class NeuralNet_2L extends NeuralNet
The NeuralNet_2L class supports multi-output, 2-layer (input and output)
Neural-Networks. It can be used for both classification and prediction,
depending on the activation functions used. Given several input vectors and output
vectors (training data), fit the weights/parameters 'bb' connecting the layers,
so that for a new input vector 'z', the net can predict the output value, i.e.,
yp_j = f (bb dot z)
where 'f' is the activation function and the parameter matrix 'bb' gives the weights between input and output layers. No batching is used for this algorithm. Note, 'b0' is treated as the bias, so 'x0' must be 1.0.
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new
NeuralNet_2L(x: MatriD, y: MatriD, eta_: Double = hp ("eta"), bSize: Int = hp ("bSize").toInt, maxEpochs: Int = hp ("maxEpochs").toInt, f1: AFF = f_sigmoid)
- x
the m-by-nx input matrix (training data consisting of m input vectors)
- y
the m-by-ny output matrix (training data consisting of m output vectors)
- eta_
the learning/convergence rate
- bSize
the batch size
- maxEpochs
the maximum number of training epochs/iterations
- f1
the activation function family for layers 1->2 (input to output)
Value Members
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final
def
!=(arg0: Any): Boolean
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##(): Int
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==(arg0: Any): Boolean
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val
_1: VectorD
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final
def
asInstanceOf[T0]: T0
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val
b: VectoD
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def
clone(): AnyRef
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def
crossVal(k: Int = 10, rando: Boolean = true): Unit
Perform 'k'-fold cross-validation.
Perform 'k'-fold cross-validation.
- k
the number of folds
- rando
whether to use randomized cross-validation
- Definition Classes
- NeuralNet_2L → NeuralNet
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def
crossValidate(algor: (MatriD, MatriD) ⇒ NeuralNet, k: Int = 10, rando: Boolean = true): Array[Statistic]
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- NeuralNet
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val
e: VectoD
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eq(arg0: AnyRef): Boolean
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def
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var
eta: Double
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def
eval(xx: MatriD, yy: MatriD): Unit
Evaluate the quality of the fit for the parameter/weight matrices on the test dataset.
Evaluate the quality of the fit for the parameter/weight matrices on the test dataset.
- xx
the test input data matrix
- yy
the test output response matrix
- Definition Classes
- NeuralNet
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def
eval(): Unit
Evaluate the quality of the fit for the parameter weight matrices on the the entire dataset or the training dataset.
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def
eval(xx: MatriD, yy: VectoD): Unit
Compute the error and useful diagnostics for the test dataset.
Compute the error and useful diagnostics for the test dataset.
- xx
the test data matrix
- yy
the test response vector FIX - implement in classes
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def
finalize(): Unit
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val
fitA: Array[Fit]
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def
fitLabel: Seq[String]
Return the labels for the quality of fit measures.
Return the labels for the quality of fit measures.
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- NeuralNet
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def
fitMap(): Unit
Show 'fitMap' for each y-column.
Show 'fitMap' for each y-column.
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- NeuralNet
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final
def
flaw(method: String, message: String): Unit
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def
getClass(): Class[_]
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isInstanceOf[T0]: Boolean
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val
m: Int
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final
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ne(arg0: AnyRef): Boolean
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val
nx: Int
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val
ny: Int
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def
parameter: VectoD
Return the vector of parameter/coefficient values.
Return the vector of parameter/coefficient values.
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def
predict(z: MatriD): MatriD
Given an input matrix 'z', predict the output/response matrix 'f(z)'.
Given an input matrix 'z', predict the output/response matrix 'f(z)'.
- z
the input matrix
- Definition Classes
- NeuralNet_2L → NeuralNet
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def
predict(z: VectoD): Double
Given a new input vector 'z', predict the output/response value 'f(z)'.
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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
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def
predictV(z: VectoD): VectoD
Given a new input vector 'z', predict the output/response vector 'f(z)'.
Given a new input vector 'z', predict the output/response vector 'f(z)'.
- z
the new input vector
- Definition Classes
- NeuralNet_2L → NeuralNet
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def
reset(eta_: Double): Unit
Reset the learning rate 'eta'.
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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final
def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
train(): NeuralNet_2L
Given training data 'x' and 'y', fit the parameter/weight matrix 'bb'.
Given training data 'x' and 'y', fit the parameter/weight matrix 'bb'. Minimize the error in the prediction by adjusting the weight matrix 'bb'. Iterate over several epochs, where each epoch divides the training set into 'nbat' batches. Each batch is used to update the weights.
- Definition Classes
- NeuralNet_2L → NeuralNet
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def
train(yy: VectoD): NeuralNet
Given training data 'x' and 'yy', fit the parameter/weight matrix.
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def
train0(): NeuralNet_2L
Given training data 'x' and 'y', fit the parameter/weight matrix 'bb'.
Given training data 'x' and 'y', fit the parameter/weight matrix 'bb'. Minimize the error in the prediction by adjusting the weight matrix 'bb'. The error 'ee' is simply the difference between the target value 'y' and the predicted value 'yp'. Minimize the dot product of error with itself using gradient-descent. specifically move in the opposite direction of the gradient. Iterate over several epochs (no batching).
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def
train2(): NeuralNet_2L
Given training data 'x' and 'y', fit the parameter/weight matrix 'bb'.
Given training data 'x' and 'y', fit the parameter/weight matrix 'bb'. Minimize the error in the prediction by adjusting the weight matrix 'bb'. Iterate over several epochs, where each epoch divides the training set into 'nbat' batches. Each batch is used to update the weights. This version preforms an interval search for the best 'eta' value.
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wait(): Unit
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def
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wait(arg0: Long): Unit
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def
weights: Array[MatriD]
Return the weight matrix 'bb' (array of 1).
Return the weight matrix 'bb' (array of 1).
- Definition Classes
- NeuralNet_2L → NeuralNet