/// <summary>
/// foamliu, 2008/12/30.
/// Notes: the order of presentation of training examples should be randomized from epoch
/// to epoch.
///
/// </summary>
private void ShuffleTrainSet()
{
ExampleSet t_set = this.TrainSet;
int num = t_set.Examples.Count;
Random rand = new Random();
int[] cards = new int[num];
for (int i = 0; i < num; i++)
{
cards[i] = i;
}
for (int i = 0; i < num; i++)
{
int temp;
int j = (int)(rand.NextDouble() * num); // 0 - (num-1)
temp = cards[i];
cards[i] = cards[j];
cards[j] = temp;
}
Example[] examples = new Example[num];
for (int i = 0; i < num; i++)
{
examples[i] = t_set.Examples[cards[i]];
}
t_set.Examples.Clear();
for (int i = 0; i < num; i++)
{
t_set.Examples.Add(examples[i]);
}
}
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);
}
public static void Main(string[] args)
{
var exampleSet = new ExampleSet()
.WithExample(new ParallelInvokeOneActionExample())
.WithExample(new ParallelInvokeManyActionsExample())
.WithExample(new ParallelInvokeBigAmountOfActionsExample())
.WithExample(new ParallelInvokeConfigurationExample())
.WithExample(new ParallelForExample())
.WithExample(new ParallelForEachExample())
.WithExample(new ParallelForManageStateExample())
.WithExample(new ParallelForManageLocalStateExample())
.WithExample(new ParallelForManageLocalStatePerformanceExample())
.WithExample(new EnumerableParallelExample())
.WithExample(new EnumerableParallelExceptionExample())
.WithExample(new TasksExample())
.WithExample(new TasksContinuationExample())
.WithExample(new TaskChildExample())
.WithExample(new ThreadExample())
.WithExample(new ThreadAbortExample())
.WithExample(new ThreadLocalAndStaticExample())
.WithExample(new ThreadPoolExample())
.WithExample(new AsyncExceptionExample())
.WithExample(new BlockingCollectionExample())
.WithExample(new BlockingStackExample())
.WithExample(new ConcurrentStackExample())
.WithExample(new ConcurrentDictionaryExample())
;
exampleSet.RunLast();
Console.ReadLine();
}
private void CalculateWeight(ExampleSet t_Set)
{
for (int i = 0; i < l; i++)
{
m_weight.Add((t_Set.Examples[i].Label.Id * m_Alpha[i]) * t_Set.Examples[i].X);
}
}
public LinearLeraningMachine(ClassificationProblem problem)
{
this.m_problem = problem;
this.m_t_set = problem.TrainingSet;
//this.m_problem.RetrieveVocabulary(out this.m_voc);
this.m_l = m_t_set.Examples.Count;
//this.m_weight = new SparseVector(m_voc.Count);
}
public static void Main(string[] args)
{
var examples = new ExampleSet()
.WithExample(new RequestsExample())
;
examples.RunLast();
Console.ReadLine();
}
public static void Main(string[] args)
{
var exampleSet = new ExampleSet()
.WithExample(new DelegateSubscriptionExample())
.WithExample(new EventsErrorsExample())
;
exampleSet.RunLast();
Console.ReadLine();
}
private static void Main(string[] args)
{
var exampleSet = new ExampleSet()
.WithExample(new RaceConditionExample())
.WithExample(new MonitorExample())
;
exampleSet.RunLast();
Console.ReadLine();
}
public AdaBoost(ExampleSet t_set, int t, int n)
{
m_t_set = t_set;
m_m = m_t_set.Examples.Count;
m_dist = new double[this.m_m];
m_t = t;
m_n = n;
m_a = new double[m_t];
m_h = new WeakLearn[m_t];
}
/// <summary>
/// No bias
/// </summary>
/// <param name="t_Set"></param>
/// <param name="x"></param>
/// <returns></returns>
private double Calculate_F(ExampleSet t_Set, SparseVector x)
{
double f = 0.0;
for (int i = 0; i < m_Alpha.Length; i++)
{
f += m_Alpha[i] * t_Set.Examples[i].Label.Id * m_kernel.Compute(t_Set.Examples[i].X, x);
}
return(f);
}
/// <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);
}
private static void Main(string[] args)
{
var examples = new ExampleSet()
