kNNRegressor

Instance Constructors

new kNNRegressor(json: JsValue)
new kNNRegressor(k: Int = kNNRegressor.k)

k
Number of closest neighbors to consider

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
var X_NN: List[List[Double]]
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 regressor
Provides meta-information on the regressor
returns
Map object of metric names and metric values

Definition Classes
Regressor
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 getNearest(x: List[Double], X_NN: List[List[Double]], y_NN: List[Double], nearest: List[(Double, Double)]): List[(Double, Double)]

Gets list of nearest neighbors
def getPrediction(x: List[Double]): Double

Predicts a label for a single instance
def hashCode(): Int

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

Definition Classes
Any
val name: String

The name of the regressor
The name of the regressor

Definition Classes
kNNRegressor → Regressor
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[Double]

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

Definition Classes
kNNRegressor → Regressor
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[Double]): Unit

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

Definition Classes
kNNRegressor → Regressor
def updateNearest(x: List[Double], instance: List[Double], label: Double, nearest: List[(Double, Double)]): List[(Double, Double)]

Yield new collection of nearest neighbors
Yield new collection of nearest neighbors
x
Test instance feature vector
instance
Training instance feature vector
label
Training instance label
nearest
Current collection of nearest neighbors
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 y_NN: List[Double]

Related Docs: object kNNRegressor | package regressors

class kNNRegressor extends Regressor

Instance Constructors

new kNNRegressor(json: JsValue)

new kNNRegressor(k: Int = kNNRegressor.k)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

var X_NN: List[List[Double]]

final def asInstanceOf[T0]: T0

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 getNearest(x: List[Double], X_NN: List[List[Double]], y_NN: List[Double], nearest: List[(Double, Double)]): List[(Double, Double)]

def getPrediction(x: List[Double]): Double

def hashCode(): Int

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[Double]

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

def toString(): String

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

def updateNearest(x: List[Double], instance: List[Double], label: Double, nearest: List[(Double, Double)]): List[(Double, Double)]

final def wait(): Unit

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

final def wait(arg0: Long): Unit

var y_NN: List[Double]

Inherited from Regressor

Inherited from AnyRef

Inherited from Any

Ungrouped