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"""
Utility functions for the RBM
Created on Fri May 10 2019
Adapted from pytorch-rbm project on GitHub
@author: João Henrique Rodrigues, IST
version: 1.0
"""
import torch
import numpy as np
class CategoricalRBM():
def __init__(self, n_features, n_hidden, n_diff, sum_data, cd=1, persistent=False, learning_rate=1... | {"hexsha": "22d0fd80aed7a267904a62d89984a7919f607e3b", "size": 9746, "ext": "py", "lang": "Python", "max_stars_repo_path": "rbm_categorical.py", "max_stars_repo_name": "joaor96/BLADE", "max_stars_repo_head_hexsha": "6f0cd0e7e5fe8f7d200a63719ecb347987fd9a1e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
from OpenGL.GL import *
from OpenGL.GL.shaders import compileProgram, compileShader
import json
import pyrr
import numpy as np
from utils.picker import Picker
from utils.text import TextDrawer
from utils.window import Window
class Drawer:
def __init__(self, path, win_params):
# data
with open(pa... | {"hexsha": "65c9487aebad8c7a7e661a860ac84b7cd70ef3bc", "size": 27531, "ext": "py", "lang": "Python", "max_stars_repo_path": "watcher/utils/drawer.py", "max_stars_repo_name": "cog-isa/htm-rl", "max_stars_repo_head_hexsha": "baf5b67a11283d37165bf6a29d6808a234d6d98c", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from flask import Flask
from flask import jsonify
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
import numpy as np
# setup db & reflect it
engine = create_engine("sqlite:///Resources/hawaii.sqlite")
Base = automap_bas... | {"hexsha": "bbd62b7cf510c7cc1189a255ecfab71e8bc8b3b4", "size": 2226, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "nukemecha/sqlalchemy-challenge", "max_stars_repo_head_hexsha": "e7cfb226a5639ca6a605a880a52aaba113e7c59c", "max_stars_repo_licenses": ["ADSL"], "max_stars_count": ... |
# encoding: utf8
import csv
import string
import numpy as np
import math
def load_data(filename, train_ratio):
with open(filename, "r") as f:
csv_reader = csv.reader(f)
next(csv_reader, None) # header
dataset = [(line[0], line[1]) for line in csv_reader]
np.random.shuffle(dataset)
... | {"hexsha": "ccadb929de3505d54c219d8bfc9ef214b9f235e0", "size": 3235, "ext": "py", "lang": "Python", "max_stars_repo_path": "datasets/kaggle-uciml-sms-spam/spam-detection-using-naive-bayes.py", "max_stars_repo_name": "JackonYang/paper-reading", "max_stars_repo_head_hexsha": "330c6724dc1e3a56d40de4c19b9965810698b60d", "m... |
import sys, os, argparse
import numpy as np
import cv2
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils.data import DataLoader
from torchvision import transforms
import torch.backends.cudnn as cudnn
import torchvision
import torch.nn.functional a... | {"hexsha": "0a1052398089660f5ea90515870243d75dc721a2", "size": 10534, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/test_on_video_dlib.py", "max_stars_repo_name": "chenyeheng/SmartCar-HeadPose", "max_stars_repo_head_hexsha": "c804b5ea58929885c8dcc349194d785d66cc6443", "max_stars_repo_licenses": ["Apache-2... |
# Circular planar piston
# Evaluate the acoustic field generated by a circular planar piston
# Collocation ("discrete dipole") approximation of the volume integral
# equation for 3D acoustic scattering
import os
import sys
from IPython import embed
# FIXME: figure out how to avoid this sys.path stuff
sys.path.append(... | {"hexsha": "549558e02960ab7a17164759e59f5b4d38969f83", "size": 13677, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo/hifu/circular_planar_piston.py", "max_stars_repo_name": "AndrewGibbs/vines", "max_stars_repo_head_hexsha": "4c4f75adc8f601f06e2ab12fbaa95a047ef4354e", "max_stars_repo_licenses": ["MIT"], "ma... |
import numpy as np
import matplotlib.pyplot as plt
"""
PLOTS PROBABILITY DISTRIBUTION FUNCTIONS
"""
def main():
vals = []
mu = 0.3
np.random.seed(1)
vals = np.random.poisson(mu, size=1000)
print(vals)
hist_vals = np.histogram(vals, bins=np.arange(0,100))
print(hist_vals)
plt.figur... | {"hexsha": "324d8851745a167d9b99732e8e7ff66640bc972e", "size": 434, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/testscripts/probs.py", "max_stars_repo_name": "alexberndt/mobile-AGV-optimization", "max_stars_repo_head_hexsha": "76b97fd5aa3898fd6cb6f74f8d87140555c92af5", "max_stars_repo_licenses": ["MIT... |
fname = :TRMA57_abs
c = Combi(hessian_sparse,PDataMA57,solve_modelTRDiagAbs,preprocessMA57,decreaseFact,Tparam())
include("Template.jl")
| {"hexsha": "6f6602ed713bc13d8963f39f761820108ede45fd", "size": 137, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Solvers/old/TRMA57_abs.jl", "max_stars_repo_name": "Goysa2/ARCTR.jl", "max_stars_repo_head_hexsha": "527772d3d25f0c7f45380bedd3060d720f5d0809", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
/* Generated from orogen/lib/orogen/templates/tasks/Task.cpp */
#include "Task.hpp"
#include <imu_kvh_1750/Driver.hpp>
#include <base/samples/IMUSensors.hpp>
#include <base-logging/Logging.hpp>
#include <Eigen/Geometry>
#include <boost/numeric/conversion/cast.hpp>
using namespace imu_kvh_1750;
Task::Task(std::string... | {"hexsha": "06b64aea2331a486a9075f7af5027a10c1d68281", "size": 4247, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tasks/Task.cpp", "max_stars_repo_name": "Brazilian-Institute-of-Robotics/orogen-imu_kvh_1750", "max_stars_repo_head_hexsha": "58e96581a770e9300fef4f7cb522fe7747cf5018", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma Pair_Agent: "Pair X Y \<noteq> Agent X'"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Messages.Pair X Y \<noteq> Messages.Agent X'
[PROOF STEP]
by transfer auto | {"llama_tokens": 75, "file": "Key_Agreement_Strong_Adversaries_Messages", "length": 1} |
"""
~~ bluestoned ~~
detect chroma keys in video and image files
(c) 2019 Nik Cubrilovic <git@nikcub.me>
"""
import argparse
import os
import sys
import time
import logging
import requests
import shutil
import tempfile
__version__ = '0.1.2'
try:
import cv2
except ImportError:
print("Error impor... | {"hexsha": "97b5d26eabe5ec01ebed15b3af03c9bf8ea9ba7d", "size": 15461, "ext": "py", "lang": "Python", "max_stars_repo_path": "bluestoned/main.py", "max_stars_repo_name": "nc9/bluestoned", "max_stars_repo_head_hexsha": "f39fedcc9bb61ca56242687aed36917cd9402b9a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#include <action_executor/action_executor.h>
#include <chrono>
#include <thread>
#include <boost/bind.hpp>
#include <fstream>
#define SHOOT_TYPE_DEFAULT 0
#define EXECUTOR_IDLE 200
#define K_GIMBAL 1.0
#define K_DRONE 1.0
#define K_DRONE_YAW ... | {"hexsha": "da02dce507a0b864e8da46a97e6a3e40c9601634", "size": 52187, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "action_executor/src/action_executor.cpp", "max_stars_repo_name": "grvcTeam/multidrone_planning", "max_stars_repo_head_hexsha": "421a7d81a3417cdc6bcb690d3d88bb4e9d6b6638", "max_stars_repo_licenses":... |
import unittest
import numpy as np
import tensorflow as tf
from pymatgen.core import Lattice, Structure
from m3gnet.graph import Index, MaterialGraph, RadiusCutoffGraphConverter
class TestConverter(unittest.TestCase):
@classmethod
def setUpClass(cls) -> None:
cls.s1 = Structure(Lattice.cubic(3.17), ... | {"hexsha": "c9a2df4ba46c20fcd38f749f07fa5a72e308ecf1", "size": 1309, "ext": "py", "lang": "Python", "max_stars_repo_path": "m3gnet/graph/tests/test_types.py", "max_stars_repo_name": "materialsvirtuallab/m3gnet", "max_stars_repo_head_hexsha": "94fb01d0c90d3b2bffcdc4514f7eb3cb8fab6c90", "max_stars_repo_licenses": ["BSD-3... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2022 Stéphane Caron
#
# 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 ... | {"hexsha": "d940173ff9c2754a23af0d2ffa77ea1fdb9adeb5", "size": 3770, "ext": "py", "lang": "Python", "max_stars_repo_path": "pink/solve_ik.py", "max_stars_repo_name": "tasts-robots/pink", "max_stars_repo_head_hexsha": "966385c89bee8a8004007cad666657b015a29f3a", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
import numpy as np
