text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
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import scipy
import numpy as np
import os
import sys
from data_profiler.labelers.classification_report_utils import classification_report
import warnings
from sklearn.exceptions import UndefinedMetricWarning
warnings.filterwarnings("ignore", category=UndefinedMetricWarning)
# in case of data profiler in own repo
_f... | {"hexsha": "1c2a3f3cf086618942b658e64f967e06bb6596e3", "size": 7719, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_profiler/labelers/labeler_utils.py", "max_stars_repo_name": "gme5078/data-profiler", "max_stars_repo_head_hexsha": "602cc5e4f4463f9b807000abf3893815918d0723", "max_stars_repo_licenses": ["Apa... |
import argparse
import baltic as bt
import matplotlib.pyplot as plt
import matplotlib.patheffects as path_effects
from matplotlib.gridspec import GridSpec
from matplotlib.patches import Rectangle
from matplotlib.colors import LinearSegmentedColormap
import seaborn as sns
import glob
import ast
from treetime.utils impor... | {"hexsha": "e6a18ff3874fc854bc7293b87cc8191c6cc96ed6", "size": 3639, "ext": "py", "lang": "Python", "max_stars_repo_path": "phylogenetic_analysis/scripts/plot_clades_per_week.py", "max_stars_repo_name": "Piantadosi-Lab/SARS-CoV-2_ATL_Introductions", "max_stars_repo_head_hexsha": "cf201410454536006508aafff83ad32aecee19b... |
## ----author info, include=F----------------------------------------------
## Author: Yanchang Zhao
## Email: yanchang@RDataMining.com
## Website: http://www.RDataMining.com
## Date: 9 December 2018
## ----load libraries, include=F, echo=F-----------------------------------
## load required packages
library(dtw... | {"hexsha": "4b41b74c6e119114ec1dfd10ac7fea2284a529fa", "size": 5482, "ext": "r", "lang": "R", "max_stars_repo_path": "Scripts/RDM-script-time-series-analysis.r", "max_stars_repo_name": "enriqueescobar-askida/Kinito.R.DataMining", "max_stars_repo_head_hexsha": "766ece2ad9a30a0dc78a9fa9b27efdfb1be96ace", "max_stars_repo_... |
import os
import sys
import random
import math
import numpy as np
import cv2
from mrcnn.config import Config
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn.model import log
import tensorflow as tf
class PlaneConfig(Config):
NAME="multiobject2"
GPU_COUNT=1
IMAGES_PER_GPU=2
NUM_CLASSES=... | {"hexsha": "d9b7d93e258790910250c8fac5a653383d82bd63", "size": 4540, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "ChienWong/Mask_RCNN", "max_stars_repo_head_hexsha": "f9d2592d8664a1abd7fd250fd129dc2bdb7c8c18", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
```python
# import libraries and modules
import numpy as np
import sympy as sp
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib.animation import PillowWriter
```
```python
# The symbols and the derivatives which will be used for later c... | {"hexsha": "581da1d8e8958fda4b68f862c488c657506cfeb4", "size": 113364, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "Double pendulum.ipynb", "max_stars_repo_name": "ScientificArchisman/Simulations", "max_stars_repo_head_hexsha": "b9f3e7cc5d94a150931c12dac5fa21391736c47f", "max_stars_repo_licenses"... |
export eval_apl
# eval
eval_apl(ex) = eval_apl(ex, nothing, nothing)
eval_apl(f, α, ω) = f
eval_apl(v::JlVal, α, ω) = v.val
eval_apl(::Α, α, ω) = α
eval_apl(::Ω, α, ω) = ω
eval_apl(x::Apply, α, ω) = eval_apl(x.f, α, ω)(eval_apl(x.r, α, ω))
eval_apl(x::ConcArr, α, ω) = vcat(eval_apl(x.l, α, ω), eval_apl(x.r, α, ω))
... | {"hexsha": "e5b89c4a645ec66c73ad37f549593880aa22bd40", "size": 1765, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/eval.jl", "max_stars_repo_name": "JuliaTagBot/APL.jl", "max_stars_repo_head_hexsha": "5806736476ad3547b0955f53af5992f35136a35e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 47, "max_... |
from __future__ import print_function
from __future__ import division
from hoomd import *
from hoomd import hpmc
import hoomd
import numpy
import math
import sys
import os
import unittest
import tempfile
context.initialize()
class convex_polyhedron(unittest.TestCase):
def setUp(self):
# setup the MC integ... | {"hexsha": "c6348138eefa232e1c618802e6f7ee948e7b8277", "size": 26915, "ext": "py", "lang": "Python", "max_stars_repo_path": "hoomd/hpmc/test-py/max_verts.py", "max_stars_repo_name": "kmoskovtsev/HOOMD-Blue-fork", "max_stars_repo_head_hexsha": "99560563a5ba9e082b513764bae51a84f48fdc70", "max_stars_repo_licenses": ["BSD-... |
[STATEMENT]
lemma qbs_bind_return':
assumes "x \<in> monadP_qbs_Px X"
shows "x \<bind> qbs_return X = x"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<bind> qbs_return X = x
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. x \<bind> qbs_return X = x
[PROOF STEP]
obtain \<alpha> \<mu> w... | {"llama_tokens": 1038, "file": "Quasi_Borel_Spaces_Monad_QuasiBorel", "length": 11} |
#include <boost/asio.hpp>
#include <boost/asio/spawn.hpp>
#include <iostream>
using boost::asio::ip::tcp;
using boost::asio::yield_context;
int main() {
boost::asio::io_service svc;
tcp::acceptor a(svc);
a.open(tcp::v4());
a.set_option(tcp::acceptor::reuse_address(true));
a.bind({{}, 6767}); //... | {"hexsha": "12271fad003488e42f672779db35801cc899692e", "size": 1066, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tests/cpp/hello_boost/echo.cc", "max_stars_repo_name": "resonai/ybt", "max_stars_repo_head_hexsha": "48e9f9b8bc02686c95b2afc29265b799ff9d80da", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_c... |
import numpy as np
from scipy.stats import norm
from scipy.optimize import fminbound
__all__ = ["polyserial_correlation"]
def polyserial_correlation(continuous, ordinal):
"""Computes the polyserial correlation.
Estimates the correlation value based on a bivariate
normal distribution.
