text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
[STATEMENT]
lemma [simp]: "eff i P pc et None = []"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. eff i P pc et None = []
[PROOF STEP]
(*<*)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. eff i P pc et None = []
[PROOF STEP]
by (simp add: Effect.eff_def) | {"llama_tokens": 115, "file": "JinjaDCI_Compiler_TypeComp", "length": 2} |
from . import common
import sys
import os
def show_cpu_features():
from numpy.lib.utils import _opt_info
info = _opt_info()
info = "NumPy CPU features: " + (info if info else 'nothing enabled')
# ASV wrapping stdout & stderr, so we assume having a tty here
if 'SHELL' in os.environ and sys.platform ... | {"hexsha": "7b9f1d3e688d4206e9d7de31f618fc71f86a5736", "size": 1866, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/benchmarks/__init__.py", "max_stars_repo_name": "iam-abbas/numpy", "max_stars_repo_head_hexsha": "2fb5e969fded3cd468f2ca01d5b954c953545dd9", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import sys
from pyspark import SparkConf, SparkContext
import re
import numpy as np
# Load and parse the data
def parseToFrom(line):
data = line.split(":")
pageTo = int(data[0])
# check if data[1] is empty
if not data[1]:
inLinksNum = 0
else:
# use unique links in data[1]
l... | {"hexsha": "916d5fd102afc1c510445d561655e0f19727f774", "size": 3191, "ext": "py", "lang": "Python", "max_stars_repo_path": "part4-SimRank PageRank/wez311_task2.py", "max_stars_repo_name": "weizh888/mining-massive-dataset", "max_stars_repo_head_hexsha": "d98891023a8a38db5c52512607587a47d7145920", "max_stars_repo_license... |
module Data.JSON.ToString
import Data.JSON.Value
import Data.Scientific
-- TODO: check if this is working correctly!
private
isControlChar : Char -> Bool
isControlChar c =
or [ cast c >= (0*1) && cast c <= (15*1 + 1*16)
, cast c == (15*1 + 7*16)
, cast c == (5*1 + 8*16)
]
-- TODO: this nice escaped v... | {"hexsha": "9a9d54d48929f58dc279555512f7c059994d857e", "size": 2599, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "src/Data/JSON/ToString.idr", "max_stars_repo_name": "jumper149/idris-json", "max_stars_repo_head_hexsha": "82543f1135011d0ac70a6fcd8f1eb70e1eb40fd4", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
"""Simple loader class that reads any of Carl Olsson's datasets from a folder on disk.
Authors: John Lambert
"""
import glob
import os
from pathlib import Path
from typing import Optional
import numpy as np
from gtsam import Cal3Bundler, Pose3, Rot3
from scipy.io import loadmat
import gtsfm.utils.io as io_utils
fro... | {"hexsha": "fe9af4a775eafe82522940803cfbf5fbe4400df7", "size": 5576, "ext": "py", "lang": "Python", "max_stars_repo_path": "gtsfm/loader/olsson_loader.py", "max_stars_repo_name": "PratyushaMaiti/gtsfm", "max_stars_repo_head_hexsha": "0d03dca0b6fb9293c9a3fb619a2141903168269a", "max_stars_repo_licenses": ["Apache-2.0"], ... |
%% build_system
% Build a system to solve by MaxwellFDFD.
%%% Syntax
% [osc, grid3d, s_factor_cell, eps_cell, mu_cell, J_cell] = build_system(ge, OSC, DOM, OBJ, SRC, [progmark])
% [..., obj_array, src_array, mat_array] = build_system(ge, OSC, DOM, OBJ, SRC, [pragmark])
% [..., eps_node_array, mu_node_array] = build... | {"author": "wsshin", "repo": "maxwellfdfd", "sha": "f7d583813781694c8a6f0533a91f56c2a78a9ee5", "save_path": "github-repos/MATLAB/wsshin-maxwellfdfd", "path": "github-repos/MATLAB/wsshin-maxwellfdfd/maxwellfdfd-f7d583813781694c8a6f0533a91f56c2a78a9ee5/io/build_system.m"} |
from IPython import embed
import numpy
from synospec.etc.source import OnSkyMoffat, OnSkyGaussian
def test_moffat():
fwhm = 0.7
beta = 3.5
sampling = 0.05
size = 5.
gau = OnSkyGaussian(fwhm, sampling=sampling, size=size)
mean_gau_x = numpy.sum(gau.data*gau.X)/numpy.sum(gau.data)
mean_g... | {"hexsha": "8fcec1f11019a613c55301328004151bd64c33d2", "size": 1360, "ext": "py", "lang": "Python", "max_stars_repo_path": "synospec/tests/test_source.py", "max_stars_repo_name": "kbwestfall/synospec", "max_stars_repo_head_hexsha": "1b882b3834b957a14a511db091421166ac24abfb", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
#!/usr/bin/env python
# coding: utf-8
# ## First Impressions Bayesian Deep Learning
#
# #### How does Laplace Approximation work in determining accuracy of facial impressions?
# In[1]:
#import libraries
import pandas as pd
import numpy as np
import os
import tensorflow as tf
import cv2
import glob as glob
from PI... | {"hexsha": "fbf529e9bc69c2b8e5239f3cfa57fc82239f87d4", "size": 6437, "ext": "py", "lang": "Python", "max_stars_repo_path": "FMRI and First Impressions(1).py", "max_stars_repo_name": "markhinds/research", "max_stars_repo_head_hexsha": "bcfb07e4809f8d4b12e9f663d91a82252372737d", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | {"hexsha": "f7ab34aed3bdfb95a40ac27ff8b3e09a33e12b43", "size": 11486, "ext": "py", "lang": "Python", "max_stars_repo_path": "tensorflow/python/keras/layers/einsum_dense_test.py", "max_stars_repo_name": "yage99/tensorflow", "max_stars_repo_head_hexsha": "c7fa71b32a3635eb25596ae80d007b41007769c4", "max_stars_repo_license... |
import numpy as np
import matplotlib.pyplot as plt
from skimage import io
import cv2
def equalize(img):
x_max = img.max()
s = img.size
h = np.zeros(256)
for i in range(256):
h[i] = np.count_nonzero(img == i)
out = np.zeros_like(img)
for i in range(256):
out[img == i] = x_max /... | {"hexsha": "f195fd9e8478f7feba81149ce730312509b545c6", "size": 677, "ext": "py", "lang": "Python", "max_stars_repo_path": "answers/question23.py", "max_stars_repo_name": "Yamahitsuji/Gasyori100knock", "max_stars_repo_head_hexsha": "62b3f776124c25dfb36e45a647d573b36b45d2b1", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Module for running CNN-BiLSTM vad model,
# may also be run directly as a script
# Author: Nick Wilkinson 2021
import argparse
import os
import numpy as np
import pandas as pd
import tensorflow as tf
from typing import Tuple
from tensorflow.keras import models
from voxseg import utils
from scipy.signal import medfilt
... | {"hexsha": "f73c8bb84783e010fba58c16673e44bb4d95fadd", "size": 10126, "ext": "py", "lang": "Python", "max_stars_repo_path": "voxseg/run_cnnlstm.py", "max_stars_repo_name": "NickWilkinson37/voxseg", "max_stars_repo_head_hexsha": "6402a67c0b4ee68115070b6aa870199d1f43c5a2", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
/*
* @Description: velocity 数据
* @Author: Ren Qian
* @Date: 2019-07-17 18:27:40
*/
#ifndef LIDAR_LOCALIZATION_SENSOR_DATA_VELOCITY_DATA_HPP_
#define LIDAR_LOCALIZATION_SENSOR_DATA_VELOCITY_DATA_HPP_
#include <deque>
#include <Eigen/Dense>
namespace lidar_localization {
class VelocityData {
public:
struct Li... | {"hexsha": "0fe5d1f5d972b390e9652b00feb75b7eb680eebb", "size": 819, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "LiDAR-SLAM/03-mapping-and-matching/src/lidar_localization/include/lidar_localization/sensor_data/velocity_data.hpp", "max_stars_repo_name": "lanqing30/SensorFusionCourse", "max_stars_repo_head_hexsha... |
import asyncio
import binascii
from dataclasses import asdict, dataclass
import io
import json
from pathlib import Path
from aiohttp import web
from aiohttp_index import IndexMiddleware
import numpy as np
from PIL import Image
MODULE_DIR = Path(__file__).parent.resolve()
STATIC_PATH = MODULE_DIR / 'web_static'
@da... | {"hexsha": "69a94137ff3cff083d6ca4f5d7c1cda21ba6f304", "size": 3088, "ext": "py", "lang": "Python", "max_stars_repo_path": "web_interface.py", "max_stars_repo_name": "Alienvlg/style_transfer", "max_stars_repo_head_hexsha": "9ec69db05a2522da7e4c88b2a50d8392e2b2e958", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
[STATEMENT]
lemma (in is_functor) cat_cf_obj_comma_is_arrI[cat_comma_cs_intros]:
assumes "b \<in>\<^sub>\<circ> \<BB>\<lparr>Obj\<rparr>"
and "ABF = [A, B, F]\<^sub>\<circ>"
and "A = [a, 0, f]\<^sub>\<circ>"
and "B = [a', 0, f']\<^sub>\<circ>"
and "F = [g, 0]\<^sub>\<circ>"
and "g : a \<mapsto>\<^... | {"llama_tokens": 1640, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_Comma", "length": 8} |
#########################
######## Imports ########
#########################
import matplotlib.pyplot as plt
import numpy as np
import json
import random
import networkx as nx
#########################
####### Constants #######
#########################
WEIGHT_MIN_DEFAULT = 0
WEIGHT_MAX_DEFAULT = 10
NUM_NODES_DEFAU... | {"hexsha": "d0e90be64b805d21c2db04dae71300d2fda43237", "size": 6494, "ext": "py", "lang": "Python", "max_stars_repo_path": "generator.py", "max_stars_repo_name": "parmita52/graph-generator", "max_stars_repo_head_hexsha": "670d018d2694778f25c4ee2fa052fc8b33f819b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Copyright (c) 2020 NVIDIA Corporation. All rights reserved.
