text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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\chapter*{Overview}
% \pagenumbering{roman} \setcounter{page}{}
\fluidity\ is an open source, general purpose, multi-phase CFD code capable of solving numerically the Navier-Stokes and accompanying field equations on arbitrary unstructured finite element meshes in one, two and three dimensions. It uses a moving finite... | {"hexsha": "3df91d3b75d88def90a6a6c421962340e9527b61", "size": 2889, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "software/multifluids_icferst/manual/overview.tex", "max_stars_repo_name": "msc-acse/acse-9-independent-research-project-Wade003", "max_stars_repo_head_hexsha": "cfcba990d52ccf535171cf54c0a91b184db6f... |
# Standard libraries
import os
import pathlib
import pickle
from datetime import datetime
# Scientific stack
import numpy as np
import numpy.random as rnd
import pandas as pd
import sklearn.metrics as skmetrics
# Chunked data
import dask
import zarr
# Enable multiprocessing support for Zarr
from numcodecs import blo... | {"hexsha": "41b45f245df5cf6d687cab305fe3df7473d5d6a3", "size": 18405, "ext": "py", "lang": "Python", "max_stars_repo_path": "training/utils.py", "max_stars_repo_name": "dangpzanco/dcase-task1", "max_stars_repo_head_hexsha": "72867cc5b8969d7ec55c5acfd30ebbc3a7246666", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
#include <boost/mpl/aux_/find_if_pred.hpp>
| {"hexsha": "1a626d1b15de7a471f87cb97689fa0c161392e4e", "size": 43, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_mpl_aux__find_if_pred.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-1.0... |
import pandas as pd
import numpy as np
from pathos.multiprocessing import _ProcessPool as Pool
import os
import sys
import copy
from sklearn.linear_model import BayesianRidge as BR
from sklearn.neighbors import KNeighborsRegressor as KNN
from sklearn.ensemble import AdaBoostRegressor as ABR
from sklearn.ensemble import... | {"hexsha": "5144494bc3a325de048ff352d7b294237efd344e", "size": 2493, "ext": "py", "lang": "Python", "max_stars_repo_path": "purePython/lims.py", "max_stars_repo_name": "rjb255/researchProject", "max_stars_repo_head_hexsha": "7b0c118ee1adaf0c68f83d5b4a043c6aa5a55331", "max_stars_repo_licenses": ["CC0-1.0"], "max_stars_c... |
[STATEMENT]
lemma ProcUniv: "(UNIV :: proc set) = {p0, p1}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. UNIV = {p0, p1}
[PROOF STEP]
by (metis UNIV_eq_I insert_iff proc.exhaust) | {"llama_tokens": 88, "file": "FLP_FLPExistingSystem", "length": 1} |
using ArgParseLite
function main()
my_args = Arguments()
push!(my_args, Argument("arg1"))
push!(my_args, Argument("--opt1"))
push!(my_args, Argument("--opt2", "-o"))
push!(my_args, Argument("--flag1", action=:store_true))
println("Parsed args:")
for (arg,val) in ArgParseLite.parse_args(m... | {"hexsha": "e9de5616a066f7840ccd5a5793046bcc4be9d4a5", "size": 384, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "bin/lite.jl", "max_stars_repo_name": "kescobo/ArgParseLite.jl", "max_stars_repo_head_hexsha": "99b5d4073e90bb712d33120c65dbdf4a4f0cf5f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
from __future__ import absolute_import
from numbers import Number
from collections import OrderedDict
from collections.abc import Iterable
import dama as dm
import numpy as np
__license__ = '''Copyright 2019 Philipp Eller
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file exce... | {"hexsha": "bec211a81efeb3723bec1624076225a3ad3c4640", "size": 7576, "ext": "py", "lang": "Python", "max_stars_repo_path": "dama/core/axis.py", "max_stars_repo_name": "philippeller/MilleFeuille", "max_stars_repo_head_hexsha": "962c322531e208a7d20a273a56d13b954ad80bc3", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# flake8: noqa
import time
from copy import deepcopy
import numpy as np
from numpy.testing import assert_almost_equal
from sklearn.metrics import log_loss, mean_squared_error
# for testing sigmoid
from scipy.special import expit
import torch
import torch.nn as nn
import torch.nn.functional as F
import tensorflow.k... | {"hexsha": "1f0068026a68dcb8931d503bc67996bd55e0fa0c", "size": 78222, "ext": "py", "lang": "Python", "max_stars_repo_path": "book-code/numpy-ml/numpy_ml/tests/test_nn.py", "max_stars_repo_name": "yangninghua/code_library", "max_stars_repo_head_hexsha": "b769abecb4e0cbdbbb5762949c91847a0f0b3c5a", "max_stars_repo_license... |
from numpy import *
#import Image
from PIL import Image
def minmax(x, range=None):
if range:
lo, hi = range
good = between(lo, x, hi)
x = compress(good, x)
return min(x), max(x)
def scale255minmax(data):
lo, hi = minmax(ravel(data))
scaled = (data - lo) / float(hi - lo) * 255
... | {"hexsha": "0d71b06d79a9421bba504d3de03d4578bf448c2b", "size": 2390, "ext": "py", "lang": "Python", "max_stars_repo_path": "nircam_jdox/scripts/coeim.py", "max_stars_repo_name": "aliciacanipe/nircam_jdox", "max_stars_repo_head_hexsha": "fa1c3381283bb08b870162d0dd3bc9d5e94561ea", "max_stars_repo_licenses": ["BSD-3-Claus... |
/-
Copyright (c) 2022 Eric Wieser. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Eric Wieser
-/
import data.set.pointwise.basic
import data.list.of_fn
/-!
# Pointwise operations with lists of sets
> THIS FILE IS SYNCHRONIZED WITH MATHLIB4.
> Any changes to this fil... | {"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/data/set/pointwise/list_of_fn.lean"} |
[STATEMENT]
lemma Runit_in_runit [intro]:
assumes "arr f" and "t \<in> f"
shows "\<^bold>\<r>\<^bold>[t\<^bold>] \<in> \<r>[f]"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<^bold>\<r>\<^bold>[t\<^bold>] \<in> \<r>[f]
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. \<^bold>\<r>\<^bo... | {"llama_tokens": 806, "file": "MonoidalCategory_FreeMonoidalCategory", "length": 7} |
#https://pythonbasics.org/webserver/
import os
import sys
import glob
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from tensorflow import keras
import numpy as np
import base64
from http.server import BaseHTTPRequestHandler, HTTPServer
import time
hostName = "localhost"
serverPort = 8080
def identify_image(fn):
imag... | {"hexsha": "b75bc03d95ff91dd16e52abb5e88aa50857899cf", "size": 1865, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/webserver.py", "max_stars_repo_name": "FMurphy17/SBUDRP21", "max_stars_repo_head_hexsha": "9943c04de2b83314742a6063be7f4ba41620a5c1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#
# Types supporting parameterized Timestep and Clock objects
#
abstract type AbstractTimestep <: MimiStruct end
struct FixedTimestep{FIRST, STEP, LAST} <: AbstractTimestep
t::Int
end
struct VariableTimestep{TIMES} <: AbstractTimestep
t::Int
current::Int
function VariableTimestep{TIMES}(t::Int = 1) ... | {"hexsha": "b03378bf4186d6d18fafd11067070bb37abf6362", "size": 1837, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/core/types/time.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Mimi.jl-e4e893b0-ee5e-52ea-8111-44b3bdec128c", "max_stars_repo_head_hexsha": "c9336f1076996dca728c30befd561280dfc1831... |
"""
Some utils used in all demos
Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
"""
from __future__ import print_function, division
import numpy as np
from PIL import Image, ImageDraw
import contextlib
def paletteVOC(N=256, normalized=False, PIL=False):
"""
Pascal VOC color map
"""
def bitget(byt... | {"hexsha": "9a9a09d2301518dfd92cb094cf4fb474f88b1984", "size": 2437, "ext": "py", "lang": "Python", "max_stars_repo_path": "LovaszSoftmax/demo_helpers/demo_utils.py", "max_stars_repo_name": "ljhclover/pytorch_Unet_CZI", "max_stars_repo_head_hexsha": "92a3c295077562161d747157fdcba998132d4a94", "max_stars_repo_licenses":... |
subroutine shearmod(eg,enu,temp,props,nprops)
implicit real*8(a-h,o-z)
dimension props(nprops)
emu0=props(1)
ed0=props(2)
et0=props(3)
enu=min(dabs(props(4)),0.499d0)
if(temp.gt.et0) then
eg = emu0 - ed0/(dexp(et0/temp)-1.d0)
else
eg = emu0
en... | {"hexsha": "3098222925966d3f64fc65489f27656136eefbd8", "size": 346, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "shearmodule/shearvarshni.f", "max_stars_repo_name": "jacojvr/UMATs", "max_stars_repo_head_hexsha": "878141ea5a028bccb808f1fde83c9502e374a0a3", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
using Base: @deprecate
@deprecate by{T,N}(ta::TimeArray{T,N}, t::Int; period::Function=day) when(ta, period, t)
@deprecate by{T,N}(ta::TimeArray{T,N}, t::String; period::Function=day) when(ta, period, t)
@deprecate to(ta::TimeArray, y::Int, m::Int, d::Int) to(ta, Date(y, m, d))
@deprecate from(ta::TimeArray, y::Int, ... | {"hexsha": "b92720c5a5f393e89abe970672ecbabea0cacba6", "size": 1469, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/deprecated.jl", "max_stars_repo_name": "BenjaminBorn/TimeSeries.jl", "max_stars_repo_head_hexsha": "c38509ea8427afd74cc140e0f7f6c7ca0cb39c06", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
# -*- coding: utf-8 -*-
"""SAC agent from demonstration for episodic tasks in OpenAI Gym.
