Github user myui commented on a diff in the pull request:
https://github.com/apache/incubator-hivemall/pull/92#discussion_r125170771
--- Diff:
core/src/main/java/hivemall/anomaly/SingularSpectrumTransform.java ---
@@ -41,161 +41,160 @@
final class SingularSpectrumTransform implements
SingularSpectrumTransformInterface {
- @Nonnull
- private final PrimitiveObjectInspector oi;
- @Nonnull
- private final ScoreFunction scoreFunc;
-
- @Nonnull
- private final int window;
- @Nonnull
- private final int nPastWindow;
- @Nonnull
- private final int nCurrentWindow;
- @Nonnull
- private final int pastSize;
- @Nonnull
- private final int currentSize;
- @Nonnull
- private final int currentOffset;
- @Nonnull
- private final int r;
- @Nonnull
- private final int k;
-
- @Nonnull
- private final DoubleRingBuffer xRing;
- @Nonnull
- private final double[] xSeries;
-
- @Nonnull
- private final double[] q;
-
- SingularSpectrumTransform(@Nonnull Parameters params, @Nonnull
PrimitiveObjectInspector oi) {
- this.oi = oi;
- this.scoreFunc = params.scoreFunc;
-
- this.window = params.w;
- this.nPastWindow = params.n;
- this.nCurrentWindow = params.m;
- this.pastSize = window + nPastWindow;
- this.currentSize = window + nCurrentWindow;
- this.currentOffset = params.g;
- this.r = params.r;
- this.k = params.k;
- Preconditions.checkArgument(params.k >= params.r);
-
- // (w + n) past samples for the n-past-windows
- // (w + m) current samples for the m-current-windows, starting
from offset g
- // => need to hold past (w + n + g + w + m) samples from the
latest sample
- int holdSampleSize = pastSize + currentOffset + currentSize;
-
- this.xRing = new DoubleRingBuffer(holdSampleSize);
- this.xSeries = new double[holdSampleSize];
-
- this.q = new double[window];
- double norm = 0.d;
- for (int i = 0; i < window; i++) {
- this.q[i] = Math.random();
- norm += q[i] * q[i];
- }
- norm = Math.sqrt(norm);
- // normalize
- for (int i = 0; i < window; i++) {
- this.q[i] = q[i] / norm;
- }
+ @Nonnull
+ private final PrimitiveObjectInspector oi;
+ @Nonnull
+ private final ScoreFunction scoreFunc;
+
+ @Nonnull
+ private final int window;
+ @Nonnull
+ private final int nPastWindow;
+ @Nonnull
+ private final int nCurrentWindow;
+ @Nonnull
+ private final int pastSize;
+ @Nonnull
+ private final int currentSize;
+ @Nonnull
+ private final int currentOffset;
+ @Nonnull
+ private final int r;
+ @Nonnull
+ private final int k;
+
+ @Nonnull
+ private final DoubleRingBuffer xRing;
+ @Nonnull
+ private final double[] xSeries;
+
+ @Nonnull
+ private final double[] q;
+
+ SingularSpectrumTransform(@Nonnull Parameters params, @Nonnull
PrimitiveObjectInspector oi) {
+ this.oi = oi;
+ this.scoreFunc = params.scoreFunc;
+
+ this.window = params.w;
+ this.nPastWindow = params.n;
+ this.nCurrentWindow = params.m;
+ this.pastSize = window + nPastWindow;
+ this.currentSize = window + nCurrentWindow;
+ this.currentOffset = params.g;
+ this.r = params.r;
+ this.k = params.k;
+ Preconditions.checkArgument(params.k >= params.r);
+
+ // (w + n) past samples for the n-past-windows
+ // (w + m) current samples for the m-current-windows, starting from
offset g
+ // => need to hold past (w + n + g + w + m) samples from the latest
sample
+ int holdSampleSize = pastSize + currentOffset + currentSize;
