/// <summary>
/// LoadContent will be called once per game and is the place to load
/// all of your content.
/// </summary>
protected override void LoadContent()
{
// Create a new SpriteBatch, which can be used to draw textures.
spriteBatch = new SpriteBatch(GraphicsDevice);
//basicEffect = Content.Load<Effect>("specular");
basicEffect = Content.Load <Effect>("diffuse");
var graph = SampleGenerator.CreateVoronoiGraph(1000, 30000, 2, 23);
var vertices = graph.Paint3D(new VertexPositionColorNormalFactory());
var normal = new Vector3(0, -1, 0);
meshTriangles = vertices.Count / 3;
vertices.Add(new VertexPositionColorNormal(Vector3.Zero, Color.Red, normal));
vertices.Add(new VertexPositionColorNormal(new Vector3(1.15f, 0, 0), Color.Red, normal));
vertices.Add(new VertexPositionColorNormal(Vector3.Zero, Color.Green, normal));
vertices.Add(new VertexPositionColorNormal(new Vector3(0, 1.15f, 0), Color.Green, normal));
vertices.Add(new VertexPositionColorNormal(Vector3.Zero, Color.Blue, normal));
vertices.Add(new VertexPositionColorNormal(new Vector3(0, 0, 1.15f), Color.Blue, normal));
var rawVertices = vertices.ToArray();
terrain = new VertexBuffer(GraphicsDevice, typeof(VertexPositionColorNormal), rawVertices.Length, BufferUsage.WriteOnly);
terrain.SetData(rawVertices);
// TODO: use this.Content to load your game content here
}
public void TestWeight()
{
var classification =
SampleGenerator.MakeClassification(
nSamples: 200,
nFeatures: 100,
weights: new[] { 0.833, 0.167 }.ToList(),
randomState: new Random(0));
var classWeight = ClassWeightEstimator <int> .Explicit(new Dictionary <int, double> {
{ 0, 5 }
});
Matrix x = SparseMatrix.OfMatrix(classification.X);
foreach (var clf in new IClassifier <int>[]
{
new LogisticRegression <int>(classWeightEstimator: classWeight),
//new LinearSvc(classWeight:classWeight, random_state=0),
//new Svc<int>(classWeight: classWeight)
})
{
clf.Fit(x.SubMatrix(0, 180, 0, x.ColumnCount), classification.Y.Take(180).ToArray());
var yPred = clf.Predict(x.SubMatrix(180, x.RowCount - 180, 0, x.ColumnCount));
var matchingN =
yPred.Zip(classification.Y.Skip(180), Tuple.Create).Where(t => t.Item1 == t.Item2).Count();
Assert.IsTrue(matchingN >= 11);
}
}
public void TestImportances()
{
var classification = SampleGenerator.MakeClassification(nSamples: 200,
nFeatures: 10,
nInformative: 3,
nRedundant: 0,
nRepeated: 0,
shuffle: false,
randomState: new Random(13));
//var xstr = "[" + string.Join(",", classification.X.RowEnumerator().Select(r => "[" + string.Join(",", r.Item2) + "]")) + "]";
//var ystr = "[" + string.Join(",", classification.Y) + "]";
foreach (var name in CLF_TREES)
{
var clf = CreateClassifier <int>(name, random: new Random(0));
clf.Fit(classification.X, classification.Y);
var importances = clf.FeatureImportances();
int n_important = importances.Where(v => v > 0.1).Count();
Assert.AreEqual(10, importances.Count, "Failed with {0}".Frmt(name));
Assert.AreEqual(3, n_important, "Failed with {0}".Frmt(name));
var X_new = clf.Transform(classification.X, threshold: ThresholdChoice.Mean());
Assert.IsTrue(0 < X_new.ColumnCount, "Failed with {0}".Frmt(name));
