Mercurial > pylearn
view pylearn/datasets/shapeset1.py @ 1531:88f361283a19 tip
Fix url/name to pylearn2.
author | Frederic Bastien <nouiz@nouiz.org> |
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date | Mon, 09 Sep 2013 10:08:05 -0400 |
parents | f36d68b65f2c |
children |
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""" Routines to load/access Shapeset1 """ import os import numpy from pylearn.io.amat import AMat from pylearn.datasets.config import data_root from pylearn.datasets.dataset import Dataset def _head(path, n): dat = AMat(path=path, head=n) try: assert dat.input.shape[0] == n assert dat.target.shape[0] == n except Exception , e: raise Exception("failed to read %i lines from file %s" % (n, path)) return dat.input, numpy.asarray(dat.target, dtype='int64').reshape(dat.target.shape[0]) def head_train(n=10000): """Load the first Shapeset1 training examples. Returns two matrices: x, y. x has N rows of 1024 columns. Each row of x represents the 32x32 grey-scale pixels in raster order. y is a vector of N integers between 0 and 2. Each element y[i] is the label of the i'th row of x. """ path = os.path.join(data_root(), 'shapeset1','shapeset1_1cspo_2_3.10000.train.shape.amat') return _head(path, n) def head_valid(n=5000): """Load the first Shapeset1 validation examples. Returns two matrices: x, y. x has N rows of 1024 columns. Each row of x represents the 32x32 grey-scale pixels in raster order. y is a vector of N integers between 0 and 2. Each element y[i] is the label of the i'th row of x. """ path = os.path.join(data_root(), 'shapeset1','shapeset1_1cspo_2_3.5000.valid.shape.amat') return _head(path, n) def head_test(n=5000): """Load the first Shapeset1 testing examples. Returns two matrices: x, y. x has N rows of 1024 columns. Each row of x represents the 32x32 grey-scale pixels in raster order. y is a vector of N integers between 0 and 2. Each element y[i] is the label of the i'th row of x. """ path = os.path.join(data_root(), 'shapeset1','shapeset1_1cspo_2_3.5000.test.shape.amat') return _head(path, n) def train_valid_test(ntrain=10000, nvalid=5000, ntest=5000): train_x, train_y = head_train(n=ntrain) valid_x, valid_y = head_valid(n=nvalid) test_x, test_y = head_test(n=ntest) rval = Dataset() rval.train = Dataset.Obj(x = train_x, y = train_y) rval.valid = Dataset.Obj(x = valid_x, y = valid_y) rval.test = Dataset.Obj(x = test_x, y = test_y) rval.n_classes = 3 rval.img_shape = (32, 32) return rval