1 | #! /usr/bin/python |
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2 | |
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3 | from aubio.bench.node import * |
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4 | from aubio.tasks import * |
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5 | |
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6 | |
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7 | |
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8 | |
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9 | def mmean(l): |
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10 | return sum(l)/float(len(l)) |
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11 | |
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12 | def stdev(l): |
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13 | smean = 0 |
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14 | lmean = mmean(l) |
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15 | for i in l: |
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16 | smean += (i-lmean)**2 |
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17 | smean *= 1. / len(l) |
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18 | return smean**.5 |
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19 | |
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20 | class benchonset(bench): |
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21 | |
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22 | valuenames = ['orig','missed','Tm','expc','bad','Td'] |
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23 | valuelists = ['l','labs'] |
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24 | printnames = [ 'mode', 'thres', 'dist', 'prec', 'recl', 'Ttrue', 'Tfp', 'Tfn', 'Tm', 'Td', |
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25 | 'aTtrue', 'aTfp', 'aTfn', 'aTm', 'aTd', 'mean', 'smean', 'amean', 'samean'] |
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26 | |
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27 | formats = {'mode': "%12s" , |
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28 | 'thres': "%5.4s", |
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29 | 'dist': "%5.4s", |
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30 | 'prec': "%5.4s", |
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31 | 'recl': "%5.4s", |
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32 | |
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33 | 'Ttrue': "%5.4s", |
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34 | 'Tfp': "%5.4s", |
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35 | 'Tfn': "%5.4s", |
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36 | 'Tm': "%5.4s", |
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37 | 'Td': "%5.4s", |
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38 | |
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39 | 'aTtrue':"%5.4s", |
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40 | 'aTfp': "%5.4s", |
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41 | 'aTfn': "%5.4s", |
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42 | 'aTm': "%5.4s", |
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43 | 'aTd': "%5.4s", |
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44 | |
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45 | 'mean': "%5.40s", |
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46 | 'smean': "%5.40s", |
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47 | 'amean': "%5.40s", |
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48 | 'samean': "%5.40s"} |
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49 | |
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50 | def file_gettruth(self,input): |
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51 | from os.path import isfile |
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52 | ftrulist = [] |
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53 | # search for match as filetask.input,".txt" |
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54 | ftru = '.'.join(input.split('.')[:-1]) |
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55 | ftru = '.'.join((ftru,'txt')) |
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56 | if isfile(ftru): |
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57 | ftrulist.append(ftru) |
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58 | else: |
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59 | # search for matches for filetask.input in the list of results |
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60 | for i in range(len(self.reslist)): |
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61 | check = '.'.join(self.reslist[i].split('.')[:-1]) |
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62 | check = '_'.join(check.split('_')[:-1]) |
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63 | if check == '.'.join(input.split('.')[:-1]): |
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64 | ftrulist.append(self.reslist[i]) |
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65 | return ftrulist |
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66 | |
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67 | def file_exec(self,input,output): |
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68 | filetask = self.task(input,params=self.params) |
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69 | computed_data = filetask.compute_all() |
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70 | ftrulist = self.file_gettruth(filetask.input) |
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71 | for i in ftrulist: |
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72 | #print i |
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73 | filetask.eval(computed_data,i,mode='rocloc',vmode='') |
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74 | for i in self.valuenames: |
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75 | self.v[i] += filetask.v[i] |
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76 | for i in filetask.v['l']: |
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77 | self.v['l'].append(i) |
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78 | for i in filetask.v['labs']: |
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79 | self.v['labs'].append(i) |
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80 | |
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81 | def dir_exec(self): |
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82 | """ run file_exec on every input file """ |
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83 | self.l , self.labs = [], [] |
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84 | self.v = {} |
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85 | for i in self.valuenames: |
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86 | self.v[i] = 0. |
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87 | for i in self.valuelists: |
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88 | self.v[i] = [] |
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89 | self.v['thres'] = self.params.threshold |
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90 | act_on_files(self.file_exec,self.sndlist,self.reslist, \ |
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91 | suffix='',filter=sndfile_filter) |
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92 | |
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93 | def dir_eval(self): |
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94 | totaltrue = self.v['expc']-self.v['bad']-self.v['Td'] |
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95 | totalfp = self.v['bad']+self.v['Td'] |
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96 | totalfn = self.v['missed']+self.v['Tm'] |
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97 | self.P = 100*float(totaltrue)/max(totaltrue + totalfp,1) |
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98 | self.R = 100*float(totaltrue)/max(totaltrue + totalfn,1) |
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99 | if self.R < 0: self.R = 0 |
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100 | self.F = 2.* self.P*self.R / max(float(self.P+self.R),1) |
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101 | |
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102 | N = float(len(self.reslist)) |
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103 | |
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104 | self.v['mode'] = self.params.onsetmode |
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105 | self.v['thres'] = "%2.3f" % self.params.threshold |
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106 | self.v['dist'] = "%2.3f" % self.F |
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107 | self.v['prec'] = "%2.3f" % self.P |
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108 | self.v['recl'] = "%2.3f" % self.R |
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109 | self.v['Ttrue'] = totaltrue |
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110 | self.v['Tfp'] = totalfp |
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111 | self.v['Tfn'] = totalfn |
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112 | self.v['aTtrue'] = totaltrue/N |
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113 | self.v['aTfp'] = totalfp/N |
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114 | self.v['aTfn'] = totalfn/N |
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115 | self.v['aTm'] = self.v['Tm']/N |
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116 | self.v['aTd'] = self.v['Td']/N |
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117 | self.v['mean'] = mmean(self.v['l']) |
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118 | self.v['smean'] = stdev(self.v['l']) |
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119 | self.v['amean'] = mmean(self.v['labs']) |
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120 | self.v['samean'] = stdev(self.v['labs']) |
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121 | |
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122 | def run_bench(self,modes=['dual'],thresholds=[0.5]): |
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123 | self.modes = modes |
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124 | self.thresholds = thresholds |
