def test_distributions_uniform_random_sample(): d = UniformDistribution(0, 10) x = numpy.array([2.21993171, 8.70732306, 2.06719155, 9.18610908, 4.88411189]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
def test_distributions_uniform_random_sample(): d = UniformDistribution(0, 10) x = numpy.array([2.21993171, 8.70732306, 2.06719155, 9.18610908, 4.88411189]) assert_array_almost_equal(d.sample(5, random_state=5), x) assert_raises(AssertionError, assert_array_almost_equal, d.sample(5), x)
def test_uniform():
d = UniformDistribution(0, 10)
assert_almost_equal(d.log_probability(2.34), -2.3025850929940455, 8)
assert_equal(d.log_probability(2), d.log_probability(8))
assert_equal(d.log_probability(10), d.log_probability(3.4))
assert_equal(d.log_probability(1.7), d.log_probability(9.7))
assert_equal(d.log_probability(10.0001), float("-inf"))
assert_equal(d.log_probability(-0.0001), float("-inf"))
for i in range(10):
data = numpy.random.randn(100) * 100
d.fit(data)
assert_equal(d.parameters[0], data.min())
assert_equal(d.parameters[1], data.max())
minimum, maximum = data.min(), data.max()
for i in range(100):
sample = d.sample()
assert_less_equal(minimum, sample)
assert_less_equal(sample, maximum)
d = UniformDistribution(0, 10)
d.fit([-5, 20], inertia=0.5)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.fit([-100, 100], inertia=1.0)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.summarize([0, 50, 2, 24, 28])
d.summarize([-20, 7, 8, 4])
d.from_summaries(inertia=0.75)
assert_equal(d.parameters[0], -6.875)
assert_equal(d.parameters[1], 23.75)
d.summarize([0, 100])
d.summarize([100, 200])
d.from_summaries()
assert_equal(d.parameters[0], 0)
assert_equal(d.parameters[1], 200)
d.freeze()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 200])
d.thaw()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 8])
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "UniformDistribution")
assert_equal(e.parameters, [0, 8])
f = pickle.loads(pickle.dumps(e))
assert_equal(f.name, "UniformDistribution")
assert_equal(f.parameters, [0, 8])
def test_uniform():
d = UniformDistribution(0, 10)
assert_almost_equal(d.log_probability(2.34), -2.3025850929940455, 8)
assert_equal(d.log_probability(2), d.log_probability(8))
assert_equal(d.log_probability(10), d.log_probability(3.4))
assert_equal(d.log_probability(1.7), d.log_probability(9.7))
assert_equal(d.log_probability(10.0001), float("-inf"))
assert_equal(d.log_probability(-0.0001), float("-inf"))
for i in range(10):
data = numpy.random.randn(100) * 100
d.fit(data)
assert_equal(d.parameters[0], data.min())
assert_equal(d.parameters[1], data.max())
minimum, maximum = data.min(), data.max()
for i in range(100):
sample = d.sample()
assert_less_equal(minimum, sample)
assert_less_equal(sample, maximum)
d = UniformDistribution(0, 10)
d.fit([-5, 20], inertia=0.5)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.fit([-100, 100], inertia=1.0)
assert_equal(d.parameters[0], -2.5)
assert_equal(d.parameters[1], 15)
d.summarize([0, 50, 2, 24, 28])
d.summarize([-20, 7, 8, 4])
d.from_summaries(inertia=0.75)
assert_equal(d.parameters[0], -6.875)
assert_equal(d.parameters[1], 23.75)
d.summarize([0, 100])
d.summarize([100, 200])
d.from_summaries()
assert_equal(d.parameters[0], 0)
assert_equal(d.parameters[1], 200)
d.freeze()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 200])
d.thaw()
d.fit([0, 1, 6, 7, 8, 3, 4, 5, 2])
assert_equal(d.parameters, [0, 8])
e = Distribution.from_json(d.to_json())
assert_equal(e.name, "UniformDistribution")
assert_equal(e.parameters, [0, 8])
f = pickle.loads(pickle.dumps(e))
assert_equal(f.name, "UniformDistribution")
assert_equal(f.parameters, [0, 8])
def __init__(self, dim , seed=None): #
K = 9
theta0=[.5,.5]
beta=np.ones(K)
Psi = .1*np.diag(np.ones(dim))
#mu0= np.zeros(dim)
#lambd=.1,
nu=dim+2.
rstate = np.random.get_state()
np.random.seed(seed)
unif_dist = UniformDistribution(0.,1.)
self.theta0 = theta0
beta_dist = DirichletDistribution(beta)
self.dim = Psi.shape[0]
self.dists = []
#same weights for both
weights = beta_dist.sample()
mus = []
for i,_ in enumerate(theta0):
#weights = beta_dist.sample()
#print(weights)
mix = []
for j,_ in enumerate(weights):
if j%3==0:
Sigma = invwishart.rvs(df=nu, scale=Psi)
elif j%3==1:
Sigma = invwishart.rvs(df=nu, scale=.01*Psi)
else:
Sigma = invwishart.rvs(df=nu, scale=.0001*Psi)
if i==0:
mu = unif_dist.sample(self.dim)
#mu =MultivariateGaussianDistribution(mu0,Sigma/lambd).sample()
mus.append(mu)
else:
mu = mus[j]
mix.append( MultivariateGaussianDistribution(mu, Sigma) )
model = GeneralMixtureModel(mix, weights=weights)
self.dists.append(model)
for d in self.dists:
print(d)
self.rstate = np.random.get_state()
np.random.set_state(rstate)
