class NeuralNet_2L extends PredictorMat2
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 'b' connecting the layers,
so that for a new input vector 'z', the net can predict the output value, i.e.,
yp_j = f (b dot z)
where 'f' is the activation function and the parameters 'b' 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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- NeuralNet_2L
- PredictorMat2
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Instance Constructors
- new NeuralNet_2L(x: MatriD, y: MatriD, fname_: Strings = null, hparam: HyperParameter = Optimizer.hp, f: AFF = f_sigmoid, itran: FunctionV_2V = null)
- 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)
- fname_
the feature/variable names (if null, use x_j's)
- hparam
the hyper-parameters for the model/network
- f
the activation function family for layers 1->2 (input to output)
- itran
the inverse transformation function returns responses to original scale
Value Members
- final def !=(arg0: Any): Boolean
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- final def ##: Int
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- final def ==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
- val _1: VectorD
- Attributes
- protected
- Definition Classes
- PredictorMat2
- 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
- final def asInstanceOf[T0]: T0
- Definition Classes
- Any
- 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)
- Definition Classes
- PredictorMat2
- See also
Fitfor index of QoF measures.
- 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
- Definition Classes
- PredictorMat2
- See also
Fitfor index of QoF measures.
- def buildModel(x_cols: MatriD): NeuralNet_2L
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
- Definition Classes
- NeuralNet_2L → PredictorMat2
- def clone(): AnyRef
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- protected[lang]
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- AnyRef
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- @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
- 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]
- Definition Classes
- PredictorMat2
- var ee: MatriD
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- protected
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- PredictorMat2
- final def eq(arg0: AnyRef): Boolean
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- AnyRef
- def equals(arg0: AnyRef): Boolean
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- AnyRef → Any
- var eta: Double
- Attributes
- protected
- Definition Classes
- PredictorMat2
- 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
- Definition Classes
- PredictorMat2
- 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
- Definition Classes
- PredictorMat2
- 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]
- Definition Classes
- PredictorMat2
- def fitLabel: Seq[String]
Return the labels for the quality of fit measures.
Return the labels for the quality of fit measures.
- Definition Classes
- PredictorMat2
- def fitMap: Array[Map[String, String]]
Return 'fitMap' results for each y-column and print the overall 'rSq' average over all y-columns.
Return 'fitMap' results for each y-column and print the overall 'rSq' average over all y-columns.
- Definition Classes
- PredictorMat2
- final def flaw(method: String, message: String): Unit
- Definition Classes
- Error
- var fname: Strings
- Attributes
- protected
- Definition Classes
- PredictorMat2
- 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.
- 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
- Definition Classes
- PredictorMat2
- See also
Fitfor index of QoF measures.
- final def getClass(): Class[_ <: AnyRef]
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- AnyRef → Any
- Annotations
- @native() @HotSpotIntrinsicCandidate()
- 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
- 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
- def getYY: MatriD
Return the response matrix 'y'.
Return the response matrix 'y'. Mainly for derived classes where 'y' is transformed.
- Definition Classes
- PredictorMat2
- def hashCode(): Int
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- AnyRef → Any
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- @native() @HotSpotIntrinsicCandidate()
- def hparameter: HyperParameter
Return the hyper-parameters.
Return the hyper-parameters.
- Definition Classes
- PredictorMat2 → Model
- final def isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- val itran: FunctionV_2V
- val m: Int
- Attributes
- protected
- Definition Classes
- PredictorMat2
- val modelConcept: URI
An optional reference to an ontological concept
An optional reference to an ontological concept
- Definition Classes
- Model
- def modelName: String
An optional name for the model (or modeling technique)
An optional name for the model (or modeling technique)
- Definition Classes
- Model
- final def ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
- final def notify(): Unit
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- @native() @HotSpotIntrinsicCandidate()
- final def notifyAll(): Unit
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- AnyRef
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- @native() @HotSpotIntrinsicCandidate()
- val nx: Int
- Attributes
- protected
- Definition Classes
- PredictorMat2
- val ny: Int
- Attributes
- protected
- Definition Classes
- PredictorMat2
- 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 parameters: NetParams
Return the parameters 'b' (weight matrix 'b.w') (array of 1).
Return the parameters 'b' (weight matrix 'b.w') (array of 1).
- Definition Classes
- NeuralNet_2L → PredictorMat2
- 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
- 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
- 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
- def predictV(z: MatriD = x): 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 → PredictorMat2
- 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 → PredictorMat2
- 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
- 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
- Definition Classes
- PredictorMat2
- 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)
- Definition Classes
- PredictorMat2
- 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
- def residuals: MatriD
Return the matrix of residuals/errors.
Return the matrix of residuals/errors.
- Definition Classes
- PredictorMat2
- 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 = x, y_: MatriD = y): NeuralNet_2L
Given training data 'x_' and 'y_', fit the parameters 'b'.
Given training data 'x_' and 'y_', fit the parameters 'b'. Minimize the error in the prediction by adjusting the parameters 'b'. Iterate over several epochs, where each epoch divides the training set into 'nbat' batches. Each batch is used to update the weights.
- x_
the training/full data/input matrix
- y_
the training/full response/output matrix
- Definition Classes
- NeuralNet_2L → PredictorMat2
- 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
- def train0(x_: MatriD = x, y_: MatriD = y): NeuralNet_2L
Given training data 'x_' and 'y_', fit the parameters 'b'.
Given training data 'x_' and 'y_', fit the parameters 'b'. Minimize the error in the prediction by adjusting the parameters 'b'. 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. It does not use batching nor a sufficient stopping rule. In practice, use the 'train' or 'train2' methods that use better optimizers.
- x_
the training/full data/input matrix
- y_
the training/full response/output matrix
- Definition Classes
- NeuralNet_2L → PredictorMat2
- def train2(x_: MatriD = x, y_: MatriD = y): NeuralNet_2L
Given training data 'x_' and 'y_', fit the parameters 'b'.
Given training data 'x_' and 'y_', fit the parameters 'b'. Minimize the error in the prediction by adjusting the parameters 'b'. 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.
- x_
the training/full data/input matrix
- y_
the training/full response/output matrix
- Definition Classes
- NeuralNet_2L → PredictorMat2
- 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
- Definition Classes
- PredictorMat2
- 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
- Definition Classes
- PredictorMat2
- final def wait(arg0: Long, arg1: Int): Unit
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