public void AddDataIncreasesUpperBound()
{
var h = new Histogram(new[] { 1.0, 5.0, 10.0 }, 2);
h.AddData(20.0);
Assert.AreEqual(2, h[1].Count);
}
public void AddDataDecreasesLowerBound()
{
var h = new Histogram(new[] { 1.0, 5.0, 10.0 }, 2);
h.AddData(0.0);
Assert.AreEqual(3, h[0].Count);
}
public void CanAddDataSingle()
{
var h = new Histogram(new[] { 1.0, 5.0, 10.0 }, 2);
h.AddData(7.0);
Assert.AreEqual(2, h[1].Count);
}
public void CanAddDataList()
{
var h = new Histogram(new[] { 1.0, 5.0, 10.0 }, 2);
h.AddData(new[] { 7.0, 8.0 });
Assert.AreEqual(3, h[1].Count);
}
public void AddDataEqualToLowerBound()
{
var h = new Histogram(new[] { 1.0, 5.0, 10.0 }, 3, 0.0, 10.0);
Assert.DoesNotThrow(() => h.AddData(0.0));
Assert.AreEqual(2, h[0].Count);
}
public void buttonAddValue_Click(object sender, EventArgs e)
{
int dataV = 0;
string inValue;
inValue = textBoxDataV.Text;
dataV = int.Parse(inValue);
one.AddData(dataV); //using the object
}
private void MakeOneAnalysis([NotNull] List <double> areas, int bucketSize, [NotNull] string baseName,
[NotNull] string sectionDescription, [NotNull] ScenarioSliceParameters slice)
{
{
var maxArea = areas.Max();
var histogram = new Histogram();
for (var i = 0; i < bucketSize * 10; i += bucketSize)
{
histogram.AddBucket(new Bucket(i, i + bucketSize));
}
if (maxArea > bucketSize * 10)
{
histogram.AddBucket(new Bucket(bucketSize * 10, maxArea));
}
histogram.AddData(areas);
string histogramfilename = MakeAndRegisterFullFilename(baseName + "_Histogram.png", Name, sectionDescription, slice);
var bs = BarSeriesEntry.MakeBarSeriesEntry(histogram, out var colNames);
var bss = new List <BarSeriesEntry> {
bs
};
Services.PlotMaker.MakeBarChart(histogramfilename, "Anzahl von Haushalten mit Fläche in diesem Bereich", bss, colNames);
}
{
string dstFileName2 = MakeAndRegisterFullFilename(baseName + "_SortedAreas.png", Name, sectionDescription, slice);
var lse = new LineSeriesEntry("Sorted");
var sorted = areas.ToList();
sorted.Sort();
for (var i = 0; i < sorted.Count; i++)
{
lse.Values.Add(new Point(i, sorted[i]));
}
var lss = new List <LineSeriesEntry> {
lse
};
Services.PlotMaker.MakeLineChart(dstFileName2, "", lss, new List <PlotMaker.AnnotationEntry>());
}
{
string dstFileName2 = MakeAndRegisterFullFilename(baseName + "_Kumulativ.png", Name, sectionDescription, slice);
var lse = new LineSeriesEntry("Kumulativ");
var sorted = areas.ToList();
sorted.Sort();
double tempSum = 0;
for (var i = 0; i < sorted.Count; i++)
{
tempSum += sorted[i];
lse.Values.Add(new Point(i, tempSum));
}
var lss = new List <LineSeriesEntry> {
lse
};
Services.PlotMaker.MakeLineChart(dstFileName2, "", lss, new List <PlotMaker.AnnotationEntry>());
}
}
// NOTE: _numbers 를 사용하게 되면, Parallel이 불가능하기 때문에, numbers를 인자로 받고, Thread-Safe 하게 사용할 수 있도록 했다.
