text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import networkx as nx
import numpy as np
def project3d(points, direction):
"""
投影函数，将三维点集投影到二维
投影平面内的y方向为z轴投影(如果投影的法向量为z轴，则y方向为x轴投影)
:param points: 三维点集
:param direction: 投影平面的法向量(u,v,w)，投影平面通过原点(0,0,0)
"""
d = direction / np.linalg.norm(direction)
y0 = np.array([1, 0, 0]) if np.array(... | {"hexsha": "06563550a45959f7b7c60ff0e3d503579d489536", "size": 3759, "ext": "py", "lang": "Python", "max_stars_repo_path": "crystalsearch/graph/graph.py", "max_stars_repo_name": "jingshenSN2/CrystalTool", "max_stars_repo_head_hexsha": "18f07963ff5f2a54ac2c93e2fa59fada51346232", "max_stars_repo_licenses": ["MIT"], "max_... |
"""
Defines a set of regressions tests that should be run succesfully after all
major modification to the code.
"""
import sys
import math
import numpy as np
import unittest
import time
from describe.descriptors import MBTR
from describe.descriptors import CoulombMatrix
from describe.descriptors import SortedCoulombMa... | {"hexsha": "c7f3f5be7d0f156875b0a4d997803dddbb4788d6", "size": 25203, "ext": "py", "lang": "Python", "max_stars_repo_path": "describe/describe/regtests/regtests.py", "max_stars_repo_name": "MadsAW/machine-learning-on-materials", "max_stars_repo_head_hexsha": "6101c7e3d12be54b12391c78442294198a39cc9b", "max_stars_repo_l... |
/*
* Copyright 2013 Matthew Harvey
*
* 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": "5bb4b8ecc48077c9ee07539d79d7ae9a20f07983", "size": 12345, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "include/detail/sql_statement_impl.hpp", "max_stars_repo_name": "matt-harvey/sqloxx", "max_stars_repo_head_hexsha": "4bc74b8992ebf735b96d512ee879b8035659fe60", "max_stars_repo_licenses": ["Apache-2.... |
import unittest
import numpy as np
import spladtool.spladtool_forward as stf
class TestBasic(unittest.TestCase):
def test_add(self):
x = np.array([[1.0], [2.0], [3.0]])
z = x + 4
sf_x = stf.tensor([[1.0], [2.0], [3.0]])
sf_z = sf_x + 4
print('x : ', sf_x)
... | {"hexsha": "429f690863202058257ed6dde8a8cfc41bd9fe10", "size": 3688, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_basic_ops.py", "max_stars_repo_name": "cs107-rysr/cs107-FinalProject", "max_stars_repo_head_hexsha": "df4814948374dbb5defe28e8d318e43d33a2d1cb", "max_stars_repo_licenses": ["MIT"], "max... |
from typing import Optional, Tuple, Union
from grgr import _R
from grgr.dev import dict_to_rargs
from grgr.dev.typing import T, U
from grgr.ggplot2.basic import Aesthetic, GGPlot
from grgr.ggplot2.facet import Facet
from grgr.ggplot2.layer import Layer
from grgr.ggplot2.scale import Appearance
from grgr.ggplot2.theme ... | {"hexsha": "e299568d35da27e4a44008b2f0ad125e4b9f62b6", "size": 8398, "ext": "py", "lang": "Python", "max_stars_repo_path": "grgr/ggplot2/__init__.py", "max_stars_repo_name": "7cm-diameter/grgr", "max_stars_repo_head_hexsha": "514a8f56e0fae9b4374d921006aaf3fb08fa2d09", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
MODULE IMSReorderingModule
use KindModule, only: DP, I4B
private
public :: ims_genrcm, ims_odrv, ims_dperm, ims_vperm
contains
!----- subroutine ims_genrcm
!
! purpose - ims_genrcm finds the reverse cuthill-mckee
! ordering for a general graph. for each connected
... | {"hexsha": "740324485e9dc29010b828bf6058d81f3ad41c13", "size": 60817, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/Solution/SparseMatrixSolver/ims8reordering.f90", "max_stars_repo_name": "maseology/mmMODFLOW6", "max_stars_repo_head_hexsha": "c4cfd2a5b52a80886142c2048ab44e0486516ae9", "max_stars_repo_lic... |
import io
import matplotlib.pyplot as plt
import numpy as np
import telegram
import torch
import torchvision
from PIL import Image
from trixi.logger.plt.numpyseabornplotlogger import NumpySeabornPlotLogger
class TelegramLogger(NumpySeabornPlotLogger):
"""
Telegram logger, inherits the AbstractLogger and sen... | {"hexsha": "b95c3f20d1d43ad359f23c3a0fe6e654d579ad8d", "size": 4402, "ext": "py", "lang": "Python", "max_stars_repo_path": "trixi/logger/message/telegramlogger.py", "max_stars_repo_name": "pfjaeger/trixi", "max_stars_repo_head_hexsha": "53f5c03ea3a955805c26037e0e9e4d135aec7652", "max_stars_repo_licenses": ["MIT"], "max... |
from numpy import inf, nan
from sklearn.linear_model import RANSACRegressor as Op
from lale.docstrings import set_docstrings
from lale.operators import make_operator
class _RANSACRegressorImpl:
def __init__(self, **hyperparams):
self._hyperparams = hyperparams
self._wrapped_model = Op(**self._hyp... | {"hexsha": "bc7c2c03ea76adb549b97f1f9d27aec56d603745", "size": 10317, "ext": "py", "lang": "Python", "max_stars_repo_path": "lale/lib/autogen/ransac_regressor.py", "max_stars_repo_name": "mfeffer/lale", "max_stars_repo_head_hexsha": "57b58843c7c14dc2e5658244280f2c1918bf030b", "max_stars_repo_licenses": ["Apache-2.0"], ... |
#include <boost/noncopyable.hpp>
#include "burger/base/Singleton.h"
#include <thread>
#include <iostream>
#include <string>
class Test :boost::noncopyable {
public:
Test() {
std::cout << "Test tid = " << std::this_thread::get_id() << " Address = "
<< static_cast<const void *>(this) << std::e... | {"hexsha": "a11d42eb664e450d73f061d268e2fed5d2e2adb5", "size": 1280, "ext": "cc", "lang": "C++", "max_stars_repo_path": "burger/base/tests/SingletonThreadLocal_test.cc", "max_stars_repo_name": "BurgerGroup/Burger", "max_stars_repo_head_hexsha": "b9159ea8855122a32091d40eb24439456f8879a9", "max_stars_repo_licenses": ["MI... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Jérôme Eberhardt 2016-2020
# Unrolr
#
# pSPE CPU
# Author: Jérôme Eberhardt <qksoneo@gmail.com>
#
# License: MIT
import os
import sys
import numpy as np
from scipy.spatial.distance import cdist
__author__ = "Jérôme Eberhardt"
__copyright__ = "Copyright 2020, Jérôme ... | {"hexsha": "789dd8577494614b3ae0afc74034431883cc744d", "size": 6473, "ext": "py", "lang": "Python", "max_stars_repo_path": "unrolr/core/spe_cpu.py", "max_stars_repo_name": "jeeberhardt/unrolr", "max_stars_repo_head_hexsha": "76d432643525a1999a6b14d6af500b9ffb296b82", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import pytest
import numpy as np
from numpy import testing as npt
import pandas.util.testing as pdt
from ixmp import Platform
from message_ix import Scenario
from message_ix.testing import (
make_dantzig,
models,
TS_DF,
TS_DF_CLEARED,
TS_DF_SHIFT
)
def test_run_clone(tmpdir):
# this test is ... | {"hexsha": "d78cb744d2ed60a04768297317f31599bde429f1", "size": 4937, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_integration.py", "max_stars_repo_name": "GamzeUnlu95/message_ix", "max_stars_repo_head_hexsha": "ccf80600991362874424b786e10a688d8d24ca74", "max_stars_repo_licenses": ["Apache-2.0", "CC... |
PROGRAM file_pos
IMPLICIT NONE
REAL :: r
INTEGER :: status, line, index
CHARACTER :: msg
OPEN(UNIT=1, FILE='temp.dat', STATUS='NEW', ACTION='READWRITE', IOSTAT=status, IOMSG=msg)
WRITE(*, *) "Enter nonnegative real numbers to store in a temporary file."
WRITE(*, *) "Enter a negative real number to stop."
