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1 #!/usr/bin/python2
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2 # -*- coding: utf-8 -*-
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3 """
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4 Created on Thu Apr 23 11:53:17 2015
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5
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6 @author: mmxgn
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7 """
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8
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9 # This file does the cluster estimation and the removal of outliers
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10
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11 from sys import argv, exit
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12 from essentia.standard import YamlInput, YamlOutput
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13 from essentia import Pool
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14 from pca import *
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15
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16 from numpy import *
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17 from sklearn import cluster
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18 from sklearn.metrics import pairwise_distances
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19 from sklearn.cluster import KMeans, MiniBatchKMeans
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20 import matplotlib.pyplot as plt
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21 #from sklearn.mixture import GMM
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22 from sklearn.naive_bayes import GaussianNB, MultinomialNB
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23 from scipy.signal import decimate
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24 from sklearn import cross_validation
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25
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26 #from hmmlearn import hmm
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27 from hmmlearn.hmm import GMM
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28 from hmmlearn import hmm
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29 #from adpcm import adm, adm_reconstruct
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30
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31
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32 mse = lambda A,B: ((array(A)-array(B)) ** 2).mean()
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33
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34
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35 def smooth_matrix_1D(X):
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36 window = scipy.signal.gaussian(51,4)
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37 window = window/sum(window)
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38 intermx = zeros((X.shape[0],X.shape[1]+100))
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39 intermx[:, 50:-50] = X
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40
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41 for m in range(0, X.shape[0]):
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42 # print intermx.shape
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43 intermx[m,:] = convolve(ravel(intermx[m,:]), window,'same')
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44
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45 return intermx[:,50:-50]
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46
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47 def adm_reconstruct(codeword, h, dmin=.01, dmax=.28):
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48 x = zeros((1, codeword.shape[1]))
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49
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50 delta1 = dmin
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51 delta2 = dmin
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52 Sum = h
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53
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54 x[0] = h
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55 for i in range(0, codeword.shape[1]):
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56 if codeword[0,i] == 0:
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57 delta1 = dmin
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58 delta2 = dmin
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59
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60 elif codeword[0,i] == 1:
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61 delta2 = dmin
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62 Sum += delta1
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63 delta1 *= 2
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64 if delta1 > dmax:
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65 delta1 = dmax
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66
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67 elif codeword[0,i] == -1:
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68 delta1 = dmin
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69 Sum -= delta2
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70 delta2 *= 2
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71 if delta2 > dmax:
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72 delta2 = dmax
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73 x[0,i] = Sum
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74 return x
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75
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76 def adm(x, dmin=.01, dmax=.28, tol=0.0001):
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77
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78 # Adaptive delta modulation adapted by code:
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79 # (adeltamod.m)
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80 #
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81 # Adaptive Delta Modulator
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82 # by Gandhar Desai (gdesai)
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83 # BITS Pilani Goa Campus
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84 # Date: 28 Sept, 2013
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85
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86 xsig = x
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87
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88 Lx = len(x)
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89
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90 ADMout = zeros((1, Lx))
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91 codevec = zeros((1, Lx))
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92
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93
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94 Sum = x[0]
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95 delta1 = dmin
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96 delta2 = dmin
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97 mult1 = 2
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98 mult2 = 2
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99 for i in range(0, Lx):
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100 #print abs(xsig[i] - Sum)
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101 if (abs(xsig[i] - Sum) < tol):
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102 bit = 0
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103 delta2 = dmin
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104 delta1 = dmin
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105
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106
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107 elif (xsig[i] >= Sum):
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108 bit = 1
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109 delta2 = dmin
