text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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#
# simulate phenotypes from a LMM
#
using Revise
using MendelIHT
using SnpArrays
using Random
using GLM
using DelimitedFiles
using Distributions
using LinearAlgebra
using CSV
using DataFrames
using StatsBase
using TraitSimulation
using Knockoffs
BLAS.set_num_threads(1)
"""
βi ~ Uniform([-0.5, -0.45, ..., -0.05, 0... | {"hexsha": "387cae14f0e3d0c7dcab727332e4fcb8a961da98", "size": 14480, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/simulate.jl", "max_stars_repo_name": "biona001/Knockoffs.jl", "max_stars_repo_head_hexsha": "8c2d1de79e85704dd5d670e11341bbbe784986ad", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
subroutine perfc
!
! to obtain the inviscid contour of the nozzle
!
use kinddefine
use gg, only:gam,g1,g2,g4,g5,g6,g7,g8,ga,qt
use cline, only:axis,taxi,frip,zonk,seo,cse
use coord, only:s,fs,waltan,sd,wmn,ttr,dmdx,spr,dpx,secd,xbin,xcin
&,gma,gmb,gmc,gmd
use work, ... | {"hexsha": "c0e07885ad75bc2ccd20ad74ad27d5d86b82ee3a", "size": 19424, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/perfc.f", "max_stars_repo_name": "aldorona/contur", "max_stars_repo_head_hexsha": "d4197b55e28b20f905f9418f0473b2c39fadb0fd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_st... |
variables P Q R S T U : Prop
/--------------------------------------------------------------------------
``exact``
If ``P`` is the current target and
``hp`` is a proof of ``P``, then
``exact hp,`` closes the goal.
English translation:
This is exactly what we wanted to prove.
----------------------------------... | {"author": "apurvanakade", "repo": "uwo2021-CUMC", "sha": "0be9402011feda35e510725449686c0af3c3761e", "save_path": "github-repos/lean/apurvanakade-uwo2021-CUMC", "path": "github-repos/lean/apurvanakade-uwo2021-CUMC/uwo2021-CUMC-0be9402011feda35e510725449686c0af3c3761e/src/implies.lean"} |
module ModuleDoesntExport where
module A where
postulate C : Set
open A using (B; module P) renaming (D to C)
| {"hexsha": "0cc3aa5118fa01f6e1cecc78bd4ad00102d9af39", "size": 115, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "test/Succeed/ModuleDoesntExport.agda", "max_stars_repo_name": "cruhland/agda", "max_stars_repo_head_hexsha": "7f58030124fa99dfbf8db376659416f3ad8384de", "max_stars_repo_licenses": ["MIT"], "max_sta... |
import pytest
import os
import skimage.io
import glob
import numpy as np
from pathlib import Path
import zdo2021.main
import skimage.measure
from skimage.draw import polygon
# cd ZDO2021
# python -m pytest
def test_run_random():
vdd = zdo2021.main.VarroaDetector()
# Nastavte si v operačním systém proměnnou p... | {"hexsha": "049803bb238a6a5bc7c7ed739c02efadd6906bca", "size": 4100, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_zdo2021.py", "max_stars_repo_name": "zeleznyt/ZDO2021_StZe", "max_stars_repo_head_hexsha": "ed93f68c13a43eba90b9933f5b0bea689dc9f8eb", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import bz2
import os.path
import re
import numpy as np
from torch.utils.data import DataLoader
from torchtext.data import get_tokenizer
# from torchnlp.encoders.text import StaticTokenizerEncoder, stack_and_pad_tensors, pad_tensor
##############################
# read raw gros bizou2 #
#######################... | {"hexsha": "f8151d83b2f4e477f0c58774629e1c67119d4640", "size": 2949, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/data/load_bz2_nlpdata.py", "max_stars_repo_name": "samytessier/samy_mlops", "max_stars_repo_head_hexsha": "f52592d3b63d8fc11d0ea6cd2f51c80c4858ef4f", "max_stars_repo_licenses": ["MIT"], "max_s... |
# Test methods with long descriptive names can omit docstrings
# pylint: disable=missing-docstring
import unittest
import pickle
import numpy as np
from Orange.preprocess import Continuize, Normalize
from Orange.projection import PCA, SparsePCA, IncrementalPCA, TruncatedSVD
from Orange.data import Table
class TestP... | {"hexsha": "a8db0e9fdb1eadfc7ede8934bfebeb44383daae8", "size": 7356, "ext": "py", "lang": "Python", "max_stars_repo_path": "orange3/Orange/tests/test_pca.py", "max_stars_repo_name": "rgschmitz1/BioDepot-workflow-builder", "max_stars_repo_head_hexsha": "f74d904eeaf91ec52ec9b703d9fb38e9064e5a66", "max_stars_repo_licenses... |
"""
This file tests the `generate_frames` method.
"""
import sys
sys.path.insert(0, '../')
import audiosegment
import math
import numpy as np
import unittest
class TestGenerateFrames(unittest.TestCase):
"""
Test the generate_frames_* methods.
"""
def test_reconstruction_mono(self):
"""
... | {"hexsha": "f4e63eaeddf8453a858962912a0f84957c6c06e9", "size": 2549, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/frames.py", "max_stars_repo_name": "MaxStrange/AudioSegment", "max_stars_repo_head_hexsha": "9e54dd575b879711021e1536b66d2b4f48965d0a", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
"""
Created on Dec 14 2020
@author: Yi-Hui (Sophia) Chou
Updated on May 10 2021
@author: I-Chun (Bronwin) Chen
"""
import sys
sys.path.append('../../CP')
import os
import json
import time
import tqdm
import torch
import pickle
import argparse
import numpy as np
import torch... | {"hexsha": "bc60b7ed64018978354989ed1f5b0ba0a2a82e47", "size": 7239, "ext": "py", "lang": "Python", "max_stars_repo_path": "baseline/CP/sequence-level/main.py", "max_stars_repo_name": "atosystem/MIDI-BERT", "max_stars_repo_head_hexsha": "61f7efb3be85a2a847e6585237036e052235a6a0", "max_stars_repo_licenses": ["MIT"], "ma... |
"""Implementation of the series A279125 from OEIS.
The entry a(n) is decided by checking if n's binary value has any overlapping
with previous i=1, 2, 3, ..., n-1, also in binary. I.e., if n's binary value has
ones in places where any of the i's have ones, a(n) is the lowest integer that
has not yet been picked, i.e. ... | {"hexsha": "a22caf8ad6cfd9b7e985683bbcd2d2b6e56a9edc", "size": 4713, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/maths_snack/scripts/snowy.py", "max_stars_repo_name": "engeir/maths_snack", "max_stars_repo_head_hexsha": "1b35c7dfc3b5ac3d105226db2be9ce6434da0a11", "max_stars_repo_licenses": ["MIT"], "max_s... |
[STATEMENT]
lemma noDA[rule_format]:
"noDenyAll xs \<longrightarrow> s \<in> set xs \<longrightarrow> \<not> member DenyAll s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. noDenyAll xs \<longrightarrow> s \<in> set xs \<longrightarrow> \<not> member DenyAll s
[PROOF STEP]
by (induct xs, simp_all) | {"llama_tokens": 110, "file": "UPF_Firewall_FWNormalisation_NormalisationGenericProofs", "length": 1} |
# Copyright 2012 Mehmet Ali ANIL
#
# 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 applicabl... | {"hexsha": "f26235ec5f3d3bd394cc4ea7cabaf60bec4a5992", "size": 1273, "ext": "py", "lang": "Python", "max_stars_repo_path": "kreveik/network/boolfuncs/__init__.py", "max_stars_repo_name": "kreveik/Kreveik", "max_stars_repo_head_hexsha": "5144c555c526f68560d891e39c401053c5286359", "max_stars_repo_licenses": ["Apache-2.0"... |
[STATEMENT]
lemma (in vfsequence) vfsequence_vcons[intro, simp]: "vfsequence (xs #\<^sub>\<circ> x)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. vfsequence (xs #\<^sub>\<circ> x)
[PROOF STEP]
proof(intro vfsequenceI)
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. vsv (xs #\<^sub>\<circ> x)
2. \<D>\<^sub>\<ci... | {"llama_tokens": 5299, "file": "CZH_Foundations_czh_sets_CZH_Sets_FSequences", "length": 57} |
# coding=utf-8
# fft & low-pass filtering
import os
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
data_dir = '/home/murphyhuang/dev/mldata/en_ch_translate_output_ut_analy/recurret_conduct'
def frequency_analy():
record_storing_path = os.path.join(data_dir, 'ut_0509_recurrent_1024.np... | {"hexsha": "4c6199258a61887768005e956e5ce64ddea5424d", "size": 1854, "ext": "py", "lang": "Python", "max_stars_repo_path": "usr_util/recurrent_dynamic_analy.py", "max_stars_repo_name": "EstelleHuang666/tensor2tensor", "max_stars_repo_head_hexsha": "7cebe23da824e87ff2fe37cb5e506f3afaca799b", "max_stars_repo_licenses": [... |
import sys
sys.path.append('/home/zankov/dev/miqsar')
import os
import pickle
import joblib
import pkg_resources
import numpy as np
import pandas as pd
from itertools import groupby
from sklearn.pipeline import Pipeline
from CGRtools import RDFRead, RDFWrite
from CIMtools.preprocessing import Fragmentor, CGR, Equation... | {"hexsha": "556f91c97cea33c54aa6ef98cd9da8ef580fe901", "size": 3887, "ext": "py", "lang": "Python", "max_stars_repo_path": "miqssr/utils.py", "max_stars_repo_name": "dzankov/3D-MIL-QSSR", "max_stars_repo_head_hexsha": "a66dd78412188d43843cb253736af63f9318d8c8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
%!TEX root = ../dissertation_vkslm.tex
\chapter{Handwritten Signature Verification} \label{ch:sig}
In this chapter, we give a brief reference to some essential concepts related to Handwritten Signature Verification, including definitions of notation and terminology used in the following
chapters. First, we give an int... | {"hexsha": "86b146553e08848cd7e4039c36d0cfe95ffc1a64", "size": 15269, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "conteudo/ch3.tex", "max_stars_repo_name": "victormelo/dissertation", "max_stars_repo_head_hexsha": "942bd6e57796d760e152dbfcc31745950dc3fd32", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
!
