NeuralNetworkClassifier

Instance Constructors

new NeuralNetworkClassifier(json: JsValue)
new NeuralNetworkClassifier(alpha: Double = NeuralNetworkClassifier.alpha, alphaHalflife: Int = ..., alphaDecay: String = NeuralNetworkClassifier.alphaDecay, regularization: Double = ..., activation: String = NeuralNetworkClassifier.activation, batchSize: Int = NeuralNetworkClassifier.batchSize, layers: List[Int] = NeuralNetworkClassifier.layers)

alpha
Learning rate
alphaHalflife
Learning rate decay after this number of training steps
alphaDecay
Type of learning rate decay
regularization
Regularization parameter
activation
Activation function
batchSize
Number of (randomized) training instances to use for each training step
layers
Structure of the network as a list of number of neurons in each layer

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
val W: IndexedSeq[DenseMatrix[Double]]
var alphaEvolution: ListBuffer[(Double, Double)]
final def asInstanceOf[T0]: T0

Definition Classes
Any
val b: IndexedSeq[DenseVector[Double]]
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def diagnostics(): Map[String, List[(Double, Double)]]

Provides meta-information on the classifier
Provides meta-information on the classifier
returns
Map object of metric names and metric values

Definition Classes
NeuralNetworkClassifier → Classifier
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
var lossEvolution: ListBuffer[(Double, Double)]
val name: String

The name of the classifier
The name of the classifier

Definition Classes
NeuralNetworkClassifier → Classifier
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def predict(listX: List[List[Double]]): List[Int]

Applies the trained classifier to a dataset
Applies the trained classifier to a dataset
returns
List of predictions

Definition Classes
NeuralNetworkClassifier → Classifier
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
AnyRef → Any
def train(listX: List[List[Double]], listy: List[Int], sampleWeight: List[Double] = Nil): Unit

Performs the training of the classifier
Performs the training of the classifier

Definition Classes
NeuralNetworkClassifier → Classifier
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )

Related Docs: object NeuralNetworkClassifier | package classifiers

class NeuralNetworkClassifier extends Classifier

Instance Constructors

new NeuralNetworkClassifier(json: JsValue)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

val W: IndexedSeq[DenseMatrix[Double]]

var alphaEvolution: ListBuffer[(Double, Double)]

final def asInstanceOf[T0]: T0

val b: IndexedSeq[DenseVector[Double]]

def clone(): AnyRef

def diagnostics(): Map[String, List[(Double, Double)]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

var lossEvolution: ListBuffer[(Double, Double)]

val name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def predict(listX: List[List[Double]]): List[Int]

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

def train(listX: List[List[Double]], listy: List[Int], sampleWeight: List[Double] = Nil): Unit

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped