo_head_hexsha": "5703884a55f449ed737a3350d5276e29a69372f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
using FastMarching
using Images
using FileIO
function maze()
Float64.(channelview(img))
img = load(joinpath(Pkg.dir("FastMarching"),"examples/images/maze.png"))
end
maze()
| {"hexsha": "3ed961c19d2dd45caf7c8c3deeb7fe0ba00a4123", "size": 178, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/maze.jl", "max_stars_repo_name": "jgoldfar/FastMarching.jl", "max_stars_repo_head_hexsha": "ecd9bbb5b5b1120ca9e4fb88f36af679017d93c3", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_star... |
import logging
import os
import torch
import torch.nn.functional as F
from functools import partial
from torch import nn, einsum
import collections.abc as container_abcs
import numpy as np
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from timm.models.layers import DropPath, trunc_norm... | {"hexsha": "6fa12aae615259dbcd8917a61f4dffd8c5db0c1c", "size": 22590, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/cvt_v4_transformer.py", "max_stars_repo_name": "rahulmangalampalli/esvit", "max_stars_repo_head_hexsha": "5caf6e36b088ae2e7aaa4100b307eec991078e3e", "max_stars_repo_licenses": ["MIT"], "ma... |
import torch.nn as nn
import torch
import numpy as np
class GLU(nn.Module):
def __init__(self):
super(GLU, self).__init__()
# Custom Implementation because the Voice Conversion Cycle GAN
# paper assumes GLU won't reduce the dimension of tensor by 2.
def forward(self, input):
r... | {"hexsha": "8224bdce03dfd49ea932f7b8bb72e7ee99a26b98", "size": 17354, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_GLU.py", "max_stars_repo_name": "astricks/Voice-Conversion-GAN", "max_stars_repo_head_hexsha": "4ba2dc91a299413286c3976416442d54a08ec298", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: chengdicao
"""
import tensorflow as tf
import os
import numpy as np
from tqdm import tqdm
import argparse
from lib.utils import load_list, cosine_decay_lr, get_label_matrix, get_cosine_distance_matrix, get_rank_matrix
from lib.metrics import mean_average_pre... | {"hexsha": "39510ace0f2b806c82ee4d770564015794c9ef66", "size": 7756, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "DiDiDoes/MulKINet", "max_stars_repo_head_hexsha": "9afb7c56e25b8c4dd8425139eb907912eb1f880f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_star... |
The Tree of Peace was planted to the west of Mrak Hall on May 12^th^, 1984. It is a valley oaks valley oak that was planted in recognition of Native American Culture Days Native American Cultural Days by Chief Jake Swamp, an Iroquois Elder.
May the dream of the Peacemakera world without warone day come true.
| {"hexsha": "d5f1f3673175d8f9fd3a3f66e4392da10fbd180c", "size": 315, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/The_Tree_of_Peace.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""
this file is build based on the code found in evaluate_suffix_and_remaining_time.py
here the beam search (with breath-first-search) is implemented, to find compliant prediction
Author: Anton Yeshchenko
"""
from __future__ import division
import csv
import os.path
import time
from queue import PriorityQueue
from ... | {"hexsha": "90d4a0c3d32659fdf8443c0a1ccce0732e210241", "size": 12243, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/evaluation/inference_algorithms/baseline_2_cf.py", "max_stars_repo_name": "stebranchi/Incremental-predictive-monitoring-python3", "max_stars_repo_head_hexsha": "114b080df4afa0653ce03d8eb0059c... |
import numpy as np
import pandas as pd
from scipy import linalg
import scipy as sp
import matplotlib.pylab as plt
from scipy import sparse
try:
from firedrake import *
from firedrake.assemble import allocate_matrix, \
create_assembly_callable
except ImportError:
import_type = "fenics"
try:
from ... | {"hexsha": "63740c9b892b391ae2fdcfb911069f5243b5d310", "size": 5926, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/Stokes/Stokes_infsup.py", "max_stars_repo_name": "ralna/ElementSchur", "max_stars_repo_head_hexsha": "840f111a10dc80ab2367222c4a5b257e6e37af8b", "max_stars_repo_licenses": ["BSD-2-Clause"... |
import numpy as np
from lib.lif import LIF, ParamsLIF, LSM, ParamsLSM, LSM_const
n = 2 # Number of neurons
q = 100 # Number of LSM neurons
x_input = 2 # Constant input
alpha1 = 10 # Cost function params
alpha2 = 30 # Cost function params
tau_s = 0.020 # Time scal... | {"hexsha": "070a883e0c2d6a2991026e0dd5ca3448d781e54b", "size": 6109, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/learningbeta_fixedx_sweepw_banana.py", "max_stars_repo_name": "benlansdell/deep-rdd", "max_stars_repo_head_hexsha": "2f1443aa9800d0e0f3a4ce9051c1b8b9ed8c2ae9", "max_stars_repo_licenses": [... |
// Copyright (c) 2014-2015 DiMS dev-team
// Distributed under the MIT/X11 software license, see the accompanying
// file COPYING or http://www.opensource.org/licenses/mit-license.php.
