public Centroid(Centroid c)
{
// perform deep copy
this.name = new StringBuilder(c.getName());
this.vectorCnt = c.getVectorCnt();
this.centroidVector = new Dictionary <string, double>(c.getCentroidVector());
this.length = c.getLength();
}
static public double similarity(Centroid a, Centroid b)
{
Dictionary <string, double> vectorA = a.getCentroidVector();
Dictionary <string, double> vectorB = b.getCentroidVector();
double lengthA = a.getLength();
double lengthB = b.getLength();
double dotproduct = 0.0;
// compute dot product of vectors A & B
foreach (KeyValuePair <string, double> kvp in vectorA)
{
// if both vectors have the key
if (vectorB.ContainsKey(kvp.Key))
{
dotproduct += (kvp.Value * vectorB[kvp.Key]);
}
}
return(dotproduct / (lengthA * lengthB));
}
static void Main(string[] args)
{
StringBuilder dictpath = new StringBuilder(256);
StringBuilder postingpath = new StringBuilder(256);
// keyed on term returns dictionary with docIDs as keys for tf-idf
Dictionary <string, Dictionary <string, double> > termdocmatrix = new Dictionary <string, Dictionary <string, double> >(2000);
int documentCnt = 0; // this is used to help with memory estimation
char[] delim = { '+' };
DateTime start = DateTime.Now;
if (args.Length != 2)
{
System.Console.WriteLine("Invalid Parameters: <dictionaryfile.txt> <postingsfile.txt>");
return;
}
else
{
dictpath.Append(args[0]);
postingpath.Append(args[1]);
}
int recordSize = 22;
// read in dictionary and postings file
// build term document matrix
#region BuildTermDocMatrix
StreamReader dictFile = new StreamReader(dictpath.ToString());
FileStream postingslist = new FileStream(postingpath.ToString(), FileMode.Open);
string dictline = "";
// read in dictionary line by line (each line is a record of fixed length)
while ((dictline = dictFile.ReadLine()) != null)
{
// 35 chars for term + ":" + 5 for df + ":" + 7 index into posting list
string dictterm = dictline.Substring(0, 35).Trim();
dictline = dictline.Remove(0, 36);
int docFreq = Int32.Parse(dictline.Substring(0, 5).Trim());
dictline = dictline.Remove(0, 6);
int posting_index = Int32.Parse(dictline.Substring(0, 7).Trim());
//Console.WriteLine(dictterm + " : " + posting_index.ToString());
// the postings entries are in the order of the dictionary terms
byte[] record = new byte[recordSize * docFreq];
postingslist.Read(record, 0, recordSize * docFreq);
string record_as_string = System.Text.ASCIIEncoding.ASCII.GetString(record);
int recordCnt = record_as_string.Length / recordSize;
Dictionary <string, double> doctfidf = new Dictionary <string, double>(5);
// split the records into pieces
for (int i = 0; i < recordCnt; i++)
{
string tmprecord = record_as_string.Substring(0, 20);
record_as_string = record_as_string.Remove(0, 22);
string docID = tmprecord.Substring(0, 10).Trim();
double tfidf = Double.Parse(tmprecord.Substring(11, 9).Trim());
//Console.WriteLine("DocID: " + docID + " Tf-idf: " + tfidf.ToString());
doctfidf.Add(docID, tfidf);
}
termdocmatrix.Add(dictterm, doctfidf);
}
postingslist.Close();
dictFile.Close();
#endregion
// build document vectors from term document matrix
#region TermDocumentMatrix
Dictionary <string, Dictionary <string, double> > documentvectors = new Dictionary <string, Dictionary <string, double> >(200);
foreach (KeyValuePair <string, Dictionary <string, double> > kvp in termdocmatrix)
{
//Console.WriteLine(kvp.Key);
// kvp.Key is the term
// subkvp.Key is the document ID
// subkvp.Value is the tf-idf for the term
foreach (KeyValuePair <string, double> subkvp in kvp.Value)
{
//Console.WriteLine("DocID: " + subkvp.Key + " Tf-idf: " + subkvp.Value.ToString());
// this is not a new document vector
if (documentvectors.ContainsKey(subkvp.Key))
{
documentvectors[subkvp.Key].Add(kvp.Key, subkvp.Value);
}
else
{
// build new document vector
documentvectors.Add(subkvp.Key, new Dictionary <string, double>());
documentvectors[subkvp.Key].Add(kvp.Key, subkvp.Value);
}
}
}
// free some memory
termdocmatrix.Clear();
#endregion
// build centroids (initially all documents are in trivial centroids)
Dictionary <string, Centroid> centroids = new Dictionary <string, Centroid>(documentvectors.Count);
foreach (KeyValuePair <string, Dictionary <string, double> > kvp in documentvectors)
{
centroids.Add(kvp.Key, new Centroid(kvp.Key, kvp.Value));
documentCnt++;
}
// free some memory
documentvectors.Clear();
// For Information Only
#region BuildAndCompareCorpusCentroid
List <string> centroidKeys = new List <string>(centroids.Keys);
Centroid forCorpus = new Centroid(centroids[centroidKeys[0]]); // this method preserves Centroid details
// we used entry 0 to make the first entry
for (int i = 1; i < centroidKeys.Count; i++)
{
forCorpus.addDocumentVectorToCentroid(centroidKeys[i], centroids[centroidKeys[i]].getVectorCnt(), centroids[centroidKeys[i]].getCentroidVector());
}
double maxSim = -1;
string maxName = string.Empty;
foreach (KeyValuePair <string, Centroid> kvp in centroids)
{
double sim = Centroid.similarity(kvp.Value, forCorpus);
if (sim > maxSim)
{
maxSim = sim;
maxName = kvp.Key;
}
}
Console.WriteLine("Most Similar to Corpus: " + maxName + " with similarity: " + maxSim.ToString());
#endregion
// so I need a matrix that is just an upper triangle matrix.
