text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import tensorflow as tf
import numpy as np
class TextCNN(object):
"""
CNN 2-chanels model for text classification.
"""
def __init__(self, sentence_len, vocab_size, embedding_size, num_classes,
static_embedding_filter, filter_sizes, num_filters, l2_reg_lambda = 0.0):
... | {"hexsha": "0eec22c8ce754d4117bd679fc4205412bf6d3a72", "size": 3837, "ext": "py", "lang": "Python", "max_stars_repo_path": "text_CNN.py", "max_stars_repo_name": "ddajing/multilayer-cnn-text-classification", "max_stars_repo_head_hexsha": "ea97105de2e4411eb492e0c268045006a9a3669f", "max_stars_repo_licenses": ["Apache-2.0... |
function test_exponential_neuron(backend::Backend, T, eps)
println("-- Testing Exponential neuron on $(typeof(backend)){$T}...")
data = rand(T, 3,4,5,6) - convert(T, 0.5)
data_blob = make_blob(backend, data)
neuron = Neurons.Exponential()
println(" > Forward")
forward(backend, neuron, data_blob)
expe... | {"hexsha": "4ca3edd0d6540fb451cb3166aa3899d00363e849", "size": 1043, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/neurons/exponential.jl", "max_stars_repo_name": "baajur/Mocha.jl", "max_stars_repo_head_hexsha": "5e15b882d7dd615b0c5159bb6fde2cc040b2d8ee", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma inj_setminus: "inj_on uminus (A::'a set set)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. inj_on uminus A
[PROOF STEP]
by (auto simp: inj_on_def) | {"llama_tokens": 75, "file": null, "length": 1} |
import math
import numpy as np
from functools import reduce
# A utility wrapper around numpy.array
# mainly to name things how I like them
def Vec2(x, y):
return np.array([x,y], dtype=float)
def Vec3(x, y, z):
return np.array([x,y,z], dtype=float)
UCONST_Pi = 3.1415926
URotation180 = float(32768)
URotationT... | {"hexsha": "1bb392df877a841274a7bbeb33f82cd04d51ba93", "size": 3848, "ext": "py", "lang": "Python", "max_stars_repo_path": "RLBotPack/DomNomNom/NomBot_v1.0/NomBot/vector_math.py", "max_stars_repo_name": "RLMarvin/RLBotPack", "max_stars_repo_head_hexsha": "c88c4111bf67d324b471ad87ad962e7bc8c2a202", "max_stars_repo_licen... |
"""
Editor Bin H.
Quantum Optimal Control Example
One Control Parameter Model
"""
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import expm
from time import clock
class QH:
"""
Initial data/conditions of Quantum Hamiltonian and initial states.
"""
def __init__(self, H0, Hctrl, ctrl_i, phi... | {"hexsha": "6d8fb36e7a04fcf3e9e8a5dd5377acd0a9a9cd41", "size": 5513, "ext": "py", "lang": "Python", "max_stars_repo_path": "abintb/qoct/qoct.py", "max_stars_repo_name": "abyellow/abin-tight-binding", "max_stars_repo_head_hexsha": "538aef632937b1840d5ffd184f162858637b01f5", "max_stars_repo_licenses": ["MIT"], "max_stars... |
# --------------
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# code starts here
df = pd.read_csv(path)
p_a = len(df[df['fico']>700])/len(df)
print(p_a)
p_b = len(df[df['purpose']=='debt_consolidation'])/len(df)
df1 = len(df[df['purpose']=='debt_consolidation'])
p_a_b = (p_a * p_b)/p_a
p_b_a ... | {"hexsha": "558a8b5164dfc1a1f6b3f3188d80a9befba312b6", "size": 1114, "ext": "py", "lang": "Python", "max_stars_repo_path": "Probability/code.py", "max_stars_repo_name": "Sadique96645/ga-learner-dsmp-repo", "max_stars_repo_head_hexsha": "155f039083c22755b32c7dead39bfd86aff4c157", "max_stars_repo_licenses": ["MIT"], "max... |
"""
======
Cutout
======
Generate a cutout image from a .fits file
"""
try:
import astropy.io.fits as pyfits
import astropy.wcs as pywcs
except ImportError:
import pyfits
import pywcs
import numpy
try:
import coords
except ImportError:
pass # maybe should do something smarter here, but I want a... | {"hexsha": "e05b74ceec4c93bc98c0f1d44f3f07a60ea2b53e", "size": 4918, "ext": "py", "lang": "Python", "max_stars_repo_path": "agpy/cutout.py", "max_stars_repo_name": "keflavich/agpy", "max_stars_repo_head_hexsha": "fb3a42d9909b7cd1ba74247530bcc8742f5aaeb1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 16, "max_... |
# https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6
# https://keras.io/layers/core/
# gsettings set org.gnome.desktop.interface cursor-size 32
# open image
# open csv
import tensorflow as tf
import numpy as np
import pandas as pd
from collections import Counter
from sklearn.model_selectio... | {"hexsha": "f30151323cf137a320977eb1da4b99e8bdf0acfd", "size": 1440, "ext": "py", "lang": "Python", "max_stars_repo_path": "tictacnet.py", "max_stars_repo_name": "angelasof25/TicTacToeML", "max_stars_repo_head_hexsha": "655ce609ec1ab371d1646b3443921b5a1569e27a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
[STATEMENT]
lemma exp_of_minus_half_pi:
fixes x:: real
assumes "x = pi/2"
shows "exp (-(\<i> * complex_of_real x)) = -\<i>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. exp (- (\<i> * complex_of_real x)) = - \<i>
[PROOF STEP]
using assms cis_conv_exp cis_minus_pi_half
[PROOF STATE]
proof (prove)
using this:... | {"llama_tokens": 222, "file": "Isabelle_Marries_Dirac_Basics", "length": 2} |
import Base.convert
abstract type DynamicSystem end
show(io::IO, ds::DynamicSystem) = print(
io,
"""$(typeof(ds))
x = $(get_x(ds))
dx/dt = $(get_x_dot(ds))
"""
)
get_x(ds::DynamicSystem) = ds.x
get_x(ds::DynamicSystem, state::State) = get_x(convert(typeof(ds), state))
get_x_dot(ds::DynamicSy... | {"hexsha": "c63b601318f6b02e78ea0c39dcbe7bd0c8c8aaec", "size": 7605, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/models/dynamic_systems.jl", "max_stars_repo_name": "AlexS12/FlightMechanics.jl", "max_stars_repo_head_hexsha": "12bc740341aee3fd61f5a43f1253b598725989fd", "max_stars_repo_licenses": ["MIT"], "m... |
# coding=utf-8
"""Evaluate embeddings on downstream tasks."""