class BatchHeatmapUtils:
@classmethod
def flatten_batch_heatmaps(self, batch_heatmaps: np.ndarray, batch_size: int, num_joints: int) -> np.ndarray:
return batch_heatmaps.reshape((batch_size, num_joints, -1))
@classmethod
def find_flattened_heatmap_maxvals(self, flattened_hea... | {"hexsha": "89b96687e0447f41d278df65d25849e5bbc4aaa2", "size": 2823, "ext": "py", "lang": "Python", "max_stars_repo_path": "common_utils/ml_utils/heatmap/heatmap.py", "max_stars_repo_name": "cm107/common_utils", "max_stars_repo_head_hexsha": "4b911efe9f8cdec16ecb2a983e16f772be05076c", "max_stars_repo_licenses": ["MIT"]... |
#ifndef TRACKER_GMD_H
#define TRACKER_GMD_H
#include "tracker.h"
#include <stdlib.h> /* srand, rand */
#include <time.h> /* time */
#include <gsl/gsl_rng.h>
#include <gsl/gsl_randist.h> /* GAUSSIAN*/
#include "helper/Constants.h"
#include <limits.h>
#include "helper/high_res_timer.h"
#include "helper/bound... | {"hexsha": "a503929b6d07b7834110fb81d978710052ea3915", "size": 4090, "ext": "h", "lang": "C", "max_stars_repo_path": "src/tracker/tracker_gmd.h", "max_stars_repo_name": "Jim61C/GMD_Tracker", "max_stars_repo_head_hexsha": "6c522b26f664c259bd371214e44c9c2cd32c51d0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
import sys
from simtk import unit
from benchmark import DATA_PATH
from benchmark.experiments.driver import ExperimentDescriptor, Experiment
from benchmark.testsystems import dhfr_constrained
import numpy as np
scale_factors = np.arange(1.0, 4.01, 0.25)
dt_range = np.arange(0.5, 8.01, 0.5)
splittings = {"O... | {"hexsha": "ccea17d78c439a95c774688aee8d0656ac1d26da", "size": 2212, "ext": "py", "lang": "Python", "max_stars_repo_path": "benchmark/experiments/submission_scripts/5_hmr.py", "max_stars_repo_name": "choderalab/integrator-benchmark", "max_stars_repo_head_hexsha": "bb307e6ebf476b652e62e41ae49730f530732da3", "max_stars_r... |
# Copyright 2021 Ibrahim Ayed, Emmanuel de Bézenac, Mickaël Chen, Jean-Yves Franceschi, Sylvain Lamprier, Patrick Gallinari
# 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.ap... | {"hexsha": "5383e8e3f0f227419b704e8c8840197045527d74", "size": 3217, "ext": "py", "lang": "Python", "max_stars_repo_path": "gantk2/data/factory.py", "max_stars_repo_name": "emited/gantk2", "max_stars_repo_head_hexsha": "157ad5d7e4b3b46da3d8b6d7f1db5cfc80ebb426", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
[STATEMENT]
lemma MGT_CALL1: "\<forall>p. {} |\<turnstile>\<^sub>t {MGT\<^sub>t(CALL p)}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<forall>p. {} |\<turnstile>\<^sub>t {MGT\<^sub>t (CALL p)}
[PROOF STEP]
by(fastforce intro:MGT_CALL[THEN ConjE]) | {"llama_tokens": 119, "file": "Abstract-Hoare-Logics_Procs_PsHoareTotal", "length": 1} |
\problemname{Quality-Adjusted Life-Year}
%% Image URL: https://www.pexels.com/photo/sunset-sunshine-travel-wings-103127/
%% Image License: https://www.pexels.com/photo-license/
\illustration{0.33}{balcony.jpg}{~}
The Quality-Adjusted Life-Year (QALY) is a way to measure a person's
quality of life that includes bot... | {"hexsha": "8dad911101b0b18649e3d6ab591fca59933d30fb", "size": 1575, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "problems/qaly/problem_statement/problem.tex", "max_stars_repo_name": "icpc/na-rocky-mountain-2018-public", "max_stars_repo_head_hexsha": "416a94258f99ab68ff7d9777faca55c94cdaf5f5", "max_stars_repo_l... |
from setuptools import (
setup,
find_packages,
Extension
)
from setupext import check_for_openmp
import os
import numpy as np
from Cython.Build import cythonize
if check_for_openmp():
omp_args = ['-fopenmp']
else:
omp_args = None
if os.name == "nt":
std_libs = []
else:
std_libs = ["m"]
ex... | {"hexsha": "31cf28e7b7fcef379cfcee18169d77094c15830a", "size": 927, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "themousepotato/ewah_bool_utils", "max_stars_repo_head_hexsha": "eb1bc18e62d02a715a441a6731cf29a38aad158d", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
#!/usr/bin/python3.4
import os
import bpy
import glob
import argparse
import numpy as np
from bpy import context
scene = context.scene
import sys
argv = sys.argv
argv = argv[argv.index("-P") + 1:] # get all args after "--"
argv.remove("--")
sys.argv = argv
print(argv) # --> ['example', 'args', '123']
def is_valid... | {"hexsha": "bc6c016495971dd30d1083181342be2830f5cf42", "size": 7285, "ext": "py", "lang": "Python", "max_stars_repo_path": "mud_me_a_video.py", "max_stars_repo_name": "ososinski/video_blender", "max_stars_repo_head_hexsha": "70bee9dfd1d74caf6eb0076035a9209041117f91", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
# UE computation
function updatechoices!()#(ECᵢ, DTC, SR)
global ECᵢ#, DTC, SR
newDTC = zero(DTC)
#newSR = SR
newSR = Dict(d => Dict(i => zeros(T, nsinks, nclasses) for i in 1:2) for d in divs);
λ = 1e-4
for (srcid,src) in enumerate(srcs)
i = outlinkids(net, src)[1]
for (snkid,s... | {"hexsha": "a96f0e8f6bd81dc4d0495dda571c2d6b67a8bf9f", "size": 2367, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "equilibrium.jl", "max_stars_repo_name": "SaiKiran92/dta_julia", "max_stars_repo_head_hexsha": "42ef131bb3ea562ea73646ffd5f8be6314afc687", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
## Usage
## copy paste it into the whole file of sawywer_push_nips.py
from collections import OrderedDict
import numpy as np
from gym.spaces import Box, Dict
import mujoco_py
import random
from multiworld.core.serializable import Serializable
from multiworld.envs.env_util import (
get_stat_in_paths,
create_sta... | {"hexsha": "6afa394083946c136e271b8c6fb46519a81ef6fc", "size": 20015, "ext": "py", "lang": "Python", "max_stars_repo_path": "multiworld/envs/mujoco/sawyer_xyz/sawyer_push_nips_get_DATA.py", "max_stars_repo_name": "luuckiest/multiworld", "max_stars_repo_head_hexsha": "3878b5480579eda169482fd013d2097956f3d083", "max_star... |
import logging
import random
import numpy as np
import torch
from fastprogress.fastprogress import progress_bar
from torch.utils.data import DataLoader, SequentialSampler
from transformers import ElectraForSequenceClassification, ElectraTokenizer
logger = logging.getLogger(__name__)
class GrandChallengeTextClassifi... | {"hexsha": "c850de12a2a2ca68533664ae8b93f14991da04a6", "size": 1677, "ext": "py", "lang": "Python", "max_stars_repo_path": "text_classification/predictor.py", "max_stars_repo_name": "voithru/asr-text_classification-pipeline", "max_stars_repo_head_hexsha": "720106cc7e06e7d7a811dee14f660be2b4fb97fc", "max_stars_repo_lice... |
from tequila.circuit import gates
from tequila.objective import ExpectationValue
from tequila.objective.objective import Variable
from tequila.hamiltonian import paulis
from tequila import simulate
import tequila
from tequila.circuit.noise import BitFlip,PhaseDamp,PhaseFlip,AmplitudeDamp,PhaseAmplitudeDamp,Depolarizing... | {"hexsha": "6eb563abead4986d8dd67e2ca82eb3b66aac90c1", "size": 7985, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_noise.py", "max_stars_repo_name": "akpc/margarita", "max_stars_repo_head_hexsha": "171fc160b32b60b30ff31e8ec62c7517da3d240d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#include "transaction.h"
#include "base58.h"
#include "bignum.h"
#include "block.h"
#include "checkpoints.h"
#include "init.h"
#include "main.h"
#include "txindex.h"
#include "txmempool.h"
#include "util.h"
#include <boost/foreach.hpp>
void CTransaction::SetNull()
{
nVersion = CTransaction::CURRENT_VERSION;
n... | {"hexsha": "99d0d4c741f8ac13585e91ed528f4a294c9d28f9", "size": 36571, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "wallet/transaction.cpp", "max_stars_repo_name": "NeblioTeam/neblio", "max_stars_repo_head_hexsha": "12d5fda35b528c57eee29098de63607bda419693", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
