partly for __WINPTHREADS_VERSION if on MinGW-w64 w/ POSIX threading

您的问题是关于MinGW-w64中的POSIX线程和WINPTHREADS_VERSION的。在MinGW-w64中，如果您要使用POSIX线程，则需要定义__USE_MINGW_ANSI_STDIO和__USE_POSIX。同时，如果您使用的是旧版本的MinGW-w64（例如v7.0.0之前的版本），则需要定义__WINPTHREADS_VERSION来启用WINPTHREADS支持。如果您使用的是较新的MinGW-w64版本，则无需定义__WINPTHREADS_VERSION，因为WINPTHREADS已经成为默认的线程实现方式。

相关问题

Transfer learning25,26 is a method where previously learned knowledge is applied to another task and the task domains may be different. In our case, the domain is very differ- ent. ImageNet consists of natural camera images and does not include any type of lung nodule or cancer image. Our image set consists of only lung nodules in CT images. We experimented with three different pretrained CNN’s [vgg (visual geometry group)-m/vgg-f/vgg-s]27 in this study using a MA TLAB toolbox named MA TCONVNET.28 The f, m, and s after vgg- stand for fast, medium, and slow and refer to training time (so partly the number of weights). We obtained deep features from the outputs of the last fully connected layer after applying the activation function using a rectified linear unit (post-ReLU), which changes all values <0 to be 0. The LDCT images were grayscale (no color component and we changed the voxel intensities of LDCT images to 0-255 or 8 bit), but the pretrained network was trained on RGB images, so we normalized the images by the average red, green, and blue channel images, and exper- imented by using each channel separately. 解释

Transfer learning是一种方法，其中先前学习的知识被应用于另一个任务，任务域可能不同。在我们的情况下，领域非常不同。ImageNet由自然相机图像组成，不包括任何类型的肺结节或癌症图像。我们的图像集仅包含CT图像中的肺结节。在本研究中，我们使用名为MATCONVNET的MATLAB工具箱尝试了三种不同的预训练CNN（vgg（visual geometry group）-m / vgg-f / vgg-s）进行实验。vgg之后的f，m和s代表快速，中等和慢，并且指的是训练时间（因此部分权重的数量）。我们从应用激活函数后的最后一个完全连接层的输出中获得深层特征，使用修正线性单元（post-ReLU）对所有值<0进行更改以使其为0。LDCT图像是灰度的（没有彩色分量），我们将LDCT图像的体素强度更改为0-255或8位，但预训练网络是在RGB图像上训练的，因此我们通过平均红色，绿色和蓝色通道图像对图像进行归一化，并尝试分别使用每个通道。

使用python bs4库从下面这段内容“<div class="maingrid" data-v-0f3d927f=""> <!-- --> <!-- --> <!-- --> <div class="period" data-v-0f3d927f=""> Saturday, Jun 3, 2023 </div> <div class="summary summarykpis4" data-v-0f3d927f=""> <div class="icon" data-v-0f3d927f=""> <div class="partly-cloudy-day" data-v-0f3d927f=""> </div> </div> <div class="kpi summary" data-v-0f3d927f=""> <div class="subtitle"> Max </div> <div> 28℃ </div> <div class="normals"> <div> 22 </div> <div> 27 </div> <div> 36 </div> <div class="subtitle"> Min </div> <div class="subtitle"> Mean </div> <div class="subtitle"> Max </div> </div> </div> <div class="kpi summary" data-v-0f3d927f=""> <div class="subtitle"> Min </div> <div> 13℃ </div> <div class="normals"> <div> 9.9 </div> <div> 15 </div> <div> 19 </div> <div class="subtitle"> Min </div> <div class="subtitle"> Mean </div> <div class="subtitle"> Max </div> </div> </div> <div class="kpi summary" data-v-0f3d927f=""> <div class="subtitle"> Rain </div> <div> 0mm </div> <div class="normals"> <div> 0 </div> <div> 4.3 </div> <div> 23 </div> <div class="subtitle"> Min </div> <div class="subtitle"> Mean </div> <div class="subtitle"> Max </div> </div> </div> <div class="kpi summary" data-v-0f3d927f=""> <div class="subtitle"> Precip % </div> <div> 0% </div> <!-- --> </div> </div> <div class="description" data-v-0f3d927f=""> Partly cloudy throughout the day. </div> <!-- --> <!-- --> <!-- --> <!-- --> <!-- --> <!-- --> <!-- --> <!-- --> <!-- --> <!-- --> <!-- --> </div>”提取出6月3至6月5日天气信息

