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SUBROUTINE DT_XHOINI
C SUBROUTINE DT_PHOINI
IMPLICIT NONE
SAVE
INCLUDE 'inc/dtflka'
END SUBROUTINE
| {"hexsha": "b1de53cecd0b5868ac0a3036146f246970d7db35", "size": 141, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/dpmjet/DT_XHOINI.f", "max_stars_repo_name": "pzhristov/DPMJET", "max_stars_repo_head_hexsha": "946e001290ca5ece608d7e5d1bfc7311cda7ebaa", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars... |
import argparse
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets,transforms,models
from workspace_utils import active_session
import json
import numpy as np
from PIL import Image
def save_checkpoint(checkpoint_pat... | {"hexsha": "1e1ddb48f9da869c08dcac4f517f0d3fdf88857b", "size": 2222, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "saurav7277/Image-Classifier", "max_stars_repo_head_hexsha": "4c2bf9f62e09b234feae97450bd3bf847b61dad0", "max_stars_repo_licenses": ["FTL", "CNRI-Python"], "max_s... |
"""
Classes for mass-unvariate tuning analyses
"""
from numpy import array, sum, inner, dot, angle, abs, exp, asarray
from thunder.rdds.series import Series
from thunder.utils.common import loadMatVar
class TuningModel(object):
"""
Base class for loading and fitting tuning models.
Parameters
------... | {"hexsha": "052370561ab2e56921e09ad251ab8c2e7873b03b", "size": 3542, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/thunder/regression/tuning.py", "max_stars_repo_name": "Andrewosh/thunder", "max_stars_repo_head_hexsha": "500892f80f758313e3788d50d2c281da64561cbf", "max_stars_repo_licenses": ["Apache-2.0"... |
import pytest
import numpy as np
import tensorflow.keras as keras
from latent.layers import *
nx = 600
nd = 200
X = np.random.uniform(low=0, high=30, size=(nx, nd)).astype(np.float32)
cond = np.random.randint(3, size=nx).astype(np.float32)
ld = 20
def test_colwise_mult():
x = np.array([[1,2,3], [1,2,3], [3,2,1]])
... | {"hexsha": "4c5eeb235bc122efc022b1ee65eb69cf642feddc", "size": 1513, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_layers.py", "max_stars_repo_name": "quadbiolab/latent-lego", "max_stars_repo_head_hexsha": "691b256ce1784a4bae2b7cdf27c8efcbbcd0ba65", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
from __future__ import division, print_function, absolute_import
import numpy as np
from numpy.testing import assert_array_almost_equal, run_module_suite, assert_
from scipy.sparse import csr_matrix
def _check_csr_rowslice(i, sl, X, Xcsr):
np_slice = X[i, sl]
csr_slice = Xcsr[i, sl]
assert_array_almost_e... | {"hexsha": "eafd9b417ec98a82319b0342e748ea6cd0a3ccdc", "size": 1499, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/build/scipy/scipy/sparse/tests/test_csr.py", "max_stars_repo_name": "crougeux/-a-i_v1.6.3_modif", "max_stars_repo_head_hexsha": "b499a812e79f335d082d3f9b1070e0465ad67bab", "max_stars_repo_li... |
# https://stackoverflow.com/questions/37500713/opencv-image-recognition-setting-up-ann-mlp
import cv2
import numpy as np
from sklearn.metrics import accuracy_score
# XOR
data = np.array([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]], dtype=np.float32)
target = np.array([0.0, 1.0, 1.0, 0.0], dtype=np.float32)
# Pr... | {"hexsha": "52476db1019130f5cc20e7e4053f732e13ebfea3", "size": 1182, "ext": "py", "lang": "Python", "max_stars_repo_path": "NeuralNetworks/Python/neural_networks.py", "max_stars_repo_name": "vladiant/OpenCVMLsamples", "max_stars_repo_head_hexsha": "c32689c5bd251ef65b317fafb2426d92857e20a0", "max_stars_repo_licenses": [... |
'''
It implements example 3.4 from https://arxiv.org/abs/2103.0132
Author: Aleyna Kara(@karalleyna)
'''
from jax import jit, random, tree_leaves, tree_map
import jax.numpy as jnp
from jax.scipy.stats import norm
from jax.random import split
import flax.linen as nn
from flax.core.frozen_dict import unfreeze, freeze
im... | {"hexsha": "b3b18416f016ef28863947c1cefd4ac786bf048a", "size": 3420, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/vb_gauss_cholesky_logreg_mroz_demo.py", "max_stars_repo_name": "peterchang0414/pyprobml", "max_stars_repo_head_hexsha": "4f5bb63e4423ecbfc2615b5aa794f529a0439bf8", "max_stars_repo_licenses... |
import argparse
import os
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from lib.config.ConfigParams import ConfigParams
from lib.data.Preprocessing import Preprocessing
from lib.data.DatasetFactory import DatasetFactory
from lib.data.DataLoaderFactory import DataLoaderFactory
from l... | {"hexsha": "3b56cd36ccca0112c98d9cc08b068dae48388c71", "size": 5940, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "SlipknotTN/MovieCaptioning", "max_stars_repo_head_hexsha": "62224764648e093d94de8f11c0590106a4bd910c", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
## 1. Recap ##
import pandas as pd
import numpy as np
np.random.seed(1)
dc_listings = pd.read_csv('dc_airbnb.csv')
dc_listings = dc_listings.loc[np.random.permutation(len(dc_listings))]
stripped_commas = dc_listings['price'].str.replace(',', '')
stripped_dollars = stripped_commas.str.replace('$', '')
dc_listings['pri... | {"hexsha": "dd57c67a835eab298f2d2ab3549834ea60c79e17", "size": 3187, "ext": "py", "lang": "Python", "max_stars_repo_path": "6. Machine Learning Introduction/machine-learning-fundamentals/Multivariate K-Nearest Neighbors-140.py", "max_stars_repo_name": "bibekuchiha/dataquest", "max_stars_repo_head_hexsha": "c7d8a2966fe2... |
from numpy import sort
from sympy import isprime
from sympy import factorint
from sympy import primefactors
def num_subplots(n):
"""
p, n = num_subplots(n)
Purpose
Calculate how many rows and columns of sub-plots are needed to
neatly display n subplots.
Inputs
n - the desired number of s... | {"hexsha": "769931eb579ad78359ba49670e761076e64b7563", "size": 1568, "ext": "py", "lang": "Python", "max_stars_repo_path": "py_eegepe/summary/num_subplots.py", "max_stars_repo_name": "jrmxn/py_eegepe", "max_stars_repo_head_hexsha": "40d4c20295828f0131b790440be05571df914140", "max_stars_repo_licenses": ["MIT"], "max_sta... |
##################################### datasets.py #######################################
# This file contains the class overrides of the Dataset superclass, specifying the
# training and testing datasets, along with the transformations they operate on the data.
#
# This file is distributed under the following license:... | {"hexsha": "7e22862d34003f153fe27df1739774135f40a6cf", "size": 4047, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/datasets.py", "max_stars_repo_name": "Minauras/deepdefresneling", "max_stars_repo_head_hexsha": "e17168e9a8d322201998c73da54efbd334b0ffb9", "max_stars_repo_licenses": ["BSD-2-Clause"], "max... |
C$ Disclaimer
C
C THIS SOFTWARE AND ANY RELATED MATERIALS WERE CREATED BY THE
C CALIFORNIA INSTITUTE OF TECHNOLOGY (CALTECH) UNDER A U.S.
