--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/datasets/shapeset1.py	Thu Oct 30 11:27:04 2008 -0400
@@ -0,0 +1,61 @@
+"""
+Routines to load/access Shapeset1
+"""
+
+from __future__ import absolute_import
+
+import os
+import numpy
+
+from ..amat import AMat
+from .config import data_root
+
+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.  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.  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.  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):
+    return head_train(n=ntrain), head_valid(n=nvalid), head_test(n=ntest)
+
+