Author: scooter
Date: 2012-08-09 21:51:16 -0700 (Thu, 09 Aug 2012)
New Revision: 30155
Added:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachClustererOld.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMCluster.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMClusterer.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMCluster.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMClusterer.java
Removed:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/treeview/CancelableSettingsDialog.java
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ClusterMaker.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/AbstractAttributeClusterAlgorithm.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/BaseMatrix.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/Clusters.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/DistanceMatrix.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/Hopach.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAM.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/types/Hopachable.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAM.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAMTest.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAM.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMTest.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/silhouette/MSplitSilhouetteCalculator.java
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ui/KnnView.java
Log:
Added PAM and HOPACH
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ClusterMaker.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ClusterMaker.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ClusterMaker.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -67,8 +67,10 @@
import clusterMaker.algorithms.ClusterAlgorithm;
import clusterMaker.algorithms.attributeClusterers.autosome.AutoSOMECluster;
import
clusterMaker.algorithms.attributeClusterers.hierarchical.HierarchicalCluster;
+import clusterMaker.algorithms.attributeClusterers.hopach.HopachPAMClusterer;
import clusterMaker.algorithms.attributeClusterers.kmeans.KMeansCluster;
import clusterMaker.algorithms.attributeClusterers.kmedoid.KMedoidCluster;
+import clusterMaker.algorithms.attributeClusterers.pam.PAMClusterer;
import
clusterMaker.algorithms.attributeClusterers.FeatureVector.FeatureVectorCluster;
import clusterMaker.algorithms.networkClusterers.TransClust.TransClustCluster;
@@ -133,6 +135,8 @@
addClusterAlgorithm(menu, new HierarchicalCluster());
addClusterAlgorithm(menu, new KMeansCluster());
addClusterAlgorithm(menu, new KMedoidCluster());
+ addClusterAlgorithm(menu, new PAMClusterer());
+ addClusterAlgorithm(menu, new HopachPAMClusterer());
// addClusterAlgorithm(menu, new QTCluster());
menu.addSeparator();
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/AbstractAttributeClusterAlgorithm.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/AbstractAttributeClusterAlgorithm.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/AbstractAttributeClusterAlgorithm.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -359,8 +359,11 @@
if (halted()) return "Halted by user";
// Cluster
- int ifound = kcluster(nClusters, nIterations, matrix, metric,
clusters);
+ int nClustersFound = kcluster(nClusters, nIterations, matrix,
metric, clusters);
if (halted()) return "Halted by user";
+
+ // TODO Change other algorithms s.t. the number of clusters
found is returned
+ if (nClusters == 0) nClusters = nClustersFound;
// OK, now run our silhouette on our final result
Silhouettes sResult = SilhouetteCalculator.calculate(matrix,
metric, clusters);
@@ -375,14 +378,24 @@
this isn't working...
renumberClusters(nClusters, clusters);
*/
+ // NB HOPACH clusters should not be re-ordered
rowOrder = matrix.indexSort(clusters, clusters.length);
// System.out.println(Arrays.toString(rowOrder));
// Update the network attributes
updateAttributes(algorithm);
+
+ // FIXME For HOPACH, nClusters is determined by the algorithm,
and is neither estimated nor predefined...
String resultString = "Created "+nClusters+" clusters with
average silhouette = "+sResult.getMean();
logger.info(resultString);
+
+ String s = "Clusters: ";
+ for (int i = 0; i < clusters.length; ++i) {
+ s += clusters[i] + ", ";
+ }
+ logger.info(s);
+
return resultString;
}
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/BaseMatrix.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/BaseMatrix.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/BaseMatrix.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -37,6 +37,7 @@
/**
* BaseMatrix is a basic numeric matrix.
+ * Independent of Cytoscape.
*/
public class BaseMatrix {
protected int nRows;
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/Clusters.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/Clusters.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/Clusters.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -5,6 +5,7 @@
/**
* Clusters store clustering results.
+ * Independent of Cytoscape.
* @author djh.shih
*
*/
@@ -32,6 +33,21 @@
}
/**
+ * Special constructor for building singleton clusters.
+ * @param k number of singleton clusters
+ */
+ public Clusters(int k) {
+ int[] t = new int[k];
+ for (int i = 0; i < k; ++i) {
+ t[i] = k;
+ }
+ this.index = t;
+ this.orderedLabels = Arrays.copyOf(t, t.length);
+ this.k = k;
+ this.cost = 0.0;
+ }
+
+ /**
* Constructor. No cluster label reassignment. No error checking.
* @param assignments cluster index/labels of each element (clusters
must be labeled 0 to k-1)
* @param k number of clusters
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/DistanceMatrix.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/DistanceMatrix.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/DistanceMatrix.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -7,6 +7,7 @@
/**
* Distance matrix. Matrix of distances between data elements.
+ * Independent of Cytoscape.
* @author djh.shih
*/
public class DistanceMatrix {
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/Hopach.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/Hopach.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/Hopach.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -9,6 +9,7 @@
/**
* Hopach performs HOPACH using a Hopachable partitioner.
+ * Independent of Cytoscape.
