2018-05-25 21:22:36 +02:00
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# 1. CPP-NLMS-Testreihe
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2018-05-25 19:35:43 +02:00
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#### Paramter:
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- Input: 500.000 Pixel (-n 500000)
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2018-05-25 21:57:29 +02:00
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- WindowSize: 5 (Standard Value)
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2018-05-25 19:35:43 +02:00
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- Lernrate von 0.1 bis 1.0 (increased by 0.1)
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- -i cathedral.ppm
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- random seed generator : 3 (-s 3)
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2018-05-25 21:22:36 +02:00
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### 1. Local Mean
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2018-05-25 19:35:43 +02:00
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Lernrate | meanerror | devitation
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--- | --- | ---
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0.1 | 0.017622| 264.808714
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0.2 | 0.035715| 563.756509
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0.3 | 0.054145| 1058.390726
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0.4 | 0.072960| 1749.137624
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0.5 | 0.092207| 2636.592222
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0.6 | 0.111931| 3721.514220
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0.7 | 0.132180| 5004.829359
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0.8 | 0.153002| 6487.641093
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0.9 | 0.174440| 8171.260237
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1.0 | 0.196529| 10057.264574
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2018-05-25 21:22:36 +02:00
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### 2. direct Predecessor
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2018-05-25 19:35:43 +02:00
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Lernrate | meanerror | devitation
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--- | --- | ---
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0.1 | 0.034605| 1165.639836
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0.2 | 0.061290| 5143.795751
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0.3 | 0.081941| 14101.954880
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0.4 | 0.098059| 33151.61923
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0.5 | 0.111109| 73575.962146
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0.6 | 0.123070| 159449.145873
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0.7 | 0.138492| 347221.927325
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0.8 | 519.742508| 8159987385389.823242
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0.9 | -7547134082896249132941312.000000| 3877153348329978955993364733716443880442465820831329026048.000000
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1.0 | 3761239144093188417284117395383469067145028584884793393044299775046771047758036992.000000| 2074572983654522674987602010560539857768301297996124872466118751856211301657828168212459204392861396853691477732320683100737172969672730282637731961860360049654132492992512.000000
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2018-05-25 21:22:36 +02:00
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### 3. differential Predecessor
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2018-05-25 19:35:43 +02:00
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Lernrate | meanerror | devitation
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--- | --- | ---
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0.1 | 0.002158| 35.172878
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0.2 | 0.003306| 33.315664
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0.3 | 0.003656| 32.549577
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0.4 | 0.003311| 33.097449
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0.5 | 0.002340| 35.717416
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0.6 | 0.000869| 43.040214
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0.7 | -0.000213| 71.249809
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0.8 | 0.013732| 1363.663734
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0.9 | -0.071016| 4623896.100036
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1.0 | -211924.365595| 556770831730198528.000000
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2018-05-25 21:22:36 +02:00
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___
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# 2. CS-NLMS-Testreihe
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#### Parameter:
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-N:4000 Pixel
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-M: 5
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-Learnrate: 0.1 bis 1.0 (increased by 0.1)
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-Image: boats.y.png
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--> Gewichte sind random, daher nicht reproduzierbar!
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### 1. local mean
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Lernrate | Average error | Variance of the error
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--- | --- | ---
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0.1 | -0,0223811013862385| 5337,82462718957
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0.2 | -0,0622259728578006| 7363,56898611733
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0.3 | -0,0298976210637875| 9614,92806843726
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0.4 | 0,0849844894447272| 12088,2433018213
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0.5 | 0,299055989866592| 14819,6805234785
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0.6 | 0,585283074155977| 17874,0914703503
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0.7 | 0,979106442634296| 21337,9978781998
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0.8 | 1,46391408202542| 25322,8680485397
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0.9 | 2,05872807071837| 29964,9719994441
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1,0 | 2,79656379441662| 35436,9340804565
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### 2. direct predecessor
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Lernrate | Average error | Variance of the error
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--- | --- | ---
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0.1 | 0,151005103097187| 5471,5876773699
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0.2 | 0,606943328690361| 7729,18121268243
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0.3 | 1,14390553779214| 10465,2060324616
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0.4 | 1,76810718228813| 13695,8876087231
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0.5 | 2,5193083296817| 17431,0433982945
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0.6 | 3,38144695588288| 21696,5188824818
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0.7 | 4,36367842697108| 26566,2513099756
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0.8 | 5,51257464442153| 32193,8755290981
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0.9 | 6,83374986915135| 38850,115776936
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1.0 | 8,38129514988831| 46958,0983013679
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### 3. differential predecessor
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Lernrate | Average error | Variance of the error
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--- | --- | ---
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0.1 | -0,0160622191672049| 4750,33040669542
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0.2 | 0,00640864710641419| 6041,46065665648
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0.3 | 0,0432263456982662| 7412,09833871273
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0.4 | 0,106075295986603| 8891,97578539296
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0.5 | 0,174689052473554| 10532,273473301
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0.6 | 0,241331954473674| 12394,8177286223
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0.7 | 0,359973579958283| 14552,6708886431
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0.8 | 0,494261683495002| 17102,3682973733
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0.9 | 0,671346960651145 | 20166,0023256068
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1.0 | 0,892045554010017| 23907,2852270274
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