! CalculiX - A 3-dimensional finite element program
! Copyright (C) 1998-2020 Guido Dhondt
!
! This program is free software; you can redistribute it and/or
! modify it under the terms of the GNU General Public License as
! published by the Free Software Foundation(version 2);
!
!
! ... | {"hexsha": "ddd733a4afc3f007e81fc818d0144ef0d929adb6", "size": 2859, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ccx_prool/CalculiX/ccx_2.17/src/calcspringforc.f", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars_repo_... |
# -*- coding: utf-8 -*-
"""
The following module provides the framework for setting parameter objects -
both parameters to be used in the model itself, as well as references to
evidence that can subsequently be used to determine these parameter
distributions.
Created on Thu Nov 12 13:45:23 2015
@author: JTrauer
"""
i... | {"hexsha": "ed1d9a8a64b69fc2cfeb2708c0bf02c5681a23b2", "size": 17846, "ext": "py", "lang": "Python", "max_stars_repo_path": "autumn/settings/parameter.py", "max_stars_repo_name": "monash-emu/Legacy-AuTuMN", "max_stars_repo_head_hexsha": "513bc14b4ea8c29c5983cc90fb94284e6a003515", "max_stars_repo_licenses": ["BSD-2-Clau... |
# -*- coding: utf-8 -*-
"""Functionality built on top of LocaleDB data."""
import numpy as np
import psycopg2
import psycopg2.extras
import sys
from numpy import linalg
__all__ = ['LocaleDB']
# ----------------------------------------------------------------------------------------------------------------------
c... | {"hexsha": "8cd2b26ef62005a171f4d27e4e42241af3fe8512", "size": 17516, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/localedb/localedb.py", "max_stars_repo_name": "scotthaleen/localedb", "max_stars_repo_head_hexsha": "328102eaea717db63437acf6049a5df012b76cdb", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import os
import json
from pkg_resources import resource_filename
import numpy as np
from astrometry.util.fits import fits_table
from mappings import petal_id_to_gfa_num
class PetalMetrology(object):
def __init__(self, fids, gfa_trans):
self.fids = fids
I = np.flatnonzero(fids.gif_num == 1)
... | {"hexsha": "6da73accb4e60c4568cd6ba3b8139e4f054f6760", "size": 4559, "ext": "py", "lang": "Python", "max_stars_repo_path": "petal_metrology.py", "max_stars_repo_name": "dstndstn/desi-commish", "max_stars_repo_head_hexsha": "71d95c0e20a1a730dbd75bdd1731c9baace6a0ed", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
module CUTENSOR
using ..APIUtils
using ..CUDA
using ..CUDA: CUstream, cudaDataType
using ..CUDA: libcutensor, @retry_reclaim
using CEnum: @cenum
const cudaDataType_t = cudaDataType
# core library
include("libcutensor_common.jl")
include("error.jl")
include("libcutensor.jl")
# low-level wrappers
include("tensor.... | {"hexsha": "1b92ab717a684932f320c2927a5e58f65833f7d4", "size": 1405, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "lib/cutensor/CUTENSOR.jl", "max_stars_repo_name": "eschnett/CUDA.jl", "max_stars_repo_head_hexsha": "717a0d55cdbe80cd1d135cf8710cb1263cf8829d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""Define trials for the experiment."""
import numpy as np
def gen_trial(rng, nsamples):
"""Generate trials for a participant.
A trial consists out of `nsamples` samples, which are
digits between 1 and 9 that are of two colors - here
indicated by the sign (negative or positive). Each trial
contai... | {"hexsha": "65ad98adcb0657be0cc077db4fd552860a6a26f6", "size": 6798, "ext": "py", "lang": "Python", "max_stars_repo_path": "ecomp_experiment/define_trials.py", "max_stars_repo_name": "sappelhoff/ecomp_experiment", "max_stars_repo_head_hexsha": "6f5ae81d6fd1fe55b876d84badc0f5bccd8ced03", "max_stars_repo_licenses": ["MIT... |
import glob
import os
import mmcv
import numpy as np
from tqdm import tqdm
from mmhuman3d.core.conventions.keypoints_mapping import convert_kps
from mmhuman3d.data.data_structures.human_data import HumanData
from .base_converter import BaseModeConverter
from .builder import DATA_CONVERTERS
@DATA_CONVERTERS.register... | {"hexsha": "56a3d6e725197ec22b9abcb00d4cb7c9fe7eb250", "size": 3514, "ext": "py", "lang": "Python", "max_stars_repo_path": "mmhuman3d/data/data_converters/posetrack.py", "max_stars_repo_name": "ttxskk/mmhuman3d", "max_stars_repo_head_hexsha": "f6d39e24a2d5cc216448fc3bd82832ff45eee436", "max_stars_repo_licenses": ["Apac... |
\documentclass[12pt]{article}
%\usepackage{fullpage}
%\usepackage[top=1in, bottom=1in, left=1in, left=1in, right=1in]{geometry}
\usepackage[margin=1in, paperwidth=8.5in, paperheight=11in]{geometry}
\usepackage{graphicx}
\usepackage{subcaption}
\usepackage{listings}
\usepackage{color}
\definecolor{dkgreen}{rgb}{0,0.6,... | {"hexsha": "2c0ff4c8a7a3529938fdcbc4b777eb56c7d3305d", "size": 5890, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "HW2/P5/Summary/details.tex", "max_stars_repo_name": "katebsaber96/UT-Machine-Learning-2019", "max_stars_repo_head_hexsha": "e6330266a7927c00024f2d1c862bfe52d2656ff4", "max_stars_repo_licenses": ["MI... |
# Copyright (c) 2012-2014, Max Zwiessele
# Licensed under the BSD 3-clause license (see LICENSE.txt)
class StochasticStorage(object):
'''
This is a container for holding the stochastic parameters,
such as subset indices or step length and so on.