import os
import shutil
import math
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pprint
import torch
import numpy as np
import pickle
from itertools import chain
from tqdm import tqdm
from torch.utils.tensorboard import Sum... | {"hexsha": "be9b3537bee886f05261858cb0d2204f2edf6aeb", "size": 14321, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluate_finegym.py", "max_stars_repo_name": "minghchen/CARL_code", "max_stars_repo_head_hexsha": "c574312969c35963fc0050a5b5dbd3e4aa590318", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# linear algebra
import numpy as np
import pandas as pd
# text vectorizer
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
# for nearest neighbor
from sklearn.neighbors import NearestNeighbors
# cosine similarity
from sklearn.metrics.pairwise import linear_kernel as cosine_similarity
# n... | {"hexsha": "f88107e79b9f6c73ff9ec20c4984b31f39e7afcd", "size": 9729, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis.py", "max_stars_repo_name": "Hikari9/Matching", "max_stars_repo_head_hexsha": "5bf8b9b229db507679d5c839f205a3d85864c504", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": nul... |
[STATEMENT]
lemma mono_closed_real:
fixes S :: "real set"
assumes mono: "\<forall>y z. y \<in> S \<and> y \<le> z \<longrightarrow> z \<in> S"
and "closed S"
shows "S = {} \<or> S = UNIV \<or> (\<exists>a. S = {a..})"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. S = {} \<or> S = UNIV \<or> (\<exists>a. S... | {"llama_tokens": 4317, "file": null, "length": 54} |
/*
*Author:GeneralSandman
*Code:https://github.com/GeneralSandman/TinyWeb
*E-mail:generalsandman@163.com
*Web:www.dissigil.cn
*/
/*---XXX---
*
****************************************
*
*/
#include <tiny_base/api.h>
#include <tiny_base/log.h>
#include <tiny_base/sync.h>
#include <tiny_core/eventloop.h>
#incl... | {"hexsha": "65e2359f859b2c5fb0d513324d25563f633823e6", "size": 4246, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/tiny_core/process.cc", "max_stars_repo_name": "swaroop0707/TinyWeb", "max_stars_repo_head_hexsha": "79d20f2bb858581cc5a0ea229bba3a54e5d1bd3e", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import pandas as pd
import os
import numpy as np
import cv2
datapath1='D:/Minor Project/COVID-19 Detection/Files/Covid-19 prediction using X-Ray images/covid-chestxray-dataset-master/'
dataset_path='D:/Minor Project/COVID-19 Detection/Files/Covid-19 prediction using X-Ray images/dataset'
categories=os.listd... | {"hexsha": "01ad161e174b4fec185952d1f4096c4c21b41a6f", "size": 1563, "ext": "py", "lang": "Python", "max_stars_repo_path": "CreatingDataset.py", "max_stars_repo_name": "amitd307/Covid-19-prediction-using-X-Ray-images", "max_stars_repo_head_hexsha": "2a12f6975b3301466957d41e08899940ebd44840", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma subset_fst_imageI: "A \<times> B \<subseteq> S \<Longrightarrow> y \<in> B \<Longrightarrow> A \<subseteq> fst ` S"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>A \<times> B \<subseteq> S; y \<in> B\<rbrakk> \<Longrightarrow> A \<subseteq> fst ` S
[PROOF STEP]
unfolding image_def subset_... | {"llama_tokens": 224, "file": null, "length": 2} |
AgeFitsSrv <- function(dat=am1, case_label="2010 assessment",f=1) {
subtle.color <- "gray40"
attach(dat)
#ages <- c(1,11) #age range
tmp1 <- paste("phat_srv_",f,sep="")
tmp2 <- paste("pobs_srv_",f,sep="")
tmp3 <- paste("pobs_srv_",f,sep="")
print(tmp1)
pred.data = get(tmp1)[,-1]
obs.... | {"hexsha": "5c31f5c1f4c02be6aad66d428aa070daf580d130", "size": 2835, "ext": "r", "lang": "R", "max_stars_repo_path": "examples/atka/R/af_srv.r", "max_stars_repo_name": "NMFS-toolbox/AMAK", "max_stars_repo_head_hexsha": "701d016cf26943050ee42488f5b5f328f79ce5d6", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
"""
Script for extracting an analyzing the filaments attached to a membrane from several input datasets
DEPRECATED: USE mb_graph_batch.py instead
Input: - Density map tomogram
- Segmentation tomogram
Output: - Connectors clusters
"""
__author__ = 'Antonio Martinez-Sanchez'
# #########... | {"hexsha": "8bb988e9de953cb79db484887e8c91759f0bf11c", "size": 9246, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/pyseg/psd/mb_fil_batch.py", "max_stars_repo_name": "anmartinezs/pyseg_system", "max_stars_repo_head_hexsha": "5bb07c7901062452a34b73f376057cabc15a13c3", "max_stars_repo_licenses": ["Apache-2.... |
'''
Loads a trained model, and classifies an image
argv[1]: path to hdf5 model to load
argv[2]: path to image to classify
'''
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import load_img, img_to_array, array_to_img
import sys
import time
import numpy as np
model = load_mo... | {"hexsha": "252fc2f92c1b8bef9f0f2a60ea6df1b7e6038598", "size": 861, "ext": "py", "lang": "Python", "max_stars_repo_path": "predict.py", "max_stars_repo_name": "jakogut/brass-sorter", "max_stars_repo_head_hexsha": "34db44c34241c32dbb38759d3e0201fc0a0c19b8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max_... |
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
import cv2
from utils import calc_pairwise_distance_3d
# from hrnet.init_hrnet import cls_hrnet_w32, pose_hrnet_w32
from config import Config
################# Bilinear Pooling Reasoning Module ###################
class... | {"hexsha": "3948dd17578beca2963ecad0ae5677942fd6e4dd", "size": 18235, "ext": "py", "lang": "Python", "max_stars_repo_path": "infer_module/TCE_STBiP_module.py", "max_stars_repo_name": "ch-its/DIN-Group-Activity-Recognition-Benchmark", "max_stars_repo_head_hexsha": "02d29decc7ed8c6c85bf53436956ef36f76e4872", "max_stars_r... |
/*
* websocket_ConnectionListener.cpp
*/
#include <boost/asio/ip/tcp.hpp>
#include "logging/log_Logger.h"
#include "websocket_WebsocketDriver.h"
#include "websocket_RawHttpConnection.h"
#include "websocket_ConnectionListener.h"
namespace mutgos
{
namespace websocket
{
// --------------------------------------... | {"hexsha": "760e3d26442e8d76ba151e105c60d9594af3b9a3", "size": 4078, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/websocketcomm/websocket_ConnectionListener.cpp", "max_stars_repo_name": "hyena/mutgos_server", "max_stars_repo_head_hexsha": "855c154be840f70c3b878fabde23d2148ad028b3", "max_stars_repo_licenses"... |
[STATEMENT]
lemma is_shortest_path_onD1 [forward]:
"is_shortest_path_on G m n p V \<Longrightarrow> p \<in> path_set_on G m n V"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. is_shortest_path_on G m n p V \<Longrightarrow> p \<in> path_set_on G m n V
[PROOF STEP]
by auto2 | {"llama_tokens": 117, "file": "Auto2_Imperative_HOL_Functional_Dijkstra", "length": 1} |
import porespy as ps
import numpy as np
import pytest
class DNSTest():
def setup_class(self):
np.random.seed(10)
def test_tortuosity_2D_lattice_spheres_axis_1(self):
im = ps.generators.lattice_spheres(shape=[200, 200], radius=8, offset=5)
t = ps.dns.tortuosity(im=im, axis=1)
... | {"hexsha": "73a715be57df5ece82ba5716c91463a3358894a4", "size": 1261, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/unit/test_dns.py", "max_stars_repo_name": "hfathian/porespy", "max_stars_repo_head_hexsha": "8747e675ba8e6410d8448492c70f6911e0eb816a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
#!/usr/bin/python
"""
Output table of instrumental specifications.
