NicolasTr

#!/bin/bash -e

# Fast Docker builds on CircleCI
# See this blog post: https://www.tresegnie.com/docker-circleci-speed-cost/

export CONTEXT_FOLDER=???
export IMAGE_NAME=???
export COMMIT_TAG=${CIRCLE_SHA1}
export BRANCH_TAG=${CIRCLE_BRANCH}

NicolasTr

mkvirtualenv test_matrix_completion
workon test_matrix_completion
pip install numpy
pip install scipy
pip install ipython
pip install tornado
pip install pyzmq
pip install --upgrade git+git://github.com/NicolasTr/scikit-learn.git@blog_201308_test_matrix_completion#egg=scikit-learn

NicolasTr

import os
import sys
import pprint
import itertools

sys.path = [os.path.join(os.getcwd(), 'matrix_completion_base')] + sys.path

import numpy as np
import scipy.sparse
rng = np.random.RandomState(0)

NicolasTr

#!/usr/bin/env python

from sklearn.random_projection import sparse_random_matrix
from sklearn.preprocessing import \
    Imputer, \
    _in1d_with_nan_handling, \
    _in2d_with_nan_handling, \
    _sparse_missing_positions_iterator

import numpy as np

NicolasTr

from sklearn.random_projection import sparse_random_matrix
from sklearn.preprocessing import Imputer, _in1d_with_nan_handling

import numpy as np
import scipy.sparse as sp

M = sparse_random_matrix( 10000, 10000, density=0.10)

print "Ideal time"
%timeit M.mean(axis=0)

NicolasTr

import numpy as np
import scipy.sparse as sp
from sklearn.preprocessing import Imputer

M = np.array([
    [1, 0, 0, 0, 0],
    [0, 2, 0, 0, 0],
    [0, 0, 0, 0, 5],
    [0, 0, 3, 0, 0],
    [0, 0, 0, 4, 0],

NicolasTr

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
from sklearn.feature_extraction.image import PatchExtractor
from copy import deepcopy

n_images = 2
image_height = 1024
image_width = 1024

NicolasTr

test