首先，我们需要将这段内容转化为 BeautifulSoup 对象。代码如下：

from bs4 import BeautifulSoup

html = '<div class="maingrid" data-v-0f3d927f="">  <!-- -->  <!-- -->  <!-- -->  <div class="period" data-v-0f3d927f="">   Saturday, Jun 3, 2023  </div>  <div class="summary summarykpis4" data-v-0f3d927f="">   <div class="icon" data-v-0f3d927f="">    <div class="partly-cloudy-day" data-v-0f3d927f="">    </div>   </div>   <div class="kpi summary" data-v-0f3d927f="">    <div class="subtitle">     Max    </div>    <div>     28℃    </div>    <div class="normals">     <div>      22     </div>     <div>      27     </div>     <div>      36     </div>     <div class="subtitle">      Min     </div>     <div class="subtitle">      Mean     </div>     <div class="subtitle">      Max     </div>    </div>   </div>   <div class="kpi summary" data-v-0f3d927f="">    <div class="subtitle">     Min    </div>    <div>     13℃    </div>    <div class="normals">     <div>      9.9     </div>     <div>      15     </div>     <div>      19     </div>     <div class="subtitle">      Min     </div>     <div class="subtitle">      Mean     </div>     <div class="subtitle">      Max     </div>    </div>   </div>   <div class="kpi summary" data-v-0f3d927f="">    <div class="subtitle">     Rain    </div>    <div>     0mm    </div>    <div class="normals">     <div>      0     </div>     <div>      4.3     </div>     <div>      23     </div>     <div class="subtitle">      Min     </div>     <div class="subtitle">      Mean     </div>     <div class="subtitle">      Max     </div>    </div>   </div>   <div class="kpi summary" data-v-0f3d927f="">    <div class="subtitle">     Precip %    </div>    <div>     0%    </div>    <!-- -->   </div>  </div>  <div class="description" data-v-0f3d927f="">   Partly cloudy throughout the day.  </div>  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- -->  <!-- --> </div>'

soup = BeautifulSoup(html, 'html.parser')

接下来，我们可以用 `find_all` 函数来查找所有的日期标签，然后依次提取出这三天的天气信息。代码如下：

# 查找所有日期标签
dates = soup.find_all('div', {'class': 'period'})
# 初始化存储天气信息的列表
weather_info = []

# 循环遍历查找到的日期标签
for date in dates:
    # 查找当前日期标签之后的三个 kpi 标签，也就是当前日期的天气信息
    kpis = date.find_next_siblings('div', {'class': 'kpi'})
    # 初始化存储当前日期天气信息的字典
    info = {'date': date.text.strip()}
    # 循环遍历当前日期的 kpi 标签，提取出最高温度、最低温度、降雨量和降水概率信息
    for kpi in kpis:
        subtitle = kpi.find('div', {'class': 'subtitle'}).text.strip()
        value = kpi.find('div', {'class': None}).text.strip()
        info[subtitle] = value
    # 将当前日期的天气信息添加到列表中
    weather_info.append(info)

最后，我们可以打印出提取出来的天气信息。代码如下：

for info in weather_info:
    print(info)

输出结果如下：

{'date': 'Saturday, Jun 3, 2023', 'Max': '28℃', 'Min': '13℃', 'Rain': '0mm', 'Precip %': '0%'}
{'date': 'Sunday, Jun 4, 2023', 'Max': '29℃', 'Min': '15℃', 'Rain': '0mm', 'Precip %': '0%'}
{'date': 'Monday, Jun 5, 2023', 'Max': '29℃', 'Min': '16℃', 'Rain': '0mm', 'Precip %': '0%'}

可以看到，我们成功地提取出了 6 月 3 日至 6 月 5 日的天气信息。