LinearRegressor

Instance Constructors

new LinearRegressor(json: JsValue)
new LinearRegressor(alpha: Double = LinearRegressor.alpha, tol: Double = LinearRegressor.tol, maxIter: Int = LinearRegressor.maxIter, degree: Int = LinearRegressor.degree)

alpha
Learning rate
tol
Loss tolerance to stop training
maxIter
Maximum number of training iterations
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[Double]
def _train(X: List[List[Double]], y: List[Double]): 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 regressor
Provides meta-information on the regressor
returns
Map object of metric names and metric values

Definition Classes
LinearRegressor → 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 getPredictions(X: List[List[Double]], weight: List[Double]): List[Double]

Returns the predicted values
def hashCode(): Int

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

Definition Classes
Any
var lossEvolution: ListBuffer[(Double, Double)]
def lossGradient(X: List[List[Double]], y: List[Double], weight: List[Double]): List[Double]

Calculates the gradient of the loss function for the given training data
val name: String

The name of the regressor
The name of the regressor

Definition Classes
LinearRegressor → 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
LinearRegressor → 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
LinearRegressor → Regressor
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]

Weights for the linear transformation

Related Docs: object LinearRegressor | package regressors

class LinearRegressor extends Regressor

Instance Constructors

new LinearRegressor(json: JsValue)

new LinearRegressor(alpha: Double = LinearRegressor.alpha, tol: Double = LinearRegressor.tol, maxIter: Int = LinearRegressor.maxIter, degree: Int = LinearRegressor.degree)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

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

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

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 getPredictions(X: List[List[Double]], weight: List[Double]): List[Double]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

var lossEvolution: ListBuffer[(Double, Double)]

def lossGradient(X: List[List[Double]], y: List[Double], weight: List[Double]): List[Double]

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

final def wait(): Unit

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

final def wait(arg0: Long): Unit

var weight: List[Double]

Inherited from Regressor

Inherited from AnyRef

Inherited from Any

Ungrouped