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110 Sum += delta1
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111 delta1 *= mult1
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112 if delta1 > dmax:
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113 delta1 = dmax
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114
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115
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116 else:
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117 bit = -1
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118 delta1 = dmin
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119 Sum -= delta2
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120 delta2 *= mult2
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121 if delta2 > dmax:
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122 delta2 = dmax
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123
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124
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125
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126 ADMout[0, i] = Sum
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127 codevec[0, i]= bit
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128
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129 return ADMout,codevec, x[0]
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130
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131 if __name__=="__main__":
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132 if len(argv) != 2:
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133 print "[EE] Wrong number of arguments"
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134 print "[II] Correct syntax is:"
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135 print "[II] \t%s <training_file>"
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136 print "[II] where <training_file> is a .yaml file containing the"
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137 print "[II] features of the dataset (try output2_stage/fulltraining-last.yaml)"
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138 exit(-1)
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139
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140
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141 n_clusters = 3
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142 UpsamplingFactor = 1
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143 dmin = 0.001
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144 dmax = 0.28
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145 tol = 0.001
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146
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147 infile = argv[1]
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148
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149 features_pool = YamlInput(filename = infile)()
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150
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151
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152
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153 feature_captions = features_pool.descriptorNames()
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154 parameter_captions = []
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155
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156
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157 for c in features_pool.descriptorNames():
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158 if c.split('.')[0] == 'parameter':
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159 parameter_captions.append(c)
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160 if c.split('.')[0] == 'metadata' or c.split('.')[0] == 'parameter':
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161 feature_captions.remove(c)
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162
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163
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164
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165 close('all')
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166
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167 print "[II] Loaded training data from %s (%s) " % (infile, features_pool['metadata.date'][0])
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168 print "[II] %d Features Available: " % len(feature_captions)
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169
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170
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171
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172 print str(feature_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
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173
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174 nfeatures_in = len(feature_captions)
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175 nparameters_in = len(parameter_captions)
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176 features_vector = zeros((nfeatures_in, len(features_pool[feature_captions[0]])))
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177
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178 parameters_vector = zeros((nparameters_in, len(features_pool[parameter_captions[0]])))
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179
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180
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181 for i in range(0, nfeatures_in):
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182 features_vector[i, :] = features_pool[feature_captions[i]].T
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183 for i in range(0, nparameters_in):
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184 parameters_vector[i, :] = features_pool[parameter_captions[0]].T
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185
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186 print "[II] %d parameters used:" % len(parameter_captions)
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187 print str(parameter_captions).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7].replace('parameter.','')
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188
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189 print "[II] Marking silent parts"
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190
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191 silent_parts = zeros((1, len(features_pool[feature_captions[i]].T)))
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192
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193 rms = features_vector[feature_captions.index('rms'), :]
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194
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195 # Implementing Hysteresis Gate -- High threshold is halfway between
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196 # the mean and the max and Low is halfway between the mean dn the min
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197
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198 rms_threshold_mean = mean(rms)
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199
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200 rms_threshold_max = max(rms)
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201 rms_threshold_min = min(rms)
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202
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203 rms_threshold_high = 0.1 * rms_threshold_mean
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204 rms_threshold_low = 0.01 * rms_threshold_mean
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205
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206 for n in range(1, len(rms)):
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207 prev = rms[n-1]
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208 curr = rms[n]
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209
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210 if prev >= rms_threshold_high:
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211 if curr < rms_threshold_low:
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212 silent_parts[0,n] = 1
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213 else:
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214 silent_parts[0,n] = 0
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215 elif prev <= rms_threshold_low:
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216 if curr > rms_threshold_high:
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217 silent_parts[0,n] = 0
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218 else:
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219 silent_parts[0,n] = 1
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220 else:
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221 silent_parts[0,n] = silent_parts[0,n-1]
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222
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223
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224 if silent_parts[0,1] == 1:
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225 silent_parts[0, 0] = 1
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226
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227
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228 # plot(rms)
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229 # plot(silent_parts.T)
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230 # plot(ones((len(rms), 1))*rms_threshold_high)
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231 # plot(ones((len(rms), 1))*rms_threshold_low)
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232
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233 active_index = invert(silent_parts.flatten().astype(bool))
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234
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235 # Keep only active parts
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236
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237 # Uncomment this
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238 features_vector = features_vector[:, active_index]
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239 # parameters_vector = parameters_vector[:, active_index]
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240
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241 moments_vector = zeros((features_vector.shape[0], 2))
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242
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243 print "[II] Storing moments vector"
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244 for i in range(0, features_vector.shape[0]):
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245 mean_ = mean(features_vector[i,:])
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246 std_ = std(features_vector[i,:], ddof=1)
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247 moments_vector[i,0] = mean_
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248 moments_vector[i,1] = std_
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249
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250 features_vector[i,:] = (features_vector[i,:] - mean_)/std_
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251
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252 features_vector_original = features_vector
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253
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254
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255 print "[II] Extracting PCA configuration "
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256
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257 kernel, q, featurelist = extract_pca_configuration_from_data(features_vector)
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258
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259 print "[II] Optimal number of PCs to keep: %d" % q
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260
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261 feature_captions_array = array(feature_captions)
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262
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263 # features_to_keep = features_vector
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264
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265 features_to_keep = list(feature_captions_array[featurelist])
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266 print "[II] Decided to keep %d features:" % len(features_to_keep)
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267 print str(features_to_keep).replace("', ","\n").replace('[','').replace("'","[II]\t ")[:-7]
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268
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269
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270
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271 # Keep the desired features
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272 #Uncomment this
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273 features_kept_data = features_vector[featurelist,:]
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274 # features_kept_data = features_vector
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275 # features_kept_data = features_vector
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276
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277 # Generate the parameter clusters using k-means
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278
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279 # Uncomment this
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280 features_vector = (kernel.T*features_kept_data)[0:q,:]
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281 #features_vector = log(features_vector+0.001)
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282 # features_vector = features_vector_original
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283
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284
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285 # parameters_k_means = KMeans(n_clusters = parameters_k, init='k-means++', max_iter=300, tol=0.0000001, verbose = 1)
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286 parameters_k_means = KMeans(init='k-means++', n_init=10, max_iter=300, tol=0.0000001, verbose = 0)
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287 # # parameters_k_means = MiniBatchKMeans(init='k-means++', max_iter=300, tol=0.00001, verbose = 1)
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288 #
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289 # # Quantize the differences of the parameters instead of the parameters themselves
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290 # parameters_vector_diff = concatenate((zeros((shape(parameters_vector)[0],1)),diff(parameters_vector, axis=1)),axis=1)
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291 # features_vector_diff = concatenate((zeros((shape(features_vector)[0],1)),diff(features_vector,axis=1)),axis=1)
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292 #
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293 # # Delete this afterwards
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294 # # features_vector = features_vector_diff
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295 # parameters_k_means.fit(parameters_vector_diff.T)
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296
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297 print "[II] Trying ADM-coded parameters"
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298 print "[II] Upsampling features and parameters by a factor of %d" % UpsamplingFactor
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299
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300
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301 # Upsampled features and parameters
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302 features_vector_upsampled = repeat(features_vector,UpsamplingFactor, axis=1)
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303
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304 # feature_labels_upsampled = repeat(features_clustering_labels,UpsamplingFactor, axis=0)
