def generate_data(N, D, K, seed=1, spread=3):
"""
Generate data from a mixture of Gaussians model
"""
np.random.seed(seed)
mu = spread*np.random.randn(K, D)
# Lambda is actually precision matrix (inverse covariance)
Lambda = random.covariance(D, size=K, nu=2*D)
pi = random.dirichlet(5*np.ones(K))
y = np.zeros((N,D))
for n in range(N):
ind = nodes.Categorical(pi).random()
y[n] = nodes.Gaussian(mu[ind], Lambda[ind]).random()
np.savetxt('mog-data-%02d.csv' % seed, y, delimiter=',', fmt='%f')
return y
def random(self, *phi, plates=None):
r"""
Draw a random sample from the distribution.
"""
return random.dirichlet(phi[0], size=plates)
def random(self, *phi, plates=None):
"""
Draw a random sample from the distribution.
"""
return random.dirichlet(phi[0], size=plates)
def random(self):
"""
Draw a random sample from the distribution.
"""
return random.dirichlet(self.phi[0], size=self.plates)
