public ContinousOptUnit(string id, DoubleRange valueRange)
: base(id)
{
Contract.Requires(!string.IsNullOrEmpty(id));
ValueRange = valueRange;
}
/// <summary>
/// Initializes a new instance of the <see cref="UniformGenerator"/> class.
/// </summary>
///
/// <param name="range">Random numbers range.</param>
/// <param name="seed">Seed value to initialize random numbers generator.</param>
///
public UniformGenerator(DoubleRange range, int seed)
{
rand = new UniformOneGenerator(seed);
min = range.Min;
length = range.Length;
}
private void parseRanges()
{
if (ranges.Count == 0)
{
SourceMask = SourceMask.Replace("\n", "");
var strArray = SourceMask.Split(',').Select(x => x.Trim()).Where(y => !string.IsNullOrEmpty(y));
foreach (var currItem in strArray)
{
var strItem = currItem;
if (!strItem.Contains("-"))
strItem += "-" + strItem;
var rangeArr = strItem.Split('-').Select(x => x.Trim()).ToArray();
if (rangeArr.Length == 2)
{
var strMin = rangeArr[0];
var strMax = rangeArr[1];
if (!strMax.Contains("."))
strMax = strMax + @".99";
var strPattern = @"[A-Z]\d{2}.*";
if (Regex.IsMatch(strMin, strPattern) && Regex.IsMatch(strMax, strPattern))
{
var newRange = new DoubleRange();
newRange.MinValue = mkbToDouble(strMin);
newRange.MaxValue = mkbToDouble(strMax);
ranges.Add(newRange);
}
}
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="AxisScale"/> class with the given range.
/// </summary>
/// <param name="min">The min value.</param>
/// <param name="max">The max value.</param>
/// <exception cref="ArgumentException">
/// [<paramref name="min"/>, <paramref name="max"/>] is not a valid range.
/// </exception>
protected AxisScale(double min, double max)
{
if (!(min < max))
throw new ArgumentException("min must be less than max.");
_range = new DoubleRange(min, max);
}
public void ConstructorTest()
{
var range = new DoubleRange(-1.2, +3.4);
Assert.AreEqual(-1.2, range.Min);
Assert.AreEqual(+3.4, range.Max);
}
public void MassRangeFromDANegative()
{
var range1 = new DoubleRange(10, new Tolerance(ToleranceUnit.DA, 4));
var range2 = new DoubleRange(10, new Tolerance(ToleranceUnit.DA, -4));
Assert.AreEqual(range1, range2);
}
/// <summary>
/// Initializes a new instance of the <see cref="AxisScale"/> class with the given range.
/// </summary>
/// <param name="range">The range of the axis.</param>
/// <exception cref="ArgumentException">
/// <paramref name="range"/> is invalid.
/// </exception>
protected AxisScale(DoubleRange range)
{
if (!(range.Min < range.Max))
throw new ArgumentException("Invalid range.", "range");
_range = range;
}
public BinaryFeatureDescription(string id, Type arrayType, int arrayLength, DoubleRange? itemValueRange = null, object context = null)
: this(id, GetItemType(arrayType), arrayLength, itemValueRange, context)
{
Contract.Requires(!string.IsNullOrEmpty(id));
Contract.Requires(itemValueRange == null || !itemValueRange.Value.IsFixed);
Contract.Requires(arrayLength > 0);
Contract.Requires(arrayType != null);
}
public RangedAdjustablePropertyValue(DoubleRange range, double value)
{
Contract.Requires(!range.IsFixed);
Contract.Requires(range.IsIn(value));
Range = range;
this.value = value;
}
private static DoubleRange? EnsureItemValueRange(DoubleRange? itemValueRange, BinaryItemType itemType)
{
Contract.Ensures(Contract.Result<DoubleRange?>() != null);
if (itemValueRange == null)
{
switch (itemType)
{
case BinaryItemType.Boolean:
return new DoubleRange(0.0, 1.0);
case BinaryItemType.Byte:
return new DoubleRange(0.0, byte.MaxValue);
case BinaryItemType.Int16:
return new DoubleRange(short.MinValue, short.MaxValue);
case BinaryItemType.Int32:
return new DoubleRange(int.MinValue, int.MaxValue);
case BinaryItemType.Int64:
return new DoubleRange(long.MinValue, long.MaxValue);
case BinaryItemType.UInt16:
return new DoubleRange(ushort.MinValue, ushort.MaxValue);
case BinaryItemType.UInt32:
return new DoubleRange(uint.MinValue, uint.MaxValue);
case BinaryItemType.UInt64:
return new DoubleRange(ulong.MinValue, ulong.MaxValue);
case BinaryItemType.Float:
return new DoubleRange(float.MinValue, float.MaxValue);
case BinaryItemType.Double:
return new DoubleRange(double.MinValue, double.MaxValue);
}
}
else
{
switch (itemType)
{
case BinaryItemType.Boolean:
return Bordered(itemValueRange.Value, new DoubleRange(0.0, 1.0));
case BinaryItemType.Byte:
return Bordered(itemValueRange.Value, new DoubleRange(0.0, byte.MaxValue));
case BinaryItemType.Int16:
return Bordered(itemValueRange.Value, new DoubleRange(short.MinValue, short.MaxValue));
case BinaryItemType.Int32:
return Bordered(itemValueRange.Value, new DoubleRange(int.MinValue, int.MaxValue));
case BinaryItemType.Int64:
return Bordered(itemValueRange.Value, new DoubleRange(long.MinValue, long.MaxValue));
case BinaryItemType.UInt16:
return Bordered(itemValueRange.Value, new DoubleRange(ushort.MinValue, ushort.MaxValue));
case BinaryItemType.UInt32:
return Bordered(itemValueRange.Value, new DoubleRange(uint.MinValue, uint.MaxValue));
case BinaryItemType.UInt64:
return Bordered(itemValueRange.Value, new DoubleRange(ulong.MinValue, ulong.MaxValue));
case BinaryItemType.Float:
return Bordered(itemValueRange.Value, new DoubleRange(float.MinValue, float.MaxValue));
case BinaryItemType.Double:
return Bordered(itemValueRange.Value, new DoubleRange(double.MinValue, double.MaxValue));
}
}
throw new ArgumentException(itemType + " is unknown.", "itemType");
}
protected FeatureDescription(string id, DoubleRange originalValueRange, object context = null)
{
Contract.Requires(!string.IsNullOrEmpty(id));
Contract.Requires(!originalValueRange.IsFixed);
ID = id;
Context = context;
OriginalValueRange = originalValueRange;
}
public BinaryFeatureDescription(string id, BinaryItemType itemType, int itemCount, DoubleRange? itemValueRange = null, object context = null)
: base(id, EnsureItemValueRange(itemValueRange, itemType).Value, context)
{
Contract.Requires(!string.IsNullOrEmpty(id));
Contract.Requires(itemValueRange == null || !itemValueRange.Value.IsFixed);
Contract.Requires(itemCount > 0);
ItemType = itemType;
ItemCount = itemCount;
}
public ScoreDistribution(DoubleRange oneStar,
DoubleRange twoStar,
DoubleRange threeStar,
DoubleRange fourStar,
DoubleRange fiveStar)
{
_oneStar = oneStar;
_twoStar = twoStar;
_threeStar = threeStar;
_fourStar = fourStar;
_fiveStar = fiveStar;
}
public static bool ValidatePriceRange(SearchCriteria criteria, DoubleRange priceRange)
{
// If the user specified a price range, but the item has no price information, fail
if (criteria.PriceRange.HasRangeSpecified && !priceRange.HasRangeSpecified) { return false; }
if (criteria.PriceRange != null && criteria.PriceRange.HasRangeSpecified)
{
if (priceRange == null) return false;
if (!criteria.PriceRange.Overlaps(priceRange)) return false;
}
return true;
}
public ScoreDistribution(double[] scores)
{
// Ensure that there are five categories being supplied.