# This work is licensed under a NVIDIA Open Source Non-commercial license
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.modules.utils as utils
from torch.utils.checkpoint import ch... | {"hexsha": "2b8796b9f1b3d53f78d5d1a46f6b8d89a4466598", "size": 10987, "ext": "py", "lang": "Python", "max_stars_repo_path": "conv-tt-lstm/code_opt/utils/convlstmcell.py", "max_stars_repo_name": "kidrabit/Data-Visualization-Lab-RND", "max_stars_repo_head_hexsha": "baa19ee4e9f3422a052794e50791495632290b36", "max_stars_re... |
# Author: Vincent Arel-Bundock
# Contact: varel@umich.edu
# License: BSD-3
# Original code from the python statsmodels project
# https://github.com/statsmodels/statsmodels
function qreg_coef(y, X::Matrix, q::Real, method::IRLS)
n, p = size(X)
xstar = copy(X)
diff = Inf
beta0 = zeros(p)
beta = zer... | {"hexsha": "9ad5b37b1452f0f637565d4f1d0bd0bde6fb83f7", "size": 1239, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/IRLS.jl", "max_stars_repo_name": "andreasnoack/QuantileRegressions.jl", "max_stars_repo_head_hexsha": "581981d0ee599c43cad8c3917f39018f2ef12720", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import os.path
import shutil
import unittest
import warnings
from abc import ABC, abstractmethod
from typing import Any, Callable, Dict, Optional, Type, Union
import fsspec
import numpy as np
import xarray as xr
import xcube.core.mldataset
from test.s3test import MOTO_SERVER_ENDPOINT_URL
from test.s3test import S3Tes... | {"hexsha": "c35cf5e7374172c16dbf0cb5e4a787364cc00476", "size": 15411, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/core/store/fs/test_registry.py", "max_stars_repo_name": "bcdev/xcube", "max_stars_repo_head_hexsha": "9d275ef3baef8fbcea5c1fbbfb84c3d0164aecd3", "max_stars_repo_licenses": ["MIT"], "max_star... |
using Test
using Bigsimr
using LinearAlgebra
using Distributions
@testset "Correlation Functions" begin
r_negdef = [
1.00 0.82 0.56 0.44
0.82 1.00 0.28 0.85
0.56 0.28 1.00 0.22
0.44 0.85 0.22 1.00
]
@testset "Nearest positive definite correlation matrix" begin
r = ... | {"hexsha": "44ea4535361dbaeb7325d95b3c7e97982cd1f262", "size": 8181, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/Correlation.jl", "max_stars_repo_name": "adknudson/MvSim.jl", "max_stars_repo_head_hexsha": "6c3085289a5e23441f5f0db90f2b3cbbb9b49afc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
#!/usr/bin/env python
import sys
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import argparse
from matplotlib.patches import Rectangle
from sklearn.cluster import KMeans
from collections import Counter
import math
from anti_instagram.calcLstsqTransform import calcTransform
from anti_instagram... | {"hexsha": "c8ba72a1780db77ffdff49350ee858c429118054", "size": 18485, "ext": "py", "lang": "Python", "max_stars_repo_path": "catkin_ws/src/10-lane-control/anti_instagram/sandbox/KMeansTrialczuidema.py", "max_stars_repo_name": "johnson880319/Software", "max_stars_repo_head_hexsha": "045894227f359e0a3a3ec5b7a53f8d1ebc06a... |
# from fairseq.data import BertDictionary
import os
import torch
import math
import numpy as np
import logging
import itertools
from fairseq import search, utils
from fairseq.tasks import register_task
from fairseq.tasks.translation import TranslationTask
from fairseq.data import (
AppendTokenDataset,
ConcatD... | {"hexsha": "da121e9daa82b6144ae5304ad550de126d04b79a", "size": 18238, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/translation_segment2.py", "max_stars_repo_name": "a414351664/TRAB-IKE", "max_stars_repo_head_hexsha": "3dd07221e1854c974127d7f6d0d95779a25166c0", "max_stars_repo_licenses": ["MIT"], "max_star... |
# Copyright 2020 - 2021 MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in wri... | {"hexsha": "683cd600bce3888a3749e6eab575bfff6c8ae02a", "size": 9533, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/deepedit/test_transforms.py", "max_stars_repo_name": "IntroAI-termproject/MONAILabel", "max_stars_repo_head_hexsha": "6a0fcc797e24aff1a1582088bae71973b2b6582e", "max_stars_repo_licenses... |
from functools import partial
from typing import Optional, Dict
import datasets
import numpy as np
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from transformers import BertTokenizerFast
from rouge_score import rouge_scorer
class RACEDataModule... | {"hexsha": "f367ba802002656eb97a937ba79702a116d026d6", "size": 5244, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/RACEDataModuleForTinyChoice.py", "max_stars_repo_name": "iamNCJ/bert-race-pytorch-lightning", "max_stars_repo_head_hexsha": "93abcc5d5c80790e16114fb021870593cb60f1f2", "max_stars_repo_license... |
# Copyright 2016-2020 The Van Valen Lab at the California Institute of
# Technology (Caltech), with support from the Paul Allen Family Foundation,
# Google, & National Institutes of Health (NIH) under Grant U24CA224309-01.
# All rights reserved.
#
# Licensed under a modified Apache License, Version 2.0 (the "License");... | {"hexsha": "e89fc800a73404a1babfdd27c80afa283b42f0eb", "size": 13796, "ext": "py", "lang": "Python", "max_stars_repo_path": "caliban_toolbox/reshape_data_test.py", "max_stars_repo_name": "vanvalenlab/deepcell-data-engineering", "max_stars_repo_head_hexsha": "a0dca3839f341841cb4a983923fd0eac21225736", "max_stars_repo_li... |
[STATEMENT]
lemma max_depth_0: "max_depth \<phi> = 0 = (\<exists>n. \<phi> = Atom n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (max_depth \<phi> = 0) = (\<exists>n. \<phi> = Atom n)
[PROOF STEP]
by (cases \<phi>) auto | {"llama_tokens": 101, "file": "Abstract_Completeness_Propositional_Logic", "length": 1} |
import json
import os
import numpy as np
from utils.FScore import F1Score
from Identification.LoadDescriptors import loadDescriptors
from Identification.PreprocessingDescriptors import preprocessDescriptors
from Identification.svm import trainSVM
__author__ = 'andres'
format_extension = '.json'
# DescribeSounds.get... | {"hexsha": "47d75ed17ff3c0867ac44ebbbe8a718bb17ff056", "size": 1884, "ext": "py", "lang": "Python", "max_stars_repo_path": "Evaluation/testNewSounds.py", "max_stars_repo_name": "andimarafioti/AIAMI", "max_stars_repo_head_hexsha": "37ea2bf61e85bf879a0f4a1014f2e93b87301582", "max_stars_repo_licenses": ["MIT"], "max_stars... |
"""
Predicting the good/bad classification of depth pixels, based solely on the current view.
