text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
\documentclass[11pt]{article}
\usepackage[utf8]{inputenc}
\usepackage{graphicx}
\usepackage{listings}
\usepackage{amsfonts}
\usepackage[utf8]{inputenc}
\usepackage{listings}
\usepackage{hyperref}
\usepackage{float}
\usepackage{color}
\usepackage{url}
\usepackage{amsmath}
\definecolor{backcolour}{rgb}{0.96,0.96,0.96}
\d... | {"hexsha": "42ff5f0b7c142a086c0adee8994ef0193c89877d", "size": 4904, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "a5/answers.tex", "max_stars_repo_name": "EllaDing/nlp-with-deep-learning", "max_stars_repo_head_hexsha": "783a238830bafddc3bd35dca614f9b0d2dbdfc53", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
import os
import tflearn
import tensorflow as tf
import random
import pickle
import nltk
from pymongo import MongoClient
from nltk.stem.lancaster import LancasterStemmer
# import our chat-bot intents data from mongo
MONGO_HOST = os.environ.get("MONGO_HOST")
MONGO_PORT = os.environ.get("MONGO_PORT")
... | {"hexsha": "632788f40811eba6381ec7339e0cc139c345a2e2", "size": 2918, "ext": "py", "lang": "Python", "max_stars_repo_path": "nlp/training.py", "max_stars_repo_name": "gary-ai/gary-docker", "max_stars_repo_head_hexsha": "5c6e7c4aa06a1bee38fc868612d1df3eed2bc3e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#!/usr/bin/python
# Copyright (c) 2014, Blind Motion Project
# All rights reserved.
import csv
import sys
import datetime
from optparse import OptionParser
from datetime import timedelta
from scipy.interpolate import interp1d
ACC_KEY = '1'
GYR_KEY = '4'
ACC_KEY_INT = 1
GYR_KEY_INT = 4
GEO_KEY = 'geo'
TIME_KEY = 'time... | {"hexsha": "17163be14baba8fec0bf41e388d1b752fb5c6eec", "size": 7620, "ext": "py", "lang": "Python", "max_stars_repo_path": "data_prepaire/generate_events.py", "max_stars_repo_name": "blindmotion/detector", "max_stars_repo_head_hexsha": "e833896ef0f78f5b9672e24ffd7062c7ffec783a", "max_stars_repo_licenses": ["BSD-3-Claus... |
# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | {"hexsha": "9879afc76fc1c85105721101190681b7fe098acb", "size": 11068, "ext": "py", "lang": "Python", "max_stars_repo_path": "snerg/snerg/baking.py", "max_stars_repo_name": "pedersor/google-research", "max_stars_repo_head_hexsha": "6fa751dd261b3f6d918fd2cd35efef5d8bf3eea6", "max_stars_repo_licenses": ["Apache-2.0"], "ma... |
# MIT License
#
# Copyright (c) 2020 Didan Deng
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, p... | {"hexsha": "84fa00891e2aea6afdcc0d5141e25f34cb691d67", "size": 6799, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/compare_all.py", "max_stars_repo_name": "wtomin/SteerablePyramid_Torch_TF_Numpy", "max_stars_repo_head_hexsha": "bb805b736b874567f61f7f23d8a104575729f86e", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (c) 2019 Primoz Ravbar UCSB
# Licensed under BSD 2-Clause [see LICENSE for details]
# Written by Primoz Ravbar
"""
Modified on Sat Oct 5 10:02:58 2019
@author: Augusto Escalante
"""
import numpy as np
import matplotlib.pyplot as plt
import os
import pickle
i... | {"hexsha": "6b1518cc014ed1cdb7ffab7a1b544401ce203ce4", "size": 1781, "ext": "py", "lang": "Python", "max_stars_repo_path": "visualize_ST3C_images.py", "max_stars_repo_name": "auesro/ABRS", "max_stars_repo_head_hexsha": "980ec3e225021a6d1566d51fe0e3a03443233d9f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
# download the NMRData repository at https://github.com/AI4DBiological-Systems/NMRData
import NMRDataSetup
using FFTW
import PyPlot
import BSON
PyPlot.close("all")
fig_num = 1
PyPlot.matplotlib["rcParams"][:update](["font.size" => 22, "font.family" => "serif"])
# creates the save path if it doesn't exist.
functio... | {"hexsha": "db08b21b0d9a5c0e0c41946d98409e5edc26fff5", "size": 4852, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/batch_load_experiments.jl", "max_stars_repo_name": "AI4DBiological-Systems/NMRDataSetup.jl", "max_stars_repo_head_hexsha": "aba4854ac0557dea5f9c5b3fa1ab9ec517ee0e91", "max_stars_repo_licen... |
#!/usr/bin/env python
# coding: utf-8
# # Begrenzen des Datasets auf 1000
#
# Dieses Notebook begrenzt jede Klasse auf 1000.
# Klassen > 1000 werden auf 1000 beschränkt, dafür wird erst nach timestamp sortiert, anschliessend jedes xte Element gewählt.
# Klassen < 1000 werden auf 1000 augmented und anschliessend überzä... | {"hexsha": "f48a424164d1be0989cc56c42fc51c76c17ddfec", "size": 7005, "ext": "py", "lang": "Python", "max_stars_repo_path": "example-scripts/preprocessing_lokiDataset_deepLearning_step1_classSize.py", "max_stars_repo_name": "o2a-data/o2a-data-ml", "max_stars_repo_head_hexsha": "61ca97e25890e65ba35175f65493ed358a847d7b",... |
[STATEMENT]
lemma quality_increases_msg_fresh [elim]:
assumes qinc: "\<forall>j. quality_increases (\<sigma> j) (\<sigma>' j)"
and "msg_fresh \<sigma> m"
shows "msg_fresh \<sigma>' m"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. msg_fresh \<sigma>' m
[PROOF STEP]
using assms(2)
[PROOF STATE]
proof (pro... | {"llama_tokens": 14506, "file": "AODV_Quality_Increases", "length": 79} |
import networkx as nx
import copy
import pylab
import numpy as np
import time
import itertools
def debug(str):
#print(str)
pass
def MIN_FDIST():
return np.sqrt(2)/2
class Config:
FuelCapacity=2
class Location():
def __init__(self, id, x, y,al=1,be=0):
self.id=id
... | {"hexsha": "2e849ea6c765d6f782c433bfbc43e765e67feae7", "size": 44193, "ext": "py", "lang": "Python", "max_stars_repo_path": "frouting.py", "max_stars_repo_name": "lilj999/FuelAwareRouting", "max_stars_repo_head_hexsha": "d635b30ca6020dc815fb80ecc310c2010086e444", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_co... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as tck
import matplotlib.font_manager as fm
from mpl_toolkits.basemap import Basemap, addcyclic, ... | {"hexsha": "edaa6cf69e47a1b438b5e8c1a3c17f2062c8e622", "size": 13363, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tesis_GOES_PorcPixelNubado.py", "max_stars_repo_name": "cmcuervol/Estefania", "max_stars_repo_head_hexsha": "13b564261dfc786b93c77fbc442a568018f87cc9", "max_stars_repo_licenses": ["MIT"], "max_st... |
# Copyright (C) 2020 Tsuda Laboratory
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, d... | {"hexsha": "78a74e0d8922a7e992e54f9e13bf17bc4f07613a", "size": 6830, "ext": "py", "lang": "Python", "max_stars_repo_path": "ver-python2.7/PrefInt/visuallization.py", "max_stars_repo_name": "sxl324/PrefInt", "max_stars_repo_head_hexsha": "9a307dcf84b1c071f413e87e8bd572d3ae9bbcd2", "max_stars_repo_licenses": ["MIT"], "ma... |
import spacy
from utils import load_jsonl
import numpy as np
import annoy
# import faiss
from annoy import AnnoyIndex
import random
if False:
f = 7
t = AnnoyIndex(f) # Length of item vector that will be indexed
for i in range(1000):
v = [random.gauss(0, 1) for z in range(f)]
t.add_item(i, ... | {"hexsha": "e73c7ff24da57be6cf99a5af6e99635234184e35", "size": 3294, "ext": "py", "lang": "Python", "max_stars_repo_path": "narrow.py", "max_stars_repo_name": "peterwilliams97/ToneRanger", "max_stars_repo_head_hexsha": "61ab8b5a96bbf3f82b8e6a07e470831189afff8c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
module Interpolation
Base.Experimental.@optlevel 3
export AbstractInterpolation
export AbstractInterpolation1D, AbstractInterpolation2D
export AbstractLinearInterpolation, AbstractBilinearInterpolation
export NoInterpolation
export LinearInterpolation, BilinearInterpolation
export AbstractInterp1DOrNone, AbstractInte... | {"hexsha": "33d7acf868469644359b7023670795ddf2fdc369", "size": 4277, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Interpolation.jl", "max_stars_repo_name": "HomodyneCT/MartaCT.jl", "max_stars_repo_head_hexsha": "090fce88c91b79a4a8326589faee6cd2f869456e", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
//==============================================================================
//
// (c) Copyright, 2013 University Corporation for Atmospheric Research (UCAR).
