Build dynamic iOS apps with the Create ML framework

// define training data source
let data = MLStyleTransfer.DataSource.images(styleImage: styleUrl, contentDirectory: contentUrl)

// define session parameter
let sessionParameters = MLTrainingSessionParameters(sessionDirectory: sessionUrl)

// define training job
let job = try MLStyleTransfer.train(trainingData: data, sessionParameters: sessionParameters)

// dispatch training job 
// save out model upon receiving successful completion, compile for later use
// make prediction with CoreML model
try model.write(to: writeToUrl)
let compiledURL = try MLModel.compileModel(at: writeToUrl)
let mlModel = try MLModel(contentsOf: compiledURL)
let inputImage = try MLDictionaryFeatureProvider(dictionary: ["image": image])
let stylizedImage = try mlModel.prediction(from: inputImage)

func featuresFromMealAndKeywords(meal: String, keywords: [String]) -> [String: Double] {

    // Capture interactions between content (the dish keywords) and context (meal) by
    // adding a copy of each keyword modified to include the meal.
    let featureNames = keywords + keywords.map { meal + ":" + $0 }
    
    // For each keyword, create an entry in a dictionary of features with a value of 1.0.
    return featureNames.reduce(into: [:]) { features, name in
        features[name] = 1.0
    }
}

var trainingKeywords: [[String: Double]] = []
var trainingTargets: [Double] = []

for item in userPurchasedItems {
    // Add in the positive example.
    trainingKeywords.append(
       featuresFromMealAndKeywords(meal: item.meal, keywords: item.keywords))
    trainingTargets.append(1.0)
            
    // Add in the negative example.
    let negativeKeywords = allKeywords.subtracting(item.keywords)
    trainingKeywords.append(
       featuresFromMealAndKeywords(meal: item.meal, keywords: Array(negativeKeywords)))
    trainingTargets.append(-1.0)
}

// Create the training data.
var trainingData = DataFrame()
trainingData.append(column: Column(name: "keywords" contents: trainingKeywords))
trainingData.append(column: Column(name: "target", contents: trainingTargets))

// Create the model.
let model = try MLLinearRegressor(trainingData: trainingData, targetColumn: "target")

// Setup the data to run an inference on.
var inputData = DataFrame()
inputData.append(column: Column(name: "keywords", contents: dishKeywords))

// Call predictions on the trained model with the data.
let predictions = try model.predictions(from: inputData)