config.go

// Copyright (c) 2020, The Emergent Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package main

import "github.com/emer/axon/v2/axon"

// ParamConfig has config parameters related to sim params
type ParamConfig struct {

	// network parameters
	Network map[string]any

	// Extra Param Sheet name(s) to use (space separated if multiple) -- must be valid name as listed in compiled-in params or loaded params
	Sheet string

	// extra tag to add to file names and logs saved from this run
	Tag string

	// user note -- describe the run params etc -- like a git commit message for the run
	Note string

	// Name of the JSON file to input saved parameters from.
	File string `nest:"+"`

	// Save a snapshot of all current param and config settings in a directory named params_<datestamp> (or _good if Good is true), then quit -- useful for comparing to later changes and seeing multiple views of current params
	SaveAll bool `nest:"+"`

	// for SaveAll, save to params_good for a known good params state.  This can be done prior to making a new release after all tests are passing -- add results to git to provide a full diff record of all params over time.
	Good bool `nest:"+"`
}

// RunConfig has config parameters related to running the sim
type RunConfig struct {

	// mem % correct level (proportion) above which training on current list stops (switch from AB to AC or stop on AC)
	StopMem float32 `default:"0.9"`

	// use the GPU for computation -- generally faster even for small models if NData ~16
	GPU bool `default:"true"`

	// number of parallel threads for CPU computation -- 0 = use default
	NThreads int `default:"0"`

	// starting run number -- determines the random seed -- runs counts from there -- can do all runs in parallel by launching separate jobs with each run, runs = 1
	Run int `default:"0"`

	// total number of runs to do when running Train
	Runs int `default:"5" min:"1"`

	// total number of epochs per run
	Epochs int `default:"100"`

	// total number of trials per epoch.  Should be an even multiple of NData.
	NTrials int `default:"20"`

	// number of data-parallel items to process in parallel per trial -- works (and is significantly faster) for both CPU and GPU.  Results in an effective mini-batch of learning.
	NData int `default:"10" min:"1"`

	// how often to run through all the test patterns, in terms of training epochs -- can use 0 or -1 for no testing
	TestInterval int `default:"1"`
}

// LogConfig has config parameters related to logging data
type LogConfig struct {

	// if true, save final weights after each run
	SaveWts bool

	// if true, save train epoch log to file, as .epc.tsv typically
	Epoch bool `default:"true" nest:"+"`

	// if true, save run log to file, as .run.tsv typically
	Run bool `default:"true" nest:"+"`

	// if true, save train trial log to file, as .trl.tsv typically. May be large.
	Trial bool `default:"false" nest:"+"`

	// if true, save testing epoch log to file, as .tst_epc.tsv typically.  In general it is better to copy testing items over to the training epoch log and record there.
	TestEpoch bool `default:"false" nest:"+"`

	// if true, save testing trial log to file, as .tst_trl.tsv typically. May be large.
	TestTrial bool `default:"false" nest:"+"`

	// if true, save network activation etc data from testing trials, for later viewing in netview
	NetData bool
}

// PatConfig have the pattern parameters
type PatConfig struct {

	// minimum difference between item random patterns, as a proportion (0-1) of total active
	MinDiffPct float32

	// use drifting context representations -- otherwise does bit flips from prototype
	DriftCtxt bool

	// proportion (0-1) of active bits to flip for each context pattern, relative to a prototype, for non-drifting
	CtxtFlipPct float32

	// percentage of active bits that drift, per step, for drifting context
	DriftPct float32
}

func (pp *PatConfig) Defaults() {
	pp.MinDiffPct = 0.5
	pp.CtxtFlipPct = .25
}

type ModConfig struct {

	// percent connectivity from Input to EC2
	InToEc2PCon float32

	// percent activation in EC pool, used in patgen for input generation
	// percent activation in EC pool, used in patgen for input generation
	ECPctAct float32

	// memory threshold
	MemThr float64
}

func (mod *ModConfig) Defaults() {
	// patgen
	mod.ECPctAct = 0.2

	// input to EC2 pcon
	mod.InToEc2PCon = 0.25

	// // theta EDL in CA1
	// mod.ThetaLow = 0.9 // doesn't have strong effect at low NTrials but shouldn't go too low (e.g., 0.3)
	// mod.ThetaHigh = 1

	// // EDL in CA3
	// mod.MossyDel = 4
	// mod.MossyDelTest = 3

	// memory threshold
	mod.MemThr = 0.34
}

// Config is a standard Sim config -- use as a starting point.
type Config struct {

	// specify include files here, and after configuration, it contains list of include files added
	Includes []string

	// open the GUI -- does not automatically run -- if false, then runs automatically and quits
	GUI bool `default:"true"`

	// log debugging information
	Debug bool

	// misc model parameters
	Mod ModConfig `view:"inline"`

	// Hippocampus sizing parameters
	Hip axon.HipConfig

	// parameters for the input patterns
	Pat PatConfig

	// parameter related configuration options
	Params ParamConfig `view:"add-fields"`

	// sim running related configuration options
	Run RunConfig `view:"add-fields"`

	// data logging related configuration options
	Log LogConfig `view:"add-fields"`
}

func (cfg *Config) Defaults() {
	cfg.Mod.Defaults()
	cfg.Hip.Defaults()
	cfg.Pat.Defaults()
}

func (cfg *Config) IncludesPtr() *[]string { return &cfg.Includes }