def test_sampling_distribution(self):
"""
test if the sampling distribution function is working as expected.
"""
trials = 1000
distribution = [('H',0.5),('T',0.5)]
num_heads = ml.sample_distribution(distribution,trials).count('H')
result = [binom_test(num_heads,trials,1.0/2) for x in range(trials)]
mean = sum(result) / len(result)
# NOTE: Maybe this number should be higher. What is a good number?
self.assertTrue(mean > 0.05)
def test_convergence(self):
categories = ml.recommend_categories(self.user)
#print "Categories: ", categories
picked_category = ml.sample_distribution(categories.items())[0]
#print "picked category: ", picked_category
picked_aggr = EventActionAggregate(user=self.user, category=picked_category)
lst = []
ctree = CachedCategoryTree()
parents = ctree.parents(picked_category)
count = 0
while count < 100:
count +=1
print "Round: %d\r"%count,
sys.stdout.flush()
# recommend a new set of categories
recommendation_scores = ml.recommend_categories(self.user)
cats = ml.sample_category_distribution(recommendation_scores.items(),
settings.N)
found_count = cats.count(picked_category)
#print "Categories: ",cats
#print "ID: ", picked_category.id
cats = set(cats)
cats.discard(picked_category.id)
# # G(oto) picked category
picked_aggr.g += found_count
picked_aggr.save()
# X all other categories
for c in cats:
if c in parents:
continue
try:
eaa = EventActionAggregate.objects.get(user=self.user, category=c)
except EventActionAggregate.DoesNotExist:
eaa = EventActionAggregate(user=self.user, category=c)
eaa.x += 1
eaa.save()
lst.append(found_count*100.0/settings.N)
plt.plot(lst,color="blue")
plt.title("Rate of learning one category")
plt.xlabel("Trials")
plt.ylabel("% of all Recommendations")
plt.savefig("learning/test_results/test.pdf")
plt.cla()
self.assertTrue(True)
