Merge pull request #4 from DataSketches/remove_mtj
Remove MTJ code
diff --git a/src/main/java/com/yahoo/sketches/vector/decomposition/MatrixOps.java b/src/main/java/com/yahoo/sketches/vector/decomposition/MatrixOps.java
index 078f4b9..b0dfcf1 100644
--- a/src/main/java/com/yahoo/sketches/vector/decomposition/MatrixOps.java
+++ b/src/main/java/com/yahoo/sketches/vector/decomposition/MatrixOps.java
@@ -52,10 +52,6 @@
mo = new MatrixOpsImplOjAlgo(n, d, algo, k);
break;
- case MTJ:
- mo = new MatrixOpsImplMTJ(n, d, algo, k);
- break;
-
default:
throw new IllegalArgumentException("Unknown MatrixType: " + A.getMatrixType().toString());
}
diff --git a/src/main/java/com/yahoo/sketches/vector/decomposition/MatrixOpsImplMTJ.java b/src/main/java/com/yahoo/sketches/vector/decomposition/MatrixOpsImplMTJ.java
deleted file mode 100644
index eebf1f9..0000000
--- a/src/main/java/com/yahoo/sketches/vector/decomposition/MatrixOpsImplMTJ.java
+++ /dev/null
@@ -1,333 +0,0 @@
-/* Portions derived from LGPL'd Matrix Toolkit for Java:
- * https://github.com/fommil/matrix-toolkits-java/blob/master/src/main/java/no/uib/cipr/matrix/SVD.java
- */
-
-package com.yahoo.sketches.vector.decomposition;
-
-import java.util.concurrent.ThreadLocalRandom;
-
-import org.netlib.util.intW;
-
-import com.github.fommil.netlib.BLAS;
-import com.github.fommil.netlib.LAPACK;
-import com.yahoo.sketches.vector.matrix.Matrix;
-import com.yahoo.sketches.vector.matrix.MatrixImplMTJ;
-import com.yahoo.sketches.vector.matrix.MatrixType;
-import no.uib.cipr.matrix.DenseMatrix;
-import no.uib.cipr.matrix.MatrixEntry;
-import no.uib.cipr.matrix.NotConvergedException;
-import no.uib.cipr.matrix.QR;
-import no.uib.cipr.matrix.SVD;
-import no.uib.cipr.matrix.SymmDenseEVD;
-import no.uib.cipr.matrix.UpperSymmDenseMatrix;
-import no.uib.cipr.matrix.sparse.CompDiagMatrix;
-import no.uib.cipr.matrix.sparse.LinkedSparseMatrix;
-
-/**
- * Computes singular value decompositions
- */
-class MatrixOpsImplMTJ extends MatrixOps {
-
- /**
- * The singular values
- */
- private final double[] sv_;
-
- /**
- * Singular vectors, sparse version of singular value matrix
- */
- private DenseMatrix Vt_;
- private CompDiagMatrix S_;
-
- /**
- * Work arrays for full SVD
- */
- private double[] work_;
- private int[] iwork_;
-
- /**
- * Work arrays for SISVD
- */
- private DenseMatrix block_;
- private DenseMatrix T_;
-
- /**
- * Work objects for SymmEVD
- */
- private SymmDenseEVD evd_;
- private LinkedSparseMatrix rotS_;
-
- /**
- * Creates an empty MatrixOps
- *
- * @param n Number of rows in matrix
- * @param d Number of columns in matrix
- * @param algo SVD algorithm to apply
- * @param k Target number of dimensions for any reduction operations
- */
- //MatrixOpsImplMTJ(final MatrixImplMTJ A, final SVDAlgo algo, final int k) {
- MatrixOpsImplMTJ(final int n, final int d, final SVDAlgo algo, final int k) {
- super(n, d, algo, k);
-
- // Allocate space for the decomposition
- sv_ = new double[Math.min(n_, d_)];
- Vt_ = null; // lazy allocation
- }
-
- @Override
- void svd(final Matrix A, final boolean computeVectors) {
- assert A.getMatrixType() == MatrixType.MTJ;
-
- if (A.getNumRows() != n_) {
- throw new IllegalArgumentException("A.numRows() != n_");
- } else if (A.getNumColumns() != d_) {
- throw new IllegalArgumentException("A.numColumns() != d_");
- }
-
- if (computeVectors && Vt_ == null) {
- Vt_ = new DenseMatrix(n_, d_);
-
- final int[] diag = {0}; // only need the main diagonal
- S_ = new CompDiagMatrix(n_, n_, diag);
