text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 19 22:25:29 2021
@author: milin
"""
#importing all the libraires
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
fr... | {"hexsha": "9fb5d90b7f42f41158379dbada5c8f57a69d0a41", "size": 1586, "ext": "py", "lang": "Python", "max_stars_repo_path": "Model_Building/DA model building.py", "max_stars_repo_name": "shrutijain0/Data-Analyst-Salary", "max_stars_repo_head_hexsha": "092877509663f89c7b642fd75d0403ce74f2a5a8", "max_stars_repo_licenses":... |
#-*-coding: UTF-8-*-
####################################################################################
"""
successfully executed in python 3.6
"""
####################################################################################
import numpy as np
import os
import math
import copy
from Statistic import ... | {"hexsha": "cd73e86ae3cb7bce2086fd3aa6eb6e97671d48e4", "size": 8982, "ext": "py", "lang": "Python", "max_stars_repo_path": "WuImprovedBaselineCorrection.py", "max_stars_repo_name": "Junjun1guo/improvedWuBaselineCorrection", "max_stars_repo_head_hexsha": "06d2dee77b7fe4cbe746140500f1ededd5ceedf0", "max_stars_repo_licens... |
import sys
import argparse
import numpy as np
import tensorflow as tf
from tensorflow.keras.preprocessing.text import tokenizer_from_json
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Model
from tensorflow.keras.models import load_model
from parsecorpus import r... | {"hexsha": "72db81d199630854403be458e8d31a6d8651e8ed", "size": 1847, "ext": "py", "lang": "Python", "max_stars_repo_path": "gen_sentences.py", "max_stars_repo_name": "hhmaia/textgen-tensorflow", "max_stars_repo_head_hexsha": "45bcfca89f7e9c74a19d7d5d1e35bc0951cd6e14", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# Functions for data collection
# mymean(a) = isempty(a) ? missing : mean(a)
mymean(a) = isempty(a) ? 0.0 : mean(a)
mystd(a) = isempty(a) ? 0.0 : std(a)
mymedian(a) = isempty(a) ? 0.0 : median(a)
mysum(a) = isempty(a) ? 0.0 : sum(a)
"""
knowledge output of the researcher given the problems it has worked on and their ... | {"hexsha": "3aff41bb525682dc41b5790dd7ac4f36ad379ca6", "size": 2149, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/data_collection.jl", "max_stars_repo_name": "kavir1698/Science_ABM.jl", "max_stars_repo_head_hexsha": "6a5ea8d723a62ac85b4e992ea957f61240318431", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
/-
Copyright (c) 2015, 2017 Jeremy Avigad. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Jeremy Avigad, Robert Y. Lewis, Johannes Hölzl, Mario Carneiro, Sébastien Gouëzel
! This file was ported from Lean 3 source module topology.metric_space.basic
! leanprover-commun... | {"author": "leanprover-community", "repo": "mathlib3port", "sha": "62505aa236c58c8559783b16d33e30df3daa54f4", "save_path": "github-repos/lean/leanprover-community-mathlib3port", "path": "github-repos/lean/leanprover-community-mathlib3port/mathlib3port-62505aa236c58c8559783b16d33e30df3daa54f4/Mathbin/Topology/MetricSpac... |
#!/usr/bin/env python
"""
Simple binning search algorithm
"""
__author__ = "Keith Bechtol"
# Python libraries
import sys
import os
import glob
import yaml
from matplotlib import mlab
import numpy as np
import healpy as hp
import astropy.io.fits as pyfits
import fitsio as fits
# Ugali libraries
import ugali.utils.heal... | {"hexsha": "6f9ab445feebb29977309fc341e15bf474e51aae", "size": 11334, "ext": "py", "lang": "Python", "max_stars_repo_path": "simple/search_algorithm.py", "max_stars_repo_name": "sidneymau/simple", "max_stars_repo_head_hexsha": "df1fb1d8cfdbf6f4964a6554072b56609baf5245", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
# python3
#
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | {"hexsha": "4f07e34d603aba5f754bb143214a9376e542d7b3", "size": 6346, "ext": "py", "lang": "Python", "max_stars_repo_path": "sensor.py", "max_stars_repo_name": "cfreeman/hota-alinta", "max_stars_repo_head_hexsha": "70f07a853800bf051e17a51e099b9d8069a35561", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count": nu... |
import os
import uuid
import pathlib
import numpy as np
import torch
import baselines.methods.bulkandcut as bnc
from baselines.problems import get_flowers
from baselines.problems import flowers
from baselines.problems import get_fashion
from baselines.problems import fashion
from baselines import save_experiment
def... | {"hexsha": "39c015ef8fcd8acf4e510c018713769fdb72bf2f", "size": 2227, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/bulkandcut.py", "max_stars_repo_name": "serizba/multi-obj-baselines", "max_stars_repo_head_hexsha": "b5c97b8009ed32f0eb9a08454cf0d4c6620a2286", "max_stars_repo_licenses": ["Apache-2.0"], ... |
[STATEMENT]
lemma (in node_histories) prefix_elem_to_carriers:
assumes "xs prefix of i"
and "x \<in> set xs"
shows "x \<in> set (history i)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. x \<in> set (history i)
[PROOF STEP]
using assms
[PROOF STATE]
proof (prove)
using this:
xs prefix of i
x \<in> set x... | {"llama_tokens": 186, "file": "CRDT_Network", "length": 2} |
import sys
import numpy as np
import tensorflow
from tensorflow.keras.models import Model
def openset_predict(model: Model, X_test: np.ndarray) -> np.ndarray:
predictions = model.predict(X_test)
openset_predictions = predict_on_threshold(predictions)
return openset_predictions
def predict_on_threshold(... | {"hexsha": "aa4c7a4942ceade8b16f3b1d847b0cb285ac9aad", "size": 1338, "ext": "py", "lang": "Python", "max_stars_repo_path": "demo.py", "max_stars_repo_name": "Machine-Listeners-Valencia/fsl_osr_dataset_baseline", "max_stars_repo_head_hexsha": "06c99e2e715be550ac0c85c5c1e4f713228d5450", "max_stars_repo_licenses": ["MIT"]... |
"Symbolic integration via external software"
from sage.symbolic.expression import Expression
from sage.symbolic.ring import SR
def maxima_integrator(expression, v, a=None, b=None):
"""
sage: from sage.symbolic.integration.external import maxima_integrator
sage: maxima_integrator(sin(x), x)
... | {"hexsha": "481ee9c01dee266af872e46c2f0c6eb48ee14ea5", "size": 4452, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/sage/symbolic/integration/external.py", "max_stars_repo_name": "switzel/sage", "max_stars_repo_head_hexsha": "7eb8510dacf61b691664cd8f1d2e75e5d473e5a0", "max_stars_repo_licenses": ["BSL-1.0"],... |
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import matplotlib.gridspec as gridspec
import scipy.io as scio
import numpy as np
from minisom import MiniSom
import math
import torch
fro... | {"hexsha": "53fb0d5bb337e2d154b060ab83e345f0e9031057", "size": 7846, "ext": "py", "lang": "Python", "max_stars_repo_path": "main.py", "max_stars_repo_name": "KGTKD/EE7207", "max_stars_repo_head_hexsha": "8fde6310aba96b58631f1652e2c32663d621a0d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars_r... |
# Copyright 2021 Franklin Siqueira.
# SPDX-License-Identifier: Apache-2.0
'''
Created on Jul 12, 2019
@author: franklincarrilho
'''
from pandas_datareader import data
from dateutil.relativedelta import relativedelta
import datetime
from datetime import date, time
import yfinance as yf
import numpy as np
# import matp... | {"hexsha": "f0c9e1098b78d9cc62b981fb9e7164b9eeb50c77", "size": 6863, "ext": "py", "lang": "Python", "max_stars_repo_path": "app18/stocks.py", "max_stars_repo_name": "Franklin-Siqueira/Python-Generic-lLab", "max_stars_repo_head_hexsha": "c56208d14196b4ed2046ecf604c2b535fbdcf2c9", "max_stars_repo_licenses": ["MIT"], "max... |
#!/usr/bin/env python
# -*- coding: utf8 -*-
"""Combine Normalized DRACS Specta.
