/// <summary>
/// Set Data To GraphInfo with out redraw grid
/// </summary>
/// <param name="graphInfo"></param>
public void SetDataToGraphInfo(GraphLib.GraphInfo graphInfo)
{
this.graphViewer.UpdateGraphInfo(graphInfo, false, false);
}
/// <summary>
/// OnGridCreated
/// </summary>
/// <param name="graphGridLine"></param>
private void OnGridCreated(GraphLib.Model.GraphGridLine graphGridLine)
{
try
{
//add gridmodel to canvas
if (this.gridAxis.Children != null)
{
Canvas gridcanvas = null;
Canvas labelxcanvas = null;
Canvas labelycanvas = null;
if (this.gridAxis.Children.Count == 3)
{
gridcanvas = this.gridAxis.Children[0] as Canvas;
labelxcanvas = this.gridAxis.Children[1] as Canvas;
labelycanvas = this.gridAxis.Children[2] as Canvas;
}
else
{
this.gridAxis.Children.Clear();
gridcanvas = new Canvas();
labelxcanvas = new Canvas();
labelycanvas = new Canvas();
this.gridAxis.Children.Add(gridcanvas);
this.gridAxis.Children.Add(labelxcanvas);
this.gridAxis.Children.Add(labelycanvas);
}
//Grid items
if (gridcanvas != null && labelxcanvas != null && labelycanvas != null)
{
if (gridcanvas.Children.Count >= 11)
{
((Button)gridcanvas.Children[4]).Click -= new RoutedEventHandler(this.BtnZoomInX_Click);
((Button)gridcanvas.Children[5]).Click -= new RoutedEventHandler(this.BtnZoomOutX_Click);
((Button)gridcanvas.Children[6]).Click -= new RoutedEventHandler(this.BtnZoomInY_Click);
((Button)gridcanvas.Children[6]).PreviewMouseLeftButtonUp -= new MouseButtonEventHandler(this.BtnZoomInY_MouseUp);
((Button)gridcanvas.Children[6]).PreviewMouseLeftButtonDown -= new MouseButtonEventHandler(this.BtnZoomInY_MouseDown);
((Button)gridcanvas.Children[7]).Click -= new RoutedEventHandler(this.BtnZoomOutY_Click);
((Button)gridcanvas.Children[7]).PreviewMouseLeftButtonUp -= new MouseButtonEventHandler(this.BtnZoomOutY_MouseUp);
((Button)gridcanvas.Children[7]).PreviewMouseLeftButtonDown -= new MouseButtonEventHandler(this.BtnZoomOutY_MouseDown);
((Button)gridcanvas.Children[8]).Click -= new RoutedEventHandler(this.BtnMeasureX_Click);
((Button)gridcanvas.Children[9]).Click -= new RoutedEventHandler(this.BtnMeasureY_Click);
((Button)gridcanvas.Children[10]).Click -= new RoutedEventHandler(this.BtnMeasureY2_Click);
}
gridcanvas.Children.Clear();
gridcanvas.Children.Add(graphGridLine.AxisShapeData);
gridcanvas.Children.Add(graphGridLine.GridShapeData);
gridcanvas.Children.Add(graphGridLine.AxisLabelX);
gridcanvas.Children.Add(graphGridLine.AxisLabelY);
gridcanvas.Children.Add(graphGridLine.ButtonZoomInX);
gridcanvas.Children.Add(graphGridLine.ButtonZoomOutX);
gridcanvas.Children.Add(graphGridLine.ButtonZoomInY);
gridcanvas.Children.Add(graphGridLine.ButtonZoomOutY);
gridcanvas.Children.Add(graphGridLine.ButtonMeasureX);
gridcanvas.Children.Add(graphGridLine.ButtonMeasureY);
gridcanvas.Children.Add(graphGridLine.ButtonMeasureY2);
//For Test Speed
//gridcanvas.Children.Add(graphGridLine.CreateSpeedCheckLabel());
//For Test Speed
//button click event
graphGridLine.ButtonMeasureX.Click += new RoutedEventHandler(this.BtnMeasureX_Click);
graphGridLine.ButtonMeasureY.Click += new RoutedEventHandler(this.BtnMeasureY_Click);
graphGridLine.ButtonMeasureY2.Click += new RoutedEventHandler(this.BtnMeasureY2_Click);
graphGridLine.ButtonZoomInX.Click += new RoutedEventHandler(this.BtnZoomInX_Click);
graphGridLine.ButtonZoomOutX.Click += new RoutedEventHandler(this.BtnZoomOutX_Click);
graphGridLine.ButtonZoomInY.Click += new RoutedEventHandler(this.BtnZoomInY_Click);
graphGridLine.ButtonZoomInY.PreviewMouseLeftButtonDown += new MouseButtonEventHandler(this.BtnZoomInY_MouseDown);
