text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
include("inputs_struct.jl")
include("constraints/constraints.jl")
include("neighbourhood_struct.jl")
include("output_struct.jl")
include("models/models.jl")
| {"hexsha": "ce2417ab2a2335be40761a649673a3a47371d874", "size": 157, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/structs/structs.jl", "max_stars_repo_name": "giadasp/ATA.jl", "max_stars_repo_head_hexsha": "8ec4227c418784521e8d14623626e072a348fc79", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, ... |
# This file was generated, do not modify it. # hide
searchtime = @elapsed sknns, sdists = searchbatch(G, db, k; parallel=true)
nothing # hide | {"hexsha": "7fae59bfd18733d158b9ee1cc8b17160aac4ce33", "size": 141, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/assets/tutorial/allknn/code/ex5.jl", "max_stars_repo_name": "sadit/SimilaritySearchDemos", "max_stars_repo_head_hexsha": "0924a140ea3d8efee1f9ccecfe514d17a891160c", "max_stars_repo_licenses": [... |
import cv2
import math
import numpy
def psnr(target, ref):
# assume RGB image
target_data = numpy.array(target, dtype=float)
ref_data = numpy.array(ref, dtype=float)
diff = ref_data - target_data
diff = diff.flatten('C')
rmse = math.sqrt(numpy.mean(diff ** 2.))
return 20 * math.log10(25... | {"hexsha": "f863b53c97be17d1eb2292939409056b65b1a735", "size": 984, "ext": "py", "lang": "Python", "max_stars_repo_path": "Super-Resolution-CNN-for-Image-Restoration-master/SRCNN-keras-master/psnr.py", "max_stars_repo_name": "AhsanulIslam/Thesis_Computer_Vision", "max_stars_repo_head_hexsha": "c308cce15146a33a3e474790b... |
import numpy as np
class Loads():
'''
This Class calculates the load to be applied on the structure
'''
def __init__(self, main_load_dict):
self.main_load_dict = main_load_dict
self.static_draft = main_load_dict['static_draft']
self.poly_third = main_load_dict['poly_third']
... | {"hexsha": "df71329bf45c140a8c765672bf3ed2288b101440", "size": 16758, "ext": "py", "lang": "Python", "max_stars_repo_path": "testdata/calc_loads.py", "max_stars_repo_name": "Konstantin8105/py4go", "max_stars_repo_head_hexsha": "7c9f3cf0d939ad94d13abe109d15c885ca849cda", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import streamlit as st
import pandas as pd
from math import sqrt
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.... | {"hexsha": "49897a130341f5644b3b853c32831418abb383cd", "size": 19229, "ext": "py", "lang": "Python", "max_stars_repo_path": "gap.py", "max_stars_repo_name": "ayanbag/Graduate_Admission_Prediction", "max_stars_repo_head_hexsha": "f7fc8a1ee9b8e23977e3b3909363352d04b9dd07", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
module airfoil_m
#ifdef dnad
use dnadmod
#define real type(dual)
#endif
use dataset_m
implicit none
type airfoil_t
character(100) :: name
character(20) :: properties_type
integer :: has_data_file
integer :: has_geom_file
real :: aL0
... | {"hexsha": "c0c523e78e2fdbcf9cc5d84654f2bd33a35439ea", "size": 3662, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "common/airfoil.f90", "max_stars_repo_name": "antzor10/MachUp", "max_stars_repo_head_hexsha": "dcc484d8aa1ddd74a55fc8fb767bc7e6ff56f8a0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
/***************************************************************************
* Copyright 1998-2020 by authors (see AUTHORS.txt) *
* *
* This file is part of LuxCoreRender. *
* ... | {"hexsha": "06655688c3dda959ab4f964a51a184764cd4ed07", "size": 88459, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/slg/engines/pathoclbase/compiletextures.cpp", "max_stars_repo_name": "Silverlan/LuxCore", "max_stars_repo_head_hexsha": "00c4eee95625a05290da0b3e3ae738fce2fdfdb5", "max_stars_repo_licenses": ["... |
'''Test two-stage segmentation'''
from pathlib import Path
import shutil
import tempfile
import torch
import numpy as np
from PIL import Image
from albumentations.augmentations.functional import center_crop
from torchvision.transforms.functional import to_tensor
from road_roughness_prediction.segmentation.datasets im... | {"hexsha": "9486c1c42342580b1b3f8914db9ec02560b976ba", "size": 2398, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/segmentation/test_segmentation_two_stage.py", "max_stars_repo_name": "mknz/dsr-road-roughness-prediction", "max_stars_repo_head_hexsha": "5f56b6ba5da70a09f2c967b7f32c740072e20ed1", "max_star... |
[STATEMENT]
lemma f_join_drop: "xs \<up> n \<Join>\<^sub>f I = xs \<Join>\<^sub>f (I \<oplus> n)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. xs \<up> n \<Join>\<^sub> I = xs \<Join>\<^sub> (I \<oplus> n)
[PROOF STEP]
apply (case_tac "length xs \<le> n")
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. length x... | {"llama_tokens": 1283, "file": "AutoFocus-Stream_IL_AF_Stream", "length": 13} |
#!/usr/bin/env python3
import pandas as pd
import socket
import stars
import numpy as np
from angle import angle_between
HOST = "myspace.satellitesabove.me"
PORT = 5016
TICKET = 'ticket{golf97715papa:___a bunch of unguessable stuff___}'
# Known from previous tries.
# The output of this script is deliberately unstabl... | {"hexsha": "d88f82761bd40c0d59240ac1c8b5e09db263c47d", "size": 3814, "ext": "py", "lang": "Python", "max_stars_repo_path": "Astronomy Astrophysics Astrometry Astrodynamics AAAA/My 0x20/myspace.py", "max_stars_repo_name": "errir503/ADDVulcan", "max_stars_repo_head_hexsha": "df5d818cb9eb7b4165d1d21533c689bedc5941ff", "ma... |
[STATEMENT]
lemma cancellative:
assumes "Some a = b \<oplus> x"
and "Some a = b \<oplus> y"
and "|x| = |y|"
shows "x = y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x = y
[PROOF STEP]
by (metis add_masks_cancellative assms(1) assms(2) assms(3) core_defined(1) plus_charact(1) state_ext) | {"llama_tokens": 136, "file": "Combinable_Wands_PartialHeapSA", "length": 1} |
#!/usr/bin/env python
# ----------------------------------------------------------------------------
# Copyright 2015-2016 Nervana Systems Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#... | {"hexsha": "3be70f0cf9da350cb09c031c22a90f4a8d0deb80", "size": 3199, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/conv_autoencoder.py", "max_stars_repo_name": "theGreenJedi/neon", "max_stars_repo_head_hexsha": "b85ba0fbbb0458d8a8599e5ead335959b10318c1", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
[STATEMENT]
lemma C_def: "Fr_1 \<F> \<Longrightarrow> Fr_2 \<F> \<Longrightarrow> Fr_4 \<F> \<Longrightarrow> \<forall>A p. (\<C> A p) \<longleftrightarrow> (\<forall>E. (nbhd E p) \<longrightarrow> \<not>(Disj E A))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>Fr_1 \<F>; Fr_2 \<F>; Fr_4 \<F>\<rbrakk> \<... | {"llama_tokens": 554, "file": "Topological_Semantics_topo_frontier_algebra", "length": 2} |
from typing import Any
import numpy as np
import xarray as xr
from xarray import Dataset
from sgkit.stats.aggregation import count_call_alleles, count_variant_alleles
from sgkit.testing import simulate_genotype_call_dataset
from sgkit.typing import ArrayLike
def get_dataset(calls: ArrayLike, **kwargs: Any) -> Datas... | {"hexsha": "291b81aca7563424aa72f6ad3e0c5b73d34d05f2", "size": 6344, "ext": "py", "lang": "Python", "max_stars_repo_path": "sgkit/tests/test_aggregation.py", "max_stars_repo_name": "jerowe/sgkit", "max_stars_repo_head_hexsha": "ff5a0a01ec6ae41d262ece14cc06a0b8c73ca342", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
# Introduction and Background
By definition, a **decorator** is a function that takes another function and extends the behavior of the latter function without explicitly modifying it. Decorators provide a simple syntax for calling [higher-order functions](https://en.wikipedia.org/wiki/Higher-order_function).