Script that take one imput parameter which is the path to the normobs directory
Needs functions/modules to get the filenames, export to a fits tablefile, change fits hdr
"""
import argparse
import fnmatch
import os
import time
import I... | {"hexsha": "87c727d692217e620b8c0cce1036fa61c75bdc7b", "size": 7398, "ext": "py", "lang": "Python", "max_stars_repo_path": "octotribble/Combine_nod_spectra.py", "max_stars_repo_name": "jason-neal/equanimous-octo-tribble", "max_stars_repo_head_hexsha": "a8788909331034725afe38ae96c83584b17c9fbd", "max_stars_repo_licenses... |
[STATEMENT]
lemma lzip_trans'_fusion [stream_fusion]:
"lunstream' (lzip_trans' g h) (sg, sh, None) = lzip (lunstream' g sg) (lunstream' h sh)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. lunstream' (lzip_trans' g h) (sg, sh, None) = lzip (lunstream' g sg) (lunstream' h sh)
[PROOF STEP]
by transfer(rule lzip_tra... | {"llama_tokens": 146, "file": "Stream_Fusion_Code_Stream_Fusion_LList", "length": 1} |
import os, sys, inspect
# realpath() with make your script run, even if you symlink it :)
cmd_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile( inspect.currentframe() ))[0]) + "/../fdasrsf")
if cmd_folder not in sys.path:
sys.path.insert(0, cmd_folder)
import h5py
import numpy as np
import cu... | {"hexsha": "5871bf8695c1c4040fdef8f368a02902a0ee5194", "size": 1332, "ext": "py", "lang": "Python", "max_stars_repo_path": "debug/test_oc_mlogit.py", "max_stars_repo_name": "gitter-badger/fdasrsf_python", "max_stars_repo_head_hexsha": "f59bd74b570662c17a1a042556d4887e6a75fa3e", "max_stars_repo_licenses": ["BSD-3-Clause... |
"""
Euler deconvolution methods for potential fields.
**Implementations**
* :class:`~fatiando.gravmag.euler.Classic`: The classic 3D solution to Euler's
equation for potential fields (Reid et al., 1990). Runs on the whole dataset.
**Solution selection procedures**
* :class:`~fatiando.gravmag.euler.ExpandingWindow... | {"hexsha": "5aaf66dee3f45b9f24381861c9d5c83ce909e39d", "size": 8902, "ext": "py", "lang": "Python", "max_stars_repo_path": "fatiando/gravmag/euler.py", "max_stars_repo_name": "silky/fatiando", "max_stars_repo_head_hexsha": "5041c6b29758a5e73e9d7b2b906fa5e493fd9aba", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
/*
* Copyright 2021 Assured Information Security, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by a... | {"hexsha": "73fab73a3e7a6b941335aa35d8352965d3828099", "size": 2998, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/windows/libraries/ws2_32/functions/WSASendTo.cc", "max_stars_repo_name": "srpape/IntroVirt", "max_stars_repo_head_hexsha": "fe553221c40b8ef71f06e79c9d54d9e123a06c89", "max_stars_repo_licenses": [... |
import numpy as np
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine, func
import datetime as dt
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_en... | {"hexsha": "0a220d1d87b201fe8a15abc2cc0ac24145bde64c", "size": 4423, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "alalufcan/sqlalchemy-challenge", "max_stars_repo_head_hexsha": "0f0644ef763d67a7013ed0e470c9cbb9ed25890d", "max_stars_repo_licenses": ["ADSL"], "max_stars_count": ... |
# -*- coding: utf-8 -*-
"""
Created on Sun Jun 24 11:45:06 2018
Administrator
"""
import cv2
import numpy as np
o = cv2.imread(r'../image/contours.bmp')
gray = cv2.cvtColor(o,cv2.COLOR_BGR2GRAY)
ret, binary = cv2.threshold(gray,127,255,cv2.THRESH_BINARY)
image,contours, hierarchy = cv2.findContours(binary,cv2.RETR... | {"hexsha": "6f53669ea3a38d8e44f3aa1b99d4c340acc28156", "size": 502, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_simple/13.\u56fe\u50cf\u8f6e\u5ed3/13.1.1\u56fe\u50cf\u8f6e\u5ed3.py", "max_stars_repo_name": "ztfmars/OpenCV_Tutorial", "max_stars_repo_head_hexsha": "2c9cf57f469dfec2aca8356f59f7473e5a506988",... |
# -*- coding: utf-8 -*-
# Author: Hao Chun Chang <changhaochun84@gmail.comm>
#
# LIME: Local Interpretable Model-agnostic Explanations
#
# Ref:
# 1. https://arxiv.org/abs/1602.04938
# 2. https://github.com/marcotcr/lime
from functools import partial
import numpy as np
import scipy as sp
from sklearn.linear_model impo... | {"hexsha": "d767b4ab6bb85f4106dc0205ef8c98f1c9d5bf94", "size": 16511, "ext": "py", "lang": "Python", "max_stars_repo_path": "interpret/LIME.py", "max_stars_repo_name": "haochunchang/explain-ECG-diagnosis", "max_stars_repo_head_hexsha": "6799ad38a71e2392192919d123548bdce5059541", "max_stars_repo_licenses": ["MIT"], "max... |
function _fatal_err(f::Function, gwt::GWTask)
try; f()
catch err
_printerr(err)
_print_eotask(gwt)
_flush()
if is_worker_mode(gwt)
_up_task_status!(gwt, _GW_TASK_ERROR_STATUS)
# TODO: del proc reg
end
exit()
end
end
function _expr_... | {"hexsha": "3a31f92936c76aa6941412885349ab7597cdc9a7", "size": 1727, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Tasks/utils.jl", "max_stars_repo_name": "josePereiro/GitWorkers", "max_stars_repo_head_hexsha": "1228521db61f601ee8a21dcfa2a8b1f2b8517587", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
#!/usr/bin/env python2
"""
bittrex_autotrader.py
Bittrex currency exchange autotrading script in a nutshell.
Copyright 2017, Marc S. Brooks (https://mbrooks.info)
Licensed under the MIT license:
http://www.opensource.org/licenses/mit-license.php
Dependencies:
humanfriendly
numpy
requests
N... | {"hexsha": "f97cf6938ccf3420e98ab5c4638abd63ebf13d8d", "size": 25492, "ext": "py", "lang": "Python", "max_stars_repo_path": "bittrex_autotrader.py", "max_stars_repo_name": "Daiwv/bittrex_autotrader", "max_stars_repo_head_hexsha": "d4d6ba78c23e3b6edf2f52bf190077356f0573f0", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import random
from PIL import Image
import torch
from torchvision import transforms
import torch.nn as nn
import numpy as np
class GaussianBlur(object):
"""Blur a single image on CPU.
"""
def __init__(self, kernel_size, sigma_min=0.1, sigma_max=2.0):
radius = kernel_size // 2
kernel_size... | {"hexsha": "c7eeae71679c4e378ec00d45803f2a6593d77b3f", "size": 2453, "ext": "py", "lang": "Python", "max_stars_repo_path": "augment/gaussian_blur.py", "max_stars_repo_name": "merlinarer/scrl", "max_stars_repo_head_hexsha": "f5bc426ed6eef130d44dd3a5609dc0772da59613", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 4 10:58:22 2022
@author: sarangbhagwat
A module with examples of task-based unit process specifications.
"""
import numpy as np
import biosteam as bst
import thermosteam as tmo
from thermosteam import Stream
from biosteam.process_tools import BoundedNumericalSpecifica... | {"hexsha": "eb78becc6d4d6dac6afae08c269b0c4b16708345", "size": 5643, "ext": "py", "lang": "Python", "max_stars_repo_path": "BioSTEAM 2.x.x/biorefineries/make_a_biorefinery/example_process_specifications.py", "max_stars_repo_name": "yoelcortes/Bioindustrial-Complex", "max_stars_repo_head_hexsha": "d39edfec88e443ef7a6221... |
/-
Copyright (c) 2018 Johannes Hölzl. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Johannes Hölzl, Floris van Doorn, Gabriel Ebner, Yury Kudryashov
-/
import data.nat.enat
import order.conditionally_complete_lattice
/-!
