| 247 | | So for each quantity we maintain the following object: |
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| 248 | | {{{ |
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| 249 | | #define MIN_SAMPLES 20 |
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| 250 | | // after this many samples, use exponentially averaged version |
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| 251 | | #define SAMPLE_WEIGHT 0.001 |
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| 252 | | // new samples get this weight in exp avg |
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| 253 | | #define SAMPLE_LIMIT 10 |
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| 254 | | // cap samples at recent_mean*10 |
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| 255 | | |
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| 256 | | struct STATS { |
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| 257 | | int nsamples; |
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| 258 | | double mean; |
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| 259 | | double sum_var; |
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| 260 | | double recent_mean; |
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| 261 | | double recent_var; |
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| 262 | | |
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| 263 | | void update(double sample) { |
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| 264 | | if (sample < 0) return; |
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| 265 | | if (nsamples > MIN_SAMPLES) { |
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| 266 | | if (sample > recent_mean*SAMPLE_LIMIT) { |
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| 267 | | sample = recent_main*SAMPLE_LIMIT; |
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| 268 | | } |
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| 269 | | } |
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| 270 | | // see http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance |
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| 271 | | nsamples++; |
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| 272 | | double delta = sample - mean; |
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| 273 | | mean += delta/nsamples; |
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| 274 | | sum_var += delta*(sample-mean); |
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| 275 | | |
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| 276 | | if (nsamples < MIN_SAMPLES) { |
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| 277 | | recent_mean = mean; |
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| 278 | | recent_var = sum_var/nsamples; |
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| 279 | | } else { |
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| 280 | | // update recent averages |
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| 281 | | delta = sample - recent_mean; |
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| 282 | | recent_mean += SAMPLE_WEIGHT*delta; |
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| 283 | | double d2 = delta*delta - recent_var; |
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| 284 | | recent_var += SAMPLE_WEIGHT*d2; |
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| 285 | | } |
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| 286 | | } |
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| 287 | | }; |
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| 288 | | }}} |
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| | 248 | The code that does all this is |
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| | 249 | [http://boinc.berkeley.edu/trac/browser/trunk/boinc/lib/average.h here]. |
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