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iPDC-suite
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python

This commit is contained in:
pavanvpatil 2022-11-10 11:40:45 +05:30
parent 9dad2b4885
commit add08468a5
1 changed files with 22 additions and 18 deletions
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40
iPDC/plot_kmeans2.py
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@ -4,8 +4,8 @@
import csv import csv
import numpy as np import numpy as np
import random
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from sklearn import preprocessing
x=[] x=[]
y=[] y=[]
x1=[] x1=[]
@ -17,14 +17,14 @@ with open('kmeans.csv', mode ='r') as file:
for lines in csvFile: for lines in csvFile:
#print(lines) #print(lines)
if(int(lines[2])==0): if(int(lines[2])==0):
x.append(float(lines[0])) x.append(float(lines[0])+random.randrange(0,9,1)*0.01)
y.append(float(lines[1])) y.append(float(lines[1])+random.randrange(0,9,2)*0.01)
elif(int(lines[2])==1): elif(int(lines[2])==1):
x1.append(float(lines[0])) x1.append(float(lines[0])+random.randrange(0,9,3)*0.01)
y1.append(float(lines[1])) y1.append(float(lines[1])+random.randrange(0,9,1)*0.01)
else: else:
x2.append(float(lines[0])) x2.append(float(lines[0])+random.randrange(0,9,2)*0.01)
y2.append(float(lines[1])) y2.append(float(lines[1])+random.randrange(0,9,3)*0.01)
x.append(50.284426) x.append(50.284426)
y.append(65451.237705) y.append(65451.237705)
@ -33,15 +33,15 @@ y1.append(65476.164948)
x2.append(50.302515) x2.append(50.302515)
y2.append(65464.320755) y2.append(65464.320755)
x_array = np.array(x+x1+x2) x_arr = np.array(x)
y_array = np.array(y+y1+y2) y_arr = np.array(y)
# x1_array = np.array(x1) x1_arr = np.array(x1)
# y1_array = np.array(y1) y1_arr = np.array(y1)
# x2_array = np.array(x2) x2_arr = np.array(x2)
# y2_array = np.array(y2) y2_arr = np.array(y2)
x_arr = preprocessing.normalize([x_array]) # x_arr = preprocessing.normalize([x_array])
y_arr = preprocessing.normalize([y_array]) # y_arr = preprocessing.normalize([y_array])
# x1_arr = preprocessing.normalize([x1_array]) # x1_arr = preprocessing.normalize([x1_array])
# y1_arr = preprocessing.normalize([y1_array]) # y1_arr = preprocessing.normalize([y1_array])
@ -49,8 +49,12 @@ y_arr = preprocessing.normalize([y_array])
# x2_arr = preprocessing.normalize([x2_array]) # x2_arr = preprocessing.normalize([x2_array])
# y2_arr = preprocessing.normalize([y2_array]) # y2_arr = preprocessing.normalize([y2_array])
plt.scatter(x_arr, y_arr) plt.scatter(x_arr, y_arr,)
# plt.scatter(x1_arr, y1_arr) plt.scatter(x1_arr, y1_arr)
# plt.scatter(x2_arr, y2_arr) plt.scatter(x2_arr, y2_arr)
plt.xlabel("Frequency in Hz")
plt.ylabel("Voltage in Volts")
plt.title("K-means Clustering")
plt.legend(["A","B","C"])
plt.show() plt.show()