Hi everyone, I attempted to use the MultivariateLinearRegressor from the Create ML Components framework to fit some multi-dimensional data linearly (4 dimensions in my example). I aim to obtain multi-dimensional output points (2 points in my example). However, when I fit the model with my training data and test it, it appears that only the first element of my training data is used for training, regardless of whether I use CreateMLComponents.AnnotatedBatch or [CreateMLComponents.AnnotatedFeature, CoreML.MLShapedArray>] as input.
let sourceMatrix: [[Double]] = [
[0,0.1,0.2,0.3],
[0.5,0.2,0.6,0.2]
]
let referenceMatrix: [[Double]] = [
[0.2,0.7],
[0.9,0.1]
]
Here is a test code to test the function (ios 18.0 beta, Xcode 16.0 beta)
In this example I train the model to learn 2 multidimensional points (4 dimensions) and here are the results of the predictions:
▿ 2 elements
▿ 0 : AnnotatedPrediction<MLShapedArray<Double>, MLShapedArray<Double>>
▿ prediction : 0.20000000298023224 0.699999988079071
▿ _storage : <StandardStorage<Double>: 0x600002ad8270>
▿ annotation : 0.2 0.7
▿ _storage : <StandardStorage<Double>: 0x600002b30600>
▿ 1 : AnnotatedPrediction<MLShapedArray<Double>, MLShapedArray<Double>>
▿ prediction : 0.23158159852027893 0.9509953260421753
▿ _storage : <StandardStorage<Double>: 0x600002ad8c90>
▿ annotation : 0.9 0.1
▿ _storage : <StandardStorage<Double>: 0x600002b55f20>
0.23158159852027893 0.9509953260421753 is totally random and should be far more closer to [0.9,0.1].
Here is the test code : ( i run it on "My mac, Designed for Ipad")
ContentView.swift
import CoreImage
import CoreImage.CIFilterBuiltins
import UIKit
import CoreGraphics
import Accelerate
import Foundation
import CoreML
import CreateML
import CreateMLComponents
func createMLShapedArray(from array: [Double], shape: [Int]) -> MLShapedArray<Double> {
return MLShapedArray<Double>(scalars: array, shape: shape)
}
func calculateTransformationMatrixWithNonlinearity(sourceRGB: [[Double]], referenceRGB: [[Double]], degree: Int = 3) async throws -> MultivariateLinearRegressor<Double>.Model {
let annotatedFeatures2 = zip(sourceRGB, referenceRGB).map { (featureArray, targetArray) -> AnnotatedFeature<MLShapedArray<Double>, MLShapedArray<Double>> in
let featureMLShapedArray = createMLShapedArray(from: featureArray, shape: [featureArray.count])
let targetMLShapedArray = createMLShapedArray(from: targetArray, shape: [targetArray.count])
return AnnotatedFeature(feature: featureMLShapedArray, annotation: targetMLShapedArray)
}
// Flatten the sourceRGBPoly into a single-dimensional array
var flattenedArray = sourceRGB.flatMap { $0 }
let featuresMLShapedArray = createMLShapedArray(from: flattenedArray, shape: [2, 4])
flattenedArray = referenceRGB.flatMap { $0 }
let targetMLShapedArray = createMLShapedArray(from: flattenedArray, shape: [2, 2])
// Create AnnotatedFeature instances
/* let annotatedFeatures2: [AnnotatedFeature<MLShapedArray<Double>, MLShapedArray<Double>>] = [
AnnotatedFeature(feature: featuresMLShapedArray, annotation: targetMLShapedArray)
]*/
let annotatedBatch = AnnotatedBatch(features: featuresMLShapedArray, annotations: targetMLShapedArray)
var regressor = MultivariateLinearRegressor<Double>()
regressor.configuration.learningRate = 0.1
regressor.configuration.maximumIterationCount=5000
regressor.configuration.batchSize=2
let model = try await regressor.fitted(to: annotatedBatch,validateOn: nil)
//var model = try await regressor.fitted(to: annotatedFeatures2)
// Proceed to prediction once the model is fitted
let predictions = try await model.prediction(from: annotatedFeatures2)
// Process or use the predictions
print(predictions)
print("Predictions:", predictions)
return model
}
struct ContentView: View {
var body: some View {
VStack {}
.onAppear {
Task {
do {
let sourceMatrix: [[Double]] = [
[0,0.1,0.2,0.3],
[0.5,0.2,0.6,0.2]
]
let referenceMatrix: [[Double]] = [
[0.2,0.7],
[0.9,0.1]
]
let model = try await calculateTransformationMatrixWithNonlinearity(sourceRGB: sourceMatrix, referenceRGB: referenceMatrix, degree: 2
)
print("Model fitted successfully:", model)
} catch {
print("Error:", error)
}
}
}
}
}
