def pca2d(pointList):
'''sets up the pca for a list of points in 2d. solves eig problem'''
matrixList = [[0, 0], [0, 0]] # init to 0
avgPt = geometry.getAverageArbitraryDimension(pointList, 2)
diffs = [0, 0]
for point in pointList:
for index in range(2):
diffs[index] = point[index] - avgPt[index]
#matrix is old plus a2 ab
# ba b2
# only compute upper diagonal now
matrixList[0][0] += diffs[0] * diffs[0] # just hardcode it
matrixList[0][1] += diffs[0] * diffs[1]
matrixList[1][1] += diffs[1] * diffs[1]
#make symmetric
matrixList[1][0] = matrixList[0][1]
actualMatrix = Matrix(matrixList)
val, vec = eigenvectors(actualMatrix)
return val, vec
def pcaN3d(pointListList):
'''sets up the pca for a list of list of points in 3d. solves eig problem.
the pointListList is a list of sets of points of the same length.'''
length = len(pointListList[0]) * 3 # know 3d points times length(list of pts)
#print 'making a square matrix of size', length
oneList = [0. for count in range(length)] # bunch of 0.0s
matrixList = [oneList[:] for count in range(length)] # copy list of 0.0s
#let's go ahead and flatten the input list!
#print 'makin flattened list of size', length
flattenedList = flatten(pointListList)
avgPt = geometry.getAverageArbitraryDimension(
flattenedList, dimension=length)
diffs = oneList[:]
#print 'filling a square matrix of size', length
for point in flattenedList: # this point has length dimensions
for index in xrange(length):
diffs[index] = point[index] - avgPt[index]
#matrix is old plus a2 ab ac ad etc
# ba b2 bc bd etc
# ca cb c2 cd etc
# da db dc d2 etc
# etc etc etc etc etc (etc)
#only compute upper diagonal now
#can't get away with hardcoding anymore
for row in xrange(length):
for col in xrange(row, length):
matrixList[row][col] += diffs[row] * diffs[col]
#make symmetric
for row in xrange(length):
for col in xrange(0, row):
matrixList[row][col] = matrixList[col][row]
#print "mat" #debugging madness
#for row in xrange(length):
# for col in xrange(length):
# print matrixList[row][col],
# print " "
actualMatrix = Matrix(matrixList)
#print 'running eigenvector calculation', type(actualMatrix)
val, vec = eigenvectors(actualMatrix)
return val, vec
