scalation.analytics

Perceptron

class Perceptron extends Predictor with Error

The Perceptron class supports single-valued 2-layer (input and output) Neural-Networks. Given several input vectors and output values (training data), fit the weights 'w' connecting the layers, so that for a new input vector 'zi', the net can predict the output value 'zo', i.e., 'zi --> zo = f (w dot zi)'. Note, w0 is treated as the bias, so x0 must be 1.0.

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Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new Perceptron(x: MatrixD, y: VectorD, eta: Double = 1.0)

    x

    the input matrix (training data consisting of m input vectors)

    y

    the output vector (training data consisting of m output values)

    eta

    the learning/convergence rate

Value Members

  1. final def !=(arg0: Any): Boolean

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

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

    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  5. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean

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

    Definition Classes
    AnyRef → Any
  8. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  9. def fit: VectorD

    Return the fit, the weigth vector 'w'.

  10. def flaw(method: String, message: String): Unit

    Show the flaw by printing the error message.

    Show the flaw by printing the error message.

    method

    the method where the error occurred

    message

    the error message

    Definition Classes
    Error
  11. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  12. def hashCode(): Int

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

    Definition Classes
    Any
  14. def minimizeError(): Unit

    Minimize the error in the prediction by adjusting the weight vector 'w'.

    Minimize the error in the prediction by adjusting the weight vector 'w'. The error 'e' is simply the difference between the target value 'y' and the predicted value 'z'. Mininize 1/2 of the dot product of error with itself using gradient-descent. The gradient is '-x.t * (e * z * (_1 - z))', so move in the opposite direction of the gradient.

  15. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  16. final def notify(): Unit

    Definition Classes
    AnyRef
  17. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  18. def predict(zi: MatrixD): VectorD

    Given several new input vectors stored as rows in a matrix 'zi', predict all output/response vector 'zo'

    Given several new input vectors stored as rows in a matrix 'zi', predict all output/response vector 'zo'

    zi

    the matrix containing row vectors to use for prediction

    Definition Classes
    PerceptronPredictor
  19. def predict(zi: VectorD): Double

    Given a new input vector 'zi', predict the output/response value 'zo'.

    Given a new input vector 'zi', predict the output/response value 'zo'.

    zi

    the new input vector

    Definition Classes
    PerceptronPredictor
  20. def predict(z: VectorI): Double

    Given a new discrete data vector z, predict the y-value of f(z).

    Given a new discrete data vector z, predict the y-value of f(z).

    z

    the vector to use for prediction

    Definition Classes
    Predictor
  21. def setWeights(i: Int = 0): Unit

    Set the initial weight vector 'w' with values in (0, 1) before training.

    Set the initial weight vector 'w' with values in (0, 1) before training.

    i

    the random number stream to use

  22. def setWeights(w0: VectorD): Unit

    Set the initial weight matrix w manually before training.

    Set the initial weight matrix w manually before training.

    w0

    the initial weights for w

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

    Definition Classes
    AnyRef
  24. def toString(): String

    Definition Classes
    AnyRef → Any
  25. def train(): Unit

    Given training data x and y, fit the weight vector w.

    Given training data x and y, fit the weight vector w.

    Definition Classes
    PerceptronPredictor
  26. final def wait(): Unit

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

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

    Definition Classes
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    @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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