- }
-
- switch (algo_) {
- case FULL:
- // make a copy if not computing vectors to avoid changing the data
- final DenseMatrix mtx = computeVectors ? (DenseMatrix) A.getRawObject()
- : new DenseMatrix((DenseMatrix) A.getRawObject());
- computeFullSVD(mtx, computeVectors);
- return;
-
- case SISVD:
- computeSISVD((DenseMatrix) A.getRawObject(), computeVectors);
- return;
-
- case SYM:
- computeSymmEigSVD((DenseMatrix) A.getRawObject(), computeVectors);
- return;
-
- default:
- throw new RuntimeException("SVDAlgo type not (yet?) supported: " + algo_.toString());
- }
- }
-
- // Because exact SVD destroys A, need to reconstruct it for MTJ
- @Override
- public double[] getSingularValues(final Matrix A) {
- svd(A, false);
- return getSingularValues();
- }
-
- @Override
- public double[] getSingularValues() {
- return sv_;
- }
-
- @Override
- Matrix getVt() {
- return MatrixImplMTJ.wrap(Vt_);
- }
-
- @Override
- double reduceRank(final Matrix A) {
- svd(A, true);
-
- double svAdjustment = 0.0;
- S_.zero();
-
- if (sv_.length >= k_) {
- double medianSVSq = sv_[k_ - 1]; // (l_/2)th item, not yet squared
- medianSVSq *= medianSVSq;
- svAdjustment += medianSVSq; // always track, even if not using compensative mode
- for (int i = 0; i < k_ - 1; ++i) {
- final double val = sv_[i];
- final double adjSqSV = val * val - medianSVSq;
- S_.set(i, i, adjSqSV < 0 ? 0.0 : Math.sqrt(adjSqSV));
- }
- for (int i = k_ - 1; i < S_.numColumns(); ++i) {
- S_.set(i, i, 0.0);
- }
- //nextZeroRow_ = k_;
- } else {
- for (int i = 0; i < sv_.length; ++i) {
- S_.set(i, i, sv_[i]);
- }
- for (int i = sv_.length; i < S_.numColumns(); ++i) {
- S_.set(i, i, 0.0);
- }
- //nextZeroRow_ = sv_.length;
- throw new RuntimeException("Running with d < 2k not yet supported");
- }
-
- // store the result back in A
- S_.mult(Vt_, (DenseMatrix) A.getRawObject());
-
- return svAdjustment;
- }
-
- @Override
- Matrix applyAdjustment(final Matrix A, final double svAdjustment) {
- // copy A before decomposing
- final DenseMatrix result = new DenseMatrix((DenseMatrix) A.getRawObject(), true);
- svd(Matrix.wrap(result), true);
-
- for (int i = 0; i < k_ - 1; ++i) {
- final double val = sv_[i];
- final double adjSV = Math.sqrt(val * val + svAdjustment);
- S_.set(i, i, adjSV);
- }
- for (int i = k_ - 1; i < S_.numColumns(); ++i) {
- S_.set(i, i, 0.0);
- }
-
- S_.mult(Vt_, result);
-
- return Matrix.wrap(result);
- }
-
- private void allocateSpaceFullSVD(final boolean vectors) {
- // Find workspace requirements
- iwork_ = new int[8 * Math.min(n_, d_)];
-
- // Query optimal workspace
- final double[] workSize = new double[1];
- final intW info = new intW(0);
- LAPACK.getInstance().dgesdd("S", n_, d_, new double[0],
- n_, new double[0], new double[0], n_,
- new double[0], n_, workSize, -1, iwork_, info);
-
- // Allocate workspace
- int lwork;
- if (info.val != 0) {
- if (vectors) {
- lwork = 3
- * Math.min(n_, d_)
- * Math.min(n_, d_)
- + Math.max(
- Math.max(n_, d_),
- 4 * Math.min(n_, d_) * Math.min(n_, d_) + 4
- * Math.min(n_, d_));
- } else {
- lwork = 3
- * Math.min(n_, d_)
- * Math.min(n_, d_)
- + Math.max(
- Math.max(n_, d_),
- 5 * Math.min(n_, d_) * Math.min(n_, d_) + 4
- * Math.min(n_, d_));
- }
- } else {
- lwork = (int) workSize[0];
- }
-
- lwork = Math.max(lwork, 1);
- work_ = new double[lwork];
- }
-
- private void allocateSpaceSISVD() {
- block_ = new DenseMatrix(d_, k_);
- T_ = new DenseMatrix(n_, k_);
- // TODO: should allocate space for QR and final SVD here?
