text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
from showml.losses import MeanSquaredError, BinaryCrossEntropy
from showml.losses.loss_functions import CrossEntropy
def r2_score(y: np.ndarray, z: np.ndarray) -> float:
"""Calculate the r^2 (coefficient of determination) score of the model.
Args:
y (np.ndarray): The true values.
... | {"hexsha": "a5a1200754607888b5baaa1bb56fdc831d3056a4", "size": 2098, "ext": "py", "lang": "Python", "max_stars_repo_path": "showml/losses/metrics.py", "max_stars_repo_name": "shubhomoy/ShowML", "max_stars_repo_head_hexsha": "9fbc366941ad910f1fbd7d91da823616c34fd400", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5 import QtCore
from PyQt5.QtCore import *
import numpy as np
class QThumbnail(QLabel):
mpsignal = pyqtSignal(list, int)
def __init__(self, parent):
super(QLabel, self).__init__(parent)
self.setMinimumSize(1, 1)
self.s... | {"hexsha": "999d19a8a43649274503401a733cff8ecc32a78f", "size": 1568, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/qthumbnail.py", "max_stars_repo_name": "wenyalintw/Nodule-CADx", "max_stars_repo_head_hexsha": "dd0b3d1d672141f8dfabde1a05ef33f87681f8e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import math
import random
import numpy as np
from parse.ast_node import ASTNode
# From here on, classes describing various mathematical operations
# TODO: minScale, scale, trimScale, widthBucket
class Abs(ASTNode):
def __init__(self, exp, line, column, graph_ref):
ASTNode.__init__(self, line, column)
... | {"hexsha": "e2bfa611c84add6622f0b2960d1eb0e8d5a5c5d3", "size": 9223, "ext": "py", "lang": "Python", "max_stars_repo_path": "parser/team03/parse/expressions/expressions_math.py", "max_stars_repo_name": "18SebastianVC/tytus", "max_stars_repo_head_hexsha": "2b22f4339356b6cf46e3235a5219f68e5ba5573b", "max_stars_repo_licens... |
function ME = myfunc(x)
% Break Test
for i = 1:100
try
x = x - 1;
if x < 0
error('x has become negative')
end
catch ME
fprintf('x become negative')
break
end
end
| {"author": "wme7", "repo": "Aero-matlab", "sha": "9430008f2e3b84f28633775a44dff534e780fbac", "save_path": "github-repos/MATLAB/wme7-Aero-matlab", "path": "github-repos/MATLAB/wme7-Aero-matlab/Aero-matlab-9430008f2e3b84f28633775a44dff534e780fbac/NumericalMethods/myfunc.m"} |
section \<open> Blocks (Abstract Local Variables) \<close>
theory utp_blocks
imports utp_rel_laws utp_wp
begin
subsection \<open> Extending and Contracting Substitutions \<close>
definition subst_ext :: "('\<alpha> \<Longrightarrow> '\<beta>) \<Rightarrow> ('\<alpha>, '\<beta>) psubst" ("ext\<^sub>s") where
\<comm... | {"author": "isabelle-utp", "repo": "utp-main", "sha": "27bdf3aee6d4fc00c8fe4d53283d0101857e0d41", "save_path": "github-repos/isabelle/isabelle-utp-utp-main", "path": "github-repos/isabelle/isabelle-utp-utp-main/utp-main-27bdf3aee6d4fc00c8fe4d53283d0101857e0d41/utp/utp_blocks.thy"} |
from abbrev import abbreviations
from absl import logging
import csv
import numpy as np
import spacy
import tensorflow_hub as hub
nlp = spacy.load("en_core_web_lg")
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4" # @param ["https://tfhub.dev/google/universal-sentence-encoder/4", "https://tfhub.de... | {"hexsha": "1de069ca97ab84ed39568422fdaaf547e8a18ef8", "size": 3278, "ext": "py", "lang": "Python", "max_stars_repo_path": "script_tf.py", "max_stars_repo_name": "SombiriX/csvcompare", "max_stars_repo_head_hexsha": "26d7d9288b702af8e350fed7f832f6360cefdaaf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
# This file is a part of BAT.jl, licensed under the MIT License (MIT).
include("bat_sample.jl")
include("mcmc/mcmc.jl")
include("sampled_density.jl")
include("importance/importance_sampler.jl")
include("partitioned_sampling/partitioned_sampling.jl")
| {"hexsha": "7bb94d0e24ee8ea7650a8fc12f0d9facd7fe4400", "size": 251, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/samplers/samplers.jl", "max_stars_repo_name": "Cornelius-G/BAT.jl", "max_stars_repo_head_hexsha": "1bb577c8d976066c1f52070984d86020728f599c", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# Copyright 2015-2016 ARM Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in w... | {"hexsha": "71f2c5a28c12ce7413ec652b98ffbc067a57a4ff", "size": 3360, "ext": "py", "lang": "Python", "max_stars_repo_path": "external/bart/tests/test_signal.py", "max_stars_repo_name": "JaimeVHArm/lisa", "max_stars_repo_head_hexsha": "e5dcb7d54f73d57d4071da87c7c8095ba351a899", "max_stars_repo_licenses": ["Apache-2.0"], ... |
# Author: Bichen Wu (bichen@berkeley.edu) 02/20/2017
# -*- coding: utf-8 -*-
"""Utility functions."""
import numpy as np
import time
# ed: label, pred_cls에서 class가 정해진 좌표에 colorize(=visualize)를 해서 리턴하는 함수
def visualize_seg(label_map, mc, one_hot=False):
if one_hot:
label_map = np.argmax(label_map, axis=-1)
... | {"hexsha": "07a9b83673409e274443703dae6c16a2ce8ac6ad", "size": 5274, "ext": "py", "lang": "Python", "max_stars_repo_path": "DEEPLEARNING/DL_SQUEEZESEG/src/squeezeseg_cpp_preprocessing/script/squeezeseg/utils/util.py", "max_stars_repo_name": "Hqss/DINK", "max_stars_repo_head_hexsha": "5fecaa65e2f9da48eb8ac38ef709aa555fc... |
[STATEMENT]
lemma less_setsD: "\<lbrakk>A \<lless> B; a \<in> A; b \<in> B\<rbrakk> \<Longrightarrow> a < b"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>A \<lless> B; a \<in> A; b \<in> B\<rbrakk> \<Longrightarrow> a < b
[PROOF STEP]
by (auto simp: less_sets_def) | {"llama_tokens": 124, "file": "Nash_Williams_Nash_Extras", "length": 1} |
