Ejemplo n.º 1
0
class MFMixGaussian(Likelihood):
    """
    q(z | lambda ) = q(pi) prod_{k=1}^K q(mu_k) q(sigma_k)
        q(pi) = Dirichlet(alpha')
        q(mu_k) = N(mu'_k, Sigma'_k)
        q(sigma_k) = Inv-Gamma(a'_k, b'_k)

    where z = {pi, mu, sigma}
    """
    def __init__(self, K, D):
        self.K = K
        self.D = D
        self.dirichlet = MFDirichlet(1, K)
        self.gaussian = MFGaussian(K*D)
        self.invgamma = MFInvGamma(K*D)

        Likelihood.__init__(self, self.dirichlet.num_vars + \
                                  self.gaussian.num_vars +  \
                                  self.invgamma.num_vars)
        self.num_params = self.dirichlet.num_params + \
                          self.gaussian.num_params + \
                          self.invgamma.num_params

    def mapping(self, x):
        return [self.dirichlet.mapping(x),
                self.gaussian.mapping(x),
                self.invgamma.mapping(x)]

    def set_params(self, params):
    	self.dirichlet.set_params(params[0])
        self.gaussian.set_params(params[1])
        self.invgamma.set_params(params[2])

    def print_params(self, sess):
    	self.dirichlet.print_params(sess)
        self.gaussian.print_params(sess)
        self.invgamma.print_params(sess)

    def sample(self, size, sess):
        """z ~ q(z | lambda)"""
        z_dirichlet = self.dirichlet.sample((size[0], self.dirichlet.num_vars), sess)
        z_gaussian = sess.run(self.gaussian.sample((size[0], self.gaussian.num_vars), sess))
        z_invgamma = self.invgamma.sample((size[0], self.invgamma.num_vars), sess)
        return np.concatenate((z_dirichlet, z_gaussian, z_invgamma), axis=1)

    def log_prob_zi(self, i, z):
        """log q(z_i | lambda_i)"""
        if i < self.dirichlet.num_vars:
            return self.dirichlet.log_prob_zi(i, z[:, 0:self.dirichlet.num_vars])
        elif i < self.dirichlet.num_vars + self.gaussian.num_vars:
            i = i - self.dirichlet.num_vars
            return self.gaussian.log_prob_zi(i,
                       z[:, self.dirichlet.num_vars:(self.dirichlet.num_vars+self.gaussian.num_vars)])
        elif i < self.num_vars:
            i = i - self.dirichlet.num_vars - self.gaussian.num_vars
            return self.invgamma.log_prob_zi(i,
                       z[:, (self.dirichlet.num_vars+self.gaussian.num_vars):])
        else:
            raise
Ejemplo n.º 2
0
    def __init__(self, K, D):
        self.K = K
        self.D = D
        self.dirichlet = MFDirichlet(1, K)
        self.gaussian = MFGaussian(K*D)
        self.invgamma = MFInvGamma(K*D)

        Likelihood.__init__(self, self.dirichlet.num_vars + \
                                  self.gaussian.num_vars +  \
                                  self.invgamma.num_vars)
        self.num_params = self.dirichlet.num_params + \
                          self.gaussian.num_params + \
                          self.invgamma.num_params