... | {"hexsha": "b86dbcb8d3e0537437d53ceef27d4c3e2f8f0193", "size": 658, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "src/chap5/file_pos.f90", "max_stars_repo_name": "evanmacbride/fortran-practice", "max_stars_repo_head_hexsha": "1d9d851c35baedf52444db65157bd9a987dec60d", "max_stars_repo_licenses": ["MIT"], "max... |
#include <iostream>
#include <iomanip>
#include <fstream>
#include <chrono>
#include <boost/algorithm/string.hpp>
#include "estimation.hpp"
#include "utils.hpp"
#include "pointmatcher/PointMatcher.h"
typedef PointMatcher<double> PM;
typedef PM::DataPoints DP;
using namespace PointMatcherSupport; // NOLINT
int main(i... | {"hexsha": "24a99c3310f4c7d25ff37fc4fbe8f70e96e45ab3", "size": 4209, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/odometry.cpp", "max_stars_repo_name": "keenan-burnett/leslie_lidar_mapping", "max_stars_repo_head_hexsha": "004f3b552c27aa87931b3e3a851a836d703682e7", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
# third party imports
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.metrics import roc_auc_score
from sklearn.linear_model import LogisticRegression
# custom imports
import cobra.utils as utils
class LogisticRegressionModel:
"""Wrapper around the LogisticRegression class, with addit... | {"hexsha": "b3261119a35a7581d9ce9e9c04a60c89c8b335a4", "size": 6994, "ext": "py", "lang": "Python", "max_stars_repo_path": "cobra/model_building/models.py", "max_stars_repo_name": "MatthiasRoels/cobra", "max_stars_repo_head_hexsha": "474650f1ba3f36aa87f3fd7e77724a10d5484401", "max_stars_repo_licenses": ["MIT"], "max_st... |
###Differential Evolution Validation###
##Brief explanation of the method##
"""
Validation will be done for various functions.
"""
__author__ = "Yarilis Gómez Martínez (yarilisgm@gmail.com)"
__date__ = "2021"
__copyright__ = "Copyright (C) 2021 Yarilis Gómez Martínez"
__license__ = "GNU GPL Version 3.0"
##M... | {"hexsha": "eb5294325a5f2af9521c451a0c37891225596f22", "size": 11468, "ext": "py", "lang": "Python", "max_stars_repo_path": "DE_Test_Function.py", "max_stars_repo_name": "Yarapy/Copula_Cosimulation", "max_stars_repo_head_hexsha": "90731920463a77952adf46b8444bba060362b9e4", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
using FrameFun.FrameFunInterface, FrameFun.Platforms, FrameFun.ApproximationProblems,
Test, LinearAlgebra, BasisFunctions, FrameFun.ParameterPaths, FrameFun.WeightedSumPlatforms,
FrameFun.ExtensionFramePlatforms
ap1 = approximationproblem(platform(Fourier(10)),10)
ap2 = approximationproblem(platform(Fourier(1... | {"hexsha": "3fe8fc00d2b3d23038e2ee4efc36d3ae92116203", "size": 9477, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/test_framefuninterface.jl", "max_stars_repo_name": "GeorgAUT/FrameFun.jl", "max_stars_repo_head_hexsha": "769c342ae76de06fa986662862ab448e48c1849c", "max_stars_repo_licenses": ["MIT"], "max_st... |
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from neupy import layers, algorithms, environment
environment.reproducible()
environment.speedup()
def make_dataset():
data, target = make_classific... | {"hexsha": "00c813945787ce4f7617d0bee0ca928ca17450ac", "size": 3326, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/mlp/mix_categorical_numerical_inputs.py", "max_stars_repo_name": "FGDBTKD/neupy", "max_stars_repo_head_hexsha": "1f5e1ae9364e8c7816df79678a4648c689d2a5d1", "max_stars_repo_licenses": ["MI... |
"""
This example shows how to connect events in one window, for example, a mouse
press, to another figure window.
If you click on a point in the first window, the z and y limits of the
second will be adjusted so that the center of the zoom in the second
window will be the x,y coordinates of the clicked point.
Note th... | {"hexsha": "6a6826011e441146ba5bc54963801f22945cdcbe", "size": 1146, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/event_handling/zoom_window.py", "max_stars_repo_name": "nkoep/matplotlib", "max_stars_repo_head_hexsha": "6ed04252994443a4cecf95f0da0efedb6d514b38", "max_stars_repo_licenses": ["MIT", "BS... |
#this file simulate omics data for nonlinear system
# the QTLs ("kk"), QTL effects ("u.txt"), QTL position ("qtl.txt"), omics effects ("alpha.txt") in Christensenet al.(2021) are required
# above data can be found in Christensenet al.(2021) or below link:
# http://genoweb.toulouse.inra.fr/~alegarra/GOBLUP/
using JWAS,... | {"hexsha": "c27a1bc9a67b499d1c730132115e35da46a5fae7", "size": 3981, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "Part2_NNLMM_nonlinear/data/data_simu_nonlinear.jl", "max_stars_repo_name": "zhaotianjing/NN-LMM", "max_stars_repo_head_hexsha": "cac8e597d7a783b64bde9b897408be500e1615a8", "max_stars_repo_licenses"... |
import logging
import os
import coloredlogs
import imageio
import numpy as np
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui
from skimage.color import rgb2gray
from skimage.exposure import rescale_intensity
from skimage.transform import rescale, rotate
from skimage.util import pad
logging.... | {"hexsha": "bb9d0c0d14f72d253f486ba5b7398716900d1534", "size": 1722, "ext": "py", "lang": "Python", "max_stars_repo_path": "workspace/generate_hashimoto_image_pattern.py", "max_stars_repo_name": "cbc-group/pattern", "max_stars_repo_head_hexsha": "84c3c69015d860430c84114aabdd9ceea151704d", "max_stars_repo_licenses": ["A... |
import struct
import numpy as np
import pandas as pd
df_train = pd.read_csv('data/train_data.csv')
df_valid = pd.read_csv('data/valid_data.csv')
df_test = pd.read_csv('data/test_data.csv')
feature_cols = list(df_train.columns[:-1])
target_col = df_train.columns[-1]
X_train = df_train[feature_cols].values
y_train = d... | {"hexsha": "5e5bac8ed85072e3318e3570b00ed728402e3f71", "size": 884, "ext": "py", "lang": "Python", "max_stars_repo_path": "bh_tsne/prep_data.py", "max_stars_repo_name": "mr4jay/numerai", "max_stars_repo_head_hexsha": "a07b2dcafe9f078df8578d150d585f239fe73c51", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 306,... |
SUBROUTINE classico(uint,vint,wint)
USE velpre
USE parametros
IMPLICIT NONE
!===================================================================================================================
real(8), dimension(nx1,ny,nz) :: uint
real(8), dimension(nx,ny1,nz) :: vint
real(8), dimension(nx,ny,nz1) :: wint
... | {"hexsha": "1f58d5afd3e49df649874d79224d875f1efaf687", "size": 16655, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "SuLi_ic/7_advectivo.f90", "max_stars_repo_name": "pemiguell/SuLi", "max_stars_repo_head_hexsha": "8d9feb8f89bc44e2f870fb68023ef3b90e5ecfa6", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import caffe
import numpy as np
import sys
import triplet.config as cfg
global mean_file
mean_file='/home/frank/triplet-master/data/models/softmax/mean.binaryproto'
if __name__ == '__main__':
proto_data = open(mean_file, "rb").read()
mean_blob = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
