/// <summary>
/// Generates a complete configuration for the simple item generator.
/// </summary>
/// <param name="preConfig">The pre-configuration defining characteristics of the actual configuration.</param>
/// <returns>The complete configuration.</returns>
public static SimpleItemGeneratorConfiguration GenerateSimpleItemConfiguration(SimpleItemGeneratorPreConfiguration preConfig)
{
// Init
SimpleItemGeneratorConfiguration config = new SimpleItemGeneratorConfiguration()
{
DefaultWeight = preConfig.DefaultWeight,
DefaultCoWeight = preConfig.DefaultCoWeight,
ProbToUseCoWeight = preConfig.ProbToUseCoWeight
};
RandomizerSimple randomizer = new RandomizerSimple(0);
List <SimpleItemDescription> itemDescriptions = new List <SimpleItemDescription>();
List <Tuple <SimpleItemDescription, double> > itemDescriptionWeights = new List <Tuple <SimpleItemDescription, double> >();
List <Tuple <SimpleItemDescription, SimpleItemDescription, double> > itemDescriptionCoWeights = new List <Tuple <SimpleItemDescription, SimpleItemDescription, double> >();
// Add comment
config.Description = string.Join(",", typeof(SimpleItemGeneratorPreConfiguration).GetFields().Select(f =>
{
string fieldValue;
if (f.GetValue(preConfig) is double)
{
fieldValue = ((double)f.GetValue(preConfig)).ToString(IOConstants.FORMATTER);
}
else
{
fieldValue = f.GetValue(preConfig).ToString();
}
return(f.Name + "=" + fieldValue);
}));
// Generate a set of item-descriptions
for (int i = 0; i < preConfig.ItemDescriptionCount; i++)
{
// Generate next item
SimpleItemDescription description = new SimpleItemDescription(null)
{
ID = i
};
// Randomly weight the item
double itemDescriptionWeight = 0;
switch (preConfig.WeightDistributionType)
{
case ItemDescriptionWeightDistributionType.Normal:
itemDescriptionWeight = randomizer.NextNormalDouble(preConfig.ItemWeightMu, preConfig.ItemWeightSigma, preConfig.ItemWeightLB, preConfig.ItemWeightUB);
break;
case ItemDescriptionWeightDistributionType.Uniform:
itemDescriptionWeight = randomizer.NextDouble(preConfig.ItemWeightLB, preConfig.ItemWeightUB);
break;
default: throw new ArgumentException("Unknown distribution: " + preConfig.WeightDistributionType);
}
description.Weight = itemDescriptionWeight;
// Randomly determine bundle size of the item
if (preConfig.SupplyBundleSize)
{
int itemDescriptionBundleSize = 0;
switch (preConfig.BundleSizeDistributionType)
{
case ItemDescriptionBundleSizeDistributionType.Normal:
itemDescriptionBundleSize = randomizer.NextNormalInt(preConfig.BundleSizeMu, preConfig.BundleSizeSigma, preConfig.BundleSizeLB, preConfig.BundleSizeUB);
break;
case ItemDescriptionBundleSizeDistributionType.Uniform:
itemDescriptionBundleSize = randomizer.NextInt(preConfig.BundleSizeLB, preConfig.BundleSizeUB + 1);
break;
default: throw new ArgumentException("Unknown distribution: " + preConfig.BundleSizeDistributionType);
}
description.BundleSize = itemDescriptionBundleSize;
}
// Add a random hue value to distinguish the item from others
description.Hue = randomizer.NextDouble(360);
// Add it
itemDescriptions.Add(description);
// Set a weight for the probability of the item
double weight = 0;
switch (preConfig.ProbWeightDistributionType)
{
case ItemDescriptionProbabilityWeightDistributionType.Constant:
weight = preConfig.ProbabilityWeightConstant;
break;
case ItemDescriptionProbabilityWeightDistributionType.Uniform:
weight = randomizer.NextDouble(preConfig.ProbabilityWeightUniformMin, preConfig.ProbabilityWeightUniformMax);
break;
case ItemDescriptionProbabilityWeightDistributionType.Normal:
weight = randomizer.NextNormalDouble(preConfig.ProbabilityWeightNormalMu, preConfig.ProbabilityWeightNormalSigma, preConfig.ProbabilityWeightLB, preConfig.ProbabilityWeightUB);
break;
case ItemDescriptionProbabilityWeightDistributionType.Exponential:
weight = randomizer.NextExponentialDouble(preConfig.ProbabilityWeightExpLambda, preConfig.ProbabilityWeightLB, preConfig.ProbabilityWeightUB);
break;
case ItemDescriptionProbabilityWeightDistributionType.Gamma:
weight = randomizer.NextGammaDouble(preConfig.ProbabilityWeightGammaK, preConfig.ProbabilityWeightGammaTheta, preConfig.ProbabilityWeightLB, preConfig.ProbabilityWeightUB);
break;
default: throw new ArgumentException("Unknown distribution: " + preConfig.ProbWeightDistributionType);
}
itemDescriptionWeights.Add(new Tuple <SimpleItemDescription, double>(description, weight));
}
// Equally distribute items over two-dimensional space
Dictionary <SimpleItemDescription, Tuple <double, double> > itemDescriptionPosition = new Dictionary <SimpleItemDescription, Tuple <double, double> >();
foreach (var description in itemDescriptions)
{
itemDescriptionPosition[description] = new Tuple <double, double>(randomizer.NextDouble(), randomizer.NextDouble());
}
// Plot the distribution for reference
GnuPlotter.Plot2DPoints(
"itemdistribution",
new List <Tuple <string, IEnumerable <Tuple <double, double> > > >()
{
new Tuple <string, IEnumerable <Tuple <double, double> > >("Item locations in 2D", itemDescriptionPosition.Values)
},
"item distribution for co-probability emulation");
// Set conditional weights
double maxDistance = Distances.CalculateEuclid(0, 0, 1, 1);
foreach (var description in itemDescriptions.OrderBy(d => randomizer.NextDouble()).Take((int)(itemDescriptions.Count * preConfig.GivenCoWeights)))
{
foreach (var otherDescription in itemDescriptions.OrderBy(d => randomizer.NextDouble()).Take((int)(itemDescriptions.Count * preConfig.GivenCoWeights)))
{
itemDescriptionCoWeights.Add(new Tuple <SimpleItemDescription, SimpleItemDescription, double>(
description,
otherDescription,
maxDistance - Distances.CalculateEuclid(
itemDescriptionPosition[description].Item1, itemDescriptionPosition[description].Item2,
itemDescriptionPosition[otherDescription].Item1, itemDescriptionPosition[otherDescription].Item2)));
}
}
// Submit all
config.ItemDescriptions = itemDescriptions.Select(d => new Skvp <int, double>()
{
Key = d.ID, Value = d.Hue
}).ToList();
config.ItemDescriptionWeights = itemDescriptions.Select(d => new Skvp <int, double>()
{
Key = d.ID, Value = d.Weight
}).ToList();
if (preConfig.SupplyBundleSize)
{
config.ItemDescriptionBundleSizes = itemDescriptions.Select(d => new Skvp <int, int>()
{
Key = d.ID, Value = d.BundleSize
}).ToList();
}
config.ItemWeights = itemDescriptionWeights.Select(d => new Skvp <int, double>()
{
Key = d.Item1.ID, Value = d.Item2
}).ToList();
config.ItemCoWeights = itemDescriptionCoWeights.Select(d => new Skkvt <int, int, double>()
{
Key1 = d.Item1.ID, Key2 = d.Item2.ID, Value = d.Item3
}).ToList();
// Name it
config.Name = config.GetMetaInfoBasedName();
// Return it
return(config);
}
public static void TestGenerateNormalDistribution()
{
// Prepare
RandomizerSimple randomizer = new RandomizerSimple(0);
int randomNumberCount = 5000;
// --> Double related
List <List <double> > randomNumbers = new List <List <double> >();
List <List <int> > randomNumbersRounded = new List <List <int> >();
List <Tuple <double, double, double, double> > meanStdTuples = new List <Tuple <double, double, double, double> >()
{
