randomSeed
TypeScriptget randomSeed(): (number) | nullSwift declarationvar randomSeed: Int?A seed to generate reproducible results from random operations.
struct · iOS 18.0+ · SDK 27.1
struct TimeSeriesClassifierConfigurationThe configuration for a time-series classifier.
get randomSeed(): (number) | nullSwift declarationvar randomSeed: Int?A seed to generate reproducible results from random operations.
set randomSeed(value: (number) | null)Swift declarationvar randomSeed: Int?A seed to generate reproducible results from random operations.
get learningRate(): numberSwift declarationvar learningRate: FloatThe starting learning rate. Defaults to 0.005.
set learningRate(value: number)Swift declarationvar learningRate: FloatThe starting learning rate. Defaults to 0.005.
get maximumIterationCount(): numberSwift declarationvar maximumIterationCount: IntThe maximum number of allowed passes through the data. More passes over the data can result in a more accurately trained model. Consider increasing this if the training accuracy is low. Defaults to 25. - Note: This parameter is only used by the `fitted` method. When using the `update` method it's up to you to decide when to stop.
set maximumIterationCount(value: number)Swift declarationvar maximumIterationCount: IntThe maximum number of allowed passes through the data. More passes over the data can result in a more accurately trained model. Consider increasing this if the training accuracy is low. Defaults to 25. - Note: This parameter is only used by the `fitted` method. When using the `update` method it's up to you to decide when to stop.
get maximumSequenceLength(): numberSwift declarationvar maximumSequenceLength: IntThe maximum number of samples that can be classified. This configuration parameter is used as the input size when exporting a Core ML model.
set maximumSequenceLength(value: number)Swift declarationvar maximumSequenceLength: IntThe maximum number of samples that can be classified. This configuration parameter is used as the input size when exporting a Core ML model.
get minimumSequenceLength(): numberSwift declarationvar minimumSequenceLength: IntThe minimum number of samples required to produce a classification. This configuration parameter controls the size of the model. Set it to the largest value that is reasonable for your task. For example if your input is accelerometer data sampled at 100Hz and you want to recognize actions, the minimum action duration may be one second, so the minimum sequence length is `100Hz * 1s = 100`. Defaults to 100.
set minimumSequenceLength(value: number)Swift declarationvar minimumSequenceLength: IntThe minimum number of samples required to produce a classification. This configuration parameter controls the size of the model. Set it to the largest value that is reasonable for your task. For example if your input is accelerometer data sampled at 100Hz and you want to recognize actions, the minimum action duration may be one second, so the minimum sequence length is `100Hz * 1s = 100`. Defaults to 100.
get earlyStoppingTolerance(): numberSwift declarationvar earlyStoppingTolerance: FloatThe early-stopping tolerance. The tolerance is used by the `fitted` method to decide when progress is no longer being made, in which case the training process will stop before the specified maximum number of iterations (known as early stopping). Significant progress happens when the validation loss decreases by at least the tolerance. Defaults to 0.01. - Note: Early stopping only happens when using the `fitted` method with validation data.
set earlyStoppingTolerance(value: number)Swift declarationvar earlyStoppingTolerance: FloatThe early-stopping tolerance. The tolerance is used by the `fitted` method to decide when progress is no longer being made, in which case the training process will stop before the specified maximum number of iterations (known as early stopping). Significant progress happens when the validation loss decreases by at least the tolerance. Defaults to 0.01. - Note: Early stopping only happens when using the `fitted` method with validation data.
get earlyStoppingIterationCount(): numberSwift declarationvar earlyStoppingIterationCount: IntThe number of iterations to use when evaluating whether to stop early. The `fitted` method will stop if no significant progress is made for this many iterations. Significant progress happens when the validation error decreases by at least `convergenceThreshold`. - Note: Early stopping only happens when using the `fitted` method with validation data.
set earlyStoppingIterationCount(value: number)Swift declarationvar earlyStoppingIterationCount: IntThe number of iterations to use when evaluating whether to stop early. The `fitted` method will stop if no significant progress is made for this many iterations. Significant progress happens when the validation error decreases by at least `convergenceThreshold`. - Note: Early stopping only happens when using the `fitted` method with validation data.
get batchSize(): numberSwift declarationvar batchSize: IntThe number of examples in each training batch. - Note: This parameter is only used by the `fitted` method.
set batchSize(value: number)Swift declarationvar batchSize: IntThe number of examples in each training batch. - Note: This parameter is only used by the `fitted` method.
static init(): TimeSeriesClassifierConfigurationSwift declarationinit()Creates a configuration.
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