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# Copyright 2017 The TensorFlow 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

mehdidc

Cython

Cython has two major benefits:

1. Making python code faster, particularly things that can't be done in scipy/numpy
2. Wrapping/interfacing with C/C++ code

Cython gains most of it's benefit from statically typing arguments. However, statically typing is not required, in fact, regular python code is valid cython (but don't expect much of a speed up). By incrementally adding more type information, the code can speed up by several factors. This gist just provides a very basic usage of cython.

mehdidc

# docker build -t ubuntu1604py36
FROM ubuntu:16.04

RUN apt-get update
RUN apt-get install -y software-properties-common vim
RUN add-apt-repository ppa:jonathonf/python-3.6
RUN apt-get update

RUN apt-get install -y build-essential python3.6 python3.6-dev python3-pip python3.6-venv
RUN apt-get install -y git

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# remap prefix from 'C-b' to 'C-a'
unbind C-b
set-option -g prefix C-a
bind-key C-a send-prefix
# Start window numbering at 1
set -g base-index 1

set -g @plugin 'tmux-plugins/tpm'
set -g @plugin 'tmux-plugins/tmux-sensible'
set -g @plugin 'tmux-plugins/tmux-copycat'

mehdidc

mehdidc

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

mehdidc

import pickle
import sys
import numpy as np
import os
from subprocess import call

import pandas as pd
from skimage.io import imsave

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from subprocess import call
import numpy as np
import os

import pandas as pd
from skimage.io import imsave

def download(url):
    fname = os.path.basename(url)
    if not os.path.exists(fname):

mehdidc

import PIL.Image
from cStringIO import StringIO
import IPython.display
import numpy as np
def showarray(a, fmt='png'):
    a = np.uint8(a)
    f = StringIO()
    PIL.Image.fromarray(a).save(f, fmt)
    IPython.display.display(IPython.display.Image(data=f.getvalue()))

mehdidc

import numpy as np
from scipy.spatial.distance import cdist
from lapjv import lapjv

def grid_embedding(h):
    assert int(np.sqrt(h.shape[0])) ** 2 == h.shape[0], 'Nb of examples must be a square number'
    size = np.sqrt(h.shape[0])
    grid = np.dstack(np.meshgrid(np.linspace(0, 1, size), np.linspace(0, 1, size))).reshape(-1, 2)
    cost_matrix = cdist(grid, h, "sqeuclidean").astype('float32')
    cost_matrix = cost_matrix * (100000 / cost_matrix.max())