#include "init.h"
#ifdef WIN32
#define MIN_CORE_FILEDESCRIPTORS 0
#else
#define MIN_CORE_FILEDESCRIPTORS 150
#endif
#if defined(HAVE_... | {"hexsha": "c5475e9830b14a8edfd77f561692196f5187b866", "size": 27471, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/monitor/init.cpp", "max_stars_repo_name": "salarii/dims", "max_stars_repo_head_hexsha": "b8008c49edd10a9ca50923b89e3b469c342d9cee", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0, "... |
"""
This code is modified from Hengyuan Hu's repository.
https://github.com/hengyuan-hu/bottom-up-attention-vqa
Reads in a tsv file with pre-trained bottom up attention features and
stores it in HDF5 format. Also store {image_id: feature_idx}
as a pickle file.
Hierarchy of HDF5 file:
{ 'image_features': num_images... | {"hexsha": "c668783dad78c64f8cfe41eb15a5879ecf5a33f1", "size": 3910, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/detection_features_converter_target.py", "max_stars_repo_name": "Shaobo-Xu/ban-vqa", "max_stars_repo_head_hexsha": "9b2f2a2acc91c542b80b756aed23fbb380e69f3a", "max_stars_repo_licenses": ["MI... |
import collections
import itertools
import json
import os
import shutil
from copy import deepcopy
import click
import joblib
import numpy as np
import pandas as pd
import torch
from ceem import logger, utils
from ceem.dynamics import *
from ceem.learner import *
from ceem.opt_criteria import *
from ceem.ceem import C... | {"hexsha": "6f16fb2439b55242329646607ebece817d18c27f", "size": 5089, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/lorenz/convergence_experiment_pem.py", "max_stars_repo_name": "sisl/CEEM", "max_stars_repo_head_hexsha": "6154587fe3cdb92e8b7f70eedb1262caa1553cc8", "max_stars_repo_licenses": ["MIT"],... |
/*
// Copyright (c) 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 applicable law or... | {"hexsha": "0634328701a932def82619510ead97cd39c00d2a", "size": 5850, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "redfish-core/include/event_service_manager.hpp", "max_stars_repo_name": "ztai-goog/bmcweb", "max_stars_repo_head_hexsha": "881e50b775fcccbc447fc39f40671574e0fa4157", "max_stars_repo_licenses": ["Apa... |
import numpy as np
import pdb
def running_cost(sys, x, u, xf_current):
"""
:param sys: system from gym environment this stores the
:param x: state trajectory
:param u: control trajectory
:return: gradients and Hessians of the loss function with respect to states and controls
"""
xf = np.s... | {"hexsha": "83c6e05b00d4371dec9d931da9c99ce25ca62d22", "size": 4735, "ext": "py", "lang": "Python", "max_stars_repo_path": "ddp/mpc_ddp_functions_circlequad.py", "max_stars_repo_name": "rebeccali/ese650-project", "max_stars_repo_head_hexsha": "0d96ff707384e67afdfb0ff259d5629257b0a78b", "max_stars_repo_licenses": ["MIT"... |
from __future__ import print_function
import os
from argparse import ArgumentParser
import numpy as np
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
if __name__ == '__main__':
parser = ArgumentParser("")
parser.add_argument("feats", help="Path to the npy features.")
