scalation.analytics

NeuralNet

Related Doc: package analytics

class NeuralNet extends Predictor with Error

The NeuralNet class supports basic 3-layer (input, hidden and output) Neural Networks. Given several input and output vectors (training data), fit the weights connecting the layers, so that for a new input vector 'zi', the net can predict the output vector 'zo' ('zh' is the itermediate value at the hidden layer), i.e.,

zi --> zh = f (w * zi) --> zo = g (v * zh)

Note, w_0 and v_0 are treated as biases, so zi_0 and zh_0 must be 1.0.

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Error, Predictor, AnyRef, Any
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Instance Constructors

  1. new NeuralNet(x: MatrixD, y: MatrixD, h: Int, eta: Double = 1.0)

    x

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

    y

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

    h

    the number of neurons in the hidden layer

    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 backProp(): Unit

    Use back propogation to adjust the weight matrices 'w' and 'v' to make the predictions more accurate.

    Use back propogation to adjust the weight matrices 'w' and 'v' to make the predictions more accurate. First adjust the 'v' weights (hidden to output layer) and then move back to adjust the 'w' weights (input to hidden layer).

    See also

    http://ufldl.stanford.edu/wiki/index.php/Backpropagation_Algorithm

    http://www4.rgu.ac.uk/files/chapter3%20-%20bp.pdf

  6. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  7. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  8. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  9. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  10. def fit: (MatrixD, MatrixD)

    Return the fit (weigth matrix 'w' and weigth matrix 'v').

  11. 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

  12. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  15. def minimizeError(xx: MatrixD, yy: MatrixD, ww: MatrixD): Double

    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 'eo' is simply the difference between the target value 'yi' and the predicted value 'zo'. Mininize 1/2 of the dot product of error with itself using gradient-descent.

    xx

    the effective input layer training data/matrix

    yy

    the effective output layer training data/matrix

    ww

    the weights between these two layers

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

    Definition Classes
    AnyRef

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  19. def predict(zi: Matrix): VectorD

    Given several input vectors 'zi', predict the output/response vector 'zo(0)'.

    Given several input vectors 'zi', predict the output/response vector 'zo(0)'. May use this method if the output is one dimensional or interested in 1st value.

    zi

    the new input vectors (stored as rows in a matrix)

    Definition Classes
    NeuralNet → Predictor

  20. def predict(zi: VectorD): Double

    Given an input vector 'zi', predict the output/response scalar 'zo(0)'.

    Given an input vector 'zi', predict the output/response scalar 'zo(0)'. May use this method if the output is one dimensional or interested in 1st value.

    zi

    the new input vector

    Definition Classes
    NeuralNet → Predictor

  21. 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

  22. def predictAll(zi: Matrix): MatrixD

    Given several input vectors 'zi', predict the output/response vectors 'zo'.

    Given several input vectors 'zi', predict the output/response vectors 'zo'.

    zi

    the new input vectors (stored as rows in a matrix)

  23. def predictAll(zi: VectorD): VectorD

    Given an input vector 'zi', predict the output/response vector 'zo'.

    Given an input vector 'zi', predict the output/response vector 'zo'. For the hidden to output layer bias, prepend the hidden values with a one (_11).

    zi

    the new input vector

  24. def setWeights(i: Int = 0): Unit

    Set the initial weight matrices 'w' and 'v' randomly with a value in (0, 1) before training.

    Set the initial weight matrices 'w' and 'v' randomly with a value in (0, 1) before training.

    i

    the random number stream to use

  25. def setWeights(w0: MatrixD, v0: MatrixD): Unit

    Set the initial weight matrices 'w and 'v' manually before training.

    Set the initial weight matrices 'w and 'v' manually before training.

    w0

    the initial weights for w

    v0

    the initial weights for v

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

    Definition Classes
    AnyRef

  27. def toString(): String

    Definition Classes
    AnyRef → Any

  28. def train(): Unit

    Given training data 'x' and 'y', fit the weight matrices 'w' and 'v'.

    Given training data 'x' and 'y', fit the weight matrices 'w' and 'v'.

    Definition Classes
    NeuralNet → Predictor

  29. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
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    @throws( ... )

  31. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
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    @throws( ... )

Inherited from Error

Inherited from Predictor

Inherited from AnyRef

Inherited from Any

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