# 1. CPP-NLMS-Testreihe
#### 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	|	devitation
---	|	---	|	---
0.1	|	0.017622|	264.808714
0.2	|	0.035715|	563.756509
0.3	|	0.054145|	1058.390726
0.4	|	0.072960|	1749.137624
0.5	|	0.092207|	2636.592222
0.6	|	0.111931|	3721.514220
0.7	|	0.132180|	5004.829359
0.8	|	0.153002|	6487.641093
0.9	|	0.174440|	8171.260237
1.0	|	0.196529|	10057.264574

### 2. direct Predecessor

Lernrate	|	meanerror	|	devitation
---	|	---	|	---
0.1	|	0.034605|	1165.639836
0.2	|	0.061290|	5143.795751
0.3	|	0.081941|	14101.954880
0.4	|	0.098059|	33151.61923
0.5	|	0.111109|	73575.962146
0.6	|	0.123070|	159449.145873
0.7	|	0.138492|	347221.927325
0.8	|	519.742508|	8159987385389.823242
0.9	|	-7547134082896249132941312.000000|	3877153348329978955993364733716443880442465820831329026048.000000
1.0	|	3761239144093188417284117395383469067145028584884793393044299775046771047758036992.000000|	2074572983654522674987602010560539857768301297996124872466118751856211301657828168212459204392861396853691477732320683100737172969672730282637731961860360049654132492992512.000000

### 3. differential Predecessor

Lernrate	|	meanerror	|	devitation
---	|	---	|	---
0.1	|	0.002158|	35.172878
0.2	|	0.003306|	33.315664
0.3	|	0.003656|	32.549577
0.4	|	0.003311|	33.097449
0.5	|	0.002340|	35.717416
0.6	|	0.000869|	43.040214
0.7	|	-0.000213|	71.249809
0.8	|	0.013732|	1363.663734
0.9	|	-0.071016|	4623896.100036	
1.0	|	-211924.365595|	556770831730198528.000000

___

# 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