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1 /* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
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2
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3 /*
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4 Vamp
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5
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6 An API for audio analysis and feature extraction plugins.
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7
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8 Centre for Digital Music, Queen Mary, University of London.
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9 Copyright 2006-2008 Chris Cannam and QMUL.
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10
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11 Permission is hereby granted, free of charge, to any person
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12 obtaining a copy of this software and associated documentation
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13 files (the "Software"), to deal in the Software without
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14 restriction, including without limitation the rights to use, copy,
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15 modify, merge, publish, distribute, sublicense, and/or sell copies
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16 of the Software, and to permit persons to whom the Software is
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17 furnished to do so, subject to the following conditions:
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18
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19 The above copyright notice and this permission notice shall be
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20 included in all copies or substantial portions of the Software.
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21
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22 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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23 EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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24 MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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25 NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR
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26 ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
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27 CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
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28 WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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29
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30 Except as contained in this notice, the names of the Centre for
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31 Digital Music; Queen Mary, University of London; and Chris Cannam
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32 shall not be used in advertising or otherwise to promote the sale,
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33 use or other dealings in this Software without prior written
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34 authorization.
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35 */
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36
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37 #include "PluginSummarisingAdapter.h"
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38
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39 #include <map>
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40 #include <cmath>
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41
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42 namespace Vamp {
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43
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44 namespace HostExt {
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45
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46 class PluginSummarisingAdapter::Impl
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47 {
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48 public:
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49 Impl(Plugin *plugin, float inputSampleRate);
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50 ~Impl();
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51
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52 FeatureSet process(const float *const *inputBuffers, RealTime timestamp);
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53 FeatureSet getRemainingFeatures();
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54
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55 void setSummarySegmentBoundaries(const SegmentBoundaries &);
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56
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57 FeatureList getSummary(int output, SummaryType type);
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58
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59 protected:
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60 Plugin *m_plugin;
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61
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62 SegmentBoundaries m_boundaries;
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63
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64 typedef std::vector<float> ValueList;
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65 typedef std::map<int, ValueList> BinValueMap;
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66
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67 struct OutputAccumulator {
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68 int count;
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69 BinValueMap values;
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70 };
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71
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72 typedef std::map<int, OutputAccumulator> OutputAccumulatorMap;
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73 OutputAccumulatorMap m_accumulators;
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74
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75 struct OutputBinSummary {
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76 float minimum;
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77 float maximum;
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78 float median;
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79 float mode;
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80 float sum;
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81 float variance;
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82 int count;
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83 };
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84
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85 typedef std::map<int, OutputBinSummary> OutputSummary;
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86 typedef std::map<RealTime, OutputSummary> SummarySegmentMap;
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87 typedef std::map<int, SummarySegmentMap> OutputSummarySegmentMap;
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88
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89 OutputSummarySegmentMap m_summaries;
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90
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91 RealTime m_lastTimestamp;
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92
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93 void accumulate(const FeatureSet &fs, RealTime);
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94 void accumulate(int output, const Feature &f, RealTime);
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95 void reduce();
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96 };
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97
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98 PluginSummarisingAdapter::PluginSummarisingAdapter(Plugin *plugin) :
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99 PluginWrapper(plugin)
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100 {
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101 m_impl = new Impl(plugin, m_inputSampleRate);
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102 }
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103
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104 PluginSummarisingAdapter::~PluginSummarisingAdapter()
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105 {
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106 delete m_impl;
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107 }
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108
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109 Plugin::FeatureSet
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110 PluginSummarisingAdapter::process(const float *const *inputBuffers, RealTime timestamp)
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111 {
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112 return m_impl->process(inputBuffers, timestamp);
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113 }
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114
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115 Plugin::FeatureSet
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116 PluginSummarisingAdapter::getRemainingFeatures()
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117 {
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118 return m_impl->getRemainingFeatures();
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119 }
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120
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121 Plugin::FeatureList
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122 PluginSummarisingAdapter::getSummary(int output, SummaryType type)
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123 {
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124 return m_impl->getSummary(output, type);
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125 }
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126
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127 PluginSummarisingAdapter::Impl::Impl(Plugin *plugin, float inputSampleRate) :
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128 m_plugin(plugin)
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129 {
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130 }
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131
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132 PluginSummarisingAdapter::Impl::~Impl()
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133 {
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134 }
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135
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136 Plugin::FeatureSet
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137 PluginSummarisingAdapter::Impl::process(const float *const *inputBuffers, RealTime timestamp)
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138 {
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139 FeatureSet fs = m_plugin->process(inputBuffers, timestamp);
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140 accumulate(fs, timestamp);
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141 m_lastTimestamp = timestamp;
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142 return fs;
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143 }
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144
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145 Plugin::FeatureSet
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146 PluginSummarisingAdapter::Impl::getRemainingFeatures()
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147 {
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148 FeatureSet fs = m_plugin->getRemainingFeatures();
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149 accumulate(fs, m_lastTimestamp);
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150 reduce();
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151 return fs;
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152 }
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153
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154 Plugin::FeatureList
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155 PluginSummarisingAdapter::Impl::getSummary(int output, SummaryType type)
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156 {
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157 //!!! need to ensure that this is only called after processing is
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158 //!!! complete (at the moment processing is "completed" in the
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159 //!!! call to getRemainingFeatures, but we don't want to require
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160 //!!! the host to call getRemainingFeatures at all unless it
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161 //!!! actually wants the raw features too -- calling getSummary
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162 //!!! should be enough -- we do need to ensure that all data has
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163 //!!! been processed though!)
