Iopt
- class pyoptex.doe.cost_optimal.metric.Iopt(n=10000, cov=None, complete=True)[source]
The I-optimality criterion. Computes the average (average) prediction variance if multiple Vinv are provided.
Note
The covariance function is called by passing random=True for initialization. The function should not use grouping or costs in this case.
Attributes
- covfunc(Y, X, Zs, Vinv, costs)
A function computing the covariate parameters and potential extra random effects.
- momentsnp.array(2d)
The moments matrix.
- samplesnp.array(2d)
The covariate expanded samples for the moments matrix.
- nint
The number of samples.
- completebool
Whether to initialize the samples between -1 and 1, or from the given coordinates.
- __init__(n=10000, cov=None, complete=True)[source]
Creates the metric
Parameters
- nint
The number of samples for computing the moments matrix.
- covfunc(Y, X, Zs, Vinv, costs)
The covariance function
- completebool
Whether to use the fixed coordinates or initialize the moments matrix from completely random samples.
Methods
Iopt.call(Y, X, Zs, Vinv, costs)Computes the I-optimal metric for a given design.
Iopt.init(params)Initializes the I-optimal metric if not yet initialized.