.WithExample(new CodeDomExample())
.WithExample(new ConditionalExample())
.WithExample(new GCExample())
.WithExample(new StringReaderWriterExample())
;
examples.RunLast();
Console.ReadLine();
}
public void PredictText(ExampleSet t_Set, Example text, ref ClassificationResult result)
{
double f;
f = Calculate_F(t_Set, text.X);
if (f >= 0)
{
result.ResultCategoryId = +1;
}
else
{
result.ResultCategoryId = -1;
}
}
public static void Main(string[] args)
{
var examples = new ExampleSet()
.WithExample(new JsonExample())
.WithExample(new RegexExample())
.WithExample(new RegexCompilationExample())
.WithExample(new ValidationExample())
.WithExample(new TraceExample())
.WithExample(new TraceSourceExample())
.WithExample(new CounterReadExample())
.WithExample(new CounterCreateExample())
;
examples.RunLast();
Console.ReadLine();
}
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));
}
}
public Binary_SVM_SMO(ClassificationProblem problem)
{
this.m_problem = problem;
this.m_t_set = this.m_problem.TrainingSet;
// this.m_problem.RetrieveVocabulary(out this.m_voc);
this.m_l = m_t_set.Examples.Count;
this.m_alpha = new double[m_l];
this.m_error = new double[m_l];
this.m_kernel = new LinearKernel();
this.m_NonBound = new List <int>();
this.m_rand = new Random();
this.m_weight = new SparseVector(problem.Dimension);
// foamliu, 2009/01/12, default values
this.m_c = Constants.SVM_C;
this.m_eta = Constants.SVM_Eta;
this.m_tolerance = Constants.SVM_Tolerance;
this.m_epsilon = Constants.SVM_Epsilon;
}
private double CalculateSVM_W(ExampleSet t_Set)
{
double W = 0.0;
double temp = 0.0;
for (int i = 0; i < m_Alpha.Length; i++)
{
temp += m_Alpha[i];
}
W = temp;
temp = 0.0;
for (int i = 0; i < m_Alpha.Length; i++)
{
for (int j = 0; j < m_Alpha.Length; j++)
{
temp += t_Set.Examples[i].Label.Id * t_Set.Examples[j].Label.Id * m_Alpha[i] * m_Alpha[j] * m_kernel.Compute(t_Set.Examples[i].X, t_Set.Examples[j].X);
}
}
W = W - temp / 2;
return(W);
}
public ID3(ExampleSet t_set, Vocabulary voc)
{
this.m_t_set = t_set;
this.m_voc = voc;
this.m_wordGainList = new List <WordGainPair>();
}
/// <summary>
/// Adds a new example set to the vault with the given name and the given
/// storage tag.
/// </summary>
/// <param name="eset">The example set to store</param>
/// <param name="name">The name of the example set</param>
/// <param name="tag">The Data Storage Tag for the example set</param>
public void AddDataFile(ExampleSet eset, DataStorageTag tag, String name) {
myManager.AddObject(eset, tag, name);
}
public ID3(ExampleSet t_set, Vocabulary voc)
{
this.m_t_set = t_set;
this.m_voc = voc;
this.m_wordGainList = new List<WordGainPair>();
}
/// <summary>
/// Adds a new example set to the vault. Calls the <code>addDataFile</code>
/// method with a new empty Data Storage Tag.
/// </summary>
/// <param name="eset">The example set to store</param>
/// <param name="name">The name of the example set</param>
public void AddDataFile(ExampleSet eset, String name) {
AddDataFile(eset, new DataStorageTag(), name);
}
/// <summary>
/// Begins the work.
/// </summary>
protected override void BeginWork()
{
BootParameters.ShouldNotBe(null);
base.BeginWork();
_trainingSetParameter = (ExampleSet)XmlUtility.DeserializeObject(BootParameters, typeof(ExampleSet));
}
/// <summary>
/// Updates the given data file and data storage tag.
/// </summary>
/// <param name="eset">The data to update</param>
/// <param name="name">The name of the data set</param>
/// <param name="tag">The data storage tag for the data</param>
public void UpdateDataFile(ExampleSet eset, DataStorageTag tag, String name)
{
myManager.UpdateFile(eset, tag, name);
}
/// <summary>
/// Begins the work.
