public static void AddRange <TKey, TBin>( this IHistogram <TKey, TBin> hist, IEnumerable <TKey> keys) where TBin : struct { AddRange(hist, keys, hist.DefaultIncrement); }
public static HistogramVisitor Collect(this IHistogram histogram) { var visitor = new HistogramVisitor(); histogram.Visit(visitor); return(visitor); }
public ProceduralCardGenerator(IHistogram m, ImageGlossary i, CreatureModelIndex creatureModels, NameModel name) { model = m; images = i; creatureModelIndex = creatureModels; nameModel = name; }
public HistogramStopwatch(IHistogram histogram, bool useTicks = false) { Contract.Requires(histogram != null, nameof(histogram) + " is null."); this.histogram = histogram; this.useTicks = useTicks; watch = Stopwatch.StartNew(); }
public static async Task <T> Measure <T>(Func <Task <T> > action, IHistogram metric, ICounter errorCounter = null) { var stopwatch = new Stopwatch(); stopwatch.Start(); T result; try { result = await action(); } catch (Exception) { errorCounter?.Inc(); throw; } finally { stopwatch.Stop(); metric.Observe(stopwatch.ElapsedTicks / (double)Stopwatch.Frequency); } return(result); }
static private CardDescription GenerateTrapCard(System.Random random, IHistogram model, ImageGlossary images, CreatureModelIndex creatureModels, NameModel nameModel, CardGenerationFlags flags) { CardDescription card = ScriptableObject.CreateInstance(typeof(CardDescription)) as CardDescription; card.cardType = CardType.TRAP; card.manaCost = (int)ProceduralUtils.GetRandomValue <ManaCost>(random, model); double powerBudget = PowerBudget.ManaPowerBudgets[card.manaCost]; double powerMargin = PowerBudget.ManaPowerMargin[card.manaCost]; double powerLimit = PowerBudget.ManaPowerLimit[card.manaCost]; card.cardName = "A trap card"; //card.name += "(" + powerBudget.ToString() + ")"; GenerateCardEffects(random, model, creatureModels, card, powerBudget, powerMargin, powerLimit); // Revise the mana cost based on what effects we actually did generate int revisedMana = PowerBudget.PowerLevelToMana(card.PowerLevel()); if (revisedMana != card.manaCost) { Debug.Log("Had to revise the mana cost from " + card.manaCost.ToString() + " to " + revisedMana.ToString()); card.manaCost = revisedMana; } card.image = ProceduralUtils.GetRandomTexture(random, images.GetTrapImages()); CardTags tags = CardTagging.GetCardTags(card); card.cardName = nameModel.GenerateName(random, tags); return(card); }
private void buttonLoadImage_Click(object sender, EventArgs e) { if (openFileDialog1.ShowDialog() == DialogResult.OK) { ResetForm(); this.labelLoading.Text = "Loading image..."; Cursor.Current = Cursors.WaitCursor; this.Update(); ImageManager.SelectImagePath(openFileDialog1.FileName); try { OriginalImage = new Bitmap(ImageManager.CurrentImagePath); } catch (ArgumentException ex) { Console.WriteLine("Not an image" + ex); this.labelLoading.Text = ""; return; } pictureBoxOriginal.Image = OriginalImage; Histogram = new Histogram(OriginalImage); this.textBoxImageName.Text = ImageManager.ImageName; this.buttonHistogramOrignal.Enabled = true; this.buttonStretch.Enabled = true; this.labelLoading.Text = ""; Cursor.Current = Cursors.Default; } }
/// <summary> /// Initializes a new instance of the <see cref="Histogram"/> class. /// </summary> /// <param name="histogram">A histogram.</param> /// <param name="name">A name.</param> /// <param name="help">Help text.</param> /// <param name="appInsightsMetric">The ApplicationInsights <see cref="Metric"/> object.</param> public Histogram(IHistogram histogram, string name, string help, Metric?appInsightsMetric) { this.histogram = histogram; this.Name = name; this.Help = help; this.appInsightsMetric = appInsightsMetric; }
public void SetupParameters(System.Random r, IHistogram m, double minBudget, double maxBudget) { random = r; model = m; Assert.IsTrue(maxBudget >= minBudget); minAllocatedBudget = minBudget; maxAllocatedBudget = maxBudget; }