C GOVERNMENT CONTRACT WITH THE NATIONAL AERONAUTICS AND SPACE
C ADMINISTRATION (NASA). THE SOFTWARE IS TECHNOLOGY AND SOFTWARE
C PUBLICLY AVAILABLE UNDER U.S. EXP... | {"hexsha": "d3881ca0bc360665e0d79bd62f84e521d4f14891", "size": 2612, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "source/nasa_f/objget.f", "max_stars_repo_name": "agforero/FTFramework", "max_stars_repo_head_hexsha": "6caf0bc7bae8dc54a62da62df37e852625f0427d", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""Groebner bases algorithms. """
from sympy.core.symbol import Dummy
from sympy.polys.monomials import monomial_mul, monomial_lcm, monomial_divides, term_div
from sympy.polys.orderings import lex
from sympy.polys.polyerrors import DomainError
from sympy.polys.polyconfig import query
def groebner(seq, ring, method=N... | {"hexsha": "40bd680bab1cc9a4333b4bde8b530e97d177422e", "size": 23339, "ext": "py", "lang": "Python", "max_stars_repo_path": "sympy/polys/groebnertools.py", "max_stars_repo_name": "skieffer/sympy", "max_stars_repo_head_hexsha": "23ab5c14881aef21409918939e0c8b78b7fcb06f", "max_stars_repo_licenses": ["BSD-3-Clause"], "max... |
//---- tests/config.cc ------------------------------------- -*- C++ -*- ----//
//
// Snap
//
// Copyright (c) 2016 Rob Clucas
// Distributed under the MIT License
// (See accompanying file LICENSE o... | {"hexsha": "9e15daee4b1a83c243e8e65de5c4336fd84b0d2e", "size": 1060, "ext": "cc", "lang": "C++", "max_stars_repo_path": "tests/config_tests.cc", "max_stars_repo_name": "robclu/snap", "max_stars_repo_head_hexsha": "a717bc153a1ab97ab478e20c67b4ba38ffd4af95", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
library(dplyr)
library(data.table)
library(stringr)
library(glue)
library(Matrix)
# home_dir = '/d0-bayes/home/tenggao'
home_dir = '/home/tenggao'
con_washu = readRDS(glue("{home_dir}/paper_data/conos_objects/conos_WASHU.rds"))
cell_annot = fread(glue('{home_dir}/paper_data/cell_annotations/cell_annot_WASHU_march.tsv... | {"hexsha": "8191478963494ee73ff9143631d8ea29254349cf", "size": 2550, "ext": "r", "lang": "R", "max_stars_repo_path": "scripts/bench_subclone_copykat.r", "max_stars_repo_name": "kharchenkolab/NumbatAnalysis", "max_stars_repo_head_hexsha": "4aa8b7735e256f11ef3644440ad795cc6febd70f", "max_stars_repo_licenses": ["MIT"], "m... |
import pandas as pd
import numpy as np
import statsmodels.api as sm
import scipy.stats
import itertools
from collections import Counter
DEFAULT_BINS = 2
class RobustRegressionTest():
def __init__(self, y, x, z, data, alpha):
self.regression = sm.RLM(data[y], data[x+z])
self.result = self.regressio... | {"hexsha": "94765f3e67ea6865d0e223729f2bec36e89bb1e0", "size": 4755, "ext": "py", "lang": "Python", "max_stars_repo_path": "causality/inference/independence_tests/__init__.py", "max_stars_repo_name": "vishalbelsare/causality", "max_stars_repo_head_hexsha": "40df9eed3f0877e7c6e344fad3f6c352977706cb", "max_stars_repo_lic... |
/*
* pmh_pr.cpp
*
* Created on: 2-feb-2017
* Author: M. El-Kebir
*/
#include <iostream>
#include "utils.h"
#include "clonetree.h"
#include <fstream>
#include <lemon/arg_parser.h>
#include "old_ilps/ilpsolver.h"
#include "old_ilps/ilpbinarizationsolver.h"
#include "migrationgraph.h"
#include "migrationtree.h... | {"hexsha": "4f3e5eba958bbccb5c68f7239a1098fff158a096", "size": 7295, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/pmh_tr.cpp", "max_stars_repo_name": "raphael-group/machina", "max_stars_repo_head_hexsha": "ac71282ffe84ff38fef5dd7ba930727f86fb97a5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_cou... |
# -*- coding: utf-8 -*-
import os
import numpy as np
from torch.utils.data import Dataset
from skimage import io, transform
class ChromosomeDataset(Dataset):
def __init__(self, data_dir, transform=None):
self.data_dir = data_dir
self.img_list = [ele for ele in os.listdir(self.data_dir) if "img" i... | {"hexsha": "2e15222723529b22267e2eee7bd48ac8d2ce3be3", "size": 1189, "ext": "py", "lang": "Python", "max_stars_repo_path": "OverlapSep/chromosome_dataset.py", "max_stars_repo_name": "PingjunChen/ChromosomeSeg", "max_stars_repo_head_hexsha": "4b5e576696e9998558478fd4ec6b74809ceea49c", "max_stars_repo_licenses": ["MIT"],... |
import unittest
import dedupe
import numpy
import random
import warnings
class RandomPairsTest(unittest.TestCase) :
def test_random_pair(self) :
self.assertRaises(ValueError, dedupe.core.randomPairs, 1, 10)
assert dedupe.core.randomPairs(10, 10)
random.seed(123)
numpy.random.seed(1... | {"hexsha": "6970322d117dd1bbcdd1cf02c0b3198456f80865", "size": 8162, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_core.py", "max_stars_repo_name": "neozhangthe1/dedupe", "max_stars_repo_head_hexsha": "aff99e6bd027291eecfb78eae08aa73877f4fff0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
from chinese_checkers.TinyGUI import GUI
from chinese_checkers.TinyChineseCheckersGame import ChineseCheckersGame as Game
game = Game()
gui = GUI(1)
# 0 1 2 3 4 5 6 7 8 9 10 11 12
board = np.array([[4, 4, 4, 4, 4, 4, 0, 4, 4], # 0
[4, 4, 4, 4, 4, 0, 0, 4, 4], # 1
... | {"hexsha": "18b156fe9728d90af731e22fc96edc80e226cb29", "size": 728, "ext": "py", "lang": "Python", "max_stars_repo_path": "construct_graphics.py", "max_stars_repo_name": "davidschulte/alpha-thesis", "max_stars_repo_head_hexsha": "a9d6d2f0b91a2c8d6ae8605db1e3e92586cc1866", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
/**
* 3D NDT-MCL Node.
* This application runs the ndt-mcl localization based on a map and laser scanner and odometry.
*
* The initialization is based now on ground truth pose information (should be replaced with manual input).
*
* The visualization depends on mrpt-gui
*
* More details about the algorithm:
* Jar... | {"hexsha": "b2e43ffc56a97027f708c9a4095326bab6eb0d1f", "size": 12918, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "perception_oru-port-kinetic/ndt_mcl/src/3d_ndt_mcl_node.cpp", "max_stars_repo_name": "lllray/ndt-loam", "max_stars_repo_head_hexsha": "331867941e0764b40e1a980dd85d2174f861e9c8", "max_stars_repo_lic... |
"""
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.
gather feature paths
copied/modified from HERO
(https://github.com/linjieli222/HERO)
"""
import os
import numpy as np
import pickle as pkl
import argparse
from tqdm import tqdm
from cytoolz import curry
import multiprocessing as m... | {"hexsha": "ca2103cb50aede4ebbb368d5c28190db85c9fcb3", "size": 4261, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/collect_video_feature_paths.py", "max_stars_repo_name": "MikeWangWZHL/StarterCode", "max_stars_repo_head_hexsha": "e271c5dff2d9c4344c2fecd81bb0b5dfe2749cf2", "max_stars_repo_licenses": ["M... |
import os
import numpy as np
import sys
import cStringIO
import re
import scipy.io as sio
import copy
def cell2strtable(celltable, delim='\t'):
''' convert a cell table into a string table that can be printed nicely
Parameters:
celltable - array-like, ndarray with rows and columns in desired order... | {"hexsha": "b77354cd87cea7be4764fcaf52375db489d73cf9", "size": 21552, "ext": "py", "lang": "Python", "max_stars_repo_path": "pebl/functions/utils.py", "max_stars_repo_name": "jdtheiss/pebl", "max_stars_repo_head_hexsha": "afbc5674e24495ced230f0c6269a061c70afbd00", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
import numpy as np
import matplotlib.pyplot as plt
import itertools
from scipy.spatial import Voronoi
from autolens.data.array.plotters import plotter_util, grid_plotters
from autolens.model.inversion import mappers
from autolens.data.plotters import ccd_plotters
def plot_image_and_mapper(ccd_data, mapper, mask=None... | {"hexsha": "380df3f58fb5394f7ba52e5c3d9fd25e207c02fb", "size": 16463, "ext": "py", "lang": "Python", "max_stars_repo_path": "autolens/model/inversion/plotters/mapper_plotters.py", "max_stars_repo_name": "AshKelly/PyAutoLens", "max_stars_repo_head_hexsha": "043795966338a655339e61782253ad67cc3c14e6", "max_stars_repo_lice... |
program bridge_main
use bridge_module, only: hp_t, epson_t, mac_t, windows_t
implicit none
type(hp_t) :: hp_printer
type(epson_t) :: epson_printer
type(mac_t) :: mac_computer
type(windows_t) :: windows_computer
call mac_computer%set_printer(hp_printer)
call mac_computer%print()
c... | {"hexsha": "3fe7a4c48b5300dbfbfdaa70886b3bede2308eb8", "size": 840, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/structural/bridge/bridge_main.f90", "max_stars_repo_name": "zoziha/Fortran-Design-Patterns", "max_stars_repo_head_hexsha": "ee88eb5d17d509254af41ae43197922309a0a726", "max_stars_repo_licenses... |
# -*- coding: utf-8 -*-
import os
import numpy as np
import pandas as pd
from skimage.transform import resize
import build_model
ORIG_ROW = 420
ORIG_COL = 580
def run_len_encoding(img):
"""Compress image using run-length encoding.