// All rights reserved.
// Do not copy or distribute without authorization.
//
// File: $RCSfile: rwx_vector_collection_nc.cc,v $
// ... | {"hexsha": "945e966f4eb885bc016a1e1fbf7d1efcbb446605", "size": 15247, "ext": "cc", "lang": "C++", "max_stars_repo_path": "libs/rwx/rwx_vector_collection_nc.cc", "max_stars_repo_name": "OSADP/Pikalert-Vehicle-Data-Translator-", "max_stars_repo_head_hexsha": "295da604408f6f13af0301b55476a81311459386", "max_stars_repo_lic... |
import numpy as np
import matplotlib.pyplot as plt
import datetime
from qutip import Qobj, identity, sigmax, sigmay, sigmaz, sigmam, tensor
from qutip.superoperator import liouvillian, sprepost
from qutip.qip.operations import cnot, cr
import qutip.logging_utils as logging
logger = logging.get_logger()
#Set this to Non... | {"hexsha": "e82d93b446ac8040e549e125ffce4e8438b80fcb", "size": 5235, "ext": "py", "lang": "Python", "max_stars_repo_path": "cr_gate_lindbladian.py", "max_stars_repo_name": "esm-qiskit21/qutip", "max_stars_repo_head_hexsha": "fe0d45a28343a36457f9b6f140842d35ab578b1a", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_st... |
Wine into Water is a wine tasting and silent auction fundraiser with live music, appetizers, raffles, exhibits, and more. It supports water and sanitation projects in developing communities, and is hosted by Engineers Without Borders at UC Davis.
Second Annual Event
Sunday, April 14th, 2013
International House
10 ... | {"hexsha": "7e5349c84570bb7376e9d006f671a4a6ac74bd05", "size": 1098, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Wine_into_Water.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
def Zeros(inp):
return np.zeros(inp)
def AlphaRandom(inp, alpha=0.01):
"""
Random weight initialization with multiplication by constant alpha.
"""
return np.random.randn(inp)*alpha
def RandomNormal(inp):
"""
Random normally distributed weight initialization.
"""
... | {"hexsha": "cfc03db564af5bfab92951df9b07702b80e5a82c", "size": 1438, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/initialization.py", "max_stars_repo_name": "khknopp/langur-python", "max_stars_repo_head_hexsha": "45ed8324a5229466cb9535a3b74e68be1b32ee36", "max_stars_repo_licenses": ["MIT"], "max_stars... |
@testset "Constraint" begin
@testset "bound constraints" begin
let bounds = [-10.0 10.0; 2.4 8.2; 0 3.4], r = [-11, 10.0, 1.23]
bounce_back!(r, [-5.3, 0, 0], bounds)
@test -10 <= r[1] <= -5.3 && 0 <= r[2] <= 8.2 && r[3] == 1.23
end
end
end | {"hexsha": "f9fa091dcac8448ab559906ffa2b0f9f156ed54b", "size": 288, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/constraint_test.jl", "max_stars_repo_name": "ShuhuaGao/DifferentialEvolution.jl", "max_stars_repo_head_hexsha": "bb8de3bcb7f6f2c2748b880329ec508802dc128d", "max_stars_repo_licenses": ["MIT"], "... |
"""Module for ML Algorithms"""
import json
import pandas as pd
import numpy as np
import pickle
from sklearn.metrics.pairwise import cosine_similarity
def nmf_recommender(user_input, no_of_recommendations, path = ""):
user_input_cleaned={}
for i in user_input:
if (user_input[i] != "Rate the game.... | {"hexsha": "5e4ebf31108cc94cf54c92aea86c9aec2f9f363b", "size": 2649, "ext": "py", "lang": "Python", "max_stars_repo_path": "flask_app/ml_models.py", "max_stars_repo_name": "Ruebe92/BoredLameRecommender", "max_stars_repo_head_hexsha": "ff6b1070359442ca9155641170ed2193fd637e67", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
Matplotlib Animation Example
author: Jake Vanderplas
email: vanderplas@astro.washington.edu
website: http://jakevdp.github.com
license: BSD
Please feel free to use and modify this, but keep the above information. Thanks!
"""
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
... | {"hexsha": "d6a74a02b867616d01aa61d268adced93939845c", "size": 2140, "ext": "py", "lang": "Python", "max_stars_repo_path": "raster_demo.py", "max_stars_repo_name": "pure-water/RastDemo", "max_stars_repo_head_hexsha": "abf0512b1b9cc5ec3b8ab91160ba1d4b8d3ee715", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from typing import Optional, Tuple
import numpy as np
from .metrics import sdr_loss, sdr, bss_eval_sources
def si_sdr_loss(
est: np.ndarray,
ref: np.ndarray,
zero_mean: Optional[bool] = False,
clamp_db: Optional[float] = None,
pairwise: Optional[bool] = False,
) -> np.ndarray:
return sdr_loss... | {"hexsha": "81cd774236afa4e57a45a9b4b3f45006bbe4180f", "size": 5919, "ext": "py", "lang": "Python", "max_stars_repo_path": "fast_bss_eval/numpy/scale_invariant.py", "max_stars_repo_name": "fakufaku/fast_bss_eval", "max_stars_repo_head_hexsha": "6e8a9a99aa7947c8075bc216fa007ef391e97f66", "max_stars_repo_licenses": ["MIT... |
import keras.layers as kl
from keras.engine.topology import Layer
import tensorflow as tf
from concise.utils.helper import get_from_module
from concise.layers import SplineWeight1D
from keras.models import Model, Sequential
import numpy as np
import gin
@gin.configurable
class GlobalAvgPoolFCN:
def __init__(self... | {"hexsha": "5a7d3be0f9b9543c7ed21194383e7306ac380141", "size": 8605, "ext": "py", "lang": "Python", "max_stars_repo_path": "bpnet/layers.py", "max_stars_repo_name": "mlweilert/bpnet", "max_stars_repo_head_hexsha": "dcc9e8d805f9de774ae9dcc62c20504915be614f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 93, "ma... |
import torch
import torch
import torch.nn as nn
import functools
from torch.autograd import Variable
import numpy as np
import torch.nn.init as init
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
##m.weight.data.normal_(0.0, 0.02)
init.kaiming_uniform_(m.w... | {"hexsha": "138f60779b7a125182861c97df0e4a6000a0f9ab", "size": 3207, "ext": "py", "lang": "Python", "max_stars_repo_path": "models/Encoder.py", "max_stars_repo_name": "Caoang327/GAN_Compression", "max_stars_repo_head_hexsha": "6cfdd771de58c7b90bbe39d99d9dfcaeb2591c02", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import datetime
class Shifter(dict):
def __init__(self, name, df_runs, shifts, **kwargs):
self["name"] = name
self["df_runs"] = df_runs
self["shifts"] = shifts
self["daytime_time"] = kwargs.pop("daytime_time", {"morning":"06:30:00",
... | {"hexsha": "63d6f7fd6045f39440877c8f1069eaf9781b5c5b", "size": 7658, "ext": "py", "lang": "Python", "max_stars_repo_path": "shiftsummary/__init__.py", "max_stars_repo_name": "Kecksdose/shiftsum", "max_stars_repo_head_hexsha": "b5d6c6cde84601e1e92a9354fd9bf8ee08ca6d7e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 1 12:46:24 2020
"""
#%%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datascrape import data
#%%
st = pd.DataFrame(data)
st.set_index(['state', 'date'])
#%%
st.sort_values(by=['positive'], ascend... | {"hexsha": "7bb6c9aa81927946c5813dca86a6db20f7f5a0a2", "size": 356, "ext": "py", "lang": "Python", "max_stars_repo_path": "graphs.py", "max_stars_repo_name": "labray24/Covid-US", "max_stars_repo_head_hexsha": "c22d7457d10a4fd073c7520be60a3b3387e69d66", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": null, ... |
SUBROUTINE HISTGM(XORIG,YORIG,BARWID,VALUES,ISTART,ISTOP)
C
C ------------------------------------------------
C ROUTINE NO. ( 81) VERSION (A8.8) 14:JUL:88
C ------------------------------------------------
C
C THIS DRAWS A HISTOGRAM OF A GIVEN SET OF VALUES.
