c语言写出SOFM神经网络
时间: 2023-06-21 09:13:58 浏览: 65
SOFM神经网络是一种自组织特征映射神经网络,它可以用来对高维数据进行降维和聚类。下面是一个用C语言实现SOFM神经网络的示例代码:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define ROWS 10
#define COLS 10
#define INPUT_DIM 2
#define LEARNING_RATE 0.1
#define NUM_EPOCHS 100
float input[ROWS][COLS][INPUT_DIM];
float weights[ROWS][COLS][INPUT_DIM];
void init_network() {
int i, j, k;
for (i = 0; i < ROWS; i++) {
for (j = 0; j < COLS; j++) {
for (k = 0; k < INPUT_DIM; k++) {
weights[i][j][k] = rand() / (float)RAND_MAX;
}
}
}
}
void update_weights(float* input_vec, int row, int col) {
int i, j, k;
float dist, dist2, influence;
for (i = 0; i < ROWS; i++) {
for (j = 0; j < COLS; j++) {
dist2 = 0;
for (k = 0; k < INPUT_DIM; k++) {
dist2 += powf(input_vec[k] - weights[i][j][k], 2);
}
dist = sqrtf(dist2);
influence = expf(-dist / LEARNING_RATE);
for (k = 0; k < INPUT_DIM; k++) {
weights[i][j][k] += influence * (input_vec[k] - weights[i][j][k]);
}
}
}
}
void train_network(float input_data[][INPUT_DIM], int num_samples) {
int epoch, i, j;
for (epoch = 0; epoch < NUM_EPOCHS; epoch++) {
for (i = 0; i < num_samples; i++) {
float* input_vec = input_data[i];
int best_row = 0, best_col = 0;
float best_dist = INFINITY;
for (j = 0; j < ROWS * COLS; j++) {
int row = j / COLS, col = j % COLS;
float dist2 = 0;
int k;
for (k = 0; k < INPUT_DIM; k++) {
dist2 += powf(input_vec[k] - weights[row][col][k], 2);
}
float dist = sqrtf(dist2);
if (dist < best_dist) {
best_row = row;
best_col = col;
best_dist = dist;
}
}
update_weights(input_vec, best_row, best_col);
}
LEARNING_RATE *= 0.5;
}
}
int main() {
init_network();
float input_data[][INPUT_DIM] = {
{0.1, 0.1},
{0.2, 0.2},
{0.3, 0.3},
{0.4, 0.4},
{0.5, 0.5},
{0.6, 0.6},
{0.7, 0.7},
{0.8, 0.8},
{0.9, 0.9},
{1.0, 1.0}
};
train_network(input_data, ROWS * COLS);
int i, j;
for (i = 0; i < ROWS; i++) {
for (j = 0; j < COLS; j++) {
printf("(%f, %f) ", weights[i][j][0], weights[i][j][1]);
}
printf("\n");
}
return 0;
}
```
这个示例代码实现了一个10x10的SOFM神经网络,输入向量的维度为2。在`main()`函数中,我们定义了一个包含10个样本、每个样本有2个特征的数据集,并对网络进行了训练。在训练结束后,打印出了每个神经元的权重。