Assert.IsTrue(X_new.ColumnCount < classification.X.ColumnCount, "Failed with {0}".Frmt(name));
}
}
public Mitchell2D(SampleGenerator g, int k = 5)
{
Generator = g;
K = Math.Max(1, k);
samples = new List <PointF>();
}
public void SomeCloneTests()
{
var event1 = SampleGenerator.GameEventTest1();
var event2 = event1.CloneJson();
Assert.AreEqual(event1.Properties.Count, event2.Properties.Count);
Assert.AreEqual(event1.Properties["stat_kill_creepy"], event2.Properties["stat_kill_creepy"]);
}
public string GetJson()
{
var sampleList = SampleGenerator.GenerateCustomSampleList(3050000, 0, 20);
var json = JsonConvert.SerializeObject(sampleList);
var size = Encoding.Unicode.GetByteCount(json); // just to check size
return(json);
}
public void Test3_IfSampleListCorrect()
{
var sampleList = SampleGenerator.GenerateCustomSampleList(400000, 1, 20);
int qtyTotal = (from s in sampleList
select s.Qty).Sum();
var result = ParallelExercise(sampleList);
Assert.Equal(qtyTotal, result);
}
public void Test3_Cancellation()
{
var sampleList = SampleGenerator.GenerateCustomSampleList(1000, 1, 10);
long qtyTotal = (from s in sampleList
select s.Qty).Sum();
var result = ParallelExercise(sampleList, true);
// Assert: should fail and throw OperationCanceledException (check test explorer message)
}
public void TwoVarsTreeOps()
{
var service = new SampleGenerator(2, 3, 0.001);
var formula = service.GetFormula();
var noiseFormula = formula.Clone<INode>();
NoisyConstants(noiseFormula);
var alg = new RegressionAlgorithm(noiseFormula, service.InSamples, service.ExactResult);
alg.Run();
Assert.AreNotEqual(alg.GetResult(), null);
}
public void Test3_OutOfRangeException()
{
var sampleList = SampleGenerator.GenerateCustomSampleList(1000, 20, 30);
long qtyTotal = (from s in sampleList
select s.Qty).Sum();
var result = ParallelExercise(sampleList);
// Assert: should fail and throw ArgumentOutOfRangeException (check test explorer message)
}
public void TestLiblinearRandomState()
{
var classification = SampleGenerator.MakeClassification(nSamples: 20);
var lr1 = new LogisticRegression <int>(random: new Random(0));
lr1.Fit(classification.X, classification.Y);
var lr2 = new LogisticRegression <int>(random: new Random(0));
lr2.Fit(classification.X, classification.Y);
Assert.IsTrue(lr1.Coef.AlmostEquals(lr2.Coef));
}
public void DatacontractSerializerHelperTest()
{
var gameEvent = SampleGenerator.GameEventTestLarge();
var content = DatacontractSerializerHelper.Serialize(gameEvent);
Assert.IsTrue(content.Length > 0);
var gameEvent2 = DatacontractSerializerHelper.Deserialize <GameEvent>(content);
Assert.AreEqual(gameEvent.Properties.Count, gameEvent2.Properties.Count);
Assert.AreEqual(gameEvent.Properties["stat_22"], gameEvent2.Properties["stat_22"]);
}
private void AudioCallback(IntPtr userData, IntPtr samples, int bufferSize)
{
unsafe
{
var span = new Span <byte>(
samples.ToPointer(),
bufferSize
);
SampleGenerator.Invoke(span, Format);
}
}
public void Reset(SampleGenerator g = null, int k = 0)
{
if (g != null)
{
Generator = g;
}
if (k > 0)
{
K = k;
}
samples.Clear();
}
static void Main(string[] args)
{
Console.WriteLine("Hello World!");
Console.WriteLine("This is ...");