Args:... | {"hexsha": "f7ab4cf38688f965b63c2bf3c5d3ba9403d12bf5", "size": 1966, "ext": "py", "lang": "Python", "max_stars_repo_path": "common/polyserial.py", "max_stars_repo_name": "eribean/GIRTH", "max_stars_repo_head_hexsha": "daf22773aa9cd1c819bf732e1061ebf5cc4dc40e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 43, ... |
# coding=utf-8
# Copyright 2021 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed ... | {"hexsha": "934dfeb9d891b5ec6e9953fc9880aff57b36d120", "size": 5907, "ext": "py", "lang": "Python", "max_stars_repo_path": "learned_optimization/outer_trainers/full_es_test.py", "max_stars_repo_name": "google/learned_optimization", "max_stars_repo_head_hexsha": "1c9ee0159c97815fc6afe79a76224fb28b199053", "max_stars_rep... |
include("../src/includes.jl")
const TEST_EXCHANGE = "testExchange"
struct IntSource <: Source{Int}
pollFn::Function
IntSource(coll) = new(() -> length(v) > 0 ? pop!(coll) : nothing)
end
v = collect(1:10000)
@async source!(IntSource(v)) |> sink!("testExchange")
readline()
| {"hexsha": "f68a78065e6e1b3708467c461719b5285eebd0e7", "size": 285, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/test-pub-with-config.jl", "max_stars_repo_name": "garethhu/ReactiveAmqp.jl", "max_stars_repo_head_hexsha": "2a916a2965c90d25ed1229bde7c2eb8db202c799", "max_stars_repo_licenses": ["MIT"], "m... |
import numpy as np
input_data = np.array([2,3])
weights = {
'node_0': np.array([1,1]),
'node_1': np.array([-1,1]),
'output': np.array([2,-1])
}
node_0_val = np.dot(input_data,weights['node_0'])
node_1_val = np.dot(input_data,weights['node_1'])
node_2_val = np.dot(np.array([node_0_val,... | {"hexsha": "510cc2d608983c70234a4b4f564e619895454ceb", "size": 393, "ext": "py", "lang": "Python", "max_stars_repo_path": "Deep-Learning-In-Python/Module-1/forward-propagation.py", "max_stars_repo_name": "vishwesh5/Datacamp-Courses", "max_stars_repo_head_hexsha": "f074ec25e373c3d1d2edb1629c5568001aeadec1", "max_stars_r... |
[STATEMENT]
lemma both_mono1':
"t \<sqsubseteq> t' \<Longrightarrow> t \<otimes>\<otimes> t'' \<sqsubseteq> t' \<otimes>\<otimes> t''"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. t \<sqsubseteq> t' \<Longrightarrow> t \<otimes>\<otimes> t'' \<sqsubseteq> t' \<otimes>\<otimes> t''
[PROOF STEP]
using both_mono1[... | {"llama_tokens": 244, "file": "Call_Arity_TTree-HOLCF", "length": 2} |
-- <html>
-- <head>
-- <BASE HREF="http://www.numeric-quest.com/haskell/Orthogonals.html">
-- <title>
-- Indexless linear algebra algorithms
-- </title>
-- </head>
-- <body>
-- <ul>
-- <center>
-- <h1>
-- ***
-- </h1>
-- <h1>
-- Indexless linear algebra algorithms
-- </h1>
-- <b... | {"hexsha": "a54d8b00f0d433b414de1bfd766b5ffd6e12f7fe", "size": 65137, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "src/Orthogonals.hs", "max_stars_repo_name": "rzil/wLPAs", "max_stars_repo_head_hexsha": "d8cde11e4ff40c802d1f79d423f0e676ccd49d59", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
module Mandelbrot where
import Diagrams.Backend.Cairo.CmdLine
import Diagrams.Prelude
import Data.Complex
quadratic :: Complex Double -> Complex Double -> Complex Double
quadratic c z = z * z + c
orbit :: Complex Double -> Complex Double -> [Complex Double]
orbit c = iterate (quadratic c)
criticalOrbit :: Complex ... | {"hexsha": "6055a8ead0c2234c028e6ae540c9cb96d75d71ca", "size": 1321, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "src/Mandelbrot.hs", "max_stars_repo_name": "FayeAlephNil/diagrams-fun", "max_stars_repo_head_hexsha": "a59e35a602ef0eb1c8511c86b10a42d3e96c4692", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
#!/usr/bin/env python
# coding: utf-8
""" BSD 3-Clause License
Copyright (c) 2020, Fred Kellerman
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the abov... | {"hexsha": "cdc40706667bd3b96e6116a7fae975bc4d1530c7", "size": 4398, "ext": "py", "lang": "Python", "max_stars_repo_path": "axidma.py", "max_stars_repo_name": "FredKellerman/pynq-juliabrot", "max_stars_repo_head_hexsha": "c79165e021a0e50b0bc1318b54090c1de708e700", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
"""
AbstractMod{T}
Abstract type for all Modueles of type T
"""
abstract type AbstractMod{T} end
"""
Mod{T} <: AbstractMod{T}
Structure to store one specific modules of type{T}
"""
# problem input as array in unique gives an error if dims = 2 not added... ne
struct Mod{T} <: AbstractMod{T}
m::T
end
... | {"hexsha": "ea62bd4a5d88d1e112c8265b762b0b5cee1528a8", "size": 2477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/design/Mod.jl", "max_stars_repo_name": "MichielStock/BOMoD.jl", "max_stars_repo_head_hexsha": "b2b9b3cda9e010c5ba1c0815ed3e8a31ae232f99", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import logging
import time
import numpy
from cqcpy import ft_utils
from cqcpy.ov_blocks import one_e_blocks
from cqcpy.ov_blocks import two_e_blocks
from cqcpy.ov_blocks import two_e_blocks_full
from pyscf import lib
from . import ft_cc_energy
from . import ft_cc_equations
from . import quadrature
einsum = lib.einsum
... | {"hexsha": "8ff56292136f2ea65dca497fbd944735f6a798bb", "size": 108567, "ext": "py", "lang": "Python", "max_stars_repo_path": "kelvin/cc_utils.py", "max_stars_repo_name": "MoleOrbitalHybridAnalyst/kelvin", "max_stars_repo_head_hexsha": "99538f8360975e2f80941446d8fbf2e848f74cf9", "max_stars_repo_licenses": ["MIT"], "max_... |
import cv2
import numpy as np
import picamera
import serial
import time
def identifySq(pt, w, h):
tlx = 80
tly = 210
ppx = 94
ppy = 82
sqx = (pt[0]-(tlx-ppx/2))/ppx
sqy = (pt[1]-(tly-ppy/2))/ppy
# print ("ID",pt, w, h, sqx, sqy)
if sqx < 0 or sqx >= 4 or sqy < 0 or sqy >= 4:
re... | {"hexsha": "0aa75cc857ce6615a7c8a612ace54be1b4f547fa", "size": 7970, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tests/TestImgRecognitionAndMotorControl/Test2048Detect5.py", "max_stars_repo_name": "robdobsn/RobotPlay2048", "max_stars_repo_head_hexsha": "0715fd67313ccf6015871c2a73f38de3ca014f10", "max_stars_r... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
__weights_dict = dict()
def load_weights(weight_file):
if weight_file == None:
return
try:
weights_dict = np.load(weight_file).item()
except:
weights_dict = np.load(weight_file, encod... | {"hexsha": "9a7a842b68c4f61e8944a20671fa0b27cc9644dc", "size": 8502, "ext": "py", "lang": "Python", "max_stars_repo_path": "models_old/tf_to_pytorch_vgg16.py", "max_stars_repo_name": "jiangyangzhou/Non-targeted-Attack-IJCAI2019-ColdRiver", "max_stars_repo_head_hexsha": "f9f26b4e00241c7831a2e46a0a2c965457fe99e5", "max_s... |
import numpy as np
import numpy.matlib
def get_rigid_transform(A, B):
cenA = np.mean(A, 0) # 3
cenB = np.mean(B, 0) # 3
N = A.shape[0] # 24
H = np.dot((B - np.matlib.repmat(cenB, N, 1)).transpose(), (A - np.matlib.repmat(cenA, N, 1)))
[U, _, V] = np.linalg.svd(H)
R = np.dot(U, V) # matlab... | {"hexsha": "96c5665eda74a7ad5e0837fa5e2870c6c62e0084", "size": 899, "ext": "py", "lang": "Python", "max_stars_repo_path": "meshreg/datasets/coordutils.py", "max_stars_repo_name": "pgrady3/handobjectconsist", "max_stars_repo_head_hexsha": "9651c569c328707cc1ad1e4797b9e4b32083c446", "max_stars_repo_licenses": ["MIT"], "m... |
function metID = findMetIDs(model, metList)
% Finds metabolite numbers in a model
%
% USAGE:
%
% metID = findMetIds(model, metList)
%
% INPUTS:
% model: COBRA model structure
% metList: List of metabolites
%
% OUTPUT:
% metID: List of metabolite IDs corresponding to `metList`
%
% .. Author: - J... | {"author": "opencobra", "repo": "cobratoolbox", "sha": "e60274d127f65d518535fd0814d20c53dc530f73", "save_path": "github-repos/MATLAB/opencobra-cobratoolbox", "path": "github-repos/MATLAB/opencobra-cobratoolbox/cobratoolbox-e60274d127f65d518535fd0814d20c53dc530f73/src/analysis/exploration/findMetIDs.m"} |
subsection {* Weakest precondition calculus *}
theory utp_wp
imports "../hoare/utp_hoare"
begin
text {* A very quick implementation of wp -- more laws still needed! *}