#mean = ... | {"hexsha": "97ea27932bbda84bef2951e398669adc1b55aa13", "size": 468, "ext": "py", "lang": "Python", "max_stars_repo_path": "meanproto2npy.py", "max_stars_repo_name": "gustavkkk/image-classifier", "max_stars_repo_head_hexsha": "4991c9e828daf793b5b8378bf989d8fb89519204", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
[STATEMENT]
lemma perp_inter_perp_in_n:
assumes "A B Perp C D"
shows "\<exists> P. Col A B P \<and> Col C D P \<and> P PerpAt A B C D"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. \<exists>P. Col A B P \<and> Col C D P \<and> P PerpAt A B C D
[PROOF STEP]
by (simp add: assms perp_inter_perp_in) | {"llama_tokens": 135, "file": "IsaGeoCoq_Tarski_Neutral", "length": 1} |
[STATEMENT]
lemma tabulate_parametric:
assumes [transfer_rule]: "bi_unique A"
shows "(list_all2 A ===> (A ===> B) ===> A ===> rel_option B)
(\<lambda>ks f. (map_of (map (\<lambda>k. (k, f k)) ks))) (\<lambda>ks f. (map_of (map (\<lambda>k. (k, f k)) ks)))"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (list... | {"llama_tokens": 222, "file": null, "length": 1} |
import sys
import numpy as np
import pandas as pd
def kese_indicators():
pass
def _neb_raw_data_merge(df_bfs, df_pep, df_bds, df_bfs_march):
return df_bfs. \
merge(df_pep.drop('region', 1), how='left', on=['fips', 'time']).\
merge(df_bds.drop('region', 1), how='left', on=['fips', 'time']).\
... | {"hexsha": "6bf2a6baf936101837e20422a2d699c84b2c4544", "size": 3051, "ext": "py", "lang": "Python", "max_stars_repo_path": "kauffman/tools/_indicators.py", "max_stars_repo_name": "EMKF/downwardata", "max_stars_repo_head_hexsha": "60e4f547df69796f52a7ac7dd9417c44a733b396", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
from Adafruit_IO import *
import RPi.GPIO as GPIO
import time as yotimma
import numpy as np
import sounddevice as sd
#Connectie met de adafruit api
aio = Client('Nizari' , '')
#setten van de pins
GPIO.setmode(GPIO.BCM)
GPIO.setwarnings(False)
PIR_PIN = 3
GPIO.setup(PIR_PIN, GPIO.IN)
#print dat de code ready is
print... | {"hexsha": "03cfe4cf6e3bc0bae0bc9cf8073d808aba937b93", "size": 1464, "ext": "py", "lang": "Python", "max_stars_repo_path": "geluidleves.py", "max_stars_repo_name": "stijnana/druktemeter", "max_stars_repo_head_hexsha": "cdb9030e54985028ef75677c477c11caf7989a12", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
#Create line graph of loss chart. x-axis = # of epochs, y-axis = loss
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
train_num = 940+2350 #760 for flickr, 1880 for mscoco outdoor decoder
val_num = 90+220 #140 for flickr, 180 for mscoco outdoor decoder
x_data_train = np.arange(1, 21, step=20/... | {"hexsha": "cc75ef3e5d1d5e91c1fb6dfaab9aa4b13756ff91", "size": 4611, "ext": "py", "lang": "Python", "max_stars_repo_path": "metrics_graphs.py", "max_stars_repo_name": "mayankiitg/a-PyTorch-Tutorial-to-Image-Captioning", "max_stars_repo_head_hexsha": "13b2ea499b1ad11d308640df82f1502ddde0e6f6", "max_stars_repo_licenses":... |
from functools import lru_cache
import numpy as np
import pytest
from copulae import GaussianCopula
from copulae.core import cov2corr
@lru_cache(3)
def gen_corr(d=3) -> np.ndarray:
np.random.seed(10)
a = np.random.uniform(size=d * d).reshape(d, d)
return cov2corr(a @ a.T)
def test_set_parameter():
... | {"hexsha": "cdaa696373cd2973a25aac966e3a3fef64ea594e", "size": 1614, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/elliptical/test_elliptical.py", "max_stars_repo_name": "CrisDS81/copulae", "max_stars_repo_head_hexsha": "2a312c2b849f95cfb2b40b381d34bc790d9d80c5", "max_stars_repo_licenses": ["MIT"], "max_... |
#
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
#
from enum import Enum
from typing import List
import numpy as np
import pandas as pd
from scipy.stats import t
class Prediction:
""" General Prediction class used to capture output from surrogate model .predict() methods
... | {"hexsha": "f6dcb77a4e99835ccb82b4c19abc5514b9305b75", "size": 9847, "ext": "py", "lang": "Python", "max_stars_repo_path": "source/Mlos.Python/mlos/Optimizers/RegressionModels/Prediction.py", "max_stars_repo_name": "amueller/MLOS", "max_stars_repo_head_hexsha": "8f79bfa27a6fd09c3e00187bae8d7177eaf55247", "max_stars_rep... |
///////////////////////////////////////////////////////////////////////////////////////////////////
/// \file formic/utils/numeric.cpp
///
/// \brief implementation file for miscellaneous functions related to numbers
///
/////////////////////////////////////////////////////////////////////////////////////////////////... | {"hexsha": "049012874b5c0ed79eb3bc2ab7d38447b3026926", "size": 10736, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/formic/utils/numeric.cpp", "max_stars_repo_name": "eugeneswalker/qmcpack", "max_stars_repo_head_hexsha": "352ff27f163bb92e0c232c48bec8ae7951ed9d8c", "max_stars_repo_licenses": ["NCSA"], "max_st... |
import numpy as np
def get_board(state, b0=None):
if b0 is None:
repr_array = np.empty(9, dtype=np.int8)
else:
repr_array = b0.ravel()
for n in range(0, 8):
new_state = state // (3**(8-n))
repr_array[8-n] = new_state
state -= new_state * (3**(8-n))
repr_array[0] = state
if b0 is None:
return repr_arra... | {"hexsha": "08bfa7f3c855337f7c5851a915b51d387ed38993", "size": 806, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/functions.py", "max_stars_repo_name": "Ollehto/ox_lib", "max_stars_repo_head_hexsha": "2326aad94be4635b43d70fbedc63e669b2643019", "max_stars_repo_licenses": ["BSD-2-Clause"], "max_stars_count":... |
from typing import Dict, List, NamedTuple, Tuple
from bs4 import BeautifulSoup, Tag
from matplotlib.path import Path
from numpy import ndarray
from shapely.geometry import MultiPolygon, Polygon
from shapely.ops import unary_union
from svgpath2mpl import parse_path
from .logs.log import get_logger
LOGGER = get_logger... | {"hexsha": "9ce226a348ece8c08b9c5de73ca34490dd85d0e8", "size": 3026, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/ukraine_war_map_twitter_bot/analyze.py", "max_stars_repo_name": "a2435191/ukraine-war-map-twitter-bot", "max_stars_repo_head_hexsha": "3218e0d1754b5a21bc995781c572e05e97151959", "max_stars_rep... |
include 'VICMAIN_FOR'
subroutine main44
c Revision History:
c 02 Jan 1995 ... CRI ... MSTP S/W Conversion (VICAR Porting)
c------ program CAMPARAM
c------ Program CAMPARAM will fill the LOCAL variables;
c------ "sc", "scan", "camera", "filter", "fds" and "exprng"
c------ and return the variables t... | {"hexsha": "8de21e955bc1c7746814c4073baacee9f9d22146", "size": 3044, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "vos/p2/prog/camparam/camparam.f", "max_stars_repo_name": "NASA-AMMOS/VICAR", "max_stars_repo_head_hexsha": "4504c1f558855d9c6eaef89f4460217aa4909f8e", "max_stars_repo_licenses": ["BSD-3-Clause"], ... |
using Dates: Hour, Minute, Second, Millisecond, days, hour, minute, second, millisecond
"""
timezone(::ZonedDateTime) -> TimeZone
Returns the `TimeZone` used by the `ZonedDateTime`.
