/// <summary>
/// Calculates the rotation and translation from a set of points in one coordinate system (orig) to another (transf).
/// You can use to create a new Matrix4 and transform points from (orig) to (trans) or take the inverse of the
/// resulting Matrix4
/// and transform (transf) back to (orig).
/// </summary>
/// <param name="orig">the points in the first coordinate system</param>
/// <param name="transf">theh points in the second coordinate system</param>
/// <returns>a tuple containing the rotation matrix as Item1 and the translation vector as Item2</returns>
public static Matrix4 TransformBetween(IEnumerable <Vector3> orig, IEnumerable <Vector3> transf)
{
int rowCountA = orig.Count();
int rowCountB = transf.Count();
if (rowCountA != rowCountB)
{
throw new Exception("Data must be paired. The number of rows should be equal in both input matrices!");
}
Vector3 aCentroid = orig.GetCentroid();
Vector3 bCentroid = transf.GetCentroid();
double[,] aCentroidMatrix = aCentroid.RepMat(rowCountA);
double[,] bCentroidMatrix = bCentroid.RepMat(rowCountB);
double[,] a = orig.ToRowMatrix();
double[,] b = transf.ToRowMatrix();
double[,] h = (a.Subtract(aCentroidMatrix)).Transpose().Multiply(b.Subtract(bCentroidMatrix));
SvdResult svd = h.Svd();
double[,] U = svd.U;
double[,] V = svd.Vt.Transpose();
//TODO Consider reflection
double[,] r = V.Multiply(U.Transpose());
double[,] first = r.Multiply(-1);
double[] second = first.Multiply(aCentroid);
double[] t = r.Multiply(-1).Multiply(aCentroid).Add(bCentroid);
return(new Matrix4(new Matrix3(r), new Vector3(t)));
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserArticleRatingsTable(rater);
SingularValueDecomposition factorizer = new SingularValueDecomposition(numFeatures, learningIterations);
svd = factorizer.FactorizeMatrix(ratings);
numUsers = ratings.UserIndexToID.Count;
numArticles = ratings.ArticleIndexToID.Count;
}
public void Train(UserBehaviorDatabase db)
{
UserBehaviorTransformer ubt = new UserBehaviorTransformer(db);
ratings = ubt.GetUserArticleRatingsTable(rater);
if (latentUserFeatureCount > 0)
{
SingularValueDecomposition svd = new SingularValueDecomposition(latentUserFeatureCount, 100);
SvdResult results = svd.FactorizeMatrix(ratings);
ratings.AppendUserFeatures(results.UserFeatures);
}
}
public void Load(string file)
{
ratings = new UserArticleRatingsTable();
using (FileStream fs = new FileStream(file, FileMode.Open))
using (GZipStream zip = new GZipStream(fs, CompressionMode.Decompress))
using (StreamReader r = new StreamReader(zip))
{
numUsers = int.Parse(r.ReadLine());
numArticles = int.Parse(r.ReadLine());
numFeatures = int.Parse(r.ReadLine());
double averageGlobalRating = double.Parse(r.ReadLine());
double[] userBiases = new double[numUsers];
for (int userIndex = 0; userIndex < numUsers; userIndex++)
{
userBiases[userIndex] = double.Parse(r.ReadLine());
}
double[] articleBiases = new double[numArticles];
for (int articleIndex = 0; articleIndex < numArticles; articleIndex++)
{
articleBiases[articleIndex] = double.Parse(r.ReadLine());
}
double[][] userFeatures = new double[numUsers][];
for (int userIndex = 0; userIndex < numUsers; userIndex++)
{
userFeatures[userIndex] = new double[numFeatures];
for (int featureIndex = 0; featureIndex < numFeatures; featureIndex++)
{
userFeatures[userIndex][featureIndex] = double.Parse(r.ReadLine());
}
}
double[][] articleFeatures = new double[numArticles][];
for (int articleIndex = 0; articleIndex < numUsers; articleIndex++)
{
articleFeatures[articleIndex] = new double[numFeatures];
for (int featureIndex = 0; featureIndex < numFeatures; featureIndex++)
{
articleFeatures[articleIndex][featureIndex] = double.Parse(r.ReadLine());
}
}
svd = new SvdResult(averageGlobalRating, userBiases, articleBiases, userFeatures, articleFeatures);
for (int i = 0; i < numUsers; i++)
{
int userId = int.Parse(r.ReadLine());
UserArticleRatings uat = new UserArticleRatings(userId, numArticles);
for (int x = 0; x < numArticles; x++)
{
uat.ArticleRatings[x] = double.Parse(r.ReadLine());
}
ratings.Users.Add(uat);
}
for (int i = 0; i < numUsers; i++)
{
ratings.UserIndexToID.Add(int.Parse(r.ReadLine()));
}
for (int i = 0; i < numArticles; i++)
{
ratings.ArticleIndexToID.Add(int.Parse(r.ReadLine()));
}
}
}