graphGridLine.ButtonZoomInY.PreviewMouseLeftButtonUp += new MouseButtonEventHandler(this.BtnZoomInY_MouseUp);
graphGridLine.ButtonZoomOutY.Click += new RoutedEventHandler(this.BtnZoomOutY_Click);
graphGridLine.ButtonZoomOutY.PreviewMouseLeftButtonDown += new MouseButtonEventHandler(this.BtnZoomOutY_MouseDown);
graphGridLine.ButtonZoomOutY.PreviewMouseLeftButtonUp += new MouseButtonEventHandler(this.BtnZoomOutY_MouseUp);
if (_IsAxisXZoomEnable)
{
graphGridLine.ButtonZoomInX.Visibility = System.Windows.Visibility.Visible;
graphGridLine.ButtonZoomOutX.Visibility = System.Windows.Visibility.Visible;
}
else
{
graphGridLine.ButtonZoomInX.Visibility = System.Windows.Visibility.Hidden;
graphGridLine.ButtonZoomOutX.Visibility = System.Windows.Visibility.Hidden;
}
if (_IsMeasureButtonShow)
{
graphGridLine.ButtonMeasureX.Visibility = System.Windows.Visibility.Visible;
graphGridLine.ButtonMeasureY.Visibility = System.Windows.Visibility.Visible;
graphGridLine.ButtonMeasureY2.Visibility = System.Windows.Visibility.Visible;
}
else
{
graphGridLine.ButtonMeasureX.Visibility = System.Windows.Visibility.Hidden;
graphGridLine.ButtonMeasureY.Visibility = System.Windows.Visibility.Hidden;
graphGridLine.ButtonMeasureY2.Visibility = System.Windows.Visibility.Hidden;
}
//label x
labelxcanvas.Children.Clear();
for (int i = 0; i < graphGridLine.LabelValueX.Length; i++)
{
if (_IsShowValueLabelX)
graphGridLine.LabelValueX[i].Visibility = System.Windows.Visibility.Visible;
else
graphGridLine.LabelValueX[i].Visibility = System.Windows.Visibility.Hidden;
labelxcanvas.Children.Add(graphGridLine.LabelValueX[i]);
}
if (_GraphInfo.ShowDateTimeAxisX)
{
UpdateLabelValueX();
}
//label y
labelycanvas.Children.Clear();
for (int i = 0; i < graphGridLine.LabelValueY.Length; i++)
{
if (_IsShowValueLabelY)
graphGridLine.LabelValueY[i].Visibility = System.Windows.Visibility.Visible;
else
graphGridLine.LabelValueY[i].Visibility = System.Windows.Visibility.Hidden;
labelycanvas.Children.Add(graphGridLine.LabelValueY[i]);
}
}
}
//add measuremodel to canvas
if (this.gridMain.Children != null)
{
Canvas measurecontainer = null;
Canvas currentlinecanvas = null;
if (this.gridMain.Children.Count == 3)
{
measurecontainer = new Canvas();
currentlinecanvas = new Canvas();
// index 2 is legend extender
this.gridMain.Children.Insert(2, measurecontainer);
this.gridMain.Children.Insert(3, currentlinecanvas);
}
else if (this.gridMain.Children.Count == 5)
{
measurecontainer = this.gridMain.Children[2] as Canvas;
currentlinecanvas = this.gridMain.Children[3] as Canvas;
}
//Measure Model
if (measurecontainer != null)
{
if (measurecontainer.Children.Count >= 9)
{
((Canvas)measurecontainer.Children[0]).MouseDown -= new MouseButtonEventHandler(this.UpperMeasureModelY_MouseDown);
((Canvas)measurecontainer.Children[1]).MouseDown -= new MouseButtonEventHandler(this.UpperMeasureModelY_MouseDown);
((Canvas)measurecontainer.Children[3]).MouseDown -= new MouseButtonEventHandler(this.UpperMeasureModelX_MouseDown);
((Canvas)measurecontainer.Children[4]).MouseDown -= new MouseButtonEventHandler(this.UpperMeasureModelX_MouseDown);
((Canvas)measurecontainer.Children[6]).MouseDown -= new MouseButtonEventHandler(this.UpperMeasureModelY2_MouseDown);
((Canvas)measurecontainer.Children[7]).MouseDown -= new MouseButtonEventHandler(this.UpperMeasureModelY2_MouseDown);
}
measurecontainer.Children.Clear();
double leftpos = (grid1.Width - _GraphModel.MeasureLabelY.Model.Width) / 2 + _GraphModel.GridLineData.Margin.Left;
