写一个FT64F0A3的低电压检测代码C语言
时间: 2023-04-06 07:01:56 浏览: 109
好的,我可以回答这个问题。以下是FT64F0A3的低电压检测代码C语言:
#include <stdio.h>
#include <stdlib.h>
#include <ft64f0a3.h>
int main()
{
// 初始化FT64F0A3
FT64F0A3_Init();
// 设置低电压检测阈值
FT64F0A3_LVD_SetThreshold(FT64F0A3_LVD_THRESHOLD_2V0);
// 启用低电压检测
FT64F0A3_LVD_Enable();
// 检测低电压状态
if (FT64F0A3_LVD_GetStatus() == FT64F0A3_LVD_STATUS_LOW)
{
printf("低电压状态\n");
}
else
{
printf("正常电压状态\n");
}
return 0;
}
相关问题
用c语言写一个lstm预测的代码
LSTM(Long Short-Term Memory)是一种循环神经网络(RNN)的变体,常用于处理序列数据。下面是一个简单的C语言LSTM模型的代码示例,可以用于预测未来的序列数据。
首先,我们需要定义一些常量和超参数:
```c
#define INPUT_SIZE 1
#define OUTPUT_SIZE 1
#define HIDDEN_SIZE 16
#define LEARNING_RATE 0.1
#define EPOCHS 1000
```
这里我们设置输入大小为1,输出大小为1,隐层大小为16,学习率为0.1,迭代次数为1000。
接下来,定义LSTM结构体:
```c
typedef struct {
double Wf[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
double Wi[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
double Wc[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
double Wo[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
double bf[HIDDEN_SIZE];
double bi[HIDDEN_SIZE];
double bc[HIDDEN_SIZE];
double bo[HIDDEN_SIZE];
double ct[HIDDEN_SIZE];
double ht[HIDDEN_SIZE];
} LSTM;
```
其中,Wf、Wi、Wc、Wo是输入、遗忘、细胞状态和输出门的权重矩阵;bf、bi、bc、bo是对应的偏置向量;ct和ht为细胞状态和隐层状态。
接下来,定义一些辅助函数:
```c
double sigmoid(double x) {
return 1.0 / (1.0 + exp(-x));
}
double tanh(double x) {
return (exp(x) - exp(-x)) / (exp(x) + exp(-x));
}
```
这里用到了sigmoid函数和tanh函数。
接下来,定义前向传播函数:
```c
void forward(LSTM *lstm, double *x) {
double xf[INPUT_SIZE + HIDDEN_SIZE];
for (int i = 0; i < INPUT_SIZE; i++)
xf[i] = x[i];
for (int i = 0; i < HIDDEN_SIZE; i++)
xf[INPUT_SIZE + i] = lstm->ht[i];
double ft[HIDDEN_SIZE], it[HIDDEN_SIZE], ct[HIDDEN_SIZE], ot[HIDDEN_SIZE], ct_new[HIDDEN_SIZE], ht_new[HIDDEN_SIZE];
for (int i = 0; i < HIDDEN_SIZE; i++) {
ft[i] = sigmoid(dot(lstm->Wf[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bf[i]);
it[i] = sigmoid(dot(lstm->Wi[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bi[i]);
ct[i] = tanh(dot(lstm->Wc[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bc[i]);
ct_new[i] = ft[i] * lstm->ct[i] + it[i] * ct[i];
ot[i] = sigmoid(dot(lstm->Wo[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bo[i]);
ht_new[i] = ot[i] * tanh(ct_new[i]);
}
for (int i = 0; i < HIDDEN_SIZE; i++) {
lstm->ct[i] = ct_new[i];
lstm->ht[i] = ht_new[i];
}
}
```
这里首先将输入和隐层状态串联起来形成一个新的向量xf。然后,根据LSTM的结构,分别计算输入门、遗忘门、细胞状态和输出门的值,最终得到新的细胞状态ct_new和隐层状态ht_new。
接下来,定义反向传播函数:
```c
void backward(LSTM *lstm, double *x, double *y) {
double xf[INPUT_SIZE + HIDDEN_SIZE];
for (int i = 0; i < INPUT_SIZE; i++)
xf[i] = x[i];
for (int i = 0; i < HIDDEN_SIZE; i++)
xf[INPUT_SIZE + i] = lstm->ht[i];
double ft[HIDDEN_SIZE], it[HIDDEN_SIZE], ct[HIDDEN_SIZE], ot[HIDDEN_SIZE], ct_new[HIDDEN_SIZE], ht_new[HIDDEN_SIZE];
double dht[HIDDEN_SIZE], dct[HIDDEN_SIZE];
double dWf[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE], dWi[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE], dWc[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE], dWo[HIDDEN_SIZE][INPUT_SIZE + HIDDEN_SIZE];