"""
timezone(zdt::ZonedDateTime) = zdt.timezone
Dates.days(zdt::ZonedDateTime) = days(DateTime(zdt))
for period in (:Hour, :Minute... | {"hexsha": "edf36494ab85a4970d4b961c4b1cf64a612252cd", "size": 605, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/accessors.jl", "max_stars_repo_name": "NHDaly/TimeZones.jl", "max_stars_repo_head_hexsha": "71178fefd23a8ad00f43aacfcde74720f1abfd07", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 49, ... |
"""
Tests of neo.io.axonaio
"""
import unittest
from neo.io.axonaio import AxonaIO
from neo.test.iotest.common_io_test import BaseTestIO
from neo.io.proxyobjects import (AnalogSignalProxy,
SpikeTrainProxy, EventProxy, EpochProxy)
from neo import (AnalogSignal, SpikeTrain)
import quantities as pq
impo... | {"hexsha": "cea32570871534de50c0c0e762dc23ba74a7d7c2", "size": 572, "ext": "py", "lang": "Python", "max_stars_repo_path": "neo/test/iotest/test_axonaio.py", "max_stars_repo_name": "teogale/python-neo", "max_stars_repo_head_hexsha": "cd4226ddcfbace080c4734f562f706423979f2dc", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
"""
Library Features:
Name: lib_snowblending_generic
Author(s): Francesco Avanzi (francesco.avanzi@cimafoundation.org), Fabio Delogu (fabio.delogu@cimafoundation.org)
Date: '20210525'
Version: '1.0.0'
"""
######################################################################################... | {"hexsha": "7c8bcff54c1487a239398e23bd6cf108eef55e9b", "size": 6035, "ext": "py", "lang": "Python", "max_stars_repo_path": "apps/satellite/blending/snow/lib_snowblending_generic.py", "max_stars_repo_name": "c-hydro/hyde", "max_stars_repo_head_hexsha": "3a3ff92d442077ce353b071d5afe726fc5465201", "max_stars_repo_licenses... |
{-# OPTIONS --cubical --no-import-sorts --safe #-}
module Cubical.Relation.Binary.Base where
open import Cubical.Core.Everything
open import Cubical.Foundations.Prelude
open import Cubical.Foundations.HLevels
open import Cubical.Data.Sigma
open import Cubical.HITs.SetQuotients.Base
open import Cubical.HITs.Propositio... | {"hexsha": "be51c02b7d27169d2918a6db3250cfc876bc93e6", "size": 2028, "ext": "agda", "lang": "Agda", "max_stars_repo_path": "Cubical/Relation/Binary/Base.agda", "max_stars_repo_name": "apabepa10/cubical", "max_stars_repo_head_hexsha": "3a9bb56260c25a6f2e9c20af8d278de0fe8d9e05", "max_stars_repo_licenses": ["MIT"], "max_s... |
"""
Example usage:
$ python issmile.py --show %userprofile%\scikit_learn_data\lfw_home\lfw_funneled\Arnold_Schwarzenegger\Arnold_Schwarzenegger_0006.jpg
$ python issmile.py %userprofile%\scikit_learn_data\lfw_home\lfw_funneled\Yoko_Ono\Yoko_Ono_0003.jpg
"""
import argparse
import numpy as np
from keras.models import ... | {"hexsha": "bd46f1d024e381ee7034d6b72656693173aa1448", "size": 1556, "ext": "py", "lang": "Python", "max_stars_repo_path": "issmile.py", "max_stars_repo_name": "MartinKondor/IsRealSmile", "max_stars_repo_head_hexsha": "b45a57589b9ef8ccded5e475c81f4786a935c177", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nul... |
// [[Rcpp::depends(RcppArmadillo)]]
#define ARMA_DONT_PRINT_ERRORS
#include <iostream>
#include <fstream>
#include <cmath>
#include <armadillo>
#include <errno.h>
#include <RcppArmadillo.h>
//' Get observation location in 2D space
//'
//' @param time time to return observer position
//' @param strip_size size of stri... | {"hexsha": "436cd11ff3f0bb55efe44f950287843a0f7340e2", "size": 41050, "ext": "cc", "lang": "C++", "max_stars_repo_path": "src/moveds.cc", "max_stars_repo_name": "r-glennie/moveds", "max_stars_repo_head_hexsha": "3fb04969cd0548e65b230ee4dcfb750ee1560b46", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
from keras.datasets import mnist
import matplotlib.pyplot as plt
import numpy as np
from keras.preprocessing import image
######################################### veri setini yükleyelim
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
################################################## Veri... | {"hexsha": "189d5a4e3f61f56ca5cadf51956d84e9e26457ce", "size": 5481, "ext": "py", "lang": "Python", "max_stars_repo_path": "mnist_karakter_tanima/mnist_karakter_tanima.py", "max_stars_repo_name": "seyfullahuysal/Derin-renme-Uygulamalar-", "max_stars_repo_head_hexsha": "8c09f5caed9fd0eaf56d348ba32f3752a5fb3b9d", "max_st... |
import base64
import io
import tarfile
import numpy as np
from numpy.testing import assert_array_equal
import pytest
import tiledb
from tiledb.tests.common import DiskTestCase
# This test writes to local filesystem, skip
# TODO: unskip if we support transparent file ops on a VFS
@pytest.mark.skipif(
pytest.ti... | {"hexsha": "f01249c6a28718f16deced1950898a313e394bea", "size": 9893, "ext": "py", "lang": "Python", "max_stars_repo_path": "tiledb/tests/test_compat.py", "max_stars_repo_name": "vishalbelsare/TileDB-Py", "max_stars_repo_head_hexsha": "9b1bf3c18fbe9d0de27ab26915f57779d3ea3635", "max_stars_repo_licenses": ["MIT"], "max_s... |
\chapter{crand Example of Extraction/Switch-Level Simulation}
\section{Introduction}
\label{PEintro}
In this example, we will be studying a random counter circuit.
We will see how Space is used for circuit extraction.
And how you can do a switch-level simulation of the circuit.
\\[1 ex]
The layout looks as follows, usi... | {"hexsha": "f8b64b0c99e055f86f076ccd8cc7a654fcfa5579", "size": 5821, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "doc/manuals/examples/crand/demo.tex", "max_stars_repo_name": "yrrapt/cacd", "max_stars_repo_head_hexsha": "696f5a22cb71b83eabbb9de199f1972d458fa9e9", "max_stars_repo_licenses": ["ISC"], "max_stars_c... |
import gc
print("############################################")
print("## 4.1. 결합, 마스터 테이블에서 정보 얻기 ")
print("############################################")
import pandas as pd
import numpy as np
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', 100)
reserve_tb=pd.read_csv('./data/re... | {"hexsha": "5a11d846fd7699b693ea13789d714a520e2da535", "size": 6886, "ext": "py", "lang": "Python", "max_stars_repo_path": "chapter4_study.py", "max_stars_repo_name": "gusdyd98/py_datapreprocessingwar", "max_stars_repo_head_hexsha": "907e4e8e2a9619bd33a34bcb4af1760f6f7ca3ee", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import cv2 as cv
import numpy as np
class StartState(object):
def action(self, ball):
ball.state = PlayState()
def update(self, ball, player, bricks, walls):
ball.pos = np.float32([player.x+player.w//2, player.y-ball.rad])
def die(self, ball):
pass
class PlayState(object):
... | {"hexsha": "0297692e3350c8efb337eb3dd3343ecf8b21037e", "size": 4856, "ext": "py", "lang": "Python", "max_stars_repo_path": "breakout.py", "max_stars_repo_name": "mikeswhitney33/face-detection-breakout-python", "max_stars_repo_head_hexsha": "6d6b0202564ba9585d46eb971fd0b8179caf5bc0", "max_stars_repo_licenses": ["MIT"], ... |
from styx_msgs.msg import TrafficLight
import cv2
#import rospy
import numpy as np
#from std_msgs.msg import Int32
class TLClassifier(object):
def __init__(self):
#TODO load classifier
pass
def get_classification(self, image):
"""Determines the color of the traffic light in the image
... | {"hexsha": "6ca51c4d2ae00de721a8d2886a1e8626862f3c82", "size": 1263, "ext": "py", "lang": "Python", "max_stars_repo_path": "ros/src/tl_detector/light_classification/tl_classifier.py", "max_stars_repo_name": "truongconghiep/CarND-Capstone-1", "max_stars_repo_head_hexsha": "e523adeaa7525f8d0cef94810db855d8331cddec", "max... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
from copy import deepcopy
from bg import Multicolor, KBreak, BreakpointGraph, GRIMMReader, NewickReader, BGGenome
import itertools
import networkx as nx
import os
__author__ = "Sergey Aganezov"
__email__ = "aganezov(at)gwu.edu"
##########################################... | {"hexsha": "0816aacbfcc5dbf846f23a48de2be9831b705330", "size": 31507, "ext": "py", "lang": "Python", "max_stars_repo_path": "gos/tmp/scaffolding_no_repeats.py", "max_stars_repo_name": "sergey-aganezov-jr/gos", "max_stars_repo_head_hexsha": "fb4d210284f3037c5321250cb95f3901754feb6b", "max_stars_repo_licenses": ["MIT"], ... |
#ifndef N_BODY_RANDOM_BODY_HPP
#define N_BODY_RANDOM_BODY_HPP
#include "communication.hpp"
#include "data.hpp"
#include "logging.hpp"
#include <boost/mpi/collectives.hpp>
#include <cstddef>
#include <functional>
namespace n_body::random::body {
template <typename T, std::size_t Dimension>
using BodyGenerator = std::... | {"hexsha": "226bc757240335ebc536caf1f341a85383f88d57", "size": 1352, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/random_body.hpp", "max_stars_repo_name": "linyinfeng/n-body", "max_stars_repo_head_hexsha": "e40c859689d76a3f36cd08e072d7ee24685e8be4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1.0... |
[STATEMENT]
lemma conemem_expansion_estimate:
fixes u v u' v'::"'a::euclidean_space"
assumes "t \<in> {0 .. pi / 2}"
assumes angle_pos: "0 < vangle u v" "vangle u v < pi / 2"
assumes angle_le: "(vangle u' v') \<le> (vangle u v)"
assumes "norm u = 1" "norm v = 1"
shows "norm (conemem u' v' t) \<ge> min (norm... | {"llama_tokens": 14032, "file": "Ordinary_Differential_Equations_IVP_Cones", "length": 105} |
using BenchmarkTools
using DataFrames
using PooledArrays
using Random
@show Threads.nthreads()
Random.seed!(1234)
ref_dfi = DataFrame(rand(1:10^4, 10^7, 4), :auto)
ref_dfs = string.(ref_dfi)
ref_dfp = mapcols(PooledArray, ref_dfs)
res = DataFrame(rows=Int[],cols=Int[], type=String[], op=String[], time=Float64[])
fo... | {"hexsha": "920297ed60f30c80d6c21d63f469f41a3a234ecd", "size": 1400, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "benchmarks/constructor_and_indexing/constructor_and_indexing_performance.jl", "max_stars_repo_name": "ericphanson/DataFrames.jl", "max_stars_repo_head_hexsha": "2f5536e92ed9d7002e3a19fa573ffad1d20d... |
[STATEMENT]
lemma unit\<^sub>0_simp:
assumes "C.obj a"
shows "EQ\<^sub>DoEQ\<^sub>U\<^sub>VoEQ\<^sub>C'.unit\<^sub>0 a =
C\<^sub>U.E (F\<^sub>U\<^sub>V.G (D\<^sub>V.\<eta>\<^sub>0 (D\<^sub>V.src (F (C\<^sub>U.P a))))) \<star>\<^sub>C C\<^sub>U.E (C\<^sub>U.P\<^sub>0 (src\<^sub>C a))
\<st... | {"llama_tokens": 904, "file": "Bicategory_EquivalenceOfBicategories", "length": 2} |
# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import numpy as np
import schema
from nni import ClassArgsValidator
from nni.common.hpo_utils import format_search_space, deformat_parameters
from nni.tuner import Tuner
class RandomTuner(Tuner):
def __init__(self, seed=None):
self.... | {"hexsha": "30318cf27b0ab5277b0988adc150245459279f6a", "size": 1627, "ext": "py", "lang": "Python", "max_stars_repo_path": "nni/algorithms/hpo/random_tuner.py", "max_stars_repo_name": "chyan0411/nni", "max_stars_repo_head_hexsha": "2064bba03da468ee9093b9015e98e62ca4262113", "max_stars_repo_licenses": ["MIT"], "max_star... |
C$Attribute setting:
C+PGCOLOUR -- set standard colour tables
SUBROUTINE PGCOLOUR
C-----------------------------------------------------------------------
C Sets standard colour tables, for devices supporting colour graphics.