... | {"hexsha": "b844e3f1daf05c31ab6d47c5b357a7c583f02cf8", "size": 1299, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocessing/pca.py", "max_stars_repo_name": "enricovian/GraphSAGE", "max_stars_repo_head_hexsha": "0cdda29dbc075fb8f3441c15638d1b06de992a57", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import os, json, copy, pickle
import numpy as np
import pandas as pd
from collections import defaultdict
from scipy import stats
from utils.metrics import Metrics
# this dir contains CLINC and SNIPS partial fewshot experiments
# EXPERIMENTS_DIR = "/path/to/partial-fewshot/savedir/"
# this dir contains CLINC, SNIPS, ... | {"hexsha": "b12d598b8283cda1183706ce5c88e929611e1602", "size": 15822, "ext": "py", "lang": "Python", "max_stars_repo_path": "runners/compile_results.py", "max_stars_repo_name": "ElementAI/data-augmentation-with-llms", "max_stars_repo_head_hexsha": "23673ab55cfb72295468e92ae58d0906f5dc7b05", "max_stars_repo_licenses": [... |
"""
NETALIGNMR
----------
solve the network alignment problem with Klau's algorithm
"""
function netalignmr(S::SparseMatrixCSC{Int64,Int64},w::Vector{Float64},
a::Int64,b::Int64,li::Vector{Int64},lj::Vector{Int64},
gamma::Float64,stepm::Int64,rtype::Int64,maxiter::Int... | {"hexsha": "78b16e16c3cad7668a6c4e9313fbf3ba9a4af903", "size": 5233, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/netalignmr.jl", "max_stars_repo_name": "nassarhuda/NetworkAlign", "max_stars_repo_head_hexsha": "2f1e2501b9c2fbb7dc97ae95da8fa3f1de503d45", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import os
import galsim
## Compute lensed ellipticities from shear and convergence
def calc_lensed_ellipticity_1(es1, es2, gamma1, gamma2, kappa):
gamma = gamma1 + gamma2*1j # shear (as a complex number)
es = es1 + es2*1j # intrinsic ellipticity (as a complex number)
g = gamma / (1.0 ... | {"hexsha": "50c3ac8e23146f3d3c40a945a8ec354d68a7287f", "size": 4891, "ext": "py", "lang": "Python", "max_stars_repo_path": "script/generate_noiseless.py", "max_stars_repo_name": "BastienArcelin/dc2_img_generation", "max_stars_repo_head_hexsha": "e3b84625afcd6a3127c98246841a9f5825c1262a", "max_stars_repo_licenses": ["BS... |
import sympy
from typing import List, Union
import logging
def test_homogeneity(exprs: Union[List[sympy.And], sympy.Matrix], vars: List[sympy.Symbol], trigger=None):
"""
Tests whether dynamics are homogeneous. If so, return also the homogeneity degree.
@param exprs: List of sympy expressions representing t... | {"hexsha": "d37ecad824b0c72b770927eb87abb1e69c42fe74", "size": 4926, "ext": "py", "lang": "Python", "max_stars_repo_path": "ETCetera/util/homogeneous.py", "max_stars_repo_name": "ggleizer/ETCetera", "max_stars_repo_head_hexsha": "8fa9f3c82fd1944507a0c02d52a236244821f3ca", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# -*- coding: utf-8 -*-
import numpy as np
from functools import lru_cache
from .base import Predictor
from ..base import Property
from ..functions import gauss2sigma, unscented_transform
from ..types.prediction import GaussianStatePrediction
from ..types.state import State
class KalmanPredictor(Predictor):
"""... | {"hexsha": "905ebc5b7b1c3e6e8417021dc86d756202fa9414", "size": 15584, "ext": "py", "lang": "Python", "max_stars_repo_path": "stonesoup/predictor/kalman.py", "max_stars_repo_name": "GSORF/Stone-Soup", "max_stars_repo_head_hexsha": "0aa730929fa6a1630a5279516c3377867e49b9b9", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
print_freq = 5
v1 = [0.34208, 0.23126, 0.20223, 0.18834, 0.18191, 0.17678, 0.17864, 0.17478, 0.17649, 0.17908, 0.17846, 0.18053, 0.20251, 0.18986, 0.18758, 0.1906, 0.19082, 0.19142]
# v2 = []
v3 = [0.32738, 0.28121, 0.2635, 0.26... | {"hexsha": "8d3af9d82d908b38163f9fd536b5adc4238d2b19", "size": 1734, "ext": "py", "lang": "Python", "max_stars_repo_path": "exp4/exp4_plot.py", "max_stars_repo_name": "Haunter17/MIR_SU17", "max_stars_repo_head_hexsha": "0eaefb8cab78ca896c1ed0074892c296110eb161", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import gym
import numpy as np
from rlkit.envs.pygame import pnp_util
from rlkit.torch.sets import set_creation
from multiworld.envs.pygame import PickAndPlaceEnv
from rlkit.envs.images import EnvRenderer
from multiworld import register_all_envs
def main():
register_all_envs()
# env = PickAndPlaceEnv(
# ... | {"hexsha": "94ab34deb8ddb077c0b92a0956d4ea33688d3579", "size": 1949, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/vitchyr/goal_distribution/representation_learning/set_creation/pygame_create_imgs.py", "max_stars_repo_name": "Asap7772/railrl_evalsawyer", "max_stars_repo_head_hexsha": "baba8ce634d32... |
//=================================================================================================
// Copyright (c) 2013, Johannes Meyer, TU Darmstadt
// All rights reserved.