using Distributions, Compat.Test, Random, LinearAlgebra
using Distributions: Product
@testset "Testing Product distributions" begin
let
rng, D = MersenneTwister(123456), 11
# Construct independent distributions and `Product` distribution from these.
μ = randn(rng, D)
ds = Normal.(μ, 1.0)
x = rand.... | {"hexsha": "d3f050e1ad6108ca0c282ce8e1df8ca6a0516cac", "size": 695, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/product.jl", "max_stars_repo_name": "jarredbarber/Distributions.jl", "max_stars_repo_head_hexsha": "a6a9ef2799e8772feb0809b4396149d5b6e9a244", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
[STATEMENT]
lemma zsplit0_I:
"\<And>n a. zsplit0 t = (n, a) \<Longrightarrow>
(Inum ((x::int) # bs) (CN 0 n a) = Inum (x # bs) t) \<and> numbound0 a"
(is "\<And>n a. ?S t = (n,a) \<Longrightarrow> (?I x (CN 0 n a) = ?I x t) \<and> ?N a")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<And>n a. zsplit0 t = (... | {"llama_tokens": 44236, "file": null, "length": 146} |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 4 17:41:56 2018
@author: hubert kyeremateng-boateng
"""
import numpy as np
import pandas as pd
recipes = pd.read_csv('arp_dataset.csv', header=None)
recipes.rename(columns={0: 'name'}, inplace=True)
print(np.transpose(recipes)) | {"hexsha": "8ecf4e191c51888fe96cf70ed22b287feafa153f", "size": 300, "ext": "py", "lang": "Python", "max_stars_repo_path": "SVM_example.py", "max_stars_repo_name": "COSC-Research-BowieState/ARP_Simulator", "max_stars_repo_head_hexsha": "ec109253bc3cc6903252f6f24b1205b8e7bab189", "max_stars_repo_licenses": ["MIT"], "max_... |
#Importo pandas y numpy para crear un DataFrame
import pandas as pd
import numpy as np
#Creo un DataFrame con dos columnas, Celsius y Kelvin, ambas con datos iguales
data = {'Celsius':[22, 36, 20, 26, 30, 38],
'Kelvin':[22, 36, 20, 26, 30, 38]}
#Creo el DataFrame con el su index y las columnas Celsius y Kelvi... | {"hexsha": "884a1e66482fd16546d1b9384ab280cd89c375bd", "size": 622, "ext": "py", "lang": "Python", "max_stars_repo_path": "Lambda/lamda.py", "max_stars_repo_name": "BrianMarquez3/Python-Course", "max_stars_repo_head_hexsha": "2622b4ddfd687505becfd246e82a2ed0cb9b76f3", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
(*
* SPDX-License-Identifier: MIT
*
*)
Inductive label {A} : Type :=
Silent : label
| Action : A -> label
.
Inductive type : Set :=
| Access
| Bool
| Nat
| Unit
| TPair (t1 t2 : type)
.
Module Type GRANT_ACCESS.
Parameter access : Set.
End GRANT_ACCESS.
Module Messages (GA : GRANT_ACCESS).
Inductive mess... | {"author": "spicy-paper", "repo": "spicy", "sha": "14b766c24bb546861e623b6681b2e71653234681", "save_path": "github-repos/coq/spicy-paper-spicy", "path": "github-repos/coq/spicy-paper-spicy/spicy-14b766c24bb546861e623b6681b2e71653234681/src/Messages.v"} |
import sys
import os
import numpy as np
import pytest
sys.path.append('..')
import autodiff as ad
def test_composite():
#Test some more complicated functions / identities, including some multivariate ones.
x = ad.Scalar('x', 2)
z = (5 * (x + 20) / 10) ** 2
d = z.getGradient(['x'])
assert(z.getV... | {"hexsha": "68776ac24c27c3348fdcb5c57b604f3cf11f13cf", "size": 7032, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_composite.py", "max_stars_repo_name": "cs207FinalProjectGroup/cs207-FinalProject", "max_stars_repo_head_hexsha": "faa78f023df43c13f2ccd4711835c4313f193c9b", "max_stars_repo_licenses": [... |
# Univariate
const VectorOfUnivariate = Distributions.Product
function arraydist(dists::AbstractVector{<:UnivariateDistribution})
return Product(dists)
end
function Distributions.logpdf(dist::VectorOfUnivariate, x::AbstractMatrix{<:Real})
size(x, 1) == length(dist) ||
throw(DimensionMismatch("Inconsi... | {"hexsha": "6b3a76d97a63dd0aa202e4bc76f5f6cb5bcae256", "size": 2652, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/arraydist.jl", "max_stars_repo_name": "bmharsha/DistributionsAD.jl", "max_stars_repo_head_hexsha": "fe2070012167b78c84a49733a0a64997e9533812", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
ET Correction Tool:
This script creates evapotranspiration Dfs2 from single/multiple reference ET
time-series, and applies spatially, monthly varying solar radiation correction
factors to the reference ET data and creates the MIKE SHE input ET Dfs2 file.
Created on Wed Apr 28 15:50:07 2021
@author: Shu... | {"hexsha": "43265f7fe1295abed8009d2f37a1720ff93f8391", "size": 17421, "ext": "py", "lang": "Python", "max_stars_repo_path": "ET_CorrectionTool.py", "max_stars_repo_name": "Shubbee/ET-Correction-Tool", "max_stars_repo_head_hexsha": "37d35ba7899aa6fc490f2e8b908baf5d82e170d3", "max_stars_repo_licenses": ["MIT"], "max_star... |
function [Fr_bin, str_Fr, Fr_dec] = Fr_dec2bin (dec)
% by Sundar Krishnan
% 2003, Edited in June, 2004
%
% Description :
% This function Fr_dec2bin.m will convert a POSITIVE Decimal system
% Fraction (dec) to Binary system Fraction Fr_bin.