- }
-
- private void allocateSpaceSymmEigSVD() {
- T_ = new DenseMatrix(n_, n_);
- rotS_ = new LinkedSparseMatrix(n_, n_); // only need if computing vectors, but only O(n_) size
- evd_ = new SymmDenseEVD(n_, true, true);
- }
-
- private void computeFullSVD(final DenseMatrix A, final boolean computeVectors) {
- if (work_ == null) {
- allocateSpaceFullSVD(computeVectors);
- }
-
- final intW info = new intW(0);
- final String jobType = computeVectors ? "S" : "N";
- LAPACK.getInstance().dgesdd(jobType, n_, d_, A.getData(),
- n_, sv_, new double[0],
- n_, computeVectors ? Vt_.getData() : new double[0],
- n_, work_, work_.length, iwork_, info);
-
- if (info.val > 0) {
- throw new RuntimeException("Did not converge after a maximum number of iterations");
- } else if (info.val < 0) {
- throw new IllegalArgumentException();
- }
- }
-
- private void computeSISVD(final DenseMatrix A, final boolean computeVectors) {
- if (block_ == null) {
- allocateSpaceSISVD();
- }
-
- // want block_ filled as ~Normal(0,1))
- final ThreadLocalRandom rand = ThreadLocalRandom.current();
- for (MatrixEntry entry : block_) {
- entry.set(rand.nextGaussian());
- }
- // TODO: in-line QR with direct LAPACK call
- final QR qr = new QR(block_.numRows(), block_.numColumns());
- block_ = qr.factor(block_).getQ(); // important for numeric stability
-
- for (int i = 0; i < numSISVDIter_; ++i) {
- A.mult(block_, T_);
- A.transAmult(T_, block_);
- block_ = qr.factor(block_).getQ(); // again, for stability
- }
-
- // Rayleigh-Ritz postprocessing
- A.mult(block_, T_);
-
- // TODO: use LAPACK directly
- final SVD svd = new SVD(T_.numRows(), T_.numColumns(), computeVectors);
- try {
- svd.factor(T_);
- } catch (final NotConvergedException e) {
- throw new RuntimeException(e.getMessage());
- }
- System.arraycopy(svd.getS(), 0, sv_, 0, svd.getS().length); // sv_ is final
-
- if (computeVectors) {
- // V^T = (block * V^T)^T = (V^T)^T * block^T
- // using BLAS directly since Vt is (n_ x d_) but result here is only (k_ x d_)
- BLAS.getInstance().dgemm("T", "T", k_, d_, k_,
- 1.0, svd.getVt().getData(), k_, block_.getData(), d_,
- 0.0, Vt_.getData(), n_);
- }
- }
-
- private void computeSymmEigSVD(final DenseMatrix A, final boolean computeVectors) {
- if (T_ == null) {
- allocateSpaceSymmEigSVD();
- }
-
- // want left singular vectors U, aka eigenvectors of AA^T -- so compute that
- A.transBmult(A, T_);
- try {
- // TODO: direct LAPACK call lets us get only the top k values/vectors rather than all
- evd_.factor(new UpperSymmDenseMatrix(T_, false));
- } catch (final NotConvergedException e) {
- throw new RuntimeException(e.getMessage());
- }
-
- // TODO: can we only use k_ values?
- // EVD gives values low-to-high; SVD does high-to-low and we want that order. Reverse
- // the list when extracting SVs from eigenvalues, and generate a diagonal rotation matrix
- // to save on an extra matrix multiply if we need to compute vectors later.
- final double[] ev = evd_.getEigenvalues();
- for (int i = 0; i < ev.length; ++i) {
- final double val = Math.sqrt(ev[i]);
- sv_[n_ - i - 1] = val;
- if (val > 0) {
- rotS_.set(n_ - i - 1, i, 1 / val);
- }
- }
-
- if (computeVectors) {
- rotS_.transBmult(evd_.getEigenvectors(), T_);
- T_.mult(A, Vt_);
- }
- }
-
-}
diff --git a/src/main/java/com/yahoo/sketches/vector/matrix/Matrix.java b/src/main/java/com/yahoo/sketches/vector/matrix/Matrix.java
index d6b6de2..a1eb8ef 100644
--- a/src/main/java/com/yahoo/sketches/vector/matrix/Matrix.java
+++ b/src/main/java/com/yahoo/sketches/vector/matrix/Matrix.java
@@ -12,7 +12,6 @@
import com.yahoo.memory.Memory;
import com.yahoo.sketches.vector.MatrixFamily;
-import no.uib.cipr.matrix.DenseMatrix;
/**
* Provides an implementation-agnostic wrapper around Matrix classes.
@@ -36,8 +35,6 @@
switch (type) {
case OJALGO:
return MatrixImplOjAlgo.heapifyInstance(srcMem);
- case MTJ:
- return MatrixImplMTJ.heapifyInstance(srcMem);
default:
return null;
}
@@ -54,8 +51,6 @@
return null;
} else if (mtx instanceof PrimitiveDenseStore) {
return MatrixImplOjAlgo.wrap((PrimitiveDenseStore) mtx);
- } else if (mtx instanceof DenseMatrix) {
- return MatrixImplMTJ.wrap((DenseMatrix) mtx);
}
else {
throw new IllegalArgumentException("wrap() does not currently support "
diff --git a/src/main/java/com/yahoo/sketches/vector/matrix/MatrixBuilder.java b/src/main/java/com/yahoo/sketches/vector/matrix/MatrixBuilder.java
index 73f0751..cbc7a39 100644
--- a/src/main/java/com/yahoo/sketches/vector/matrix/MatrixBuilder.java
+++ b/src/main/java/com/yahoo/sketches/vector/matrix/MatrixBuilder.java
@@ -45,11 +45,8 @@
case OJALGO:
return MatrixImplOjAlgo.newInstance(numRows, numCols);
- case MTJ:
- return MatrixImplMTJ.newInstance(numRows, numCols);
-
default:
- throw new IllegalArgumentException("OJALGO and MTJ are currently the only supported MatrixTypes");
+ throw new IllegalArgumentException("OJALGO is currently the only supported MatrixTypes");
}
}
}
diff --git a/src/main/java/com/yahoo/sketches/vector/matrix/MatrixImplMTJ.java b/src/main/java/com/yahoo/sketches/vector/matrix/MatrixImplMTJ.java
deleted file mode 100644
index 5834b1a..0000000
--- a/src/main/java/com/yahoo/sketches/vector/matrix/MatrixImplMTJ.java
+++ /dev/null
@@ -1,226 +0,0 @@
-/*
- * Copyright 2017, Yahoo! Inc.
- * Licensed under the terms of the Apache License 2.0. See LICENSE file at the project root
- * for terms.
- */
-
-package com.yahoo.sketches.vector.matrix;
-
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.COMPACT_FLAG_MASK;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractFamilyID;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractFlags;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractNumColumns;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractNumColumnsUsed;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractNumRows;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractNumRowsUsed;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractPreLongs;
-import static com.yahoo.sketches.vector.matrix.MatrixPreambleUtil.extractSerVer;
-
-import com.yahoo.memory.Memory;
-import com.yahoo.memory.WritableMemory;
-import com.yahoo.sketches.vector.MatrixFamily;
-import no.uib.cipr.matrix.DenseMatrix;
-
-public final class MatrixImplMTJ extends Matrix {
- private DenseMatrix mtx_;
-
- private MatrixImplMTJ(final int numRows, final int numCols) {
- mtx_ = new DenseMatrix(numRows, numCols);
- numRows_ = numRows;
- numCols_ = numCols;
- }
-
- private MatrixImplMTJ(final DenseMatrix mtx) {
- mtx_ = mtx;
- numRows_ = mtx.numRows();
- numCols_ = mtx.numColumns();
- }
-
- static Matrix newInstance(final int numRows, final int numCols) {
- return new MatrixImplMTJ(numRows, numCols);
- }
-