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are... | {"hexsha": "7f2037f24aa3e4d797c2e9a74ca9fbd1ea50e689", "size": 14663, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Source Files/hector_quadrotor_tutorial/src/hector_quadrotor/hector_quadrotor_controller/src/twist_controller.cpp", "max_stars_repo_name": "AntoineHX/OMPL_Planning", "max_stars_repo_head_hexsha": "6... |
[STATEMENT]
lemma rot_circle_cube_is_type_II:
shows "typeII_twoCube rot_circle_cube"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. typeII_twoCube rot_circle_cube
[PROOF STEP]
using d_gt_0 swap_typeI_is_typeII circle_cube_is_type_I
[PROOF STATE]
proof (prove)
using this:
0 < d
typeI_twoCube ?C \<Longrightarrow> ty... | {"llama_tokens": 226, "file": "Green_CircExample", "length": 2} |
# Copyright (c) 2013: Joey Huchette and contributors
#
# Use of this source code is governed by an MIT-style license that can be found
# in the LICENSE.md file or at https://opensource.org/licenses/MIT.
using CPLEX
using Test
@testset "MathOptInterface Tests" begin
for file in readdir("MathOptInterface")
... | {"hexsha": "2c62595b9bf9a5a36968ce153dfb7ef3786d15df", "size": 376, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "JuliaOpt/CPLEX.jl", "max_stars_repo_head_hexsha": "e2f15e06b767b33941dce62873a048d18e6a844f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 52, "m... |
(* Title: Imperative_HOL_Time/Array_Time.thy
Author: Maximilian P. L. Haslbeck & Bohua Zhan, TU Muenchen
*)
section \<open>Monadic arrays\<close>
text \<open>This theory is an adaptation of \<open>HOL/Imperative_HOL/Array.thy\<close>,
adding time bookkeeping.\<close>
theory Array_Time
imports Heap_Time... | {"author": "isabelle-prover", "repo": "mirror-afp-devel", "sha": "c84055551f07621736c3eb6a1ef4fb7e8cc57dd1", "save_path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel", "path": "github-repos/isabelle/isabelle-prover-mirror-afp-devel/mirror-afp-devel-c84055551f07621736c3eb6a1ef4fb7e8cc57dd1/thys/Van_Emde_Boas... |
@testset "independent" begin
x = MOInput([rand(5) for _ in 1:4], 3)
y = MOInput([rand(5) for _ in 1:4], 3)
k = IndependentMOKernel(GaussianKernel())
@test k isa IndependentMOKernel
@test k isa MOKernel
@test k isa Kernel
@test k.kernel isa Kernel
@test k(x[2], y[2]) isa Real
@test ... | {"hexsha": "354b207f300afcb2af381dfa19b90f259b7592a4", "size": 656, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/mokernels/independent.jl", "max_stars_repo_name": "simonschoelly/KernelFunctions.jl", "max_stars_repo_head_hexsha": "600df21de4465c50a0bb73be344a9bf95e6212f6", "max_stars_repo_licenses": ["MIT"... |
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import threading
import time
import tools
import webbrowser
def tb_view(model, logdir=None, cmd=None):
"""Visualises a :model: in TensorBoard. (That is, everything in the model's Graph, which may actually be much larger
than the model ... | {"hexsha": "6061c66786b9ca3d5b4c0672861641ec238c2263", "size": 3191, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/visualise.py", "max_stars_repo_name": "patrick-kidger/ktools", "max_stars_repo_head_hexsha": "9b31ed348ce011781576a1e194c9126e2937982f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
import numpy as np
import matplotlib.pyplot as plt
import time
import _pickle as cPickle
# Function used for loading the CIFAR10 dataset
def unpickle(file):
with open(file, 'rb') as fo:
dict = cPickle.load(fo)
return dict
# Compute the softmax function of the output
def softmax(y):
max_of_rows = ... | {"hexsha": "c04700abe7e0f1c03dc65060f3481c69759df459", "size": 15095, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "A-Katopodis/Neural-Network-Implemenation", "max_stars_repo_head_hexsha": "4e28f695fba57c63aa9e3d5b7ac6036a341d97d5", "max_stars_repo_licenses": ["Apache-2.0"], "... |