Args:
img: binary array of image
Returns: string of encoded ima... | {"hexsha": "4e49c79147bc52a4da49889383885deeb714262f", "size": 2222, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/models/predict_model.py", "max_stars_repo_name": "jenny-chou/Kaggle-Ultrasound_Segmentation", "max_stars_repo_head_hexsha": "55967d3538362340276376fa709d1ed572d52c40", "max_stars_repo_licenses... |
# from typing import Callable
# import scipy.misc
# import scipy.optimize
# from dsalgo.type import Numeric
def binary_search() -> None:
...
def ternary_search() -> None:
...
# def find_root_newton(
# y: Numeric,
# n=2,
# x0=1.0,
# tol: float = 1e-8,
# ):
# def f(x):
# return... | {"hexsha": "a9660f5dfe8a379f39d8c57bd0ef604a51a96e93", "size": 648, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/dsalgo/analysis_search.py", "max_stars_repo_name": "kagemeka/python-algorithms", "max_stars_repo_head_hexsha": "dface89b8c618845cf524429aa8e97c4b2b10ceb", "max_stars_repo_licenses": ["MIT"], "m... |
%http://cs.pugetsound.edu/~jross/courses/cs240/project/requirements/
%Animation Group
\documentclass[12pt]{article}
\usepackage{graphicx}
\begin{document}
% Front Page
\begin{titlepage}
\begin{center}
\huge Edith \\
\vspace*{\fill}%
\huge \textsc{\textbf{Animation System \\Intermediate Report} }
\bigskip
\ru... | {"hexsha": "6055ba68c4e25a47cb9b510f34f5c5f7ae9a5f0e", "size": 6528, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "docs/Animation_Intermediate_Report/ASReport.tex", "max_stars_repo_name": "kcanfieldpugetsound/edith", "max_stars_repo_head_hexsha": "2c674f6feb333571eae240dc1d094936b8c59c71", "max_stars_repo_licens... |
using MRC
using Documenter
makedocs(;
modules=[MRC],
authors="Seth Axen <seth.axen@gmail.com> and contributors",
sitename="MRC.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://sethaxen.github.io/MRC.jl",
assets=String[],
),
p... | {"hexsha": "2dfea114126937164914a04f6b8619b03d0dd1b4", "size": 419, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/make.jl", "max_stars_repo_name": "ehgus/MRC.jl", "max_stars_repo_head_hexsha": "f5a0981c1346d1a1ab42619746efd812f0ae1f62", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 5, "max_stars_r... |
import numpy as np
import cv2
import random
face_cascade = cv2.CascadeClassifier('D:\python36\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('D:\python36\Lib\site-packages\cv2\data\haarcascade_eye.xml')
#先检测人脸，存到face.jpg中
cap=cv2.VideoCapture(0)
while(T... | {"hexsha": "fa07c813d7442bdd20056463ab719e52d2c3fcfb", "size": 6872, "ext": "py", "lang": "Python", "max_stars_repo_path": "tttchess.py", "max_stars_repo_name": "kq18G8/mcmk", "max_stars_repo_head_hexsha": "07a263298f14798cd4410dcb273448fefaa93d6f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_star... |
#!/usr/bin/python3 -B
# Copyright 2015-2019 Josh Pieper, jjp@pobox.com. 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... | {"hexsha": "219333f4efe998f36d364ea0116466f5072fa867", "size": 39642, "ext": "py", "lang": "Python", "max_stars_repo_path": "moteus/tool/tview.py", "max_stars_repo_name": "annhan/moteus", "max_stars_repo_head_hexsha": "03cafe4472da1fe018ed90e24a2b1f00667c688e", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_coun... |
import json
import numpy as np
class MeanCalculator:
def __init__(self, source_file, target_file, threshold):
self.source_file = source_file
self.target_file = target_file
self.threshold = threshold
def run(self):
with open(self.source_file, "r") as input_file:
cl... | {"hexsha": "b1a75666299553eea7b05a0e0246e41419b20a18", "size": 982, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/mean_calculator.py", "max_stars_repo_name": "jonasmue/satisfaction.observer", "max_stars_repo_head_hexsha": "d7f1a09515639f60994e5b3a390d4897d2b03893", "max_stars_repo_licenses": ["MIT"], ... |
from skimage import morphology
import Algorithm.cropAndPaste as cropAndPaste
from Algorithm.propagation import propagationSegment
import os
import numpy as np
import cv2
import time
from Algorithm.propagationLabel import getEdgesFromLabel,evaluateMeritForEdge
from skimage.measure import label
from shutil import rmtree
... | {"hexsha": "0c364ed1a583851eb790be9fff58220596034ad8", "size": 3681, "ext": "py", "lang": "Python", "max_stars_repo_path": "fastFineCutDemo.py", "max_stars_repo_name": "clovermini/Fast-FineCut", "max_stars_repo_head_hexsha": "c9690d72bfa368257a33ce8f0d2d55d0672fec8b", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma fresh_star_restrictA[intro]: "a \<sharp>* \<Gamma> \<Longrightarrow> a \<sharp>* AList.restrict V \<Gamma>"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. a \<sharp>* \<Gamma> \<Longrightarrow> a \<sharp>* AList.restrict V \<Gamma>
[PROOF STEP]
by (induction \<Gamma>) (auto simp add: fresh_star_Con... | {"llama_tokens": 118, "file": "MiniSail_Nominal-Utils", "length": 1} |
/*
Copyright [2021] [IBM Corporation]
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to i... | {"hexsha": "f2a16d84033022745de7d811fcc27af341309846", "size": 18047, "ext": "h", "lang": "C", "max_stars_repo_path": "src/mm/mm_plugin_itf.h", "max_stars_repo_name": "omriarad/mcas", "max_stars_repo_head_hexsha": "f47aab12754c91ebd75b0e1881c8a7cc7aa81278", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_count": ... |
[STATEMENT]
lemma msg_fresh_inc_sn [simp, elim]:
"msg_fresh \<sigma> m \<Longrightarrow> rreq_rrep_sn m"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. msg_fresh \<sigma> m \<Longrightarrow> rreq_rrep_sn m
[PROOF STEP]
by (cases m) simp_all | {"llama_tokens": 102, "file": "AODV_variants_e_all_abcd_E_Quality_Increases", "length": 1} |
"""Simple webservice which tells a user whether K2 is observing a point in the sky.
Example usage
-------------
The url:
/is-k2-observing?ra=129.9885&dec=14.6993&campaign=16
Will return 'yes' or 'no'.