"""
import numpy as np
import radiofisher as rf
e = rf.experiments
expt_list = [
( 'exptS', e.exptS ), # 0
( 'iexptM', e.exptM ), # 1
( 'exptL', e.exptL ), # 2
( 'iexptL', e... | {"hexsha": "6c4a7bffec346760adb96b6f47a61111cb839dcb", "size": 3795, "ext": "py", "lang": "Python", "max_stars_repo_path": "specs_table.py", "max_stars_repo_name": "sjforeman/RadioFisher", "max_stars_repo_head_hexsha": "fe25f969de9a700c5697168ba9e0d2645c55ed81", "max_stars_repo_licenses": ["AFL-3.0"], "max_stars_count"... |
/*
* Copyright (c) 2016 George Ungureanu <ugeorge@kth.se>
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright notice,
* ... | {"hexsha": "a2b447b86b405c5797246cba514a99d73f306127", "size": 7359, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/settings/config.hpp", "max_stars_repo_name": "forsyde/DeSyDe", "max_stars_repo_head_hexsha": "48c55861ed78dd240451787258ee286b0f46aea5", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_c... |
[STATEMENT]
lemma delete_type:
"t \<in> B h \<Longrightarrow> delete x t \<in> B h \<union> B(h-1)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. t \<in> B h \<Longrightarrow> delete x t \<in> B h \<union> B (h - 1)
[PROOF STEP]
unfolding delete_def
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. t \<in> B h \<... | {"llama_tokens": 201, "file": null, "length": 2} |
"""Parameter or state variable as random variable
"""
from __future__ import division
import json
import logging
import sys
import itertools
from collections import OrderedDict as odict
import numpy as np
import runner.xparams as xp
from runner.xparams import XParams
from runner.lib.doelhs import lhs
from runner.tools... | {"hexsha": "c7006d02c64a6e9919f3c559645785b53b3d0e69", "size": 8358, "ext": "py", "lang": "Python", "max_stars_repo_path": "runner/param.py", "max_stars_repo_name": "alex-robinson/runner", "max_stars_repo_head_hexsha": "5e992ef7eaf82b4a69be8c6db9e572421323bc69", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2,... |
function write_surf(fname, vert, face)
% write_surf - FreeSurfer I/O function to write a surface file
%
% write_surf(fname, vert, face)
%
% writes a surface triangulation into a binary file
% fname - name of file to write
% vert - Nx3 matrix of vertex coordinates
% face - Mx3 matrix of face triangulation indices
%... | {"author": "fieldtrip", "repo": "fieldtrip", "sha": "c2039be598a02d86b39aae76bfa7aaa720f9801c", "save_path": "github-repos/MATLAB/fieldtrip-fieldtrip", "path": "github-repos/MATLAB/fieldtrip-fieldtrip/fieldtrip-c2039be598a02d86b39aae76bfa7aaa720f9801c/external/freesurfer/write_surf.m"} |
from collections import defaultdict
from torchseq.metric_hooks.base import MetricHook
from torchseq.utils.tokenizer import Tokenizer
from torchseq.utils.metrics import bleu_corpus, meteor_corpus, ibleu_corpus
from torchseq.utils.sari import SARIsent
import torch
import numpy as np
class TextualMetricHook(MetricHook):... | {"hexsha": "b5e03b87df91f0f68ffbce5817971bcb58946e06", "size": 2174, "ext": "py", "lang": "Python", "max_stars_repo_path": "torchseq/metric_hooks/textual.py", "max_stars_repo_name": "tomhosking/torchseq", "max_stars_repo_head_hexsha": "1b08c16822a553ecb77b96289fb21eb0a13d9c6b", "max_stars_repo_licenses": ["Apache-2.0"]... |
import math
import numpy as np
DEFAULT_FRAMERATE = 44100
import matplotlib.pyplot as pl
DoublePI = math.pi * 2
import wave
import matplotlib.pyplot as plt
import sounddevice as sd
import scipy.stats
import scipy.fftpack
def serial_corr(wave, lag = 1):
n = len(wave)
y1 = wave[lag:]
y2 = wave[:n-lag]
... | {"hexsha": "fe4a7b410404f46b057150bc38cc716c79d55352", "size": 16107, "ext": "py", "lang": "Python", "max_stars_repo_path": "19-q2/endpoint_detection/volume.py", "max_stars_repo_name": "18325391772/blog-code-example", "max_stars_repo_head_hexsha": "8ea0cc00855334da045b4566072611c24c14c844", "max_stars_repo_licenses": [... |
import numpy as np
import os
import shutil
import glob
import argparse
import os.path as osp
import xml.etree.ElementTree as et
from xml.dom import minidom
import pickle
import random
import copy
import cv2
import pickle
def loadMesh(name ):
vertices = []
faces = []
with open(name, 'r') as meshIn:
... | {"hexsha": "dc9b8b9f5a47b7055b75b8240f53bca74cca3330", "size": 12140, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils_OR/DatasetCreation/renderLSImage.py", "max_stars_repo_name": "Jerrypiglet/Total3DUnderstanding", "max_stars_repo_head_hexsha": "655d00a988c839af3b73f8ab890c3f70c1500147", "max_stars_repo_li... |
import os
import re
import html
import nltk
import numpy as np
from math import log
from collections import Counter,defaultdict
from scipy import sparse
import pickle
import logging
logging.basicConfig(format='%(asctime)s %(levelname)-8s %(message)s', \
level=logging.INFO, \
da... | {"hexsha": "21c5a2269e5b8fb843cba99c30d2fedd7aebfee4", "size": 4489, "ext": "py", "lang": "Python", "max_stars_repo_path": "features_generator_bow.py", "max_stars_repo_name": "tellesleandro/mo444_final_project", "max_stars_repo_head_hexsha": "66cf996c8add125272040777edc5c64b3a7323c8", "max_stars_repo_licenses": ["MIT"]... |
import numpy as np
import jax
import jax.numpy as jnp
import jax.scipy as jsp
import jaxtorch
from jaxtorch import PRNG, Context, Module, nn, init
from diffusion_models.common import *
from diffusion_models.schedules import cosine
class ResidualBlock(nn.Module):
def __init__(self, main, skip=None):
super(... | {"hexsha": "6b5fa8057437a36d4a0131f4c016d83427d43415", "size": 4758, "ext": "py", "lang": "Python", "max_stars_repo_path": "jax-diffusion/jax-guided-diffusion/diffusion_models/pixelart.py", "max_stars_repo_name": "Baughn/nixgan", "max_stars_repo_head_hexsha": "20639e37f8263187ef3928fa91974e9d9d0848d8", "max_stars_repo_... |
import unittest
import pandas as pd
import os
import numpy as np
from mlapp.utils.metrics.pandas import regression
from mlapp.utils.general import get_project_root
from mlapp.managers import ModelManager, DataManager
from mlapp.managers.io_manager import IOManager
from mlapp.utils.automl import AutoMLResults
from pyspa... | {"hexsha": "246e4f5465600e99d8907f312cd34c6433c46895", "size": 7357, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_managers.py", "max_stars_repo_name": "kerenleibovich/mlapp", "max_stars_repo_head_hexsha": "0b8dfaba7a7070ab68cb29ff61dd1c7dd8076693", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
import numpy as np
import csv
from datetime import datetime
from PyQt5 import uic, QtCore, QtWidgets, QtGui
from PyQt5.QtWidgets import QDialog
import matplotlib
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
matp... | {"hexsha": "5b5a0f5ff633cc0332ade7b9162b7bcf12d96beb", "size": 13654, "ext": "py", "lang": "Python", "max_stars_repo_path": "SingleIRdetection/gui/dialogs/dialog_live_graphs.py", "max_stars_repo_name": "biqute/QTLab2122", "max_stars_repo_head_hexsha": "4d53d4c660bb5931615d8652e698f6d689a4dead", "max_stars_repo_licenses... |
# ----------------------------------------
# Written by Yude Wang
# ----------------------------------------
import random
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.data import DataLoader
from lib.utils.test_utils import single_gpu_test
from lib.utils.DenseC... | {"hexsha": "5ff78b1f98c1cf0b506b1e0fa32309953fda20ab", "size": 4077, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiment/deeplabv3_voc/test.py", "max_stars_repo_name": "giussepi/semantic-segmentation-codebase", "max_stars_repo_head_hexsha": "163b0edfa30a8e1147b532a737d0784ea09f4fc2", "max_stars_repo_licen... |
import numba as nb
import numpy as np
FASTMATH=True
PARALLEL = True
CACHE=True
njitSerial = nb.njit(fastmath=FASTMATH,cache=CACHE)
jitSerial = nb.jit(fastmath=FASTMATH,cache=CACHE)
njitParallel = nb.njit(fastmath=FASTMATH,cache=CACHE,parallel=PARALLEL)
jitParallel = nb.jit(fastmath=FASTMATH,cache=CACHE,parallel=PARALLE... | {"hexsha": "a409c109418516b2d975ed28eb033da81dcefcbe", "size": 1603, "ext": "py", "lang": "Python", "max_stars_repo_path": "Legacy/mcmc/autocorr.py", "max_stars_repo_name": "puat133/MCMC-MultiSPDE", "max_stars_repo_head_hexsha": "2beca39f32c0cdd7664baeacd495b193850d8e7d", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
from flask import Flask, render_template, request
app = Flask(__name__)
@app.route('/')
def index():
return render_template("index.html")
@app.route('/inputs')
def inputs():
return render_template("inputs.html")
@app.route('/lookup')
def lookup():
return render_template("lookup.html")
... | {"hexsha": "83e4d24f1c10a980d975e98cc21be7ac4b7366f7", "size": 4949, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "xdhacksteam/xdhacksteam", "max_stars_repo_head_hexsha": "52c1922e59a3846772da887e0da2e0546a9e0d33", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4, "max_... |
import numpy as np
import subprocess
import os.path
import sys
class MetisGraphHelper(object):
def __init__(self, metisUndirectedGraphFile, partitionNum, machineNum, machineScale, hyperMetisGraphFiles, metisDistFiles):
self.metisUndirectedGraphFile = metisUndirectedGraphFile
self.metisUndirectedGr... | {"hexsha": "9857823e711ffb0a49d62ce703f407a48c9f0317", "size": 22171, "ext": "py", "lang": "Python", "max_stars_repo_path": "metis_graph_helper.py", "max_stars_repo_name": "HolyLow/Balanced_Graph_Partitioning", "max_stars_repo_head_hexsha": "72aecb42c1edbd7973babf4779d3aea4ad6cdf6b", "max_stars_repo_licenses": ["Apache... |
using DataStructures: OrderedDict
using DataFrames: DataFrame
using Dates: AbstractTime
struct Simulation
base::Union{String,Symbol,Nothing}
index::OrderedDict{Symbol,Any}
target::OrderedDict{Symbol,Any}
mapping::OrderedDict{Symbol,Any}
meta::OrderedDict{Symbol,Any}
result::DataFrame
end
simul... | {"hexsha": "3fab5431f94ddf9ecd659d48dd1646e1ef39fb34", "size": 9376, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/util/simulate.jl", "max_stars_repo_name": "tomyun/Cropbox.jl", "max_stars_repo_head_hexsha": "10d2180d213919c91c911b6718460057a4c8be9d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2... |
import os
import numpy as np
import xarray as xr
from echopype.convert import Convert
from echopype.model import EchoData
# ek60_raw_path = './echopype/test_data/ek60/2015843-D20151023-T190636.raw' # Varying ranges
ek60_raw_path = './echopype/test_data/ek60/DY1801_EK60-D20180211-T164025.raw' # Constant ranges
ek... | {"hexsha": "5ee472753c2158c773c8c150d24d811de3cf3f35", "size": 4994, "ext": "py", "lang": "Python", "max_stars_repo_path": "echopype/tests/test_ek60_model.py", "max_stars_repo_name": "leewujung/echopype-lfs-test", "max_stars_repo_head_hexsha": "b76dcf42631d0ac9cef0efeced9be4afdc15e659", "max_stars_repo_licenses": ["Apa... |
[STATEMENT]
lemma Qs_member_iff [simp]:
"q |\<in>| Qs A \<longleftrightarrow> (\<exists> f ps p. TA_rule (Some f) ps p |\<in>| rules A \<and> (p = q \<or> (p, q) |\<in>| (eps A)|\<^sup>+|))" (is "?Ls \<longleftrightarrow> ?Rs")
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (q |\<in>| Qs A) = (\<exists>f ps p. So... | {"llama_tokens": 2591, "file": "Regular_Tree_Relations_RRn_Automata", "length": 17} |
#!/usr/bin/env python
#-*- coding: utf-8 -*-
"""
meep_materials.py
This script contains definitions of materials suitable for the FDTD algorithm.