Dictionary <string, double> similarities = new Dictionary <string, double>(2000);
// centroids is the hash table of all centroids keyed on centroid name
// So I can track how many similarity checks I do
int cnt = 0;
// Compute Initial Similarity Matrix (Upper Triangle Matrix with no Diagonals)
#region InitialCompute
List <string> cen = new List <string>(centroids.Keys);
for (int i = 0; i < cen.Count; i++)
{
for (int j = i + 1; j < cen.Count; j++)
{
cnt++;
double sim = Centroid.similarity(centroids[cen[i]], centroids[cen[j]]);
similarities.Add(cen[i] + " + " + cen[j], sim);
}
} // for each centroid go along by row then column
#endregion
Console.WriteLine("Number of Initial Similarity Calculations: " + cnt.ToString());
#region DetermineMostAndLeastSimilar
// Run through initial similarity scores seeking out the largest and smallest
double mostSimVal = -1;
string mostSimName = string.Empty;
double leastSimVal = 2;
string leastSimName = string.Empty;
foreach (KeyValuePair <string, double> kvp in similarities)
{
if (kvp.Value > mostSimVal)
{
mostSimVal = kvp.Value;
mostSimName = kvp.Key;
}
if (kvp.Value < leastSimVal)
{
leastSimVal = kvp.Value;
leastSimName = kvp.Key;
}
}
Console.WriteLine("Most Similar Documents: " + mostSimName + " with similarity: " + mostSimVal.ToString());
Console.WriteLine("Least Similar Documents: " + leastSimName + " with similarity: " + leastSimVal.ToString());
#endregion
#region ClusterAllDocuments
while (centroids.Count > 1)
{
// so I know who I'm joining
string maxI = string.Empty;
string maxJ = string.Empty;
double maxValue = -1;
// Find max Similarity
foreach (KeyValuePair <string, double> kvp in similarities)
{
if (kvp.Value > maxValue)
{
maxValue = kvp.Value;
string[] names = kvp.Key.Split(delim);
maxI = names[0].Trim();
maxJ = names[1].Trim();
}
}
Console.WriteLine("Merging: " + maxI + " and " + maxJ + " of similarity: " + maxValue.ToString());
if (String.Compare(maxI, string.Empty) == 0)
{
Console.WriteLine("Emptry String detected, centroids in system:" + centroids.Count.ToString());
break;
}
// New Centroid -- When creating Centroid with Centroid it preserves the VectorCount and Such
Centroid centroid_x = new Centroid(centroids[maxI]);
centroid_x.addDocumentVectorToCentroid(centroids[maxJ].getName(), centroids[maxJ].getVectorCnt(), centroids[maxJ].getCentroidVector());
// when you merge two centroids the weight of the centroid being added is reduced completely...
// need to consider fixing (luckily this will be abstracted away
// Remove Ones I just merged from the list
centroids.Remove(maxI);
centroids.Remove(maxJ);
// Remove all references in similarlity Table
List <string> valuesToRemove = new List <string>(2 * documentCnt);
foreach (string k in similarities.Keys)
{
string[] names = k.Split(delim);
string name_i = names[0].Trim();
string name_j = names[1].Trim();
if (String.Compare(name_i, maxI) == 0 ||
String.Compare(name_i, maxJ) == 0 ||
String.Compare(name_j, maxI) == 0 ||
String.Compare(name_j, maxJ) == 0)
{
valuesToRemove.Add(k);
}
}
// Clean out Similarity Values
foreach (string r in valuesToRemove)
{
similarities.Remove(r);
}
// Calculate New similarity of everyone against new Centroid
foreach (string k in centroids.Keys)
{
double sim = Centroid.similarity(centroids[k], centroid_x);
cnt++;
similarities.Add(centroids[k].getName() + " + " + centroid_x.getName(), sim);
}
// add new centroid to x
centroids.Add(centroid_x.getName(), centroid_x);
}
#endregion
Console.WriteLine("Final Number of Similarity Calculations made: " + cnt.ToString());
DateTime stop = DateTime.Now;
Console.WriteLine("Total time: " + (stop - start).ToString());
}
