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-rw-r--r--src/de/lmu/ifi/dbs/elki/math/dimensionsimilarity/HSMDimensionSimilarity.java73
1 files changed, 37 insertions, 36 deletions
diff --git a/src/de/lmu/ifi/dbs/elki/math/dimensionsimilarity/HSMDimensionSimilarity.java b/src/de/lmu/ifi/dbs/elki/math/dimensionsimilarity/HSMDimensionSimilarity.java
index e73d02af..02511047 100644
--- a/src/de/lmu/ifi/dbs/elki/math/dimensionsimilarity/HSMDimensionSimilarity.java
+++ b/src/de/lmu/ifi/dbs/elki/math/dimensionsimilarity/HSMDimensionSimilarity.java
@@ -4,7 +4,7 @@ package de.lmu.ifi.dbs.elki.math.dimensionsimilarity;
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
- Copyright (C) 2012
+ Copyright (C) 2014
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
@@ -28,11 +28,10 @@ import de.lmu.ifi.dbs.elki.database.Database;
import de.lmu.ifi.dbs.elki.database.ids.DBIDIter;
import de.lmu.ifi.dbs.elki.database.ids.DBIDs;
import de.lmu.ifi.dbs.elki.database.relation.Relation;
+import de.lmu.ifi.dbs.elki.database.relation.RelationUtil;
import de.lmu.ifi.dbs.elki.math.SinCosTable;
-import de.lmu.ifi.dbs.elki.utilities.DatabaseUtil;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
import de.lmu.ifi.dbs.elki.utilities.optionhandling.AbstractParameterizer;
-import de.lmu.ifi.dbs.elki.utilities.pairs.Pair;
/**
* Compute the similarity of dimensions by using a hough transformation.
@@ -52,8 +51,11 @@ import de.lmu.ifi.dbs.elki.utilities.pairs.Pair;
* @author Erich Schubert
* @author Robert Rödler
*/
-@Reference(authors = "A. Tatu, G. Albuquerque, M. Eisemann, P. Bak, H. Theisel, M. A. Magnor, and D. A. Keim.", title = "Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data", booktitle = "IEEE Trans. Visualization and Computer Graphics, 2011", url = "http://dx.doi.org/10.1109/TVCG.2010.242")
-public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector<?>> {
+@Reference(authors = "A. Tatu, G. Albuquerque, M. Eisemann, P. Bak, H. Theisel, M. A. Magnor, and D. A. Keim", //
+title = "Automated Analytical Methods to Support Visual Exploration of High-Dimensional Data", //
+booktitle = "IEEE Trans. Visualization and Computer Graphics, 2011", //
+url = "http://dx.doi.org/10.1109/TVCG.2010.242")
+public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector> {
/**
* Static instance.
*/
@@ -79,36 +81,35 @@ public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector<
}
@Override
- public void computeDimensionSimilarites(Database database, Relation<? extends NumberVector<?>> relation, DBIDs subset, DimensionSimilarityMatrix matrix) {
+ public void computeDimensionSimilarites(Database database, Relation<? extends NumberVector> relation, DBIDs subset, DimensionSimilarityMatrix matrix) {
final int dim = matrix.size();
final int resolution = 512;
boolean[][][][] pics = new boolean[dim][dim][][]; // [resolution][resolution];
// Initialize / allocate "pictures":
- for (int i = 0; i < dim - 1; i++) {
- for (int j = i + 1; j < dim; j++) {
+ for(int i = 0; i < dim - 1; i++) {
+ for(int j = i + 1; j < dim; j++) {
pics[i][j] = new boolean[resolution][resolution];
}
}
// FIXME: Get/keep these statistics in the relation, or compute for the
// sample only.
