public void SaveAndLoad_DocWithChildren()
{
var doc = new BinaryVectorDocument();
doc.Version = 2;
doc.Vectors.Add(ColumnVector1D.Create(1.2, 7.4));
var doc1 = new BinaryVectorDocument();
doc1.Vectors.Add(ColumnVector1D.Create(8.2432, 89.4));
doc1.Vectors.Add(ColumnVector1D.Create(20, 0.14));
doc.Children.Add(doc1);
using (var store = new InMemoryBlobStore())
{
store.Store("a", doc);
var doc2 = store.Restore("a", new BinaryVectorDocument());
Assert.That(doc.Vectors[0][1], Is.EqualTo(7.4));
Assert.That(doc.Children[0].Vectors[0][0], Is.EqualTo(8.2432));
Assert.That(doc.Children[0].Vectors[1][1], Is.EqualTo(0.14));
}
}
public void SaveAndLoad_BasicDoc()
{
var doc = new BinaryVectorDocument();
doc.Version = 1;
doc.Properties["title"] = "abc";
doc.Properties["value1"] = "12";
//doc.Properties["null"] = null;
doc.Vectors.Add(ColumnVector1D.Create(1.2, 7.4));
doc.Vectors.Add(ColumnVector1D.Create(0.2, 3.9));
doc.Vectors.Add(ColumnVector1D.Create(4.6, 5.1, 8.1));
Console.WriteLine(doc.Checksum);
using (var store = new InMemoryBlobStore())
{
store.Store("a", doc);
var doc2 = store.Restore("a", new BinaryVectorDocument());
Assert.That(doc2.Properties.Count, Is.EqualTo(2));
Assert.That(doc2.Properties["title"], Is.EqualTo("abc"));
Assert.That(doc2.Properties["value1"], Is.EqualTo("12"));
//Assert.That(doc2.Properties["null"], Is.EqualTo(""));
Assert.That(doc.Vectors[1][1], Is.EqualTo(3.9));
}
}
public async Task CreateClassifier_SavesOutput()
{
var bytes = BitConverter.GetBytes(0);
Assert.That(bytes.Length, Is.EqualTo(4));
var endpoint = new Uri("tcp://localhost:9212");
var data = Functions.NormalRandomDataset(3, 10).Select(x => new
{
x = x,
y = Math.Log(x)
}).AsQueryable();
using (var blobs = new InMemoryBlobStore())
using (var server = new RemoteClassifierTrainingServer(endpoint, blobs))
using (var client = new RemoteClassifierTrainingClient(endpoint))
{
#if DEBUGGING
client.Timeout = 20000;
#endif
server.Start();
var trainingSet = data.CreatePipeline().AsTrainingSet(x => x.x > 10 ? 'a' : 'b');
var keyAndclassifier = await client.CreateClassifier(trainingSet, true);
// We should be able to restore the raw blob back into a network
var nn = new MultilayerNetwork(new NetworkParameters(1, 1));
blobs.Restore(new string(keyAndclassifier.Key.PathAndQuery.Skip(1).ToArray()), nn);
// We should receive a valid classifier object back
var cls = keyAndclassifier.Value.Classify(new
{
x = 12.432,
y = Math.Log(12.432)
});
foreach (var c in cls)
{
Console.WriteLine(c);
}
Assert.That(cls.Any());
}
}
