text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
"""
Connectivity{T}
Stores connection data between cells. Connections are stored in a compressed
sparse column (CSC) adjacency matrix: only non-zero values are stored.
Primarily, this consist of two vectors:
* the row value vector holds the cell indices of neighbors
* the column pointers marks the start and end in... | {"hexsha": "cee252817a665f0ebeb23a45d10aee60fa753003", "size": 3655, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/groundwater/connectivity.jl", "max_stars_repo_name": "DirkEilander/Wflow.jl", "max_stars_repo_head_hexsha": "18b6203d8e90e566998928808a84c906c322fd5d", "max_stars_repo_licenses": ["MIT"], "max_... |
(***************************** LIFLF - TPX ************************************)
(************* Evaluation pratique en temps limité : 30' **********************)
(******************************************************************************)
Require Import List.
Import ListNotations.
(***************************** ... | {"author": "KevinFroissart", "repo": "coqTP", "sha": "f050bf832a49be9262aea70f4844a7394d112817", "save_path": "github-repos/coq/KevinFroissart-coqTP", "path": "github-repos/coq/KevinFroissart-coqTP/coqTP-f050bf832a49be9262aea70f4844a7394d112817/liflf/liflf_B.v"} |
\section{Statistical postulates}
<<<<<<< HEAD
So far we have looked at the macroscopic properties of a thermodynamics system and at some ways of calculating properties of random processes that obey some given probability distribution. Now it is time to combine these ideas and have a first attempt at linking the microsc... | {"hexsha": "721bcec4f36cea3231937916331c954c6145d6f3", "size": 6243, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "04-statisticalPostulates.tex", "max_stars_repo_name": "notdroneale/708Notes2018", "max_stars_repo_head_hexsha": "4808fc20291758193ffb24c1201844816858932d", "max_stars_repo_licenses": ["MIT"], "max_s... |
from datetime import datetime
from random import randint
import numpy as np
import pandas as pd
import pytest
from cognite.v05 import dto, timeseries
TS_NAME = None
dps_params = [
{"start": 1522188000000, "end": 1522620000000},
{"start": datetime(2018, 4, 1), "end": datetime(2018, 4, 2)},
{"start": datet... | {"hexsha": "8ed593491f4e3fb85a19dbcb309143356d7ebe3d", "size": 10914, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/v05/test_timeseries.py", "max_stars_repo_name": "boyeah/cognite-sdk-python", "max_stars_repo_head_hexsha": "39abf5c98d758c59609cb33f5f3e2c009712005d", "max_stars_repo_licenses": ["Apache-2.... |
// Copyright (C) 2015, Pawel Tomulik <ptomulik@meil.pw.edu.pl>
//
// 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)
#define BOOST_TEST_MODULE test_txpl_vm_object_find
#include <txpl/test_config.hpp>
#include <bo... | {"hexsha": "0d0c0bb74e647dcbd37e4803c4bc619b1e418c75", "size": 5524, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/txpl/vm/object_find_test.cpp", "max_stars_repo_name": "ptomulik/txpl", "max_stars_repo_head_hexsha": "109b5847abe0d46c598ada46f411f98ebe8dc4c8", "max_stars_repo_licenses": ["BSL-1.0"], "max_sta... |
import os
import argparse
from io import BytesIO
from tqdm.auto import tqdm
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, Subset
from torchvision import transforms
from PIL import Image
import lmdb
from torch_tools.utils import numerical_order, wrap_with_tqdm
def _filename(path):
... | {"hexsha": "7e382c1a02591ac00096109f2d051061a1f0e6ca", "size": 7916, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_tools/data.py", "max_stars_repo_name": "anon-auth-2022/i2i_synth", "max_stars_repo_head_hexsha": "e5ef30c57f336240bd1e14f4008cfbf455c52069", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
[STATEMENT]
lemma has_field_derivative_bernpoly:
"(bernpoly (Suc n) has_field_derivative
(of_nat (n + 1) * bernpoly n x :: 'a :: real_normed_field)) (at x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (bernpoly (Suc n) has_field_derivative of_nat (n + 1) * bernpoly n x) (at x)
[PROOF STEP]
proof -
[PROOF ... | {"llama_tokens": 1738, "file": "Bernoulli_Bernoulli", "length": 12} |
import tensorflow as tf
import tensorflow.contrib as tc
import pickle
import numpy as np
class VNect():
def __init__(self, input_size, is_training=False):
self.is_training = is_training
self.input_holder = tf.placeholder(dtype=tf.float32,
shape=(None, in... | {"hexsha": "b75a17fc1d4521188d9394a2261b7f59a496e705", "size": 12461, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/vnect_model.py", "max_stars_repo_name": "rrbarioni/VNect-tensorflow", "max_stars_repo_head_hexsha": "2137172dd61df5f83ce3fbe0cf972950b3cb23f7", "max_stars_repo_licenses": ["Apache-2.0"], "... |
# Author: Uygar Sumbul, Olga Gliko, Rohan Gala
# Allen Institute
import numpy as np
import keras
import scipy as sp
import scipy.io as sio
from scipy.stats import norm
from keras.layers import Input, Dense, Lambda, Layer, Dropout, BatchNormalization
from keras.models import Model
from keras import backend as K
from ke... | {"hexsha": "ad7fa1c3fe83b81bd251ae0a915adf89aab21cf7", "size": 4833, "ext": "py", "lang": "Python", "max_stars_repo_path": "dualAE_inputZ1_47genes.py", "max_stars_repo_name": "elifesciences-publications/PeptidergicNetworks", "max_stars_repo_head_hexsha": "1e7c2c56dc57c789272282aff32559c7cc51f23f", "max_stars_repo_licen... |
from numba import jit
''' Lecture1 '''
@jit
def lec1_first_forward(n, dx, f, df_approx_f):
for i in range(1, n):
df_approx_f[i] = (f[i + 1] - f[i]) / dx
return
@jit
def lec1_second_central(n, dx, f, df_approx_c):
for i in range(1, n):
df_approx_c[i] = (f[i + 1] - f[i - 1]) / dx / 2
... | {"hexsha": "c34f819110c4ba382b0b8e58c0ad74f2bed3c61b", "size": 1430, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/func/operation.py", "max_stars_repo_name": "wakky927/Computational-Engineering-B", "max_stars_repo_head_hexsha": "3720d96668a32dc73f38ed0bc8afe4705452de9e", "max_stars_repo_licenses": ["MIT"],... |
//////////////////////////////////////////////////////////////////////////////
//
// (C) Copyright Ion Gaztanaga 2004-2007. Distributed under the Boost
// Software License, Version 1.0. (See accompanying file
// LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
//
// See http://www.boost.org/libs/interpr... | {"hexsha": "8b313cb3f682fdfff039aa77a3a3983000979270", "size": 8906, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libs/interprocess/test/semaphore_test_template.hpp", "max_stars_repo_name": "mike-code/boost_1_38_0", "max_stars_repo_head_hexsha": "7ff8b2069344ea6b0b757aa1f0778dfb8526df3c", "max_stars_repo_licens... |
Jed Alexander is an local artists artist, illustrator and educator who lives in the Davis area. He is the founding member and original organizer of The Davis Figure Drawing Group. His work has been shown at the Pence Gallery and a number of local businesses. As an illustrator hes done covers for Sacramento News & Revie... | {"hexsha": "186455481a1f40687fc954536572e56434d66fd0", "size": 609, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Jed_Alexander.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
import time # temp timer
from classy import Class
from scipy.optimize import fsolve
from scipy import special # bessel functions
# constants
h = 0.67
c = 299793. # km/s
H0 = 100*h
omega_m = 0.27 + 0.049
omega_rad = 2.47e-05/(h*h)
omega_lambda = 1 - omega_m - omega_rad
T_nu = 2.7255*(4/11)**(1... | {"hexsha": "7845c76466bd510cbe1a5bcde034290c3f79dfde", "size": 22412, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnb_utils.py", "max_stars_repo_name": "gemyxzhang/cnb-anisotropies", "max_stars_repo_head_hexsha": "f087e5f18dd253d413f4a47a3265bec516ae3612", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""Calculate area of a mask."""