However, only the materials from the sections "Generic" and "Prepared for FDTD" are
ready to be fed to MEEP, and they are valid only for a given spectral range.
On the cont... | {"hexsha": "b0e8948bc1df02b313472d79cb84846c7f367992", "size": 25704, "ext": "py", "lang": "Python", "max_stars_repo_path": "meep_materials.py", "max_stars_repo_name": "LeiDai/meep_metamaterials", "max_stars_repo_head_hexsha": "2ca51c861e1f69e638e920c9bdacc7c583e22aed", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
'''
Copyright (C) 2016 The Crown (i.e. Her Majesty the Queen in Right of Canada)
This file is an add-on to RAVE.
RAVE is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(a... | {"hexsha": "80355bd42492e6f8f0f5c08ee5d7ef50cb979f4b", "size": 4835, "ext": "py", "lang": "Python", "max_stars_repo_path": "rave_ec/test/pytest/ECDopvolFilterTest.py", "max_stars_repo_name": "DanielMichelson/drqc_article", "max_stars_repo_head_hexsha": "cd7df2f7290adedb557bbc6ba484d30039a23ce2", "max_stars_repo_license... |
#utility functions for debugging and plotting
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
#*NOTE* constants definition used for finding lines
WIN_NUM = 12
WIN_MARGIN = 100
MIN_PIX = 50
X_CORNER = 9
Y_CORNER = 6
#*NOTE* pre-requisite: image has already been converte... | {"hexsha": "d1cc9e229fedd0ea0863ee186d4abb3bd7504de6", "size": 2376, "ext": "py", "lang": "Python", "max_stars_repo_path": "utilities.py", "max_stars_repo_name": "zhengweiqi/AdvancedLaneDetection", "max_stars_repo_head_hexsha": "a443b8c12a79ac8ae8b43dfd5f7a8cf9f9547314", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
\section{Conclusion}
This study presents an approach to account for plastic deformation in
a velocity based formulation.
In the introduced method, the plastic deformation takes place if the force or moment exceeds the given
limit, the deformation absorbs energy and the joint breaks if plastic capacity is exceeded.
... | {"hexsha": "c59885121d381ac12767a6d9df46285164e1995c", "size": 1815, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "pdocs/thesis/article-conclusion.tex", "max_stars_repo_name": "simo-11/bullet3", "max_stars_repo_head_hexsha": "af7753f5d7fbc0030a3abbe43356d9a9ea784a62", "max_stars_repo_licenses": ["Zlib"], "max_st... |
# from sensible_raw.loaders import loader
from world_viewer.synthetic_world import SyntheticWorld
from world_viewer.glasses import Glasses
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import networkx as nx
from matplotlib.colors import LogNorm
from sklearn.utils import shuffle
import pickle
d... | {"hexsha": "c4428f7c9bb62e63e16889a1aa0c0355abef4336", "size": 1218, "ext": "py", "lang": "Python", "max_stars_repo_path": "CreateSurrogates/AVM_surrogate_time-edges-complete.py", "max_stars_repo_name": "pik-copan/pydrf", "max_stars_repo_head_hexsha": "998072d655331ca6669c71bb6df665292e8972e7", "max_stars_repo_licenses... |
#1 -)ARRAYS
# reversing numpy array
import numpy
def arrays(arr):
return numpy.array(arr, float)[::-1]
arr = input().strip().split(' ')
result = arrays(arr)
print(result)
#2 -)SHAPE AND RESHAPE
# reshaping with reshape function
import numpy
print(numpy.array(list(map(int, input().rstrip().split()))).reshape(3... | {"hexsha": "033a442a5c4b9688955004fa52efd4ea5d31026b", "size": 3651, "ext": "py", "lang": "Python", "max_stars_repo_path": "hw1scripts/numpy.py", "max_stars_repo_name": "vedatk67/ADM-HW1", "max_stars_repo_head_hexsha": "21da1ba0c4f08a8d3fb14d40a65476b39761b4f9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import pybullet as p
import time
import math
from datetime import datetime
from numpy import *
from pylab import *
import struct
import sys
import os, fnmatch
import argparse
from time import sleep
def readLogFile(filename, verbose = True):
f = open(filename, 'rb')
print('Opened'),
print(filename)
keys = f... | {"hexsha": "5b02f1b72d11b97b9ce90be4f9aa07a3d20bf2d7", "size": 1835, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/pybullet/examples/kuka_with_cube_playback.py", "max_stars_repo_name": "frk2/bullet3", "max_stars_repo_head_hexsha": "225d823e4dc3f952c6c39920c3f87390383e0602", "max_stars_repo_licenses": ... |
[STATEMENT]
lemma eval_preserves_sconf:
"\<lbrakk> wf_C_prog P; P,E \<turnstile> \<langle>e,s\<rangle> \<Rightarrow> \<langle>e',s'\<rangle>; P,E \<turnstile> e::T; P,E \<turnstile> s \<surd> \<rbrakk> \<Longrightarrow> P,E \<turnstile> s' \<surd>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>wf_C_prog ... | {"llama_tokens": 262, "file": "CoreC++_TypeSafe", "length": 1} |
from scipy.spatial import cKDTree
from deduplication.duplicatefinder.NearDuplicateImageFinder import NearDuplicateImageFinder
class cKDTreeFinder(NearDuplicateImageFinder):
valid_metrics = [
'manhattan',
'euclidean'
]
def __init__(self, img_file_list, distance_metric, leaf_size=40, paral... | {"hexsha": "dd1cf0b316eaef90169ca452a16ca36cb8d7a155", "size": 2855, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/deduplication/duplicatefinder/cKDTreeFinder.py", "max_stars_repo_name": "rebinsilva/fast-near-duplicate-image-search", "max_stars_repo_head_hexsha": "eed7776c423e9b870b8f3c08b3e4f56019bace10",... |
import json
import sys
import numpy as np
import networkx as nx
from tqdm import tqdm
from util.config import JAVADOC_GLOBAL_NAME, EXP_DEUS_X_MACHINA_CONCEPT_SEARCH_RESULT_STORE_PATH, EXP_DEUS_X_MACHINA_LITERAL_STRICT_SEARCH_RESULT_STORE_PATH,EXP_DEUX_X_MACHINA_LITERAL_SEARCH_RESULT_STORE_PATH, base_dir
from util.conc... | {"hexsha": "ba727a9ac977e8f5bad3125cec25d5aa2a00286c", "size": 8930, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/DEUS-X-MACHINA_EXP_2-statistic_min_concept_map_distance.py", "max_stars_repo_name": "delavet/SOworkspace", "max_stars_repo_head_hexsha": "74bbcfa62c7e293b2b02f23249ac408aa22b44af", "max_stars_... |
///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2013, PAL Robotics S.L.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// * Redistributions of source code must reta... | {"hexsha": "ed97684c3a1f4d9e56a8978e285e9527affe560e", "size": 5001, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "play_motion/play_motion/test/play_motion_test.cpp", "max_stars_repo_name": "hect1995/Robotics_intro", "max_stars_repo_head_hexsha": "1b687585c20db5f1114d8ca6811a70313d325dd6", "max_stars_repo_licens... |
%%%%%%%%%%%%%%%%%%%%%definitions%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\input{../header.tex}
\input{../newcommands.tex}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%DOCUMENT%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{document}
%\preprint{}
\title{The parallel derivative on structured grids}
\author{M.~Wiesenberger and M.~ Held... | {"hexsha": "94ff9fe721db16b8d4dac710dd51d9f942c07cd5", "size": 33819, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/related_pages/parallel/parallel.tex", "max_stars_repo_name": "gregordecristoforo/feltor", "max_stars_repo_head_hexsha": "d3b7b296e6f5be3a9ff9d602d98461ed9c60033a", "max_stars_repo_licenses": ["... |
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras import backend as K
import os
target_size = (512, 512)
batch_size = 2
path_train = 'data/train'
path_test = 'data/test'
# generate train data
def gen_train_data():
data_gen_args = dict(
rotation_range=... | {"hexsha": "5a63f4003452e4c0b094a7f7aa9b1488b3c4cda4", "size": 2571, "ext": "py", "lang": "Python", "max_stars_repo_path": "U-net-re/data_generator.py", "max_stars_repo_name": "BillChan226/In-Field-Crop-Disease-Regnition-via-Domain-Adaptation", "max_stars_repo_head_hexsha": "4500e7149a51eab66778471750b84b09d415e578", "... |
import os
import sys
import torch
import argparse
import collections
import numpy as np
sys.path.append('.')
