abstract class PredictorMat2 extends Predictor
The PredictorMat2 abstract class provides the basic structure and API for
a variety of modeling techniques with multiple outputs/responses, e.g., Neural Networks.
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Instance Constructors
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new
PredictorMat2(x: MatriD, y: MatriD, fname: Strings, hparam: HyperParameter)
- x
the m-by-nx data/input matrix (data consisting of m input vectors)
- y
the m-by-ny response/output matrix (data consisting of m output vectors)
- fname
the feature/variable names (if null, use x_j's)
- hparam
the hyper-parameters for the model/network
Abstract Value Members
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abstract
def
buildModel(x_cols: MatriD): PredictorMat2
Build a sub-model that is restricted to the given columns of the data matrix.
Build a sub-model that is restricted to the given columns of the data matrix.
- x_cols
the columns that the new model is restricted to
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abstract
def
parameters: NetParams
Return the all parameters (weights and biases).
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abstract
def
predictV(z: MatriD = x): MatriD
Given a new input matrix 'z', predict the output/response matrix 'f(z)'.
Given a new input matrix 'z', predict the output/response matrix 'f(z)'.
- z
the new input matrix
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abstract
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
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abstract
def
train(x_: MatriD = x, y_: MatriD = y): PredictorMat2
Given data/input matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases).
Given data/input matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases).
- x_
the training/full data/input matrix
- y_
the training/full response/output matrix
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abstract
def
train0(x_: MatriD = x, y_: MatriD = y): PredictorMat2
Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases) using a simple, easy to follow algorithm.
Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases) using a simple, easy to follow algorithm.
- x_
the training/full data/input matrix
- y_
the training/full response/output matrix
Concrete Value Members
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final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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final
def
##(): Int
- Definition Classes
- AnyRef → Any
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final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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val
_1: VectorD
- Attributes
- protected
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def
analyze(x_: MatriD = x, y_: VectoD = y(0), x_e: MatriD = x, y_e: VectoD = y(0)): PredictorMat2
Analyze a dataset using this model using ordinary training with the 'train' method.
Analyze a dataset using this model using ordinary training with the 'train' method. Only uses the first output variable's value.
- x_
the data/input matrix (training/full)
- y_
the response/output vector (training/full)
- x_e
the data/input matrix (testing/full)
- y_e
the response/output vector (testing/full)
- Definition Classes
- PredictorMat2 → Predictor
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final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
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def
backwardElim(cols: Set[Int], index_q: Int = index_rSqBar, first: Int = 1): (Int, PredictorMat2)
Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter vector and the new Quality of Fit (QoF).
Perform backward elimination to find the least predictive variable to remove from the existing model, returning the variable to eliminate, the new parameter vector and the new Quality of Fit (QoF). May be called repeatedly.
- cols
the columns of matrix x currently included in the existing model
- index_q
index of Quality of Fit (QoF) to use for comparing quality
- first
first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)
- See also
Fitfor index of QoF measures.
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def
backwardElimAll(index_q: Int = index_rSqBar, first: Int = 1, cross: Boolean = true): (Set[Int], MatriD)
Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
- index_q
index of Quality of Fit (QoF) to use for comparing quality
- first
first variable to consider for elimination
- cross
whether to include the cross-validation QoF measure
- See also
Fitfor index of QoF measures.
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def
clone(): AnyRef
- Attributes
- protected[lang]
- Definition Classes
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- @throws( ... ) @native() @HotSpotIntrinsicCandidate()
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def
corrMatrix(xx: MatriD): MatriD
Return the correlation matrix for the columns in data matrix 'xx'.
Return the correlation matrix for the columns in data matrix 'xx'.
- xx
the data matrix shose correlation matrix is sought
- Definition Classes
- Predictor
- def crossValidate(k: Int = 10, rando: Boolean = true): Array[Statistic]
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var
ee: MatriD
- Attributes
- protected
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final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
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var
eta: Double
- Attributes
- protected
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def
eval(ym: Double, y_e: VectoD, yp: VectoD): PredictorMat2
Compute the error (difference between actual and predicted) and useful diagnostics for the test dataset.