if (scores.Length != 5)
{
throw new ArgumentException("Score distribution must have five scores.");
}
_oneStar = new DoubleRange(scores[0],scores[0]);
_twoStar = new DoubleRange(scores[1], scores[1]);
_threeStar = new DoubleRange(scores[2], scores[2]);
_fourStar = new DoubleRange(scores[3], scores[3]);
_fiveStar = new DoubleRange(scores[4], scores[4]);
}
public ValueRangeControl(string name, DoubleRange range)
{
this._range = range;
// Set up grid with four coulmns, one row
ColumnDefinition col = new ColumnDefinition();
col.Width = new GridLength(20, GridUnitType.Star);
this.ColumnDefinitions.Add(col);
col = new ColumnDefinition();
col.Width = new GridLength(20, GridUnitType.Star);
this.ColumnDefinitions.Add(col);
col = new ColumnDefinition();
col.Width = new GridLength(20, GridUnitType.Star);
this.ColumnDefinitions.Add(col);
col = new ColumnDefinition();
col.Width = new GridLength(20, GridUnitType.Star);
this.ColumnDefinitions.Add(col);
RowDefinition row = new RowDefinition();
row.Height = new GridLength(100, GridUnitType.Star);
// TODO: remove the set row if it's not necessary
TextBlock from = new TextBlock();
from.Text = name + " from :";
this.Children.Add(from);
Grid.SetColumn(from,0);
Grid.SetRow(from,0);
TextBoxPlus low = new TextBoxPlus();
low.Text = range.Low.ToString();
this.Children.Add(low);
Grid.SetColumn(low, 1);
Grid.SetRow(low,0);
TextBlock to = new TextBlock();
to.Text = " to : ";
this.Children.Add(to);
Grid.SetColumn(to, 2);
Grid.SetRow(to, 0);
TextBoxPlus high = new TextBoxPlus();
if (range.High < double.MaxValue)
{ high.Text = range.High.ToString(); }
Grid.SetColumn(high, 3);
Grid.SetRow(high, 0);
}
internal IEnumerable<double?> GetFeatureValues(Feature feature, DoubleRange normalizationRange)
{
Contract.Requires(feature != null);
Contract.Requires(!normalizationRange.IsFixed);
foreach (double? value in GetFeatureValues(feature))
{
if (value.HasValue)
{
yield return OriginalValueRange.Normalize(value.Value, normalizationRange);
}
else
{
yield return null;
}
}
}
public SearchCriteria(string searchText,
double numberOfResults,
DoubleRange priceRange,
ScoreDistribution distribution,
double minNumberReviews = 0,
bool matchAllSearchTerms = false,
bool strictPrimeEligibility = false
)
{
_searchText = searchText;
_numberOfResults = numberOfResults;
_matchAllSearchTerms = matchAllSearchTerms;
_minNumberReviews = minNumberReviews;
_strictPrimeEligibility = strictPrimeEligibility;
_distribution = distribution;
_priceRange = priceRange;
}
public AmazonItem(string name,
int reviewCount,
DoubleRange priceRange,
ScoreDistribution reviewDistribution,
Uri url,
double rating,
bool primeEligible,
BitmapImage image)
{
_name = name;
_reviewCount = reviewCount;
_priceRange = priceRange;
_reviewDistribution = reviewDistribution;
_url = url;
_rating = rating;
_primeEligible = primeEligible;
_image = image;
}
public static Chromatogram GetClosetsPeakChromatogram(this IEnumerable<ISpectrumTime> spectra, DoubleRange range)
{
if (range == null)
{
throw new ArgumentException("A range must be declared for a m/z range chromatogram");
}
List<double> times = new List<double>();
List<double> intensities = new List<double>();
foreach (ISpectrumTime spectrum in spectra)
{
times.Add(spectrum.Time);
var peak = spectrum.GetClosestPeak(range);
intensities.Add((peak != null) ? peak.Intensity : 0);
}
return new MassRangeChromatogram(times.ToArray(), intensities.ToArray(), range);
}
public static Chromatogram GetExtractedIonChromatogram(this IEnumerable<ISpectrumTime> spectra, DoubleRange range)
{
if (range == null)
{
throw new ArgumentException("A range must be declared for a m/z range chromatogram");
}
List<double> times = new List<double>();
List<double> intensities = new List<double>();
foreach (ISpectrumTime spectrum in spectra)
{
double intensity;
spectrum.TryGetIntensities(range, out intensity);
times.Add(spectrum.Time);
intensities.Add(intensity);
}
return new MassRangeChromatogram(times.ToArray(), intensities.ToArray(), range);
}
public void EqualityTest()
{
// ReSharper disable SuspiciousTypeConversion.Global
var range0 = new DoubleRange(-1.2, +3.4);
var range1 = new DoubleRange(-1.2, +3.4);
var range2 = new DoubleRange(0, +3.4);
var range3 = new DoubleRange(-1.2, +3.5);
var range4 = new DoubleRange(double.NaN, +3.4);
var range5 = new DoubleRange(double.NaN, double.NaN);
// Equals(DoubleRange)
Assert.IsTrue(range0.Equals(range0));
Assert.IsTrue(range0.Equals(range1));
Assert.IsFalse(range0.Equals(range2));
Assert.IsFalse(range0.Equals(range3));
Assert.IsFalse(range0.Equals(range4));
Assert.IsTrue(range5.Equals(range5));
// Equals(object)
Assert.IsTrue(range0.Equals((object)range0));
Assert.IsTrue(range0.Equals((object)range1));
Assert.IsFalse(range0.Equals((object)range2));
Assert.IsFalse(range0.Equals((object)range3));
Assert.IsFalse(range0.Equals((object)range4));
Assert.IsTrue(range5.Equals((object)range5));
Assert.IsFalse(range0.Equals(null));
Assert.IsFalse(range0.Equals(range0.ToString()));
// == operator
Assert.IsTrue(range0 == range1);
Assert.IsFalse(range0 == range2);
Assert.IsFalse(range0 == range3);
Assert.IsFalse(range0 == range4);
// != operator
Assert.IsFalse(range0 != range1);
Assert.IsTrue(range0 != range2);
Assert.IsTrue(range0 != range3);
Assert.IsTrue(range0 != range4);
// ReSharper restore SuspiciousTypeConversion.Global
}
protected internal override Feature CreateFeature(IEnumerator<double?> valueEnumerator, DoubleRange valueNormalizationRange)
{
byte[] bytes;
MemoryStream ms;
using (var writer = new BinaryWriter(ms = new MemoryStream()))
{
for (int idx = 0; idx < ItemCount; idx++)
{
double? value = GetNext(valueEnumerator);
Write(writer, value, valueNormalizationRange);
}
bytes = ms.ToArray();
}
return new BinaryFeature(this, bytes);
}
private double DeterminePurity(MSDataScan scan, double mz, int charge, DoubleRange isolationRange)
{
var miniSpectrum = scan.GetReadOnlySpectrum().Extract(isolationRange);
double expectedSpacing = Constants.C13C12Difference/charge;
double min = expectedSpacing*0.95;
double max = expectedSpacing*1.05;
double totalIntensity = 0;
double precursorIntensity = 0;
foreach (MZPeak peak in miniSpectrum)
{
double difference = (peak.MZ - mz) * charge;
double difference_Rounded = Math.Round(difference);
double expected_difference = difference_Rounded * Constants.C13C12Difference;
double Difference_ppm = (Math.Abs((expected_difference - difference) / (mz * charge))) * 1000000;
if (Difference_ppm <= 25)
{
precursorIntensity += peak.Intensity;
}
totalIntensity += peak.Intensity;
}
return precursorIntensity / totalIntensity;
}
private static DoubleRange Bordered(DoubleRange range, DoubleRange borderRange)
{
double min = range.MinValue, max = range.MaxValue;
if (min < borderRange.MinValue) min = borderRange.MinValue;
if (max > borderRange.MaxValue) max = borderRange.MaxValue;
return new DoubleRange(min, max);
}
/// <summary>
/// Scales a variable from a scale into another, effectively
/// performing a rule of three on scales bundaries.