"""
import os
import numpy as np
import pandas as pd
import flask
from flask import Flask, request
from . import... | {"hexsha": "2e89a116f1ca66cf7ca5bc2f22925fb0bc730dd2", "size": 1731, "ext": "py", "lang": "Python", "max_stars_repo_path": "k2app/k2app.py", "max_stars_repo_name": "KeplerGO/k2-visibility-app", "max_stars_repo_head_hexsha": "321180d3c33c3ae44fb0c8b721cebaa563064e09", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dropout, Dense, Flatten
import matplotlib.pyplot as plt
import numpy as np
import random
def get_label_color(val1, val2):
if val1 == val2:
return 'black'
else:
return 'red'
pixe... | {"hexsha": "60641ef4e63b02e80e53c98c3ee1677bdfba26a8", "size": 2308, "ext": "py", "lang": "Python", "max_stars_repo_path": "handwriting_classifier.py", "max_stars_repo_name": "ngp111/digit_classification_keras", "max_stars_repo_head_hexsha": "65d6b72d6d38bc257c5cde55d92d2210865f74d6", "max_stars_repo_licenses": ["MIT"]... |
module randomf
use curand
use parameters
use, intrinsic :: iso_fortran_env, only: output_unit
implicit none
real, parameter, private :: sqrt3 = sqrt(3.0)
public initialize_rng, fill_vec
contains
subroutine initialize_rng(rng)
type(curandGenerator), intent(inout) :: rng
... | {"hexsha": "6be8415ebd5ab8bde5da9dbfe0734fbd7ffb8852", "size": 2813, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "include/random.f90", "max_stars_repo_name": "edwinb-ai/brownian-cuda", "max_stars_repo_head_hexsha": "549be5d255bb3c559b01b9e0b0091dd8aa5e55c0", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
module TestQuadratic
using Test
using TreeParzen
objective(params) = (params[:x] - 3)^2
best = fmin(objective, Dict(:x => HP.Uniform(:x, -5.0, 5.0)), 50)
@test abs(best[:x] - 3) < .25
end
true
| {"hexsha": "9d6f1e0f9f222de60c7438317478cab1e594804d", "size": 197, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/fmin/quadratic.jl", "max_stars_repo_name": "lhnguyen-vn/TreeParzen.jl", "max_stars_repo_head_hexsha": "d6b4181a45167663e8844330220f0c62c715c75f", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
"""Test .tab format functionalities"""
import pathlib
import tempfile
import numpy as np
import afmformats
data_path = pathlib.Path(__file__).resolve().parent / "data"
def test_open_0_13_3():
fdat = afmformats.load_data(data_path / "fmt-hdf5-fd_version_0.13.3.h5")[0]
assert fdat.metadata["imaging mode"] =... | {"hexsha": "96a7641287864eee7e52c345584c77cf0634e700", "size": 1667, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_fmt_hdf5.py", "max_stars_repo_name": "AFM-analysis/afmformats", "max_stars_repo_head_hexsha": "7cbacd94ca27e42973ec14e55d3b0b00024bd934", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import typing
import numpy as np
import pandas as pd
Files = typing.Union[
str, typing.Sequence[str], pd.Index, pd.Series,
]
Timestamps = typing.Union[
float,
int,
str,
pd.Timedelta,
typing.Sequence[typing.Union[float, int, str, pd.Timedelta]],
pd.Index,
pd.Series,
]
Values = typing.... | {"hexsha": "7df09e6fe64eddd08a68c36f38cab6b9b4dd832a", "size": 476, "ext": "py", "lang": "Python", "max_stars_repo_path": "audformat/core/typing.py", "max_stars_repo_name": "audeering/audformat", "max_stars_repo_head_hexsha": "a9ffce03e333e21a1ceb0db1d13e9f1fb5b61cca", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
"""
Copyright 2018 Ashar <ashar786khan@gmail.com>
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicab... | {"hexsha": "041f72a7ae11b461fbdee9e3c426bb30ab7b1289", "size": 10022, "ext": "py", "lang": "Python", "max_stars_repo_path": "transfer.py", "max_stars_repo_name": "coder3101/neural-style-transfer", "max_stars_repo_head_hexsha": "1b10efbd10859a545494c9d2dba7cbfcd79d40e5", "max_stars_repo_licenses": ["Apache-2.0"], "max_s... |
import unittest
from pkg_resources import resource_filename
from collections import Counter
import numpy as np
from desimeter.circles import fit_circle,robust_fit_circle
class TestCircles(unittest.TestCase):
def test_fit_circle(self):
print("Testing fit circle")
xc=12
yc=24
r=3.
... | {"hexsha": "b59ae1247d499ce78b610704ee2808f8ff53e739", "size": 1178, "ext": "py", "lang": "Python", "max_stars_repo_path": "py/desimeter/test/test_circles.py", "max_stars_repo_name": "julienguy/desimeter", "max_stars_repo_head_hexsha": "9dd40251b0c483b525a952051a73587ac9180e8a", "max_stars_repo_licenses": ["BSD-3-Claus... |
# Copyright 2020 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | {"hexsha": "f6a615a68a98deaf73a322adfeaff98fcf54face", "size": 9185, "ext": "py", "lang": "Python", "max_stars_repo_path": "bin/eval.py", "max_stars_repo_name": "pacificlion/world_models", "max_stars_repo_head_hexsha": "dff58d80466f0b0fcee59ca25581ea986f8663d5", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
using PartialRejectionSampling
using Random
using Plots
using GraphPlot, Colors
using LightGraphs
const LG = LightGraphs
using Cairo, Compose
function plot(
graph::LG.AbstractGraph,
dims::Vector{Int}=zeros(Int, 2),
path="";
kwargs...
)
if isempty(path)
if any(dims .== 0)
p = ... | {"hexsha": "99fa7e74724dfabcd2b63cb1b6fdd6dba02282d1", "size": 10125, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "docs/plots/pedagogy_graph.jl", "max_stars_repo_name": "guilgautier/PartialRejectionSampling.jl", "max_stars_repo_head_hexsha": "b9b586b9347430cd5375e34ff6f1d37193555997", "max_stars_repo_licenses"... |
#!/usr/bin/python
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.size'] = 20
plt.rcParams['font.family'] = 'serif'
plt.rcParams['axes.linewidth'] = 1.0
plt.rcParams['figure.figsize'] = (10, 8)
plt.rcParams['figure.dpi'] = 120
data = np.loadtxt('cores.dat')
orbit_data_0 = np.loadtxt('../orbit0... | {"hexsha": "e5b691e40e651e03cfd33109b54da7399e366249", "size": 1661, "ext": "py", "lang": "Python", "max_stars_repo_path": "contrib/figs/plotdata.py", "max_stars_repo_name": "archman/genopt", "max_stars_repo_head_hexsha": "388c900fa4b7b9ec16c9c490797b8cca0683e251", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# implementation of the GPUCompiler interfaces for generating Metal code
const Metal_LLVM_Tools_jll = LazyModule("Metal_LLVM_Tools_jll", UUID("0418c028-ff8c-56b8-a53e-0f9676ed36fc"))
## target
export MetalCompilerTarget
Base.@kwdef struct MetalCompilerTarget <: AbstractCompilerTarget
macos::VersionNumber
end
f... | {"hexsha": "d9aab126fd58544ab70ecc69246a0567e994a50b", "size": 34685, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/metal.jl", "max_stars_repo_name": "maleadt/GPUCompiler.jl", "max_stars_repo_head_hexsha": "8fb9b50d85df93183aba852c59dd7ae8e58c0abc", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
C=======================================================================
C OpFloodN, Subroutine
C
C Generates output for simulated data
C=======================================================================
SUBROUTINE OpFloodN (CONTROL, ISWITCH,
& ALGACT, ALI, AMLOSS, BD1, EF, FLDH3C, FLDH4, ... | {"hexsha": "fdc8891f9fc6a81a8df775d1975f1203908e330c", "size": 3974, "ext": "for", "lang": "FORTRAN", "max_stars_repo_path": "Soil/FloodN/OPFLOODN.for", "max_stars_repo_name": "vstocca/dssat-csm-os", "max_stars_repo_head_hexsha": "fec0f313a3b7c1119093e61ad87b392dd3f75863", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
# -*- coding: utf-8 -*-
"""
==========================================================
Create a new coordinate class (for the Sagittarius stream)
==========================================================
This document describes in detail how to subclass and define a custom spherical
