def pca(ID, biniter):
tree = Display.getFront().getLayerSet().findById(ID)
#Calculate the center of mass
center = Matrix( [[0,0,0]] )
coords = Matrix(getNodeCoordinates(tree))
m=coords.getRowDimension()
for i in range(0, coords.getRowDimension()):
center += Matrix([coords.getRow( i )])
center /= float(m)
#print center
"""
x, y, z = 0, 0, 0
nc = getNodeCoordinates(tree)
for c in nc:
x += c[0]
y += c[1]
z += c[2]
print "center:", x/len(nc), y/len(nc), z/len(nc)
"""
#Define covvariance matrix
cova = Matrix([[0,0,0],[0,0,0],[0,0,0]])
diff= Matrix ( [[0,0,0]])
for i in range(0, m):
diff = Matrix([coords.getRow( i )]) - center
cova += diff.transpose() * diff
cova /= float(m)
#Evaluedecomposition
evaluedecomp = cova.eig()
evalues = evaluedecomp.getRealEigenvalues()
evectors = evaluedecomp.getV() # is a Matrix instance
#Find maximum Eigenvector for maximal Eigenvalue
maxevaluepos = -1
maxevalue = 0
for i in range(0, len(evalues)):
if evalues[i] > maxevalue or maxevaluepos==-1:
maxevalue=evalues[i]
maxevaluepos=i
maxevector=evectors.getColumn(maxevaluepos) #HAVE TO GET VALUES OF COLUMN of evector matrix i NOT ROW
#--------------------TEST if Eigenvaluedecomposition is correct-----------
#vectormatrix=Matrix([evectors.getColumn(maxevaluepos)])
#test=cova * vectormatrix.transpose()
#test2=vectormatrix*maxevalue
#print test
#print test2
#-------------------------------------------------------------------------
#Define a vector over the point cloud and count points in defined interval
#Normalize vector
length=sqrt(maxevector[0]**2 + maxevector[1]**2 + maxevector[2]**2)
normvector = map(lambda x: x / length, maxevector)
normvectormatrix = Matrix([normvector])
pca=[]
m=coords.getRowDimension()
for i in range(0, m):
pcastore = (Matrix([coords.getRow( i )]) - center ) * normvectormatrix.transpose()
pca.append(pcastore.getRow(0))
#Count number of nodes which fall in defined interval of pca projection -> has to be fixed globally when to compare PCAs!
counter = 0
histovector = []
pca=[ x[0] for x in pca] #get it back in array form (get rid of list in list)
binleft = -12500 #binleft = min(pca)
iterations = biniter
length = int(25000/iterations + 0.5) #int((max(pca)-min(pca))/iterations + 0.5)
binright = binleft + length
for i in range(0,iterations):
for i in range(0, len(pca)):
if pca[i] <= binright and pca[i] >= binleft:
counter += 1
histovector.append(counter)
counter=0
binleft+=length
binright+=length
#print "The histogram vector is:", histovector
return histovector
coords = getNodeCoordinates(tree) for coord in coords: center += coord center /= float(len(coords)) print center #Define covvariance matrix # [[row1], [row2], [row3]] cova = Matrix([[0,0,0],[0,0,0],[0,0,0]]) for coord in coords: diff = coord - center cova += diff * diff.transpose() #Evaluedecomposition evaluedecomp = cova.eig() evalues = evaluedecomp.getRealEigenvalues() evectors = evaluedecomp.getV() # is a Matrix instance #Find maximum Eigenvector for maximal Eigenvalue maxevaluepos = -1 maxevalue = 0 for i in range(0, len(evalues)): if evalues[i] > maxevalue or maxevaluepos==-1: maxevalue=evalues[i] maxevaluepos=i maxevector=evectors.getRow(maxevaluepos) # its a list of 3 values #Define a vector over the point cloud and count points in defined interval #Normalize vector