/// </summary>
/// <param name="x">The variable to be scaled.</param>
/// <param name="from">The original scale of the variable.</param>
/// <param name="to">The destination scale of the variable.</param>
/// <returns>The scaled variable.</returns>
public static double Scale(double x, DoubleRange from, DoubleRange to)
{
return ((x - from.Min) * to.Length / from.Length) + to.Min;
}
private void UpdateMarkers()
{
if (DataPointTemplate == null)
{
// This is a line chart without markers.
return;
}
double markerThreshold = MarkerThreshold;
if (!Numeric.IsNaN(markerThreshold))
{
int numberOfDataPoints = _endIndexExclusive - _startIndex;
int allowedNumberOfDataPoints = (int)(XAxis.Length * markerThreshold);
if (numberOfDataPoints > allowedNumberOfDataPoints)
{
// The number of data points exceeds the MarkerThreshold.
return;
}
}
var yAxis = YAxis;
var originY = yAxis.OriginY;
var length = yAxis.Length;
var yRange = new DoubleRange(originY - length, originY);
// Loop over data points and create markers.
for (int i = _startIndex; i < _endIndexExclusive; i++)
{
// Draw only data which is in the visible data range.
if (!yRange.Contains(_yPositions[i]))
continue;
// Create marker
DataPoint data = Data[i];
FrameworkElement marker = CreateDataPoint(data.DataContext ?? data);
if (marker == null)
return;
Canvas.Children.Add(marker);
// Save index in tag for PositionMarker().
// Alternatively, we could set the property as an attached property.
marker.Tag = i;
#if SILVERLIGHT
// In Silverlight: Position marker immediately, because some elements do not raise a
// SizeChanged event.
PositionMarker(marker);
#endif
// Position the marker as soon as it is measured.
marker.SizeChanged += MarkerSizeChanged;
}
}
/// <summary>
/// Displays a scatter plot.
/// </summary>
///
/// <param name="title">The title for the plot window.</param>
/// <param name="function">The function to plot.</param>
/// <param name="range">The functions argument range to be plotted.</param>
///
public static DataSeriesBox Show(String title, Func <double, double> function, DoubleRange range)
{
return(show(title, function, range.Min, range.Max, null, null));
}
/// <summary>
/// Constructs a new <see cref="RombergMethod">Romberg's integration method</see>.
/// </summary>
///
/// <param name="steps">The number of steps used in Romberg's method. Default is 6.</param>
///
public RombergMethod(int steps)
{
this.s = new double[steps];
Range = new DoubleRange(0, 1);
}
public ProcVarOutOfRangeForm(INumericFormat iNumericFormat, ProcessVar var, object varSpecifiedValue, ErrorMessage error)
{
//
// Required for Windows Form Designer support
//
InitializeComponent();
this.Text = error.Title;
this.var = var;
// display the message
this.labelMessage.Text = error.Message;
// dispaly the variable
this.labelProcVarName.InitializeVariable(var);
this.labelProcVarName.BorderStyle = BorderStyle.None;
this.textBoxExistingValue.InitializeVariable(iNumericFormat, var);
// note: the specified value is already converted to the current unit system,
// it needs only to be formated
if (var is ProcessVarDouble)
{
double val = (double)varSpecifiedValue;
if (val == Constants.NO_VALUE)
{
this.textBoxSpecifiedValue.Text = "";
}
else
{
this.textBoxSpecifiedValue.Text = val.ToString(iNumericFormat.NumericFormatString);
}
}
else if (var is ProcessVarInt)
{
int val = (int)varSpecifiedValue;
if (val == Constants.NO_VALUE_INT)
{
this.textBoxSpecifiedValue.Text = "";
}
else
{
this.textBoxSpecifiedValue.Text = val.ToString(UI.DECIMAL);
}
}
string averageValStr = "";
object minMaxValues = error.ProcessVarsAndValues.GetVarRange(var);
// note: the min and max values are not converted to the current unit system,
// and they also needs formating
if (var is ProcessVarDouble)
{
DoubleRange doubleRange = (DoubleRange)minMaxValues;
double valMin = doubleRange.MinValue;
double val2Min = 0.0;
val2Min = UnitSystemService.GetInstance().ConvertFromSIValue(var.Type, valMin);
if (val2Min == Constants.NO_VALUE)
{
this.textBoxMinValue.Text = "";
}
else
{
this.textBoxMinValue.Text = val2Min.ToString(iNumericFormat.NumericFormatString);
}
double valMax = doubleRange.MaxValue;
double val2Max = 0.0;
val2Max = UnitSystemService.GetInstance().ConvertFromSIValue(var.Type, valMax);
if (val2Max == Constants.NO_VALUE)
{
this.textBoxMaxValue.Text = "";
}
else
{
this.textBoxMaxValue.Text = val2Max.ToString(iNumericFormat.NumericFormatString);
}
double averageVal = (val2Min + val2Max) / 2;
averageValStr = averageVal.ToString(iNumericFormat.NumericFormatString);
}
else if (var is ProcessVarInt)
{
IntRange intRange = (IntRange)minMaxValues;
int valMin = intRange.MinValue;
if (valMin == Constants.NO_VALUE_INT)
{
this.textBoxMinValue.Text = "";
}
else
{
this.textBoxMinValue.Text = valMin.ToString(UI.DECIMAL);
}
int valMax = intRange.MaxValue;
if (valMax == Constants.NO_VALUE_INT)
{
this.textBoxMaxValue.Text = "";
}
else
{
this.textBoxMaxValue.Text = valMax.ToString(UI.DECIMAL);
}
int averageVal = (int)((valMin + valMax) / 2);
averageValStr = averageVal.ToString(iNumericFormat.NumericFormatString);
}
// as convenience, initialize the new value with the recommended value
this.textBoxNewValue.Text = averageValStr;
this.textBoxExistingValue.BackColor = Color.Gainsboro;
this.textBoxSpecifiedValue.BackColor = Color.Gainsboro;
this.textBoxMinValue.BackColor = Color.Gainsboro;
this.textBoxMaxValue.BackColor = Color.Gainsboro;
}
/// <summary>
/// Paints the control.
