Package com.jml.linear_models

Class PolynomialRegressionSGD

java.lang.Object

com.jml.core.Model<double[],double[]>

com.jml.linear_models.PolynomialRegression

com.jml.linear_models.PolynomialRegressionSGD

public class PolynomialRegressionSGD
extends PolynomialRegression

Model for least squares regression of polynomials using Stochastic Gradient Descent.

PolynomialRegression fits a model y = b₀ + b₁x + b₂x² + ... + b_nxⁿ to the datasets by minimizing the residuals of the sum of squares between the values in the target dataset and the values predicted by the model. This is solved using Stochastic Gradient Descent.

Constructor Summary

Constructors

Constructor	Description
PolynomialRegressionSGD()	Creates a PolynomialRegressionSGD model.
PolynomialRegressionSGD(int degree)	Creates a PolynomialRegressionSGD model.
PolynomialRegressionSGD(int degree, double learningRate)	Creates a PolynomialRegressionSGD model.
PolynomialRegressionSGD(int degree, double learningRate, int maxIterations)	Creates a PolynomialRegressionSGD model.
PolynomialRegressionSGD(int degree, double learningRate, int maxIterations, double threshold)	Creates a PolynomialRegressionSGD model.

Method Summary

Modifier and Type	Method	Description
PolynomialRegressionSGD	fit(double[] features, double[] targets)	Fits or trains the model with the given features and targets.
double[]	getLossHist()	Gets the loss history from training.

Methods inherited from class com.jml.linear_models.PolynomialRegression

getParams, inspect, predict, predict, saveModel, toString

Methods inherited from class com.jml.core.Model

load

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Details

PolynomialRegressionSGD

public PolynomialRegressionSGD()

Creates a PolynomialRegressionSGD model. This will use a default learning rate of 0.002.

PolynomialRegressionSGD

public PolynomialRegressionSGD(int degree,
 double learningRate,
 int maxIterations,
 double threshold)

Creates a PolynomialRegressionSGD model. When the fit method is called, Stochastic Gradient Descent will use the provided learning rate and will stop if it does not converge within the threshold by the specified number of max iterations.

Parameters:

degree - Degree of the polynomial to fit.

learningRate - Learning rate to use during Stochastic Gradient Descent

threshold - Threshold for early stopping during Stochastic Gradient Descent. If the loss is less than the specified threshold, gradient descent will stop early.

maxIterations - Maximum number of iterations to run for during Stochastic Gradient Descent.

PolynomialRegressionSGD

public PolynomialRegressionSGD(int degree,
 double learningRate,
 int maxIterations)

Creates a PolynomialRegressionSGD model. When the fit method is called, Stochastic Gradient Descent will use the provided learning rate and will stop if it does not converge by the specified number of max iterations.

Parameters:

degree - Degree of the polynomial to fit.

learningRate - Learning rate to use during Stochastic Gradient Descent.

maxIterations - Maximum number of iterations to run for during Stochastic Gradient Descent.

PolynomialRegressionSGD

public PolynomialRegressionSGD(int degree,
 double learningRate)

Creates a PolynomialRegressionSGD model. When the fit method is called, Stochastic Gradient Descent will use the provided learning rate and will stop if it does not converge by the specified number of max iterations.

Parameters:

degree - Degree of the polynomial to fit.

learningRate - Learning rate to use during Stochastic Gradient Descent.

PolynomialRegressionSGD

public PolynomialRegressionSGD(int degree)

Creates a PolynomialRegressionSGD model. When the fit method is called, Stochastic Gradient Descent will fit a polynomial of the specified degree using gradient descent.

Parameters:

degree - Degree of the polynomial to fit.

Method Details

fit

public PolynomialRegressionSGD fit(double[] features,
 double[] targets)

Fits or trains the model with the given features and targets.

Overrides:

fit in class PolynomialRegression

Parameters:

features - The features of the training set.

targets - The targets of the training set.

Returns:

Returns details of the fitting / training process.

Throws:

IllegalArgumentException - Can be thrown for the following reasons
- If key, value pairs in args are unspecified or invalid arguments.
- If the features and targets are not correctly sized per the specification when the model was compiled.

getLossHist

public double[] getLossHist()

Gets the loss history from training.

Returns:

The loss of every iteration stored in a List.