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made prototypecluster generic, in ml module, and added randominitialization
author Nicolas Saunier <nicolas.saunier@polymtl.ca>
date Tue, 11 Aug 2015 11:38:05 -0400
parents da1352b89d02
children 1d4dcb5c8708
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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
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7 class Model(object):
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8 '''Abstract class for loading/saving model'''
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9 def load(self, filename):
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10 from os import path
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11 if path.exists(filename):
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12 self.model.load(filename)
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13 else:
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14 print('Provided filename {} does not exist: model not loaded!'.format(filename))
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15
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16 def save(self, filename):
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17 self.model.save(filename)
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18
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19 class SVM(Model):
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20 '''wrapper for OpenCV SimpleVectorMachine algorithm'''
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21
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22 def __init__(self):
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23 import cv2
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24 self.model = cv2.SVM()
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25
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26 def train(self, samples, responses, svm_type, kernel_type, degree = 0, gamma = 1, coef0 = 0, Cvalue = 1, nu = 0, p = 0):
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27 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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28 self.model.train(samples, responses, params = self.params)
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29
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30 def predict(self, hog):
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31 return self.model.predict(hog)
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32
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33
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34 class Centroid(object):
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35 'Wrapper around instances to add a counter'
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36
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37 def __init__(self, instance, nInstances = 1):
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38 self.instance = instance
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39 self.nInstances = nInstances
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40
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41 # def similar(instance2):
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42 # return self.instance.similar(instance2)
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43
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44 def add(self, instance2):
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45 self.instance = self.instance.multiply(self.nInstances)+instance2
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46 self.nInstances += 1
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47 self.instance = self.instance.multiply(1/float(self.nInstances))
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48
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49 def average(c):
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50 inst = self.instance.multiply(self.nInstances)+c.instance.multiply(instance.nInstances)
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51 inst.multiply(1/(self.nInstances+instance.nInstances))
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52 return Centroid(inst, self.nInstances+instance.nInstances)
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53
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54 def plot(self, options = ''):
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55 from matplotlib.pylab import text
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56 self.instance.plot(options)
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57 text(self.instance.position.x+1, self.instance.position.y+1, str(self.nInstances))
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58
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59 def kMedoids(similarityMatrix, initialCentroids = None, k = None):
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60 '''Algorithm that clusters any dataset based on a similarity matrix
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61 Either the initialCentroids or k are passed'''
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62 pass
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63
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64 def assignCluster(data, similarFunc, initialCentroids = None, shuffleData = True):
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65 '''k-means algorithm with similarity function
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66 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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67 The number of clusters will be determined accordingly
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68
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69 data: list of instances
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70 averageCentroid: '''
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71
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72 from random import shuffle
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73 from copy import copy, deepcopy
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74 localdata = copy(data) # shallow copy to avoid modifying data
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75 if shuffleData:
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76 shuffle(localdata)
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77 if initialCentroids is None:
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78 centroids = [Centroid(localdata[0])]
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79 else:
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80 centroids = deepcopy(initialCentroids)
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81 for instance in localdata[1:]:
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82 i = 0
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83 while i<len(centroids) and not similarFunc(centroids[i].instance, instance):
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84 i += 1
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85 if i == len(centroids):
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86 centroids.append(Centroid(instance))
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87 else:
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88 centroids[i].add(instance)
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89
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90 return centroids
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91
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92 # TODO recompute centroids for each cluster: instance that minimizes some measure to all other elements
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93
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94 def spectralClustering(similarityMatrix, k, iter=20):
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95 '''Spectral Clustering algorithm'''
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96 n = len(similarityMatrix)
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97 # create Laplacian matrix
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98 rowsum = np.sum(similarityMatrix,axis=0)
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99 D = np.diag(1 / np.sqrt(rowsum))
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100 I = np.identity(n)
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101 L = I - np.dot(D,np.dot(similarityMatrix,D))
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102 # compute eigenvectors of L
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103 U,sigma,V = np.linalg.svd(L)
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104 # create feature vector from k first eigenvectors
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105 # by stacking eigenvectors as columns
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106 features = np.array(V[:k]).T
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107 # k-means
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108 from scipy.cluster.vq import kmeans, whiten, vq
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109 features = whiten(features)
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110 centroids,distortion = kmeans(features,k, iter)
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111 code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster)
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112 return code,sigma
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113
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114 def prototypeCluster(instances, similarityMatrix, minSimilarity, minClusterSize = None, randomInitialization = False):
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115 '''Finds exemplar (prototype) instance that represent each cluster
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116 Returns the prototype indices (in the instances list) and the cluster label of each instance
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117
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118 the elements in the instances list must have a length (method __len__), or one can use the random initialization
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119 the positions in the instances list corresponds to the similarityMatrix
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120
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121 if an instance is different enough (<minSimilarity),
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122 it will become a new prototype.
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123 Non-prototype instances will be assigned to an existing prototype
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124 if minClusterSize is not None, the clusters will be refined by removing iteratively the smallest clusters
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125 and reassigning all elements in the cluster until no cluster is smaller than minClusterSize'''
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126
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127 # sort instances based on length
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128 indices = range(similarityMatrix.shape[0])
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129 if randomInitialization:
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130 indices = np.random.permutation(indices)
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131 else:
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132 def compare(i, j):
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133 if len(instances[i]) > len(instances[j]):
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134 return -1
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135 elif len(instances[i]) == len(instances[j]):
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136 return 0
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137 else:
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138 return 1
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139 indices.sort(compare)
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140 # go through all instances
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141 prototypeIndices = [indices[0]]
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142 for i in indices[1:]:
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143 if similarityMatrix[i][prototypeIndices].max() < minSimilarity:
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144 prototypeIndices.append(i)
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145
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146 # assignment
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147 indices = [i for i in range(similarityMatrix.shape[0]) if i not in prototypeIndices]
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148 assign = True
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149 while assign:
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150 labels = [-1]*similarityMatrix.shape[0]
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151 for i in prototypeIndices:
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152 labels[i] = i
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153 for i in indices:
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154 prototypeIndex = similarityMatrix[i][prototypeIndices].argmax()
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155 labels[i] = prototypeIndices[prototypeIndex]
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156 clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices}
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157 smallestClusterIndex = min(clusterSizes, key = clusterSizes.get)
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158 assign = (clusterSizes[smallestClusterIndex] < minClusterSize)
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159 if assign:
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160 prototypeIndices.remove(smallestClusterIndex)
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161 indices.append(smallestClusterIndex)
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162
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163 return prototypeIndices, labels
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164
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165 def motionPatternLearning(objects, maxDistance):
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166 '''
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167 Option to use only the (n?) longest features per object instead of all for speed up
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168 TODO'''
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169 pass