def test_anderson_samples(self):
"""
Sample from the Gamma scipy distribution and from ours using Anderson's
method.
"""
cnt = 10000
rng = np.random.RandomState()
alpha = 1.34
beta = 0.18
theta = 1.0 / beta
te = 0.2
now = 1.1
exp_dist = distributions.GammaDistribution(alpha, beta, te)
samples = distributions.anderson_sample_tester(exp_dist, now, cnt, rng)
emp_dist = distributions.EmpiricalDistribution(samples)
system = np.zeros(cnt)
for i in range(cnt):
v = now - 1
while v < now:
v = scipy.stats.gamma.rvs(a=alpha,
scale=1.0 / beta,
loc=0,
size=1)
system[i] = v
system_dist = distributions.EmpiricalDistribution(system)
ks_fit = emp_dist.compare_empirical(system_dist)
logger.debug("Gamma test_samples ks {0}".format(ks_fit))
self.assertTrue(ks_fit < 1.63)
def test_anderson_samples(self):
"""
Sample from the scipy distribution and from ours using Anderson's
method.
"""
cnt = 10000
rng = np.random.RandomState()
lam = 2.0
te = 0.7
now = 1.1
exp_dist = distributions.ExponentialDistribution(lam, te)
samples = distributions.anderson_sample_tester(exp_dist, now, cnt, rng)
emp_dist = distributions.EmpiricalDistribution(samples)
system = scipy.stats.expon.rvs(scale=1. / lam, loc=now, size=cnt)
system_dist = distributions.EmpiricalDistribution(system)
ks_fit = emp_dist.compare_empirical(system_dist)
logger.debug("Exponential test_samples ks {0}".format(ks_fit))
self.assertTrue(ks_fit < 1.63)
def test_samples(self):
"""
Sample from the scipy distribution and from ours. Compare.
"""
cnt = 10000
rng = np.random.RandomState()
samples = np.zeros(cnt)
lam = 2.0
te = 0.7
now = 1.1
exp_dist = distributions.ExponentialDistribution(lam, te)
for i in range(cnt):
samples[i] = exp_dist.sample(now, rng)
emp_dist = distributions.EmpiricalDistribution(samples)
system = scipy.stats.expon.rvs(scale=1. / lam, loc=now, size=cnt)
system_dist = distributions.EmpiricalDistribution(system)
ks_fit = emp_dist.compare_empirical(system_dist)
logger.debug("Exponential test_samples ks {0}".format(ks_fit))
self.assertTrue(ks_fit < 1.63)
def test_anderson_samples(self):
"""
Sample from the Uniform scipy distribution and from ours using Anderson's
method.
"""
cnt = 10000
rng = np.random.RandomState()
a = 1.0
b = 2.0
te = 0.5
now = 2.3
exp_dist = distributions.UniformDistribution(a, b, te)
samples = distributions.anderson_sample_tester(exp_dist, now, cnt, rng)
emp_dist = distributions.EmpiricalDistribution(samples)
system = scipy.stats.uniform.rvs(loc=now, scale=b + te - now, size=cnt)
system_dist = distributions.EmpiricalDistribution(system)
ks_fit = emp_dist.compare_empirical(system_dist)
logger.debug("Uniform test_samples ks {0}".format(ks_fit))
self.assertTrue(ks_fit < 1.63)
def test_samples(self):
"""
Sample from the Uniform scipy distribution and from ours. Compare.
"""
cnt = 10000
rng = np.random.RandomState()
samples = np.zeros(cnt)
a = 1.0
b = 2.0
te = 0.5
now = 2.3
exp_dist = distributions.UniformDistribution(a, b, te)
for i in range(cnt):
samples[i] = exp_dist.sample(now, rng)
emp_dist = distributions.EmpiricalDistribution(samples)
system = scipy.stats.uniform.rvs(loc=now, scale=b + te - now, size=cnt)
system_dist = distributions.EmpiricalDistribution(system)
ks_fit = emp_dist.compare_empirical(system_dist)
logger.debug("Uniform test_samples ks {0}".format(ks_fit))
self.assertTrue(ks_fit < 1.63)