'''
def __init__(self, model):
"""
In... | {"hexsha": "dc71d53934dc41daa27b12ee1115155fc44475e3", "size": 1685, "ext": "py", "lang": "Python", "max_stars_repo_path": "GPy/inference/optimization/stochastics.py", "max_stars_repo_name": "strongh/GPy", "max_stars_repo_head_hexsha": "775ce9e64c1e8f472083b8f2430134047d97b2fa", "max_stars_repo_licenses": ["BSD-3-Claus... |
[STATEMENT]
lemma steps_z_beta_complete':
"A \<turnstile> \<langle>l, Z\<rangle> \<leadsto>* \<langle>l',Z'\<rangle> \<Longrightarrow> valid_abstraction A X k \<Longrightarrow> Z \<subseteq> V \<Longrightarrow> Z' \<noteq> {}
\<Longrightarrow> \<exists> Z''. A \<turnstile> \<langle>l, Z\<rangle> \<leadsto>\<^sub>\<... | {"llama_tokens": 597, "file": "Timed_Automata_Approx_Beta", "length": 2} |
[STATEMENT]
lemma fwi_len:
"\<exists> ys. set ys \<subseteq> set xs \<union> {k} \<and> len (fwi m n k n n) i j xs = len m i j ys"
if "i \<le> n" "j \<le> n" "k \<le> n" "m k k \<ge> 0" "set xs \<subseteq> {0..n}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>ys. set ys \<subseteq> set xs \<union> {k} ... | {"llama_tokens": 4938, "file": "Floyd_Warshall_Floyd_Warshall", "length": 23} |
import numpy as np
import tensorflow as tf
from baselines.a2c.utils import conv, conv_without_bias, fc, conv_to_fc, batch_to_seq, seq_to_batch, lstm, lnlstm, mse, cat_entropy
from baselines.common.distributions import make_pdtype
def nature_cnn_h3(unscaled_images, first_layer_mode='', trainable=True, conv1_fn=lambda x... | {"hexsha": "3f3b13026aef3feda3e82d72e19e0bdbc3b58e92", "size": 26321, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/a2c/policies.py", "max_stars_repo_name": "vik-goel/MOREL", "max_stars_repo_head_hexsha": "55c8bb81b25de7c2dfba451db61564c352cdb5e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
r"""
Check for pynormaliz
"""
from . import PythonModule
from .join_feature import JoinFeature
class PyNormaliz(JoinFeature):
r"""
A :class:`sage.features.Feature` describing the presence of the
Python package ``PyNormaliz``.
EXAMPLES::
sage: from sage.features.normaliz import PyNormaliz
... | {"hexsha": "ceb67875b018e5ca24caf27e80eb22891fff91c2", "size": 783, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/features/normaliz.py", "max_stars_repo_name": "kliem/sage-test-27122", "max_stars_repo_head_hexsha": "cc60cfebc4576fed8b01f0fc487271bdee3cefed", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
program prog
integer vals
dimension vals(2)
dimension abc(4)
write(6, *) vals(1)
write(6, *) abc(3)
end
| {"hexsha": "1365fe5d9eb4a6db83e9fb6885c0ac85817feb77", "size": 146, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "fable/test/valid/combine_decl_1.f", "max_stars_repo_name": "rimmartin/cctbx_project", "max_stars_repo_head_hexsha": "644090f9432d9afc22cfb542fc3ab78ca8e15e5d", "max_stars_repo_licenses": ["BSD-3-Cl... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import random
import jams
import numpy as np
import copy
import datetime
import pandas as pd
import soundfile as psf
import librosa.util
import os
from .utils import find_files_in_dirs
from .utils import read_audio
PERC_VOICE_SET = ['kicks',
'snares',
... | {"hexsha": "a56456ef43f4db949cea4c69b26353fada3ce8a5", "size": 19148, "ext": "py", "lang": "Python", "max_stars_repo_path": "large_vocab_adt_dafx2018/percussion_synth.py", "max_stars_repo_name": "mcartwright/dafx2018_adt", "max_stars_repo_head_hexsha": "057ac6b1e39cd0c80554d52535cc9d88b6316c74", "max_stars_repo_license... |
struct Bingo
table::Matrix{String}
end
Bingo() = Bingo([cast() for _ in 1:5, _ in 1:5])
string(rand('a':'z', 10)...)
Base.setindex!(b::Bingo, v::String, inds...) = b.table[inds...] = v
Base.getindex(b::Bingo, inds...) = b.table[inds...]
Base.lastindex(b::Bingo) = lastindex(b.table)
save(b::Bingo) = save("bingo.p... | {"hexsha": "9281e086a41a193a5d19f0f50baaa5e2939acd02", "size": 2079, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/bingo.jl", "max_stars_repo_name": "Roger-luo/PinGo.jl", "max_stars_repo_head_hexsha": "a382ac8c9856614f5f3e3af5293582d0cd438ca7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_... |
From MatchingLogic Require Export Syntax
IndexManipulation
Semantics
ProofSystem
StringSignature
wftactics
DerivedOp... | {"author": "harp-project", "repo": "AML-Formalization", "sha": "ee6fd737632e1bb2737b22cbbbca3b8a3e68f89d", "save_path": "github-repos/coq/harp-project-AML-Formalization", "path": "github-repos/coq/harp-project-AML-Formalization/AML-Formalization-ee6fd737632e1bb2737b22cbbbca3b8a3e68f89d/matching-logic/src/Logic.v"} |
# Copyright (c) 2020, NVIDIA CORPORATION.
import itertools
import warnings
import numpy as np
import pandas as pd
import cudf
from cudf import _lib as libcudf
from cudf._lib.join import compute_result_col_names
from cudf.core.dtypes import CategoricalDtype
class Merge(object):
def __init__(
self,
... | {"hexsha": "0aadcf875cb647b8e11674403fb9c7759ffa1b41", "size": 19468, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cudf/cudf/core/join/join.py", "max_stars_repo_name": "isVoid/cudf", "max_stars_repo_head_hexsha": "1a3b3f217be93a55b47af3a9d0da29f0fcb7c7e9", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
function nlp3(oct::Bool = false)
m = JuMP.Model()
@variable(m, 0 <= x[1:10])
for i = 1:8
JuMP.set_lower_bound(x[[1, 2, 4, 6, 7, 8, 9, 10][i]],
[1,1,1,85,90,3,1.2,145][i])
end
for i = 1:10
JuMP.set_upper_bound(x[i], [2000, 16000, 120, 5000, 2000,
... | {"hexsha": "973a558ad1b7f87491d956b5ccc06958af7f3052", "size": 2470, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "data/baron/nlp3.jl", "max_stars_repo_name": "1ozturkbe/OCTHaGOn.jl", "max_stars_repo_head_hexsha": "222a73c8da834c8e4114b6f29492d8ab917f6722", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
module Subst
-- Substitution inside untyped lambda calculus terms.
import Term
%default total
%access public export
shift : (cutoff : Nat) -> (distance : Nat) ->
Nat -> Nat
shift Z distance k = distance+k
shift (S c) distance Z = Z
shift (S c) distance (S k) = S $ shift c distance k
... | {"hexsha": "9bfba2eb8cfef41af74ce7466dee3bcc6d85f0c6", "size": 2294, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "totality/src/Subst.idr", "max_stars_repo_name": "normanrink/PCF", "max_stars_repo_head_hexsha": "6b817263fc7d64f0ed0e5535261814d572292192", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
"""Utilities for training a GP fed from the MEGNet Concatenation layer for a pretrained model."""