new Tuple <double, double, double, double>(1, 2, double.NegativeInfinity, double.PositiveInfinity),
new Tuple <double, double, double, double>(10, 0.5, double.NegativeInfinity, double.PositiveInfinity),
new Tuple <double, double, double, double>(-7, 5, double.NegativeInfinity, double.PositiveInfinity),
new Tuple <double, double, double, double>(15, 5, 14.5, double.PositiveInfinity),
new Tuple <double, double, double, double>(30, 2, 29, 32),
};
// Draw random double numbers
for (int j = 0; j < meanStdTuples.Count; j++)
{
randomNumbers.Add(new List <double>());
for (int i = 0; i < randomNumberCount; i++)
{
randomNumbers[j].Add(randomizer.NextNormalDouble(meanStdTuples[j].Item1, meanStdTuples[j].Item2, meanStdTuples[j].Item3, meanStdTuples[j].Item4));
}
}
// Round them to get ints
for (int j = 0; j < meanStdTuples.Count; j++)
{
randomNumbersRounded.Add(new List <int>());
for (int i = 0; i < randomNumbers[j].Count; i++)
{
randomNumbersRounded[j].Add((int)Math.Round(randomNumbers[j][i]));
}
}
// Write them
string fileNameBase = "normaldistribution";
for (int i = 0; i < meanStdTuples.Count; i++)
{
using (StreamWriter sw = new StreamWriter(fileNameBase + i.ToString() + ".dat"))
{
foreach (var numberGroup in randomNumbersRounded[i].GroupBy(e => e).OrderBy(g => g.Key))
{
sw.WriteLine(numberGroup.Key.ToString() + " " + numberGroup.Count().ToString());
}
}
}
// Write plot script
using (StreamWriter sw = new StreamWriter(fileNameBase + ".gp"))
{
sw.WriteLine("reset");
sw.WriteLine("# Output definition");
sw.WriteLine("set terminal postscript clip color eps \"Arial\" 14");
sw.WriteLine("# Parameters");
sw.WriteLine("set key left top Left");
sw.WriteLine("set xlabel \"Value\"");
sw.WriteLine("set ylabel \"Count\"");
sw.WriteLine("set grid");
sw.WriteLine("set style fill solid 0.25");
sw.WriteLine("# Line-Styles");
sw.WriteLine("set style line 1 linetype 1 linecolor rgb \"#474749\" linewidth 1");
sw.WriteLine("set style line 2 linetype 1 linecolor rgb \"#7090c8\" linewidth 1");
sw.WriteLine("set style line 3 linetype 1 linecolor rgb \"#42b449\" linewidth 1");
sw.WriteLine("set style line 4 linetype 1 linecolor rgb \"#f7cb38\" linewidth 1");
sw.WriteLine("set style line 5 linetype 1 linecolor rgb \"#db4a37\" linewidth 1");
sw.WriteLine("set title \"Normal distribution - Test\"");
sw.WriteLine("set output \"" + fileNameBase + ".eps\"");
sw.WriteLine("plot \\");
for (int i = 0; i < meanStdTuples.Count; i++)
{
if (i < meanStdTuples.Count - 1)
{
sw.WriteLine("\"" + fileNameBase + i.ToString() + ".dat\" u 1:2 w boxes linestyle " + ((i % 5) + 1) +
" t \"mean: " + meanStdTuples[i].Item1.ToString(IOConstants.FORMATTER) +
" std: " + meanStdTuples[i].Item2.ToString(IOConstants.FORMATTER) +
" lb: " + (double.IsNegativeInfinity(meanStdTuples[i].Item3) ? "na" : meanStdTuples[i].Item3.ToString(IOConstants.FORMATTER)) +
" ub: " + (double.IsPositiveInfinity(meanStdTuples[i].Item4) ? "na" : meanStdTuples[i].Item4.ToString(IOConstants.FORMATTER)) + "\", \\");
}
else
{
sw.WriteLine("\"" + fileNameBase + i.ToString() + ".dat\" u 1:2 w boxes linestyle " + ((i % 5) + 1) +
" t \"mean: " + meanStdTuples[i].Item1.ToString(IOConstants.FORMATTER) +
" std: " + meanStdTuples[i].Item2.ToString(IOConstants.FORMATTER) +
" lb: " + (double.IsNegativeInfinity(meanStdTuples[i].Item3) ? "na" : meanStdTuples[i].Item3.ToString(IOConstants.FORMATTER)) +
" ub: " + (double.IsPositiveInfinity(meanStdTuples[i].Item4) ? "na" : meanStdTuples[i].Item4.ToString(IOConstants.FORMATTER)) + "\"");
}
}
sw.WriteLine("reset");
sw.WriteLine("exit");
}