- Author: Curt Park
- Contact: curt.park@medipixel.io
- Paper: https://arxiv.org/pdf/1801.01290.pdf
https://arxiv.org/pdf/1812.05905.pdf
https://arxiv.org/pdf/1511.05952.pdf
https://arxiv.org/pdf/1707.0... | {"hexsha": "0a674e7d4537fac8ae6500cc17d1116f3899c093", "size": 14967, "ext": "py", "lang": "Python", "max_stars_repo_path": "algorithms/fd/sac_agent.py", "max_stars_repo_name": "ur1ove/rl_algorithms", "max_stars_repo_head_hexsha": "3e7a554d5ea83b4c19bad7a51d4867cacc986aa9", "max_stars_repo_licenses": ["MIT"], "max_star... |
import io
import sys
import os
from cassandra.cluster import Cluster, BatchStatement, ConsistencyLevel
from cassandra.auth import PlainTextAuthProvider
import boto3
import pandas as pd
import zipfile
import numpy as np
import datetime
now = datetime.datetime.now()
print("{} Starting Finnhub preloader".format(now.strft... | {"hexsha": "55db527c8de6e62596158784a001e92469a8d02a", "size": 4568, "ext": "py", "lang": "Python", "max_stars_repo_path": "foobar/preloader/preload_finnhub.py", "max_stars_repo_name": "brijow/foobar-gamestop", "max_stars_repo_head_hexsha": "54302ab330ff5a2099c7300f943f271ea9d30b52", "max_stars_repo_licenses": ["MIT"],... |
#! /usr/bin/Rscript
args <- commandArgs(trailingOnly = TRUE)
headersTXT = c(
"Miranda_score",
"miR_ID",
"mRNA_ID",
"Start_position",
"End_position",
"Seed_match_6mer2",
"miR_match_P01",
"Seed_match_7mer2",
"Seed_match_7mer1",
"Seed_MFE",
"X3p_MFE",
"Target_UC_comp",
"miR_match_P09",
"miR_match_P02",
"Seed_GU",
"miR_ma... | {"hexsha": "528da104a21a42bd224de5690288d08ced957875", "size": 2352, "ext": "r", "lang": "R", "max_stars_repo_path": "Core/calc_utr_features_selected.r", "max_stars_repo_name": "lanagarmire/MirMark", "max_stars_repo_head_hexsha": "19339ee7dbff9bdfd627b642f3df81358cf8c5f6", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
from typing import List, Tuple, Dict
from dataclasses import dataclass
import numpy as np
import cv2
from img_proc.padding import calc_pad_size, pad
BASE_IMG_SIZE = 300
@dataclass
class BBox:
x1: int
y1: int
x2: int
y2: int
def calc_line_size(img: np.ndarray) -> int:
h, w = img.shape[:2]
... | {"hexsha": "844a44be0cb80d93d39b3a827f6ca06c79d85618", "size": 2338, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/img_proc/bbox.py", "max_stars_repo_name": "rewolfiluac/convert-torch2trt-demo", "max_stars_repo_head_hexsha": "9b9a3646bdf8b82af2149e73b4cd57939c6729cd", "max_stars_repo_licenses": ["MIT"], "m... |
# MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# r... | {"hexsha": "6b2b65e2610d6b577d0e15df8bb9827c2cd55dc8", "size": 5860, "ext": "py", "lang": "Python", "max_stars_repo_path": "art/attacks/evasion/hclu.py", "max_stars_repo_name": "meghana-sesetti/adversarial-robustness-toolbox", "max_stars_repo_head_hexsha": "6a5ce9e4142734ad9004e5c093ef8fa754ea6b39", "max_stars_repo_lic... |
#!/usr/bin/python
########################################################################################################################
#
# Copyright (c) 2014, Regents of the University of California
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permi... | {"hexsha": "d4fd1e6c0865110706b10ac166f281618532975d", "size": 19216, "ext": "py", "lang": "Python", "max_stars_repo_path": "laygo/generators/splash/adc_sar_sar_wsamp_layout_generator_bb_doubleSA_pe.py", "max_stars_repo_name": "tinapiao/Software-IC-Automation", "max_stars_repo_head_hexsha": "74b23cd94aa6e4658b110e93b5d... |
import tensorflow as tf
import tensorflow_quantum as tfq
import cirq
import sympy
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
def f(x):
vqe.set_weights(np.array([x]))
ret = vqe(tfq.convert_to_tensor([cirq.Circuit()]))
return ret.numpy()[0][0]
def anz... | {"hexsha": "8f0e92f15751e663fd850f7283b0e3f919a99d85", "size": 3556, "ext": "py", "lang": "Python", "max_stars_repo_path": "TFQ/VQE/vqe_multi.py", "max_stars_repo_name": "Project-Fare/quantum_computation", "max_stars_repo_head_hexsha": "fc182007d0cf7cca170efdbcb442576fde5927ff", "max_stars_repo_licenses": ["MIT"], "max... |
// Copyright Matt Overby 2021.
// Distributed under the MIT License.