- static Matrix heapifyInstance(final Memory srcMem) {
- final int minBytes = MatrixFamily.MATRIX.getMinPreLongs() * Long.BYTES;
- final long memCapBytes = srcMem.getCapacity();
- if (memCapBytes < minBytes) {
- throw new IllegalArgumentException("Source Memory too small: " + memCapBytes
- + " < " + minBytes);
- }
-
- final int preLongs = extractPreLongs(srcMem);
- final int serVer = extractSerVer(srcMem);
- final int familyID = extractFamilyID(srcMem);
-
- if (serVer != 1) {
- throw new IllegalArgumentException("Invalid SerVer reading srcMem. Expected 1, found: "
- + serVer);
- }
- if (familyID != MatrixFamily.MATRIX.getID()) {
- throw new IllegalArgumentException("srcMem does not point to a Matrix");
- }
-
- final int flags = extractFlags(srcMem);
- final boolean isCompact = (flags & COMPACT_FLAG_MASK) > 0;
-
- int nRows = extractNumRows(srcMem);
- int nCols = extractNumColumns(srcMem);
-
- final MatrixImplMTJ matrix;
-
- if (isCompact) {
- matrix = new MatrixImplMTJ(nRows, nCols);
-
- nRows = extractNumRowsUsed(srcMem);
- nCols = extractNumColumnsUsed(srcMem);
-
- int memOffset = preLongs * Long.BYTES;
- for (int c = 0; c < nCols; ++c) {
- for (int r = 0; r < nRows; ++r) {
- matrix.mtx_.set(r, c, srcMem.getDouble(memOffset));
- memOffset += Double.BYTES;
- }
- }
- } else {
- final int nElements = nRows * nCols;
- final double[] data = new double[nElements];
- srcMem.getDoubleArray(preLongs * Long.BYTES, data, 0, nElements);
-
- matrix = new MatrixImplMTJ(new DenseMatrix(nRows, nCols, data, false));
- }
-
- return matrix;
- }
-
- static Matrix wrap(final DenseMatrix mtx) {
- return new MatrixImplMTJ(mtx);
- }
-
- @Override
- public Object getRawObject() {
- return mtx_;
- }
-
- @Override
- public byte[] toByteArray() {
- final int preLongs = 2;
- final long numElements = numRows_ * numCols_;
- assert numElements == (mtx_.numRows() * mtx_.numColumns());
-
- final int outBytes = (int) ((preLongs * Long.BYTES) + (numElements * Double.BYTES));
- final byte[] outByteArr = new byte[outBytes];
- final WritableMemory memOut = WritableMemory.wrap(outByteArr);
- final Object memObj = memOut.getArray();
- final long memAddr = memOut.getCumulativeOffset(0L);
-
- MatrixPreambleUtil.insertPreLongs(memObj, memAddr, preLongs);
- MatrixPreambleUtil.insertSerVer(memObj, memAddr, MatrixPreambleUtil.SER_VER);
- MatrixPreambleUtil.insertFamilyID(memObj, memAddr, MatrixFamily.MATRIX.getID());
- MatrixPreambleUtil.insertFlags(memObj, memAddr, 0);
- MatrixPreambleUtil.insertNumRows(memObj, memAddr, numRows_);
- MatrixPreambleUtil.insertNumColumns(memObj, memAddr, numCols_);
- memOut.putDoubleArray(preLongs << 3, mtx_.getData(), 0, (int) numElements);
-
- return outByteArr;
- }
-
- @Override
- public byte[] toCompactByteArray(final int numRows, final int numCols) {
- // TODO: row/col limit checks
-
- final int preLongs = 3;
-
- // for non-compact we can do an array copy, so save as non-compact if using the entire matrix
- final long numElements = (long) numRows * numCols;
- final boolean isCompact = numElements < (mtx_.numRows() * mtx_.numColumns());
- if (!isCompact) {
- return toByteArray();
- }
-
- final int outBytes = (int) ((preLongs * Long.BYTES) + (numElements * Double.BYTES));
- final byte[] outByteArr = new byte[outBytes];
- final WritableMemory memOut = WritableMemory.wrap(outByteArr);
- final Object memObj = memOut.getArray();
- final long memAddr = memOut.getCumulativeOffset(0L);
-
- MatrixPreambleUtil.insertPreLongs(memObj, memAddr, preLongs);
- MatrixPreambleUtil.insertSerVer(memObj, memAddr, MatrixPreambleUtil.SER_VER);
- MatrixPreambleUtil.insertFamilyID(memObj, memAddr, MatrixFamily.MATRIX.getID());
- MatrixPreambleUtil.insertFlags(memObj, memAddr, COMPACT_FLAG_MASK);
- MatrixPreambleUtil.insertNumRows(memObj, memAddr, mtx_.numRows());
