def eval_assertion(request, lang, concept1, reltype, concept2):
c1 = Concept.get(concept1, lang)
c2 = Concept.get(concept2, lang)
svd = get_svd_results(lang)
from csc.conceptnet4.analogyspace import eval_assertion
lval, rval = eval_assertion(svd, relationtype=reltype, ltext=c1.text, rtext=c2.text)
return {'lfeat_val': lval,
'rfeat_val': rval}
def category_similarity(request, lang, cat1, cat2):
from math import sqrt
svd = get_svd_results(lang)
cat1_vec = category_from_urlcategory(svd, lang, cat1)
cat2_vec = category_from_urlcategory(svd, lang, cat2)
return {
'similarity':
cat1_vec*cat2_vec / (sqrt(cat1_vec*cat1_vec) * sqrt(cat2_vec*cat2_vec))
}
def similar_concepts(request, lang, category):
# Default to retrieving 10 items.
count = int(request.GET.get('count', 10))
svd = get_svd_results(lang)
cat = category_from_urlcategory(svd, lang, category)
items = svd.u_distances_to(cat).top_items(count)
return {
'similar':
[{
'text': canonical_form(item[0], lang),
'score': item[1],
} for item in items]
}
def similar_features(request, lang, category):
# Default to retrieving 10 items.
count = int(request.GET.get('count', 10))
fmt = request.GET.get('format', 'frame_blank')
svd = get_svd_results(lang)
cat = category_from_urlcategory(svd, lang, category)
items = svd.v_distances_to(cat).top_items(count)
def feature_to_dict(feature_tup, score):
feature = Feature.from_tuple(feature_tup)
return dict(
raw = feature_tup,
logical = str(feature),
text = feature.nl_statement('__'),
score = score
)
return {
'similar':
[feature_to_dict(feature, score) for (feature, score) in items]
}
def get_predictions(lang, concepts):
count = 100
tensor = get_tensor(lang)
svd = get_svd_results(lang)
cat = make_category_failsoft(svd, concepts, [], concepts)
return get_prediction_vector(lang, cat)
def get_tops_of_axes(lang, num):
svd = get_svd_results(lang)
tops_of_axes = [svd.u[:, n].top_items(1)[0][0] for n in xrange(num)]
return [Concept.objects.get(text=text, language=lang) for text in tops_of_axes]