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125 | |
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126 | self.pretty_titles() |
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127 | for mode in self.modes: |
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128 | self.params.onsetmode = mode |
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129 | for threshold in self.thresholds: |
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130 | self.params.threshold = threshold |
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131 | self.dir_exec() |
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132 | self.dir_eval() |
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133 | self.pretty_print() |
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134 | #print self.v |
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135 | |
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136 | def pretty_print(self,sep='|'): |
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137 | for i in self.printnames: |
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138 | print self.formats[i] % self.v[i], sep, |
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139 | print |
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140 | |
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141 | def pretty_titles(self,sep='|'): |
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142 | for i in self.printnames: |
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143 | print self.formats[i] % i, sep, |
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144 | print |
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145 | |
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146 | def auto_learn(self,modes=['dual'],thresholds=[0.1,1.5]): |
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147 | """ simple dichotomia like algorithm to optimise threshold """ |
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148 | self.modes = modes |
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149 | self.pretty_titles() |
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150 | for mode in self.modes: |
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151 | steps = 10 |
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152 | lesst = thresholds[0] |
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153 | topt = thresholds[1] |
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154 | self.params.onsetmode = mode |
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155 | |
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156 | self.params.threshold = topt |
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157 | self.dir_exec() |
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158 | self.dir_eval() |
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159 | self.pretty_print() |
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160 | topF = self.F |
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161 | |
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162 | self.params.threshold = lesst |
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163 | self.dir_exec() |
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164 | self.dir_eval() |
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165 | self.pretty_print() |
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166 | lessF = self.F |
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167 | |
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168 | for i in range(steps): |
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169 | self.params.threshold = ( lesst + topt ) * .5 |
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170 | self.dir_exec() |
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171 | self.dir_eval() |
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172 | self.pretty_print() |
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173 | if self.F == 100.0 or self.F == topF: |
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174 | print "assuming we converged, stopping" |
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175 | break |
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176 | #elif abs(self.F - topF) < 0.01 : |
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177 | # print "done converging" |
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178 | # break |
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179 | if topF < self.F: |
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180 | #lessF = topF |
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181 | #lesst = topt |
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182 | topF = self.F |
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183 | topt = self.params.threshold |
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184 | elif lessF < self.F: |
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185 | lessF = self.F |
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186 | lesst = self.params.threshold |
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187 | if topt == lesst: |
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188 | lesst /= 2. |
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189 | |
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190 | def auto_learn2(self,modes=['dual'],thresholds=[0.00001,1.0]): |
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191 | """ simple dichotomia like algorithm to optimise threshold """ |
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192 | self.modes = modes |
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193 | self.pretty_titles([]) |
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194 | for mode in self.modes: |
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195 | steps = 10 |
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196 | step = 0.4 |
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197 | self.params.onsetmode = mode |
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198 | self.params.threshold = thresholds[0] |
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199 | cur = 0 |
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200 | |
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201 | for i in range(steps): |
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202 | self.dir_exec() |
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203 | self.dir_eval() |
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204 | self.pretty_print() |
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205 | new = self.P |
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206 | if self.R == 0.0: |
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207 | #print "Found maximum, highering" |
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208 | step /= 2. |
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209 | self.params.threshold -= step |
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210 | elif new == 100.0: |
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211 | #print "Found maximum, highering" |
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212 | step *= .99 |
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213 | self.params.threshold += step |
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214 | elif cur > new: |
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215 | #print "lower" |
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216 | step /= 2. |
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217 | self.params.threshold -= step |
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218 | elif cur < new: |
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219 | #print "higher" |
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220 | step *= .99 |
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221 | self.params.threshold += step |
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222 | else: |
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223 | print "Assuming we converged" |
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224 | break |
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225 | cur = new |
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226 | |
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227 | |
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228 | if __name__ == "__main__": |
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229 | import sys |
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230 | if len(sys.argv) > 1: datapath = sys.argv[1] |
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231 | else: print "ERR: a path is required"; sys.exit(1) |
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232 | modes = ['complex', 'energy', 'phase', 'specdiff', 'kl', 'mkl', 'dual'] |
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233 | #modes = [ 'phase' ] |
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234 | thresholds = [ 0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2] |
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235 | #thresholds = [1.5] |
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236 | |
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237 | #datapath = "%s%s" % (DATADIR,'/onset/DB/*/') |
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238 | respath = '/var/tmp/DB-testings' |
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239 | |
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240 | benchonset = benchonset(datapath,respath,checkres=True,checkanno=True) |
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241 | benchonset.params = taskparams() |
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242 | benchonset.task = taskonset |
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243 | benchonset.valuesdict = {} |
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244 | |
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245 | |
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246 | try: |
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247 | #benchonset.auto_learn2(modes=modes) |
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248 | benchonset.run_bench(modes=modes,thresholds=thresholds) |
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249 | except KeyboardInterrupt: |
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250 | sys.exit(1) |
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