| Package | Description |
|---|---|
| org.aika | |
| org.aika.lattice | |
| org.aika.neuron | |
| org.aika.neuron.activation | |
| org.aika.training |
| Modifier and Type | Field and Description |
|---|---|
Document[] |
Model.docs |
| Modifier and Type | Method and Description |
|---|---|
Document |
Model.createDocument(String txt) |
Document |
Model.createDocument(String txt,
int threadId) |
| Modifier and Type | Method and Description |
|---|---|
int |
Document.compareTo(Document doc) |
static boolean |
Converter.convert(Model m,
int threadId,
Document doc,
INeuron neuron,
Collection<Synapse> modifiedSynapses) |
T |
Provider.get(Document doc) |
| Modifier and Type | Field and Description |
|---|---|
Document |
NodeActivation.doc |
| Modifier and Type | Method and Description |
|---|---|
void |
InputNode.addActivation(Document doc,
Activation inputAct) |
void |
OrNode.addActivation(Document doc,
Integer ridOffset,
NodeActivation inputAct) |
static <T extends Node,A extends NodeActivation<T>> |
Node.addActivation(Document doc,
NodeActivation.Key<T> ak,
Collection<NodeActivation<?>> inputActs)
Add a new activation to this logic node and further propagate this activation through the network.
|
void |
Node.clearActivations(Document doc) |
static SortedMap<AndNode.Refinement,Provider<? extends Node>> |
AndNode.computeNextLevelParents(Model m,
int threadId,
Document doc,
Node pa,
AndNode.Refinement ref,
PatternDiscovery.Config config) |
Activation |
OrNode.createActivation(Document doc,
NodeActivation.Key ak) |
protected NodeActivation<InputNode> |
InputNode.createActivation(Document doc,
NodeActivation.Key ak) |
protected NodeActivation<AndNode> |
AndNode.createActivation(Document doc,
NodeActivation.Key ak) |
static AndNode |
AndNode.createNextLevelNode(Model m,
int threadId,
Document doc,
Node n,
AndNode.Refinement ref,
PatternDiscovery.Config config) |
Collection<A> |
Node.getActivations(Document doc) |
Activation |
OrNode.lookupOrOption(Document doc,
Range r,
boolean create) |
void |
Node.postCreate(Document doc) |
void |
Node.processChanges(Document doc)
Process all added or removed activation for this logic node.
|
void |
OrNode.reprocessInputs(Document doc) |
abstract void |
Node.reprocessInputs(Document doc) |
void |
InputNode.reprocessInputs(Document doc) |
void |
AndNode.reprocessInputs(Document doc) |
String |
NodeActivation.toString(Document doc) |
| Constructor and Description |
|---|
NodeActivation(int id,
Document doc,
NodeActivation.Key<T> key) |
NodeActivation(int id,
Document doc,
T n,
Range pos,
Integer rid) |
| Modifier and Type | Method and Description |
|---|---|
Activation |
Neuron.addInput(Document doc,
Activation.Builder inputAct)
Propagate an input activation into the network.
|
Activation |
INeuron.addInput(Document doc,
Activation.Builder input)
Propagate an input activation into the network.
|
Activation |
Neuron.addInput(Document doc,
int begin,
int end)
Propagate an input activation into the network.
|
Activation |
Neuron.addInput(Document doc,
int begin,
int end,
Integer relationalId)
Propagate an input activation into the network.
|
void |
Neuron.addSynapse(Document doc,
Synapse.Builder input) |
void |
INeuron.clearActivations(Document doc) |
static Synapse |
Synapse.createOrLookup(Document doc,
Synapse.Key synapseKey,
Neuron inputNeuron,
Neuron outputNeuron) |
Collection<Activation> |
INeuron.getActivations(Document doc) |
Collection<Activation> |
Neuron.getFinalActivations(Document doc)
getFinalActivations is a convenience method to retrieve all activations of the given neuron that
are part of the final interpretation. |
Collection<Activation> |
INeuron.getFinalActivations(Document doc) |
Stream<Activation> |
INeuron.getFinalActivationsStream(Document doc)
getFinalActivations is a convenience method to retrieve all activations of the given neuron that
are part of the final interpretation. |
Activation |
INeuron.getFirstActivation(Document doc) |
boolean |
Neuron.init(Document doc,
double bias,
String activationFunctionKey,
INeuron.Type type,
Collection<Synapse.Builder> inputs) |
static Neuron |
Neuron.init(Document doc,
Neuron n,
double bias,
INeuron.Type type,
Synapse.Builder... inputs)
Creates a neuron with the given bias.
|
static Neuron |
Neuron.init(Document doc,
Neuron n,
double bias,
String activationFunctionKey,
INeuron.Type type,
Collection<Synapse.Builder> inputs)
Initializes a neuron with the given bias.
|
static Neuron |
Neuron.init(Document doc,
Neuron n,
double bias,
String activationFunctionKey,
INeuron.Type type,
Synapse.Builder... inputs)
Creates a neuron with the given bias.
|
void |
Synapse.update(Document doc,
double weight,
double bias) |
static boolean |
INeuron.update(Model m,
int threadId,
Document doc,
Neuron pn,
Double bias,
Collection<Synapse> modifiedSynapses) |
void |
Synapse.updateDelta(Document doc,
double weightDelta,
double biasDelta) |
| Modifier and Type | Method and Description |
|---|---|
static Activation |
Selector.get(Document doc,
INeuron n,
Integer rid,
Range r,
Range.Relation rr) |
static Activation |
Selector.get(Document doc,
INeuron n,
NodeActivation.Key ak) |
static void |
SearchNode.search(Document doc,
SearchNode root,
long v,
Long timeoutInMilliSeconds)
Searches for the best interpretation for the given document.
|
static Stream<Activation> |
Selector.select(Document doc,
INeuron n,
Integer rid,
Range r,
Range.Relation rr) |
| Constructor and Description |
|---|
Activation(int id,
Document doc,
NodeActivation.Key key) |
SearchNode(Document doc,
SearchNode selParent,
SearchNode exclParent,
int level) |
| Modifier and Type | Field and Description |
|---|---|
Document |
SupervisedTraining.doc |
| Modifier and Type | Method and Description |
|---|---|
static void |
PatternDiscovery.discover(Document doc,
PatternDiscovery.Config config) |
static void |
LongTermLearning.longTermDepression(Document doc,
LongTermLearning.Config config,
Activation act,
boolean dir)
The long-term depression algorithm decreases the strength of a synapse if only one side of the synapse is
firing.
|
static void |
LongTermLearning.longTermPotentiation(Document doc,
LongTermLearning.Config config,
Activation act)
The long-term potentiation algorithm is a variant of the Hebb learning rule.
|
static void |
MetaNetwork.train(Document doc) |
static void |
LongTermLearning.train(Document doc,
LongTermLearning.Config config) |
| Constructor and Description |
|---|
SupervisedTraining(Document doc) |
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