C ... | {"hexsha": "92282d0aaa7e9388707fde50ef20522d03549703", "size": 3945, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/lib/histgm.f", "max_stars_repo_name": "ZedThree/GHOST", "max_stars_repo_head_hexsha": "cba30b43bdcc73fb87cff0724337a7d3a1bd7812", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#' communiter.
#'
#' @name communiter
#' @docType package
NULL
| {"hexsha": "a140cfc0608e69f3bd3c833b4ab506bbaf8c12ed", "size": 63, "ext": "r", "lang": "R", "max_stars_repo_path": "R/communiter-package.r", "max_stars_repo_name": "davharris/communiter", "max_stars_repo_head_hexsha": "50dcedbf59dfd53d3b38c43d5e619b2c9a0eed47", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
###############################################################################
# apogee.spec.window: routines for dealing with the individual element windows
###############################################################################
import os, os.path
import numpy
from apogee.tools.read import modelspecOnApStarWa... | {"hexsha": "c30598224cbf0ba09e0a017f20fbae485473f217", "size": 9323, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/jobovy/apogee/apogee/spec/window.py", "max_stars_repo_name": "dnidever/apogee", "max_stars_repo_head_hexsha": "83ad7496a0b4193df9e2c01b06dc36cb879ea6c1", "max_stars_repo_licenses": ["BSD-3-... |
# encoding: utf8
"""
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label
Classification. André F. T. Martins, Ramón Fernandez Astudillo
In: Proc. of ICML 2016, https://arxiv.org/abs/1602.02068
"""
from __future__ import division
import numpy as np
import torch
from torch import nn
from .base impor... | {"hexsha": "ce06da82b90098f11273aea52f992d57e950b5d4", "size": 1443, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchsparseattn/sparsemax.py", "max_stars_repo_name": "dhruvdcoder/sparse-structured-attention", "max_stars_repo_head_hexsha": "6b9d2703014d4ec17899ac0e153a57f5cc36b950", "max_stars_repo_licenses"... |
from __future__ import print_function
from os import stat
import os.path as ops
from typing import Union
import numpy as np
import cv2
import time
from scipy.special import expit
import tensorrt as trt
# import cuda functions
import pycuda.autoinit
import pycuda.driver as cuda
def _aspectaware_resize_padding(image,... | {"hexsha": "71a02c4c292c5cbb6bfdf0d21134c6b1f0aad759", "size": 20753, "ext": "py", "lang": "Python", "max_stars_repo_path": "unittest_yolov5_head.py", "max_stars_repo_name": "HtutLynn/alto_unittests", "max_stars_repo_head_hexsha": "f741b14ee47508a21af2554d4604463466713b58", "max_stars_repo_licenses": ["MIT"], "max_star... |
function M=json2mat(J)
%JSON2MAT converts a javscript data object (JSON) into a Matlab structure
% using s recursive approach. J can also be a file name.
%
%Example: lala=json2mat('{lele:2,lili:4,lolo:[1,2,{lulu:5,bubu:[[1,2],[3,4],[5,6]]}]}')
% notice lala.lolo{3}.bubu is read as a 2D matrix.
%
% Jon... | {"author": "covartech", "repo": "PRT", "sha": "4305e612af048e7dbf3d9392efc7436db125b1fc", "save_path": "github-repos/MATLAB/covartech-PRT", "path": "github-repos/MATLAB/covartech-PRT/PRT-4305e612af048e7dbf3d9392efc7436db125b1fc/+prtExternal/+json/json2mat.m"} |
"""
Very useful librray
"""
import numpy as np
class MeshLoader:
def __init__(self, params):
"""Only list in list, without оборачиваетелй numpy
# mesh_alpha = np.arange(10)
# mesh_beta = np.arange(10)
# for k,z in enumerate(MeshLoader([[0.1,0.2,0.3],['a','b']])):
# print(z)
"""
self.p... | {"hexsha": "dbd46b3f5b35d08f75e84781a709ef69f1a4e61a", "size": 1890, "ext": "py", "lang": "Python", "max_stars_repo_path": "tsad/useful/iterators.py", "max_stars_repo_name": "waico/DL-anomaly-detection", "max_stars_repo_head_hexsha": "c69eeecfd3376e6e3544ab34e36d6360dbebeb94", "max_stars_repo_licenses": ["BSD-3-Clause"... |
import logging
import tempfile
import zipfile
from enum import Enum
from pathlib import Path
import numpy as np
from PIL import Image
from scipy.io import loadmat
from . import download
from .enums import Split
logger = logging.getLogger(__name__)
class MPII:
FOLDER_NAME = "mpii_human_pose_v1"
IMG_URL = "h... | {"hexsha": "4fe3dc25c1ed0728c056dd9bd3fd1d02c543f447", "size": 1235, "ext": "py", "lang": "Python", "max_stars_repo_path": "torch_keypoints/datasets/mpii.py", "max_stars_repo_name": "latkins/torch_keypoints", "max_stars_repo_head_hexsha": "7e0ad2618cef18a80eb6cf96acf624b6f67e3d51", "max_stars_repo_licenses": ["MIT"], "... |
from abc import ABC, abstractmethod
import numpy as np
import pandas as pd
from hdmf.common.table import DynamicTable
from pynwb import ProcessingModule
from src.bsl_python.utils import flatten_dict
def build_dataframe(nwb_file):
spike_times = []
unit_indices = []
electrodes = []
all_units = nwb_fil... | {"hexsha": "5837c95e5ccdb48dc39fc9170fc27f309cdd7d3c", "size": 4062, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/bsl_python/preprocessing/experiments/experiment.py", "max_stars_repo_name": "Rechenmann-Data-EIRL/BSLPy", "max_stars_repo_head_hexsha": "45f037d00c7f171bc5f63784e181f82eb909c8c4", "max_stars_r... |
# # Bessel function
# An Bessel function of the first kind of order ``\nu`` can be computed using Taylor expansion
# ```math
# J_\nu(z) = \sum\limits_{n=0}^{\infty} \frac{(z/2)^\nu}{\Gamma(k+1)\Gamma(k+\nu+1)} (-z^2/4)^{n}
# ```
# where ``\Gamma(n) = (n-1)!`` is the Gamma function. One can compute the accumulated... | {"hexsha": "c336f0c92ff72730349aacc977fd5c3b64f5e597", "size": 5736, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/besselj.jl", "max_stars_repo_name": "johnnychen94/NiLang.jl", "max_stars_repo_head_hexsha": "81fbe77d1f499003153857be8367de2024c797a5", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
from sympy import symbols, diff, exp, sqrt, factor, Symbol, printing, simplify, acos, sin, cos, pretty_print
x, y, z, r, k, theta, phi = symbols('x y z r k theta phi')
r = sqrt(x*x+y*y+z*z)
theta = acos(z/r)
R = Symbol('r')
def associatedLaguerre(X, p, q):
if q==0:
return 1
elif q==1:
... | {"hexsha": "f7d7a26a1e53312a26d93c33e0360a78da6d02a0", "size": 3550, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/generateHydrogenOrbitals.py", "max_stars_repo_name": "evenmn/VMaChine", "max_stars_repo_head_hexsha": "f38de342d95c25eb22a4a61260b3fa47f92d9174", "max_stars_repo_licenses": ["MIT"], "max_s... |
using LinearAlgebra
eye(n) = Matrix{Float64}(I, n, n)
function gaussnewton(x0, f, tol=1e-6, maxit = 100)
alpha = 1.0e-4
it = 1
xc = x0
fc, jac, gc = f(xc)
n_f = 1
n_g = 1
n_h = 0
while norm(gc) > tol && it < maxit
dc = (jac'*jac)\gc
lambda = 1.0
xt = xc - lambda*... | {"hexsha": "13985d49197314723b94dbf1162c2abffa1cb6ef", "size": 2325, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datafitting.jl", "max_stars_repo_name": "hessianguo/NumericalMethod.jl", "max_stars_repo_head_hexsha": "bd6c00a88c8168e39b2ba1894466a6b6f6e24984", "max_stars_repo_licenses": ["MIT"], "max_stars... |
// Boost.Geometry (aka GGL, Generic Geometry Library)
// Unit Test
// Copyright (c) 2007-2012 Barend Gehrels, Amsterdam, the Netherlands.