# Conditionally complete linear order structur... | {"author": "jjaassoonn", "repo": "projective_space", "sha": "11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce", "save_path": "github-repos/lean/jjaassoonn-projective_space", "path": "github-repos/lean/jjaassoonn-projective_space/projective_space-11fe19fe9d7991a272e7a40be4b6ad9b0c10c7ce/src/data/nat/lattice.lean"} |
import numpy as np
import pytest
from imitation.data import types
from imitation.data.buffer import Buffer, ReplayBuffer
def _fill_chunk(start, chunk_len, sample_shape, dtype=np.float):
fill_vals = np.arange(start, start + chunk_len, dtype=dtype)
fill_vals = np.reshape(fill_vals, (-1,) + (1,) * len(sample_sh... | {"hexsha": "655f5f4af9c7a3d7c7cdb210129c9fa52c714af6", "size": 8946, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_buffer.py", "max_stars_repo_name": "bbrito/imitation-1", "max_stars_repo_head_hexsha": "c1c1f01ce7a141a501238adcdffa73fe8acceb73", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
import re
import sys
import nltk
import string
import math
import numpy as np
import seaborn as sns
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import sklearn
from sklearn.model_selection import cross_val_predict, KFold
from sklearn.metrics import classifi... | {"hexsha": "29166acfe8a979fbf9d4968f127d1f1c8b6ed6dc", "size": 15139, "ext": "py", "lang": "Python", "max_stars_repo_path": "AutomaticStructuring/CRF Baseline/CRF_training_testing_baseline.py", "max_stars_repo_name": "ShreyasiPathak/AutomaticStructuringBreastCancerReports", "max_stars_repo_head_hexsha": "a7e109b515e99f... |
import numpy as np
import copy
from scipy.stats import entropy
from scipy.special import logsumexp
from numpy.random import choice
from scipy.special import softmax
#from scipy.stats import entropy
#import LinkedList as LL
#class LinkedList(object):
# def __init__(self, head=None):
# self.head = head
... | {"hexsha": "e05d70f3991a5597e03a64e9351014fcca7d8279", "size": 13342, "ext": "py", "lang": "Python", "max_stars_repo_path": "figure5/MethodsTrueData_M.py", "max_stars_repo_name": "QianLab/WMOCU_AL", "max_stars_repo_head_hexsha": "b8f095366e731a730ba6e043a7fa57ef66510a33", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Automatically adapted for numpy.oldnumeric Jul 23, 2007 by
# taken from Pmv/measureCommands.py
def torsion(x1, x2, x3, x4):
"""
Compute the torsion angle between x1, x2, x3, x4.
All coordinates are cartesian; result is in degrees.
Raises a ValueError if angle is not defined.
"""
from math i... | {"hexsha": "a4a12df7bddd6e774632ca4a99d6ee9a98ece2cb", "size": 1474, "ext": "py", "lang": "Python", "max_stars_repo_path": "mgl_tools/mglutil/math/torsion.py", "max_stars_repo_name": "mesoscope/cellpack", "max_stars_repo_head_hexsha": "ec6b736fc706c1fae16392befa814b5337a3a692", "max_stars_repo_licenses": ["MIT"], "max_... |
[STATEMENT]
lemma tm_ntcf_arrows_in_cf_arrows[cat_map_cs_intros]:
assumes "\<NN> \<in>\<^sub>\<circ> tm_ntcf_arrows \<alpha> \<AA> \<BB>"
shows "\<NN> \<in>\<^sub>\<circ> ntcf_arrows \<alpha> \<AA> \<BB>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<NN> \<in>\<^sub>\<circ> ntcf_arrows \<alpha> \<AA> \<BB>
[P... | {"llama_tokens": 308, "file": "CZH_Elementary_Categories_czh_ecategories_CZH_DG_FUNCT", "length": 2} |
# -*- coding: utf-8 -*-
"""
Collection of tools for writing formatted text to files.
"""
import numpy as np
from pyyeti import ytools
def getith(i, args, fncs):
"""
Return list with i'th value from each input, typically called by
:func:`vecwrite`.
Parameters
----------
i : integer
Sp... | {"hexsha": "0c2ac1e16c3a2e2c73f049eb0c91be9038465e23", "size": 12367, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyyeti/writer.py", "max_stars_repo_name": "twmacro/pyye", "max_stars_repo_head_hexsha": "c4febd44be836bd87368da13c1fb0cf82838b687", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count":... |
#! /usr/bin/env python
from operator import is_
from random import randint
import gym
from gym import spaces
from gym.spaces import space
from typing import Union
from stable_baselines3.common.env_checker import check_env
import yaml
from rl_agent.utils.observation_collector import ObservationCollector
from rl_agent.ut... | {"hexsha": "2f39a885692e51d3a1deb63bedba8645800c4703", "size": 7741, "ext": "py", "lang": "Python", "max_stars_repo_path": "navigation/arena_local_planner/learning_based/arena_local_planner_drl/rl_agent/envs/flatland_gym_env.py", "max_stars_repo_name": "kilinmao/sarl_star", "max_stars_repo_head_hexsha": "dde9bb2b690c70... |
"""
see: get_sorted_connected_regions
input a 3D mask numpy array, output a dict, with key 1, 2, 3, ... (int), which conforms to the ranking of the volume of
the connected component. The value of the dict is lists of locations like {1: [(x1, y1, z1), (x2, y2, z2), ...], ...}
"""
import numpy as np
import Tool_Functions... | {"hexsha": "523fe12cd2f7be941300025c3595ab62035390bc", "size": 24271, "ext": "py", "lang": "Python", "max_stars_repo_path": "Analysis/connect_region_detect.py", "max_stars_repo_name": "LongxiZhou/DLPE-method", "max_stars_repo_head_hexsha": "ed20abc91e27423c7ff677a009cfd99314730217", "max_stars_repo_licenses": ["BSD-3-C... |
import sys
count = 0;
total = 0
data = sys.stdin.read().splitlines()
for i in range(len(data)) :
val = int(data[i],16)
if (val >= 555555 and val <= 777777) :
total += 1
count += 1
print(float(total/count))
#print(val)
#print(type(val))
#print(count)
#print(total)
#print(val)
#print(data[0])
"""
i... | {"hexsha": "b2123c225bea4aedea7ae6132698b1894870f2d6", "size": 1985, "ext": "py", "lang": "Python", "max_stars_repo_path": "unsorted.py", "max_stars_repo_name": "TotallyNotTito/concurrency", "max_stars_repo_head_hexsha": "151bbabe4daac1b1e72e75fdbe2724d0863cc4a9", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
"""
Given annotations, segment images to extract lines, words, and letters.
"""