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305 parameters_vector_upsampled = repeat(parameters_vector,UpsamplingFactor, axis=1)
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306
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307 # parameters_vector_upsampled = smooth_matrix_1D(parameters_vector_upsampled)
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308
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309 parameters_vector_upsampled_adm = matrix(zeros(shape(parameters_vector_upsampled)))
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310 parameters_vector_upsampled_code = matrix(zeros(shape(parameters_vector_upsampled)))
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311 parameters_vector_upsampled_firstval = matrix(zeros((parameters_vector_upsampled.shape[0],1)))
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312
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313 # Reconstructed parameters
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314
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315 parameters_vector_upsampled_reconstructed = matrix(zeros(shape(parameters_vector_upsampled)))
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316
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317
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318
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319
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320 def adm_matrix(X, dmin=0.001,dmax=0.28,tol=0.001):
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321
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322 out = matrix(zeros(shape(X)))
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323 code = matrix(zeros(shape(X)))
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324 firstval = matrix(zeros((X.shape[0], 1)))
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325
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326 for i in range(0, X.shape[0]):
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327 out[i,:], code[i,:], firstval[i,0] = adm(X[i,:],dmin=dmin,dmax=dmax,tol=tol)
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328
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329 return out,code,firstval
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330
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331 # parameters_vector_upsampled_reconstructed[i,:] = adm_reconstruct(parameters_vector_upsampled_code[i,:],parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
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332
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333 def adm_matrix_reconstruct(code, firstval, dmin=0.001, dmax=0.28):
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334 X = matrix(zeros(shape(code)))
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335 for i in range(0, code.shape[0]):
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336 X[i,:] = adm_reconstruct(code[i,:], firstval[i,0], dmin=dmin, dmax=dmax)
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337
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338 return X
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339
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340
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341 parameters_vector_upsampled_adm, parameters_vector_upsampled_code, parameters_vector_upsampled_firstval = adm_matrix(parameters_vector_upsampled, dmin, dmax, tol)
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342
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343
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344 def diff_and_pad(X):
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345 return concatenate((
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346 zeros((
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347 shape(X)[0],
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348 1
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349 )),
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350 diff(X, axis=1)),
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351 axis=1)
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352
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353
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354 # features_vector_upsampled = features_vector_upsampled
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355
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356
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357 features_vector_upsampled = diff_and_pad(features_vector_upsampled)
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358
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359
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360 # features_vector_diff = concatenate((zeros((shape(features_vector)[0],1)),diff(features_vector,axis=1)),axis=1)
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361
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362
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363
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364 # Segmentation stuff
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365
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366
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367
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368 # for i in range(0, parameters_vector_upsampled.shape[0]):
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369 # out, code, h = adm(parameters_vector_upsampled[i,:],dmin=dmin,dmax=dmax,tol=tol)
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370 # parameters_vector_upsampled_adm[i,:] = out
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371 # parameters_vector_upsampled_code[i,:] = code
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372 # parameters_vector_upsampled_firstval[i, 0] = h
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373
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374 # parameters_k_means.fit(parameters_vector.T)
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375 ##
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376 # parameters_k_means_centers = parameters_k_means.cluster_centers_
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377 # parameters_k_means_labels = parameters_k_means.labels_
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378 ##
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379 # parameters_vector_estimated = zeros(shape(parameters_vector))
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380 ##
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381 # for n in range(0, len(parameters_vector_estimated[0])):
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382 # parameters_vector_estimated[:,n] = parameters_k_means_centers[parameters_k_means_labels[n]]
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383 ##
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384 ### plot(parameters_vector[0])
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385 ## # plot(parameters_vector_estimated[0])
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386 ##
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387 ## # PROvLIMA EDW
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388 # print "[II] Parameters MSE for %d clusters: %.3f" % (len(parameters_k_means.cluster_centers_), mse(parameters_vector, parameters_vector_estimated))
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389 #
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390 ##
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391 print "[II] Clustering features."
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392 #
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393 features_clustering = GMM(n_components = n_clusters, covariance_type='full')
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394 #
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395 features_clustering.fit( features_vector_upsampled.T, y=parameters_vector_upsampled_code)
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396 #