using (StreamWriter sw = new StreamWriter(fileNameBase + ".cmd"))
{
sw.WriteLine("gnuplot " + fileNameBase + ".gp");
}
// --> Int related
int randomNumberCountInt = 200;
List <List <int> > randomNumbersInt = new List <List <int> >();
List <Tuple <double, double, int, int> > meanStdTuplesInt = new List <Tuple <double, double, int, int> >()
{
new Tuple <double, double, int, int>(1, 1.5, 1, 4),
new Tuple <double, double, int, int>(4, 0.5, 0, int.MaxValue),
new Tuple <double, double, int, int>(-7, 3, int.MinValue, int.MaxValue),
};
// Draw random int numbers
for (int j = 0; j < meanStdTuplesInt.Count; j++)
{
randomNumbersInt.Add(new List <int>());
for (int i = 0; i < randomNumberCountInt; i++)
{
randomNumbersInt[j].Add(randomizer.NextNormalInt(meanStdTuplesInt[j].Item1, meanStdTuplesInt[j].Item2, meanStdTuplesInt[j].Item3, meanStdTuplesInt[j].Item4));
}
}
// Write them
string fileNameBaseInt = "normaldistributionint";
for (int i = 0; i < meanStdTuplesInt.Count; i++)
{
using (StreamWriter sw = new StreamWriter(fileNameBaseInt + i.ToString() + ".dat"))
{
foreach (var numberGroup in randomNumbersInt[i].GroupBy(e => e).OrderBy(g => g.Key))
{
sw.WriteLine(numberGroup.Key.ToString() + " " + numberGroup.Count().ToString());
}
}
}
// Write plot script
using (StreamWriter sw = new StreamWriter(fileNameBaseInt + ".gp"))
{
sw.WriteLine("reset");
sw.WriteLine("# Output definition");
sw.WriteLine("set terminal postscript clip color eps \"Arial\" 14");
sw.WriteLine("# Parameters");
sw.WriteLine("set key left top Left");
sw.WriteLine("set xlabel \"Value\"");
sw.WriteLine("set ylabel \"Count\"");
sw.WriteLine("set grid");
sw.WriteLine("set style fill solid 0.25");
sw.WriteLine("# Line-Styles");
sw.WriteLine("set style line 1 linetype 1 linecolor rgb \"#474749\" linewidth 1");
sw.WriteLine("set style line 2 linetype 1 linecolor rgb \"#7090c8\" linewidth 1");
sw.WriteLine("set style line 3 linetype 1 linecolor rgb \"#42b449\" linewidth 1");
sw.WriteLine("set style line 4 linetype 1 linecolor rgb \"#f7cb38\" linewidth 1");
sw.WriteLine("set style line 5 linetype 1 linecolor rgb \"#db4a37\" linewidth 1");
sw.WriteLine("set title \"Normal distribution (int) - Test\"");
sw.WriteLine("set output \"" + fileNameBaseInt + ".eps\"");
sw.WriteLine("plot \\");
for (int i = 0; i < meanStdTuplesInt.Count; i++)
{
if (i < meanStdTuplesInt.Count - 1)
{
sw.WriteLine("\"" + fileNameBaseInt + i.ToString() + ".dat\" u 1:2 w boxes linestyle " + ((i % 5) + 1) +
" t \"mean: " + meanStdTuplesInt[i].Item1.ToString(IOConstants.FORMATTER) +
" std: " + meanStdTuplesInt[i].Item2.ToString(IOConstants.FORMATTER) +
" lb: " + ((int.MinValue == meanStdTuplesInt[i].Item3) ? "na" : meanStdTuplesInt[i].Item3.ToString(IOConstants.FORMATTER)) +
" ub: " + ((int.MaxValue == meanStdTuplesInt[i].Item4) ? "na" : meanStdTuplesInt[i].Item4.ToString(IOConstants.FORMATTER)) + "\", \\");
}
else
{
sw.WriteLine("\"" + fileNameBaseInt + i.ToString() + ".dat\" u 1:2 w boxes linestyle " + ((i % 5) + 1) +
" t \"mean: " + meanStdTuplesInt[i].Item1.ToString(IOConstants.FORMATTER) +
" std: " + meanStdTuplesInt[i].Item2.ToString(IOConstants.FORMATTER) +
" lb: " + ((int.MinValue == meanStdTuplesInt[i].Item3) ? "na" : meanStdTuplesInt[i].Item3.ToString(IOConstants.FORMATTER)) +
" ub: " + ((int.MaxValue == meanStdTuplesInt[i].Item4) ? "na" : meanStdTuplesInt[i].Item4.ToString(IOConstants.FORMATTER)) + "\"");
}
}
sw.WriteLine("reset");
sw.WriteLine("exit");
}
using (StreamWriter sw = new StreamWriter(fileNameBaseInt + ".cmd"))
{
sw.WriteLine("gnuplot " + fileNameBaseInt + ".gp");
}
}