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164 FeatureList fl;
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165 for (SummarySegmentMap::const_iterator i = m_summaries[output].begin();
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166 i != m_summaries[output].end(); ++i) {
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167 Feature f;
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168 f.hasTimestamp = true;
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169 f.timestamp = i->first;
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170 f.hasDuration = false;
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171 for (OutputSummary::const_iterator j = i->second.begin();
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172 j != i->second.end(); ++j) {
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173
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174 // these will be ordered by bin number, and no bin numbers
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175 // will be missing except at the end (because of the way
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176 // the accumulators were initially filled in accumulate())
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177
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178 const OutputBinSummary &summary = j->second;
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179 float result = 0.f;
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180
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181 switch (type) {
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182
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183 case Minimum:
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184 result = summary.minimum;
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185 break;
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186
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187 case Maximum:
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188 result = summary.maximum;
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189 break;
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190
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191 case Mean:
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192 if (summary.count) {
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193 result = summary.sum / summary.count;
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194 }
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195 break;
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196
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197 case Median:
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198 result = summary.median;
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199 break;
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200
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201 case Mode:
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202 result = summary.mode;
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203 break;
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204
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205 case Sum:
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206 result = summary.sum;
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207 break;
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208
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209 case Variance:
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210 result = summary.variance;
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211 break;
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212
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213 case StandardDeviation:
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214 result = sqrtf(summary.variance);
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215 break;
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216
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217 case Count:
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218 result = summary.count;
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219 break;
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220 }
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221 }
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222
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223 fl.push_back(f);
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224 }
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225 return fl;
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226 }
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227
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228 void
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229 PluginSummarisingAdapter::Impl::accumulate(const FeatureSet &fs,
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230 RealTime timestamp)
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231 {
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232 for (FeatureSet::const_iterator i = fs.begin(); i != fs.end(); ++i) {
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233 for (FeatureList::const_iterator j = i->second.begin();
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234 j != i->second.end(); ++j) {
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235 accumulate(i->first, *j, timestamp);
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236 }
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237 }
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238 }
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239
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240 void
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241 PluginSummarisingAdapter::Impl::accumulate(int output,
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242 const Feature &f,
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243 RealTime timestamp)
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244 {
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245 //!!! use timestamp to determine which segment we're on
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246 m_accumulators[output].count++;
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247 for (int i = 0; i < int(f.values.size()); ++i) {
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248 m_accumulators[output].values[i].push_back(f.values[i]);
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249 }
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250 }
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251
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252 void
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253 PluginSummarisingAdapter::Impl::reduce()
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254 {
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255 RealTime segmentStart = RealTime::zeroTime; //!!!
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256
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257 for (OutputAccumulatorMap::iterator i = m_accumulators.begin();
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258 i != m_accumulators.end(); ++i) {
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259
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260 int output = i->first;
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261 OutputAccumulator &accumulator = i->second;
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262
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263 for (BinValueMap::iterator j = accumulator.values.begin();
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264 j != accumulator.values.end(); ++j) {
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265
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266 int bin = j->first;
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267 ValueList &values = j->second;
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268
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269 OutputBinSummary summary;
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270 summary.minimum = 0.f;
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271 summary.maximum = 0.f;
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272 summary.median = 0.f;
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273 summary.mode = 0.f;
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274 summary.sum = 0.f;
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275 summary.variance = 0.f;
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276 summary.count = accumulator.count;
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277 if (summary.count == 0 || values.empty()) continue;
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278
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279 std::sort(values.begin(), values.end());
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280 int sz = values.size();
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281
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282 summary.minimum = values[0];
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283 summary.maximum = values[sz-1];
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284
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285 if (sz % 2 == 1) {
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286 summary.median = values[sz/2];
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287 } else {
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288 summary.median = (values[sz/2] + values[sz/2 + 1]) / 2;
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289 }
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290
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291 std::map<float, int> distribution;
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292
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293 for (int k = 0; k < sz; ++k) {
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294 summary.sum += values[k];
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295 ++distribution[values[k]];
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296 }
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297
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298 int md = 0;
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299
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300 //!!! I don't like this. Really the mode should be the
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301 //!!! value that spans the longest period of time, not the
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302 //!!! one that appears in the largest number of distinct
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303 //!!! features. I suppose that a median by time rather
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304 //!!! than number of features would also be useful.
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305
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306 for (std::map<float, int>::iterator di = distribution.begin();
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307 di != distribution.end(); ++di) {
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308 if (di->second > md) {
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309 md = di->second;
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310 summary.mode = di->first;
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311 }
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312 }
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313
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314 distribution.clear();
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315
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316 float mean = summary.sum / summary.count;
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317
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318 for (int k = 0; k < sz; ++k) {
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319 summary.variance += (values[k] - mean) * (values[k] - mean);
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320 }
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321 summary.variance /= summary.count;
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322
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323 m_summaries[output][segmentStart][bin] = summary;
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324 }
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325 }
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326
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327 m_accumulators.clear();
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328 }
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329
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330
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331 }
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332
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333 }
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334
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