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397 features_clustering_means = features_clustering.means_
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398 features_clustering_labels = features_clustering.predict(features_vector_upsampled.T)
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399 features_clustering_sigmas = features_clustering.covars_
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400 #
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401 features_vector_upsampled_estimated = zeros(shape(features_vector_upsampled))
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402 #
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403 #
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404 for n in range(0, len(features_vector_upsampled_estimated[0])):
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405 features_vector_upsampled_estimated[:,n] = features_clustering_means[features_clustering_labels[n]]
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406 #
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407 # # for n in range(0,features_vector.shape[0]):
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408 # # hist(features_vector[1]-features_vector_estimated[1], 256)
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409 # std(features_vector[1]-features_vector_estimated[1], ddof=1)
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410 # mean(features_vector[1]-features_vector_estimated[1])
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411 #
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412 print "[II] Features MSE for %d clusters: %.3f" % (n_clusters, mse(features_vector_upsampled, features_vector_upsampled_estimated))
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413
|
|
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414
|
|
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415
|
|
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416 def cross_validate_clustering(data, estimator):
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|
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417 print "[II] Crossvalidating... "
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418 estimator_fulldata = estimator
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419 estimator_fulldata.fit(data)
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420
|
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421 # labels = estimator_fulldata.predict(data)
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422 means = estimator_fulldata.means_
|
|
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423 # print means
|
|
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|
424
|
|
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|
425 percents = arange(0.1,0.6,0.1)
|
|
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|
426 MSEs = []
|
|
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|
427 reconstructed = zeros(shape(data))
|
|
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|
428 labels = estimator.predict(data)
|
|
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|
429 for n in range(0, len(reconstructed)):
|
|
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|
430 reconstructed[n,:] = means[labels[n]]
|
|
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|
431
|
|
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|
432 MSEs.append(mse(data,reconstructed))
|
|
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|
433 for p in percents:
|
|
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|
434 train,test = cross_validation.train_test_split(data,test_size=p,random_state=0)
|
|
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|
435 train = matrix(train)
|
|
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|
436 test = matrix(test)
|
|
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|
437 # print shape(train)
|
|
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|
438 # print shape(test)
|
|
e@0
|
439 estimator.fit(train)
|
|
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|
440 means = estimator.means_
|
|
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|
441 labels = estimator.predict(test)
|
|
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|
442 reconstructed = zeros(shape(test))
|
|
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|
443 for n in range(0, len(reconstructed)):
|
|
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|
444 reconstructed[n,:] = means[labels[n]]
|
|
e@0
|
445
|
|
e@0
|
446 m = mse(test,reconstructed)
|
|
e@0
|
447
|
|
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|
448 print "[II] MSE for clustering crossvalidated data %.2f-%.2f: %.5f" % ((1-p), p, m)
|
|
e@0
|
449 MSEs.append(m)
|
|
e@0
|
450
|
|
e@0
|
451 print "[II] Crossvalidation complete"
|
|
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|
452
|
|
e@0
|
453 return MSEs
|
|
e@0
|
454
|
|
e@0
|
455 # print "[II] Trying Cross Validation"
|
|
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|
456
|
|
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|
457 # cross_validate_clustering(features_vector_upsampled.T, features_clustering)
|
|
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|
458
|
|
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|
459
|
|
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|
460 # Construct parameters alphabet, each symbol is going to be a different column vector
|
|
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|
461 # in parameter code matrix
|
|
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|
462
|
|
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|
463
|
|
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|
464 def vector_to_states(X):
|
|
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|
465 """
|
|
e@0
|
466 Input: a vector MxN with N samples and M variables
|
|
e@0
|
467 Output: a codeword dictionary `parameters_alphabet',
|
|
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|
468 state_seq, inverse `parameters_alphabet_inv' """
|
|
e@0
|
469
|
|
e@0
|
470
|
|
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|
471 parameters_alphabet = {}
|
|
e@0
|
472 n = 0
|
|
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|
473
|
|
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|
474 for i in range(0, X.shape[1]):
|
|
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|
475 vec = tuple(ravel(X[:,i]))
|
|
e@0
|
476 if vec not in parameters_alphabet:
|
|
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|
477 parameters_alphabet[vec] = n
|
|
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|
478 n += 1
|
|
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|
479
|
|
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|
480 parameters_alphabet_inv = dict([(parameters_alphabet[m],m) for m in parameters_alphabet])
|
|
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|
481
|
|
e@0
|
482 state_seq = array([parameters_alphabet[tuple(ravel(X[:,m]))] for m in range(0, parameters_vector_upsampled_code.shape[1])] )
|
|
e@0
|
483
|
|
e@0
|
484 return state_seq, parameters_alphabet, parameters_alphabet_inv
|
|
e@0
|
485
|
|
e@0
|
486
|
|
e@0
|
487 def states_to_vector(predicted, parameters_alphabet_inv):
|
|
e@0
|
488 estimated = matrix(zeros((len(parameters_alphabet_inv[0]), len(predicted))))
|
|
e@0
|
489 for i in range(0, len(state_seq)):
|
|
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|
490 estimated[:, i] = matrix(parameters_alphabet_inv[predicted[i]]).T
|
|
e@0
|
491
|
|
e@0
|
492 return estimated
|
|
e@0
|
493
|
|
e@0
|
494 state_seq, parameters_alphabet, parameters_alphabet_inv = vector_to_states(parameters_vector_upsampled_code)
|
|
e@0
|
495
|
|
e@0
|
496
|
|
e@0
|
497 parameters_change_variable = matrix(diff_and_pad(parameters_vector_upsampled)!=0).astype(int)
|
|
e@0
|
498
|
|
e@0
|
499 changes_state_seq, changes_parameters_alphabet, changes_parameters_alphabet_inv = vector_to_states(parameters_change_variable)
|
|
e@0
|
500
|
|
e@0
|
501
|
|
e@0
|
502 # This is an hmm that just codes the changes"
|
|
e@0
|
503 # We have only two states, change and stay the same.
