Struct ReservoirTrainingData 

pub struct ReservoirTrainingData {
    pub inputs: Vec<Array1<f64>>,
    pub targets: Vec<Array1<f64>>,
    pub timestamps: Vec<f64>,
    pub features: Option<Vec<Array1<f64>>>,
    pub labels: Option<Vec<usize>>,
    pub sequence_lengths: Option<Vec<usize>>,
    pub missing_mask: Option<Vec<Array1<bool>>>,
    pub sample_weights: Option<Vec<f64>>,
    pub metadata: Option<Vec<HashMap<String, String>>>,
}

Enhanced training data for reservoir computing

Fields

inputs: Vec<Array1<f64>>

Input time series

targets: Vec<Array1<f64>>

Target outputs

timestamps: Vec<f64>

Time stamps

features: Option<Vec<Array1<f64>>>

Additional features

labels: Option<Vec<usize>>

Data labels for classification

sequence_lengths: Option<Vec<usize>>

Sequence lengths for variable-length sequences

missing_mask: Option<Vec<Array1<bool>>>

Missing data indicators

sample_weights: Option<Vec<f64>>

Data weights for importance sampling

metadata: Option<Vec<HashMap<String, String>>>

Metadata for each sample

Implementations

impl ReservoirTrainingData

pub fn new( inputs: Vec<Array1<f64>>, targets: Vec<Array1<f64>>, timestamps: Vec<f64>, ) -> Self

Create new training data

pub fn with_features(self, features: Vec<Array1<f64>>) -> Self

Add features to training data

pub fn with_labels(self, labels: Vec<usize>) -> Self

Add labels for classification

pub fn with_weights(self, weights: Vec<f64>) -> Self

Add sample weights

pub fn len(&self) -> usize

Get data length

pub fn is_empty(&self) -> bool

Check if data is empty

pub fn train_test_split(&self, test_ratio: f64) -> (Self, Self)

Split data into train/test sets

Trait Implementations

impl Clone for ReservoirTrainingData

fn clone(&self) -> ReservoirTrainingData

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ReservoirTrainingData

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.