using gitea now

update, correct and expand our measurement results

README.md

@@ -2,13 +2,13 @@
 
 # Objective
 
-The goal of our task was to implement three kinds of a least mean square filter.
+The goal of our task was to implement four variants of a least mean square filter.
 According to the given task we created implementations in ANSI C, C++ and C#.
-Input of these filters are image files, namely BMP, GIF, EXIF, JPG, PNG and TIFF in C#.
+Input given to these filters are image files, namely BMP, GIF, EXIF, JPG, PNG and TIFF in C#.
 PPM  in ANSI C and C++ _only_.
 In each of these implementations there are log files and graphs as an output.
 
-More information can be found inside the according folders.
+More information can be found in according folders.
 
 # Structure
 ```

bin/input.c

@@ -0,0 +1,90 @@
+#include  <stdio.h>
+#include <stdio.h>
+
+#define RGB_COLOR 255
+
+typdef struct{
+	unsigned char red, green, blue;
+} colorChannel_t;
+
+typdef struct{
+	int x,y;
+	colorChannel *data;
+} imagePixel_t;
+
+static imagePixel_t *readPPM( const char *fileName ) {
+	char buffer[16];
+	imagePixel_t *image;
+	int c, rgbColor;
+
+	FILE *fp = fopen(fileName, "r");
+	if( !fp ) {
+		exit(EXIT_FAILURE);
+	}
+	if( !fgets(buffer, sizeof(buffer), fp);
+			perror(filename);
+		exit(EXIT_FAILURE);
+	}
+	if( buffer[0] != 'P' || buffer[1] != '6' ) {
+		fprintf(stderr, "Invalid file format \n");
+		exit(EXIT_FAILURE);
+	}
+	image = (imagePixel_t *) malloc(sizeof(imagePixel_t));
+	if( !image ) {
+		fprintf(stderr, "malloc() failed");
+	}
+	c = getc(fp);
+	while( c == '#' ) {
+		while( getc(fp) !='\n' ) {
+			c = getc(fp);
+	}
+	ungetc(c, fp);
+	if( scanf(fp, "%d %d", $image->, &img->) != 2 ) {
+		fprintf(stderr, "Invalid image size in %s\n)", filename);
+		exit(EXIT_FAILURE);
+	}
+	if( rgbColor != RGB_COLOR ) {
+		fprintf(stderr,"Invalid image color range in %s\n", filename);
+		exit(EXIT_FAILURE);
+	}
+	while(fgetc(fp) != '\n') {
+		image.data = (imagePixel_t *) malloc(image.x * image.y * sizeof(imagePixel_t));
+		if ( !img ) { 
+			fprintf(stderr, "malloc() failed");
+			exit(EXIT_FAILURE);
+		}
+		if ( fread( image.data, 3 * image.x, image.y, fp ) != img.y ) {
+			fprintf(stderr, "Loading image failed");
+			exit(EXIT_FAILURE);
+		}
+	}
+	fclose( fp );
+	return image;
+}
+
+int *mkPpmTestArray (int* testarray, imagePixel_t *image ) {
+	int i;
+	int output [image.x * image.y]
+	if ( !image ) {
+		for ( i = 0; i < image.x * image.y>; i++ ) {
+		//	image.data[i].red = RGB_COLOR - image[i].red;
+		//	image.data[i].green = RGB_COLOR - image.data[i].green;
+		//	image.data[i].blue = RGB_COLOR - image.data[i].blue;
+			output[i] = image.data[i].red;
+		}
+	}
+}
+
+
+void main ( void ) {
+	char *fN = "beaches.ppm"
+	int testarray [1024];
+	imagePixel_t result;
+	result = readPPM(fN);
+	mkPpmTestArray(testarray, result);
+	for (int i = 0; i < 1023; i++ ) {
+		printf("%d\n", testArray[i];
+	}
+}
+	
+	

doc/Messprotokoll.md

@@ -1,7 +1,8 @@
 # 1. CPP-NLMS-Testreihe
+## 1.1 Veränderung der Lernrate
 #### Paramter: 
 				- Input: 500.000 Pixel (-n 500000)
-				- WindowSize: 5 (Standard Value)
+				- WindowSize: 5 (Standart Value)
 				- Lernrate von 0.1 bis 1.0 (increased by 0.1)
 				- -i cathedral.ppm 
 				- random seed generator : 3 (-s 3)
@@ -9,51 +10,92 @@
 
 ### 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
+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
+Lernrate	|	meanerror	|	variance
 ---	|	---	|	---
-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
+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
+Lernrate	|	meanerror	|	variance
 ---	|	---	|	---
-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
+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	|	variance
+---	|	---	|	---
