shrinkage.py 文件源码

def shrink_bgest(r,rvar,theta):
    """Bernoulli-Gaussian MMSE estimator
    Perform MMSE estimation E[x|r]
    for x ~ BernoulliGaussian(lambda,xvar1)
        r|x ~ Normal(x,rvar)

    The parameters theta[0],theta[1] represent
        The variance of non-zero x[i]
            xvar1 = abs(theta[0])
        The probability of nonzero x[i]
            lamba = 1/(exp(theta[1])+1)
    """
    xvar1 = abs(theta[...,0])
    loglam = theta[...,1] # log(1/lambda - 1)
    beta = 1/(1+rvar/xvar1)
    r2scale = r*r*beta/rvar
    rho = tf.exp(loglam - .5*r2scale ) * tf.sqrt(1 +xvar1/rvar)
    rho1 = rho+1
    xhat = beta*r/rho1
    dxdr = beta*((1+rho*(1+r2scale) ) / tf.square( rho1 ))
    dxdr = tf.reduce_mean(dxdr,0)
    return (xhat,dxdr)