_GraphModel.MeasureLabelY.Model.Margin = new Thickness(leftpos, 0, 0, 0);
AddMeasureModule(_IsMeasureYShown, "Y", measurecontainer);
double toppos = (grid1.Height - _GraphModel.MeasureLabelX.Model.Height) / 2 + _GraphModel.GridLineData.Margin.Top;
_GraphModel.MeasureLabelX.Model.Margin = new Thickness(0, toppos, 0, 0);
AddMeasureModule(_IsMeasureXShown, "X", measurecontainer);
double leftpos2 = (grid1.Width - _GraphModel.MeasureLabelY2.Model.Width) / 2 + _GraphModel.GridLineData.Margin.Left;
_GraphModel.MeasureLabelY2.Model.Margin = new Thickness(leftpos2, 0, 0, 0);
AddMeasureModule(_IsMeasureY2Shown, "Y2", measurecontainer);
}
//currentline model
if (currentlinecanvas != null)
{
currentlinecanvas.Children.Clear();
if (!_IsCurrentLine)
{
_GraphModel.CurrentLineModel.Visibility = System.Windows.Visibility.Collapsed;
}
else
{
_GraphModel.CurrentLineModel.Visibility = System.Windows.Visibility.Visible;
}
currentlinecanvas.Children.Add(_GraphModel.CurrentLineModel);
}
}
this.Background = new SolidColorBrush(_GraphModel.GraphBackgroundColor);
this.Background.Freeze();
_IsCreateGrid = true;
}
catch (Exception ex)
{
_Log4NetClass.ShowError(ex.ToString(), "OnGridCreated");
}
}
static void Main(string[] args)
{
WriteLine("Execution begins");
var fn = @"c:\DEMO\Data\train.csv";
var f = File.ReadLines(fn);
var data = from t in f.Skip(1)
let zz = t.Split(',').Select(float.Parse)
select new Digit
{
Label = (int)zz.First(),
Image = zz.Skip(1).Select(x => x / 256f).ToArray()
};
var train = data.Take(40000).ToArray();
var test = data.Skip(40000).Take(1000).ToArray();
WriteLine("Creating network");
DeviceDescriptor device = DeviceDescriptor.CPUDevice;
int inputDim = 784;
int outputDim = 10;
var inputShape = new NDShape(1, inputDim);
var outputShape = new NDShape(1, outputDim);
Variable features = Variable.InputVariable(inputShape, DataType.Float);
Variable label = Variable.InputVariable(outputShape, DataType.Float);
var W = new Parameter(new [] { outputDim, inputDim }, DataType.Float, CNTKLib.GlorotUniformInitializer(), device, "w");
var b = new Parameter(new [] { outputDim }, DataType.Float, 0, device, "b");
var z = CNTKLib.Times(W, features) + b;
var loss = CNTKLib.CrossEntropyWithSoftmax(z, label);
var evalError = CNTKLib.ClassificationError(z, label);
CNTK.TrainingParameterScheduleDouble learningRatePerSample = new CNTK.TrainingParameterScheduleDouble(0.02, 1);
IList <Learner> parameterLearners =
new List <Learner>()
{
Learner.SGDLearner(z.Parameters(), learningRatePerSample)
};
var trainer = Trainer.CreateTrainer(z, loss, evalError, parameterLearners);
int minibatchSize = 64;
int numMinibatchesToTrain = 500;
var feat = new BatchSource <float[]>((from x in train select x.Image).ToArray(), minibatchSize);
var labl = new BatchSource <float[]>((from x in train select x.Label.ToOneHot10(10).ToFloatArray()).ToArray(), minibatchSize);
var gr = new List <float>();
// train the model
for (int ep = 0; ep < numMinibatchesToTrain; ep++)
{
Value ft, lb;
feat.MoveNext(); labl.MoveNext();
ft = Value.CreateBatchOfSequences <float>(inputShape, feat.Current, device);
lb = Value.CreateBatchOfSequences <float>(outputShape, labl.Current, device);
trainer.TrainMinibatch(
new Dictionary <Variable, Value>()
{
{ features, ft }, { label, lb }
}, device);
if (ep % 50 == 0)
{
var _loss = trainer.PreviousMinibatchLossAverage();
var _eval = trainer.PreviousMinibatchEvaluationAverage();
WriteLine($"Epoch={ep}, loss={_loss}, eval={_eval}");
gr.Add((float)_loss);
}
}
var G = new GraphLib();