import argparse
import math
import logging
import sys
from osgeo import gdal
from osgeo import osr
import pygeoprocessing
import numpy
gdal.SetCacheMax(2**27)
logging.basicConfig(
level=logging.DEBUG,
format=(
'%(asctime)s (%(relativeCreated)d) %(levelname)s %(name)s... | {"hexsha": "5c5bed94245fbd6c5cf80444cb8da625da8ae87f", "size": 3586, "ext": "py", "lang": "Python", "max_stars_repo_path": "area_of_mask.py", "max_stars_repo_name": "richpsharp/raster_calculations", "max_stars_repo_head_hexsha": "28b18c34f49c2c275c46e332d7021a27703053cd", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
#!/usr/bin/env python
# -*-coding:utf-8-*-
# @Author : Weiqun Wu
# @Time : 2018-11-23
import math
import random
import os
import cv2 as cv
import numpy as np
from scipy.io import loadmat
import matplotlib.pyplot as plt
np.set_printoptions(threshold=np.inf)
def fspecial(ksize, sigma):
"""
Generates 2d Gaussi... | {"hexsha": "1b9a63503d11a7ac86a5150cfa7f7c1ad8b7ae76", "size": 12545, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/lib/utils/density.py", "max_stars_repo_name": "Anothorld/FairMOT", "max_stars_repo_head_hexsha": "6dbd7bbfac4c665c664baeeb9c1dd8f292e53cbe", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#pragma once
#include <boost/filesystem.hpp>
#include <fstream>
#include <stdint.h>
#include <cstddef>
#include <eosio/chain/block_header.hpp>
#include <eosio/chain/combined_database.hpp>
#include <eosio/chain/exceptions.hpp>
#include <eosio/chain/log_catalog.hpp>
#include <eosio/chain/log_data_base.hpp>
#include <eo... | {"hexsha": "595628da155674e3e4c558dcd8aaacf0e7859557", "size": 8538, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "libraries/state_history/include/eosio/state_history/log.hpp", "max_stars_repo_name": "forfreeday/eos", "max_stars_repo_head_hexsha": "11d35f0f934402321853119d36caeb7022813743", "max_stars_repo_licen... |
module Builtin.Reflection where
open import Prelude hiding (abs)
open import Prelude.Equality.Unsafe
open import Builtin.Float
open import Container.Traversable
open import Control.Monad.Zero
open import Agda.Builtin.Reflection as Builtin
open Builtin public
hiding ( primQNameEquality
; primQNameLess
... | {"hexsha": "d483be0e226f8ff5c93ab3131ca6fb43a17ab366", "size": 9402, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "src/Builtin/Reflection.agda", "max_stars_repo_name": "lclem/agda-prelude", "max_stars_repo_head_hexsha": "75016b4151ed601e28f4462cd7b6b1aaf5d0d1a6", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import json
import re
import sys
import time
from multiprocessing import Process
import cv2
import imutils
import numpy as np
from imutils.video import FileVideoStream
from kafka import KafkaProducer, TopicPartition
from kafka.partitioner import RoundRobinPartitioner, Murmur2Partitioner
from .utils import np_to_json
... | {"hexsha": "8a7c3768054afa6e38df2eed1409bcf2f3a15fb3", "size": 7868, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/frame_producer.py", "max_stars_repo_name": "karanveersingh5623/pico-test", "max_stars_repo_head_hexsha": "b24ec8835193c9c71579686bebf55d5993cea0a9", "max_stars_repo_licenses": ["MIT"], "max_st... |
# <-- encoding UTF-8 -->
# Empirical study on NHSS dataset (county level)
# -------------------------------------
## DOC STRING
#
#
# Tianhao Zhao (GitHub: Clpr)
# Dec 2018
# -------------------------------------
# -------------------------------------
## SECTION 0: ENVIRONMENT
library(sqldf) # sql enquiry
library(... | {"hexsha": "f162fecfad0b687fe89fcefb814882a5f9bee349", "size": 11558, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/proc_2_NHSS.r", "max_stars_repo_name": "Clpr/HealthInequality2018Dec", "max_stars_repo_head_hexsha": "d88d80c97e46f3e0b10c2c15e83eb0932957e69d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
# Copyright 2018 Anthony H Thomas and Arun Kumar
# 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 t... | {"hexsha": "0a893bfb5b631146e65924ef953183a0642786c1", "size": 4748, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/SimpleMatrixOps (Single Node Dense)/src/madlib_matrix_ops.py", "max_stars_repo_name": "ADALabUCSD/SLAB", "max_stars_repo_head_hexsha": "86d71b345c50b3a73eefcad3da39dc8d919d9652", "max_stars_... |
from numpy import arange
class AggregateSelector(object):
@staticmethod
def deciles_approx(
column: str,
min_decile: float = 0.0,
max_decile: float = 1.0,
as_name: str = None
) -> str:
if as_name is None:
as_name = f'{column}__deciles'
... | {"hexsha": "5c3b3f7f74ce61993bb5213012a160998c406c5a", "size": 1042, "ext": "py", "lang": "Python", "max_stars_repo_path": "aws_managers/athena/selectors/aggregate_selector.py", "max_stars_repo_name": "vahndi/aws-managers", "max_stars_repo_head_hexsha": "bdbfb2b8a9258a53e3ea4dfbbfe5491a34113899", "max_stars_repo_licens... |
/**
* author: Jochen K"upper
* created: Jan 2002
* file: pygsl/src/statisticsmodule.c
* $Id: floatmodule.c,v 1.8 2004/03/24 08:40:45 schnizer Exp $
*
* "
*/
#include <Python.h>
#include <gsl/gsl_statistics.h>
#include <pygsl/error_helpers.h>
#include <pygsl/block_helpers.h>
/* include real functions for defa... | {"hexsha": "3866d0671ed4692897ae5064981bf6222d6a115e", "size": 755, "ext": "c", "lang": "C", "max_stars_repo_path": "production/pygsl-0.9.5/src/statistics/floatmodule.c", "max_stars_repo_name": "juhnowski/FishingRod", "max_stars_repo_head_hexsha": "457e7afb5cab424296dff95e1acf10ebf70d32a9", "max_stars_repo_licenses": [... |
from PIL import Image
from torch.utils.data import Dataset
import numpy as np
import torch
def default_loader(path):
return Image.open(path).convert('RGB')
class csv_Dataset(Dataset):
def __init__(self, label_list, transform=None, target_transform=None, loader=default_loader):
imgs = []
for i... | {"hexsha": "cf4ec19ac609802cdc73f71e0da026b26637d1b3", "size": 5986, "ext": "py", "lang": "Python", "max_stars_repo_path": "ZXX_utils/load_csv_data.py", "max_stars_repo_name": "RiyaoDong/HGSL", "max_stars_repo_head_hexsha": "19fa984b3bfde0e3b7acbce87dd40177cd64f9b0", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
\xname{setup}
\chapter{Setting up a Java Program for Analysis}
\label{chap:setup}
This chapter describes how to setup a Java program for analysis using Chord.
Suppose the program has the following directory structure:
\begin{framed}
\begin{verbatim}
example/
src/
foo/
Main.java
... | {"hexsha": "6ebcda5d1224aa96aaa0bfbc20c97c801c46efbc", "size": 4134, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/setup.tex", "max_stars_repo_name": "CSA-PLLab/STAND", "max_stars_repo_head_hexsha": "2e41f21b842ab43f23aecbf5527f6043ce837b29", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 4,... |
from django.shortcuts import render
from django.http import *
import numpy as np
from . import autoencoder
from . import models
import json as js
import cv2, base64, utils
bad = HttpResponseBadRequest(js.dumps('nope'), content_type='application/json')
def submitFace(res):
#validate data
if not res.is_ajax() or no... | {"hexsha": "8d577b775370be154b4dc1074ae5595da68bcccb", "size": 3767, "ext": "py", "lang": "Python", "max_stars_repo_path": "main/rest.py", "max_stars_repo_name": "x13machine/facega", "max_stars_repo_head_hexsha": "eadff498344b35e3f413927ac72b88098f812268", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
function [A, b] = sllinrega(X, Y, varargin)
%SLLINREGA Performs Augmented Multivariate Linear Regression
%
% $ Syntax $
% - [A, b] = sllinrega(X, Y, ...)