from pathlib import Path
from typing import Dict, Iterator, List, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
import tensorflow.python.util.deprecation as deprecation
import tensorflow_probability as... | {"hexsha": "5ef231fca4cf3dd232cd2b514d15c15f0f6344eb", "size": 11965, "ext": "py", "lang": "Python", "max_stars_repo_path": "unlockgnn/gp/gp_trainer.py", "max_stars_repo_name": "CalvinCYY/unlockGNN", "max_stars_repo_head_hexsha": "f620f12f2ba823b64895e6c7a6d435320223eb06", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
import matplotlib.pyplot as plt
def get_data(num_x, num_k, x_mean, y_mean, sigma):
num = int(num_k * num_x)
X = np.zeros(num)
Y = np.zeros(num)
for i in range(num_k):
for j in range(num_x):
X[i * num_x + j] = np.random.normal(x_mean[i], sigma[i])
Y[i *... | {"hexsha": "952d360bde73de606c9e272689888d02b0c3380b", "size": 866, "ext": "py", "lang": "Python", "max_stars_repo_path": "GMM/makedata.py", "max_stars_repo_name": "MartrixG/machine-learning", "max_stars_repo_head_hexsha": "10f1b4aa2c723023a0b6d5c7904654b13f1bdaa8", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
% Codes for CVPR-15 work `Face Alignment by Coarse-to-Fine Shape Searching'
% Any question please contact Shizhan Zhu: zhshzhutah2@gmail.com
% Released on July 25, 2015
function T = getTransViaRotateGivenCenter(theta_vector,center,rotatorLength)
%T = getTransViaRotateGivenCenter(theta_vector,win_size)
% T: m*1 t_con... | {"author": "zhusz", "repo": "CVPR15-CFSS", "sha": "11b8d0b28a4a3e954741a4dae2f114df7b644d4e", "save_path": "github-repos/MATLAB/zhusz-CVPR15-CFSS", "path": "github-repos/MATLAB/zhusz-CVPR15-CFSS/CVPR15-CFSS-11b8d0b28a4a3e954741a4dae2f114df7b644d4e/codes_release/trans/getTransViaRotateGivenCenter.m"} |
// ==========================================================================
// SeqAn - The Library for Sequence Analysis
// ==========================================================================
//
// Copyright (c) 2006-2018, Knut Reinert, FU Berlin
// Copyright (c) 2016-2018, Knut Reinert & MPI M... | {"hexsha": "b33f3ed16a2a19b01c743824acbd9048bd714811", "size": 6429, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/performance/std/charconv_from_chars_benchmark.cpp", "max_stars_repo_name": "FirstLoveLife/seqan3", "max_stars_repo_head_hexsha": "ac2e983e0a576515c13ebb2c851c43c1eba1ece1", "max_stars_repo_lice... |
import os
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import confusion_matrix
from sklearn.pipeline import Pipeline
from sklearn.model_selection imp... | {"hexsha": "3622fed4029894a0a8877cb4100297b0f91cae85", "size": 2782, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/soc_classifier.py", "max_stars_repo_name": "caseyhartnett/SOC_classifier", "max_stars_repo_head_hexsha": "cc293dabf35d58c1f62ba04f163c9b199483dc77", "max_stars_repo_licenses": ["MIT"], "max_st... |
[STATEMENT]
lemma lunstream_simps:
"g s = Done \<Longrightarrow> lunstream s = LNil"
"g s = Skip s' \<Longrightarrow> lunstream s = lunstream s'"
"g s = Yield x s' \<Longrightarrow> lunstream s = LCons x (lunstream s')"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (g s = Done \<Longrightarrow> local.lunstrea... | {"llama_tokens": 201, "file": "Stream_Fusion_Code_Stream_Fusion_LList", "length": 1} |
!* Copyright (c) 2018, NVIDIA CORPORATION. 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... | {"hexsha": "1b2e9138e705bb96b38f9beb8d0aedd01501f673", "size": 958, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "test/mp_correct/src/src/nv2041349.f90", "max_stars_repo_name": "kammerdienerb/flang", "max_stars_repo_head_hexsha": "8cc4a02b94713750f09fe6b756d33daced0b4a74", "max_stars_repo_licenses": ["Apache... |
from base.base_predictor import BasePredictor
import os
import numpy as np
class WavClassifyPredictor(BasePredictor):
def __init__(self, model, data, config):
super(WavClassifyPredictor, self).__init__(model, data, config)
def predict(self):
class_list = ['baby cry', 'siren', 'etc']
s... | {"hexsha": "a5fa182ba1afcbd579273178c8cc196632666ac0", "size": 757, "ext": "py", "lang": "Python", "max_stars_repo_path": "predictors/wav_classify_predictor.py", "max_stars_repo_name": "dizwe/VibrationFromWarningForHearingImpaired_ML", "max_stars_repo_head_hexsha": "d585fbf63fb47d27f35ca4025fdb952e4bd50574", "max_stars... |
from bs4 import BeautifulSoup as BS
from selenium import webdriver
from functools import reduce
import pandas as pd
import time
import matplotlib.pyplot as plt
from selenium.webdriver.firefox.options import Options
import numpy as np
options = Options()
options.add_argument('--headless')
def render_page(url):
... | {"hexsha": "8065f94061a3ad7bdb27fd35494de9ff83f61655", "size": 6293, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/WScrape.py", "max_stars_repo_name": "sanjin94/HTool-dev", "max_stars_repo_head_hexsha": "b4a13d2af82c12ae21337ac2313d8e732891a7cd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
import os
import argparse
import scipy.misc
import scipy
import torch
import torchvision
import numpy as np
import _pickle as cp
import os.path as osp
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data
import torch.multiprocessing as mp
import torch.backends.cudnn as cudnn
import torch.... | {"hexsha": "6eab01d2030e03b06d77bffa3f8dee4f9c2fd055", "size": 6431, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "Chuhanxx/FontAdaptor", "max_stars_repo_head_hexsha": "dd086ce705216a92babd03d9113d5392bd992a4c", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": 116... |
import pandas as pd # 데이터프레임
import numpy as np # 행렬처리
from tkinter import filedialog
from tkinter import messagebox
import tkinter as tk
import tkinter.ttk as ttk
from winreg import *
import os
def central_box(root):
# Gets the requested values of the height and widht.
windowWidth = root.winfo_reqwidth()
... | {"hexsha": "fd045bb4d81fe19b5ce54322ac92baced1b1845b", "size": 13181, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "DataNetworkAnalysis/QuotaSampling", "max_stars_repo_head_hexsha": "7661ac1040d3af39530f067ab2c27e54f79f49db", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
## ObjectiveFunc.py -- Perform Gradient Estimation and Evaluation for a Given Function
##
## Copyright (C) 2018, IBM Corp
## PaiShun Ting <paishun@umich.edu>
## Pin-Yu Chen <Pin-Yu.Chen@ibm.com>
## Sijia Liu <sijia.liu@ibm.com>
##
## Licensed under the ... | {"hexsha": "90094df65c7b7674d55ab7e82b3e8d34930a6698", "size": 3818, "ext": "py", "lang": "Python", "max_stars_repo_path": "optimization_methods/ObjectiveFunc.py", "max_stars_repo_name": "zkbfdzp/IBM7", "max_stars_repo_head_hexsha": "48fc2b74c666c1a2acb9303c825236da8b513f63", "max_stars_repo_licenses": ["Apache-2.0"], ... |
from PySide2 import QtCore
from PySide2.QtWebEngineWidgets import QWebEngineView
from PySide2.QtWidgets import (QMainWindow, QWidget, QApplication, QAction,
QPushButton, QLineEdit, QTextEdit, QVBoxLayout,
QGridLayout, QSplitter, QLabel, QFileDialog,
... | {"hexsha": "5d6ad294d1cd73f19d78cd3166463a744d70e1c4", "size": 28658, "ext": "py", "lang": "Python", "max_stars_repo_path": "singer_lab_to_nwb/gui/nwb_conversion_gui.py", "max_stars_repo_name": "stephprince/singer-lab-to-nwb-hackathon", "max_stars_repo_head_hexsha": "d357c1ebf948d8b49d7bc132dc892610be155e46", "max_star... |
#include "StaticSound.h"
#include <cassert>
#include <boost/scoped_ptr.hpp>
#include "../../include/gameaudio/IFileReader.h"
#include "../../include/gameaudio/Error.h"
#include "WavDecoder.h"
#include "OggVorbisDecoder.h"
using namespace gameaudio;
StaticSound::StaticSound(boost::shared_ptr<IFileReader> reader, en... | {"hexsha": "e64b26c7889096ac85b752810d0f906717beb217", "size": 1596, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/gameaudio/StaticSound.cpp", "max_stars_repo_name": "YosukeM/GameAudio", "max_stars_repo_head_hexsha": "49d4f5b56058b6f99d3f33438139adc07f56547a", "max_stars_repo_licenses": ["Unlicense", "MIT"],... |
//
// Created by Quentin Liardeaux on 12/19/19.