|
|
e@0
|
504
|
|
e@0
|
505 print "[II] (changes hmm-chain) Creating emission probability mixtures for every state "
|
|
e@0
|
506
|
|
e@0
|
507 gmm_list_changes = []
|
|
e@0
|
508 for n in range(0, 2):
|
|
e@0
|
509 vectors = features_vector_upsampled[:,changes_state_seq == n]
|
|
e@0
|
510 gmm = GMM(n_components=n_clusters, covariance_type = 'diag')
|
|
e@0
|
511 gmm.fit(vectors.T)
|
|
e@0
|
512 gmm_list_changes.append(gmm)
|
|
e@0
|
513
|
|
e@0
|
514
|
|
e@0
|
515 hmm_changes = hmm.GMMHMM(n_components=2, gmms=array(gmm_list_changes),n_mix=n_clusters)
|
|
e@0
|
516 hmm_changes.fit([array(features_vector_upsampled).T])
|
|
e@0
|
517
|
|
e@0
|
518 subplot(211)
|
|
e@0
|
519 plot(parameters_change_variable.T)
|
|
e@0
|
520 subplot(212)
|
|
e@0
|
521
|
|
e@0
|
522 changes_predicted_states = hmm_changes.predict(array(features_vector_upsampled.T))
|
|
e@0
|
523 predicted_changes_estimated = states_to_vector(changes_predicted_states, changes_parameters_alphabet_inv)
|
|
e@0
|
524
|
|
e@0
|
525 plot(predicted_changes_estimated.T)
|
|
e@0
|
526
|
|
e@0
|
527
|
|
e@0
|
528
|
|
e@0
|
529
|
|
e@0
|
530 # End of changes HMM here
|
|
e@0
|
531
|
|
e@0
|
532
|
|
e@0
|
533 print "[II] Creating emission probability mixtures for every state"
|
|
e@0
|
534 gmm_list = []
|
|
e@0
|
535 for n in range(0, 3):
|
|
e@0
|
536 vectors = features_vector_upsampled[:,state_seq == n]
|
|
e@0
|
537 gmm = GMM(n_components=n_clusters,covariance_type = 'diag')
|
|
e@0
|
538 gmm.fit(vectors.T)
|
|
e@0
|
539 gmm_list.append(gmm)
|
|
e@0
|
540
|
|
e@0
|
541 hmm1 = hmm.GMMHMM(n_components=3, gmms=array(gmm_list),n_mix=n_clusters)
|
|
e@0
|
542 hmm1.fit([array(features_vector_upsampled).T])
|
|
e@0
|
543
|
|
e@0
|
544 figure()
|
|
e@0
|
545 subplot(221)
|
|
e@0
|
546 plot(parameters_vector_upsampled_code.T)
|
|
e@0
|
547
|
|
e@0
|
548 predicted = hmm1.predict(array(features_vector_upsampled.T))
|
|
e@0
|
549
|
|
e@0
|
550 code_estimated = matrix(zeros((len(parameters_vector_upsampled), len(state_seq))))
|
|
e@0
|
551
|
|
e@0
|
552 # for i in range(0, len(state_seq)):
|
|
e@0
|
553 # code_estimated[:, i] = matrix(parameters_alphabet_inv[predicted[i]]).T
|
|
e@0
|
554
|
|
e@0
|
555 code_estimated = states_to_vector(predicted,parameters_alphabet_inv)
|
|
e@0
|
556 subplot(222)
|
|
e@0
|
557 plot(code_estimated.T)
|
|
e@0
|
558
|
|
e@0
|
559 reconstructed_original = adm_matrix_reconstruct(parameters_vector_upsampled_code, parameters_vector_upsampled_firstval)
|
|
e@0
|
560 subplot(223)
|
|
e@0
|
561 plot(reconstructed_original.T)
|
|
e@0
|
562 subplot(224)
|
|
e@0
|
563 reconstructed_estimated = adm_matrix_reconstruct(code_estimated, parameters_vector_upsampled_firstval)
|
|
e@0
|
564 plot(reconstructed_estimated.T)
|
|
e@0
|
565
|
|
e@0
|
566 # scatter(features_vector_upsampled[0,:],features_vector_upsampled_estimated[0,:])
|
|
e@0
|
567 # scatter(features_vector_upsampled[1,:],features_vector_upsampled_estimated[1,:])
|
|
e@0
|
568 #
|
|
e@0
|
569 # xlabel('Original Features on Principal Components')
|
|
e@0
|
570 # ylabel('Estimated Features on Principal Components')
|
|
e@0
|
571 # title('Original vs Estimated Features')
|
|
e@0
|
572 # savefig('original_vs_estimated.png')
|
|
e@0