* @author djh.shih
*
*/
@@ -27,11 +28,14 @@
ArrayList<Clusters> splits;
// maximum number of partitioning levels
- int maxLevel;
+ int maxLevel = 9;
// minimum cost reduction
- double minCostReduction;
+ double minCostReduction = 0;
+ // whether to force split the initial level
+ boolean forceInitSplit = false;
+
/*
* Initial set of parameters implemented
* clusters = best
@@ -43,13 +47,14 @@
*/
public Hopach(Hopachable partitioner) {
- this(partitioner, 9, 0);
+ this.partitioner = partitioner;
+ initialize();
}
- public Hopach(Hopachable partitioner, int maxLevel, int
minCostReduction) {
- this.partitioner = partitioner;
+ public void setParameters(int maxLevel, int minCostReduction, boolean
forceInitSplit) {
this.maxLevel = maxLevel;
this.minCostReduction = minCostReduction;
+ this.forceInitSplit = forceInitSplit;
initialize();
}
@@ -124,14 +129,14 @@
* @return possibility of continuing split
*/
Clusters initLevel() {
- Clusters split = partitioner.split();
+ Clusters split = partitioner.split(forceInitSplit);
sortInitLevel(split);
splits.set(0, split);
return split;
}
/**
- * Attempt to collapse clusters in the specified level.
+ * Attempt to collapse clusters at the specified level.
* @param level
*/
Clusters collapse(int level) {
@@ -239,13 +244,18 @@
boolean rightNeighbour = j < nClusters - 1;
int[] partition = partitions[i];
+ if (partition.length == 0) {
+ // partition is empty (partitioner returned
fewer partitions than requested)
+ continue;
+ }
+
int neighbourIndex = rightNeighbour ? i+1 : i-1;
// split partition
// TODO cache sub-partitioner here and in MSS calculator
// or, cache partition and split results
Hopachable sub = partitioner.subset(partition);
- Clusters subsplit = sub.split();
+ Clusters subsplit = sub.split(false);
int subk = subsplit.getNumberOfClusters();
@@ -277,13 +287,10 @@
costs[i] = subsplit.getCost();
// copy over new cluster index
- int js = 0;
- int jj;
- for (jj = j; jj < j + sub.size(); ++jj) {
- clusterIndex[jj] =
subsplit.getClusterIndex(js++) + k;
+ for (int jj = 0; jj < sub.size(); ++jj) {
+ clusterIndex[ partition[jj] ] =
subsplit.getClusterIndex(jj) + k;
}
- j = jj;
-
+ j += sub.size();
k += subk;
}
@@ -323,7 +330,7 @@
*/
void sortInitLevel(Clusters split) {
if (split.getNumberOfClusters() <= 2) {
- // order of 2 or fewer clusters cannot be optiimized
+ // order of 2 or fewer clusters cannot be optimized
return;
}
Added:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachClustererOld.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachClustererOld.java
(rev 0)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachClustererOld.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -0,0 +1,135 @@
+/* vim: set ts=2: */
+/**
+ * Copyright (c) 2011 The Regents of the University of California.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ * 1. Redistributions of source code must retain the above copyright
+ * notice, this list of conditions, and the following disclaimer.
+ * 2. Redistributions in binary form must reproduce the above
+ * copyright notice, this list of conditions, and the following
+ * disclaimer in the documentation and/or other materials provided
+ * with the distribution.
+ * 3. Redistributions must acknowledge that this software was
+ * originally developed by the UCSF Computer Graphics Laboratory
+ * under support by the NIH National Center for Research Resources,
+ * grant P41-RR01081.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER "AS IS" AND ANY
+ * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+ * CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT
+ * OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
+ * BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
+ * WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
+ * OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
+ * EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ */
+package clusterMaker.algorithms.attributeClusterers.hopach;
+
+import java.awt.GridLayout;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Comparator;
+import java.util.List;
+import javax.swing.JPanel;
+
+// Cytoscape imports
+import cytoscape.Cytoscape;
+import cytoscape.data.CyAttributes;
+import cytoscape.layout.Tunable;
+import cytoscape.logger.CyLogger;
+import cytoscape.task.TaskMonitor;
+
+import clusterMaker.algorithms.AbstractClusterAlgorithm;
+import clusterMaker.algorithms.ClusterAlgorithm;
+import clusterMaker.algorithms.attributeClusterers.AbstractAttributeClusterer;
+import clusterMaker.algorithms.attributeClusterers.DistanceMetric;
+import clusterMaker.ui.ClusterViz;
+import clusterMaker.ui.KnnView;
+
+
+/**
+ * HopachCluster implements the HOPACH (Hierarchical Ordered Partitioning and
Collapsing Hybrid)
+ * algorithm of van der Laan and Pollard. The algorithm can be basically
outlined as:
+ * while (minClusterSize > 3)
+ * foreach (level)
+ * k = averageSilhouette(K); // Get the average
silhouette for k=2,...,K
+ * partitionClusters(k); // Partition the clusters with
PAM
+ * orderClusters(); // Order the
new clusters
+ * collapseClusters(); // Collapse clusters
+ * level++;
+ *
+ * where partitionClusters() uses PAM (Partition About Medoids)
+ * algorithm (we actually use our k-medoid implementation)
+ *
+ * User inputs:
+ * K - the maximum number of levels in the tree (15)
+ * kmax - the maximum number of children at at each node in the tree (1-9)
+ * coll - collapse approach