% Matlab itself has bin2dec.m and dec2bin.m, but there seems to be
% no standard... | {"author": "Sable", "repo": "mcbench-benchmarks", "sha": "ba13b2f0296ef49491b95e3f984c7c41fccdb6d8", "save_path": "github-repos/MATLAB/Sable-mcbench-benchmarks", "path": "github-repos/MATLAB/Sable-mcbench-benchmarks/mcbench-benchmarks-ba13b2f0296ef49491b95e3f984c7c41fccdb6d8/5396-conversion-of-fractions-from-binary-to-... |
import json
import random
import pdb
import rdkit.Chem as Chem
import numpy as np
from tqdm import tqdm
import utils.data_utils as data_utils
from template.rdchiral.main import rdchiralRun, rdchiralReaction, rdchiralReactants
def main():
with open('template/templates_train.json', 'r+') as template_file:
... | {"hexsha": "ee05143f8c04f02f6270a0364eecfb949daafba7", "size": 1642, "ext": "py", "lang": "Python", "max_stars_repo_path": "template/count_freq.py", "max_stars_repo_name": "sw32-seo/GTA", "max_stars_repo_head_hexsha": "86b102a14b78f6c8b50d742a56445c748e59b51e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, ... |
# coding=utf-8
# Copyright 2021 RigL 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 applicable law or agree... | {"hexsha": "759c18390427c19e476ce857086bdeeecd299411", "size": 4060, "ext": "py", "lang": "Python", "max_stars_repo_path": "rigl/experimental/jax/datasets/dataset_factory_test.py", "max_stars_repo_name": "vishalbelsare/rigl", "max_stars_repo_head_hexsha": "f18abc7d82ae3acc6736068408a0186c9efa575c", "max_stars_repo_lice... |
using Test
using jInvVis
using jInv.Mesh
# tests for regular mesh
domain = [0 1.1 0 1.0 0 1.1]
n = [8 5 3]
Mr = getRegularMesh(domain,n)
xc = getCellCenteredGrid(Mr)
println("=== test viewSlice2D ===")
figure(1); clf()
subplot(1,3,1)
viewSlice2D(xc[:,1],Mr,Int(round(n[3]/2)))
xlabel("x, intensity increas... | {"hexsha": "9c7d7bae6de7719a67505d5421e5ec9431661b04", "size": 665, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/testViewSlice2D.jl", "max_stars_repo_name": "JuliaInv/jInvVis.jl", "max_stars_repo_head_hexsha": "49b3a041734b043ed945ee06e41a3988052554f2", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
""" Testing CULVERT (Changing from Horizontal Abstraction to Vertical Abstraction
This example includes a Model Topography that shows a TYPICAL Headwall Configuration
The aim is to change the Culvert Routine to Model more precisely the abstraction
from a vertical face.
The inflow must include the impact of Approach ... | {"hexsha": "714eabd22daeb6b4f1a8f63f38be6f5a85ec8118", "size": 6336, "ext": "py", "lang": "Python", "max_stars_repo_path": "anuga/culvert_flows/tests/run_culvert_flat_water_lev.py", "max_stars_repo_name": "samcom12/anuga_core", "max_stars_repo_head_hexsha": "f4378114dbf02d666fe6423de45798add5c42806", "max_stars_repo_li... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import joblib
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from ... | {"hexsha": "4a1f8b8544c98cf17015e9a1dad8f06fe3bfabc1", "size": 3663, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/data_analysis/others/clf_models.py", "max_stars_repo_name": "mcpeixoto/Sentrade", "max_stars_repo_head_hexsha": "55f65508d6b565b99840c9ce5d757185f5027164", "max_stars_repo_licenses": ["MIT"],... |
function [propValue, srcObj] = get(obj, propName)
% Accessor for reading BstPanel attributes.
% @=============================================================================
% This function is part of the Brainstorm software:
% https://neuroimage.usc.edu/brainstorm
%
% Copyright (c) University of Southern California... | {"author": "brainstorm-tools", "repo": "brainstorm3", "sha": "a892cfaabde1eaa2f9a3ac015c05b73f3739433a", "save_path": "github-repos/MATLAB/brainstorm-tools-brainstorm3", "path": "github-repos/MATLAB/brainstorm-tools-brainstorm3/brainstorm3-a892cfaabde1eaa2f9a3ac015c05b73f3739433a/toolbox/gui/@BstPanel/get.m"} |
#!/usr/bin/env python
import sys
import argparse
sys.path.append('.')
from scripts.py_featextr_server.base_server import BaseQueryHandler, start_query_server
import numpy as np
from scripts.py_featextr_server.utils import load_embeddings, create_embed_map, robust_cosine_simil
# Exclusive==True means that only one ... | {"hexsha": "be171beac128416167eee97c9a6608464421fe2b", "size": 4347, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/py_featextr_server/wordembed_cosine_server.py", "max_stars_repo_name": "prateeksingh0001/FlexNeuART", "max_stars_repo_head_hexsha": "ebc82ca4fe01436374c595db2429bc49fb9e1dd0", "max_stars_r... |
[STATEMENT]
lemma wt_int:
assumes wtE: "wtE \<xi>" and wt: "wt T"
shows "intT (tpOf T) (int \<xi> T)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. intT (tpOf T) (int \<xi> T)
[PROOF STEP]
using wt
[PROOF STATE]
proof (prove)
using this:
wt T
goal (1 subgoal):
1. intT (tpOf T) (int \<xi> T)
[PROOF STEP]
apply(ind... | {"llama_tokens": 498, "file": "Sort_Encodings_M", "length": 4} |
from datetime import datetime
import os
from typing import List
import numpy as np
import pandas as pd
import pytest
from drift_report.domain.statistical_report.statistical_feature_report import (
HeatMapData,
)
from drift_report.domain.statistical_report.statistical_report import StatisticalReport
import drift_re... | {"hexsha": "7c80b43d8f72dab853080a55d1f840bdc7fb9f42", "size": 2630, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_report.py", "max_stars_repo_name": "rufusnufus/drift-report-plugin", "max_stars_repo_head_hexsha": "37019491a82e3478d6bfc718962a477266e1fa26", "max_stars_repo_licenses": ["Apache-2.0"],... |
#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import multiprocessing
from enum import Enum, auto
from itertools import count, product
from numbers import Number
from typing import (
Any,
Callable,
Iterable,
Iterator,
Optional,
Union,
List,
Tuple,
Dic... | {"hexsha": "a32ecd98c061241b3d58a8773dd28285c247a8c4", "size": 19583, "ext": "py", "lang": "Python", "max_stars_repo_path": "signal_processing/utils/data_view/array_utils.py", "max_stars_repo_name": "dtemir/labgraph", "max_stars_repo_head_hexsha": "34e12f203ba1254291a6b3b08458ede75f1d4383", "max_stars_repo_licenses": [... |
G = CImGui
using Printf
DoGui() do
@cstatic f=Cfloat(0.0) counter=Cint(0) one=false two=false clear_color=copy(CImGuiFrontEnd.default_clear_color) begin
G.Begin("Hello, world!") # create a window called "Hello, world!" and append into it.
TreeNode("First section") do
G.Text("This is... | {"hexsha": "5b11b1255111dfed2a030341a8234ee8cad17ff2", "size": 1317, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/context_wrappers.jl", "max_stars_repo_name": "pengwyn/CImGuiFrontEnd.jl", "max_stars_repo_head_hexsha": "8bb5f0c840c07f5a5cbdf2b1588dab61b6d77704", "max_stars_repo_licenses": ["MIT"], "max... |
# Copyright 2021 The CLVR 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 applicable law or agreed to in writ... | {"hexsha": "98a30a0e5aeb25d4e40240809c2222eb38930cb6", "size": 2708, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "scripts/run_solver.jl", "max_stars_repo_name": "ericlincc/Efficient-GLP", "max_stars_repo_head_hexsha": "9c9fce117ac4ff645cc82ed4ed84c75a52b83dea", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
.. codeauthor:: Dominik Höchemer <dominik.hoechemer@tu-ilmenau.de>
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
import argparse as ap
from datetime import datetime
import os
import pickle
import sys
import warnings
import numpy as np
import skle... | {"hexsha": "899171e5e7bd83c05a254e59345e179586948a4c", "size": 18636, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "TUI-NICR/multi-task-person-perception", "max_stars_repo_head_hexsha": "81666eb42be9522fd726448e82e8bbf04138ffa3", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
[STATEMENT]
lemma generic_poly_closed:
"generic_poly R n \<in> carrier (coord_ring R (Suc (Suc n)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. generic_poly R n \<in> carrier (R [\<X>\<^bsub>Suc (Suc n)\<^esub>])
[PROOF STEP]
apply(induction n)
[PROOF STATE]
proof (prove)
goal (2 subgoals):
1. generic_poly R 0 ... | {"llama_tokens": 2149, "file": "Padic_Field_Ring_Powers", "length": 15} |
#== # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
#
# Compute the satellite position.
#
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # ==#
export satellite_position_i
"""
satellite_position_i(a::Number, e::Number, i::Number, RAAN::Number... | {"hexsha": "714b32880ab815d6fa175ae2a5ffd52bd2547b56", "size": 2169, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/transformations/position.jl", "max_stars_repo_name": "disberd/SatelliteToolbox.jl", "max_stars_repo_head_hexsha": "441470938af978e9d5653a9c4b36ccc107023960", "max_stars_repo_licenses": ["MIT"],... |
import re
import numpy as np
from numba import jit
from collections import deque
ALPHABET = """abcdefghijklmnopqrstuvwxyz1234567890,.()[]"' -\n"""
class Tokenizer(object):
def __init__(self, alphabet=ALPHABET, unk="~"):
assert unk not in alphabet, "please keep UNK character not part of alphabet"
... | {"hexsha": "d798f8daa81fd53347fd1cf2676b779a6f23db89", "size": 2538, "ext": "py", "lang": "Python", "max_stars_repo_path": "char2vec/utils.py", "max_stars_repo_name": "sonlamho/Char2Vec", "max_stars_repo_head_hexsha": "88141ecf33cd48ad84c14f8999cb3988d51d205b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, ... |
"""
act.retrievals.cbh
------------------
Module that calculates cloud base heights in various ways.
"""
import numpy as np
import xarray as xr
from scipy import ndimage
def generic_sobel_cbh(obj, variable=None, height_dim=None,
var_thresh=None, fill_na=None,
return_thre... | {"hexsha": "fbd3444711c9f466bc70aaefeec0e0cadf8cf7c9", "size": 3953, "ext": "py", "lang": "Python", "max_stars_repo_path": "act/retrievals/cbh.py", "max_stars_repo_name": "rcjackson/ACT", "max_stars_repo_head_hexsha": "c57fb55094b142bbbef63e7069d4024049996139", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_co... |
# -*- coding: utf-8 -*-
"""
Meteorological, 2D{1,1,2,1,1} dataset
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
"""
# %%
# The following dataset is obtained from `NOAA/NCEP Global Forecast System (GFS)
# Atmospheric Model
# <https://coastwatch.pfeg.noaa.gov/erddap/griddap/NCEP_Global_Best.graph?ugrd10m[(2017-09-17T12:0... | {"hexsha": "0bb74914da58fead57cf9c02b03cd53f38c90e0b", "size": 4660, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/correlated_examples/plot_1_meteorology.py", "max_stars_repo_name": "DeepanshS/csdmpy", "max_stars_repo_head_hexsha": "ae8d20dd09f217bb462af67a3145bb6fcb025def", "max_stars_repo_licenses":... |
import comet_ml
import pickle
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = "3"
from os.path import dirname, realpath
import sys
import git
sys.path.append(dirname(dirname(realpath(__file__))))
import torch
import torch.distributed as dist
import sandstone.datasets.factory as dataset_factory
import sandstone.models.fa... | {"hexsha": "9c4a9277f0cb85d640b8129b375966265f4b281d", "size": 5556, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/create_submission.py", "max_stars_repo_name": "yala/NeuraCrypt", "max_stars_repo_head_hexsha": "6c9862d1076095d76779af03a3a9ffd2cfec748a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
import random
import copy
from collections import namedtuple, deque
from model import Actor, Critic
from configuration import Configuration
import torch
import torch.nn.functional as F
import torch.optim as optim
class DDPGAgent():
"""A class to create DDPG agents that interact and learn from... | {"hexsha": "843de20a45a544f507a1183ca51af079f7e67f09", "size": 5384, "ext": "py", "lang": "Python", "max_stars_repo_path": "ddpg.py", "max_stars_repo_name": "riley-mld/UnityTennis_DRLN", "max_stars_repo_head_hexsha": "a0a78b76870b46d2c75105591140357871b4fab3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
#include <boost/test/unit_test.hpp>
#include "../Coverage/SymbolNameUtils.h"
#include "TestUtils.h"
#include "GLib/compat.h"
#include "GLib/Cpp/HtmlGenerator.h"
#include <fstream>
namespace GLib::Cpp
{
std::ostream & operator<<(std::ostream & s, const Fragment & f)
{
return s << "State: " << f.first << ", Valu... | {"hexsha": "1f34b70a7c0a086b7e4b4a5d2167010c7e1f1682", "size": 16924, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Tests/CppIteratorTests.cpp", "max_stars_repo_name": "glen-summers/GLibCopy", "max_stars_repo_head_hexsha": "3a0a7d0fc17f1ea98b723120d8394257047d4386", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# docker attach kairi_nvidia
# conda activate train
import random
import numpy as np
import logging
import time
from requests.api import get
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
from torch import nn
from torch.nn.utils.rnn import pack_padded_sequence
from models import Decode... | {"hexsha": "5fe59f94d4e06a8d88db218964723fcac49fe187", "size": 20865, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "seobeomjin/vilbert-multi-task", "max_stars_repo_head_hexsha": "20deed798f3ed4c4fcc736742e382e3528d61914", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
make object! [
doc: "Returns the total hits (display requests) for the snips in the space."
handle: func [/local total name] [
total: 0
foreach name space-dir [
total: total + to-integer space-meta-get snip "displays"
]
to-string total
]
]
| {"hexsha": "070d5e499ca43cb57a6dcc057dcce227e5bfc5d7", "size": 308, "ext": "r", "lang": "R", "max_stars_repo_path": "code/apps/stats/total-views.r", "max_stars_repo_name": "earl/vanilla", "max_stars_repo_head_hexsha": "76e3b6196820810690aa113362dc1c4f34381b85", "max_stars_repo_licenses": ["AFL-2.0"], "max_stars_count":... |
# Copyright 2020 Huawei Technologies Co., Ltd
#
# 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... | {"hexsha": "0ed368bcffad56e2c0bee2ca5722192e4377c538", "size": 5315, "ext": "py", "lang": "Python", "max_stars_repo_path": "research/nlp/gpt2/create_lambada_data.py", "max_stars_repo_name": "leelige/mindspore", "max_stars_repo_head_hexsha": "5199e05ba3888963473f2b07da3f7bca5b9ef6dc", "max_stars_repo_licenses": ["Apache... |
from detectron2.utils.visualizer import ColorMode
from detectron2 import model_zoo
from detectron2.modeling import build_model
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data.datasets import register_coco_instan... | {"hexsha": "5184b5329292a4b6e351bcb46018f57590b5ea23", "size": 6025, "ext": "py", "lang": "Python", "max_stars_repo_path": "detection/visualize.py", "max_stars_repo_name": "Nkechinyere-Agu/AnaXnet-Original", "max_stars_repo_head_hexsha": "7719bd9f90d0f0ca4506d41def8ebc1e21f8f2c1", "max_stars_repo_licenses": ["MIT"], "m... |
// Copyright (c) 2019 Bitcoin Association
// Distributed under the Open BSV software license, see the accompanying file LICENSE.