C
C 16-Dec-1988 - new routine for Lexidata image processor [DJT].
C------------------... | {"hexsha": "cdce3aa7e13b4a5a3c07f39fb48e8e160e29ebb1", "size": 436, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "graphic_lib/pgcolour.f", "max_stars_repo_name": "CavendishAstrophysics/anmap", "max_stars_repo_head_hexsha": "efb611d7f80a3d14dc55e46cd01e8a622f6fd294", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
"""Find the synapse's pulse extender speed
Derived from check_max_synapse_rates.py
For each synapse:
Set up the Tag Action Table to send +1 and -1 spikes to an individual synapse for each input spike
generated from one of the FPGA's spike generators.
Send a high rate to the synapse, well above its maximum possible in... | {"hexsha": "aa3a9553ad564db8b60bb9379b416af8e5f2155f", "size": 10815, "ext": "py", "lang": "Python", "max_stars_repo_path": "pystorm/calibration/syn_pulse_extender.py", "max_stars_repo_name": "Stanford-BIS/pystorm", "max_stars_repo_head_hexsha": "4acaaee78a04b69ad17554126018016800e5a140", "max_stars_repo_licenses": ["M... |
import copy
import numpy as np
import hashlib
import collections
class Individual(object):
def __init__(self, id, params, n_var, genome = []):
self.id = id
self.acc = -1
self.flop = -1
self.params = params
self.n_var = n_var
self.rank = np.inf
self.crowding =... | {"hexsha": "63a7cb936cabdbbb3d4a37e97659f91fe21c8152", "size": 2248, "ext": "py", "lang": "Python", "max_stars_repo_path": "BenchENAS_linux_platform/algs/nsga_net/genetic/population.py", "max_stars_repo_name": "benchenas/BenchENAS", "max_stars_repo_head_hexsha": "776cd1dd035d73c4af369d0106d010b932f64782", "max_stars_re... |
import numpy as np
from pyglib.model import circauxi
import shutil,subprocess
cmd = ['/home/ykent/WIEN_GUTZ/bin2/CyGutz', '-r', '-1']
for i,u in enumerate(np.arange(1.0,0.9,-10)):
print(' Running with u = {}'.format(u))
circauxi.gutz_model_setup(u=u, nmesh=5000, norb=3, tiny=0.0, mu=0.0)
subprocess.call(c... | {"hexsha": "6e41bba2f326ea490fdd75eeb08fad2e8a9cafab", "size": 446, "ext": "py", "lang": "Python", "max_stars_repo_path": "ComRISB/pyglib/pyglib/model/test/test_semicircular_metal_2g/REF/scan.py", "max_stars_repo_name": "comscope/comsuite", "max_stars_repo_head_hexsha": "d51c43cad0d15dc3b4d1f45e7df777cdddaa9d6c", "max_... |
function kern = nddisimKernParamInit(kern)
% NDDISIMKERNPARAMINIT NDDISIM kernel parameter initialisation.
% The driven input single input motif (DISIM) kernel is specifically designed for
% working with gene networks where there is assumed to be a single
% transcription factor controlling several genes. This transcri... | {"author": "SheffieldML", "repo": "GPmat", "sha": "4b5914a38ecbad9fb7a13a3392970bfc28c9d911", "save_path": "github-repos/MATLAB/SheffieldML-GPmat", "path": "github-repos/MATLAB/SheffieldML-GPmat/GPmat-4b5914a38ecbad9fb7a13a3392970bfc28c9d911/kern/nddisimKernParamInit.m"} |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2018, 2019.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any... | {"hexsha": "db6fa0ee3be1332d3e2e622fda3a687844557315", "size": 3858, "ext": "py", "lang": "Python", "max_stars_repo_path": "qiskit/optimization/ising/partition.py", "max_stars_repo_name": "IanJoel/qiskit-aqua", "max_stars_repo_head_hexsha": "7707172d01f0539358f1ce2406f307e830105303", "max_stars_repo_licenses": ["Apache... |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import json
import sys
from elq.index.faiss_indexer import DenseFlatIndexer, DenseHNSWFlatIndexer, DenseIV... | {"hexsha": "54e52bd548e9bb31c2a4e505fe1dee058b37bb1d", "size": 42181, "ext": "py", "lang": "Python", "max_stars_repo_path": "elq/main_dense.py", "max_stars_repo_name": "lorenzo-sasso/BLINK", "max_stars_repo_head_hexsha": "ec26995da10a30d3ab0c520b0377faef329a7620", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
Require Import Coq.Bool.Bool.
Require Import Coq.ZArith.ZArith.
Require Import Coq.Lists.List. Import ListNotations.
Require Import bedrock2.MetricLogging.
Require Import coqutil.Macros.unique.
Require Import bedrock2.Memory.
Require Import compiler.util.Common.
Require Import coqutil.Decidable.
Require Import coqutil.... | {"author": "mit-plv", "repo": "bedrock2", "sha": "7f2d764ed79f394fe715505a04301d0fb502407f", "save_path": "github-repos/coq/mit-plv-bedrock2", "path": "github-repos/coq/mit-plv-bedrock2/bedrock2-7f2d764ed79f394fe715505a04301d0fb502407f/compiler/src/compiler/FlatImp.v"} |
//
// server.cpp
// ~~~~~~~~~~
//
// Copyright 2012 Red Hat, Inc.
// Copyright (c) 2003-2012 Christopher M. Kohlhoff (chris at kohlhoff 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)
//
#include <stdio... | {"hexsha": "e9f6b3a095f984a76f1574f422e6cdeeba144394", "size": 4265, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/server.cpp", "max_stars_repo_name": "dancal/as_proxyd", "max_stars_repo_head_hexsha": "a8f4eeb41dbffb49071d55b966f0ecf705104d2a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2.0, "max... |
import numpy as np
from collections import OrderedDict
from sklearn.metrics import average_precision_score
def str2ind(categoryname,classlist):
return [i for i in range(len(classlist)) if categoryname==classlist[i].decode('utf-8')][0]
def strlist2indlist(strlist, classlist):
return [str2ind(s,classlist) f... | {"hexsha": "9a858a6fac1314665e070adebc27dd6bec506e81", "size": 6322, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "salesforce/woad-pytorch", "max_stars_repo_head_hexsha": "405fa0c56271ae241f4789d9e1150330fddca3e5", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_count... |
(*****************************************************************************
* Featherweight-OCL --- A Formal Semantics for UML-OCL Version OCL 2.5
* for the OMG Standard.
