static async void Main()
{
// New BigML client with username and API key
Console.Write("user: "******"key: "); var ApiKey = Console.ReadLine();
// set true the development mode
var client = new Client(User, ApiKey, true);
// create a source
var remoteURL = @"azure://csv/iris.csv?AccountName=bigmlpublic";
Source.Arguments remoteArguments = new Source.Arguments();
remoteArguments.Add("remote", remoteURL);
var sourceFromAzure = await client.Create(remoteArguments);
while ((sourceFromAzure = await client.Get(sourceFromAzure)).StatusMessage.NotSuccessOrFail()) await Task.Delay(10);
Console.WriteLine(sourceFromAzure.StatusMessage.ToString());
var dataset = await client.Create(sourceFromAzure);
// 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());
// Customized cluster from dataset. 3 is the desired number of cluster. Sets cluster name: "my cluster"
var cluster = await client.CreateCluster(dataset, "my cluster", 3);
while ((cluster = await client.Get(cluster)).StatusMessage.NotSuccessOrFail()) await Task.Delay(10);
Console.WriteLine(cluster.StatusMessage.ToString());
}
static async void Main()
{
// New BigML client with username and API key
Console.Write("user: "******"key: "); var ApiKey = Console.ReadLine();
var client = new Client(User, ApiKey);
// Create a source from a file in azure storage
var remoteURL = @"azure://csv/iris.csv?AccountName=bigmlpublic";
Source.Arguments remoteArguments = new Source.Arguments();
remoteArguments.Remote = remoteURL;
remoteArguments.Name = "Iris from Azure";
var sourceFromAzure = await client.Create(remoteArguments);
while ((sourceFromAzure = await client.Get(sourceFromAzure)).StatusMessage.NotSuccessOrFail()) await Task.Delay(10);
// Create a dataset from this sources
var dataset = await client.Create(sourceFromAzure);
while ((dataset = await client.Get(dataset)).StatusMessage.NotSuccessOrFail()) await Task.Delay(10);
Console.WriteLine(dataset.StatusMessage.ToString());
// Default model from this 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.NotSuccessOrFail()) await Task.Delay(10);
Console.WriteLine(model.StatusMessage.ToString());
}
/// <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");
}
static async void Main()
{
// New BigML client with username and API key
Console.Write("user: "******"key: "); var ApiKey = Console.ReadLine();
var client = new Client(User, ApiKey);
// retrieve a anomaly detector with a known ID
Anomaly anomaly;
string anomalyId = "anomaly/54daa82eaf447f5daa000XXY"; //Put your ID here
if ((anomaly = await client.Get<Anomaly>(anomalyId)).StatusMessage.StatusCode != Code.Finished) {
Console.WriteLine("Error retrieving anomaly " + anomalyId);
} else {
Console.WriteLine(anomaly.StatusMessage.ToString());
}
//Input the data and calculate the score
var parameters = new AnomalyScore.Arguments();
parameters.Anomaly = anomaly.Resource;
parameters.InputData.Add("000000", 7.9);
parameters.InputData.Add("000001", 3.8);
parameters.InputData.Add("000002", 6.4);
parameters.InputData.Add("000003", 2);
parameters.InputData.Add("000004", "virginica");
AnomalyScore score;
while ((score = await client.Create<AnomalyScore>(parameters)).StatusMessage.StatusCode != Code.Finished) await Task.Delay(10);
Console.WriteLine(score.StatusMessage.ToString());
}