# Copyright (c) 2015 Ultimaker B.V.
# Cura is released under the terms of the LGPLv3 or higher.
import numpy
from PyQt5.QtGui import QImage, qRed, qGreen, qBlue
from PyQt5.QtCore import Qt
from UM.Mesh.MeshReader import MeshReader
from UM.Mesh.MeshBuilder import MeshBuilder
from UM.Math.Vector import Vector
from UM.... | {"hexsha": "5195b61595b002c82ac4e53e139a517eff13c2b3", "size": 7928, "ext": "py", "lang": "Python", "max_stars_repo_path": "Fracktory3-3.0_b11/plugins/ImageReader/ImageReader.py", "max_stars_repo_name": "ganeshmev/Fracktory3-3.0_b11_KLE", "max_stars_repo_head_hexsha": "16066e6993b96a880aa1a2f044a27930cbd0787d", "max_st... |
import os,sys
import os.path
import numpy as np
import pandas as pd
import torch
import torch.utils.data
from torchvision import datasets,transforms
from sklearn.utils import shuffle
import urllib.request
from PIL import Image
import pickle
import utils
####################################################... | {"hexsha": "25c1ea73571470ae024588a9bebd0500e536a68b", "size": 25096, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dataloaders/mixture.py", "max_stars_repo_name": "felixnext/dwa", "max_stars_repo_head_hexsha": "a37ea57ac247f00c5bf2d2b32a3a3cf9c2597b9f", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from torch.utils.data import Dataset
from pythia.common.registry import registry
class MultiTask(Dataset):
def __init__(self, dataset_type, config):
super(MultiTask, self).__init__()
self.config = config
self.dataset_t... | {"hexsha": "d15759bcc617ea80d39b5da691628842f81dafd1", "size": 2755, "ext": "py", "lang": "Python", "max_stars_repo_path": "pythia/tasks/multi_task.py", "max_stars_repo_name": "mandliya/pythia_updated", "max_stars_repo_head_hexsha": "e986c4dff7cc3a9f6b85ffe8e7d45ea53ab36e95", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import numpy as np
import tensorflow as tf
import vgg16
import utils
import cv2
BATCH_SIZE = 100
def mkbatch():
files = open('material_dataset.txt').readlines()
cnt = len(files) // BATCH_SIZE
if len(files) % BATCH_SIZE != 0:
cnt += 1
files = [item.split()[0] for item in files]
batchlist ... | {"hexsha": "8dfeaf609be1c4a844154e82e40952e3ca079f75", "size": 1707, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/tensorflow-vgg-master/vgg16_fabri.py", "max_stars_repo_name": "leix28/ML-Fabri", "max_stars_repo_head_hexsha": "6776f1b93cc84ab40569af3052ffc30bee7f8910", "max_stars_repo_licenses": ["MI... |
# For extracting unique MMSI from multiple input files
ExtractUniqueMMSI <- function(infiles){
uniqueMMSI <- c() # dataframe to store unique MMSIs
shipcount <- 0 #counter for unique ships
shiplist <- NA # start with a null ship list to compare first ship to
for(i in 1:length(infiles)){ ... | {"hexsha": "07bbe1e280ab839dc594edceb70c59c40bf9d96c", "size": 1349, "ext": "r", "lang": "R", "max_stars_repo_path": "R_Functions/ExtractUniqueMMSI.r", "max_stars_repo_name": "Pacific-CEBP/AIS-processing", "max_stars_repo_head_hexsha": "6704511cf69ae51fb14b61c4d53771031b5ac962", "max_stars_repo_licenses": ["MIT"], "max... |
# -*- coding: utf-8 -*-
"""
This script makes plots of relevant data.
@author: Jonathan Dumas
"""
import yaml
import os
import pandas as pd
import energyscope as es
import numpy as np
import matplotlib.pyplot as plt
from sys import platform
from energyscope.utils import make_dir, load_config, get_FEC_from_sankey
f... | {"hexsha": "b92d0e01927bfa275063b4d7bc9b792c38c12242", "size": 22145, "ext": "py", "lang": "Python", "max_stars_repo_path": "projects/eroi_study/utils_res.py", "max_stars_repo_name": "energyscope/EnergyScope_multi_criteria", "max_stars_repo_head_hexsha": "438ca2d3a8502110ce45ed6a1165eb0ff7c2d57c", "max_stars_repo_licen... |
from pocovidnet.utils_butterfly_data import (
get_processing_info, get_paths, label_to_dir
)
import os
import cv2
import numpy as np
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-data', type=str, default="butterfly")
parser.add_argument('-out', ty... | {"hexsha": "9754a64995c3ed348715e0ca4d17da52f2480b54", "size": 4194, "ext": "py", "lang": "Python", "max_stars_repo_path": "pocovidnet/scripts/process_butterfly_videos.py", "max_stars_repo_name": "983632847/covid19_pocus_ultrasound", "max_stars_repo_head_hexsha": "3625e95bbf189926dbd12966ef59ee71ed10e453", "max_stars_r... |
[STATEMENT]
lemma "\<lfloor>P \<^bold>\<rightarrow> \<^bold>O\<^sub>aP\<rfloor>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>c. P c \<sqsubseteq> (\<^bold>O\<^sub>aP) c
[PROOF STEP]
nitpick
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>c. P c \<sqsubseteq> (\<^bold>O\<^sub>aP) c
[PROOF STEP]... | {"llama_tokens": 170, "file": "GewirthPGCProof_CJDDLplus", "length": 2} |
[STATEMENT]
lemma diamond_fin_word_inf_word:
assumes "Ind (set v) (sset w)" "path v p" "run w p"
shows "run w (fold ex v p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. run w (target v p)
[PROOF STEP]
using diamond_inf_word_step assms
[PROOF STATE]
proof (prove)
using this:
\<lbrakk>Ind {?a} (sset ?w)... | {"llama_tokens": 225, "file": "Partial_Order_Reduction_Transition_System_Traces", "length": 2} |
from .truthdiscoverer import TruthDiscoverer
import pandas as pd
import numpy as np
class MajorityVoting(TruthDiscoverer):
"""Find truths by majority voting."""
def discover(self, claims, auxiliary_data=None):
return (self._majority_vote(claims), None)
def _majority_vote(self, claims):
""... | {"hexsha": "2836306a6ed223dedec1667c791104ac7931f32f", "size": 1435, "ext": "py", "lang": "Python", "max_stars_repo_path": "spectrum/judge/majority.py", "max_stars_repo_name": "totucuong/spectrum", "max_stars_repo_head_hexsha": "77628c14251f3078b83a505260d71e46ec56775b", "max_stars_repo_licenses": ["Apache-2.0"], "max_... |
/**
* @file llfloaterregioninfo.cpp
* @author Aaron Brashears
* @brief Implementation of the region info and controls floater and panels.
*
* $LicenseInfo:firstyear=2004&license=viewerlgpl$
* Second Life Viewer Source Code
* Copyright (C) 2010, Linden Research, Inc.
*
* This library is free software; you can... | {"hexsha": "4f81af2755400b58db1a6c7570bb831fe023e30f", "size": 131558, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "indra/newview/llfloaterregioninfo.cpp", "max_stars_repo_name": "SaladDais/LLUDP-Encryption", "max_stars_repo_head_hexsha": "8a426cd0dd154e1a10903e0e6383f4deb2a6098a", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 26 14:21:02 2016
@author: Sebastijan Mrak <smrak@gmail.com>
"""
import numpy as np
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
#%% Keograms
def plotKeogram(t, y, kg, title=None, legend=None, ylim=None, pcolorbar=None,
... | {"hexsha": "4be8c5c59f220f6b74e6f1ec422394d473047da4", "size": 43989, "ext": "py", "lang": "Python", "max_stars_repo_path": "gsit/plotting.py", "max_stars_repo_name": "aldebaran1/gsit", "max_stars_repo_head_hexsha": "d4309799d0d7bc0d670a34e8983c6ac0eb17569b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "m... |
"""DataFrame-level subtraction operations.
Subtract one set of regions from another, returning the one-way difference.
The functions here operate on pandas DataFrame and Series instances, not
GenomicArray types.
