hyperparameter
MuyGPyS.gp.hyperparameters module reference.
- class MuyGPyS.gp.hyperparameter.scalar.ScalarHyperparameter(val, bounds='fixed')[source]
A MuyGPs kernel or model Hyperparameter.
Hyperparameters are defined by a value and optimization bounds. Values must be scalar numeric types, and bounds are either a len == 2 iterable container whose elements are numeric scalars in increasing order, or the string
fixed
. Ifbounds == "fixed"
(the default behavior), the hyperparameter value will remain fixed during optimization.val
must remain within the range of the upper and lower bounds, if notfixed
.- Parameters:
val (
Union
[str
,float
]) – A scalar within the range of the upper and lower bounds (if given). val can also be the strings"sample"
or"log_sample"
, which will result in randomly sampling a value within the range given by the bounds.bounds (
Union
[str
,Tuple
[float
,float
]]) – Iterable container of len 2 containing lower and upper bounds (in that order), or the string"fixed"
.
- Raises:
ValueError – Any
bounds
string other than"fixed"
will produce an error.ValueError – A non-iterable non-string type for
bounds
will produce an error.ValueError – A
bounds
iterable of len other than 2 will produce an error.ValueError – Iterable
bounds
values of non-numeric types will produce an error.ValueError – A lower bound that is not less than an upper bound will produce an error.
ValueError –
val == "sample" or val == "log_sample"
will produce an error ifself._bounds == "fixed"
.ValueError – Any string other than
"sample"
or"log_sample"
will produce an error.ValueError – A
val
outside of the range specified byself._bounds
will produce an error.
- __call__()[source]
Value accessor.
- Return type:
float
- Returns:
The current value of the hyperparameter.
- class MuyGPyS.gp.sigma_sq.SigmaSq(response_count=1)[source]
A \(\sigma^2\) covariance scale parameter.
\(\sigma^2\) is a scaling parameter that one multiplies with the found diagonal variances of a
MuyGPyS.gp.muygps.MuyGPS
orMuyGPyS.gp.muygps.MultivariateMuyGPS
regression in order to obtain the predicted posterior variance. Trained values assume a number of dimensions equal to the number of response dimensions, and correspond to scalar scaling parameters along the corresponding dimensions.- Parameters:
response_count (
int
) – The integer number of response dimensions.
- __call__()[source]
Value accessor.
- Return type:
ndarray
- Returns:
The current value of the hyperparameter.
- property shape: Tuple[int, ...]
Report the shape of the SigmaSq value.
- Returns:
The shape of the SigmaSq value.
- property trained: bool
Report whether the value has been set.
- Returns:
True
if trained,False
otherwise.