#ifndef GINI_MESHTXT_HPP
#define GINI_MESHTXT_HPP 1
#include <iostream>
#include <fstream>
#include <sstream>
#include <Eigen/Geometry>
#include <vector>
#include <iomanip>
namespace mcl
{
// Simple, slow, plain text
//
// X is n x DIM vertices (D... | {"hexsha": "7ffb416ab14506c18ceba9e6f67d7eafc0191536", "size": 3837, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/MCL/MeshTXT.hpp", "max_stars_repo_name": "mattoverby/mclgeom", "max_stars_repo_head_hexsha": "d3ecd2a878900f33ba1412b8d82e643895201e51", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
from sklearn.datasets import make_regression
from scipy.stats import norm, itemfreq
import pandas as pd
from pandas.io import sql
import sys
import time
import argparse
import os
from sqlalchemy import create_engine
import random
parser = argparse.ArgumentParser()
parser.add_argument(
'RowCount'... | {"hexsha": "19124bbe59f502ba2a3b1e711a2ada38a219d15e", "size": 2406, "ext": "py", "lang": "Python", "max_stars_repo_path": "docker-environment/init_mariadb.py", "max_stars_repo_name": "Quer-io/Quer.io-reference", "max_stars_repo_head_hexsha": "f4fd3505587143d5407b9b49ec81a9e7b94a0583", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
import numpy as np
import tensorflow as tf
from zhusuan.distributions.base import *
from zhusuan.distributions.utils import \
maybe_explicit_broadcast, \
assert_same_float_dtype, \
... | {"hexsha": "fabe5bf180c41970c61d4cb297d5c8519304d76c", "size": 42038, "ext": "py", "lang": "Python", "max_stars_repo_path": "zhusuan/distributions/univariate.py", "max_stars_repo_name": "ycguo028/zhusuan", "max_stars_repo_head_hexsha": "244536d93c55e486a3587e53229f0a7e1b19bef0", "max_stars_repo_licenses": ["MIT"], "max... |
from __future__ import annotations
import logging
import os
from collections import defaultdict
from functools import reduce
from typing import Any, Dict, List, Optional, Set, Tuple, Type
import matplotlib.pyplot as plt
import numpy as np
import numpy.typing as npt
import PIL.Image
from cachetools import LRUCache, ca... | {"hexsha": "f2800d5676a10a0ca23ac406ccf1c19695f726fb", "size": 13501, "ext": "py", "lang": "Python", "max_stars_repo_path": "nuplan/database/nuplan_db/nuplandb.py", "max_stars_repo_name": "MCZhi/nuplan-devkit", "max_stars_repo_head_hexsha": "3c4f5b8dcd517b27cfd258915ca5fe5c54e3cb0c", "max_stars_repo_licenses": ["Apache... |
[STATEMENT]
lemma pivot_unsat_core_id: "\<lbrakk>\<triangle> (\<T> s); x\<^sub>i \<in> lvars (\<T> s); x\<^sub>j \<in> rvars_of_lvar (\<T> s) x\<^sub>i\<rbrakk> \<Longrightarrow> \<U>\<^sub>c (pivot x\<^sub>i x\<^sub>j s) = \<U>\<^sub>c s"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>\<triangle> (\<T> s);... | {"llama_tokens": 534, "file": "Simplex_Simplex", "length": 2} |
"""
Train shadow net script
"""
import argparse
import functools
import itertools
import os
import os.path as ops
import sys
import time
import numpy as np
import tensorflow as tf
import pprint
import shadownet
import six
from six.moves import xrange # pylint: disable=redefined-builtin
sys.path.append('/data/')
fr... | {"hexsha": "fbf23a32edea1c76b286e1eb5b7cddd3cfc77494", "size": 17504, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/tensorflow/train/crnn_chinese/code_multi/tools/train_shadownet_multi.py", "max_stars_repo_name": "soar-zhengjian/uai-sdk", "max_stars_repo_head_hexsha": "e195bd3fb2b97aca7dac6722d332c25b... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import pandas as pd
import numpy as np
import scipy, scipy.stats
from datetime import date, datetime, timedelta
from dateutil.relativedelta import relativedelta
from sklearn import linear_model
import h5py
import plotly.graph_objs as go
import plotly.f... | {"hexsha": "18bd633cf89b3b71f409a330a0e5f663309dc09f", "size": 38570, "ext": "py", "lang": "Python", "max_stars_repo_path": "dash/futures/commodity_futures_app.py", "max_stars_repo_name": "jingmouren/QuantResearch", "max_stars_repo_head_hexsha": "7a17e567b0e95481894ed37524c041b30155b6cb", "max_stars_repo_licenses": ["M... |
\documentclass[preprint,pre,floats,aps,amsmath,amssymb]{revtex4}
\usepackage{graphicx}
\usepackage{bm}
\begin{document}
\title{Band Structure of Silver Chloride(AgCl) using LDA(linear density approximation with ABINIT}
\author{Jaswinder Singh (Roll No-2016PHY1059)}
\date{\today}
\begin{abstract}
The band structure o... | {"hexsha": "47d17785d0845c42528e2c87375bc96e6709560a", "size": 26569, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Project Report(Tex File)/Abinit final.tex", "max_stars_repo_name": "singh-sudo098/Band-Structure-of-AgCl-using-Abinit", "max_stars_repo_head_hexsha": "7b8bbfe1a2eedea6e8de8e6d748e173b0be1bdae", "ma... |
Require Import VerdiRaft.Raft.
Require Import VerdiRaft.RaftRefinementInterface.
Require Import VerdiRaft.CommonTheorems.
Require Import VerdiRaft.SpecLemmas.
Require Import VerdiRaft.RefinementSpecLemmas.
Local Arguments update {_} {_} _ _ _ _ _ : simpl never.
Require Import VerdiRaft.InLogInAllEntriesInterface.
S... | {"author": "uwplse", "repo": "verdi-raft", "sha": "7c8e4d53d27f7264ec4d3de72944dc0368e065f0", "save_path": "github-repos/coq/uwplse-verdi-raft", "path": "github-repos/coq/uwplse-verdi-raft/verdi-raft-7c8e4d53d27f7264ec4d3de72944dc0368e065f0/raft-proofs/InLogInAllEntriesProof.v"} |
%% Gabor Filter demo
%
% A GUI to interact with the 5 different Gabor filter parameters, while
% visualizing the resulting filter.
%
function varargout = gabor_filter_gui(ksize)
% create the UI
if nargin < 1, ksize = [121 121]; end
h = buildGUI(ksize);
if nargout > 0, varargout{1} = h; end
end
functio... | {"author": "kyamagu", "repo": "mexopencv", "sha": "d29007b2a484d0fd92e6e941dc5fd4750014fa6a", "save_path": "github-repos/MATLAB/kyamagu-mexopencv", "path": "github-repos/MATLAB/kyamagu-mexopencv/mexopencv-d29007b2a484d0fd92e6e941dc5fd4750014fa6a/samples/gabor_filter_gui.m"} |
"""
Title: English-to-Spanish translation with a sequence-to-sequence Transformer
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2021/05/26
Last modified: 2021/05/26
Description: Implementing a sequence-to-sequene Transformer and training it on a machine translation task.
"""
"""
## Introduction
In thi... | {"hexsha": "1b5428af0370ec819ab5f637f45b5ced3bf9cc77", "size": 15083, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/nlp/neural_machine_translation_with_transformer.py", "max_stars_repo_name": "floscha/keras-io", "max_stars_repo_head_hexsha": "fb064c551eda7aea631ceaa548c4411b9a1193cb", "max_stars_repo_... |
#-*- coding: utf-8 -*-
import sys
sys.path.append("..")
import codecs
import numpy as np
from utils.nlp_util import NlpUtil
from search_dialog import config
from seq2seq_dialog.infer import get_infer_model, predict_sent_emb
class SentEmbSearch(object):
sent_emb_index = np.load(config.sent_emb_index_path +... | {"hexsha": "e9032788059ea3d8cfae10c184e72c50447851fc", "size": 1525, "ext": "py", "lang": "Python", "max_stars_repo_path": "search_dialog/sent_emb_search.py", "max_stars_repo_name": "HouchangX-AI/Dialog-Solution", "max_stars_repo_head_hexsha": "1f68f847d9c9c4a46ef0b5fc6a78014402a4dd7a", "max_stars_repo_licenses": ["MIT... |
from sklearn.datasets import load_iris
iris_dataset=load_iris()
import pandas as pd
import numpy
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(iris_dataset['data'],iris_dataset['target'],random_state=0)
print("dimensions of X_train: {}".format(X_train.shape))
#75... | {"hexsha": "cd472adebec3ef8f985a7d30eb9a423a266b2736", "size": 801, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_test_split.py", "max_stars_repo_name": "Pl4gue/Iris-ML", "max_stars_repo_head_hexsha": "41aa30cc5138132ca5feb3a17bbb91a1c54fefbb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
from typing import List, Dict, Set
import numpy as np
from bidict import bidict
from sklearn.preprocessing import OneHotEncoder
from collections import defaultdict
from .embed import BaseEmbed
from .logging import getLogger
from .recommendation_base import RecommendationBase, NodeType, Node, Edge, FeatureName
from .u... | {"hexsha": "5cded1a744d2c7a645aa56ef0a342b148dd1ac27", "size": 5354, "ext": "py", "lang": "Python", "max_stars_repo_path": "hwer/content_recommender.py", "max_stars_repo_name": "faizanahemad/Hybrid-Weighted-Embedding-Recommender", "max_stars_repo_head_hexsha": "904a27c4b0126935735aee689408b2b6acf4af9a", "max_stars_repo... |
/**
\file
\author Datta Ramadasan
//==============================================================================
// Copyright 2015 INSTITUT PASCAL UMR 6602 CNRS/Univ. Clermont II
//
// Distributed under the Boost Software License, Version 1.0.