%
% $ Arguments $
% - X: The sample matrix of x
% - Y: The sample matrix of y
% - A: The solved transform matrix
% - b: The solv... | {"author": "lmthang", "repo": "nmt.hybrid", "sha": "50d5c025f18ed280ff0fd2e2adce327f4170a2c3", "save_path": "github-repos/MATLAB/lmthang-nmt.hybrid", "path": "github-repos/MATLAB/lmthang-nmt.hybrid/nmt.hybrid-50d5c025f18ed280ff0fd2e2adce327f4170a2c3/code/wordsim/code/sltoolbox_r101/sltoolbox_r101/sltoolbox/regression/s... |
# Maximum feasibility of 0.4769921436588103 for [1,3,5,8]
# Mean feasibility of 0.3221046443268665
include("CenterOfMass.jl")
include("setup_parameters.jl")
mass_resolution = 0.1
masses = collect(min_mass:mass_resolution:max_mass)
resolution = 0.1
max_robots = 4
actuator_limit = 6.0
#########################
# Myop... | {"hexsha": "b1a77172d5d80e0d4a4148a28b3661ed7e625a21", "size": 2280, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "maximal_initial_feasibility.jl", "max_stars_repo_name": "mcorah/CenterOfMass", "max_stars_repo_head_hexsha": "84bd9124b042ed0cd4f5cd6ab3a705f72046f3ca", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
# Neighbors in 1D
function has_neighbor(m::Mesh1D, cell, face)
m.isperiodic && return true
if cell.coord == 1 && face == :l
return false
elseif cell.coord == length(m.elements)-1 && face == :r
return false
else
return true
end
end
function neighbor(m::Mesh1D, cell, face)
... | {"hexsha": "ccef4880dc35d0bc25876836702eac843104db67", "size": 3612, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/neighbors.jl", "max_stars_repo_name": "Keno/AC274.jl", "max_stars_repo_head_hexsha": "9eafcba152019a563b4501c9626f1699814e37b4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_s... |
import tensorflow as tf
import tensorflow_addons as tfa
import random
import numpy as np
import itertools
def augment(*images: tf.Tensor, mask_image = False, size=None):
p1 = np.random.uniform((), 0, 1)
p2 = np.random.uniform((), 0, 1)
# p3 = tf.random.uniform((), 0, 1)
random_state = np.random.RandomS... | {"hexsha": "91f0674aa44582a8e740f4de2a7b3f504a6dc6ff", "size": 8151, "ext": "py", "lang": "Python", "max_stars_repo_path": "augmentations.py", "max_stars_repo_name": "donikv/IlluminationBase", "max_stars_repo_head_hexsha": "4aade52bb8a1065f10b94ffda09645a681d8160c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
// Copyright (c) Facebook, Inc. and its affiliates.
#include <glog/logging.h>
#include <sys/socket.h>
#include <zlib.h>
#include <array>
#include <boost/uuid/uuid.hpp>
#include <boost/uuid/uuid_io.hpp>
#include <iostream>
#include <memory>
#include <thread>
#include "../lib/rapidjson/include/rapidjson/document.h"
#i... | {"hexsha": "b14fea84559d51a39bf068677e7aeadbd0f4ed2d", "size": 17919, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "client/src/packet_reader.cpp", "max_stars_repo_name": "satyamedh/craftassist", "max_stars_repo_head_hexsha": "d97cbc14bc25149d3ef41737231ab9f3cb7e392a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma sheaf_spec_on_open_is_comm_ring:
assumes "is_zariski_open U"
shows "comm_ring (\<O> U) (add_sheaf_spec U) (mult_sheaf_spec U) (zero_sheaf_spec U) (one_sheaf_spec U)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. comm_ring (\<O> U) (add_sheaf_spec U) (mult_sheaf_spec U) (zero_sheaf_spec U) (one... | {"llama_tokens": 25695, "file": "Grothendieck_Schemes_Comm_Ring", "length": 147} |
import os
import pickle
from collections import defaultdict
import numpy as np
def get_paths(root_folder):
"""
Creating a path dictionary for the features in the dataset.
"""
path_dict = defaultdict(list)
folders = os.listdir(root_folder)
for feature in folders:
file_names = os.list... | {"hexsha": "0eed43f4aa064cdd8b35e229eb84793f76e02545", "size": 5479, "ext": "py", "lang": "Python", "max_stars_repo_path": "read_dataset.py", "max_stars_repo_name": "Tommy-Johannessen/MovementRecognition", "max_stars_repo_head_hexsha": "be84d7d014a272987dd20d03194336a9244eb900", "max_stars_repo_licenses": ["MIT"], "max... |
import torch
from torch.utils.data import Dataset
from PIL import Image, ImageFile
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
import albumentations
from albumentations.pytorch import ToTensorV2
import operator
ImageFile.LOAD_TRUNCATED_IMAGES = True
class DatasetUtil... | {"hexsha": "222d384a572ad5dd137d8b96a21e25c2b222f51d", "size": 6276, "ext": "py", "lang": "Python", "max_stars_repo_path": "MyVision/dataset/Dataset.py", "max_stars_repo_name": "Abhiswain97/MyVision", "max_stars_repo_head_hexsha": "2f8dd5c57d979b2bec365d637575e839e4b2427b", "max_stars_repo_licenses": ["MIT"], "max_star... |
#include <boost/test/utils/iterator/token_iterator.hpp>
| {"hexsha": "6bc50dfd9839219d31b28c42f253afa3657cd300", "size": 56, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_test_utils_iterator_token_iterator.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_license... |
import numpy as np
from numpy.testing import assert_almost_equal
import filecmp
from ..molecules.protein import Protein
seq_reference = 'GG'
prot = Protein(seq_reference)
def test_coords():
coords = prot.coords
# this test is valid for GG
ref_coords = np.array([[-3.28713324, 1.37438873, -0.25902808],
... | {"hexsha": "f5ac374bcc14472594e866f3b0e7e27e847a702f", "size": 3048, "ext": "py", "lang": "Python", "max_stars_repo_path": "bomeba0/tests/test_protein.py", "max_stars_repo_name": "aloctavodia/bomeba0", "max_stars_repo_head_hexsha": "e212986d8ee60be1da91d63a7a889db14ec851c3", "max_stars_repo_licenses": ["Apache-2.0"], "... |
(** Generated by coq-of-ocaml *)
Require Import OCaml.OCaml.
Local Set Primitive Projections.
Local Open Scope string_scope.
Local Open Scope Z_scope.
Local Open Scope type_scope.
Import ListNotations.
Unset Positivity Checking.
Unset Guard Checking.
Inductive nat : Set :=
| O : nat
| S : nat -> nat.