//
#ifndef R_TYPE_CLIENT_HPP
#define R_TYPE_CLIENT_HPP
#include <string>
#include <optional>
#include <queue>
#include "protocol.hpp"
#include "Message.hpp"
#include "Protocol/Packet.hpp"
#include "ClientHandler.hpp"
#include "GameRoom.hpp"
#include "Lobby.hpp"
#includ... | {"hexsha": "dbbc19642ae8b3e1bb0338ea6805cf9e12898624", "size": 3290, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "Server/includes/Client.hpp", "max_stars_repo_name": "LiardeauxQ/r-type", "max_stars_repo_head_hexsha": "8a77164c276b2d5958cd3504a9ea34f1cf6823cf", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#include <boost/foreach.hpp>
#include <framework/framework.h>
#include <framework/graphics.h>
#include <framework/bitmap.h>
#include <framework/texture.h>
#include <framework/exception.h>
#include <game/world/terrain_helper.h>
#include <game/editor/editor_terrain.h>
namespace ed {
static const int sp... | {"hexsha": "a38db99e299bf72de2103d59763e33a3c9bc903a", "size": 4090, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/game/editor/editor_terrain.cc", "max_stars_repo_name": "codeka/ravaged-planets", "max_stars_repo_head_hexsha": "ab20247b3829414e71b58c9a6e926bddf41f1da5", "max_stars_repo_licenses": ["Apache-2.0"... |
from tqdm import tqdm
import os
from glob import glob
from multiprocessing.dummy import Pool as ThreadPool
from PIL import Image as IM
import scipy.misc
import imageio as io
import numpy as np
IMAGE_PATH = "../../../dataset/celebA/*.jpg"
SAVE_PATH = "../../../dataset/celebA_crop"
NUM_THREAD = 16
def ce... | {"hexsha": "ec8cf902fbad7d0b1a10c0f47b3df97a1b0416c1", "size": 1648, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/data_process.py", "max_stars_repo_name": "dev6969/DCGAN_face", "max_stars_repo_head_hexsha": "ff373c8521b5023ba30fee2c99cf905977090e14", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#ifndef NOTIFICATIONFUNCTIONTYPETRAITS_HPP_
#define NOTIFICATIONFUNCTIONTYPETRAITS_HPP_
#include "Config.hpp"
#include "Widgets.hpp"
#include <boost/function.hpp>
#include <boost/bind.hpp>
struct NotificationFunctionTypeTraitsTracing
{
};
template< typename NotificationFunction >
struct NotificationFunctionTypeTrait... | {"hexsha": "a30735aadfb6f7bf0ae2a10bada33d2807e516ca", "size": 883, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/NotificationFunctionTypeTraits.hpp", "max_stars_repo_name": "MustafaSabur/RobotWereld", "max_stars_repo_head_hexsha": "e696e6e7ad890abb719a78fc1a0c111a680d27e0", "max_stars_repo_licenses": ["BSD-... |
import os
import unittest
import numpy as np
from monty.serialization import loadfn
from pymatgen.core import Lattice, Structure
from pymatgen.analysis.diffusion.aimd.rdf import RadialDistributionFunctionFast
tests_dir = os.path.dirname(os.path.abspath(__file__))
class RDFTest(unittest.TestCase):
def test_rdf(... | {"hexsha": "86b561eda41e88b9ce64b006a1540e5da20a331b", "size": 1521, "ext": "py", "lang": "Python", "max_stars_repo_path": "pymatgen/analysis/diffusion/aimd/tests/test_rdf.py", "max_stars_repo_name": "JiQi535/pymatgen-analysis-diffusion", "max_stars_repo_head_hexsha": "3600e70549e9462f30e104e083410e7b4544a4b2", "max_st... |
import numpy as np
import statsmodels.api as sm
nsample = 100
#这里，我们想要 x1 的值从 0 到 10 等差排列。
x = np.linspace(0, 10, nsample)
# 使用 sm.add_constant() 在 array 上加入一列常项1。
X = sm.add_constant(x)
# 然后设置模型里的 β0,β1 β0,β1，这里要设置成 1,10 。
beta = np.array([1, 10])
# 然后还要在数据中加上误差项，所以生成一个长度为k的正态分布样本。
e = np.random.normal(size=n... | {"hexsha": "fa13e1e71e871dc3d81a0198ae6e820ba7edfaba", "size": 679, "ext": "py", "lang": "Python", "max_stars_repo_path": "References/numpy/\u56de\u5f52\u5206\u6790.py", "max_stars_repo_name": "royqh1979/python_libs_usage", "max_stars_repo_head_hexsha": "57546d5648d8a6b7aca7d7ff9481aa7cd4d8f511", "max_stars_repo_licens... |
#! /usr/bin/env python
# -*- coding:utf-8 -*-
"""Generate SN Ia toy models for Weizmann workshop code-comparison study
(Radiation Transfer and Explosive Thermonuclear Burning in Supernovae,
17-28 June 2018)
The model is defined by its total mass (--mtot) and asymptotic kinetic
energy (--ekin; alternatively it... | {"hexsha": "852429c0ca57ad88bdb2e2a84f5956263a908c7d", "size": 51080, "ext": "py", "lang": "Python", "max_stars_repo_path": "mk_snia_toy_model.py", "max_stars_repo_name": "sblondin2605/snia_toy_model", "max_stars_repo_head_hexsha": "b9ea7e4f4af78147a7df494bbebc25ea2b487b6b", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import pandas as pd
import numpy as np
import sys
sys.path.append('./')
from train_base import write_csv, read_info, convert_to_loader, _run_language
from util import argparser
full_results = [['lang', 'artificial', 'avg_len', 'test_shannon', 'test_loss',
'test_acc', 'val_loss', 'val_acc', 'best_epoc... | {"hexsha": "a454e2ab72d06fa2eb9341ae81746261c70849b3", "size": 5701, "ext": "py", "lang": "Python", "max_stars_repo_path": "learn_layer/train_artificial.py", "max_stars_repo_name": "tpimentelms/phonotactic-complexity", "max_stars_repo_head_hexsha": "70d0a9e45943096d7640eaf7277033e3920408c9", "max_stars_repo_licenses": ... |
from math import ceil
import os
import colorcet as cc
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
from dataset import add_chunk_to_arr, get_test_data, reconstruct
from model import get_model
from utils import ex, round_down
# Ignores TensorFlow CPU messa... | {"hexsha": "580700dd07cc77f4d368be2af57716791d505eca", "size": 9545, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "sandialabs/bcnn", "max_stars_repo_head_hexsha": "a64dd8e4dc439d77a700c8e35048ac7ebfc49ef3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 47, "max_stars_... |
"""Basic operations using matplotlib plots and synthetic
trig data.
"""
import numpy as np #python's array proccesing / linear algebra library
import pandas as pd #data processing / stats library
import matplotlib.pyplot as plt #data visualization
import matplotlib.dates as dates
import... | {"hexsha": "59ad51d453a146a6671b2ed3b2bb9efe37403ef8", "size": 2384, "ext": "py", "lang": "Python", "max_stars_repo_path": "dkr-py310/docker-student-portal-310/course_files/pandas/py_pandas_time_series_2.py", "max_stars_repo_name": "pbarton666/virtual_classroom", "max_stars_repo_head_hexsha": "a9d0dc2eb16ebc4d2fd451c3a... |
# Copyright (c) 2018-2019, NVIDIA CORPORATION
# Copyright (c) 2017- Facebook, Inc
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the BSD 3-Clause License (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://open... | {"hexsha": "ee61fe073dcd00cbefc3d6fc2e1bb2f5bef1493e", "size": 7906, "ext": "py", "lang": "Python", "max_stars_repo_path": "built-in/PyTorch/Official/cv/image_classification/ResNet50_for_PyTorch/DistributedResnet50/image_classification/dataloaders.py", "max_stars_repo_name": "Ascend/modelzoo", "max_stars_repo_head_hexs... |
"""
Aggregate results for a single dataset.