- double[] off = new double[dim], scale = new double[dim];
+ double[] off, scale;
{
- Pair<? extends NumberVector<?>, ? extends NumberVector<?>> mm = DatabaseUtil.computeMinMax(relation);
- NumberVector<?> min = mm.first;
- NumberVector<?> max = mm.second;
- for (int d = 0; d < dim; d++) {
- off[d] = min.doubleValue(matrix.dim(d));
- final double m = max.doubleValue(matrix.dim(d));
- scale[d] = (m > off[d]) ? 1. / (m - off[d]) : 1;
+ double[][] mm = RelationUtil.computeMinMax(relation);
+ off = mm[0];
+ scale = mm[1];
+ for(int d = 0; d < dim; d++) {
+ scale[d] -= off[d];
+ scale[d] = (scale[d] > 0.) ? 1. / scale[d] : 1.;
}
}
// Iterate dataset
- for (DBIDIter id = subset.iter(); id.valid(); id.advance()) {
- NumberVector<?> pvec = relation.get(id);
- for (int i = 0; i < dim - 1; i++) {
+ for(DBIDIter id = subset.iter(); id.valid(); id.advance()) {
+ NumberVector pvec = relation.get(id);
+ for(int i = 0; i < dim - 1; i++) {
double xi = (pvec.doubleValue(matrix.dim(i)) - off[i]) * scale[i];
- for (int j = i + 1; j < dim; j++) {
+ for(int j = i + 1; j < dim; j++) {
double xj = (pvec.doubleValue(matrix.dim(j)) - off[j]) * scale[j];
drawLine(0, (int) (resolution * xi), resolution - 1, (int) (resolution * xj), pics[i][j]);
}
@@ -116,8 +117,8 @@ public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector<
}
final double stepsq = (double) STEPS * (double) STEPS;
- for (int x = 0; x < dim; x++) {
- for (int y = x + 1; y < dim; y++) {
+ for(int x = 0; x < dim; x++) {
+ for(int y = x + 1; y < dim; y++) {
int[][] hough = houghTransformation(pics[x][y]);
pics[x][y] = null; // Release picture
// The original publication said "median", but judging from the text,
@@ -139,9 +140,9 @@ public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector<
*/
private long sumMatrix(int[][] mat) {
long ret = 0;
- for (int i = 0; i < mat.length; i++) {
+ for(int i = 0; i < mat.length; i++) {
final int[] row = mat[i];
- for (int j = 0; j < row.length; j++) {
+ for(int j = 0; j < row.length; j++) {
ret += row[j];
}
}
@@ -157,10 +158,10 @@ public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector<
*/
private int countAboveThreshold(int[][] mat, double threshold) {
int ret = 0;
- for (int i = 0; i < mat.length; i++) {
+ for(int i = 0; i < mat.length; i++) {
int[] row = mat[i];
- for (int j = 0; j < row.length; j++) {
- if (row[j] >= threshold) {
+ for(int j = 0; j < row.length; j++) {
+ if(row[j] >= threshold) {
ret++;
}
}
@@ -179,12 +180,12 @@ public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector<
final double tscale = STEPS * .5 / (xres + yres);
final int[][] ret = new int[STEPS][STEPS];
- for (int x = 0; x < mat.length; x++) {
- for (int y = 0; y < mat[0].length; y++) {
- if (mat[x][y]) {
- for (int i = 0; i < STEPS; i++) {
+ for(int x = 0; x < mat.length; x++) {
+ for(int y = 0; y < mat[0].length; y++) {
+ if(mat[x][y]) {
+ for(int i = 0; i < STEPS; i++) {
final int d = (STEPS >> 1) + (int) (tscale * (x * table.cos(i) + y * table.sin(i)));
- if (d > 0 && d < STEPS) {
+ if(d > 0 && d < STEPS) {
ret[d][i]++;
}
}
@@ -217,18 +218,18 @@ public class HSMDimensionSimilarity implements DimensionSimilarity<NumberVector<
// Error counter
int err = dx + dy;
- for (;;) {
+ for(;;) {
pic[x0][y0] = true;
- if (x0 == x1 && y0 == y1) {
+ if(x0 == x1 && y0 == y1) {
break;
}
final int e2 = err << 1;
- if (e2 > dy) {
+ if(e2 > dy) {
err += dy;
x0 += sx;
}
- if (e2 < dx) {
+ if(e2 < dx) {
err += dx;
y0 += sy;
}
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