# Copyright (c) 2017 Ben Zimmer. All rights reserved.
import cv2
import numpy as np
from handwriting import geom
VISUALIZE = False
def extract_line_image(line, image, px_above, px_below):
"""extract line annotation image slic... | {"hexsha": "b954e5b28c4a01dd605113033f6e1ae7caaf02fe", "size": 2551, "ext": "py", "lang": "Python", "max_stars_repo_path": "handwriting/extract.py", "max_stars_repo_name": "bdzimmer/handwriting", "max_stars_repo_head_hexsha": "eaa22c12a7a3210bbbee67b3d35ab0bb50bb38ad", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_... |
"""
Created on Sun Feb 2 13:28:48 2020
@author: matias
"""
import numpy as np
from scipy.interpolate import interp1d
from scipy.constants import c as c_luz #metros/segundos
c_luz_km = c_luz/1000
import sys
import os
from os.path import join as osjoin
from pc_path import definir_path
path_git, path_datos_global = def... | {"hexsha": "7a93ceb60ea7904e68011f4548b0bf2c1931a204", "size": 4820, "ext": "py", "lang": "Python", "max_stars_repo_path": "Software/Funcionales/funciones_alternativos_odintsov.py", "max_stars_repo_name": "matiasleize/tesis_licenciatura", "max_stars_repo_head_hexsha": "5df6e341314583702b466b8ed7977d410f0ee457", "max_st... |
using SparseArrays
using LinearAlgebra
using UnicodePlots
include("diagonal_sbp.jl")
# flatten tuples to arrays
flatten_tuples(x) = reshape(collect(Iterators.flatten(x)), length(x[1]),
length(x))
⊗(A,B) = kron(A, B)
const BC_DIRICHLET = 1
const BC_NEUMANN = 2
const BC_LOC... | {"hexsha": "f98678ee3621e376107a90ba196d3a574d32a0af", "size": 29954, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "global_curved.jl", "max_stars_repo_name": "brittany-erickson/HybridSBP", "max_stars_repo_head_hexsha": "29dc9fe084c561ea0c4007e5097baf456fd86462", "max_stars_repo_licenses": ["Unlicense"], "max_st... |
from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import cv2
import imageio
import utils
import numpy as np
import pickle
import scipy.misc
import skimage
import skimage.io
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.opt... | {"hexsha": "400b81c22567e5ff7eac76f589dc30d9f4ea62ab", "size": 22431, "ext": "py", "lang": "Python", "max_stars_repo_path": "references/video_classification/test_mem_online.py", "max_stars_repo_name": "ajabri/vision", "max_stars_repo_head_hexsha": "f69847cc761937891be77e1780bc4ddea46dbe68", "max_stars_repo_licenses": [... |
# Packages
import pandas as pd
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
from pprint import pprint
import argparse
# Functions
def plotHeatmap(data, fname, xlabs, ylabs):
cm = "hot_r"
fig = plt.figure(figsize=(3*len(xlabs), len(ylabs)))
sns.set(... | {"hexsha": "8baaff9ed23d7a12dc3625e93b607f2ef7e794f8", "size": 4012, "ext": "py", "lang": "Python", "max_stars_repo_path": "plotLDSC_2.py", "max_stars_repo_name": "mwoodbri/ldsc_wdl", "max_stars_repo_head_hexsha": "b9497b9cdd23c990f8003adde368e852d2349a0c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
#! /usr/bin/env python3
#
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | {"hexsha": "753fbb9d1d798b0d0862d6ee620ff4357d93d8fd", "size": 5729, "ext": "py", "lang": "Python", "max_stars_repo_path": "difflogic/envs/graph/graph.py", "max_stars_repo_name": "isabella232/neural-logic-machines", "max_stars_repo_head_hexsha": "3f8a8966c54d13d2658c77c03793a9a98a283e22", "max_stars_repo_licenses": ["A... |
//
// Copyright (c) 2016-2019 Vinnie Falco (vinnie dot falco at gmail dot com)
//
// Distributed under the Boost Software License, Version 1.0. (See accompanying
// file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
//
// Official repository: https://github.com/boostorg/beast
//
// Test that header ... | {"hexsha": "21a5fecb415781caacf6de3e7dc55606e330557c", "size": 1867, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/beast/core/dynamic_buffer_ref.cpp", "max_stars_repo_name": "DokuEnterprise/boost", "max_stars_repo_head_hexsha": "f2dc8be896eabd5f5da18d3b78abec2f5a04d4e5", "max_stars_repo_licenses": ["BSL-1.0... |
module Niancat
include("Users.jl")
include("Http.jl")
include("Gameface.jl")
include("Languages.jl")
include("Formatters.jl")
include("Persistence.jl")
include("Scores.jl")
include("GameServiceImpl.jl")
include("Instances.jl")
module Games
include("Games/NiancatGames.jl")
end
include("service.jl")
end # module
| {"hexsha": "9d2db918d0aeb5ad5c894e44c8b6394ab41be900", "size": 316, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Niancat.jl", "max_stars_repo_name": "erikedin/Niancat.jl", "max_stars_repo_head_hexsha": "e5c94bbbc957dae1a9896dadf285cd7e900dde7b", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count":... |
import numpy as np
from time import clock
from pygeonet_rasterio import *
from pygeonet_plot import *
# Skeleton by thresholding one grid measure e.g. flow or curvature
def compute_skeleton_by_single_threshold(inputArray, threshold):
skeletonArray = np.zeros((inputArray.shape))
skeletonArray[np.where... | {"hexsha": "d54f14f065df4435923284eb08dcf3efe8981afa", "size": 3451, "ext": "py", "lang": "Python", "max_stars_repo_path": "pygeonet_skeleton_definition.py", "max_stars_repo_name": "uva-hydroinformatics/wetland_identification", "max_stars_repo_head_hexsha": "21b797eec1f4babe5c4fb53441bc256385dc2094", "max_stars_repo_li... |
(* This file is generated by Why3's Coq driver *)
(* Beware! Only edit allowed sections below *)
Require Import BuiltIn.
Require BuiltIn.
Require HighOrd.
Require int.Int.
Require int.Abs.
Require int.MinMax.
Require int.EuclideanDivision.
Require real.Real.
Require map.Map.
Axiom set : forall (a:Type), Type.
Param... | {"author": "Heim-AI", "repo": "qbricks.github.io", "sha": "0aed52b81eab137cb3dde15d02ed59a36e532247", "save_path": "github-repos/coq/Heim-AI-qbricks.github.io", "path": "github-repos/coq/Heim-AI-qbricks.github.io/qbricks.github.io-0aed52b81eab137cb3dde15d02ed59a36e532247/Case_studies/deutsch-jozsa/deutschmnjozsa_Deutsc... |
# -*- coding: utf-8 -*-
r"""
A Francy Widget for the Jupyter Notebook.
AUTHORS ::
Odile Bénassy
"""
from ipywidgets import register
from ipywidgets.widgets.widget_string import Text
from traitlets import Any
try:
from .francy_adapter import FrancyAdapter
except:
from francy_adapter import FrancyAdapter #... | {"hexsha": "5bf9c5a3f19420b36edc7a550ed4ad333ca97e21", "size": 7370, "ext": "py", "lang": "Python", "max_stars_repo_path": "francy_widget/francy_widget.py", "max_stars_repo_name": "zerline/sage-francy", "max_stars_repo_head_hexsha": "876419b73788e2e635bea2c2d4c49866e6cf8fc4", "max_stars_repo_licenses": ["BSL-1.0"], "ma... |
import torch
import random
import numpy as np
class GeneratorData(object):
def __init__(self, training_data_path, tokens=None, start_token='<',
end_token='>', max_len=120, use_cuda=None, **kwargs):
super(GeneratorData, self).__init__()
if 'cols_to_read' not in kwargs:
... | {"hexsha": "feae7a81cc113cda5939af74bee054c649a7d587", "size": 4037, "ext": "py", "lang": "Python", "max_stars_repo_path": "ml_models/files/util_funcs.py", "max_stars_repo_name": "joeym-09/Synbiolic", "max_stars_repo_head_hexsha": "8a81e362b40b8497124318b42439df8c0e560adf", "max_stars_repo_licenses": ["MIT"], "max_star... |
"""
Utility functions related to concat
"""
import numpy as np
import pandas.core.common as com
import pandas.tslib as tslib
from pandas import compat
from pandas.compat import map
def get_dtype_kinds(l):
"""
Parameters
----------
l : list of arrays
Returns
-------
a set of kinds that ex... | {"hexsha": "53db9ddf79a5c277d004ba0f8f22512c4bc9949a", "size": 12481, "ext": "py", "lang": "Python", "max_stars_repo_path": "pandas/types/concat.py", "max_stars_repo_name": "ranaroussi/pandas", "max_stars_repo_head_hexsha": "8f8d75d315f5a61bbf2060ff821bff9b282a1582", "max_stars_repo_licenses": ["PSF-2.0", "Apache-2.0",... |
// Copyright (c) 2019 Graphcore Ltd. All rights reserved.
#define BOOST_TEST_MODULE ScatterTest
#include <poplibs_support/TestDevice.hpp>
#include <iostream>
#include <boost/test/unit_test.hpp>
#include <poplar/Engine.hpp>
#include <popops/ElementWise.hpp>
#include <popops/Scatter.hpp>
#include <popops/codelets.hpp>... | {"hexsha": "6665f3b1ec124d81c8051e4c6421b5a652432460", "size": 3128, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/popops/ScatterUpdateTest.cpp", "max_stars_repo_name": "graphcore/poplibs", "max_stars_repo_head_hexsha": "3fe5a3ecafe995eddb72675d1b4a7af8a622009e", "max_stars_repo_licenses": ["MIT"], "max_st... |
// Copyright (c) 2018-present Baidu, Inc. All Rights Reserved.
