Index

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All Classes|All Packages|Constant Field Values

M

max(double...) - Static method in class com.jml.core.Stats

Finds the maximum value in a dataset.

mean(double...) - Static method in class com.jml.core.Stats

Computes the arithmetic mean.

meanNormalize(double[]) - Static method in class com.jml.preprocessing.Normalize

Applies meanNormalize normalization to the data.

median(double...) - Static method in class com.jml.core.Stats

Computes the median.

Metrics - Class in com.jml.core

min(double...) - Static method in class com.jml.core.Stats

Finds the minimum value in a dataset.

minIndex(double[]) - Static method in class com.jml.core.Stats

Finds index of minimum value in an array.

minIndices(double[], int) - Static method in class com.jml.core.Stats

Finds indices of the k smallest values in an array.

minMaxScale(double[]) - Static method in class com.jml.preprocessing.Normalize

Applies min-max feature scaling to data.

minMaxScale(double[], double, double) - Static method in class com.jml.preprocessing.Normalize

Applies min-max feature scaling to data.

mode(double...) - Static method in class com.jml.core.Stats

Computes the mode of a dataset.

Model<X,Y> - Class in com.jml.core

This interface specifies the requirements for a machine learning model.

Model() - Constructor for class com.jml.core.Model

MODEL_TYPE - Enum constant in enum class com.jml.classifiers.ClassifierTags

MODEL_TYPE - Enum constant in enum class com.jml.linear_models.LinearModelTags

MODEL_TYPE - Enum constant in enum class com.jml.neural_network.ModelTags

ModelTags - Enum Class in com.jml.neural_network

ModelTypes - Enum Class in com.jml.core

Momentum - Class in com.jml.optimizers

The momentum based gradient descent optimizer.

Momentum(double) - Constructor for class com.jml.optimizers.Momentum

Creates a Momentum optimizer with specified learning rate.

Momentum(double, double) - Constructor for class com.jml.optimizers.Momentum

Creates a Momentum optimizer with specified learning rate and momentum.

mse - Static variable in class com.jml.losses.LossFunctions

The sum of mean squared-errors loss function.
That is mse = (1/n)*sum(xi - yi)2 where x and y are datasets of length n, and x is the actual data and y is the predicted data.

MULTIPLE_LINEAR_REGRESSION - Enum constant in enum class com.jml.core.ModelTypes

MULTIPLE_LINEAR_REGRESSION_SGD - Enum constant in enum class com.jml.core.ModelTypes

MultipleLinearRegression - Class in com.jml.linear_models

Model for least squares linear regression of multiple variables by least squares.

MultipleLinearRegression fits a model y = b₀ + b₁x₁ + ...

MultipleLinearRegression() - Constructor for class com.jml.linear_models.MultipleLinearRegression

MultipleLinearRegressionSGD - Class in com.jml.linear_models

Model for least squares linear regression of multiple variables by stochastic gradient descent.

MultipleLinearRegressionSGD fits a model y = b₀ + b₁x₁ + ...

MultipleLinearRegressionSGD() - Constructor for class com.jml.linear_models.MultipleLinearRegressionSGD

Creates a MultipleLinearRegressionSGD model.

MultipleLinearRegressionSGD(double) - Constructor for class com.jml.linear_models.MultipleLinearRegressionSGD

Creates a MultipleLinearRegressionSGD model.

MultipleLinearRegressionSGD(double, int) - Constructor for class com.jml.linear_models.MultipleLinearRegressionSGD

Creates a MultipleLinearRegressionSGD model.

MultipleLinearRegressionSGD(double, int, double) - Constructor for class com.jml.linear_models.MultipleLinearRegressionSGD

Creates a MultipleLinearRegressionSGD model.

MultipleLinearRegressionSGD(int) - Constructor for class com.jml.linear_models.MultipleLinearRegressionSGD

Creates a MultipleLinearRegressionSGD model.

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All Classes|All Packages|Constant Field Values