+
+ this.xRing = new DoubleRingBuffer(holdSampleSize);
+ this.xSeries = new double[holdSampleSize];
+
+ this.q = new double[window];
+ double norm = 0.d;
+ for (int i = 0; i < window; i++) {
+ this.q[i] = Math.random();
+ norm += q[i] * q[i];
}
-
- @Override
- public void update(@Nonnull final Object arg, @Nonnull final double[]
outScores)
- throws HiveException {
- double x = PrimitiveObjectInspectorUtils.getDouble(arg, oi);
- xRing.add(x).toArray(xSeries, true /* FIFO */);
-
- // need to wait until the buffer is filled
- if (!xRing.isFull()) {
- outScores[0] = 0.d;
- } else {
- // create past trajectory matrix and find its left singular
vectors
- RealMatrix H = new Array2DRowRealMatrix(window, nPastWindow);
- for (int i = 0; i < nPastWindow; i++) {
- H.setColumn(i, Arrays.copyOfRange(xSeries, i, i + window));
- }
-
- // create current trajectory matrix and find its left singular
vectors
- RealMatrix G = new Array2DRowRealMatrix(window,
nCurrentWindow);
- int currentHead = pastSize + currentOffset;
- for (int i = 0; i < nCurrentWindow; i++) {
- G.setColumn(i,
- Arrays.copyOfRange(xSeries, currentHead + i,
currentHead + i + window));
- }
-
- switch (scoreFunc) {
- case svd:
- outScores[0] = computeScoreSVD(H, G);
- break;
- case ika:
- outScores[0] = computeScoreIKA(H, G);
- break;
- default:
- throw new IllegalStateException("Unexpected score
function: " + scoreFunc);
- }
- }
+ norm = Math.sqrt(norm);
+ // normalize
+ for (int i = 0; i < window; i++) {
+ this.q[i] = q[i] / norm;
}
-
- /**
- * Singular Value Decomposition (SVD) based naive scoring.
- */
- private double computeScoreSVD(@Nonnull final RealMatrix H, @Nonnull
final RealMatrix G) {
- SingularValueDecomposition svdH = new
SingularValueDecomposition(H);
- RealMatrix UT = svdH.getUT();
-
- SingularValueDecomposition svdG = new
SingularValueDecomposition(G);
- RealMatrix Q = svdG.getU();
-
- // find the largest singular value for the r principal components
- RealMatrix UTQ = UT.getSubMatrix(0, r - 1, 0, window - 1).multiply(
- Q.getSubMatrix(0, window - 1, 0, r - 1));
- SingularValueDecomposition svdUTQ = new
SingularValueDecomposition(UTQ);
- double[] s = svdUTQ.getSingularValues();
-
- return 1.d - s[0];
+ }
+
+ @Override
+ public void update(@Nonnull final Object arg, @Nonnull final double[]
outScores)
+ throws HiveException {
+ double x = PrimitiveObjectInspectorUtils.getDouble(arg, oi);
+ xRing.add(x).toArray(xSeries, true /* FIFO */);
+
+ // need to wait until the buffer is filled
+ if (!xRing.isFull()) {
+ outScores[0] = 0.d;
+ } else {
+ // create past trajectory matrix and find its left singular vectors
+ RealMatrix H = new Array2DRowRealMatrix(window, nPastWindow);
+ for (int i = 0; i < nPastWindow; i++) {
+ H.setColumn(i, Arrays.copyOfRange(xSeries, i, i + window));
+ }
+
+ // create current trajectory matrix and find its left singular
vectors
+ RealMatrix G = new Array2DRowRealMatrix(window, nCurrentWindow);
+ int currentHead = pastSize + currentOffset;
+ for (int i = 0; i < nCurrentWindow; i++) {
+ G.setColumn(i, Arrays.copyOfRange(xSeries, currentHead + i,
currentHead + i + window));
+ }
+
+ switch (scoreFunc) {
+ case svd:
--- End diff --
`case` better to be indented as seen in Google coding style.
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