## Funct... | {"hexsha": "736604cc1b66dd84a7c27099840849f94894b690", "size": 48467, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Python/decorator.ipynb", "max_stars_repo_name": "bingrao/notebook", "max_stars_repo_head_hexsha": "4bd74a09ffe86164e4bd318b25480c9ca0c6a462", "max_stars_repo_licenses": ["MIT"], "max... |
KDRT DJs include or have included:
Former KDRT DJs
| {"hexsha": "33664cd362fb878ed1abb8c75d4d2a9845102184", "size": 57, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/KDRT_DJs.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
day_response.py
a bar plot for day log response
http://matplotlib.org/examples/api/barchart_demo.html
Copyright (c) 2016年 li3huo.com All rights reserved.
"""
import argparse
import numpy as np
import matplotlib.pyplot as plt
# labels = (u'0点', u'1点', u'2点', u'3点', u... | {"hexsha": "94b6f31206f43ee1f1ba1c3eacd8cc14b77cd2f9", "size": 5043, "ext": "py", "lang": "Python", "max_stars_repo_path": "log_to_graphs/day_response.py", "max_stars_repo_name": "twotwo/tools-python", "max_stars_repo_head_hexsha": "b9e7a97e58fb0a3f3fb5e8b674e64a997669c2c4", "max_stars_repo_licenses": ["MIT"], "max_sta... |
[STATEMENT]
lemma mset_set_set_mset_subseteq:
"mset_set (set_mset M) \<subseteq># M"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. mset_set (set_mset M) \<subseteq># M
[PROOF STEP]
proof (induct M)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. mset_set (set_mset {#}) \<subseteq># {#}
2. \<And>x M. mset_set ... | {"llama_tokens": 2833, "file": "Card_Multisets_Card_Multisets", "length": 27} |
[STATEMENT]
lemma rerename_subst_noop:
"freshenLC from ` V \<inter> subst_lconsts s = {} \<Longrightarrow> subst_renameLCs (rerename V from to f) s = subst_renameLCs f s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. freshenLC from ` V \<inter> subst_lconsts s = {} \<Longrightarrow> subst_renameLCs (rerenam... | {"llama_tokens": 162, "file": "Incredible_Proof_Machine_Abstract_Formula", "length": 1} |
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 6 13:53:55 2021
@author: jpeacock
"""
import numpy as np
from xml.etree import cElementTree as et
class TransferFunction:
"""
Deal with the complex XML format
"""
def __init__(self):
self.index_dict = {"hx": 0, "hy": 1, "ex": 0, "ey": 1, "hz": ... | {"hexsha": "c3714d0af0b490cef92b73d3c3368ea8fdae6666", "size": 6468, "ext": "py", "lang": "Python", "max_stars_repo_path": "mt_metadata/transfer_functions/emtf_xml/data.py", "max_stars_repo_name": "kujaku11/mt_metadata", "max_stars_repo_head_hexsha": "92081e77550b737619f6c40c4ecb56e2e4d4d870", "max_stars_repo_licenses"... |
from typing import Union, Optional, Any, List
import numpy as np
import eagerpy as ep
from ..devutils import atleast_kd
from ..models import Model
from ..distances import Distance
from ..criteria import Criterion
from .base import FlexibleDistanceMinimizationAttack
from .base import T
from .base import get_criteri... | {"hexsha": "9903df4e6067cb418e56fa6d2ec6cf557618fc6a", "size": 3201, "ext": "py", "lang": "Python", "max_stars_repo_path": "foolbox/attacks/dataset_attack.py", "max_stars_repo_name": "SamplingAndEnsemblingSolvers/foolbox", "max_stars_repo_head_hexsha": "788ea92b314cd974b39047000ed692a48c7e5bf1", "max_stars_repo_license... |
(** 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_commut_91_mult_succ/goal33c... |
"""
ModelNet10(;kwargs...)
Returns ModelNet10 dataset.
### Optional Key Arguments:
* `root::String=default_root` - Root directory of dataset
* `train::Bool=true` - Specifies the trainset
* `transform=nothing` - Transform to be applied to data point.
* `categories::Vector{Str... | {"hexsha": "e3dd84547868c9c80befd19683f443863adaa8fb", "size": 892, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datasets/modelnet/mn10.jl", "max_stars_repo_name": "kool7d/Flux3D.jl", "max_stars_repo_head_hexsha": "cbb6e64344ed5767b79cae36556957f1233cd84e", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 6 11:24:49 2018
@author: rhou
"""
import warnings
warnings.filterwarnings("ignore")
import argparse
import pandas as pd
import os, sys, glob
from scipy import io
def CalCords(savefolder, em, visMethod):
cordFile = os.path.join(savefolder, 'co... | {"hexsha": "55d93fa6ffab41676755b7037f2db739f2f90e47", "size": 7320, "ext": "py", "lang": "Python", "max_stars_repo_path": "visAnnos.py", "max_stars_repo_name": "kant/scMatch", "max_stars_repo_head_hexsha": "bc116c867ec2d1e635245dbc975078d6b641012a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
def neuronal_network(X,y,num_labels,hidden_size,learning_rate,max_iter):
import numpy as np
from sklearn.preprocessing import OneHotEncoder
X = np.matrix(X)
y = np.matrix(y)
encoder = OneHotEncoder(sparse=False)
y_2D=y.reshape(-1,1)
y_onehot = encoder.fit_transfo... | {"hexsha": "c7c0b53c4e627c73a3155b70a8404d2800df2671", "size": 5277, "ext": "py", "lang": "Python", "max_stars_repo_path": "NeuronalNetwork2.py", "max_stars_repo_name": "Joseph-Garzon/MachineLearningGUI", "max_stars_repo_head_hexsha": "12ee5a2f7f8309de0b5b6a17ad6d6731da70de70", "max_stars_repo_licenses": ["MIT"], "max_... |
using OrderedCollections, Test
using OrderedCollections: FrozenLittleDict, UnfrozenLittleDict
@testset "LittleDict" begin
@testset "Type Aliases" begin
FF1 = LittleDict{Int,Int, NTuple{10, Int}, NTuple{10, Int}}
@test FF1 <: FrozenLittleDict{<:Any, <:Any}
@test FF1 <: FrozenLittleDict
... | {"hexsha": "46d54a05de4cea5dcb89c9b8fa8acf4c2617f712", "size": 16076, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_little_dict.jl", "max_stars_repo_name": "mcognetta/OrderedCollections.jl", "max_stars_repo_head_hexsha": "342792ccb4d14bf7c49b0812b0375981a1448b9f", "max_stars_repo_licenses": ["MIT"], "... |
import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.models as models
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from PIL import Image
from google.cloud import storage
def ge... | {"hexsha": "aaa468f645aca2900bf27891018f4fa147e9e746", "size": 7501, "ext": "py", "lang": "Python", "max_stars_repo_path": "Training/regression_training.py", "max_stars_repo_name": "hammad-mohi/FacialAgeEstimator", "max_stars_repo_head_hexsha": "1372e4a72afb94b5ae32394109fae9e9982c8b0b", "max_stars_repo_licenses": ["MI... |
#! /usr/bin/env julia
write(STDOUT, "I'm writing some stuff to STDOUT\n")
f = open("stream_test.txt", "w")
write(f, "I'm writing some stuff to a file\n")
close(f)
write(STDOUT, "I should have just wrote to a file.\n")
| {"hexsha": "abf158b157e1f6ea45235be2471de44d2da6d64a", "size": 223, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "attic/streams/stream_writing.jl", "max_stars_repo_name": "sjkelly/JuliaPlayground", "max_stars_repo_head_hexsha": "7636a33d6f24c0bd56041a69822a7982b88c5b71", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
"""
fit a binomial distribution with several parameterizations
"""
import sys,os
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as scs
plt.style.use('bmh')
## declare variables
font_size = 11
font_name = 'sans-serif'
n = 10000
fig = plt.figure(figsize=(10,6),dpi=300)
splo... | {"hexsha": "27be479f372e5824e730ba9e386fdecfa8e0c7ce", "size": 1089, "ext": "py", "lang": "Python", "max_stars_repo_path": "binomial-distn.py", "max_stars_repo_name": "jackbenn/probabilistic-programming-intro", "max_stars_repo_head_hexsha": "b68f141481052dd53ebd5a2675b053740d745239", "max_stars_repo_licenses": ["BSD-3-... |