Inductive natu... | {"author": "yalhessi", "repo": "lemmaranker", "sha": "53bc2ad63ad7faba0d7fc9af4e1e34216173574a", "save_path": "github-repos/coq/yalhessi-lemmaranker", "path": "github-repos/coq/yalhessi-lemmaranker/lemmaranker-53bc2ad63ad7faba0d7fc9af4e1e34216173574a/benchmark/clam/_lfind_clam_lf_goal33_mult_succ_80_plus_succ/goal33con... |
using BinaryProvider
# Parse some basic command-line arguments
const verbose = "--verbose" in ARGS
const prefix = Prefix(get([a for a in ARGS if a != "--verbose"], 1, joinpath(@__DIR__, "usr")))
products = Product[
# Instantiate products here, e.g.:
LibraryProduct(prefix, "libgumbo", :libgumbo),
]
# Download b... | {"hexsha": "bf8ce3b10bc37452c9e947b98a7606bbd20a7186", "size": 2562, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "deps/build.jl", "max_stars_repo_name": "stev47/Gumbo.jl", "max_stars_repo_head_hexsha": "cc7864e819ef2af6791d6f6633f5675400b75741", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
from math import *
from functools import reduce
import networkx as nx
import matplotlib as mpl
import matplotlib.pyplot as plt
def GlobalAxialMapAnalysis(graph):
global k, TD, MD, RA, RRA, IntV
d = nx.all_pairs_dijkstra_path_length(graph)
k = len(d)
TD = {i: reduce(lambda x, y: x + y, d[i].values()) f... | {"hexsha": "95efb46d76e11821d09c4b346314286bd671148f", "size": 3108, "ext": "py", "lang": "Python", "max_stars_repo_path": "ch4_1/axialmap3.py", "max_stars_repo_name": "o-kei/design-computing-aij", "max_stars_repo_head_hexsha": "954b46fb5f2192ab79fc003a2ca3a259e41dc7a4", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
function [mg,nu,sig,info] = spm_rice_mixture(h,x,K)
% Fit a mixture of Ricians to a histogram
% FORMAT [mg,nu,sig] = spm_rice_mixture(h,x,K)
% h - histogram counts
% x - bin positions (plot(x,h) to see the histogram)
% K - number of Ricians
% mg - integral under each Rician
% nu - "mean" parameter of each ... | {"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/toolbox/Longitudinal/spm_rice_mixture.m"} |
# Tests for Mamlmquist DEA Model
@testset "MalmquistDEAModel" begin
## Test Mamlmquist DEA Model with 1 input and 1 output
X = Array{Float64,3}(undef, 5, 1, 2)
X[:, :, 1] = [2; 3; 5; 4; 4];
X[:, :, 2] = [1; 2; 4; 3; 4];
Y = Array{Float64,3}(undef, 5, 1, 2)
Y[:, :, 1] = [1; 4; 6; 3; 5];
Y[:... | {"hexsha": "1a650cc0557dd451dd22953cd65aaa70c1963002", "size": 3556, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/malmquist.jl", "max_stars_repo_name": "simeonschaub/DataEnvelopmentAnalysis.jl", "max_stars_repo_head_hexsha": "ef5caf760c212460402b94db3f7c8623ee114f63", "max_stars_repo_licenses": ["MIT"], "... |
#include <iostream>
#include <cstdlib>
#include <cmath>
#include <boost/pending/disjoint_sets.hpp>
#include <vector>
#include <queue>
#include <map>
using namespace std;
template <class T>
class AffinityGraphCompare{
private:
const T * mEdgeWeightArray;
public:
AffinityGraphCompare(const T * EdgeWeightArray){... | {"hexsha": "7dba696326ae988e10f671cba2662211d8dffb71", "size": 6163, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dataset_06/malis/malis_cpp.cpp", "max_stars_repo_name": "naibaf7/caffe_neural_models", "max_stars_repo_head_hexsha": "9d372c4bc599029902185e19f89e5c39f842fff7", "max_stars_repo_licenses": ["BSD-2-Cl... |
import pickle
import unittest
from collections import OrderedDict
import numpy as np
from qtt.instrument_drivers.virtual_gates import VirtualGates, extend_virtual_gates, update_cc_matrix
from qtt.instrument_drivers.virtual_instruments import VirtualIVVI
from qtt.measurements.scans import instrumentName
class TestVi... | {"hexsha": "b1dd020868463db39829fe2473d865480a7264d6", "size": 4182, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tests/unittests/instrument_drivers/test_virtual_gates.py", "max_stars_repo_name": "codecrap/qtt", "max_stars_repo_head_hexsha": "39a8bf21f7bcab94940a66f4d553a14bf34f82b0", "max_stars_repo_lice... |
subroutine foo(f1,f2,f3,f4,f5,f6,f7,f8,f9,f0,g1,g2,g3)
implicit none
integer f4,f3,f2,f1
integer g4,g5,g6,g7,g8,g9
integer i1,i2,i3,i4,i5
real*8 g1(5,f3,f2,f1),g2(5,5,f3,f2,f1),g3(5,f3,f2,f1)
real*8 f0(5,5,f3,f2,f1),f9(5,5,f3,f2,f1),f8(5,5,f3,f2,f1)
real*8 f7(5,5,f3,f2,f... | {"hexsha": "d19cf70c1661d9a1189aa2f40d381acd8729eb70", "size": 1551, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "validation_tests/llvm/f18/gfortran.dg/graphite/interchange-1.f", "max_stars_repo_name": "brugger1/testsuite", "max_stars_repo_head_hexsha": "9b504db668cdeaf7c561f15b76c95d05bfdd1517", "max_stars_r... |
% -----------------------------------------------------------------------------
% Author : Bimalka Piyaruwan Thalagala
% GitHub : https://github.com/bimalka98
% Date Created : 11/8/2021
% Last Modified :
% -----------------------------------------------------------------------------
\documentc... | {"hexsha": "b3a2801386ef2ae79ba230ee8667246707e0fed5", "size": 2325, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Assignment 01/LaTeX Report/EN3053_180631J_A1.tex", "max_stars_repo_name": "bimalka98/EN3053-Digital-Communications-I", "max_stars_repo_head_hexsha": "723d984fc12e2d27743855a2a791f999c2148426", "max_... |
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
class MNISTCNNModel(nn.Module):
def __init__(self):
super(MNISTCNNModel, self).__init__()
# ONE LAYER
self.layer1 = torch.nn.Sequential(torch.nn.Conv2d(1, 16, 5, 1, 4), # output space (16, 16, ... | {"hexsha": "b4c806201d2093d6a0098d322cb60904df6a9015", "size": 2950, "ext": "py", "lang": "Python", "max_stars_repo_path": "ML/Pytorch/mnist_cnn_model.py", "max_stars_repo_name": "DistributedML/Biscotti", "max_stars_repo_head_hexsha": "dfba71b3924e1bafd2ab2545881fb741193f224e", "max_stars_repo_licenses": ["BSD-2-Clause... |
import cv2
import numpy as np
def test_transform(fnames):
imgs = []
for fname in fnames:
img = cv2.imread(fname)
imgs.append(cv2.resize(img, (224, 224)))
return (np.float32(imgs) - 128.)/128.
| {"hexsha": "99d5d673f05b7934b94ab297dc296df56db8d3e5", "size": 221, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepface/datasets/augmentation_policies.py", "max_stars_repo_name": "MatheusAD95/fg2020-faceunderstanding", "max_stars_repo_head_hexsha": "95a3d04f68c2c3207137a9f3b9fb3f8e2134fe8e", "max_stars_repo... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 Alibaba Group Holding 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-... | {"hexsha": "c3623019992e0bd4fd3a19033d5eddb5bfe13b81", "size": 4485, "ext": "py", "lang": "Python", "max_stars_repo_path": "mars/tensor/reduction/nansum.py", "max_stars_repo_name": "tomzhang/mars-1", "max_stars_repo_head_hexsha": "6f1d85e37eb1b383251314cb0ba13e06288af03d", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#include "Util.h"
#include "platform.h"
#include <boost/algorithm/string/classification.hpp>
#include <boost/algorithm/string/split.hpp>
#include <boost/filesystem/operations.hpp>
namespace fs = boost::filesystem;
std::string strToUpper(const char* from)
{
std::string str(from);
for(unsigned int i = 0; i < str.siz... | {"hexsha": "3a52d484324c9ca1acb1415b7d30f67951089bba", "size": 6520, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "es-core/src/Util.cpp", "max_stars_repo_name": "Odroid-RetroArena/EmulationStation", "max_stars_repo_head_hexsha": "62c92b5ec76561d543a213acf51495220189b8e6", "max_stars_repo_licenses": ["Apache-2.0"... |
/**
* @license BSD 3-Clause
* @copyright Pawel Okas
* @version $Id$
* @brief
*
* @authors Pawel Okas
* created on: 30-03-2019
*
* @copyright Copyright (c) 2019, Pawel Okas
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without modification, are permitted prov... | {"hexsha": "0ceeeaac071404bfc8510dc5f19319464124766f", "size": 6291, "ext": "h", "lang": "C", "max_stars_repo_path": "drivers/Atmel/AT24MAC/driver/at24mac.h", "max_stars_repo_name": "microHAL/microhal-drivers", "max_stars_repo_head_hexsha": "09925a9696e4794f9ca0b2e9b5e61908ac99b84b", "max_stars_repo_licenses": ["BSD-3-... |
# coding: utf-8
import os
import numpy as np
def shuffle_array(*args):
"""
Shuffle the given data. Keeps the relative associations arr_j[i] <-> arr_k[i].
Params
------
args: (numpy arrays tuple) arr_1, arr_2, ..., arr_n to be shuffled.
Return
------
X, y : the shuffled arrays.
... | {"hexsha": "4811bd35f72bea00390c0f5578e5d68a5762fc44", "size": 2193, "ext": "py", "lang": "Python", "max_stars_repo_path": "datawarehouse/pizza.py", "max_stars_repo_name": "victor-estrade/datawarehouse", "max_stars_repo_head_hexsha": "9ae342bf6f9c3622eb841c2ee770519b12cde1c3", "max_stars_repo_licenses": ["MIT"], "max_s... |
import numpy as np
from src.compute_corr_coef import compute_corr_coef
from utils.plotting import plot_similarities
def compute_trust_values(dsk, do_plot=False):
"""
Compute trust values following formula 6
k:= number of blendshapes
n:= num_features (num_markers*3)
:param dsk: delta_sk vector (k... | {"hexsha": "f1c021de79d124febfa8a831e976cd4dc12aeed9", "size": 1647, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/compute_trust_values.py", "max_stars_repo_name": "johndpope/FacialRetargeting", "max_stars_repo_head_hexsha": "5fb0c1da6af6c3d59aef264f567bfa7a244d0764", "max_stars_repo_licenses": ["MIT"], "m... |
"""
Test for nested class Parent
This file contains a discussion, examples, and tests about nested
classes and parents. It is kept in a separate file to avoid import
loops.
