public void ClassifyDataSourceResult(DataSourceResult news)
{
BayesianClassifier classifier =
new BayesianClassifier(this, new DefaultTokenizer(), new CustomizableStopWordProvider());
List<Category> mainCategories = this.Data.Categories.All().DistinctBy(s => s.BaseCategoryId).ToList();
var sequenceEnum = news.Data.GetEnumerator();
while (sequenceEnum.MoveNext())
{
var article = (sequenceEnum.Current as NewsItemViewModel);
decimal? categoryPosibiity = 0;
decimal? maxPropability = 0;
string maxProbCategory = "";
for (int i = 0; i < mainCategories.Count; i++)
{
bool isMatch = classifier.IsMatch(mainCategories[i].Name, article.Header, ref categoryPosibiity, mainCategories[i].Id);
if (isMatch)
{
article.ClassificationCategory = mainCategories[i].Name;
article.ClassificationProbability = categoryPosibiity.Value;
break;
}
else
{
if (categoryPosibiity > maxPropability)
{
maxPropability = categoryPosibiity;
maxProbCategory = mainCategories[i].Name;
}
}
}
if (string.IsNullOrEmpty( article.ClassificationCategory))
{
article.ClassificationCategory = maxProbCategory;
article.ClassificationProbability = maxPropability.Value;
}
}
}
public string ClassifyArticle(int articleId)
{
BayesianClassifier classifier =
new BayesianClassifier(this, new DefaultTokenizer(), new CustomizableStopWordProvider());
NewsItem article = this.Data.NewsItems.FirstOrDefault(s => s.Id == articleId);
List<Category> mainCategories = this.Data.Categories.All().DistinctBy(s => s.BaseCategoryId).ToList();
string category = "";
decimal? probResult = 0;
decimal? maxProbResult = 0;
for (int i = 0; i < mainCategories.Count; i++)
{
bool isMatch = classifier.IsMatch(mainCategories[i].Name, article.Header, ref probResult, mainCategories[i].Id);
if (isMatch)
{
category = mainCategories[i].Name;
break;
}
else
{
if (probResult>maxProbResult)
{
maxProbResult = probResult;
category= mainCategories[i].Name;
}
}
}
return category;
}
