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182 lines (146 loc) · 4.04 KB
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from svmutil import *
my_file1 = open("tdata.txt", "w")
my_file4 = open("tdata_test.txt", "w")
with open('data.txt', 'r') as f:
data = f.readlines()
#print(data)
#print(" length data ")
numberOfData = len(data)
#Scale of Training Data
arr1 = []
arr2 = []
arr3 = []
for line in data:
arr1.append(line[:-1])
mini = 1000000
maxi = 0
last = len(arr1);
for i in range(0,last):
words1 = arr1[i].split()
for j in range(0,7):
data1 = float(words1[j])
if data1<mini:
mini = data1
if data1>maxi:
maxi = data1
upper = 1
lower = 0
for i in range(0,last):
words1 = arr1[i].split()
for j in range(0,8):
data1 = float(words1[j])
data1 = lower + (upper-lower)/(maxi-mini)*(data1-mini)
words1[j] = str(data1)
arr2.append(words1)
#print("scaled \n")
#print(arr1)
#print(arr2)
#print(arr2[3])
#print("printing")
with open('x500.txt', 'r') as g:
xdata = g.readlines()
#print(xdata)
for line in xdata:
arr3.append(line[:-1])
#print(arr3)
for i in range(0,last):
words2 = arr3[i]
# print("this is starting the writing task")
# print(words2)
my_file1.write(words2+ " ")
k = 1
for j in arr2[i]:
# print(' '+str(k)+':'+j),
my_file1.write(str(k)+":"+str(j)+" ")
k=k+1
my_file1.write("\n");
# print("\n")
my_file1.close()
#Scaling of data to be predicted
with open('randomdata500.txt', 'r') as f:
data = f.readlines()
#print(data)
#print(" length data ")
numberOfTestData = len(data)
#Scale The Data
arr1 = []
arr2 = []
arr3 = []
for line in data:
arr1.append(line[:-1])
mini = 1000000
maxi = 0
last = len(arr1);
for i in range(0,last):
words1 = arr1[i].split()
for j in range(0,8):
data1 = float(words1[j])
if data1<mini:
mini = data1
if data1>maxi:
maxi = data1
upper = 1
lower = 0
for i in range(0,last):
words1 = arr1[i].split()
for j in range(0,8):
data1 = float(words1[j])
data1 = lower + (upper-lower)/(maxi-mini)*(data1-mini)
words1[j] = str(data1)
arr2.append(words1)
#print("scaled \n")
#print(arr1)
#print(arr2)
#print(arr2[3])
with open('randomx500.txt', 'r') as g:
xdata = g.readlines()
#print(xdata)
for line in xdata:
arr3.append(line[:-1])
#print(arr3)
for i in range(0,last):
words2 = arr3[i]
#print("this is starting the writing task")
#print(words2)
my_file4.write(words2+ " ")
k = 1
for j in arr2[i]:
#print(' '+j),
my_file4.write(str(k)+":"+str(j)+" ")
k=k+1
my_file4.write("\n");
#print("\n")
my_file4.close()
y, x = svm_read_problem('tdata.txt')
C = max(y) - min(y);
T = 2; # RBF kernel
gamma =[0.0078125, 0.015625, 0.03125, 0.0625, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64, 128]; # range of the gamma parameter
epsilon = [0, 1, 2, 3, 4, 5]; # range of the epsilon parameter
best_mse = 100000000
for j in range(0,15):
G = gamma[j]
for k in range(0,6):
E = epsilon[k]
mse = svm_train(y[:numberOfData], x[:numberOfData], '-q -v 5 -s 3 -t 2 -b 1 -c ' + str(C) + ' -g ' + str(G) + ' -p ' + str(E)) # build model on Learning data
#print ("mse")
#print (mse)
if mse <= best_mse:
best_mse = mse
bestG = G
bestE = E
print("best parameters mse G E")
print(best_mse, bestG, bestE)
model = svm_train(y[:numberOfData], x[:numberOfData], '-q -s 3 -t 2 -c ' + str(C) + ' -g ' + str(bestG) + ' -p ' + str(bestE))
y, x = svm_read_problem('tdata_test.txt')
#print("input")
#print(y)
#print(x)
p_label, p_acc, p_val = svm_predict(y[0:], x[0:], model)
#print("p_label")
#print(p_label)
#print(p_acc)
#print(p_val)
my_file2 = open("resultsx.txt", "w")
for i in range(0,numberOfTestData):
my_file2.write(str(p_label[i])+"\n")
my_file2.close()