Console.WriteLine("The Voice Of The Station!");
Console.WriteLine("...");
Console.WriteLine("...");
Console.WriteLine("input datetime: 3pm 12 Dec 2018");
// ProcessADailyFile_SampleTEST();
// ProcessADailyFile_Sample1();
// ProcessFlowFile_SampleTEST();
// ProcessFlowFile_Sample1();
// ProcessFlowFile_Sample2();
// ProcessFlowFile_Sample3();
SampleGenerator sg = new SampleGenerator();
var dataset = sg.GenerateAPISample("03", null);
Console.WriteLine("OUTPUT:");
Console.WriteLine("");
foreach (var data in dataset)
{
Console.WriteLine(" ");
Console.WriteLine(" ");
Console.WriteLine("Year: " + data.Year);
Console.WriteLine("Month: " + data.Month);
Console.WriteLine("Day: " + data.Day);
Console.WriteLine("Hour: " + data.Hour);
Console.WriteLine("Minute: " + data.Minute);
Console.WriteLine("Second: " + data.Second);
Console.WriteLine("MilliSecond: " + data.MilliSecond);
Console.WriteLine("UserId: " + data.UserId);
Console.WriteLine("Latitude: " + data.Latitude);
Console.WriteLine("Longitude: " + data.Longitude);
Console.WriteLine("NeigbourhoodSize: " + data.NeigbourhoodSize);
Console.WriteLine("AcceptableNeighbourhoodSize: " + data.AcceptableNeighbourhoodSize);
Console.WriteLine("IsAcceptable: " + data.IsAcceptable);
Console.WriteLine(" ");
Console.WriteLine("IsHappyFace: " + data.IsHappyFace);
Console.WriteLine(" ");
Console.WriteLine(" ");
}
// Console.ReadKey();
Console.WriteLine("Hello World! AGAIN!");
}
public void Sinus100Hz_Spectrum()
{
//var audioContainer = AudioLoader.Load(Samples.Wave100hz);
var container = SampleGenerator.GenerateSineSignalContainer(100, 2048, 2048);
var fft = new FastFourierTransformCPU(container.Samples).CreateTransform();
var slice = FastFourierTransformCPU.ConvertToSpectrumSlice(fft);
var amplitude50 = slice.GetAmplitudeForFrequency(50);
var amplitude100 = slice.GetAmplitudeForFrequency(100);
var amplitude150 = slice.GetAmplitudeForFrequency(150);
Assert.True(amplitude50 < 0.1f);
Assert.True(amplitude100 > 0.9f);
Assert.True(amplitude150 < 0.1f);
}
public void GameEventEncryptionTest()
{
CommonConfiguration.Instance.EncryptionConfiguration.IsEncryptionEnabled = true;
CommonConfiguration.Instance.EncryptionConfiguration.Salt = "o6806642kbM7c5";
var event1 = SampleGenerator.GameEventTest1();
var packet = SampleGenerator.GamePacketTest1();
packet.GameEvent = event1;
// is the content encrypted
Assert.IsTrue(packet.Content.Contains("76561198024856042") == false);
Assert.AreEqual(event1.Properties["stat_avg_level_1"], packet.GameEvent.Properties["stat_avg_level_1"]);
}
static void Main()
{
var sampleGenerator = new SampleGenerator(2, 6, 0.001);
var randomFormula = sampleGenerator.GetFormula();
Console.WriteLine("Formula before making constants noisy: {0}", randomFormula);
GenerateNoisyConstantsForNodeLeafes(randomFormula);
Console.WriteLine("Formula after making constants noisy: {0}", randomFormula);
Console.WriteLine("Press any key to start regression...");
Console.ReadKey(true);
var alg = new RegressionAlgorithm(randomFormula, sampleGenerator.InSamples, sampleGenerator.ExactResult);
ConsoleGui.Run(alg, 5, "");
Console.WriteLine("Result: {0}", alg.GetResult());
}
public void Test1()