//
private static void DisplayDistribution(string name, Histogram histogram, double[] numbers)
{
log.Debug(@"-------------------------------------------");
histogram.ResetData();
histogram.AddData(numbers);
log.Debug(@"{0} Histogram:\r\n{1}", name, histogram.StemLeaf(50));
double avg, stdev;
numbers.AverageAndStDev(out avg, out stdev);
log.Debug(@"Avg = {0} ; StDev = {1}", avg, stdev);
log.Debug(@"-------------------------------------------");
log.Debug(string.Empty);
}
public void CanAddDataList()
{
var h = new Histogram(new[] {1.0, 5.0, 10.0}, 2);
h.AddData(new[] {7.0, 8.0});
Assert.AreEqual(3, h[1].Count);
}
public void AddDataIncreasesUpperBound()
{
var h = new Histogram(new[] {1.0, 5.0, 10.0}, 2);
h.AddData(20.0);
Assert.AreEqual(2, h[1].Count);
}
public void AddDataDecreasesLowerBound()
{
var h = new Histogram(new[] {1.0, 5.0, 10.0}, 2);
h.AddData(0.0);
Assert.AreEqual(3, h[0].Count);
}
public void CanAddDataSingle()
{
var h = new Histogram(new[] {1.0, 5.0, 10.0}, 2);
h.AddData(7.0);
Assert.AreEqual(2, h[1].Count);
}
public static void Run()
{
RILogManager.Default?.SendDebug("MNIST Data Loading...");
MnistData mnistData = new MnistData(28);
RILogManager.Default?.SendDebug("Training Start...");
int neuronCount = 28;
FunctionStack nn = new FunctionStack("Test19",
new Linear(true, neuronCount * neuronCount, N, name: "l1 Linear"), // L1
new BatchNormalization(true, N, name: "l1 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l1 LeakyReLU"),
new Linear(true, N, N, name: "l2 Linear"), // L2
new BatchNormalization(true, N, name: "l2 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l2 LeakyReLU"),
new Linear(true, N, N, name: "l3 Linear"), // L3
new BatchNormalization(true, N, name: "l3 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l3 LeakyReLU"),
new Linear(true, N, N, name: "l4 Linear"), // L4
new BatchNormalization(true, N, name: "l4 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l4 LeakyReLU"),
new Linear(true, N, N, name: "l5 Linear"), // L5
new BatchNormalization(true, N, name: "l5 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l5 LeakyReLU"),
new Linear(true, N, N, name: "l6 Linear"), // L6
new BatchNormalization(true, N, name: "l6 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l6 LeakyReLU"),
new Linear(true, N, N, name: "l7 Linear"), // L7
new BatchNormalization(true, N, name: "l7 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l7 ReLU"),
new Linear(true, N, N, name: "l8 Linear"), // L8
new BatchNormalization(true, N, name: "l8 BatchNorm"),
new LeakyReLU(slope: 0.000001, name: "l8 LeakyReLU"),
new Linear(true, N, N, name: "l9 Linear"), // L9
new BatchNormalization(true, N, name: "l9 BatchNorm"),
new PolynomialApproximantSteep(slope: 0.000001, name: "l9 PolynomialApproximantSteep"),
new Linear(true, N, N, name: "l10 Linear"), // L10
new BatchNormalization(true, N, name: "l10 BatchNorm"),
new PolynomialApproximantSteep(slope: 0.000001, name: "l10 PolynomialApproximantSteep"),
new Linear(true, N, N, name: "l11 Linear"), // L11
new BatchNormalization(true, N, name: "l11 BatchNorm"),