"""Makes a .joblib file containing the trained model
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import time
import numpy as np
import logging
import tensorflow as tf
from tensorflow.python.platform import app, flags
from clev... | {"hexsha": "5760177d740faa35d2226dbb5d0220d82127d270", "size": 2017, "ext": "py", "lang": "Python", "max_stars_repo_path": "cleverhans_v3.1.0/cleverhans/model_zoo/madry_lab_challenges/make_cifar10_joblib.py", "max_stars_repo_name": "xu-weizhen/cleverhans", "max_stars_repo_head_hexsha": "c83898f5c6e6077ba6f3057dce9adcc4... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 7 18:32:28 2019
@author: stayal0ne
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.preprocessing import Imputer, LabelEncoder, OneHotEncoder, StandardScaler
from sklearn.model_selection import train_test_spl... | {"hexsha": "fe918f3bcf2aba5ac6e6b76bd24f4c3412be5e05", "size": 4351, "ext": "py", "lang": "Python", "max_stars_repo_path": "research_extension/slope.py", "max_stars_repo_name": "zelzhan/Linear-algebra-with-python", "max_stars_repo_head_hexsha": "a58042c9f29f67aafcd2c1c4c1300a0e9223a650", "max_stars_repo_licenses": ["MI... |
"""
Tools to perform analyses by shuffling in time, as in Landau & Fries (2012) and
Fiebelkorn et al. (2013).
"""
import os
import yaml
import numpy as np
import statsmodels.api as sm
from statsmodels.stats.multitest import multipletests
from .utils import avg_repeated_timepoints, dft
# Load the details of the behavi... | {"hexsha": "32eaa0a294af2308ff208fed9c050fd370b31fec", "size": 8526, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis_methods/shuff_time.py", "max_stars_repo_name": "gbrookshire/simulated_rhythmic_sampling", "max_stars_repo_head_hexsha": "5c9ed507847a75dbe38d10d78b54441ae83f5831", "max_stars_repo_license... |
import numpy as np
import random
from sklearn.metrics import mean_squared_error
from sklearn.neural_network import MLPClassifier
def one_hot_generator(length):
a = []
for i in range(0, length):
i = random.randint(0, 7)
a.append(i)
output = np.eye(8)[a]
return output
n_... | {"hexsha": "52fe2e5fc023e7af3c88139db0ec3389ea064a1a", "size": 1197, "ext": "py", "lang": "Python", "max_stars_repo_path": "MachineLearning/hw3_q1_sklearn/once.py", "max_stars_repo_name": "SeanSyue/SklearnReferences", "max_stars_repo_head_hexsha": "a2770a7108947877e772f3525bc915c5de4114bb", "max_stars_repo_licenses": [... |
# ----------------------------------------------------------------------------
# Copyright (c) 2016-2017, QIIME 2 development team.
#
# Distributed under the terms of the Modified BSD License.
#
# The full license is in the file LICENSE, distributed with this software.
# ------------------------------------------------... | {"hexsha": "3cb16290451e0fd7452feb771084663d9cd501ba", "size": 16119, "ext": "py", "lang": "Python", "max_stars_repo_path": "q2_diversity/tests/test_alpha.py", "max_stars_repo_name": "gregcaporaso/q2-diversity", "max_stars_repo_head_hexsha": "3b03b4c1e47b2893668f14c91612507e4864c34e", "max_stars_repo_licenses": ["BSD-3... |
import os
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
import pickle
from pdb import set_trace as stop
from dataloaders.data_utils import get_unk_mask_indices,image_loader
class VGDataset(torch.utils.data.Dataset):
def __init__(self, img_dir, img_list, image_transform,label_pat... | {"hexsha": "b5d38d32aa3118d60d96b05b3789be42697bca7e", "size": 1489, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataloaders/vg500_dataset.py", "max_stars_repo_name": "sorrowyn/C-Tran", "max_stars_repo_head_hexsha": "236d785952c59210e6812b4ad5ee12bab585ce4c", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
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