coordinate frame, as discussed in ... | {"hexsha": "0b276de9fc768aab920131e5af2da0650f74263c", "size": 10549, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/coordinates/plot_sgr-coordinate-frame.py", "max_stars_repo_name": "jayvdb/astropy", "max_stars_repo_head_hexsha": "bc6d8f106dd5b60bf57a8e6e29c4e2ae2178991f", "max_stars_repo_licenses": [... |
import pandas as pd
import numpy as np
from utils.cross_val import get_cv_results
import matplotlib.pyplot as plt
from utils.nemenyi import nemenyi, nemenyi_unrolled_plot
plot_pars = {"size": (5, 2.5),
"font_scale": 0.7,
"w": 0.3,
"h": 0.2,
"b": 0.2}
# -------------... | {"hexsha": "8fd34dd4fae00d39479705940dd05c898311c0cb", "size": 4479, "ext": "py", "lang": "Python", "max_stars_repo_path": "analysis/statistics.py", "max_stars_repo_name": "supsi-dacd-isaac/mbtr_experiments", "max_stars_repo_head_hexsha": "276e011b303435513961923ad1fb74e1b76cf5b7", "max_stars_repo_licenses": ["MIT"], "... |
from numpy import array,zeros,append
import numpy as np
def gausselim(A,b):
#AUGMENTED MATRIX
augA = np.c_[A,b]
p1 = augA [1,:] - augA [0,:] * (augA [1,0]/augA [0,0])
p2 = augA [2,:] - augA [0,:] * (augA [2,0]/augA [0,0])
temp = append(augA[0,:],p1)
augA1 = append(temp,p2).reshape(3,4)
p3... | {"hexsha": "e66dd4ba0ba1810b3809c2d7a8ab3c31538951fb", "size": 854, "ext": "py", "lang": "Python", "max_stars_repo_path": "18_4.py", "max_stars_repo_name": "rursvd/pynumerical2", "max_stars_repo_head_hexsha": "4b2d33125b64a39099ac8eddef885e0ea11b237d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
class Metrics:
def __init__(self):
self.data = {
"losses": [],
"loss_actor": [],
"returns": [],
"collisions": []
}
self.loss_buffer = []
self.returns_buffer = []
self.loss_actor = []
self.collision_... | {"hexsha": "fcdd551959fd7fa6327470dd8ed5bb4dce968dd9", "size": 1899, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/metrics.py", "max_stars_repo_name": "bdvllrs/marl-patroling-agents", "max_stars_repo_head_hexsha": "f2924d88a412acd27c02e3889aedc648a6d7400e", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import numpy as np
def Transform2eMO(C, Vee):
VeeMO = np.zeros((len(Vee),len(Vee),len(Vee),len(Vee)))
MO1 = np.zeros((len(Vee),len(Vee),len(Vee),len(Vee)))
MO2 = np.zeros((len(Vee),len(Vee),len(Vee),len(Vee)))
MO3 = np.zeros((len(Vee),len(Vee),len(Vee),len(Vee)))
for s in range(0, le... | {"hexsha": "ae5b3d79b0c52da295d840302a42c7f18536c983", "size": 1947, "ext": "py", "lang": "Python", "max_stars_repo_path": "slowquant/integraltransformation/IntegralTransform.py", "max_stars_repo_name": "Melisius/Hartree-Fock", "max_stars_repo_head_hexsha": "46bf811dfcf217ce0c37ddec77d34ef00da769c3", "max_stars_repo_li... |
Add Search Blacklist "Private_" "_subproof".
Set Printing Depth 50.
Remove Search Blacklist "Private_" "_subproof".
Add Search Blacklist "Private_" "_subproof".
Add LoadPath "../..".
Require Import BetaJulia.BasicPLDefs.Identifier.
Require Import BetaJulia.Sub0280a.BaseDefs.
Require Import BetaJulia.Sub0280a.BaseProps.... | {"author": "uwplse", "repo": "analytics-data", "sha": "64d3fccac3a25230d1adb59fcf1aded3f375029a", "save_path": "github-repos/coq/uwplse-analytics-data", "path": "github-repos/coq/uwplse-analytics-data/analytics-data-64d3fccac3a25230d1adb59fcf1aded3f375029a/diffs-annotated-fixed-2/7/user-7-session-145.v"} |
(** Type-safety proofs.
Authors: Steve Zdancewic and Karl Mazurak.
Table of contents:
- #<a href="##subtyping">Properties of subtyping</a>#
- #<a href="##typing">Properties of typing</a>#
- #<a href="##preservation">Preservation</a>#
- #<a href="##progress">Progress</a># *)
Require E... | {"author": "Zdancewic", "repo": "linearity", "sha": "b2662939b2e8fb8fbe34f7ec0d3c69ef1bdff916", "save_path": "github-repos/coq/Zdancewic-linearity", "path": "github-repos/coq/Zdancewic-linearity/linearity-b2662939b2e8fb8fbe34f7ec0d3c69ef1bdff916/linf/LinF_Soundness.v"} |
# !/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Compute the cost of a trajectory.
"""
from sklearn.pipeline import Pipeline
import numpy as np
import pickle
from .projection import coef_to_trajectory
from scipy.integrate import trapz
def load_model(name):
"""
Load model saved in a .pkl format.
Inpu... | {"hexsha": "80c769ca742a8da43bd8e7670f618247518cec6c", "size": 6797, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyrotor/cost_functions.py", "max_stars_repo_name": "bguedj/pyrotor", "max_stars_repo_head_hexsha": "31620de00f69d4ff2e0c4c8b03f38f80742c1d44", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
"""
This module provides functions to retrieve system information.
"""
import datetime
import platform
from sunpy.extern.distro import linux_distribution
__all__ = ['get_sys_dict', 'system_info']
def get_sys_dict():
"""
Test which packages are installed on system.
Returns
-------
`dict`
... | {"hexsha": "fe73f1ec133f1201126633200692d3d6bae42115", "size": 4659, "ext": "py", "lang": "Python", "max_stars_repo_path": "sunpy/util/sysinfo.py", "max_stars_repo_name": "johan12345/sunpy", "max_stars_repo_head_hexsha": "56e1ab0c2c992f99e0fe3e6bff468b731a51228c", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars... |
# Copyright (c) 2017-present, Facebook, 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 applicable law or agreed... | {"hexsha": "7c867266aaa299e20ed4caab5dad7f4b35b4829f", "size": 11905, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/modeling/retinanet_heads.py", "max_stars_repo_name": "xieshuqin/RefineNet", "max_stars_repo_head_hexsha": "94d7f53db82aec2e3969850fe7c1d1b76ce7ad48", "max_stars_repo_licenses": ["Apache-2.0"]... |
The Blue Mango Restaurant was a vegetarian restaurant located on G Street G St in the 1980s. It was collectively owned by the workers. It was open Tuesday to Sunday, with Mondays for general or team meetings, maintenance, and hanging out. In Robert Crumbs book My Troubles with Women he visits the Blue Mango.
There w... | {"hexsha": "e13c1982fd1c964c8571fd7d97660cfb2b58c36c", "size": 2572, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Blue_Mango.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
from collections import Counter
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import cv2
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
ROAD_CLASSES = ('road', 'lane markings', 'undrivable', 'movable', 'my car')
CLASS_VALUES = {(64, 32, 32) : 0,
(255,... | {"hexsha": "88c6e0260b645164c771f86c18124924163a332a", "size": 3067, "ext": "py", "lang": "Python", "max_stars_repo_path": "dataset.py", "max_stars_repo_name": "greerviau/RoadSegNet", "max_stars_repo_head_hexsha": "1ff9b04a18e66b67869dfbea910138222fbf8bd1", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
# Created on 2017
# Satyam Mukherjee <satyam.mukherjee@gmail.com>
# As a Red Hat Research Fellow in Research Center for Open Digital Innovation (RCODI), Purdue University
# Principal Investigator: Prof Sabine Brunswicker, RCODI
"""Codes to generate network of files.
Two files are connected if there is a fu... | {"hexsha": "983f6de50d72191cae4e3fd3d1a585c71efce138", "size": 22269, "ext": "py", "lang": "Python", "max_stars_repo_path": "Github_for_RH/Codes/Python/04_code_complexity_motifs.py", "max_stars_repo_name": "RedHatRCODI/ReddHatFellowNetworks", "max_stars_repo_head_hexsha": "839e99bbf87f60ede0bf4c14edff5bb9623313fa", "ma... |
[STATEMENT]
lemma servTicket_authentic_Kas:
"\<lbrakk> Crypt (shrK B) \<lbrace>Agent A, Agent B, Key servK, Number Ts\<rbrace>
\<in> parts (spies evs); B \<noteq> Tgs; B \<notin> bad;
evs \<in> kerbV \<rbrakk>
\<Longrightarrow> \<exists>authK Ta.