# Copyright 2019-present NAVER Corp.
# CC BY-NC-SA 3.0
# Available only for non-commercial use
import os, pdb
import numpy as np
from PIL import Image
from .dataset import Dataset, CatDataset
from tools.transforms import instanciate_transformation
from tools.transforms_tools import persp_apply
class PairDataset (Da... | {"hexsha": "aeed98b6700e0ba108bb44abccc20351d16f3295", "size": 10058, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyslam/thirdparty/r2d2/datasets/pair_dataset.py", "max_stars_repo_name": "dysdsyd/VO_benchmark", "max_stars_repo_head_hexsha": "a7602edab934419c1ec73618ee655e18026f834f", "max_stars_repo_licenses... |
from pyroad import *
from road.road_builder import RoadBuilder
import numpy as np
import collections
import random
class Road(PyRoad):
def get_description(self):
return 'A square region with random intersecting trajectories'
def get_asciiart(self):
return \
'''
|----A----|
... | {"hexsha": "113082c1233f61c60e31b445751ae773fefb238f", "size": 2762, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_roads/random-conflict-square.py", "max_stars_repo_name": "jadnohra/daisy", "max_stars_repo_head_hexsha": "105c0f37c6adbe85ce830375c5e2fc89cbcc6cc9", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import h5py
import os
#import sys
from glob import glob
from tqdm import tqdm
import numpy as np
import resampy
from metrics import read_wav_scipy_float64, get_mfcc_pw, eval_nn_mcd
#from metrics import get_f0_pw_sptk, eval_rmse_f0
from metrics import eval_pesq_8k
from metrics import eval_pesq
#filename=sys.argv[1]
#ou... | {"hexsha": "60b19d932e0f3b207b602a38906cce67606b5603", "size": 8309, "ext": "py", "lang": "Python", "max_stars_repo_path": "cmp_all_wave.py", "max_stars_repo_name": "deciding/speech_metrics", "max_stars_repo_head_hexsha": "87ef769a81efd060f4505bb4b3c09357ac53432e", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
@testset "lasing" begin
# Create a system.
Ngrid = [3,3,3]
N = 3prod(Ngrid)
ind_in = 5:7
εc = ones(ComplexF64, N)
εc[ind_in] .= 12+0.1im
m = 1
ωₘ = 100.0
ωₐ = 1.0
γ˔ = 1.0
D₀ = fill(0.01, N)
D₀[ind_in] .= 1.0
gp = GainProfile(gen_gain_2lv(ωₐ,γ˔), D₀)
# Create a solution.
M = 3
ω = rand(M)
ω[m] = ωₘ
a² = rand(M)
abs2g... | {"hexsha": "d4a33d6ab4bad756d0492f513f1a1287badbe799", "size": 1385, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/lasing.jl", "max_stars_repo_name": "wsshin/SALTBase.jl", "max_stars_repo_head_hexsha": "7e649196ebe80045e17a3227280011fb3fab1cb3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "ma... |
import numpy as np
import gym
class NormalizeObservWrapper(gym.Wrapper):
"""
Wrapper to normalize the observation space.
:param env: (gym.Env) Gym environment that will be wrapped
"""
def __init__(self, env):
# Retrieve the observation space
observation_space = env.observation_spa... | {"hexsha": "3d9f4e05536a22f08781c35599ceb98a2fe7e341", "size": 1977, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/frobs_rl/wrappers/NormalizeObservWrapper.py", "max_stars_repo_name": "jmfajardod/gym_gazebo_sb3", "max_stars_repo_head_hexsha": "72afcd4943c2c145e7e01bfce842f2d09b5b7978", "max_stars_repo_lice... |
from datetime import datetime
import time
import os
import sys
import random
import tensorflow as tf
import numpy as np
import pickle
import data_utils
import utils
import scorer
import logging
import copy
from scheduler import ReduceLROnPlateau
import sprnn_model as model
tf.app.flags.DEFINE_string('data_dir', '../d... | {"hexsha": "f8e6e608ce2e03031909c6d4626abf66402323f8", "size": 18657, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/sdp-lstm/dependency-kbp/train-greedy.py", "max_stars_repo_name": "Milozms/feedforward-RE", "max_stars_repo_head_hexsha": "a0415b6b835287d7257936c7cbb03abb467a17a8", "max_stars_repo_lice... |
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\chapter{Theoretical background}\label{chap:1}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Introduction}\lab... | {"hexsha": "c1d7328520defc8495a62d32ab3baf19f34565db", "size": 101883, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report_computational/chapters/chapter_1.tex", "max_stars_repo_name": "nikikilbertus/report_sky-moca", "max_stars_repo_head_hexsha": "e7f4cbb06e2df01192f571d5d62d7539d0fb256e", "max_stars_repo_lice... |
import os
import sys
curr_path = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(curr_path, ".."))
import argparse
from glob import glob
from matplotlib import pyplot as plt
import numpy as np
import mxnet as mx
from mxnet.gluon.data import DataLoader, ArrayDataset
from mxnet.gluon.data.vision... | {"hexsha": "0f9c58a7add1a0ec6a176c4f4026b7ccf49e9bf5", "size": 3302, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/show_record.py", "max_stars_repo_name": "haleuh/ArcFace-MXNet-Gluon", "max_stars_repo_head_hexsha": "503cf49c84fed51fc035e5d91031ac7733bb43fa", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
#!/usr/bin/python
import numpy as np
from scipy.spatial.distance import pdist, squareform
# three points
points = np.array([[-1, 1], [1, 0], [2, 0]])
# distance between all points
d = squareform(pdist(points, 'euclidean'))
print(d)
| {"hexsha": "6bcb0cbbbe3c4063b05534c46775d16e49866111", "size": 235, "ext": "py", "lang": "Python", "max_stars_repo_path": "datascience/numpy/points_distance.py", "max_stars_repo_name": "janbodnar/Python-Course", "max_stars_repo_head_hexsha": "51705ab5a2adef52bcdb99a800e94c0d67144a38", "max_stars_repo_licenses": ["BSD-2... |
#!/usr/bin/env python3
import logging
# import numpy as np
import pystella.model.sn_eve as sneve
import pystella.rf.light_curve_plot as lcp
from pystella import phys
try:
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
mpl_logger = logging.getLogger('matplotlib')
mpl_logger.setLeve... | {"hexsha": "d1022694d40323e123fa022f334cf1ca7fa932d5", "size": 16632, "ext": "py", "lang": "Python", "max_stars_repo_path": "eve.py", "max_stars_repo_name": "baklanovp/pystella", "max_stars_repo_head_hexsha": "47a8b9c3dcd343bf80fba80c8468b803f0f842ce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
// -----------------------------------------------------------------------
// pion-common: a collection of common libraries used by the Pion Platform
// -----------------------------------------------------------------------
// Copyright (C) 2007-2008 Atomic Labs, Inc. (http://www.atomiclabs.com)
//
// Distributed und... | {"hexsha": "b49290cc5d95cbb54d2bae621a192124bc84ae84", "size": 9949, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "common/include/pion/PionLockedQueue.hpp", "max_stars_repo_name": "marshallmcmullen/pion", "max_stars_repo_head_hexsha": "7dcbe769e7076f5cc983bb4a5b5d2bc83cadb3a2", "max_stars_repo_licenses": ["BSL-1... |
import spikeextractors as si
import os
import time
import numpy as np
from os.path import join
from subprocess import Popen, PIPE
import shlex
def spyking_circus(
recording,
output_folder=None, # Temporary working directory
probe_file=None,
file_name=None,
detect_sign=-1, # -... | {"hexsha": "f7d90a2613bcc48c1cb0713310e58e5b50dd7836", "size": 5237, "ext": "py", "lang": "Python", "max_stars_repo_path": "spikeforest_batch_run/sorters/spyking_circus/spyking_circus.py", "max_stars_repo_name": "magland/spikeforest_batch_run", "max_stars_repo_head_hexsha": "7b6bbf6e3a108c1e83ed69ccc4540f3285da8bc5", "... |
