public override void Predict(TextClassificationProblem problem, TextExample text, ref ClassificationResult result)
{
int j = 0, k; // j is index in categories, while k is index in vocabulary.
double logv;
Vocabulary voc;
voc = problem.TrainingSetVocabulary;
foreach (Category c in m_CategoryCollection.Collection)
{
logv = 0.0; // reset
logv += Math.Log(m_prob_vj[j]);
// for all the word (token) in the text
foreach (string token in text.Tokens.Keys)
{
if (voc.WordBag.ContainsKey(token))
{
// Get the position of this token
// in m_TotalTrainingSetTokens.
k = voc.WordPositionMap[token];
// Look up the probability in the table.
logv += m_prob_wk_vj_log[k, j];
}
}
result.CategoryName2LogVMap.Add(c.Name, logv);
j++; // next category
}
result.Normalize();
}
public double CrossValidate()
{
ExampleSet v_Set; // validation set
//Logging.Info("Retrieving validation set");
v_Set = this.m_problem.ValidationSet;
int numExample = v_Set.Examples.Count;
int numCorrect = 0;
//Logging.Info("Cross Validating on validation set");
foreach (Example example in v_Set.Examples)
{
ClassificationResult result = new ClassificationResult();
if (this.PredictText(example) == example.Label.Id)
{
numCorrect++;
}
}
double correctRatio = 1.0 * numCorrect / numExample;
Logger.Info(string.Format("Correct ratio: {0}", correctRatio));
return correctRatio;
}
public void CrossValidate(TextClassificationProblem problem, out double correctRatio, out double falsePositive, out double falseNegative)
{
ExampleSet v_Set; // validation set
//Logging.Info("Retrieving validation set");
v_Set = problem.ValidationSet;
int numExample = v_Set.Examples.Count;
int numCorrect = 0;
int numFP = 0, numFN = 0;
//Logging.Info("Cross Validating on validation set");
foreach (TextExample example in v_Set.Examples)
{
ClassificationResult result = new ClassificationResult();
this.Predict(problem, example, ref result);
string res = this.MinCostClassName(result);
if (res == example.Label.Name)
{
numCorrect++;
}
else if (res == className[0] && example.Label.Name == className[1])
{
numFP++;
}
else if (res == className[1] && example.Label.Name == className[0])
{
numFN++;
}
}
correctRatio = 1.0 * numCorrect / numExample;
falsePositive = 1.0 * numFP / numExample;
falseNegative = 1.0 * numFN / numExample;
Logger.Info("Correct ratio: {0}", correctRatio);
Logger.Info("False Positive: {0}", falsePositive);
Logger.Info("False Negative: {0}", falseNegative);
}
public double CrossValidate(TextClassificationProblem problem)
{
ExampleSet v_Set; // validation set
//Logging.Info("Retrieving validation set");
v_Set = problem.ValidationSet;
int numExample = v_Set.Examples.Count;
int numCorrect = 0;
//Logging.Info("Cross Validating on validation set");
foreach (TextExample example in v_Set.Examples)
{
ClassificationResult result = new ClassificationResult();
this.Predict(problem, example, ref result);
if (result.Vnb == example.Label.Name)
{
numCorrect++;
}
}
double correctRatio = 1.0 * numCorrect / numExample;
Logger.Info(string.Format("Correct ratio: {0}", correctRatio));
return correctRatio;
}
private string MinCostClassName(ClassificationResult result)
{
double[] cost = new double[2];
for (int i = 0; i < 2; i++)
{
cost[i] = 0;
for (int j = 0; j < 2; j++)
{
cost[i] += costMat[i, j] * result.CategoryName2LogVMap[className[j]];
}
}
// find classname with minimized cost
if (cost[0] <= cost[1])
return className[0];
else
return className[1];
}
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;
}
}