def _test_scipy_stats(name):
if name in known_failures:
raise SkipTest('known failure')
dist = getattr(scipy.stats, name)
try:
params = default_params[name]
except KeyError:
params = [tuple(1.0 + rand(dist.numargs))]
for param in params:
print 'param = {}'.format(param)
dim = get_dim(dist.rvs(*param))
sample_count = 100 + 1000 * dim
samples = list(dist.rvs(*param, size=sample_count))
if hasattr(dist, 'pmf'):
probs = [dist.pmf(sample, *param) for sample in samples]
probs_dict = dict(izip(samples, probs))
gof = discrete_goodness_of_fit(samples, probs_dict, plot=True)
else:
probs = [dist.pdf(sample, *param) for sample in samples]
gof = auto_density_goodness_of_fit(samples, probs, plot=True)
assert_greater(gof, TEST_FAILURE_RATE)
gof = mixed_density_goodness_of_fit(samples, probs, plot=True)
assert_greater(gof, TEST_FAILURE_RATE)
def test_good_fit(self):
for param in self.dist_params():
dim = get_dim(self.dist.rvs(*param, size=2)[0])
sample_count = NUM_BASE_SAMPLES + NUM_SAMPLES_SCALE * dim
samples = self.dist.rvs(*param, size=sample_count)
samples = list(map(self._sample_postprocessing, samples))
probabilities = [self.pdf(sample, *param) for sample in samples]
gof = self.goodness_of_fit(samples, probabilities)
self.assertGreater(gof, TEST_FAILURE_RATE)
def test_good_fit(self):
for param in self.dist_params():
dim = get_dim(self.dist.rvs(*param, size=2)[0])
sample_count = NUM_BASE_SAMPLES + NUM_SAMPLES_SCALE * dim
samples = self.dist.rvs(*param, size=sample_count)
samples = list(map(self._sample_postprocessing, samples))
probabilities = [self.pdf(sample, *param) for sample in samples]
gof = self.goodness_of_fit(samples, probabilities)
self.assertGreater(gof, TEST_FAILURE_RATE)