/// <summary>
/// Trains a classifier. Training requires a zip file of 10 positive examples and a zip file of 10 negative examples.
/// The name of the positive examples shoud be prepended with the class name (ie className_positive_examples).
/// Negative examples should be named negative_examples.
/// </summary>
/// <returns><c>true</c>, if classifier was trained, <c>false</c> otherwise.</returns>
/// <param name="classifierName">Classifier name.</param>
/// <param name="className">Class name.</param>
/// <param name="positiveExamplesPath">Positive examples path.</param>
/// <param name="negativeExamplesPath">Negative examples path.</param>
/// <param name="callback">Callback.</param>
public bool TrainClassifier(string classifierName, string className, string positiveExamplesPath, string negativeExamplesPath, OnTrainClassifier callback)
{
if (string.IsNullOrEmpty(mp_ApiKey))
{
mp_ApiKey = Config.Instance.GetAPIKey(SERVICE_ID);
}
if (string.IsNullOrEmpty(mp_ApiKey))
{
throw new WatsonException("GetClassifier - VISUAL_RECOGNITION_API_KEY needs to be defined in config.json");
}
if (string.IsNullOrEmpty(classifierName))
{
throw new ArgumentNullException("ClassifierName");
}
if (string.IsNullOrEmpty(positiveExamplesPath))
{
throw new ArgumentNullException("positiveExamplesPath");
}
if (string.IsNullOrEmpty(negativeExamplesPath))
{
throw new ArgumentNullException("negativeExamplesPath");
}
if (callback == null)
{
throw new ArgumentNullException("callback");
}
byte[] positiveExamplesData = null;
byte[] negativeExamplesData = null;
if (LoadFile != null)
{
positiveExamplesData = LoadFile(positiveExamplesPath);
negativeExamplesData = LoadFile(negativeExamplesPath);
}
else
{
#if !UNITY_WEBPLAYER
positiveExamplesData = File.ReadAllBytes(positiveExamplesPath);
negativeExamplesData = File.ReadAllBytes(negativeExamplesPath);
#endif
}
if (positiveExamplesData == null || negativeExamplesData == null)
{
Log.Error("VisualRecognition", "Failed to upload {0} or {1}!", positiveExamplesPath, negativeExamplesPath);
}
return(UploadClassifier(classifierName, className, positiveExamplesData, negativeExamplesData, callback));
}
private bool UploadClassifier(string classifierName, string className, byte[] positiveExamplesData, byte[] negativeExamplesData, OnTrainClassifier callback)
{
if (string.IsNullOrEmpty(mp_ApiKey))
{
mp_ApiKey = Config.Instance.GetAPIKey(SERVICE_ID);
}
if (string.IsNullOrEmpty(mp_ApiKey))
{
throw new WatsonException("GetClassifier - VISUAL_RECOGNITION_API_KEY needs to be defined in config.json");
}
if (string.IsNullOrEmpty(classifierName))
{
throw new ArgumentNullException("ClassifierName");
}
if (positiveExamplesData == null)
{
throw new ArgumentNullException("positiveExamplesData");
}
if (negativeExamplesData == null)
{
throw new ArgumentNullException("negativeExamplesData");
}
if (callback == null)
{
throw new ArgumentNullException("callback");
}
RESTConnector connector = RESTConnector.GetConnector(SERVICE_ID, SERVICE_CLASSIFIERS);
if (connector == null)
{
return(false);
}
TrainClassifierReq req = new TrainClassifierReq();
req.Callback = callback;
req.OnResponse = OnTrainClassifierResp;
req.Timeout = REQUEST_TIMEOUT;
req.Parameters["api_key"] = mp_ApiKey;
req.Parameters["version"] = VisualRecognitionVersion.Version;
req.Forms = new Dictionary <string, RESTConnector.Form>();
req.Forms["name"] = new RESTConnector.Form(classifierName);
req.Forms[className + "_positive_examples"] = new RESTConnector.Form(positiveExamplesData, className + "_positive_examples.zip", "application/zip");
req.Forms["negative_examples"] = new RESTConnector.Form(negativeExamplesData, "negative_examples.zip", "application/zip");
return(connector.Send(req));
}
/// <summary>
/// Train a new classifier.
/// </summary>
/// <param name="classifierName">A name to give the classifier.</param>
/// <param name="language">Language of the classifier.</param>
/// <param name="trainingData">CSV training data.</param>
/// <param name="callback">Callback to invoke with the results.</param>
/// <returns>Returns true if training data was submitted correctly.</returns>
public bool TrainClassifier(string classifierName, string language, string trainingData, OnTrainClassifier callback)
{
if (string.IsNullOrEmpty(classifierName))
{
throw new ArgumentNullException("classifierId");
}
if (string.IsNullOrEmpty(language))
{
throw new ArgumentNullException("language");
}
if (string.IsNullOrEmpty(trainingData))
{
throw new ArgumentNullException("trainingData");
}
if (callback == null)
{
throw new ArgumentNullException("callback");
}
RESTConnector connector = RESTConnector.GetConnector(SERVICE_ID, "/v1/classifiers");
if (connector == null)
{
return(false);
}
Dictionary <string, object> trainingMetaData = new Dictionary <string, object>();
trainingMetaData["language"] = language;
trainingMetaData["name"] = classifierName;
TrainClassifierReq req = new TrainClassifierReq();
req.Callback = callback;
req.OnResponse = OnTrainClassifierResp;
req.Forms = new Dictionary <string, RESTConnector.Form>();
req.Forms["training_metadata"] = new RESTConnector.Form(Encoding.UTF8.GetBytes(Json.Serialize(trainingMetaData)));
req.Forms["training_data"] = new RESTConnector.Form(Encoding.UTF8.GetBytes(trainingData));
return(connector.Send(req));
}