API Reference - Models - UnscentedKalmanFilterDataPredictVariant
UnscentedKalmanFilterDataPredictVariant is a supervised machine learning model that predicts continuous values (e.g. 1.2, -32, 90, -1.2 and etc.). It uses iterative calculations to find the best model parameters.
Stored Model Parameters
Contains a matrix.
- ModelParameters[I][J]: Value of matrix at row I and column J. The rows are the features.
Constructors
new()
Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
UnscentedKalmanFilterDataPredictVariant.new(alpha: number, beta: number: kappa: number, noiseValue: number, lossFunction: string, useJosephForm: boolean, epsilon: number, processNoiseCovarianceMatrix: matrix, observationNoiseCovarianceMatrix: matrix): ModelObject
Parameters:
-
alpha: The spread of the sigma points around the mean state. Typically set to a small positive value. Controls the distribution of the sigma points. [Default: 1e-3]
-
beta: Used to incorporate prior knowledge of the distribution. For Gaussian distributions, beta = 2 is optimal. [Default: 2]
-
kappa: A secondary scaling parameter that determines the distance of the sigma points from the mean. Usually set to 0 or 3 - n, where n is the state dimension. [Default: 0]
-
noiseValue: The noise value to be used by observationNoiseCovarianceMatrix and processNoiseCovarianceMatrix. [Default: 1]
-
lossFunction: The function to calculate the cost of each training. Available options are:
| Function | Input Range (From Predicted Label Vector And Label Vector) | Output Range | Characteristics | Use Cases |
|---|---|---|---|---|
| L2 (Default) | (-∞, ∞) | (0, ∞) | Quadratic penalty, sensitive to outliers | Regression with normally distributed errors, when outliers are minimal |
| L1 | (-∞, ∞) | (0, ∞) | Linear penalty, robust to outliers | Robust regression, when dataset contains outliers, median prediction |
| Mahalanobis | (-∞, ∞) | (0, ∞) | Accounts for correlations between variables, scale-invariant, uses covariance matrix | Regression with correlated features, multivariate outlier detection, when feature dependencies matter |
-
useJosephForm: Set whether or not to use a more numerically accurate representation in exchange for lower performance [Default: true]
-
epsilon: Used for Jacobian approximation. [Default: 1e-5]
-
stateTransitionFunction: The non-linear state transition function that predicts the next state given current state and control vector. This function defines how the system evolves over time without noise. [Default: linear state transition]
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observationFunction: The nonlinear observation function that maps the state space to the observation space. This function defines how measurements relate to the state. [Default: linear observation]
-
processNoiseCovarianceMatrix: The process noise covariance matrix representing the uncertainty in the state transition model. [Default: identity matrix]
-
observationNoiseCovarianceMatrix: The observation noise covariance matrix representing the uncertainty in the observation model. [Default: identity matrix]
Returns:
- ModelObject: The generated model object.
Functions
train()
Train the model.
UnscentedKalmanFilterDataPredictVariant:train(previousStateMatrix: matrix, currentStateMatrix: matrix): number[]
Parameters:
-
previousStateMatrix: A matrix containing all previous state data.
-
currentStateMatrix: A matrix containing all current state data.
Returns:
- costArray: An array containing cost values.
predict()
Predict the value for a given data.
UnscentedKalmanFilterDataPredictVariant:predict(previousStateMatrix: matrix): matrix
Parameters:
- previousStateMatrix: A matrix containing all previous state data.
Returns:
- predictedNextStateMatrix: A matrix containing all next state data that are predicted by the model.