Uses of Class
com.aliasi.stats.LogisticRegression

Packages that use LogisticRegression

Package	Description
com.aliasi.classify	Classes for classifying data and evaluation.
com.aliasi.stats	Classes for handling basic statical distributions and estimators.

Uses of LogisticRegression in com.aliasi.classify

Methods in com.aliasi.classify that return LogisticRegression

Modifier and Type	Method and Description
LogisticRegression	LogisticRegressionClassifier.model() Returns the logistic regression model underlying this classifier.

Uses of LogisticRegression in com.aliasi.stats

Methods in com.aliasi.stats that return LogisticRegression

Modifier and Type	Method and Description
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, AnnealingSchedule annealingSchedule, Reporter reporter, double minImprovement, int minEpochs, int maxEpochs) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, int blockSize, LogisticRegression hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegression> handler, Reporter reporter) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.

Methods in com.aliasi.stats with parameters of type LogisticRegression

Modifier and Type	Method and Description
static LogisticRegression	LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, int blockSize, LogisticRegression hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegression> handler, Reporter reporter) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.
static double	LogisticRegression.log2Likelihood(Vector[] inputs, int[] cats, LogisticRegression regression) Returns the log (base 2) likelihood of the specified inputs with the specified categories using the specified regression model.

Method parameters in com.aliasi.stats with type arguments of type LogisticRegression

Modifier and Type

Method and Description

static LogisticRegression

LogisticRegression.estimate(Vector[] xs, int[] cs, RegressionPrior prior, int blockSize, LogisticRegression hotStart, AnnealingSchedule annealingSchedule, double minImprovement, int rollingAverageSize, int minEpochs, int maxEpochs, ObjectHandler<LogisticRegression> handler, Reporter reporter) Estimate a logistic regression model from the specified input data using the specified Gaussian prior, initial learning rate and annealing rate, the minimum improvement per epoch, the minimum and maximum number of estimation epochs, and a reporter.