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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val
b: VectoD
Coefficient/parameter vector [b_0, b_1, ...
Coefficient/parameter vector [b_0, b_1, ... b_k]
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def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
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val
e: VectoD
Residual/error vector [e_0, e_1, ...
Residual/error vector [e_0, e_1, ... e_m-1]
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def
fit: VectorD
Return the fit, the weight vector 'w'.
Return the fit, the weight vector 'w'.
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def
fitLabels: Array[String]
Return the labels for the fit.
Return the labels for the fit. Override when necessary.
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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
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isInstanceOf[T0]: Boolean
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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.
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notify(): Unit
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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
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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
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def
residual: VectoD
Return the vector of residuals/errors.
Return the vector of residuals/errors.
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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
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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
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def
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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.
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wait(arg0: Long): Unit
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