fit_curve return schema

[soxofaan]

(related to other "ml" tickets like e.g. #396 (comment))

fit_curve currently returns a datacube:

openeo-processes/proposals/fit_curve.json

Lines 79 to 84 in f8f6731

	"returns": {
	"description": "A data cube with the optimal values for the parameters.",
	"schema": {
	"type": "object",
	"subtype": "datacube"
	}

But I think it should return the array of "trained" parameters. It should basically correspond (in number of items) to the parameters parameter of the function callback

openeo-processes/proposals/fit_curve.json

Lines 52 to 59 in f8f6731

	"name": "parameters",
	"description": "The parameters for the model function, contains at least one parameter.",
	"schema": {
	"type": "array",
	"minItems": 1,
	"items": {
	"type": "number"
	}

Unless fit_curve is intended to do the "training" and "prediction" within the same process: you provide a input data cube and a model (least squares cure fitting), the model is trained and immediately applied to the same input data to "smooth" it in some way?

[soxofaan]

Likewise, the parameters argument of predict_curve should also be just an array

openeo-processes/proposals/predict_curve.json

Lines 19 to 26 in 0dd3ab0

	{
	"name": "parameters",
	"description": "A data cube with optimal values from a result of e.g. ``fit_curve()``.",
	"schema": {
	"type": "object",
	"subtype": "raster-cube"
	}
	},

[m-mohr]

@clausmichele Do you agree? I think we made it a data cube to allow large scale fitting on data cubes?

[clausmichele]

@m-mohr yes indeed. If we would modify the process as @soxofaan seggests, I guess that then it should be called inside a reduce_dimension or an apply_dimension process.
I am using as a reference the old notebook for the use case I prepared here https://github.com/openEOPlatform/SRR2_notebooks/blob/main/UC6%20-%20Forest%20Dynamics.ipynb

Maybe let's check also if @ValentinaHutter has an opinion on this, since she was the last one working on the implementation of it.

[m-mohr]

reduce_dimension doesn't work, but apply_dimension may work.