+ * attributeList - the names of the features to cluster on
+ * metric - the distance metric (Euclidean distance is used in HOPACH)
+ *
+ * Output:
+ * Ordered cluster tree
+ *
+ * Reference:
+ * M.J. van der Laan, K.S. Pollard (2001). Hybrid clustering of
gene expression
+ * data with visualization and the bootstrap. Journal of
Statistical Planning and
+ * Inference, 2003, 117: 275-303
+ *
+ * TODO: Get R source code to figure out exact algorithm
+ */
+public class HopachClustererOld extends AbstractAttributeClusterer {
+
+ String[] attributeArray = new String[1];
+
+ TaskMonitor monitor = null;
+ CyLogger logger = null;
+
+ public HopachClustererOld() {
+ super();
+ logger = CyLogger.getLogger(HopachClustererOld.class);
+ initializeProperties();
+ }
+
+ public String getShortName() {return "hopach";};
+ public String getName() {return "HOPACH cluster";};
+
+ public JPanel getSettingsPanel() {
+ return null;
+ }
+
+ public ClusterViz getVisualizer() {
+ return null;
+ }
+
+ public void initializeProperties() {
+ super.initializeProperties();
+ updateSettings(true);
+ }
+
+ public void updateSettings() {
+ updateSettings(false);
+ }
+
+ public void updateSettings(boolean force) {
+ clusterProperties.updateValues();
+ super.updateSettings(force);
+
+ }
+
+ public void doCluster(TaskMonitor monitor) {
+ this.monitor = monitor;
+ }
+
+ public boolean isAvailable() {
+ return true;
+ }
+
+}
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAM.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAM.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAM.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -7,6 +7,7 @@
/**
* HopachPAM is a specialized a specialized Hopach algorithm that uses the
* PAM partitioner (Hopach-PAM).
+ * Independent of Cytoscape.
* @author djh.shih
*
*/
Added:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMCluster.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMCluster.java
(rev 0)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMCluster.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -0,0 +1,36 @@
+package clusterMaker.algorithms.attributeClusterers.hopach;
+
+import cytoscape.logger.CyLogger;
+import cytoscape.task.TaskMonitor;
+import
clusterMaker.algorithms.attributeClusterers.AbstractAttributeClusterAlgorithm;
+import clusterMaker.algorithms.attributeClusterers.Clusters;
+import clusterMaker.algorithms.attributeClusterers.DistanceMetric;
+import clusterMaker.algorithms.attributeClusterers.Matrix;
+
+public class HopachPAMCluster extends AbstractAttributeClusterAlgorithm {
+
+ public HopachPAMCluster(String weightAttributes[], DistanceMetric
metric, CyLogger logger, TaskMonitor monitor) {
+ this.logger = logger;
+ this.weightAttributes = weightAttributes;
+ this.metric = metric;
+ this.monitor = monitor;
+ resetAttributes();
+ }
+
+ @Override
+ public int kcluster(int nClusters, int nIterations, Matrix matrix,
DistanceMetric metric, int[] clusterId) {
+
+ if (monitor != null) monitor.setPercentCompleted(0);
+
+ HopachPAM hopachPam = new HopachPAM(matrix, metric);
+ hopachPam.setParameters(9, 0, true);
+ Clusters c = hopachPam.run();
+
+ // copy results into clusterId
+ for (int i = 0; i < c.size(); ++i) {
+ clusterId[i] = c.getClusterIndex(i);
+ }
+
+ return c.getNumberOfClusters();
+ }
+}
Added:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMClusterer.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMClusterer.java
(rev 0)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/HopachPAMClusterer.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -0,0 +1,200 @@
+package clusterMaker.algorithms.attributeClusterers.hopach;
+
+import java.util.Arrays;
+
+import javax.swing.JPanel;
+
+import cytoscape.layout.Tunable;
+import cytoscape.logger.CyLogger;
+import cytoscape.task.TaskMonitor;
+import clusterMaker.algorithms.ClusterAlgorithm;
+import clusterMaker.algorithms.attributeClusterers.AbstractAttributeClusterer;
+import clusterMaker.algorithms.attributeClusterers.BaseMatrix;
+import clusterMaker.ui.ClusterViz;
+import clusterMaker.ui.KnnView;
+
+public class HopachPAMClusterer extends AbstractAttributeClusterer {
+
+ public HopachPAMClusterer() {
+ logger = CyLogger.getLogger(HopachPAMClusterer.class);
+ initializeProperties();
+ }
+
+ @Override
+ public void initializeProperties() {
+ super.initializeProperties();
+
+ clusterProperties.add(
+ new Tunable(
+ "dMetric",
+ "Distance Metric",
+ Tunable.LIST, new Integer(0),
+ (Object)BaseMatrix.distanceTypes, null, 0
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "attributeListGroup",
+ "Source for array data",
+ Tunable.GROUP, new Integer(1)
+ )
+ );
+
+ attributeArray = getAllAttributes();
+ clusterProperties.add(
+ new Tunable(
+ "attributeList",
+ "Array sources",
+ Tunable.LIST, "",
+ (Object)attributeArray, null,
Tunable.MULTISELECT
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "selectedOnly",
+ "Only use selected nodes/edges for cluster",
+ Tunable.BOOLEAN, new Boolean(selectedOnly)
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "clusterAttributes",
+ "Cluster attributes as well as nodes",
+ Tunable.BOOLEAN, new Boolean(clusterAttributes)
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "createGroups",
+ "Create groups from clusters",
+ Tunable.BOOLEAN, new Boolean(createGroups)
+ )
+ );
+
+ clusterProperties.initializeProperties();
+ updateSettings(true);
+ }
+
+ @Override