"""
from __future__ import print_function, absolute_import, division
import logging
import numpy as np
... | {"hexsha": "0a14ef3aaf4d301ec1991ad9f63f83fe7f0be726", "size": 2652, "ext": "py", "lang": "Python", "max_stars_repo_path": "skgenome/subtract.py", "max_stars_repo_name": "jeremy9959/cnvkit", "max_stars_repo_head_hexsha": "b839a2b323113a7d318d216f61a0ed6657c70ed4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
from pathlib import Path
import numpy as np
from skimage.draw import disk
from skimage.io import imsave
from PIL import Image
from tqdm import tqdm
import tables
from skimage.transform import rescale, resize, downscale_local_mean
import sys
sys.path.append("../")
from derive_dataset import get_max_r2
def generat... | {"hexsha": "de8c431c401ba06d4babaeae4d87cee7c7ed5a9f", "size": 9972, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/generate_packlab_mst_hyperflow.py", "max_stars_repo_name": "patrickmineault/brain-scorer", "max_stars_repo_head_hexsha": "5e882bafb323ff58028ade2394d18176e6c02e80", "max_stars_repo_license... |
### Julia OpenStreetMapX Package ###
### MIT License ###
### Copyright 2014 ###
### Default Speed Limits in Kilometers Per Hour ###
const SPEED_ROADS_URBAN = Dict{Int,Float64}(
1 => 100, # Motorway
2 => 90, # Trunk
3 => 90, # Primary
4 => 70, # Secondary
5 =... | {"hexsha": "37d07d703dab1158f079df5b74d94dd5a7d1d956", "size": 594, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/speeds.jl", "max_stars_repo_name": "arash-dehghan/OpenStreetMapX.jl", "max_stars_repo_head_hexsha": "179251a5cfa4a62c123dbf793674c0374a07f841", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# Autogenerated wrapper script for xrootdgo_jll for x86_64-w64-mingw32
export xrootdgo
JLLWrappers.@generate_wrapper_header("xrootdgo")
JLLWrappers.@declare_library_product(xrootdgo, "xrootdgo.dll")
function __init__()
JLLWrappers.@generate_init_header()
JLLWrappers.@init_library_product(
xrootdgo,
... | {"hexsha": "544439dcadd6a5b954e4d802bb811c1910705ee4", "size": 446, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/wrappers/x86_64-w64-mingw32.jl", "max_stars_repo_name": "JuliaBinaryWrappers/xrootdgo_jll.jl", "max_stars_repo_head_hexsha": "3d6747353e4c39f3dc30d32905fa9b5658bbd26f", "max_stars_repo_licenses"... |
import json
import os
import glob
import random
from typing import Union
try:
import xarray as xr
except ModuleNotFoundError:
xr = None
import numpy as np
import pandas as pd
from .datasets import Datasets
from .utils import check_attributes, download, sanity_check
from ai4water.utils.utils import dateandtim... | {"hexsha": "daa7d26d703b2e6fd2855c3dde932b88c4d35034", "size": 69842, "ext": "py", "lang": "Python", "max_stars_repo_path": "ai4water/datasets/camels.py", "max_stars_repo_name": "csiro-hydroinformatics/AI4Water", "max_stars_repo_head_hexsha": "cdb18bd4bf298f77b381f1829045a1e790146985", "max_stars_repo_licenses": ["MIT"... |
[STATEMENT]
lemma leadsTo_common:
"[| \<forall>m. F \<in> {m} Co (maxfg m);
\<forall>m \<in> -common. F \<in> {m} LeadsTo (greaterThan m);
n \<in> common |]
==> F \<in> (atMost (LEAST n. n \<in> common)) LeadsTo common"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<for... | {"llama_tokens": 930, "file": null, "length": 5} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright 2019 The FATE 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/lic... | {"hexsha": "b966c406b9fa1ab3989b10ea36d5ffcc58734d87", "size": 3579, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/federatedml/util/abnormal_detection.py", "max_stars_repo_name": "hubert-he/FATE", "max_stars_repo_head_hexsha": "6758e150bd7ca7d6f788f9a7a8c8aea7e6500363", "max_stars_repo_licenses": ["Apac... |
'''
inventoryanalytics: a Python library for Inventory Analytics
Author: Roberto Rossi
MIT License
Copyright (c) 2018 Roberto Rossi
'''
from typing import List
from inventoryanalytics.utils import memoize as mem
import scipy.stats as sp
import json
class State:
"""
The state of the inventory system.
... | {"hexsha": "9766966c529d8c4784b8b98883201a3c1a262075", "size": 7252, "ext": "py", "lang": "Python", "max_stars_repo_path": "inventoryanalytics/lotsizing/stochastic/nonstationary/sdp.py", "max_stars_repo_name": "vishalbelsare/inventoryanalytics", "max_stars_repo_head_hexsha": "85feff8f1abaf2c29414e066eed096ac3a74973b", ... |
import argparse
import numpy as np
import subprocess as sp
import os
combiner_bin = "/home/lars/work/combiner/bin/"
def main():
args = parseCmd()
braker2_level = ['species_excluded', 'family_excluded', 'order_excluded']
with open(args.data + '/species.tab', 'r') as file:
species_list = file.read().... | {"hexsha": "5a1db67b2fafd3d4f3fee8d62ac84ef4085eab03", "size": 2612, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/dev/data2ucsc.py", "max_stars_repo_name": "LarsGab/PrEvCo", "max_stars_repo_head_hexsha": "55461001685b33cbf49d1f8fef93c387ee85b284", "max_stars_repo_licenses": ["ClArtistic"], "max_stars_coun... |
[STATEMENT]
lemma Sublists_Un [simp]: "Sublists (A \<union> B) = Sublists A \<union> Sublists B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Sublists (A \<union> B) = Sublists A \<union> Sublists B
[PROOF STEP]
by (auto simp: Sublists_altdef) | {"llama_tokens": 97, "file": "Regular-Sets_Regexp_Constructions", "length": 1} |
# Utility functions for processing VLSV data.
"""
getcell(meta, location) -> UInt
Return cell ID containing the given spatial `location` in meter, excluding domain
boundaries. Only accept 3D location.
"""
function getcell(meta::MetaVLSV, loc)
(;coordmin, coordmax, dcoord, ncells, cellid, maxamr) = meta
for... | {"hexsha": "306e5fbbd86f9a633ccdfef9e16545254e967861", "size": 36257, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/vlsv/vlsvutility.jl", "max_stars_repo_name": "alhom/Vlasiator.jl", "max_stars_repo_head_hexsha": "615333705b5346522479ab72398f059cb94ab026", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import argparse
import os
import numpy as np
import skimage.io as io
import tqdm
from pycocotools.coco import COCO
from skimage.draw import pol... | {"hexsha": "606d9ab53da428894d2ad3f38ca2eddb650052b0", "size": 2476, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/coco_generate_instance_map.py", "max_stars_repo_name": "NguyenHoangAn0511/gan-compression", "max_stars_repo_head_hexsha": "6512c067d4adebc7451635991418b54ab76dd711", "max_stars_repo_licen... |
import itertools
import numpy as np
import pandas as pd
import pytest
from hamcrest import assert_that, none, not_none, calling, raises, close_to
import cifrum as lib
from conftest import decimal_places, delta
from cifrum._portfolio.currency import PortfolioCurrencyFactory
from cifrum.common.enums import Currency
__... | {"hexsha": "2408579a2686ab51c71de352007aef3a5aaae2e8", "size": 2958, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_inflation.py", "max_stars_repo_name": "31337mbf/yapo", "max_stars_repo_head_hexsha": "b790e112efccfb8f818dc7711989a9174b2c65fb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
from dagster_pandas.data_frame import create_dagster_pandas_dataframe_type
from dagster_pandas.validation import PandasColumn
from numpy import mean, median, ndarray
from pandas import Timestamp
from dagster import DagsterType, EventMetadataEntry, TypeCheck
def compute_trip_dataframe_event_metadata(dataframe):
r... | {"hexsha": "6ac5ddff0450d15cda4978e23eeef7695838052b", "size": 6979, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/legacy_examples/dagster_examples/bay_bikes/types.py", "max_stars_repo_name": "bitdotioinc/dagster", "max_stars_repo_head_hexsha": "4fe395a37b206b1a48b956fa5dd72bf698104cca", "max_stars_re... |
import numpy as np
from dataclasses import dataclass
from material import Material
@dataclass
class ConversionMatrices:
S: np.ndarray
S_reduced: np.ndarray
S_bar: np.ndarray
S_bar_reduced: np.ndarray
C: np.ndarray
C_reduced: np.ndarray
Q_bar: np.ndarray
Q_bar_reduced: np.ndarray
... | {"hexsha": "588e4fdc7eeb7c20f68868940842f7fa8f80e690", "size": 7653, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/conversion.py", "max_stars_repo_name": "echaffey/Compysite", "max_stars_repo_head_hexsha": "bf56f8fa641f39c747ce7be1d35dd198ea5a09e0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import time
import sys, os
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder
import scipy.stats
import tracemalloc
import umap
import hdbscan
from inspect import Parameter
import weblogo
from weblogo import *
def onehot_enc(row, expected_length=21):
"""Encode the data with one-... | {"hexsha": "17ca1708ccdc1b617e2cc05b400c863d36c57113", "size": 5779, "ext": "py", "lang": "Python", "max_stars_repo_path": "iMVP_utils/iMVP_utils/interactive_functions.py", "max_stars_repo_name": "jhfoxliu/iMVP", "max_stars_repo_head_hexsha": "741c355fbaae3a610cb31f0e34965734f0cd19a4", "max_stars_repo_licenses": ["Unli... |
import os
import random
from .libenv import CVecEnv
import numpy as np
from .build import build
SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
ENV_NAMES = [
"bigfish",
"bossfight",
"caveflyer",
"chaser",
"climber",
"coinrun",
"dodgeball",
"fruitbot",
"heist",
"jumper"... | {"hexsha": "b46544a73ca86cb55021ff2530acc5628a7e3700", "size": 5713, "ext": "py", "lang": "Python", "max_stars_repo_path": "procgen/env.py", "max_stars_repo_name": "KarlXing/procgen", "max_stars_repo_head_hexsha": "937de8c350dff5c7cb0f6b9639a0b0815a8f3689", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
module SStools
import Distributions.MvNormal
export kalman_filter, fast_state_smoother, simulate
"""
Perform Kalman filtering on the data y.
