[Pkg-javascript-commits] [science.js] 20/87: Add stats.hcluster.

[bhuvan krishna]

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commit 889c1d5e8456870b09470025feb70623c16eb6b1
Author: Jason Davies <jason at jasondavies.com>
Date:   Thu Aug 25 20:23:23 2011 +0100

    Add stats.hcluster.
---
 src/stats/hcluster.js | 87 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)

diff --git a/src/stats/hcluster.js b/src/stats/hcluster.js
new file mode 100644
index 0000000..39372a5
--- /dev/null
+++ b/src/stats/hcluster.js
@@ -0,0 +1,87 @@
+science.stats.hcluster = function() {
+  var distance = science.stats.distance.euclidean;
+
+  function hcluster(vectors) {
+    var n = vectors.length;
+    var dMin = [];
+    var cSize = [];
+
+    var matrixObj = new figue.Matrix(N,N);
+    var distMatrix = matrixObj.mtx ;
+    var clusters = [];
+
+    var c1, c2, c1Cluster, c2Cluster, i, j, p, root , newCentroid ;
+
+    var i,
+        j;
+
+    // Initialize distance matrix and vector of closest clusters
+    i = -1; while (++i < n) {
+      dMin[i] = 0;
+      j = -1; while (++j < n) {
+        distMatrix[i][j] = i === j ? Infinity : distance(vectors[i] , vectors[j]);
+        if (distMatrix[i][dMin[i]] > distMatrix[i][j]) dMin[i] = j ;
+      }
+    }
+
+    // create leaves of the tree
+    i = -1; while (++i < n) {
+      clusters[i] = [];
+      clusters[i][0] = new Node(labels[i], null, null, 0, vectors[i]);
+      cSize[i] = 1;
+    }
+
+    // Main loop
+    for (p = 0; p < N-1; p++) {
+      // find the closest pair of clusters
+      c1 = 0 ;
+      for (i = 0 ; i < N ; i++) {
+        if (distMatrix[i][dMin[i]] < distMatrix[c1][dMin[c1]]) c1 = i;
+      }
+      c2 = dMin[c1];
+
+      // create node to store cluster info 
+      c1Cluster = clusters[c1][0] ;
+      c2Cluster = clusters[c2][0] ;
+
+      newCentroid = calculateCentroid ( c1Cluster.size , c1Cluster.centroid , c2Cluster.size , c2Cluster.centroid ) ;
+      newCluster = new Node (-1, c1Cluster, c2Cluster , distMatrix[c1][c2] , newCentroid) ;
+      clusters[c1].splice(0,0, newCluster) ;
+      cSize[c1] += cSize[c2] ;
+
+      // overwrite row c1 with respect to the linkage type
+      for (j = 0 ; j < N ; j++) {
+              if (linkage == figue.SINGLE_LINKAGE) {
+                      if (distMatrix[c1][j] > distMatrix[c2][j])
+                              distMatrix[j][c1] = distMatrix[c1][j] = distMatrix[c2][j] ;
+              } else if (linkage == figue.COMPLETE_LINKAGE) {
+                      if (distMatrix[c1][j] < distMatrix[c2][j])
+                              distMatrix[j][c1] = distMatrix[c1][j] = distMatrix[c2][j] ;
+              } else if (linkage == figue.AVERAGE_LINKAGE) {
+                      var avg = ( cSize[c1] * distMatrix[c1][j] + cSize[c2] * distMatrix[c2][j])  / (cSize[c1] + cSize[j]) 
+                      distMatrix[j][c1] = distMatrix[c1][j] = avg ;
+              }
+      }
+      distMatrix[c1][c1] = Infinity;
+
+      // infinity ­out old row c2 and column c2
+      for (i = 0 ; i < N ; i++)
+        distMatrix[i][c2] = distMatrix[c2][i] = Infinity;
+
+      // update dmin and replace ones that previous pointed to c2 to point to c1
+      for (j = 0; j < N ; j++) {
+        if (dMin[j] == c2)
+          dMin[j] = c1;
+        if (distMatrix[c1][j] < distMatrix[c1][dMin[c1]]) 
+          dMin[c1] = j;
+      }
+
+      // keep track of the last added cluster
+      root = newCluster;
+    }
+
+    return root;
+  }
+
+  return hcluster;
+};

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