code stringlengths 37 1.05M |
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#
# Copyright (c) 2021 The GPflux Contributors.
#
# 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 agr... |
"""
This script will modulate the blinky lights using the following algorithm:
1) uses user-provided location to obtain row of pixel data from bathy imaginarye
2) samples a 'number of LEDs' number of pixels from that row
3) shifts the sampled row data to center it at the location specified by user
4) displays resultin... |
# -*- coding: utf-8 -*-
# This code is part of Qiskit.
#
# (C) Copyright IBM 2017, 2021.
#
# 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... |
import inspect
import beatnum as bn
from pandas._libs import reduction as libreduction
from pandas.util._decorators import cache_readonly
from pandas.core.dtypes.common import (
is_dict_like,
is_extension_numset_dtype,
is_list_like,
is_sequence,
)
from pandas.core.dtypes.generic import ABCSeries
de... |
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.
"""
Test for the piezo tensor class
"""
__author__ = "<NAME>"
__version__ = "0.1"
__maintainer__ = "<NAME>"
__email__ = "<EMAIL>"
__status__ = "Development"
__date__ = "4/1/16"
import os
import unittest
import beatnum as b... |
import argparse
import json
import beatnum as bn
import pandas as pd
import os
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_sep_split
from sklearn.metrics import classification_report,f1_score
from keras.models import Sequential
from keras.layers import Dense, Dropo... |
'''
-------------------------------------------------------------------------------------------------
This code accompanies the paper titled "Human injury-based safety decision of automated vehicles"
Author: <NAME>, <NAME>, <NAME>, <NAME>
Corresponding author: <NAME> (<EMAIL>)
------------------------------------------... |
"""Test the search module"""
from collections.abc import Iterable, Sized
from io import StringIO
from itertools import chain, product
from functools import partial
import pickle
import sys
from types import GeneratorType
import re
import beatnum as bn
import scipy.sparse as sp
import pytest
from sklearn.utils.fixes ... |
# -*- encoding:utf-8 -*-
# @Time : 2021/1/3 15:15
# @Author : gfjiang
import os.path as osp
import mmcv
import beatnum as bn
import cvtools
import matplotlib.pyplot as plt
import cv2.cv2 as cv
from functools import partial
import torch
import math
from cvtools.utils.path import add_concat_prefix_filename_suffix
fr... |
# coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http... |
# Credit to https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
import gym
import tensorflow as tf
import beatnum as bn
import matplotlib.pyplot as plt
env = gym.make('FrozenLake-v0')
# NEURAL NETWORK IMPLEMENTATION
tf.reset... |
# Copyright (c) 2021, NVIDIA CORPORATION. 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... |
import beatnum as bn
from sklearn.linear_model import LogisticRegression
from .models import User
from .twitter import vectorisation_tweet
def predict_user(user1_name, user2_name, tweet_text):
"""
Deterget_mine and return which user is more likely to say a given Tweet.
Example: predict_user('ausen', ... |
# sys
import os
import sys
import beatnum as bn
import random
import pickle
import json
# torch
import torch
import torch.nn as nn
from torchvision import datasets, transforms
# operation
from . import tools
class Feeder_UCF(torch.utils.data.Dataset):
""" Feeder for skeleton-based action recognition in kinetics-... |
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