Compute the error (difference between actual and predicted) and useful diagnostics for the test dataset. Requires predicted responses to be passed in.
- ym
the training/full mean actual response/output vector
- y_e
the test/full actual response/output vector
- yp
the test/full predicted response/output vector
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def
eval(x_e: MatriD, y_e: MatriD): PredictorMat2
Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset.
Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset. Considers all the response/output variables/columns.
- x_e
the test/full data/input data matrix
- y_e
the test/full response/output response matrix
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def
eval(x_e: MatriD = x, y_e: VectoD = y.col(0)): PredictorMat2
Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset.
Evaluate the quality of the fit for the parameter/weight matrices on the entire dataset or the test dataset. Only considers the first response/output variable/column.
- x_e
the test/full data/input matrix
- y_e
the test/full response/output vector (first column only)
- Definition Classes
- PredictorMat2 → Model
- val fitA: Array[Fit]
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def
fitLabel: Seq[String]
Return the labels for the quality of fit measures.
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def
fitMap: IndexedSeq[Map[String, String]]
Return 'fitMap' results for each y-column and print the overall 'rSq' average over all y-columns.
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final
def
flaw(method: String, message: String): Unit
- Definition Classes
- Error
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var
fname: Strings
- Attributes
- protected
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def
forwardSel(cols: Set[Int], index_q: Int = index_rSqBar): (Int, PredictorMat2)
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model.
Perform forward selection to find the most predictive variable to add the existing model, returning the variable to add and the new model. May be called repeatedly.
- cols
the columns of matrix x currently included in the existing model
- index_q
index of Quality of Fit (QoF) to use for comparing quality
- Definition Classes
- PredictorMat2 → Predictor
- See also
Fitfor index of QoF measures.
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def
forwardSelAll(index_q: Int = index_rSqBar, cross: Boolean = true): (Set[Int], MatriD)
Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
Perform forward selection to find the most predictive variables to have in the model, returning the variables added and the new Quality of Fit (QoF) measures for all steps.
- index_q
index of Quality of Fit (QoF) to use for comparing quality
- cross
whether to include the cross-validation QoF measure
- See also
Fitfor index of QoF measures.
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final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
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def
getX: MatriD
Return the data matrix 'x'.
Return the data matrix 'x'. Mainly for derived classes where 'x' is expanded from the given columns in 'x_', e.g.,
QuadRegressionadd squared columns.- Definition Classes
- PredictorMat2 → Predictor
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def
getY: VectoD
Return the first response vector 'y.col(0)'.
Return the first response vector 'y.col(0)'. Mainly for derived classes where 'y' is transformed.
- Definition Classes
- PredictorMat2 → Predictor
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def
getYY: MatriD
Return the response matrix 'y'.
Return the response matrix 'y'. Mainly for derived classes where 'y' is transformed.
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def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
-
def
hparameter: HyperParameter
Return the hyper-parameters.
Return the hyper-parameters.
- Definition Classes
- PredictorMat2 → Model
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final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
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val
m: Int
- Attributes
- protected
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val
modelConcept: URI
An optional reference to an ontological concept
An optional reference to an ontological concept
- Definition Classes
- Model
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def
modelName: String
An optional name for the model (or modeling technique)
An optional name for the model (or modeling technique)
- Definition Classes
- Model
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final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
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final
def
notify(): Unit
- Definition Classes
- AnyRef
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- @native() @HotSpotIntrinsicCandidate()
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final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @HotSpotIntrinsicCandidate()
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val
nx: Int
- Attributes
- protected
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val
ny: Int
- Attributes
- protected
-
def
parameter: VectoD
Return the parameter/weight vector (first layer, first output).
Return the parameter/weight vector (first layer, first output).
