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 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_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_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_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)