Says Kas A
\<lbrace>Crypt (s... | {"llama_tokens": 396, "file": null, "length": 1} |
from testfixtures import LogCapture
from unittest import mock
import numpy as np
from skimage import data
from scipy.ndimage.filters import gaussian_filter
from pyfibre.addons.shg_pl_trans.shg_reader import SHGReader
from pyfibre.utilities import (
unit_vector, numpy_remove, nanmean, ring, matrix_split,
label... | {"hexsha": "f093f5e13f48713a925fc1f90a206e9b5dfdd270", "size": 6354, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyfibre/tests/test_utilities.py", "max_stars_repo_name": "franklongford/ImageCol", "max_stars_repo_head_hexsha": "96f0db337a203c5634bebcbae10a6d85789dff2c", "max_stars_repo_licenses": ["Apache-2.0... |
import ctypes
from . import ogg
from . import opus
from .pyogg_error import PyOggError
class OpusFile:
def __init__(self, path):
# Open the file
error = ctypes.c_int()
of = opus.op_open_file(
ogg.to_char_p(path),
ctypes.pointer(error)
)
# Check for ... | {"hexsha": "01abd37f64e433fbb29db3dfe62ec9fac736dd1b", "size": 3597, "ext": "py", "lang": "Python", "max_stars_repo_path": "pyogg/opus_file.py", "max_stars_repo_name": "lstolcman/PyOgg", "max_stars_repo_head_hexsha": "8be17d09942ee9245527b20eed92ef4795964bd4", "max_stars_repo_licenses": ["BSD-3-Clause", "Unlicense"], "... |
#!/usr/bin/env python3
"""
Author : eg
Date : 2021-04-29
Purpose: Rock the Casbah
"""
import argparse
import os
import sys
import numpy as np
import subprocess
import re
from datetime import datetime
# --------------------------------------------------
def get_args():
"""Get command-line arguments"""
parse... | {"hexsha": "b3603afe0893480e774350139a67e4e30ad10b48", "size": 3664, "ext": "py", "lang": "Python", "max_stars_repo_path": "stereoTopRGB/run_pipeline.py", "max_stars_repo_name": "LyonsLab/PhytoOracle", "max_stars_repo_head_hexsha": "89ede8ede388fee8e8518cadecdc1d5bf43b3d76", "max_stars_repo_licenses": ["MIT"], "max_sta... |
# Copyright (C) 2019 Intel Corporation
#
# SPDX-License-Identifier: MIT
import copy
import numpy as np
from scipy.optimize import linear_sum_assignment
from shapely import geometry
from . import models
class DataManager:
def __init__(self, data):
self.data = data
def merge(self, data, start_frame,... | {"hexsha": "7da117c1f0473c4a2119be7e59c3ffe18aaea746", "size": 15691, "ext": "py", "lang": "Python", "max_stars_repo_path": "cvat/apps/engine/data_manager.py", "max_stars_repo_name": "javoweb/cvat", "max_stars_repo_head_hexsha": "684544d2a06c192e7155f655897e6360b4a3be37", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
# Copyright (c) 2021 PaddlePaddle 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | {"hexsha": "900a2eab26e4414b487d6d7858381ee302a107e8", "size": 1979, "ext": "py", "lang": "Python", "max_stars_repo_path": "modules/audio/keyword_spotting/kwmlp_speech_commands/feature.py", "max_stars_repo_name": "AK391/PaddleHub", "max_stars_repo_head_hexsha": "a51ab7447e089776766becb3297e560dfed98573", "max_stars_rep... |
import streamlit as st
import pandas as pd
import numpy as np
import streamlit.components.v1 as components
import string
import nltk
import re
nltk.download('conll2000')
nltk.download('averaged_perceptron_tagger')
from nltk.corpus import conll2000
from nltk.chunk.util import tree2conlltags,conlltags2tree
fro... | {"hexsha": "f081406ac89c6dbe056f4f59db4276bc5eeadff3", "size": 26037, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "bagaboi/English-sentence-to-SQL-Query-Converter", "max_stars_repo_head_hexsha": "68450eef33ba87f5eb7cd71167c32744590c66e4", "max_stars_repo_licenses": ["MIT"], "m... |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Twenty Seconds Resume/CV
% LaTeX Template
% Version 1.0 (14/7/16)
%
% Original author:
% Carmine Spagnuolo (cspagnuolo@unisa.it) with major modifications by
% Vel (vel@LaTeXTemplates.com) and Harsh (harsh.gadgil@gmail.com)
% Further Modifications by Brian (brianleepollack@gm... | {"hexsha": "8bd8534bcd6d50e7cc0b6c7e71e6f97b615b1374", "size": 8874, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "template.tex", "max_stars_repo_name": "brovercleveland/Data-Engineer-Resume-LaTeX", "max_stars_repo_head_hexsha": "c5c3b7bf2a75f65378320eeb5de1149497e051f0", "max_stars_repo_licenses": ["Apache-2.0"... |
########################
#Import Dependencies
########################
import numpy as np
import pandas as pd
import datetime as dt
# Python SQL toolkit and Object Relational Mapper
import sqlalchemy
from sqlalchemy.ext.automap import automap_base
from sqlalchemy.orm import Session
from sqlalchemy import create_engine... | {"hexsha": "3b371cab84aa6e3f310c39391a6463fe4db90293", "size": 5251, "ext": "py", "lang": "Python", "max_stars_repo_path": "app.py", "max_stars_repo_name": "MarkusShipley/sqlalchemy-challenge", "max_stars_repo_head_hexsha": "bdb8aeee853c68f810997e1b1862948e753d7c51", "max_stars_repo_licenses": ["ADSL"], "max_stars_coun... |
function [ pc, normal ] = circle_pppr2imp_3d ( p1, p2, p3, r )
%*****************************************************************************80
%
%% CIRCLE_PPR2IMP_3D converts a circle from PPR to implicit form in 3D.
%
% Discussion:
%
% The PPPR form of a circle in 3D is:
%
% The circle of radius R passing t... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/geometry/circle_pppr2imp_3d.m"} |
\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage[ngerman]{babel}
% Convenience improvements
\usepackage{csquotes}
\usepackage{enumitem}
\setlist[enumerate,1]{label={\alph*)}}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{mathtools}
\usepackage{tabularx}
% Proper tables and centering for overful... | {"hexsha": "1b6ad2836e08102d96bc90ef8cc4deb25b22899d", "size": 7980, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "2022s/algebra/ue/sheet3.tex", "max_stars_repo_name": "LW2904/jku", "max_stars_repo_head_hexsha": "aa6a72381cc499e8c628e4d57a883647c8201d08", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
import numpy as np
from typing import List, Tuple, Union, Sequence, Dict, Any, Callable, Iterable
import threading
from time import sleep, perf_counter
import traceback
import logging
from datetime import datetime
from qcodes.station import Station
from qcodes.data.data_set import new_data, DataSet
from qcodes.data.da... | {"hexsha": "d1956f9968c1ac692af3dd08c057c9e339669240", "size": 48555, "ext": "py", "lang": "Python", "max_stars_repo_path": "qcodes/measurement.py", "max_stars_repo_name": "nulinspiratie/Qcodes", "max_stars_repo_head_hexsha": "d050d38ac83f532523a39549c3247dfa6096a36e", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
import numpy as np
import math
import pySUTtoIO.sut as st
import pySUTtoIO.matrix_inverter as mi
from pySUTtoIO.secondary_flows import make_secondary
class TransformationModel0:
default_rel_tol = 1E-3
def __init__(self, sut, env_extensions, make_secondary=False):
assert type(sut) is st.Sut
s... | {"hexsha": "34556288da933847124120796917eb820782c2c7", "size": 2574, "ext": "py", "lang": "Python", "max_stars_repo_path": "pySUTtoIO/transformation_model_0.py", "max_stars_repo_name": "CMLPlatform/pysuttoio", "max_stars_repo_head_hexsha": "4d0c2de6c7d6d90e9caf4b5b361e5046828bf113", "max_stars_repo_licenses": ["MIT"], ... |
import os
import json
from csv import DictReader, DictWriter
import numpy as np
from numpy import array
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import train_test_split
from sklearn.base import BaseEstimato... | {"hexsha": "48f78fd81915e47f82fce14b2716a58872246e14", "size": 8400, "ext": "py", "lang": "Python", "max_stars_repo_path": "fall_2017/hw2/feature_eng.py", "max_stars_repo_name": "bdmckean/MachineLearning", "max_stars_repo_head_hexsha": "b6cc30b1302e7228ef8fb74750414c9551fb9474", "max_stars_repo_licenses": ["MIT"], "max... |
"""Compute the Choi matrix of a list of Kraus operators."""
