diff options
Diffstat (limited to 'mllib')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala | 11 |
1 files changed, 8 insertions, 3 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala index e55ef26858..7c7d900af3 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/RowMatrix.scala @@ -109,7 +109,8 @@ class RowMatrix @Since("1.0.0") ( } /** - * Computes the Gramian matrix `A^T A`. + * Computes the Gramian matrix `A^T A`. Note that this cannot be computed on matrices with + * more than 65535 columns. */ @Since("1.0.0") def computeGramianMatrix(): Matrix = { @@ -150,7 +151,8 @@ class RowMatrix @Since("1.0.0") ( * - s is a Vector of size k, holding the singular values in descending order, * - V is a Matrix of size n x k that satisfies V' * V = eye(k). * - * We assume n is smaller than m. The singular values and the right singular vectors are derived + * We assume n is smaller than m, though this is not strictly required. + * The singular values and the right singular vectors are derived * from the eigenvalues and the eigenvectors of the Gramian matrix A' * A. U, the matrix * storing the right singular vectors, is computed via matrix multiplication as * U = A * (V * S^-1^), if requested by user. The actual method to use is determined @@ -320,7 +322,8 @@ class RowMatrix @Since("1.0.0") ( } /** - * Computes the covariance matrix, treating each row as an observation. + * Computes the covariance matrix, treating each row as an observation. Note that this cannot + * be computed on matrices with more than 65535 columns. * @return a local dense matrix of size n x n */ @Since("1.0.0") @@ -374,6 +377,8 @@ class RowMatrix @Since("1.0.0") ( * The row data do not need to be "centered" first; it is not necessary for * the mean of each column to be 0. * + * Note that this cannot be computed on matrices with more than 65535 columns. + * * @param k number of top principal components. * @return a matrix of size n-by-k, whose columns are principal components */ |