+ public void updateSettings() {
+ updateSettings(false);
+ }
+
+ public void updateSettings(boolean force) {
+ clusterProperties.updateValues();
+ super.updateSettings(force);
+
+ Tunable t;
+
+ t = clusterProperties.get("dMetric");
+ if ((t != null) && (t.valueChanged() || force)) {
+ distanceMetric = BaseMatrix.distanceTypes[((Integer)
t.getValue()).intValue()];
+ }
+
+ t = clusterProperties.get("clusterAttributes");
+ if ((t != null) && (t.valueChanged() || force)) {
+ clusterAttributes = ((Boolean)
t.getValue()).booleanValue();
+ }
+
+ t = clusterProperties.get("selectedOnly");
+ if ((t != null) && (t.valueChanged() || force)) {
+ selectedOnly = ((Boolean) t.getValue()).booleanValue();
+ }
+
+ t = clusterProperties.get("createGroups");
+ if ((t != null) && (t.valueChanged() || force)) {
+ createGroups = ((Boolean) t.getValue()).booleanValue();
+ }
+
+ t = clusterProperties.get("attributeList");
+ if ((t != null) && (t.valueChanged() || force)) {
+ dataAttributes = (String) t.getValue();
+ }
+ }
+
+ @Override
+ public String getShortName() {
+ return "HOPACH";
+ }
+
+ @Override
+ public String getName() {
+ return "HOPACH-PAM";
+ }
+
+ @Override
+ public ClusterViz getVisualizer() {
+ return new KnnView();
+ }
+
+ @Override
+ public JPanel getSettingsPanel() {
+ // update attributes
+ Tunable attributeTunable =
clusterProperties.get("attributeList");
+ attributeArray = getAllAttributes();
+ attributeTunable.setLowerBound((Object)attributeArray);
+
+ return clusterProperties.getTunablePanel();
+ }
+
+ @Override
+ public void doCluster(TaskMonitor monitor) {
+ this.monitor = monitor;
+
+ if (debug) {
+ logger.debug("Performing HOPACH-PAM cluster with k = "
+ kNumber + " using " + distanceMetric + " and attributes: " + dataAttributes);
+ }
+
+ // sanity check of parameters
+ if (dataAttributes == null || dataAttributes.length() == 0) {
+ if (monitor != null) {
+ monitor.setException(null, "Error: no attribute
list selected");
+ logger.warning("Must have an attribute list to
use for cluster weighting");
+ } else {
+ logger.error("Must have an attribute list to
use or cluster weighting");
+ }
+ return;
+ }
+
+ // get attributes to be used for clustering
+ String attributeArray[] = getAttributeArray(dataAttributes);
+ Arrays.sort(attributeArray);
+
+ HopachPAMCluster algo = new HopachPAMCluster(attributeArray,
distanceMetric, logger, monitor);
+ algo.setCreateGroups(createGroups);
+ algo.setIgnoreMissing(true);
+ algo.setSelectedOnly(selectedOnly);
+ algo.setDebug(debug);
+ algo.setUseSilhouette(useSilhouette);
+ algo.setKMax(kMax);
+ algo.setClusterInterface(this);
+
+ String resultsString = getName() + " results:";
+
+ int nIterations = 0;
+
+ // Cluster the attributes
+ if (clusterAttributes && attributeArray.length > 1) {
+ if (monitor != null) {
+ monitor.setStatus("Clustering attributes");
+ }
+ resultsString = "\nAttributes: " +
algo.cluster(kNumber, nIterations, true, getShortName());
+ }
+
+ // Cluster the nodes
+ if (monitor != null) {
+ monitor.setStatus("Clustering nodes");
+ }
+ resultsString = "\nNodes: " + algo.cluster(kNumber,
nIterations, false, getShortName());
+ if (monitor != null) {
+ monitor.setStatus(resultsString);
+ }
+
+ // signal completion to listeners
+ pcs.firePropertyChange(ClusterAlgorithm.CLUSTER_COMPUTED, null,
this);
+ }
+}
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/types/Hopachable.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/types/Hopachable.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/hopach/types/Hopachable.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -12,7 +12,7 @@
// Split elements into partitions
// A class implementing Hopachable is free to choose the best k, and
can use simply choose k = argmin_k MSS
// by calling a separately provided functions to calculate min MSS
- Clusters split();
+ Clusters split(boolean forceSplit);
// Return a matrix with distances from each element to each cluster
double[][] segregations(Clusters clusters);
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAM.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAM.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAM.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -52,8 +52,8 @@
}
@Override
- public Clusters split() {
- return
MSplitSilhouetteCalculator.splitByMedianSplitSilhouette(this, maxK, maxL);
+ public Clusters split(boolean forceSplit) {
+ return
MSplitSilhouetteCalculator.splitByMedianSplitSilhouette(this, maxK, maxL,
forceSplit);
}
@Override
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAMTest.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAMTest.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/HopachablePAMTest.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -92,7 +92,7 @@
HopachablePAM pam = new HopachablePAM(mat,
DistanceMetric.CITYBLOCK);
pam.setParameters(9, 9);
- Clusters c = pam.split();
+ Clusters c = pam.split(false);
// check that data are split into expected number of clusters
assertEquals(c.getNumberOfClusters(), k);
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAM.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAM.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAM.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -13,6 +13,7 @@
* Partitioning Around Medoids. Cluster data elements by aiming to minimize
the average
* dissimilarity of objects to their closest selected element (medoid).