// See accompanying file LICENSE.txt or ... | {"hexsha": "80b167c560fe418cabc98c49a0340bf974433cb1", "size": 1361, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/libv/lma/ttt/fusion/at.hpp", "max_stars_repo_name": "bezout/LMA", "max_stars_repo_head_hexsha": "9555e41eed5f44690c5f6e3ea2d22d520ff1a9d2", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_cou... |
(* (c) Copyright 2006-2016 Microsoft Corporation and Inria. *)
(* Distributed under the terms of CeCILL-B. *)
Require Import mathcomp.ssreflect.ssreflect.
From mathcomp
Require Import ssrbool ssrfun eqtype ssrnat seq div.
From mathcomp
Require Import fintype finset prim... | {"author": "math-comp", "repo": "odd-order", "sha": "663e1827836cf0dedebb99f0ab6b232bab9bffd0", "save_path": "github-repos/coq/math-comp-odd-order", "path": "github-repos/coq/math-comp-odd-order/odd-order-663e1827836cf0dedebb99f0ab6b232bab9bffd0/theories/BGsection5.v"} |
# -*- coding: utf-8 -*-
# file: example.py
# date: 2021-08-01
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()
import os
import numpy as np
import cv2
import random
import numpy as np
#from google.colab.patches import cv2_imshow
from detectron2 import model_zoo
from detectron2.engin... | {"hexsha": "412cdd073aa2a35381fe0f6167d7f4516b3da092", "size": 2531, "ext": "py", "lang": "Python", "max_stars_repo_path": "wiki4codes/ML/CV/object_detection/py_example_venv/detectron2_example.py", "max_stars_repo_name": "innerNULL/wiki4codes", "max_stars_repo_head_hexsha": "b707557de24befba0cd9dcacf66d74e5c122bb18", "... |
from typing import Sequence, Optional
import haiku as hk
import jax.nn as jnn
import jax.numpy as jnp
from tensorflow_probability.substrates import jax as tfp
from dreamer.utils import initializer
tfd = tfp.distributions
tfb = tfp.bijectors
class Encoder(hk.Module):
def __init__(self, depth: int, kernels: Sequen... | {"hexsha": "92a76fcddde9f5e95244af61a4684c020afce36f", "size": 4093, "ext": "py", "lang": "Python", "max_stars_repo_path": "dreamer/blocks.py", "max_stars_repo_name": "yardenas/jax-dreamer", "max_stars_repo_head_hexsha": "b3f3945b389cc9153f8e06ad416252977bda488a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
(* Title: Kleene algebra with tests
Author: Alasdair Armstrong, Victor B. F. Gomes, Georg Struth
Maintainer: Georg Struth <g.struth at sheffield.ac.uk>
*)
header {* Transformation Theorem for while Loops *}
theory FolkTheorem
imports Conway KAT DRAT
begin
text {*
We prove Kozen's transformation th... | {"author": "Josh-Tilles", "repo": "AFP", "sha": "f4bf1d502bde2a3469d482b62c531f1c3af3e881", "save_path": "github-repos/isabelle/Josh-Tilles-AFP", "path": "github-repos/isabelle/Josh-Tilles-AFP/AFP-f4bf1d502bde2a3469d482b62c531f1c3af3e881/thys/KAT_and_DRA/SingleSorted/FolkTheorem.thy"} |
theory BDD_select
imports Main BDD_basic
begin
definition select :: "nat \<Rightarrow> BDD \<Rightarrow> BDD \<Rightarrow> BDD" where
"select a t e = (if t = e then t else Select a t e)"
lemma select_noop [simp]: "norm n t \<Longrightarrow> norm n e \<Longrightarrow> t \<noteq> e \<Longrightarrow> select v t e = ... | {"author": "jmaessen", "repo": "bdd-subtyping", "sha": "49852f5841dadfdb86ba87be923b42fb547810ef", "save_path": "github-repos/isabelle/jmaessen-bdd-subtyping", "path": "github-repos/isabelle/jmaessen-bdd-subtyping/bdd-subtyping-49852f5841dadfdb86ba87be923b42fb547810ef/BDD_select.thy"} |
# -*- coding: utf-8 -*-
from scipy.stats import rv_continuous
from scipy.stats import rv_discrete
from scipy.stats import _continuous_distns as crv_helper
from scipy.stats import _discrete_distns as drv_helper
import scipy.special as special
import numpy as np
import matplotlib.pyplot as plt
def plothistogram(values... | {"hexsha": "28bbcdbacb43377b76b59c9ffd544aee56733509", "size": 5119, "ext": "py", "lang": "Python", "max_stars_repo_path": "spectrocrunch/math/distributions.py", "max_stars_repo_name": "woutdenolf/spectrocrunch", "max_stars_repo_head_hexsha": "fde4b6e0f462f464ce7af6a942b355d3d8f39f77", "max_stars_repo_licenses": ["MIT"... |
#!/usr/bin/env python
# matteo: use subprocess.getoutput if available
# use os.path.join instead of +
from __future__ import print_function
import sys, os, platform
from setuptools import setup
from setuptools import Extension
import distutils.sysconfig
from Cython.Build import cythonize
import numpy
pri... | {"hexsha": "32c2a395d839e21f5be00308749331153d9bac77", "size": 4597, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "bshapiroalbert/libstempo", "max_stars_repo_head_hexsha": "e5e6231e9d9897aa161080baedd0ea210780460e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
# coding=utf-8
# Copyright 2022 The ML Fairness Gym Authors.
#
# 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 applicab... | {"hexsha": "608886bf2e96f6332b6ce9f66903c97f3f05055b", "size": 5372, "ext": "py", "lang": "Python", "max_stars_repo_path": "agents/recommenders/evaluation.py", "max_stars_repo_name": "jackblandin/ml-fairness-gym", "max_stars_repo_head_hexsha": "dce1feaacf2588e0a2d6187e896796241a25ed81", "max_stars_repo_licenses": ["Apa... |
# -*- coding: utf-8 -*-
import numbers
import numpy as np
from filterpy.common import Q_discrete_white_noise
class Process:
"""The Process class:
Define the F, Q, B and u matrices.
:param dim: The dimension.
"""
def __init__(self, dt, state):
"""
"""
self.F = np.a... | {"hexsha": "fa99ebc196a53ea1c307c0c4ba5c97a08ce8a43c", "size": 484, "ext": "py", "lang": "Python", "max_stars_repo_path": "zolware/process.py", "max_stars_repo_name": "zolware/zolware_API", "max_stars_repo_head_hexsha": "653e0f71cff440c5ff409b69bdb20b619af0b8bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import os
import numpy
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
module = 'cshepard'
setup(cmdclass = {'build_ext': build_ext},
name=module,
version='1.0',
ext_modules=[Extension(module,
[module + "... | {"hexsha": "778434e611ca69006381b40b64f273c5372e53b6", "size": 442, "ext": "py", "lang": "Python", "max_stars_repo_path": "csetup.py", "max_stars_repo_name": "sdickreuter/ToneGen", "max_stars_repo_head_hexsha": "69c554c7207563a69479202349061e1f8ef4f328", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_st... |
import xml.etree.cElementTree as Elem
import re
import nltk
import string
import numpy as np
import sys
import sklearn
import pickle
from sklearn.model_selection import cross_val_predict, ShuffleSplit, KFold
from nltk.tokenize import RegexpTokenizer
#from sklearn.grid_search import RandomizedSearchCV
import seaborn a... | {"hexsha": "95e6e354bae89133176b1c806e6ec727054e2072", "size": 29097, "ext": "py", "lang": "Python", "max_stars_repo_path": "AutomaticStructuring/CRF Model A/CRF_advancedmodel1_onpredicted.py", "max_stars_repo_name": "ShreyasiPathak/AutomaticStructuringBreastCancerReports", "max_stars_repo_head_hexsha": "a7e109b515e99f... |
subroutine amrex_probinit (init,name,namlen,problo,probhi) bind(c)
use amrex_fort_module, only : rt => amrex_real
use probdata_module
implicit none
integer init, namlen
integer name(namlen)
real(rt) :: problo(3), probhi(3)
integer untin,i
namelist /fortin/ probt... | {"hexsha": "431a13cfadd55f341f7dd2b7ce857dc7b7600ad9", "size": 7888, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Exec/HydroTests/Sedov/Prob_3d.f90", "max_stars_repo_name": "burlen/Nyx", "max_stars_repo_head_hexsha": "d31397361115bc9268da4e6addd3d3e77cc4798e", "max_stars_repo_licenses": ["BSD-3-Clause-LBNL"... |
!