"""
import os
import sys
import argparse
from datetime import datetime
from itertools import product
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import sem
from tqdm import tqdm
here = os.path.abspath(os.path... | {"hexsha": "6f21dd0df1b3f6b91bca49341251c510dbc2b4a3", "size": 3107, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/postprocess/noise_set.py", "max_stars_repo_name": "jjbrophy47/tree_influence", "max_stars_repo_head_hexsha": "245ff369ed3f4df3ddba243c7e3172423f385505", "max_stars_repo_licenses": ["Apache... |
#!/usr/bin/env python
import os
import rospy
import cv2
import numpy as np
from nav_msgs.srv import GetMap, GetMapRequest
class MapLoader:
def __init__(self, start=None, target=None, crop_image=False):
self.occupancy_grid = self.request_occupancy_grid()
self.start = start # tuple with x and y coor... | {"hexsha": "e5ed9070f7915de5a612bb565b5dc64d0c8b705b", "size": 6616, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/map_loader.py", "max_stars_repo_name": "Robotcraft19/amazebot-pathfinding", "max_stars_repo_head_hexsha": "f84f966959cb396e05cf121313362edf4c0bb41c", "max_stars_repo_licenses": ["MIT"], "max_s... |
\documentclass[10pt]{beamer}
\usepackage[utf8]{inputenc}
\usepackage{url}
\usepackage{listings}
\usepackage{drawstack}
\lstset{
basicstyle=\ttfamily\scriptsize,
showtabs=false,
showspaces=false,
showstringspaces=false,
columns=fixed,
showstringspaces=false,
extendedchars=true,
}
\usetheme{Copenhagen}
\... | {"hexsha": "1f7c2d1094636980fb451fdd2479db2f70e0832b", "size": 14526, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "presentation/slides.tex", "max_stars_repo_name": "ahovgaard/ret2libc", "max_stars_repo_head_hexsha": "5542c7c84e83cde99d0b785a4917852a7ed9ac24", "max_stars_repo_licenses": ["Unlicense"], "max_stars... |
from brightics.common.report import ReportBuilder, strip_margin, plt2MD, dict2MD
from brightics.function.utils import _model_dict
import numpy as np
import pandas as pd
from sklearn.neighbors import LocalOutlierFactor
from brightics.common.groupby import _function_by_group
from brightics.common.utils import check_requi... | {"hexsha": "b6173cc52887691d88bed7c106e2a60a8900b14c", "size": 5329, "ext": "py", "lang": "Python", "max_stars_repo_path": "function/python/brightics/function/manipulation/outlier_detection.py", "max_stars_repo_name": "power4454/studio", "max_stars_repo_head_hexsha": "d8115a8f483edab8d674f567e277863ea1bb3f79", "max_sta... |
# Ok, here we read parses from the CATH8 corpus and we try to reconstruct them.
# Let's give it a try.
import cky_constituent_copy
import pandas as pd
import numpy as np
# First, let's read the grammar
dat = pd.DataFrame.from_csv('input/00001_fitted_grammars.txt',sep=' ').reset_index()
rule_probabilities = d... | {"hexsha": "539e61b29d38bc5dacaf1d47a1a70a1e82374d96", "size": 7097, "ext": "py", "lang": "Python", "max_stars_repo_path": "ckypy/read_cath8_parses.py", "max_stars_repo_name": "megodoonch/birdsong", "max_stars_repo_head_hexsha": "582e7ddecf6c9c1b75f17418097f7bcbf6784d31", "max_stars_repo_licenses": ["BSD-3-Clause-Clear... |
# -*- coding: utf-8 -*-
# dcf
# ---
# A Python library for generating discounted cashflows.
#
# Author: sonntagsgesicht, based on a fork of Deutsche Postbank [pbrisk]
# Version: 0.7, copyright Sunday, 22 May 2022
# Website: https://github.com/sonntagsgesicht/dcf
# License: Apache License 2.0 (see LICENSE file)
... | {"hexsha": "95ada81b4536acdca691d887a9703c86a87923c9", "size": 27683, "ext": "py", "lang": "Python", "max_stars_repo_path": "dcf/curves/curve.py", "max_stars_repo_name": "pbrisk/dcf", "max_stars_repo_head_hexsha": "c585e173e5ea3b529be7463787ddcd5cb93fffd3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
/******************************************************************************
* Copyright 2017 Baidu Robotic Vision 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 L... | {"hexsha": "e70149d9b4f1c95dc26082b12eb14ea2e93c985a", "size": 50283, "ext": "cc", "lang": "C++", "max_stars_repo_path": "Frontend/feature_utils.cc", "max_stars_repo_name": "zjcs/ICE-BA", "max_stars_repo_head_hexsha": "b004bb5afc0d554d49742aae8503d231213f7e6d", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
using Printf
using Random
using LinearAlgebra
using Distributed
using MAT
@everywhere using DistributedArrays
@everywhere using RCAM
@everywhere using Random
@everywhere Random.seed!(123)
#Load the data
fid = matopen("../data/X.mat")
d = read(fid)
X = d["X"]
function pMNtest(X)
# Choltest is just a wrapper for this s... | {"hexsha": "d53d1be418541b7ce2702093e8da6878998da439", "size": 925, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/pMNsimpletest.jl", "max_stars_repo_name": "slimgroup/RCAM.jl", "max_stars_repo_head_hexsha": "54c4ff3891087300b3e46b5643b38e8eafa2234e", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import numpy as np
import matplotlib.pyplot as plt
from seaborn import kdeplot
import matplotlib.patheffects as mpe
import utils
from sklearn.metrics import precision_score, recall_score, roc_auc_score, label_ranking_average_precision_score
from sklearn.metrics import label_ranking_loss, confusion_matrix, average_pre... | {"hexsha": "bb171d2fd37871054d4614432eddd33473fec20a", "size": 1335, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/replicate_AVE_figures.py", "max_stars_repo_name": "ljmartin/fp_generalizability", "max_stars_repo_head_hexsha": "091a34a6f19f68cc6245345083dc4c15fcbbcbfc", "max_stars_repo_licenses": ["MIT"],... |
[STATEMENT]
lemma sumset_empty [simp]: "sumset A {} = {}" "sumset {} A = {}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. sumset A {} = {} &&& sumset {} A = {}
[PROOF STEP]
by (auto simp: sumset_eq) | {"llama_tokens": 86, "file": "Pluennecke_Ruzsa_Inequality_Pluennecke_Ruzsa_Inequality", "length": 1} |
# coding=utf-8
"""mathematical algorithms for the particle pusher, Leapfrog and Boris"""
import numpy as np
from numba import jit, njit
@jit()
def boris_velocity_kick(v, eff_q, E, B, dt, eff_m):
"""
The velocity update portion of the Boris pusher. Updates the velocity in place so as to conserve memory.
Pa... | {"hexsha": "f1c1ed571f2b09a0e623e9ced2f59271d1ef3d1c", "size": 5316, "ext": "py", "lang": "Python", "max_stars_repo_path": "pythonpic/algorithms/particle_push.py", "max_stars_repo_name": "StanczakDominik/PIC3", "max_stars_repo_head_hexsha": "583262cff0edfaee48b9540505bcd68983ec53ec", "max_stars_repo_licenses": ["BSD-3-... |
function scatterbar3(X,Y,Z,width)
%SCATTERBAR3 3-D scatter bar graph.
% SCATTERBAR3(X,Y,Z,WIDTH) draws 3-D bars of height Z at locations X and Y with width WIDTH.
%
% X, Y and Z must be of equal size. If they are vectors, than bars are placed
% in the same fashion as the SCATTER3 or PLOT3 functions.
%
% If t... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/1420-scatterbar3/scatterbar3.m"} |
module Simple
using MLIR
test0 = () -> begin
println("---- TEST 0 ----\n")