/// </summary>
protected override void BeginWork()
{
BootParameters.ShouldNotBe(null);
base.BeginWork();
_trainingSetParameter = (ExampleSet)XmlUtility.DeserializeObject(BootParameters, typeof(ExampleSet));
}
/// <summary>
/// Updates the given data file and data storage tag.
/// </summary>
/// <param name="eset">The data to update</param>
/// <param name="name">The name of the data set</param>
/// <param name="tag">The data storage tag for the data</param>
public void UpdateDataFile(ExampleSet eset, DataStorageTag tag, String name) {
myManager.UpdateFile(eset, tag, name);
}
/// <summary>
/// Calls update data file but does not update the data storage tag.
/// </summary>
/// <param name="eset">The example set to update</param>
/// <param name="name">The name of the example set</param>
public void updateDataFile(ExampleSet eset, String name) {
myManager.UpdateFile(name, eset);
}
protected virtual void AppendExampleSetLine(StringBuilder result, GherkinDialect dialect, ExampleSet exampleSet)
{
result.AppendFormat("{0}: {1}", dialect.GetBlockKeywords(GherkinBlockKeyword.Examples).First(), exampleSet.Title);
AppendLine(result);
}
/// <summary>
/// Adds a new example set to the vault with the given name and the given
/// storage tag.
/// </summary>
/// <param name="eset">The example set to store</param>
/// <param name="name">The name of the example set</param>
/// <param name="tag">The Data Storage Tag for the example set</param>
public void AddDataFile(ExampleSet eset, DataStorageTag tag, String name)
{
myManager.AddObject(eset, tag, name);
}
private void GenerateScenarioOutlineTestVariant(CodeTypeDeclaration testType, ScenarioOutline scenarioOutline, string testMethodName, List <KeyValuePair <string, string> > paramToIdentifier, string exampleSetTitle, Row row, Tags exampleSetTags, string variantName, ExampleSet exampleSet)
{
CodeMemberMethod testMethod = GetTestMethodDeclaration(testType, scenarioOutline, exampleSetTags);
testMethod.Name = string.IsNullOrEmpty(exampleSetTitle)
? string.Format("{0}_{1}", testMethod.Name, variantName)
: string.Format("{0}_{1}_{2}", testMethod.Name, exampleSetTitle, variantName);
//call test implementation with the params
List <CodeExpression> argumentExpressions = new List <CodeExpression>();
foreach (var paramCell in row.Cells)
{
argumentExpressions.Add(new CodePrimitiveExpression(paramCell.Value));
}
argumentExpressions.Add(GetStringArrayExpression(exampleSetTags));
testMethod.Statements.Add(
new CodeMethodInvokeExpression(
new CodeThisReferenceExpression(),
testMethodName,
argumentExpressions.ToArray()));
List <KeyValuePair <string, string> > arguments = new List <KeyValuePair <string, string> >();
for (int rowIndex = 0; rowIndex < exampleSet.Table.Body.Length; rowIndex++)
{
arguments.Add(new KeyValuePair <string, string>(exampleSet.Table.Header.Cells[rowIndex].Value, row.Cells[rowIndex].Value));
}
testGeneratorProvider.SetTestVariant(testMethod, scenarioOutline.Title, exampleSetTitle, arguments);
}
public WeakLearn(int index, ExampleSet t_set)
{
m_index = index;
m_t_set = t_set;
m_m = t_set.Count;
}
public SimpleLLM(ExampleSet t_set, int d)
{
this.m_t_set = t_set;
this.m_l = t_set.Examples.Count;
this.m_weight = new SparseVector(d);
}
/// <summary>
/// Calls update data file but does not update the data storage tag.
/// </summary>
/// <param name="eset">The example set to update</param>
/// <param name="name">The name of the example set</param>
public void updateDataFile(ExampleSet eset, String name)
{
myManager.UpdateFile(name, eset);
}
/// <summary>
/// Adds a new example set to the vault. Calls the <code>addDataFile</code>
/// method with a new empty Data Storage Tag.
/// </summary>
/// <param name="eset">The example set to store</param>
/// <param name="name">The name of the example set</param>
public void AddDataFile(ExampleSet eset, String name)
{
AddDataFile(eset, new DataStorageTag(), name);
}