! 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": "cc291b317d29d43b9c0a70d6538352fb8e79c51d", "size": 2730, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "ccx_prool/CalculiX/ccx_2.17/src/mafillp.f", "max_stars_repo_name": "alleindrach/calculix-desktop", "max_stars_repo_head_hexsha": "2cb2c434b536eb668ff88bdf82538d22f4f0f711", "max_stars_repo_license... |
"""
Usage: python schedule.py < input
See README.md for input format
"""
from itertools import islice
import networkx as nx
import numpy as np
import itertools
import math
import sys
import re
from absl import app
from .. import log
def _main(_argv):
log.init()
reading_tasks = False # else reading edges
... | {"hexsha": "82a1cfc67af8789bdff48e742c70d98aeeb876ce", "size": 4261, "ext": "py", "lang": "Python", "max_stars_repo_path": "fdd/main/schedule.py", "max_stars_repo_name": "vlad17/fdd", "max_stars_repo_head_hexsha": "fadf2a1595a31bf5eea4d750a986b3ffee0bfc06", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
"""
Check captioner says something about background for waterbirds.
python -m explainx.waterbird_check
"""
from typing import List
import fire
import numpy as np
import torch
import tqdm
from swissknife import utils
from .common import make_image2text_model, make_vqa_model
from .misc import load_image_tensor
devic... | {"hexsha": "0799c2afde1fafa7ab79aae47f3cdb1fa13d0e51", "size": 7976, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/explainx/waterbird_check.py", "max_stars_repo_name": "lxuechen/swissknife", "max_stars_repo_head_hexsha": "43dbd36f1e998ebe29c0b85fafd0de765dfb5de8", "max_stars_repo_licenses": ["MIT"]... |
subroutine dump(dumpfile)
! Write out a raw binary file containing all variables needed to continue computation
!-------------------------------------------------------------------------------------
! GLOBALS
use global
use zone
IMPLICIT NONE
! LOCALS
CHARACTER(LEN=3) :: dumpfile
CHARACTER(LEN=1) :: sf1,sf2,cha... | {"hexsha": "1b4ce5bf14572f23bdfc5aad639013172efa8977", "size": 2878, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "VH1/src/Patch/f2py/dump.f90", "max_stars_repo_name": "samgeen/Weltgeist", "max_stars_repo_head_hexsha": "c7d52e879bb3473cecbb06651b5e76dac3020da6", "max_stars_repo_licenses": ["MIT"], "max_stars... |
[STATEMENT]
lemma finite_Fvars_fm[simp]:
fixes A :: fm
shows "finite (Fvars A)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. finite (Fvars A)
[PROOF STEP]
by (induct A rule: fm.induct) auto | {"llama_tokens": 85, "file": "Goedel_HFSet_Semanticless_Instance", "length": 1} |
from numpy.random import RandomState
# import imageio
def test_bboxes():
PRNG = RandomState()
PRNG2 = RandomState()
if args.seed > 0:
PRNG.seed(args.seed)
PRNG2.seed(args.seed)
transform = Compose([
[ColorJitter(prob=0.5)], # or write [ColorJitter(), None]
... | {"hexsha": "a76c509cb3e4c9bf356455dbae4fc80d1f30709c", "size": 1909, "ext": "py", "lang": "Python", "max_stars_repo_path": "test_bounding_boxes.py", "max_stars_repo_name": "uoip/transforms", "max_stars_repo_head_hexsha": "80e00bc9f1a789c71d9da4efdde789b7526a6554", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import data.real.irrational
import topology.basic
import algebra.order.floor
--OUTPUT 1
theorem irrational_orbit_dense {α : ℝ} (hα_irrat : irrational α) : closure ((λ m : ℤ, int.fract (α * ↑m)) '' (@set.univ ℤ)) = set.Icc 0 1 :=
begin
--Let $\alpha$ be an irrational number. Then for distinct $i, j \in \mathbb{Z}$, ... | {"author": "ayush1801", "repo": "Autoformalisation_benchmarks", "sha": "51e1e942a0314a46684f2521b95b6b091c536051", "save_path": "github-repos/lean/ayush1801-Autoformalisation_benchmarks", "path": "github-repos/lean/ayush1801-Autoformalisation_benchmarks/Autoformalisation_benchmarks-51e1e942a0314a46684f2521b95b6b091c536... |
import sys
import theano.tensor as T
from mylog.mylog import mylog
from utility.utility import *
from data_processor.data_manager import *
from data_processor.data_loader import data_loader
from build_model.build_model import build_model, build_sampler
from build_model.parameters import *
from generation.generation ... | {"hexsha": "e904c83ced780cd20dbdee94f7de22f048f838b9", "size": 5770, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate.py", "max_stars_repo_name": "KaiQiangSong/Structure-Infused-Copy-Mechanism", "max_stars_repo_head_hexsha": "da159ea47516894829d34d3db05bd87b0398bb02", "max_stars_repo_licenses": ["BSD-3-C... |
"""
Filename: test_tauchen.py
Authors: Chase Coleman
Date: 07/22/2014
Tests for ricatti.py file
"""
import sys
import os
import unittest
import numpy as np
from numpy.testing import assert_allclose
from quantecon.riccati import dare
class TestDoubling(unittest.TestCase):
def setUp(self):
self.A, self.B... | {"hexsha": "c638ec3ff3368e40592b364b81f43fb73aff62d1", "size": 1156, "ext": "py", "lang": "Python", "max_stars_repo_path": "quantecon/tests/test_ricatti.py", "max_stars_repo_name": "sglyon/quant-econ", "max_stars_repo_head_hexsha": "67d44ed719c9e6202c53f3b18d16ddf7e666e58b", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
import torch
from torch.autograd import Variable
import utils
import dataset
from PIL import Image
import cv2 as cv
import os
import numpy as np
import models.crnn as crnn
debug = False
model_path = './data/crnn.pth'
gt_path = './data/res/'
img_path = '/data/home/zjw/pythonFile/masktextspotter.caffe2/lib/datasets/dat... | {"hexsha": "6b08caa1e563301a560a4dc314296c548bbabba9", "size": 2179, "ext": "py", "lang": "Python", "max_stars_repo_path": "testEvalOneImage.py", "max_stars_repo_name": "zhengjiawen/crnn.pytorch", "max_stars_repo_head_hexsha": "0721deb8c2914a5a090b231a644c2331d2fc9bd9", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import netCDF4 as netcdf
import numpy as np
f = netcdf.Dataset('data/md-solvent-langevin.nc', 'r')
dis = f.variables['distance']
chunksize = 50000
data = []
maxstep = dis.shape[0]
i = range(0, maxstep + chunksize, chunksize)
for k in xrange(len(i)-1):
print i[k], i[k+1]
data.append(dis[i[k]:i[k+1]])
d = np... | {"hexsha": "c691d4db571253199923dbbb48a9f149d05a31a3", "size": 388, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/examples/wca-dimer_openmm/bruteforce/extract_distance.py", "max_stars_repo_name": "ajoshpratt/westpa", "max_stars_repo_head_hexsha": "545a42a5ae4cfa77de0e125a38a5b1ec2b9ab218", "max_stars_repo_... |
import datetime
from datetime import date
import pytz
import string
import random
import pandas as pd
import numpy as np
import h5py
import math
import os
from skimage import io
from skimage.draw import polygon
#import matplotlib.pyplot as plt
#from nwbwidgets import nwb2widget
from pynwb import NWBFile, TimeSerie... | {"hexsha": "117a54a80b4d705117e523d0e241211a81899924", "size": 11402, "ext": "py", "lang": "Python", "max_stars_repo_path": "eln2nwb/convert2nwb.py", "max_stars_repo_name": "DSegebarth/DCL_to_NWB", "max_stars_repo_head_hexsha": "71025ece4ccc227eb58ad9b2e5db05b7a53bc621", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
import itertools
from pwtools import crys, common, atomic_data, num
from pwtools.crys import Structure, Trajectory
from pwtools.test import tools
rand = np.random.rand
syms = itertools.cycle(atomic_data.symbols[1:])
def test_scell():
cell = np.identity(3)
coords_frac = np.array([[0.5, 0.5, ... | {"hexsha": "221ba4387584df623b8dd64dcd6f0770e4e331d9", "size": 8327, "ext": "py", "lang": "Python", "max_stars_repo_path": "pwtools/test/test_scell.py", "max_stars_repo_name": "elcorto/pwtools", "max_stars_repo_head_hexsha": "cee068d1c7984d85e94ace243f86de350d3a1dba", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_s... |
[STATEMENT]
lemma LIMSEQ_linear: "X \<longlonglongrightarrow> x \<Longrightarrow> l > 0 \<Longrightarrow> (\<lambda> n. X (n * l)) \<longlonglongrightarrow> x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>X \<longlonglongrightarrow> x; 0 < l\<rbrakk> \<Longrightarrow> (\<lambda>n. X (n * l)) \<longlonglon... | {"llama_tokens": 294, "file": null, "length": 2} |
Image(imageDukeMcAdow.jpg, right, thumbnail) Duke McAdow moved to Northern California from Southern California Los Angeles in 2001 because he had grown tired of life in the big city.
