PerceptronClassifier

Instance Constructors

new PerceptronClassifier(json: JsValue)
new PerceptronClassifier(alpha: Double = PerceptronClassifier.alpha, degree: Int = PerceptronClassifier.degree)

alpha
Learning rate
degree
Order of polynomial features to add to the instances (1 for no addition)

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
def _predict(X: List[List[Double]]): List[Int]
def _train(X: List[List[Double]], y: List[Int]): Unit
final def asInstanceOf[T0]: T0

Definition Classes
Any
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
Classifier
def distToPlane(instance: List[Double], weight: List[Double]): Double

Calculates the distance to the decision decision hyperplane
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 getPrediction(instance: List[Double], weight: List[Double]): Int

Gets the prediction for this instance
Gets the prediction for this instance
Predicts if instance is above or below the decision hyperplane
def hashCode(): Int

Definition Classes
AnyRef → Any
def isCorrect(instance: List[Double], label: Int, weight: List[Double]): Boolean

Determines if an instance is classified correctly
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val name: String

The name of the classifier
The name of the classifier

Definition Classes
PerceptronClassifier → 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(X: List[List[Double]]): List[Int]

Applies the trained classifier to a dataset
Applies the trained classifier to a dataset
X
List of data instances
returns
List of predictions

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

Definition Classes
AnyRef
def toString(): String

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

Performs the training of the classifier
Performs the training of the classifier
X
List of training instances
y
List of training labels

Definition Classes
PerceptronClassifier → 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( ... )
var weight: List[Double]

The weights with the intercept as the head element

Related Docs: object PerceptronClassifier | package classifiers

class PerceptronClassifier extends Classifier

Instance Constructors

new PerceptronClassifier(json: JsValue)

new PerceptronClassifier(alpha: Double = PerceptronClassifier.alpha, degree: Int = PerceptronClassifier.degree)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

def _predict(X: List[List[Double]]): List[Int]

def _train(X: List[List[Double]], y: List[Int]): Unit

final def asInstanceOf[T0]: T0

def clone(): AnyRef

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

def distToPlane(instance: List[Double], weight: List[Double]): Double

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getPrediction(instance: List[Double], weight: List[Double]): Int

def hashCode(): Int

def isCorrect(instance: List[Double], label: Int, weight: List[Double]): Boolean

final def isInstanceOf[T0]: Boolean

val name: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

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

def toString(): String

def train(X: List[List[Double]], y: 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

var weight: List[Double]

Inherited from Classifier

Inherited from AnyRef

Inherited from Any

Ungrouped