metrics = [{'func': norms.l1, 'invert': True, 'name': 'l1'},
{'func': norms.l2, 'invert': True, 'name': 'l2'},
{'func': norms.l3, 'invert': True, 'name': 'l3'},
metrics = [{'func': norms.l1, 'invert': True, 'name': 'l1'},
{'func': norms.l2, 'invert': True, 'name': 'l2'},
{'func': norms.l3, 'invert': True, 'name': 'l3'},
- {'func': norms.cosine_distance, 'invert': False, 'name': 'cosine_distance'},
- {'func': norms.harmonic_mean, 'invert': True, 'name': 'harmonic_mean'},
- {'func': norms.geometric_mean, 'invert': True, 'name': 'geometric_mean'}]
+ {'func': norms.cosine_similarity, 'invert': False, 'name': 'cosine_similarity'}]
+ # {'func': norms.harmonic_mean, 'invert': True, 'name': 'harmonic_mean'},
+ # {'func': norms.geometric_mean, 'invert': True, 'name': 'geometric_mean'}]
scalings = [{'corpus_frequency': normalised_english_counts,
'scaling': norms.normalise,
'name': 'normalised'},
{'corpus_frequency': euclidean_scaled_english_counts,
'scaling': norms.euclidean_scale,
'name': 'euclidean_scaled'}]
scalings = [{'corpus_frequency': normalised_english_counts,
'scaling': norms.normalise,
'name': 'normalised'},
{'corpus_frequency': euclidean_scaled_english_counts,
'scaling': norms.euclidean_scale,
'name': 'euclidean_scaled'}]
-message_lengths = [300, 100, 50, 30, 20, 10, 5]
+message_lengths = [100, 50, 30, 20, 10, 5]
print(scoring_function['name'], message_length)
if scoring_function['name'] not in scores:
scores[scoring_function['name']] = collections.defaultdict(int)
print(scoring_function['name'], message_length)
if scoring_function['name'] not in scores:
scores[scoring_function['name']] = collections.defaultdict(int)
for _ in range(trials):
sample_start = random.randint(0, corpus_length - message_length)
sample = corpus[sample_start:(sample_start + message_length)]
for _ in range(trials):
sample_start = random.randint(0, corpus_length - message_length)
sample = corpus[sample_start:(sample_start + message_length)]