named_theorems wp
method wp_tac = (simp add: wp)
consts
uwp :: "'a \<Rightarrow> 'b \<Rightarrow> 'c" (infix "wp" 60)
definition wp_upred :: "('... | {"author": "git-vt", "repo": "orca", "sha": "92bda0f9cfe5cc680b9c405fc38f07a960087a36", "save_path": "github-repos/isabelle/git-vt-orca", "path": "github-repos/isabelle/git-vt-orca/orca-92bda0f9cfe5cc680b9c405fc38f07a960087a36/Archive/Programming-Languages-Semantics/WP11-C-semantics/src/orca/utp/utp_wp.thy"} |
import numpy as np
from multiprocessing import Pool
import os
from sklearn.feature_extraction import image
def _denoise_pixel(img, x, y, K, L, sig):
def getBlock(x, y):
return img[x - halfK: x + halfK + 1, y - halfK: y + halfK + 1]
# def mse(block):
# return np.mean((block - target... | {"hexsha": "edcef0c537fb57e44c80a68a94ef9ead44d9a0e2", "size": 3493, "ext": "py", "lang": "Python", "max_stars_repo_path": "lpg_pca_impl.py", "max_stars_repo_name": "delmarrerikaine/LPG-PCA", "max_stars_repo_head_hexsha": "deb631ee2c4c88190ce4204fcbc0765ae5cd8f53", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from wordcloud import WordCloud
import numpy as np
import jieba
from PIL import Image
from scipy.misc import imread
import os
from os import path
import matplotlib.pyplot as plt
def draw_wordCloud():
comment_text = open('text.txt', 'r').read()
cut_text = "".join(jieba.cut(comment_text))
color_mask = imread... | {"hexsha": "17fd2019deb2b7d429dc226f83dac98e47419fb3", "size": 820, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "mental2008/wordcloud", "max_stars_repo_head_hexsha": "ff9c2d83ddc438e7663d2315860915ca1106d334", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_... |
import Mercury as Hg
import ProtoBuf
using Sockets
using ZMQ
using BenchmarkTools
using Test
using Logging
Logging.disable_logging(Logging.Info)
# Generate ProtoBuf julia files
outdir = joinpath(@__DIR__, "jlout")
if !isdir(outdir)
Base.Filesystem.mkdir(outdir)
end
protodir = joinpath(@__DIR__, "proto")
msgfile = ... | {"hexsha": "8de25d28410bc3f3c03e289cc254d23420bc0e5d", "size": 619, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "RoboticExplorationLab/Mercury.jl", "max_stars_repo_head_hexsha": "8d000b623ee1a2d5ca676ea10847de3abe6f46b5", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import logging
import ransac.core as ransac
import ransac.models.circle as circle_model
import random
import math
import matplotlib.pyplot as plt
import numpy as np
logging.basicConfig(level=logging.INFO, format='%(asctime)-15s %(levelname)s \t%(message)s')
def main():
logging.info("create_circle_modeler.py main... | {"hexsha": "30c3228bc6dc2fd713a50be8117295f9074c1d04", "size": 2702, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/create_circle_modeler.py", "max_stars_repo_name": "sebastiengilbert73/ransac", "max_stars_repo_head_hexsha": "4c4d683e58b6b73e7877b18d9700b7c63045710a", "max_stars_repo_licenses": ["MIT"], "... |
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import SGDClassifier
from sklearn import... | {"hexsha": "f294cb2b0652e0eecc17fd7120b15a25f42a484b", "size": 2702, "ext": "py", "lang": "Python", "max_stars_repo_path": "alpha/prediction/or.py", "max_stars_repo_name": "MingJerry/Guide", "max_stars_repo_head_hexsha": "0ac6ee9d20a579a93bcf9a90c53937179fdf6875", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma aL_circ_ext:
"|x\<^sup>\<star>]y \<le> |aL * x\<^sup>\<circ>]y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. | x\<^sup>\<star> ] y \<le> | aL * x\<^sup>\<circ> ] y
[PROOF STEP]
by (simp add: aL_circ box_left_antitone) | {"llama_tokens": 115, "file": "Correctness_Algebras_Hoare_Modal", "length": 1} |
import numpy as np
from ..utils import fix_dim_gmm
from .base import Acquisition, AcquisitionWeighted
class IVR(Acquisition):
"""A class for Integrated Variance Reduction.
Parameters
----------
model, inputs : see parent class (Acquisition)
Attributes
----------
model, inputs : see Param... | {"hexsha": "f1130173d164418d864cbc6827304b98d585ef52", "size": 4636, "ext": "py", "lang": "Python", "max_stars_repo_path": "gpsearch/core/acquisitions/ivr.py", "max_stars_repo_name": "Fluid-Dynamics-Group/gpsearch", "max_stars_repo_head_hexsha": "8c5758c9fb2b623ef79952c3e9c113cb157d79bc", "max_stars_repo_licenses": ["M... |
From sflib Require Import sflib.
From Paco Require Import paco.
Require Import Coq.Classes.RelationClasses Lia Program.
From Fairness Require Export ITreeLib WFLibLarge FairBeh NatStructsLarge Mod pind.
Set Implicit Arguments.
Module WMod.
Variant output (state: Type) (ident: ID) (mident: ID) (R: Type) :=
| no... | {"author": "snu-sf", "repo": "fairness", "sha": "170bd1ade88d32ac6ab661ed0c272af8a00d9ea1", "save_path": "github-repos/coq/snu-sf-fairness", "path": "github-repos/coq/snu-sf-fairness/fairness-170bd1ade88d32ac6ab661ed0c272af8a00d9ea1/src/semantics/Wrapper.v"} |
# Importing stock libraries
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader, RandomSampler, SequentialSampler
import json
from typing import List
# Importing the GPT2 modules from huggingface/transformers
from transformers import GPT2... | {"hexsha": "23c99f53efc52c496d05c9f34a6ebff0d11b131d", "size": 10278, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/comet_atomic2020_gpt2/comet_gpt2.py", "max_stars_repo_name": "anudeep23/CS7634-FinalProject-COMET2020", "max_stars_repo_head_hexsha": "ef86531719a9016f2597516d84dbcf010fb8699c", "max_stars... |
% SPDX-FileCopyrightText: © 2021 Martin Michlmayr <tbm@cyrius.com>
% SPDX-License-Identifier: CC-BY-4.0
\setchapterimage[9.5cm]{images/code}
\chapter{Licensing and copyright}
\labch{copyright}
The licensing of open source projects is a widely discussed topic. The choice of a license can greatly influence the impac... | {"hexsha": "7452815be12f5db12c6cd46914c76639acc774c8", "size": 2369, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/legal/copyright.tex", "max_stars_repo_name": "tbm/foss-foundations-primer", "max_stars_repo_head_hexsha": "1c7370b86f9ea5133f6a077d9b7b0105729f21ac", "max_stars_repo_licenses": ["CC-BY-4.0"... |
import csv
import math
import pprint
import time
from argparse import ArgumentParser
import numpy as np
import airsim
import setup_path
class DroneEnv:
def __init__(self):
self.client = airsim.CarClient()
self.client.confirmConnection()
self.client.enableApiControl(True)
car_cont... | {"hexsha": "3727ad164466bbea23d762b797b5e10bec926067", "size": 3978, "ext": "py", "lang": "Python", "max_stars_repo_path": "env_car.py", "max_stars_repo_name": "ysbsb/code_demo", "max_stars_repo_head_hexsha": "d6ed52506439b7b0fecc01f7c831f257064f97f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_star... |
import os
import numpy as np
def parse_icd9_range(range_: str) -> (str, str, int, int):
ranges = range_.lstrip().split('-')
if ranges[0][0] == 'V':
prefix = 'V'
format_ = '%02d'
start, end = int(ranges[0][1:]), int(ranges[1][1:])
elif ranges[0][0] == 'E':
prefix = 'E'
... | {"hexsha": "07eb121b53c5428e32450879474b504d0883d89f", "size": 3782, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess/auxiliary.py", "max_stars_repo_name": "LuChang-CS/sherbet", "max_stars_repo_head_hexsha": "d1061aca108eab8e0ccbd2202460e25261fdf1d5", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import glob
import json
import os
import shutil
import operator
import sys
import argparse
import math
import numpy as np
from copy import deepcopy
parser = argparse.ArgumentParser()
parser.add_argument('-na', '--no-animation', help="no animation is shown.", action="store_true")
parser.add_argument('-np', '--no-plot'... | {"hexsha": "a020629b55ab6abfc8cf8bf22253da2b57e92a2b", "size": 35019, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "vadimen/mAP", "max_stars_repo_head_hexsha": "6b284707d91706d5e261da69c9c7376cd57ee386", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "max_... |
"""Helper methods for deep learning.
--- NOTATION ---
The following letters will be used throughout this module.