double dbf[HIDDEN_SIZE], dbi[HIDDEN_SIZE], dbc[HIDDEN_SIZE], dbo[HIDDEN_SIZE];
for (int i = 0; i < HIDDEN_SIZE; i++) {
ft[i] = sigmoid(dot(lstm->Wf[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bf[i]);
it[i] = sigmoid(dot(lstm->Wi[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bi[i]);
ct[i] = tanh(dot(lstm->Wc[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bc[i]);
ct_new[i] = ft[i] * lstm->ct[i] + it[i] * ct[i];
ot[i] = sigmoid(dot(lstm->Wo[i], xf, INPUT_SIZE + HIDDEN_SIZE) + lstm->bo[i]);
ht_new[i] = ot[i] * tanh(ct_new[i]);
}
for (int i = 0; i < OUTPUT_SIZE; i++) {
double dht_total = 0;
for (int j = 0; j < HIDDEN_SIZE; j++)
dht_total += (ht_new[j] - y[i]) * ot[j] * (1 - tanh(ct_new[j]) * tanh(ct_new[j])) * lstm->Wc[j][INPUT_SIZE + i];
dht[i] = dht_total;
}
for (int i = 0; i < HIDDEN_SIZE; i++) {
double dct_total = 0;
for (int j = 0; j < OUTPUT_SIZE; j++)
dct_total += (ht_new[i] - y[j]) * ot[i] * (1 - tanh(ct_new[i]) * tanh(ct_new[i])) * lstm->Wo[i][INPUT_SIZE + j];
for (int j = 0; j < HIDDEN_SIZE; j++)
dct_total += dct[j] * ft[i] * lstm->Wf[i][INPUT_SIZE + j];
dct[i] = dct_total;
}
for (int i = 0; i < HIDDEN_SIZE; i++) {
for (int j = 0; j < INPUT_SIZE + HIDDEN_SIZE; j++) {
dWf[i][j] = dct[i] * lstm->ct[i] * ft[i] * (1 - ft[i]) * xf[j];
dWi[i][j] = dct[i] * ct[i] * it[i] * (1 - it[i]) * xf[j];
dWc[i][j] = dct[i] * it[i] * (1 - ct[i] * ct[i]) * xf[j];
dWo[i][j] = dht[i] * tanh(ct_new[i]) * ot[i] * (1 - ot[i]) * xf[j];
}
dbf[i] = dct[i] * lstm->ct[i] * ft[i] * (1 - ft[i]);
dbi[i] = dct[i] * ct[i] * it[i] * (1 - it[i]);
dbc[i] = dct[i] * it[i] * (1 - ct[i] * ct[i]);
dbo[i] = dht[i] * tanh(ct_new[i]) * ot[i] * (1 - ot[i]);
}
for (int i = 0; i < HIDDEN_SIZE; i++) {
for (int j = 0; j < INPUT_SIZE + HIDDEN_SIZE; j++) {
lstm->Wf[i][j] -= LEARNING_RATE * dWf[i][j];
lstm->Wi[i][j] -= LEARNING_RATE * dWi[i][j];
lstm->Wc[i][j] -= LEARNING_RATE * dWc[i][j];
lstm->Wo[i][j] -= LEARNING_RATE * dWo[i][j];
}
lstm->bf[i] -= LEARNING_RATE * dbf[i];
lstm->bi[i] -= LEARNING_RATE * dbi[i];
lstm->bc[i] -= LEARNING_RATE * dbc[i];
lstm->bo[i] -= LEARNING_RATE * dbo[i];
}
}
```
这里首先进行一次前向传播,然后计算输出误差对隐层状态和细胞状态的偏导数。接下来,根据公式计算各个权重的偏导数和偏置的偏导数,并更新它们的值。
最后,定义训练函数:
```c
void train(double *input, double *output) {
LSTM lstm;
init(&lstm);
for (int epoch = 0; epoch < EPOCHS; epoch++) {
for (int i = 0; i < INPUT_SIZE; i++) {
double x[INPUT_SIZE], y[OUTPUT_SIZE];
x[0] = input[i];
y[0] = output[i];
forward(&lstm, x);
backward(&lstm, x, y);
}
}
}
```
这里我们首先初始化LSTM模型,然后进行多次迭代,每次迭代都遍历整个输入序列,对每个输入输出对进行训练。
这就是一个简单的C语言LSTM模型的代码示例。需要注意的是,这个模型只处理输入大小为1的序列,如果需要处理更长的序列,需要相应地调整模型的参数。
用Python语言写一个FT算法程序
好的,以下是一个Python实现的FT算法程序:
```python
import math
def FT(x):
N = len(x)
if N == 1:
return x
else:
X_even = FT(x[0::2])
X_odd = FT(x[1::2])
X = [0] * N
for k in range(N//2):
X[k] = X_even[k] + X_odd[k] * math.e**(-2*math.pi*1j*k/N)
X[k+N//2] = X_even[k] - X_odd[k] * math.e**(-2*math.pi*1j*k/N)
return X
```
其中,这个FT函数实现了递归计算FFT,并返回FFT计算结果。输入参数x是一个长度为N的实数序列,输出结果是一个长度为N的复数序列。在这个函数中,我们使用了Python中的切片操作,`x[0::2]`表示从序列x中取出下标为偶数的元素,而`x[1::2]`则表示从序列x中取出下标为奇数的元素。
这个程序的时间复杂度为O(NlogN)。