{
var sampleList1 = SampleGenerator.GenerateCustomSampleList(500000, 0, 20);
var sampleList2 = SampleGenerator.GenerateSampleList2();
var newSampleList = (from s1 in sampleList1
join s2 in sampleList2
on s1.Id equals s2.Id
select new Sample
{
Id = s1.Id,
Content = s1.Content,
Qty = s1.Qty
}).ToList();
Assert.NotEmpty(newSampleList);
}
public void TestMakeLowRankMatrix()
{
Matrix <double> x = SampleGenerator.MakeLowRankMatrix(
numSamples: 50,
numFeatures: 25,
effectiveRank: 5,
tailStrength: 0.01,
randomState: new Random(0));
Assert.AreEqual(50, x.RowCount, "X shape mismatch");
Assert.AreEqual(25, x.ColumnCount, "X shape mismatch");
Svd svd = x.Svd(true);
double sum = svd.S().Sum();
Assert.IsTrue(Math.Abs(sum - 5) < 0.1, "X rank is not approximately 5");
}
public void EncryptionTests()
{
CommonConfiguration.Instance.EncryptionConfiguration.IsEncryptionEnabled = true;
CommonConfiguration.Instance.EncryptionConfiguration.Salt = "o6806642kbM7c5";
var sw = new Stopwatch();
sw.Start();
var value = SampleGenerator.GameEventTestLarge();
var p1 = sw.ElapsedMilliseconds;
var serialised = JsonSerializerHelper.Serialize(value);
var p2 = sw.ElapsedMilliseconds;
var encrypted = GamePacket.EncryptStringAES(serialised);
var p3 = sw.ElapsedMilliseconds;
var dSerialisation = p2 - p1;
var dEncryption = p3 - p2;
}
static void Main()
{
var sampleGenerator = new SampleGenerator(2, 6, 0.001);
var randomFormula = sampleGenerator.GetFormula();
Console.WriteLine("Formula before making constants noisy: {0}", randomFormula);
GenerateNoisyConstantsForNodeLeafes(randomFormula);
Console.WriteLine("Formula after making constants noisy: {0}", randomFormula);
Console.WriteLine("Press any key to start regression...");
Console.ReadKey(true);
var alg = new RegressionAlgorithm(randomFormula, sampleGenerator.InSamples, sampleGenerator.ExactResult);
ConsoleGui.Run(alg, 5, "");
Console.WriteLine("Result: {0}", alg.GetResult());
}
private void LoadGen(Sf2Region region, AssetManager assets)
{
SampleDataAsset sda = assets.SampleAssetList[region.Generators[(int)GeneratorEnum.SampleID]];
SampleGenerator gen = new SampleGenerator();
gen.EndPhase = sda.End + region.Generators[(int)GeneratorEnum.EndAddressOffset] + 32768 * region.Generators[(int)GeneratorEnum.EndAddressCoarseOffset];
gen.Frequency = sda.SampleRate;
gen.KeyTrack = region.Generators[(int)GeneratorEnum.ScaleTuning];
gen.LoopEndPhase = sda.LoopEnd + region.Generators[(int)GeneratorEnum.EndLoopAddressOffset] + 32768 * region.Generators[(int)GeneratorEnum.EndLoopAddressCoarseOffset];
switch (region.Generators[(int)GeneratorEnum.SampleModes] & 0x3)
{
case 0x0:
case 0x2:
gen.LoopMode = LoopModeEnum.NoLoop;
break;
case 0x1:
gen.LoopMode = LoopModeEnum.Continuous;
break;
case 0x3:
gen.LoopMode = LoopModeEnum.LoopUntilNoteOff;
break;
}
gen.LoopStartPhase = sda.LoopStart + region.Generators[(int)GeneratorEnum.StartLoopAddressOffset] + 32768 * region.Generators[(int)GeneratorEnum.StartLoopAddressCoarseOffset];
gen.Offset = 0;
gen.Period = 1.0;
if (region.Generators[(int)GeneratorEnum.OverridingRootKey] > -1)
{
gen.RootKey = region.Generators[(int)GeneratorEnum.OverridingRootKey];
}
else
{