# Constructors.
ctx = MLIR.IR.Context()
println(ctx)
loc = MLIR.IR.Location(ctx)
println(loc)
mod = MLIR.IR.Module(loc)
println(mod)
op_state = MLIR.IR.OperationState("foo", loc)
println(op_state)
o... | {"hexsha": "b0b4e10cd3ad58ea5999f57ecc16d64cc5765952", "size": 4060, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/simple.jl", "max_stars_repo_name": "femtomc/MLIR.jl", "max_stars_repo_head_hexsha": "f3b7eefdbd8cdc1ad0a3df50a8138ecadfd9c062", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "... |
import random
from datetime import datetime
from math import ceil
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.exceptions import NotFittedError
from common.util.log_helper import LogHelper
he_init = tf.contrib.layers.vari... | {"hexsha": "c042f14c2253d3387e8b57eedab35d55092c8c9a", "size": 20378, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/rte_pac/deep_models/USE_BiLSTM_2.py", "max_stars_repo_name": "UKPLab/conll2019-snopes-experiments", "max_stars_repo_head_hexsha": "102f4a05cfba781036bd3a7b06022246e53765ad", "max_stars_repo_l... |
# installing tm
install.packages('devtools', lib="C:/R/Packages")
library(devtools)
slam_url <- "https://cran.r-project.org/src/contrib/Archive/slam/slam_0.1-37.tar.gz"
install_url(slam_url)
dest <- "C:/Data/Test Folder"
mytxtfiles <- list.files(path = dest, pattern = "txt", full.names = TRUE)
library(tm)
mycorpus... | {"hexsha": "1266b274e61a3aa436f1512f84e40b64de899b90", "size": 2201, "ext": "r", "lang": "R", "max_stars_repo_path": "ElectricArchaeology_Examples/tm.r", "max_stars_repo_name": "HHS/capstone.arthur.pignotti", "max_stars_repo_head_hexsha": "85b7c5809f4c96777c27a69c8b04a371f94bef09", "max_stars_repo_licenses": ["Unlicens... |
# Clean the original data
# coding=utf-8
# import neccessary packages
import numpy as np
import pandas as pd
import csv
import pymongo as pm
image2KData = pd.read_csv('single2k_metadata.csv',encoding="utf-8")
image410Data = pd.read_csv('targets410_metadata.csv',encoding="latin1")['filename']
del image2KData['url'... | {"hexsha": "653c0aca5603bb80e04924f9094ab9b8804ba2e5", "size": 893, "ext": "py", "lang": "Python", "max_stars_repo_path": "document/data mining/FileClean.py", "max_stars_repo_name": "LiruiErnest/VisMemo", "max_stars_repo_head_hexsha": "4ff84dbb3e9d892025fa9a54e41d2c96a5ae3482", "max_stars_repo_licenses": ["Apache-2.0"]... |
using Redux
using CImGui
include("Counter.jl")
using .Counter
include("../Renderer.jl")
using .Renderer
const store = create_store(Counter.counter, Counter.State(0))
function counter_ui(store)
flag = CImGui.ImGuiWindowFlags_NoTitleBar |
CImGui.ImGuiWindowFlags_NoResize |
CImGui.ImGuiWindow... | {"hexsha": "742b4492b8e2c7790a2ab3436ec4a4e1ef11b35b", "size": 1104, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/counter/app.jl", "max_stars_repo_name": "ianshmean/Redux.jl", "max_stars_repo_head_hexsha": "4c078aeb1137b9820e86198f9bafae652cb31953", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
Decline Curve Models
Copyright © 2020 David S. Fulford
Author
------
David S. Fulford
Derrick W. Turk
Notes
-----
Created on August 5, 2019
"""
from math import exp, log, log1p, ceil as ceiling, floor
import warnings
import dataclasses as dc
from dataclasses import dataclass
from numpy import ndarray
import nu... | {"hexsha": "200e1fc435859369c039481eca65b5090dc2d3ba", "size": 4369, "ext": "py", "lang": "Python", "max_stars_repo_path": "petbox/dca/bourdet.py", "max_stars_repo_name": "mwentzWW/dca", "max_stars_repo_head_hexsha": "338ba696e9e6081f9549d284a3dae64318a5b6cc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "... |
from mrjob.job import MRJob
from mrjob.protocol import PickleProtocol, PickleValueProtocol
import numpy as np
import lxmls.readers.pos_corpus as pcc
from lxmls.sequences.hmm import HMM
import pickle
from emstep import load_sequence, predict_sequence, load_parameters
# A single iteration of the distributed EM algorith... | {"hexsha": "6fce815a27d46f572929c7faa92476045ac4af4d", "size": 2783, "ext": "py", "lang": "Python", "max_stars_repo_path": "lxmls/big_data_em/emstep_split.py", "max_stars_repo_name": "mtreviso/lxmls-toolkit", "max_stars_repo_head_hexsha": "7b135d98c8bde592649fface8e6f24f112939937", "max_stars_repo_licenses": ["MIT"], "... |
# coding: utf-8
import chainer
import chainer.functions as F
class Stack(chainer.Chain):
def forward(self, x, y):
y1 = F.stack((x, y))
return y1
class StackAxis0(chainer.Chain):
def forward(self, x, y):
y1 = F.stack((x, y), axis=0)
return y1
class StackAxis1(chainer.Chain)... | {"hexsha": "0efcd3bce35464c39292aff01ecd395c66a1c389", "size": 987, "ext": "py", "lang": "Python", "max_stars_repo_path": "testcases/ch2o_tests/node/Stack.py", "max_stars_repo_name": "vermashresth/chainer-compiler", "max_stars_repo_head_hexsha": "5f5ad365d14398d6ae0214fa012eb10360db8e7e", "max_stars_repo_licenses": ["M... |
from __future__ import absolute_import, division, print_function
from ..accumulators import Mean, WeightedMean, WeightedSum
import numpy as np
class View(np.ndarray):
__slots__ = ()
def __getitem__(self, ind):
sliced = super(View, self).__getitem__(ind)
# If the shape is empty, return the ... | {"hexsha": "de6383e1b00aafc1e9109ccba81916667b414b88", "size": 3127, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/boost_histogram/_internal/view.py", "max_stars_repo_name": "HDembinski/boost-histogram", "max_stars_repo_head_hexsha": "6071588d8b58504938f72818d22ff3ce2a5b45dc", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
# This code plot the horizontal illuminance inside a room using a SISO array.
# Semi-angle at half illuminance (degree)
tethaHalf = 70
# Lambertian emission order (adimensional)
m = -np.log(2)/np.log10(... | {"hexsha": "dad773c10429da03e6f3bdf84bcc33e30e0f1c08", "size": 1443, "ext": "py", "lang": "Python", "max_stars_repo_path": "horizontalIlluminance.py", "max_stars_repo_name": "sophiekovalevsky/Visible-Light-Communication", "max_stars_repo_head_hexsha": "91f0634ce9a66a9fb4995dfa2c0730bbf3bc675b", "max_stars_repo_licenses... |
import enum
import typing as tp
import jax
import jax.numpy as jnp
import numpy as np
from treex import types
from treex.metrics.metric import Metric
class Reduction(enum.Enum):
sum = enum.auto()
sum_over_batch_size = enum.auto()
weighted_mean = enum.auto()
class Reduce(Metric):
"""Encapsulates me... | {"hexsha": "fc0a71b43456ca38e6043e7e7f817d4ec8ddb918", "size": 3691, "ext": "py", "lang": "Python", "max_stars_repo_path": "treex/metrics/reduce.py", "max_stars_repo_name": "BioGeek/treex", "max_stars_repo_head_hexsha": "fcbee17fcbc069ff5d33554013ce00e49405f872", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
from __future__ import unicode_literals, division
import os
import threading
import numpy as np
from gensim.models import Word2Vec
from base.document import Document
from config import BATCH_SIZE, SAMPLE_LENGTH, EMBEDDING_SIZE
from utils import get_answers_for_doc
def get_data_for_model(train_dir, labels, test_dir... | {"hexsha": "fbc9d324bedf90022090ac088d43a23044fa00a4", "size": 4596, "ext": "py", "lang": "Python", "max_stars_repo_path": "nn/input_data.py", "max_stars_repo_name": "ammarinjtk/Multi-Label-Text-Classification", "max_stars_repo_head_hexsha": "9098351c8ad47b30b3c41f5b9d0eed753a9ae960", "max_stars_repo_licenses": ["MIT"]... |
#include "Client.h"
#include "SystemTool.h"
#include <iostream>
#include <boost/bind.hpp>
#include <iostream>
#include <utility>
#include <thread>
#include <chrono>
#include <functional>
#include <atomic>
IOServiceType iosev;
void ServiceRun()
{
iosev.run();
}
using namespace std;
int main(int argc, const char*... | {"hexsha": "23dd4d05c420b812362bfdde038f28fd65bd7a0e", "size": 1182, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/asionet/TestBoostClient.cpp", "max_stars_repo_name": "SeeForward/SysMonitor", "max_stars_repo_head_hexsha": "fdaac5eacf28b62739c4e050e27abd1fdbfd18c4", "max_stars_repo_licenses": ["MIT"], "... |
@doc """
simple_estimator(model::Ising, T::Real, Js::AbstractArray)
Returns the following observables as `Dict{String, Any}`
# Observables
- `"Energy"`
- energy density
- `"Energy^2"`
- square of energy density
- `"Magnetization"`
- magnetization density
- `"|Magnetization|"`
- absolute value of m... | {"hexsha": "f945e7cbd11e2400167a69d86c00f3a74e876821", "size": 3165, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/model/Ising/estimator.jl", "max_stars_repo_name": "UnofficialJuliaMirror/SpinMonteCarlo.jl-71c4a2d3-ecf8-5cd9-ab6a-09a504837b4f", "max_stars_repo_head_hexsha": "fdd23854c33846b4e61396add4787c02... |
import numpy as np
from envs.babyai.oracle.teacher import Teacher
class DemoCorrections(Teacher):
def reset(self):
self.env.compute_obj_infos()
empty_path = np.zeros((self.env.grid.height + self.env.grid.width, 2))
path = self.oracle.shortest_path_obj()
empty_path[:len(path)] = path... | {"hexsha": "3cad8ac584fd422e1cb1bccf82ef93317ac1672c", "size": 1525, "ext": "py", "lang": "Python", "max_stars_repo_path": "envs/babyai/oracle/demo_corrections.py", "max_stars_repo_name": "AliengirlLiv/babyai", "max_stars_repo_head_hexsha": "51421ee11538bf110c5b2d0c84a15f783d854e7d", "max_stars_repo_licenses": ["MIT"],... |
// Boost Includes ==============================================================
#include <boost/python.hpp>
#include <boost/cstdint.hpp>
// Includes ====================================================================
#include <Magick++/Drawable.h>
// Declarations ===================================================... | {"hexsha": "04e932f71bcdb59fdb490a78ed0b79eb55e4f11c", "size": 1385, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "dev/Tools/Python/2.7.12/windows/Lib/site-packages/PythonMagick-0.9.19/pythonmagick_src/_DrawableStrokeLineCap.cpp", "max_stars_repo_name": "jeikabu/lumberyard", "max_stars_repo_head_hexsha": "07228c... |
/-
Copyright (c) 2020 Jannis Limperg. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Jannis Limperg
-/
import tactic.core
/-!