* http://www.brucker.ch/projects/hol-testgen/
*
* Design_OCL.thy --- OCL Contracts and an Example... | {"author": "data61", "repo": "PSL", "sha": "2a71eac0db39ad490fe4921a5ce1e4344dc43b12", "save_path": "github-repos/isabelle/data61-PSL", "path": "github-repos/isabelle/data61-PSL/PSL-2a71eac0db39ad490fe4921a5ce1e4344dc43b12/SeLFiE/Example/afp-2020-05-16/thys/Featherweight_OCL/examples/Employee_Model/Design/Design_OCL.th... |
# 10.1.3 一変数ガウス分布の変分推論
#%%
# 10.1.3項で利用するライブラリ
import numpy as np
from scipy.stats import norm, gamma # 1次元ガウス分布, ガンマ分布
import matplotlib.pyplot as plt
#%%
## 真の分布(1次元ガウス分布)の設定
# 真の平均パラメータを指定
mu_truth = 5.0
# 真の精度パラメータを指定
tau_truth = 0.5
print(np.sqrt(1.0 / tau_truth)) # 標準偏差
# 作図用のxの値を作成
x_line = np.linspace(
... | {"hexsha": "501c8510523a51bc7e4ac96ef1f47bc45f2777a4", "size": 11859, "ext": "py", "lang": "Python", "max_stars_repo_path": "Code_Python/ch10_1_3.py", "max_stars_repo_name": "anemptyarchive/PRML", "max_stars_repo_head_hexsha": "58cbb35ae65d66b6faf436c70a6cbc9d54d4589f", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import string
import re
from os import walk
globaldict = dict()
def print_dict(d):
for key in list(d.keys()):
print(key, ":", d[key])
def read_data(textfile):
d = dict()
text = open(textfile).read().split()
for word... | {"hexsha": "bbd78fc5032f0369fa1fbb527bc627d38b5b3c60", "size": 2046, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/hi_clustering/prepare_data.py", "max_stars_repo_name": "piotrkoziar/AGH-python-labs", "max_stars_repo_head_hexsha": "a474c5f6ebe17b625d449ccbb0e6e9ef8a3755cb", "max_stars_repo_licenses": ["MIT... |
[STATEMENT]
lemma chain_subdiv_path_singleton:
shows "chain_subdiv_path \<gamma> {(1,\<gamma>)}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. chain_subdiv_path \<gamma> {(1, \<gamma>)}
[PROOF STEP]
proof -
[PROOF STATE]
proof (state)
goal (1 subgoal):
1. chain_subdiv_path \<gamma> {(1, \<gamma>)}
[PROOF STEP]
h... | {"llama_tokens": 910, "file": "Green_Paths", "length": 10} |
[GOAL]
R : Type u_1
M : Type u_2
inst✝² : CommSemiring R
σ : Type u_3
inst✝¹ : AddCommMonoid M
inst✝ : SemilatticeSup M
w : σ → M
p : MvPolynomial σ R
⊢ weightedTotalDegree' w p = ⊥ ↔ p = 0
[PROOFSTEP]
simp only [weightedTotalDegree', Finset.sup_eq_bot_iff, mem_support_iff, WithBot.coe_ne_bot, MvPolynomial.eq_zero_iff]... | {"mathlib_filename": "Mathlib.RingTheory.MvPolynomial.WeightedHomogeneous", "llama_tokens": 26398} |
using DualNumbers
using Random
using StaticArrays
using Test
Random.seed!(0)
@testset "Ring" begin
T = Int
randint() = T(rand(-100:100))
for iter in 1:100
n = Dual(T(0))
e = Dual(T(1))
x = Dual(randint(), randint())
y = Dual(randint(), randint())
z = Dual(randint()... | {"hexsha": "b8a2b7118ad37cba7e1e58cffb99ff3179462866", "size": 6300, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/runtests.jl", "max_stars_repo_name": "eschnett/DualNumbers.jl", "max_stars_repo_head_hexsha": "e46e17c2c7c2321fb8b79e878fd13e2afcd71e7c", "max_stars_repo_licenses": ["MIT"], "max_stars_count":... |
# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright © Climate Data Preprocessing Tool Project Contributors
# https://github.com/cgq-qgc/climate-data-preprocessing-tool
#
# This file is part of Climate Data Preprocessing Tool.
# Licensed under the terms of ... | {"hexsha": "ba8407cbafb60165081aef538cfdf02e389c326e", "size": 47488, "ext": "py", "lang": "Python", "max_stars_repo_path": "cdprep/gapfill_data/gapfill_weather_algorithm.py", "max_stars_repo_name": "cgq-qgc/climate-data-preprocessing-tool", "max_stars_repo_head_hexsha": "59aaf214127f00f92c6cacc8f988c5f05de6c0f6", "max... |
"""
Code based loosely on implementation:
https://github.com/openai/baselines/blob/master/baselines/common/models.py
Under MIT license.
"""
import numpy as np
import torch.nn as nn
import torch.nn.init as init
import vel.util.network as net_util
from vel.api.base import LinearBackboneModel, ModelFactory
class MLP... | {"hexsha": "131004f2ef02dca6e500566e082f0a883c157b62", "size": 2066, "ext": "py", "lang": "Python", "max_stars_repo_path": "vel/rl/models/backbone/mlp.py", "max_stars_repo_name": "cclauss/vel", "max_stars_repo_head_hexsha": "78a6a20af80ff613898d2983c83fdb223634aaad", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
import multiprocessing
import pytest
import numpy as np
import scipy as sp
import scipy.stats as st
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
from pyabc import (ABCSMC, RV, Distribution,
MedianEpsilon,
PercentileDistance, SimpleModel,
... | {"hexsha": "1288edd62b60a6111564c86fd8ba78dd7319bcc3", "size": 8043, "ext": "py", "lang": "Python", "max_stars_repo_path": "test/test_samplers.py", "max_stars_repo_name": "Pat-Laub/pyABC", "max_stars_repo_head_hexsha": "f23f0ff8d430a8ce0a0c8253b45e19add9121992", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
# This code is based off the DUET algorithm presented in:
# O. Yilmaz and S. Rickard, "Blind separation of speech mixtures via time-frequency masking."
# S. Rickard, "The DUET Blind Source Separation Algorithm"
#
# At this time, the algorithm is not working when returning to the time domain
# and, to be honest, I haven... | {"hexsha": "10bdb71ed6a0629f81dc4130589e00db4592648a", "size": 3493, "ext": "py", "lang": "Python", "max_stars_repo_path": "msspy/msspy.py", "max_stars_repo_name": "adambnoel/msspy", "max_stars_repo_head_hexsha": "8f3f3283e11b2ded141636abacdc7a3279ed45e5", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "m... |
import pathlib, sys
home_path = pathlib.Path('.').resolve()
while home_path.name != 'membership_inference_attack':
home_path = home_path.parent
reports_path = home_path/'reports'
from sklearn.metrics import confusion_matrix, classification_report, balanced_accuracy_score, roc_auc_score, \
... | {"hexsha": "acdbd6b0a16544a38d89329eab681028cef1371e", "size": 17176, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/utils/statistics.py", "max_stars_repo_name": "DisaitekAI/membership_inference_attack", "max_stars_repo_head_hexsha": "3cd77529e7584b3195a532d848bbea5f5cb6304d", "max_stars_repo_licenses": ["M... |
import numpy as np
import operator as op
from functools import reduce
def ncr(n, r):
"""n choose r"""
r = min(r, n - r) # This works since it's symmetric
numer = reduce(op.mul, range(n, n - r, -1), 1)
denom = reduce(op.mul, range(1, r + 1), 1)
return numer / denom
def bernstein_poly(i, n, t... | {"hexsha": "e17a1819d59e78cc88beda39e7be6c4c4fc04b30", "size": 1325, "ext": "py", "lang": "Python", "max_stars_repo_path": "sim_assets/ext.py", "max_stars_repo_name": "AvanaPY/SimSims", "max_stars_repo_head_hexsha": "6f74ed93f642a4238f98969a3f34ea8bccd83a87", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
import os, vtk
import numpy as np
from pymicro.view.scene3d import Scene3D
from pymicro.view.vtk_utils import *
from vtk.util.colors import white, grey, black, lamp_black
'''
Create a 3d scene with a tomographic view of a polymer foam.