import darknet
import shufflenetv2
import yolov3
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str,
help='path to the model')
parser.add... | {"hexsha": "d7918458d7885cf3768d0a21957c00f700d903f4", "size": 1635, "ext": "py", "lang": "Python", "max_stars_repo_path": "tools/valid_tensorrt_output.py", "max_stars_repo_name": "Royzon/JDE", "max_stars_repo_head_hexsha": "76258ffbfc51d20ebdd2a1fc152a91fe43a12f1c", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
"""
Generates some prediction plots for the Herschel I paper.
:author: Sami-Matias Niemi
:contact: sammy@sammyniemi.com
:version: 0.5
"""
import matplotlib
#matplotlib.use('Cairo')
matplotlib.use('Agg')
matplotlib.rc('text', usetex=True)
matplotlib.rcParams['font.size'] = 17
matplotlib.rc('xtick', labelsize=14)
matpl... | {"hexsha": "b18370c41d4bd6369beb8716a89405f761501116", "size": 23995, "ext": "py", "lang": "Python", "max_stars_repo_path": "herschel/plotmergersPaper.py", "max_stars_repo_name": "sniemi/SamPy", "max_stars_repo_head_hexsha": "e048756feca67197cf5f995afd7d75d8286e017b", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_s... |
import pytest
import pandas as pd
import numpy as np
from mlserver.codecs.pandas import PandasCodec, _to_response_output
from mlserver.types import (
InferenceRequest,
InferenceResponse,
RequestInput,
Parameters,
ResponseOutput,
)
@pytest.mark.parametrize(
"series, expected",
[
(
... | {"hexsha": "fe24b34bdd7a9e07f92c985bf8f140a270ac6d2b", "size": 3968, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/codecs/test_pandas.py", "max_stars_repo_name": "JakeNeyer/MLServer", "max_stars_repo_head_hexsha": "a283d3c0008c944b28cdd39c2ffec73f59296603", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
# %load_ext rpy2.ipython
# %matplotlib inline
from fbprophet import Prophet
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import logging
logging.getLogger('fbprophet').setLevel(logging.ERROR)
import warnings
warnings.filterwarnings("ignore")
df = pd.read_csv('../examples/example_wp_peyton_... | {"hexsha": "c3029f11dfeea829a410911ed9588a68312c419c", "size": 2151, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebook/seasonality_and_holiday_effects.py", "max_stars_repo_name": "wuliuyuedetian/prophet", "max_stars_repo_head_hexsha": "32c623721315eba562e422a7ef341ff2e5182eb4", "max_stars_repo_licenses": ... |
#!/usr/bin/env python
from __future__ import division, absolute_import, print_function
import numpy as np
"""
Defines signatures of discharge time series.
They are:
autocorrelation
flow duration curves
rising and declining limb densities
maximum monthly flow
moments
... | {"hexsha": "07f8d97e61a3f2caf220456248890d4ccf28274b", "size": 21511, "ext": "py", "lang": "Python", "max_stars_repo_path": "jams/qa/signatures.py", "max_stars_repo_name": "MuellerSeb/jams_python", "max_stars_repo_head_hexsha": "1bca04557da79d8f8a4c447f5ccc517c40ab7dfc", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/python2.7
# _*_ coding: utf-8 _*_
"""
@Author: MarkLiu
"""
import AdaBoostAndNavieBayes.AdaboostNavieBayes as boostNaiveBayes
import random
import numpy as np
def trainingAdaboostGetDS(iterateNum=40):
"""
测试分类的错误率
:param iterateNum:
:return:
"""
filename = '../emails/training/SMSCo... | {"hexsha": "ab1c160cb08d5d9610903b4fbd47607124f29f81", "size": 3543, "ext": "py", "lang": "Python", "max_stars_repo_path": "AdaBoostAndNavieBayes/training.py", "max_stars_repo_name": "steveatgit/Bayes", "max_stars_repo_head_hexsha": "7e64789466dd29ed1e5b1590c21573903aaa2cc5", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
import math
from scipy.spatial.transform import Rotation as R
from math import ceil,trunc,floor,sin,cos,atan,acos,sqrt
EPS = 1e-6
def angle_axis_from_quaternion(quater):
angle = 2 * acos(quater[3])
axis = quater[:3]/(sin(angle/2)+EPS)
return angle * axis
def angle_axis_from_quaternion_batch(... | {"hexsha": "a0f882be83ae029ea1dc63a8259785bc6ac552d4", "size": 13412, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/rotation_lib.py", "max_stars_repo_name": "yifan-you-37/omnihang", "max_stars_repo_head_hexsha": "c80b699b2cf2cf3422201cc8c3fa572d0e01d5a2", "max_stars_repo_licenses": ["MIT"], "max_star... |
# This file is a part of AstroLib.jl. License is MIT "Expat".
# Copyright (C) 2016 Mosè Giordano.
function kepler_solver(_M::Real, e::Real)
@assert 0 <= e <= 1 "eccentricity must be in the range [0, 1]"
# M must be in the range [-pi, pi], see Markley (1995), page 2.
M = rem2pi(_M, RoundNearest)
T = flo... | {"hexsha": "c64170410edaf4f82954661722b89151389f9da6", "size": 3477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/kepler_solver.jl", "max_stars_repo_name": "TobiasHeinicke/AstroLib.jl", "max_stars_repo_head_hexsha": "c3dbc7437dd95d3deb42d23f8d5f6c9cc1abfbe5", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# %%
import numpy as np
import tensorflow as tf
from tensorflow import keras
# %%
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
x_train = x_train.reshape((60000, 28, 28, 1)) / 255
x_test = x_test.reshape((10000, 28, 28, 1)) / 255
n_train = x_train.shape[0]
x_train_expanded = np.zeros((5*n_... | {"hexsha": "715edeb3b18546eef7ff7deddc16bf9134d3a3b3", "size": 2387, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/convolutional.py", "max_stars_repo_name": "thinety/neural-networks-and-deep-learning", "max_stars_repo_head_hexsha": "c6485a68b12522f1d1d489a76f68964ecff917ea", "max_stars_repo_licenses": ["MI... |
#include "worker.hpp"
#include <beasty-tepee/request_header_handler.hpp>
#include <boost/asio/io_context_strand.hpp>
#include <boost/coroutine2/coroutine.hpp>
#include <boost/beast/http/write.hpp>
#include <boost/beast/core/span.hpp>
namespace tepee::server
{
worker::worker(boost::asio::io_context& ... | {"hexsha": "677f91692e74564a02f6c89cc4de13b068687a37", "size": 4205, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/worker.cpp", "max_stars_repo_name": "syoliver/beasty-tepee", "max_stars_repo_head_hexsha": "64240264ae16885894e1372e1a98176935b5594e", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count": ... |
// ------------------------------------------------------------
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License (MIT). See License.txt in the repo root for license information.