//
// 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 require... | {"hexsha": "80c7a81cebe88680b1c2df0722aee29b62599f9c", "size": 47670, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/meta_server/cluster_manager.cpp", "max_stars_repo_name": "lvxinup/BaikalDB", "max_stars_repo_head_hexsha": "e1ca1e273156e64fd0c3aef047d6d9b639be0c7b", "max_stars_repo_licenses": ["Apache-2.0"],... |
# Create by Packetsss
# Personal use is allowed
# Commercial use is prohibited
r"""
USAGE:
python .\utils\plot.py --log-dir "path/to/log
"""
import argparse
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from typing import Callable, List, Optional, Tuple
from stable_baselines3.common.mo... | {"hexsha": "c7e5a6480f073f80efdc6a9b52328dd3861c8425", "size": 5942, "ext": "py", "lang": "Python", "max_stars_repo_path": "Python Tutorial Reinforcement Learning/9_muzero/games/utils/plot.py", "max_stars_repo_name": "PaulPan00/donkey_wrapper", "max_stars_repo_head_hexsha": "a03cf0f42f65625fbce792b06c98acd153c5d6c8", "... |
/-
Copyright (c) 2021 Eric Wieser. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Eric Wieser
-/
import data.set.pointwise.smul
import group_theory.submonoid.membership
import order.well_founded_set
/-! # Pointwise instances on `submonoid`s and `add_submonoid`s
> THI... | {"author": "leanprover-community", "repo": "mathlib", "sha": "5e526d18cea33550268dcbbddcb822d5cde40654", "save_path": "github-repos/lean/leanprover-community-mathlib", "path": "github-repos/lean/leanprover-community-mathlib/mathlib-5e526d18cea33550268dcbbddcb822d5cde40654/src/group_theory/submonoid/pointwise.lean"} |
[STATEMENT]
lemma aform_val_msum_aform:
assumes "degree_aform f \<le> n"
shows "aform_val e (msum_aform n f g) = aform_val e f + aform_val (\<lambda>i. e (i + n)) g"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. aform_val e (msum_aform n f g) = aform_val e f + aform_val (\<lambda>i. e (i + n)) g
[PROOF STEP]
us... | {"llama_tokens": 263, "file": "Affine_Arithmetic_Affine_Form", "length": 2} |
'''
We replicate the structure of an MA(q) model with a neural network here,
simulate a simple AR(1) processs and fit said model to it.
'''
from __future__ import print_function
import numpy as np
from neuralforecast.models import NeuralMA
from neuralforecast.data_generator import arma_sample
np.random.seed(337)
sam... | {"hexsha": "74c468371a3d430353e7110282f7ae34879814ed", "size": 508, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/ma.py", "max_stars_repo_name": "maxpumperla/neuralforecast", "max_stars_repo_head_hexsha": "61d730a28454f89843defb823430daee2ff64912", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 20 22:02:27 2020
@author: Yuri
Frequcnsies in [radians/s]
"""
import numpy as np
import math
from math import pi
from math import tan
from math import sin
def timetofreqspectrumFun(Wpars, Freqs, modelting, Pars):
# y = np.zeros([len(Freqs),2])
... | {"hexsha": "12f52d0e81da62647612ecf92314e98ff16934d7", "size": 2237, "ext": "py", "lang": "Python", "max_stars_repo_path": "timetofreqspectrumFun.py", "max_stars_repo_name": "yu-efremov/ViscoIndent", "max_stars_repo_head_hexsha": "b9658509fdf9ed232cf682cb554f668572735e4d", "max_stars_repo_licenses": ["MIT"], "max_stars... |
import data.nat.basic
import .ch01_basics
open basics (evenb oddb sub_two leb)
open nat (add mul)
namespace poly
/-
Inductive boollist : Type :=
| bool_nil
| bool_cons (b : bool) (l : boollist).
-/
inductive boollist : Type
| bool_nil
| bool_cons (b : bool) (l : boollist)
/-
Inductive list (X:Type) : Type :=
... | {"author": "michens", "repo": "learn-lean", "sha": "f38fc342780ddff5a164a18e5482163dea506ccd", "save_path": "github-repos/lean/michens-learn-lean", "path": "github-repos/lean/michens-learn-lean/learn-lean-f38fc342780ddff5a164a18e5482163dea506ccd/sf/v1/ch04_poly.lean"} |
"""Summary
"""
import os
import yaml
import getopt
import sys
import time
import numpy as np
from tensorflow.keras import backend as K
from action_predict_attention import action_prediction
# from new_model import NewModel, HybridModel, MultiRNN3D, MultiRNN3D_MATT
from jaad_data import JAAD
# if use PIE data:
from pi... | {"hexsha": "366a36ed3a4af883221962fa01cc1641c6ee9918", "size": 7896, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_test_attention.py", "max_stars_repo_name": "sgt390/Pedestrian_Crossing_Intention_Prediction", "max_stars_repo_head_hexsha": "ef1a956ae603b8d042253a225744a88bf07c30a4", "max_stars_repo_licens... |
"""
Author: Shraey Bhatia
Date: October 2016
File: train_svm_model.py
This file is to generate the trained svm model. You can specify your own datset. By default
will take our dataset. Note a trained svm model for our datset is already in place in model_run/support_files.
You will need bin... | {"hexsha": "c4b1f2d9f4c52c5520675cde0e755054080e01b6", "size": 9060, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_svm_model.py", "max_stars_repo_name": "wpfjtmwls/CookieMonster", "max_stars_repo_head_hexsha": "2215d20d4fcf9482d988eb89dbb4d998794aae7b", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import pytest
import numpy as np
import numpy.testing as npt
import toppra as ta
import toppra.constraint as constraint
from toppra.constants import JACC_MAXU
@pytest.fixture(params=[1, 2, 6], name='acceleration_pc_data')
def create_acceleration_pc_fixtures(request):
""" Parameterized Acceleration path constraint... | {"hexsha": "c0621f529421cb16b4eb7d116b58c8dc2538902b", "size": 2945, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/constraint/test_joint_acceleration.py", "max_stars_repo_name": "mrunaljsarvaiya/toppra", "max_stars_repo_head_hexsha": "468d81d23f37fdee06ea1aa3f1d445d747fad7f6", "max_stars_repo_licenses": ... |
/*
* Copyright (C) 2019, Xilinx Inc - All rights reserved
* Xilinx Resource Manger U30 Encoder Plugin
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may
* not use this file except in compliance with the License. A copy of the
* License is ... | {"hexsha": "ba6424bda07a81ce50e790d5c8462ec4cccecc1b", "size": 2474, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "codec-encoder-xrm-plg-u30.hpp", "max_stars_repo_name": "Xilinx/codec-encoder-xrm-plg-u30", "max_stars_repo_head_hexsha": "fcba0e2ca5125760212f2a22b3039911d7a61c15", "max_stars_repo_licenses": ["Apac... |
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
from torch.nn import functional as F
from Relation_batch import *
# Recurrent neural network (many-to-many)
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, num_classes, batch=4, df=64, dk=32, Nr... | {"hexsha": "7bc1a789f4f3bce72d2767d42a0cfd1f20593745", "size": 5697, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/model_ST.py", "max_stars_repo_name": "weiyi1991/UA_Concurrent", "max_stars_repo_head_hexsha": "11238c778c60095abf326800d6e6a13a643bf071", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from vbhmm.hmm.vbhmm import VBHMM
import numpy as np
import matplotlib.pyplot as plt
from time import time
import sys
sys.path.append('.')
# data = np.load('vbhmm/data/hmm_data.npz')
# labels, obs = data['labels'], data['obs']
# obs = [obs]
data = np.load('vbhmm/data/params.npz', allow_pickle=True)
sigs, mus, obs, la... | {"hexsha": "bf377b730e709f5b1e838c3a023ce3a7b9c6aa69", "size": 1439, "ext": "py", "lang": "Python", "max_stars_repo_path": "sds_bayesian_numpy/tests/test_vbhmm.py", "max_stars_repo_name": "thlautenschlaeger/sds", "max_stars_repo_head_hexsha": "710e7c6b1b15afd0476b6f0f60c03415571bebeb", "max_stars_repo_licenses": ["MIT"... |
C
C
real*4 function ct_standard( rgb, ci, cimin, cimax )
C ----------------------------------------------------
C
C Function defining an standard colour-scale.
