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PNN.py
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PNN.py
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# coding: utf-8
#Dzulfiqar Ridha 1301154298 IF-39-04
# In[75]:
import matplotlib.pyplot as plt
import math
import numpy as np
import operator
#load data
data_train = np.genfromtxt("data_train_PNN.txt",skip_header=1)
data_test = np.genfromtxt("data_test_PNN.txt",skip_header=1)
#visualization data train
xyz = plt.figure().add_subplot(111, projection='3d')
xyz.set_xlabel("att1")
xyz.set_ylabel("att2")
xyz.set_zlabel("att3")
xyz.scatter(data_train[:,0], data_train[:,1], data_train[:,2], c=data_train[:,3])
plt.show()
# In[76]:
#menghitung PDF
def PDF(test,train,s):
return math.exp(-((((test[0]-train[0])**2)+((test[1]-train[1])**2)+((test[2]-train[2])**2))/(2*(s)**2)))
# In[77]:
#Klasifikasi
def klasifikasi(dtest,dtrain,s):
kelas = {0:0.0,1:0.0,2:0.0}
hasil = []
for test in dtest:
for train in dtrain:
kelas[int(train[3])] = kelas[int(train[3])] + PDF(test,train,s)
hasil.append(max(kelas.iteritems(), key=operator.itemgetter(1))[0])
kelas = {0:0.0,1:0.0,2:0.0}
return np.array(hasil)
# In[78]:
#menghitung akurasi
def tepat():
n=0
smooth = []
persenan = []
while n<1:
n+=0.05
akurasi = klasifikasi(data_train,data_train,n)
sum = 0
for i in range(len(data_train)):
cek = akurasi[i] == int(data_train[:,3][i])
if cek:
sum+=1
persen = float(sum)/len(data_train)*100
persenan.append(persen)
smooth.append(n)
print "smoothing:",n,", akurasi:",persen
print "nilai smoothing terbaik:",smooth[persenan.index(max(persenan))],"dengan akurasi: ",max(persenan)
plt.xlabel("nilai smoothing")
plt.ylabel("akurasi (%)")
plt.plot(smooth,persenan)
plt.show()
return smooth[persenan.index(max(persenan))]
# In[79]:
if __name__ == '__main__':
kelas = np.concatenate((data_test,klasifikasi(data_test,data_train,tepat())[:,None]),axis=1)
#visualisasi hasil======
abc = plt.figure().add_subplot(111, projection='3d')
abc.set_xlabel("att1")
abc.set_ylabel("att2")
abc.set_zlabel("att3")
abc.scatter(kelas[:,0], kelas[:,1], kelas[:,2], c=kelas[:,3])
plt.show()
#visualisasi hasil======
f = open('prediksi.txt','w')
f.write("\n".join(map(lambda x: str(x), kelas)))
f.close()