The shape is displayed using a simple contour filter. Bounding box
and axes are al... | {"hexsha": "a3273e563068f38e0e64996d536218d06f8cdf7c", "size": 1559, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/3d_visualisation/mousse_3d.py", "max_stars_repo_name": "heprom/pymicro", "max_stars_repo_head_hexsha": "176bf3a829dbf67796a3d4471f18868a3da229a7", "max_stars_repo_licenses": ["MIT"], "max... |
import tensorflow as tf
import numpy as np
from train import model_to_tflite
from gdrive import saveModel, saveTFLiteModel
from mates import rmse
class DetectorInterface:
def train(self, trainData):
"""Train using many sessions"""
pass
def trainSession(self, session):
"""Train parameters... | {"hexsha": "1dc74fe9645bb32806f855c8ddaeac60ddcd9832", "size": 4945, "ext": "py", "lang": "Python", "max_stars_repo_path": "classes.py", "max_stars_repo_name": "aberaza/tfm2020Scripts", "max_stars_repo_head_hexsha": "5bd996c8cf3e1637f08669fee8b052ffefd09d64", "max_stars_repo_licenses": ["Unlicense"], "max_stars_count":... |
import argparse
import statistics
import scipy.stats as stats
''' //////////// CLASSI ////////////'''
class Caller():
GT=''
AO=''
RO=''
AO_f=''
AO_r=''
DP_f=''
DP_r=''
DP=''
QB=''
Call=''
AF=''
StrandBias=''
class Freebayes(Caller):
RO_f=''
RO_r=''
class Vardict(Caller):
RO_f=''
RO_r=''
ODDRATI... | {"hexsha": "d9ac79e69e7eb58f95c143337e4a894f2d852ad1", "size": 15598, "ext": "py", "lang": "Python", "max_stars_repo_path": "feature_extraction_indel.py", "max_stars_repo_name": "BonizzoniLab/SVD", "max_stars_repo_head_hexsha": "95ed967ae385ed0a339030763a07ea7acfa0c1d3", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
import os
import re
import math
import hashlib
import argparse
import numpy as np
import pandas as pd
import firecloud.api as fapi
from google.cloud import bigquery
from google.cloud import storage
from google.api_core.exceptions import NotFound
from collections import OrderedDict
import xmlt... | {"hexsha": "ef2df10d38e73e9d6489ebcc89bbef94680139da", "size": 14244, "ext": "py", "lang": "Python", "max_stars_repo_path": "terra/scripts/update_pacbio_tables.py", "max_stars_repo_name": "CloudyTrees/long-read-pipelines", "max_stars_repo_head_hexsha": "c6fdfa8f4f7ba06a92598e6febb7a6b81d78e946", "max_stars_repo_license... |
import pyrealsense2 as rs
import numpy as np
import cv2
import os
#import keyboard
import time
# Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Star... | {"hexsha": "29b75bf35a9bd3fc7b656c8bf20b1ffb297aa2e9", "size": 1821, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/capture.py", "max_stars_repo_name": "GRobled0/CenterNet", "max_stars_repo_head_hexsha": "740ecf06a96897b3545249bbb239264394283565", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
"""Abstract type for container modules -Modules that specify an
execution structure for a given group of modules"""
abstract type KnetContainer <: KnetModule end
"""
Sequential <: KnetModule
# Constructor
Sequential(ls...) adds layers in ls.
# Fields
layers::Array{Union{KnetModule, Function}, 1}
# Usage
... | {"hexsha": "48846e685279b7ac277dade6afcfef84491d56a1", "size": 1647, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/container.jl", "max_stars_repo_name": "cangumeli/KnetModules.jl", "max_stars_repo_head_hexsha": "ecf9d1dce785a9fafd2e2cd7cb354db10b1a032d", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
'''
Demo to show use of the engineering Formatter.
'''
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import EngFormatter
fig, ax = plt.subplots()
ax.set_xscale('log')
formatter = EngFormatter(unit='Hz', places=1)
ax.xaxis.set_major_formatter(formatter)
xs = np.logspace(1, 9, 100)
ys = (0... | {"hexsha": "a2c5d3003b16b04404a7c188818da46755b1d995", "size": 402, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/api/engineering_formatter.py", "max_stars_repo_name": "jbbrokaw/matplotlib", "max_stars_repo_head_hexsha": "86ec1b6fc5628bfb2d09797c58d7eed0ca8c2427", "max_stars_repo_licenses": ["MIT", "B... |
import os
import cv2
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import preprocess
# Return a list of all image names with a given extension in a given folder
def listImages(dir, extension):
res = []
for img in os.listdir(dir):
if img.endswith(extension):
res... | {"hexsha": "12fdeef5d26bbb94352e8d3eab0790bd1137b9c1", "size": 14709, "ext": "py", "lang": "Python", "max_stars_repo_path": "util.py", "max_stars_repo_name": "Koen-Git/ColorSymDetect", "max_stars_repo_head_hexsha": "5d6bb6734063f4a09c9a153527a446ce5c02a5b0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1, "ma... |
function mrAnatSetNiftiXform(niftiFile, outFile);
%
% mrAnatSetNiftiXform([niftiFile=uigetfile],[outFile=uiputfile])
%
% Allows you to set the qto xform in a nifti file.
%
% REQUIRES:
% * Stanford anatomy tools (eg. /usr/local/matlab/toolbox/mri/Anatomy)
%
% HISTORY:
% 2006.10.25 RFD (bob@white.stanford.edu) wrote it.... | {"author": "vistalab", "repo": "vistasoft", "sha": "7f0102c696c091c858233340cc7e1ab02f064d4c", "save_path": "github-repos/MATLAB/vistalab-vistasoft", "path": "github-repos/MATLAB/vistalab-vistasoft/vistasoft-7f0102c696c091c858233340cc7e1ab02f064d4c/mrAnatomy/VolumeUtilities/mrAnatSetNiftiXform.m"} |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# # # # # # # # # # # # # # # # # # # # # # # #
# @Author: ZhuangYuZhou
# @E-mail: 605540375@qq.com
# @Time: 22-4-20
# @Desc:
# # # # # # # # # # # # # # # # # # # # # # # #
import torch
import numpy as np
import math
import torch.nn.functional as F
def get_order_value... | {"hexsha": "ffe17a1ce5c6d65390e87c7d576cc718aef5de25", "size": 1953, "ext": "py", "lang": "Python", "max_stars_repo_path": "lib/dataloader/medical_loader_utils.py", "max_stars_repo_name": "Healingl/3DAPRNet", "max_stars_repo_head_hexsha": "7c5e0028ae844df4e1f26327e8b438532ca0745f", "max_stars_repo_licenses": ["BSD-2-Cl... |
import re
import numpy as np
from sqlalchemy import create_engine
def check_input_data(data):
"""
This function is used to check if input data are accepted or not
Args:
data: input data
Returns: True or false
"""
if 'uri' in data.keys() and 'type' in data.keys() and 'part' in data.... | {"hexsha": "7e84eea5626ed06368227c1c981da9d3227fa9cc", "size": 2530, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "QualiChain/analyzer", "max_stars_repo_head_hexsha": "e854479af374bc15823e7e930564b3ec88b83096", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max... |
from sympy.parsing.sympy_parser import parse_expr as parse
from sympy.parsing.sympy_parser import standard_transformations,implicit_multiplication_application
from sympy import latex
from strg import hasq
from sympy import sympify
def gotec(q,mode="equation"):
#def hasq(q,key):#returns if q has (<key> or <key></key>... | {"hexsha": "9ad4caed603b28d79e3d58a2d66f60ce68583213", "size": 822, "ext": "py", "lang": "Python", "max_stars_repo_path": "formula.py", "max_stars_repo_name": "psorus/xtech", "max_stars_repo_head_hexsha": "da856b3254e6885353636b1e3b163d5562c502b3", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_stars... |