# The `unify_equations` tactic
This module defines `unify_equations`, a first-order unification tactic that
unifies one or more equations in ... | {"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/tactic/unify_equations.lean"} |
import numpy as np
from tqdm import tqdm_notebook as tqdm
import spectral
def grid_search(X, param_grid):
"""
Compute all error rates for the given combinations of parameters
Parameters
----------
param_grid : sklearn model_selection ParameterGrid
grid of parameters (all combinations to try)... | {"hexsha": "fab617aba0e903efa22c390071b042489175ca5b", "size": 635, "ext": "py", "lang": "Python", "max_stars_repo_path": "learning.py", "max_stars_repo_name": "ali-h/GraphLang", "max_stars_repo_head_hexsha": "d3fdab912be967a3642708df4d222a5bf1df992c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_star... |
!> Pressure Load for nonlinear elasticity in a total lagrangian formulation
!!
!!
!! @param iShiftU = nint(CommonPar(1))
!! @param iShiftDeltaU = nint(CommonPar(2))
!! @param iFemType = nint(CommonPar(3)) !!! of the associated volume element (one dimension higher)
!! @param iLoadProg = nint(CommonPar(4... | {"hexsha": "679417526e44f53315fec47f620654599d02c701", "size": 2816, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/FiniteStrain/NeumannRefTraction.f90", "max_stars_repo_name": "felipefr/gpmaterials", "max_stars_repo_head_hexsha": "db9c4b2c348a85c1af01d8d3256a243fb7b59a21", "max_stars_repo_licenses": ["MI... |
subroutine fermiBreakUpInterface (gsmObj, residINC, gsmRxn)
! ======================================================================
!
! Fermi break-up calculation of nuclei with A<13 in Preco and Evap
!
! Called from PRECOF
!
! Written by K.K. Gudima, 06/23/06
! Modified by SGM, 07/09/06
! Edited by... | {"hexsha": "1f4e6367fd8f0fa531fc0c6e34c895b8ec0c212a", "size": 6113, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/GeneralizedSpallation/fermiBreakUpInterface.f90", "max_stars_repo_name": "lanl/generalized-spallation-model", "max_stars_repo_head_hexsha": "4a2f01a873d2e8f2304b8fd1474d43d1ce8d744d", "max_s... |
# -*- coding: utf-8 -*-
"""
@author: ZhiyuanLi
"""
import numpy as np
import tensorflow as tf
import pandas as pd
import collections
from tensorflow.keras import Sequential, layers, optimizers
from xgboost import XGBClassifier
from sklearn.model_selection import LeaveOneOut
from sklearn.metrics import roc_... | {"hexsha": "813337f93b8491e3415ab80b23268e004987e168", "size": 10232, "ext": "py", "lang": "Python", "max_stars_repo_path": "OAP_EL_Early_Prediction/OAP_EL.py", "max_stars_repo_name": "jiaolang771/aicad", "max_stars_repo_head_hexsha": "f683fb7c3adeecebff3d36b660fe180a8f8b8c1d", "max_stars_repo_licenses": ["MIT"], "max_... |
#bhuvan's submission for eyantra hactober fest (image processing)
import numpy as np
import cv2
cap = cv2.VideoCapture(0)#opens the camera
# Capture frame-by-frame
def greenCircleDetect():# to detect and draw green contour around green circle
a=0;b=255;c=0;#describes color of contour
lower_green = np.a... | {"hexsha": "2764469778aaced84bcaf0f3ef2ee607bbe34910", "size": 3760, "ext": "py", "lang": "Python", "max_stars_repo_path": "color_detect.py", "max_stars_repo_name": "bhuvanjhamb/image-processing", "max_stars_repo_head_hexsha": "b89ee42db793ba90e2eb40cc03f6262f56c0574b", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
module pcre_constants
use, intrinsic :: iso_c_binding, only : c_int
implicit none
! Extension
integer(c_int), parameter :: PCRE_SUCCESS = 0
!! The code ran successfully
integer(c_int), parameter :: PCRE_ERROR_NOMATCH = -1
!! The subject string did not match the pattern.
inte... | {"hexsha": "19a7b58e801baac8be354f8b6d198cf3a3bf3c23", "size": 13170, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "lib/pcre_constants.f90", "max_stars_repo_name": "14NGiestas/fregex", "max_stars_repo_head_hexsha": "1e20f084fb33b2b9dda8ca887bafae3c847e08c6", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma h1b_helper_leq:
"(\<forall>((a::real), (b::real), (c::real))\<in>set leq. \<exists>x. \<forall>y<x. a * y\<^sup>2 + b * y + c \<le> 0) \<Longrightarrow> (\<exists>y.\<forall>x<y. (\<forall>(a, b, c)\<in>set leq. a * x\<^sup>2 + b * x + c \<le> 0))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. ... | {"llama_tokens": 7738, "file": "Virtual_Substitution_QE", "length": 37} |
# -*- coding: utf-8 -*-
# Copyright (c) 2012, Sergio Callegari
# All rights reserved.
# This file is part of PyDSM.
# PyDSM is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, o... | {"hexsha": "ac61c7830fa29d88a43d364c6a71ed2cb955b9db", "size": 8068, "ext": "py", "lang": "Python", "max_stars_repo_path": "pydsm/delsig/_ds.py", "max_stars_repo_name": "EnjoyLifeFund/macHighSierra-py36-pkgs", "max_stars_repo_head_hexsha": "5668b5785296b314ea1321057420bcd077dba9ea", "max_stars_repo_licenses": ["BSD-3-C... |
import contextlib
from datetime import datetime, timezone
import getpass
import io
import json
import pathlib
import uuid
import pickle
import hashlib
import subprocess
from os.path import join, exists
import numpy as np
import sqlalchemy as sqla
from sqlalchemy.ext.declarative import declarative_base as sqla_declarat... | {"hexsha": "f77e5ba401b2a1a42643ec90597574f08944aac4", "size": 50605, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/mldb/model_repository.py", "max_stars_repo_name": "modestyachts/imagenet-testbed", "max_stars_repo_head_hexsha": "f4083a29524fe9a9e029bf34d1476cea5a497132", "max_stars_repo_licenses": ["MIT"]... |
using CoinbasePro
using Test
using DataFrames
@testset "CoinbasePro" begin
for file in filter(x->occursin("test_", x), readdir("."))
include(file)
end
end | {"hexsha": "30e24704bf9b293b76002d4319f13475ea2d0187", "size": 159, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "stimakov123/CoinbasePro.jl", "max_stars_repo_head_hexsha": "03be51feb9b4d73236a4fa7f66765fbe2624ccda", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
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