def score(self, imgs, labels):
idx = np.in1d(labels, self.digits)
imgs, labels = imgs[idx], labels[idx]
# Compute the digit counts for each top-level pattern.
machine = self.machine
sample_count = 10
self.digit_dists = digit_dists = {}
for img, label in izip(imgs, labels):
samples = machine.sample_recognition_dist(img, size=sample_count)
for top_sample in samples[0]:
pattern = bit_vector_to_str(top_sample)
dist = digit_dists.setdefault(pattern, np.zeros(10))
dist[label] += 1
# Tranform digit counts to distributions.
total_count = len(imgs) * sample_count
self.pattern_dist = pattern_dist = {}
for pattern, digit_dist in digit_dists.iteritems():
pattern_count = digit_dist.sum()
pattern_dist[pattern] = pattern_count / total_count
digit_dist /= pattern_count
# Compute the normalized digit specificity score.
score = 0.0
n = len(self.digits)
for pattern, weight in pattern_dist.iteritems():
p = digit_dists[pattern]
p = p[p != 0] # Set p log p = 0 when p == 0.
score += weight * np.sum(p * np.log(n*p))
score /= np.log(n)
self.specificity = score
return score
def compare(v, s):
assert_equal(bit_vector_to_str(v), s)
