network

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 Go to latest Published: Jul 26, 2021 License: MIT Imports: 6 Imported by: 0

Documentation

Overview

Package network provides data structures and utilities to describe Artificial Neural Network and network solvers.

Index

Constants

This section is empty.

Variables

View Source 
var (
	// NetErrExceededMaxActivationAttempts The error to be raised when maximal number of network activation attempts exceeded
	NetErrExceededMaxActivationAttempts = errors.New("maximal network activation attempts exceeded")
	// NetErrUnsupportedSensorsArraySize The error to be raised when unsupported sensors data array size provided
	NetErrUnsupportedSensorsArraySize = errors.New("the sensors array size is unsupported by network solver")
	// NetErrDepthCalculationFailedLoopDetected The error to be raised when depth calculation failed due to the loop in network
	NetErrDepthCalculationFailedLoopDetected = errors.New("depth can not be determined for network with loop")
)

Functions

func ActivateModule 

func ActivateModule(module *NNode, a *neatmath.NodeActivatorsFactory) error

ActivateModule Method to activate neuron module presented by provided node. As a result of execution the activation values of all input nodes will be processed by corresponding activation function and corresponding activation values of output nodes will be set. Will panic if unsupported activation type requested.

func ActivateNode 

func ActivateNode(node *NNode, a *neatmath.NodeActivatorsFactory) error

ActivateNode Method to calculate activation for specified neuron node based on it's ActivationType field value. Will return error and set -0.0 activation if unsupported activation type requested.

func NeuronTypeName 

func NeuronTypeName(nlayer NodeNeuronType) string

NeuronTypeName Returns human readable neuron type name for given constant

func NodeTypeName 

func NodeTypeName(nType NodeType) string

NodeTypeName Returns human readable NNode type name for given constant value

Types

type FastControlNode 

type FastControlNode struct {
	// The activation function for control node
	ActivationType neatmath.NodeActivationType
	// The indexes of the input nodes
	InputIndexes []int
	// The indexes of the output nodes
	OutputIndexes []int
}

FastControlNode The module relay (control node) descriptor for fast network

type FastModularNetworkSolver 

type FastModularNetworkSolver struct {
	// A network id
	Id int
	// Is a name of this network */
	Name string
	// contains filtered or unexported fields
}

FastModularNetworkSolver is the network solver implementation to be used for large neural networks simulation.

func NewFastModularNetworkSolver 

func NewFastModularNetworkSolver(biasNeuronCount, inputNeuronCount, outputNeuronCount, totalNeuronCount int,
	activationFunctions []neatmath.NodeActivationType, connections []*FastNetworkLink,
	biasList []float64, modules []*FastControlNode) *FastModularNetworkSolver

NewFastModularNetworkSolver Creates new fast modular network solver

func (*FastModularNetworkSolver) Flush 

func (s *FastModularNetworkSolver) Flush() (bool, error)

Flush Flushes network state by removing all current activations. Returns true if network flushed successfully or false in case of error.

func (*FastModularNetworkSolver) ForwardSteps 

func (s *FastModularNetworkSolver) ForwardSteps(steps int) (res bool, err error)

ForwardSteps Propagates activation wave through all network nodes provided number of steps in forward direction. Returns true if activation wave passed from all inputs to the outputs.

func (*FastModularNetworkSolver) LinkCount 

func (s *FastModularNetworkSolver) LinkCount() int

LinkCount Returns the total number of links between nodes in the network

func (*FastModularNetworkSolver) LoadSensors 

func (s *FastModularNetworkSolver) LoadSensors(inputs []float64) error

LoadSensors Set sensors values to the input nodes of the network

func (*FastModularNetworkSolver) NodeCount 

func (s *FastModularNetworkSolver) NodeCount() int

NodeCount Returns the total number of neural units in the network

func (*FastModularNetworkSolver) ReadOutputs 

func (s *FastModularNetworkSolver) ReadOutputs() []float64

ReadOutputs Read output values from the output nodes of the network

func (*FastModularNetworkSolver) RecursiveSteps 

func (s *FastModularNetworkSolver) RecursiveSteps() (res bool, err error)

RecursiveSteps Propagates activation wave through all network nodes provided number of steps by recursion from output nodes Returns true if activation wave passed from all inputs to the outputs. This method is preferred method of network activation when number of forward steps can not be easy calculated and no network modules are set.

func (*FastModularNetworkSolver) Relax 

func (s *FastModularNetworkSolver) Relax(maxSteps int, maxAllowedSignalDelta float64) (relaxed bool, err error)

Relax Attempts to relax network given amount of steps until giving up. The network considered relaxed when absolute value of the change at any given point is less than maxAllowedSignalDelta during activation waves propagation. If maxAllowedSignalDelta value is less than or equal to 0, the method will return true without checking for relaxation.

func (*FastModularNetworkSolver) String 

func (s *FastModularNetworkSolver) String() string

Stringer 

type FastNetworkLink struct {
	// The index of source neuron
	SourceIndex int
	// The index of target neuron
	TargetIndex int
	// The weight of this link
	Weight float64
	// The signal relayed by this link
	Signal float64
}

