Packages

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. val b: VectoD

    Coefficient/parameter vector [b_0, b_1, ...

    Coefficient/parameter vector [b_0, b_1, ... b_k]

    Attributes
    protected
    Definition Classes
    Predictor
  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. def coefficient: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  8. val e: VectoD

    Residual/error vector [e_0, e_1, ...

    Residual/error vector [e_0, e_1, ... e_m-1]

    Attributes
    protected
    Definition Classes
    Predictor
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def fit: VectorD

    Return the fit, the weight vector 'w'.

    Return the fit, the weight vector 'w'.

    Definition Classes
    PerceptronPredictor
  13. def fitLabels: Array[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    Predictor
  14. final 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
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  16. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  17. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  18. 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'. Minimize 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.

  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. final def notify(): Unit
    Definition Classes
    AnyRef
  21. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  22. def predict(zi: VectoD): 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
  23. 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
  24. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  25. 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

  26. 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

  27. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  28. def toString(): String
    Definition Classes
    AnyRef → Any
  29. 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
  30. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  32. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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