Index
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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
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Sets the parameters for this layer.
- setParams(Matrix...) - Method in interface com.jml.neural_network.layers.TrainableLayer
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Sets the parameters for this layer.
- setScheduler(Scheduler) - Method in class com.jml.optimizers.Optimizer
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Sets the learning rate scheduler for this optimizer.
- shuffle(double[]) - Static method in class com.jml.util.ArrayUtils
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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
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The sigmoid activation function.
- sigmoid() - Static method in class com.jml.neural_network.activations.Activations
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Creates and returns a new instance of the sigmoid activation function.
- Sigmoid - Class in com.jml.neural_network.activations
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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
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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 issse = sum(xi - yi)2
wherex
andy
are datasets of length
n
, andx
is the actual data andy
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
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Computes the sum of squares total of a dataset.
- Stats - Class in com.jml.core
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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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