class Perceptron extends PredictorMat
The Perceptron class supports single-output, 2-layer (input and output)
Neural-Networks. Although perceptrons are typically used for classification,
this class is used for prediction. Given several input vectors and output
values (training data), fit the weights/parameters 'b' connecting the layers,
so that for a new input vector 'z', the net can predict the output value, i.e.,
z = f (b dot z)
The parameter vector 'b' (w) gives the weights between input and output layers. Note, b0 is treated as the bias, so x0 must be 1.0.
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- PredictorMat
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Instance Constructors
-
new
Perceptron(x: MatriD, y: VectoD, eta: Double = 0.1, f1: FunctionS2S = sigmoid, f1D: FunctionV_2V = sigmoidDV)
- 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 (requires adjustment)
- f1
the activation function (mapping scalar => scalar)
Value Members
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def
coefficient: VectoD
Return the vector of coefficient/parameter values.
Return the vector of coefficient/parameter values.
- Definition Classes
- Predictor
-
def
crossVal(k: Int = 10): Unit
Perform 'k'-fold cross-validation.
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def
crossValidate(algor: (MatriD, VectoD) ⇒ PredictorMat, k: Int = 10): Array[Statistic]
- Definition Classes
- PredictorMat
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val
df: (Double, Double)
- Definition Classes
- Fit
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def
diagnose(e: VectoD, w: VectoD = null, yp: VectoD = null): Unit
Given the error/residual vector, compute the quality of fit measures.
Given the error/residual vector, compute the quality of fit measures.
- e
the corresponding m-dimensional error vector (y - yp)
- w
the weights on the instances
- yp
the predicted response vector (x * b)
- Definition Classes
- Fit
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def
eval(): Unit
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'.
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'.
- Definition Classes
- Perceptron → PredictorMat → Predictor
-
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
- Definition Classes
- PredictorMat → Predictor
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def
f_(z: Double): String
Format a double value.
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def
fit: VectoD
Return the quality of fit including 'sst', 'sse', 'mse0', rmse', 'mae', 'rSq', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'.
Return the quality of fit including 'sst', 'sse', 'mse0', rmse', 'mae', 'rSq', 'df._2', 'rBarSq', 'fStat', 'aic', 'bic'. Note, if 'sse > sst', the model introduces errors and the 'rSq' may be negative, otherwise, R^2 ('rSq') ranges from 0 (weak) to 1 (strong). Note that 'rSq' is the number 5 measure. Override to add more quality of fit measures.
- Definition Classes
- 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. Override to add more quality of fit measures.
- Definition Classes
- Fit
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def
fitMap: Map[String, String]
Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered).Build a map of quality of fit measures (use of
LinedHashMapmakes it ordered). Override to add more quality of fit measures.- Definition Classes
- Fit
-
final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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val
index_rSq: Int
- Definition Classes
- Fit
-
def
mse_: Double
Return the mean of squares for error (sse / df._2).
Return the mean of squares for error (sse / df._2). Must call diagnose first.
- Definition Classes
- Fit
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def
predict(z: MatriD): VectoD
Given a new input matrix 'z', predict the output/response value 'f(z)'.
Given a new input matrix 'z', predict the output/response value 'f(z)'.
- z
the new input matrix
- Definition Classes
- Perceptron → PredictorMat
-
def
predict(z: VectoD): Double
Given a new input vector 'z', predict the output/response value 'f(z)'.
Given a new input vector 'z', predict the output/response value 'f(z)'.
- z
the new input vector
- Definition Classes
- Perceptron → PredictorMat → Predictor
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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
- Predictor
-
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.
- Definition Classes
- Predictor
-
def
setWeights(ymin: Double = 0.0, ymax: Double = 1.0, stream: Int = 0): Unit
Randomly initialize the parameter/weight vector 'b' with values in '(ymin, ymax)' before training.
Randomly initialize the parameter/weight vector 'b' with values in '(ymin, ymax)' before training.
- ymin
the minimum value to produce
- ymax
the maximum value to produce
- stream
the random number stream to use
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def
setWeights(w0: VectoD): Unit
Set the initial parameter/weight vector 'b' manually before training.
Set the initial parameter/weight vector 'b' manually before training.
- w0
the initial weights for b
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def
sumCoeff(b: VectoD, stdErr: VectoD = null): String
Produce the summary report portion for the cofficients.
Produce the summary report portion for the cofficients.
- b
the parameters/coefficients for the model
- Definition Classes
- Fit
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def
summary(): Unit
Compute diagostics for the regression model.
Compute diagostics for the regression model.
- Definition Classes
- PredictorMat
-
def
summary(b: VectoD, stdErr: VectoD = null): String
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
Produce a summary report with diagnostics for each predictor 'x_j' and the overall quality of fit.
- b
the parameters/coefficients for the model
- Definition Classes
- Fit
-
def
train(yy: VectoD = y): Perceptron
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'.
Given training data 'x' and 'yy', fit the parameter/weight vector 'b'. Minimize the error in the prediction by adjusting the weight vector 'b'. The error 'e' is simply the difference between the target value 'yy' and the predicted value 'yp'. Minimize the dot product of error with itself using gradient-descent (move in the opposite direction of the gradient).
- yy
the output vector
- Definition Classes
- Perceptron → PredictorMat → Predictor
-
def
train(): PredictorMat
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
Given a set of data vectors 'x's and their corresponding responses 'y's, passed into the implementing class, train the prediction function 'y = f(x)' by fitting its parameters.
- Definition Classes
- PredictorMat