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Author SHA1 Message Date
pavanvpatil 3213347dcb
Merge pull request #11 from karthikmurakonda/testing-kmeans 
added kmeans2
 2022-11-01 22:50:41 +05:30
pavanvpatil ef9fd10e2f added kmeans2 2022-11-01 22:49:13 +05:30
6 changed files with 10444 additions and 9 deletions
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3
iPDC/inc/Kmeans2.h Normal file
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int *getRandoms(int lower, int upper, int count);
long double distance(struct Point* A, struct Point* B);
void Kmeans2(struct data_frame *df);

500
iPDC/kmeans.csv Normal file
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iPDC/kmeans.txt Normal file
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56
iPDC/plot_kmeans2.py Normal file
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# 0 : 50.284426, 65451.237705
# 1 : 50.370104, 65476.164948
# 2 : 50.302515, 65464.320755
import csv
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
x=[]
y=[]
x1=[]
y1=[]
x2=[]
y2=[]
with open('kmeans.csv', mode ='r') as file:
csvFile = csv.reader(file)
for lines in csvFile:
#print(lines)
if(int(lines[2])==0):
x.append(float(lines[0]))
y.append(float(lines[1]))
elif(int(lines[2])==1):
x1.append(float(lines[0]))
y1.append(float(lines[1]))
else:
x2.append(float(lines[0]))
y2.append(float(lines[1]))
x.append(50.284426)
y.append(65451.237705)
x1.append(50.370104)
y1.append(65476.164948)
x2.append(50.302515)
y2.append(65464.320755)
x_array = np.array(x+x1+x2)
y_array = np.array(y+y1+y2)
# x1_array = np.array(x1)
# y1_array = np.array(y1)
# x2_array = np.array(x2)
# y2_array = np.array(y2)
x_arr = preprocessing.normalize([x_array])
y_arr = preprocessing.normalize([y_array])
# x1_arr = preprocessing.normalize([x1_array])
# y1_arr = preprocessing.normalize([y1_array])
# x2_arr = preprocessing.normalize([x2_array])
# y2_arr = preprocessing.normalize([y2_array])
plt.scatter(x_arr, y_arr)
# plt.scatter(x1_arr, y1_arr)
# plt.scatter(x2_arr, y2_arr)
plt.show()

204
iPDC/src/Kmeans2.c Normal file
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#include "parser.h"
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <time.h>
#include <string.h>
#include <stdbool.h>
struct Point
{
long double x, y; // coordinates
int cluster; // no default cluster
long double minDist; // default infinite dist to nearest cluster
};
int count = 0;
struct Point *P;
long double distance(struct Point* A, struct Point* B)
{
return (((A->x - B->x) * (A->x - B->x)) + ((A->y - B->y) * (A->y - B->y)));
}
int *getRandoms(int lower, int upper, int count)
{
srand(time(0));
int *p = (int *)malloc(sizeof(int) * count);
int track = 0;
while (1)
{
if (track == count)
{
return p;
}
int num = (rand() % (upper - lower + 1)) + lower;
if (track == 0)
{
p[track] = num;
track++;
continue;
}
int flag = 0;
for (int j = 0; j <= track - 1; j++)
{
if (num == p[j])
{
flag = 1;
break;
}
}
if (flag == 0)
{
p[track] = num;
track++;
}
}
}
void Kmeans2(struct data_frame *df)
{
printf("count: %d\n",count);
if(count==0)
{
P = (struct Point *)malloc(sizeof(struct Point) * 500);
}
if (count != 500)
{
float CURR_FREQ;
if (df->dpmu[0]->fmt->freq == '0')
{
CURR_FREQ = 50 + to_intconvertor(df->dpmu[0]->freq) * 1e-3;
}
else
{
CURR_FREQ = decode_ieee_single(df->dpmu[0]->freq);
}
float CURR_vol;
if (df->dpmu[0]->fmt->phasor == '0')
{
if (df->dpmu[0]->fmt->polar == '0')
{
unsigned char s1[2];
unsigned char s2[2];
strncpy(s1, df->dpmu[0]->phasors[0], 2);
strncpy(s2, df->dpmu[0]->phasors[0] + 2, 2);
long double v1 = to_intconvertor(s1);
long double v2 = to_intconvertor(s2);
CURR_vol = sqrt((v1 * v1) + (v2 * v2));
}
else
{
unsigned char s1[2];
strncpy(s1, df->dpmu[0]->phasors[0], 2);
CURR_vol = to_intconvertor(s1);
}
}
else
{
if (df->dpmu[0]->fmt->polar == '0')
{
unsigned char s1[4];
unsigned char s2[4];
strncpy(s1, df->dpmu[0]->phasors[0], 4);
strncpy(s2, df->dpmu[0]->phasors[0] + 2, 4);
long double v1 = decode_ieee_single(s1);
long double v2 = decode_ieee_single(s2);
CURR_vol = sqrt((v1 * v1) + (v2 * v2));
}
else
{
unsigned char s1[4];
strncpy(s1, df->dpmu[0]->phasors[0], 4);
CURR_vol = decode_ieee_single(s1);
}
}
P[count].x = CURR_FREQ;
P[count].y = CURR_vol;
P[count].cluster = -1;
P[count].minDist = __DBL_MAX__;
count++;
}
else
{
int no_of_clusters = 5;
int epochs = 20;
int *c = getRandoms(0, 499, no_of_clusters);
struct Point *Centroids = (struct Point *)malloc(sizeof(struct Point) * no_of_clusters);
for (int i = 0; i < no_of_clusters; i++)
{
Centroids[i].x = P[c[i]].x;
Centroids[i].y = P[c[i]].y;
Centroids[i].minDist = __DBL_MAX__;
Centroids[i].cluster = -1;
}
free(c);
while (epochs--)
{
for (int i = 0; i < no_of_clusters; i++)
{
for (int j = 0; j < 500; j++)
{
long double dist = distance(&Centroids[i], &P[j]);
if (P[j].minDist > dist)
{
P[j].minDist = dist;
P[j].cluster = i;
}
}
}
int *nPoints = (int *)malloc(sizeof(int) * no_of_clusters);
long double *Sumx = (long double *)malloc(sizeof(long double) * no_of_clusters);
long double *Sumy = (long double *)malloc(sizeof(long double) * no_of_clusters);
for (int i = 0; i < no_of_clusters; i++)
{
nPoints[i] = 0;
Sumx[i] = 0;
Sumy[i] = 0;
}
for (int i = 0; i < 500; i++)
{
nPoints[P[i].cluster]++;
Sumx[P[i].cluster] += P[i].x;
Sumy[P[i].cluster] += P[i].y;
P[i].minDist = __DBL_MAX__;
}
for (int i = 0; i < no_of_clusters; i++)
{
Centroids[i].x = Sumx[i] / nPoints[i];
Centroids[i].y = Sumy[i] / nPoints[i];
}
free(nPoints);
free(Sumx);
free(Sumy);
}
count = 0;
FILE *fp;
fp = fopen("kmeans.txt","a");
for(int i=0;i<500;i++)
{
fprintf(fp,"%Lf, %Lf, %d\n",P[i].x,P[i].y,P[i].cluster);
}
fprintf(fp,"\n\n");
for(int i=0;i<no_of_clusters;i++)
{
fprintf(fp,"%d : %Lf, %Lf\n",i,Centroids[i].x,Centroids[i].y);
}
fprintf(fp,"\n\n");
fclose(fp);
free(Centroids);
free(P);
}
}