E = number of examples (storm objects)
M = number of rows per radar image
N = number of columns per radar image
H_r = number of heights per radar image
F_r = number of radar fields (not including differen... | {"hexsha": "af731b4838f3ea3d542e20d80ee02c8cbf06fb52", "size": 43975, "ext": "py", "lang": "Python", "max_stars_repo_path": "gewittergefahr/deep_learning/deep_learning_utils.py", "max_stars_repo_name": "dopplerchase/GewitterGefahr", "max_stars_repo_head_hexsha": "4415b08dd64f37eba5b1b9e8cc5aa9af24f96593", "max_stars_re... |
##### Beginning of file
function _is_filesystem_root(path::AbstractString)::Bool
path::String = abspath(strip(path))
if path == dirname(path)
return true
else
return false
end
end
function _is_package_directory(path::AbstractString)::Bool
path::String = abspath(strip(path))
if ... | {"hexsha": "233b515cac8dd06b1fa786482809fd550355eb0e", "size": 4876, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/package_directory.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Snapshots.jl-44eb87bc-f37b-45e8-9f53-3bcb453a652d", "max_stars_repo_head_hexsha": "7d31297350f9ad4af022d8735c19a783... |
function DEM_demo_fMRI_HMM
% Demonstration of Hidden Markov models for fMRI
%__________________________________________________________________________
% This demonstration routine illustrates the modelling of state
% transitions generating resting state fMRI timeseries. The hidden states
% are modelled as a hidden ... | {"author": "spm", "repo": "spm12", "sha": "3085dac00ac804adb190a7e82c6ef11866c8af02", "save_path": "github-repos/MATLAB/spm-spm12", "path": "github-repos/MATLAB/spm-spm12/spm12-3085dac00ac804adb190a7e82c6ef11866c8af02/toolbox/DEM/DEM_demo_fMRI_HMM.m"} |
import os
import argparse
import time
import numpy as np
import pickle
import torch
from torch.autograd import Variable
from PIL import Image
from yolov2 import Yolov2
from dataset.factory import get_imdb
from dataset.roidb import RoiDataset
from yolo_eval import yolo_eval
from util.visualize import draw_detection_boxe... | {"hexsha": "0c8706c654e9383989e0c18406a17c8f85deff78", "size": 5405, "ext": "py", "lang": "Python", "max_stars_repo_path": "test.py", "max_stars_repo_name": "minji-o-j/YOLO", "max_stars_repo_head_hexsha": "5f2d12a80879c80d4b04b4b9acd937c290d0fbd8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_stars_re... |
// Copyright Ricardo Calheiros de Miranda Cosme 2018.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#pragma once
#include <boost/fusion/include/as_vector.hpp>
#include <boost/mpl/vector.hpp>
#include <occi.h>
#incl... | {"hexsha": "36cd24d9e5b8659d8b824aae6cfe7f837143c745", "size": 2349, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/picapau/oracle/core/result_set.hpp", "max_stars_repo_name": "ricardocosme/picapau", "max_stars_repo_head_hexsha": "751b946b3911f3ff15e19d177b0b561412c5f8d1", "max_stars_repo_licenses": ["BSL... |
[STATEMENT]
lemma eval_red_Hcomp:
assumes "Ide a" and "Ide b"
shows "\<lbrace>(a \<^bold>\<star> b)\<^bold>\<down>\<rbrace> = \<lbrace>\<^bold>\<lfloor>a\<^bold>\<rfloor> \<^bold>\<Down> \<^bold>\<lfloor>b\<^bold>\<rfloor>\<rbrace> \<cdot> (\<lbrace>a\<^bold>\<down>\<rbrace> \<star> \<lbrace>b\<^bold>\<down>\<r... | {"llama_tokens": 5392, "file": "Bicategory_Coherence", "length": 26} |
#ifndef STAN_MATH_PRIM_SCAL_PROB_UNIFORM_RNG_HPP
#define STAN_MATH_PRIM_SCAL_PROB_UNIFORM_RNG_HPP
#include <boost/random/uniform_real_distribution.hpp>
#include <boost/random/variate_generator.hpp>
#include <stan/math/prim/scal/err/check_consistent_sizes.hpp>
#include <stan/math/prim/scal/err/check_finite.hpp>
#includ... | {"hexsha": "40ddbbd61a89ed0b7c15f54942680113851a4870", "size": 1261, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "stan/math/prim/scal/prob/uniform_rng.hpp", "max_stars_repo_name": "sakrejda/math", "max_stars_repo_head_hexsha": "3cc99955807cf1f4ea51efd79aa3958b74d24af2", "max_stars_repo_licenses": ["BSD-3-Clause... |
// __BEGIN_LICENSE__
// Copyright (c) 2006-2013, United States Government as represented by the
// Administrator of the National Aeronautics and Space Administration. All
// rights reserved.
//
// The NASA Vision Workbench is licensed under the Apache License,
// Version 2.0 (the "License"); you may not use this f... | {"hexsha": "e925f49edbcfa4c32a5e99f2700ba464212b59eb", "size": 2191, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/vw/Cartography/PointImageManipulation.cc", "max_stars_repo_name": "maxerbubba/visionworkbench", "max_stars_repo_head_hexsha": "b06ba0597cd3864bb44ca52671966ca580c02af1", "max_stars_repo_licenses"... |
import tensorflow as tf
import tensorflow_io as tfio
import numpy as np
import matplotlib.pyplot as plt
from scipy.fftpack import fft
from scipy.io import wavfile as wav
'''
Tensorflow conversion to spectrograms
Maybe use if we go with Mel spectrograms
'''
def print_FFT(song_path):
rate, data = wav.read(song_... | {"hexsha": "ad7d908389e071f2d689cd1b2d7c4520758f4d25", "size": 1141, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python Files/Dataset_Formating/Tensor_audio_formating.py", "max_stars_repo_name": "brennanMosher/Music-Genre-Recognition-using-a-Machine-Learning-Appraoch", "max_stars_repo_head_hexsha": "7834fe5d... |
# coding: utf-8
# Copyright (c) Materials Virtual Lab
# Distributed under the terms of the BSD License.
import numpy as np
class Preprocessing(object):
"""
Preprocessing class used for spectrum preprocessing.
"""
def __init__(self, spectrum):
"""
Create an Preprocessing object
... | {"hexsha": "949a5975e0f1bbcde2799fac9b149a31761ac288", "size": 5913, "ext": "py", "lang": "Python", "max_stars_repo_path": "veidt/elsie/preprocessing.py", "max_stars_repo_name": "yimingchen95/veidt", "max_stars_repo_head_hexsha": "90f201f856d2f71c578f74b7391c0c9ff284986b", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
###########################################################################################
# Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py #
# Mainly changed the model forward() function #
############################################... | {"hexsha": "aad6c46523b0e8a743f17dd3fc5b60c3e4c4fba2", "size": 21760, "ext": "py", "lang": "Python", "max_stars_repo_path": "nets/resnet.py", "max_stars_repo_name": "nicksum107/thesiswork", "max_stars_repo_head_hexsha": "5d175d0e110b08b7da2926fc64287086f503e086", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import pandas as pd
