Performs a spline based line fit to a set of data.
Given a set of training parameters and observations (x and y values) along with a parameter controlling the smoothness of the required output, the function returns a set of values that make up a curve that fits to the parameters and observations.
| Number | Type | Compulsory | Default | Description |
| 1 | real array | Yes | Training parameters (x values) | |
| 2 | real array | Yes | Training observations (y values) | |
| 3 | real array | Yes | Smoothness parameter | |
| 4 | real array | Yes | Result parameters (x values) |
The parameters for the training data. This would normally be the values on the x-axis of a graph. The values must be ordered from lowest to highest value.
The observations for the training data. This would normally be the values on the y-axis of a graph.
Parameter that controls how smooth the fit to the data will be. Value must be 0-positive, where the smoothness of the fit increases as the parameter increases.
At the extremes, a value of 0 produces a result made up of straight lines between each training point in order, whilst a value tending towards infinity produces a single straight line through the whole of the data.
The parameters to fit the resulting curve to.
Return type: real array
Vector the same length as parameter 4 (Result parameters (x values)) , with fitted values for each parameter in order.
| ▲Function Summary▲ | ||
| ◄ MLRidgeRegressionFit | MLVector ▶ | |