- MatrixPreambleUtil.insertNumColumns(memObj, memAddr, mtx_.numColumns());
- MatrixPreambleUtil.insertNumRowsUsed(memObj, memAddr, numRows);
- MatrixPreambleUtil.insertNumColumnsUsed(memObj, memAddr, numCols);
-
- // write elements in column-major order
- long offsetBytes = preLongs * Long.BYTES;
- for (int c = 0; c < numCols; ++c) {
- for (int r = 0; r < numRows; ++r) {
- memOut.putDouble(offsetBytes, mtx_.get(r, c));
- offsetBytes += Double.BYTES;
- }
- }
-
- return outByteArr;
- }
-
- @Override
- public double getElement(final int row, final int col) {
- return mtx_.get(row, col);
- }
-
- @Override
- public double[] getRow(final int row) {
- final int cols = mtx_.numColumns();
- final double[] result = new double[cols];
- for (int c = 0; c < cols; ++c) {
- result[c] = mtx_.get(row, c);
- }
- return result;
- }
-
- @Override
- public double[] getColumn(final int col) {
- final int rows = mtx_.numRows();
- final double[] result = new double[rows];
- for (int r = 0; r < rows; ++r) {
- result[r] = mtx_.get(r, col);
- }
- return result;
- }
-
- @Override
- public void setElement(final int row, final int col, final double value) {
- mtx_.set(row, col, value);
- }
-
- @Override
- public void setRow(final int row, final double[] values) {
- if (values.length != mtx_.numColumns()) {
- throw new IllegalArgumentException("Invalid number of elements for row. Expected "
- + mtx_.numColumns() + ", found " + values.length);
- }
-
- for (int i = 0; i < mtx_.numColumns(); ++i) {
- mtx_.set(row, i, values[i]);
- }
- }
-
- @Override
- public void setColumn(final int column, final double[] values) {
- if (values.length != mtx_.numRows()) {
- throw new IllegalArgumentException("Invalid number of elements for column. Expected "
- + mtx_.numRows() + ", found " + values.length);
- }
-
- for (int i = 0; i < mtx_.numRows(); ++i) {
- // TODO: System.arraycopy()?
- mtx_.set(i, column, values[i]);
- }
- }
-
- @Override
- public MatrixType getMatrixType() {
- return MatrixType.MTJ;
- }
-}
diff --git a/src/main/java/com/yahoo/sketches/vector/matrix/MatrixType.java b/src/main/java/com/yahoo/sketches/vector/matrix/MatrixType.java
index 8a3dac2..7ca7f2b 100644
--- a/src/main/java/com/yahoo/sketches/vector/matrix/MatrixType.java
+++ b/src/main/java/com/yahoo/sketches/vector/matrix/MatrixType.java
@@ -1,8 +1,7 @@
package com.yahoo.sketches.vector.matrix;
public enum MatrixType {
- OJALGO(1, "ojAlgo"),
- MTJ(2, "MTJ");
+ OJALGO(1, "ojAlgo");
private int id_;
private String name_;
diff --git a/src/test/java/com/yahoo/sketches/vector/matrix/MatrixBuilderTest.java b/src/test/java/com/yahoo/sketches/vector/matrix/MatrixBuilderTest.java
index eb071cd..4beb32b 100644
--- a/src/test/java/com/yahoo/sketches/vector/matrix/MatrixBuilderTest.java
+++ b/src/test/java/com/yahoo/sketches/vector/matrix/MatrixBuilderTest.java
@@ -20,9 +20,6 @@
Matrix m = builder.build(128, 512);
assertNotNull(m);
- builder.setType(MatrixType.MTJ);
- assertEquals(builder.getBackingType(), MatrixType.MTJ);
-
m = builder.build(128, 512);
assertNotNull(m);
}
@@ -34,10 +31,6 @@
assertEquals(type, MatrixType.OJALGO); // default type
assertEquals(type.getId(), MatrixType.OJALGO.getId());
assertEquals(type.getName(), MatrixType.OJALGO.getName());
-
- builder.setType(MatrixType.MTJ);
- assertEquals(builder.getBackingType(), MatrixType.MTJ);
- assertEquals(builder.getBackingType().toString(), "MTJ");
}
}
diff --git a/src/test/java/com/yahoo/sketches/vector/matrix/MatrixImplMTJTest.java b/src/test/java/com/yahoo/sketches/vector/matrix/MatrixImplMTJTest.java
deleted file mode 100644
index b23b961..0000000
--- a/src/test/java/com/yahoo/sketches/vector/matrix/MatrixImplMTJTest.java
+++ /dev/null
@@ -1,211 +0,0 @@
-/*
- * Copyright 2017, Yahoo, Inc.