EXAMPLES:
Currently pickling fails for parents using nested classes (typically
for categories), but deriving only from Parent::
sage: from ... | {"hexsha": "9c714f13889b710c0ac1d7fc887f0e20868825a7", "size": 6057, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/misc/nested_class_test.py", "max_stars_repo_name": "fchapoton/sage", "max_stars_repo_head_hexsha": "765c5cb3e24dd134708eca97e4c52e0221cd94ba", "max_stars_repo_licenses": ["BSL-1.0"], "max... |
export PrioritizedSweepingSamplingModel
using DataStructures: PriorityQueue, dequeue!
import StatsBase: sample
"""
PrioritizedSweepingSamplingModel(θ::Float64=1e-4)
See more details at Section (8.4) on Page 168 of the book *Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT pre... | {"hexsha": "d8852283a1de1aaaff8ae6980b8f6717245a319f", "size": 1994, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/ReinforcementLearningZoo/src/algorithms/tabular/dyna_agents/env_models/prioritized_sweeping_sampling_model.jl", "max_stars_repo_name": "LaarsOman/ReinforcementLearning.jl", "max_stars_repo_head... |
using StatsBase
input = joinpath(@__DIR__, "input")
lines = readlines(input)
function endpoints(line)
components = map(n -> parse(Int, n), split(line, r",| -> "))
return ((components[1], components[2]), (components[3], components[4]))
end
function orthogonals(coords)
map(c -> begin
(start, fi... | {"hexsha": "021f603ed4d69b0da528e48290fed27ebeaf29a4", "size": 1944, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "bin/five/run.jl", "max_stars_repo_name": "talentdeficit/aoc2021", "max_stars_repo_head_hexsha": "6dbc52d2ad096584641aab629b29a0cdadedd5a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
subroutine axial
!
! to obtain the axial distributiun of velocity and/or mach number
!
use kinddefine
use fg, only:gc,gd,ge,gf,gh,gi,hb,hc,he
use gg, only:gam,gm,g2,g4,g5,g6,g7,g8,g9,ga,rga,qt
use cline, only:wip,x1,frip,zonk,seo,cse,axis,taxi
use prop, only:sfoa,conv
... | {"hexsha": "462c4c4cc015ad41166d3a78582491b7550778dd", "size": 28001, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/axial.f", "max_stars_repo_name": "aldorona/contur", "max_stars_repo_head_hexsha": "d4197b55e28b20f905f9418f0473b2c39fadb0fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_st... |
import gym
import numpy as np
from marathon_envs.envs import MarathonEnvs
from timeit import default_timer as timer
from datetime import timedelta
import os
env_names = [
'Hopper-v0',
# 'Walker2d-v0',
# 'Ant-v0',
# 'MarathonMan-v0',
# 'MarathonManSparse-v0'
]
for env_name in env_names:
... | {"hexsha": "8517336346fc59391fcc9bce2989fcf6a3a8071f", "size": 973, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_marathon_envs.py", "max_stars_repo_name": "Sohojoe/plan2exploreMarathonEnvs", "max_stars_repo_head_hexsha": "d5ea00e0d24d5bf2447df4921681a5e3bfc398c1", "max_stars_repo_licenses": ["Apache-2.0"... |
module GeoCost
using FillArrays
using StaticArrays
using Distances
using OffsetArrays
using DataStructures
using Statistics
const sqrt2 = sqrt(2.0)
const neib_8 = @SMatrix[1. 1 1; 1 0 1; 1 1 1]
const distance_8 = @SMatrix[sqrt2 1 sqrt2; 1 Inf 1; sqrt2 1 sqrt2]
const distance_4 = @SMatrix[Inf 1 Inf; 1 Inf 1; Inf 1 Inf... | {"hexsha": "12668fc838a29c680589c766301ffb9dd1a41abf", "size": 13829, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/GeoCost.jl", "max_stars_repo_name": "evetion/GeoCost.jl", "max_stars_repo_head_hexsha": "98b39c6fc5808d686cbfc2c5ef5738b7a35c3e5e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
'''
This script divides copies of the focal plane into individual rafts
This script is designed to be called from the command line as:
python hp2fp_tiler.py [fpID] [chunkSize]
- fpID refers to the index of the focal plane we are writing in the list stored in utils/pointingList.obj
- chunkSize is not required, and is... | {"hexsha": "fef76ca1db26261ec9d9122eed7981f853a69c8d", "size": 3357, "ext": "py", "lang": "Python", "max_stars_repo_path": "fp2raft_tiler.py", "max_stars_repo_name": "cahebert/read-noise", "max_stars_repo_head_hexsha": "c8c7972bb9dcdd8758a4f48313f73011fe533937", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
import pickle
import numpy as np
import sys
with open(sys.argv[1], 'rb') as handle:
dict_out = pickle.load(handle)
TD = np.array(dict_out['2 metre dewpoint temperature']['values'])
T = np.array(dict_out['2 metre temperature']['values'])
RH = 100*(np.exp((17.625*TD)/(243.04+TD))/np.exp((17.625*T)/(243.04+T)))
... | {"hexsha": "f8fb0d4bd3607a9e82c93825da02b5744d4414bb", "size": 487, "ext": "py", "lang": "Python", "max_stars_repo_path": "WINGSWorkflowComponents/GeneralDataPreparation/deprecated/netCDF_simple/code/library/calculateRH/calculateRH.py", "max_stars_repo_name": "mintproject/MINT-WorkflowDomain", "max_stars_repo_head_hexs... |
#AIXPM - AIX Package Manager, by Michael Felt aka aixtools
# Copyright 2020
# An Ansible 'project' that is to evolve from a role to a module
## History and Motivation to develop AIXPM
AIX, since roughly the year 2000 and the development of AIX 5.0 (alpha test), the concept
of 'geninstall' generic installer. This was ... | {"hexsha": "cb25c05def3e3b857cfe6f3210a640c1df99e5e4", "size": 2559, "ext": "rd", "lang": "R", "max_stars_repo_path": "AIXPM.rd", "max_stars_repo_name": "aixtools/aixpm", "max_stars_repo_head_hexsha": "8c00c5241b38b16d7f6bfe8a16699e288c0c4642", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_st... |
import pickle
import numpy as np
import os
def export_linear(x, weight, bias):
z = x @ weight + bias #can change this to a RELU function instead too
return sigmoid(z)
def sigmoid(x):
return 1/(1+np.exp(-x))
#weights
model_weights = pickle.load(open(os.getcwd() + "/MLP_scratch/model_weights.pickle", "rb"... | {"hexsha": "ea1271774c6066773111349dd372fd65eebe1148", "size": 1121, "ext": "py", "lang": "Python", "max_stars_repo_path": "MLP_scratch/model_export.py", "max_stars_repo_name": "christopherjgan/fsdl-text-recognizer-2021-labs", "max_stars_repo_head_hexsha": "c0ecdbc579094f802bfddab30206699d71a50d9a", "max_stars_repo_lic... |
[STATEMENT]
lemma fundamental_theorem_of_algebra:
assumes nc: "\<not> constant (poly p)"
shows "\<exists>z::complex. poly p z = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>z. poly p z = 0
[PROOF STEP]
using nc
[PROOF STATE]
proof (prove)
using this:
\<not> constant (poly p)
goal (1 subgoal):
1. ... | {"llama_tokens": 23721, "file": null, "length": 238} |
\chapter{Future developments}
\label{chap:futuredevelopments}
Here we list briefly some of the developments that we are working on. We also discuss a few suboptimal features of the current version of the toolbox.