+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

src/ansi_c_implementation/NLMSvariants.c

@@ -202,7 +202,7 @@ void standardNLMS( mldata_t *mlData, point_t points[] ) {
 
 	char fileName[512];
 	const unsigned xErrorLength = mlData->samplesCount;	
-	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount);
+	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount + 1);
 
 	unsigned i, xCount = 0;
 	mkFileName ( fileName, sizeof(fileName), STANDARD_NLMS);
@@ -280,7 +280,7 @@ void localMean ( mldata_t *mlData, point_t points[] ) {
 
 	char fileName[512];
 	const unsigned xErrorLength = mlData->samplesCount;                                   
-	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount);
+	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount + 1);
 
     	unsigned i, xCount = 0; 			            						// Runtime vars
 
@@ -363,7 +363,7 @@ void directPredecessor( mldata_t *mlData, point_t points[]) {
 	
 	char fileName[512];
     	const unsigned xErrorLength = mlData->samplesCount;
-	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount);
+	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount + 1);
    	
 	unsigned xCount = 0, i;
 	double xActual = 0.0;
@@ -443,7 +443,7 @@ void differentialPredecessor ( mldata_t *mlData, point_t points[] ) {
 	memcpy(localWeights, mlData->weights, sizeof(double) * mlData->windowSize + 1);
 	const unsigned xErrorLength = mlData->samplesCount; 
 	char fileName[512]; 
-	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount);
+	double *xError = (double *) malloc ( sizeof(double) *mlData->samplesCount + 1);
 
 	unsigned xCount = 0, i;
 	double xPredicted = 0.0;

src/ansi_c_implementation/README.md

@@ -29,7 +29,7 @@ There are a bunch of options you can predefine but do not have to. The only para
 | -w        | Size of  M (window).     	    | 5      |
 | -c        | Choose RGB color channel, green has least noise. | green  |
 | -l        | Learnrate of machine learning.| 0.4    |
-| -g        | include graph building. Choose for amount of input data lower than 1200. If the template is located in another folder use its path otherwise use true. Do not use whitespace in path to folder.| none|
+| -g        | include graph building. Choose for any amount of input data lower than 1200 pixels. If the template is located in another folder use its __path__, otherwise use __true__. Do not use whitespace in path to folder.| none|
 | -s        | Seed randomizing weights. Choose for repoducability. | time(NULL)| 
 
 This code is ANSI aka C89 compatible. No POSIX, C99, C11 or GNU libs, because it had to be windows compatible . There are way easier methods like getline() for file parsing or getopt() as an args parser, because of compatibility reasons things have been kept simple.

src/ansi_c_implementation/signedunsigned.c

@@ -0,0 +1,14 @@
+#include <stdio.h>
+
+void main ( void ) {
+	unsigned 	ui_one  = 1;
+	signed 		i_one = 1;
+	signed 		short s_minus_one = -1;
+
+	if ( s_minus_one > ui_one ) {
+		printf("-1 > 1\n");
+	}
+	if ( s_minus_one < i_one ) {
+		printf("-1 < 1\n");
+	}
+}

src/cpp_implementation/NLMSvariants.cpp

@@ -270,6 +270,7 @@ void standardNLMS(mldata_t *mlData, point_t points[]) {
 	fprintf(fp01, "\nQuadratische Varianz(x_error): %f\nMittelwert:(x_error): %f\n\n", deviation, mean);
 
 	free(localWeights);
+	free(xError);
 
 }
 
@@ -354,6 +355,7 @@ void localMean(mldata_t *mlData, point_t points[]) {
 	fclose(fp4);
 
 	free(localWeights);
+	free(xError);
 }
 
 /*
@@ -435,6 +437,7 @@ void directPredecessor(mldata_t *mlData, point_t points[]) {
 	fclose(fp3);
 
 	free(localWeights);
+	free(xError);
 }
 
 /*
@@ -522,6 +525,7 @@ void differentialPredecessor(mldata_t *mlData, point_t points[]) {
 	fclose(fp6);
 
 	free(localWeights);
+	free(xError);
 }
 
 /*