He works at UC Davis solely to keep his two cats supplied with the expensive food, toys, scratchers and plush beds they demand. Althoug... | {"hexsha": "bc0d0a74e2b07d5d1546e85742c8e5c6c5219119", "size": 3606, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/DukeMcAdow.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
#getLog(mt) : Returns the natural log of the matrix.
import numpy as np
from .isPSDMd import isPSD
__all__ = ['getLog']
def getLog(M, eps=1e-15):
r"""Takes as input a matrix M and returns the natural log of M.
Parameters
----------
M : numpy.ndarray
2-d array representing a... | {"hexsha": "cd9a270db06869d77aba723936d043df67928ccc", "size": 1258, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/qinfpy/basic/getLogMd.py", "max_stars_repo_name": "vsiddhu/qinfpy", "max_stars_repo_head_hexsha": "f8f29070c31cc5577e66cad093b0686108d237d4", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
from hoomd import *
from hoomd import md
import numpy as np
sigma = 4.629 #A
la = 6.00
lr = 19.61
kBby10 = 0.83144622
eps = 414.90 * kBby10
ms = 3
mass = 142.2817 / ms
#equals to 1668 molecules of 3 beads
N = 1668
Lx = 57.63
Ly = Lx
Lz = 345.78
Nequilibrium = 5000000
Nproduction = 12000000
Ntotal = Nequilibrium ... | {"hexsha": "be3dd46504c7d046236068ee47161fc62145a262", "size": 3504, "ext": "py", "lang": "Python", "max_stars_repo_path": "input_files/HOOMD/CGC10/config_IK.py", "max_stars_repo_name": "livecomsjournal/BPIPMDS", "max_stars_repo_head_hexsha": "7491ed8a66acaf4a879cacc6ae29e4220d268a1c", "max_stars_repo_licenses": ["CC-B... |
# Copyright 2020 Xilinx Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "025dde600737576655b9d90658e57217aaccf120", "size": 9422, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/unit/_libpyxir/test_xgraph.py", "max_stars_repo_name": "pankajdarak-xlnx/pyxir", "max_stars_repo_head_hexsha": "a93b785a04b6602418c4f07a0f29c809202d35bd", "max_stars_repo_licenses": ["Apache... |
import numpy as np
class Optimizer:
def __init__(self, optimizer, learning_rate,
param1, param2):
self.optimizer = optimizer
self.learning_rate = learning_rate
self.param1 = param1
self.param2 = param2
self.moment1 = 0
self.moment2 = 0
self.m... | {"hexsha": "e980a830ea248ba4c6cd1252c0ddc03662b5311c", "size": 4050, "ext": "py", "lang": "Python", "max_stars_repo_path": "SimpleNN/Optimizer.py", "max_stars_repo_name": "joel2411/Simple-Neural-Network", "max_stars_repo_head_hexsha": "b8de22f57073944541a5c2df4c6918c9a665abb3", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/usr/bin/env python
# We use Python 2 instead of python3 bacause ROS uses Python 2.
# ref. https://numpy.org/doc/1.18/numpy-user.pdf
# Single Beam Sonar
# Sonar Point-Scatter Model
# Contributors: Andreina Rascon, Derek Olson, Woeng-Sug Choi
from random import random
from math import sqrt, sin, cos, pi, log, acos
... | {"hexsha": "ae5966aae8526b26e185fa69b4bc49c93af6e98f", "size": 9644, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/sonar_equations.py", "max_stars_repo_name": "daewok/nps_uw_sensors_gazebo", "max_stars_repo_head_hexsha": "f49df01ca54ac7911888a56b2291af6813000b93", "max_stars_repo_licenses": ["Apache-2.... |
import numpy as np
from math import floor
import random
from keras.models import Model
from keras.layers import Dense, Input
from keras.models import load_model
import sys
from sklearn.cluster import KMeans
import csv
autoencoder_model_path = 'hw6_autoencoder.h5'
encoder_model_path = 'hw6_encoder.h5'
random.seed(24)... | {"hexsha": "e7d96cc0b38eec2f53fd954584f98098623940f3", "size": 4256, "ext": "py", "lang": "Python", "max_stars_repo_path": "2017 Fall/EE5184 - Machine Learning/homework/homework_06/hw6.py", "max_stars_repo_name": "Hsins/NTUCourse", "max_stars_repo_head_hexsha": "5a623a52761ceb649621b4c3f140697c8cdb5d88", "max_stars_rep... |
# -*- coding: utf-8 -*-
"""
Created on Tue May 28 12:41:11 2019
@author: Nikita
"""
import pandas as pd
import numpy as np
# create a data frame - dictionary is used here where keys get converted to column names and values to row values.
data = pd.DataFrame({'Country': ['Russia', 'Colombia', 'Chile', 'Equador... | {"hexsha": "ef3a3e9c6e7b4a1f4e725edd03cb2d936e8c647c", "size": 3563, "ext": "py", "lang": "Python", "max_stars_repo_path": "DataSciencePractice/DataScience/pandas.py", "max_stars_repo_name": "47shubh/blog", "max_stars_repo_head_hexsha": "79f349411c7cfbbc52010f5627401b33c74cc40b", "max_stars_repo_licenses": ["Apache-2.0... |
'''
Created on 14/6/2020
@author: Neil Symington
This script is for converting aseg-gdf EM data to a netcdf file. The netcdf file will also include some additional
AEM system metadata.
'''
from geophys_utils.netcdf_converter import aseg_gdf2netcdf_converter
import netCDF4
import os, math
import numpy as np
# SO we can... | {"hexsha": "44d9a206c6ade5b3e2fab7896cb78201ffca8516", "size": 2061, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/conversion/lci_data_conversion.py", "max_stars_repo_name": "Neil-Symington/aem_interp_dash", "max_stars_repo_head_hexsha": "f7c6f385838b455a2c1e9d3a1db5f675a327b8dd", "max_stars_repo_license... |
import numpy as num
from decimal import *
import scipy as sci
from numpy.polynomial import polynomial as pol
def euler(f,a,b,n ,y_0):
h=Decimal((b-a))/Decimal(n)
vals = []
vals.append(y_0)
print ("Indice\t | t | Aproximado(u) ")
print("0\t | 0 |\t"+str(y_0))
for i in range (0, n-1):
... | {"hexsha": "9618b6567a2b2ba2b7280ca8ca19eb3ca8076de7", "size": 573, "ext": "py", "lang": "Python", "max_stars_repo_path": "practica3/1.py", "max_stars_repo_name": "danipozo/practicas-mnii", "max_stars_repo_head_hexsha": "f4afe725316c694a4cd06e2ce3c0019f4f68652f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from diac_h2h.network... | {"hexsha": "b4c9df5757f0733fb0968c043bb0ee60afb9f693", "size": 3349, "ext": "py", "lang": "Python", "max_stars_repo_path": "networks/iotnets/random_net_resnet.py", "max_stars_repo_name": "dengliming/iotnets", "max_stars_repo_head_hexsha": "db744e56769c799dbf765a27fc5aa91e3edeaaa3", "max_stars_repo_licenses": ["MIT"], "... |
#ifndef _ROBOT_PLUGIN_HH_
#define _ROBOT_PLUGIN_HH_
#include <ros/ros.h>
#include <ros/callback_queue.h>
#include <ros/subscribe_options.h>
#include <gazebo/gazebo.hh>
#include <gazebo/physics/physics.hh>
#include <gazebo_braitenberg_robot/Sensor.h>
#include <thread>
#include <math.h>
#include <Eigen/Dense>
using nam... | {"hexsha": "849bcb52041130c7285668db2b2479275903822b", "size": 4634, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/robot_plugin.cpp", "max_stars_repo_name": "merlin24u/Gazebo_Braitenberg_Robot", "max_stars_repo_head_hexsha": "5c58d64411c6aee5d071b67f498977205d1490f8", "max_stars_repo_licenses": ["BSD-3-Claus... |
export datachunk
export split_into_n, split_tod_mpi, get_chunk_properties
import Healpix
import CorrNoise
using Random
using FITSIO
try
import MPI
catch
end
"""
This structure holds a number of parameters relative to a certain chunk of data.
