def train_with_glyphs(self):
"""Parse font and load data as binary vectors.
FUNCTION PRONE TO FAILURE. make sure size and label are elements
of the keys of metrics.labels and metrics.metrics.
Also make sure all bitmaps have been rendered.
See metrics.get_filename for proper locations."""
# open file, read as sequence of space separated strings
with open(metrics.get_filename(self.size, self.label)) as f:
words = []
for line in f:
words += line.split()
# measure each glyph
image_height = int(words[2])
self.shape = sh = (image_height, metrics.metrics[self.size])
# get the meaning of each expected pattern
self.patterns = metrics.labels[self.label]
# image data to -1 or 1
bits = list(map(lambda x: -1 if x=="0" else 1, words[3:]))
# save parsed image
self.show_matrix(resize(array(bits, int), (image_height, int(words[1]))))
# create pattern vectors
self.pattern_vectors = reshape(array(bits, int),
(sh[0], len(bits) / sh[0]))
# fill in missing columns at end with zeros
missing = max(0, len(self.patterns) * sh[1] - len(bits) / sh[0])
self.pattern_vectors = hstack([self.pattern_vectors,
-1 * ones((image_height, missing))])
# save bit vectors
self.data = bits
self.pattern_vectors = hsplit(self.pattern_vectors, len(self.patterns))
self.patterns = self.filter_patterns(self.patterns)
self.pattern_vectors = self.filter_patterns(self.pattern_vectors)
self.show_matrix(vstack(self.pattern_vectors), "patterns")
self.pattern_vectors = list(map(self.process_pattern, self.pattern_vectors))
self.show_matrix(self.make_pattern_matrix(), "pattern_matrix")
# train network
self.W = hop.train(self.make_pattern_matrix())
self.show_matrix(self.W, "W_matrix")
def train_with_vectors(self):
lsz = self.logsize
self.shape = (1, self.size) # (2 ** (lsz // 2), 2 ** (lsz - lsz // 2))
self.pattern_vectors = walsh.walsh_system(self.nvectors, 2 ** lsz)
self.patterns = []
for i, v in enumerate(self.pattern_vectors):
self.patterns.append(str(i) + "_" + "".join([
"0" if i != 1 else "1" for i in v[0:5]]))
# convert to numpy vectors
self.pattern_vectors = list(map(lambda w: array([w]), self.pattern_vectors))
self.W = hop.train(self.make_pattern_matrix())
self.show_matrix(vstack(self.pattern_vectors), "patterns")
self.show_matrix(self.make_pattern_matrix(), "pattern_matrix")
self.show_matrix(self.W, "W_matrix")