public ExampleSet GetProof(SparseVector x)
{
ExampleSet res = new ExampleSet();
List <ExampleDistancePair> list = new List <ExampleDistancePair>();
foreach (Example e in m_t_set.Examples)
{
list.Add(new ExampleDistancePair(e, SparseVector.Distance(x, e.X)));
}
list.Sort();
int[] votes = new int[m_catnum];
for (int i = 0; i < votes.Length; i++)
{
votes[i] = 0;
}
for (int i = 0; i < this.m_k; i++)
{
ExampleDistancePair pair = list[i];
res.AddExample(pair.Example);
}
return(res);
}
/// <summary>
/// foamliu, 2009/04/15, 生成样本.
/// </summary>
/// <param name="set"></param>
private static ExampleSet GetExamples(CategoryCollection collect)
{
const int Rows = 4;
const int Columns = 4;
const int CellWidth = 100;
const int CellHeight = 100;
const int ExampleNumber = 640;
ExampleSet set = new ExampleSet();
set.Examples.Clear();
Random rand = new Random();
for (int i = 0; i < ExampleNumber; i++)
{
int x = (int)(rand.NextDouble() * Columns * CellWidth);
int y = (int)(rand.NextDouble() * Rows * CellHeight);
Example e = new Example();
e.X = new SparseVector(2);
e.X[0] = x;
e.X[1] = y;
e.Label = collect.GetCategoryById(
GetCat(x, y, CellWidth, CellHeight));
set.AddExample(e);
}
return(set);
}
/// <summary>
/// foamliu, 2009/12/21, please make sure you've uncompressed "2_newsgroups.7z" in the "data" folder.
/// </summary>
/// <returns></returns>
private static ClassificationProblem CreateText()
{
const string DataFolder = @"..\data\2_newsgroups";
ClassificationProblem problem = new ClassificationProblem();
ExampleSet t_set = new ExampleSet();
ExampleSet v_set = new ExampleSet();
CategoryCollection collect = new CategoryCollection();
collect.Add(new Category(+1, "+1"));
collect.Add(new Category(-1, "-1"));
problem.Dimension = 2;
problem.CategoryCollection = collect;
DirectoryInfo dataFolder = new DirectoryInfo(DataFolder);
DirectoryInfo[] subfolders = dataFolder.GetDirectories();
int count = 0;
for (int i = 0; i < subfolders.Count(); i++)
{
DirectoryInfo categoryFolder = subfolders[i];
int cat = i * 2 - 1;
// for all the text files in each category
FileInfo[] files = categoryFolder.GetFiles();
count = 0;
int trainSetCount = Convert.ToInt32(Constants.TrainingSetRatio * files.Count());
for (int j = 0; j < files.Count(); j++)
{
FileInfo textFile = files[j];
Example e = new Example();
if (++count < trainSetCount)
{
t_set.AddExample(e);
}
else
{
v_set.AddExample(e);
}
}
}
problem.TrainingSet = t_set;
problem.ValidationSet = v_set;
return(problem);
}
public void Test()
{
int l = 30;
int k = 10;
double ratioSeparable = 0;
int numSeparable = 0;
ExampleSet set = new ExampleSet();
for (int d = 10; d < 50; d = d + 10)
{
numSeparable = 0;
for (int n = 0; n < k; n++)
{
set.Examples.Clear();
for (int i = 0; i < l; i++)
{
SparseVector x = new SparseVector(d);
for (int j = 0; j < d; j++)
{
x[j] = m_rand.NextDouble();
}
Category c = GetRandCategory();
Example e = new Example(c);
e.X = x;
set.AddExample(e);
}
SimpleLLM llm = new SimpleLLM(set, d);
//Logging.Info(string.Format("IsLinearSeparable: {0}", llm.IsLinearSeparable()));
//System.Console.WriteLine(string.Format("IsLinearSeparable: {0}", llm.IsLinearSeparable()));
if (llm.IsLinearSeparable())
{
numSeparable++;
}
}
ratioSeparable = 1.0 * numSeparable / k;
System.Console.WriteLine(string.Format("d: {0}, l: {1}, Separable ratio: {2}", d, l, ratioSeparable));
}
}