gen.RootKey = sda.RootKey;
}
gen.StartPhase = sda.Start + region.Generators[(int)GeneratorEnum.StartAddressOffset] + 32768 * region.Generators[(int)GeneratorEnum.StartAddressCoarseOffset];
gen.Tune = (short)(sda.Tune + region.Generators[(int)GeneratorEnum.FineTune] + 100 * region.Generators[(int)GeneratorEnum.CoarseTune]);
gen.VelocityTrack = 0;
gen.Samples = sda.SampleData;
this.genList[0] = gen;
}
public void TestLinearRegressionSparseMultipleOutcome()
{
var random = new Random(0);
var r = SampleGenerator.MakeSparseUncorrelated(random: random);
Matrix x = SparseMatrix.OfMatrix(r.X);
Vector <double> y = r.Y.Column(0);
Matrix y1 = DenseMatrix.OfColumns(y.Count, 2, new[] { y, y });
int nFeatures = x.ColumnCount;
var ols = new LinearRegression();
ols.Fit(x, y1);
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Assert.AreEqual(Tuple.Create(2, nFeatures), ols.Coef.Shape());
Matrix <double> yPred = ols.Predict(x);
ols.Fit(x, y);
Matrix <double> yPred1 = ols.Predict(x);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}
public void TestMakeRegression()
{
var r = SampleGenerator.MakeRegression(
numSamples: 100,
numFeatures: 10,
numInformative: 3,
shuffle: false,
effectiveRank: 5,
coef: true,
bias: 0.0,
noise: 1.0,
random: new Random(0));
Assert.AreEqual(Tuple.Create(100, 10), r.X.Shape(), "X shape mismatch");
Assert.AreEqual(Tuple.Create(100, 1), r.Y.Shape(), "y shape mismatch");
Assert.AreEqual(Tuple.Create(10, 1), r.Coef.Shape(), "coef shape mismatch");
Assert.AreEqual(3, r.Coef.Column(0).Count(v => v != 0), "Unexpected number of informative features");
// Test that y ~= np.dot(X, c) + bias + N(0, 1.0)
Matrix <double> matrix = r.Y - (r.X * r.Coef);
Assert.IsTrue(Math.Abs(matrix.Column(0).StandardDeviation() - 1.0) < 0.2);
}
public void TestLinearRegressionMultipleOutcome()
{
var result = SampleGenerator.MakeRegression(shuffle: false, random: new Random(0));
Matrix y = DenseMatrix.OfColumns(
result.Y.RowCount,
2,
new[] { result.Y.Column(0), result.Y.Column(0) });
var numFeatures = result.X.RowCount;
var clf = new LinearRegression(fitIntercept: true);
clf.Fit(result.X, y);
Assert.AreEqual(Tuple.Create(2, numFeatures), clf.Coef.Shape());
Matrix <double> yPred = clf.Predict(result.X);
clf.Fit(result.X, result.Y);
Matrix <double> yPred1 = clf.Predict(result.X);
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(0)));
Assert.IsTrue(yPred1.Column(0).AlmostEquals(yPred.Column(1)));
}
private void InitializiseSampleGenerator()
{
generator = new SampleGenerator(time, layer, filepath, volume);
// we need to save this object in order to generate the storyboard when all parsing processes finished
GlobalMemory.Instance.RegisterStoryboardGenerator(generator);
}
static void Main(string[] args)
{
Bootstrapper.Initialize();
SampleGenerator.CreateVoronoiGraphAndSave();
}
public SamplesService(IUserService userService, IProfileService profileService, IPhotosService photosService, IConversationService conversationService, IVisitService visitService, IResourceService resourceService)
{
_generator = new SampleGenerator(profileService, userService, photosService, conversationService, visitService, resourceService);
}
