# 1. CPP-NLMS-Testreihe
## 1.1 Veränderung der Lernrate
#### Paramter: 
				- Input: 500.000 Pixel (-n 500000)
				- WindowSize: 5 (Standart Value)
				- Lernrate von 0.1 bis 1.0 (increased by 0.1)
				- -i cathedral.ppm 
				- random seed generator : 3 (-s 3)


### 1. Local Mean

Lernrate	|	meanerror	|	variance 
---	|	---	|	---	
0.1	|	0.020861|	373.760784
0.2	|	-0.015231|	1261.386282
0.3	|	-0.054507|	2574.244199
0.4	|	-0.088173|	4229.104809
0.5	|	-0.116079 |	6169.762185
0.6	|	-0.139313 |	8360.777760
0.7	|	-0.158889 |	10783.351638
0.8	|	-0.175600 |	13432.957565
0.9	|	-0.190101 |	16318.529330
1.0	|	-0.202958 |	19463.062580

### 2. direct Predecessor

Lernrate	|	meanerror	|	devitation
---	|	---	|	---
0.1	|	0.024560	|	608.448633
0.2	|	-0.030306	|	2007.513911
0.3	|	-0.084634	|	3959.201147
0.4	|	-0.126798	|	6328.009325
0.5	|	-0.157719	|	9057.241991
0.6	|	-0.179736	|	12137.852301
0.7	|	-0.194805	|	15592.808948
0.8	|	-0.204488	|	19471.279015
0.9	|	-0.210085	|	23849.267714
1.0	|	-0.212681	|	28835.466521

### 3. differential Predecessor

Lernrate	|	meanerror	|	devitation
---	|	---	|	---
0.1	|	0.046246 |	237.604268
0.2	|	0.058362 |	838.182243
0.3	|	0.060553 |	1834.829054
0.4	|	0.060390 |	3227.671286
0.5	|	0.060858 |	5016.829374
0.6	|	0.063120 |	7202.687465
0.7	|	0.067694 |	9785.843689
0.8	|	0.074840 |	12767.049840
0.9	|	0.084710 |	16147.248088
1.0	|	0.097443 |	19927.694764


### 4. NLMS

Lernrate	|	meanerror	|	devitation
---	|	---	|	---
0.1	|	-0.865235	|	51.897994
0.2	|	-0.916315 |	67.761425
0.3	|	-0.950006 |	82.291526
0.4	|	-0.975096 |	96.554001
0.5	|	-0.995089 |	111.236008
0.6	|	-1.011618 |	126.820482
0.7	|	-1.025538 |	143.713619
0.8	|	-1.037385 |	162.334219
0.9	|	-1.047550 |	183.192878
1.0	|	-1.056358 |	206.993456

## 1.2 Veränderung der WindowSize
#### Paramter: 
				- Input: 500.000 Pixel (-n 500000)
				- WindowSize: -w {1,3,5, 8, 10, 15, 20, 25, 50, 100}
				- Lernrate: -l 0.6
				- -i cathedral.ppm 
				- random seed generator : 3 (-s 3)

>Tabelle beinhaltet jeweils den 'mean error' der jeweiligen Variante


|  Windowsize | local Mean  | direct Predecessor  |  differential Predecessor | NLMS  |
|---|---|---|---|---|
|  1 | 0.330125  |  0.330131 | 0.330131  |  -1.063652 |
|  3 |  0.444605 |  0.412069 |  0.413190 | -1.089547  |
|  5 | -0.139313  | -0.179736  | 0.063120  |  -1.011618 |
|  8 | 0.181180  | 0.042986  | 0.221078  | -0.873373  |
|  10 | 0.279242  | 0.188497  | 0.291145  |  -0.811906 |
|  15 | 0.215564  | 0.222018  | 0.265400  | -0.696719  |
|  20 | 0.273161 |  0.383155 | 0.194116  | -0.615447  |
|  25 |  0.213731 | 0.276315  | 0.185892  | -0.576340  |
|  50 |  0.467920 | 0.463972  |  0.188099 | -0.479407  |
|  100 | -0.331102  | -0.412149  |  -0.018204 | -0.506369  |

___


# 2. CS-NLMS-Testreihe
#### Parameter:
				-N:4000 Pixel
				-M: 5
				-Learnrate: 0.1 bis 1.0 (increased by 0.1)
				-Image: boats.y.png
				--> Gewichte sind random, daher nicht reproduzierbar!


### 1. local mean

Lernrate	|	Average error	|	Variance of the error
---	|	---	|	---
0.1	|	-0,0223811013862385|	5337,82462718957
0.2	|	-0,0622259728578006|	7363,56898611733
0.3	|	-0,0298976210637875|	9614,92806843726
0.4	|	0,0849844894447272|	12088,2433018213
0.5	|	0,299055989866592|	14819,6805234785
0.6	|	0,585283074155977|	17874,0914703503
0.7	|	0,979106442634296|	21337,9978781998
0.8	|	1,46391408202542|	25322,8680485397
0.9	|	2,05872807071837|	29964,9719994441
1,0	|	2,79656379441662|	35436,9340804565

### 2. direct predecessor

Lernrate	|	Average error	|	Variance of the error
---	|	---	|	---
0.1	|	0,151005103097187|	5471,5876773699
0.2	|	0,606943328690361|	7729,18121268243
0.3	|	1,14390553779214|	10465,2060324616
0.4	|	1,76810718228813|	13695,8876087231
0.5	|	2,5193083296817|	17431,0433982945
0.6	|	3,38144695588288|	21696,5188824818
0.7	|	4,36367842697108|	26566,2513099756
0.8	|	5,51257464442153|	32193,8755290981
0.9	|	6,83374986915135|	38850,115776936
1.0	|	8,38129514988831|	46958,0983013679

### 3. differential predecessor

Lernrate	|	Average error	|	Variance of the error
---	|	---	|	---
0.1	|	-0,0160622191672049|	4750,33040669542
0.2	|	0,00640864710641419|	6041,46065665648
0.3	|	0,0432263456982662|	7412,09833871273
0.4	|	0,106075295986603|	8891,97578539296
0.5	|	0,174689052473554|	10532,273473301
0.6	|	0,241331954473674|	12394,8177286223
0.7	|	0,359973579958283|	14552,6708886431
0.8	|	0,494261683495002|	17102,3682973733
0.9	|	0,671346960651145	|	20166,0023256068
1.0	|	0,892045554010017|	23907,2852270274