import numpy as np
import xgboost as xgb
import lightgbm as lgb
import pyarrow as pa
import pyarrow.parquet as pq
import json
import traceback
from utils import *
import argparse
# specify the version.
parser = argparse.ArgumentParser()
parser.add_argument('--version', '-v', default=1, help='versio... | {"hexsha": "5821b92614b4d0040f62076c17262ab22757fa75", "size": 3402, "ext": "py", "lang": "Python", "max_stars_repo_path": "yuki/avito/src/lgbm_with_stack_seed_average.py", "max_stars_repo_name": "RandLive/Avito-Demand-Prediction-Challenge", "max_stars_repo_head_hexsha": "eb2955c6cb799907071d8bbf7b31b73b163c604f", "max... |
# Orthogonal polynomials
Copyright (C) 2020 Andreas Kloeckner
<details>
<summary>MIT License</summary>
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 limit... | {"hexsha": "aeb5d48d99398b895b8b12191e8997e85b9e1577", "size": 8701, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "cleared-demos/interpolation/Orthogonal Polynomials.ipynb", "max_stars_repo_name": "xywei/numerics-notes", "max_stars_repo_head_hexsha": "70e67e17d855b7bb06a0de7e3570d40ad50f941b", "ma... |
import copy
from liegroups import SO3, SE3
import transforms3d as tf3d
from transforms3d.quaternions import mat2quat
from numpy.linalg import lstsq
import numpy as np
TASK_DIM = 6
JOINT_NAMES = 1
JOINT_ACTIVE = 3
LINK_NAMES = 12
ZERO_DISP = [0, 0, 0]
POS = range(0, 3)
ROT = range(3, 6)
KI = .01
# originally from ht... | {"hexsha": "4e83fe92a15113e803278c7ffeef93f58c769270", "size": 26902, "ext": "py", "lang": "Python", "max_stars_repo_path": "manipulator_learning/sim/robots/manipulator.py", "max_stars_repo_name": "utiasSTARS/manipulator_learning", "max_stars_repo_head_hexsha": "9a0e0c66c0a3c07124331f010bd04bb52eaf95bb", "max_stars_rep... |
[STATEMENT]
lemma mult_minus_eq_nat:
fixes x::nat and y ::nat and z::nat
assumes " x+y = z"
shows " -x-y = -z "
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. - int x - int y = - int z
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
x + y = z
goal (1 subgoal):
1. - int x - int y = - int z
... | {"llama_tokens": 155, "file": "Amicable_Numbers_Amicable_Numbers", "length": 2} |
export parallel
parallel() = schedule_on(ThreadsScheduler())
| {"hexsha": "e0966ea7ae0b903d7543c22f728afea3852a79af", "size": 62, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/operators/parallel.jl", "max_stars_repo_name": "hgeorgako/Rocket.jl", "max_stars_repo_head_hexsha": "9661dad340e9a079ebd6ed57dcf9e5db31af637f", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Copyright 2021 Mechanics of Microstructures Group
# at The University of Manchester
#
# 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
#
#... | {"hexsha": "10e3a72b02ffc19c0e561d284b451942da95bdcc", "size": 25820, "ext": "py", "lang": "Python", "max_stars_repo_path": "defdap/inspector.py", "max_stars_repo_name": "MechMicroMan/DefDAP", "max_stars_repo_head_hexsha": "d8769c9255b6a64ab528d99057afa5c05b8f5cac", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
[STATEMENT]
lemma trace_ft_append: "trace_between s (tr1@tr2) s'
\<longleftrightarrow> (\<exists>sh. trace_between s tr1 sh \<and> trace_between sh tr2 s')"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. trace_between s (tr1 @ tr2) s' = (\<exists>sh. trace_between s tr1 sh \<and> trace_between sh tr2 s')
[PROOF ST... | {"llama_tokens": 652, "file": "CoCon_Traceback_Properties", "length": 5} |
#generate community
p = random_micrm_params(2,2,0.5)
#convert to ODESystem
@named sys = micrm_system(p)
@testset "MTK system" begin
@test length(states(sys)) == 4
@test length(parameters(sys)) == 14
end
#convert to problem
#define starting mass
u0 = fill(0.1, 4)
u0 = [states(sys)[i] => u0[i] for i = eachind... | {"hexsha": "20cd60a0ec582172c618b69046c4efc2900842e1", "size": 434, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/MTK_test.jl", "max_stars_repo_name": "CleggTom/MiCRM.jl", "max_stars_repo_head_hexsha": "578a31774b81927a444eb39c459e4af4281448b7", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#include <Eigen/Core>
#include <iostream>
using namespace Eigen;
using namespace std;
void PolygonToEquations(const MatrixX2d& pts, MatrixX2d& ab, VectorXd& c) {
// ax + by + c <= 0
// assume polygon is convex
Vector2d p0 = pts.row(0);
for (int i=0; i < pts.rows(); ++i) {
int i1 = (i+1) % pts.rows();
... | {"hexsha": "176d0b83e605d5d5c3ec9862f4f5338b3efe44a5", "size": 748, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/sandbox/polygon_expt.cpp", "max_stars_repo_name": "HARPLab/trajopt", "max_stars_repo_head_hexsha": "40e2260d8f1e4d0a6a7a8997927bd65e5f36c3a4", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_st... |
@memoize function result(x, y=True, maxdepth=2, truth_table=truths) # if x ⟹ y it will return true, false or missing
# hardcoded things
if y == True && class(x) <:ASubset
sub, super = args(x)
if sub == super
return True
elseif super == Ω
return True
end
... | {"hexsha": "d6979bc6ea76cb14c551649505ce2141d38064a2", "size": 1493, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/tree_type3/proving/Proving.jl", "max_stars_repo_name": "Maelstrom6/Breadth.jl", "max_stars_repo_head_hexsha": "5ccb6ec063e1d0337856257608ad887a7bd53eb8", "max_stars_repo_licenses": ["MIT"], "ma... |
[STATEMENT]
lemma alternativelistconc2[rule_format]:
"a \<in> set (net_list_aux [x]) \<longrightarrow> a \<in> set (net_list_aux [y,x])"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a \<in> set (net_list_aux [x]) \<longrightarrow> a \<in> set (net_list_aux [y, x])
[PROOF STEP]
by (induct y, simp_all) | {"llama_tokens": 132, "file": "UPF_Firewall_FWNormalisation_NormalisationGenericProofs", "length": 1} |
#include "CorePch.h"
#include <rtp++/network/TcpRtpConnection.h>
#include <boost/bind.hpp>
#include <boost/asio/ip/multicast.hpp>
#include <boost/asio/ip/udp.hpp>
#include <boost/asio/placeholders.hpp>
#include <boost/make_shared.hpp>
#include <cpputil/OBitStream.h>
#include <rtp++/RtpTime.h>
#include <rtp++/n... | {"hexsha": "a91cac243d49b01c169fbea522276642f5cc7192", "size": 20257, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/Lib/network/TcpRtpConnection.cpp", "max_stars_repo_name": "miseri/rtp_plus_plus", "max_stars_repo_head_hexsha": "244ddd86f40f15247dd39ae7f9283114c2ef03a2", "max_stars_repo_licenses": ["BSD-3-Cl... |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: directory_service
using AWS.Compat
using AWS.UUIDs
"""
AcceptSharedDirectory()
Accepts a directory sharing request that was sent from the directory owner account.
# Required Parameters
- `SharedDirectoryId`: Identifier of the shared... | {"hexsha": "7668e3b52e59c57c9a5c5ef9b4f11111b8f86af6", "size": 67414, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/services/directory_service.jl", "max_stars_repo_name": "sean-bennett112/AWS.jl", "max_stars_repo_head_hexsha": "08347ed4afdfeb70009369630b4f2de70d8f7b81", "max_stars_repo_licenses": ["MIT"], "... |
#! /usr/bin/env python
# Author: S.Rodney
# Created : 2014.04.21
def reportDone( pid, dayspan=1, emailto='',emailuser='',emailpass='',
logfile=None, verbose=False ):
""" Check for visits executed in the last <ndays> days.
Fetch the visit status page, parse the visit info, print a report to stdo... | {"hexsha": "ec91ace4a5b745db877e8d4fbdcd070f7084b9e5", "size": 14464, "ext": "py", "lang": "Python", "max_stars_repo_path": "hstMonitor.py", "max_stars_repo_name": "srodney/hstsntools", "max_stars_repo_head_hexsha": "a36e0cc89dece4c992bb312df1af1dc5de595619", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "m... |
function allen_ccf_2pi(tv,av,st)
% allen_ccf_2pi(tv,av,st)
% written by Samuel Picard (samuel.picard@ucl.ac.uk)
% based on original allen_ccf_npx tool written by Andy Peters (peters.andrew.j@gmail.com)
%
% GUI for planning 2pi chronic window implant with the Allen CCF
% Part of repository: https://github.com/cortex-lab... | {"author": "cortex-lab", "repo": "allenCCF", "sha": "0bbff55fc906fd3f023da81ce1d0e4b8726d4fd0", "save_path": "github-repos/MATLAB/cortex-lab-allenCCF", "path": "github-repos/MATLAB/cortex-lab-allenCCF/allenCCF-0bbff55fc906fd3f023da81ce1d0e4b8726d4fd0/Browsing Functions/allen_ccf_2pi.m"} |
module Main
import Collie
import Interface
import Data.Version
import System.Directory.Extra
import Command
%hide Collie.(.handleWith)
exitError : HasIO io => String -> io a
exitError err = do
putStrLn ""
putStrLn err
putStrLn ""
exitFailure
exitSuccess : HasIO io => String -> io a
exitSuccess msg = do
p... | {"hexsha": "5ad97c534ca4b0f70dae15190aca648dba7a7dcb", "size": 1980, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "cli/src/Main.idr", "max_stars_repo_name": "memoryruins/idv", "max_stars_repo_head_hexsha": "7631bd1c0bdea2cfb672fa178918b4e6191a738c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
! path: $Source: /storm/rc1/cvsroot/rc/rrtmg_lw/src/mcica_subcol_gen_lw.1col.f90,v $
! author: $Author: mike $
! revision: $Revision: 1.5 $
! created: $Date: 2009/05/22 21:04:30 $
!