/// </summary>
///
protected override void OnPaint(PaintEventArgs e)
{
Graphics g = e.Graphics;
int clientWidth = ClientRectangle.Width;
int clientHeight = ClientRectangle.Height;
// fill with background
g.FillRectangle(backgroundBrush, 0, 0, clientWidth - 1, clientHeight - 1);
// draw borders
g.DrawRectangle(bordersPen, 0, 0, clientWidth - 1, clientHeight - 1);
if (DesignMode)
{
return;
}
DoubleRange rangeClientX = new DoubleRange(0, clientWidth);
DoubleRange rangeClientY = new DoubleRange(clientHeight, 0);
// walk through all data series
foreach (Waveform waveform in waveTable.Values)
{
// get data of the waveform
float[] data = waveform.data;
// check for available data
if (data == null)
{
continue;
}
using (Pen pen = new Pen(waveform.color, waveform.width))
{
if (SimpleMode)
{
int blockSize = waveform.samples / clientWidth;
for (int x = 0; x < clientWidth; x++)
{
double max = data.RootMeanSquare(x * blockSize, blockSize);
int y = clientHeight / 2 + (int)(max * clientHeight);
g.DrawLine(pen, x, clientHeight - y, x, y);
}
}
else
{
int xPrev = 0;
int yPrev = (int)rangeY.Scale(rangeClientY, data[0]);
for (int x = 0; x < clientWidth; x++)
{
int index = (int)rangeClientX.Scale(rangeX, x);
if (index < 0 || index >= data.Length)
{
index = data.Length - 1;
}
int y = (int)rangeY.Scale(rangeClientY, data[index]);
g.DrawLine(pen, xPrev, yPrev, x, y);
xPrev = x;
yPrev = y;
}
}
}
}
}
public DoubleValueRange(DoubleRange inRange)
{
_range = inRange;
}
/// <summary>
/// Initializes a new instance of the <see cref="GaussianFunction"/> class.
/// </summary>
///
public GaussianFunction(double alpha, DoubleRange range)
{
this.Alpha = alpha;
this.Range = range;
}
/// <summary>
/// Estimates appropriate values for sigma given a data set.
/// </summary>
///
/// <remarks>
/// This method uses a simple heuristic to obtain appropriate values
/// for sigma in a radial basis function kernel. The heuristic is shown
/// by Caputo, Sim, Furesjo and Smola, "Appearance-based object
/// recognition using SVMs: which kernel should I use?", 2002.
/// </remarks>
///
/// <param name="kernel">The inner kernel.</param>
/// <param name="inputs">The data set.</param>
/// <param name="samples">The number of random samples to analyze.</param>
/// <param name="range">The range of suitable values for sigma.</param>
///
/// <returns>A Gaussian kernel initialized with an appropriate sigma value.</returns>
///
public static Gaussian <T> Estimate <T>(T kernel, double[][] inputs, int samples, out DoubleRange range)
where T : IDistance, IKernel, ICloneable
{
if (samples > inputs.Length)
{
throw new ArgumentOutOfRangeException("samples");
}
double[] distances = Distances <T>(kernel, inputs, samples);
double q1 = Math.Sqrt(distances[(int)Math.Ceiling(0.15 * distances.Length)] / 2.0);
double q9 = Math.Sqrt(distances[(int)Math.Ceiling(0.85 * distances.Length)] / 2.0);
double qm = Math.Sqrt(BestCS.Statistics.Tools.Median(distances, alreadySorted: true) / 2.0);
range = new DoubleRange(q1, q9);
return(new Gaussian <T>(kernel, sigma: qm));
}
/// <summary>
/// Estimate appropriate values for sigma given a data set.
/// </summary>
///
/// <remarks>
/// This method uses a simple heuristic to obtain appropriate values
/// for sigma in a radial basis function kernel. The heuristic is shown
/// by Caputo, Sim, Furesjo and Smola, "Appearance-based object
/// recognition using SVMs: which kernel should I use?", 2002.
/// </remarks>
///
/// <param name="kernel">The inner kernel.</param>
/// <param name="inputs">The data set.</param>
/// <param name="range">The range of suitable values for sigma.</param>
///
/// <returns>A Gaussian kernel initialized with an appropriate sigma value.</returns>
///
public static Gaussian <T> Estimate <T>(T kernel, double[][] inputs, out DoubleRange range)
where T : IDistance, IKernel, ICloneable
{
return(Estimate(kernel, inputs, inputs.Length, out range));
}
/// <summary>
/// Estimate appropriate values for sigma given a data set.
/// </summary>
///
/// <remarks>
/// This method uses a simple heuristic to obtain appropriate values
/// for sigma in a radial basis function kernel. The heuristic is shown
/// by Caputo, Sim, Furesjo and Smola, "Appearance-based object
/// recognition using SVMs: which kernel should I use?", 2002.