C
C defining:
C x = (ci-cimin)/(cimax-cimin)
C then:
C red = sin(power(red)*x)**2,
C green = sin(power(g... | {"hexsha": "4326ebb85a638053a72fcba507d68c19a24cd188", "size": 606, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "graphic_lib/ct_standard.f", "max_stars_repo_name": "CavendishAstrophysics/anmap", "max_stars_repo_head_hexsha": "efb611d7f80a3d14dc55e46cd01e8a622f6fd294", "max_stars_repo_licenses": ["BSD-3-Clause... |
import numpy as np
import pandas as pd
# adding path to GPR_for_IM directory in order to import relevant scripts
import sys
sys.path.append('../')
import pk_tools as pk
def test_pk_perp_run():
#### Loading data ####
data = pd.read_pickle('../Data/data.pkl')
HI_data = data.beam.HI
#### ... | {"hexsha": "bdb2e5e73e748a671d15ad7c1db53e29c5abd225", "size": 1326, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_pk_perp.py", "max_stars_repo_name": "LBJ-Wade/gpr4im", "max_stars_repo_head_hexsha": "c1ea849b4af7807cd3928b86b3692bd272e6f7d3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4... |
# Note that this will erase your nvidia cache, ~/.nv/ComputeCache This may or may not be an undesirable side-effect for you. For example, cutorch will take 1-2 minutes or so to start after this cache has been emptied.
from __future__ import print_function, division
import time
import string
import random
import nump... | {"hexsha": "a74e6b79d71bd419696ad6fe959e8732e782e60d", "size": 6967, "ext": "py", "lang": "Python", "max_stars_repo_path": "gpuexperiments/old/optimization_shortcutting.py", "max_stars_repo_name": "hughperkins/gpu-experiments", "max_stars_repo_head_hexsha": "3e5064e45682494be97190558807672b602f1c76", "max_stars_repo_li... |
using Performance
using BenchmarkTools
using Test
@testset "structs" begin
##
## https://docs.julialang.org/en/v1/manual/performance-tips/#Type-declarations
##
# Unfortunately, this problem is not Revise-friendly. You'll have to restart your
# Julia session when you make substantive changes to the ... | {"hexsha": "756ecfb5c685663d4469de16d8530f98af7357d7", "size": 1376, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/structs.jl", "max_stars_repo_name": "nlaird/Performance.jl", "max_stars_repo_head_hexsha": "16b222fd1df7c5055ed6285377cca91aefb5a75b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 24... |
import numpy as np
from .VariableUnitTest import VariableUnitTest
from gwlfe import enums
from gwlfe.Input.WaterBudget import Grow
class TestGrow(VariableUnitTest):
def test_Grow(self):
z = self.z
np.testing.assert_array_almost_equal(
Grow.Grow_f(z.Grow_0),
Grow.Grow(z.Gr... | {"hexsha": "07fac60a1108b0531ebcae83d1c68792a9d88d66", "size": 362, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/unittests/test_Grow.py", "max_stars_repo_name": "rajadain/gwlf-e", "max_stars_repo_head_hexsha": "ba2fb9dbc08a3d7a4ced4b83b6f0f1307814e2a3", "max_stars_repo_licenses": ["Apache-2.0"], "max_sta... |
import socket
import cv2
import numpy as np
soc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)#ソケットオブジェクト作成
soc.connect(("127.0.0.1", 9001))#サーバー側のipと使用するポート(ポートはサーバーと同じにする。)
print("接続完了")
while(1):
data = soc.recv(921600)#引数は下記注意点参照
data = np.fromstring(data, dtype=np.uint8)#バイトデータ→ndarray変換
dat... | {"hexsha": "2cef898029950deee6b17cd7778cdbe152859009", "size": 598, "ext": "py", "lang": "Python", "max_stars_repo_path": "sample/imageClient.py", "max_stars_repo_name": "jettom/JtSpider", "max_stars_repo_head_hexsha": "7e2cb32415ca5d439b117c0277a7f7b2b27fa0bf", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
module SemiLattice where
open import Prelude
open import PartialOrder as PO
import Chain
private
module IsSemiLat
{A : Set}(po : PartialOrder A)(_⊓_ : A -> A -> A) where
private open module PO = PartialOrder po
record IsSemiLattice : Set where
field
⊓-lbL : forall {x y} -> (x ⊓ y) ≤ x
⊓-lb... | {"hexsha": "1b05377a3f6d6dbe285aa9616964f13937efaea6", "size": 2992, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "examples/outdated-and-incorrect/lattice/SemiLattice.agda", "max_stars_repo_name": "asr/agda-kanso", "max_stars_repo_head_hexsha": "aa10ae6a29dc79964fe9dec2de07b9df28b61ed5", "max_stars_repo_licens... |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from compas.geometry import Frame
from compas.geometry import Transformation
from pytest import fixture
from pytest import raises
from compas_mobile_robot_reloc.xforms import _coerce_cg_xform
from compas_mobil... | {"hexsha": "eb4536a35751d2640337c692452d427542fd411b", "size": 2088, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_xforms.py", "max_stars_repo_name": "gramaziokohler/total_station_robot_localization", "max_stars_repo_head_hexsha": "aa4baa070d4d93a8058a0286615580ed8f95567f", "max_stars_repo_licenses"... |
[STATEMENT]
lemma fpxs_val_compose_power [simp]:
assumes "r > 0"
shows "fpxs_val (fpxs_compose_power f r) = fpxs_val f * r"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. fpxs_val (fpxs_compose_power f r) = fpxs_val f * r
[PROOF STEP]
proof (cases "f = 0")
[PROOF STATE]
proof (state)
goal (2 subgoals):
1. f =... | {"llama_tokens": 1507, "file": "Formal_Puiseux_Series_Formal_Puiseux_Series", "length": 16} |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
""" sick, the spectroscopic inference crank. """
__author__ = "Andy Casey <arc@ast.cam.ac.uk>"
import argparse
import cPickle as pickle
import logging
import os
from glob import glob
import numpy as np
import yaml
import json
impo... | {"hexsha": "67876cf2ba5aafdb10785604c89ff72908fd5a7b", "size": 24078, "ext": "py", "lang": "Python", "max_stars_repo_path": "sick/clis/run.py", "max_stars_repo_name": "andycasey/sick", "max_stars_repo_head_hexsha": "6c37686182794c4cafea45abf7062b30b789b1a2", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 8, "ma... |
import logging
from typing import List, Dict, Tuple, Optional, Any
import numpy as np
OUT_RECALL = 0.9
OUT_PRECISION = 0.8
logger = logging.getLogger(__name__)
def span_to_label(tokens: List[str],
labeled_spans: Dict[Tuple[int, int], Any]) -> List[str]:
"""
Convert label spans to
:par... | {"hexsha": "a329702d3804a4ae4099468f2112006961098f28", "size": 7968, "ext": "py", "lang": "Python", "max_stars_repo_path": "wrench/seq_labelmodel/chmm_src/Src/DataAssist.py", "max_stars_repo_name": "Stranger469/ARS2", "max_stars_repo_head_hexsha": "b6d62b66997180abe01b676d5359c20daa42b7ad", "max_stars_repo_licenses": [... |
import numpy as np
class MeanShift:
def __init__(self, radius=2):
self.radius = radius
def fit(self, data):
centroids = self.initialize_starting_centroids(data)
self.centroids = self.make_centroids(centroids, data)
def initialize_starting_centroids(self, data):
centroids ... | {"hexsha": "3da7fc4300dabd09ec4c470043ea127780e60a3b", "size": 2450, "ext": "py", "lang": "Python", "max_stars_repo_path": "EyePatterns/clustering_algorithms/custom_mean_shift.py", "max_stars_repo_name": "Sale1996/Pattern-detection-of-eye-tracking-scanpaths", "max_stars_repo_head_hexsha": "15c832f26dce98bb95445f9f39f45... |
\section{Blockchain}\label{sec:blockchain}
Recently, cryptocurrency has attracted extensive attention from both the industry and the academy. Bitcoin, often called the first cryptocurrency, had massive success, with the capital market coming to \$ 10 billion in 2016 \cite{coindesk}. Blockchain is the central mechanism... | {"hexsha": "9e273b7131db099296131a5ea0d6f68b19392364", "size": 6665, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "chapters/2_THEORETICAL_BACKGROUND/sections/3_blockchain.tex", "max_stars_repo_name": "juniorug/Master-thesis", "max_stars_repo_head_hexsha": "c7e7e1da620ea717619c058c170db46cee8e9970", "max_stars_re... |
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
from __future__ import division
import numpy as np
from testing import *
from pyecs import *
from pycompupipe.other import Event
from pycompupipe.components import (
GuiManager,GuiElement,
Selectable, FetchMouseCallbacksWhileSelected,
... | {"hexsha": "d79ae7298f57b746be26a896aacc84e656c77dc0", "size": 2826, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/components/gui/interaction/test_draggable.py", "max_stars_repo_name": "xaedes/PyCompuPipe", "max_stars_repo_head_hexsha": "c5243a875bb007fc67b02e1ed08b1d62b6ddc483", "max_stars_repo_licenses... |
/**
* @copyright Copyright 2018 The J-PET Framework Authors. All rights reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may find a copy of the License in the LICENCE file.