\section{Load balancer}
Experiments are distributed throughout the workers in a fully random way. We pla... | {"hexsha": "35e94891dc786050a64e083509093cbf15df1d5a", "size": 695, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "manual/Manual Source Code/chapters/appendixB.tex", "max_stars_repo_name": "ispamm/Lynx-Toolbox", "max_stars_repo_head_hexsha": "c018ee3dbad4bfc75315732a883ccfd44e15f18a", "max_stars_repo_licenses": [... |
function run!(model::elmod3d)
path="/home/lzh/Dropbox/Zhenhua/Ongoing/Seisimu/deps/builds/el3d_openmp.so"
ccall((:el3d_openmp,path),
Void,
(Ptr{Cdouble}, Cint, Cint, Cint, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble},
Ptr{Cdouble}, Cint, Cint, Cint, Ptr{Cdouble}, Ptr{Cdouble}, Ptr{Cdouble},
Ptr{Cdouble}, Cin... | {"hexsha": "95d64cce3d0c06a017660030e65c0af77da6cc20", "size": 5468, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/extrap/run_base.jl", "max_stars_repo_name": "zhenhua3/Seisimu", "max_stars_repo_head_hexsha": "357b6a5c1ecfe8e6afd390fd3b295e878044a21d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from dnn_utils import sigmoid,sigmoid_backward,relu,relu_backward
def initialize_two_layer(n_x,n_h,n_y):
W1 = np.random.randn(n_h,n_x) * 0.01
b1 = np.zeros(n_h,1)
W2 = np.random.randn(n_y,n_h) * 0.01
b2 = np.zeros(n_y,1)
param = {"W1":W1,"b1":b1,"W2":W2,"b2":b2}
return param
def initialize... | {"hexsha": "f727406dcaa18843458f6c479462d8f14bb82493", "size": 2802, "ext": "py", "lang": "Python", "max_stars_repo_path": "DLCoursera_part1_week4_1.py", "max_stars_repo_name": "zhouhan921001/DeepLearning-homework", "max_stars_repo_head_hexsha": "20562dc49ca5898b531a678c0e54c8d985fcc72f", "max_stars_repo_licenses": ["M... |
import os
import numpy as np
import torch
import torch.autograd
import torch.optim as optim
import torch.nn as nn
from torch.autograd import Variable
class ReplayBuffer:
"""
Buffer to store trajectories.
"""
def __init__(self, size):
self.state_buf = list()
self.act_buf =... | {"hexsha": "7a31acae5dea23028a3229c2f053afa69e4d2fbd", "size": 12631, "ext": "py", "lang": "Python", "max_stars_repo_path": "core/MADDPG.py", "max_stars_repo_name": "zisikons/deep-rl", "max_stars_repo_head_hexsha": "3c39a194d048618a2a3962cdf5f4b1825e789a22", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7, "ma... |
from sklearn.grid_search import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from scipy.stats import randint as sp_randint
from time import time
import numpy as np
def grid_search_parameter(clf, X, y):
param = {'max_depth':[3,4,5,6,7,8]}
grid_search = GridSearchCV(clf, param, cv=5, sc... | {"hexsha": "6e786f458516627733d69955381ae85d3777bbd9", "size": 1693, "ext": "py", "lang": "Python", "max_stars_repo_path": "activity_recognition/parameter.py", "max_stars_repo_name": "linw7/Activity-Recognition", "max_stars_repo_head_hexsha": "f76a327268c48f6e3cbe5ff25576f49d8c4927cf", "max_stars_repo_licenses": ["MIT"... |
from scipy.spatial import cKDTree as KDTree
import numpy as np
class NormalVectorEstimator(object):
def __init__(self, simplices, points):
self.simplices = simplices
self.points = points
self.centroids = self._facet_centroids()
self.centroid_tree = KDTree(self.centroids)
sel... | {"hexsha": "0ca022637e1696d62eb92392ada5da768ef58dc9", "size": 3084, "ext": "py", "lang": "Python", "max_stars_repo_path": "cemc/tools/normal_vector.py", "max_stars_repo_name": "davidkleiven/WangLandau", "max_stars_repo_head_hexsha": "0b253dd98033c53560fe95c76f5e38257834bdf6", "max_stars_repo_licenses": ["MIT"], "max_s... |
import argparse
import json
from pathlib import Path
import skimage.transform
import torch
import visdom
from skimage.io import imread
from torch.nn import functional as F
import numpy as np
from terial import models
from terial.classifier.inference.utils import compute_weighted_scores_single
from terial.classifier.... | {"hexsha": "bf6dca1a07d9b5a7e6b4c91dbd4a0b612a10c84e", "size": 6191, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/terial/classifier/inference/infer_one.py", "max_stars_repo_name": "keunhong/photoshape", "max_stars_repo_head_hexsha": "6e795512e059bc5a6bdac748fda961f66d51c6f6", "max_stars_repo_licenses": ["... |
Artists and performers from Campus campus, the Davis community, and beyond showcase their original work in a huge, welcoming, and free open forum for artistic expression.
Like the name says, Fridays @ 4 happens every Friday at (you guessed it) 4:00 PM at Cafe Roma, on the corner of 3rd and University, next to Navins.
... | {"hexsha": "17e5743cc496ed43cb886a9494d7d8ef0b4cf30e", "size": 850, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Fridays_at_Four.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import flameplot as flameplot
from sklearn import (manifold, decomposition)
import numpy as np
# %%
# Load libraries
from sklearn import (manifold, decomposition)
import pandas as pd
import numpy as np
# Import library
import flameplot as flameplot
# Load mnist example data
X,y = flameplot.import_example()
# PCA: 5... | {"hexsha": "f11bc45bb9cd4eb81b7f0146d419fc663b409e98", "size": 1666, "ext": "py", "lang": "Python", "max_stars_repo_path": "flameplot/examples.py", "max_stars_repo_name": "rohankumardubey/flameplot", "max_stars_repo_head_hexsha": "fe24f0e47ea721222112a765d8955d10b4491a86", "max_stars_repo_licenses": ["MIT"], "max_stars... |
theory NP4_Simple_Action_Values
imports
NP4_Simple_Action_Syntax
"~~/src/HOL/Word/Word"
"~~/src/HOL/Word/Word_Bitwise"
(* These files contain a minimalistic semantics of P4's action constructs. More complex concepts
like switch statements are left out. The purpose of this verification effort is to showcase... | {"author": "Johanmyst", "repo": "Nano-P4", "sha": "fc3720d7115d0bac5d719cfe6c73a024aae7f9c4", "save_path": "github-repos/isabelle/Johanmyst-Nano-P4", "path": "github-repos/isabelle/Johanmyst-Nano-P4/Nano-P4-fc3720d7115d0bac5d719cfe6c73a024aae7f9c4/Theory_Files/Simple_Action_Verification/NP4_Simple_Action_Values.thy"} |
import pytest
from solo import hashsolo
from anndata import AnnData
import numpy as np
def test_cell_demultiplexing():
from scipy import stats
import random
random.seed(52)
signal = stats.poisson.rvs(1000, 1, 990)
doublet_signal = stats.poisson.rvs(1000, 1, 10)
x = np.reshape(stats.poisson.... | {"hexsha": "f4487674f55eefee01895a830355161e8c91775d", "size": 1582, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/hashsolo_tests.py", "max_stars_repo_name": "Elhl93/solo", "max_stars_repo_head_hexsha": "76b158f203ac9af6704304d1d21543fa561d3ed2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 63,... |
import cv2
import mediapipe as mp
import math
# FOR CHECKING THE FRAME RATE
import time
import numpy as np
class HandDetector():
def __init__(self, mode=False, maxHands=2, detectionCon=0.5, trackCon=0.5):
self.mode = mode
self.maxHands = maxHands
self.detectionCon = detectionCo... | {"hexsha": "550720200c37ba69bed9bf1fb0beb26127d75282", "size": 6700, "ext": "py", "lang": "Python", "max_stars_repo_path": "Virtual Mouse/handtrackingmodule.py", "max_stars_repo_name": "yadavujwal/virtual-mouse", "max_stars_repo_head_hexsha": "548a4ef74239006ac3cd2edbe0655f46961ec0ad", "max_stars_repo_licenses": ["MIT"... |
"""Specify the jobs to run via config file.
A simple experiment comparing Thompson sampling to greedy algorithm. Finite
armed bandit with 3 arms. Greedy algorithm premature and suboptimal
exploitation.