/// </remarks>
///
/// <param name="inputs">The data set.</param>
/// <param name="range">The range of suitable values for sigma.</param>
///
/// <returns>A Gaussian kernel initialized with an appropriate sigma value.</returns>
///
public static Gaussian Estimate(double[][] inputs, out DoubleRange range)
{
return(Estimate(inputs, inputs.Length, out range));
}
private static void ReportInverseSolverROfRho(double drho,
double[] rhoRange,
InverseFitType IFT,
string projectName,
string inputPath,
ForwardSolverType[] forwardSolverTypes,
OptimizerType[] optimizerTypes,
IEnumerable <OpticalProperties> guessOps,
IEnumerable <OpticalProperties> realOps,
int ratioDetectors,
double noisePercentage,
bool stepByStep)
{
Console.WriteLine("#############################################");
Console.WriteLine("####### REPORT INVERSE SOLVER: ROfRho #######");
Console.WriteLine("#############################################");
//path definition
string spaceDomainFolder = "Real";
string timeDomainFolder = "SteadyState";
string problemFolder = "drho" + drho.ToString() + "/" + "ratioD" + ratioDetectors.ToString() + "/" +
"noise" + noisePercentage.ToString() + "/" + rhoRange[0].ToString() + "_" + rhoRange[1].ToString();
problemFolder = problemFolder.Replace(".", "p");
//rhos based on range
int numberOfPoints = Convert.ToInt32((rhoRange[1] - rhoRange[0]) / drho) + 1;
var rhos = new DoubleRange(rhoRange[0], rhoRange[1], numberOfPoints).AsEnumerable().ToArray();
double[] R = new double[numberOfPoints];
double[] S = new double[numberOfPoints];
//based on range evaluate the index of first and last points to use
int firstInd = Convert.ToInt32((rhoRange[0] + drho / 2.0) / drho) - 1;
int lastInd = Convert.ToInt32((rhoRange[1] + drho / 2) / drho) - 1;
//execute
foreach (var fST in forwardSolverTypes)
{
Console.WriteLine("Forward Solver Type: {0}", fST.ToString());
foreach (var oT in optimizerTypes)
{
Console.WriteLine("Optimizer Type: {0}", oT.ToString());
if (stepByStep)
{
Console.WriteLine("Press enter to continue");
}
Console.WriteLine("=================================================");
if (stepByStep)
{
Console.ReadLine();
}
foreach (var rOp in realOps)
{
//output
double bestMua = 0.0;
double meanMua = 0.0;
double guessBestMua = 0.0;
double bestMusp = 0.0;
double meanMusp = 0.0;
double guessBestMusp = 0.0;
double bestChiSquared = 10000000000000.0; //initialize very large to avoid if first
double meanChiSquared = 0.0;
DateTime start = new DateTime(); //processing start time
DateTime end = new DateTime(); //processing finish time
double elapsedSeconds; //processing time
//set filename based on real optical properties
var filename = "musp" + rOp.Musp.ToString() + "mua" + rOp.Mua.ToString();
filename = filename.Replace(".", "p");
Console.WriteLine("Looking for file {0}", filename);
if (File.Exists(inputPath + spaceDomainFolder + "/" + timeDomainFolder + "/" + filename + "R"))
{
Console.WriteLine("The file has been found");
//read binary files
var Rtot = (IEnumerable <double>)FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + filename + "R", projectName, 88);
var Stot = (IEnumerable <double>)FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + filename + "S", projectName, 88);
// extract points within range
for (int i = firstInd; i <= lastInd; i++)
{
R[i - firstInd] = Rtot.ToArray()[i];
S[i - firstInd] = Stot.ToArray()[i];
}
// reduce number of measurements
var mrhos = FilterArray(rhos, ratioDetectors);
var mR = FilterArray(R, ratioDetectors);
var mS = FilterArray(S, ratioDetectors);
// add noise
if (noisePercentage != 0.0)
{
mR.AddNoise(noisePercentage);
}
start = DateTime.Now;
int covergedCounter = 0;
foreach (var gOp in guessOps)
{
bool converged;
//if fitting only one parameter change the guess to the true value
if (IFT == InverseFitType.Mua)
{
gOp.Musp = rOp.Musp;
}
if (IFT == InverseFitType.Musp)
{
gOp.Mua = rOp.Mua;
}
//solve inverse problem
double[] fit = ComputationFactory.SolveInverse(fST, oT, SolutionDomainType.ROfRho, mR, mS, IFT, new object[] { new[] { gOp }, mrhos });
if (fit[0] != 0 && fit[1] != 0)
{
converged = true;
}
else
{
converged = false;
}
// fitted op
if (converged)
{
OpticalProperties fOp = new OpticalProperties(fit[0], fit[1], gOp.G, gOp.N);
//calculate chi squared and change values if it improved
double chiSquared = EvaluateChiSquared(mR, SolverFactory.GetForwardSolver(fST).ROfRho(fOp.AsEnumerable(), mrhos).ToArray(), mS);
if (chiSquared < bestChiSquared)
{
guessBestMua = gOp.Mua;
bestMua = fit[0];
guessBestMusp = gOp.Musp;
bestMusp = fit[1];
bestChiSquared = chiSquared;
}
meanMua += fit[0];
meanMusp += fit[1];
meanChiSquared += chiSquared;
covergedCounter += 1;
}
}
end = DateTime.Now;
meanMua /= covergedCounter;
meanMusp /= covergedCounter;
meanChiSquared /= covergedCounter;
elapsedSeconds = (end - start).TotalSeconds;
MakeDirectoryIfNonExistent(new string[] { spaceDomainFolder, timeDomainFolder, problemFolder, fST.ToString(), oT.ToString(), IFT.ToString() });
//write results to array
double[] inverseProblemValues = FillInverseSolverValuesArray(bestMua, meanMua, guessBestMua,
bestMusp, meanMusp, guessBestMusp,
bestChiSquared, meanChiSquared,
elapsedSeconds, mR.Count());
// write array to binary
LocalWriteArrayToBinary(inverseProblemValues, @"Output/" + spaceDomainFolder + "/" +
timeDomainFolder + "/" + problemFolder + "/" + fST.ToString() + "/" +
oT.ToString() + "/" + IFT.ToString() + "/" + filename, FileMode.Create);
Console.WriteLine("Real MUA = {0} - best MUA = {1} - mean MUA = {2}", rOp.Mua, bestMua, meanMua);
Console.WriteLine("Real MUSp = {0} - best MUSp = {1} - mean MUSp = {2}", rOp.Musp, bestMusp, meanMusp);
if (stepByStep)
{
Console.ReadLine();
}
}
else
{
Console.WriteLine("The file has not been found.");
}
Console.Clear();
}
}
}
}
/// <summary>
/// Initializes a new instance of the <see cref="LinearFunction"/> class.
/// </summary>
///
public LinearFunction(DoubleRange range)
{
this.Range = range;
}
/// <summary>
/// Initializes a new instance of the <see cref="UniformGenerator"/> class.
/// </summary>
///
/// <param name="range">Random numbers range.</param>
///
/// <remarks>Initializes random numbers generator with zero seed.</remarks>
///
public UniformGenerator(DoubleRange range) :
this(range, 0)
{
}
/// <summary>
/// Computes the circular quartiles of the given circular angles.
/// </summary>
///
/// <param name="angles">A double array containing the angles in radians.</param>
/// <param name="range">The sample quartiles, as an out parameter.</param>
/// <param name="wrap">
/// Whether range values should be wrapped to be contained in the circle. If
/// set to false, range values could be returned outside the [+pi;-pi] range.
/// </param>
///
/// <returns>The median of the given angles.</returns>
///
public static double Quartiles(double[] angles, out DoubleRange range, bool wrap = true)
{
return(Quartiles(angles, out range, Median(angles), wrap));
}
/// <summary>
/// Creates an interval vector.
/// </summary>
///
public static double[] Interval(this DoubleRange range, double stepSize)
{
return(Interval(range.Min, range.Max, stepSize));
}
/// <summary>
/// Creates a new column options.