// Copyright (c) 2008-2012 Bruno Lalande, Paris, France.
// Copyright (c) 2009-2012 Mateusz Loskot, London, UK.
// This file was modified by Oracle on 2020.
// Modifications copyri... | {"hexsha": "b40b14fe44ad56431cb8da9ea94766d5872711e5", "size": 3813, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/geometries/segment.cpp", "max_stars_repo_name": "jkerkela/geometry", "max_stars_repo_head_hexsha": "4034ac88b214da0eab8943172eff0f1200b0a6cc", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars... |
from matplotlib.pyplot import *
from numpy import array
from raysect.primitive import Sphere, Box, Cylinder, Union, Intersect, Subtract
from raysect.optical import World, translate, rotate, Point3D, d65_white, InterpolatedSF
from raysect.optical.observer import OrthographicCamera
from raysect.optical.material.emitte... | {"hexsha": "a1449e63cf17739174493fc7e152ff0e281b5659", "size": 2805, "ext": "py", "lang": "Python", "max_stars_repo_path": "demos/observers/orthographic.py", "max_stars_repo_name": "Gjacquenot/source", "max_stars_repo_head_hexsha": "5f9b86bbb44c25b5096d637d65e41e257a9bda3c", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
\section{Floquet theory}
Since we describe the lifetime of an electron in certain Landau level using conventianal perturbation theeory, now we can apply the Floquet theory to identify the difference of these methods.
\noindent
First we need to identify the \textit{quasienergies} and periodic \textit{Floquet modes} fo... | {"hexsha": "270e5a892681c93968132bb244a34612ce98df0c", "size": 15473, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "theory/sec_03.tex", "max_stars_repo_name": "KosalaHerath/magnetic-2DEG-conductivity", "max_stars_repo_head_hexsha": "91c5df1b018579b4b9c91d84f2d60ee482a001de", "max_stars_repo_licenses": ["MIT"], "... |
#include <boost/compute/algorithm/rotate.hpp>
| {"hexsha": "0d8307aaed8b9d1f0851b5a42f3515d8632c4219", "size": 46, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_compute_algorithm_rotate.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["BSL-... |
import atexit
import logging as l
import sys
import numpy as np
from argparse import ArgumentParser
from enum import Enum
from pathlib import Path
from time import sleep, time
import RPi.GPIO as GPIO
from marcs.CubeSolver.logger import log, set_log_level
from marcs.CubeSolver.stepper import Stepper
from marcs.RubiksCu... | {"hexsha": "0b4e884ae67d9f63040406b341cec383ecb0da56", "size": 13116, "ext": "py", "lang": "Python", "max_stars_repo_path": "solver.py", "max_stars_repo_name": "SebastienLavoie/CubeSolver", "max_stars_repo_head_hexsha": "7f55a905708718e0ed83b6548003922269255224", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
[STATEMENT]
lemma eqvtI[eqvt]:
fixes P :: pi
and Q :: pi
and perm :: "name prm"
assumes "P \<approx>\<^sup>s Q"
shows "(perm \<bullet> P) \<approx>\<^sup>s (perm \<bullet> Q)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. perm \<bullet> P \<approx>\<^sup>s perm \<bullet> Q
[PROOF STEP]
using assms
[... | {"llama_tokens": 202, "file": "Pi_Calculus_Weak_Early_Bisim_Subst", "length": 2} |
# Aliev_Panfilov_2D_plot_sequence_v1.jl
# ===================================== u.p. 5.8.21 / 5.9.21
# Aliev-Panfilov model
# load data and plot sequence with phase singularities
using DrWatson
@quickactivate "NonlinearDynamicsTextbook"
include(srcdir("style.jl"))
using DynamicalSystems, PyPlot, OrdinaryDiffEq
usi... | {"hexsha": "7d9edb125112b6986e1519116007ba53a627525c", "size": 1640, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "figure_generation/11/11.8_plot.jl", "max_stars_repo_name": "JuliaDynamics/NonlinearDynamicsTextbook", "max_stars_repo_head_hexsha": "bfae8cf867f458f00151da089332f2ce3bea5dd0", "max_stars_repo_licen... |
from Stream.IPStream import IPStream
from Stream.TransportStream import TransportStream
import numpy
from scipy.stats import stats
class StreamAnalyzer:
def do(self, stream, operation):
pass
| {"hexsha": "a9067e82b44518155f303eee33623266cdc578ed", "size": 207, "ext": "py", "lang": "Python", "max_stars_repo_path": "Core/StreamAnalyzer.py", "max_stars_repo_name": "SeuSQ/SimpleShark", "max_stars_repo_head_hexsha": "a7da2aa26b3e6f67f160008c0a19078ccfd3ab98", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
"""
Copyright (C) 2018-2021 Intel Corporation
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 i... | {"hexsha": "8a6339bc0b824d0b3f664bec440895ab261f8251", "size": 60252, "ext": "py", "lang": "Python", "max_stars_repo_path": "model-optimizer/mo/middle/passes/fusing/fuse_linear_seq_test.py", "max_stars_repo_name": "calvinfeng/openvino", "max_stars_repo_head_hexsha": "11f591c16852637506b1b40d083b450e56d0c8ac", "max_star... |
# -*- coding: utf-8 -*-
"""
Spyder Editor
This is a temporary script file.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import urllib.request
import numpy as np
import tensorflow as tf
import scipy
import spacy
print()
iris_training = "i... | {"hexsha": "a0a35a8a25ab038067c90bb8c8e2093d50e67b63", "size": 1038, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python/NLP/DL/dl_sample.py", "max_stars_repo_name": "vbsteja/code", "max_stars_repo_head_hexsha": "0c8f4dc579f5de21b6c55fe6e65c3c8eb5473687", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
from mpi4py import MPI
import time
import torch
import torch.distributed as dist
import numpy as np
import deepspeed
from deepspeed.runtime.comm.mpi import MpiBackend
# Configure wall clock timer
from deepspeed.utils.timer import SynchronizedWallClockTimer
from statistics import mean
timers = SynchronizedWallClockT... | {"hexsha": "6017ec873c21f8e076a257800e703cc4364f4119", "size": 2150, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/onebit/test_mpi_perf.py", "max_stars_repo_name": "ConnollyLeon/DeepSpeed", "max_stars_repo_head_hexsha": "2d84d1c185ef0345eaf43a7240d61b33eda43497", "max_stars_repo_licenses": ["MIT"], "max_... |
Maveric is a UC Davis graduate (with degrees in English and Psychology), a former California Aggie arts writer, and a Delta Lambda Phi alumnus. Friends call him Mav and know him for is sardonic humor, great sense of style, affectionate nature, and sweet moves on the dance floor.