- * Licensed under the terms of the Apache License 2.0. See LICENSE file at the project root for terms.
- */
-
-package com.yahoo.sketches.vector.matrix;
-
-import static org.testng.Assert.assertEquals;
-import static org.testng.Assert.fail;
-
-import org.testng.annotations.Test;
-
-import com.yahoo.memory.Memory;
-import com.yahoo.memory.WritableMemory;
-import no.uib.cipr.matrix.DenseMatrix;
-
-public class MatrixImplMTJTest {
- @Test
- public void checkInstantiation() {
- final int nRows = 10;
- final int nCols = 15;
- final Matrix m = MatrixImplMTJ.newInstance(nRows, nCols);
- assertEquals(m.getNumRows(), nRows);
- assertEquals(m.getNumColumns(), nCols);
-
- final DenseMatrix pds = (DenseMatrix) m.getRawObject();
- assertEquals(pds.numRows(), nRows);
- assertEquals(pds.numColumns(), nCols);
-
- final Matrix wrapped = Matrix.wrap(pds);
- MatrixTest.checkMatrixEquality(wrapped, m);
- assertEquals(wrapped.getRawObject(), pds);
- }
-
- @Test
- public void updateAndQueryValues() {
- final int nRows = 5;
- final int nCols = 5;
- final Matrix m = generateIncreasingEye(nRows, nCols); // tests setElement() in method
-
- for (int i = 0; i < nRows; ++i) {
- for (int j = 0; j < nCols; ++j) {
- final double val = m.getElement(i, j);
- if (i == j) {
- assertEquals(val, i + 1.0);
- } else {
- assertEquals(val, 0.0);
- }
- }
- }
- }
-
- @Test
- public void checkStandardSerialization() {
- final int nRows = 3;
- final int nCols = 7;
- final Matrix m = generateIncreasingEye(nRows, nCols);
-
- final byte[] mtxBytes = m.toByteArray();
- assertEquals(mtxBytes.length, m.getSizeBytes());
-
- final Memory mem = Memory.wrap(mtxBytes);
- final Matrix tgt = MatrixImplMTJ.heapifyInstance(mem);
- MatrixTest.checkMatrixEquality(tgt, m);
- }
-
- @Test
- public void checkCompactSerialization() {
- final int nRows = 4;
- final int nCols = 7;
- final Matrix m = generateIncreasingEye(nRows, nCols);
-
- byte[] mtxBytes = m.toCompactByteArray(nRows - 1, 7);
- assertEquals(mtxBytes.length, m.getCompactSizeBytes(nRows - 1, 7));
-
- Memory mem = Memory.wrap(mtxBytes);
- Matrix tgt = MatrixImplMTJ.heapifyInstance(mem);
- for (int c = 0; c < nCols; ++c) {
- for (int r = 0; r < (nRows - 1); ++r) {
- assertEquals(tgt.getElement(r, c), m.getElement(r, c)); // equal here
- }
- // assuming nRows - 1 so check only the last row as being 0
- assertEquals(tgt.getElement(nRows - 1, c), 0.0);
- }
-
- // test without compacting
- mtxBytes = m.toCompactByteArray(nRows, nCols);
- assertEquals(mtxBytes.length, m.getSizeBytes());
- mem = Memory.wrap(mtxBytes);
- tgt = MatrixImplMTJ.heapifyInstance(mem);
- MatrixTest.checkMatrixEquality(tgt, m);
- }
-
- @Test
- public void matrixRowOperations() {
- final int nRows = 7;
- final int nCols = 5;
- final Matrix m = generateIncreasingEye(nRows, nCols);
-
- final int tgtCol = 2;
- final double[] v = m.getRow(tgtCol); // diagonal matrix, so this works ok
- for (int i = 0; i < v.length; ++i) {
- assertEquals(v[i], (i == tgtCol ? i + 1.0 : 0.0));
- }
-
- assertEquals(m.getElement(6, tgtCol), 0.0);
- m.setRow(6, v);
- assertEquals(m.getElement(6, tgtCol), tgtCol + 1.0);
- }
-
- @Test
- public void matrixColumnOperations() {
- final int nRows = 9;
- final int nCols = 4;
- final Matrix m = generateIncreasingEye(nRows, nCols);