*
* Unless required by applicable la... | {"hexsha": "499cde6a5c66bda7897b0aeccb7736e32e520ff5", "size": 5996, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "tests/Core/JPetGeomMapping/JPetGeomMappingTest.cpp", "max_stars_repo_name": "BlurredChoise/j-pet-framework", "max_stars_repo_head_hexsha": "f6728e027fae2b6ac0bdf274141254689894aa08", "max_stars_repo... |
import json
import pickle
import fire
import matplotlib.image as mpimg
import numpy as np
from cidan.LSSC.functions.data_manipulation import load_tif_stack, \
reshape_to_2d_over_time
from cidan.TimeTrace.spatial_temporal_denoising import calculate_spatial_time_denoising
from plotting_functions import time_graph
... | {"hexsha": "d8ec230b31f6f84f10c2433358cc3757a9d2645a", "size": 3795, "ext": "py", "lang": "Python", "max_stars_repo_path": "explorations/time_trace_extract_test.py", "max_stars_repo_name": "Mishne-Lab/cidan", "max_stars_repo_head_hexsha": "3f579b6d5a49e17690e9aa07dfb60d3e8c05e681", "max_stars_repo_licenses": ["MIT"], "... |
""" file management, copying etc. """
import inspect
import json
import os
from shutil import copyfile
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from stable_baselines3.common.monitor import load_results
from .utils import plot_curves, ts2xy
def write_env_config(destination_directory, vec_... | {"hexsha": "5bbc9bb822f6005406b9ea69ac9af3d687e2f443", "size": 4950, "ext": "py", "lang": "Python", "max_stars_repo_path": "quadruped_spring/utils/file_utils.py", "max_stars_repo_name": "francescovezzi/quadruped_spring", "max_stars_repo_head_hexsha": "23848496ac7a4508e8a0f527e961c7956fd12f95", "max_stars_repo_licenses"... |
"""
This code is a extension version of KNNGraph + SEAL, which aims to incorporate metric learning to compute the
distance while constructing the KNN Graph.
"""
import torch
import torch_geometric.nn
from torch.nn import BCEWithLogitsLoss
import torch.nn.functional as F
from torch_geometric import seed_everythi... | {"hexsha": "c7a1fbb29dda1afa1e3e96778dc4252ada0fcefc", "size": 13839, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/MLKG+SEAL.py", "max_stars_repo_name": "BishengLi0327/Topology-Restoration-Based-on-GNN", "max_stars_repo_head_hexsha": "99ede7abbab1ce06f094d79ad18e58f30fc7fc4b", "max_stars_repo_licenses": ... |
#redirect Howard Johnson Hotel
| {"hexsha": "e971b23d852c380d0a4014e9824726e41d1e2fc7", "size": 31, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Howard_Johnson_Inn.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import logging
import numpy as np
import reagent.core.types as rlt
import torch
import torch.nn as nn
from reagent.core.dataclasses import field
from reagent.model_utils.seq2slate_utils import Seq2SlateMode
from reagent.mode... | {"hexsha": "bb160253d88f19e38a1dbf160a3d6d513b1143b7", "size": 6103, "ext": "py", "lang": "Python", "max_stars_repo_path": "reagent/training/ranking/seq2slate_attn_trainer.py", "max_stars_repo_name": "dmitryvinn/ReAgent", "max_stars_repo_head_hexsha": "f98825b9d021ec353a1f9087840a05fea259bf42", "max_stars_repo_licenses... |
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 19 19:16:33 2018
@author: solis
Funciones que realizan en análisis de una serie temporal (piezométrica) en
puntos que tienen definidos umbrales de referencia
"""
import log_file as lf
FLOAT_PRECISION = 'float64'
file_xml_ini = 'piezo_CTH.xml'
def ... | {"hexsha": "878749daf60cca15d211b55236d92ef40afb5681", "size": 10176, "ext": "py", "lang": "Python", "max_stars_repo_path": "piezo_CTH.py", "max_stars_repo_name": "solisgb/PIEZO-THRESHOLDS", "max_stars_repo_head_hexsha": "2c7c5f296a8c58b8e36c93940774e7626f96664e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
using DataFrames
using KernelDensity
KWArgT = Union{Dict, NamedTuple, Nothing}
update_args(args::Union{Dict, NamedTuple}, update::Nothing) = args
update_args(args::Union{Dict, NamedTuple}, update::Union{Dict, NamedTuple}) =
merge([Dict{Symbol, Any}(zip(keys(d), values(d))) for d in (args, update)]...)
estimate_d... | {"hexsha": "0be7dc376bb541fafa53082ea4fa9d9c42ba3e81", "size": 10556, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/utils/utils.jl", "max_stars_repo_name": "YinuoJin/Baysor", "max_stars_repo_head_hexsha": "a7ee9bd8e38c6c572dcd20f0dc0bbd6591c87d37", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null... |
from hydroDL import kPath, utils
from hydroDL.app import waterQuality
from hydroDL.master import basins
from hydroDL.data import usgs, gageII, gridMET, ntn
from hydroDL.master import slurm
from hydroDL.post import axplot, figplot
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
codeLst... | {"hexsha": "8621f72b881f76404f35e7b49d0aaeecc013cb63", "size": 2454, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/waterQual/stableSites/sbW/box_WRTDS.py", "max_stars_repo_name": "fkwai/geolearn", "max_stars_repo_head_hexsha": "30cb4353d22af5020a48100d07ab04f465a315b0", "max_stars_repo_licenses": ["MIT"], ... |
#! /usr/bin/env python
import numpy as np
import json,cv2,os,time,glob,copy,re
from color import *
def cv2ver():
major = cv2.__version__.split('.')[0]
return int(major)
class ImageSource:
EmptyData = {
"frames": [],
"input_file": ""
}
EmptyFrame = {
"frame_... | {"hexsha": "e0dd2cc0d901b131fa572cfaee8c71565d83b128", "size": 12336, "ext": "py", "lang": "Python", "max_stars_repo_path": "plugins/flask/annotator/ImageSource.py", "max_stars_repo_name": "readicculus/VIAME", "max_stars_repo_head_hexsha": "3e65fa4ec9139f73f8f9f4b3c0f1b18ba6209733", "max_stars_repo_licenses": ["BSD-3-C... |
'''
Created on Jun 12, 2013
Modelled upon LpdFemDataReceiver.py
@author: ckd27546
'''
from lpd.gui.data_containers import LpdImageContainer, LpdFrameContainer
import os, sys, time, socket
import numpy as np
from PyQt4 import QtCore
# Import HDF5 Library; Disable its use if library not installed on PC
try:
... | {"hexsha": "60744495de15159bac1949bb1b658b41d4e381fc", "size": 20180, "ext": "py", "lang": "Python", "max_stars_repo_path": "app/lpd/tests/redundant_files/uni_receive.py", "max_stars_repo_name": "stfc-aeg/lpd-detector", "max_stars_repo_head_hexsha": "7bc0d9700a3d3992a4541f05e1434aba0d056dcc", "max_stars_repo_licenses":... |
import os
import sys
import re
import contextlib
import numpy as np
import cv2
import easygui
from imgphon import imgphon
def interface(windowName, canvas, tp, frame_index, file_name, result_dict, clicks_list, working_dir, sort_fn, label_num_constr: int, ext):
output_imgs_dir = "output_imgs"
with contextlib... | {"hexsha": "d58bb3b7f9587a8c566562d2b699946903ae2b94", "size": 4119, "ext": "py", "lang": "Python", "max_stars_repo_path": "imgphon/handlabel.py", "max_stars_repo_name": "shunjiewang/imgphon", "max_stars_repo_head_hexsha": "b7d82c1dde39a332191e40dc87e88248ac300b4c", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_sta... |
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#
# Description
# ==============================================================================
#
# This file contains functions reltated to input handling. This code was
# adapted from the on in TextUserInterfaces.jl.
#
# # # # # # # ... | {"hexsha": "fa2df1f752ecc08106db08e3e37cc41f672fa2dc", "size": 4722, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/input/input.jl", "max_stars_repo_name": "TheCedarPrince/TerminalPager.jl", "max_stars_repo_head_hexsha": "a94d727d20310076697ceb7b18c6dccf9e85363f", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Copyright (C) 2020-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
"""Federated averaging module."""