import os
import collections
import numpy as np
import json
from deephyper.search import util
from deephyper.search.nas import NeuralArchitectureSearch
from deephyper.core.parser import add_arguments_from_signature
from deephyper.core.logs.logging import JsonMessage as jm
from deephyper.evaluator.evaluate import Encod... | {"hexsha": "402950e84d51e905d487ca11ab754649fbe97e75", "size": 6680, "ext": "py", "lang": "Python", "max_stars_repo_path": "deephyper/search/nas/regevo.py", "max_stars_repo_name": "bigwater/deephyper", "max_stars_repo_head_hexsha": "4427e30c7b76b6f87cc417d0871768efe078f850", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import numpy as np
from scipy.spatial.distance import pdist, squareform
def scalar_dpp_diversity(x, max_distance=1.):
x = np.array(x)[:,None]
K = max_distance - squareform(pdist(x))
K /= max_distance
return np.linalg.det(K)
def scalar_mean_pdist_diversity(x):
x = np.array(x)[:,None]
return n... | {"hexsha": "80782c0d496ea3484416bd2e855bfb6ba8e8bb5e", "size": 427, "ext": "py", "lang": "Python", "max_stars_repo_path": "suggestion/diversity.py", "max_stars_repo_name": "kcarnold/sentiment-slant-gi18", "max_stars_repo_head_hexsha": "6028b42627e3eec14a1f27986f8925d8b1e6ad9c", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jan 22 13:05:03 2017
@author: dionisis
"""
file='example'
from numpy import zeros, ones
def read_input():
from numpy import zeros
f = open('input_files/'+ file + '.in','r')
line = f.readline()
noOfIngredients = 2
fline = l... | {"hexsha": "5b925a2cde87f7e814c0f5b0d718c81e5ea6e168", "size": 3353, "ext": "py", "lang": "Python", "max_stars_repo_path": "hashcode/pizza/CORE.py", "max_stars_repo_name": "nonsensedwarf/mcube", "max_stars_repo_head_hexsha": "1aadbd41c93de516af38c7313a1578327c4c850d", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from brl_gym.envs.crosswalk_vel import CrossWalkVelEnv
import numpy as np
env = CrossWalkVelEnv()
env.reset()
goals = env.goals
peds = env.pedestrians
pose = env.pose
ped_speeds = env.pedestrian_speeds
print("Car 37, 38, 35")
print("Peds :\n", np.around(peds,1))
print("Ped speeds:\n", np.around(ped_speeds,2))
pr... | {"hexsha": "a9c7a1bb7d4cb52e8276db48814c90777f6661e9", "size": 772, "ext": "py", "lang": "Python", "max_stars_repo_path": "brl_gym/scripts/crosswalk_vel/generate_initial_conditions.py", "max_stars_repo_name": "gilwoolee/brl_gym", "max_stars_repo_head_hexsha": "9c0784e9928f12d2ee0528c79a533202d3afb640", "max_stars_repo_... |
subroutine enddet(cnpart,ielmt,i)
c
c + + + PURPOSE + + +
c
c SR ENDDET determines the point where detachment ends.
c
c Called from: SR CASE34
c Author(s): Ascough II, R. van der Zweep, V. Lopes
c Reference in User Guide:
c
c Version:
c Date recoded:
c Recoded by: Jim Ascough II
c
... | {"hexsha": "88b25b4a5a8bf3054ff261435add60cfac1b9314", "size": 2697, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "src/wepp2012-src/enddet.for", "max_stars_repo_name": "jarad/dep", "max_stars_repo_head_hexsha": "fe73982f4c70039e1a31b9e8e2d9aac31502f803", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import tensorflow as tf
from bvlc_alexnet_fc7 import AlexNet
import fine_tune_nt
import numpy as np
import os
import time
import cv2
import image_io
# the dimension of the final layer = feature dim
NN_DIM = 100
LABEL_DIM = 3
TRAIN_TXT = 'file_list_fine_tune_train.txt'
# TEST_TXT = 'file_list_test.txt'
TRAIN = True
S... | {"hexsha": "533345bfd4a1b64b10707e7e90709e587fee76ea", "size": 5198, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn/fine_tune_nn.py", "max_stars_repo_name": "polltooh/FineGrainedAction", "max_stars_repo_head_hexsha": "4582b4179e643119448c7c20ab06044fb211163e", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
using LinearAlgebra
# positions
x⃗ = [-1, 0, 1] .* 1.0
# position operator
x̂ = Diagonal(x⃗)
# construct differentiation matrix / position operator
X⃗ = [x⃗[i]^(j-1) for i in eachindex(x⃗), j in eachindex(x⃗)]
dX⃗ = [(j-1) * x⃗[i]^(j-2) for i in eachindex(x⃗), j in eachindex(x⃗)]
dX⃗[:,1] .= 0.0 # get rid of NaNs
# ... | {"hexsha": "f321f9a41516ea957b9fef66c7d9cf582c770173", "size": 560, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "quantum_mechanics.jl", "max_stars_repo_name": "sandreza/HeldSuarezVisualizationScripts", "max_stars_repo_head_hexsha": "904ce7f44e965618b0ba6f5fa89015ba02aaf44a", "max_stars_repo_licenses": ["MIT"],... |
from __future__ import absolute_import, division, print_function
import csv
import logging
import os
import sys
from io import open
import json
from nltk.tokenize import sent_tokenize
import numpy as np
from proof_utils import get_proof_graph, get_proof_graph_with_fail
logger = logging.getLogger(__name__)
class I... | {"hexsha": "0b603806eb9cd32a14e1a0e0a755104c9f361b9b", "size": 31097, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils_iterative_mprover.py", "max_stars_repo_name": "swarnaHub/multiPRover", "max_stars_repo_head_hexsha": "ac2f1094bab8390f47aeb8bad7dcde9b4b19240b", "max_stars_repo_licenses": ["MIT"], "max_sta... |
#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Manuel Guenther <Manuel.Guenther@idiap.ch>
# @date: Thu May 24 10:41:42 CEST 2012
#
# Copyright (C) 2011-2012 Idiap Research Institute, Martigny, Switzerland
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of ... | {"hexsha": "2f205baf90b06ea62ce2611516a6fcb5747bd45a", "size": 6467, "ext": "py", "lang": "Python", "max_stars_repo_path": "bob/bio/face/test/test_extractors.py", "max_stars_repo_name": "bioidiap/bob.bio.face", "max_stars_repo_head_hexsha": "2341e6423ca5a412ebe23fa18acacd69ea1ef914", "max_stars_repo_licenses": ["BSD-3-... |
# Deprecated!
import csv
import os
import numpy as np
from constants import PROJECT_HOME, INDEX_RETURN_INDICATOR_NUMBER
from data.combine_data import COMBINATION_COLUMN_RANGE_KEY_FUND_RETURN, \
COMBINATION_COLUMN_RANGE_KEY_FUND_BENCHMARK_RETURN, COMBINATION_COLUMN_RANGE_KEY_INDEX_RETURN, \
combination_column... | {"hexsha": "aee9d0ffdd31315c72f44d87f248f977fe9b65d0", "size": 2767, "ext": "py", "lang": "Python", "max_stars_repo_path": "program/predict_and_save_result__without_generator.py", "max_stars_repo_name": "donyori/2018ccf_bdci_inter_fund_correlation_prediction", "max_stars_repo_head_hexsha": "6e06a3e192e05ae1e9822111cf32... |
import argparse
parser = argparse.ArgumentParser(description="control voa and take megadata")
parser.add_argument("--power", default=5, type=float, help="On voltage of VOA")
parser.add_argument("--delay", default=10, type=float, help="Delay between starting megadaq and turning on voa (in ms)")
parser.add_argument(... | {"hexsha": "1304534d80b2dff7e32c9acf5d7838faeb29410b", "size": 3700, "ext": "py", "lang": "Python", "max_stars_repo_path": "voa_and_megadata.py", "max_stars_repo_name": "cdoolin/labtools", "max_stars_repo_head_hexsha": "867a2726f5a707a8f5e698bf4a6bb4de40cfbe27", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
#include <boost/interprocess/sync/named_sharable_mutex.hpp>
| {"hexsha": "4c90a666bdab6e0ae6252d8b0f7e5ac720ead1cc", "size": 60, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_interprocess_sync_named_sharable_mutex.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_lic... |
C Copyright (c) 2014, Sandia Corporation.
C Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
C the U.S. Government retains certain rights in this software.
C
C Redistribution and use in source and binary forms, with or without
C modification, are permitted provided that the foll... | {"hexsha": "6c3c1ea58fe89224be7cf0b48c3adf797166f70f", "size": 4926, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/seacas/applications/fastq/skinit.f", "max_stars_repo_name": "milljm/seacas", "max_stars_repo_head_hexsha": "4990651554b336901e260304067ff91c7284531f", "max_stars_repo_licenses": ["NetCDF"... |
#ifndef BOOST_GEOMETRY_PROJECTIONS_GSTMERC_HPP
#define BOOST_GEOMETRY_PROJECTIONS_GSTMERC_HPP
// Boost.Geometry - extensions-gis-projections (based on PROJ4)
// This file is automatically generated. DO NOT EDIT.
// Copyright (c) 2008-2015 Barend Gehrels, Amsterdam, the Netherlands.
// Use, modification and distribut... | {"hexsha": "b73ef80a226e862cdc32ba4d3c58746ad08e1ebd", "size": 9230, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "3party/boost/boost/geometry/extensions/gis/projections/proj/gstmerc.hpp", "max_stars_repo_name": "bowlofstew/omim", "max_stars_repo_head_hexsha": "8045157c95244aa8f862d47324df42a19b87e335", "max_sta... |
import csv
import os
import pandas as pd
import torch
import numpy as np
# WARNING: TAKES A LONG TIME TO RUN, HAS EVERY POSSIBLE CONNECTION BETWEEN AIRPORTS, EVEN ONES WITHOUT FLIGHTS
# ALSO: THIS FILE MIGHT BE USELESS. WHATEVER FILE USES THE MATRIX CAN JUST CHECK IF A SPECIFIC ENTRY EXISTS IN IT
# IF THE ENTR... | {"hexsha": "6e8aaf33867e8c49916865f05f325392e5a7e95d", "size": 2471, "ext": "py", "lang": "Python", "max_stars_repo_path": "Scripts/FullMatrix.py", "max_stars_repo_name": "BrenoPin/AirTrafficSTGCN", "max_stars_repo_head_hexsha": "ba7e7421a15f56d96c62ee85dbd29b63205053e7", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import torch
import numpy as np
eps = np.finfo(np.float32).eps
PI = 3.141592
chrom0 = torch.tensor([[1.0], [1.0], [1.0]])
chrom0 /= torch.norm(chrom0, p=2)
temp = torch.tensor([[0.0], [0.0], [1.0]])
chrom1 = torch.cross(chrom0, temp)
chrom1 /= torch.norm(chrom1, p=2)
chrom2 = torch.cross(chrom0, chrom1)
chrom2 /= to... | {"hexsha": "9954254e825e31066be1f76613b7d383d63472ac", "size": 3773, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "kakumarabhishek/Illumination-based-Transformations-Skin-Lesion-Segmentation", "max_stars_repo_head_hexsha": "26e3ddf3b5ff4c994e3cf77bdd84f3a4fc66a25b", "max_star... |
from __future__ import print_function
# COMPARE STRINGS WHICH MIGHT CONTAIN UNICODES
############################################################################
def insensitive(string):
"""Given a string, returns its lower/upper case insensitive string"""
if getattr(str,'casefold',None) is not None:
i... | {"hexsha": "675fafde8f969dd1c162202327ae998b915177c4", "size": 4954, "ext": "py", "lang": "Python", "max_stars_repo_path": "Florence/Utils/Utils.py", "max_stars_repo_name": "jdlaubrie/florence", "max_stars_repo_head_hexsha": "830dca4a34be00d6e53cbec3007c10d438b27f57", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma cos_one_sin_zero:
fixes x :: "'a::{real_normed_field,banach}"
assumes "cos x = 1"
shows "sin x = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sin x = (0::'a)
[PROOF STEP]
using sin_cos_squared_add [of x, unfolded assms]
[PROOF STATE]
proof (prove)
using this:
(sin x)\<^sup>2 + (1::'a)\<^... | {"llama_tokens": 191, "file": null, "length": 2} |
[GOAL]
α : Type u
β : Type v
δ : Type w
s : Stream' α
i : ℕ
⊢ (head s :: tail s) i = s i
[PROOFSTEP]
cases i
[GOAL]
case zero
α : Type u
β : Type v
δ : Type w
s : Stream' α
⊢ (head s :: tail s) zero = s zero
[PROOFSTEP]
rfl
[GOAL]
case succ
α : Type u
β : Type v
δ : Type w
s : Stream' α
n✝ : ℕ
⊢ (head s :: tail s) (suc... | {"mathlib_filename": "Mathlib.Data.Stream.Init", "llama_tokens": 20635} |
// Copyright (c) 2007-2015 Hartmut Kaiser
// Copyright (c) 2016 Thomas Heller
// Copyright (c) 2011 Bryce Adelstein-Lelbach
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
#ifndef HPX_LCOS_DETAIL_... | {"hexsha": "881081bcac09ddbcdfbdc54af9e508227246844b", "size": 12050, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "hpx/lcos/detail/promise_base.hpp", "max_stars_repo_name": "atrantan/hpx", "max_stars_repo_head_hexsha": "6c214b2f3e3fc58648513c9f1cfef37fde59333c", "max_stars_repo_licenses": ["BSL-1.0"], "max_star... |
# from classifier.feed_forward_neural_network import FeedForwardNeuralNetwork
from classifier.feed_forward_neural_network import FeedForwardNeuralNetwork
import pandas as pd
from sklearn.preprocessing import LabelEncoder
import numpy as np
from sklearn import datasets
data_tennis = pd.read_csv("tennis.csv", dtype="c... | {"hexsha": "73843ac928820d6c6c5ab8df3b6347c1c4a7a894", "size": 1637, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "reeechart/raklassifier", "max_stars_repo_head_hexsha": "f2bdc8dd12350b52ecfa3084f4346284b419bbf8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
#!/usr/bin/env python
import numpy as np
from pycrazyswarm import *
Z = 1.0
if __name__ == "__main__":
swarm = Crazyswarm()
timeHelper = swarm.timeHelper
allcfs = swarm.allcfs
pos0s = [cf.position() for cf in allcfs.crazyflies]
allcfs.takeoff(targetHeight=Z, duration=1.0+Z)
timeHelper.s... | {"hexsha": "c7c5b1b0d43e9cb9fa9eebb5f32e482b41666031", "size": 655, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros_ws/src/crazyswarm/scripts/niceHover.py", "max_stars_repo_name": "dasc-lab/crazyswarm", "max_stars_repo_head_hexsha": "f768c80bbfc25a757897030300d567c683623aaa", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma Max_union_Max_out:
assumes "finite Y" and "\<forall>y \<in> Y. finite (f y)" and "\<forall>y \<in> Y. f y \<noteq> {}" and "Y \<noteq> {}"
shows "Max (\<Union>y\<in>Y. {Max (f y)}) = Max (\<Union>y\<in>Y. f y)" (is "?M1=_")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Max (\<Union>y\<in>Y. {M... | {"llama_tokens": 3103, "file": "Query_Optimization_Dtree", "length": 27} |
import unittest
import pytest
try:
import scipy.sparse
scipy_available = True
except ImportError:
scipy_available = False
from cupy import testing
from cupyx.scipy import sparse
if scipy_available:
class DummySparseCPU(scipy.sparse.spmatrix):
def __init__(self, maxprint=50, shape=None, nnz=... | {"hexsha": "1cece0ea471052c8da91f34a934b1bd4fc10e2de", "size": 2174, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/cupyx_tests/scipy_tests/sparse_tests/test_base.py", "max_stars_repo_name": "prkhrsrvstv1/cupy", "max_stars_repo_head_hexsha": "ea86c8225b575af9d2855fb77a306cf86fd098ea", "max_stars_repo_lice... |
import pyclesperanto_prototype as cle
import numpy as np
def test_maximum_sphere_1():
test = cle.push(np.asarray([
[1, 1, 1],
[1, 2, 1],
[1, 1, 1]
]))
test2 = cle.create(test)
cle.maximum_sphere(test, test2, 1, 1, 1)
a = cle.pull(test2)
assert (np.min(a) == 1)
asse... | {"hexsha": "063d0fa7712d527884ac622a994264e5a236717f", "size": 831, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_maximum_sphere.py", "max_stars_repo_name": "elsandal/pyclesperanto_prototype", "max_stars_repo_head_hexsha": "7bda828813b86b44b63d73d5e8f466d9769cded1", "max_stars_repo_licenses": ["BSD-... |
"""
---
Returns the vector of allocated subcarriers associated to Long Term evolution frequency mapping.
