async Task FetchBatchResults(int num)
{
var batch = new MLArrayBatchProvider(inputs.Take(num).ToArray());
var options = new MLPredictionOptions()
{
UsesCpuOnly = false
};
Stopwatch stopWatch = Stopwatch.StartNew();
await Task.Run(() =>
{
model.GetPredictions(batch, options, out NSError error);
});
stopWatch.Stop();
batchMilliseconds = stopWatch.ElapsedMilliseconds;
}
/// <summary>
/// Make a prediction using the standard interface
/// </summary>
/// <param name="input">an instance of hate_coremlInput to predict from</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public hate_coremlOutput GetPrediction(hate_coremlInput input, MLPredictionOptions options, out NSError error)
{
if (input == null)
{
throw new ArgumentNullException(nameof(input));
}
if (options == null)
{
throw new ArgumentNullException(nameof(options));
}
var prediction = model.GetPrediction(input, options, out error);
if (prediction == null)
{
return(null);
}
var output1Value = prediction.GetFeatureValue("output1").MultiArrayValue;
return(new hate_coremlOutput(output1Value));
}
/// <summary>
/// Make a prediction using the standard interface
/// </summary>
/// <param name="input">an instance of MarsHabitatPricerInput to predict from</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public MarsHabitatPricerOutput GetPrediction(MarsHabitatPricerInput input, MLPredictionOptions options, out NSError error)
{
if (input == null)
{
throw new ArgumentNullException(nameof(input));
}
if (options == null)
{
throw new ArgumentNullException(nameof(options));
}
var prediction = model.GetPrediction(input, options, out error);
if (prediction == null)
{
return(null);
}
var priceValue = prediction.GetFeatureValue("price").DoubleValue;
return(new MarsHabitatPricerOutput(priceValue));
}
/// <summary>
/// Make a prediction using the standard interface
/// </summary>
/// <param name="input">an instance of CookHappyJuneInput to predict from</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public CookHappyJuneOutput GetPrediction(CookHappyJuneInput input, MLPredictionOptions options, out NSError error)
{
if (input == null)
{
throw new ArgumentNullException(nameof(input));
}
if (options == null)
{
throw new ArgumentNullException(nameof(options));
}
var prediction = model.GetPrediction(input, options, out error);
if (prediction == null)
{
return(null);
}
var lossValue = prediction.GetFeatureValue("loss").DictionaryValue;
var classLabelValue = prediction.GetFeatureValue("classLabel").StringValue;
return(new CookHappyJuneOutput(lossValue, classLabelValue));
}
/// <summary>
/// Make a prediction using the standard interface
/// </summary>
/// <param name="input">an instance of FreeSoundsModel25Input to predict from</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public FreeSoundsModel25Output GetPrediction(FreeSoundsModel25Input input, MLPredictionOptions options, out NSError error)
{
if (input == null)
{
throw new ArgumentNullException(nameof(input));
}
if (options == null)
{
throw new ArgumentNullException(nameof(options));
}
var prediction = model.GetPrediction(input, options, out error);
if (prediction == null)
{
return(null);
}
var classLabelProbsValue = prediction.GetFeatureValue("classLabelProbs").DictionaryValue;
var classLabelValue = prediction.GetFeatureValue("classLabel").StringValue;
return(new FreeSoundsModel25Output(classLabelProbsValue, classLabelValue));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="audioSamples">Input audio samples to be classified as 15600 1-dimensional array of floats</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public FreeSoundsPlusModel25Output GetPrediction(MLMultiArray audioSamples, MLPredictionOptions options, out NSError error)
{
var input = new FreeSoundsPlusModel25Input(audioSamples);
return(GetPrediction(input, options, out error));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="data"> as color (kCVPixelFormatType_32BGRA) image buffer, 224 pizels wide by 224 pixels high</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public CookHappyJuneOutput GetPrediction(CVPixelBuffer data, MLPredictionOptions options, out NSError error)
{
var input = new CookHappyJuneInput(data);
return(GetPrediction(input, options, out error));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="data"> as color (kCVPixelFormatType_32BGRA) image buffer, 224 pizels wide by 224 pixels high</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public RamenOutput GetPrediction(CVPixelBuffer data, MLPredictionOptions options, out NSError error)
{
var input = new RamenInput(data);
return(GetPrediction(input, options, out error));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="input1"> as 1 1-dimensional array of doubles</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public hate_coremlOutput GetPrediction(MLMultiArray input1, MLPredictionOptions options, out NSError error)
{
var input = new hate_coremlInput(input1);
return(GetPrediction(input, options, out error));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="data"> as color (kCVPixelFormatType_32BGRA) image buffer, 224 pizels wide by 224 pixels high</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public jankenmodelOutput GetPrediction(CVPixelBuffer data, MLPredictionOptions options, out NSError error)
{
var input = new jankenmodelInput(data);
return(GetPrediction(input, options, out error));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="data"> as color (kCVPixelFormatType_32BGRA) image buffer, 224 pizels wide by 224 pixels high</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public coil100Model_CoreMLOutput GetPrediction(CVPixelBuffer data, MLPredictionOptions options, out NSError error)
{
var input = new coil100Model_CoreMLInput(data);
return(GetPrediction(input, options, out error));
}
/// <summary>
/// Make a prediction using the convenience interface
/// </summary>
/// <param name="solarPanels">Number of solar panels as double</param>
/// <param name="greenhouses">Number of greenhouses as double</param>
/// <param name="size">Size in acres as double</param>
/// <param name="options">prediction options</param>
/// <param name="error">If an error occurs, upon return contains an NSError object that describes the problem.</param>
public MarsHabitatPricerOutput GetPrediction(double solarPanels, double greenhouses, double size, MLPredictionOptions options, out NSError error)
{
var input = new MarsHabitatPricerInput(solarPanels, greenhouses, size);
return(GetPrediction(input, options, out error));
}