#include "test/test_bitcoin.h"
#include "checkqueuepool.h"
#include "taskcancellation.h"
#include <boost/test/unit_test.hpp>
#include <boost/thread/thread.hpp>
#include <array>
#include <a... | {"hexsha": "4812e003faddf6a70b68017bfa3982952ea563a3", "size": 8183, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/test/checkqueue_tests.cpp", "max_stars_repo_name": "bxlkm1/yulecoin", "max_stars_repo_head_hexsha": "3605faf2ff2e3c7bd381414613fc5c0234ad2936", "max_stars_repo_licenses": ["OML"], "max_stars_cou... |
import tensorflow as tf
import cv2
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
model_pred = tf.keras.models.load_model('CKmodel.h5')
model_pred.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
face_cascade = cv2.CascadeC... | {"hexsha": "93264d095e9c6004150c1e29a9f1d790900ddb94", "size": 3174, "ext": "py", "lang": "Python", "max_stars_repo_path": "model_working.py", "max_stars_repo_name": "abhinavg8/Real-Time--Face-Analysis", "max_stars_repo_head_hexsha": "7b18a1308347743ea12d29fc7c3039ad7b172f32", "max_stars_repo_licenses": ["MIT"], "max_s... |
\section{Recurrent Neural Networks}
\label{sec:rnn}
Recurrent Neural Networks (RNNs) are one of the most commonly used typology of neural networks~\cite{lecun2015deep}. In recent years, thanks to advancements in their architecture~\cite{hochreiter1997long,chung2014empirical} and in computational power, they have become... | {"hexsha": "6c45741717b1b7eec0715e642e40e568b47454c9", "size": 13500, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "paper/sections/rnn.tex", "max_stars_repo_name": "D2KLab/recsys18_challenge", "max_stars_repo_head_hexsha": "5cd47d1b9df2a2bccad2889ba1d570d5a8dd0f8d", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
#include <boost/test/unit_test.hpp>
#include <cpp-utils/pattern/registry.h>
#include <cpp-utils/algorithm/container.h>
using namespace cpp;
namespace {
class Module
{
public:
virtual ~Module() = default;
virtual void init() = 0;
int init_called = 0;
};
class MyModule1 : public Module
{
void init() overri... | {"hexsha": "f3140907da54e34e384f1822a5768483fd8dce27", "size": 1328, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/pattern/registry.cpp", "max_stars_repo_name": "R1tschY/cpp-utils", "max_stars_repo_head_hexsha": "d03c16cf0c2503770a201b9499a9aebbcdd0a694", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
from typing import List, Dict
import numpy as np
import torch as t
import torch.nn.functional as F
from keras_preprocessing import sequence
from sklearn.feature_extraction.text import HashingVectorizer
from unify_eval.model.mixins.classification import Classifier
from unify_eval.model.types import Tensor
from unify_e... | {"hexsha": "c4e3b814aeaf43f5347aeb1ce8a16011895966d7", "size": 4334, "ext": "py", "lang": "Python", "max_stars_repo_path": "unify_eval/model/ensemble.py", "max_stars_repo_name": "goesslfabian/unify-eval", "max_stars_repo_head_hexsha": "ced486e44ca57ed31b552fd20b53cae61015e486", "max_stars_repo_licenses": ["Apache-2.0"]... |
import numpy as np
import pandas as pd
import mesa_reader as mr
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import glob
from cosmic.sample.initialbinarytable import InitialBinaryTable
from cosmic.evolve import Evolve
#-------------------------------------------------------------------#
## fi... | {"hexsha": "5fa7679360e342ece4dcd767093700cef8d680c9", "size": 6849, "ext": "py", "lang": "Python", "max_stars_repo_path": "MESA_plotting.py", "max_stars_repo_name": "celiotine/CIERA_code", "max_stars_repo_head_hexsha": "c4fd6b5dec9397fed4aa2a73dae311edcde6ed81", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
import numpy as np
import pytest
from orix.quaternion.orientation import Orientation, Misorientation
from orix.quaternion.symmetry import C1, C2, C3, C4, D2, D3, D6, T, O
from orix.vector import Vector3d
@pytest.fixture
def vector(request):
return Vector3d(request.param)
@pytest.fixture(params=[(0.5, 0.5, 0.5,... | {"hexsha": "ad7da85a0bd50af4c7011598aae5d06b21a22c93", "size": 3829, "ext": "py", "lang": "Python", "max_stars_repo_path": "orix/tests/test_orientation.py", "max_stars_repo_name": "JoonatanL/orix", "max_stars_repo_head_hexsha": "b8e6dfe4c3ac053e923b001b9bccec717db3e56c", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
This example shows how to do instance segmentation on an image using net pretrained
on the PASCAL VOC2012 dataset. For an introduction to what instance segmentation is,
see the accompanying header file dnn_instan... | {"hexsha": "b864015b992bec3e33bc3aac63a1e670914782dd", "size": 7506, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "examples/dnn_instance_segmentation_ex.cpp", "max_stars_repo_name": "babic95/dlib", "max_stars_repo_head_hexsha": "285f0255f6deef4e59e97f93023de112594c0741", "max_stars_repo_licenses": ["BSL-1.0"], "... |
'''The barebone-essentials of weighted ordinary least squares and
a RANSAC-wrapper of it.
'''
import random
from collections import namedtuple
import numpy as np
from scipy import stats
WLSSolution = namedtuple("WLSSolution", [
'yhat', 'parameters', 'data', 'weights', 'residuals',
'projection_matrix', 'rss',... | {"hexsha": "4cc46f0f8b9d73d2297803ed3809aea404e7c666", "size": 6333, "ext": "py", "lang": "Python", "max_stars_repo_path": "glycan_profiling/scoring/elution_time_grouping/linear_regression.py", "max_stars_repo_name": "mstim/glycresoft", "max_stars_repo_head_hexsha": "1d305c42c7e6cba60326d8246e4a485596a53513", "max_star... |
module ModuleMacro where
record ⊤
: Set
where
module M where
module N where
postulate
A
: Set
B
: Set
module O
= M
module P
= M
module Q
= P
module R
(x : ⊤)
= N
using (A)
module S
= N
renaming
( A
to A'
; B
to B'
)
y
: ⊤
y
= record {O}
C
: ⊤
... | {"hexsha": "3b827bb55c0f1dff9c234869bfc33061f70373bb", "size": 359, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "data/declaration/ModuleMacro.agda", "max_stars_repo_name": "msuperdock/agda-unused", "max_stars_repo_head_hexsha": "f327f9aab8dcb07022b857736d8201906bba02e9", "max_stars_repo_licenses": ["MIT"], "m... |
[STATEMENT]
lemma combined_restrict_perm:
assumes "supp \<pi> \<sharp>* S" and [simp]: "finite S"
shows "combined_restrict S (\<pi> \<bullet> p) = combined_restrict S p"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. combined_restrict S (\<pi> \<bullet> p) = combined_restrict S p
[PROOF STEP]
proof(cases p)
[PRO... | {"llama_tokens": 1250, "file": "Call_Arity_CoCallAnalysisImpl", "length": 15} |
using JuMP, EAGO
m = Model()
EAGO.register_eago_operators!(m)
@variable(m, -1 <= x[i=1:5] <= 1)
@variable(m, -6.148474362391325 <= q <= 10.677081718106185)
add_NL_constraint(m, :(softplus(-0.2518902526786948 + ... | {"hexsha": "61530c0dbe7fb6f6aedc32a7f7ec684b06ded632", "size": 6369, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "solver_benchmarking/MINLPLib.jl/instances/ANN_Env/06_softplus_5_3_3.jl", "max_stars_repo_name": "PSORLab/RSActivationFunctions", "max_stars_repo_head_hexsha": "0bf8b4500b21144c076ea958ce93dbdd19a53... |
'''SGD classifier-- linear SVM. Try RADIAL BASIS FUNCTION SVM??? https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html
cd bcws_psu_research/recursive_classifier/
mkdir out
python3 recursive_sgd.py stack.bin out/
todo: write inputs, accuracy etc, to log file!!!!!'''
