Packages

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 intermediate 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.

Linear Supertypes
Error, Predictor, AnyRef, Any
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  1. NeuralNet
  2. Error
  3. Predictor
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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. val b: VectoD
    Attributes
    protected
    Definition Classes
    Predictor
  6. def backProp(): Unit

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

    Use back propagation 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

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

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    Predictor
  9. def diagnose(yy: VectoD): Unit

    Compute diagostics for the predictor.

    Compute diagostics for the predictor. Override to add more diagostics. Note, for 'rmse', 'sse' is divided by the number of instances 'm' rather than degrees of freedom.

    yy

    the response vector

    Definition Classes
    Predictor
    See also

    en.wikipedia.org/wiki/Mean_squared_error

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

    Return the quality of fit.

    Return the quality of fit.

    Definition Classes
    NeuralNet → Predictor
  15. def fit2: (MatrixD, MatrixD)

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

    Return the fit (weight matrix 'w' and weight matrix 'v'). FIX - make compatible with Predictor

  16. def fitLabels: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit.

    Definition Classes
    NeuralNet → Predictor
  17. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
  19. def hashCode(): Int
    Definition Classes
    AnyRef → Any
  20. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  21. val mae: Double
    Attributes
    protected
    Definition Classes
    Predictor
  22. 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'. Minimize 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

  23. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  24. final def notify(): Unit
    Definition Classes
    AnyRef
  25. final def notifyAll(): Unit
    Definition Classes
    AnyRef
  26. def predict(zi: VectoD): 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
  27. def predict(z: VectoI): 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
  28. def predictAll(zi: MatriD): 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)

  29. def predictAll(zi: VectoD): VectoD

    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

  30. val rSq: Double
    Attributes
    protected
    Definition Classes
    Predictor
  31. def residual: VectoD

    Return the vector of residuals/errors.

    Return the vector of residuals/errors.

    Definition Classes
    Predictor
  32. val rmse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  33. 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

  34. 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

  35. val sse: Double
    Attributes
    protected
    Definition Classes
    Predictor
  36. val ssr: Double
    Attributes
    protected
    Definition Classes
    Predictor
  37. val sst: Double
    Attributes
    protected
    Definition Classes
    Predictor
  38. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  39. def toString(): String
    Definition Classes
    AnyRef → Any
  40. 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
  41. def train(yy: VectoD): 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'.

    yy

    the response vector

    Definition Classes
    NeuralNet → Predictor
  42. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  43. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  44. 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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