G.Plot(gr, System.Windows.Forms.DataVisualization.Charting.SeriesChartType.Line);
int count = 0, correct = 0;
// Test the model
foreach (var x in test)
{
var imap = new Dictionary <Variable, Value> {
{ features, Value.CreateBatch(inputShape, x.Image, device) }
};
var omap = new Dictionary <Variable, Value> {
{ z, null }
};
z.Evaluate(imap, omap, device);
var o = omap[z].GetDenseData <float>(z).First();
var res = o.MaxIndex();
WriteLine("{0} => {1}", x.Label, res);
if (x.Label == res)
{
correct++;
}
count++;
}
WriteLine("Done, {0} of {1} correct ({2}%)", correct, count, (double)correct / (double)count * 100);
Console.ReadKey();
}
public void Main()
{
WriteLine("Starting");
var G = new GraphLib();
WriteLine("Generating sample data");
// Создаём случайные точки двух типов
float cx1 = -1.0f, cy1 = -1.0f;
float cx2 = 1.0f, cy2 = 1.0f;
var Rnd = new Random();
var x = new List <float>();
var y = new List <float>();
var lab = new List <float>();
var lab_oh = new List <float[]>();
for (int i = 0; i < 200; i++)
{
x.Add(cx1 + Rnd.Next(-2.0, 2.0)); y.Add(cy1 + Rnd.Next(-2.0, 2.0));
lab.Add(-1.0f); lab_oh.Add(new float[] { 1.0f, 0.0f });
x.Add(cx2 + Rnd.Next(-2.0, 2.0)); y.Add(cy2 + Rnd.Next(-2.0, 2.0));
lab.Add(1.0f); lab_oh.Add(new float[] { 0.0f, 1.0f });
}
G.Plot(x, y);
WriteLine("Doing data split");
var x_train = x.Take(150).ToArray();
var y_train = y.Take(150).ToArray();
var l_train = lab.Take(150).ToArray();
var l_oh_train = lab_oh.Take(150).ToArray();
var x_test = x.Skip(150).ToArray();
var y_test = y.Skip(150).ToArray();
var l_test = lab.Skip(150).ToArray();
var l_oh_test = lab_oh.Skip(150).ToArray();
WriteLine("Creating network");
DeviceDescriptor device = DeviceDescriptor.CPUDevice;
// Create network model
int inputDim = 2;
int outputDim = 2;
Variable features = Variable.InputVariable(inputDim.AsArray(), DataType.Float);
Variable label = Variable.InputVariable(outputDim.AsArray(), DataType.Float);
var W = new Parameter(new int[] { outputDim, inputDim }, DataType.Float, 1, device, "w");
var b = new Parameter(new int[] { outputDim }, DataType.Float, 0, device, "b");
var z = CNTKLib.Times(W, features) + b;
var loss = CNTKLib.CrossEntropyWithSoftmax(z, label);
var evalError = CNTKLib.ClassificationError(z, label);
// prepare for training
CNTK.TrainingParameterScheduleDouble learningRatePerSample = new CNTK.TrainingParameterScheduleDouble(0.02, 1);
IList <Learner> parameterLearners =
new List <Learner>()
{
Learner.SGDLearner(z.Parameters(), learningRatePerSample)
};
var trainer = Trainer.CreateTrainer(z, loss, evalError, parameterLearners);
int minibatchSize = 64;
int numMinibatchesToTrain = 1000;
int k = 0; // current position in dataset
// train the model
for (int ep = 0; ep < numMinibatchesToTrain; ep++)
{
Value f, l;
var fa = new float[minibatchSize * inputDim];
var la = new float[minibatchSize * outputDim];
for (int j = 0; j < minibatchSize; j++)
{
fa[j * inputDim] = x_train[k];
fa[j * inputDim + 1] = y_train[k];
la[j * outputDim] = l_oh_train[k][0];
la[j * outputDim + 1] = l_oh_train[k][1];
k++;
if (k == x_train.Length)
{
k = 0;
}
}
f = Value.CreateBatch <float>(inputDim.AsArray(), fa, device);
l = Value.CreateBatch <float>(outputDim.AsArray(), la, device);
trainer.TrainMinibatch(
new Dictionary <Variable, Value>()
{
{ features, f }, { label, l }
}, device);
if (ep % 50 == 0)
{
var _loss = trainer.PreviousMinibatchLossAverage();
var _eval = trainer.PreviousMinibatchEvaluationAverage();
WriteLine($"Epoch={ep}, loss={_loss}, eval={_eval}");
}
}
WriteLine("Press any key to exit");
Console.ReadKey();
}