import sys; sys.path.append("..... | {"hexsha": "aa1588c0f8bc77c40da69efda107d92f84512d9d", "size": 8221, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/recursive_supervised/recursive_sgd.py", "max_stars_repo_name": "breadcrumbbuilds/bcws-psu-research", "max_stars_repo_head_hexsha": "e541ffc050807186160a6b8d7cc6ac78fc9f3ddc", "max_stars_repo_li... |
from astropy.io import fits
def hdr3Dto2D(hdr3D,verbose=True):
"""
Removing the wavelength component of a header, i.e., converting
the hdr from 3D (lambda, dec, ra) to 2D (dec, ra)
--- INPUT ---
hdr3d The header object to convert from (lambda, dec, ra) to (dec, ra)
verbose Toggle verbosity
"""
for... | {"hexsha": "cc7bcc61eea01d6b6caa85a4b8f4231c03bcb13f", "size": 1901, "ext": "py", "lang": "Python", "max_stars_repo_path": "MIRAGE/arx/v0/slice.py", "max_stars_repo_name": "kxxdhdn/MISSILE", "max_stars_repo_head_hexsha": "89dea38aa9247f20c444ccd0b832c674be275fbf", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
# Load modules
import csv
import copy
import cv2
import numpy as np
import sklearn
import matplotlib.pyplot as plt
import scipy.stats
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from keras.models import Sequential, load_model
from keras.layers.core import Dense, Flatten, Lambd... | {"hexsha": "d51dcd21cdcc6e2d63f5085f3013d73003dfad83", "size": 12004, "ext": "py", "lang": "Python", "max_stars_repo_path": "model.py", "max_stars_repo_name": "dalacan/behavioral-cloning", "max_stars_repo_head_hexsha": "d312380d5b1d1d655b2ffd7b9eb42f5c56795fdd", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
# !!! The order of these imports needs to be preserved !!!
import imglyb
from imglyb import util
from jnius import autoclass, cast
# !!!
import multiprocessing
import numpy as np
import vigra
import h5py
def apply_wsgray(img):
# TODO we properly want grayscale types instead
#RealARGBConverter = autoclass( 'ne... | {"hexsha": "671eb259525194ed8b6d75708b80083035bd0984", "size": 1553, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/imglyb/test_imglyb.py", "max_stars_repo_name": "constantinpape/watersheds", "max_stars_repo_head_hexsha": "9fde72b2df5aa0e3531969361b3a6c37be77ba8a", "max_stars_repo_licenses": ["BSD-3-Claus... |
# coding: utf-8
import hashlib
import numpy as np
import cv2
import os
def four_point_transform(image, pts):
rect = order_points(pts)
(tl, tr, br, bl) = rect
widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
maxWidt... | {"hexsha": "f2c5a01b78a78f026d5da43685f980086fdb9e11", "size": 1001, "ext": "py", "lang": "Python", "max_stars_repo_path": "idmatch/idcardocr/core/processing/utils.py", "max_stars_repo_name": "javierherrera1996/idmatch", "max_stars_repo_head_hexsha": "8bb27dafaa12b7b0bdb745071e81e6b940b7553a", "max_stars_repo_licenses"... |
import pygrib
import pytest
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from cartopy.util import add_cyclic_point
import cartopy.crs as ccrs
grbs = pygrib.open('../sampledata/reduced_latlon_surface.grib2')
grb = grbs.readline()
data = grb.values
lats, lons = grb.latlons()
lons1 = lons[0,:]; la... | {"hexsha": "bd287185b6dc8dfbf8bfc8d5cd170036f7e3b4ec", "size": 872, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_reduced_ll.py", "max_stars_repo_name": "FelixDQ/pygrib", "max_stars_repo_head_hexsha": "5d1761f86b684661788d8297c9299f91b4bd714e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
(*
Author(s):
Felix Jahn (1)
Yannick Forster (1)
Affiliation(s):
(1) Saarland University, Saarbrücken, Germany
*)
Require Export Undecidability.Axioms.EA.
Require Export Undecidability.Shared.Pigeonhole.
Require Export Undecidability.Shared.FinitenessFacts.
Require Export Undecidability.Synthetic.redu... | {"author": "uds-psl", "repo": "coq-synthetic-computability", "sha": "dc6eaeef99c76f4ff2903b8c07e2928622ee36ba", "save_path": "github-repos/coq/uds-psl-coq-synthetic-computability", "path": "github-repos/coq/uds-psl-coq-synthetic-computability/coq-synthetic-computability-dc6eaeef99c76f4ff2903b8c07e2928622ee36ba/Syntheti... |
[STATEMENT]
lemma iT_Plus_image_conv: "I \<oplus> k = (\<lambda>n.(n + k)) ` I"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. I \<oplus> k = (\<lambda>n. n + k) ` I
[PROOF STEP]
by (simp add: iT_Plus_def) | {"llama_tokens": 97, "file": "Nat-Interval-Logic_IL_IntervalOperators", "length": 1} |
# Timestep conversion function
function getindexfromyear_dice_2013(year)
baseyear = 2010
if rem(year - baseyear, 5) != 0
error("Invalid year")
end
return div(year - baseyear, 5) + 1
end
# Get parameters from DICE2013 excel sheet
# range is the range of cell values on the excel sheet and mus... | {"hexsha": "f591357a6a2e4f01b67b4152caf5ad1270e11f5e", "size": 1080, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/helpers.jl", "max_stars_repo_name": "anthofflab/MimiDICE2013.jl", "max_stars_repo_head_hexsha": "ae2d83788d55697d25a08e46fb872476c18c20f7", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
Fourier Transform
-Find Fourier Transform of images using OpenCV
-utilize FFT functions in Numpy
-FT applications
functions:
cv2.
dft()
idft()
FT used to analyze freq characteristics of filters
for images
2D Discrete Fourier Transform used to find frequency domain
FFT calculates DFT
sinusoidal signal... | {"hexsha": "d0b47a7d518e7862e5d0b67a09610a50524966bc", "size": 4853, "ext": "py", "lang": "Python", "max_stars_repo_path": "opencv/tutorials/imageProcessing/transform/fourier.py", "max_stars_repo_name": "SSG-DRD-IOT/commercial-iot-security-system", "max_stars_repo_head_hexsha": "0c3d89b35d0468d4d3cc5ce2653b3f0ac82652a9... |
\documentclass[output=paper]{langsci/langscibook}
% % \ChapterDOI{10.5281/zenodo.4680306} %initial publication
\ChapterDOI{10.5281/zenodo.5530358} %corrected publication
\author{Henk C. van Riemsdijk\affiliation{Tilburg University}}
\title{Case mismatches and match fixing cases}
\abstract{Matching and mismatching are... | {"hexsha": "7372a12f33d019475264b400409156f64aa58308", "size": 43852, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/15.tex", "max_stars_repo_name": "langsci/277", "max_stars_repo_head_hexsha": "80f4daa0d585057e668d6581927bb35c73e51828", "max_stars_repo_licenses": ["CC-BY-4.0"], "max_stars_count": null, ... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 20 13:17:09 2020
@author: mateusz
"""
import torch.nn as nn
import random
import torch
import copy
from collections import namedtuple
import numpy as np
from utils import dictionary_of_actions, dict_of_actions_revert_q
class DQN(object):
de... | {"hexsha": "91559b842822c1c10c4b75532519011cfb7b948d", "size": 6276, "ext": "py", "lang": "Python", "max_stars_repo_path": "agents/DeepQ.py", "max_stars_repo_name": "empyriumz/QAS_RL", "max_stars_repo_head_hexsha": "1f44f46acd9e61a8ed501cc7f0462c7217f46316", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Copyright 2018 Open Energy Efficiency, 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/LICE... | {"hexsha": "1c32d80c636c7007f06dbb384a20862f86d2b290", "size": 8945, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_segmentation.py", "max_stars_repo_name": "goitom/eemeter", "max_stars_repo_head_hexsha": "bb05d5b776546858f8f3a8d3a95bec202728d9f0", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import sys, pdb
import sqlalchemy as sa
from sqlalchemy.orm import Session
from sqlalchemy.ext.declarative import declarative_base
from obspy.core import trace
from obspy.core import Stream
from obspy.core.util import AttribDict
from datetime import datetime
import numpy as np
from numpy import append
