private static void CalculateLshForListingSet(List <Listing> listings, string job_id, Dictionary <long, long> duplicates)
{
var numSimilarityBuckets = (int)Math.Ceiling(listings.Count / 100M);
// First make 2 dimensional array (docs by min-hashes)
var matrix = new int[listings.Count, minHashCount];
for (int listing = 0; listing < listings.Count; listing++)
{
for (int hash = 0; hash < listings[listing].minhash_description.Count; hash++)
{
matrix[listing, hash] = (int)listings[listing].minhash_description[hash];
}
}
// Now set LSH
var lsh = new LSH(matrix, numSimilarityBuckets);
lsh.Calc();
// Set closes duplicate on each listing
var duplicatesFound = new Dictionary <long, long>();
var singleItemProgress = ProgressManager.CalculateLoopIncrement(listings.Count(), 0.2M);
for (int listing = 0; listing < listings.Count; listing++)
{
ProgressManager.IncrementJobPercentBy(job_id, singleItemProgress);
var nearest = lsh.GetNearest(listing);
if (!nearest.Any())
{
continue;
}
var thisListing = listings[listing];
var nearestListing = listings[nearest[0]];
var priceRatio = nearestListing.buy_now_price / thisListing.buy_now_price;
if (priceRatio < 0.8M || priceRatio > 1.2M)
{
continue;
}
if (duplicatesFound.ContainsKey(nearestListing.id))
{
continue;
}
listings[listing].likely_duplicate_id_by_description = nearestListing.id;
listings[listing].similarity_description = Jaccard.Calc(ArrayHelpers.GetRow <int>(matrix, listing).ToList(), nearest);
duplicates[nearestListing.id] = thisListing.id;
duplicates[thisListing.id] = nearestListing.id;
}
}
public void GenerateSSGraph(List <string> docs)
{
/* 1) decomposite the document represetned by fileName into sentences
* 2) generate the sentence similarity graph via minhashing and LSH
* 3) describe the graph by neiborhood list NB and offset list OFF
*/
Stopwatch sw = new Stopwatch();
sw.Start();
List <string> docsOrg = new List <string>(docs);
for (int i = 0; i < docs.Count; i++)
{
senteceNames[i] = docs[i];
}
int r = docs.Count;
int n = 40;
int rows = 2; // b= n / r;
int[][] minHashes = new int[r][];
for (int i = 0; i < r; i++)
{
//minHashes[i] = getShingleVec(parseWords(docs[i]).ToList()).ToArray();
minHashes[i] = GetShingleVec(docs[i]).ToArray();
}
MinHash mh = new MinHash(r, n);
int[,] minhashes = new int[r, n];
for (int i = 0; i < r; i++)
{
List <int> doc = minHashes[i].ToList();
List <uint> hvs = mh.GetMinHash(doc).ToList();
for (int j = 0; j < hvs.Count; j++)
{
minhashes[i, j] = (int)hvs[j];
}
}
OFF.Add(0);
int conCount = 0;
LSH lsh = new LSH(minhashes, rows);
lsh.Calc();
int idx = 0;
for (int k = 0; k < minhashes.GetUpperBound(0); k++)
{
List <int> nearest = lsh.GetNearest(k);
if (!nodes.Contains(k))
{
nodes.Add(k);
}
//Console.Write("\n" + k+" ");
foreach (int i in nearest)
{
//Console.Write(near + ", ");
if (!nodes.Contains(i))
{
nodes.Add(i);
}
if (i == idx)
{
continue;
}
NB.Add(i);
conCount++;
++idx;
}
OFF.Add(conCount);
}
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds / (double)1000);
}
public void GenerateSSGraphForComparativeSum(List <string> docs, List <int> offsets)
{
/*
* same as the first just applied to comparative sum
* here we have the set of different documents
*/
Stopwatch sw = new Stopwatch();
sw.Start();
List <string> docsOrg = new List <string>(docs);
for (int i = 0; i < docs.Count; i++)
{
senteceNames[i] = docs[i];
}
int r = docs.Count;
int n = 100;
int rows = 5; // n / r;
int[][] minHashes = new int[r][];
for (int i = 0; i < r; i++)
{
//minHashes[i] = getShingleVec(parseWords(docs[i]).ToList()).ToArray();
minHashes[i] = GetShingleVec(docs[i]).ToArray();
}
MinHash mh = new MinHash(r, n);
int[,] minhashes = new int[r, n];
for (int i = 0; i < r; i++)
{
List <int> doc = minHashes[i].ToList();
List <uint> hvs = mh.GetMinHash(doc).ToList();
for (int j = 0; j < hvs.Count; j++)
{
minhashes[i, j] = (int)hvs[j];
}
}
OFF.Add(0);
int conCount = 0;
LSH lsh = new LSH(minhashes, rows);
lsh.Calc();
int idx = 0;
for (int k = 0; k < minhashes.GetUpperBound(0); k++)
{
List <int> nearest = lsh.GetNearest(k);
if (!nodes.Contains(k))
{
nodes.Add(k);
}
//Console.Write("\n" + k+" ");
foreach (int i in nearest)
{
//Console.Write(near + ", ");
if (!nodes.Contains(i))
{
nodes.Add(i);
}
if (i == idx)
{
continue;
}
NB.Add(i);
if (Helpers.AreFromSameGroup(k, i, offsets))
{
SIGN.Add(1);
}
else
{
SIGN.Add(-0.5f);
}
conCount++;
++idx;
}
OFF.Add(conCount);
}
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds / (double)1000);
}
