<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[L_F0724的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/L_F0724</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; L_F0724]]></copyright><item><title><![CDATA[自定义数据集 使用scikit-learn中svm的包实现svm分类]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145443726</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145443726</guid><author>L_F0724</author><pubDate>Tue, 04 Feb 2025 19:36:07 +0800</pubDate><description><![CDATA[【代码】自定义数据集 使用scikit-learn中svm的包实现svm分类。]]></description><category></category></item><item><title><![CDATA[自定义数据集，使用scikit-learn 中K均值包 进行聚类]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145443582</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145443582</guid><author>L_F0724</author><pubDate>Tue, 04 Feb 2025 19:31:21 +0800</pubDate><description><![CDATA[【代码】自定义数据集，使用scikit-learn 中K均值包 进行聚类。]]></description><category></category></item><item><title><![CDATA[自定义数据集 ，使用朴素贝叶斯对其进行分类]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145431662</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145431662</guid><author>L_F0724</author><pubDate>Mon, 03 Feb 2025 17:37:09 +0800</pubDate><description><![CDATA[【代码】自定义数据集 ，使用朴素贝叶斯对其进行分类。]]></description><category></category></item><item><title><![CDATA[自定义数据集 使用pytorch框架实现逻辑回归并保存模型，然后保存模型后再加载模型进行预测，对预测结果计算精确度和召回率及F1分数]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145431597</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145431597</guid><author>L_F0724</author><pubDate>Mon, 03 Feb 2025 17:30:48 +0800</pubDate><description><![CDATA[【代码】【无标题】]]></description><category></category></item><item><title><![CDATA[自定义数据集 使用paddlepaddle框架实现逻辑回归]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145431457</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145431457</guid><author>L_F0724</author><pubDate>Mon, 03 Feb 2025 17:15:21 +0800</pubDate><description><![CDATA[【代码】【无标题】]]></description><category></category></item><item><title><![CDATA[自定义数据集 使用tensorflow框架实现逻辑回归]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145430192</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145430192</guid><author>L_F0724</author><pubDate>Mon, 03 Feb 2025 17:03:23 +0800</pubDate><description><![CDATA[【代码】自定义数据集 使用tensorflow框架实现逻辑回归。]]></description><category></category></item><item><title><![CDATA[自定义数据集 使用pytorch框架实现逻辑回归]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145421112</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145421112</guid><author>L_F0724</author><pubDate>Sun, 02 Feb 2025 17:25:29 +0800</pubDate><description><![CDATA[结果会生成名为model_pth和entire_model.pth的模型文件，在后续实验中可以直接调用。]]></description><category></category></item><item><title><![CDATA[自定义数据集使用框架的线性回归方法对其进行拟合]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145357976</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145357976</guid><author>L_F0724</author><pubDate>Sat, 25 Jan 2025 19:03:12 +0800</pubDate><description><![CDATA[【代码】自定义数据集使用框架的线性回归方法对其进行拟合。]]></description><category></category></item><item><title><![CDATA[自定义数据集使用scikit-learn中的包实现线性回归方法对其进行拟合]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145346142</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145346142</guid><author>L_F0724</author><pubDate>Fri, 24 Jan 2025 20:27:31 +0800</pubDate><description><![CDATA[创建自定义数据集# 假设我们有一个简单的线性关系 y = 2x + 1，并添加一些随机噪声np.random.seed(42) # 为了结果的可重复性设置随机种子X = 2 * np.random.rand(100, 1) # 100个样本，每个样本1个特征（随机生成在0到2之间的数）y = 4 + 3 * X + np.random.randn(100, 1) # 目标变量，添加了一些随机噪声。]]></description><category></category></item><item><title><![CDATA[使用scikit-learn中的KNN包实现对鸢尾花数据集的预测。]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/145323164</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/145323164</guid><author>L_F0724</author><pubDate>Thu, 23 Jan 2025 14:38:05 +0800</pubDate><description><![CDATA[【代码】使用scikit-learn中的KNN包实现对鸢尾花数据集的预测。]]