internal RasterHistogram(IHistogram histogram) { if (histogram == null) { throw new ArgumentNullException("histogram"); } _histogram = histogram; }
public Node( IStatistic statistic, IHistogram histogram ) { _statistic = statistic; _histogram = histogram; Distributions = new List <IDistribution>(); }
/// Create an instance of the receiver with parameters estimated from /// the given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h DhbScientificCurves.Histogram /// @exception ArgumentOutOfRangeException /// when no suitable parameter can be found. public StudentDistribution(IHistogram h) { double variance = h.Variance; if (variance <= 0) throw new ArgumentOutOfRangeException( Resource.Student_distribution_is_only_defined_for_positive_variance); DefineParameters((int)System.Math.Max(1, System.Math.Round(2 / (1 - 1 / variance)))); }
public static List <KeyValuePair <TKey, TBin> > GetPeaks <TKey, TBin>( this IHistogram <TKey, TBin> hist) where TBin : struct { var peaks = new List <KeyValuePair <TKey, TBin> >(); GetPeaks(hist, peaks); return(peaks); }
/// Create an instance of the receiver with parameters estimated from /// the given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h DhbScientificCurves.Histogram /// @exception ArgumentOutOfRangeException /// when no suitable parameter can be found. public ExponentialDistribution(IHistogram h) { if (h.Minimum < 0) throw new ArgumentOutOfRangeException(Resource.Exponential_distribution_is_only_defined_for_non_negative_values); if (h.Average < 0) throw new ArgumentOutOfRangeException(Resource.Exponential_distribution_is_only_defined_for_positive_scale); this.Scale = h.Average; }
public static HistogramValue GetCurrentValue(IHistogram metric) { var implementation = metric as IHistogramMetric; if (implementation != null) { return(implementation.Value); } return(EmptyHistogram); }
public static void AddRange <TKey, TBin>( this IHistogram <TKey, TBin> hist, IEnumerable <KeyValuePair <TKey, TBin> > keysAndIncrements) where TBin : struct { foreach (var kvp in keysAndIncrements) { hist.Add(kvp); } }
public Portfolio( IStatistic statistics, IHistogram histogram, IPortfolioSimulator portfolioSimulator ) { _statistics = statistics; _histogram = histogram; _portfolioSimulator = portfolioSimulator; }
public static void CopyTo <TKey, TBin>( this IHistogram <TKey, TBin> hist, ICollection <KeyValuePair <TKey, TBin> > dest) where TBin : struct { foreach (var kvp in hist) { dest.Add(kvp); } }
public ContactMetrics(IMetrics metrics) { _writeDurations = metrics.Histogram() .Name("myservice_contact_write_duration_seconds") .Help("Average duration of persistence write operations for contacts") .Register(); _pokes = metrics.Counter() .Name("myservice_contact_pokes_total") .Help("Number of times contacts were poked") .Register(); }
public static void AddRange <TKey, TBin>( this IHistogram <TKey, TBin> hist, IEnumerable <TKey> keys, TBin increment) where TBin : struct { foreach (var key in keys) { hist.Add(key, increment); } }
public void Setup() { _histogramDefaultBuckets = OurMetricFactory.CreateHistogram("testhistogram1", HelpText); _histogramManyBuckets = OurMetricFactory.CreateHistogram("testhistogram2", HelpText, false, _bucketsMany); _theirHistogramDefaultBuckets = TheirMetricFactory.CreateHistogram("testhistogram1", HelpText); _theirHistogramManyBuckets = TheirMetricFactory.CreateHistogram("testhistogram2", HelpText, new Their.Prometheus.HistogramConfiguration() { Buckets = _bucketsMany }); }
/// Create an instance of the receiver with parameters estimated from /// the given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h Histogram /// @exception ArgumentOutOfRangeException /// when no suitable parameter can be found. public ChiSquareDistribution(IHistogram h) { if (h.Minimum < 0) throw new ArgumentOutOfRangeException( Resource.Chi_square_distribution_is_only_defined_for_non_negative_values); int dof = (int)System.Math.Round(h.Average); if (dof <= 0) throw new ArgumentOutOfRangeException( Resource.Chi_square_distribution_is_only_defined_for_positive_degrees_of_freedom); DegreesOfFreedom = dof; }