from typing import List
import numpy as np
from toqito.states import max_entangled
from toqito.channel_ops import partial_channel
def kraus_to_choi(kraus_ops: List[List[np.ndarray]], sys: int = 2) -> np.ndarray:
r"""
Compute the Choi matrix of a lis... | {"hexsha": "7894a2c92796ac715bf5aae12a12042c398b29d7", "size": 2369, "ext": "py", "lang": "Python", "max_stars_repo_path": "toqito/channel_ops/kraus_to_choi.py", "max_stars_repo_name": "paniash/toqito", "max_stars_repo_head_hexsha": "ab67c2a3fca77b3827be11d1e79531042ea62b82", "max_stars_repo_licenses": ["MIT"], "max_st... |
c routines for determining length of multipole and
c local expansions based on size of box in wavelengths
c
c-----------------------------------------------------------------------------
c
c h3dterms - determine number of terms in mpole expansions for box
c of size "size" with Helmholtz p... | {"hexsha": "bcc2825c2647e8dc30a6fe6143b1fa1d32043912", "size": 3902, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/Helmholtz/h3dterms.f", "max_stars_repo_name": "jmark/FMM3D", "max_stars_repo_head_hexsha": "fd26f2b71f5dfd6e20bf797d3d01b8bb98dbc602", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_cou... |
[STATEMENT]
lemma config'_n_bv: fixes qs init n
shows " map_pmf (snd \<circ> snd) init = return_pmf s0
\<Longrightarrow> map_pmf (fst \<circ> snd) init = bv (length s0)
\<Longrightarrow> map_pmf (snd \<circ> snd) (config'_rand (BIT_init, BIT_step) init qs) = return_pmf s0
\<and> map_pmf (fst \<ci... | {"llama_tokens": 6334, "file": "List_Update_BIT", "length": 28} |
import cv2
import numpy as np
import torch
import torch.nn as nn
from torch.nn import functional as F
class GradCAM():
def __init__(self, model, target_layer, use_cuda):
self.model = model.eval()
self.target_layer = target_layer
self.use_cuda = use_cuda
self.feature_map = 0
... | {"hexsha": "7fadca2a0a9dc0accddc3d2175a76d03abd1dd0e", "size": 2123, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/gradCAM.py", "max_stars_repo_name": "kamata1729/visualize-pytorch", "max_stars_repo_head_hexsha": "ec1b3fe0952c5db187a5d4875cd1539a1b7a1270", "max_stars_repo_licenses": ["MIT"], "max_stars_cou... |
function circVar=findCircVar(x,w)
%%FINDCIRCVAR Given a set of samples of a circular distribution, determine
% the circular variance. As in Chapter 2.3.1 of [1], this is
% just 1 minus the resultant length of the first trigonometric
% moment.
%INPUTS: x The 1XN or NX1 vector of possi... | {"author": "USNavalResearchLaboratory", "repo": "TrackerComponentLibrary", "sha": "9f6e329de5be06a371757c4b853200beb6def2d0", "save_path": "github-repos/MATLAB/USNavalResearchLaboratory-TrackerComponentLibrary", "path": "github-repos/MATLAB/USNavalResearchLaboratory-TrackerComponentLibrary/TrackerComponentLibrary-9f6e3... |
@testset "MOMA" begin
model = test_toyModel()
sol = [looks_like_biomass_reaction(rid) ? 0.5 : 0.0 for rid in reactions(model)]
moma = minimize_metabolic_adjustment_analysis_dict(
model,
sol,
OSQP.Optimizer;
modifications = [silence, change_optimizer_attribute("polish", true... | {"hexsha": "2759d1cfc8e598ba2cd6883b53aecf18c7a84710", "size": 419, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/analysis/minimize_metabolic_adjustment.jl", "max_stars_repo_name": "LCSB-BioCore/COBREXA.jl", "max_stars_repo_head_hexsha": "cfe20e2a9d5e98cd097cf9f62c5d32f07c1199b0", "max_stars_repo_licenses"... |
import numpy as np
import time
from astropy.io import fits
import matplotlib.pyplot as plt
from running_mean_std_FITS import running_stats
def median_bins_fits(filenames,B):
mean, std = running_stats(filenames)
smaller = np.zeros(mean.shape) #200x200
bins = np.zeros((mean.shape[0],mean.shape[1],B))
minval = m... | {"hexsha": "592a7fc8d440f7a6ced18f40506078ed666e1297", "size": 1579, "ext": "py", "lang": "Python", "max_stars_repo_path": "Week1/binapprox_FITS.py", "max_stars_repo_name": "vinayak1998/Data_Driven_Astronomy", "max_stars_repo_head_hexsha": "1d0dd82b2e9066759c442807c30c70bef096d719", "max_stars_repo_licenses": ["MIT"], ... |
include("../src/Shapes.jl")
using Test
using StaticArrays
PX = Shapes.PolygonXor{Float64}
rect(a,b,c=0,d=0) = PX([SA[c,d],SA[c+a,d],SA[c+a,d+b],SA[c,d+b]])
r1 = rect(2,1)
r2 = rect(1,2)
r3 = rect(3,3)
r4 = rect(1,1,4,4)
nva(s::Shapes.PolygonXor) = (length.(s.paths), Shapes.area(s))
@testset "Union" begin#««
@test nva... | {"hexsha": "d989a055fa55a767836b3751ad819c75f16245c3", "size": 1085, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/shapes.jl", "max_stars_repo_name": "plut/ConstructiveGeometry.jl", "max_stars_repo_head_hexsha": "0f384d6307513ec83ee030161d22937d3b65294b", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
# -*- coding: utf-8 -*-
"""
Defines unit tests for :mod:`colour.algebra.matrix` module.
"""
from __future__ import division, unicode_literals
import numpy as np
import unittest
from colour.algebra import is_identity
__author__ = 'Colour Developers'
__copyright__ = 'Copyright (C) 2013-2019 - Colour Developers'
__lic... | {"hexsha": "4a9dc85999cd3093abf55821bb8e72e9cba88572", "size": 1266, "ext": "py", "lang": "Python", "max_stars_repo_path": "colour/algebra/tests/test_matrix.py", "max_stars_repo_name": "MaxSchambach/colour", "max_stars_repo_head_hexsha": "3f3685d616fda4be58cec20bc1e16194805d7e2d", "max_stars_repo_licenses": ["BSD-3-Cla... |
""" This file defines utilities for the ROS agents. """
import numpy as np
import rospy
from gps.algorithm.policy.lin_gauss_policy import LinearGaussianPolicy
from gps_agent_pkg.msg import ControllerParams, LinGaussParams, TfParams, CaffeParams, TfActionCommand
from gps.sample.sample import Sample
from gps.proto.gps_... | {"hexsha": "c3c73f9ef5d990cd1f081b3ad041af10372a5ccb", "size": 4959, "ext": "py", "lang": "Python", "max_stars_repo_path": "baselines/gps/agent/ros/ros_utils.py", "max_stars_repo_name": "DengYuelin/baselines-assembly", "max_stars_repo_head_hexsha": "d40171845349395f0ed389d725873b389b08f94f", "max_stars_repo_licenses": ... |
# -*- coding: utf-8 -*-
## Sample file to show the implementation of variotherm data extraction from .irb video
### AUTHOR : VISWAMBHAR REDDY YASA
### MATRICULATION NUMBER : 65074
### STUDENT PROJECT TUBF: Projekt LaDECO (Machine learning on thermographic videos)
import numpy as np
from thermograms.Data_extraction impo... | {"hexsha": "59734bccfe88daa600a4def35e404de74f7ac036", "size": 784, "ext": "py", "lang": "Python", "max_stars_repo_path": "Variotherm_data_extraction.py", "max_stars_repo_name": "viswambhar-yasa/LaDECO", "max_stars_repo_head_hexsha": "0172270a86c71e8c32913005ec07fd63293af0f7", "max_stars_repo_licenses": ["MIT"], "max_s... |
import tensorflow as tf
import numpy as np
#import mnist_data
batch_size = 128
test_size = 256
img_size = 28
num_classes = 10
def init_weights(shape):
return tf.Variable(tf.random_normal(shape, stddev=0.01))
def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
conv1 = tf.nn.conv2d(X, w,\
... | {"hexsha": "606a6884f291d1217051f46e6c947e0656c8db3e", "size": 6166, "ext": "py", "lang": "Python", "max_stars_repo_path": "Chapter04/MNIST_CNN/Python 3.5/mnist_cnn_1.py", "max_stars_repo_name": "littlealexchen/Deep-Learning-with-TensorFlow-master", "max_stars_repo_head_hexsha": "44018b1940736b26700660dca2b6d38b13c1acf... |
#-------------------------------------------------------------------------------
# Name:Recursive least squares
# Author: m_tsutsui
#-------------------------------------------------------------------------------
#Library_Import#######################... | {"hexsha": "70061e6da59646590a080e844f948f76767af6d4", "size": 1751, "ext": "py", "lang": "Python", "max_stars_repo_path": "playground/fir_sofa_ipynb/filters/test/RLS_Algorithm/af_Recursive_least_squares.py", "max_stars_repo_name": "tetsuzawa/go-research", "max_stars_repo_head_hexsha": "e76f820b92b58b35825a8cbb28452b7d... |
""" README:
This script allows operation (move or delete) on fits files
based on their stored fits keyword.