new PolynomialApproximantSteep(slope: 0.000001, name: "l11 PolynomialApproximantSteep"),
new Linear(true, N, N, name: "l12 Linear"), // L12
new BatchNormalization(true, N, name: "l12 BatchNorm"),
new PolynomialApproximantSteep(slope: 0.000001, name: "l12 PolynomialApproximantSteep"),
new Linear(true, N, N, name: "l13 Linear"), // L13
new BatchNormalization(true, N, name: "l13 BatchNorm"),
new PolynomialApproximantSteep(slope: 0.000001, name: "l13 PolynomialApproximantSteep"),
new Linear(true, N, N, name: "l14 Linear"), // L14
new BatchNormalization(true, N, name: "l14 BatchNorm"),
new PolynomialApproximantSteep(slope: 0.000001, name: "l14 PolynomialApproximantSteep"),
new Linear(true, N, 10, name: "l15 Linear") // L15
);
nn.SetOptimizer(new AdaGrad());
//nn.SetOptimizer(new Adam());
RunningStatistics stats = new RunningStatistics();
Histogram lossHistogram = new Histogram();
Histogram accuracyHistogram = new Histogram();
Real totalLoss = 0;
long totalLossCounter = 0;
Real highestAccuracy = 0;
Real bestLocalLoss = 0;
Real bestTotalLoss = 0;
// First skeleton save
ModelIO.Save(nn, nn.Name);
for (int epoch = 0; epoch < 1; epoch++)
{
RILogManager.Default?.SendDebug("epoch " + (epoch + 1));
RILogManager.Default?.ViewerSendWatch("epoch", (epoch + 1));
for (int i = 1; i < TRAIN_DATA_COUNT + 1; i++)
{
RILogManager.Default?.SendInformation("batch count " + i + "/" + TRAIN_DATA_COUNT);
TestDataSet datasetX = mnistData.GetRandomXSet(BATCH_DATA_COUNT, 28, 28);
Real sumLoss = Trainer.Train(nn, datasetX.Data, datasetX.Label, new SoftmaxCrossEntropy());
totalLoss += sumLoss;
totalLossCounter++;
stats.Push(sumLoss);
lossHistogram.AddBucket(new Bucket(-10, 10));
accuracyHistogram.AddBucket(new Bucket(-10.0, 10));
if (sumLoss < bestLocalLoss && sumLoss != Double.NaN)
{
bestLocalLoss = sumLoss;
}
if (stats.Mean < bestTotalLoss && sumLoss != Double.NaN)
{
bestTotalLoss = stats.Mean;
}
try
{
lossHistogram.AddData(sumLoss);
}
catch (Exception)
{
}
if (i % 20 == 0)
{
RILogManager.Default?.SendDebug("\nbatch count " + i + "/" + TRAIN_DATA_COUNT);
RILogManager.Default?.SendDebug("Total/Mean loss " + stats.Mean);
RILogManager.Default?.SendDebug("local loss " + sumLoss);
RILogManager.Default?.SendInformation("batch count " + i + "/" + TRAIN_DATA_COUNT);
RILogManager.Default?.ViewerSendWatch("batch count", i);
RILogManager.Default?.ViewerSendWatch("Total/Mean loss", stats.Mean);
RILogManager.Default?.ViewerSendWatch("local loss", sumLoss);
RILogManager.Default?.SendDebug("");
RILogManager.Default?.SendDebug("Testing...");
TestDataSet datasetY = mnistData.GetRandomYSet(TEST_DATA_COUNT, 28);
Real accuracy = Trainer.Accuracy(nn, datasetY.Data, datasetY.Label);
if (accuracy > highestAccuracy)
{
highestAccuracy = accuracy;
}
RILogManager.Default?.SendDebug("Accuracy: " + accuracy);
RILogManager.Default?.ViewerSendWatch("Accuracy", accuracy);
try
{
accuracyHistogram.AddData(accuracy);
}
catch (Exception)
{
}
}
}
}
RILogManager.Default?.SendDebug("Best Accuracy: " + highestAccuracy);
RILogManager.Default?.SendDebug("Best Total Loss " + bestTotalLoss);
