--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/amat.py	Mon Jun 16 17:47:36 2008 -0400
@@ -0,0 +1,123 @@
+"""load PLearn AMat files"""
+
+import sys, numpy, array
+
+path_MNIST = '/u/bergstrj/pub/data/mnist.amat'
+
+
+class AMat:
+    """DataSource to access a plearn amat file as a periodic unrandomized stream.
+
+    Attributes:
+
+    input -- minibatch of input
+    target -- minibatch of target
+    weight -- minibatch of weight
+    extra -- minitbatch of extra
+
+    all -- the entire data contents of the amat file
+    n_examples -- the number of training examples in the file
+
+    AMat stands for Ascii Matri[x,ces]
+
+    """
+
+    marker_size = '#size:'
+    marker_sizes = '#sizes:'
+    marker_col_names = '#:'
+
+    def __init__(self, path, head=None, update_interval=0, ofile=sys.stdout):
+
+        """Load the amat at <path> into memory.
+        
+        path - str: location of amat file
+        head - int: stop reading after this many data rows
+        update_interval - int: print '.' to ofile every <this many> lines
+        ofile - file: print status, msgs, etc. to this file
+
+        """
+        self.all = None
+        self.input = None
+        self.target = None
+        self.weight = None
+        self.extra = None
+
+        self.header = False
+        self.header_size = None
+        self.header_rows = None
+        self.header_cols = None
+        self.header_sizes = None
+        self.header_col_names = []
+
+        data_started = False
+        data = array.array('d')
+        
+        f = open(path)
+        n_data_lines = 0
+        len_float_line = None
+
+        for i,line in enumerate(f):
+            if n_data_lines == head:
+                #we've read enough data, 
+                # break even if there's more in the file
+                break
+            if len(line) == 0 or line == '\n':
+                continue
+            if line[0] == '#':
+                if not data_started:
+                    #the condition means that the file has a header, and we're on 
+                    # some header line
+                    self.header = True
+                    if line.startswith(AMat.marker_size):
+                        info = line[len(AMat.marker_size):]
+                        self.header_size = [int(s) for s in info.split()]
+                        self.header_rows, self.header_cols = self.header_size
+                    if line.startswith(AMat.marker_col_names):
+                        info = line[len(AMat.marker_col_names):]
+                        self.header_col_names = info.split()
+                    elif line.startswith(AMat.marker_sizes):
+                        info = line[len(AMat.marker_sizes):]
+                        self.header_sizes = [int(s) for s in info.split()]
+            else:
+                #the first non-commented line tells us that the header is done
+                data_started = True
+                float_line = [float(s) for s in line.split()]
+                if len_float_line is None:
+                    len_float_line = len(float_line)
+                    if (self.header_cols is not None) \
+                            and self.header_cols != len_float_line:
+                        print >> sys.stderr, \
+                                'WARNING: header declared %i cols but first line has %i, using %i',\
+                                self.header_cols, len_float_line, len_float_line
+                else:
+                    if len_float_line != len(float_line):
+                        raise IOError('wrong line length', i, line)
+                data.extend(float_line)
+                n_data_lines += 1
+
+                if update_interval > 0 and (ofile is not None) \
+                        and n_data_lines % update_interval == 0:
+                    ofile.write('.')
+                    ofile.flush()
+
+        if update_interval > 0:
+            ofile.write('\n')
+        f.close()
+
+        # convert from array.array to numpy.ndarray
+        nshape = (len(data) / len_float_line, len_float_line)
+        self.all = numpy.frombuffer(data).reshape(nshape)
+        self.n_examples = self.all.shape[0]
+
+        # assign
+        if self.header_sizes is not None:
+            if len(self.header_sizes) > 4:
+                print >> sys.stderr, 'WARNING: ignoring sizes after 4th in %s' % path
+            leftmost = 0
+            #here we make use of the fact that if header_sizes has len < 4
+            # the loop will exit before 4 iterations
+            attrlist = ['input', 'target', 'weight', 'extra']
+            for attr, ncols in zip(attrlist, self.header_sizes): 
+                setattr(self, attr, self.all[:, leftmost:leftmost+ncols])
+                leftmost += ncols
+