This network returns the depth estimate and the trust both.
"""
import math
import numpy as np
import torch
from experiment_handler import ExperimentHandler
from network_architectures.network import Network, UNet
class Dept... | {"hexsha": "4e44d931f367626d0ad2be7fee437d11bcfe03bc", "size": 1951, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/network_architectures/DepthMapInitialTrust.py", "max_stars_repo_name": "simon-donne/defusr", "max_stars_repo_head_hexsha": "fa4275070af4024eea128e99d7c6df2358d129a5", "max_stars_repo_licenses... |
from keras.models import Sequential
from keras.layers import Dense
import numpy as np
import sys, os, string
characters = string.printable
char_indices = dict((c, i) for i, c in enumerate(characters))
indices_char = dict((i, c) for i, c in enumerate(characters))
INPUT_VOCAB_SIZE = len(characters)
WINDOW_SIZE = 3
def... | {"hexsha": "6b6e0f1ba0c385b1e103e98d11457bf825b6f3b7", "size": 2646, "ext": "py", "lang": "Python", "max_stars_repo_path": "39-sliding-window/tf-39.py", "max_stars_repo_name": "cyyeh/exercises-in-programming-style", "max_stars_repo_head_hexsha": "70c4c3a346c77eb207c247b8c717d490099cce4f", "max_stars_repo_licenses": ["M... |
library(grid)
library(extrafont)
library(extrafontdb)
library(ggthemes)
font_import()
loadfonts(device = "win")
theme_Publication <- function(base_size=14, base_family="Arial") {
(theme_foundation(base_size=base_size, base_family=base_family)
+ theme(plot.title = element_text(face = "bold",
... | {"hexsha": "f54266cdc64eb284bb10e536842591308dcfaf3c", "size": 1984, "ext": "r", "lang": "R", "max_stars_repo_path": "Raw data/publication_theme.r", "max_stars_repo_name": "tshalev/WRC-genome-paper", "max_stars_repo_head_hexsha": "8fbd7d389ee75ea08a0a978f74c4910cd51a4904", "max_stars_repo_licenses": ["CC0-1.0"], "max_s... |
(*
Copyright 2014 Cornell University
Copyright 2015 Cornell University
Copyright 2016 Cornell University
This file is part of VPrl (the Verified Nuprl project).
VPrl is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Sof... | {"author": "vrahli", "repo": "NuprlInCoq", "sha": "0c3d7723836d3f615ea47f56e58b2ea6173e7d98", "save_path": "github-repos/coq/vrahli-NuprlInCoq", "path": "github-repos/coq/vrahli-NuprlInCoq/NuprlInCoq-0c3d7723836d3f615ea47f56e58b2ea6173e7d98/cequiv/approx_props1.v"} |
// smooth_feedback: Control theory on Lie groups
// https://github.com/pettni/smooth_feedback
//
// Licensed under the MIT License <http://opensource.org/licenses/MIT>.
//
// Copyright (c) 2021 Petter Nilsson
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and asso... | {"hexsha": "24427fd83c01f1ed5332bda669e3f719205a0e2b", "size": 2408, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/test_ocp_flatten.cpp", "max_stars_repo_name": "tgurriet/smooth_feedback", "max_stars_repo_head_hexsha": "1f926cb4269741ddc09ba048af5bea5e0390a053", "max_stars_repo_licenses": ["MIT"], "max_sta... |
double precision susycoup
common/susycoup/susycoup
| {"hexsha": "65b221ec1d7baa5c4a31c48b8135558c44547a7f", "size": 63, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/src/Inc/susycoup.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# libraries
import networkx as nx
import matplotlib.pyplot as plt
import matplotlib as mpl
from src import graphs
def run1():
N = 8
K = 2
# Build your graph
G = nx.DiGraph()
for i in range(1, N + 1):
G.add_node(i)
A = graphs.generate_n_cycle_d_regular_graph_by_degree(N, K)
for ... | {"hexsha": "98cde4740295972213f44fb5a0ad6a9167badf98", "size": 2923, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_plot_graphs.py", "max_stars_repo_name": "gianmarcocalbi/asyncdsm-sym", "max_stars_repo_head_hexsha": "21a82c5fafada360faef340db805a772cec61530", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#!/usr/bin/env python
import mango
import mango.io
import mango.image
import mango.application.mdss as mdss
import mango.mpi as mpi
import scipy as sp
import logging
import re
import os
import sys
haveArgParse = False
try:
import argparse
haveArgParse = True
except:
import optparse
logger, rootLogger =... | {"hexsha": "3ce1b7f67ca7a4e4a7989554cd37f1cae8617240", "size": 16574, "ext": "py", "lang": "Python", "max_stars_repo_path": "misc/python/mango/application/srskull/batchdownsample.py", "max_stars_repo_name": "pymango/pymango", "max_stars_repo_head_hexsha": "b55f831f0194b214e746b2dfb4d9c6671a1abc38", "max_stars_repo_lice... |
# Copyright (c) MMEditing Authors.
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.vision.ops import DeformConv2D
from ...utils.download import get_path_from_url
from ...modules.init import kaiming_normal_, constant_
from .builder import GENERATORS
@paddle.no_gra... | {"hexsha": "73567032d968d42844d9f0010be52248aa8a901b", "size": 22879, "ext": "py", "lang": "Python", "max_stars_repo_path": "paddlers/models/ppgan/models/generators/basicvsr.py", "max_stars_repo_name": "huilin16/PaddleRS", "max_stars_repo_head_hexsha": "ca0d6223d8e56cd3bd3cbd3a033c89f1718ce26a", "max_stars_repo_license... |
"""
Dynamical Systems needed to create the data for the experiments.
Gabriele Abbati, Machine Learning Research Group, University of Oxford
February 2019
"""
# Libraries
from abc import ABC, abstractmethod
import numpy as np
from typing import Union, Tuple
from scipy.integrate import ode
from scipy.optimize import f... | {"hexsha": "ac73c3616af6dc5ead777e4c1e6ffdf703dcf3a6", "size": 39922, "ext": "py", "lang": "Python", "max_stars_repo_path": "odin/utils/dynamical_systems.py", "max_stars_repo_name": "sdi1100041/SLEIPNIR", "max_stars_repo_head_hexsha": "02dd3eca8574899fd3f0e287b1a050e76e5ba0de", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
# The 'fit.py' module is unchanged from the previous tutorial.
class FitDataset:
# noinspection PyUnresolvedReferences
def __init__(self, masked_dataset, model_data):
"""Class to fit a dataset with model data.
Parameters
-----------
masked_dat... | {"hexsha": "a56f4bf5ed2e1c5614380340f39d1735b912c2a8", "size": 7640, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_autofit/integration/src/fit/fit.py", "max_stars_repo_name": "arfon/PyAutoFit", "max_stars_repo_head_hexsha": "5926b13eefd97e089ee468cbec33452766edbd22", "max_stars_repo_licenses": ["MIT"], "m... |
(** * imp: a formalisation of the IMP programming language on top of KAT *)
(* We formalise the IMP language (whose programs are also known as
"while programs"). We give a big step semantics as an inductive
predicate, and using KAT, and we show that the two versions
actually coincide.
