SimpleExpRegression

scalation.modeling.SimpleExpRegression
See theSimpleExpRegression companion object
class SimpleExpRegression(x: MatrixD, y: VectorD, fname_: Array[String], hparam: HyperParameter, nonneg: Boolean) extends Predictor, Fit, NoSubModels

The SimpleExpRegression class supports exponential regression. In this case, x is [1, x_1]. Fit the parameter vector b in the exponential regression equation log (mu (x)) = b dot x = b_0 + b_1 * x_1

Value parameters

fname_

the feature/variable names (defaults to null)

hparam

the hyper-parameters (currently none)

nonneg

whether to check that responses are nonnegative (defaults to true)

x

the data/input matrix

y

the response/output vector

Attributes

See also
Companion
object
Graph
Supertypes
trait NoSubModels
trait Fit
trait FitM
trait Predictor
trait Model
class Object
trait Matchable
class Any
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Members list

Type members

Inherited classlikes

case class BestStep(col: Int, qof: VectorD, mod: Predictor & Fit)

The BestStep is used to record the best improvement step found so far.

The BestStep is used to record the best improvement step found so far.

Value parameters

col

the column/variable to ADD/REMOVE for this step

mod

the model including selected features/variables for this step

qof

the Quality of Fit (QoF) for this step

Attributes

Inherited from:
Predictor
Supertypes
trait Serializable
trait Product
trait Equals
class Object
trait Matchable
class Any
Show all

Value members

Concrete methods

def f(xi: Double, b: VectorD): Double

The estimated response value at point xi.

The estimated response value at point xi.

Value parameters

b

the given parameter values

xi

the point to evaluate

Attributes

override def predict(z: VectorD): Double

Predict the value of y = f(z) by evaluating the formula y = exp (b dot z), e.g., exp (b_0, b_1) dot (1, z_1).

Predict the value of y = f(z) by evaluating the formula y = exp (b dot z), e.g., exp (b_0, b_1) dot (1, z_1).

Value parameters

z

the new vector to predict

Attributes

Definition Classes
def test(x_: MatrixD, y_: VectorD): (VectorD, VectorD)

Test a predictive model y_ = f(x_) + e and return its QoF vector. Testing may be be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test.

Test a predictive model y_ = f(x_) + e and return its QoF vector. Testing may be be in-sample (on the training set) or out-of-sample (on the testing set) as determined by the parameters passed in. Note: must call train before test.

Value parameters

x_

the testing/full data/input matrix (defaults to full x)

y_

the testing/full response/output vector (defaults to full y)

Attributes

def train(x_: MatrixD, y_: VectorD): Unit

Train the predictor by fitting the parameter vector (b-vector) in the simple exponential regression equation.

Train the predictor by fitting the parameter vector (b-vector) in the simple exponential regression equation.

Value parameters

x_

the training/full data/input matrix

y_

the training/full response/output vector

Attributes

Inherited methods

def backwardElim(cols: LinkedHashSet[Int], idx_q: Int, first: Int): BestStep

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.

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.

Value parameters

cols

the columns of matrix x currently included in the existing model

first

first variable to consider for elimination (default (1) assume intercept x_0 will be in any model)

idx_q

index of Quality of Fit (QoF) to use for comparing quality

Attributes

See also

Fit for index of QoF measures.

Inherited from:
Predictor
def backwardElimAll(idx_q: Int, first: Int, cross: Boolean): (LinkedHashSet[Int], MatrixD)

Perform backward elimination to find the least predictive variables to remove from the full model, returning the variables left and the new Quality of Fit (QoF) measures for all steps.

Perform backward elimination to find the least predictive variables to remove from the full model, returning the variables left and the new Quality of Fit (QoF) measures for all steps.

Value parameters

cross

whether to include the cross-validation QoF measure

first

first variable to consider for elimination

idx_q

index of Quality of Fit (QoF) to use for comparing quality

Attributes

See also

Fit for index of QoF measures.

Inherited from:
Predictor
def buildModel(x_cols: MatrixD): Predictor & Fit

Build a sub-model that is restricted to the given columns of the data matrix. Must be implemented for models that support feature selection. NOT SUPPORTED for this model, so throw an EXCEPTION.

Build a sub-model that is restricted to the given columns of the data matrix. Must be implemented for models that support feature selection. NOT SUPPORTED for this model, so throw an EXCEPTION.

