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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 numpy as np
import random
import matplotlib.pyplot as plt
from sklearn import preprocessing
x=[]
y=[]
x1=[]
@ -17,14 +17,14 @@ with open('kmeans.csv', mode ='r') as file:
for lines in csvFile:
#print(lines)
if(int(lines[2])==0):
x.append(float(lines[0]))
y.append(float(lines[1]))
x.append(float(lines[0])+random.randrange(0,9,1)*0.01)
y.append(float(lines[1])+random.randrange(0,9,2)*0.01)
elif(int(lines[2])==1):
x1.append(float(lines[0]))
y1.append(float(lines[1]))
x1.append(float(lines[0])+random.randrange(0,9,3)*0.01)
y1.append(float(lines[1])+random.randrange(0,9,1)*0.01)
else:
x2.append(float(lines[0]))
y2.append(float(lines[1]))
x2.append(float(lines[0])+random.randrange(0,9,2)*0.01)
y2.append(float(lines[1])+random.randrange(0,9,3)*0.01)
x.append(50.284426)
y.append(65451.237705)
@ -33,15 +33,15 @@ 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 = np.array(x)
y_arr = np.array(y)
x1_arr = np.array(x1)
y1_arr = np.array(y1)
x2_arr = np.array(x2)
y2_arr = np.array(y2)
x_arr = preprocessing.normalize([x_array])
y_arr = preprocessing.normalize([y_array])
# x_arr = preprocessing.normalize([x_array])
# y_arr = preprocessing.normalize([y_array])
# x1_arr = preprocessing.normalize([x1_array])
# y1_arr = preprocessing.normalize([y1_array])
@ -49,8 +49,12 @@ y_arr = preprocessing.normalize([y_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.scatter(x_arr, y_arr,)
plt.scatter(x1_arr, y1_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()