FastNetworkLink The connection descriptor for fast network 

type Link struct {
	// Weight of connection
	Weight float64
	// NNode inputting into the link
	InNode *NNode
	// NNode that the link affects
	OutNode *NNode
	// If TRUE the link is recurrent
	IsRecurrent bool
	// If TRUE the link is time delayed
	IsTimeDelayed bool

	// Points to a trait of parameters for genetic creation
	Trait *neat.Trait

	/* ************ LEARNING PARAMETERS *********** */
	// The following parameters are for use in neurons that learn through habituation,
	// sensitization, or Hebbian-type processes
	Params []float64
	// The amount of weight adjustment
	AddedWeight float64
}

Link is a connection from one node to another with an associated weight. It can be marked as recurrent. 

func NewLink(weight float64, inputNode, outNode *NNode, recurrent bool) *Link

NewLink Creates new link with specified weight, input and output neurons connected recurrently or not.

func NewLinkCopy 

func NewLinkCopy(l *Link, inputNode, outNode *NNode) *Link

NewLinkCopy The copy constructor to create new link with parameters taken from provided ones and connecting specified nodes

func NewLinkWithTrait 

func NewLinkWithTrait(trait *neat.Trait, weight float64, inputNode, outNode *NNode, recurrent bool) *Link

NewLinkWithTrait Creates new Link with specified Trait

func (*Link) IsEqualGenetically 

func (l *Link) IsEqualGenetically(ol *Link) bool

IsEqualGenetically Checks if this link is genetically equal to provided one, i.e. connects nodes with the same IDs and has equal recurrent flag. I.e. if both links represent the same Gene.

func (*Link) String 

func (l *Link) String() string

The Link methods implementation

type NNode 

type NNode struct {
	// The ID of the node
	Id int

	// The type of node activation function (SIGMOID, ...)
	ActivationType math.NodeActivationType
	// The neuron type for this node (HIDDEN, INPUT, OUTPUT, BIAS)
	NeuronType NodeNeuronType

	// The node's activation value
	Activation float64
	// The number of activations for current node
	ActivationsCount int32
	// The activation sum
	ActivationSum float64

	// The list of all incoming connections
	Incoming []*Link
	// The list of all outgoing connections
	Outgoing []*Link
	// The trait linked to the node
	Trait *neat.Trait

	// Used for Gene decoding by referencing analogue to this node in organism phenotype
	PhenotypeAnalogue *NNode

	/* ************ LEARNING PARAMETERS *********** */
	// The following parameters are for use in neurons that learn through habituation,
	// sensitization, or Hebbian-type processes  */
	Params []float64
	// contains filtered or unexported fields
}

NNode is either a NEURON or a SENSOR.

  • If it's a sensor, it can be loaded with a value for output
  • If it's a neuron, it has a list of its incoming input signals ([]*Link is used)

Use an activation count to avoid flushing

func NewNNode 

func NewNNode(nodeId int, neuronType NodeNeuronType) *NNode

NewNNode Creates new node with specified ID and neuron type associated (INPUT, HIDDEN, OUTPUT, BIAS)

func NewNNodeCopy 

func NewNNodeCopy(n *NNode, t *neat.Trait) *NNode

NewNNodeCopy Construct a NNode off another NNode with given trait for genome purposes

func NewNetworkNode 

func NewNetworkNode() *NNode

NewNetworkNode The default constructor

func (*NNode) Depth 

func (n *NNode) Depth(d int) (int, error)

Depth Find the greatest depth starting from this neuron at depth d

func (*NNode) Flushback 

func (n *NNode) Flushback()

Flushback Recursively deactivate backwards through the network

func (*NNode) FlushbackCheck 

func (n *NNode) FlushbackCheck() error

FlushbackCheck is to verify flushing for debugging

func (*NNode) GetActiveOut 

func (n *NNode) GetActiveOut() float64

GetActiveOut Returns activation for a current step

func (*NNode) GetActiveOutTd 

func (n *NNode) GetActiveOutTd() float64

GetActiveOutTd Returns activation from PREVIOUS time step

func (*NNode) IsNeuron 

func (n *NNode) IsNeuron() bool

IsNeuron returns true if this node is NEURON

func (*NNode) IsSensor 

func (n *NNode) IsSensor() bool

IsSensor Returns true if this node is SENSOR

func (*NNode) NodeType 

func (n *NNode) NodeType() NodeType

NodeType Convenient method to check network's node type (SENSOR, NEURON)

func (*NNode) Print 

func (n *NNode) Print() string

Print Prints all node's fields to the string

func (*NNode) SensorLoad 

func (n *NNode) SensorLoad(load float64) bool

SensorLoad If the node is a SENSOR, returns TRUE and loads the value

func (*NNode) String 

func (n *NNode) String() string

type Network 

type Network struct {
	// A network id
	Id int
	// Is a name of this network */
	Name string
	// NNodes that output from the network
	Outputs []*NNode
	// contains filtered or unexported fields
}

Network is a collection of all nodes within an organism's phenotype, which effectively defines Neural Network topology. The point of the network is to define a single entity which can evolve or learn on its own, even though it may be part of a larger framework.

