/// <summary>
/// Simple sample that runs through all steps to explicitly create a
/// local prediction from a csv file with the classic iris data.
/// </summary>
static async Task MainAsync()
{
// New BigML client with username and API key
Console.Write("user: "******"key: ");
var ApiKey = Console.ReadLine();
var client = new Client(User, ApiKey);
// New source from in-memory stream, with separate header. That's the header
var source = await client.CreateSource(iris, "Iris.csv", "sepal length, sepal width, petal length, petal width, species");
// No push, so we need to busy wait for the source to be processed.
while ((source = await client.Get(source)).StatusMessage.NotSuccessOrFail())
{
await Task.Delay(10);
}
Console.WriteLine(source.StatusMessage.ToString());
// Default dataset from source
var dataset = await client.CreateDataset(source);
// No push, so we need to busy wait for the dataset to be processed.
while ((dataset = await client.Get(dataset)).StatusMessage.NotSuccessOrFail())
{
await Task.Delay(10);
}
Console.WriteLine(dataset.StatusMessage.ToString());
// Default model from dataset
var model = await client.CreateModel(dataset);
// No push, so we need to busy wait for the source to be processed.
while ((model = await client.Get(model)).StatusMessage.NotSuccessOrFail())
{
await Task.Delay(10);
}
Console.WriteLine(model.StatusMessage.ToString());
Console.WriteLine("Creating local model");
Dictionary<string, dynamic> inputData = new Dictionary<string, dynamic>();
inputData.Add("000002", 3);
inputData.Add("000003", 1.5);
var localModel = model.ModelStructure();
var nodeResult = localModel.predict(inputData);
Console.WriteLine("Predict:\n" + nodeResult);
}