See Figure 3 from https://arxiv.org/abs/1707.02038
"""
import collections
import functools
from base.config_lib imp... | {"hexsha": "4e7cd583a4cbeac1066e483a44c1a3c9db8f68d3", "size": 1291, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/finite_arm/config_simple_rand.py", "max_stars_repo_name": "AbhinavGopal/ts_tutorial", "max_stars_repo_head_hexsha": "147ff28dc507172774693f225071f8e244e5994e", "max_stars_repo_licenses": ["MIT... |
import os
import cv2
import string
from tqdm import tqdm
import click
import numpy as np
import editdistance
import glob
import torch
from torch.autograd import Variable
import utils
import dataset
from PIL import Image
import models.crnn as crnn
#model_path = './data/crnn.pth'
#img_path = '../TextBoxes_plusplus/doc... | {"hexsha": "88b36528453c1ad9dcf0b5880ea4c9542eaa3749", "size": 2526, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval.py", "max_stars_repo_name": "moskiteau/crnn.pytorch", "max_stars_repo_head_hexsha": "934667baf19d89bc593fc859bfedf90c8bc2c1eb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import numpy as np
from shap_fork.utils import MaskedModel
from shap_fork import links
from shap_fork.models import Model
from .._explainer import Explainer
class Random(Explainer):
""" Simply returns random (normally distributed) feature attributions.
This is only for benchmark comparisons. It supports both ... | {"hexsha": "c58be50a0154b03dece9dfadb355962fc3cce7cb", "size": 3337, "ext": "py", "lang": "Python", "max_stars_repo_path": "shap_fork/explainers/other/_random.py", "max_stars_repo_name": "thbuerg/shap_fork", "max_stars_repo_head_hexsha": "bb82becb3295f31a8eed5dedc47d515ecf16e503", "max_stars_repo_licenses": ["MIT"], "m... |
defaultfigure(;kwargs...) = Figure(
;resolution = (800, 800),
background = RGBA(0, 0, 0, 0),
kwargs...)
# ## Plotting interface definition
"""
plotsample(method, sample)
"""
function plotsample(method, sample)
f = defaultfigure(resolution = (300, 150))
plotsample!(f, method, sample)
retur... | {"hexsha": "488d59983a8e5f8c4c34209af8f86fb253d4c854", "size": 2458, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/plotting.jl", "max_stars_repo_name": "dave7895/FastAI.jl", "max_stars_repo_head_hexsha": "8246d165ab4f9e0830ea9288564503d5d37fcfec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "m... |
# Version: 2020.02.21
#
# MIT License
#
# Copyright (c) 2018 Jiankang Deng and Jia Guo
#
# 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 limitatio... | {"hexsha": "e8229e9de0f7f070bf93091922899f8cd665818c", "size": 4959, "ext": "py", "lang": "Python", "max_stars_repo_path": "embedding-calculator/srcext/insightface/src/utils/benchmark.py", "max_stars_repo_name": "drawdy/CompreFace", "max_stars_repo_head_hexsha": "143b7955536f406a622248fad2d2108dfb5dd4f6", "max_stars_re... |
# -*- coding: utf-8 -*-
"""
Wind Setbacks tests
"""
from click.testing import CliRunner
import json
import numpy as np
import os
import pytest
import shutil
import tempfile
import traceback
from rex.utilities.loggers import LOGGERS
from reVX import TESTDATADIR
from reVX.handlers.geotiff import Geotiff
from reVX.wind_... | {"hexsha": "04ee87126e47cba05ba80f95f7584975f8f6488e", "size": 5739, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_setbacks.py", "max_stars_repo_name": "NREL/reVX", "max_stars_repo_head_hexsha": "4d62eb2c003c3b53b959f7a58bdc342d18098884", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function # Required for stderr output, must be the first import
import os
import random
import math
import argparse
import multiprocessing as mp
import networkx as nx
import numpy as np
import igraph as ig
import community as cm # python-louva... | {"hexsha": "d0f39dce18068f8b156786149ddbba5693d37639", "size": 14112, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithms/fast_consensus.py", "max_stars_repo_name": "eXascaleInfolab/clubmark", "max_stars_repo_head_hexsha": "5c329a5308a39d53f77db790a31d621245a7c693", "max_stars_repo_licenses": ["Apache-2.0... |
@testset "Degree Independent Set" begin
g0 = SimpleGraph(0)
for g in testgraphs(g0)
c = @inferred(independent_set(g, DegreeIndependentSet()))
@test isempty(c)
end
g1 = SimpleGraph(1)
for g in testgraphs(g1)
c = @inferred(independent_set(g, DegreeIndependentSet()))
@... | {"hexsha": "77e1c8d7b739de120398d53caa133195dbdb907c", "size": 1261, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/independentset/degree_ind_set.jl", "max_stars_repo_name": "SyxP/LightGraphs.jl", "max_stars_repo_head_hexsha": "6c488a872b991d99cc794f59d0ae617d5bf204a1", "max_stars_repo_licenses": ["MIT"], "... |
import os
import time
import itertools
import numpy as np
from matplotlib import colors as mcolors
from PyQt5 import QtWidgets
from otk.sdb import lookat, projection
from otk import zemax, trains
from otk import ri
from otk.sdb import npscalar
from otk.sdb import numba as sdb_numba
from otk.rt2 import rt2_scalar_qt as ... | {"hexsha": "46dbdae8a26a39b7ce7f98b2bf2a7f4a24298c10", "size": 1828, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/rt2/benchmark.py", "max_stars_repo_name": "draustin/otk", "max_stars_repo_head_hexsha": "c6e91423ec79b85b380ee9385f6d27c91f92503d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
//
// Copyright (c) 2017 Michele Segata <msegata@disi.unitn.it>
//
// This program is free software: you can redistribute it and/or modify
// it under the terms of the GNU Lesser General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later ver... | {"hexsha": "7d471a1b8658b95ca2a618f1f4d435de9234a108", "size": 7231, "ext": "h", "lang": "C", "max_stars_repo_path": "src/matrix_utils.h", "max_stars_repo_name": "michele-segata/mpclib", "max_stars_repo_head_hexsha": "a030421cbbcca8d3eb5e50b7cd0335bac0057fb0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 22.0... |
// program-options.cpp - application options
// written by Elijah Zarezky
// GNU libc headers
#include <limits.h>
#include <unistd.h>
// STL headers
#include <exception>
#include <iostream>
#include <string>
// Boost headers
#include <boost/program_options.hpp>
// our headers
#include "common-defs.h"
// shortcuts
... | {"hexsha": "5f5ba64609e8ee32b358fb69e9dbf1c94a12eb45", "size": 2470, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/program-options.cpp", "max_stars_repo_name": "SchweinDeBurg/Confident", "max_stars_repo_head_hexsha": "541ebb6d3d72b576b1bd0853f49585cc705d0006", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 16 05:43:41 2016
@author: aman
"""
import numpy as np
import cv2
from matplotlib import pyplot as plt
filename = '/home/aman/Pictures/Computer_Vision/Project/1.jpg'
img = cv2.imread(filename)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
corners =... | {"hexsha": "0868890bafe17d12d748ba8403866563ed9f58fa", "size": 528, "ext": "py", "lang": "Python", "max_stars_repo_path": "Testing Code/goodFeatures.py", "max_stars_repo_name": "amanwalia92/VisionChess", "max_stars_repo_head_hexsha": "c57219b3b7ce1fd98b27573aa0a8658ceabd0593", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Transform images with multiprocessing
# Author: David Young, 2019, 2020
"""Transform large images with multiprocessing, including up/downsampling
and image transposition.
"""
from time import time
from typing import Sequence
import numpy as np
from skimage import transform
from magmap.cv import chunking, cv_nd
fr... | {"hexsha": "5b11c8750cd5f32b3c68f8cd560b0364324ea203", "size": 15443, "ext": "py", "lang": "Python", "max_stars_repo_path": "magmap/atlas/transformer.py", "max_stars_repo_name": "sanderslab/magellanmapper", "max_stars_repo_head_hexsha": "16d55df6dc1f0e5baf3938a30edcdd634e0ffd85", "max_stars_repo_licenses": ["BSD-3-Clau... |
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
import torch.optim as optim
from torch.optim.lr_scheduler import MultiStepLR
import logging
import argparse
import os
import pandas as pd
import datetime
current_time = date... | {"hexsha": "ac5b18893303f368ee989ca13d7de32f5ce43ed8", "size": 11943, "ext": "py", "lang": "Python", "max_stars_repo_path": "Frame_Level_Speech_recognition/Frame_Level_Speech_Recognition/src/train.py", "max_stars_repo_name": "MonitSharma/Data-Science-Projects", "max_stars_repo_head_hexsha": "b78df36061a9877240763bf3e71... |
"""
AbstractMenu
The supertype for all Menu types.
