hip

type CHLParams struct {

	// if true, use CHL learning instead of standard XCAL learning -- allows easy exploration of CHL vs. XCAL
	On bool `desc:"if true, use CHL learning instead of standard XCAL learning -- allows easy exploration of CHL vs. XCAL"`

	// [def: 0.001] [min: 0] [max: 1] amount of hebbian learning (should be relatively small, can be effective at .0001)
	Hebb float32 `def:"0.001" min:"0" max:"1" desc:"amount of hebbian learning (should be relatively small, can be effective at .0001)"`

	// [def: 0.999] [min: 0] [max: 1] amount of error driven learning, automatically computed to be 1-Hebb
	Err float32 `def:"0.999" min:"0" max:"1" inactive:"+" desc:"amount of error driven learning, automatically computed to be 1-Hebb"`

	// if true, use ActQ1 as the minus phase -- otherwise ActM
	MinusQ1 bool `desc:"if true, use ActQ1 as the minus phase -- otherwise ActM"`

	// [def: 0.4:0.8] [min: 0] [max: 1] proportion of correction to apply to sending average activation for hebbian learning component (0=none, 1=all, .5=half, etc)
	SAvgCor float32 `` /* 161-byte string literal not displayed */

	// [def: 0.001] [min: 0] threshold of sending average activation below which learning does not occur (prevents learning when there is no input)
	SAvgThr float32 `` /* 145-byte string literal not displayed */
}

type CHLPrjn struct {
	leabra.Prjn // access as .Prjn

	// [view: inline] parameters for CHL learning -- if CHL is On then WtSig.SoftBound is automatically turned off -- incompatible
	CHL CHLParams `` /* 129-byte string literal not displayed */
}

type EcCa1Prjn struct {
	leabra.Prjn // access as .Prjn
}