RILogManager.Default?.SendDebug("Best Local Loss " + bestLocalLoss);
RILogManager.Default?.ViewerSendWatch("Best Accuracy:", highestAccuracy);
RILogManager.Default?.ViewerSendWatch("Best Total Loss", bestTotalLoss);
RILogManager.Default?.ViewerSendWatch("Best Local Loss", bestLocalLoss);
// Save all with training data
ModelIO.Save(nn, nn.Name);
}
protected override void MakeVisualization([NotNull] ScenarioSliceParameters slice,
bool isPresent)
{
var dbHouse = Services.SqlConnectionPreparer.GetDatabaseConnection(Stage.Houses, slice);
var houses = dbHouse.Fetch <House>();
var households = dbHouse.Fetch <Household>();
if (households.Count == 0)
{
throw new FlaException("Not a single household");
}
MakeAgeHistogram();
MakePeopleCountMap2();
MakeFamilySizeSankey();
MakePeopleCountMap();
MakeAgeMap();
void MakeAgeHistogram()
{
List <Occupant> occupants = households.SelectMany(x => x.Occupants).ToList();
double maxAge = occupants.Max(x => x.Age);
var histogram = new Histogram();
const int bucketSize = 5;
for (var i = 0; i < maxAge; i += bucketSize)
{
histogram.AddBucket(new Bucket(i, i + bucketSize));
}
var ages = occupants.Select(x => (double)x.Age).ToList();
histogram.AddData(ages);
var filename = MakeAndRegisterFullFilename("Altersverteilung.png", slice);
var bs = BarSeriesEntry.MakeBarSeriesEntry(histogram, out var colNames);
var bss = new List <BarSeriesEntry> {
bs
};
Services.PlotMaker.MakeBarChart(filename, "Anzahl von Personen in diesem Altersbereich", bss, colNames);
}
void MakeFamilySizeSankey()
{
var ssa = new SingleSankeyArrow("Households", 1000, MyStage, SequenceNumber,
Name, slice, Services);
ssa.AddEntry(new SankeyEntry("Households", houses.Count, 5000, Orientation.Straight));
var counts = households.Select(x => x.Occupants.Count).ToList();
var maxSize = counts.Max();
var filename = MakeAndRegisterFullFilename("HouseholdSizeHistogram.png", slice);
var names = new List <string>();
var barSeries = new List <BarSeriesEntry>();
for (var i = 0; i < maxSize + 1; i++)
{
names.Add(i.ToString());
var j = i; // because of closure
var count = counts.Count(x => x == j);
barSeries.Add(BarSeriesEntry.MakeBarSeriesEntry(i + "Personen", count, i));
}
Services.PlotMaker.MakeBarChart(filename, "HouseholdSizeHistogram", barSeries, names);
}
void MakePeopleCountMap()
{
RGB GetColor(House h)
{
var hhs = households.Where(x => x.HouseGuid == h.Guid).ToList();
var peopleCount = hhs.Select(x => x.Occupants.Count).Sum();
if (peopleCount == 0)
{
return(new RGB(128, 128, 128));
}
if (peopleCount == 1)
{
return(Constants.Green);
}
if (peopleCount == 2)
{
return(Constants.Blue);
}
return(Constants.Red);
}
var mapPoints = houses.Select(x => x.GetMapPoint(GetColor)).ToList();
var filename = MakeAndRegisterFullFilename("AnzahlPersonenProHaushalt.svg", slice);
var legendEntries = new List <MapLegendEntry> {
new MapLegendEntry("Keine Einwohner", new RGB(128, 128, 128)),
new MapLegendEntry("Ein Einwohner", Constants.Green),
new MapLegendEntry("Zwei Einwohner", Constants.Blue),
new MapLegendEntry("Drei+ Einwohner", Constants.Red)
};
Services.PlotMaker.MakeMapDrawer(filename, Name, mapPoints, legendEntries);