-
- final int tgtRow = 3;
- final double[] v = m.getColumn(tgtRow); // diagonal matrix, so this works ok
- for (int i = 0; i < v.length; ++i) {
- assertEquals(v[i], (i == tgtRow ? i + 1.0 : 0.0));
- }
-
- assertEquals(m.getElement(tgtRow, 0), 0.0);
- m.setColumn(0, v);
- assertEquals(m.getElement(tgtRow, 0), tgtRow + 1.0);
- }
-
- @Test
- public void invalidRowColumnOperations() {
- final int nRows = 9;
- final int nCols = 4;
- final Matrix m = generateIncreasingEye(nRows, nCols);
-
- final double[] shortRow = new double[nCols - 2];
- try {
- m.setRow(1, shortRow);
- fail();
- } catch (final IllegalArgumentException e) {
- // expected
- }
-
- final double[] longColumn = new double[nRows + 2];
- try {
- m.setColumn(1, longColumn);
- fail();
- } catch (final IllegalArgumentException e) {
- // expected
- }
- }
-
- @Test
- public void invalidSerVer() {
- final int nRows = 3;
- final int nCols = 3;
- final Matrix m = generateIncreasingEye(nRows, nCols);
- final byte[] sketchBytes = m.toByteArray();
- final WritableMemory mem = WritableMemory.wrap(sketchBytes);
- MatrixPreambleUtil.insertSerVer(mem.getArray(), mem.getCumulativeOffset(0L), 0);
-
- try {
- MatrixImplMTJ.heapifyInstance(mem);
- fail();
- } catch (final IllegalArgumentException e) {
- // expected
- }
- }
-
- @Test
- public void invalidFamily() {
- final int nRows = 3;
- final int nCols = 3;
- final Matrix m = generateIncreasingEye(nRows, nCols);
- final byte[] sketchBytes = m.toByteArray();
- final WritableMemory mem = WritableMemory.wrap(sketchBytes);
- MatrixPreambleUtil.insertFamilyID(mem.getArray(), mem.getCumulativeOffset(0L), 0);
-
- try {
- MatrixImplMTJ.heapifyInstance(mem);
- fail();
- } catch (final IllegalArgumentException e) {
- // expected
- }
- }
-
- @Test
- public void insufficientMemoryCapacity() {
- final byte[] bytes = new byte[6];
- final Memory mem = Memory.wrap(bytes);
- try {
- MatrixImplMTJ.heapifyInstance(mem);
- fail();
- } catch (final IllegalArgumentException e) {
- // expected
- }
- }
-
- /**
- * Creates a scaled I matrix, where the diagonal consists of increasing integers,
- * starting with 1.0.
- * @param nRows number of rows
- * @param nCols number of columns
- * @return PrimitiveDenseStore, suitable for direct use or wrapping
- */
- private static Matrix generateIncreasingEye(final int nRows, final int nCols) {
- final Matrix m = MatrixImplMTJ.newInstance(nRows, nCols);
- for (int i = 0; (i < nRows) && (i < nCols); ++i) {
- m.setElement(i, i, 1.0 + i);
- }
- return m;
- }
-}
diff --git a/src/test/java/com/yahoo/sketches/vector/matrix/MatrixTest.java b/src/test/java/com/yahoo/sketches/vector/matrix/MatrixTest.java
index a2262d7..8be75cc 100644
--- a/src/test/java/com/yahoo/sketches/vector/matrix/MatrixTest.java
+++ b/src/test/java/com/yahoo/sketches/vector/matrix/MatrixTest.java
@@ -26,13 +26,9 @@
final Memory mem = Memory.wrap(bytes);
println(MatrixPreambleUtil.preambleToString(mem));
- Matrix tgt = Matrix.heapify(mem, MatrixType.OJALGO);
+ final Matrix tgt = Matrix.heapify(mem, MatrixType.OJALGO);
assertTrue(tgt instanceof MatrixImplOjAlgo);
checkMatrixEquality(m, tgt);
-
- tgt = Matrix.heapify(mem, MatrixType.MTJ);
- assertTrue(tgt instanceof MatrixImplMTJ);
- checkMatrixEquality(m, tgt);
}
@Test