module mcica_subcol_gen_lw
! -------------------------------------------------------------------------... | {"hexsha": "1af55f35ec8ac551799ddd8b78f7dfb2ff3d9e4e", "size": 26312, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "rrtmg_lw/src/mcica_subcol_gen_lw.1col.f90", "max_stars_repo_name": "danielkoll/PyRADS_vs_RRTMG", "max_stars_repo_head_hexsha": "72361b22fbebd96022f9082c306ac30fb8f46b6b", "max_stars_repo_licens... |
from nose.tools import raises
import networkx as nx
# smoke tests for exceptions
@raises(nx.NetworkXException)
def test_raises_networkx_exception():
raise nx.NetworkXException
@raises(nx.NetworkXError)
def test_raises_networkx_error():
raise nx.NetworkXError
@raises(nx.NetworkXPointlessConcept)... | {"hexsha": "78923ef7c8d57158d71fd027baf4317899917572", "size": 837, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/prism-fruit/Games-DQL/examples/games/car/networkx/tests/test_exceptions.py", "max_stars_repo_name": "kushgrover/apt-vs-dift", "max_stars_repo_head_hexsha": "250f64e6c442f6018cab65ec6979d9568a84... |
[STATEMENT]
lemma measurable_bind2:
assumes "f \<in> measurable M (subprob_algebra N)" and "g \<in> measurable N (subprob_algebra R)"
shows "(\<lambda>x. bind (f x) g) \<in> measurable M (subprob_algebra R)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<lambda>x. f x \<bind> g) \<in> M \<rightarrow>\<^sub>M ... | {"llama_tokens": 252, "file": null, "length": 2} |
#!/usr/bin/env python3
import sys
import numpy
import pylab
pylab.rcParams["font.size"]=8
pylab.rcParams["legend.fontsize"]=8
#pylab.rcParams["lines.linewidth"]=1
#pylab.rcParams["axes.linewidth"]=2
#pylab.rcParams["axes.labelsize"]="large"
#pylab.rcParams["axes.labelweight"]="bold"
pylab.rcParams["xtick.major.size"]... | {"hexsha": "8e8c70a570140873f2f3e5b2b1133e4ec943ffc0", "size": 1773, "ext": "py", "lang": "Python", "max_stars_repo_path": "Fig1/ADPmod/plot_spike.py", "max_stars_repo_name": "TatsuyaHaga/reversereplaymodel_codes", "max_stars_repo_head_hexsha": "503d545449efab603e18d224fc2f94158d967530", "max_stars_repo_licenses": ["MI... |
"""Mass budget-related quantities."""
try:
from animal_spharm import SpharmInterface
except ImportError:
pass
from aospy.constants import grav
from aospy.utils.vertcoord import (d_deta_from_pfull, d_deta_from_phalf,
to_pfull_from_phalf, dp_from_ps, int_dp_g,
... | {"hexsha": "6cad343ed6f0a202290eac77297896b37706d938", "size": 13401, "ext": "py", "lang": "Python", "max_stars_repo_path": "aospy_user/calcs/mass.py", "max_stars_repo_name": "spencerahill/aospy-obj-lib", "max_stars_repo_head_hexsha": "76803806e8c6b0042c901735eed1c88042d4e4ed", "max_stars_repo_licenses": ["Apache-2.0"]... |
import matplotlib.pyplot as plt
import numpy as np
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('data', help='Arquivo com as contagens das palavras')
parser.add_argument('title', help='Titulo do gráfico')
args = parser.parse_args()
image_name = args.title.lower().replace(' ', '_') + '.png'
w... | {"hexsha": "5e3e4db961805bb5b58ea04dc1478c2e015000db", "size": 1257, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/plot_cont.py", "max_stars_repo_name": "Vnicius/filter", "max_stars_repo_head_hexsha": "0e478c5bc02c5152151308a1ca750c458c982135", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
/*
* Distributed under the Boost Software License, Version 1.0.
* (See accompanying file LICENSE_1_0.txt or copy at
* http://www.boost.org/LICENSE_1_0.txt)
*
* (C) Copyright 2013 Andrey Semashev
*/
/*!
* \file exceptions.hpp
*
* \brief This header includes all exception types.
*/
#ifndef BOOST... | {"hexsha": "25f5c14fc4a8c2fb8773fcebcbb4cfbcd2b0564b", "size": 789, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/sync/exceptions.hpp", "max_stars_repo_name": "ballisticwhisper/boost", "max_stars_repo_head_hexsha": "f72119ab640b564c4b983bd457457046b52af9ee", "max_stars_repo_licenses": ["BSL-1.0"], "max_sta... |
/*
* @name BookFiler Library - Sort Filter Table Widget
* @author Branden Lee
* @version 1.00
* @license MIT
* @brief sqlite3 based table widget.
*/
#ifndef BOOKFILER_LIBRARY_SORT_FILTER_TABLE_WIDGET_MAIN_WIDGET_H
#define BOOKFILER_LIBRARY_SORT_FILTER_TABLE_WIDGET_MAIN_WIDGET_H
// config
#include "../core/confi... | {"hexsha": "3e97c9ee73682ba26e003666a14f0476ef7177e7", "size": 4274, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/UI/MainWidget.hpp", "max_stars_repo_name": "bradosia/BookFiler-Lib-Sort-Filter-Table-Widget", "max_stars_repo_head_hexsha": "6d4b99ed27eb6b43f6ac0495a8adb02bec5c801e", "max_stars_repo_licenses":... |
using EasyDataAugmentation
using Documenter
DocMeta.setdocmeta!(EasyDataAugmentation, :DocTestSetup, :(using EasyDataAugmentation); recursive=true)
makedocs(;
modules=[EasyDataAugmentation],
authors="lilianabs <lilianabsmath@google.com> and contributors",
repo="https://github.com/lilianabs/EasyDataAugment... | {"hexsha": "e3bb8ae4269ebdd2446ff47d0d0b033ee74c92d7", "size": 732, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "lilianabs/EasyDataAugmentationNLP.jl", "max_stars_repo_head_hexsha": "bb54e163ef74f10f8dc4e21a1bbc04bb35a3e24a", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import numpy as np
from scipy import stats, linalg
import os
import pandas as pd
import neurolab as nl
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
from sklearn.utils import shuffle
import matplotlib.pyplot as plt
import pickle... | {"hexsha": "7ec6d98eaa1508addabc5b008369c19b8eb6e354", "size": 1526, "ext": "py", "lang": "Python", "max_stars_repo_path": "f_data_prep.py", "max_stars_repo_name": "jungminshan/drosophila", "max_stars_repo_head_hexsha": "8efccfdaaac1404811eac2d81a90f5f42b1d24c1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
/*
* libasiotap - A portable TAP adapter extension for Boost::ASIO.
* Copyright (C) 2010-2011 Julien KAUFFMANN <julien.kauffmann@freelan.org>
*
* This file is part of libasiotap.
*
* libasiotap is free software; you can redistribute it and/or modify it
* under the terms of the GNU General Public License as
* pu... | {"hexsha": "0c43ce2b983d57ba1887439ac449e43445f8dc63", "size": 9320, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "blades/freelan/libs/asiotap/include/asiotap/base_dns_servers_manager.hpp", "max_stars_repo_name": "krattai/AEBL", "max_stars_repo_head_hexsha": "a7b12c97479e1236d5370166b15ca9f29d7d4265", "max_stars... |
"""
Reimplementing segan paper as close as possible.
Deepak Baby, UGent, June 2018.