Conventions are as in Durbin and Koopman (2012).
Relevant dimensions are:
- Nt: number of time points
- Np: dimension of observation space
- Nm: dimension of state space
- Nr: ... | {"hexsha": "e6aba09a9c99aa9e1462282d41664d28991f34af", "size": 8125, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/SStools.jl", "max_stars_repo_name": "jmxpearson/labcr", "max_stars_repo_head_hexsha": "ec9560004d81ecb912500d811b86829135a81782", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "ma... |
#! /usr/bin/julia
# Rosetta Code, Find common directory path
function commonpath{T<:String}(ds::Array{T,1}, delim::Char='/')
0 < length(ds) || return convert(T, "")
1 < length(ds) || return ds[1]
p = split(ds[1], delim)
mcnt = length(p)
for d in ds[2:end]
q = split(d, delim)
mcnt =... | {"hexsha": "10739205e767bfd8a3ca1847d5316b6e1160f1c7", "size": 884, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia/completed/find_common_directory_path.jl", "max_stars_repo_name": "MichaeLeroy/rosetta-code", "max_stars_repo_head_hexsha": "cb0f45f79704912967cbd37c0c9bdc1e78c964b5", "max_stars_repo_licenses"... |
function [x,zo]=overlapadd(f,win,inc)
%OVERLAPADD join overlapping frames together X=(F,WIN,INC)
%
% Inputs: F(NR,NW) contains the frames to be added together, one
% frame per row.
% WIN(NW) contains a window function to multiply each frame.
% WIN may be omitted to use a d... | {"author": "decouples", "repo": "Matlab_deep_learning", "sha": "1b823b82686080e32b03e1f1a4648896bd6e3c44", "save_path": "github-repos/MATLAB/decouples-Matlab_deep_learning", "path": "github-repos/MATLAB/decouples-Matlab_deep_learning/Matlab_deep_learning-1b823b82686080e32b03e1f1a4648896bd6e3c44/\u7b2c 19 \u7ae0 \u57fa\... |
const QuasiArrayMulArray{p, q, T, V} =
Applied{<:Any, typeof(*), <:Tuple{<:AbstractQuasiArray{T,p}, <:AbstractArray{V,q}}}
const ArrayMulQuasiArray{p, q, T, V} =
Applied{<:Any, typeof(*), <:Tuple{<:AbstractArray{T,p}, <:AbstractQuasiArray{V,q}}}
const QuasiArrayMulQuasiArray{p, q, T, V} =
Applied{<:Any, t... | {"hexsha": "a844d9117d93cd2f509344e1c32c57338729b7d9", "size": 9373, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/matmul.jl", "max_stars_repo_name": "UnofficialJuliaMirror/QuasiArrays.jl-c4ea9172-b204-11e9-377d-29865faadc5c", "max_stars_repo_head_hexsha": "db22aeeaa768d5995b9b09028e5422dcda273668", "max_st... |
"""
Name : c11_16_VaR_sorting_10day.py
Book : Python for Finance (2nd ed.)
Publisher: Packt Publishing Ltd.
Author : Yuxing Yan
Date : 6/6/2017
email : yany@canisius.edu
paulyxy@hotmail.com
"""
import numpy as np
import pandas as pd
from scipy.stats import norm
from matplotli... | {"hexsha": "c43db825432cc8038dd1bcb58a2e0b1e069dd260", "size": 1584, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter11/c11_16_VaR_sorting_10days.py", "max_stars_repo_name": "John-ye666/Python-for-Finance-Second-Edition", "max_stars_repo_head_hexsha": "dabef09bcdd7b0ec2934774741bd0a7e1950de73", "max_stars... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Class for managing seismic refraction data and doing inversions"""
from math import pi
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import pygimli as pg
import pygimli.meshtools as mt
from pygimli.viewer.mpl imp... | {"hexsha": "4c6f26e667291dce8dac6c5a1d554e0b264daf99", "size": 37090, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygimli/physics/traveltime/refraction.py", "max_stars_repo_name": "baender/gimli", "max_stars_repo_head_hexsha": "eb9a2204669cf11209b9577472f61ac70217a191", "max_stars_repo_licenses": ["Apache-2.... |
import cocos.device
import cocos.numerics as cn
import numpy as np
import pytest
test_data = [np.array([[1, -1],
[0, 1]],
dtype=np.int32),
np.array([[0.2, 1.0, 0.5],
[0.4, 0.5, 0.6],
[0.7, 0.2, 0.25]],
... | {"hexsha": "25cb8e9f5e83856291e1f8e6f37c07367c1132e0", "size": 2161, "ext": "py", "lang": "Python", "max_stars_repo_path": "cocos/tests/test_numerics/test_arith/test_trigonometric.py", "max_stars_repo_name": "michaelnowotny/cocos", "max_stars_repo_head_hexsha": "3c34940d7d9eb8592a97788a5df84b8d472f2928", "max_stars_rep... |
import numpy as np
import miepy
nm = 1e-9
v = 10
u = 0
n = 10
m = 0
ftype = 'electric'
N,M = miepy.vsh.VSH(n, m)
if ftype == 'magnetic':
func = M
elif ftype == 'electric':
func = N
k = 2*np.pi/(600*nm)
r = 600*nm
origin_1 = np.array([0,0,0])
THETA, PHI = miepy.coordinates.sphere_mesh(800)
E = func(r, THE... | {"hexsha": "bc0389fd122789ac4dda72bc5ed118ba46512c22", "size": 1258, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/tests/vsh_translation.py", "max_stars_repo_name": "johnaparker/MiePy", "max_stars_repo_head_hexsha": "5c5bb5a07c8ab79e9e2a9fc79fb9779e690147be", "max_stars_repo_licenses": ["MIT"], "max_s... |
#ipython --pylab
import scipy
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
from netCDF4 import Dataset
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import pandas
import pylab
np.set_printoptions(threshold=np.nan)
plt.rc('font', family='serif', serif='Times New Roma... | {"hexsha": "f5888fdf0fd798a48801004d0bbf449e4b81907f", "size": 3056, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyCode/IUCN_southernbluefin_spawning.py", "max_stars_repo_name": "kallisons/CMIP5_p50", "max_stars_repo_head_hexsha": "ee8e078720d1a009cfb9355a9cadb07455b674ba", "max_stars_repo_licenses": ["MIT"]... |
"""Generic training script that trains a model using a given dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import numpy as np
import tensorflow as tf
import model
import data
from utils import tfmri
import utils.logging
# Data dimens... | {"hexsha": "22a9e7ed0653fb0dde4b3d67be604203ce1ab6d4", "size": 16427, "ext": "py", "lang": "Python", "max_stars_repo_path": "recon_train.py", "max_stars_repo_name": "MRSRL/dl-cs", "max_stars_repo_head_hexsha": "df8b541b8797ffceb65cdbc06c93c377a22d777a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 36, "max_st... |
/*
@copyright Louis Dionne 2014
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
#include <boost/hana/detail/assert.hpp>
#include <boost/hana/ext/std/integral_constant.hpp>
#include <boost/hana/functional.hpp>
#include <boost/... | {"hexsha": "474a3a83d7b36729b2bfb5a4f88a3205e081e3dd", "size": 973, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "example/searchable/find.cpp", "max_stars_repo_name": "rbock/hana", "max_stars_repo_head_hexsha": "2b76377f91a5ebe037dea444e4eaabba6498d3a8", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count":... |
"""
`x, y = noisy_function(fun, x; noise = 0.01, f_rand = randn)`
Generates a noisy response `y` for the given function `fun`
by adding `noise .* f_randn(length(x))` to the result of `fun(x)`.
