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fuzzyCluster.py
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fuzzyCluster.py
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## numpy-oldnumeric calls replaced by custom script; 09/06/2016
## Automatically adapted for numpy-oldnumeric Mar 26, 2007 by alter_code1.py
##
## Biskit, a toolkit for the manipulation of macromolecular structures
## Copyright (C) 2002-2004; Wolfgang Rieping
## Copyright (C) 2005; Raik Gruenberg & Johan Leckner
##
## This program is free software; you can redistribute it and/or
## modify it under the terms of the GNU General Public License as
## published by the Free Software Foundation; either version 3 of the
## License, or any later version.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## General Public License for more details.
##
## You find a copy of the GNU General Public License in the file
## license.txt along with this program; if not, write to the Free
## Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
"""
Implementation of the fuzzy c-means algorithm
Author: Wolfgang Rieping 1998, 2002
Reference::
Heather L. Gordon, Rajmund L. Somorjai
Fuzzy Cluster Analysis of Molecular Dynamics Trajectories
PROTEINS: Structure, Function and Genetics 14:249-264 1992
"""
import Biskit.oldnumeric as N0
import Biskit.mathUtils as MU
import tools
import numpy.random.mtrand as R # seed, random / converted from oldnumeric/random_array
## def average(x):
## return N0.sum(N0.array(x)) / len(x)
## def variance(x, avg = None):
## if avg is None:
## avg = N0.average(x)
## return N0.sum(N0.power(N0.array(x) - avg, 2)) / (len(x) - 1.)
## def standardDeviation(x, avg = None):
## return N0.sqrt(variance(x, avg))
def squared_distance_matrix(x, y):
d1 = N0.diagonal(N0.dot(x, N0.transpose(x)))
d2 = N0.diagonal(N0.dot(y, N0.transpose(y)))
a1 = N0.add.outer(d1,d2)
a2 = N0.dot(x, N0.transpose(y))
return a1 - 2 * a2
def distance_matrix(x, y):
return N0.sqrt(squared_distance_matrix(x, y))
class FuzzyCluster:
def __init__(self, data, n_cluster, weight, seedx = 0, seedy = 0):
"""
@param data: cluster this
@type data: [float] OR array
@param n_cluster: number of clusters
@type n_cluster: int
@param weight: fuzziness weigth
@type weight: float
@param seedx: random seed value for RandomArray.seed (default: 0)
@type seedx: int OR 0
@param seedy: random seed value for RandomArray.seed
(default: 0, set seed from clock)
@type seedy: int OR 0
"""
self.data = N0.array(data, N0.Float)
self.w = weight
self.n_cluster = n_cluster
self.npoints, self.dimension = N0.shape(data)
self.seedx = seedx
self.seedy = seedy
def calc_membership_matrix(self, d2):
## remove 0s (if a cluster center is exactly on one item)
d2 = N0.clip( d2, N0.power(1e200, 1-self.w), 1e300 )
q = N0.power(d2, 1. / (1. - self.w))
return q / N0.sum(q)
def calc_cluster_center(self, msm):
p = N0.power(msm, self.w)
ccenter = N0.transpose(N0.dot(p, self.data))
return N0.transpose(ccenter / N0.sum(p, 1))
def updateDistanceMatrix(self):
return squared_distance_matrix(self.cluster_center, self.data)
def iterate(self, centers):
"""
@param centers: array with cluster centers
@type centers: array('f')
@return: distance to the centers, membership matrix, array of cenetrs
@rtype: array, array, array
"""
d2 = squared_distance_matrix(centers, self.data)
msm = self.calc_membership_matrix(d2)
centers = self.calc_cluster_center(msm)
return d2, msm, centers
def error(self, msm, d2):
"""
@param msm: membership matrix
@type msm: array('f')
@param d2: distance from data to the centers
@type d2: array('f')
@return: weighted error
@rtype: float
"""
p = N0.power(msm, self.w)
product = N0.dot(p, N0.transpose(d2))
return N0.trace(product)
def create_membership_matrix(self):
"""
Create a random membership matrix.
@return: random array of shape length of data to
cluster times number of clusters
@rtype: array('f')
"""
## default signature has changed oldnumeric->numpy
if (self.seedx==0 or self.seedy==0):
R.seed()
else:
R.seed((self.seedx, self.seedy))
r = R.random_sample((self.npoints, self.n_cluster))
return N0.transpose(r / N0.sum(r))
def go(self, errorthreshold, n_iterations=1e10, nstep=10, verbose=1):
"""
Start the cluestering. Run until the error is below the error
treshold or the max number of iterations have been run.
@param errorthreshold: treshold value for error
@type errorthreshold: float
@param n_iterations: treshold value for number of iterations
(default: 1e10)
@type n_iterations: int
@param nstep: print information for every n'th step in the iteration
@type nstep: int
@return: array with cluster centers
@rtype: array('f')
"""
iteration = 0
rel_err = 1e10
error = 1.e10
msm = self.create_membership_matrix()
centers = self.calc_cluster_center(msm)
while rel_err > errorthreshold and iteration < n_iterations:
d2, msm, centers = self.iterate(centers)
old_error = error
error = self.error(msm, d2)
rel_err = abs(1. - error/old_error)
iteration = iteration+1
if not iteration % nstep and verbose:
tools.errWrite( "%i %f\n" % (iteration, error) )
self.centers = centers
self.msm = msm
self.d2 = d2
return centers
def clusterEntropy(self):
centropy = N0.diagonal(N0.dot(self.msm,
N0.transpose(N0.log(self.msm))))
return -1/float(self.npoints)*centropy
def entropy(self):
return N0.sum(self.clusterEntropy())
def nonFuzzyIndex(self):
p = N0.power(self.msm, self.w)
return (self.n_cluster*N0.sum(N0.sum(p))-
self.npoints)/(self.npoints*(self.n_cluster-1))
def clusterPartitionCoefficient(self):
return N0.sum(N0.power(self.msm, self.w), 1)/self.npoints
def partitionCoefficient(self):
return N0.sum(self.clusterPartitionCoefficient())
def getMembershipMatrix(self):
return self.msm
def getClusterCenter(self):
return self.cluster_center
def entropySD(self):
centropy = N0.sum(-N0.log(self.msm)*\
self.msm)/float(self.n_cluster)
return MU.SD(centropy)
def standardDeviation(self):
sd = MU.SD(self.msm)
return sd
#############
## TESTING
#############
import Biskit.test as BT
class Test(BT.BiskitTest):
"""FuzzyCluster test"""
def test_FuzzyCluster( self):
"""FuzzyCluster test"""
import gnuplot as G
x1 = R.random_sample((500,2))
x2 = R.random_sample((500,2)) + 1
x3 = R.random_sample((500,2)) + 2
self.x = N0.concatenate((x1, x2, x3))
self.fuzzy = FuzzyCluster(self.x, n_cluster=5, weight=1.5)
self.centers = self.fuzzy.go(1.e-30, n_iterations=50, nstep=10,
verbose=self.local)
if self.local:
print "cluster centers are displayed in green"
G.scatter( self.x, self.centers )
self.assertEqual( N0.shape(self.centers), (5, 2) )
if __name__ == '__main__':
BT.localTest()