/// </summary>
///
public Options(String name)
: base(name)
{
this.SourceRange = new DoubleRange(0, 1);
this.OutputRange = new DoubleRange(-1, 1);
}
public List <PSM> crunch(List <PSM> psms)
{
var rawFile = new ThermoRawFile(rawpath);
rawFile.Open();
List <int> msOneScanNumbers = new List <int>();
foreach (var scan in rawFile)
{
if (scan.MsnOrder == 1)
{
msOneScanNumbers.Add(scan.SpectrumNumber);
}
}
//ReadInTargets(peptidesPath, rawFile);
foreach (PSM psm in psms)
{
LFPeptide pep = psmToLFPeptide(psm, rawFile);
targetPeptides.Add(pep);
}
// Question: how do we sync the list of PSMs with the intensity of the LFPeptide
// They will be in same order
List <LFPeptide> pepsWithSmooth = new List <LFPeptide>();
if (rawFile.GetMzAnalyzer(msOneScanNumbers[0]).Equals(MZAnalyzerType.Orbitrap))
{
var firstSpectrumRT = rawFile.GetRetentionTime(rawFile.FirstSpectrumNumber);
var lastSpectrumRT = rawFile.GetRetentionTime(rawFile.LastSpectrumNumber);
foreach (var pep in targetPeptides)
{
var startTime = Math.Max(pep.parentMS1Time - 2, firstSpectrumRT);
var stopTime = Math.Min(pep.parentMS1Time + 2, lastSpectrumRT);
pep.startLookupTime = startTime;
pep.stopLookupTime = stopTime;
pep.FirstScan = rawFile.GetSpectrumNumber(pep.startLookupTime);
pep.LastScan = rawFile.GetSpectrumNumber(pep.stopLookupTime);
pep.lookupRange = DoubleRange.FromPPM(pep.UserMZ, 10);
}
foreach (var ms1 in msOneScanNumbers)
{
var spectrum = rawFile.GetSpectrum(ms1);
GetXICs(spectrum, ms1, rawFile.GetRetentionTime(ms1));
rawFile.ClearCachedScans();
List <LFPeptide> peptidesForExtract = targetPeptides.Where(x => x.doneBuildingXIC && !x.extractedXIC).ToList();
foreach (var pep in peptidesForExtract)
{
try
{
pep.SmoothLibrary = Smoothing.GetRollingAveragePeaks(pep, 11, true);
if (pep.SmoothLibrary.Count != 0)
{
ExtractFeatures.GetApexPeak(pep, true);
ExtractFeatures.GetFWHMWindow(pep);
LFPeptide pepSmooth = (LFPeptide)pep.Clone();
pepsWithSmooth.Add(pepSmooth);
pep.XICLibrary.Clear();
}
}catch (Exception e)
{/**
* System.Windows.Forms.MessageBox.Show("XICLibraryCount: " + pep.XICLibrary.Count
+ "Peptide: " + pep.sequence
+ "\n" + e.ToString());
**/
}
}
}
List <LFPeptide> peptidesToWrite = targetPeptides.Where(x => x.doneBuildingXIC && !x.extractedXIC).ToList();
//Changed from psms.Count to pepswithsmooth.Count
//for(int i = 0; i < psms.Count; i++)
for (int i = 0; i < pepsWithSmooth.Count; i++)
{
/**
* if(psms[i].scanTime == 20.45)
* {
* double[] xs = new double[pepsWithSmooth[i].SmoothLibrary.Count];
* double[] ys = new double[pepsWithSmooth[i].SmoothLibrary.Count];
*
* StreamWriter writer = new StreamWriter(@"C:\Users\gwilson\Desktop\GlycoQuant - Injs\TLN[NGlycan_1216.42286271]CSGAHVK_0.5_2_PolynomialFit.csv");
* for (int k = 0; k < pepsWithSmooth[i].SmoothLibrary.Count; k++)
* {
* xs[k] = pepsWithSmooth[i].SmoothLibrary[k].RT;
* ys[k] = pepsWithSmooth[i].SmoothLibrary[k].Intensity;
*
* //writer.WriteLine(pepsWithSmooth[i].SmoothLibrary[k].RT + "," + pepsWithSmooth[i].SmoothLibrary[k].Intensity);
*
*
* if (pepsWithSmooth[i].SmoothLibrary[k].RT > targetPeptides[i].LeftFWHM.RT && pepsWithSmooth[i].SmoothLibrary[k].RT < targetPeptides[i].RightFWHM.RT)
* {
* writer.WriteLine(pepsWithSmooth[i].SmoothLibrary[k].RT + "," + pepsWithSmooth[i].SmoothLibrary[k].Intensity);
* }
*
* }
*
* double[] p = Fit.Polynomial(xs, ys, 3)
*
* for(int j = 0; j < p.Length; j++)
* {
* //writer.WriteLine()
* }
*
* writer.Close();
* }
**/
double intensity = 0;
try
{
for (int j = 0; j < pepsWithSmooth[i].SmoothLibrary.Count - 1; j++)
{
// Intensity is the sum of peak intensities from Left FWHM to Right FWHM
/**
* if (pepsWithSmooth[i].SmoothLibrary[j].RT > targetPeptides[i].LeftFWHM.RT && pepsWithSmooth[i].SmoothLibrary[j].RT < targetPeptides[i].RightFWHM.RT)
* {
* intensity += pepsWithSmooth[i].SmoothLibrary[j].Intensity;
* }
**/
// Retention times
double RT1 = pepsWithSmooth[i].SmoothLibrary[j].RT;
double RT2 = pepsWithSmooth[i].SmoothLibrary[j + 1].RT;
// Intensities
double int1 = pepsWithSmooth[i].SmoothLibrary[j].Intensity;
double int2 = pepsWithSmooth[i].SmoothLibrary[j + 1].Intensity;
//Rectangle area
double rectArea = (RT2 - RT1) * Math.Min(int1, int2);
//Triangle Area
double triArea = ((RT2 - RT1) * Math.Abs(int1 - int2)) / 2;
intensity += rectArea + triArea;
}
}
catch (Exception e)
{
Console.ReadKey();
}
psms[i].intensity = intensity;
//psms[i].intensity = targetPeptides[i].apexPeakLibrary.Intensity;
}
rawFile.Dispose();
return(psms);
}
rawFile.Dispose();
return(psms);
}
/// <summary>
/// Displays a scatter plot.
