// Extract and store features
public static void extractFeatures(string folderPath, int M, string output = "articles.feat")
{
DocCollection collection = new DocCollection();
// Collect all files
foreach (string filepath in Directory.GetFiles(folderPath, "*.txt"))
{
string content = File.ReadAllText(filepath);
collection.collect(content);
}
List<DocVector> docVect = collectionProcessing(collection, 20);
// Store features
using (StreamWriter writer = new StreamWriter(output, false))
{
writer.WriteLine(Directory.GetFiles(folderPath, "*.txt").Count()); // Number of files
writer.WriteLine(M); // Number of keywords
// Store bag of words
for (int i = 0; i < M; ++i)
writer.Write(keywords[i] + " ");
writer.WriteLine();
// Store feature for each document
foreach (DocVector vector in docVect)
{
for (int i = 0; i < M; ++i)
writer.Write(vector.Tf_idf[i] + " ");
writer.WriteLine();
}
}
}
//private static Regex r = new Regex("([ \\t{}()\",:;. \n])");
private static List<DocVector> collectionProcessing(DocCollection collection, int M)
{
Dictionary<string, int> globalTerms = new Dictionary<string, int>();
/*
* Remove stopwords
* Stemming
* Chose M keywords
* Compute tf-idf
*/
// Chose global keywords base on document frequency value
foreach (string documentContent in collection.RawDocs)
{
// Remove stopwords
string[] words = StopwordRemoval.removeStopword(documentContent);
// Stemming
words = stemmer.stem_list(words);
// Construct a set of distinct terms of current document
HashSet<string> distinctTerms = new HashSet<string>();
foreach (string word in words)
distinctTerms.Add(word);
// Update represented value of document frequency for found distinct terms
foreach (string term in distinctTerms)
{
if (globalTerms.ContainsKey(term))
globalTerms[term]++;
else
globalTerms.Add(term, 1);
}
}
// Sort globalTerms dictionary and use the top M pair as keywords
var keywords_df = from pair in globalTerms
orderby Math.Abs(pair.Value - collection.RawDocs.Count / 5) ascending
select pair;
List<DocVector> docVectorSpace = new List<DocVector>();
float[] space = new float[M];
foreach (string document in collection.RawDocs)
{
// Calculate tf-idf
int topRange = M * 5;
HashSet<int> indices = new HashSet<int>();
Random r = new Random((int)DateTime.Now.Ticks & 0x0000FFFF);
do
{
int pos = r.Next(0, topRange);
indices.Add(pos);
} while (indices.Count < M);
DocVector _docVector = new DocVector(M);
int i = 0;
foreach (int pos in indices)
{
KeyValuePair<string, int> pair = keywords_df.ElementAt(pos);
_docVector.Tf_idf[i] = (float)(Math.Log(collection.RawDocs.Count * 1.0 / pair.Value) * calc_tf(document, pair.Key));
++i;
}
//DocVector _docVector = new DocVector(M);
//int i = 0;
//foreach (KeyValuePair<string, int> pair in keywords_df)
//{
// _docVector.Tf_idf[i] = (float)(Math.Log(collection.RawDocs.Count * 1.0 / pair.Value) * calc_tf(document, pair.Key));
// ++i;
// if (i >= M)
// break;
//}
docVectorSpace.Add(_docVector);
}
// Store keywords
keywords.Clear();
foreach (KeyValuePair<string, int> pair in keywords_df)
keywords.Add(pair.Key);
// Return vector space of whole collection
return docVectorSpace;
}