* Data elements are indexable.
+ * Independent of Cytoscape.
* NB This class is an implementation of the PAM algorithm described in the
course notes
* at <www.cs.umb.edu/cs738/pam1.pdf> (accessed 2012-07-31).
* There appears to be different variants of the PAM algorithm varying in
details
@@ -22,6 +23,7 @@
* The cluster results from current implementation can differ from the
implementation
* in R's cluster::pam. (See PAMTest for details.)
* @author djh.shih
+ * @comment
*
*/
public class PAM implements KClusterable {
@@ -62,6 +64,8 @@
// required since Java's HashSet cannot use native types
Integer[] elements;
+ int maxSwaps = 1000;
+
public PAM(BaseMatrix data, DistanceMetric metric) {
this(data, metric, null, null);
}
@@ -95,11 +99,15 @@
@Override
public Clusters cluster(int k) {
- if (size() == 0) {
+ int n = size();
+ if (n == 0) {
throw new IllegalArgumentException("No data elements
are indexed.");
}
- if (k >= size()) {
+ if (k > n) {
throw new IllegalArgumentException("Number of clusters
must be less than the number of data elements.");
+ } else if (k == n) {
+ // build trivial single clusters
+ return new Clusters(k);
}
this.nClusters = k;
@@ -239,12 +247,15 @@
*/
private void swapPhase() {
boolean notConverged = true;
+ boolean continueLoop = true;
+ int nSwaps = 0;
- while (notConverged) {
+ while (notConverged && continueLoop) {
notConverged = false;
+ continueLoop = false;
Iterator<Integer> medIt = medoids.iterator();
- while (medIt.hasNext()) {
+ while (medIt.hasNext() && continueLoop) {
int ii = medIt.next().intValue();
Iterator<Integer> nonmedIt =
nonmedoids.iterator();
@@ -287,10 +298,15 @@
if (change < 0) {
// distance to nearest medoid
summed over all nonmedoids is improved: swap
swap(hh, ii);
- // non-convergence if any swap
occurs
- notConverged = true;
//System.out.print("Swap " + hh
+ " and " + ii + " for change = " + change + "\n");
+ // non-convergence if any swap
occurs, up to a maximum number of swaps (to guard against swap cycles)
+ if (nSwaps++ < maxSwaps) {
+ notConverged = true;
+ } else {
+ continueLoop = false;
+ }
+
// reset iterator
medIt = medoids.iterator();
// break out of inner loop to
consider next medoid
Added:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMCluster.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMCluster.java
(rev 0)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMCluster.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -0,0 +1,36 @@
+package clusterMaker.algorithms.attributeClusterers.pam;
+
+import cytoscape.logger.CyLogger;
+import cytoscape.task.TaskMonitor;
+
+import
clusterMaker.algorithms.attributeClusterers.AbstractAttributeClusterAlgorithm;
+import clusterMaker.algorithms.attributeClusterers.Clusters;
+import clusterMaker.algorithms.attributeClusterers.DistanceMetric;
+import clusterMaker.algorithms.attributeClusterers.Matrix;
+
+public class PAMCluster extends AbstractAttributeClusterAlgorithm {
+
+ public PAMCluster(String weightAttributes[], DistanceMetric metric,
CyLogger log, TaskMonitor monitor) {
+ this.logger = log;
+ this.weightAttributes = weightAttributes;
+ this.metric = metric;
+ this.monitor = monitor;
+ resetAttributes();
+ }
+
+ @Override
+ public int kcluster(int nClusters, int nIterations, Matrix matrix,
DistanceMetric metric, int[] clusterId) {
+
+ if (monitor != null) monitor.setPercentCompleted(0);
+
+ PAM pam = new PAM(matrix, metric);
+ Clusters c = pam.cluster(nClusters);
+
+ // copy results into clusterId
+ for (int i = 0; i < c.size(); ++i) {
+ clusterId[i] = c.getClusterIndex(i);
+ }
+
+ return c.getNumberOfClusters();
+ }
+}
Added:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMClusterer.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMClusterer.java
(rev 0)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMClusterer.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -0,0 +1,217 @@
+package clusterMaker.algorithms.attributeClusterers.pam;
+
+import java.util.Arrays;
+
+import javax.swing.JPanel;
+
+import cytoscape.layout.Tunable;
+import cytoscape.logger.CyLogger;
+import cytoscape.task.TaskMonitor;
+
+import clusterMaker.algorithms.ClusterAlgorithm;
+import clusterMaker.algorithms.attributeClusterers.AbstractAttributeClusterer;
