shrinkage.py 文件源码

def pwlin_grid(r_,rvar_,theta_,dtheta = .75):
    """piecewise linear with noise-adaptive grid spacing.
    returns xhat,dxdr
    where
        q = r/dtheta/sqrt(rvar)
        xhat = r * interp(q,theta)

    all but the  last dimensions of theta must broadcast to r_
    e.g. r.shape = (500,1000) is compatible with theta.shape=(500,1,7)
    """
    ntheta = int(theta_.get_shape()[-1])
    scale_ = dtheta / tf.sqrt(rvar_)
    ars_ = tf.clip_by_value( tf.expand_dims( tf.abs(r_)*scale_,-1),0.0, ntheta-1.0 )
    centers_ = tf.constant( np.arange(ntheta),dtype=tf.float32 )
    outer_distance_ = tf.maximum(0., 1.0-tf.abs(ars_ - centers_) ) # new dimension for distance to closest bin centers (or center)
    gain_ = tf.reduce_sum( theta_ * outer_distance_,axis=-1) # apply the gain (learnable)
    xhat_ = gain_ * r_
    dxdr_ = tf.gradients(xhat_,r_)[0]
    return (xhat_,dxdr_)