1.命令函数部分:
clear%清屏
clc
X =load('data.txt')
n = length(X)%总样本数量
y = X(:,4)%类别标志
X = X(:,1:3)
TOL = 0.0001%精度要求
C = 1%参数,对损失函数的权重
b = 0%初始设置截距b
Wold = 0%未更新a时的W(a)
Wnew = 0%更新a后的W(a)
for i = 1 : 50%设置类别标志为1或者-1
y(i) = -1
end
a = zeros(n,1)%参数a
for i = 1 : n%随机初始化a,a属于[0,C]
a(i) = 0.2
end
%为简化计算,减少重复计算进行的计算
K = ones(n,n)
for i = 1 :n%求出K矩阵,便于之后的计算
for j = 1 : n
K(i,j) = k(X(i,:),X(j,:))
end
end
sum = zeros(n,1)%中间变量,便于之后的计算,sum(k)=sigma a(i)*y(i)*K(k,i)
for k = 1 : n
for i = 1 : n
sum(k) = sum(k) + a(i) * y(i) * K(i,k)
end
end
while 1%迭代过程
%启发式选点
n1 = 1%初始化,n1,n2代表选择的2个点
n2 = 2
%n1按照第一个违反KKT条件的点选择
while n1 <= n
if y(n1) * (sum(n1) + b) == 1 &&a(n1) >= C &&a(n1) <= 0
break
end
if y(n1) * (sum(n1) + b) >1 &&a(n1) ~= 0
break
end
if y(n1) * (sum(n1) + b) <1 &&a(n1) ~=C
break
end
n1 = n1 + 1
end
%n2按照最大化|E1-E2|的原则选取
E1 = 0
E2 = 0
maxDiff = 0%假设的最大误差
E1 = sum(n1) + b - y(n1)%n1的误差
for i = 1 : n
tempSum = sum(i) + b - y(i)
if abs(E1 - tempSum)>maxDiff
maxDiff = abs(E1 - tempSum)
n2 = i
E2 = tempSum
end
end
%以下进行更新
a1old = a(n1)
a2old = a(n2)
KK = K(n1,n1) + K(n2,n2) - 2*K(n1,n2)
a2new = a2old + y(n2) *(E1 - E2) / KK%计算新的a2
%a2必须满足约束条件
S = y(n1) * y(n2)
if S == -1
U = max(0,a2old - a1old)
V = min(C,C - a1old + a2old)
else
U = max(0,a1old + a2old - C)
V = min(C,a1old + a2old)
end
if a2new >V
a2new = V
end
if a2new <U
a2new = U
end
a1new = a1old + S * (a2old - a2new)%计算新的a1
a(n1) = a1new%更新a
a(n2) = a2new
%更新部分值
sum = zeros(n,1)
for k = 1 : n
for i = 1 : n
sum(k) = sum(k) + a(i) * y(i) * K(i,k)
end
end
Wold = Wnew
Wnew = 0%更新a后的W(a)
tempSum = 0%临时变量
for i = 1 : n
for j = 1 : n
tempSum= tempSum + y(i )*y(j)*a(i)*a(j)*K(i,j)
end
Wnew= Wnew+ a(i)
end
Wnew= Wnew - 0.5 * tempSum
%以下更新b:通过找到某一个支持向量来计算
support = 1%支持向量坐标初始化
while abs(a(support))<1e-4 &&support <= n
support = support + 1
end
b = 1 / y(support) - sum(support)
%判断停止条件
if abs(Wnew/ Wold - 1 ) <= TOL
break
end
end
%输出结果:包括原分类,辨别函数计算结果,svm分类结果
for i = 1 : n
fprintf('第%d点:原标号 ',i)
if i <= 50
fprintf('-1')
else
fprintf(' 1')
end
fprintf('判别函数值%f 分类结果',sum(i) + b)
if abs(sum(i) + b - 1) <0.5
fprintf('1\n')
else if abs(sum(i) + b + 1) <0.5
fprintf('-1\n')
else
fprintf('归类错误\n')
end
end
end
2.名为f的功能函数部分:
function y = k(x1,x2)
y = exp(-0.5*norm(x1 - x2).^2)
end
3.数据:
0.8871 -0.34918.3376 0
1.25191.20836.5041 0
-1.19251.93381.8790 0