Any condition can be set by the user on all fits keyword.
For example:
- delete file with Eccentricity too high
- move files having same Gain and Offset to another dir
"""
import sys
impo... | {"hexsha": "78288a55d2069bfad6ad590b4dd153a33ad7a5f0", "size": 7036, "ext": "py", "lang": "Python", "max_stars_repo_path": "fits_manager.py", "max_stars_repo_name": "fenriques/astro_tools", "max_stars_repo_head_hexsha": "ac65005a69eb4905c97864e431284bdaa4415c98", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
# Copyright (c) 2018-2021, Carnegie Mellon University
# See LICENSE for details
#P Permutations
#P ------------
#P
#P Under different circumstances different objects are called permutations,
#P even in the context of linear algrebra.
#P
#P Following list describes these objects and their representation in SPIRAL:
#P... | {"hexsha": "5eeb94fc7b4ab3f15f8391facbe6595d572a87ba", "size": 6927, "ext": "gi", "lang": "GAP", "max_stars_repo_path": "namespaces/spiral/spl/PermClass.gi", "max_stars_repo_name": "sr7cb/spiral-software", "max_stars_repo_head_hexsha": "349d9e0abe75bf4b9a4690f2dbee631700f8361a", "max_stars_repo_licenses": ["BSD-2-Claus... |
import cv2
import numpy as np
import matplotlib.pyplot as plt
# EXAMPLE HOMOGRAPHY TRANSFORM
# Read source image.
im_src = cv2.imread('book2.jpg')
# Four corners of the book in source image
pts_src = np.array([[141, 131], [480, 159], [493, 630],[64, 601]])
# Read destination image.
im_dst = cv2.imread('book1.jpg')
#... | {"hexsha": "664c38236695844aa3782d5b221f0efe24a19a6d", "size": 897, "ext": "py", "lang": "Python", "max_stars_repo_path": "pertemuan_12/homography.py", "max_stars_repo_name": "Muhammad-Yunus/Jetson-Nano-OpenCV-Learn", "max_stars_repo_head_hexsha": "933cb2594539a877030fb82dc3e6867409c1a557", "max_stars_repo_licenses": [... |
[STATEMENT]
lemma holds_set_list: "\<lbrakk>holds pre l nxt; x \<in> set l\<rbrakk> \<Longrightarrow> \<exists> p y . P p x y"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<lbrakk>holds pre l nxt; x \<in> set l\<rbrakk> \<Longrightarrow> \<exists>p y. P p x y
[PROOF STEP]
by (metis TW.holds_append holds_Cons_P sp... | {"llama_tokens": 137, "file": "IsaNet_infrastructure_Take_While", "length": 1} |
import robotics
import numpy as np
if __name__ == "__main__":
# define a collection of transforms, each with parents and children
baseLink = robotics.Transform(name="base_link")
link1 = robotics.Transform(
0.1, 1.5, -0.5, 0, 0, parent="base_link", child="link1", name="bTo1"
)
link2 = roboti... | {"hexsha": "619140680c5417a8bd2f6e2825fd70343f3cf516", "size": 1256, "ext": "py", "lang": "Python", "max_stars_repo_path": "docs/samples/sample_kinematic_tree.py", "max_stars_repo_name": "bkolligs/pyrobo", "max_stars_repo_head_hexsha": "341687cbed96f839fae682f9ec1c58524d7b35b4", "max_stars_repo_licenses": ["MIT"], "max... |
import matplotlib.pyplot as plt
import numpy as np
import os
import config_arm_project as config
import mxnet as mx
from mxboard import SummaryWriter
import scipy.spatial.distance as distance
import shutil, cv2
from gluoncv.model_zoo import get_model
from mxnet import image, init, nd, gluon, ndarray
from mxnet.gluon.da... | {"hexsha": "922d430a2f7516f56ae804c3e54833dbe4a6517a", "size": 19113, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/classification/arm_project/data_analyze.py", "max_stars_repo_name": "titikid/gluon-cv", "max_stars_repo_head_hexsha": "e3d5b5a0a71e0a0cf20671914b5f74004b491d3a", "max_stars_repo_licenses"... |
import numpy as np
from sklearn.preprocessing import MultiLabelBinarizer
def labels2seq(word2type, all_words, word_list, is_train):
ls = [-1, 0, 1, 19, 20, 21, 22, 41, 42, 43, 44, 45, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133,
134, 135, 136, 137,
138, 139, 140, 141... | {"hexsha": "8cea6922b7abfec82fddee8e96a4672b78009159", "size": 6522, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils/data_transform.py", "max_stars_repo_name": "thunlp/AutoCLIWC", "max_stars_repo_head_hexsha": "3fd594911cde210cf0d3a06226975449c0c00083", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
module apply_matrix_module
use ml_layout_module
use multifab_module
use define_bc_module
use div_and_grad_module
use stag_applyop_module
use div_and_grad_module
use bc_module
use multifab_physbc_module
use multifab_physbc_stag_module
implicit none
private
public :: apply_matrix
contains
... | {"hexsha": "981ea67a2a0ad8c04a86f19a09d4616ce6cbc2c9", "size": 3955, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src_gmres/apply_matrix.f90", "max_stars_repo_name": "BoxLib-Codes/LowMachMixtures", "max_stars_repo_head_hexsha": "0eacbd80fd0810f9fcfd6747e6b1a2a2cf6d9c12", "max_stars_repo_licenses": ["BSD-3-C... |
const config = Dict{String, Any}(
# whether to debug AWS requests and responses
"dbg" => false,
# AWS credentials to connect with
# If not set, they are taken from environment or .aws/* files
#"id" => "",
#"key" => "",
#"region" => AWS.US_WEST_2,
#"availability-zones" => ["us-west-2a", ... | {"hexsha": "f3ab20bd1a3dc4faaaeeda216d291f6315e4152a", "size": 866, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/config.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/AWS.jl-fbe9abb3-538b-5e4e-ba9e-bc94f4f92ebc", "max_stars_repo_head_hexsha": "b7d717848632591e7be29869d14051326b915ba3", "max_s... |
# -*- coding: utf-8 -*-
__author__ = ["Junhao Wen", "Jorge Samper-Gonzalez"]
__copyright__ = "Copyright 2016-2018 The Aramis Lab Team"
__credits__ = ["Junhao Wen"]
__license__ = "See LICENSE.txt file"
__version__ = "0.1.0"
__status__ = "Development"
import os
from os import path
import numpy as np
from clinica.pipeli... | {"hexsha": "8f95867eaf6081283bb808078365fd7fb183edea", "size": 6488, "ext": "py", "lang": "Python", "max_stars_repo_path": "Paper_Specific_Versions/2018_OHBM_DTI/Code/clinica_ml_dwi/mlworkflow_dwi.py", "max_stars_repo_name": "adamwild/AD-ML", "max_stars_repo_head_hexsha": "e4ac0b7d312ab482b9b52bb3f5c6745cc06431e9", "ma... |
import numpy as np
import pandas as pd
from lightgbm import LGBMClassifier
from scipy.stats import rankdata
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
from encoders import MultipleEncoder, DoubleValidationEncoderNumerical
class Model:
def __init__(
s... | {"hexsha": "b897d4a4c497a4edc6521afefa3f567d9248bf15", "size": 5627, "ext": "py", "lang": "Python", "max_stars_repo_path": "Research/model.py", "max_stars_repo_name": "ylzhang29/GAN-for-tabular-data", "max_stars_repo_head_hexsha": "2ba28e8c6fd20143850aa329fe6c68e6aa5af436", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
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