codemillmatt

public class App : Application
{
		AkaConsumer consumer;

		public App()
		{
			// The root page of your application
			consumer = new AkaConsumer();

			var contentLabel = new Label() { HorizontalTextAlignment = TextAlignment.Center };

codemillmatt

public async Task PushAsync<T>(Action<T> initialize = null) where T : BaseViewModel 
{
	T viewModel;

	// First instantiate the view model
	// "IoC" is a static AutoFac IContainer property in the main Xam Forms Application class
	using (var scope = App.IoC.BeginLifetimeScope ()) {
		viewModel = App.IoC.Resolve<T> ();

	        if (initialize != null)

codemillmatt

public async Task PopAsync<T>(Action<T> reInitialize = null) where T : BaseViewModel
{
	await PopAsync();

	var topPage = FormsNavigation.NavigationStack.Last() as IViewFor<T>;

	if (topPage != null)
		reInitialize?.Invoke(topPage.ViewModel);
}

codemillmatt

public void PopTo<T>() where T : BaseViewModel
{
	var pagesToRemove = new List<Page>();
	var upper = FormsNavigation.NavigationStack.Count - 1;

	// Loop through the nav stack backwards
	for (int i = upper; i >= 0; i--)
	{
		var currentPage = FormsNavigation.NavigationStack[i] as IViewFor;

codemillmatt

To compile XAML

This should be in the PCL's AssemblyInfo.cs

using Xamarin.Forms.XAML;

[assembly: XamlCompilation(XamlCompilationOptions.Compile)]

To selectively skip XAML compilation

This will be in the code behind of the XAML page

codemillmatt

[ContentProperty("FriendlyName")]
    public class FriendlyThickness : IMarkupExtension
    {
        public FriendlyThickness()
        {
        }

        public string FriendlyName { get; set; }

        public object ProvideValue(IServiceProvider serviceProvider)

codemillmatt

Always Wanted To Know About Machine Learning ... But Were Too Afraid To Ask

Abstract

Classification algorithms, supervised learning, neural networks, generative models, and multivariate linear regression! The terminology of machine learning can be intimidating... then you get to the math. Machine learning doesn't need to live in the halls of academia however, and this session aims to answer some of the questions you have about machine learning. In this session, you'll see how to use generic algorithms to make "intelligent" predictions about sets of data. How that algorithm can be reused across domains, to predict entirely different scenarios. Then we'll get into the fun stuff, using deep learning to recognize objects. You'll also learn where machine learning can fall short and when it can produce less than desirable results. And you'll also see that sometimes we have no idea how the machine gets the answer ... it just does! By the end of this session, you'll have a foundational level of understanding

codemillmatt

{
    "id": "f58f93de-4f3c-464c-a72f-61c770c870ef",
    "movieName": "Ant Man and the Wasp",
    "starRating": 2,
    "reviewText": "I'm embarassed I even watched the trailer.",
    "isPremium": true
}

codemillmatt

[FunctionName("MovieReviewPermission")]
public static async Task<HttpResponseMessage> Run(
    [HttpTrigger(AuthorizationLevel.Anonymous, "get", "post", Route = null)]HttpRequestMessage req,
    [DocumentDB(databaseName: "moviereview-db", collectionName: "reviews", ConnectionStringSetting = "CosmosConnectionString")] DocumentClient client,
    TraceWriter log)

codemillmatt

string userId = GetUserId(log);

Permission token = await GetPartitionPermission(userId, client, dbName, collectionName);

return req.CreateResponse<string>(HttpStatusCode.OK, token.Token);