func NewModularNetwork 

func NewModularNetwork(in, out, all, control []*NNode, netId int) *Network

NewModularNetwork Creates new modular network with control nodes

func NewNetwork 

func NewNetwork(in, out, all []*NNode, netId int) *Network

NewNetwork Creates new network

func (*Network) Activate 

func (n *Network) Activate() (bool, error)

Activate Activates the net such that all outputs are active

func (*Network) ActivateSteps 

func (n *Network) ActivateSteps(maxSteps int) (bool, error)

ActivateSteps Attempts to activate the network given number of steps before returning error.

func (*Network) AllNodes 

func (n *Network) AllNodes() []*NNode

AllNodes Returns all nodes in the network

func (*Network) Complexity 

func (n *Network) Complexity() int

Complexity Returns complexity of this network which is sum of nodes count and links count

func (*Network) FastNetworkSolver 

func (n *Network) FastNetworkSolver() (Solver, error)

FastNetworkSolver Creates fast network solver based on the architecture of this network. It's primarily aimed for big networks to improve processing speed.

func (*Network) Flush 

func (n *Network) Flush() (res bool, err error)

func (*Network) ForwardSteps 

func (n *Network) ForwardSteps(steps int) (res bool, err error)

func (*Network) IsRecurrent 

func (n *Network) IsRecurrent(inNode, outNode *NNode, count *int, thresh int) bool

IsRecurrent This checks a POTENTIAL link between a potential in_node and potential out_node to see if it must be recurrent. Use count and thresh to jump out in the case of an infinite loop.

func (*Network) LinkCount 

func (n *Network) LinkCount() int

func (*Network) LoadSensors 

func (n *Network) LoadSensors(sensors []float64) error

func (*Network) MaxDepth 

func (n *Network) MaxDepth() (int, error)

MaxDepth Find the maximum number of neurons between an output and an input

func (*Network) NodeCount 

func (n *Network) NodeCount() int

func (*Network) OutputIsOff 

func (n *Network) OutputIsOff() bool

OutputIsOff If at least one output is not active then return true

func (*Network) PrintActivation 

func (n *Network) PrintActivation() string

PrintActivation Prints the values of network outputs to the console

func (*Network) PrintInput 

func (n *Network) PrintInput() string

PrintInput Print the values of network inputs to the console

func (*Network) ReadOutputs 

func (n *Network) ReadOutputs() []float64

func (*Network) RecursiveSteps 

func (n *Network) RecursiveSteps() (bool, error)

func (*Network) Relax 

func (n *Network) Relax(_ int, _ float64) (bool, error)

type NodeIdGenerator 

type NodeIdGenerator interface {
	// NextNodeId is to get next unique node ID
	NextNodeId() int
}

NodeIdGenerator definition of the unique IDs generator for network nodes.

type NodeNeuronType 

type NodeNeuronType byte

NodeNeuronType NeuronType defines the type of neuron to create 

const (
	// HiddenNeuron The node is in hidden layer
	HiddenNeuron NodeNeuronType = iota
	// InputNeuron The node is in input layer
	InputNeuron
	// OutputNeuron The node is in output layer
	OutputNeuron
	// BiasNeuron The node is bias
	BiasNeuron
)

These are NNode layer type

func NeuronTypeByName 

func NeuronTypeByName(name string) (NodeNeuronType, error)

NeuronTypeByName Returns neuron node type from its name

type NodeType 

type NodeType byte

NodeType NNodeType defines the type of NNode to create 

const (
	// NeuronNode The neuron type
	NeuronNode NodeType = iota
	// SensorNode The sensor type
	SensorNode
)

Predefined NNode types

type Solver 

type Solver interface {
	// ForwardSteps Propagates activation wave through all network nodes provided number of steps in forward direction.
	// Returns true if activation wave passed from all inputs to outputs.
	ForwardSteps(steps int) (bool, error)

	// RecursiveSteps Propagates activation wave through all network nodes provided number of steps by recursion from output nodes
	// Returns true if activation wave passed from all inputs to outputs.
	RecursiveSteps() (bool, error)

	// Relax Attempts to relax network given amount of steps until giving up. The network considered relaxed when absolute
	// value of the change at any given point is less than maxAllowedSignalDelta during activation waves propagation.
	// If maxAllowedSignalDelta value is less than or equal to 0, the method will return true without checking for relaxation.
	Relax(maxSteps int, maxAllowedSignalDelta float64) (bool, error)

	// Flush Flushes network state by removing all current activations. Returns true if network flushed successfully or
	// false in case of error.
	Flush() (bool, error)

	// LoadSensors Set sensors values to the input nodes of the network
	LoadSensors(inputs []float64) error
	// ReadOutputs Read output values from the output nodes of the network
	ReadOutputs() []float64

	// NodeCount Returns the total number of neural units in the network
	NodeCount() int
	// LinkCount Returns the total number of links between nodes in the network
	LinkCount() int
}

Solver defines network solver interface, which allows propagation of the activation waves through the underlying network graph.

Source Files

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