// ------------------------------------------------------------
#include "stdafx.h"
#include ... | {"hexsha": "e6876c257478cd5a9e3b9333f40d4923d87c9852", "size": 112919, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/prod/src/data/tstore/Merge.Test.cpp", "max_stars_repo_name": "vishnuk007/service-fabric", "max_stars_repo_head_hexsha": "d0afdea185ae932cc3c9eacf179692e6fddbc630", "max_stars_repo_licenses": [... |
\pagebreak
\section*{\underline{Abstract}}
This document is an example of one-page abstract for the book of abstracts
of the Modelica Conference 2011. The full conference proceedings will be only published
electronically on a memory stick and on the Web. However, for the conference
attendant's convenience, a smaller ... | {"hexsha": "7a020f9e86358490afad4125a19dacb3ce976731", "size": 2288, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "Chapter/abstract.tex", "max_stars_repo_name": "drwalles/Project-II-Book", "max_stars_repo_head_hexsha": "9ec82150d5151a1159336b0d5ad0a6ca6776e646", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
#!/usr/bin/env python3
'''Generate a rollout using a random policy. This is useful for getting data to
trian the VAE.
'''
import argparse
import cv2
import json
import gym
import matplotlib.pyplot as plt
import numpy as np
import os
import torch
import settings
from PIL import Image
from gym.envs.box2d.car_racing im... | {"hexsha": "4d12c2cee730f3bee5faaa8e94f42372467b4697", "size": 4694, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset/car_racing/rollout.py", "max_stars_repo_name": "prakharsingh95/Model-vs-Model-Free-RL", "max_stars_repo_head_hexsha": "1ddcc54bab66905a44997c48df1d2034c3b8b903", "max_stars_repo_licenses":... |
"""
Date: 07/11/2020
Author: Carlo Cena
Implementation of minmax algorithm with alpha-beta pruning.
"""
import numpy as np
import time
from tablut.state.tablut_state import State
from tablut.utils.state_utils import MAX_VAL_HEURISTIC
from threading import Thread, Lock
lock_2 = Lock()
#TODO: remove num_state_visited,... | {"hexsha": "f4f992d209a831d7b6d93fdc50b5894f84534736", "size": 13981, "ext": "py", "lang": "Python", "max_stars_repo_path": "tablut/search/min_max_parallel_2.py", "max_stars_repo_name": "carlo98/tablut-THOR", "max_stars_repo_head_hexsha": "b990d49c66735c40afbfa7d26aeec7694a80a729", "max_stars_repo_licenses": ["MIT"], "... |
using LinearAlgebra
function get_module_3_excersise_4_author_name()
return "YOUR NAME HERE"
end
function problem_4(a)
A = [
a 1 0 0 0 1
1 a 1 0 0 0
0 1 a 1 0 0
0 0 1 a 1 0
0 0 0 1 a 1
1 0 0 0 1 a
]
eigen_vals = []
eigen_vectors = []
return eigen_vals, eigen_vect... | {"hexsha": "99c8094b3b1c918edcb1c70085a82c9de4975846", "size": 328, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "module3/module-3-exercise-4.jl", "max_stars_repo_name": "j-hayes/chem-324-programming-tutorials", "max_stars_repo_head_hexsha": "ff9a72685a9d1000026941abb1a680fffb927468", "max_stars_repo_licenses":... |
import numpy as np
import matplotlib.pyplot as plt
def step(space):
neighbor_count = sum(np.roll(space, (dr, dc), (0, 1)) for dr in (-1,0,1) for dc in (-1,0,1)) - space
return ((neighbor_count == 3) | (space & (neighbor_count == 2))).astype(np.int)
def show(space):
for i in range(space.shape[0]):
... | {"hexsha": "1c69252f3b76b223473e6824d4245ab5434dae34", "size": 883, "ext": "py", "lang": "Python", "max_stars_repo_path": "game_of_life.py", "max_stars_repo_name": "cosmo-jana/numerics-physics-stuff", "max_stars_repo_head_hexsha": "f5fb35c00c84ca713877e20c1d8186e76883cd28", "max_stars_repo_licenses": ["MIT"], "max_star... |
import h5py
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
import keras
import h5py
import numpy as np
from keras.layers import Input, Dense, Conv1D, MaxPooling2D, MaxPooling1D, BatchNormalization
from keras.layers.core import Dropout, Activation, Flatten
from keras.layers.merge import... | {"hexsha": "7e1402108bb7de514c010b6024727269126e3752", "size": 4364, "ext": "py", "lang": "Python", "max_stars_repo_path": "dcnn/Basset/Basset/basset.py", "max_stars_repo_name": "wzthu/NeuronMotif", "max_stars_repo_head_hexsha": "0f7f786e4b75916039388824d04d2041747fd299", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
# Generate a random Latin Hypercube Design (LHD)
def rLHD(nrows,ncols,unit_cube=False):
""" Generate a random Latin Hypercube Design (LHD)
Args:
nrows (int): A positive integer specifying the number of rows
ncols (int): A postive integer specifying the number of columns
unit_... | {"hexsha": "44f77b7ff3577b686be7967dc9000beb047b6c89", "size": 1882, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyLHD/base_designs.py", "max_stars_repo_name": "toledo60/pyLHD", "max_stars_repo_head_hexsha": "40df7f2015e06e9e1190cce49c68f17068b86070", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1,... |
# MIT License - Copyright Petri Laarne and contributors
# See the LICENSE.md file included in this source code package
"""A simplified version of the case study in the documentation."""
from __future__ import annotations
from ennemi import estimate_mi, pairwise_mi
import numpy as np
import pandas as pd
import unittes... | {"hexsha": "a1493d6097216bbc63d9b726e80264e7a7ecedfb", "size": 3131, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/pandas/test_pandas_workflow.py", "max_stars_repo_name": "polsys/ennemi", "max_stars_repo_head_hexsha": "2c3fe839cb2f416e9f7132fd38277ab97f7c53a0", "max_stars_repo_licenses": ["MIT"], "max_st... |
import json, logging, pickle, os, sys, time
from shapely import geometry
from pulp import *
import pandas as pd
import geopandas as gpd
import numpy as np
from geopy.distance import geodesic
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
tic = time.time()
from shapely.strtree import STRtree
im... | {"hexsha": "2a437c8daf51b86cd3231d80bc80ba2be120b8f8", "size": 11638, "ext": "py", "lang": "Python", "max_stars_repo_path": "solarpv/analysis/matching/match_region.py", "max_stars_repo_name": "shivareddyiirs/solar-pv-global-inventory", "max_stars_repo_head_hexsha": "9940a454de88a39ca92dbabf07e98d8623f0ec8b", "max_stars... |
#!/usr/bin/env python3
# Written by Christopher Hempel (hempelc@uoguelph.ca) on 22 Jul 2021
# This script processes the output from the script "metrics_generation.py" and
# determines the Euclidean distance of pipelines to the reference mock community,
# the correaltion between Euclidean distance and tools, and clust... | {"hexsha": "54b46704c7e40c6659406f8074405b695c9130dc", "size": 13061, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/statistics/eucdist_correlation_clustering_metrics.py", "max_stars_repo_name": "hempelc/metagenomics-vs-totalRNASeq", "max_stars_repo_head_hexsha": "ff2c70351f09654455be056fb0edefc1f0b0f3c... |
module Writer
export write_pddl, write_domain, write_problem
export save_domain, save_problem
using Julog
using ..PDDL:
IMPLIED_REQUIREMENTS, Domain, Problem, Action,
get_name, get_requirements, get_typetree, get_constants, get_constypes,
get_predicates, get_functions, get_actions, get_axioms,
get_dom... | {"hexsha": "09eec86675a5b181bba8bc5880c90acd5be264a5", "size": 8597, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/writer/writer.jl", "max_stars_repo_name": "Leticia-maria/PDDL.jl", "max_stars_repo_head_hexsha": "f462be51f94c6f97b1ba70c992987094116b0cdb", "max_stars_repo_licenses": ["Apache-2.0"], "max_star... |
import pytest
import numpy as np
import os, sys
os.path.join(os.path.dirname(os.path.abspath(__file__)),'../../..')
