# HG changeset patch
# User goldfinger
# Date 1272390453 14400
# Node ID 116b2de2c0a4701707454b2c2de54b0ee63c4433
# Parent  f61a04074723c1f3e8c8abb11a739be360811705
utils for the amazon MT code

diff -r f61a04074723 -r 116b2de2c0a4 AMT/utils.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/AMT/utils.py	Tue Apr 27 13:47:33 2010 -0400
@@ -0,0 +1,125 @@
+""" This file contains different utility functions that are not connected 
+in anyway to the networks presented in the tutorials, but rather help in 
+processing the outputs into a more understandable way. 
+
+For example ``tile_raster_images`` helps in generating a easy to grasp 
+image from a set of samples or weights.
+"""
+
+
+import numpy
+
+
+def scale_to_unit_interval(ndar,eps=1e-8):
+    """ Scales all values in the ndarray ndar to be between 0 and 1 """
+    ndar = ndar.copy()
+    ndar -= ndar.min()
+    ndar *= 1.0 / (ndar.max()+eps)
+    return ndar
+
+
+def tile_raster_images(X, img_shape, tile_shape,tile_spacing = (0,0), 
+              scale_rows_to_unit_interval = True, output_pixel_vals = True):
+    """
+    Transform an array with one flattened image per row, into an array in 
+    which images are reshaped and layed out like tiles on a floor.
+
+    This function is useful for visualizing datasets whose rows are images, 
+    and also columns of matrices for transforming those rows 
+    (such as the first layer of a neural net).
+
+    :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can 
+    be 2-D ndarrays or None;
+    :param X: a 2-D array in which every row is a flattened image.
+
+    :type img_shape: tuple; (height, width)
+    :param img_shape: the original shape of each image
+
+    :type tile_shape: tuple; (rows, cols)
+    :param tile_shape: the number of images to tile (rows, cols)
+    
+    :param output_pixel_vals: if output should be pixel values (i.e. int8
+    values) or floats
+
+    :param scale_rows_to_unit_interval: if the values need to be scaled before
+    being plotted to [0,1] or not
+
+
+    :returns: array suitable for viewing as an image.  
+    (See:`PIL.Image.fromarray`.)
+    :rtype: a 2-d array with same dtype as X.
+
+    """
+ 
+    assert len(img_shape) == 2
+    assert len(tile_shape) == 2
+    assert len(tile_spacing) == 2
+
+    # The expression below can be re-written in a more C style as 
+    # follows : 
+    #
+    # out_shape    = [0,0]
+    # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -
+    #                tile_spacing[0]
+    # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -
+    #                tile_spacing[1]
+    out_shape = [(ishp + tsp) * tshp - tsp for ishp, tshp, tsp 
+                        in zip(img_shape, tile_shape, tile_spacing)]
+
+    if isinstance(X, tuple):
+        assert len(X) == 4
+        # Create an output numpy ndarray to store the image 
+        if output_pixel_vals:
+            out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype='uint8')
+        else:
+            out_array = numpy.zeros((out_shape[0], out_shape[1], 4), dtype=X.dtype)
+
+        #colors default to 0, alpha defaults to 1 (opaque)
+        if output_pixel_vals:
+            channel_defaults = [0,0,0,255]
+        else:
+            channel_defaults = [0.,0.,0.,1.]
+
+        for i in xrange(4):
+            if X[i] is None:
+                # if channel is None, fill it with zeros of the correct 
+                # dtype
+                out_array[:,:,i] = numpy.zeros(out_shape,
+                        dtype='uint8' if output_pixel_vals else out_array.dtype
+                        )+channel_defaults[i]
+            else:
+                # use a recurrent call to compute the channel and store it 
+                # in the output
+                out_array[:,:,i] = tile_raster_images(X[i], img_shape, tile_shape, tile_spacing, scale_rows_to_unit_interval, output_pixel_vals)
+        return out_array
+
+    else:
+        # if we are dealing with only one channel 
+        H, W = img_shape
+        Hs, Ws = tile_spacing
+
+        # generate a matrix to store the output
+        out_array = numpy.zeros(out_shape, dtype='uint8' if output_pixel_vals else X.dtype)
+
+
+        for tile_row in xrange(tile_shape[0]):
+            for tile_col in xrange(tile_shape[1]):
+                if tile_row * tile_shape[1] + tile_col < X.shape[0]:
+                    if scale_rows_to_unit_interval:
+                        # if we should scale values to be between 0 and 1 
+                        # do this by calling the `scale_to_unit_interval`
+                        # function
+                        this_img = scale_to_unit_interval(X[tile_row * tile_shape[1] + tile_col].reshape(img_shape))
+                    else:
+                        this_img = X[tile_row * tile_shape[1] + tile_col].reshape(img_shape)
+                    # add the slice to the corresponding position in the 
+                    # output array
+                    out_array[
+                        tile_row * (H+Hs):tile_row*(H+Hs)+H,
+                        tile_col * (W+Ws):tile_col*(W+Ws)+W
+                        ] \
+                        = this_img * (255 if output_pixel_vals else 1)
+        return out_array
+
+
+