He moved out of town in April 2006 but... | {"hexsha": "75e6e3c7b3a1a90ca909d09705b509c1fdc1c5e0", "size": 352, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Maveric_Vu.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import os
import numpy as np
import json
import EggNet
import EggNet.Reader
from util import perform_real_quant, init_network_from_weights, plot_confusion_matrix, evaluate_network_full, \
quant2float, init_fake_network_from_weights, read_np_keras, \
init_quant_network_from_weights
def quant_to_int(x, b, f):... | {"hexsha": "f8e3ffb6416cd8cde12dd1220bf0a65b8406e346", "size": 9413, "ext": "py", "lang": "Python", "max_stars_repo_path": "net/quantize.py", "max_stars_repo_name": "marbleton/FPGA_MNIST", "max_stars_repo_head_hexsha": "4b4a30e0adca35de9adcad7b3fec08c516260790", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 7,... |
import spacy
import re
import numpy as np
from sklearn.base import TransformerMixin
from html.parser import HTMLParser
import dill
import sys, os
nlp = spacy.load("en_core_web_sm", parser=False, entity=False)
class SpacyTokenTransformer(TransformerMixin):
__symbols = set("!$%^&*()_+|~-=`{}[]:\";'<>?,./-")
d... | {"hexsha": "62b881252637c8423436e0b1e40a0b4e61fef28a", "size": 2567, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/models/sklearn_spacy_text/ml_utils.py", "max_stars_repo_name": "juldou/seldon-core", "max_stars_repo_head_hexsha": "34021ee3ead41c729ff57efd1964ab3f0d37861e", "max_stars_repo_licenses": [... |
import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
from utils import montage_tf, get_variables_to_train, assign_from_checkpoint_fn, remove_missing, weights_montage
from constant... | {"hexsha": "13147ad998e12e617e0e2cb138a90b7a0e6cc12b", "size": 9344, "ext": "py", "lang": "Python", "max_stars_repo_path": "train/AlexNet_NN_search.py", "max_stars_repo_name": "SimuJenni/Correspondences", "max_stars_repo_head_hexsha": "384c0272e438ad3e7c936f5ae78fe6154b188c54", "max_stars_repo_licenses": ["MIT"], "max_... |
####################################################################
# Copyright (c) 2019 Nobuyuki Umetani #
# #
# This source code is licensed under the MIT license found in the #
# LICENSE file in the root directory of this... | {"hexsha": "f96fa0d88898a4f4712ab0ee122cdd0df830ab22", "size": 10151, "ext": "py", "lang": "Python", "max_stars_repo_path": "PyDelFEM2/msh.py", "max_stars_repo_name": "stnoh/pydelfem2", "max_stars_repo_head_hexsha": "40224736dda9576a39d2b5a753a1ca1e4f906299", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "m... |
# -*- coding: utf-8 -*-
#
# Copyright 2018 isobar. All Rights Reserved.
#
# Usage:
# python teeth-whitening.py pic.jpg
#
import os
import sys
import argparse
import cv2
import dlib
import numpy as np
from skimage import io
from PIL import Image
from scipy.spatial import distance
import IsobarImg
DEBUG = False
... | {"hexsha": "236d75bfba66c5a6021c55f43297133cb8cd1b13", "size": 6918, "ext": "py", "lang": "Python", "max_stars_repo_path": "teeth-whitening.py", "max_stars_repo_name": "wwwins/TeethWhitening", "max_stars_repo_head_hexsha": "b9b19bfccdc3a27813b24772038153da7db8133d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
import pylab as plt
np.random.seed(17)
plt.rc('font', size=8)
nproblem = 8 # magic
nstudent = 150 # magic
problems = [r"""\begin{problem}
(From Problem Set 3)
In the Galaxy rest frame, Alpha Centauri is 4.4 light-years away from us.
My friend travels at $0.5\,c$ (relative to this frame) from us to A... | {"hexsha": "f3fac0a9841d8b17919a55a5615fcfbffb9c22a6", "size": 2317, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/make_exam3.py", "max_stars_repo_name": "davidwhogg/EinsteinsUniverse", "max_stars_repo_head_hexsha": "91babed322a5985a45ec827c030564cacbd49354", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import torch
from torch import nn
import numpy as np
from music21 import midi
from music21 import converter
from music21 import note, stream, duration, tempo
class MidiDataset(torch.utils.data.Dataset):
def __init__(self, path, subset='train',
n_bars=2, n_steps_per_bar=16):
self.n_bars=n_... | {"hexsha": "2db67cb05431aca629a4ab7b6c46a18716500dac", "size": 2856, "ext": "py", "lang": "Python", "max_stars_repo_path": "data/utils.py", "max_stars_repo_name": "yeong35/musegan", "max_stars_repo_head_hexsha": "5a226fe246bb85f4d91317908a2fe413cde8ca56", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "max_s... |
from deep_sort.deep_sort import nn_matching
from deep_sort.deep_sort.tracker import Tracker
from deep_sort.application_util.preprocessing import non_max_suppression
from deep_sort.deep_sort.detection import Detection
import numpy as np
class deepsort_rbc():
def __init__(self):
self.metric = nn_matching.... | {"hexsha": "4d3e1eac061485a16974368cf494d7b8394840e5", "size": 2339, "ext": "py", "lang": "Python", "max_stars_repo_path": "deepsort.py", "max_stars_repo_name": "jvech/DeepSort_Yolo", "max_stars_repo_head_hexsha": "16ce856f7b56a9fa8d660fad7d2dc086c26152b2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max... |
# --------------
# Import packages
import numpy as np
import pandas as pd
from scipy.stats import mode
# code starts here
path = pd.read_csv(path)
bank = pd.DataFrame(path)
print(bank.head())
categorical_var = bank.select_dtypes(include='object')
print(categorical_var.head())
numerical_var = bank.select_dtypes(i... | {"hexsha": "cfc56821ff557dd3807cdf6f37e4e6fecee7e8ae", "size": 1662, "ext": "py", "lang": "Python", "max_stars_repo_path": "Guided-Project-:-Loan-Approval-Analysis/code.py", "max_stars_repo_name": "HarshBedmutha/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "acce0db9952700288a58ceee66fd173b5662ca1a", "max_stars_... |
#!/usr/bin/env python
# coding: utf-8
# In[1]:
'''
Student_name: Reddivinod Reddy
Student_ID : 21244404
github link for repo: https://github.com/Reddivinod/ARC.git
'''
#!/usr/bin/python
import os, sys
import json
import numpy as np
import re
### YOUR CODE HERE: write at least three functions which solve
### speci... | {"hexsha": "f8187917e007a005c76b9671c756a8bce5737faa", "size": 7777, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/manual_solve.py", "max_stars_repo_name": "Reddivinod/ARC", "max_stars_repo_head_hexsha": "e00e9b8c22982bbc7b889a214d2742c9cd84c1d0", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
import numpy as np
import pandas as pd
from ..punctuation_count import PunctuationCount
from ..utils import PrimitiveT, find_applicable_primitives, valid_dfs
class TestPunctuationCount(PrimitiveT):
primitive = PunctuationCount
def test_punctuation(self):
x = pd.Series(['This is a test file.',
... | {"hexsha": "3ff653f084b00c9b5a37c1e1b9130fe06f5c1720", "size": 1654, "ext": "py", "lang": "Python", "max_stars_repo_path": "nlp_primitives/tests/test_punctuation_count.py", "max_stars_repo_name": "mikewcasale/nlp_primitives", "max_stars_repo_head_hexsha": "e42ff518f78fe2398c156e559b6d0fe222fd5cdd", "max_stars_repo_lice... |
// $Id$
/***********************************************************************
Moses - factored phrase-based language decoder
Copyright (c) 2006 University of Edinburgh
All rights reserved.