from tests.embeddings_pipelines.model.test_models import AbstractTestMultipleWordsEmbeddingModel
sys.path.append( os.path.join(os.path.dirname(os.path.abspath(__file__)),'../../../src') )
from embeddings_... | {"hexsha": "49d020eadee94fbae7347879418b5d088b88a438", "size": 1022, "ext": "py", "lang": "Python", "max_stars_repo_path": "playground/oliver_ilnicki/embeddings_pipelines/tests/embeddings_pipelines/models/test_multiple_words_embedding_models.py", "max_stars_repo_name": "toedtli/rg_text_to_sound", "max_stars_repo_head_h... |
#!/usr/bin/env python3
import csv
import numpy as np
def main():
with open('../data/abbreviations.csv', 'r') as read_csv:
reader = csv.reader(read_csv, delimiter=' ')
abbreviations = []
for line in reader:
abbreviations.append(line[0])
read_csv.close()
with open('../dat... | {"hexsha": "ae3770e3e9ed4dc9281901507f3c67418d8f94c3", "size": 1319, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/feature_generator.py", "max_stars_repo_name": "michaelneuder/plant_clustering", "max_stars_repo_head_hexsha": "38a32f800b4a6dafaa2c40dbce6333fc6db3a1fa", "max_stars_repo_licenses": ["MIT"], "m... |
function new_PreequilibriumData(projBaryons, projProtons, &
& targBaryons, targProtons, kinEnergyMeV, &
& clientFissBarr, clientIO) &
& result(preeqData)
! ======================================================================
!
! Constructor for the PreequilibriumData class.
!
! USE:
! pree... | {"hexsha": "92bf333aac27ded71a0b1c2133e8f19839b28c50", "size": 3924, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/Preequilibrium/ModelData/new_PreequilibriumDataClass.f90", "max_stars_repo_name": "lanl/generalized-spallation-model", "max_stars_repo_head_hexsha": "4a2f01a873d2e8f2304b8fd1474d43d1ce8d744d... |
from unittest import TestCase
from unittest.mock import Mock, create_autospec
import numpy as np
import pandas as pd
from acnportal.acnsim import Simulator
from acnportal.acnsim.network import ChargingNetwork
from acnportal.algorithms import BaseAlgorithm
from acnportal.acnsim.events import EventQueue, Event
from dat... | {"hexsha": "856eb04f856c4dd73be35a05100c075f0020e98c", "size": 2919, "ext": "py", "lang": "Python", "max_stars_repo_path": "acnportal/acnsim/tests/test_simulator.py", "max_stars_repo_name": "irasus-technologies/acnportal", "max_stars_repo_head_hexsha": "f6ac7b9ddb28ab48177c51a676f1619e88ea91e0", "max_stars_repo_license... |
# Utilities
## repeat each element in a vector
function repeach{T}(x::AbstractVector{T}, n::Integer)
k = length(x)
r = Array(T, k * n)
p = 0
@inbounds for i = 1:k
xi = x[i]
for j = 1:n
r[p += 1] = xi
end
end
return r
end
function repeach{T}(x::AbstractVect... | {"hexsha": "eb7cb17b92fac53ac29d30aff77204ac49ed3cc5", "size": 2438, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils.jl", "max_stars_repo_name": "yuehhua/MLBase.jl", "max_stars_repo_head_hexsha": "438a2cbbfc6d3071d113c3e9703adc4fb86cb84c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
import os
import pickle
import cv2
import numpy as np
import open3d as o3d
# import pyrealsense2 as rs2
import config
def load_kntcalibmat(amat_path=os.path.join(config.ROOT, "./camcalib/data/"), f_name="knt_calibmat.pkl"):
amat = pickle.load(open(amat_path + f_name, "rb"))
return amat
def map_depth2pcd(d... | {"hexsha": "c560f92f07324af10d03eeb7cae9630342a41905", "size": 3213, "ext": "py", "lang": "Python", "max_stars_repo_path": "0000_students_work/2021tro/gaussian_surface_bug/vision_utils.py", "max_stars_repo_name": "takuya-ki/wrs", "max_stars_repo_head_hexsha": "f6e1009b94332504042fbde9b39323410394ecde", "max_stars_repo_... |
import numpy as np
class Casino:
"""
Occasionally dishonest casino from Machine Learning: A Probabilistic Perspective, Chapter 17.
"""
Z_HONEST = 0
Z_DISHONEST = 1
A = np.array([
[0.95, 0.05],
[0.1, 0.9]
])
PX = np.array([
[1/6, 1/6, 1/6, 1/6, 1/6, 1/6],
... | {"hexsha": "8496daae82b0750aec20ba97bf5448b44ebfe1fc", "size": 923, "ext": "py", "lang": "Python", "max_stars_repo_path": "hmm/casino.py", "max_stars_repo_name": "ondrejba/hmm", "max_stars_repo_head_hexsha": "1e9fe47a6057d93e7c77614016a89d5d46959e97", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_st... |
\documentclass[12pt]{article}
\newcommand{\VERSION}{1.8.0}
\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{csquotes}
\usepackage{xcolor}
\usepackage{listings, lstautogobble}
\usepackage{booktabs}
\setlength{\heavyrulewidth}{1.5pt}
\setlength{\abovetopsep}{4pt}
\lstset{language=bash,
keywordst... | {"hexsha": "97f0b228043362a04795ade82cd54e0a85af8b34", "size": 16940, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/tex/DTarray_pro-Userguide.tex", "max_stars_repo_name": "ajmaurais/DTarray_pro", "max_stars_repo_head_hexsha": "5601660b43ccfa4e46e4a3c503eb251f062d34b4", "max_stars_repo_licenses": ["MIT"], "ma... |
"""Pumps Module:
This module calculates and produces pump curves based on mfg's data points
"""
from __future__ import print_function, division
import matplotlib.pyplot as plt
from numpy import linspace, any, interp, array
import sqlite3
from os import path
BASE_DIR = path.dirname(path.abspath(__file__))
db_path ... | {"hexsha": "0faa2afd9fb2d72894a2fec60004715fc9dfc29f", "size": 17093, "ext": "py", "lang": "Python", "max_stars_repo_path": "Water/pumps.py", "max_stars_repo_name": "hasemar/water", "max_stars_repo_head_hexsha": "b8cac349f4bf86be50ac29ae8c31a2fe74d71acf", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "ma... |
from . import zernike
#from .test.multiproc import MultiprocPipeline
from mindboggle.mio.vtks import read_vtk
import numpy as np
import argparse
import logging
#import profilehooks
def example1():
# >>> # Example 1: simple cube (decimation results in a Segmentation Fault):
# >>> from mindboggle.shapes.z... | {"hexsha": "21165cfb23be805ee74ce7929a5c19ba63605408", "size": 2830, "ext": "py", "lang": "Python", "max_stars_repo_path": "mindboggle/shapes/zernike/__main__.py", "max_stars_repo_name": "cemlyn007/mindboggle", "max_stars_repo_head_hexsha": "947d4b3f41fb7a24c079550c7255c4d16939d740", "max_stars_repo_licenses": ["CC-BY-... |
(* Title: HOL/Auth/n_germanSymIndex_lemma_on_inv__45.thy
Author: Yongjian Li and Kaiqiang Duan, State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
Copyright 2016 State Key Lab of Computer Science, Institute of Software, Chinese Academy of Sciences
*)
header{*The ... | {"author": "lyj238Gmail", "repo": "newParaVerifier", "sha": "5c2d49bf8e6c46c60efa53c98b0ba5c577d59618", "save_path": "github-repos/isabelle/lyj238Gmail-newParaVerifier", "path": "github-repos/isabelle/lyj238Gmail-newParaVerifier/newParaVerifier-5c2d49bf8e6c46c60efa53c98b0ba5c577d59618/examples/n_germanSymIndex/n_german... |
import numpy as np
import plotly.graph_objects as go
from pathlib import Path
from src.utils.cmd_parser import parsing_params
# Tested wave files
# Path("data/a0e55.csv")
# Path("data/a0e45_a-90e3.csv")
# Path("data/a0e45_a270e3_a90e42.csv")
class PlotlySphere:
def __init__(self, r=1):
self.r = r
... | {"hexsha": "a9d5146729ab92be4afef1498da9ee1ea3c04dc4", "size": 5160, "ext": "py", "lang": "Python", "max_stars_repo_path": "srp_visualizer.py", "max_stars_repo_name": "BrownsugarZeer/Multi_SSL", "max_stars_repo_head_hexsha": "f0ce429d95680e4eb56d99f04a4bbccf33c27cc9", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/python
# -*- coding: latin-1 -*-
"""
Cameras produce the initial ray from the camera position through the currently
rendered pixel and into the scene.