+import clusterMaker.algorithms.attributeClusterers.BaseMatrix;
+import clusterMaker.ui.ClusterViz;
+import clusterMaker.ui.KnnView;
+
+/**
+ *
+ * @author DavidS
+ *
+ */
+public class PAMClusterer extends AbstractAttributeClusterer {
+
+ public PAMClusterer() {
+ logger = CyLogger.getLogger(PAMClusterer.class);
+ initializeProperties();
+ }
+
+ @Override
+ public void initializeProperties() {
+ super.initializeProperties();
+
+ addKTunables();
+
+ // TODO Number of iterations
+
+ clusterProperties.add(
+ new Tunable(
+ "dMetric",
+ "Distance Metric",
+ Tunable.LIST, new Integer(0),
+ (Object)BaseMatrix.distanceTypes, null, 0
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "attributeListGroup",
+ "Source for array data",
+ Tunable.GROUP, new Integer(1)
+ )
+ );
+
+ attributeArray = getAllAttributes();
+ clusterProperties.add(
+ new Tunable(
+ "attributeList",
+ "Array sources",
+ Tunable.LIST, "",
+ (Object)attributeArray, null,
Tunable.MULTISELECT
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "selectedOnly",
+ "Only use selected nodes/edges for cluster",
+ Tunable.BOOLEAN, new Boolean(selectedOnly)
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "clusterAttributes",
+ "Cluster attributes as well as nodes",
+ Tunable.BOOLEAN, new Boolean(clusterAttributes)
+ )
+ );
+
+ clusterProperties.add(
+ new Tunable(
+ "createGroups",
+ "Create groups from clusters",
+ Tunable.BOOLEAN, new Boolean(createGroups)
+ )
+ );
+
+ clusterProperties.initializeProperties();
+ updateSettings(true);
+ }
+
+ @Override
+ public void updateSettings() {
+ updateSettings(false);
+ }
+
+ public void updateSettings(boolean force) {
+ clusterProperties.updateValues();
+ super.updateSettings(force);
+ updateKTunables(force);
+
+ Tunable t;
+
+ // TODO Number of iterations
+
+ t = clusterProperties.get("dMetric");
+ if ((t != null) && (t.valueChanged() || force)) {
+ distanceMetric = BaseMatrix.distanceTypes[((Integer)
t.getValue()).intValue()];
+ }
+
+ t = clusterProperties.get("clusterAttributes");
+ if ((t != null) && (t.valueChanged() || force)) {
+ clusterAttributes = ((Boolean)
t.getValue()).booleanValue();
+ }
+
+ t = clusterProperties.get("selectedOnly");
+ if ((t != null) && (t.valueChanged() || force)) {
+ selectedOnly = ((Boolean) t.getValue()).booleanValue();
+ }
+
+ t = clusterProperties.get("createGroups");
+ if ((t != null) && (t.valueChanged() || force)) {
+ createGroups = ((Boolean) t.getValue()).booleanValue();
+ }
+
+ t = clusterProperties.get("attributeList");
+ if ((t != null) && (t.valueChanged() || force)) {
+ dataAttributes = (String) t.getValue();
+ }
+ }
+
+ @Override
+ public String getShortName() {
+ return "PAM";
+ }
+
+ @Override
+ public String getName() {
+ return "Partitioning Around Medoids";
+ }
+
+ @Override
+ public ClusterViz getVisualizer() {
+ return new KnnView();
+ }
+
+ @Override
+ public JPanel getSettingsPanel() {
+ // update attributes
+ Tunable attributeTunable =
clusterProperties.get("attributeList");
+ attributeArray = getAllAttributes();
+ attributeTunable.setLowerBound((Object)attributeArray);
+
+ // update estimate
+ updateKEstimates();
+
+ return clusterProperties.getTunablePanel();
+ }
+
+ @Override
+ public void doCluster(TaskMonitor monitor) {
+ this.monitor = monitor;
+
+ if (debug) {
+ logger.debug("Performing PAM cluster with k = " +
kNumber + " using " + distanceMetric + " and attributes: " + dataAttributes);
+ }
+
+ // sanity check of parameters
+ if (dataAttributes == null || dataAttributes.length() == 0) {
+ if (monitor != null) {
+ monitor.setException(null, "Error: no attribute
list selected");
+ logger.warning("Must have an attribute list to
use for cluster weighting");
+ } else {
+ logger.error("Must have an attribute list to
use or cluster weighting");
+ }
+ return;
+ }
+
+ // get attributes to be used for clustering
+ String attributeArray[] = getAttributeArray(dataAttributes);
+ Arrays.sort(attributeArray);
+
+ PAMCluster algo = new PAMCluster(attributeArray,
distanceMetric, logger, monitor);
+ algo.setCreateGroups(createGroups);
+ algo.setIgnoreMissing(true);
+ algo.setSelectedOnly(selectedOnly);
+ algo.setDebug(debug);
+ algo.setUseSilhouette(useSilhouette);
+ algo.setKMax(kMax);
+ algo.setClusterInterface(this);
+
+ String resultsString = "PAM results:";
