/// <summary>
/// Simple sample that runs through all steps to explicitly create a prediction
/// from a csv file with the classic iris data.
/// </summary>
static async Task MainAsync()
{
// New BigML client using username and API key.
Console.Write("user: "******"key: "); var ApiKey = Console.ReadLine();
var client = new Client(User, ApiKey);
Ordered<Source.Filterable, Source.Orderable, Source> result
= (from s in client.ListSources()
orderby s.Created descending
select s);
var sources = await result;
foreach(var src in sources)
{
Console.WriteLine(src.ToString());
}
// New source from in-memory stream, with separate header.
var source = await client.Create(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.StatusCode != Code.Finished) await Task.Delay(10);
Console.WriteLine(source.StatusMessage.ToString());
// Default dataset from source
var dataset = await client.Create(source);
// No push, so we need to busy wait for the source to be processed.
while ((dataset = await client.Get(dataset)).StatusMessage.StatusCode != Code.Finished) await Task.Delay(10);
Console.WriteLine(dataset.StatusMessage.ToString());
// Default model from dataset
var model = await client.Create(dataset);
// No push, so we need to busy wait for the source to be processed.
while ((model = await client.Get(model)).StatusMessage.StatusCode != Code.Finished) await Task.Delay(10);
Console.WriteLine(model.StatusMessage.ToString());
// The model description is what we are really after
var description = model.ModelDescription;
Console.WriteLine(description.ToString());
// First convert it to a .NET expression tree
var expression = description.Expression();
Console.WriteLine(expression.ToString());
// Then compile the expression tree into MSIL
var predict = expression.Compile() as Func<double,double,double,double,string>;
// And try the first flower of the example set.
var result2 = predict(5.1, 3.5, 1.4, 0.2);
Console.WriteLine("result = {0}, expected = {1}", result2, "setosa");
}
/// <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);
}
private Task<Source> DoGetAsync()
{
Client c = new Client(userName, apiKey);
return c.Get<Source>("source/57e4206ba2e47604db001b20");
}
