private void OnClassifyImage(object sender, EventArgs e)
{
try
{
_loggingService.Log("Classify drawing has started");
var imagePreprocessor = new ImagePreprocessor();
double[] pixels = imagePreprocessor.Preprocess(_uploadImageView.Image);
IPredictionModel predictionModel = Global.PredictionModel;
double[] prediction = predictionModel.Predict(pixels);
_uploadImageView.ProcessPrediction(prediction);
_loggingService.Log("Classify drawing has completed");
}
catch (NullReferenceException exception)
{
_loggingService.Log(exception);
_messageService.ShowMessage("No image was uploaded. Please upload an image and try again.", "Upload error", icon: MessageBoxIcon.Information);
}
catch (Exception exception)
{
_loggingService.Log(exception);
_messageService.ShowMessage("An error ocurred while classyfing the drawing. Please try again.", "Classification error", icon: MessageBoxIcon.Information);
}
}
public double computeRMSE(Users validationUsers, Items validationItems, IPredictionModel model)
{
double sse = 0;
double actualRating;
double predictedRating;
int n = 0;
foreach (User user in validationUsers)
{
foreach (string itemId in user.GetRatedItems())
{
Item item = validationItems.GetItemById(itemId);
actualRating = user.GetRating(itemId);
predictedRating = model.Predict(user, item);
if (predictedRating == -1) //in case the user and item are not exist the prediction is the average rating
{
predictedRating = avgRating;
}
sse += Math.Pow(actualRating - predictedRating, 2);
n++;
}
}
return(Math.Sqrt(sse / n));
}
private void OnClassifyDrawing(object sender, EventArgs e)
{
try
{
_loggingService.Log("Classify drawing has started");
var imagePreprocessor = new ImagePreprocessor();
IPredictionModel predictionModel = Global.PredictionModel;
Image img = _slidingWindowView.Drawing;
foreach (Size windowSize in WindowSizes)
{
foreach (BoundingBox boundingBox in ImageUtilities.SlidingWindow(img, windowSize, 112))
{
try
{
double[] pixels = imagePreprocessor.Preprocess(boundingBox.Image);
double[] prediction = predictionModel.Predict(pixels);
// If classification is over 99% draw a bounding box at this location
int predicted = prediction.ArgMax();
double predictedAccuracy = prediction[prediction.ArgMax()];
if (predictedAccuracy >= 0.95)
{
_slidingWindowView.DrawBoundingBox(boundingBox, predicted, predictedAccuracy);
}
}
catch (Exception exception)
{
_loggingService.Log(exception);
}
}
}
_loggingService.Log("Classify drawing has completed");
}
catch (Exception exception)
{
_loggingService.Log(exception);
_messageService.ShowMessage("An error ocurred while classyfing the drawing. Please try again.", "Classification error", icon: MessageBoxIcon.Information);
}
}
private List <string> GetTopItems(IPredictionModel predictionModel, string sUserId, int cRecommendations)
{
var currentUser = testUsers.getUserById(sUserId);
var currentItems = trainUsers.getUserById(sUserId).GetRatedItems(); //in case the user is also in the training set we want to filter out those rated items from train set
var candidateItems = trainItems.GetAllItemsIds().Except(currentItems); //select items that current user is not yet rated
var candidateItemsDic = candidateItems.ToDictionary(item => item, item => predictionModel.Predict(currentUser, trainItems.GetItemById(item)));
var orderByPrediction = candidateItemsDic.OrderByDescending(item => item.Value);
return(orderByPrediction.Select(item => item.Key).Take(cRecommendations).ToList());
}
public Tuple <double, double> computeConfidence(Users validationUsers, Items validationItems, IPredictionModel modelA, IPredictionModel modelB)
{
double aCounter = 0;
double bCounter = 0;
double aPrediction;
double bPrediction;
double aError;
double bError;
double actualRating;
// calcualte number wins for each model
foreach (User user in validationUsers)
{
foreach (string itemId in user.GetRatedItems())
{
Item item = validationItems.GetItemById(itemId);
actualRating = user.GetRating(itemId);
aPrediction = modelA.Predict(user, item);
bPrediction = modelB.Predict(user, item);
aError = Math.Abs(actualRating - aPrediction);
bError = Math.Abs(actualRating - bPrediction);
if (aError < bError)
{
aCounter++;
}
else if (aError > bError)
{
bCounter++;
}
else
{
aCounter += 0.5;
bCounter += 0.5;
}
}
}
int n = (int)(aCounter + bCounter);
// calcualte pA
double sum = 0;
for (int i = (int)aCounter; i < n; i++)
{
sum += MathUtils.Factorial(n) / (MathUtils.Factorial(n - i) * MathUtils.Factorial(i));
}
double pA = (1 - Math.Pow(0.5, n) * sum);
// calculate pB
sum = 0;
for (int i = (int)bCounter; i < n; i++)
{
sum += MathUtils.Factorial(n) / (MathUtils.Factorial(n - i) * MathUtils.Factorial(i));
}
double pB = (1 - Math.Pow(0.5, n) * sum);
return(Tuple.Create(pA, pB));
}