19
iPDC/src/map_vis.c
View File

@ -13,6 +13,7 @@
#include "connections.h"
#include "livechart.h"
#include "utility_tools.h"
#include "Kmeans2.h"
// debug
@ -44,17 +45,17 @@ gboolean update_images(gpointer* pars){
float lat;
float lon;
loops++;
printf("loops: %d\n", loops);
// printf("loops: %d\n", loops);
id = to_intconvertor(df->idcode);
printf("id = %d\n",id);
// printf("id = %d\n",id);
pthread_mutex_lock(&mutex_cfg);
temp_cfg = cfgfirst;
// Check for the IDCODE in Configuration Frame
while(temp_cfg != NULL){
if(id == to_intconvertor(temp_cfg->idcode)){
cfg_match = 1;
printf("Matched - id : %d\n",id);
// printf("Matched - id : %d\n",id);
freq_fmt = temp_cfg->pmu[0]->fmt->freq;
anal_fmt = temp_cfg->pmu[0]->fmt->analog;
phas_fmt = temp_cfg->pmu[0]->fmt->phasor;
@ -69,7 +70,7 @@ gboolean update_images(gpointer* pars){
// get data from df.
if(freq_fmt == '1'){
freq = decode_ieee_single(df->dpmu[i]->freq);
printf("freq = %f\n",freq);
// printf("freq = %f\n",freq);
}else{
freq = to_intconvertor(df->dpmu[i]->freq)*1e-6+50;
}
@ -80,15 +81,15 @@ gboolean update_images(gpointer* pars){
strncpy(last2bytes, df->dpmu[i]->phasors[0]+2, 2);
vol_magnitude = to_intconvertor(first2bytes);
float imaginary = to_intconvertor(last2bytes);
printf("vol = %f imag = %f\n",vol_magnitude, imaginary);
// printf("vol = %f imag = %f\n",vol_magnitude, imaginary);
vis_ptr = vis_data_head;
match = 0;
while(vis_ptr != NULL){
printf("vis_ptr->id = %d\n",vis_ptr->id);
// printf("vis_ptr->id = %d\n",vis_ptr->id);
if(vis_ptr->id == id){
match = 1;
printf("Matched - id : %d\n",id);
// printf("Matched - id : %d\n",id);
break;
}
vis_ptr = vis_ptr->next;
@ -102,7 +103,7 @@ gboolean update_images(gpointer* pars){
live_chart_serie_add(vis_ptr->serie_dfreq, dfreq);
if(match == 1 && cfg_match == 1){
printf("lat = %f, lon = %f, freq = %f\n",lat,lon,freq);
// printf("lat = %f, lon = %f, freq = %f\n",lat,lon,freq);
if(vis_ptr->last_image != 0){
osm_gps_map_image_remove(parameters->util_map, vis_ptr->last_image);
}
@ -153,7 +154,7 @@ gboolean update_images(gpointer* pars){
}else if (algorithm==1 && dimmension == 1){
}else if (algorithm==1 && dimmension == 2){
Kmeans2(df);
}else if (algorithm==2 && dimmension == 0){
if(!DTWvolDistance(df)){
vis_ptr->last_image = osm_gps_map_image_add(parameters->util_map,lat, lon, parameters->g_red_image);