See AbstractMenu.jl for descriptions of functions mentioned in this
doc string.
# Functions
The following functions can be called on all <:AbstractMenu types.
Details can be found in
## Exported
- `request(m::AbstractMenu)`
- `request(msg::AbstractString... | {"hexsha": "508178f50f8a6b2361216a25506f8ca1f31d674a", "size": 7947, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "ext/TerminalMenus/src/AbstractMenu.jl", "max_stars_repo_name": "KristofferC/Pkg.jl", "max_stars_repo_head_hexsha": "c5785d4293dd4fc26b5e03205424d7155342e7ad", "max_stars_repo_licenses": ["MIT"], "m... |
/*
* channel_element_base.hpp - micros base channel element
* Copyright (C) 2015 Zaile Jiang
*
* This program 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 Software Foundation; either version 2
* of the License, ... | {"hexsha": "ef58e723ccdf7fd025359c53fbd7dfc7e0487f73", "size": 4906, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/micros_rtt/oro/channel_element_base.hpp", "max_stars_repo_name": "sukha-cn/hpcl_rtt", "max_stars_repo_head_hexsha": "2fc67fa103011c7683762f26bbeb9a2937087ef5", "max_stars_repo_licenses": ["B... |
import numpy as np
from numpy.testing import assert_equal
from h5py._hl.selections import Selection
from ..slicetools import spaceid_to_slice
def test_spaceid_to_slice(h5file):
shape = 10
a = h5file.create_dataset('a', data=np.arange(shape))
for start in range(0, shape):
for count in range(0, sh... | {"hexsha": "48fa33f218315e8c05e63fd74a995545bc23b2c7", "size": 1615, "ext": "py", "lang": "Python", "max_stars_repo_path": "versioned_hdf5/tests/test_slicetools.py", "max_stars_repo_name": "takluyver/versioned-hdf5", "max_stars_repo_head_hexsha": "6fda5c803346e9be1dff459080566863f71cdc78", "max_stars_repo_licenses": ["... |
\documentclass{article}
\def\COMM{0}
\usepackage[nottoc]{tocbibind}
\usepackage{verbatim}
\usepackage{fullpage}
\usepackage{times}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{multirow}
\usepackage{xcolor}
\usepackage{fancyhdr}
\usepackage{float}
\usepackage{hyperref}
\usepackage{framed}
\usepackage{graphic... | {"hexsha": "48b9845fa0acb67093e9d59a803e422667a19fe7", "size": 1142, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Documentation/Documentation.tex", "max_stars_repo_name": "vlaskinvlad/SCALE-MAMBA", "max_stars_repo_head_hexsha": "7d318088bfe9110e50b22d7155b9f1775ef3df80", "max_stars_repo_licenses": ["BSD-2-Claus... |
import pyvisa as visa
import json
import time
from decimal import Decimal
from threading import Lock
import numpy as np
import math
class generic_driver_visa_gpib(object):
def __init__(self, spec):
self.spec = spec
self.operations = spec['operations']
port = spec["port"]
w_term = ... | {"hexsha": "545dd3da14343a7c3a6e49bfbda05b2c72414ddf", "size": 11115, "ext": "py", "lang": "Python", "max_stars_repo_path": "hs-logger/drivers/generic_driver_visa_gpib.py", "max_stars_repo_name": "b-sherson/hs-logger", "max_stars_repo_head_hexsha": "537865e44c93a4d234c9a96e9ad784a735869bcc", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma obs_a_extTA2J_eq_obs_a_extTA2J0 [simp]: "\<lbrace>extTA2J P ta\<rbrace>\<^bsub>o\<^esub> = \<lbrace>extTA2J0 P ta\<rbrace>\<^bsub>o\<^esub>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrace>extTA2J P ta\<rbrace>\<^bsub>o\<^esub> = \<lbrace>extTA2J0 P ta\<rbrace>\<^bsub>o\<^esub>
[PROOF STEP]... | {"llama_tokens": 170, "file": "JinjaThreads_Compiler_J0", "length": 1} |
From Coq Require Import Lists.List.
Import ListNotations.
Inductive sublist {T : Type} : list T -> nat -> nat -> list T -> Prop :=
| SHeadIncluded head tail j subtail : sublist subtail 0 j tail ->
sublist (head::subtail) 0 (S j) (head::tail)
| SHeadExcluded head tail i j sub : ... | {"author": "astOwOlfo", "repo": "PetitC", "sha": "449bc594f698eaf476faac0943e65fb34e36a63f", "save_path": "github-repos/coq/astOwOlfo-PetitC", "path": "github-repos/coq/astOwOlfo-PetitC/PetitC-449bc594f698eaf476faac0943e65fb34e36a63f/Sublist.v"} |
# coding: utf-8
import numpy as np
import torch
import os
import pickle
from PIL import Image, ImageOps, ImageEnhance
from argparse import ArgumentParser
from torch.optim import SGD, Adam
from torch.autograd import Variable
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Normaliz... | {"hexsha": "fef0826663ddc10dfea739393e2688b73f8c3059", "size": 7406, "ext": "py", "lang": "Python", "max_stars_repo_path": "train/tool.py", "max_stars_repo_name": "ydxb7/graduate", "max_stars_repo_head_hexsha": "836c47f881ff6c4edfdf1a0ee23bd04602788ca3", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null... |
import validation_tool.server.data_request as rs_data
import pandas as pd
import numpy as np
import cStringIO
import os
import json
from pytesmo.validation_framework.validation import Validation
from pytesmo.validation_framework.metric_calculators import BasicMetricsPlusMSE
from pytesmo.validation_framework.temporal... | {"hexsha": "f8a8705ccb0a16fa4bf609cb498f101e0c17de9f", "size": 13318, "ext": "py", "lang": "Python", "max_stars_repo_path": "validation_tool/views.py", "max_stars_repo_name": "TUW-GEO/web-validation-tool", "max_stars_repo_head_hexsha": "e73faeeda0a5abe4366f1dd39c77d2e63d8bae93", "max_stars_repo_licenses": ["MIT"], "max... |
# attr_list = []
# for func in (atomics..., contour)
# Typ = to_type(func)
# attr = keys(default_theme(nothing, Typ))
# push!(attr_list, attr...)
# end
# attr_list = string.(sort!(unique(attr_list)))
# # filter out fxaa attribute
# attr_list = filter!(x -> x ≠ "fxaa", attr_list)
const plot_attr_desc = Dict... | {"hexsha": "b5baca7c80c39299654bec8fc93286f3e7561ee2", "size": 3375, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/plot_attr_desc.jl", "max_stars_repo_name": "MaximeRivest/Makie.jl", "max_stars_repo_head_hexsha": "331f183c024b031a1ec425a4ccb3c25583f130b2", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
import json
import csv
from music21 import note, chord, stream, instrument
import numpy as np
from model import lstm_model
time_steps = 32
# load notes dict
with open('notes.json', 'r') as file:
notes_dict = json.load(file)
############
# Load music data from csv produced by mid_to_csv.py
with open('mozart.csv',... | {"hexsha": "21f24528d701542aeb535d3b7fb4c72051cf6d10", "size": 2932, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate.py", "max_stars_repo_name": "g95wang/music-generator", "max_stars_repo_head_hexsha": "528ce6d6dfd1c5aac15749f9f4eac8735078ab46", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
Require Utf8.
Require Import S00_setoid_basics S01_Wty S02_PolyFun S10_Wstd_Obj
S30_IdType S31_DiscStd S32_IdWty S33_PtwEq S34_Eqvr.
(* The setoid of extensional trees is isomorphic to
the subsetoid of pointwise equal trees on the equivariant ones. *)
Section Extensional_as_Equivariant.
Context {X : Se... | {"author": "j-emmen", "repo": "W-types-in-setoids", "sha": "d1d8028217532c77308227453fa5be0867407120", "save_path": "github-repos/coq/j-emmen-W-types-in-setoids", "path": "github-repos/coq/j-emmen-W-types-in-setoids/W-types-in-setoids-d1d8028217532c77308227453fa5be0867407120/S35_freeWstd.v"} |
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