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following con... | {"hexsha": "fe703a57ad42310b7d3935fdcafb938eb5818b26", "size": 6620, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/decoder-phrasebased/src/native/decoder/moses/IOWrapper.cpp", "max_stars_repo_name": "ugermann/MMT", "max_stars_repo_head_hexsha": "715ad16b4467f44c8103e16165261d1391a69fb8", "max_stars_repo_lice... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import os
import math
from gv_tools.util.visual import draw_regions
import argparse
from gv_tools.tracking.tracking_region import TrackingRegion
from gv_tools.util.text i... | {"hexsha": "5bccebedc040bf6dc85d4a00069ab4787033db9f", "size": 4886, "ext": "py", "lang": "Python", "max_stars_repo_path": "mot.py", "max_stars_repo_name": "xuehaouwa/pysot", "max_stars_repo_head_hexsha": "091c8ce2f63233f84505d2d7e45241bfaa5441f4", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": null, "ma... |
"""
A few convenience methods for quickly extracting/changing data in
netCDFs
"""
import os
from datetime import datetime
import numpy as np
from netCDF4 import Dataset
from netCDF4 import num2date
import netCDF4.utils
import netcdftime
import uuid
# import netCDF4_utils, netcdftime # these make cx_freeze work
import p... | {"hexsha": "26ad2f3e6c49c1a9a0db6ffb9e30295a78fa6665", "size": 26824, "ext": "py", "lang": "Python", "max_stars_repo_path": "netCDF_Utils/nc.py", "max_stars_repo_name": "shape87/WaveLab", "max_stars_repo_head_hexsha": "70eb6299dbceafacf95f2298ba469bb53881e7ec", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
[STATEMENT]
lemma Sup_pres_Inf_pres:
fixes f :: "'a::complete_boolean_algebra_alt_with_dual \<Rightarrow> 'b::complete_boolean_algebra_alt_with_dual"
shows "(Sup_pres f) = (Inf_pres (\<partial>\<^sub>F f))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. Sup_pres f = Inf_pres (\<partial>\<^sub>F f)
[PROOF STEP]
... | {"llama_tokens": 143, "file": "Transformer_Semantics_Sup_Inf_Preserving_Transformers", "length": 1} |
"""
Test the basic example found in the README
"""
import numpy as np
import reclab
def test_basic_example():
"""
Test the basic example in the READMe
"""
n_users = 1000
n_topics = 10
n_items = 20
env = reclab.make(
'topics-dynamic-v1',
num_topics=n_topics,
num_user... | {"hexsha": "1edf9537a7b483bff645ec337de01e69601f5bb2", "size": 858, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_simple_example.py", "max_stars_repo_name": "lematt1991/RecLab", "max_stars_repo_head_hexsha": "7ba212ac2ae346fb6dfeec232eef652d7f26e193", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
"""Example of computing precision recall curve
"""
#%%
import matplotlib.pyplot as plt
import numpy as np
import sklearn.metrics as skm
y_true = np.array([0, 0, 0, 0, 0, 1, 1, 1])
y_score = np.array([0.1, 0.4, 0.35, 0.7, 0.2, 0.3, 0.6, 0.8])
# tresholding, above 0.4
y_pred = (y_score>=0.4).astype(int)
print(y_pred... | {"hexsha": "b87067603f6b205ecaa64e03f4a4e6b7f342d7e8", "size": 1642, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics/precision_recall_curve.py", "max_stars_repo_name": "ksopyla/scikit-learn-tutorial", "max_stars_repo_head_hexsha": "3a1a3df1fc39dcb8da5eec65b0297b28788b6a25", "max_stars_repo_licenses": ["M... |
#!/usr/bin/env python
"""
Created by howie.hu at 2021/4/25.
Description：数据加载工具类，参考：https://github.com/mhjabreel/CharCNN 感谢
Changelog: all notable changes to this file will be documented
"""
import numpy as np
import pandas as pd
class DataUtils:
"""
此类用于加载原始数据
"""
def __init__(
s... | {"hexsha": "2ef830df881b8385a77ce172724d9a0b3d8fc307", "size": 4844, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/classifier/model_lib/char_cnn/data_utils.py", "max_stars_repo_name": "LeslieLeung/2c", "max_stars_repo_head_hexsha": "6b3a17cd614833e536b90ee130427d4cc538c1db", "max_stars_repo_licenses": ["Ap... |
import numpy as np
from text_selection.kld.kld_iterator import is_valid_counts_or_weights
# region empty
def test_s0_empty__returns_true():
counts = np.array([], dtype=np.int32)
result = is_valid_counts_or_weights(counts, axis=0)
assert result
def test_s0x0_empty__returns_true():
counts = np.empty(shape=(0,... | {"hexsha": "8855172d0d6966a0d378cf6d1df6ff14b39b853a", "size": 3717, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/text_selection_tests/kld/kld_iterator_py/test_is_valid_counts_or_weights.py", "max_stars_repo_name": "stefantaubert/text-selection", "max_stars_repo_head_hexsha": "4b3b49005cbeb2e9212ed94686d8... |
"""Q-Learning using Bellman Equation to solve a Reinforcement Learning problem.
@author
Victor I. Afolabi
Artificial Intelligence & Software Engineer.
Email: javafolabi@gmail.com | victor.afolabi@zephyrtel.com
GitHub: https://github.com/victor-iyiola
@project
File: q_learning.py
Cr... | {"hexsha": "41745c7cb82b5aabc1ebb92800cf03013ca1c2ed", "size": 6105, "ext": "py", "lang": "Python", "max_stars_repo_path": "rl/policy/q_learning.py", "max_stars_repo_name": "victor-iyiola/deep-RL", "max_stars_repo_head_hexsha": "91f614676d375d9938976392dcff5eba933693ca", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma single_leadsETo_I:
"(\<And>x. x \<in> A ==> F \<in> {x} leadsTo[CC] B) \<Longrightarrow> F \<in> A leadsTo[CC] B"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (\<And>x. x \<in> A \<Longrightarrow> F \<in> {x} leadsTo[CC] B) \<Longrightarrow> F \<in> A leadsTo[CC] B
[PROOF STEP]
by (subst UN_... | {"llama_tokens": 154, "file": null, "length": 1} |
_throw_table_error() = throw(ArgumentError("Please specify the column that contains the targets explicitly, or provide a target-extraction-function as first parameter. see parameter 'f' in ?targets."))
# required data container interface
LearnBase.nobs(dt::AbstractDataFrame) = DataFrames.nrow(dt)
LearnBase.getobs(dt::... | {"hexsha": "a0ab5fc0937522f5c161a274bce3a3f06f89dea8", "size": 2125, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/datapattern.jl", "max_stars_repo_name": "ppalmes/MLDataUtils.jl", "max_stars_repo_head_hexsha": "d2bfa0eefd44ede1976d4931f792a8f5e314a686", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy as np
sourcefile1 = ["./src/mqc/el_prop/el_propagator.pyx", "./src/mqc/el_prop/rk4.c"]
sourcefile2 = ["./src/mqc/el_prop/el_propagator_xf.pyx", "./src/mqc/el_prop/rk4_xf.c"]
sourcefile3 = [".... | {"hexsha": "5819fd7c7edc1e4f838d4c8ccf3f3ae055ec15b3", "size": 914, "ext": "py", "lang": "Python", "max_stars_repo_path": "setup.py", "max_stars_repo_name": "hkimaf/unixmd", "max_stars_repo_head_hexsha": "616634c720d0589fd600e3268afab9da957e18bb", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 9, "max_stars_rep... |
*--#[ log:
* $Id: ffxd0h.f,v 1.6 1996/01/22 13:33:49 gj Exp $
* $Log: ffxd0h.f,v $
c Revision 1.6 1996/01/22 13:33:49 gj
c Added the word 'error' to print statements in ffxuvw that u,v,w were wrong
c
c Revision 1.5 1995/12/08 10:48:32 gj
c Changed xloss to xlosn to prevent spurious error messages.
c
c Revision 1.... | {"hexsha": "fc6c62bf41e3a19b0438b356d0e4c02602cfdd0e", "size": 25859, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "MCFM-JHUGen/QCDLoop/ff/ffxd0h.f", "max_stars_repo_name": "tmartini/JHUGen", "max_stars_repo_head_hexsha": "80da31668d7b7eb5b02bb4cac435562c45075d24", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
import numpy
import numpy.linalg
def distance_sum(inputs, references):
"""Sum of all distances between inputs and references
Each element should be in a row!
