我用zephyr写程序的时候遇到问题,printf无法输出浮点数
时间: 2023-06-12 08:07:57 浏览: 125
Zephyr是一个嵌入式操作系统,它的printf函数默认是不支持浮点数输出的。但是你可以使用以下几种方法来解决这个问题:
1. 使用C标准库的sprintf函数来格式化浮点数输出到一个字符串中,然后使用puts函数输出这个字符串。例如:
```c
char buf[32];
sprintf(buf, "%f", 3.14);
puts(buf);
```
2. 使用第三方的printf库,例如Newlib或者µClibc++,它们支持浮点数输出。你需要在编译时链接这些库,并且在代码中包含它们的头文件。例如:
```c
#include <stdio.h>
#include <stdlib.h>
#include <newlib.h>
float f = 3.14;
printf("%f", f);
```
3. 如果你的Zephyr版本支持C11标准,你可以使用%F格式化符号来输出浮点数。例如:
```c
float f = 3.14;
printf("%F", f);
```
这些方法都可以解决在Zephyr中无法输出浮点数的问题,你可以选择适合自己的方法。
相关问题
我用zephyr开发esp32C3,帮我写一段输出PPM信号的代码
好的,以下是一份简单的代码示例,可以用于在 ESP32C3 上生成 PPM 信号:
```c
#include <zephyr.h>
#include <device.h>
#include <drivers/gpio.h>
#include <drivers/pwm.h>
// PPM signal properties
#define PPM_FRAME_LENGTH 22500 // Length of one PPM frame in microseconds
#define PPM_SYNC_PULSE_LENGTH 3000 // Length of sync pulse in microseconds
#define PPM_CHANNEL_COUNT 8 // Number of PPM channels
#define PPM_CHANNEL_OFFSET 300 // Time offset between channels in microseconds
// Pin configuration
#define PPM_PIN DT_ALIAS_PWM_0_GPIOS_PIN
void main(void) {
struct device *pwm_dev;
int ret;
// Open PWM device
pwm_dev = device_get_binding(DT_ALIAS_PWM_0_LABEL);
if (!pwm_dev) {
printk("Failed to get PWM device\n");
return;
}
// Configure PWM device
ret = pwm_pin_set_usec(pwm_dev, PPM_PIN, PPM_FRAME_LENGTH, 0);
if (ret) {
printk("Failed to configure PWM device\n");
return;
}
// Generate PPM signal
while (1) {
// Start of PPM frame
pwm_pin_set_usec(pwm_dev, PPM_PIN, PPM_SYNC_PULSE_LENGTH, PPM_FRAME_LENGTH);
// Generate PPM channels
for (int i = 0; i < PPM_CHANNEL_COUNT; i++) {
// TODO: Calculate PPM channel value
int channel_value_us = 1500;
pwm_pin_set_usec(pwm_dev, PPM_PIN, channel_value_us, PPM_CHANNEL_OFFSET);
}
// Wait for end of PPM frame
k_sleep(K_MSEC(20));
}
}
```
请注意,此代码示例仅演示了如何使用 Zephyr 和 ESP32C3 的 PWM 驱动程序生成 PPM 信号。您需要根据您的具体需求修改代码以生成正确的 PPM 信号。例如,您需要计算每个 PPM 通道的值,并在适当的时间设置 PWM 输出。
帮我写一个SAKT模型代码用paddle实现
import paddle
import paddle.nn as nn
class SAKTModel(nn.Layer):
def __init__(self, num_items, num_skills, hidden_dim):
super(SAKTModel, self).__init__()
self.embedding = nn.Embedding(num_items, hidden_dim)
self.skill_embedding = nn.Embedding(num_skills, hidden_dim)
self.transformer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=8)
self.fc = nn.Linear(hidden_dim, 1)
def forward(self, question_ids, skill_ids):
question_emb = self.embedding(question_ids)
skill_emb = self.skill_embedding(skill_ids)
input_emb = question_emb + skill_emb
output = self.transformer(input_emb)
logits = self.fc(output)
return logits.squeeze(-1)
# 定义模型的输入和输出维度
num_items = 1000 # 假设有1000个题目
num_skills = 50 # 假设有50个知识点
hidden_dim = 128
# 创建SAKT模型实例
sakt_model = SAKTModel(num_items, num_skills, hidden_dim)
# 定义损失函数和优化器
loss_fn = nn.BCEWithLogitsLoss()
optimizer = paddle.optimizer.Adam(learning_rate=0.001, parameters=sakt_model.parameters())
# 假设有训练数据和标签,训练模型
train_data = paddle.to_tensor(train_data)
train_labels = paddle.to_tensor(train_labels)
for epoch in range(num_epochs):
# 前向传播
logits = sakt_model(train_data)
# 计算损失
loss = loss_fn(logits, train_labels)
# 反向传播
loss.backward()
# 更新参数
optimizer.step()
optimizer.clear_grad()
# 使用训练好的模型进行预测
test_data = paddle.to_tensor(test_data)
logits = sakt_model(test_data)
predictions = paddle.where(logits > 0, paddle.ones_like(logits), paddle.zeros_like(logits))
# 输出预测结果
print(predictions)<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* [SAKT:self attentive knowledge tracing知识跟踪模型](https://blog.csdn.net/zephyr_wang/article/details/111828319)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
- *3* [SAKT:pytorch实施的“知识跟踪的自我专注模型”](https://download.csdn.net/download/weixin_42135753/16717758)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_2"}}] [.reference_item style="max-width: 50%"]
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