Field | Type | Meaning
:----------------- |:... | {"hexsha": "6f19bb17289d8744c097720b5271d6da7f1046d8", "size": 8756, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tod_splitter.jl", "max_stars_repo_name": "fincardona/Stripeline.jl", "max_stars_repo_head_hexsha": "e4dd169f9952e26b16292dccd44ce64cf69db67e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
!
! File iomex.f90
!
! snPRNT ioTRIM snREAD
!
!+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
subroutine snPRNT ( mode, string, iw, leniw )
implicit none
character*(*) string
integer mode, leniw, iw... | {"hexsha": "429007b297156f07416936360045c1728ced291d", "size": 6703, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "mex/iomex.f90", "max_stars_repo_name": "sh-cau/snopt-matlab", "max_stars_repo_head_hexsha": "b2222596b0d02347f9c3708ac7e6a8f727bc35bc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 49,... |
module stpdwmod
!$$$ module documentation block
! . . . .
! module: stpdwmod module for stpdw and its tangent linear stpdw_tl
! prgmmr:
!
! abstract: module for stpdw and its tangent linear stpdw_tl
!
! program history log:
! 2005-05-18 Yanqiu zhu - w... | {"hexsha": "162cab8d57d92c6a1a2e8a2099d12b9caa6e3ad7", "size": 6194, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "GEOSaana_GridComp/GSI_GridComp/stpdw.f90", "max_stars_repo_name": "GEOS-ESM/GEOSana_GridComp", "max_stars_repo_head_hexsha": "cf33607613754313a2383bb7e7b3d29c856b9daf", "max_stars_repo_licenses"... |
#=
def linear(a, b, x):
return b + a*x
=#
linear(a, b, x) = b + a * x
#=
# a linear demand function is generated for every
# pair of coefficients in vectors a_vec and b_vec
def demand_hypotheses(a_vec, b_vec):
for a, b in itertools.product(a_vec, b_vec):
yield {
'd': functools.partial(l... | {"hexsha": "d96a3dab2cff4392e8978eb8fba201ca00fc0cb9", "size": 778, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "approaches/dynamic_pricing/dynamic_pricing/price_demand_models.jl", "max_stars_repo_name": "StatisticalRethinkingJulia/DynamicPricingExamples.jl", "max_stars_repo_head_hexsha": "a6fae1736bf30f7aeed2... |
function FieldEventsLogger()
return FieldEventsLogger(())
end
function clear_logged_events(obj::FieldEventsLogger)
return jcall(obj, "clearLoggedEvents", void, ())
end
function get_logged_events(obj::FieldEventsLogger)
return jcall(obj, "getLoggedEvents", List, ())
end
function monitor_detector(obj::Fiel... | {"hexsha": "427194c5c9d259e640c292cb3cae21e80099a2bd", "size": 456, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "gen/OrekitWrapper/PropagationWrapper/EventsWrapper/field_events_logger.jl", "max_stars_repo_name": "JuliaAstrodynamics/Orekit.jl", "max_stars_repo_head_hexsha": "e2dd3d8b2085dcbb1d2c75471dab42d6ddf5... |
/-
Copyright (c) 2021 Bhavik Mehta. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Bhavik Mehta, Alena Gusakov, Yaël Dillies
-/
import data.finset.slice
import logic.function.iterate
/-!
# Shadows
> THIS FILE IS SYNCHRONIZED WITH MATHLIB4.
> Any changes to this file ... | {"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/combinatorics/set_family/shadow.lean"} |
[STATEMENT]
lemma little_Fermat_int:
fixes a :: int and p :: nat
assumes "prime p" "\<not>p dvd a"
shows "[a ^ p = a] (mod p)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. [a ^ p = a] (mod int p)
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. [a ^ p = a] (mod int p)
[PROOF STEP]
hav... | {"llama_tokens": 2512, "file": "Mersenne_Primes_Lucas_Lehmer_Auxiliary", "length": 37} |
from collections import Counter
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from wordcloud import WordCloud, ImageColorGenerator
from ..utils.file_handling import read_df_from_file
entities = read_df_from_file("data/dataframes/merged_entities_10k_df.jsonl")
entities_duplicated = []
for... | {"hexsha": "c3d622983dc5452acd1b3b3a593cd80feb0b48e3", "size": 981, "ext": "py", "lang": "Python", "max_stars_repo_path": "ner/entity_processing/clouding.py", "max_stars_repo_name": "BonnierNews/lukas-ner-model", "max_stars_repo_head_hexsha": "1f7f688f9b0f1e7b7cb66c42f188358d27a0be09", "max_stars_repo_licenses": ["MIT"... |
import numpy as np
class oneR:
#Constructor
def __init__(self):
self.rule = []
self.accuracy = 0
self.fitShape = []
self.targets = []
def _checkInputs(self,X,y) -> bool:
"""
Internal function to ensure input is valid.
