K-Means

Hi all,

Here is an implementation of K-Means for grasshopper I wrote for an aspect of my thesis.

What does it do? K-Means Algorthims are a automated way of grouping data. For now my implementation only works on points but other types should work in theory. The VB script is uses alot of loops and i am sure that is far from optimzed.

Check out the K-Means Page on wikipedia

And the general page on data clustering...

The code goes like this:

Private Sub RunScript(ByVal P As DataTree(Of Point3d), ByVal N As DataTree(Of Integer), ByVal D As Double, ByRef G As Object)
'your code goes here…
Dim t As Integer = 0
Dim i As Integer = 0
Dim j As Integer = 0
Dim k As Integer = 0
Dim numGroups As Integer
Dim groupNum As Integer
Dim X As Double
Dim Y As Double
Dim Z As Double
Dim minDist As Double
Dim dist As Double
'Dim cluster As Integer
'initialize random number generator
Dim r As New Random(System.DateTime.Now.Millisecond)
Dim goupings As New List(Of List(Of Integer))
Dim oldCentroids As New List(Of Point3d)
Dim newCentroids As New List(Of Point3d)
Dim pathList As New List(Of GH_Path)
Dim newCentroid As Point3d
Dim pt As Point3d
Dim isStillMoving As Boolean
Dim groupingsTree As New DataTree(Of Point3d)
Dim inputpts As New List(Of Point3d)

For t = 0 To P.BranchCount - 1

'this mess dose data matching for inputs P and N
'this if is ture is the input branches are equal
If N.BranchCount >= P.BranchCount Then
'check to see if, in the case the branches are equal, that there is only one item in each branch of N
If N.Branch(t).Count = 1 Then
numGroups = N.Branch(t)(0)
'I don't know what to do if the above statment isn't true so let's exit the program
Else
Print("Error: data missmatch between inputs P and N, Please reformat inputs")
Exit Sub
End If
'here we check if it is a simple list...
Else If N.Branch(0).Count = P.BranchCount
numGroups = N.Branch(0)(t)
'here we check if it is a simple list but is shorter or longer than the tree P
Else If N.Branch(0).Count < P.BranchCount Then
If t <= N.Branch(0).Count - 1 Then
numGroups = N.Branch(0)(t)
Else
numGroups = N.Branch(0)(N.Branch(0).Count - 1)
End If
'if all else fails exit the program!
Else
Print("Error: data missmatch between inputs P and N, Please reformat inputs")
Exit Sub
End If

inputpts = P.Branch(t)
print("--------Accessing Branch " & t & "--------")
'***************** Initialize K-Means Algorithm ****************************
print("--------Initializing " & numGroups & " groupings-------")


For i = 0 To numGroups - 1
'Dim pt As Point3d
'Dim group As New List(Of Integer)
Dim tempPath As New GH_Path
goupings.Add(New List(Of Integer))
newCentroids.add(pt)
oldCentroids.Add(pt)

tempPath.FromString(t & ";" & i)
pathList.Add(tempPath)
print("creating grouping " & i)
Next
'radomly assign points from the input list (intialze the k-mean groups)
print("--------Initializing group assinments (random)-------")
For j = 0 To inputpts.Count - 1
groupNum = RandomNumber(r, numGroups - 1)
goupings(groupNum).Add(j)
print("Adding index value " & goupings(groupNum)(goupings(groupNum).Count - 1) & " to group " & groupNum & ".")
Next

'***************** Do K-Means Algorithm ************************************
print("--------entering main k-means loop--------")
isStillMoving = True
Do While isStillMoving
isStillMoving = False


'find new centroids
print(".....finding new centroids")
For i = 0 To numGroups - 1
oldCentroids(i) = newCentroids(i)
For j = 0 To goupings(i).Count - 1
pt = inputpts(goupings(i)(j))
X = X + pt.X
Y = Y + pt.Y
Z = Z + pt.Z
Next
X = X / (j + 1)
Y = Y / (j + 1)
Z = Z / (j + 1)
newCentroid.X = X
newCentroid.Y = Y
newCentroid.Z = Z
newCentroids(i) = newCentroid
print("new centroid for group " & i & " is " & newCentroids(i).ToString)
goupings(i).Clear
dist = newCentroid.DistanceTo(oldCentroids(i))
'print("dist = " & dist)
If dist > D Then
print("need to do more work")
isStillMoving = True
End If
Next

'create new groupings
print(".....creating new groupings")
For j = 0 To inputpts.Count - 1
pt = inputpts(j)
minDist = pt.DistanceTo(newCentroids(0))
For i = 0 To numGroups - 1
dist = pt.DistanceTo(newCentroids(i))
If minDist >= dist Then
groupNum = i
minDist = dist
'print(minDist)
End If

Next
'Print("Adding index value " & j & " to group " & groupNum & ".")
goupings(groupNum).Add(j)
If isStillMoving = False Then
groupingsTree.Add(pt, pathList(groupNum))
End If
Next

If isStillMoving = False Then
print("***************all groups setled exiting loop********************")
print("***************total loop count is " & k & "************************")
End If

'a hard limit on the number of loops to execute....
If k > 1000 Then
isStillMoving = False
End If

k = k + 1
Loop
goupings.Clear
newCentroids.Clear
oldCentroids.Clear
pathList.Clear
Next
G = groupingsTree
End Sub

'<Custom additional code>
'function taken from http://www.freevbcode.com/ShowCode.Asp?ID=4451 (and slightly modified)
Public Function RandomNumber(ByVal r As Random, ByVal MaxNumber As Integer, Optional ByVal MinNumber As Integer = 0) As Integer

'if passed incorrect arguments, swap them
'can also throw exception or return 0

If MinNumber > MaxNumber Then
Dim t As Integer = MinNumber
MinNumber = MaxNumber
MaxNumber = t
End If

Return r.Next(MinNumber, MaxNumber)

End Function

... So any thoughts?