HsRangeCa CalculateHsRangeCa(string bucketCounts, int [] samplesCount, bool isVerbose)
{
ClusterTree rt = new ClusterTree();
Props parameters = GetParams(bucketCounts);
parameters.Set("IsCreatingClusterTree", "true");
HsRangeCa ca = new HsRangeCa(parameters);
CaMcGen gen = new CaMcGen
{
Clusterizer = ca,
IsVerbose = isVerbose,
// IsVerboseSamples = true,
RngSeed = 0,
SamplesCount = samplesCount
};
rt.Root = gen.Generate();
string fileName = Path.Combine(_outDir, bucketCounts.Replace(' ', 'x')) + ".dat";
rt.Write(fileName);
parameters.Set("ClusterTreeFile", fileName);
parameters.Set("IsCreatingClusterTree", "false");
HsRangeCa ca1 = new HsRangeCa(parameters);
return(ca1);
}
public void Test_ReadWrite()
{
ClusterTree rt1 = new ClusterTree();
RangeNode root = new RangeNode(5);
rt1.Root = root;
for (int i = 0; i < 5; ++i)
{
root.Children[i] = new RangeNode(0)
{
UpperLimit = 0.1f * i
};
}
rt1.Version.UserDescription = "Bla bla";
string fileName = Path.Combine(_outDir, "ClusterTree.dat");
rt1.Write(fileName);
ClusterTree rt2 = ClusterTree.Read(fileName);
Assert.AreEqual(rt1.Version, rt2.Version);
Assert.IsTrue(CompareTrees <int, int> .Compare(rt1, rt1.Root, rt2, rt2.Root,
(t1, n1, t2, n2) =>
{
return(((RangeNode)n1).UpperLimit == ((RangeNode)n2).UpperLimit);
}));
}
HsSdKMeansAdaptiveCa CalculateCa(Props parameters, int[] samplesCount, int rngSeed)
{
ClusterTree rt = new ClusterTree();
parameters.Set("IsCreatingClusterTree", "true");
HsSdKMeansAdaptiveCa ca = new HsSdKMeansAdaptiveCa(parameters);
CaMcGen gen = new CaMcGen
{
Clusterizer = ca,
IsVerbose = false,
// IsVerboseSamples = true,
RngSeed = rngSeed,
SamplesCount = samplesCount
};
rt.Root = gen.Generate();
string fileName = Path.Combine(_outDir, "ca-hssd-km.dat");
rt.Write(fileName);
parameters.Set("ClusterTreeFile", fileName);
parameters.Set("IsCreatingClusterTree", "false");
HsSdKMeansAdaptiveCa ca1 = new HsSdKMeansAdaptiveCa(parameters);
return(ca1);
}
static int Main(string[] args)
{
if (!Parser.ParseArgumentsWithUsage(args, _cmdLine))
{
return(1);
}
if (_cmdLine.DebuggerLaunch)
{
Debugger.Launch();
}
Props caProps = XmlSerializerExt.Deserialize <Props>(_cmdLine.ChanceAbstractionFile);
caProps.Set("IsCreatingClusterTree", "true");
IChanceAbstraction ca = ChanceAbstractionHelper.CreateFromProps(caProps);
Console.WriteLine("CA: {0}", ca.Name);
List <int> samplesCount = new List <int>();
foreach (string sc in _cmdLine.SamplesCount.Split(new char[] { ',' }, StringSplitOptions.RemoveEmptyEntries))
{
samplesCount.Add(int.Parse(sc));
}
int rngSeed = _cmdLine.RngSeed == 0 ? (int)DateTime.Now.Ticks : _cmdLine.RngSeed;
Console.WriteLine("RNG seed: {0}", rngSeed);
CaMcGen gen = new CaMcGen
{
Clusterizer = (IClusterizer)ca,
IsVerbose = true,
// IsVerboseSamples = true,
RngSeed = rngSeed,
SamplesCount = samplesCount.ToArray()
};
ClusterTree rt = new ClusterTree();
rt.Root = gen.Generate();
string dir = Path.GetDirectoryName(_cmdLine.ChanceAbstractionFile);
string file = Path.GetFileNameWithoutExtension(_cmdLine.ChanceAbstractionFile) + ".dat";
string fileName = Path.Combine(dir, file);
Console.WriteLine("Writing range tree to {0}", fileName);
rt.Write(fileName);
return(0);
}