一、libsvm的安装与测试
1. 安装libsvm
官方网站：LIBSVM -- A Library for Support Vector Machines (ntu.edu.tw)
pip install -U libsvm-official
2. 测试libsvm
测试libsvm是否可以使用：
注意包含包的时候不能直接写
from svmutil import *
而要写
from libsvm.svmutil import *
代码如下：
#! /usr/bin/env python3.10
# encoding: utf-8
import sys
from libsvm.svmutil import *
path='C:/Users/30296/Documents/libsvm-3.31/python' # 测试代码：（注意libsvm-3.31/python路径）
sys.path.append(path)
train_label,train_pixel = svm_read_problem('C:/Users/30296/Documents/libsvm-3.31/heart_scale')
model = svm_train(train_label[:200],train_pixel[:200],'-c 4')
print("result:")
p_label, p_acc, p_val = svm_predict(train_label[200:], train_pixel[200:], model);
print(p_acc)得到如下结果：
*.*
optimization finished, #iter = 257
nu = 0.351161
obj = -225.628984, rho = 0.636110
nSV = 91, nBSV = 49
Total nSV = 91
result:
Accuracy = 84.2857% (59/70) (classification)
(84.28571428571429, 0.6285714285714286, 0.463744141163496)二、使用libsvm
1.数据预处理：用python转换txt
libsvm的数据需要是：
label 1：value1 2：value2 3：value3 …
1 1:1 2:2 3:3
-1 1:1 2:2 3:3
而我们的数据格式一般是：
value1 value2 value3 … label
1 2 3 1
1 2 3 -1
需要提前转换一下：
"""
original data form:
value1 value2 value3 value4 ......label
target data form:
label index1:value1 index2:value2 ......
"""
import numpy as np
def readFromTxt(filename):
# o_data = np.loadtxt(filename,encoding='utf-8')
o_data = np.genfromtxt(filename,delimiter=',')#,dtype=[float, float, float, float, float, float, int]
# print(o_data)
lines = o_data.shape[0]
columns = o_data.shape[1]
print(lines,columns)
new_data = np.zeros([lines, 2*columns-1], dtype=list)
i = 0
while i < lines:
j = 1
new_data[i][0] = o_data[i][columns-1]
while j < columns:
new_data[i][2*j-1] = j
new_data[i][2*j] = o_data[i][j-1]
j = j+1
i = i+1
return new_data,lines,2*columns-1
# 替换成自己的文件名
filename = "collision_results_-1.900000.txt"
newfilename = "new-collision_results_-1.900000.txt"
new_data,newlines,newcolumns = readFromTxt(filename)
f = open(newfilename,"w+")
i = 0
while i < newlines:
# if classification --> label shall be int
f.write(str(int(new_data[i][0])))
# if regression --> label shall be real
# f.write(str(new_data[i][0]))
f.write(" ")
j = 1
while j < newcolumns:
f.write(str(new_data[i][j]))
if j % 2 == 1:
f.write(":")
else:
f.write(" ")
j = j+1
f.write("\n")
i = i+1
f.close()2. 学习训练
#! /usr/bin/env python3.10
# encoding: utf-8
import sys
from libsvm.svmutil import *
import numpy as np
# path='C:/Users/30296/Documents/libsvm-3.31/python' # 测试代码：（注意libsvm-3.31/python路径）
# sys.path.append(path)
train_label,train_pixel = svm_read_problem('C:/Users/30296/Documents/libsvm-3.31/heart_scale')
per_80=int(np.floor(0.8*len(train_label)))
print(per_80)
print(train_label[:per_80])
model = svm_train(train_label[:per_80],train_pixel[:per_80],'-c 4')
print("result:")
p_label, p_acc, p_val = svm_predict(train_label[per_80:], train_pixel[per_80:], model)
print(p_label,'下一个', p_acc,'下一个', p_val)得到输出：
216
[1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, -1.0, -1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, 1.0, -1.0, 1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, 1.0, -1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, -1.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, -1.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, -1.0, 1.0, -1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, 1.0, -1.0, -1.0, 1.0, -1.0, -1.0]
*.*
optimization finished, #iter = 236
nu = 0.350163
obj = -245.091761, rho = 0.725105
nSV = 96, nBSV = 57
Total nSV = 96
result:
Accuracy = 83.3333% (45/54) (classification)
[-1.0, 1.0, 1.0, -1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, 1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, 1.0, -1.0, -1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, 1.0, -1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, -1.0, 1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0, 1.0] 下一个 (83.33333333333334, 0.6666666666666666, 0.4376577840112202) 下一个 [[-1.6124757728862074], [0.0886991748765581], [0.4663204240189208], [-1.809307063660131], [1.789713658958068], [1.4263019450015681], [-1.3657682586413893], [1.9107841719202647], [-1.396542713524683], [-0.1876978331664505], [0.15542694054439632], [0.7089826475868414], [-1.4615037457023727], [-1.130433148293036], [0.3205752603012352], [0.415261195248798], [-0.976906868341664], [0.9029986716445745], [1.956591639825957], [0.7847881477664195], [-1.1472189675661113], [1.076648795606011], [-0.8582718654160085], [-1.8068212282248122], [0.6840602464863493], [-1.101835886542706], [-1.2402235520592604], [-0.5678741375735478], [-0.8835802514321], [1.0834647106286988], [1.5292489100848137], [-1.4540902207078812], [-1.313175746493155], [2.6895483406734924], [1.1512582225370713], [-1.639392753733577], [0.11443306053238411], [-1.0278685255180595], [-1.2251953985069255], [-1.033632513651953], [0.4120354239632241], [1.9451408840796356], [-1.315608704047866], [-0.9240953214882549], [-2.108813048050079], [2.5980107155518404], [-1.732737578667451], [-1.71260757809163], [-0.5738742753188778], [-1.0641364858922406], [-0.8518389292541279], [-1.8430304080540507], [-0.001770943242147327], [2.450684766756411]]