from .interface import AggregationFunctionInterface
import numpy as np
def weighted_average(tensors, weights):
"""Compute average."""
return np.average(tensors, weights=weights, axis=0)
clas... | {"hexsha": "e7ab596b4d5965ab6cfcebaaa076ed76fb25c861", "size": 1058, "ext": "py", "lang": "Python", "max_stars_repo_path": "openfl/component/aggregation_functions/weighted_average.py", "max_stars_repo_name": "walteriviera/openfl", "max_stars_repo_head_hexsha": "1f5d6e7b7131dd519baf51ecdb9ab29de0e9308b", "max_stars_repo... |
import numpy as np
import pandas as pd
import niimpy.preprocessing.survey
df1 = pd.DataFrame(
{"time": ['2019-01-01']*3 + ['2019-01-02']*3 + ['2019-01-03']*3,
"answer": [0, 1, 2, 3, 4, 5, 6, 7, np.nan],
"id": ["S1_1", "S1_2", "S1_3"] * 3,
})
df1['time'] = pd.to_datetime(df1['time'])
d... | {"hexsha": "8c649104623eebd00db9b5a20d04b77faf3637c9", "size": 1263, "ext": "py", "lang": "Python", "max_stars_repo_path": "niimpy/preprocessing/test_survey.py", "max_stars_repo_name": "lnknguyen/niimpy", "max_stars_repo_head_hexsha": "2aebd1d59b0b7562103128d5eaff90f20091f6fd", "max_stars_repo_licenses": ["MIT"], "max_... |
program make_starmask
use fieldinfo_
use sub_
implicit none
! idum: set arbitrary value for initial seed for ran2
integer :: idum=-152103
! inf: input file of shear distribution
! outf: mask field [1:ni,1:nj](real)
! outf2: flag data describing which data is masked or not [1:ntot] (logical*1)... | {"hexsha": "3ad747248029785d7f6ba4ae5137be24ef1d484d", "size": 6549, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "starmask.f90", "max_stars_repo_name": "chiaki-hikage/pseudoCl_flatsky", "max_stars_repo_head_hexsha": "9bcef43a78346d4062756f9798ec0cf66fd61e5f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
from easydict import EasyDict as edict
import numpy as np
__C = edict()
cfg = __C
__C.TRAIN = edict()
__C.IMG_WIDTH = 300
__C.IMG_HEIGHT = 300
__C.IMG_CHANNEL = 3
__C.CLASS_NUM = 21
__C.BACKGROUND_ID = 0
# training settings
__C.TRAIN.LEARNING_RATE = 0.001 / 4
__C.TRAIN.MOMENTUM = 0.9
__C.TRAIN.BATCH_SIZE = 32
__C.T... | {"hexsha": "318b9ae6798be4855b9fdabdee85f690eb3139bc", "size": 2573, "ext": "py", "lang": "Python", "max_stars_repo_path": "Demo/linux/ssd/models/vgg_ssd_net/config/pascal_voc_conf.py", "max_stars_repo_name": "daming-lu/Mobile", "max_stars_repo_head_hexsha": "849dbd3cd9076102d509ef054b8b3da72ed608a2", "max_stars_repo_l... |
@testset "LinkedList" begin
@testset "l1" begin
l = list([1,2,3]...)
@test length(l) == 3
@test head(l) == 1
@test head(tail(l)) == 2
end
end
| {"hexsha": "aa72bfa7b1d23f4f27e868fb6e42b0c40a723057", "size": 184, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_list.jl", "max_stars_repo_name": "swairshah/DataStructs.jl", "max_stars_repo_head_hexsha": "7f4503bd2d2557ff5d6868e93dec3661eba347ea", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
import numpy as np
def join_1d_np():
print("Concetante 1-D Arrays")
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])
print("First Array")
print(arr1)
print("second Array")
print(arr2)
arr = np.concatenate((arr1, arr2))
print("Array after concetenate")
print(arr)
def join_2d... | {"hexsha": "5cb99172774c1e2caca99c5db5d580610e6cf482", "size": 1993, "ext": "py", "lang": "Python", "max_stars_repo_path": "Numpy/10_Join_Numpy.py", "max_stars_repo_name": "ajeyln/W3_School_Python_Coding", "max_stars_repo_head_hexsha": "cfc598d5d0e96072303846b07b5c2b6f11d72690", "max_stars_repo_licenses": ["MIT"], "max... |
import numpy as np
import matplotlib.pyplot as mp
import matplotlib.cm as mpcm
import matplotlib.colors as mpc
import scipy.stats as ss
# plotting settings
lw = 1.5
mp.rc('font', family = 'serif')
mp.rcParams['text.latex.preamble'] = [r'\boldmath']
mp.rcParams['axes.linewidth'] = lw
mp.rcParams['lines.linewidth'] = lw... | {"hexsha": "8373339dd9a549bf7c7f2156eb693bffea51c85a", "size": 6207, "ext": "py", "lang": "Python", "max_stars_repo_path": "gw_grb_h_0_ppd_summaries.py", "max_stars_repo_name": "KamshatTazhenova/hh0", "max_stars_repo_head_hexsha": "c71845056c5a108cb95654b1a1012d63034541c2", "max_stars_repo_licenses": ["MIT"], "max_star... |
[STATEMENT]
lemma add_canc1: "D x y \<Longrightarrow> rquot (x \<cdot> y) x = y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. D x y \<Longrightarrow> rquot (x \<oplus> y) x = y
[PROOF STEP]
using rquot_prop
[PROOF STATE]
proof (prove)
using this:
D ?x ?z \<and> ?y = ?x \<oplus> ?z \<Longrightarrow> ?z = rquot ?y ?... | {"llama_tokens": 177, "file": "PSemigroupsConvolution_Partial_Semigroups", "length": 2} |
export ElectronSite,
electronSites
struct ElectronSite <: Site
s::Index
ElectronSite(I::Index) = new(I)
end
ElectronSite(n::Int) = ElectronSite(Index(4,"Site,Electron,n=$n"))
function electronSites(N::Int;kwargs...)::SiteSet
sites = SiteSet(N)
for n=1:N
set(sites,n,ElectronSite(n))
end
return s... | {"hexsha": "0918b7c337fd475a1e566afb8a43ae1c23bf4b6c", "size": 2629, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/physics/sitesets/electron.jl", "max_stars_repo_name": "Roger-luo/ITensors.jl", "max_stars_repo_head_hexsha": "8b3d7fca9cd0856d8119f5d712b675b023d563a1", "max_stars_repo_licenses": ["Apache-2.0"... |
import pickle
import numpy as np
import os.path
class MNIST:
def __init__(self):
self.dataset = {}
self.path = os.path.dirname(os.path.abspath(__file__))
def _load_img(self, filename):
with open(filename, 'rb') as file:
data = np.frombuffer(file.read(), np.uint8, offset=16)... | {"hexsha": "e0e6273c98237521544de244a611a3f8c0a82806", "size": 2748, "ext": "py", "lang": "Python", "max_stars_repo_path": "agent/data/MNIST/mnist.py", "max_stars_repo_name": "kashu98/Deep-Agent", "max_stars_repo_head_hexsha": "04dc5f8d1dc6963f1bf075cd3e67dcb39784a861", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import numpy as np
def aggregate_loss_dict(agg_loss_dict):
mean_vals = {}
for output in agg_loss_dict:
for key in output:
mean_vals[key] = mean_vals.setdefault(key, []) + [output[key]]
for key in mean_vals:
if len(mean_vals[key]) > 0:
mean_vals[key] = sum(mean_vals[key]) / len(mean_vals[key])
else:
... | {"hexsha": "678806c7c4c8b4272a7e144636aa8a89e4d0008c", "size": 741, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/train_utils.py", "max_stars_repo_name": "thienquang199x/SAM", "max_stars_repo_head_hexsha": "c18ab4eee23ee5196d772b5f57d6bf6e2708b262", "max_stars_repo_licenses": ["Apache-2.0", "BSD-2-Clause... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.