tensorflow_probability/python/sts/regression.py
def params_to_weights(self,
                        global_scale_variance,
                        global_scale_noncentered,
                        local_scale_variances,
                        local_scales_noncentered,
                        weights_noncentered):
    """Build regression weights from model parameters."""
    global_scale = (global_scale_noncentered *
                    tf.sqrt(global_scale_variance) *
                    self.weights_prior_scale)

    local_scales = local_scales_noncentered * tf.sqrt(local_scale_variances)
    return weights_noncentered * local_scales * global_scale[..., tf.newaxis]
def params_to_weights(self,
                        global_scale_variance,
                        global_scale_noncentered,
                        local_scale_variances,
                        local_scales_noncentered,
                        weights_noncentered):
    global_scale = (global_scale_noncentered *
                    tf.sqrt(global_scale_variance) *
                    self.weights_prior_scale)

    local_scales = local_scales_noncentered * tf.sqrt(local_scale_variances)
    return weights_noncentered * local_scales * global_scale[..., tf.newaxis]
def params_to_weights ( self , global_scale_variance , global_scale_noncentered , local_scale_variances , local_scales_noncentered , weights_noncentered ) : global_scale = ( global_scale_noncentered * tf . sqrt ( global_scale_variance ) * self . weights_prior_scale ) local_scales = local_scales_noncentered * tf . sqrt ( local_scale_variances ) return weights_noncentered * local_scales * global_scale [ ... , tf . newaxis ]
Build regression weights from model parameters.
Build regression weights from model parameters .