We then use the [... | {"author": "damien-pous", "repo": "relation-algebra", "sha": "13b99896782e449c7ca3910e48e18427517c8135", "save_path": "github-repos/coq/damien-pous-relation-algebra", "path": "github-repos/coq/damien-pous-relation-algebra/relation-algebra-13b99896782e449c7ca3910e48e18427517c8135/examples/imp.v"} |
import torch
import numpy as np
import pyredner
import torch
import enum
import math
from typing import Optional
class Texture:
"""
Representing a texture and its mipmap.
Args
====
texels: torch.Tensor
a float32 tensor with size C or [height, width, C]
... | {"hexsha": "3aa94441977c89e5c1e474abf9ea71f13527eabf", "size": 3780, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyredner/texture.py", "max_stars_repo_name": "brownvc/shapefromtracing", "max_stars_repo_head_hexsha": "f89226c8f20f1be9d6c8b7e46405872355accf2b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
[STATEMENT]
lemma reachable_snapshot_inv_black_heap_no_grey_refs_dequeue_Mutate:
assumes sb: "sys_mem_store_buffers (mutator m') s = mw_Mutate r f opt_r' # ws"
assumes bh: "black_heap s"
assumes ngr: "no_grey_refs s"
assumes vri: "valid_refs_inv s"
shows "mut_m.reachable_snapshot_inv m (s(sys := s sys\<lparr>... | {"llama_tokens": 3883, "file": "ConcurrentGC_Global_Noninterference", "length": 13} |
import time
#from pyftdi.ftdi import Ftdi
#ft = Ftdi()
#url = 'ftdi://ftdi:2232:FT2RTNYW/2'
#ft.open_bitbang_from_url(url, 0b00001111)
#print(ft.is_connected)
#print(ft.bitbang_enabled)
#print(ft.device_version)
#print(ft.fifo_sizes)
#print(ft.get_identifiers(url))
#print(ft.has_mpsse)
#print(ft.has_wide_port)
#print(f... | {"hexsha": "86fe186133624c909ca1eb6314f9699976682ebc", "size": 14017, "ext": "py", "lang": "Python", "max_stars_repo_path": "platform/ise/xl9_cw/sca_attack.py", "max_stars_repo_name": "kavehshamsi/scadec", "max_stars_repo_head_hexsha": "3cc1e0eba5db12be5b16aea7b7fd4909faf42714", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python3
# --------------------( LICENSE )--------------------
# Copyright (c) 2014-2021 Beartype authors.
# See "LICENSE" for further details.
'''
**Beartype code-based object validation classes** (i.e.,
:mod:`beartype`-specific classes enabling callers to define PEP-compliant
... | {"hexsha": "85473ea15452b0a452755acc718d80df167e19bc", "size": 17318, "ext": "py", "lang": "Python", "max_stars_repo_path": "beartype/vale/_valeisobj.py", "max_stars_repo_name": "jonathanmorley/beartype", "max_stars_repo_head_hexsha": "0d1207210220807d5c5848033d13657afa307983", "max_stars_repo_licenses": ["MIT"], "max_... |
#include <cctbx/boost_python/flex_fwd.h>
#include <cctbx/geometry_restraints/shared_wrapper_pickle.hpp>
#include <boost/python/class.hpp>
#include <boost/python/def.hpp>
#include <boost/python/return_value_policy.hpp>
#include <boost/python/copy_const_reference.hpp>
#include <boost/python/return_arg.hpp>
#include <boo... | {"hexsha": "dbc3e58da4c48053618ee4aa63e7698aa1eacf25", "size": 8861, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "cctbx/crystal/pair_tables_bpl.cpp", "max_stars_repo_name": "rimmartin/cctbx_project", "max_stars_repo_head_hexsha": "644090f9432d9afc22cfb542fc3ab78ca8e15e5d", "max_stars_repo_licenses": ["BSD-3-Cla... |
"""
Exit signals based on a dollar value.
"""
import numpy as np
class DollarExit():
"""
Calculate dollar value based exit signals.
"""
@classmethod
def exit_dollar(
cls, prices, trigger_value, exit_level):
"""
Calculate exit based on a dollar amount.
Paramet... | {"hexsha": "4ccc9cc4a179d95fc107be63cb1b5b53c67f3c5b", "size": 10216, "ext": "py", "lang": "Python", "max_stars_repo_path": "tradingsystems/dollar_exits.py", "max_stars_repo_name": "GBERESEARCH/tradingsystems", "max_stars_repo_head_hexsha": "5158d41d32b48d35db34a6e132c7fa2f259987c1", "max_stars_repo_licenses": ["MIT"],... |
def PlaneWaveEM(Eo, kvec, r):
import numpy as np
(L, M, N, junk) = r.shape
krx = kvec[0] * r[:, :, :, 0]
kry = kvec[1] * r[:, :, :, 1]
krz = kvec[2] * r[:, :, :, 2]
kr = krx + kry + krz
expKr = np.exp(1j * kr)
Einc = np.zeros((L, M, N, 3), dtype=np.complex128)
Einc[:, :, :, ... | {"hexsha": "24749bcdb21b551cd9744486262dd0e1420b026a", "size": 433, "ext": "py", "lang": "Python", "max_stars_repo_path": "vines/fields/plane_wave_em.py", "max_stars_repo_name": "AndrewGibbs/vines", "max_stars_repo_head_hexsha": "4c4f75adc8f601f06e2ab12fbaa95a047ef4354e", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
(* (c) Copyright Microsoft Corporation and Inria. All rights reserved. *)
Require Import ssreflect ssrbool ssrfun eqtype ssrnat seq path div choice.
Require Import fintype tuple finfun bigop prime ssralg poly finset.
Require Import fingroup morphism perm automorphism quotient finalg action.
Require Import gproduct zmod... | {"author": "math-comp", "repo": "mathcomp-history-before-github", "sha": "19ef9415e2b509a2327f9ef704268ce8570b607c", "save_path": "github-repos/coq/math-comp-mathcomp-history-before-github", "path": "github-repos/coq/math-comp-mathcomp-history-before-github/mathcomp-history-before-github-19ef9415e2b509a2327f9ef704268ce... |
import sys
import os
import pandas as pd
import numpy as np
import scipy as sp
import camoco as co
import matplotlib.pylab as plt
try:
from sklearn.neighbors import KernelDensity
except ImportError as e:
raise ImportError('This command requires sklearn')
def cistrans(args):
cob = co.COB(args.cob)
i... | {"hexsha": "fc4964107e780f00b02e313b7a98f8122926331d", "size": 1827, "ext": "py", "lang": "Python", "max_stars_repo_path": "camoco/cli/commands/cistrans.py", "max_stars_repo_name": "jonahcullen/Camoco", "max_stars_repo_head_hexsha": "2e95950f996329e27c00e5155e3768c0de9b8b7b", "max_stars_repo_licenses": ["MIT"], "max_st... |
###
# Format and filters
# Reader
support_filter_all(archive::Reader) =
@_la_call(archive_read_support_filter_all, (Ptr{Void},), archive)
support_filter_bzip2(archive::Reader) =
@_la_call(archive_read_support_filter_bzip2, (Ptr{Void},), archive)
support_filter_compress(archive::Reader) =
@_la_call(archive... | {"hexsha": "37fd9fdcc2920655b7be19b980e683a3a17be6c6", "size": 8881, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/format.jl", "max_stars_repo_name": "JuliaPackageMirrors/LibArchive.jl", "max_stars_repo_head_hexsha": "74ebbe81ff8f4f6908b38caef58767906c20308d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
"""
===================
Entropy
===================
"""
from skimage import data
from skimage.filter.rank import entropy
from skimage.morphology import disk
import numpy as np
import matplotlib.pyplot as plt
# defining a 8- and a 16-bit test images
a8 = data.camera()
a16 = data.camera().astype(np.uint16)*4
ent8 = e... | {"hexsha": "f019d79c194f3ecfee704cb5fad901f91fa9d882", "size": 899, "ext": "py", "lang": "Python", "max_stars_repo_path": "doc/examples/plot_entropy.py", "max_stars_repo_name": "RKDSOne/scikit-image", "max_stars_repo_head_hexsha": "baa67eafcace9cde1b94ad2d467e2f2e0468e759", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
# Copyright 2019-2021 Cambridge Quantum Computing
#
# 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 a... | {"hexsha": "5a70ae847e54040ee60dffb8b94f742360d24f0b", "size": 32046, "ext": "py", "lang": "Python", "max_stars_repo_path": "pytket/tests/predicates_test.py", "max_stars_repo_name": "NewGitter2017/tket", "max_stars_repo_head_hexsha": "6ff81af26280770bf2ca80bfb2140e8fa98182aa", "max_stars_repo_licenses": ["Apache-2.0"],... |
import os
import sys
import glob
import argparse
import numpy as np
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import load_model
from tensorflow.python.keras.applications.resnet50 import preprocess_input
from tensorflow.python.keras.preprocessing import image
from flask impor... | {"hexsha": "9bfd1c9b1a4afaf855dcc4c0d4dc926d37979247", "size": 1360, "ext": "py", "lang": "Python", "max_stars_repo_path": "DCrec.py", "max_stars_repo_name": "aditya210/Image-Classification-Cats-vs-Dogs", "max_stars_repo_head_hexsha": "8c96c405180ad8132243a4f00262004d4cbbb272", "max_stars_repo_licenses": ["MIT"], "max_... |