public DummyMetrics(IMetrics metrics) { _runs = metrics.Counter() .Name("myservice_dummy_runs") .Help("Number of times the dummy worker ran") .Register(); _runDurations = metrics.Histogram() .Name("myservice_dummy_duration_seconds") .Help("Average duration of a dummy worker run") .Register(); }
public static Dictionary <TKey, TBin> ToDictionary <TKey, TBin>( this IHistogram <TKey, TBin> hist) where TBin : struct { var dict = new Dictionary <TKey, TBin>(); foreach (var kvp in hist) { dict.Add(kvp.Key, kvp.Value); } return(dict); }
/// Create an instance of the receiver with parameters estimated from the /// given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h DhbScientificCurves.Histogram /// @exception ArgumentOutOfRangeException when no suitable parameter can be found. public GammaDistribution(IHistogram h) { if (h.Minimum < 0) throw new ArgumentOutOfRangeException("Gamma distribution is only defined for non-negative values"); double shape = h.Average; if (shape <= 0) throw new ArgumentOutOfRangeException("Gamma distribution must have a non-negative shape parameter"); double scale = h.Variance / shape; if (scale <= 0) throw new ArgumentOutOfRangeException("Gamma distribution must have a non-negative scale parameter"); DefineParameters(shape / scale, scale); }
/// Create an instance of the receiver with parameters estimated from /// the given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h DhbScientificCurves.Histogram /// @exception ArgumentOutOfRangeException /// when no suitable parameter can be found. public StudentDistribution(IHistogram h) { double variance = h.Variance; if (variance <= 0) { throw new ArgumentOutOfRangeException( "Student distribution is only defined for positive variance"); } DefineParameters((int)Math.Max(1, Math.Round(2 / (1 - 1 / variance)))); }
/// <summary> /// Initializes a new instance of the <see cref="SerializerDecorator" /> class. /// </summary> /// <param name="metrics">The metrics factory.</param> /// <param name="handler">The handler.</param> /// <param name="connectionInformation">The connection information.</param> public SerializerDecorator(IMetrics metrics, ISerializer handler, IConnectionInformation connectionInformation) { var name = handler.GetType().Name; _bytesToMessageTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertBytesToMessageTimer", Units.Calls); _messageToBytesTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertMessageToBytesTimer", Units.Calls); _resultSizeHistogram = metrics.Histogram($"{connectionInformation.QueueName}.{name}.ConvertMessageToBytesHistogram", Units.Bytes, SamplingTypes.LongTerm); _handler = handler; }
private double[] fnClassification(IFeatureLayer pFLayer, decimal NClasses, string strClassifiedField, string ClassifiedMethod) { IFeatureClass pFClass = pFLayer.FeatureClass; //Create Rendering of Mean Value at Target Layer int intBreakeCount = Convert.ToInt32(NClasses); ITable pTable = (ITable)pFClass; IClassifyGEN pClassifyGEN; switch (ClassifiedMethod) { case "Equal Interval": pClassifyGEN = new EqualIntervalClass(); break; case "Geometrical Interval": pClassifyGEN = new GeometricalInterval(); break; case "Natural Breaks": pClassifyGEN = new NaturalBreaksClass(); break; case "Quantile": pClassifyGEN = new QuantileClass(); break; case "StandardDeviation": pClassifyGEN = new StandardDeviationClass(); break; default: pClassifyGEN = new NaturalBreaksClass(); break; } //Need to be changed 1/29/15 ITableHistogram pTableHistogram = new TableHistogramClass(); pTableHistogram.Field = strClassifiedField; pTableHistogram.Table = pTable; IHistogram pHistogram = (IHistogram)pTableHistogram; object xVals, frqs; pHistogram.GetHistogram(out xVals, out frqs); pClassifyGEN.Classify(xVals, frqs, intBreakeCount); double[] cb = (double[])pClassifyGEN.ClassBreaks; return(cb); }