Parameters:
X: Array-... | {"hexsha": "7b06c0508775a9533f30fdb9820a0616e8b9b8c9", "size": 4902, "ext": "py", "lang": "Python", "max_stars_repo_path": "py1r.py", "max_stars_repo_name": "ErikShively/Py1R", "max_stars_repo_head_hexsha": "59b4c72083282e6e0625d8b5370fa14f07bb4bce", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
# Copyright (c) 2021, Alessandro Abate, Daniele Ahmed, Alec Edwards, Mirco Giacobbe, Andrea Peruffo
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import sympy as sp
import numpy as np
import copy
import torc... | {"hexsha": "a88c9ac5e4e96213e91a4a0d7311cc78a670cd65", "size": 6798, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/shared/system.py", "max_stars_repo_name": "oxford-oxcav/fossil", "max_stars_repo_head_hexsha": "f5b8e2bba80d8792b149ee75b51d3ee74df9b88e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
import random
import numpy as np
from playsound import playsound
import eleonora.utils.config as config
from eleonora.interact.Mindfulness import *
from eleonora.utils.input import message, warning, userInput
class Emotion(object):
def __init__(self, emotion, speech=None):
self.emotion = emotion
se... | {"hexsha": "1d60781fa0d7b33ac15890e5b37da66d04dae369", "size": 3357, "ext": "py", "lang": "Python", "max_stars_repo_path": "eleonora/modules/Interact.py", "max_stars_repo_name": "gert-janwille/Eleonora", "max_stars_repo_head_hexsha": "a979dcd9b41231ea3abc9a57d842c680314ac9ca", "max_stars_repo_licenses": ["MIT"], "max_s... |
import phase1.basic
open set with_bot
universe u
namespace con_nf
variables [params.{u}] (α : Λ) [core_tangle_cumul α] {β : Iio_index α}
{s t : set (tangle β)}
/-- An `α` code is a type index `β < α` together with a set of tangles of type `β`. -/
@[derive inhabited] def code : Type u := Σ β : Iio_index α, set (ta... | {"author": "leanprover-community", "repo": "con-nf", "sha": "f0b66bd73ca5d3bd8b744985242c4c0b5464913f", "save_path": "github-repos/lean/leanprover-community-con-nf", "path": "github-repos/lean/leanprover-community-con-nf/con-nf-f0b66bd73ca5d3bd8b744985242c4c0b5464913f/src/phase1/code.lean"} |
##predefined_condition_begin
rootdir<-"/scratch/cqs/shengq1/vickers/20170222_smallRNA_3018_61_human_v3/host_genome/deseq2_miRNA/result"
inputfile<-"3018_61.define"
showLabelInPCA<-1
showDEGeneCluster<-1
pvalue<-0.05
foldChange<-1.5
minMedianInGroup<-5
addCountOne<-0
usePearsonInHCA<-0
top25only<-0
detec... | {"hexsha": "5c3c123a5587773b07cfd0a91bb7243dfce3c913", "size": 49816, "ext": "r", "lang": "R", "max_stars_repo_path": "lib/Comparison/DESeq2.r", "max_stars_repo_name": "shengqh/ngsperl", "max_stars_repo_head_hexsha": "f81d5bf30171950583bb1ab656f51eabc1e9caf6", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
# Copyright 2017 Google Inc. and Skytruth Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agr... | {"hexsha": "c860c5619297217d292455c1217daf94559df389", "size": 1533, "ext": "py", "lang": "Python", "max_stars_repo_path": "classification/models/models_test.py", "max_stars_repo_name": "marketler/GFW_Vessel_Classification", "max_stars_repo_head_hexsha": "fb2ada9aeebe2582b42e940db86674fd4da6fb07", "max_stars_repo_licen... |
import numpy as np
import random as rnd
from sklearn.base import clone
from sklearn.model_selection import train_test_split
from tqdm import trange
from .gafs import *
__version__ = '0.0.2' | {"hexsha": "aa8c1297080b43ae6f7d0b703b1aa5896cb443b8", "size": 189, "ext": "py", "lang": "Python", "max_stars_repo_path": "gafs/__init__.py", "max_stars_repo_name": "Shemka/GAFS", "max_stars_repo_head_hexsha": "70007116b712a7f700111c04fb9fac5c21654d71", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 10, "max_st... |
[STATEMENT]
lemma ma_sqrt_main: "ma_rat r \<ge> 0 \<Longrightarrow> ma_coeff r = 0 \<Longrightarrow> sqrt (real_of r) = real_of (ma_sqrt r)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>0 \<le> ma_rat r; ma_coeff r = 0\<rbrakk> \<Longrightarrow> sqrt (real_of r) = real_of (ma_sqrt r)
[PROOF STEP]
proof (t... | {"llama_tokens": 14281, "file": "Real_Impl_Real_Impl", "length": 75} |
Program zheevr_example
! ZHEEVR Example Program Text
! Copyright (c) 2018, Numerical Algorithms Group (NAG Ltd.)
! For licence see
! https://github.com/numericalalgorithmsgroup/LAPACK_Examples/blob/master/LICENCE.md
! .. Use Statements ..
Use blas_interfaces, Only: zscal
Use lap... | {"hexsha": "f6b955af16ec294e5f24c24285afe01e41da8033", "size": 3389, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "examples/source/zheevr_example.f90", "max_stars_repo_name": "numericalalgorithmsgroup/LAPACK_examples", "max_stars_repo_head_hexsha": "0dde05ae4817ce9698462bbca990c4225337f481", "max_stars_repo_... |
// Function by the Orthanc project to load a dictionary from a memory
// buffer, which is necessary in sandboxed environments. This is an
// adapted version of DcmDataDictionary::loadDictionary().
#include <string>
#include <boost/noncopyable.hpp>
struct OrthancLinesIterator;
// This plain old C class is implemented... | {"hexsha": "b4bf8c283e18ee616ce310c9ca337058333b7e50", "size": 5301, "ext": "cc", "lang": "C++", "max_stars_repo_path": "DicomWebStorge/Orthanc-1.7.4/OrthancFramework/Resources/Patches/dcmtk-dcdict_orthanc.cc", "max_stars_repo_name": "a2609194449/Assistant-decision-making-system-for-gallbladder-cancer-", "max_stars_rep... |
cdis
cdis Open Source License/Disclaimer, Forecast Systems Laboratory
cdis NOAA/OAR/FSL, 325 Broadway Boulder, CO 80305
cdis
cdis This software is distributed under the Open Source Definition,
cdis which may be found at http://www.opensource.org/osd.html.
cdis
cdis In particular, redistributio... | {"hexsha": "547f855bf0f39c929f039bcdad625ca566ff57fa", "size": 2204, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/lib/mthermo/tcon.f", "max_stars_repo_name": "maxinye/laps-mirror", "max_stars_repo_head_hexsha": "b3f7c08273299a9e19b2187f96bd3eee6e0aa01b", "max_stars_repo_licenses": ["Intel", "Unlicense", "... |
[STATEMENT]
lemma (in loc1) [simp]: "infinite (deriv s) ==> init s ==> (contains f n (m,A)) ==> ~ is_FEx A ==> m = 0"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>infinite (deriv s); init s; contains f n (m, A); \<not> is_FEx A\<rbrakk> \<Longrightarrow> m = 0
[PROOF STEP]
apply(frule_tac n=n in index0)
[... | {"llama_tokens": 1286, "file": "Verified-Prover_Prover", "length": 8} |
"""
Module for move generation in the game Reversi.
Module for move generation in the game Reversi. The algorithm is implemented
quite inefficiently and could be improved.
"""
import numpy
BOARD_SIZE = 8
EMPTY = 2
BLACK = 0
WHITE = 1
a = ord("a")
NOTATION_CHART = {n: chr(n + a) for n in xrange(8)}
COORDINATE_CHART ... | {"hexsha": "bde8013f3781b29bee4eef46dd64a667bb125dd1", "size": 10858, "ext": "py", "lang": "Python", "max_stars_repo_path": "reversi.py", "max_stars_repo_name": "steven-xia/reversi-bot", "max_stars_repo_head_hexsha": "a645159f5a39686348d3989a75350b0feb217d03", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count"... |
import scipy
# Basic layout parameters
partDiameter = 1.4
partDepth = 0.45
params = {}
params['numParts'] = 5
params['partSpacing'] = 2.0
layoutLen = (params['numParts']-1)*params['partSpacing']
xPosArray = scipy.linspace(-0.5*layoutLen, 0.5*layoutLen,params['numParts'])
yPosArray = scipy.zeros(xPosArray.size)
para... | {"hexsha": "025eb32dd0821e26e401d073bd89b8d29441f71e", "size": 1067, "ext": "py", "lang": "Python", "max_stars_repo_path": "cnc/motor_hub/motor_hub/magnet_and_boundary/params.py", "max_stars_repo_name": "iorodeo/stir_plate_mechanics", "max_stars_repo_head_hexsha": "ad721e708d962afcb14dd69456df4231c83ffed8", "max_stars_... |
"""
Copyright (C) 2021 NVIDIA Corporation. All rights reserved.
Licensed under the NVIDIA Source Code License. See LICENSE at the main github page.
Authors: Seung Wook Kim, Jonah Philion, Antonio Torralba, Sanja Fidler
"""
import os
import sys
import numpy as np
import torch.utils.data as data_utils
import cv2
import... | {"hexsha": "2e93d816a8d5ad099b65c2a937163abcf2d916f5", "size": 13442, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/dataloader.py", "max_stars_repo_name": "LvZut/DriveGAN_code", "max_stars_repo_head_hexsha": "6fd29dc6a0bc9e4a45b7db329ff8e951bd55432a", "max_stars_repo_licenses": ["BSD-2-Clause", "MIT"], "m... |
from . import ccllib as lib
from .pyutils import check
from .pk2d import Pk2D
import numpy as np
def bcm_model_fka(cosmo, k, a):
"""The BCM model correction factor for baryons.
.. note:: BCM stands for the "baryonic correction model" of Schneider &
Teyssier (2015; https://arxiv.org/abs/1510.060... | {"hexsha": "8ccfeb1f88b68eeba9dacaffde5e06f4dc6b1443", "size": 1588, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyccl/bcm.py", "max_stars_repo_name": "Jappenn/CCL", "max_stars_repo_head_hexsha": "a37cad61f060f3928fa5d47b1e2670db3e9bce6f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count": 91, "... |
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