# 2D FWI gradient test with 4 sources
# The receiver positions and the source wavelets are the same for each of the four experiments.
# Author: Philipp Witte, pwitte@eos.ubc.ca
# Date: January 2017
#
# Mathias Louboutin, mlouboutin3@gatech.edu
# Updated July 2020
parsed_args = parse_commandline()
nlayer = parsed_args... | {"hexsha": "26958100316a11b0e16f4392e47dd28a3f6e5246", "size": 3751, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_gradient_twri.jl", "max_stars_repo_name": "nogueirapeterson/JUDI", "max_stars_repo_head_hexsha": "cc76e950929f0b7a3cf29c2dff71e432e8ea26f8", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# Zero Momentum
function lam4piflow(k, m2, T, Npi, lamda4pi)
return lamda4pi^2 * (8 + Npi) * (dkF1TildeAll(k, m2, T)
+ dkF2TildeAll(k, m2, T)
)
end
function dkVintqs_Zero(q0, k, kprim, m, T, lam4pik, Npi; kwargs...)
return lam4pik^2 *(2 + Npi) *(
k^3 *
(2 + Npi) *
(dkF... | {"hexsha": "1b33ed00d69d85f6ad9a460c65cf947d7a1e7a6a", "size": 11640, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/vertexRe.jl", "max_stars_repo_name": "Yangyang-Tan/FRGRealTime.jl", "max_stars_repo_head_hexsha": "6581b783432a5d5d08d00c887b483f9596d12fe3", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Comment from 9/14/2019 --> Wow. This is the first neural network program for classifying images.
# I was pretty clueless, but I won't touch the original comments just for history!
# My experience with the TensorFlow tutorial on "basic" classification. (i found this rly hard)
# Website: https://www.tensorflow.org/tut... | {"hexsha": "1141078304712ea161305aa255fffbd3c794e58f", "size": 5360, "ext": "py", "lang": "Python", "max_stars_repo_path": "Neural Networks/Tensorflow/FasionMNIST/fasionMNST_classifier.py", "max_stars_repo_name": "B0kCh01/Machine-Learning-COdes", "max_stars_repo_head_hexsha": "8fc442cd3a139ecd6e6d0a3f352e7bfc8470365e",... |
/*
* Copyright (c) 2010-2012 Steffen Kieß
*
* 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, ... | {"hexsha": "438729c666d23450c74fb23ee47650ffc1b466ce", "size": 2611, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/PluginHDF5/HDF5/Exception.hpp", "max_stars_repo_name": "voxie-viewer/voxie", "max_stars_repo_head_hexsha": "d2b5e6760519782e9ef2e51f5322a3baa0cb1198", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Joint TV for multi-contrast MR
This demonstration shows how to do a synergistic reconstruction of two MR images with different contrast. Both MR images show the same underlying anatomy but of course with different contrast. In order to make use of this similarity a joint total variation (TV) operator is used as a reg... | {"hexsha": "203d4fa76fd087d4b08f6e43a6c40035268c2af9", "size": 37671, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/Synergistic/cil_joint_tv_mr.ipynb", "max_stars_repo_name": "johannesmayer/SIRF-Exercises", "max_stars_repo_head_hexsha": "772f132ca3639f364189258d558a8f06a8666fb1", "max_st... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Catchment specific water flux plots.
###############################################################
runoff_et/catchment_analysis.py
Authors ESMValToolV1 Version
Philipp Sommer (philipp.sommer@mpimet.mpg.de)
Stefan Hagemann (stefan.hagemann@hzg.de)
Alexander... | {"hexsha": "3fe8997e0c7ce4d739612a42cb3114bf0ee370a5", "size": 22614, "ext": "py", "lang": "Python", "max_stars_repo_path": "esmvaltool/diag_scripts/runoff_et/catchment_analysis.py", "max_stars_repo_name": "YanchunHe/ESMValTool", "max_stars_repo_head_hexsha": "3d9238a83f11c3f01266c6eca357471b7bb02116", "max_stars_repo_... |
#ifndef HTOOL_SPMATRIX_HPP
#define HTOOL_SPMATRIX_HPP
#include <Eigen/Dense>
#include <Eigen/Sparse>
#include <cassert>
#include <complex>
#include <iostream>
#include <vector>
//#include "point.hpp"
namespace htool {
//================================//
// DECLARATIONS DE TYPE //
//========================... | {"hexsha": "0bf7fdaebea57cce3467b8244021a8f7b5d7abe2", "size": 4988, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/htool/types/sparsematrix.hpp", "max_stars_repo_name": "htool-ddm/htool", "max_stars_repo_head_hexsha": "e4dbec7c08c5008e62344fd0d5ebfdf95ef8863f", "max_stars_repo_licenses": ["MIT"], "max_st... |
[STATEMENT]
lemma has_field_derivative_powr_right [derivative_intros]:
"w \<noteq> 0 \<Longrightarrow> ((\<lambda>z. w powr z) has_field_derivative Ln w * w powr z) (at z)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. w \<noteq> 0 \<Longrightarrow> ((powr) w has_field_derivative Ln w * w powr z) (at z)
[PROOF ... | {"llama_tokens": 258, "file": null, "length": 2} |
/-
Copyright (c) 2022 Anne Baanen. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Anne Baanen, Alex J. Best
! This file was ported from Lean 3 source module linear_algebra.quotient_pi
! leanprover-community/mathlib commit 398f60f60b43ef42154bd2bdadf5133daf1577a4
! Ple... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/LinearAlgebra/Quoti... |
import os, sys
import numpy as np
execfile('JvM_correction.py')
execfile('reduction_utils.py')
execfile('ImportMS.py')
sys.path.append('../')
import diskdictionary as disk
# read which disk this is about
target = str(np.loadtxt('whichdisk.txt', dtype='str'))
# Perform the imaging
imagename = 'data/deep_'+target+'_dat... | {"hexsha": "b8d605e8a2c6a895bf0736835079d5163c5091ce", "size": 1648, "ext": "py", "lang": "Python", "max_stars_repo_path": "CSD_modeling/data_imaging.py", "max_stars_repo_name": "seanandrews/DSHARP_CPDs", "max_stars_repo_head_hexsha": "40d8f02945e0d412c5c912b050d4a3d8c6dbbfa2", "max_stars_repo_licenses": ["MIT"], "max_... |
from .base import Strategy, Transform
from .random import LHS
from summit.domain import *
from summit.utils.dataset import DataSet
import botorch
from botorch.models.model import Model
from botorch.acquisition.objective import ScalarizedObjective
import torch
from torch import Tensor
from gpytorch.mlls.exact_marginal_... | {"hexsha": "3c3a88030c309bb846fdf059ed9cb55e92c1a42c", "size": 14772, "ext": "py", "lang": "Python", "max_stars_repo_path": "summit/strategies/multitask.py", "max_stars_repo_name": "jezsadler/summit", "max_stars_repo_head_hexsha": "982de7f6424bb94da2084d4d84396b4b2673eeca", "max_stars_repo_licenses": ["MIT"], "max_star... |
# -*- coding: UTF-8 -*-
from typing import Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
import numpy as np
from .base import TK2Conv2D, TK2Linear
class TK2LeNet5(nn.Module):
def __init__(self, num_classes: int, rs: Union[list, np.ndarray]):
"""LeNet... | {"hexsha": "7edf1338dff95b2d18b0b6b1304560851e4255dd", "size": 1687, "ext": "py", "lang": "Python", "max_stars_repo_path": "tednet/tnn/tucker2/tk2_lenet.py", "max_stars_repo_name": "perryuu/tednet", "max_stars_repo_head_hexsha": "9e05d7df3b690518921b3d8c2289ab638dbc221c", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
(*
Theory: PDF_Compiler.thy
Authors: Manuel Eberl
The concrete compiler that compiles a PDF expression into a target language expression
that describes a density function on the corresponding measure space.