"""
function noisy_function(fun::Function, x::AbstractVector{T}; noise::Real = 0.01, f_rand::Function = randn) where T<:Real
... | {"hexsha": "9ae879dadeb5f16f7a3800be73de630a8d08df64", "size": 2512, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/noisy_function.jl", "max_stars_repo_name": "UnofficialJuliaMirror/MLDataUtils.jl-cc2ba9b6-d476-5e6d-8eaf-a92d5412d41d", "max_stars_repo_head_hexsha": "2f845c6482e821d56f75353318a8c3bd507f5b1d",... |
import unittest
import numpy as np
import openjij as oj
class UtilsTest(unittest.TestCase):
def test_benchmark(self):
h = {0: 1}
J = {(0, 1):-1.0, (1,2): -1.0}
sa_samp = oj.SASampler()
def solver(time_param, iteration):
sa_samp.step_num = time_param
sa_... | {"hexsha": "395c57e79a176f6a707cbb4e06e110a999769817", "size": 2062, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test.py", "max_stars_repo_name": "y-yu/OpenJij", "max_stars_repo_head_hexsha": "ed08460b7c9f8e553d4d33e08977d465472e9c44", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null,... |
import numpy as np
import matplotlib.pyplot as plt
import subprocess
import h5py
# from tensorflow.python.keras.models import Sequential
# from tensorflow.python.keras.layers import BatchNormalization, Dense, Flatten, Input, LeakyReLU, Reshape
from os.path import abspath
from keras.models import Sequential, Model
fro... | {"hexsha": "43c5811a2893638b2a514e447b00d0f08c5746fd", "size": 5646, "ext": "py", "lang": "Python", "max_stars_repo_path": "poke-dicts/artsy.py", "max_stars_repo_name": "iheartbenzene/musical-funicular", "max_stars_repo_head_hexsha": "5fc83504874243d13aeedc97bdb955d01d64844a", "max_stars_repo_licenses": ["MIT"], "max_s... |
*DECK MPMAXR
SUBROUTINE MPMAXR (X)
C***BEGIN PROLOGUE MPMAXR
C***SUBSIDIARY
C***PURPOSE Subsidiary to DQDOTA and DQDOTI
C***LIBRARY SLATEC
C***TYPE ALL (MPMAXR-A)
C***AUTHOR (UNKNOWN)
C***DESCRIPTION
C
C Sets X to the largest possible positive 'mp' number.
C
C The argument X(*) is an INTEGER arrays of... | {"hexsha": "9eaba4fecfe88a4468060363038d118f3ce01ac6", "size": 1154, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "slatec/src/mpmaxr.f", "max_stars_repo_name": "andremirt/v_cond", "max_stars_repo_head_hexsha": "6b5c364d7cd4243686488b2bd4318be3927e07ea", "max_stars_repo_licenses": ["Unlicense"], "max_stars_coun... |
from PIL import Image
import numpy as np
import torch
import torch.nn.functional as F
import torchvision
# Gaussian blur kernel
def get_gaussian_kernel(device="cpu"):
kernel = np.array([
[1, 4, 6, 4, 1],
[4, 16, 24, 16, 4],
[6, 24, 36, 24, 6],
[4, 16, 24, 16, 4],
[1, 4, 6, 4... | {"hexsha": "7ffdd93cf681cfdc3ce86fb6089158272a6f0000", "size": 6007, "ext": "py", "lang": "Python", "max_stars_repo_path": "swd.py", "max_stars_repo_name": "WestCityInstitute/swd-pytorch", "max_stars_repo_head_hexsha": "2b0c224fa4e43ab081a40380689d6a334959eb65", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 75... |
"""
To run k-means, just call the function:
kmeans(k, data, min_vals, max_vals, max_iter=50)
with parameters:
k - number of clusters
d - data set, should be ndarray of shape (n, d) where n is the number
of data entries and d is the number of dimensions of each data entry
min_vals, max_vals - array like lists tha... | {"hexsha": "a7feb4c7a83d9b312eb377d75e269d8920ef9a99", "size": 4496, "ext": "py", "lang": "Python", "max_stars_repo_path": "kmeans.py", "max_stars_repo_name": "adapiekarska/kmeans", "max_stars_repo_head_hexsha": "1b9e77646fadd6d9ab73c4b9feaacfe215d39744", "max_stars_repo_licenses": ["FSFAP"], "max_stars_count": null, "... |
###############################################################################
#
# pttableau.py - Object to represent protein tableaux and functions to
# parse output of TableauCreator program into tableau object.
#
#
# File: pttableau.py
# Author: Alex Stivala
# Created: October 2007
#
#
# $Id: ptt... | {"hexsha": "961c07c58a25bb9f557107c4b25f4e0b6bb866a2", "size": 34863, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/pttableau.py", "max_stars_repo_name": "stivalaa/cuda_satabsearch", "max_stars_repo_head_hexsha": "b947fb711f8b138e5a50c81e7331727c372eb87d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import glob
import os
from typing import Dict, Optional, Tuple, Union
import numpy as np
import rasterio
import torch
import torch.nn.functional as F
def get_paths(img_dir: str, label_dir: str) -> Tuple[list, list]:
os.chdir(label_dir)
label_paths, img_paths = [], []
for filepath in glob.glob("*_SR.tif")... | {"hexsha": "450bf54cf4bfb9c7fbc3139b1a1a137435afc791", "size": 7508, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/raster_tiles/raster_tile_io.py", "max_stars_repo_name": "rcorrero/charcoal", "max_stars_repo_head_hexsha": "bd91a6a25960acdfafa1fd6a3be0839357a9e7ee", "max_stars_repo_licenses": ["BSD-3-Clau... |
[STATEMENT]
lemma map_map_rexp:
"map_rexp f (map_rexp g r) = map_rexp (\<lambda>r. f (g r)) r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. map_rexp f (map_rexp g r) = map_rexp (\<lambda>r. f (g r)) r
[PROOF STEP]
unfolding rexp.map_comp o_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. map_rexp (\<lambda>... | {"llama_tokens": 185, "file": "Regular-Sets_Regular_Exp", "length": 2} |
import numpy as np
class Activation:
def __call__(self, incoming):
raise NotImplementedError
def delta(self, incoming, outgoing, above):
"""
Compute the derivative of the cost with respect to the input of this
activation function. Outgoing is what this function returned in th... | {"hexsha": "40d209b7ffe80afc0137d110a08a6857a7c21560", "size": 4202, "ext": "py", "lang": "Python", "max_stars_repo_path": "layered/activation.py", "max_stars_repo_name": "danijar/ffnn", "max_stars_repo_head_hexsha": "c1c09d95f90057a91ae24c80b74f415680b97338", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 265,... |
#' This Script reshapes data on the Labour Income Share from Europe commission / economic database, https://ec.europa.eu/info/business-economy-euro/indicators-statistics/economic-databases/macro-economic-database-ameco_en
#'
#' a link gives access to the set of the database pertaining to labour costs etc
#' https://ec... | {"hexsha": "6face3b32cd6f553d3f1b5c2df1014367751bfcf", "size": 5714, "ext": "r", "lang": "R", "max_stars_repo_path": "inst/doc/do/REP_EUAMECO.ANNUAL_LAP.r", "max_stars_repo_name": "dbescond/iloData", "max_stars_repo_head_hexsha": "c4060433fd0b7025e82ca3b0a213bf00c62b2325", "max_stars_repo_licenses": ["MIT"], "max_stars... |
#import tensorflow as tf
import numpy as np
import torch
import torch.nn.functional as F
#
""" Includes helper functions that are used in admm.py and model.py
Last updated: 2/22/2019
Overview:
* Padding and cropping functions
* FFT shifting functions
* Forward Model (H, Hadj)
* Soft thresholding fu... | {"hexsha": "48f7a859a4e7b53e0e3f02642934a0a2d80f4b1a", "size": 7031, "ext": "py", "lang": "Python", "max_stars_repo_path": "admm_helper_functions_torch.py", "max_stars_repo_name": "sangeetsu/LenslessLearning", "max_stars_repo_head_hexsha": "751efc614eff5616a229972620192478af2c39c1", "max_stars_repo_licenses": ["BSD-3-C... |
import torch
import torchvision.transforms as T
import numpy as np
from nn_analysis.datasets import datasets as ds
from nn_analysis.datasets import transforms
def get_custom_dataset(base_dataset_name, seed, transform_names=[], subset_indices=None, outer_dims=None):
transforms_map = {
'crop': transforms.Ra... | {"hexsha": "dec7baa13fa1db24864ee70cb0337826bb847b74", "size": 2063, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn_analysis/datasets/custom_dataset.py", "max_stars_repo_name": "hchau630/nn-analysis", "max_stars_repo_head_hexsha": "0fbe7ad7b2b4566b9f88d8f21413a6d405f96bdc", "max_stars_repo_licenses": ["MIT"]... |
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import os
import sys
from GlimpseSensor import *
from Globals import *
from CoreRNN import *
batchSize = constants['batchSize']
trainingIters = 1000000 # in terms of sample size
displayStep = 1 # how often to print ... | {"hexsha": "fd89448b1cdf6040596e03d66c5aeea8b8046416", "size": 3603, "ext": "py", "lang": "Python", "max_stars_repo_path": "MNIST/ram2/Main.py", "max_stars_repo_name": "mimikaan/Attention-Model", "max_stars_repo_head_hexsha": "079cc1b42c83f6e3e77a92aa54c1a8f9ad0d8b93", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
// Copyright (c) 2009-2010 Satoshi Nakamoto
// Copyright (c) 2014-2016 The FFF Core developers
// Original code was distributed under the MIT software license.