Value parameters

x_cols

the columns that the new model is restricted to

Attributes

Inherited from:
NoSubModels
def crossValidate(k: Int, rando: Boolean): Array[Statistic]

Attributes

Inherited from:
Predictor
override def diagnose(y: VectorD, yp: VectorD, w: VectorD): VectorD

Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

Value parameters

w

the weights on the instances (defaults to null)

y

the actual response/output vector to use (test/full)

yp

the predicted response/output vector (test/full)

Attributes

See also

Regression_WLS

Definition Classes
Fit -> FitM
Inherited from:
Fit
def fit: VectorD

Return the Quality of Fit (QoF) measures corresponding to the labels given. 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). Override to add more quality of fit measures.

Return the Quality of Fit (QoF) measures corresponding to the labels given. 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). Override to add more quality of fit measures.

Attributes

Inherited from:
Fit
def forwardSel(cols: LinkedHashSet[Int], idx_q: Int): BestStep

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.

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.

Value parameters

cols

the columns of matrix x currently included in the existing model

idx_q

index of Quality of Fit (QoF) to use for comparing quality

Attributes

See also

Fit for index of QoF measures.

Inherited from:
Predictor
def forwardSelAll(idx_q: Int, cross: Boolean): (LinkedHashSet[Int], MatrixD)

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.

Value parameters

cross

whether to include the cross-validation QoF measure

idx_q

index of Quality of Fit (QoF) to use for comparing quality

Attributes

See also

Fit for index of QoF measures.

Inherited from:
Predictor

Return the best model found from feature selection.

Return the best model found from feature selection.

Attributes

Inherited from:
Predictor
def getFname: Array[String]

Return the feature/variable names.

Return the feature/variable names.

Attributes

Inherited from:
Predictor
def getX: MatrixD

Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.

Return the used data matrix x. Mainly for derived classes where x is expanded from the given columns in x_, e.g., SymbolicRegression.quadratic adds squared columns.

Attributes

Inherited from:
Predictor
def getY: VectorD

Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.

Return the used response vector y. Mainly for derived classes where y is transformed, e.g., TranRegression, ARX.

Attributes

Inherited from:
Predictor
def help: String

Return the help string that describes the Quality of Fit (QoF) measures provided by the Fit trait. Override to correspond to fitLabel.

Return the help string that describes the Quality of Fit (QoF) measures provided by the Fit trait. Override to correspond to fitLabel.

Attributes

Inherited from:
Fit

Return the hyper-parameters.

Return the hyper-parameters.

Attributes

Inherited from:
Predictor
def importance(cols: Array[Int], rSq: MatrixD): Array[(Int, Double)]

Return the relative importance of selected variables, ordered highest to lowest, rescaled so the highest is one.

Return the relative importance of selected variables, ordered highest to lowest, rescaled so the highest is one.

Value parameters

cols

the selected columns/features/variables

rSq

the matrix R^2 values (stand in for sse)

Attributes

Inherited from:
Predictor
def ll(ms: Double, s2: Double, m2: Int): Double

The log-likelihood function times -2. Override as needed.

The log-likelihood function times -2. Override as needed.

Value parameters

ms

raw Mean Squared Error

s2

MLE estimate of the population variance of the residuals

Attributes

See also
Inherited from:
Fit
def mse_: Double

Return the mean of the squares for error (sse / df). Must call diagnose first.

Return the mean of the squares for error (sse / df). Must call diagnose first.

Attributes

Inherited from:
Fit
def numTerms: Int

Return the number of terms/parameters in the model, e.g., b_0 + b_1 x_1 + b_2 x_2 has three terms.

Return the number of terms/parameters in the model, e.g., b_0 + b_1 x_1 + b_2 x_2 has three terms.

Attributes

Inherited from:
Predictor

Return the vector of parameter/coefficient values.

Return the vector of parameter/coefficient values.

Attributes

Inherited from:
Predictor
def predict(x_: MatrixD): VectorD

Predict the value of vector y = f(x_, b), e.g., x_ * b for Regression. May override for efficiency.

Predict the value of vector y = f(x_, b), e.g., x_ * b for Regression. May override for efficiency.

Value parameters

x_

the matrix to use for making predictions, one for each row

Attributes

Inherited from:
Predictor
def rSq0_: Double

Attributes

Inherited from:
FitM
def rSq_: Double

Return the coefficient of determination (R^2). Must call diagnose first.

Return the coefficient of determination (R^2). Must call diagnose first.

Attributes

Inherited from:
FitM
def report(ftMat: MatrixD): String

Return a basic report on a trained and tested multi-variate model.

Return a basic report on a trained and tested multi-variate model.

Value parameters

ftMat

the matrix of qof values produced by the Fit trait

Attributes

Inherited from:
Model
def report(ftVec: VectorD): String

Return a basic report on a trained and tested model.

Return a basic report on a trained and tested model.