"""
from __future__ import print_function
import tensorflow as tf
from tensorflow.contrib.layers import xavier_initializer, flatten, fully_connected
import numpy as np
from keras.layers import Subtract, Activation, Input
from keras.mo... | {"hexsha": "c79696f6cc4b23b6cc0f151867546dbad929fbf3", "size": 13823, "ext": "py", "lang": "Python", "max_stars_repo_path": "run_rsgan-gp_se.py", "max_stars_repo_name": "samiulshuvo/se_relativisticgan", "max_stars_repo_head_hexsha": "5501c4d96faa03eb3c1fd776b232b68940183f4d", "max_stars_repo_licenses": ["MIT"], "max_st... |
#This is a code for thresholding the CAM image and output a mask
import numpy as np
import scipy.misc as misc
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
root = "./result/"
img_path = root+"00436515-870c-4b36-a041-de91049b9ab4-densenet121-cam.jpg"
img = mpimg.imread(img_path)
img_name = ... | {"hexsha": "239e4bd97ae40b4086438d36a175d60cdd90b56f", "size": 2204, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/thresholding.py", "max_stars_repo_name": "hizircanbayram/Explainable-Pneumonia-Learning-A-Comprehensive-Study", "max_stars_repo_head_hexsha": "56269d80ca6d5626dc7683d9f699964d6f54044a", "max_... |
import time
import networks
import pdb
from data.frankenstein_dataset import FrankensteinDataset
from data.horizon_dataset import HorizonDataset
from data.eval_dataset import EvalDataset
import matplotlib.pyplot as plt
from scipy.misc import imsave
from torch.utils.data import DataLoader
import torch.nn.functional as F... | {"hexsha": "74b9fca82746618fe516f50253b2cb96bcc604c7", "size": 4270, "ext": "py", "lang": "Python", "max_stars_repo_path": "discriminator/vanilla/generate_pano_noGAN.py", "max_stars_repo_name": "dangeng/infiniteGANorama", "max_stars_repo_head_hexsha": "92c9cbe0638cf9fcdc05020759772e36aebf788c", "max_stars_repo_licenses... |
#!/usr/bin/env python
#!python
#command='Produce.Simulated.FussyJuncs.py heterozygous --reference /mnt/EXT/Mills-scratch2/reference/GRCh37/human_g1k_v37.fasta --input-sim /mnt/EXT/Mills-scratch2/Xuefang/Simulate.FussyJunc/Simulate.het.rerun.test.20150901/het.sim --output-prefix /mnt/EXT/Mills-scratch2/Xuefang/Simulate... | {"hexsha": "54b73d4492f7b5357d54e5870dd217f28ae6f560", "size": 152415, "ext": "py", "lang": "Python", "max_stars_repo_path": "Support.Scripts/Produce.Simulated.FussyJuncs.py", "max_stars_repo_name": "mills-lab/svelter", "max_stars_repo_head_hexsha": "d318b06d588483fe8a8ebcac8c8a6c7878f2c2b3", "max_stars_repo_licenses":... |
import pymysql
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import font_manager
import pandas as pd
import numpy as np
import jieba
import jieba.analyse as analyse
username = 'root'
password = 'mysql'
url = '127.0.0.1'
port = 3306
database = 'campus'
conn = pymysql.connect(url, port=port, databas... | {"hexsha": "e690e2b33475b8ca9cc4bd7aaedbf5be4e136c6a", "size": 2066, "ext": "py", "lang": "Python", "max_stars_repo_path": "01_crawl_cases/campus_public_opinion/huitu.py", "max_stars_repo_name": "zlj-zz/anti-crawlCase", "max_stars_repo_head_hexsha": "a6ed670ad332bd456572eeff707bd5fc14186b3d", "max_stars_repo_licenses":... |
// Copyright Abel Sinkovics (abel@sinkovics.hu) 2010.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <mpllibs/metamonad/lambda.hpp>
#include <mpllibs/metamonad/lazy.hpp>
#include <mpllibs/m... | {"hexsha": "b12d5b1b56cb93939cc0302cb47bb8bfdbfb940e", "size": 2678, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/metamonad/test/lambda.cpp", "max_stars_repo_name": "sabel83/mpllibs", "max_stars_repo_head_hexsha": "8e245aedcf658fe77bb29537aeba1d4e1a619a19", "max_stars_repo_licenses": ["BSL-1.0"], "max_star... |
from autoconf import conf
import numba
"""
Depending on if we're using a super computer, we want two different numba decorators:
If on laptop:
@numba.jit(nopython=True, cache=True, parallel=False)
If on super computer:
@numba.jit(nopython=True, cache=False, parallel=True)
"""
try:
nopython = c... | {"hexsha": "2d0a64c5cb29d05754ea444598a6958d21d95e40", "size": 758, "ext": "py", "lang": "Python", "max_stars_repo_path": "autolens/decorator_util.py", "max_stars_repo_name": "rakaar/PyAutoLens", "max_stars_repo_head_hexsha": "bc140c5d196c426092c1178b8abfa492c6fab859", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/python3
# -*- coding: utf-8 -*-
from numpy import pi
from numpy.random import random
from modules.growth import spawn_curl
from modules.growth import spawn
from numpy import zeros
NMAX = 10**6
SIZE = 800
ONE = 1./SIZE
PROCS = 2
INIT_RAD = 25*ONE
INIT_NUM = 40
STP = ONE*0.4
NEARL = 6*ONE
FARL = 60*ONE... | {"hexsha": "f449fb773b8144122adcf5b18dc8454949b031c5", "size": 1698, "ext": "py", "lang": "Python", "max_stars_repo_path": "generator/main_detail_ani.py", "max_stars_repo_name": "stevejaxon/leonardo-dao-vinci", "max_stars_repo_head_hexsha": "e1074f872ac83a69a70115e5e5e4376ff4462b36", "max_stars_repo_licenses": ["MIT"],... |
"""
Visualization functions for forest of trees-based ensemble methods for Uplift modeling on Classification
Problem.
"""
from collections import defaultdict
import numpy as np
import pydotplus
def uplift_tree_string(decisionTree, x_names):
'''
Convert the tree to string for print.
Args
----
de... | {"hexsha": "1648c75c30512fd3aef04a6175c668b32fd7d314", "size": 8236, "ext": "py", "lang": "Python", "max_stars_repo_path": "causalml/inference/tree/plot.py", "max_stars_repo_name": "lleiou/causalml", "max_stars_repo_head_hexsha": "2d3cacacad5ed3b0e57b593803a33c61c554f3b2", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
import numpy as np
import itertools
import pandas as pd
import re
import json
import matplotlib.pyplot as plt
import scipy.io.wavfile
import librosa
import librosa.display
import IPython.display as ipd
from random import randint
import os
from numpy import random as rd
from pandas.api.types import is_str... | {"hexsha": "10940bf2a46497aa043bd8de25f05cb06302856a", "size": 34576, "ext": "py", "lang": "Python", "max_stars_repo_path": "functions.py", "max_stars_repo_name": "benedettacandelori/ADM4_group12", "max_stars_repo_head_hexsha": "95a3efe27ec481e1d28a96daef30fd52a9e1419d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
from distutils.extension import Extension
from Cython.Build import cythonize
from torch.utils.cpp_extension import BuildExtension
import numpy
# Get the numpy include directory.
numpy_include_dir = numpy.get_include()
# Ex... | {"hexsha": "d9d21e37c3c740161997946751fd6b3246548c52", "size": 2116, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "ray8828/occupancy_flow", "max_stars_repo_head_hexsha": "09c172262bb151895d450eb323e2383a5c88841c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 146, "m... |