+
+ int nIterations = 0;
+
+ // Cluster the attributes
+ if (clusterAttributes && attributeArray.length > 1) {
+ if (monitor != null) {
+ monitor.setStatus("Clustering attributes");
+ }
+ resultsString = "\nAttributes: " +
algo.cluster(kNumber, nIterations, true, getShortName());
+ }
+
+ // Cluster the nodes
+ if (monitor != null) {
+ monitor.setStatus("Clustering nodes");
+ }
+ resultsString = "\nNodes: " + algo.cluster(kNumber,
nIterations, false, getShortName());
+ if (monitor != null) {
+ monitor.setStatus(resultsString);
+ }
+
+ // signal completion to listeners
+ pcs.firePropertyChange(ClusterAlgorithm.CLUSTER_COMPUTED, null,
this);
+ }
+
+}
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMTest.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMTest.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/pam/PAMTest.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -9,6 +9,23 @@
import clusterMaker.algorithms.attributeClusterers.DistanceMetric;
public class PAMTest {
+
+ @Test
+ public void testSingletonCluster() {
+ Double[] data = {
+ .9, .9
+ };
+ int k = 1;
+
+ int[] ans = {0};
+
+ BaseMatrix mat = new BaseMatrix(0, 2, data);
+ PAM pam = new PAM(mat, DistanceMetric.CITYBLOCK);
+
+ Clusters c = pam.cluster(k);
+
+ assertEquals(c.getNumberOfClusters(), k);
+ }
@Test
public void testCluster() {
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/silhouette/MSplitSilhouetteCalculator.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/silhouette/MSplitSilhouetteCalculator.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/algorithms/attributeClusterers/silhouette/MSplitSilhouetteCalculator.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -131,6 +131,10 @@
}
public static Clusters splitByMeanSplitSilhouette(Subsegregatable sseg,
int K, int L) {
+ return splitByMeanSplitSilhouette(sseg, K, L, false);
+ }
+
+ public static Clusters splitByMeanSplitSilhouette(Subsegregatable sseg,
int K, int L, boolean forceSplit) {
Clusters split = null;
int m = sseg.size();
@@ -140,9 +144,11 @@
K = m / 3;
}
+ int minK = (forceSplit ? 2 : 1);
+
// minimize the mean split silhouette
double avgSplitSil = Double.POSITIVE_INFINITY;
- for (int k = 1; k <= K; k++) {
+ for (int k = minK; k <= K; k++) {
Clusters clusters = sseg.cluster(k);
double t = meanSplitSilhouette(sseg, clusters, L);
if (t < avgSplitSil) {
@@ -151,13 +157,19 @@
}
}
- if (split != null) {
- split.setCost(avgSplitSil);
- }
+ if (split == null) {
+ split = sseg.cluster(minK);
+ }
+ split.setCost(avgSplitSil);
+
return split;
}
public static Clusters splitByMedianSplitSilhouette(Subsegregatable
sseg, int K, int L) {
+ return splitByMedianSplitSilhouette(sseg, K, L, false);
+ }
+
+ public static Clusters splitByMedianSplitSilhouette(Subsegregatable
sseg, int K, int L, boolean forceSplit) {
Clusters split = null;
int m = sseg.size();
@@ -167,9 +179,11 @@
K = m / 3;
}
- // minimize the mean split silhouette
+ int minK = (forceSplit ? 2 : 1);
+
+ // minimize the median split silhouette
double avgSplitSil = Double.POSITIVE_INFINITY;
- for (int k = 1; k <= K; k++) {
+ for (int k = minK; k <= K; k++) {
Clusters clusters = sseg.cluster(k);
double t = medianSplitSilhouette(sseg, clusters, L);
if (t < avgSplitSil) {
@@ -178,9 +192,11 @@
}
}
- if (split != null) {
- split.setCost(avgSplitSil);
- }
+ if (split == null) {
+ split = sseg.cluster(minK);
+ }
+ split.setCost(avgSplitSil);
+
return split;
}
Deleted:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/treeview/CancelableSettingsDialog.java
===================================================================
---
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/treeview/CancelableSettingsDialog.java
2012-08-10 02:24:49 UTC (rev 30154)
+++
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/treeview/CancelableSettingsDialog.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -1,104 +0,0 @@
-/* BEGIN_HEADER Java TreeView
- *
- * $Author: alokito $
- * $RCSfile: CancelableSettingsDialog.java,v $
- * $Revision: 1.6 $
- * $Date: 2004/12/21 03:28:13 $
- * $Name: $
- *
- * This file is part of Java TreeView
- * Copyright (C) 2001-2003 Alok Saldanha, All Rights Reserved. Modified by
- * Alex Segal 2004/08/13. Modifications Copyright (C) Lawrence Berkeley Lab.
