forked from viccwq/ANN
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathperceptron.cpp
More file actions
169 lines (154 loc) · 3.36 KB
/
perceptron.cpp
File metadata and controls
169 lines (154 loc) · 3.36 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
#include "comm_def_col.h"
//PerceptronLearning Algorithm(PLA)
const double alpha = 1.5; //学习参数
#define COUNT 50
#define HEIGHT 512
#define WIDTH 512
#define RANDOM_X (rand() % WIDTH) //通过取余取得指定范围的随机数
#define RANDOM_Y (rand() % HEIGHT) //通过取余取得指定范围的随机数
CvMat *mat_point = NULL;
CvMat *mat_weight = NULL;
int init_data()
{
int i, j;
int *data;
//初始化样本
mat_point = cvCreateMat(COUNT, 4, CV_32SC1);
cvSetZero(mat_point);
srand(time(NULL));
for (i = 0; i< mat_point->height; i++)
{
data = (int *)(mat_point->data.ptr + i * mat_point->step);
data[0] = RANDOM_X;
data[1] = RANDOM_Y;
data[2] = 1;
if (data[0] < 200)
data[3] = 1;
else
data[3] = -1;
}
//初始化权值
mat_weight = cvCreateMat(1, 4, CV_64FC1);
cvSetZero(mat_weight);
return 0;
}
int release_data()
{
cvReleaseMat(&mat_point);
cvReleaseMat(&mat_weight);
return 0;
}
int show_update(int delay)
{
int i;
int x1 = 0, y1 = 0, x2 = 0, y2 = 0;
double x_temp;
int *point;
double *weight;
IplImage *p_image = cvCreateImage(cvSize(WIDTH, HEIGHT), IPL_DEPTH_8U, 3);
cvZero(p_image);
//绘制样本点
CvScalar scalar = cvScalar(255, 255, 255, 0);
for (i = 0; i < COUNT; i++)
{
point = (int *)(mat_point->data.ptr + mat_point->step * i);
if (point[3] == 1)
{
scalar = cvScalar(0, 0, 255, 0);
}
else
scalar = cvScalar(0, 255, 0, 0);
cvLine(p_image, cvPoint(point[0] - 2, point[1]),cvPoint(point[0] + 2, point[1]), scalar, 1);
cvLine(p_image, cvPoint(point[0], point[1] - 2),cvPoint(point[0], point[1] + 2), scalar, 1);
}
//绘制分界面
weight = mat_weight->data.db;
if (weight[0] < -0.0001 || weight[0] > 0.0001)
{
for (y1 = 0; y1 < HEIGHT; y1++)
{
x_temp = (-1 * weight[2] - y1 * weight[1])/weight[0];
if (x_temp >= 0 && x_temp < HEIGHT)
{
x1 = x_temp;
break;
}
}
}
if (x1 < 0 || x1 >= HEIGHT)
{
x1 = 0;
}
if (weight[0] < -0.0001 || weight[0] > 0.0001)
{
for (y2 = HEIGHT -1 ; y2 > 0; y2--)
{
x_temp = (-1 * weight[2] - y2 * weight[1])/weight[0];
if (x_temp >= 0 && x_temp < HEIGHT)
{
x2 = x_temp;
break;
}
}
}
if (x2 < 0 || x2 >= HEIGHT)
{
x2 = 0;
}
scalar = cvScalar(255, 0, 0, 0);
cvLine(p_image, cvPoint(x1, y1),cvPoint(x2, y2), scalar, 2);
//显示
cvNamedWindow("perceptron", 1);
cvShowImage("perceptron", p_image);
cvWaitKey(delay);
cvDestroyWindow("perceptron");
cvReleaseImage(&p_image);
return 0;
}
int compute(int *point, double *weight)
{
double sum =0.0;
int i;
for (i = 0; i < 3; ++i)
{
sum += point[i] * weight[i];
}
if(sum > 0.0)
return 1;
else
return -1;
}
int perceptron()
{
bool bLearningOK = false;
int count = 0;
int *point;
double *weight;
//感知器学习算法
init_data();
while(!bLearningOK)
{
bLearningOK = true;
for (int i = 0 ; i < COUNT ; ++i)
{
point = (int *)(mat_point->data.ptr + mat_point->step * i);
weight = mat_weight->data.db;
//计算输出，比较后矫正权值
int output = compute(point,weight);
if(output!= point[3])
{
for(int w = 0 ; w <3 ; ++w)
{
weight[w] += alpha * point[3] * point[w];
}
bLearningOK = false;
}
}
count++;
// cout<<count<<endl;
// show_update(50);
}
show_update(0);
release_data();
return 0;
}