"""
norms = numpy.zeros(inputs.shape[0])
for i in xrange(references.shape[0]):
norms += numpy.apply_along_axis(numpy.linalg.norm, 1... | {"hexsha": "955351f42a772eb848c0ae2b75d5d28ba1ff2a00", "size": 3033, "ext": "py", "lang": "Python", "max_stars_repo_path": "mir3/lib/knn.py", "max_stars_repo_name": "pymir3/pymir3", "max_stars_repo_head_hexsha": "c1bcca66a5ef1ff0ebd6373e3820e72dee6b0b70", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12, "max_... |
[STATEMENT]
lemma ma_plus:
"(real_of r1 + real_of r2) = (if ma_compatible r1 r2
then real_of (ma_plus r1 r2) else
Code.abort (STR ''different base'') (\<lambda> _. real_of r1 + real_of r2))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. real_of r1 + real_of r2 = (if ma_compatible r1 r2 then real_of (ma_pl... | {"llama_tokens": 198, "file": "Real_Impl_Real_Impl", "length": 1} |
__precompile__()
module PointClouds
import Base:
show, keys, haskey,
getindex, setindex!,
vcat, length, endof
import NearestNeighbors:
knn, inrange
using NearestNeighbors
using StaticArrays
export PointCloud,
# Point cloud data access
positions,
normals,
# Spatial indexing
knn,
... | {"hexsha": "4672a501db93513a6cbf3b5195a1002fe3a43f78", "size": 443, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/PointClouds.jl", "max_stars_repo_name": "FugroRoames/PointClouds.jl", "max_stars_repo_head_hexsha": "da034e5aef5cadbd34e3dc6b9371d4b93a36dd71", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from typing import Tuple
import numpy as np
from numpy import cos, pi, sin, sqrt
import pp
from pp.component import Component
@pp.cell
def ellipse(
radii: Tuple[float, float] = (10.0, 5.0),
angle_resolution: float = 2.5,
layer: Tuple[int, int] = pp.LAYER.WG,
) -> Component:
"""Generate an ellipse ge... | {"hexsha": "b20b9dae674f714961822b5e8460a2e06d7d4d7d", "size": 1343, "ext": "py", "lang": "Python", "max_stars_repo_path": "pp/components/ellipse.py", "max_stars_repo_name": "flaport/gdsfactory", "max_stars_repo_head_hexsha": "1f2e844c1fe27b9c6340e2d51500fd3358fa16e5", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from joblib import load
from Preprocess.Tensor import processReynoldsStress, getBarycentricMapData, expandSymmetricTensor, contractSymmetricTensor,makeRealizable
import time as t
import numpy as np
import pickle
import os
"""
User Inputs, Anything Can Be Changed Here
"""
# Name of the flow case in both ML and test
ml_... | {"hexsha": "18eff4dbdac3cdef7d4ca1c6eac2f2c683345348", "size": 13562, "ext": "py", "lang": "Python", "max_stars_repo_path": "Predict_RANS_Cluster.py", "max_stars_repo_name": "YuyangL/TurbulenceMachineLearning", "max_stars_repo_head_hexsha": "63fab542d61856df00ef176abe044cb80fdaa077", "max_stars_repo_licenses": ["MIT"],... |
\documentclass[11pt]{article}
\usepackage{graphicx}
\usepackage{amssymb}
\usepackage{multicol}
\usepackage{epstopdf}
\DeclareGraphicsRule{.tif}{png}{.png}{`convert #1 `dirname #1`/`basename #1 .tif`.png}
\textwidth = 6.5 in
\textheight = 9 in
\oddsidemargin = 0.0 in
\evensidemargin = 0.0 in
\topmargin = 0.0 in
\headhe... | {"hexsha": "4a415ed8eabddf974da033ef20d89a3403142210", "size": 3821, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "SplitDMD_KS-master/FEM code/kuramoto_1d.tex", "max_stars_repo_name": "jovanzigic/SplitDMD_KS", "max_stars_repo_head_hexsha": "ddfe2eb81ffcda58837e3e7fe63476ff578b8bf4", "max_stars_repo_licenses": ["... |
#!/usr/bin/env python
# Part of the psychopy_ext library
# Copyright 2010-2015 Jonas Kubilius
# The program is distributed under the terms of the GNU General Public License,
# either version 3 of the License, or (at your option) any later version.
"""
A library of simple models of vision
Simple usage::
import g... | {"hexsha": "cad9300bf802af6fcd78f5b8ab77e42d387898e9", "size": 88036, "ext": "py", "lang": "Python", "max_stars_repo_path": "psychopy_ext/models.py", "max_stars_repo_name": "qbilius/psychopy_ext", "max_stars_repo_head_hexsha": "1bde931f59eea95f73a4d7b635e7e98aed9ee0dd", "max_stars_repo_licenses": ["PSF-2.0"], "max_star... |
# Implmenting a Stacked Autoencoder
# Importing libraries
import numpy as np # used for working with arrays
import pandas as pd # import dataset, and create training and test set
import torch # import PyTorch
import torch.nn as nn # implement neural networks
import torch.nn.parallel # # used for parallel computatio... | {"hexsha": "34c5f0bead465f5f8f4bd7e9f6c0c9d571a68404", "size": 11394, "ext": "py", "lang": "Python", "max_stars_repo_path": "built_ae.py", "max_stars_repo_name": "rikardsaqe/Movie-Recommendation-Tools", "max_stars_repo_head_hexsha": "6f0d7f95732807b7d8d4eefaa1a1053595526731", "max_stars_repo_licenses": ["MIT"], "max_st... |
/-
Copyright (c) 2018 Mario Carneiro and Kevin Buzzard. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Mario Carneiro and Kevin Buzzard
-/
import order.order_iso
import tactic.tidy
import linear_algebra.subtype_module
import linear_algebra.quotient_module
import linea... | {"author": "khoek", "repo": "mathlib-tidy", "sha": "866afa6ab597c47f1b72e8fe2b82b97fff5b980f", "save_path": "github-repos/lean/khoek-mathlib-tidy", "path": "github-repos/lean/khoek-mathlib-tidy/mathlib-tidy-866afa6ab597c47f1b72e8fe2b82b97fff5b980f/linear_algebra/submodule.lean"} |
#ifndef BOOST_BIND_PLACEHOLDERS_HPP_INCLUDED
#define BOOST_BIND_PLACEHOLDERS_HPP_INCLUDED
// MS compatible compilers support #pragma once
#if defined(_MSC_VER) && (_MSC_VER >= 1020)
# pragma once
#endif
//
// bind/placeholders.hpp - _N definitions
//
// Copyright (c) 2002 Peter Dimov and Multi Media Ltd.
// Copyr... | {"hexsha": "d115e9c948de72e3020867de438c0d3d728eee2e", "size": 1957, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost/bind/placeholders.hpp", "max_stars_repo_name": "Liastre/uri", "max_stars_repo_head_hexsha": "f5a701d19e3dd9cec950dcc70f6a42f0d9151f89", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_c... |
using Replay
instructions = """
println("julia -q")
"""
replay(instructions, cmd="-q")
| {"hexsha": "c34311a19402f8a8b5a65a8a463c7bc0165630a0", "size": 89, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "examples/quietmode/app.jl", "max_stars_repo_name": "AtelierArith/Replay.jl", "max_stars_repo_head_hexsha": "fc1652434fa5270c4199784704120a7a9e2923ff", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
################################################################################
# The Neural Network (NN) based Speech Synthesis System
# https://svn.ecdf.ed.ac.uk/repo/inf/dnn_tts/
#
# Centre for Speech Technology Research
# University of Edinburgh, UK
# ... | {"hexsha": "e279c8babc57ef502bb9b9d5d662a1c19d56426b", "size": 9663, "ext": "py", "lang": "Python", "max_stars_repo_path": "cute/common/vocoder.py", "max_stars_repo_name": "bbepoch/TTS", "max_stars_repo_head_hexsha": "b573e611958c0984d761cae215a2f8c1f0b05109", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count... |
/*
* Copyright (c) 2014, Stanislav Vorobiov
* 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 above copyright notice, this
* list of ... | {"hexsha": "249c4389fd1d44b5e4d6bb423e940717f792d85a", "size": 5125, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "game/PlasmaComponent.cpp", "max_stars_repo_name": "Sheph/TriggerTime", "max_stars_repo_head_hexsha": "9265dee6a178e43bf7365e3aa2f7f2ca22df074f", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_sta... |
[STATEMENT]
lemma widen_NT_Class [simp]: "G \<turnstile> T \<preceq> NT \<Longrightarrow> G \<turnstile> T \<preceq> Class D"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. G \<turnstile> T \<preceq> NT \<Longrightarrow> G \<turnstile> T \<preceq> Class D
[PROOF STEP]
by (ind_cases "G \<turnstile> T \<preceq> NT", ... | {"llama_tokens": 136, "file": null, "length": 1} |
/*
@copyright Louis Dionne 2014
Distributed under the Boost Software License, Version 1.0.