// Copyright (c) 2014-2019 Coin Sciences Ltd
// FFF_Core code distributed under the GPLv3 license, see COPYING file.
#include "rpc/rpcutils.h"
#include "filter... | {"hexsha": "92674691fbd557b12ea1534586bfc7d7e6df1224", "size": 103465, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/rpc/rpcrawdata.cpp", "max_stars_repo_name": "fffnerwork/FFF_Protocol_Core", "max_stars_repo_head_hexsha": "94d75cc6b3a94e06fe6dde75967e665db26a7649", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
integrate a TaylorNModel with respect to the variable number `which`.
Optionally adds `x0` to the result.
"""
function integrate(f::TaylorNModel, which=1, x0=0)
p = integrate(f.p, which) # not necessary if an already complete Taylor series, in which case p2 == f.p
Δ = integral_bound(f, which)
g = Ta... | {"hexsha": "7bd4b35c436b21f5cd4d8009d309bd6683b167bf", "size": 880, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/TaylorN/integrate.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/TaylorModels.jl-314ce334-5f6e-57ae-acf6-00b6e903104a", "max_stars_repo_head_hexsha": "330b5b79fceef074979e100d14a56f... |
# coding: utf-8
# # Influence of parameter choice on the phase diagram
# To study to what extend the phase diagram depends on the cost of infection $c_{\rm inf}$, and on the trade-off shapes $c_{\rm def}(c_{\rm con}), c_{\rm uptake}(p_{\rm uptake})$ we plot the phase diagram for a number of different choices in the ... | {"hexsha": "b3dd75cbe1a974bd6145d872528497320d3fe776", "size": 6607, "ext": "py", "lang": "Python", "max_stars_repo_path": "figSIaltphases/figure-SIaltphases.py", "max_stars_repo_name": "andim/evolimmune", "max_stars_repo_head_hexsha": "6ffcc19e8725d343e9b10fa9c4dd77a9a485398a", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python3
import numpy as np
from pyqubo import Array, Constraint, Placeholder
def make_energy(type_matrix, weak_matrix, resist_matrix, enemy, skill):
# set the number of enemies
num_enemies = len(enemy)
# set the number of my pokemon
num_my_team = num_enemies
# set the number of type... | {"hexsha": "ca872bd8c7181e149aabf089f67a25eefe097e3f", "size": 3038, "ext": "py", "lang": "Python", "max_stars_repo_path": "make_energy.py", "max_stars_repo_name": "github-nakasho/Pokemon_opt", "max_stars_repo_head_hexsha": "abf1522fc1bf315d2018599b94d839084b421420", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <Eigen/StdVector>
#include <iostream>
#include <thread>
#include <srrg_system_utils/shell_colors.h>
#include <srrg_system_utils/parse_command_line.h>
#include <srrg_messages/message_handlers/message_file_source.h>
#include <srrg_messages/message_handlers/message_sorted_source.h>
#include <srrg_messages/messa... | {"hexsha": "d2cc1a3deb3b4aaded18209ef308f2f7846374ee", "size": 4521, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "catkin_ws/src/srrg2_core/srrg2_core_ros/src/tests/test_tf_from_source.cpp", "max_stars_repo_name": "laaners/progetto-labiagi_pick_e_delivery", "max_stars_repo_head_hexsha": "3453bfbc1dd7562c78ba06c0... |
import json
from scipy import interpolate
import copy
from ._emulator import Emulator
def transform_ES_elastance(emulator_data, factor):
"""
Transform an emulator by applying a multiplicative factor to end systolic elastance.
Parameters
----------
emulator_data : str, dict or Emulator
Emul... | {"hexsha": "db65cb05936715038febb441a6aade1c21f59b50", "size": 1840, "ext": "py", "lang": "Python", "max_stars_repo_path": "cardioemulator/_transform_emulator.py", "max_stars_repo_name": "michelebucelli/cardioemulator", "max_stars_repo_head_hexsha": "0ce8d5fce017a7251865ab01fdf3d0653490b60f", "max_stars_repo_licenses":... |
#pragma once
#include <Core/Containers/AlignedStdVector.hpp>
#include <Core/Containers/VectorArray.hpp>
#include <Eigen/Core>
#include <iostream>
namespace Ra {
namespace Core {
using ParentList = AlignedStdVector<int>;
using LevelList = AlignedStdVector<uint8_t>;
using ChildrenList = AlignedStdVector<uint8_t>;... | {"hexsha": "f892438ebd449a2af4df9760dd1208965f4e7731", "size": 3963, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/Core/Containers/AdjacencyList.hpp", "max_stars_repo_name": "grandch/Radium-Engine", "max_stars_repo_head_hexsha": "9d8c5b34c191ab3a31acff2f12cf3b0d66f613db", "max_stars_repo_licenses": ["Apache-... |
import numpy as np
import sys
import math
def read_inputs() :
feature_vec_train=np.load('train_feature.npy')
#print(feature_vec_train.shape)
train_label=np.load('train_label.npy')
#print(train_label.shape)
feature_vec_test=np.load('test_feature.npy')
#print(feature_vec_test.shape)
test_label=np.load('test_label... | {"hexsha": "092389a4ebb90504c27bf69d5dd10a92e0b6d152", "size": 5346, "ext": "py", "lang": "Python", "max_stars_repo_path": "regression.py", "max_stars_repo_name": "samiragarwala/diabetic_retinopathy_detection", "max_stars_repo_head_hexsha": "c0a134a65339098d338a998109fcab367bb00a32", "max_stars_repo_licenses": ["MIT"],... |
from collections.abc import Iterable
from contextlib import contextmanager, nullcontext
import emcee as mc
import numpy as np
import scipy.stats as st
import sklearn
from scipy.linalg import cho_solve, cholesky, solve_triangular
from sklearn.utils import check_random_state
from skopt.learning import GaussianProcessReg... | {"hexsha": "4c9aee18a5f184ff0eb9d2d4aa0ec5288f1039ce", "size": 30611, "ext": "py", "lang": "Python", "max_stars_repo_path": "bask/bayesgpr.py", "max_stars_repo_name": "kiudee/bayes-skopt", "max_stars_repo_head_hexsha": "8f1daf996e34b95af47ef0d382d57fe8a17bbae5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
using CSV, DataFrames, ScikitLearn, PyPlot
pathtodata = joinpath("julia-scripts", "model-zoo", "covid_cleaned.csv")
data = DataFrame(CSV.File(pathtodata))
X = convert(Array, data[!, Not(:covid_res)])
y = convert(Array, data[!, :covid_res])
@sk_import model_selection:train_test_split
X_train, X_test, y_train, y_test =... | {"hexsha": "ef78a6259929e843b80510f236b99e98718afde3", "size": 684, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-scripts/model-zoo/nbclassifier.jl", "max_stars_repo_name": "coinslab/ComputationalCognitiveModeling", "max_stars_repo_head_hexsha": "14c761ab8bc6685e7ec7f2bd79e7adae6abbad92", "max_stars_repo_... |
\documentclass[12pt]{article}
\usepackage[margin = 1.5in]{geometry}
\setlength{\parindent}{0in}
\usepackage{amsfonts, amssymb, amsthm, mathtools, tikz, qtree, float}
\usepackage{algpseudocode, algorithm, algorithmicx}
\usepackage{DejaVuSans}
\usepackage[T1]{fontenc}
\usepackage{ae, aecompl, color}
\usepackage[pdftex, p... | {"hexsha": "93e886a37e72f1b3d8715f92913350b32bd9ed45", "size": 43251, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Latex Notes/STAT 231/stat_231.tex", "max_stars_repo_name": "cc-shen/Handy-Tools", "max_stars_repo_head_hexsha": "1fa14867e5957e3e2ce78b8acb6976a7df140a12", "max_stars_repo_licenses": ["MIT"], "max_... |
import numpy as np
from math import sqrt, pow
from numba import njit, prange
@njit(fastmath=True)
def KineticEnergy(J, pA) -> float:
