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#!/usr/bin/env sh

sudo apt-get update -y && \
sudo apt-get upgrade -y && \
sudo apt-get dist-upgrade -y && \
sudo apt-get install build-essential software-properties-common -y && \
sudo add-apt-repository ppa:ubuntu-toolchain-r/test -y && \
sudo apt-get update -y && \
sudo apt-get install gcc-9 g++-9 -y && \
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-9 60 --slave /usr/bin/g++ g++ /usr/bin/g++-9 && \

jiqiujia

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]

jiqiujia

##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang, Rutgers University, Email: zhang.hang@rutgers.edu
## Modified by Thomas Wolf, HuggingFace Inc., Email: thomas@huggingface.co
## 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"""

jiqiujia

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

jiqiujia

1. Clone your fork:

git clone git@github.com: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

jiqiujia

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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# 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,

jiqiujia

import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
import numpy as np

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import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter

def elastic_transform(image, alpha, sigma, random_state=None):
    """Elastic deformation of images as described in [Simard2003]_.


    .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
       Convolutional Neural Networks applied to Visual Document Analysis", in

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