-0.12772.43712.6971 0
1.96973.09066.0391 0
0.76030.82411.5323 0
1.63823.55164.4694 0
1.3438 -0.45395.9366 0
-1.3361 -2.02011.6393 0
-0.38863.30418.0450 0
-0.67806.0196 -0.4084 0
0.3552 -0.10511.2458 0
1.65604.07860.8521 0
0.81173.54516.8925 0
1.4773 -1.93403.9256 0
-0.0732 -0.95260.4609 0
0.15214.37112.2600 0
1.48200.74930.3475 0
0.61404.52618.3776 0
0.57213.34603.7853 0
0.52694.14524.3900 0
1.7879 -0.53902.5516 0
0.98855.76250.1832 0
-0.33182.4373 -0.6884 0
1.35785.47093.4302 0
2.7210 -1.12684.7719 0
0.5039 -0.10252.3650 0
1.11071.68853.7650 0
0.78621.35877.3203 0
1.0444 -1.58413.6349 0
1.77951.72764.9847 0
0.67101.4724 -0.5504 0
0.23030.2720 -1.6028 0
1.7089 -1.73994.8882 0
1.00590.55575.1188 0
2.30500.85452.8294 0
1.95550.98980.3501 0
1.71411.54133.8739 0
2.27495.32804.9604 0
1.61710.52703.3826 0
3.6681 -1.84094.8934 0
1.19641.87811.4146 0
0.77882.10480.0380 0
0.79165.09063.8513 0
1.08071.88495.9766 0
0.63402.60303.6940 0
1.9069 -0.06097.4208 0
1.65994.94098.1108 0
1.37630.88993.9069 0
0.84851.46886.7393 0
3.67926.10924.9051 1
4.38127.21486.1211 1
4.39713.41397.7974 1
5.07167.7253 10.5373 1
5.30788.81386.1682 1
4.14485.51562.8731 1
5.36096.04584.0815 1
4.74526.63521.3689 1
6.02746.5397 -1.9120 1
5.31743.01346.7935 1
7.24593.69703.1246 1
6.10078.10875.5568 1
5.99246.92385.7938 1
6.02635.33337.5185 1
3.64708.09156.4713 1
3.65437.22647.5783 1
5.01146.53353.5229 1
4.43487.4379 -0.0292 1
3.60873.73513.0172 1
3.53745.53547.6578 1
6.00482.0691 10.4513 1
3.14234.00035.4994 1
3.40127.15368.3510 1
5.54715.1372 -1.5090 1
6.50895.49118.0468 1
5.45836.76745.9353 1
4.17272.97983.6027 1
5.16728.41364.8621 1
4.88083.55141.9953 1
5.49384.19983.2440 1
5.45425.88034.4269 1
4.87433.96418.1417 1
5.97626.77112.3816 1
6.69457.28581.8942 1
4.73015.76521.6608 1
4.70845.36233.2596 1
6.04083.31387.7876 1
4.60248.35170.2193 1
4.70546.6633 -0.3492 1
4.71395.63626.2330 1
4.0850 10.71183.3541 1
6.10886.16354.2292 1
4.98365.40426.7422 1
6.13876.19492.5614 1
6.07007.03733.3256 1
5.68815.13639.9254 1
7.20582.35704.7361 1
4.29727.32454.7928 1
4.77948.12353.1827 1
3.92826.4092 -0.6339 1
matlab如何svm实现图像分类输出像素点,这里分享下 *** 作方法。
设备:华硕笔记本
系统:win10
软件:matlab2012
1、首先双击桌面matlab图标,打开matlab软件。
2、然后在打开的软件中,点击左上角新建m文件。
3、然后输入如图所示的代码。
4、最后点击运行程序,即可输出两幅图像像素点。
欢迎分享,转载请注明来源:内存溢出
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