In LTE, depending on FFT size, only few subcarriers are allocated (45-55%). This function takes a FFT size as input
and returns an array of size nbSubcarriers
# --- Syntax
allocatedSubcarrier = getLTEAlloc(nFF... | {"hexsha": "2633d5dc4cd96f433d2186d8e23a68a1c88839fd", "size": 1209, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Waveforms/getLTEAlloc.jl", "max_stars_repo_name": "JuliaTelecom/DigitalComm.jl", "max_stars_repo_head_hexsha": "13c854b9c4a8864787075e06e0e3ef5a1d30beae", "max_stars_repo_licenses": ["MIT"], "m... |
#Fit dirlichet models to all functional and phylogenetic groups at once.
#No hierarchy required, as everything is observed at the site level. Each observation is a unique site.
#Missing data are allowed.
#clear environment
rm(list = ls())
library(data.table)
library(doParallel)
source('paths.r')
source('NEFI_functions/... | {"hexsha": "2787aa1a32607030b9d3c7055b2e35133973d2e8", "size": 2351, "ext": "r", "lang": "R", "max_stars_repo_path": "ITS/analysis/spatial_prior_analysis/ddirch_fit/1._prior_ddirch_fit_ted2014.r", "max_stars_repo_name": "colinaverill/NEFI_microbe", "max_stars_repo_head_hexsha": "e59ddef4aafcefdf0aff61765a8684859daad6e0... |
"""
inspired from https://github.com/lcswillems/torch-rl
"""
import numpy as np
import torch
import torch.nn.functional as F
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_rl
from argparse import Namespace
from copy import deepcopy
from agents.two_v_base_general import T... | {"hexsha": "106a581964fe617cfa657c9e7306a021939717a1", "size": 62486, "ext": "py", "lang": "Python", "max_stars_repo_path": "agents/ppo_worlds.py", "max_stars_repo_name": "DanIulian/minigrid_rl", "max_stars_repo_head_hexsha": "d7b59fd1d1e62fc99d5134c89f59c6ad16246cfa", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
/-
Copyright (c) 2019 Johannes Hölzl. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johannes Hölzl
-/
import algebra.direct_sum.module
import data.finsupp.to_dfinsupp
/-!
# Results on direct sums and finitely supported functions.
> THIS FILE IS SYNCHRONIZED WITH MAT... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/algebra/direct_sum/finsupp.lean"} |
import astropy.units as u
from astropy.io import fits
import numpy as np
from numpy import ma
import logging
from .world import Position, VelWave
class DataND:
# maybe this will help with sorting the 1D conditions?
_is_spectral = False
_is_spatial = False
def __init__(self, filename=None, data=None... | {"hexsha": "53ef0460c034f355584b4bab174711a87bcb2345", "size": 12228, "ext": "py", "lang": "Python", "max_stars_repo_path": "telassar/data.py", "max_stars_repo_name": "amiller361/telassar", "max_stars_repo_head_hexsha": "8497d9ecee3dcd6509e7c870981b67978eb278b7", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_... |
'''
Author: Junbong Jang
Creation Date: 9/21/2020
Helper blocks for deep_neural_net.py
'''
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Layer, Activation, Input, concatenate, Conv2D, MaxPooling2D, UpSampling2D, Cropping2D, Conv2DTranspose,BatchNormal... | {"hexsha": "d5a22b5eecc3c0d071692870c3b971ce004a62e8", "size": 20567, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/deep_neural_net_blocks.py", "max_stars_repo_name": "norton-chris/MARS-Net", "max_stars_repo_head_hexsha": "6f671837d0629422680c78adf9b643894debae70", "max_stars_repo_licenses": ["MIT"], "m... |
# Copyright (c) 2020 PaddlePaddle 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": "eae118fdae874ce1a83b6df296f65c923c105f53", "size": 2371, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/text_classification/rnn/utils.py", "max_stars_repo_name": "zzz2010/PaddleNLP", "max_stars_repo_head_hexsha": "fba0b29601b0e8286a9ab860bf69c9acca4481f4", "max_stars_repo_licenses": ["Apach... |
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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": "2899e3f76567cee638b07c86174896a19f51bd2f", "size": 14353, "ext": "py", "lang": "Python", "max_stars_repo_path": "dygraph/models/architectures/mobilenetv3.py", "max_stars_repo_name": "MRXLT/PaddleSeg", "max_stars_repo_head_hexsha": "52ef0ee505a00ed8c81f95759887e56062f0941b", "max_stars_repo_licenses": ["Apac... |
/***********************************************************************
Copyright (c) 2015, Carnegie Mellon University
All rights reserved.
Authors: Michael Koval <mkoval@cs.cmu.edu>
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions... | {"hexsha": "c80b1eb5bb5586f064a3309d30dce031c0244f8f", "size": 25658, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/InteractiveMarkerViewer.cpp", "max_stars_repo_name": "personalrobotics/or_interactivemarker", "max_stars_repo_head_hexsha": "742d3d0ac5e606e35a1717a1e8b2a5f69e8efb8e", "max_stars_repo_licenses"... |
from abc import ABCMeta, abstractmethod
import numpy as np
from cutils import likelihood
class BaseScorer(metaclass=ABCMeta):
""" Base class for scoring topics model. """
@staticmethod
@abstractmethod
def score(term_doc_matrix, model):
""" Returns a score of topics model that.
