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development for clustering
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Sun, 21 Jul 2013 10:23:15 -0400
parents adfd4f70ee1d
children 8bc632cb8344
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1 #! /usr/bin/env python
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2 '''Libraries for machine learning algorithms'''
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3
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4 import numpy as np
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5
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6 __metaclass__ = type
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7
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8 class Model(object):
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9 '''Abstract class for loading/saving model'''
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10 def load(self, fn):
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11 self.model.load(fn)
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12
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13 def save(self, fn):
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14 self.model.save(fn)
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15
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16 class SVM(Model):
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17 '''wrapper for OpenCV SimpleVectorMachine algorithm'''
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18
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19 def __init__(self, svm_type, kernel_type, degree = 0, gamma = 1, coef0 = 0, Cvalue = 1, nu = 0, p = 0):
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20 import cv2
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21 self.model = cv2.SVM()
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22 self.params = dict(svm_type = svm_type, kernel_type = kernel_type, degree = degree, gamma = gamma, coef0 = coef0, Cvalue = Cvalue, nu = nu, p = p)
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23
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24 def train(self, samples, responses):
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25 self.model.train(samples, responses, params = self.params)
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26
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27 def predict(self, sample):
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28 return np.float32(self.model.predict(s))
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30
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31 class Centroid:
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32 'Wrapper around instances to add a counter'
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33
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34 def __init__(self, instance, nInstances = 1):
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35 self.instance = instance
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36 self.nInstances = nInstances
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37
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38 # def similar(instance2):
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39 # return self.instance.similar(instance2)
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40
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41 def add(self, instance2):
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42 self.instance = self.instance.multiply(self.nInstances)+instance2
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43 self.nInstances += 1
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44 self.instance = self.instance.multiply(1/float(self.nInstances))
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45
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46 def average(c):
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47 inst = self.instance.multiply(self.nInstances)+c.instance.multiply(instance.nInstances)
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48 inst.multiply(1/(self.nInstances+instance.nInstances))
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49 return Centroid(inst, self.nInstances+instance.nInstances)
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50
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51 def draw(self, options = ''):
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52 from matplotlib.pylab import text
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53 self.instance.draw(options)
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54 text(self.instance.position.x+1, self.instance.position.y+1, str(self.nInstances))
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55
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56
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57 def assignCluster(data, similarFunc, initialCentroids = [], shuffleData = True):
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58 '''k-means algorithm with similarity function
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59 Two instances should be in the same cluster if the sameCluster function returns true for two instances. It is supposed that the average centroid of a set of instances can be computed, using the function.
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60 The number of clusters will be determined accordingly
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61
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62 data: list of instances
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63 averageCentroid: '''
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64
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65 from random import shuffle
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66 from copy import copy, deepcopy
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67 localdata = copy(data) # shallow copy to avoid modifying data
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68 if shuffleData:
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69 shuffle(localdata)
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70 if initialCentroids:
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71 centroids = deepcopy(initialCentroids)
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72 else:
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73 centroids = [Centroid(localdata[0])]
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74 for instance in localdata[1:]:
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75 i = 0
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76 while i<len(centroids) and not similarFunc(centroids[i].instance, instance):
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77 i += 1
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78 if i == len(centroids):
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79 centroids.append(Centroid(instance))
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80 else:
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81 centroids[i].add(instance)
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82
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83 return centroids
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84
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85 # TODO recompute centroids for each cluster: instance that minimizes some measure to all other elements
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86
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87 def spectralClustering(similarityMatrix, k, iter=20):
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88 '''Spectral Clustering algorithm'''
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89 n = len(similarityMatrix)
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90 # create Laplacian matrix
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91 rowsum = np.sum(similarityMatrix,axis=0)
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92 D = np.diag(1 / np.sqrt(rowsum))
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93 I = np.identity(n)
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94 L = I - np.dot(D,np.dot(similarityMatrix,D))
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95 # compute eigenvectors of L
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96 U,sigma,V = np.linalg.svd(L)
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97 # create feature vector from k first eigenvectors
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98 # by stacking eigenvectors as columns
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99 features = np.array(V[:k]).T
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100 # k-means
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101 from scipy.cluster.vq import kmeans, whiten, vq
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102 features = whiten(features)
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103 centroids,distortion = kmeans(features,k, iter)
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104 code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster)
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105 return code,sigma