import pisces as... | {"hexsha": "e8d547b447c39ca484c21fd27dcaefc812ec57f8", "size": 2271, "ext": "py", "lang": "Python", "max_stars_repo_path": "infrapy/utils/get_arraywaveforms.py", "max_stars_repo_name": "LANL-Seismoacoustics/infrapy", "max_stars_repo_head_hexsha": "132c1f5f9c074eca7300ab35d23109d8423a9912", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python
"""Generic utils for LoFreq
"""
__author__ = "Andreas Wilm"
__email__ = "wilma@gis.a-star.edu.sg"
__copyright__ = "2011 Genome Institute of Singapore"
__license__ = "The MIT License"
#--- standard library imports
#
from math import log10, log
import sys
from time import strftime
import string... | {"hexsha": "49d8e74a95989f983215d0d29431e7bfcecfab69", "size": 3465, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/tools/lofreq_star/utils.py", "max_stars_repo_name": "joshwkearney/lofreq", "max_stars_repo_head_hexsha": "8966e95044875ec9068d2ea4d1cf72ed96d92781", "max_stars_repo_licenses": ["MIT"], "max_st... |
import os
from collections import Counter
from typing import List, Dict
from OurPaper.myconstants import *
import numpy as np
from detectron2.data import MetadataCatalog, DatasetCatalog, \
build_detection_test_loader, build_detection_train_loader
from detectron2.data.datasets.coco import load_coco_json, convert_to_... | {"hexsha": "1b311e79e5ef17e7779e5d45dc94a142bf0be922", "size": 6868, "ext": "py", "lang": "Python", "max_stars_repo_path": "OurPaper/dataset.py", "max_stars_repo_name": "superclass-FSIS/test", "max_stars_repo_head_hexsha": "9bb2844c77704a609291135b75e94a794f235aa0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
import torch
import torchvision
from torch import nn
import logging
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import time
import os
import copy
import logging
import sys
sys.path.append('../')
from Model.Res_Seg import Res_Seg,get_norm_layer,init_weights
from Data.get_segmen... | {"hexsha": "41cdf250e495b8863bcc843916461e5c126a64db", "size": 2430, "ext": "py", "lang": "Python", "max_stars_repo_path": "Train/Train_res_seg.py", "max_stars_repo_name": "giussepi/cyto_CRLM", "max_stars_repo_head_hexsha": "4489d5d81c4270ec7b6048ceb2f2a02bfa699177", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from sklearn.cluster import KMeans
from tqdm import tqdm
import swalign
from multiprocessing import Process, Queue
from utils.color import getRandomColor
from utils.manager im... | {"hexsha": "769593451cd9a85df84017573399ec6c04cd8a30", "size": 8213, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/alignment.py", "max_stars_repo_name": "Asichurter/APISeqFewShot", "max_stars_repo_head_hexsha": "b4b7843da1f53cdc1d1711537c31305e7d5c6555", "max_stars_repo_licenses": ["MIT"], "max_stars... |
[STATEMENT]
lemma dagger_slide_var1_eq: "x\<^sup>\<dagger> \<cdot> x = x \<cdot> x\<^sup>\<dagger>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x\<^sup>\<dagger> \<cdot> x = x \<cdot> x\<^sup>\<dagger>
[PROOF STEP]
by (metis local.dagger_unfoldl_distr local.dagger_unfoldr_eq local.distrib_left local.mult_1_right ... | {"llama_tokens": 132, "file": "Kleene_Algebra_Conway", "length": 1} |
import numpy as np
import xarray
def gaussian2d(pg: xarray.Dataset, Qpeak: float, Qbackground: float) -> np.ndarray:
mlon_mean = pg.mlon.mean().item()
mlat_mean = pg.mlat.mean().item()
if "mlon_sigma" in pg.attrs and "mlat_sigma" in pg.attrs:
Q = (
Qpeak
* np.exp(-((pg.ml... | {"hexsha": "57d517cc796b064c28fb50a0f335cf3d4ccf2431", "size": 905, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gemini3d/particles/gaussian2d.py", "max_stars_repo_name": "qwsae10/pygemini", "max_stars_repo_head_hexsha": "adc6b2401ac9fc8b7cb1fc8870322f730a3383a3", "max_stars_repo_licenses": ["Apache-2.0"]... |
import numpy as np
import theano
import theano.tensor as T
import q_learning
class SGDRegressor:
def __init__(self, D):
print("Hello Theano!")
w = np.random.randn(D) / np.sqrt(D)
self.w = theano.shared(w)
self.lr = 0.1
X = T.matrix('X')
Y = T.vector('Y')
Y_hat = X.dot(self.w)
delta ... | {"hexsha": "96e048ef04a69f5a8b1914013d318bba45a54881", "size": 813, "ext": "py", "lang": "Python", "max_stars_repo_path": "udemy/lazyprogrammer/deep-reinforcement-learning-python/cartpole/theano_warmup.py", "max_stars_repo_name": "balazssimon/ml-playground", "max_stars_repo_head_hexsha": "c2eba497bebc53e5a03807bdd8873c... |
# code pour parser in
import numpy as np
INSTANCES_DIR = "instances"
instances = [
"{}/a_example.in".format(INSTANCES_DIR),
"{}/b_should_be_easy.in".format(INSTANCES_DIR),
"{}/c_no_hurry.in".format(INSTANCES_DIR),
"{}/d_metropolis.in".format(INSTANCES_DIR),
"{}/e_high_bonus.in".format(IN... | {"hexsha": "eb921a4a93e3abbe507b47b1e8e63331f43b95b1", "size": 2370, "ext": "py", "lang": "Python", "max_stars_repo_path": "Contests/2018-03-01 - Google Hash Code 2018/parser.py", "max_stars_repo_name": "NicolasBizzozzero/Competitive-Programming", "max_stars_repo_head_hexsha": "53ece89623b4aca542d096b2aff18be35b19da6d"... |
import cv2 as cv
import dlib
import numpy as np
import scipy.io as sio
import torch
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from config import device
from misc import ensure_folder
from utils.ddfa import ToTensorGjz, NormalizeGjz, _parse_param
from utils.estimate_pose import pa... | {"hexsha": "7c4557b4774d2ce66aa1457db6a7c7c79a60c6a4", "size": 3060, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo.py", "max_stars_repo_name": "foamliu/3DDFA-v2", "max_stars_repo_head_hexsha": "addadf3fdbf39d1d6ad2d8913fc9f87c7c66b53f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_stars_... |
import os.path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib import pyplot
from pandas.api.types import is_string_dtype
from sklearn import metrics
from sklearn.decomposition import PCA
from sklearn.ensemble import BaggingClassifier, AdaBoostClassifier, RandomForestClassifier, ... | {"hexsha": "d480a7c5bfb641ab06cfea4b9f1888c1ce5e65f5", "size": 11758, "ext": "py", "lang": "Python", "max_stars_repo_path": "predictions/prediction.py", "max_stars_repo_name": "alfagama/INstagramINfluencers", "max_stars_repo_head_hexsha": "cd455f5e0d2c80629fe3a29229f9308646252dfe", "max_stars_repo_licenses": ["Apache-2... |
[STATEMENT]
lemma param_foldli[param]: "(foldli, foldli)
\<in> \<langle>Re\<rangle>list_rel \<rightarrow> (Rs\<rightarrow>Id) \<rightarrow> (Re\<rightarrow>Rs\<rightarrow>Rs) \<rightarrow> Rs \<rightarrow> Rs"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (foldli, foldli) \<in> \<langle>Re\<rangle>list_rel \<rig... | {"llama_tokens": 360, "file": "Automatic_Refinement_Parametricity_Param_HOL", "length": 2} |
import sys
import math
import tensorflow.compat.v1 as tf
import tensorflow_probability as tfp
import numpy as np
## default settings
MAX_ITERATIONS = 10000 # number of iterations to perform gradient descent
MAX_INITS = 2 # number of restarts
ABORT_EARLY = True # if we can't improve anymore, abort ea... | {"hexsha": "37fe02b56836ddaf1494f57faa5c3aedbaa17590", "size": 8970, "ext": "py", "lang": "Python", "max_stars_repo_path": "rgb_attack.py", "max_stars_repo_name": "Harry1993/GhostImage", "max_stars_repo_head_hexsha": "7388ab2f48d7ab8a99c15bb65ccdbee47db7de97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "... |
export AbstractNetwork, update!, update, statespace, neighbors
"""
AbstractNetwork
A supertype for all network types.
To implement the `AbstractNetwork` interface, a concrete subtype must provide
the following methods:
* [`update!(net, dest, state)`](@ref)
* [`statespace(net)`](@ref)
* [`neighbors(net, node, di... | {"hexsha": "4faf7216fd17fe52a0680aeb88ba57700a4d9e35", "size": 3806, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/core.jl", "max_stars_repo_name": "hbsmith/Neet.jl", "max_stars_repo_head_hexsha": "72add45845a9fe3b330f79663af3d391fae689f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
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