/*
*
* -----> BUTTONS CLICK <-----
*
*/
private void modifyRssSourceButton_Click(object sender, EventArgs e)
{
if (rssSourcesGridView.SelectedRows.Count == 0)
{
MetroMessageBox.Show(this, "Para eliminar primero debe seleccionar una fuente RSS de la lista", "No hay ninguna fuente RSS seleccionada", MessageBoxButtons.OK, MessageBoxIcon.Information);
return;
}
//se hace depp copy para evitar que el formulario de fuente RSS modifique el objeto antes de guardarlo
var rssSourceForm = new RssSourceForm(
DeepCopyHelper.DeepCopy <RssSourceDTO>((RssSourceDTO)rssSourcesGridView.SelectedRows[0].DataBoundItem)
);
StyleManager.Clone(rssSourceForm);
if (rssSourceForm.ShowDialog(this) == DialogResult.OK)
{
//modificar la fuente RSS
try
{
iRssSourceService.Update(rssSourceForm.iRssSourceModel);
MetroMessageBox.Show(this, "se han modificado todos los datos", "Exito al modificar la fuente RSS", MessageBoxButtons.OK, MessageBoxIcon.Question);
reloadSearch();
}
catch (Exception ex)
{
MetroMessageBox.Show(this, "detalles del error: " + ex.Message, "Error al modificar la fuente RSS", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
}
}
static double Evaluate(FunctionStack model, int[] dataset)
{
FunctionStack predictModel = DeepCopyHelper.DeepCopy(model);
predictModel.ResetState();
Real totalLoss = 0;
long totalLossCount = 0;
for (int i = 0; i < dataset.Length - 1; i++)
{
NdArray x = new NdArray(new[] { 1 }, BATCH_SIZE);
NdArray t = new NdArray(new[] { 1 }, BATCH_SIZE);
for (int j = 0; j < BATCH_SIZE; j++)
{
x.Data[j] = dataset[j + i];
t.Data[j] = dataset[j + i + 1];
}
Real sumLoss = new SoftmaxCrossEntropy().Evaluate(predictModel.Forward(x), t);
totalLoss += sumLoss;
totalLossCount++;
}
//calc perplexity
return(Math.Exp(totalLoss / (totalLossCount - 1)));
}
private void modifyImageButton_Click(object sender, EventArgs e)
{
if (imagesGridView.SelectedRows.Count == 0)
{
MetroMessageBox.Show(this, "Para modificar primero debe seleccionar una imagen de la lista", "No hay ninguna imagen seleccionada", MessageBoxButtons.OK, MessageBoxIcon.Information);
return;
}
var imageForm = new ImageForm(
DeepCopyHelper.DeepCopy <ImageDTO>((ImageDTO)imagesGridView.SelectedRows[0].DataBoundItem),
getImageListLength()
);
StyleManager.Clone(imageForm);
if (imageForm.ShowDialog(this) == DialogResult.OK)
{
var updatedImage = imageForm.iImageModel;
var oldImage = iCampaignModel.Images.Where(i => i.Id == updatedImage.Id).First();
//checkear que no ocupe el orden que ya tenia otra imagen
if (updatedImage.Order != oldImage.Order)
{
var solapedImage = iCampaignModel.Images.Where(image => image.Order == updatedImage.Order).First();
solapedImage.Order = oldImage.Order;
}
iCampaignModel.Images[iCampaignModel.Images.IndexOf(oldImage)] = updatedImage;
refreshImagesGridView();
}
}
static Real Evaluate(FunctionStack <Real> model, int[] dataset)
{
FunctionStack <Real> predictModel = DeepCopyHelper <Real> .DeepCopy(model);
predictModel.ResetState();
Real totalLoss = 0;
long totalLossCount = 0;
for (int i = 0; i < dataset.Length - 1; i++)
{
NdArray <Real> x = new NdArray <Real>(new[] { 1 }, BATCH_SIZE);
NdArray <int> t = new NdArray <int>(new[] { 1 }, BATCH_SIZE);
for (int j = 0; j < BATCH_SIZE; j++)
{
x.Data[j] = dataset[j + i];
t.Data[j] = dataset[j + i + 1];
}
NdArray <Real> result = predictModel.Forward(x)[0];
Real sumLoss = new SoftmaxCrossEntropy <Real>().Evaluate(result, t);
totalLoss += sumLoss;
totalLossCount++;
}
//calc perplexity
return(Math.Exp(totalLoss / (totalLossCount - 1)));
}
//コピーを作成するメソッド
public Function Clone()
{
return(DeepCopyHelper.DeepCopy(this));
}
public static object DeepCopy(this object originalObject)
{
return(DeepCopyHelper.DeepCopy(originalObject, new Dictionary <object, object>(new DeepCopyReferenceEqualityComparer())));
}
public NetworkLayer Clone()
{
return(DeepCopyHelper.DeepCopy(this));
}