*)
section \<open>Concrete PDF Compiler\<close>
theory PDF_Compiler
imports PDF_Compiler_Pred PDF_Targ... | {"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/Density_Compiler/PDF_Compiler.thy"} |
subroutine unpack_contr(contr,svertex,vtx,topo,xlines,nvtx,nj_res)
implicit none
include 'stdunit.h'
include 'opdim.h'
include 'def_contraction.h'
include 'ifc_operators.h'
integer, intent(in) ::
& nj_res, nvtx
type(contraction), intent(inout) ::
& co... | {"hexsha": "3eb5cb60c86f56a73fd46e2c48fc90ec22b18fc4", "size": 2765, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "formula/unpack_contr.f", "max_stars_repo_name": "ak-ustutt/GeCCo-public", "max_stars_repo_head_hexsha": "8d43a6c9323aeba7eb54625b95553bfd4b2418c6", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#include "root_path.hpp"
#include "../common/content_format.hpp"
#include <pxp-agent/external_module.hpp>
#include <pxp-agent/module_type.hpp>
#include <pxp-agent/configuration.hpp>
#include <pxp-agent/util/process.hpp>
#include <cpp-pcp-client/protocol/chunks.hpp> // ParsedChunks
#include <leatherman/json_con... | {"hexsha": "b3d6ff9fcc4b56850c29b8aa012ecfe98ea9cd7b", "size": 14150, "ext": "cc", "lang": "C++", "max_stars_repo_path": "lib/tests/unit/external_module_test.cc", "max_stars_repo_name": "nicklewis/pxp-agent", "max_stars_repo_head_hexsha": "12a383fb3403760524008ec81e5c1fcfd9178452", "max_stars_repo_licenses": ["Apache-2... |
\section{The dataset}
We downloaded data from Kaggle\footnote{\url{https://www.kaggle.com/c/random-acts-of-pizza/download/train.json.zip}}. The dataset includes 4040 requests collected from the Reddit community Random Acts of Pizza between December 8, 2010 and September 29, 2013. Each data object is a request for a fr... | {"hexsha": "ac4c0c85f83862871576673677696e8e11f8a351", "size": 2820, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "project/proposal/data.tex", "max_stars_repo_name": "tamnguyenthe/CS5890Project", "max_stars_repo_head_hexsha": "74446d536074d369af73bf6d88e38f917c7a3ee6", "max_stars_repo_licenses": ["MIT"], "max_st... |
#include <glipf/processors/foreground-coverage-processor.h>
#include <glipf/gles-utils/shader-builder.h>
#include <glipf/gles-utils/glsl-program-builder.h>
#include <boost/variant/get.hpp>
#include <glm/gtc/type_ptr.hpp>
#include <cstring>
#define BASE_TEXTURE_WIDTH 320
#define BASE_TEXTURE_HEIGHT 240
using std:... | {"hexsha": "01c55455167fc5e5a013cae2353439b8b17bb124", "size": 13013, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "glipf/src/processors/foreground-coverage-processor.cpp", "max_stars_repo_name": "cognitivesystems/smartcamera", "max_stars_repo_head_hexsha": "5374193260e6385becfe8086a70d21d650314beb", "max_stars_... |
import challenge_notations
import challenge_prerequisites
/-!
# Liquid Tensor Experiment
## The main challenge
The main challenge of the liquid tensor experiment is
a formalisation of the first theorem in Peter Scholze's blogpost
https://xenaproject.wordpress.com/2020/12/05/liquid-tensor-experiment/
Theorem 1.1 (Cl... | {"author": "leanprover-community", "repo": "lean-liquid", "sha": "92f188bd17f34dbfefc92a83069577f708851aec", "save_path": "github-repos/lean/leanprover-community-lean-liquid", "path": "github-repos/lean/leanprover-community-lean-liquid/lean-liquid-92f188bd17f34dbfefc92a83069577f708851aec/src/challenge.lean"} |
using Test
using TestSetExtensions
using LinearAlgebra
using Qaintessent
using SparseArrays
using StatsBase
##==----------------------------------------------------------------------------------------------------------------------
isunitary(cg::CircuitGate) = (sparse_matrix(cg) * sparse_matrix(Base.adjoint(cg)) ≈ I... | {"hexsha": "dbf0b7302b07683535f8e2d20bdd656dc9451eb8", "size": 6714, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_circuitgate.jl", "max_stars_repo_name": "oguzcankirmemis/Qaintessent.jl", "max_stars_repo_head_hexsha": "6261dc5d8a9a7ea7d406ea39cac950747583f414", "max_stars_repo_licenses": ["Apache-2.0... |
# -*- coding: utf-8 -*-
""" Class average finetuning functions. Before using any of these finetuning
functions, ensure that the model is set up with nb_classes=2.
"""
from __future__ import print_function
import uuid
from time import sleep
import numpy as np
import torch
import torch.nn as nn
import torch.optim a... | {"hexsha": "919ac68a8a354a3b0626fad1b6ec465c54dda01a", "size": 12853, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchMoji/torchmoji/class_avg_finetuning.py", "max_stars_repo_name": "UmaTaru/run", "max_stars_repo_head_hexsha": "be29e4d41a4de3dee27cd6796801bfe51382d294", "max_stars_repo_licenses": ["MIT"], "... |
using LocalRegistry
using LocalRegistry: find_package_path, find_registry_path
using Test
using Random
using Pkg
const TEST_GITCONFIG = Dict(
"user.name" => "LocalRegistryTests",
"user.email" => "localregistrytests@example.com",
)
include("utils.jl")
# Since these tests will need to modify active registries ... | {"hexsha": "df7f053ecf856ec6179cf8672e433e1f08a7ceeb", "size": 14036, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "EricForgy/LocalRegistry.jl", "max_stars_repo_head_hexsha": "76a02ca3ca126a81f67a0db1d686d3f39d3dbdfe", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# https://github.com/SciML/GalacticOptim.jl/blob/master/test/rosenbrock.jl#L30
using GalacticOptim
using ForwardDiff
using Ipopt
rosenbrock(x,p) = (p[1] - x[1])^2 + p[2] * (x[2] - x[1]^2)^2
x0 = zeros(2)
p = Dict(1 => 1.0, 2=> 100.0)
function con2_c(x,p)
[x[1]^2 + x[2]^2, x[2]*sin(x[1])-x[1]]
end
optprob = Op... | {"hexsha": "15d9b10d9b838cd7d2dfb95930cfd606d9da799f", "size": 551, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "debug/galacticoptim-mwe.jl", "max_stars_repo_name": "lanl-ansi/rosetta-opf", "max_stars_repo_head_hexsha": "09e76f505c04cc788256a4f3f479033ba6abe1f0", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import pandas as pd
import numpy as np
import sys
import os
import configparser
import skimage
from skimage import io
sys.path.append(os.path.abspath('../../utils'))
import preprocessing
config = configparser.ConfigParser()
config.read('../../config.ini')
vg_json = config['PATHS']['vg-json']
data_dir = config['PATHS'... | {"hexsha": "9601ee6934abf598cc7206c9aedd593e318bf6a7", "size": 3744, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/pixelwise/pixelwise_predict.py", "max_stars_repo_name": "clause-jena/colour-term-grounding", "max_stars_repo_head_hexsha": "af5a32e1511b588a07a5b478885120d271aa7546", "max_stars_repo_licens... |