|
573
|
|
e@0
|
574
|
|
e@0
|
575
|
|
e@0
|
576 #
|
|
e@0
|
577 #
|
|
e@0
|
578 # print "[II] Testing Gaussian Naive Bayes Classifier"
|
|
e@0
|
579 ##
|
|
e@0
|
580 # gnb = GaussianNB()
|
|
e@0
|
581 # gnb.fit(features_vector_upsampled.T, parameters_vector_upsampled_code.T)
|
|
e@0
|
582 # parameters_vector_upsampled_code_estimated = gnb.predict(features_vector_upsampled.T)
|
|
e@0
|
583 #
|
|
e@0
|
584 #
|
|
e@0
|
585 ##
|
|
e@0
|
586 # print "[II] Raw Parameters - Gaussian Naive Bayes classifier testing ratio: %.4f" % (float(sum((parameters_vector_upsampled_code_estimated == parameters_vector_upsampled_code).astype(float)))/float(len(parameters_k_means_labels)))
|
|
e@0
|
587 ##
|
|
e@0
|
588 # plot(adm_matrix_reconstruct(parameters_vector_upsampled_code_estimated,parameters_vector_upsampled_firstval,dmin,dmax).T)
|
|
e@0
|
589 #
|
|
e@0
|
590 # print "[II] Trying ADM-coded parameters"
|
|
e@0
|
591 # UpsamplingFactor = 100
|
|
e@0
|
592 # print "[II] Upsampling features and parameters by a factor of %d" % UpsamplingFactor
|
|
e@0
|
593 #
|
|
e@0
|
594 #
|
|
e@0
|
595 # # Upsampled features and parameters
|
|
e@0
|
596 # features_vector_upsampled = repeat(features_vector,UpsamplingFactor, axis=1)
|
|
e@0
|
597 # feature_labels_upsampled = repeat(features_clustering_labels,UpsamplingFactor, axis=0)
|
|
e@0
|
598 # parameters_vector_upsampled = repeat(parameters_vector,UpsamplingFactor, axis=1)
|
|
e@0
|
599 #
|
|
e@0
|
600 # parameters_vector_upsampled_adm = matrix(zeros(shape(parameters_vector_upsampled)))
|
|
e@0
|
601 # parameters_vector_upsampled_code = matrix(zeros(shape(parameters_vector_upsampled)))
|
|
e@0
|
602 # parameters_vector_upsampled_firstval = matrix(zeros((parameters_vector_upsampled.shape[0],1)))
|
|
e@0
|
603 #
|
|
e@0
|
604 # # Reconstructed parameters
|
|
e@0
|
605 #
|
|
e@0
|
606 # parameters_vector_upsampled_reconstructed = matrix(zeros(shape(parameters_vector_upsampled)))
|
|
e@0
|
607 #
|
|
e@0
|
608 # dmin = 0.001
|
|
e@0
|
609 # dmax = 0.28
|
|
e@0
|
610 # tol = 0.001
|
|
e@0
|
611 # for i in range(0, parameters_vector_upsampled.shape[0]):
|
|
e@0
|
612 # out, code, h = adm(parameters_vector_upsampled[i,:],dmin=dmin,dmax=dmax,tol=tol)
|
|
e@0
|
613 # parameters_vector_upsampled_adm[i,:] = out
|
|
e@0
|
614 # parameters_vector_upsampled_code[i,:] = code
|
|
e@0
|
615 # parameters_vector_upsampled_firstval[i, 0] = h
|
|
e@0
|
616 #
|
|
e@0
|
617 #
|
|
e@0
|
618 # # Reconstruct-ADM
|
|
e@0
|
619 # parameters_vector_upsampled_reconstructed[i,:] = adm_reconstruct(parameters_vector_upsampled_code[i,:],parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
|
|
e@0
|
620 #
|
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e@0
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621 #
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622 # # plot(parameters_vector_upsampled_adm.T, 'r--')
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623 #
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624 #
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625 # # plot(parameters_vector_upsampled.T)
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626 # # plot(parameters_vector_upsampled_reconstructed.T, 'g.')
