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#from https://rosettacode.org/wiki/LU_decomposition#Python
from pprint import pprint
 
def matrixMul(A, B):
    TB = zip(*B)
    return [[sum(ea*eb for ea,eb in zip(a,b)) for b in TB] for a in A]
 
def pivotize(m):
    """Creates the pivoting matrix for m."""
    n = len(m)

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# This is an example for the CIFAR-10 dataset.
# There's a function for creating a train and validation iterator.
# There's also a function for creating a test iterator.
# Inspired by https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4

from utils import plot_images

def get_train_valid_loader(data_dir,
                           batch_size,
                           augment,

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import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim

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###byte pair encoding
###Neural Machine Translation of Rare Words with Subword Units
###from https://plmsmile.github.io/2017/10/19/subword-units/
import re
def process_raw_words(words, endtag='-'):
    '''把单词分割成最小的符号，并且加上结尾符号'''
    vocabs = {}
    for word, count in words.items():
        # 加上空格
        word = re.sub(r'([a-zA-Z])', r' \1', word)

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{
  "mappings": {
    "docs": {
      "dynamic": true,
      "properties": {
        "time": {
          "type": "date",
          "format": "yyyyMMdd",
          "store": "true"
        },

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1. Clone your fork:

git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git

2. Add remote from original repository in your forked repository:

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

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Compile the C++ code creating a shared library (or shared object in UNIX)

$ clang++ TestJNI.cpp -o libTestJNI.so -fPIC -shared -std=c++11 -I$HOME/opt/java/include -I$HOME/opt/java/include/linux

Run the application

$ scala -save load.scala 
dir = /home/archbox/opengl/jni/libTestJNI.so
Hello world java
i = 0

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##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: [email protected]
## Modified by Thomas Wolf, HuggingFace Inc., Email: [email protected]
## Copyright (c) 2017-2018
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

"""Encoding Data Parallel"""

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# -*- coding: utf-8 -*-

import struct
import os

# 由于原代码不适用python3且有大量bug
# 以及有函数没有必要使用且一些代码书写不太规范或冗余
# 所以本人在原有的大框架基本不动的情况下作了大量的细节更改。
# 使得没有乱码出现，文件夹导入更方便等等。
# Author：Ling Yue, Taiyuan U of Tech

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import numpy as np

def make_lsh_model(nb_tables, nb_bits, nb_dimensions, vector_sample):
    # vector_sample: np arr w/ shape (2 * nb_tables * nb_tables, nb_dimensions).
    # normals, midpoints: np arrs w/ shape (nb_bits, nb_dimensions)
    # thresholds: np arrs w/ shape (nb_bits)
    # all_normals, all_thresholds: lists w/ one normal, one threshold per table.
    all_normals, all_thresholds = [], []
    for i in range(0, len(vector_sample), 2 * nb_bits):
        vector_sample_a = vector_sample[i:i + nb_bits]