Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/964#discussion_r13742973
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/linalg/EigenValueDecomposition.scala
---
@@ -0,0 +1,150 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.linalg
+
+import breeze.linalg.{DenseMatrix => BDM, DenseVector => BDV}
+import com.github.fommil.netlib.ARPACK
+import org.netlib.util.{intW, doubleW}
+
+import org.apache.spark.annotation.Experimental
+
+/**
+ * :: Experimental ::
+ * Represents eigenvalue decomposition factors.
+ */
+@Experimental
+case class EigenValueDecomposition[VType](s: Vector, V: VType)
+
+@Experimental
+object EigenValueDecomposition {
+ /**
+ * Compute the leading k eigenvalues and eigenvectors on a symmetric
square matrix using ARPACK.
+ * The caller needs to ensure that the input matrix is real symmetric.
This function requires
+ * memory for `n*(4*k+4)` doubles.
+ *
+ * @param mul a function that multiplies the symmetric matrix with a
DenseVector.
+ * @param n dimension of the square matrix (maximum Int.MaxValue).
+ * @param k number of leading eigenvalues required.
+ * @param tol tolerance of the eigs computation.
+ * @return a dense vector of eigenvalues in descending order and a dense
matrix of eigenvectors
+ * (columns of the matrix). The number of computed eigenvalues
might be smaller than k.
+ */
+ private[mllib] def symmetricEigs(mul: DenseVector => DenseVector, n:
Int, k: Int, tol: Double)
+ : (BDV[Double], BDM[Double]) = {
+ // TODO: remove this function and use eigs in breeze when switching
breeze version
+ require(n > k, s"Number of required eigenvalues $k must be smaller
than matrix dimension $n")
+
+ val arpack = ARPACK.getInstance()
+
+ // tolerance used in stopping criterion
+ val tolW = new doubleW(tol)
+ // number of desired eigenvalues, 0 < nev < n
+ val nev = new intW(k)
+ // nev Lanczos vectors are generated are generated in the first
iteration
+ // ncv-nev Lanczos vectors are generated are generated in each
subsequent iteration
+ // ncv must be smaller than n
+ val ncv = scala.math.min(2 * k, n)
--- End diff --
`math.min` should be sufficient.
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