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annotate python/ml.py @ 736:967d244968a4 dev
work in progress on saving/loading prototypes
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 12 Aug 2015 08:26:59 -0400 |
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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 |
380 | 7 class Model(object): |
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)) |
380 | 15 |
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16 def save(self, filename): |
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17 self.model.save(filename) |
380 | 18 |
19 class SVM(Model): | |
20 '''wrapper for OpenCV SimpleVectorMachine algorithm''' | |
21 | |
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22 def __init__(self): |
380 | 23 import cv2 |
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): |
380 | 27 self.params = dict(svm_type = svm_type, kernel_type = kernel_type, degree = degree, gamma = gamma, coef0 = coef0, Cvalue = Cvalue, nu = nu, p = p) |
28 self.model.train(samples, responses, params = self.params) | |
29 | |
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30 def predict(self, hog): |
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31 return self.model.predict(hog) |
380 | 32 |
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 = ''): |
184
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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 |
386 | 59 def kMedoids(similarityMatrix, initialCentroids = None, k = None): |
60 '''Algorithm that clusters any dataset based on a similarity matrix | |
61 Either the initialCentroids or k are passed''' | |
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. |
183
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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) |
309 | 112 return code,sigma |
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113 |
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114 def prototypeCluster(instances, similarities, minSimilarity, similarityFunc = None, 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 similarities |
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120 if similarityFunc is provided, the similarities are calculated as needed (this is faster) if not in similarities (negative if not computed) |
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121 similarities must still be allocated with the right size |
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122 |
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123 if an instance is different enough (<minSimilarity), |
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124 it will become a new prototype. |
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125 Non-prototype instances will be assigned to an existing prototype |
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126 if minClusterSize is not None, the clusters will be refined by removing iteratively the smallest clusters |
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127 and reassigning all elements in the cluster until no cluster is smaller than minClusterSize''' |
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128 |
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129 # sort instances based on length |
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130 indices = range(len(instances)) |
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131 if randomInitialization: |
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132 indices = np.random.permutation(indices) |
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133 else: |
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134 def compare(i, j): |
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135 if len(instances[i]) > len(instances[j]): |
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136 return -1 |
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137 elif len(instances[i]) == len(instances[j]): |
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138 return 0 |
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139 else: |
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140 return 1 |
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141 indices.sort(compare) |
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142 # go through all instances |
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143 prototypeIndices = [indices[0]] |
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144 for i in indices[1:]: |
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145 if similarityFunc is not None: |
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146 for j in prototypeIndices: |
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147 if similarities[i][j] < 0: |
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148 similarities[i][j] = similarityFunc(instances[i], instances[j]) |
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149 similarities[j][i] = similarities[i][j] |
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150 if similarities[i][prototypeIndices].max() < minSimilarity: |
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151 prototypeIndices.append(i) |
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152 |
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153 # assignment |
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154 indices = [i for i in range(similarities.shape[0]) if i not in prototypeIndices] |
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155 assign = True |
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156 while assign: |
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157 labels = [-1]*similarities.shape[0] |
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158 for i in prototypeIndices: |
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159 labels[i] = i |
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160 for i in indices: |
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161 if similarityFunc is not None: |
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162 for j in prototypeIndices: |
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163 if similarities[i][j] < 0: |
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164 similarities[i][j] = similarityFunc(instances[i], instances[j]) |
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165 similarities[j][i] = similarities[i][j] |
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166 prototypeIdx = similarities[i][prototypeIndices].argmax() |
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167 if similarities[i][prototypeIndices[prototypeIdx]] >= minSimilarity: |
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168 labels[i] = prototypeIndices[prototypeIdx] |
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169 else: |
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170 labels[i] = -1 # outlier |
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171 clusterSizes = {i: sum(np.array(labels) == i) for i in prototypeIndices} |
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172 smallestClusterIndex = min(clusterSizes, key = clusterSizes.get) |
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173 assign = (clusterSizes[smallestClusterIndex] < minClusterSize) |
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174 if assign: |
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175 prototypeIndices.remove(smallestClusterIndex) |
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176 indices.append(smallestClusterIndex) |
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177 |
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178 return prototypeIndices, labels |