/// </summary>
///
/// <param name="function">The function to plot.</param>
/// <param name="range">The functions argument range to be plotted.</param>
/// <param name="step">The step size to use during plotting.</param>
///
public static ScatterplotBox Show(Func <double, double> function, DoubleRange range, double step)
{
return(show(null, function, range.Min, range.Max, step));
}
/// <summary>
/// InitializeVectorsAndInterpolators reads in reference database and initializes data
/// Note that the reference data is now in mm/ns so no conversion needed
/// </summary>
private void InitializeVectorsAndInterpolators()
{
// load R(rho,time) reference data
var rOfRhoAndTime = (dynamic)DetectorIO.ReadDetectorFromFileInResources("ROfRhoAndTime", "Modeling/Resources/" + folder, _assemblyName);
nrReference = rOfRhoAndTime.Rho.Count - 1;
drReference = rOfRhoAndTime.Rho.Delta;
ntReference = rOfRhoAndTime.Time.Count - 1;
dtReference = rOfRhoAndTime.Time.Delta;
// assume mus' used by Kienle
muspReference = 1.0;
RhoReference = new DoubleRange(drReference / 2, drReference * nrReference - drReference / 2, nrReference).AsEnumerable().ToArray();
TimeReference = new DoubleRange(dtReference / 2, dtReference * ntReference - dtReference / 2, ntReference).AsEnumerable().ToArray();
RReferenceOfRhoAndTime = new double[nrReference, ntReference];
for (int ir = 0; ir < nrReference; ir++)
{
double sum = 0.0;
for (int it = 0; it < ntReference; it++)
{
RReferenceOfRhoAndTime[ir, it] = rOfRhoAndTime.Mean[ir, it];
sum += rOfRhoAndTime.Mean[ir, it]; // debug line
}
}
// load R(fx,time) reference data
var rOfFxAndTime = (dynamic)DetectorIO.ReadDetectorFromFileInResources("ROfFxAndTime", "Modeling/Resources/" + folder, _assemblyName);
nfxReference = rOfFxAndTime.Fx.Count;
dfxReference = 1.0 / nfxReference;
FxReference = new DoubleRange(dfxReference / 2, dfxReference * nfxReference - dfxReference / 2, nfxReference).AsEnumerable().ToArray();
RReferenceOfFxAndTime = new double[nfxReference, ntReference];
for (int ifx = 0; ifx < nfxReference; ifx++)
{
for (int it = 0; it < ntReference; it++) // this only goes to 800 not 801 because ntReference determined from ROfRhoAndTime.txt
{
RReferenceOfFxAndTime[ifx, it] = rOfFxAndTime.Mean[ifx, it].Real;
}
}
////if (File.Exists("Resources/" + folder + @"R_fxt"))
//if (true)
//{
// RReferenceOfFxAndTime = (double[,])FileIO.ReadArrayFromBinaryInResources<double>("Modeling/Resources/" +
// folder + @"R_fxt", _assemblyName);
//}
//else
//{
// double[] RReferenceOfRhoAndTj = new double[nrReference];
// double[] RReferenceOfFxAndTj = new double[nfxReference];
// for (int j = 0; j < ntReference; j++)
// {
// for (int k = 0; k < nrReference; k++)
// {
// RReferenceOfRhoAndTj[k] = RReferenceOfRhoAndTime[k, j]; // get ROfRho at a particular Time Tj
// }
// for (int i = 0; i < nfxReference; i++)
// {
// RReferenceOfFxAndTime[i, j] = LinearDiscreteHankelTransform.GetHankelTransform(RhoReference,
// RReferenceOfRhoAndTj, drReference, dfxReference * i);
// }
// }
// FileIO.WriteArrayToBinary<double>(RReferenceOfFxAndTime, @"/R_fxt");
//}
}
/// <summary>
/// Displays a scatter plot.
/// </summary>
///
/// <param name="title">The title for the plot window.</param>
/// <param name="function">The function to plot.</param>
/// <param name="range">The functions argument range to be plotted.</param>
///
public static ScatterplotBox Show(String title, Func <double, double> function, DoubleRange range)
{
return(show(title, function, range.Min, range.Max, null));
}
/// <summary>
/// Scales variables from a scale into another, effectively
/// performing a rule of three on scales bundaries.
/// </summary>
/// <param name="x">The variables to be scaled.</param>
/// <param name="from">The original scale of the variables.</param>
/// <param name="to">The destination scale of the variables.</param>
/// <returns>The scaled variables.</returns>
public double[] Scale(double[] x, DoubleRange from, DoubleRange to)
{
double[] r = new double[x.Length];
for (int i = 0; i < x.Length; i++)
{
r[i] = Scale(x[i], from, to);
}
return r;
}
public PointValueRange(DoubleRange inX, DoubleRange inY, DoubleRange inZ)
{
_rangeX = inX;
_rangeY = inY;
_rangeZ = inZ;
}
/// <summary>
/// Constructs a new <see cref="TrapezoidalRule"/> integration method.
/// </summary>
///
/// <param name="steps">The number of steps into which the integration
/// interval will be divided.</param>
///
public TrapezoidalRule(int steps)
{
Steps = steps;
Range = new DoubleRange(0, 1);
}
/// <summary>Update event.</summary>
protected override void OnSizeChanged()
{
this.Confidence = GetConfidenceInterval(1.0 - Size);
}
public static double Clamp(this double value, DoubleRange valueRange)
{
return(Clamp(value, valueRange.Minimum, valueRange.Maximum));
}
//----------------------------------------------------------------------
public void SetValues(DoubleRange range)
{
SetValues(range.Min, range.Extent);
}
private static void ReportInverseSolverROfRhoAndTime(double dt,
double riseMarker,
double tailMarker,
string stDevMode,
InverseFitType IFT,
string projectName,
string inputPath,
ForwardSolverType[] forwardSolverTypes,
OptimizerType[] optimizerTypes,
IEnumerable <OpticalProperties> guessOps,
IEnumerable <OpticalProperties> realOps,
double[] rhos,
double noisePercentage,
bool stepByStep)
{
Console.WriteLine("#############################################");
Console.WriteLine("##### REPORT INVERSE SOLVER: ROfRhoAndT #####");
Console.WriteLine("#############################################");
//path definition
string spaceDomainFolder = "Real";
string timeDomainFolder = "TimeDomain";
string noiseFolder = "noise" + noisePercentage.ToString();
string problemFolder = "dt" + (dt * 1000).ToString() + "markers" + riseMarker.ToString() +
tailMarker.ToString();
problemFolder = problemFolder.Replace(".", "p");
foreach (var fST in forwardSolverTypes)
{
//initialize forward solver
Console.WriteLine("Forward Solver Type: {0}", fST.ToString());
foreach (var oT in optimizerTypes)
{
Console.WriteLine("Optimizer Type: {0}", oT.ToString());
foreach (var rho in rhos)
{
string rhoFolder = rho.ToString();
Console.WriteLine("=================================================");
Console.WriteLine("SOURCE DETECTOR SEPARETION: R = {0} mm", rhoFolder);
if (stepByStep)
{