public HistogramTests() { noLabel = new DefaultHistogram("noLabel", new HistogramOptions { Help = "help noLabels", }); labeled = new DefaultHistogram("labeled", new HistogramOptions { Help = "help labeled", LabelNames = Labeled.SingleLabels }); }
/// <summary> /// Initializes a new instance of the <see cref="ExpressionSerializerDecorator" /> class. /// </summary> /// <param name="metrics">The metrics factory.</param> /// <param name="handler">The handler.</param> /// <param name="connectionInformation">The connection information.</param> public ExpressionSerializerDecorator(IMetrics metrics, IExpressionSerializer handler, IConnectionInformation connectionInformation) { var name = "ExpressionSerializer"; _methodToBytesTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertMethodToBytesTimer", Units.Calls); _bytesToMethodTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertBytesToMethodTimer", Units.Calls); _resultMethodSizeHistogram = metrics.Histogram($"{connectionInformation.QueueName}.{name}.ConvertMethodToBytesHistogram", Units.Bytes, SamplingTypes.LongTerm); _handler = handler; }
public static KeyValuePair <TKey, TBin>[] ToSortedArray <TKey, TBin>( this IHistogram <TKey, TBin> hist) where TBin : struct { var arr = ToArray(hist); int CompareFunc(KeyValuePair <TKey, TBin> first, KeyValuePair <TKey, TBin> second) { return(hist.HistArith.Compare(first.Value, second.Value)); } Array.Sort(arr, CompareFunc); return(arr); }
public override IImage ApplyEqualizedHistogram(IHistogram histogram) { List<int> equalizedValues = new List<int>(histogram.EqualizedValues); GrayscalePixel[,] pixels = new GrayscalePixel[this.Height, this.Width]; for (int row = 0; row < histogram.Image.Height; row++) { for (int column = 0; column < histogram.Image.Width; column++) { int level = ((GrayscalePixel)this.pixels[row, column]).Level; pixels[row, column] = new GrayscalePixel((byte)equalizedValues[level]); } } return new GrayscaleImage(pixels); }
/// <summary> /// Stretch Image Histogram /// </summary> private void Stretch() { this.labelLoading.Text = "Stretching Histogram from image..."; this.Update(); int lower = (int)this.numericUpDownLower.Value; int upper = (int)this.numericUpDownUpper.Value; this.pictureBoxStretched.Image = Histogram.Stretch(lower, upper); HistogramStretched = new Histogram((Bitmap)this.pictureBoxStretched.Image); this.isStretched = true; this.labelLoading.Text = ""; }
public IHistogram <byte> Merge(IHistogram <byte> other) { if (Normalized || other.Normalized) { throw new HistogramException("Normalized histogram can not be merged"); } HueHisto h = new HueHisto(); for (byte i = 0; i < Dimension; i++) { h[i] = hhisto[i] + other[i]; } return(h); }
/// Create an instance of the receiver with parameters estimated from /// the given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h Histogram /// @exception ArgumentOutOfRangeException /// when no suitable parameter can be found. public ChiSquareDistribution(IHistogram h) { if (h.Minimum < 0) { throw new ArgumentOutOfRangeException( "Chi square distribution is only defined for non-negative values"); } int dof = (int)Math.Round(h.Average); if (dof <= 0) { throw new ArgumentOutOfRangeException( "Chi square distribution is only defined for positive degrees of freedom"); } DegreesOfFreedom = dof; }
public HistogramViewModel(MainWindowViewModel mainViewModel, IHistogram histogram) { this.mainViewModel = mainViewModel; this.histogram = histogram; this.image = histogram.Image; this.points = new PointCollection(); this.points.Add(new Point(0, histogram.Max)); int i = 0; foreach (int value in histogram.Values) { points.Add(new Point(i++, histogram.Max - value)); } // last point (lower-right corner) points.Add(new Point(i, histogram.Max)); }