cat('\n---Polygenic Burden (PB) - Large Effect Variant (LEV) SCAN (PB-LEV-SCAN)---\n')
cat('---version 1.0---\n')
library('ggplot2')
library("optparse")
options(scipen = 10)
option_list = list(
#path
make_option("--plink_path", action="store", default="plink", type='character',
he... | {"hexsha": "3f5f03505d692bb6446bfae026ab34b204d03601", "size": 14895, "ext": "r", "lang": "R", "max_stars_repo_path": "src/PB-LEV-SCAN_1.0.r", "max_stars_repo_name": "gamazonlab/Polygenic_Background_Rare_Variant_Axis", "max_stars_repo_head_hexsha": "f86f3e385ae458e738ce9dcd0c0dbaa6e376fac4", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma guarantees_imp: "(Y = UNIV guarantees Y) ==> ex_prop(Y)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Y = UNIV guarantees Y \<Longrightarrow> ex_prop Y
[PROOF STEP]
by (auto simp add: guar_def ex_prop_equiv component_of_def dest: sym) | {"llama_tokens": 98, "file": null, "length": 1} |
[STATEMENT]
lemma iT_Div_assoc:"I \<oslash> a \<oslash> b = I \<oslash> (a * b)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. I \<oslash> a \<oslash> b = I \<oslash> a * b
[PROOF STEP]
by (simp add: iT_Div_def image_image div_mult2_eq) | {"llama_tokens": 115, "file": "Nat-Interval-Logic_IL_IntervalOperators", "length": 1} |
import sys
import pandas as pd
import numpy as np
import nltk
from joblib import dump
from sqlalchemy import create_engine
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.base import BaseEstimator... | {"hexsha": "7942980b6aa13cebcca6b812f2901652441fa6ae", "size": 6483, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/train_classifier.py", "max_stars_repo_name": "jeena72/disaster-response-pipeline", "max_stars_repo_head_hexsha": "4621425a29e7fa2f162c725555787b6fc24f8010", "max_stars_repo_licenses": ["MIT... |
# https://arxiv.org/pdf/1301.1071.pdf "Direct TSQR"
import os
os.environ["OMP_NUM_THREADS"] = "24" # This is the default on my machine (Zemaitis)
import sys
import argparse
import numpy as np
import scipy.linalg
from time import perf_counter as time
# Accepts a matrix and returns a list of its blocks
# block_size row... | {"hexsha": "24df71e491b021cae5be9c31517d6f7bc44a55cb", "size": 4037, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/TSQR/qr_simple_blocked.py", "max_stars_repo_name": "UTexas-PSAAP/Parla.py", "max_stars_repo_head_hexsha": "3d92a156be41983e32576940ef6baffd78d4eb84", "max_stars_repo_licenses": ["BSD-3-Cl... |
[STATEMENT]
lemma trunc_ell2_union_disjoint: \<open>M\<inter>N = {} \<Longrightarrow> trunc_ell2 (M \<union> N) \<psi> = trunc_ell2 M \<psi> + trunc_ell2 N \<psi>\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. M \<inter> N = {} \<Longrightarrow> trunc_ell2 (M \<union> N) \<psi> = trunc_ell2 M \<psi> + trunc_... | {"llama_tokens": 150, "file": "Complex_Bounded_Operators_Complex_L2", "length": 1} |
import numpy as np
from util import util
from config.atlas_config import PnCConfig, WBCConfig
from pnc.wbc.wbc import WBC
from pnc.wbc.joint_integrator import JointIntegrator
class AtlasController(object):
def __init__(self, tf_container, robot):
self._tf_container = tf_container
self._robot = ro... | {"hexsha": "cbdffaacd157c16433b8f98f575f33558350c26c", "size": 2994, "ext": "py", "lang": "Python", "max_stars_repo_path": "pnc/atlas_pnc/atlas_controller.py", "max_stars_repo_name": "junhyeokahn/ASE389", "max_stars_repo_head_hexsha": "a57d668f968da1db56f0dfe8dadad548ad631f33", "max_stars_repo_licenses": ["MIT"], "max_... |
# -*- coding:utf8 -*-
r"""Namescope Transformer
Transformers that get rid of namescope/nodes which are not needed
for inference
"""
import re
from collections import defaultdict
from copy import deepcopy
import numpy as np
import tensorflow as tf
from utensor_cgen.frontend.tensorflow import GraphDefParser
from uten... | {"hexsha": "e6c668691f28ffee42205f518e050eb307a0e5ff", "size": 16634, "ext": "py", "lang": "Python", "max_stars_repo_path": "utensor_cgen/transformer/ns_transformer.py", "max_stars_repo_name": "dboyliao/utensor_cgen", "max_stars_repo_head_hexsha": "aacd3adf4ee2a521a8eb2e75807fe3c1c0d1e1e5", "max_stars_repo_licenses": [... |
# encoding: utf-8
"""
IndStateAnalyzer.py
"""
import sys
from lmfit import report_fit
import Meta as mt
import numba
from IndTypes import IndType
from ModellingMode import ModellingMode as mm
cumsum = lambda series : series.cumsum().iloc[:]
movavg_cumulative = lambda n, series : cumsum(series).iloc[:].rolling(window=... | {"hexsha": "7c381b1088e6c95c0c8cf538e6c8e735629953cc", "size": 3241, "ext": "py", "lang": "Python", "max_stars_repo_path": "india-covid19india-py/Ind/IndStateAnalyzer.py", "max_stars_repo_name": "vpt101/covid-19_data_analysis", "max_stars_repo_head_hexsha": "1d02385ad75b650e584e119a8891433aa70e90d8", "max_stars_repo_li... |
#include <boost/test/unit_test.hpp>
#include <scorum/chain/database/database.hpp>
#include <scorum/chain/database_exceptions.hpp>
#include <scorum/chain/schema/scorum_objects.hpp>
#include <scorum/chain/schema/account_objects.hpp>
#include <scorum/chain/services/account.hpp>
#include <scorum/chain/services/comment.h... | {"hexsha": "a24d59595e962a4b74ab3fb49dfcac5da4370d91", "size": 6496, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/chain_tests/rewards/comment_beneficiaries_apply_tests.cpp", "max_stars_repo_name": "scorum/scorum", "max_stars_repo_head_hexsha": "1da00651f2fa14bcf8292da34e1cbee06250ae78", "max_stars_repo_li... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Module for the Numerov Scrodinger equation solver
Description: This module defines all the necessary functions that are used in the main script Numerov.py
author: Félix Desrochers
email: felix.desrochers@polymtl.ca
MIT License
Copyright (c) 2017 Félix Desrochers
P... | {"hexsha": "fcc8a8ba2419620562ad332e63e2510048c01f28", "size": 36253, "ext": "py", "lang": "Python", "max_stars_repo_path": "Fct_Numerov.py", "max_stars_repo_name": "Guo-Wen-Xiang/Numerov", "max_stars_repo_head_hexsha": "035a301e50697063e0c89d84192729c198803d1d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5... |
"""VectorSpaceData tests.
Scientific Machine Learning Benchmark:
A benchmark of regression models in chem- and materials informatics.
Matthias Rupp 2019-2020, Citrine Informatics.