}
void MakePeopleCountMap2()
{
var maxCount = 0;
foreach (var house in houses)
{
var hhs = households.Where(x => x.HouseGuid == house.Guid).ToList();
var sumCount = hhs.Sum(x => x.Occupants.Count);
if (maxCount < sumCount)
{
maxCount = sumCount;
}
}
var colorStep = 250.0 / maxCount;
double color = 0;
var colorDict = new Dictionary <int, int>();
for (var i = 1; i <= maxCount; i++)
{
colorDict.Add(i, (int)color);
color += colorStep;
}
RGBWithSize GetColor(House h)
{
var hhs = households.Where(x => x.HouseGuid == h.Guid).ToList();
var peopleCount = hhs.Select(x => x.Occupants.Count).Sum();
if (peopleCount == 0)
{
return(new RGBWithSize(128, 128, 128, 10));
}
var red = colorDict[peopleCount];
var size = 10;
if (peopleCount > size)
{
size = peopleCount;
}
return(new RGBWithSize(red, 0, 0, size));
}
var mapPoints = houses.Select(x => x.GetMapPointWithSize(GetColor)).ToList();
var filename = MakeAndRegisterFullFilename("AnzahlPersonenProHaushaltRelative.svg", slice);
var legendEntries = new List <MapLegendEntry> {
new MapLegendEntry("Keine Einwohner", new RGB(128, 128, 128))
};
Services.PlotMaker.MakeMapDrawer(filename, Name, mapPoints, legendEntries);
}
void MakeAgeMap()
{
RGB GetColor(House h)
{
var hhs = households.Where(x => x.HouseGuid == h.Guid).ToList();
var houseoccupants = hhs.SelectMany(x => x.Occupants).ToList();
double averageAge = 0;
if (houseoccupants.Count > 0)
{
averageAge = houseoccupants.Average(x => x.Age);
}
if (houseoccupants.Count == 0)
{
return(new RGB(128, 128, 128));
}
var agefactor = averageAge / 100;
if (agefactor > 1)
{
agefactor = 1;
}
return(new RGB((int)(255.0 * agefactor), 0, 0));
}
var mapPoints = houses.Select(x => x.GetMapPoint(GetColor)).ToList();
var filename = MakeAndRegisterFullFilename("Durchschnittsalter.svg", slice);
var legendEntries = new List <MapLegendEntry> {
new MapLegendEntry("Keine Einwohner", new RGB(128, 128, 128)),
new MapLegendEntry("0 Jahre Durchschnittsalter", new RGB(0, 0, 0)),
new MapLegendEntry("100 Jahre Durchschnittsalter", new RGB(255, 0, 0))
};
Services.PlotMaker.MakeMapDrawer(filename, Name, mapPoints, legendEntries);
}
}
public void AddDataEqualToLowerBound()
{
var h = new Histogram(new[] { 1.0, 5.0, 10.0 }, 3, 0.0, 10.0);
Assert.DoesNotThrow(() => h.AddData(0.0));
Assert.AreEqual(2, h[0].Count);
}
public static void Run()
{
int neuronCount = 28;
RILogManager.Default?.SendDebug("MNIST Data Loading...");
MnistData mnistData = new MnistData(neuronCount);
RILogManager.Default.SendInformation("Training Start, creating function stack.");
SortedFunctionStack nn = new SortedFunctionStack();
SortedList <Function> functions = new SortedList <Function>();
ParallelOptions po = new ParallelOptions();
po.MaxDegreeOfParallelism = 4;
for (int x = 0; x < numLayers; x++)
{
Application.DoEvents();
functions.Add(new Linear(true, neuronCount * neuronCount, N, name: $"l{x} Linear"));
functions.Add(new BatchNormalization(true, N, name: $"l{x} BatchNorm"));
functions.Add(new ReLU(name: $"l{x} ReLU"));
RILogManager.Default.ViewerSendWatch("Total Layers", (x + 1));
}
;