# coding=utf-8
# main codes, call functions at stokes_flow.py
# Assuming u=u1+u2, u1 is velocity filed due to a stokeslet. u2=-u1 at boundary of a pip.
# Thus, u==0, no-slip boundary condition at the pip.
# Zhang Ji, 20170320
import sys
from typing import Any, Union
import petsc4py
petsc4py.init(sys.argv)
import num... | {"hexsha": "cc1951cb375402c392ac7b9ff3be76a5a40e84b2", "size": 15768, "ext": "py", "lang": "Python", "max_stars_repo_path": "sphereInPipe/StokesletInPipe.py", "max_stars_repo_name": "pcmagic/stokes_flow", "max_stars_repo_head_hexsha": "464d512d3739eee77b33d1ebf2f27dae6cfa0423", "max_stars_repo_licenses": ["MIT"], "max_... |
import mapchete
from mapchete.errors import MapcheteConfigError
from mapchete.formats import available_output_formats
import numpy as np
import pytest
import xarray as xr
import dateutil
import json
from mapchete.testing import get_process_mp
def test_format_available():
assert "xarray" in available_output_forma... | {"hexsha": "e9beec547509b32b7bbbfcecca5122473282d6d5", "size": 18590, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_format.py", "max_stars_repo_name": "ungarj/mapchete_xarray", "max_stars_repo_head_hexsha": "e42b2f8f38871c415ba12de0299d30f5618227c9", "max_stars_repo_licenses": ["MIT"], "max_stars_co... |
import hashlib
import math
import os
import random
from datetime import datetime, timedelta
from functools import reduce
import numpy as np
import pandas as pd
from faker import Faker
class GenerateData:
def __init__(self, num_users, num_txn, fraud_ratio, start_date, end_date):
self.num_users = num_users... | {"hexsha": "f8a87ca50c0abde19f35fa2ba5259feb015862d2", "size": 5883, "ext": "py", "lang": "Python", "max_stars_repo_path": "generate_transactions.py", "max_stars_repo_name": "Priya4607/real_time_fraudulent_transaction_detection", "max_stars_repo_head_hexsha": "ef2251295b8ff8a93bd39fcc568c9c7a3cb72153", "max_stars_repo_... |
module Meshing
using Delaunay
using DifferentialForms
# using MiniQhull
using SparseArrays
using StaticArrays
using ..Algorithms
using ..SparseOps
using ..ZeroOrOne
################################################################################
export delaunay_mesh
"""
Find the Delaunay triangulation for a set of ... | {"hexsha": "badfb8500ec8273f5c16d81d8c111e37a1bec361", "size": 5789, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/Meshing.jl", "max_stars_repo_name": "eschnett/DDF.jl", "max_stars_repo_head_hexsha": "e5a93eaef99e2143619ce81ec0e9e222f049f25b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 6, "max_s... |
import random
import time
import numpy as np
from Team import Team
from Match import Match
class Tournament:
matches_per_team = 0
number_of_matches = 0
ceiling_hits = 0
def __init__(self, te, ma):
# set up the array of teams, which will also keep track of rankings
self.teams = []
if isinstance(te, int... | {"hexsha": "b8358c8c9ebdad73433336d5e190ab6854c54502", "size": 6633, "ext": "py", "lang": "Python", "max_stars_repo_path": "Tournament.py", "max_stars_repo_name": "ftc9899/tournament-sim", "max_stars_repo_head_hexsha": "72b67488affe7cd4f649d1fc84f74c4f2fea155a", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 4,... |
#########################
# 1. Importing Packages #
#########################
import numpy as np
##################################
# 2. Helper Conversion Functions #
##################################
def dms2dec(degrees, arcminutes, arcseconds):
angle = abs(degrees) + arcminutes/60 + arcseconds/(60*60)
retu... | {"hexsha": "d3b11ded9d055833ee58c2cea1d504c3dc6375fb", "size": 2283, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "SauravMaheshkar/Cross-Matching-Methods-for-Astronomical-Catalogs", "max_stars_repo_head_hexsha": "c10f43ad9ea3ce5ced5deadd7b6aaf99e1b9afb3", "max_stars_repo_lice... |
"""
Create a subset with more frequent labels
> python notebooks/subset-dataset.py TEMP/train-from-kaggle.csv 1500
"""
import itertools
import os.path
import sys
import numpy as np
import pandas as pd
COUNT_THR = 1000
CSV_NAME = "train-from-kaggle.csv"
COL_LABELS = 'attribute_ids'
def main(path_csv: str = CSV_NAME... | {"hexsha": "d38db9cc854e99753cdd369557906997ba86ce39", "size": 1665, "ext": "py", "lang": "Python", "max_stars_repo_path": "notebooks/subset-dataset.py", "max_stars_repo_name": "Borda/kaggle_iMet-collection", "max_stars_repo_head_hexsha": "ab3f55e85029768d5cdb8bc7ee895c195010cf80", "max_stars_repo_licenses": ["MIT"], "... |
import itertools
import numpy as np
_min = np.minimum
_max = np.maximum
def union(a, *bs, k=None):
def f(p):
d1 = a(p)
for b in bs:
d2 = b(p)
K = k or getattr(b, '_k', None)
if K is None:
d1 = _min(d1, d2)
else:
h = np... | {"hexsha": "9c245e8286118b21daf04b17e120c113f70f1b7c", "size": 2925, "ext": "py", "lang": "Python", "max_stars_repo_path": "sdf/dn.py", "max_stars_repo_name": "yihong0618/sdf", "max_stars_repo_head_hexsha": "0949ae7d4468e03270290dca5ebd36847fd60192", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 926, "max_star... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "wangzhefeng"
from pyspark import SparkContext as sc
import random
import numpy as np
# **************************************************************************
# version 1
# **************************************************************************
# ---... | {"hexsha": "1b5487c202a5953c33b70d8d4c4598323adaa3b0", "size": 2121, "ext": "py", "lang": "Python", "max_stars_repo_path": "project_scala/MC_pyspark.py", "max_stars_repo_name": "wangzhefeng/spark_spr", "max_stars_repo_head_hexsha": "a8f92b2563c70c7e072668a1e82cfa50164dd30d", "max_stars_repo_licenses": ["Apache-2.0"], "... |
c Subroutine find single vector of stations
c AJ_Kettle, 22Apr2021
SUBROUTINE find_single_bigvector(l_rgh_stn,
+ l_stn2019,s_vec_stnlist2019,l_stn2020,s_vec_stnlist2020,
+ l_stn2021,s_vec_stnlist2021,
+ s_vec_stnlist_amal,i_mat_stnlist_flag)
IMPLICIT NONE
c***********************... | {"hexsha": "812318f59dbf58e2579083980e0c44df780ec59d", "size": 1185, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "r202106_usaf_update2/Y_amalgamate/Subroutine/find_single_bigvector.f", "max_stars_repo_name": "glamod/glamod-nuim", "max_stars_repo_head_hexsha": "eed6f9d7d71b0c456ef39fdea6b58677e13ab50c", "max_s... |
[STATEMENT]
lemma flag_read_reg_write:
shows "flag_read (\<sigma> with ((r :=\<^sub>r w)#updates)) f = flag_read (\<sigma> with updates) f"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. flag_read (\<sigma> with ((r :=\<^sub>r w) # updates)) f = flag_read (\<sigma> with updates) f
[PROOF STEP]
by (induct updates a... | {"llama_tokens": 139, "file": "X86_Semantics_State", "length": 1} |
from pbcore.io import (CmpH5Reader,
AlignmentSet)
import numpy as np
import os
class SequencingYield:
"""
Class for characterizing the yield of a
sequencing run
"""
def __init__(self, aset_path):
(self.aset,
self.is_cmph5) = self._openAset(aset_path)
d... | {"hexsha": "6772bdd86d3e450b51e3332532a1a983525fb3d1", "size": 2236, "ext": "py", "lang": "Python", "max_stars_repo_path": "biotk/libs/YieldAccumulation.py", "max_stars_repo_name": "knyquist/biotk", "max_stars_repo_head_hexsha": "d86bb3d8c72c8b820160ba8698c973208a72c2ca", "max_stars_repo_licenses": ["BSD-3-Clause"], "m... |
import sys
import numpy as np
def preprocess(text):
text = text.lower()
text = text.replace('.', ' .')
words = text.split(' ')
word_to_id = {}
id_to_word = {}
for word in words:
if word not in word_to_id:
new_id = len(word_to_id)
word_to_id[word] = new_id
... | {"hexsha": "82bfadc3fb6ecadc242d98b5ff421c6a52d7f0c5", "size": 4071, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/common/util.py", "max_stars_repo_name": "kanta-nakamura/jarujaru-tower-generator", "max_stars_repo_head_hexsha": "9f3cc4cc9e699b968bc573cd8198f4938a302894", "max_stars_repo_licenses": ["MIT"],... |
# Certification of Robustness using Zonotopes with DeepZ
In this notebook we will demonstrate the usage of certification using zonotopes within ART. With deterministic certification methods such as DeepZ we can have a guarantee if a datapoint could have its class changed under a given bound. This method was originally... | {"hexsha": "54a00e1ceb8b5218c681e3480af4e47712f3dc1a", "size": 63901, "ext": "ipynb", "lang": "Jupyter Notebook", "max_stars_repo_path": "notebooks/certification_deepz.ipynb", "max_stars_repo_name": "david-shmailov/adversarial-robustness-toolbox", "max_stars_repo_head_hexsha": "ad8b94d3928abe218cd6ab2eed1c5c21f1d6e420"... |
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