"""
import pytest
import numpy as np
import smlb
from test_data import validate_data_interface
def test_VectorSpaceData_instantiation... | {"hexsha": "0c6c7220ae61c04f860f3c363941933b1ccf3990", "size": 4320, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/core/test_vector_space_data.py", "max_stars_repo_name": "CitrineInformatics/smlb", "max_stars_repo_head_hexsha": "28a3689bd36aa8d51031b4faf7e2331bbd8148a9", "max_stars_repo_licenses": ["Apac... |
import numpy
def data_generator(celltypes, assays, data, n_positions, batch_size):
while True:
celltype_idxs = numpy.zeros(batch_size, dtype='int32')
assay_idxs = numpy.zeros(batch_size, dtype='int32')
genomic_25bp_idxs = numpy.random.randint(n_positions, size=batch_size)
genomic_250bp_idxs = ge... | {"hexsha": "2f6f4c79d6865a51701994c472d576dfbc74e50d", "size": 2007, "ext": "py", "lang": "Python", "max_stars_repo_path": "avocado/io.py", "max_stars_repo_name": "luizirber/avocado", "max_stars_repo_head_hexsha": "7551492d6e60ba9cc1ddcc791882d3278e69607c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
#include <boost/test/unit_test.hpp>
#include "algorithms/dp/binary_watch_time.hpp"
BOOST_AUTO_TEST_SUITE(TestBinaryWatchTime)
BOOST_AUTO_TEST_CASE(test_get_possible_time)
{
{
const int num_led_on = 0;
const std::vector<std::string> expected = {"0:00"};
BOOST_CHECK(e... | {"hexsha": "db03510eae77e8af74f16b6aa55562e0fbcb5464", "size": 1457, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/algorithms/dp/test_binary_watch_time.cpp", "max_stars_repo_name": "iamantony/CppNotes", "max_stars_repo_head_hexsha": "2707db6560ad80b0e5e286a04b2d46e5c0280b3f", "max_stars_repo_licenses": ["MI... |
import skimage
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from imageManipulation import *
from arguments import *
def main():
args = setupArguments()
img = openImage(args.imagePath)
for method in args.limiarizationMethods:
print('Applying ' + method.name)
histogr... | {"hexsha": "3c41a7b1b99f9f5cf6613f47975b9bb956c0e151", "size": 718, "ext": "py", "lang": "Python", "max_stars_repo_path": "limiarization/main.py", "max_stars_repo_name": "giovaninppc/MC920", "max_stars_repo_head_hexsha": "7d46238f4079dabc4769c72cbed44d024fcf5c97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
# Copyright (c) 2020 Greg Pintilie - gregp@slac.stanford.edu
# 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, mo... | {"hexsha": "6a444bd72062dc62167d303475b5937449faeab5", "size": 132894, "ext": "py", "lang": "Python", "max_stars_repo_path": "Segger/qscores.py", "max_stars_repo_name": "gregdp/segger", "max_stars_repo_head_hexsha": "d4c112fd43f0b088145e225f976335800874ebe5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "m... |
"""
marchonintime(W0,Z,B,I)
Solve by marching-on-in-time the causal convolution problem defined by `(W0,Z,B)`
up to timestep `I`. Here, `Z` is an array of order 3 that contains a discretisation
of a time translation invariant retarded potential operator. `W0` is the inverse of
the slice `Z[:,:,1]`.
"""
funct... | {"hexsha": "93101da382d0a4605f46d04b44408e19322b4252", "size": 1689, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/timedomain/motlu.jl", "max_stars_repo_name": "RodVoskamp/BEAST.jl", "max_stars_repo_head_hexsha": "a56309d894dcbab85e514089ddee22e1ae9e53ef", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include <cmath>
#include <iostream>
#include <fstream>
#include <assert.h>
#include <string>
#include <vector>
#include <map>
#include <boost/tokenizer.hpp>
#include <boost/algorithm/string/predicate.hpp>
#include "parse_speedup.h"
// Cores for which speedup data is available.
static const size_t NUM_SPEEDUP_POINTS... | {"hexsha": "19880de9a1d9f7f0a2cc1d1cee1de760efa4dcf9", "size": 5089, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "xiosim/pintool/parse_speedup.cpp", "max_stars_repo_name": "s-kanev/XIOSim", "max_stars_repo_head_hexsha": "9673bbd15ba72c9cce15243a462bffb5d9ded9ae", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
[STATEMENT]
lemma angle_actrans_conj: "|~ (\<langle>F \<and> G\<rangle>_v) = (\<langle>F\<rangle>_v \<and> \<langle>G\<rangle>_v)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. |~ \<langle>F \<and> G\<rangle>_v = (\<langle>F\<rangle>_v \<and> \<langle>G\<rangle>_v)
[PROOF STEP]
by (auto simp: angle_actrans_def actr... | {"llama_tokens": 148, "file": "TLA_PreFormulas", "length": 1} |
[STATEMENT]
lemma clm_decompose: "\<Turnstile>(cl(c) \<^bold>\<rightarrow> cl(c) \<^bold>\<frown> cl(c))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>ts v. 0 < \<parallel>ext v\<parallel> \<and> len v ts c = ext v \<and> restrict v (clm ts) c = lan v \<and> |lan v| = 1 \<longrightarrow> (\<exists>va u. ... | {"llama_tokens": 5831, "file": "Hybrid_Multi_Lane_Spatial_Logic_HMLSL", "length": 35} |
#to force cpu use
#import os
#os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
#os.environ["CUDA_VISIBLE_DEVICES"] = ""
import tensorflow as tf
from tensorflow import keras
from keras.utils import plot_model
from keras.models import load_model
import _pickle as pickle
import matplotlib.pyplot as plt
import numpy as np
f... | {"hexsha": "ac6338291dfa587f7ae8db1a9f453102f5865b6b", "size": 3536, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnn.py", "max_stars_repo_name": "rayanti/frame_interpolation_CNN", "max_stars_repo_head_hexsha": "65fa55886e87cb1c2f1dee0bb315e550b29d9601", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/env python
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from time import time
import numpy as np
import tensorflow.compat.v1 as tf
import blocksparse.ewops as ew
import blocksparse.transformer as trans
from tensorflow.python.ops import gradien... | {"hexsha": "228f2871e2d9c1405aad29217c018a56541c32d1", "size": 5114, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/top_k_test.py", "max_stars_repo_name": "AlumiK/blocksparse", "max_stars_repo_head_hexsha": "bc265b5e404f68b27229cb445ea48bf5b5f3bef7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
"""
Run preprocessing on the data using this script. See the README.md for more details.
"""
import sqlite3
import os
import datetime
import logging
import sys
import glob
import pandas as pd
import numpy as np
from .utils import create_adjacent_stop_pairs, bank_holidays_2018
logging.basicConfig(
filename=f"/home... | {"hexsha": "2bb3d873c884684ba1b7728c13662329ecbceb0d", "size": 7776, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_analytics/preprocessing/run_preprocessing.py", "max_stars_repo_name": "reidy-p/DublinBusPredictions", "max_stars_repo_head_hexsha": "a6b1fc8a5c28500a3292883ea0dfcde1770d78d1", "max_stars_repo... |
# Beg et al. (2007): https://doi.org/10.1073/pnas.0609845104.
module BegData
using DataFrames
import ..Chemostat_Heerden2013
const ChH = Chemostat_Heerden2013
import CSV
import UtilsJL
const UJL = UtilsJL
UJL.gen_sub_proj(@__MODULE__)
# The average crowding coefficient a was fitted to... | {"hexsha": "3e9f8ae0f675b01ed63ffeba472f459ec3a8816c", "size": 1061, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/BegData/BegData.jl", "max_stars_repo_name": "josePereiro/Chemostat_Heerden2013.jl", "max_stars_repo_head_hexsha": "00f97a57a64daf8d175b2eb22871c5fc83d2e6f0", "max_stars_repo_licenses": ["MIT"],... |
# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to... | {"hexsha": "cf2085b60b62eddebde0194df73f97803fb791fb", "size": 5949, "ext": "py", "lang": "Python", "max_stars_repo_path": "mindarmour/adv_robustness/evaluations/defense_evaluation.py", "max_stars_repo_name": "hboshnak/mindarmour", "max_stars_repo_head_hexsha": "0609a4eaea875a84667bed279add9305752880cc", "max_stars_rep... |
# RUN: SUPPORTLIB=%mlir_runner_utils_dir/libmlir_c_runner_utils%shlibext %PYTHON %s | FileCheck %s
import filecmp
import numpy as np
import os
import sys
import tempfile
_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
sys.path.append(_SCRIPT_PATH)
from tools import mlir_pytaco_api as pt
from tools import t... | {"hexsha": "af1c6ba0c4552881e2b015048450437c07f0d321", "size": 1077, "ext": "py", "lang": "Python", "max_stars_repo_path": "mlir/test/Integration/Dialect/SparseTensor/taco/test_SpMM.py", "max_stars_repo_name": "LaudateCorpus1/llvm-project", "max_stars_repo_head_hexsha": "ff2e0f0c1112558b3f30d8afec7c9882c33c79e3", "max_... |
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