private static void MakePredictionsOnTestFileAndCalculateError(MovieScorePredictor predictor, UserCache testingSetCache)
{
// Get the list of users to make predictions on
IReadOnlyDictionary <int, UserCache.UserRatingsCache> listOfUsersToPredictScoresOn = testingSetCache.GetAllUsersAndMovieRatings();
Console.WriteLine("Making predictions...");
// Predict ratings for all the users in parallel
List <MoviePrediction> predictions = listOfUsersToPredictScoresOn.AsParallel().Select(l =>
{
// Make the prediction for this users movies
var returnValue = predictor.PredictAllScores(l.Key, l.Value.GetMovieRatings(), K);
// This is simply to update the console on the current progress
l.Value.Predicted = true;
int predicted = listOfUsersToPredictScoresOn.Values.Count(n => n.Predicted);
Console.Write("\r{0}/{1}", predicted, listOfUsersToPredictScoresOn.Count);
// Return the prediction
return(returnValue);
}).SelectMany(s => s.Values).ToList();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Calculating errors...");
var rootMeanSquareError = RootMeanSquareError.Calculate(predictions);
var meanAbsoluteError = MeanAbsoluteError.Calculate(predictions);
Console.WriteLine("=========================================");
Console.WriteLine("Root mean square error: {0}", rootMeanSquareError);
Console.WriteLine("Mean absolute error: {0}", meanAbsoluteError);
}
/// <summary>
/// Add a set of evaluation metrics to the set of observations.
/// </summary>
/// <param name="metrics">The observed regression evaluation metric</param>
void IMetricsStatistics <RegressionMetrics> .Add(RegressionMetrics metrics)
{
MeanAbsoluteError.Add(metrics.MeanAbsoluteError);
MeanSquaredError.Add(metrics.MeanSquaredError);
RootMeanSquaredError.Add(metrics.RootMeanSquaredError);
LossFunction.Add(metrics.LossFunction);
RSquared.Add(metrics.RSquared);
}
static void Main(string[] args)
{
Operations K = new Operations();
//Load array to the tensor
NDArray x = new NDArray(3, 3);
x.Load(2, 4, 6, 1, 3, 5, 2, 3, 5);
x.Print("Load X Values");
NDArray y = new NDArray(3, 1);
y.Load(20, 15, 15);
y.Print("Load Y Values");
//Create two layers, one with 6 neurons and another with 1
FullyConnected fc1 = new FullyConnected(3, 6, "relu");
FullyConnected fc2 = new FullyConnected(6, 1, "relu");
//Connect input by passing data from one layer to another
fc1.Forward(x);
fc2.Forward(fc1.Output);
var preds = fc2.Output;
preds.Print("Predictions");
//Calculate the mean square error cost between the predicted and expected values
BaseCost cost = new MeanSquaredError();
var costValues = cost.Forward(preds, y);
costValues.Print("MSE Cost");
//Calculate the mean absolute metric value for the predicted vs expected values
BaseMetric metric = new MeanAbsoluteError();
var metricValues = metric.Calculate(preds, y);
metricValues.Print("MAE Metric");
Console.ReadLine();
}
internal static HandleRef getCPtr(MeanAbsoluteError obj)
{
return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr);
}
internal static HandleRef getCPtr(MeanAbsoluteError obj) {
return (obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr;
}