Argum... | {"hexsha": "311cd8929a9c293b7826c1b5ad08464139d435e3", "size": 2995, "ext": "py", "lang": "Python", "max_stars_repo_path": "scorers.py", "max_stars_repo_name": "Bellator95/topic_modeling", "max_stars_repo_head_hexsha": "0eeb7ddb2ff1959741c45b76ab301d54e204be6c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
import numpy as np ## For Linear Algebra
## For visualizations I'll be using plotly package, this creates interesting and interective visualizations.
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from datetime import datetime ## Time Series analysis.... | {"hexsha": "6ac11c1e4cf0e109ee074689f719b0d966177242", "size": 2760, "ext": "py", "lang": "Python", "max_stars_repo_path": "temperature.py", "max_stars_repo_name": "Sarah-Victor/Weather_Prediction_MLProject", "max_stars_repo_head_hexsha": "581a088da7a24222a28403c1ece24c5e96e74023", "max_stars_repo_licenses": ["MIT"], "... |
% \begin{meta-comment}
%
% $Id: mdwtools.tex,v 1.4 1996/11/19 20:55:55 mdw Exp $
%
% Common declarations for mdwtools.dtx files
%
% (c) 1996 Mark Wooding
%
%----- Revision history -----------------------------------------------------
%
% $Log: mdwtools.tex,v $
% Revision 1.4 1996/11/19 20:55:55 mdw
% Entered into RCS... | {"hexsha": "63abfc4acf47750ac3c43a510941864562ca3860", "size": 35056, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "mdwtools/mdwtools.tex", "max_stars_repo_name": "christopher-henderson/Declarative-Search-Construct-in-Imperative-Procedural-Programming-Languages", "max_stars_repo_head_hexsha": "cdb623fae8382acec2... |
import MittagLefflerFunctions.asymptotic_start
using PyPlot
β = 1.0
m = 201
α = range(0, 1, length=m)
tol = 1e-15
min_x = 35
x = Vector{Float64}(undef, m)
N = Vector{Int64}(undef, m)
for k = 1:m
x[k], N[k] = asymptotic_start(α[k], β, tol, min_x)
end
figure(1)
subplot(2, 1, 1)
plot(α, x, "o", markersize=2)
ylabel... | {"hexsha": "4a3fe42c8b58db9a21b9ddf295eafc7329999c0a", "size": 433, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_asymptotic_start.jl", "max_stars_repo_name": "billmclean/MittagLefflerFunctions.jl", "max_stars_repo_head_hexsha": "5244e7fce7efeee160edfc76eb7cab5e7624ae8e", "max_stars_repo_licenses": ["... |
import Bio.PDB.Superimposer
from Bio.PDB.Atom import Atom as BioPDBAtom
import numpy as np
import warnings
from Bio.PDB.Atom import PDBConstructionWarning
from classes.PDB import PDB
from classes.Atom import Atom
warnings.simplefilter('ignore', PDBConstructionWarning)
def biopdb_aligned_chain(pdb_fixed, chain_id_fixe... | {"hexsha": "86f77ae0a498454a6105c94539b07b8fde877538", "size": 3494, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/BioPDBUtils.py", "max_stars_repo_name": "nanohedra/nanohedra", "max_stars_repo_head_hexsha": "3921b7f5ce10e0e3393c3b675bb97ccbecb96663", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.4'
# jupytext_version: 1.2.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# <div style='background-image:... | {"hexsha": "c2d01573b0090962383721cc3bff66f333519d96", "size": 8811, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/Computational Seismology/The Pseudospectral Method/ps_fourier_acoustic_1d.py", "max_stars_repo_name": "krischer/seismo_live_build", "max_stars_repo_head_hexsha": "e4e8e59d9bf1b020e13ac91... |
"""
@Project : DuReader
@Module : pca.py
@Author : Deco [deco@cubee.com]
@Created : 5/24/18 1:16 PM
@Desc :
In Depth: Principal Component Analysis
https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html
PCA and proportion of variance explained:
https://stats.stackexc... | {"hexsha": "11c3d7a564d81d453b590fac9e35b6264a105f79", "size": 4091, "ext": "py", "lang": "Python", "max_stars_repo_path": "wiki-word2vec/pca.py", "max_stars_repo_name": "arfu2016/DuReader", "max_stars_repo_head_hexsha": "66934852c508bff5540596aa71d5ce40c828b37d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
[STATEMENT]
lemma cf_comma_proj_left_is_functor'[cat_comma_cs_intros]:
assumes "\<GG> : \<AA> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>"
and "\<HH> : \<BB> \<mapsto>\<mapsto>\<^sub>C\<^bsub>\<alpha>\<^esub> \<CC>"
and "\<AA>' = \<GG> \<^sub>C\<^sub>F\<down>\<^sub>C\<^sub>F \<HH>"
shows "\<GG>... | {"llama_tokens": 580, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_ECAT_Comma", "length": 3} |
import numpy as np
def generate_batches(data_path, char_threshold, sequence_length):
"""
Inputs:
data_path: File path to simple txt file.
char_threshold: If count of any character is below char_threshold it is
ignored and replaced with null token: ^'
sequence_length: Sequence length... | {"hexsha": "623a6056224a8f6d3e52ae2b4ba4969600d6e247", "size": 1398, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dataloader/batches_from_txt.py", "max_stars_repo_name": "kirbiyik/generate-any-text", "max_stars_repo_head_hexsha": "7f9d78e439e23f99be34681268c052f7f6df9fdb", "max_stars_repo_licenses": ["MIT... |
/*
* ElevationMap.hpp
*
* Created on: Dec 22, 2019
* Author: Peter XU
* Institute: ZJU, CSC 104
*/
#pragma once
// Grid Map
#include <grid_map_ros/grid_map_ros.hpp>
// OpenCV
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/core/core.hpp>
#include <cv_bridge/cv_bridge.h>
// Eigen
#include <Eige... | {"hexsha": "ad1a23a1a3d32af84657245fd33a8ac070eaf691", "size": 5412, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "elevation_mapping/elevation_mapping/include/elevation_mapping/ElevationMap.hpp", "max_stars_repo_name": "mcx/GEM", "max_stars_repo_head_hexsha": "e1245a59f44213edcc8c105f1e9aaa092ea97169", "max_star... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
################################################################################
#
# CoCoPy - A python toolkit for rotational spectroscopy
#
# Copyright (c) 2016 by David Schmitz (david.schmitz@chasquiwan.de).
#
# Permission is hereby granted, free of charge, to any person obt... | {"hexsha": "c39255e0451772769ffea9a3c4af1a046958658f", "size": 41877, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/analysis/spec.py", "max_stars_repo_name": "CoCoMol/CoCoPy", "max_stars_repo_head_hexsha": "66bd4deda4b80eca65ceb0660f940214e4b457fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
# points = 100 # number of points per class
# D = 2 # dimensionality
# classes = 3 # number of classes
def create_data(points, classes):
X = np.zeros((points*classes, 2)) # data matrix (each row = single example)
y = np.zeros(points*classes, dtype='uint8') # class labels
for class_numbe... | {"hexsha": "dc62c32c27b34f5288354589dd5b59a89541a1ab", "size": 798, "ext": "py", "lang": "Python", "max_stars_repo_path": "_oldmodels/Model_3/dataset.py", "max_stars_repo_name": "caelanhadley/NNFSIP", "max_stars_repo_head_hexsha": "da048af5ded549db7464b206b255104900b40ab8", "max_stars_repo_licenses": ["MIT"], "max_star... |
# SVM Regression
#----------------------------------
#
# This function shows how to use TensorFlow to
# solve support vector regression. We are going
# to find the line that has the maximum margin
# which INCLUDES as many points as possible
#
# We will use the iris data, specifically:
# y = Sepal Length
# x = Pedal W... | {"hexsha": "6f6ba9385202885e1775fb296e8a933310e33db0", "size": 3849, "ext": "py", "lang": "Python", "max_stars_repo_path": "04_Support_Vector_Machines/03_Reduction_to_Linear_Regression/03_support_vector_regression.py", "max_stars_repo_name": "rajat19/tensorflow_cookbook", "max_stars_repo_head_hexsha": "2ba60b94b81b0f2f... |
import numpy as np
from matplotlib import pyplot as plt
import imageio
# --------------- Model Class -----------------------------------
def logistic_func(x):
return 1/(1 + np.exp(-x))
class TestMS:
def __init__(self, number_of_bacteria: int, TMG_amount: float=0, GLU_amount: float=0, TMG_rate: float=0, G... | {"hexsha": "6b90e29c4c11dc77a49b45f5c6346c39d5141220", "size": 4068, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "Nemo20k/lactose_multistability_model", "max_stars_repo_head_hexsha": "e50d68bb508e243d0a775d1d562bd8e8b88b3b30", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
"""
Command-line tool to control the concavity constraining tools
Mudd et al., 2018
So far mostly testing purposes
B.G.