Value parameters

ftVec

the vector of qof values produced by the Fit trait

Attributes

Inherited from:
Model
def resetBest(): Unit

Reset the best-step to default

Reset the best-step to default

Attributes

Inherited from:
Predictor
def resetDF(df_update: (Double, Double)): Unit

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.

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.

Value parameters

df_update

the updated degrees of freedom (model, error)

Attributes

Inherited from:
Fit

Return the vector of residuals/errors.

Return the vector of residuals/errors.

Attributes

Inherited from:
Predictor
def selectFeatures(tech: SelectionTech, idx_q: Int, cross: Boolean): (LinkedHashSet[Int], MatrixD)

Perform feature 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 feature 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.

Value parameters

cross

whether to include the cross-validation QoF measure

idx_q

index of Quality of Fit (QoF) to use for comparing quality

tech

the feature selection technique to apply

Attributes

See also

Fit for index of QoF measures.

Inherited from:
Predictor
def sse_: Double

Return the sum of the squares for error (sse). Must call diagnose first.

Return the sum of the squares for error (sse). Must call diagnose first.

Attributes

Inherited from:
FitM
def stepRegressionAll(idx_q: Int, cross: Boolean): (LinkedHashSet[Int], MatrixD)

Perform stepwise regression to find the most predictive variables to have in the model, returning the variables left and the new Quality of Fit (QoF) measures for all steps. At each step it calls forwardSel and backwardElim and takes the best of the two actions. Stops when neither action yields improvement.

Perform stepwise regression to find the most predictive variables to have in the model, returning the variables left and the new Quality of Fit (QoF) measures for all steps. At each step it calls forwardSel and backwardElim and takes the best of the two actions. Stops when neither action yields improvement.

Value parameters

cross

whether to include the cross-validation QoF measure

idx_q

index of Quality of Fit (QoF) to use for comparing quality

Attributes

See also

Fit for index of QoF measures.

Inherited from:
Predictor
def summary(x_: MatrixD, fname: Array[String], b: VectorD, vifs: VectorD): String

Produce a QoF summary for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.

Produce a QoF summary for a model with diagnostics for each predictor x_j and the overall Quality of Fit (QoF). Note: `Fac_Cholesky is used to compute the inverse of xtx.

Value parameters

b

the parameters/coefficients for the model

fname

the array of feature/variable names

vifs

the Variance Inflation Factors (VIFs)

x_

the testing/full data/input matrix

Attributes

Inherited from:
Fit
inline def testIndices(n_test: Int, rando: Boolean): IndexedSeq[Int]

Return the indices for the test-set.

Return the indices for the test-set.

Value parameters

n_test

the size of test-set

rando

whether to select indices randomly or in blocks

Attributes

See also

scalation.mathstat.TnT_Split

Inherited from:
Predictor
def train2(x_: MatrixD, y_: VectorD): Unit

The train2 method should work like the train method, but should also optimize hyper-parameters (e.g., shrinkage or learning rate). Only implementing classes needing this capability should override this method.

The train2 method should work like the train method, but should also optimize hyper-parameters (e.g., shrinkage or learning rate). Only implementing classes needing this capability should override this method.

Value parameters

x_

the training/full data/input matrix (defaults to full x)

y_

the training/full response/output vector (defaults to full y)

Attributes

Inherited from:
Predictor
def trainNtest(x_: MatrixD, y_: VectorD)(xx: MatrixD, yy: VectorD): (VectorD, VectorD)

Train and test the predictive model y_ = f(x_) + e and report its QoF and plot its predictions. Return the predictions and QoF. FIX - currently must override if y is transformed, @see TranRegression

Train and test the predictive model y_ = f(x_) + e and report its QoF and plot its predictions. Return the predictions and QoF. FIX - currently must override if y is transformed, @see TranRegression

Value parameters

x_

the training/full data/input matrix (defaults to full x)

xx

the testing/full data/input matrix (defaults to full x)

y_

the training/full response/output vector (defaults to full y)

yy

the testing/full response/output vector (defaults to full y)

Attributes

Inherited from:
Predictor
def validate(rando: Boolean, ratio: Double)(idx: IndexedSeq[Int]): VectorD

Attributes

Inherited from:
Predictor
def vif(skip: Int): VectorD

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 50 indicates that over 98% 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.

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 50 indicates that over 98% 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.

Value parameters

skip

the number of columns of x at the beginning to skip in computing VIF

Attributes

Inherited from:
Predictor

Inherited fields

var modelConcept: URI

The optional reference to an ontological concept

The optional reference to an ontological concept

Attributes

Inherited from:
Model
var modelName: String

The name for the model (or modeling technique).

The name for the model (or modeling technique).

Attributes

Inherited from:
Model