MNIST extractor obtained from http://abel.ee.ucla.edu/cvxopt/_downloads/mnist.py

mnist.py

import os, struct
from array import array
from cvxopt.base import matrix

def read(digits, dataset = "training", path = "."):
    """
    Python function for importing the MNIST data set.
    """

    if dataset is "training":
        fname_img = os.path.join(path, 'train-images-idx3-ubyte')
        fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')
    elif dataset is "testing":
        fname_img = os.path.join(path, 't10k-images-idx3-ubyte')
        fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')
    else:
        raise ValueError, "dataset must be 'testing' or 'training'"

    flbl = open(fname_lbl, 'rb')
    magic_nr, size = struct.unpack(">II", flbl.read(8))
    lbl = array("b", flbl.read())
    flbl.close()

    fimg = open(fname_img, 'rb')
    magic_nr, size, rows, cols = struct.unpack(">IIII", fimg.read(16))
    img = array("B", fimg.read())
    fimg.close()

    ind = [ k for k in xrange(size) if lbl[k] in digits ]
    images =  matrix(0, (len(ind), rows*cols))
    labels = matrix(0, (len(ind), 1))
    for i in xrange(len(ind)):
        images[i, :] = img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]
        labels[i] = lbl[ind[i]]

    return images, labels