/// <summary> /// Initializes a new instance of the <see cref="ExpressionSerializerDecorator" /> class. /// </summary> /// <param name="metrics">The metrics factory.</param> /// <param name="handler">The handler.</param> /// <param name="connectionInformation">The connection information.</param> public ExpressionSerializerDecorator(IMetrics metrics, IExpressionSerializer handler, IConnectionInformation connectionInformation) { var name = handler.GetType().Name; _methodToBytesTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertMethodToBytesTimer", Units.Calls); _bytesToMethodTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertBytesToMethodTimer", Units.Calls); _resultMethodSizeHistogram = metrics.Histogram($"{connectionInformation.QueueName}.{name}.ConvertMethodToBytesHistogram", Units.Bytes, SamplingTypes.LongTerm); _functionToBytesTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertFunctionToBytesTimer", Units.Calls); _bytesToFunctionTimer = metrics.Timer($"{connectionInformation.QueueName}.{name}.ConvertBytesToFunctionTimer", Units.Calls); _resultFunctionSizeHistogram = metrics.Histogram($"{connectionInformation.QueueName}.{name}.ConvertFunctionToBytesHistogram", Units.Bytes, SamplingTypes.LongTerm); _handler = handler; }
/// Create an instance of the receiver with parameters estimated from /// the given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h Histogram /// @exception ArgumentOutOfRangeException /// when no suitable parameter can be found. public FisherSnedecorDistribution(IHistogram h) { if (h.Minimum < 0) throw new ArgumentOutOfRangeException(Resource.Fisher_Snedecor_distribution_is_only_defined_for_non_negative_values); int n2 = (int)System.Math.Round(2 / (1 - 1 / h.Average)); if (n2 <= 0) throw new ArgumentOutOfRangeException(Resource.Fisher_Snedecor_distribution_has_positive_degrees_of_freedom); var a = 1 - (n2 - 2) * (n2 - 4) * h.Variance / (2 * 2 * n2); var n1 = (int)System.Math.Round(0.7 * (n2 - 2) / a); if (n1 <= 0) throw new ArgumentOutOfRangeException(Resource.Fisher_Snedecor_distribution_has_positive_degrees_of_freedom); DefineParameters(n1, n2); }
/// <summary> /// Initializes a new instance of the <see cref="MessageInterceptorDecorator" /> class. /// </summary> /// <param name="metrics">The metrics factory.</param> /// <param name="handler">The handler.</param> /// <param name="connectionInformation">The connection information.</param> public MessageInterceptorDecorator(IMetrics metrics, IMessageInterceptor handler, IConnectionInformation connectionInformation) { _handler = handler; var name = handler.GetType().Name; _metricTimerBytes = metrics.Timer($"{connectionInformation.QueueName}.{name}.BytesToMessageTimer", Units.Calls); _metricTimerMessage = metrics.Timer($"{connectionInformation.QueueName}.{name}.MessageToBytesTimer", Units.Calls); _metricHistogram = metrics.Histogram($"{connectionInformation.QueueName}.{name}.MessageToBytesHistogram", Units.Bytes, SamplingTypes.LongTerm); _metricHistogramDelta = metrics.Histogram($"{connectionInformation.QueueName}.{name}.MessageToBytesDeltaHistogram", Units.Bytes, SamplingTypes.LongTerm); _metricHistogramOptOut = metrics.Histogram($"{connectionInformation.QueueName}.{name}.OptOutOfGraphHistogram", Units.Bytes, SamplingTypes.LongTerm); }
public override IImage ApplyEqualizedHistogram(IHistogram histogram) { // TODO: Implement this method throw new NotImplementedException(); }
public FetchRequestAndResponseMetrics(ClientIdAndBroker metricId) { this.RequestTimer = new KafkaTimer(MetersFactory.NewTimer(metricId + "FetchRequestRateAndTimeMs", TimeSpan.FromMilliseconds(1), TimeSpan.FromSeconds(1))); this.RequestSizeHist = MetersFactory.NewHistogram(metricId + "FetchResponseSize"); }
/// Create an instance of the receiver with parameters estimated from /// the given histogram using best guesses. This method can be used to /// find the initial values for a fit. /// @param h DhbScientificCurves.Histogram public NormalDistribution(IHistogram h) : this(h.Average, h.StandardDeviation) { }
/// @param f statistics.ProbabilityDensity /// @param hist curves.Histogram public ScaledProbabilityDensityFunction(ProbabilityDensityFunction f, IHistogram hist) : this(f, hist.Count, hist.BinWidth) { }