Binary matrix-matrix operations which may save result in-place, optionally
Matrix-matrix unary func
R-like operators.
R-like operators
Syntactic sugar for mahout vectors
Matlab-like operators.
R-like operators.
Compute column-wise means and stdevs.
Compute column-wise means and stdevs.
input
colMeans → colStdevs
Compute column-wise means and variances.
Compute column-wise means and variances.
colMeans → colVariances
Create dense matrix out of inline arguments -- rows -- which can be tuples, iterables of Double, or just single Number (for columnar vectors)
Create dense matrix out of inline arguments -- rows -- which can be tuples, iterables of Double, or just single Number (for columnar vectors)
Check the density of an in-core matrix based on supplied criteria.
Check the density of an in-core matrix based on supplied criteria. Returns true if we think mx is denser than threshold with at least 80% confidence.
The matrix to check density of.
the threshold of non-zero elements above which we consider a Matrix Dense
Computes Eigendecomposition of a symmetric matrix
Computes Eigendecomposition of a symmetric matrix
symmetric input matrix
(V, eigen-values-vector)
More general version of eigen decomposition
More general version of eigen decomposition
(V, eigenvalues-real-vector, eigenvalues-imaginary-vector)
QR.
QR.
Right now Mahout's QR seems to be using argument for in-place transformations, so the matrix context gets messed after this. Hence we force cloning of the argument before passing it to Mahout's QR so to keep expected semantics.
(Q,R)
Solution x of A*x = b using QR-Decomposition, where A is a square, non-singular matrix.
Solution x of A*x = b using QR-Decomposition, where A is a square, non-singular matrix.
(x)
Solution A^{-1} of A*A^{-1} = I using QR-Decomposition, where A is a square, non-singular matrix.
Solution A^{-1} of A*A^{-1} = I using QR-Decomposition, where A is a square, non-singular matrix. Here only for compatibility with R semantics.
(A^{-1})
Solution X of A*X = B using QR-Decomposition, where A is a square, non-singular matrix.
Solution X of A*X = B using QR-Decomposition, where A is a square, non-singular matrix.
(X)
Default initializes are always row-wise.
Default initializes are always row-wise. create a sparse, e.g.
m = sparse( (0,5)::(9,3)::Nil, (2,3.5)::(7,8)::Nil )
Pairwise squared distance computation.
Pairwise squared distance computation.
X, m x d
Y, n x d
pairwise squaired distances of row-wise data points in X and Y (m x n)
Compute square distance matrix.
Compute square distance matrix. We assume data points are row-wise, similar to R's dist().
computes SVD
computes SVD
svd input
(U,V, singular-values-vector)
create a sparse vector out of list of tuple2's
create a sparse vector out of list of tuple2's
cardinality
Mahout matrices and vectors' scala syntactic sugar