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S

saveModel(String) - Method in class com.jml.classifiers.KNearestNeighbors
Saves a trained model to the specified file path.
saveModel(String) - Method in class com.jml.classifiers.LogisticRegression
Saves a trained model to the specified file path.
saveModel(String) - Method in class com.jml.core.Model
Saves a trained model to the specified file path.
saveModel(String) - Method in class com.jml.linear_models.MultipleLinearRegression
Saves a trained model to the specified file path.
saveModel(String) - Method in class com.jml.linear_models.Perceptron
Saves a trained model to the specified file path.
saveModel(String) - Method in class com.jml.linear_models.PolynomialRegression
Saves a trained model to the specified file path including the name of the file.
saveModel(String) - Method in class com.jml.neural_network.NeuralNetwork
Saves a trained model to the specified file path.
schedule - Variable in class com.jml.optimizers.Optimizer
 
Scheduler - Class in com.jml.optimizers
Learning rate scheduler for optimizers.
Scheduler() - Constructor for class com.jml.optimizers.Scheduler
 
setParams(Matrix...) - Method in interface com.jml.neural_network.layers.BaseLayer
Sets parameters of this layer.
setParams(Matrix...) - Method in class com.jml.neural_network.layers.Dense
Sets the parameters for this layer.
setParams(Matrix...) - Method in class com.jml.neural_network.layers.Linear
Sets the parameters for this layer.
setParams(Matrix...) - Method in interface com.jml.neural_network.layers.TrainableLayer
Sets the parameters for this layer.
setScheduler(Scheduler) - Method in class com.jml.optimizers.Optimizer
Sets the learning rate scheduler for this optimizer.
shuffle(double[]) - Static method in class com.jml.util.ArrayUtils
Randomly shuffles arrays using the Fisher–Yates algorithm.
shuffle(double[]...) - Static method in class com.jml.util.ArrayUtils
Randomly shuffles arrays using the Fisher–Yates algorithm.

If more than one array is passed, the shuffled indices of all arrays will be the same.
shuffle(double[][]...) - Static method in class com.jml.util.ArrayUtils
Randomly shuffles 2D arrays by rows using the Fisher–Yates algorithm.

If more than one array is passed, the shuffled row indices of all arrays will be the same.
sigmoid - Static variable in class com.jml.neural_network.activations.Activations
The sigmoid activation function.
sigmoid() - Static method in class com.jml.neural_network.activations.Activations
Creates and returns a new instance of the sigmoid activation function.
Sigmoid - Class in com.jml.neural_network.activations
The sigmoid activation function.
Sigmoid() - Constructor for class com.jml.neural_network.activations.Sigmoid
 
softmax() - Static method in class com.jml.neural_network.activations.Activations
Creates and returns a new instance of the softmax activations function.
Softmax - Class in com.jml.neural_network.activations
The softmax activation function.
Softmax() - Constructor for class com.jml.neural_network.activations.Softmax
 
sse - Static variable in class com.jml.losses.LossFunctions
The sum of squared-errors loss function.
That is sse = 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.
sse(double[], double[]) - Static method in class com.jml.core.Stats
Computes the sum of square differences between two datasets.
sst(double...) - Static method in class com.jml.core.Stats
Computes the sum of squares total of a dataset.
Stats - Class in com.jml.core
The stats class is a utility class to compute various statistical information about datasets.
std(double...) - Static method in class com.jml.core.Stats
Computes the standard deviation of the dataset.
step(boolean, Matrix...) - Method in class com.jml.optimizers.Adam
Steps the optimizer a single iteration by applying the update rule of the optimizer to the matrix w.
step(boolean, Matrix...) - Method in class com.jml.optimizers.GradientDescent
Steps the optimizer a single iteration by applying the update rule of the optimizer to the matrix w.
step(boolean, Matrix...) - Method in class com.jml.optimizers.Momentum
Steps the optimizer a single iteration by applying the update rule of the optimizer to the matrix w.
step(boolean, Matrix...) - Method in class com.jml.optimizers.Optimizer
 
step(Optimizer) - Method in class com.jml.optimizers.Scheduler
Applies the specified scheduler rule to the learning rate of the optimizer.
step(Optimizer) - Method in class com.jml.optimizers.StepLearningRate
 
step(Matrix...) - Method in class com.jml.optimizers.Adam
Steps the optimizer a single iteration by applying the update rule of the optimizer to the matrix w.
step(Matrix...) - Method in class com.jml.optimizers.GradientDescent
Steps the optimizer a single iteration by applying the update rule of the optimizer to the matrix w.
step(Matrix...) - Method in class com.jml.optimizers.Momentum
Steps the optimizer a single iteration by applying the update rule of the optimizer to the matrix w.
step(Matrix...) - Method in class com.jml.optimizers.Optimizer
Steps the optimizer a single iteration by applying the update rule of the optimizer to the matrix w.
StepLearningRate - Class in com.jml.optimizers
StepLearningRate is a Scheduler which "steps" the learning rate at regular intervals during optimization.
StepLearningRate() - Constructor for class com.jml.optimizers.StepLearningRate
Creates a default StepLearningRate scheduler with factor of 0.8 and an interval of 10.
StepLearningRate(double) - Constructor for class com.jml.optimizers.StepLearningRate
Creates a StepLearningRate with specified factor and the default interval of 10.
StepLearningRate(double, int) - Constructor for class com.jml.optimizers.StepLearningRate
Creates a StepLearningRate with specified factor and the interval.
stringToFile(String, String) - Static method in class com.jml.util.FileManager
Writes a string to a file.
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