Improve scaling of inputs and optionally target values for the Gaussian process

[gkronber]

There are some issues related to scaling of data in the Gaussian process model.

1. input features are always scaled to the range [0..1]. This has the effect that the period length in the periodic covariance function cannot be set to a natural value. Assume that we have a time series with daily observations where we want to model a weekly periodic signal. In this case it would be great if we could set the period = 7 in the periodic covariance function. However, because of scaling the correct value depends on minimum and maximum observed day.
2. All hyper-parameters are initialized randomly using a log-uniform distribution. This is a good assumption for length scales if all input variables have been scaled to the same range. But for other hyper-parameters (esp. for hyper-parameters for mean functions) this initialization might not be ideal. Especially, for the constant mean function and for the noise hyper-parameter of the Gaussian likelihood it might be better to initialize these hyperparameters to the empirical mean and variance.

[gkronber]

This is also relevant for #2501 as we must assume that a regression model for the mean function expects unscaled inputs.

[gkronber]

r13118: added hidden parameter to turn on/off scaling of input variables in Gaussian process models

[gkronber]

r13119: added parameter for input scaling also to Gaussian process classification models

[mkommend]

Reviewed r13118 & r13119.

[gkronber]

r13147: merged r13118:13119 from trunk to stable