Console.WriteLine("Press enter to continue");
}
Console.WriteLine("=================================================");
if (stepByStep)
{
Console.ReadLine();
}
rhoFolder = rhoFolder.Replace(".", "p");
rhoFolder = "rho" + rhoFolder;
double[] constantVals = { rho };
foreach (var rOp in realOps)
{
//output
double bestMua = 0.0;
double meanMua = 0.0;
double guessBestMua = 0.0;
double bestMusp = 0.0;
double meanMusp = 0.0;
double guessBestMusp = 0.0;
double bestChiSquared = 10000000000000.0; //initialize very large to avoid if first
double meanChiSquared = 0.0;
DateTime start = new DateTime(); //processing start time
DateTime end = new DateTime(); //processing finish time
double elapsedSeconds; //processing time
//set filename based on real optical properties
var filename = "musp" + rOp.Musp.ToString() + "mua" + rOp.Mua.ToString();
filename = filename.Replace(".", "p");
Console.WriteLine("Looking for file {0}", filename);
if (File.Exists(inputPath + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "Range"))
{
Console.WriteLine("The file has been found for rho = {0} mm.", rho);
//read binary files
var timeRange = (double[])FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "Range", projectName, 2);
// CKH 4/20/20 quickfix to avoid exception, somehow binary read of timeRange have timeRange[1]=0
if ((timeRange[1] - timeRange[0] < 0) || (Math.Abs(timeRange[1] - timeRange[0]) > 2e9))
{
break;
}
int numberOfPoints = Convert.ToInt32((timeRange[1] - timeRange[0]) / dt) + 1;
var T = new DoubleRange(timeRange[0], timeRange[1], numberOfPoints).AsEnumerable().ToArray();
var R = (double[])FileIO.ReadArrayFromBinaryInResources <double>
("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "R", projectName, numberOfPoints);
var S = GetStandardDeviationValues("Resources/" + spaceDomainFolder + "/" + timeDomainFolder + "/" + problemFolder + "/" + rhoFolder + "/" + filename + "S",
projectName, stDevMode, numberOfPoints, R.ToArray());
// add noise
if (noisePercentage != 0.0)
{
R = R.AddNoise(noisePercentage);
}
start = DateTime.Now;
int convergedCounter = 0;
foreach (var gOp in guessOps)
{
bool converged;
if (IFT == InverseFitType.Mua)
{
gOp.Musp = rOp.Musp;
}
if (IFT == InverseFitType.Musp)
{
gOp.Mua = rOp.Mua;
}
//solve inverse problem
double[] fit = ComputationFactory.SolveInverse(fST, oT, SolutionDomainType.ROfRhoAndTime, R, S, IFT, new object[] { new[] { gOp }, constantVals, T });
if (fit[0] != 0 && fit[1] != 0)
{
converged = true;
}
else
{
converged = false;
}
if (converged)
{
OpticalProperties fOp = new OpticalProperties(fit[0], fit[1], gOp.G, gOp.N);
//calculate chi squared and change best values if it improved
double chiSquared = EvaluateChiSquared(R.ToArray(), SolverFactory.GetForwardSolver(fST).ROfRhoAndTime(fOp.AsEnumerable(), rho.AsEnumerable(), T).ToArray(), S.ToArray());
if (chiSquared < bestChiSquared)
{
guessBestMua = gOp.Mua;
bestMua = fit[0];
guessBestMusp = gOp.Musp;
bestMusp = fit[1];
bestChiSquared = chiSquared;
}
meanMua += fit[0];
meanMusp += fit[1];
meanChiSquared += chiSquared;
convergedCounter += 1;
}
}
end = DateTime.Now;
meanMua /= convergedCounter;
meanMusp /= convergedCounter;
meanChiSquared /= convergedCounter;
elapsedSeconds = (end - start).TotalSeconds;
MakeDirectoryIfNonExistent(new string[] { spaceDomainFolder, timeDomainFolder, noiseFolder, problemFolder, fST.ToString(), oT.ToString(), IFT.ToString(), rhoFolder });
//write results to array
double[] inverseProblemValues = FillInverseSolverValuesArray(bestMua, meanMua, guessBestMua,
bestMusp, meanMusp, guessBestMusp,
bestChiSquared, meanChiSquared,
elapsedSeconds, numberOfPoints);
// write array to binary
LocalWriteArrayToBinary(inverseProblemValues, @"Output/" + spaceDomainFolder + "/" +
timeDomainFolder + "/" + noiseFolder + "/" + problemFolder + "/" + fST.ToString() + "/" +
oT.ToString() + "/" + IFT.ToString() + "/" + rhoFolder + "/" + filename, FileMode.Create);
Console.WriteLine("Real MUA = {0} - best MUA = {1} - mean MUA = {2}", rOp.Mua, bestMua, meanMua);
Console.WriteLine("Real MUSp = {0} - best MUSp = {1} - mean MUSp = {2}", rOp.Musp, bestMusp, meanMusp);
if (stepByStep)
{
Console.ReadLine();
}
}
else
{
Console.WriteLine("The file has not been found.");
}
Console.Clear();
}
}
}
}
}
public static double Scale(this DoubleRange from, DoubleRange to, double x)
{
return(Accord.Math.Vector.Scale(x, from, to));
}
private void Write(BinaryWriter writer, double? value, DoubleRange valueNormalizationRange)
{
if (value == null)
{
WriteDefault(writer);
}
else
{
double currentValue = OriginalValueRange.Denormalize(valueNormalizationRange.Cut(value.Value), valueNormalizationRange);
switch (ItemType)
{
case BinaryItemType.Boolean:
writer.Write(currentValue >= 0.5);
return;
case BinaryItemType.Byte:
writer.Write((byte)Math.Round(currentValue));
return;
case BinaryItemType.Int16:
writer.Write((short)Math.Round(currentValue));
return;
case BinaryItemType.Int32:
writer.Write((int)Math.Round(currentValue));
return;
case BinaryItemType.Int64:
writer.Write((long)Math.Round(currentValue));
return;
case BinaryItemType.UInt16:
writer.Write((ushort)Math.Round(currentValue));
return;
case BinaryItemType.UInt32:
writer.Write((uint)Math.Round(currentValue));
return;
case BinaryItemType.UInt64:
writer.Write((ulong)Math.Round(currentValue));
return;
case BinaryItemType.Float:
writer.Write((float)currentValue);
return;
case BinaryItemType.Double:
writer.Write(currentValue);
return;
}
}
}
public static double[] Scale(DoubleRange from, DoubleRange to, double[] x)
{
return(Accord.Math.Vector.Scale(values: x, fromRange: from, toRange: to));
}
public static double Scale(IntRange from, DoubleRange to, int x)
{
return(Accord.Math.Vector.Scale(x, from, to));
}
/// <summary>
/// Creates an interval vector.
/// </summary>
///
public static double[] Interval(this DoubleRange range, int steps)
{
return(Interval(range.Min, range.Max, steps));
}
/// <summary>
/// Creates a new <see cref="NonAdaptiveGaussKronrod"/> integration algorithm.
/// </summary>
///
public NonAdaptiveGaussKronrod()
{
ToleranceAbsolute = 0;
ToleranceRelative = 1e-3;
range = new DoubleRange(0, 1);
}
/// <summary>
/// Initializes a new instance of the <see cref="LinearFunction"/> class.
/// </summary>
///
public LinearFunction(double alpha, DoubleRange range)
{
this.Alpha = alpha;
this.Range = range;
}
