def test_init_pmf():
"""
Expect to create a new PMF with given hypotheses.
"""
test = {0: 0.25, 0.5: 0.25, 0.75: 0.25, 1: 0.25}
hypotheses_a = init_pmf(tuple(test.keys()))
assert hypotheses_a == test
hypotheses_b = init_pmf(test)
assert hypotheses_b == test
hypotheses_c = init_pmf()
assert hypotheses_c == {}
def test_get_pmf_value():
"""
Expect to get a single number representing the best fit for the PMF.
"""
hypotheses = init_pmf((0, 0.5, 0.75, 1))
assert get_pmf_value(hypotheses) == 0.5625
def test_update_pmf():
"""
Expect to update a PMF with given data,
require and use a likelihood function to update.
"""
def likelihood(data, hypothesis):
return hypothesis
hypotheses = init_pmf((0, 0.5, 0.75, 1))
hypotheses = update_pmf(hypotheses, {}, likelihood)
assert hypotheses == {0: 0.0, 0.5: 2 / 9, 0.75: 1 / 3, 1: 4 / 9}
def get_distribution(card_parameters, kind):
"""
Parse own distribution hypotheses,
changing the keys back into numbers.
"""
key = '{kind}_distribution'.format(kind=kind)
if key in card_parameters:
distribution = card_parameters[key]
distribution = deliver_distribution(distribution)
else:
init = init_guess if kind == 'guess' else init_slip
distribution = {
h: 1 - (init - h)**2
for h in [h / precision for h in range(1, precision)]
}
card_parameters[key] = distribution
if kind == 'guess':
return init_pmf(distribution)
if kind == 'slip':
return init_pmf(distribution)
def main(num_learners=1000, num_cards=50):
d = create_responses(num_learners, num_cards)
responses, learners, cards = d['responses'], d['learners'], d['cards']
precision = 20
my_cards = [{
'name': card['name'],
'guess_distribution': init_pmf({
h: 1 - (init_guess - h) ** 2
for h in [h / precision for h in range(1, precision)]}),
'slip_distribution': init_pmf({
h: 1 - (init_slip - h) ** 2
for h in [h / precision for h in range(1, precision)]}),
'transit': init_transit,
} for card in cards]
my_learners = [{
'name': learner['name'],
'learned': init_learned,
} for learner in learners]
latest_response_per_learner = {}
for i, response in enumerate(responses):
# response keys: learner, card, time, score
if response['learner'] in latest_response_per_learner:
prev_response = latest_response_per_learner[response['learner']]
else:
prev_response = {
'time': response['time'],
'prev_learned': init_learned,
}
my_learner = get_learner(response['learner'], my_learners)
my_card = get_card(response['card'], my_cards)
response['prev_learned'] = my_learner['learned']
c = update(learned=my_learner['learned'],
guess_distribution=my_card['guess_distribution'],
slip_distribution=my_card['slip_distribution'],
score=response['score'],
time_delta=response['time'] - prev_response['time'])
my_learner['learned'] = c['learned']
my_card['guess_distribution'] = c['guess_distribution']
my_card['slip_distribution'] = c['slip_distribution']
latest_response_per_learner[response['learner']] = response
# Compute the error rates.
# Error should sqrt(sum( (o - x)^2 for o in list ))
guess_error, slip_error, transit_error = 0, 0, 0
for card in cards:
my_card = get_card(card['name'], my_cards)
my_card['guess'] = get_guess_pmf_value(my_card['guess_distribution'])
my_card['slip'] = get_slip_pmf_value(my_card['slip_distribution'])
guess_error += (my_card['guess'] - card['guess']) ** 2
slip_error += (my_card['slip'] - card['slip']) ** 2
transit_error += (my_card['transit'] - card['transit']) ** 2
print('guess_error', sqrt(guess_error / len(my_cards)))
print('slip_error', sqrt(slip_error / len(my_cards)))
print('transit_error', sqrt(transit_error / len(my_cards)))
guess_error, slip_error, transit_error = 0, 0, 0
for card in cards:
guess_error += (init_guess - card['guess']) ** 2
slip_error += (init_slip - card['slip']) ** 2
transit_error += (init_transit - card['transit']) ** 2
print('CONTROL guess_error', sqrt(guess_error / len(my_cards)))
print('CONTROL slip_error', sqrt(slip_error / len(my_cards)))
print('CONTROL transit_error', sqrt(transit_error / len(my_cards)))