RILogManager.Default.SendInformation("Adding Output Layer");
Application.DoEvents();
nn.Add(new Linear(true, N, 10, noBias: false, name: $"l{numLayers + 1} Linear"));
RILogManager.Default.ViewerSendWatch("Total Layers", numLayers);
RILogManager.Default.SendInformation("Setting Optimizer to AdaGrad");
nn.SetOptimizer(new AdaGrad());
Application.DoEvents();
RunningStatistics stats = new RunningStatistics();
Histogram lossHistogram = new Histogram();
Histogram accuracyHistogram = new Histogram();
Real totalLoss = 0;
long totalLossCounter = 0;
Real highestAccuracy = 0;
Real bestLocalLoss = 0;
Real bestTotalLoss = 0;
for (int epoch = 0; epoch < 3; epoch++)
{
RILogManager.Default?.SendDebug("epoch " + (epoch + 1));
RILogManager.Default.SendInformation("epoch " + (epoch + 1));
RILogManager.Default.ViewerSendWatch("epoch", (epoch + 1));
Application.DoEvents();
for (int i = 1; i < TRAIN_DATA_COUNT + 1; i++)
{
Application.DoEvents();
TestDataSet datasetX = mnistData.GetRandomXSet(BATCH_DATA_COUNT, neuronCount, neuronCount);
Real sumLoss = Trainer.Train(nn, datasetX.Data, datasetX.Label, new SoftmaxCrossEntropy());
totalLoss += sumLoss;
totalLossCounter++;
stats.Push(sumLoss);
lossHistogram.AddBucket(new Bucket(-10, 10));
accuracyHistogram.AddBucket(new Bucket(-10.0, 10));
if (sumLoss < bestLocalLoss && !double.IsNaN(sumLoss))
{
bestLocalLoss = sumLoss;
}
if (stats.Mean < bestTotalLoss && !double.IsNaN(sumLoss))
{
bestTotalLoss = stats.Mean;
}
try
{
lossHistogram.AddData(sumLoss);
}
catch (Exception)
{
}
if (i % 20 == 0)
{
RILogManager.Default.ViewerSendWatch("Batch Count ", i);
RILogManager.Default.ViewerSendWatch("Total/Mean loss", stats.Mean);
RILogManager.Default.ViewerSendWatch("Local loss", sumLoss);
RILogManager.Default.SendInformation("Batch Count " + i + "/" + TRAIN_DATA_COUNT + ", epoch " + epoch + 1);
RILogManager.Default.SendInformation("Total/Mean loss " + stats.Mean);
RILogManager.Default.SendInformation("Local loss " + sumLoss);
Application.DoEvents();
RILogManager.Default?.SendDebug("Testing...");
TestDataSet datasetY = mnistData.GetRandomYSet(TEST_DATA_COUNT, 28);
Real accuracy = Trainer.Accuracy(nn, datasetY?.Data, datasetY.Label);
if (accuracy > highestAccuracy)
{
highestAccuracy = accuracy;
}
RILogManager.Default?.SendDebug("Accuracy: " + accuracy);
RILogManager.Default.ViewerSendWatch("Best Accuracy: ", highestAccuracy);
RILogManager.Default.ViewerSendWatch("Best Total Loss ", bestTotalLoss);
RILogManager.Default.ViewerSendWatch("Best Local Loss ", bestLocalLoss);
Application.DoEvents();
try
{
accuracyHistogram.AddData(accuracy);
}
catch (Exception)
{
}
}
}
}
ModelIO.Save(nn, Application.StartupPath + "\\test20.nn");
RILogManager.Default?.SendDebug("Best Accuracy: " + highestAccuracy);
RILogManager.Default?.SendDebug("Best Total Loss " + bestTotalLoss);
RILogManager.Default?.SendDebug("Best Local Loss " + bestLocalLoss);
RILogManager.Default.ViewerSendWatch("Best Accuracy: ", highestAccuracy);
RILogManager.Default.ViewerSendWatch("Best Total Loss ", bestTotalLoss);
RILogManager.Default.ViewerSendWatch("Best Local Loss ", bestLocalLoss);
}