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627 #
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628 #
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629 #
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630 # parameters_vector_reconstructed = zeros(shape(parameters_vector))
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631 # for n in range(0, parameters_vector.shape[1]):
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632 # parameters_vector_reconstructed[:,n] = parameters_vector_upsampled_reconstructed[:,n*UpsamplingFactor]
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633 #
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634 #
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635 # mse_adm = mse(parameters_vector_reconstructed, parameters_vector)
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636 #
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637 # print "[II] Expected ADM reconstruction MSE: %.4f" % mse_adm
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638 #
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639 # # figure()
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640 # #plot(parameters_vector.T)
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641 # # plot(parameters_vector_reconstructed.T)
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642 #
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643 # print "[II] Building HMM transition, emission matrices and priors"
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644 #
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645 # transmat = zeros((3,3))
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646 # startprob = zeros((3,))
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647 # emissionmat = zeros((3, n_clusters))
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648 #
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649 #
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650 # state_labels = parameters_vector_upsampled_code + 1
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651 # stateseq = state_labels.T
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652 #
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653 # for n in range(0,shape(parameters_vector_upsampled_code)[1]):
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654 # if n>0:
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655 # transmat[state_labels[0,n-1],state_labels[0,n]] += 1
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656 # startprob[state_labels[0,n]] +=1
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657 # emissionmat[state_labels[0,n],feature_labels_upsampled[n]] += 1
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658 #
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659 #
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660 # for n in range(0, transmat.shape[0]):
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661 # transmat[n,:]/=sum(transmat[n,:])
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662 # emissionmat[n,:]/=sum(emissionmat[n,:])
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663 #
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664 #
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665 # transmat = matrix(transmat)
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666 # emissionmat = matrix(emissionmat)
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667 # # Prior
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668 # startprob = startprob/sum(startprob)
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669 # startprob = ravel(startprob)
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670 #
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671 # # Vocabulary
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672 #
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673 # model = hmm.GMMHMM(n_mix=n_clusters, n_components=3, covariance_type="diag")
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674 # model.means_ = features_clustering.means_
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675 # model.covars_ = features_clustering.covars_
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676 #
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677 # features_vector_array = array(features_vector)
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678 # features_vector_upsampled_array=array(features_vector_upsampled)
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679 #
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680 # model.fit([features_vector_array.T])
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681 # stateseq_estimated = model.predict(features_vector_upsampled_array.T)
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682 #
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683 # parameters_vector_upsampled_reconstructed_decoded = matrix(zeros(shape(parameters_vector_upsampled)))
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684 #
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685 #
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686 # plot(stateseq_estimated)
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687 # plot(stateseq)
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688 #
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689 # code_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
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690 # code_estimated[0,:] = stateseq_estimated - 1
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691 #
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692 #
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693 #
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694 # parameters_vector_upsampled_reconstructed_estimated = matrix(zeros(shape(parameters_vector_upsampled)))
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695 #
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696 # for i in range(0, parameters_vector_upsampled.shape[0]):
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697 # parameters_vector_upsampled_reconstructed_estimated[i,:] = adm_reconstruct(code_estimated,parameters_vector_upsampled_firstval[i,0], dmin=dmin,dmax=dmax)
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698 # figure()
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699 # plot(parameters_vector_upsampled_reconstructed_estimated.T)
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700 # plot(parameters_vector_upsampled.T) |