"""
import matplotlib;matplotlib.use("Agg")
from lsdtopytools import LSDDEM # I am telling python I will need this module to run.
from lsdtopytools import argparser_debug as AGPD... | {"hexsha": "611742d2312d9ddaac3edd390e850a0118e73cda", "size": 4662, "ext": "py", "lang": "Python", "max_stars_repo_path": "lsdtopytools/scripts_for_lsdtopytools/lsdtt_chi_ksn_knickoint_tools.py", "max_stars_repo_name": "LSDtopotools/lsdtopytools", "max_stars_repo_head_hexsha": "9809cbd368fe46b5e483085fa55f3206e4d85183... |
theory invariant
imports kernel_spec "HOL-Eisbach.Eisbach_Tools"
begin
section \<open>invariants\<close>
subsection \<open>defs of invariants\<close>
text\<open>
we consider multi-threaded execution on mono-core.
A thread is the currently executing thread iff it is in RUNNING state.
\<close>
definition inv_cur :: "... | {"author": "SunHuan321", "repo": "uc-OS-verification", "sha": "760e159857c4015d6a9e3ccbe9f8247e4518862a", "save_path": "github-repos/isabelle/SunHuan321-uc-OS-verification", "path": "github-repos/isabelle/SunHuan321-uc-OS-verification/uc-OS-verification-760e159857c4015d6a9e3ccbe9f8247e4518862a/ucOS_mem_mailbox/invarian... |
"""Class for creating a table to index characters"""
import numpy as np
class CharacterTable(object):
"""Given a set of characters:
+ Encode them to a one hot integer representation
+ Decode the one hot integer representation to their character output
+ Decode a vector of probabilities to their charac... | {"hexsha": "e67c2b0798d073c86d0c2f8b1809ea5ca86adf6a", "size": 1250, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/character_table.py", "max_stars_repo_name": "JohanvandenHeuvel/keras", "max_stars_repo_head_hexsha": "2b5a44e0315d05b0b8c61920f4b527ad1383ef4e", "max_stars_repo_licenses": ["MIT"], "max_s... |
function _precompile_()
Base.precompile(Tuple{typeof(Vcov.ranktest!),Array{Float64,2},Array{Float64,2},Array{Float64,2},Vcov.SimpleCovariance,Int64,Int64})
Base.precompile(Tuple{typeof(Vcov.ranktest!),Array{Float64,2},Array{Float64,2},Array{Float64,2},Vcov.RobustCovariance,Int64,Int64})
Base.precompile(Tupl... | {"hexsha": "ae4e765bf2e3701d3ef310705b22e446268ac830", "size": 437, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/precompile.jl", "max_stars_repo_name": "eloualiche/Vcov.jl", "max_stars_repo_head_hexsha": "ce4fe6f6aae84efcdf5738c43ca84a1a19a3a73a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "... |
import logging
import numpy as np
from arekit.contrib.networks.embeddings.base import Embedding
logger = logging.getLogger(__name__)
def create_term_embedding(term, embedding, check, word_separator=' '):
"""
Embedding algorithm based on parts (trigrams originally)
"""
assert(isinstance(term, str))
... | {"hexsha": "4dd57e8d994936c6367e0b2a755b2282125e281d", "size": 2936, "ext": "py", "lang": "Python", "max_stars_repo_path": "arekit/contrib/networks/core/input/embedding/custom.py", "max_stars_repo_name": "nicolay-r/AREk", "max_stars_repo_head_hexsha": "19c39ec0dc9a17464cade03b9c4da0c6d1d21191", "max_stars_repo_licenses... |
/****************************************************************************
* hipipe library
* Copyright (c) 2017, Cognexa Solutions s.r.o.
* Copyright (c) 2018, Iterait a.s.
* Author(s) Filip Matzner
*
* This file is distributed under the MIT License.
* See the accompanying file LICENSE.txt for the comp... | {"hexsha": "6d6d7b3221f2b0b73ae630779e021c49b4877e35", "size": 3260, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/core/thread.cpp", "max_stars_repo_name": "iterait/hipipe", "max_stars_repo_head_hexsha": "c2a6cc13857dce93e5ae3f76a86e8f029ca3f921", "max_stars_repo_licenses": ["BSL-1.0", "MIT"], "max_stars_co... |
import unittest
import numpy as np
from gpuparallel import GPUParallel, BatchGPUParallel, delayed, log_to_stderr
log_to_stderr(log_level='INFO')
def task_return_identity(value, **kwargs):
return value
def task_return_all_kwargs(value, **kwargs):
return value, kwargs
class TestBatchGPUParallel(unittest.Te... | {"hexsha": "36d38682e76708932211ea21bb1d5b26e184ea07", "size": 3708, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/tests_batch.py", "max_stars_repo_name": "vlivashkin/GPUParallel", "max_stars_repo_head_hexsha": "23dbabecaa421b535babc3ed19407ce8d2e55d63", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import pandas as pd
import random
import math
#Assign people to universes
random.seed(0)
ppl = ["A","B","C","D","E","F","G","NPC"]
random.shuffle(ppl)
universeAppl=ppl[0:4]
universeBppl=ppl[4:8]
print(ppl)
print(universeAppl, universeBppl)
#Set up bids, set up budgets, add NPC
random.seed(0)
... | {"hexsha": "509f8de8cbe884cb193434e237b1bdc0642c8e19", "size": 1976, "ext": "py", "lang": "Python", "max_stars_repo_path": "eval.py", "max_stars_repo_name": "H-B-P/a-d-and-d-sci-may", "max_stars_repo_head_hexsha": "c21867fb08be996545cbaf3dd19b57ceae3f9526", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1... |
#!/usr/bin/python3
# Main.py
# Ashish D'Souza
# December 5th, 2018
import Data
import OutlierDetection
import DeepLearning
import tensorflow as tf
import numpy as np
from datetime import datetime
import os
parameters = ["Temp", "NO2", "NOX", "NOY", "RH", "Wind Speed V", "SO2 Trace Level", "Ozone"]
soda = Data.SODA(... | {"hexsha": "f1aa4e18f5468ece472b69a2fbdfc95a934f3b1d", "size": 4685, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/python/Main.py", "max_stars_repo_name": "computer-geek64/mtd", "max_stars_repo_head_hexsha": "0b2103b60a7a9401b0dea3a0a3ddc1f96a966251", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import pytest
import os
import numpy as np
import pandas as pd
import taxcrunch.multi_cruncher as mcr
CURRENT_PATH = os.path.abspath(os.path.dirname(__file__))
def test_a18_validation():
taxcrunch_in = os.path.join(CURRENT_PATH, "taxsim_validation/taxcrunch_in_a18.csv")
crunch = mcr.Batch(taxcrunch_in)
t... | {"hexsha": "a00b4841ff5b81cd025097c7cec760356326a325", "size": 1494, "ext": "py", "lang": "Python", "max_stars_repo_path": "taxcrunch/tests/test_taxsim.py", "max_stars_repo_name": "hdoupe/Tax-Cruncher-ARP", "max_stars_repo_head_hexsha": "c8c960c085d0883915f99ac2ea4630c928af4c16", "max_stars_repo_licenses": ["MIT"], "ma... |
(* Helper functions for dealing with maps and decidable equality *)
Require Import Merges.Tactics.
Require Import Coq.Program.Equality.
Require Import Coq.Logic.FunctionalExtensionality.
Section Map.
Variable K : Type.
Variable V : Type.
Definition EqDec := forall (n m : K), { n = m } + { n <> m }.
Hint Unfold E... | {"author": "amosr", "repo": "merges", "sha": "bf8cb7bca2d859977d6fb8bf4a9d07ac780b7edd", "save_path": "github-repos/coq/amosr-merges", "path": "github-repos/coq/amosr-merges/merges-bf8cb7bca2d859977d6fb8bf4a9d07ac780b7edd/proof/Merges/Map.v"} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.