k = 0.0
# Outside of compute loop so prange can be used.
for j in prange(J):
v2 = pow(pA[j]['vx'], 2) + pow(pA[j]['vy'], 2)
k += 0.5 * pA[j]['m'] * ... | {"hexsha": "7f0bf16ff8dc41acfc1dd40d29c6ecfc995f9146", "size": 338, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/Equations/KineticEnergy.py", "max_stars_repo_name": "KoningJasper/Offshore-SPH", "max_stars_repo_head_hexsha": "558bb359249eb89b082322f7585e19df003281fb", "max_stars_repo_licenses": ["MIT"], "m... |
/**
* Copyright (c) 2019 Melown Technologies SE
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* * Redistributions of source code must retain the above copyright notice,
* this list of conditions and the f... | {"hexsha": "6e79a9e5474bb265d5238ea3d0e744a1f03a9eb1", "size": 3533, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "mapproxy/src/mapproxy/generator/tms-raster-base.hpp", "max_stars_repo_name": "melowntech/vts-mapproxy", "max_stars_repo_head_hexsha": "241ba43c1f7dcc226ec0f2089d47e11c699c2587", "max_stars_repo_lice... |
# Create figures showing donor site segments that make up the
# different sequences for a variable region.
# Currently specifically set up for choice V1 donor sites in SFig 5.
import os
import sys
import dna_features_viewer
import pandas as pd
import numpy as np
from dna_features_viewer import GraphicFeature, Graphic... | {"hexsha": "32af60bf7ffc55e761aa3137481bfb9397133f27", "size": 2698, "ext": "py", "lang": "Python", "max_stars_repo_path": "donor_sites/plot_donor_sites.py", "max_stars_repo_name": "greninger-lab/longitudinal_tprk", "max_stars_repo_head_hexsha": "769d93ec7feeb14a7640469266f3a4531c1b6d25", "max_stars_repo_licenses": ["M... |
"""
Damavand Volcano
~~~~~~~~~~~~~~~~
Visualize 3D models of Damavand Volcano, Alborz, Iran.
This is an adaption of `Alexey Pechnikov <https://orcid.org/0000-0001-9626-8615>`_ and `A.V.Durandin <https://orcid.org/0000-0001-6468-9757>`_'s `ParaView-MoshaFault <https://github.com/mobigroup/ParaView-MoshaFault>`_.
See ... | {"hexsha": "167e1c0a455e8c27330f3894358611bc5e8a6adc", "size": 3469, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyvista-examples/damavand.py", "max_stars_repo_name": "RichardScottOZ/banesullivan", "max_stars_repo_head_hexsha": "8b6a530fc7ea36a91f6aa6a5dc3d4d5557128d04", "max_stars_repo_licenses": ["MIT"], "... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import glob
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# In[50]:
df = pd.read_csv('... | {"hexsha": "ea4d6a5e3fe75ca3afebd92b6825574bfa7c8a68", "size": 5248, "ext": "py", "lang": "Python", "max_stars_repo_path": "DataBase/Neural_Networks/Resnet/resnet50.py", "max_stars_repo_name": "J0AZZ/chord-detection-challenge", "max_stars_repo_head_hexsha": "e0648d235ee0fbbf48d692911032aba7e4fedb31", "max_stars_repo_li... |
#!/usr/bin/env python
'''
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2 as cv
import math
bins = np.arange(256).reshape(256,1)
def hist_curve(im):
h = np.zeros((300,256,3))
if len(im.shape) == 2:
color = [(255,255,255)]
elif im.shape[2] == 3:... | {"hexsha": "11697342f81f1095ad633d38f90db236b182f495", "size": 3777, "ext": "py", "lang": "Python", "max_stars_repo_path": "samples/python/practice_2p1.py", "max_stars_repo_name": "jeroFlo/robotsVision_openCV", "max_stars_repo_head_hexsha": "cbf1bf440bcb6ad2e0fec9ed9d967e05c8e9d531", "max_stars_repo_licenses": ["Apache... |
-- This test ensures that implicits bound on the RHS of a
-- record update field are correctly bound by the compiler.
record Rec where
n : Nat
data T : Rec -> Type where
C : T ({ n := Z } r)
data U : Rec -> Type where
D : U ({ n $= S } r)
| {"hexsha": "8782a6cb2e0adcd8235c6604cdb5222c48021660", "size": 257, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "idris2/tests/idris2/record010/record.idr", "max_stars_repo_name": "chrrasmussen/Idris2-Erlang", "max_stars_repo_head_hexsha": "dfa38cd866fd683d4bdda49fc0bf2f860de273b4", "max_stars_repo_licenses": ... |
# coding:utf-8
import os
import sys
import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from catboost import CatBoostClassifier
from sklearn.utils import shuffle
from category_encoders import TargetEncoder
from sklearn.metrics import roc_auc_score
np.random.seed(7)
class CatBoo... | {"hexsha": "8c19a74f7bd5c0b5fcc39ed1c6694bd270efa739", "size": 5681, "ext": "py", "lang": "Python", "max_stars_repo_path": "20180617/CombineStackingAndCatBoostKfold/CatBoostKfold.py", "max_stars_repo_name": "fengjiaxin/Home_Credit_Default_Risk", "max_stars_repo_head_hexsha": "3407e76b4e5cfb8dd6056d24675b80fe0e82c123", ... |
import time
import edgeiq
import numpy
from sign_monitor import SignMonitor
"""
Simultaneously use object detection to detect human faces and classification to classify
the detected faces in terms of age groups, and output results to
shared output stream.
To change the computer vision models, follow this guide:
https:... | {"hexsha": "e4b45a0e1c053d50b747f99db4551fbe07b6ea49", "size": 4613, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "alwaysai/gesture-audio-control", "max_stars_repo_head_hexsha": "9c6450ce4abcb72e7b32b799d904b30ca24421d4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
import argparse
import bz2
import json
import os
import pickle
import random
import tempfile
import urllib.request
import pandas as pd
import glob
import pickle as pkl
import numpy as np
import boto3
import logging
from botocore.exceptions import ClientError
import xgboost
from sklearn import metrics
#from smdebug imp... | {"hexsha": "58bfc912578912e3647b92d204dac7680c2d45dd", "size": 13433, "ext": "py", "lang": "Python", "max_stars_repo_path": "container/train.py", "max_stars_repo_name": "tvkpz/ml-innovate-2021", "max_stars_repo_head_hexsha": "30ef7fed40ad70ad4e2a32d8843de0ed0e808a8a", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import numpy as np
import random
import tensorflow as tf
import matplotlib.pyplot as plt
import scipy.misc
import os
import csv
import itertools
import tensorflow.contrib.slim as slim
#imageio.plugins.ffmpeg.download()
# This is a simple function to reshape our game frames.
def processState(state1):
return np.resh... | {"hexsha": "18bad0dcc96be5a8e90f691f09d18743839f9390", "size": 8002, "ext": "py", "lang": "Python", "max_stars_repo_path": "helper2.py", "max_stars_repo_name": "Ohara124c41/DeepRL-AgentsDB", "max_stars_repo_head_hexsha": "5e5d1b1e983e1e5c1412e2c21227442050d3b555", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
%auto-ignore
%
\providecommand{\MainFolder}{..}
\documentclass[\MainFolder/Text.tex]{subfiles}
\begin{document}
\section{String topology and Chen's iterated integrals}
String topology of a manifold~$M$ is the study of the \emph{free loop space}
\[ \Loop M = \{\gamma: \Sph{1}\rightarrow M\text{ continuous}\}, \]
whic... | {"hexsha": "7467c53c5078c7874401c66d8f8b58a1c41e8b6f", "size": 55313, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Subfiles/ThesisIntroduction.tex", "max_stars_repo_name": "p135246/phd-thesis", "max_stars_repo_head_hexsha": "0e124466a3d0ff988c012225400fadb0b170aa9e", "max_stars_repo_licenses": ["CC-BY-4.0"], "m... |
\newpage
\chapter{Plug flow reactor}
\section{Introduction}
The plug flow reactor model of Camflow simulates a one dimensional plug flow reactor with gas-phase chemistry. The model can handle a number of temperature conditions such as isothermal, non-isothermal, or user defined temperature profiles.