- *
- * This software is provided under the GNU GPL Version 2. In particular,
- *
- * 1) If you modify a source file, make a comment in it containing your name
and the date.
- * 2) If you distribute a modified version, you must do it under the GPL 2.
- * 3) Developers are encouraged but not required to notify the Java TreeView
- * maintainers at [email protected] when they make a useful addition.
It would
- * be nice if significant contributions could be merged into the main
distribution.
- *
- * A full copy of the license can be found in gpl.txt or online at
- * http://www.gnu.org/licenses/gpl.txt
- *
- * END_HEADER
- */
-package clusterMaker.treeview.dendroview;
-
-import clusterMaker.treeview.SettingsPanel;
-
-import java.awt.BorderLayout;
-import java.awt.Component;
-import java.awt.event.ActionEvent;
-import java.awt.event.ActionListener;
-
-import javax.swing.JButton;
-import javax.swing.JDialog;
-import javax.swing.JFrame;
-import javax.swing.JLabel;
-import javax.swing.JOptionPane;
-import javax.swing.JPanel;
-
-/**
- * this is a dialog which displays a single cancelable settings panel
- *
- * @author Alok Saldanha <[email protected]>
- * @version @version $Revision: 1.6 $ $Date: 2004/12/21 03:28:13 $
- */
-public class CancelableSettingsDialog extends JDialog {
- SettingsPanel settingsPanel;
- JDialog settingsFrame;
-
-
- /**
- *
- * @param frame <code>JFrame</code> to block on
- * @param title a title for the dialog
- * @param panel A <code>SettingsPanel</code> to feature in the dialog.
- */
- public CancelableSettingsDialog(JFrame frame, String title,
SettingsPanel panel) {
- super(frame, title, true);
- settingsPanel = panel;
- settingsFrame = this;
- JPanel inner = new JPanel();
- inner.setLayout(new BorderLayout());
- inner.add((Component) panel, BorderLayout.CENTER);
- inner.add(new ButtonPanel(), BorderLayout.SOUTH);
- getContentPane().add(inner);
- pack();
- }
-
-
- class ButtonPanel extends JPanel {
- ButtonPanel() {
- JButton save_button = new JButton("Save");
- save_button.addActionListener(
- new ActionListener() {
- public void actionPerformed(ActionEvent
e) {
- try {
-
settingsPanel.synchronizeTo();
- settingsFrame.dispose();
- } catch
(java.lang.OutOfMemoryError ex) {
- JPanel temp = new
JPanel();
- temp. add(new
JLabel("Out of memory, try smaller pixel settings or allocate more RAM"));
- temp. add(new
JLabel("see Chapter 3 of Help->Documentation... for Out of Memory and Image
Export)"));
-
-
JOptionPane.showMessageDialog(CancelableSettingsDialog.this, temp);
- }
- }
- });
- add(save_button);
-
- JButton cancel_button = new JButton("Cancel");
- cancel_button.addActionListener(
- new ActionListener() {
- public void actionPerformed(ActionEvent
e) {
- settingsPanel.synchronizeFrom();
- settingsFrame.dispose();
- }
- });
- add(cancel_button);
- }
- }
-}
-
Modified:
csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ui/KnnView.java
===================================================================
--- csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ui/KnnView.java
2012-08-10 02:24:49 UTC (rev 30154)
+++ csplugins/trunk/ucsf/scooter/clusterMaker/src/clusterMaker/ui/KnnView.java
2012-08-10 04:51:16 UTC (rev 30155)
@@ -126,9 +126,13 @@
String netId = network.getIdentifier();
if (networkAttributes.hasAttribute(netId,
ClusterMaker.CLUSTER_TYPE_ATTRIBUTE)) {
String type =
networkAttributes.getStringAttribute(netId,
ClusterMaker.CLUSTER_TYPE_ATTRIBUTE);
- if (!networkAttributes.getStringAttribute(netId,
ClusterMaker.CLUSTER_TYPE_ATTRIBUTE).equals("kmeans") &&
- !networkAttributes.getStringAttribute(netId,
ClusterMaker.CLUSTER_TYPE_ATTRIBUTE).equals("kmedoid") &&
- !networkAttributes.getStringAttribute(netId,
ClusterMaker.CLUSTER_TYPE_ATTRIBUTE).equals("autosome_heatmap"))
+ // TODO why is KnnView deciding which algorithms are
visualizable, rather than having each algorithm decide whether itself is
visualizable?
+ if (!type.equals("kmeans") &&
+ !type.equals("kmedoid") &&
+ !type.equals("autosome_heatmap") &&
+ !type.equals("PAM") &&
+ !type.equals("HOPACH")
+ )
return false;
}
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