### CONCRETE TYPE: DIRECT PROX EVALUATION
# prox! is computed using a Cholesky factorization of A'A + I/(lambda*gamma)
# or AA' + I/(lambda*gamma), according to which matrix is smaller.
# The factorization is cached and recomputed whenever gamma changes
using LinearAlgebra
using SparseArrays
using SuiteSparse
mutable... | {"hexsha": "a2b3638949dc4d289bcdbf8183d06e1eff7703b8", "size": 5202, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/functions/leastSquaresDirect.jl", "max_stars_repo_name": "UnofficialJuliaMirror/ProximalOperators.jl-a725b495-10eb-56fe-b38b-717eba820537", "max_stars_repo_head_hexsha": "0e77f72cae83cceb27543a... |
import numpy as np
import matplotlib.pyplot as plt
'''
Equations taken from:
Rahvar, S., Mehrabi, A., & Dominik, M. 2011, MNRAS, 410, 912
'''
# speed of light, c, (au/day)
# orbital radius, a, (au)
# mass of source star, m_star, mass of lens, M (solal masses)
# inclination angle with respect to observer-lens line of ... | {"hexsha": "6500259239f3a044b2a508e735a6b95c0aa59ff1", "size": 2213, "ext": "py", "lang": "Python", "max_stars_repo_path": "troia/kartik_eli/rahvar.py", "max_stars_repo_name": "tdaylan/troia", "max_stars_repo_head_hexsha": "55751fbbcab2faddcd157b22b7a127e1afffeeae", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from sympy.core.symbol import Symbol
from .input_size import input_size
from .base import call_method_or_dispatch, create_registerer
from sklearn.base import BaseEstimator
def syms_x(estimator):
return [Symbol('x%d' % d) for d in range(input_size(estimator))]
syms_dispatcher = {
BaseEstimator: ... | {"hexsha": "102be98ba62583f8b69c26795709ca4702b8899c", "size": 472, "ext": "py", "lang": "Python", "max_stars_repo_path": "sklearntools/sym/syms.py", "max_stars_repo_name": "modusdatascience/sklearntools", "max_stars_repo_head_hexsha": "6cb87edcb501440266622fe4c738be3f9015a859", "max_stars_repo_licenses": ["BSD-3-Claus... |
from functools import partial
import haiku as hk
import jax
import jax.numpy as jnp
class EncoderBlock(hk.Module):
def __init__(
self,
n_in: int,
n_out: int,
n_layers: int,
name: str = "EncoderBlock"
):
super().__init__(name=name)
n_... | {"hexsha": "263dc0dbf9ff53c68f918d1f285f14388557958d", "size": 3272, "ext": "py", "lang": "Python", "max_stars_repo_path": "dall_e_jax/encoder.py", "max_stars_repo_name": "kingoflolz/DALL-E", "max_stars_repo_head_hexsha": "d3f3e9a57a31b1e1cc74a449a9e6e5a0442f0ac7", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
from pathlib import Path
import json
import os
import numpy as np
import torch
imp... | {"hexsha": "720253ec629410a0cd967e8e81fccee488c898a0", "size": 10033, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/coco.py", "max_stars_repo_name": "wdurhamh/detr_radiate", "max_stars_repo_head_hexsha": "2c9d53914816dd15fc4a6d176d5ea013703db7b3", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
#coding:utf-8
"""
#Author : Arijit Mukherjee
#Date : June 2016
#B.P. Poddar Institute of Management and Technology
#Inteligent Human-Computer Interaction with depth prediction using normal webcam and IR leds
#Inspired by : http://research.microsoft.com/pubs/220845/depth4free_SIGGRAPH.pdf
Demo Application to estimate h... | {"hexsha": "71d3d3d3284b0c3606c6e23a4830a2c621b083cf", "size": 4440, "ext": "py", "lang": "Python", "max_stars_repo_path": "python-code/opencv-learning/tiny-apps/handgesture/multitouch.py", "max_stars_repo_name": "juxiangwu/image-processing", "max_stars_repo_head_hexsha": "c644ef3386973b2b983c6b6b08f15dc8d52cd39f", "ma... |
# Composite pattern - Option
import numpy as np
import scipy.stats as si
class Asset:
def price(self, scenario):
raise NotImplementedError("Abstract asset does not have a price")
def volatility(self, scenario):
raise NotImplementedError("Abstract asset does not have a volatility")
@stati... | {"hexsha": "54dd2d01fa360c45930d777b83609fa205e2f3ad", "size": 5321, "ext": "py", "lang": "Python", "max_stars_repo_path": "portfolio/portfolio5-creation.py", "max_stars_repo_name": "orest-d/design-patterns-finance", "max_stars_repo_head_hexsha": "5878912dfa5b34925b00c38da978e7b9e4735a14", "max_stars_repo_licenses": ["... |
"""
Figure 4K: learning angle between
habituation and recall population vectors.
"""
import pickle
import numpy as np
from scipy.io import savemat
import matplotlib.pyplot as plt
import seaborn as sns
from src.data_utils import get_per_mouse_boutons
from src.population_utils import compute_angle, get_learning_angles
sn... | {"hexsha": "a37ff2185fe9c0c8b164ae82647368d9d5e4b2dd", "size": 2432, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/learning_angle.py", "max_stars_repo_name": "sprekelerlab/long-range-inhibition", "max_stars_repo_head_hexsha": "61aa94ee853e666304b1ac544cb300528eb3f591", "max_stars_repo_licenses": ["Apac... |
#include <boost/type_traits/is_polymorphic.hpp>
| {"hexsha": "25c0e229a4bcef4e15c16a1ec10ffa3acb1a39b9", "size": 48, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_type_traits_is_polymorphic.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BS... |
import re
import torch
import jpegio
import shutil
import numpy as np
from pathlib import Path
from functools import partial
from argus import load_model
from src.ema import ModelEma
from src import config
def deep_chunk(input, chunks, dim=0):
partial_deep_chunk = partial(deep_chunk, chunks=chunks, dim=dim)
... | {"hexsha": "bc76ff0a7092d5b657f19a89624f08bea7aba32c", "size": 3448, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils.py", "max_stars_repo_name": "lRomul/argus-alaska", "max_stars_repo_head_hexsha": "f45dca1781b4a5f1336ebf826e3102ad5a6c0aeb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "ma... |
import io
import pathlib
import re
from dataclasses import dataclass
from functools import singledispatch
from typing import Tuple, Any
import ipywidgets as widgets
import numpy as np
from PIL import Image, ImageEnhance, ImageOps
URL_REGEX = re.compile(
r"^(http:\/\/www\.|https:\/\/www\.|http:\/\/|https:\/\/)?"
... | {"hexsha": "a39f30372f5fa5465f5b1a25d9c05f0b8fd2338d", "size": 3127, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ipyannotations/images/canvases/image_utils.py", "max_stars_repo_name": "tabaspki/ipyannotations", "max_stars_repo_head_hexsha": "8253d3a0abcd5644d6e5a0c5b04557ec7f50ba4c", "max_stars_repo_lice... |
SUBROUTINE WRITCA(LUNXX,MSGT,MSGL)
C$$$ SUBPROGRAM DOCUMENTATION BLOCK
C
C SUBPROGRAM: WRITCA
C PRGMMR: J. ATOR ORG: NP12 DATE: 2004-08-18
C
C ABSTRACT: THIS SUBROUTINE IS CONSIDERED OBSOLETE AND MAY BE REMOVED
C FROM THE BUFR ARCHIVE LIBRARY IN A FUTURE VERSION. IT NOW SIMPLY
C CALLS B... | {"hexsha": "c205ba9b1f4e31d528c5cde91a468c76d1cfb15b", "size": 2320, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "observations/obs_converters/NCEP/prep_bufr/lib/writca.f", "max_stars_repo_name": "hkershaw-brown/feature-preprocess", "max_stars_repo_head_hexsha": "fe2bd77b38c63fa0566c83ebc4d2fac1623aef66", "max... |
module AddIntegersF90
using CxxInterface
const libAddIntegersF90 = joinpath(pwd(), "libAddIntegersF90")
eval(f90setup())
eval(f90newfile("AddIntegersF90.f90", ""))
eval(f90function(FnName(:add_int, "add_int", libAddIntegersF90), FnResult(Cint, "integer", Int, expr -> :(convert(Int, $expr))),
[FnArg(... | {"hexsha": "34345b5f37256f7e3db685266321aa3be3baea59", "size": 1884, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test-f90.jl", "max_stars_repo_name": "jw3126/CxxInterface.jl", "max_stars_repo_head_hexsha": "4b69da8d7e3497c10d5029c8f0c13ee81019ea13", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from datasets import load_dataset, load_metric
import transformers
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_... | {"hexsha": "837b3344ed03af149d0da7ac919592cece8e698d", "size": 14573, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/train.py", "max_stars_repo_name": "spacemanidol/RankingModelCompression", "max_stars_repo_head_hexsha": "43123fb37d97db3ae4338eb9af28520e2aaf88ea", "max_stars_repo_licenses": ["MIT"], "max_st... |
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