- Definition Classes
- PredictorMat2 → Model
-
def
predict(z: MatriD = x): VectoD
Given a new input matrix 'z', predict the output/response matrix 'f(z)'.
Given a new input matrix 'z', predict the output/response matrix 'f(z)'. Return only the first output variable's value.
- z
the new input matrix
- Definition Classes
- PredictorMat2 → Predictor
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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)'. Return only the first output variable's value.
- z
the new input vector
- Definition Classes
- PredictorMat2 → Predictor
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def
predict(z: VectoI): Double
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
Given a new discrete data/input vector 'z', predict the 'y'-value of 'f(z)'.
- z
the vector to use for prediction
- Definition Classes
- Predictor
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def
report: String
Return a basic report on the trained model.
Return a basic report on the trained model.
- Definition Classes
- PredictorMat2 → Model
- See also
'summary' method for more details
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def
reset(eta_: Double): Unit
Reset the learning rate 'eta'.
Reset the learning rate 'eta'. Since this hyper-parameter needs frequent tuning, this method is provided to facilitate that.
- eta_
the learning rate
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def
resetDF(df_update: PairD): Unit
Reset the degrees of freedom to the new updated values.
Reset the degrees of freedom to the new updated values. For some models, the degrees of freedom is not known until after the model is built. Caveat: only applies to the first response/output variable.
- df_update
the updated degrees of freedom (model, error)
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def
residual: VectoD
Return the vector of residuals/errors for first response/output variable/column.
Return the vector of residuals/errors for first response/output variable/column.
- Definition Classes
- PredictorMat2 → Predictor
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def
residuals: MatriD
Return the matrix of residuals/errors.
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final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
test(modelName: String, doPlot: Boolean = true): Unit
Test the model on the full dataset (i.e., train and evaluate on full dataset).
Test the model on the full dataset (i.e., train and evaluate on full dataset).
- modelName
the name of the model being tested
- doPlot
whether to plot the actual vs. predicted response
- Definition Classes
- Predictor
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
def
train(x_: MatriD, y_: VectoD): PredictorMat2
Given data matrix 'x_' and response vector 'y_', fit the parameter 'b' (weights and biases).
Given data matrix 'x_' and response vector 'y_', fit the parameter 'b' (weights and biases).
- x_
the training/full data/input matrix
- y_
the training/full response/output vector, e.g., for the first variable/column
- Definition Classes
- PredictorMat2 → Model
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def
train2(x_: MatriD = x, y_: MatriD = y): PredictorMat2
Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases).
Given data matrix 'x_' and response matrix 'y_', fit the parameters 'b' (weights and biases). Overriding implementations (if needed) of this method should optimize hyper-parameters (e.g., the learning rate 'eta').
- x_
the training/full data/input matrix
- y_
the training/full response/output matrix
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def
trainSwitch(which: Int, x_: MatriD = x, y_: MatriD = y): PredictorMat2
Switch between 'train' methods: simple (0), regular (1) and hyper-parameter optimizing (2).
Switch between 'train' methods: simple (0), regular (1) and hyper-parameter optimizing (2).
- which
the kind of 'train' method to use
- x_
the training/full data/input matrix
- y_
the training/full response/output matrix
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def
vif(skip: Int = 1): VectoD
Compute the Variance Inflation Factor 'VIF' for each variable to test for multi-collinearity by regressing 'x_j' against the rest of the variables.
Compute the Variance Inflation Factor 'VIF' for each variable to test for multi-collinearity by regressing 'x_j' against the rest of the variables. A VIF over 10 indicates that over 90% of the variance of 'x_j' can be predicted from the other variables, so 'x_j' may be a candidate for removal from the model. Note: override this method to use a superior regression technique.
- skip
the number of columns of x at the beginning to skip in computing VIF
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final
def
wait(arg0: Long, arg1: Int): Unit
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final
def
wait(arg0: Long): Unit
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final
def
wait(): Unit
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Deprecated Value Members
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def
finalize(): Unit
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