(See accompanying file LICENSE.md or copy at http://boost.org/LICENSE_1_0.txt)
*/
#include <boost/hana/ext/boost/mpl/list.hpp>
#include <boost/hana/detail/assert.hpp>
#include <boost/mpl/list.hpp>
using namespace boost::hana;... | {"hexsha": "948b873d03cfb831e577240b36edfbefd5a53fac", "size": 912, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/ext/boost/mpl/list/comparable/equal.cpp", "max_stars_repo_name": "rbock/hana", "max_stars_repo_head_hexsha": "2b76377f91a5ebe037dea444e4eaabba6498d3a8", "max_stars_repo_licenses": ["BSL-1.0"], "... |
import os
import subprocess
import numpy as np
filter_number = {'u':'0','g':'1','r':'2','i':'3','z':'4','Y':'5'}
def encode_obshistid(atm, telescope, sensor, wavelength, filter_name, seed):
mode = 0
if atm:
mode += 1
if telescope:
mode += 2
if sensor:
mode += 4
mode_digit ... | {"hexsha": "dab77bd970d1f63655d8941a856f2294110de7f4", "size": 3165, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/phosim/validate/monochromatic_seeing/submit_job.py", "max_stars_repo_name": "DarkEnergyScienceCollaboration/chroma", "max_stars_repo_head_hexsha": "64fc123a065334b307654f29b3bea52885b46ec8", "... |
import tkinter as tk
import vlc
import os
import cv2
import HandTracking as ht
import math
import numpy as np
import sys
import time
import platform
if sys.version_info[0] < 3:
import Tkinter as Tk
from Tkinter import *
from Tkinter.filedialog import askopenfilename
else:
import tkinter as Tk
fro... | {"hexsha": "f96b46981f5c5b8cbbc7e9bfed420a570c5b1375", "size": 7793, "ext": "py", "lang": "Python", "max_stars_repo_path": "tkinter_gesture_class.py", "max_stars_repo_name": "dhruvak99/GBVC", "max_stars_repo_head_hexsha": "dc6267b858a28208ca9d1dd32886491a0197ec07", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchsort import soft_rank
from .vits import vit_tiny, vit_small, vit_base, Head
class ResNetNoPool(torchvision.models.ResNet):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwar... | {"hexsha": "56ad45d81674202b31be2d34cdd61c36bd4cc298", "size": 17411, "ext": "py", "lang": "Python", "max_stars_repo_path": "patch_game/builder.py", "max_stars_repo_name": "kampta/PatchGame", "max_stars_repo_head_hexsha": "12411e3202643217dd47a3590c413e95e960f1fc", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
from math import gamma
import numpy as np
from numba import njit as jit
@jit
def hyp2f1b(x):
"""Hypergeometric function 2F1(3, 1, 5/2, x), see [Battin].
.. todo::
Add more information about this function
Notes
-----
More information about hypergeometric function can be checked at
ht... | {"hexsha": "822fc280aeb9edb6c1dc67ded04ec7011695ff50", "size": 1912, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/poliastro/_math/special.py", "max_stars_repo_name": "DhruvJ22/poliastro", "max_stars_repo_head_hexsha": "ac5fafc6d054b2c545e111e5a6aa32259998074a", "max_stars_repo_licenses": ["MIT"], "max_sta... |
c
c -----------------------------------------------------
c
subroutine valout (lst, lend, time, nvar, naux)
c
use amr_module
use geoclaw_module, only: rho
use multilayer_module, only: num_layers
implicit double precision (a-h,o-z)
character(len=10) :: fname1, fname2, fname3, fname4
... | {"hexsha": "5c3d3feb34f2f41a4b2fe5e30c2fda59e683a4ed", "size": 11244, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/2d/shallow/multilayer/valout.f", "max_stars_repo_name": "dlgeorge/geoclaw", "max_stars_repo_head_hexsha": "2b4ce9b1ba2532fe3ac38ee7c05297eb61e45bd1", "max_stars_repo_licenses": ["BSD-3-Clause... |
# %%
# things we need for NLP
import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()
import pickle
import pandas as pd
import numpy as np
#import tensorflow as tf
import tensorflow.compat.v1 as tf
import random
# %%
from tensorflow.keras.models import Sequential, Model
from tensorflow... | {"hexsha": "7f5f5bb12f61c2154db41c50c75aef21dbab0cb7", "size": 4620, "ext": "py", "lang": "Python", "max_stars_repo_path": "start_cmd.py", "max_stars_repo_name": "abheeshtroy/Musoassist-Chatbot", "max_stars_repo_head_hexsha": "0acdc60a273436c2385e68453b0a9411a85e60e2", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.3.4
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
from sklearn.datasets... | {"hexsha": "f207050cae0d3a10b1eee7486781a7bb21a025ea", "size": 7166, "ext": "py", "lang": "Python", "max_stars_repo_path": "z_xgboost_aki_tesing_w0.py", "max_stars_repo_name": "sxinger/xgboost", "max_stars_repo_head_hexsha": "779e8dd305c857c64edfd6b4a6f87a51986b9211", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
# Loading dependencies
import os
import numpy as np
import matplotlib.pyplot as plt
from .BaseLCA import BaseLCA
class LCA(BaseLCA):
def __init__(self, **params):
super().__init__(**params)
'''The first monte carlo analyzer in GIAMS
This module conducts life cycle analysis with the
help of a simulator
... | {"hexsha": "716a0fc570c43c7983016662aaca85b8cf31b2fe", "size": 8456, "ext": "py", "lang": "Python", "max_stars_repo_path": "LifeCycleAnalyzer/LCA.py", "max_stars_repo_name": "vd1371/GIAMS", "max_stars_repo_head_hexsha": "dd6551f344b8d0377131d4496846eb5d03b6189c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#include <boost/range.hpp>
#include <boost/range/irange.hpp>
#include <boost/range/adaptors.hpp>
#include <boost/phoenix.hpp>
#include <boost/detail/lightweight_test.hpp>
using namespace boost::phoenix::arg_names;
using namespace boost::adaptors;
int foo() { return 5; }
int main()
{
BOOST_TEST((*boost::begin(bo... | {"hexsha": "2a6d39ec12094d813e716f26bb2e3b16d7332565", "size": 398, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "boost/libs/phoenix/test/regression/bug5626.cpp", "max_stars_repo_name": "randolphwong/mcsema", "max_stars_repo_head_hexsha": "eb5b376736e7f57ff0a61f7e4e5a436bbb874720", "max_stars_repo_licenses": ["B... |
# pylint: skip-file
import sys
import mxnet as mx
import numpy as np
import tempfile
import random
import string
def test_recordio():
frec = tempfile.mktemp()
N = 255
writer = mx.recordio.MXRecordIO(frec, 'w')
for i in range(N):
if sys.version_info[0] < 3:
writer.write(str(chr(i)))... | {"hexsha": "f4489bdfe6411c3eea1858d1c8edc48b496ffb4a", "size": 2031, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/python/unittest/test_recordio.py", "max_stars_repo_name": "axbaretto/mxnet", "max_stars_repo_head_hexsha": "5f593885356ff6d14f5519fa18e79b944beb51cd", "max_stars_repo_licenses": ["Apache-2.0... |
"""
This is super!
==============
"""
super
import numpy as np
np.sin
@np.vectorize
def vectorized(x):
pass
| {"hexsha": "fdd290e2fa96606f86bcd72d6289e467933b83f7", "size": 115, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/sphinx-tree/examples/refs.py", "max_stars_repo_name": "anntzer/sphinx-exhibit", "max_stars_repo_head_hexsha": "5bdb0c41ef5bde3aea72b48e5aebe292696c53c1", "max_stars_repo_licenses": ["MIT"], "... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.