></description><category></category></item><item><title><![CDATA[Pyqt+Opencv的练习]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144782494</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144782494</guid><author>L_F0724</author><pubDate>Sat, 28 Dec 2024 10:50:46 +0800</pubDate><description><![CDATA[创建一个 PyQt 应用程序，该应用程序能够：1.使用 OpenCV 加载一张图像。2.在 PyQt 的窗口中显示这张图像。4.当用户点击按钮时，相应地更新窗口中显示的图像。QWidgetQLabelQLabelQLabelcv2filepathflagssrccodesrcflipCodePyQt6ui_filedatawidthheightformatimagepixmap效果展示从左到右，从上到下依次为：转灰度图，水平翻转，图像旋转，恢复原状。]]></description><category></category></item><item><title><![CDATA[Python中opencv的一些函数及应用]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144491998</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144491998</guid><author>L_F0724</author><pubDate>Sun, 15 Dec 2024 20:39:27 +0800</pubDate><description><![CDATA[Sobel 算子用于计算图像的梯度（变化率），常用于边缘检测。它通过对图像应用一个基于一阶导数的滤波器来强调图像中的边缘部分，特别是水平和垂直方向上的边缘。通过计算图像的梯度，可以获得图像中亮度变化较大的地方，这些地方通常是物体的边界。]]></description><category></category></item><item><title><![CDATA[opencv-python的简单练习]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144467165</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144467165</guid><author>L_F0724</author><pubDate>Sun, 15 Dec 2024 20:20:19 +0800</pubDate><description><![CDATA[【代码】opencv-python的简单练习。]]></description><category></category></item><item><title><![CDATA[python图像处理]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144433704</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144433704</guid><author>L_F0724</author><pubDate>Thu, 12 Dec 2024 20:16:10 +0800</pubDate><description><![CDATA[图像透视变换（Perspective Transformation）是一种通过数学方法将图像中的点集映射到一个新的点集上的技术。它能够对图像进行几何变换，常用于将不规则形状的区域转换为规则形状，或修正图像中的透视失真。透视变换的核心在于透视矩阵的计算。通过一组源图像中的四个点（通常是矩形的四个角）和目标图像中的四个点，我们可以得到一个透视变换矩阵，然后应用该矩阵到原始图像中，从而得到变换后的图像。]]></description><category></category></item><item><title><![CDATA[Python机器视觉的学习]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144408023</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144408023</guid><author>L_F0724</author><pubDate>Wed, 11 Dec 2024 20:55:06 +0800</pubDate><description><![CDATA[二值化图：就是将图像中的像素改成只有两种值，其操作的图像必须是灰度图。]]></description><category></category></item><item><title><![CDATA[python事件机制]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144380752</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144380752</guid><author>L_F0724</author><pubDate>Tue, 10 Dec 2024 18:50:00 +0800</pubDate><description><![CDATA[输入时间，当设置的时间与电脑时间相等时，播报下面的文本内容。]]></description><category></category></item><item><title><![CDATA[PythonQt练习]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144249265</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144249265</guid><author>L_F0724</author><pubDate>Wed, 04 Dec 2024 20:50:49 +0800</pubDate><description><![CDATA[登录成功的页面目前留白，没有搭建任何内容。下面是一个简单登录界面的搭建。调用的主页面搭建如下。]]></description><category></category></item><item><title><![CDATA[PyQt6 开发基础]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144223584</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144223584</guid><author>L_F0724</author><pubDate>Tue, 03 Dec 2024 20:57:31 +0800</pubDate><description><![CDATA[【代码】PyQt6 开发基础。]]></description><category></category></item><item><title><![CDATA[PyQt的学习]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144198820</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144198820</guid><author>L_F0724</author><pubDate>Mon, 02 Dec 2024 21:16:24 +0800</pubDate><description><![CDATA[【代码】PyQt的学习。]]></description><category></category></item><item><title><![CDATA[python算法的学习]]></title><link>https://blog.youkuaiyun.com/L_F0724/article/details/144093731</link><guid>https://blog.youkuaiyun.com/L_F0724/article/details/144093731</guid><author>L_F0724</author><pubDate>Wed, 27 Nov 2024 20:34:20 +0800</pubDate><description><![CDATA[算法就是计算机解决问题的方法或者步骤程序 = 数据结构 + 算法。]]></description><category></category></item></channel></rss>