\section{Fundame... | {"hexsha": "475ef8101dcfa1e2b311650b40468885105c3301", "size": 10335, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/supporting-information/camflow/plug.tex", "max_stars_repo_name": "sm453/MOpS", "max_stars_repo_head_hexsha": "f1a706c6552bbdf3ceab504121a02391a1b51ede", "max_stars_repo_licenses": ["MIT"], "max... |
import os
import bz2
import pickle
import numpy as np
from autodp.reader.base_reader import BaseReader
from autodp import cf
@BaseReader.register
class SQReader(BaseReader):
"""This class implements a data reader that will read a file of data sequentially without shuffling."""
def __init__(self, path, num_e... | {"hexsha": "c362440b37e2b2a9ff010e6d6b31521d0a3f1127", "size": 1391, "ext": "py", "lang": "Python", "max_stars_repo_path": "autodp/reader/sq_reader.py", "max_stars_repo_name": "IBM/automation-of-image-data-preprocessing", "max_stars_repo_head_hexsha": "a5327b1b6da3f5fc92dae4dfeb235c5f24378589", "max_stars_repo_licenses... |
# My Algorithm: draw ROI plots with boundary pts and check if inside or outside, based on this to give sliding windows with details
#
import xml.etree.ElementTree as ET
import fnmatch
import matplotlib.pyplot as plt
import numpy as np
import math
import os
#rootDir = '/Users/yanzhexu/Desktop/Research/GBM/aCGH_whole_t... | {"hexsha": "16c5d9d1f42e97284bf1c176f298b87c0d9a8b9f", "size": 10671, "ext": "py", "lang": "Python", "max_stars_repo_path": "GBM/GBMSlidingwindows_V2/GBMSlidingWindows_QualityControl/Boundarycheck.py", "max_stars_repo_name": "joshlyman/TextureAnalysis", "max_stars_repo_head_hexsha": "bfbedbd53f62396fdef383408089b37e5ab... |
"""
Benchmark an implementation of the Black–Scholes model.
"""
import math
import numpy as np
# Taken from numba.tests.test_blackscholes
# XXX this data should be shared with bench_cuda.py
# (see https://github.com/spacetelescope/asv/issues/129)
N = 16384
RISKFREE = 0.02
VOLATILITY = 0.30
A1 = 0.31938153
A2 = ... | {"hexsha": "bfc807bf8b4539cafd6f9d4a2648ae9504e4d886", "size": 2041, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmarks/bench_blackscholes.py", "max_stars_repo_name": "abitrolly/numba-benchmark", "max_stars_repo_head_hexsha": "4bea9c23276fd0399df26452d19f13810a6496c7", "max_stars_repo_licenses": ["BSD-2-... |
\PassOptionsToPackage{unicode=true}{hyperref} % options for packages loaded elsewhere
\PassOptionsToPackage{hyphens}{url}
%
\documentclass[]{article}
\usepackage{stata}
\usepackage{lmodern}
\usepackage{amssymb,amsmath}
\usepackage{ifxetex,ifluatex}
\usepackage{fixltx2e} % provides \textsubscript
\ifnum 0\ifxetex 1\fi\i... | {"hexsha": "3b3bfa3c4429161b0643284ed92fa67b84cb36b3", "size": 8485, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "stata-dynamic/stata-markdown-example.tex", "max_stars_repo_name": "cdsamii/cds-demos", "max_stars_repo_head_hexsha": "422fe62caeb961dac6b24efb3443d9475cea9318", "max_stars_repo_licenses": ["MIT"], "... |
[STATEMENT]
lemma tr_tfr:
assumes "A' \<in> set (tr A [])" and "tfr\<^sub>s\<^sub>s\<^sub>t A" and "fv\<^sub>s\<^sub>s\<^sub>t A \<inter> bvars\<^sub>s\<^sub>s\<^sub>t A = {}"
shows "tfr\<^sub>s\<^sub>t A'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. tfr\<^sub>s\<^sub>t A'
[PROOF STEP]
proof -
[PROOF STATE]
p... | {"llama_tokens": 5228, "file": "Stateful_Protocol_Composition_and_Typing_Stateful_Typing", "length": 34} |
[STATEMENT]
lemma GreatestIB:
fixes n :: \<open>nat\<close> and P
assumes a:\<open>\<exists>k\<le>n. P k\<close>
shows GreatestBI: \<open>P (GREATEST k. k\<le>n \<and> P k)\<close> and GreatestB: \<open>(GREATEST k. k\<le>n \<and> P k) \<le> n\<close>
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. P (GREATEST k. k \... | {"llama_tokens": 839, "file": "IFC_Tracking_IFC", "length": 8} |
% !TeX spellcheck = en_GB
\section{Simulations Experiments}
\subsection{Study on Response Time Limits}
Before choosing the extremes of the \textbf{mean inter-arrival time} factor, a study on the \textbf{mean response time}, by changing the latter, has been carried out by comparing limit values of other factors (all the... | {"hexsha": "50b2fe50dfbd18cf46b62c13453247599ea0eb9b", "size": 24781, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/chapters/simulation_experiments.tex", "max_stars_repo_name": "gerti98/PerformanceEvaluationGroupProject", "max_stars_repo_head_hexsha": "055c30da1352aa22c128456bc2407c6a7619d4b5", "max_stars_re... |
[STATEMENT]
lemma set_takeWhileD: "x \<in> set (takeWhile P xs) \<Longrightarrow> x \<in> set xs \<and> P x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> set (takeWhile P xs) \<Longrightarrow> x \<in> set xs \<and> P x
[PROOF STEP]
by (induct xs) (auto split: if_split_asm) | {"llama_tokens": 116, "file": null, "length": 1} |
// Copyright Oleg Maximenko 2014.
// 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://github.com/svgpp/svgpp for library home page.
#pragma once
#include <svgpp/definitions.hpp>
#include <boost/mpl... | {"hexsha": "d26e4b635083dc31eece02ed8c04e98b12217bc0", "size": 1480, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/svgpp/traits/overflow_hidden_elements.hpp", "max_stars_repo_name": "RichardCory/svgpp", "max_stars_repo_head_hexsha": "801e0142c61c88cf2898da157fb96dc04af1b8b0", "max_stars_repo_licenses": [... |
import numpy as np
import fns
from . import PLSRregressionMethods
from . import PLSRsave
import tkinter
import copy
import sklearn.model_selection
import types
from . import PLSRclassifiers
def get_buttons():
buttons=[
{'key': 'RNNtab2name', 'type': 'tabname', 'text': 'Wavelength Selection', 'tab': 2} ,
{'key': '... | {"hexsha": "7c3033396dee72ca48175006b61899349e0822a0", "size": 8090, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/libs/PLSRwavelengthSelection.py", "max_stars_repo_name": "jernelv/SpecAnalysis", "max_stars_repo_head_hexsha": "175875ea14f200ecd5de8eaa5b228c32c6621e46", "max_stars_repo_licenses": ["MIT"... |
import tensorflow as tf
import numpy as np
from data import *
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
# set up placeholders
ph_s1_x = tf.placeholder(t... | {"hexsha": "08ec9e71222ba8318e3b1f376d1e4310a7446cca", "size": 8525, "ext": "py", "lang": "Python", "max_stars_repo_path": "battleship_lstm/model.py", "max_stars_repo_name": "evanthebouncy/nnhmm", "max_stars_repo_head_hexsha": "acd76edaa1b3aa0c03d39f6a30e60d167359c6ad", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import os
import numpy as np
from enn_zoo.griddly import create_env
from entity_gym.environment import CategoricalActionSpace, DenseCategoricalActionMask
init_path = os.path.dirname(os.path.realpath(__file__))
def test_griddly_wrapper() -> None:
env_class = create_env(os.path.join(init_path, "env_descriptions/t... | {"hexsha": "b2811359567f3849ca0739f160fa780a2038a937", "size": 2751, "ext": "py", "lang": "Python", "max_stars_repo_path": "enn_zoo/enn_zoo/griddly/test_griddly_env.py", "max_stars_repo_name": "batu/incubator", "max_stars_repo_head_hexsha": "11f0f60de24102af4356c9738cbb9793ea6aa334", "max_stars_repo_licenses": ["Apache... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.