.. moduleauthor:: Adrian Köring
"""
import numpy as np
from padvinder.ray import Ray
from padvinder.util import normalize
from padvinder.util import check_... | {"hexsha": "6670e5ea54d979b0cc793cdec5acf00483d6ab72", "size": 10211, "ext": "py", "lang": "Python", "max_stars_repo_path": "padvinder/camera.py", "max_stars_repo_name": "adriankoering/padvinder", "max_stars_repo_head_hexsha": "eaebd80867f22c8ca8cc3b97bf0b726f408d928a", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
from math import log, pow
from scipy.stats import entropy
class level:
def __init__(self, start, end, depth):
self.start = start
self.end = end
self.depth = depth
def MDLPDiscretize(col, y, min_depth):
"""Performs MDLP discretization on X and y"""
order = np.ar... | {"hexsha": "b08924b6aa24fb94c8268bc5a88ce4d1fb65205f", "size": 4136, "ext": "py", "lang": "Python", "max_stars_repo_path": "mdlp/_mdlp.py", "max_stars_repo_name": "Lean-Y/mdlp-discretization", "max_stars_repo_head_hexsha": "9b9b151eeb1c5a69e4e0e150c415aff6d217e9b1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
/*
* fixp.hpp
*
* Created on: 03.01.2016
* Author: Marius
*/
#ifndef IT_FIXP_FIXP_HPP_
#define IT_FIXP_FIXP_HPP_
#include <boost/container/flat_map.hpp>
#include <array>
#include <typeinfo>
#include <typeindex>
namespace fixp
{
class security_id_source_isin_number
{
public:
constexpr char value='4';
};
... | {"hexsha": "3b635457f113a515a417577f9aced9bec92df0ae", "size": 1130, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "fixp/fixp.hpp", "max_stars_repo_name": "mdobrea/effective-fix", "max_stars_repo_head_hexsha": "0dc8013a8470366f99281f191ea4379d59ef761a", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_coun... |
!-----------------------------------------------------------------------
! Interface file for kncmbpush3.c
module kncmbpush3_h
implicit none
!
interface
subroutine ckncgbppush3lt(ppart,fxyz,bxyz,kpic,qbm,dt,dtc,ek, &
&idimp,nppmx,nx,ny,nz,mx,my,mz,nxv,nyv,nzv,mx1,my1,mxyz1,ipbc)
... | {"hexsha": "0a54ee5a1be78247bc32d68884755abc176963c3", "size": 12977, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "vectorization/vbpic3/kncmbpush3_h.f90", "max_stars_repo_name": "gcasabona/cuda", "max_stars_repo_head_hexsha": "064cfa02398e2402c113d45153d7ba36ae930f7e", "max_stars_repo_licenses": ["W3C"], "m... |
#!/usr/bin/env python
import ctypes
import datetime
from time import sleep
import cv2
import numpy as np
import sounddevice as sd
from pydub import AudioSegment
from pydub.silence import detect_nonsilent
from scipy.io import loadmat, wavfile
import csv
""" ~~~~~~~~~~~~~ TUNABLE PARAMETERS ~~~~~~~~~... | {"hexsha": "915e287cbc8ce1b57d4eb965967c2f125b42d9f3", "size": 8516, "ext": "py", "lang": "Python", "max_stars_repo_path": "color_reaction.py", "max_stars_repo_name": "NickPulsone/colors_test_with_reatction", "max_stars_repo_head_hexsha": "7698f2f126fc3d99d47c049adba5062a08ec4cac", "max_stars_repo_licenses": ["MIT"], "... |
### Day 23
## Safe Cracking
## Author: Thanasis Georgiou
workspace()
# Convert anything to integer
int(any) = parse(Int, any)
# Type union of String and Int to use in instructions
IntOrString = Union{Int, String}
# Instruction struct
immutable Instruction
opcode::String
args::Array{IntOrString}
end
# A CPU with... | {"hexsha": "a0f10d3d3713eee5d33f0dc67579088608ea4f81", "size": 3775, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/day-23.jl", "max_stars_repo_name": "sakisds/AoC-2016", "max_stars_repo_head_hexsha": "626ace2c5945daa3a5e4a7a980aae04469da3e76", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": 1, ... |
from typing import Tuple, Union
import numpy as np
from ...utils.translations import trans
from ._text_constants import Anchor
def get_text_anchors(
view_data: Union[np.ndarray, list],
ndisplay: int,
anchor: Anchor = Anchor.CENTER,
) -> np.ndarray:
# Explicitly convert to an Anchor so that string va... | {"hexsha": "2604f371945032e212db933e466a33f53decb810", "size": 4808, "ext": "py", "lang": "Python", "max_stars_repo_path": "napari/layers/utils/_text_utils.py", "max_stars_repo_name": "jojoelfe/napari", "max_stars_repo_head_hexsha": "b52a136dad392c091b0008c0b8d7fcc5ef460f66", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import numpy as np
import pandas as pd
import random as rd
import matplotlib.pyplot as plt
import seaborn as sns
import random
from math import log, gamma
from scipy.stats import expon, weibull_min
import os,sys
from datetime import datetime
import nMGA as gil_nMGA
import REGIR as gil_REGIR
""" ---------------------... | {"hexsha": "c646594b0d8de79cf36eea176da1b76605636ebd", "size": 7884, "ext": "py", "lang": "Python", "max_stars_repo_path": "REGIR/Benchmarking/Benchmarking_speed (Fig.1B).py", "max_stars_repo_name": "Aurelien-Pelissier/REGIR", "max_stars_repo_head_hexsha": "2420de16526d0aa318dee560341f6c7e850161a8", "max_stars_repo_lic... |
program cstexp
c To check that max0
parameter (i=1, j=2*i, k = max0(i,j))
real a(i), b(j), c(k)
print *, a(i)
l = k
print *,l
end
| {"hexsha": "0d7e44d865432259ee436867d145146f7aff2d6a", "size": 180, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "packages/PIPS/validation/Syntax/cstexp.f", "max_stars_repo_name": "DVSR1966/par4all", "max_stars_repo_head_hexsha": "86b33ca9da736e832b568c5637a2381f360f1996", "max_stars_repo_licenses": ["MIT"], "... |
import functools
from operator import mul
import unittest
import numpy
import six
import chainer
from chainer.backends import cuda
from chainer import functions
from chainer import testing
from chainer.testing import attr
from chainer.utils import conv
from chainer_tests.functions_tests.pooling_tests import pooling_n... | {"hexsha": "906a71b75a7ae69c80425af2d54b2babbb57abd8", "size": 7481, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/chainer_tests/functions_tests/pooling_tests/test_max_pooling_nd.py", "max_stars_repo_name": "zjzh/chainer", "max_stars_repo_head_hexsha": "e9da1423255c58c37be9733f51b158aa9b39dc93", "max_sta... |
# -*- coding: utf-8 -*-
"""HDF5 input and output."""
#------------------------------------------------------------------------------
# Imports
#------------------------------------------------------------------------------
import logging
import numpy as np
import h5py
from six import string_types
logger = logging.... | {"hexsha": "bd0bd3729bd05839aac6821f7be897840473bada", "size": 10912, "ext": "py", "lang": "Python", "max_stars_repo_path": "klusta/kwik/h5.py", "max_stars_repo_name": "hrnciar/klusta", "max_stars_repo_head_hexsha": "408e898e8d5dd1788841d1f682e51d0dc003a296", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_coun... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.