<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[aa_moon的专栏]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/aa_moon</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; aa_moon]]></copyright><item><title><![CDATA[yolov8 识别-转]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/152129515</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/152129515</guid><author>aa_moon</author><pubDate>Fri, 26 Sep 2025 16:55:58 +0800</pubDate><description><![CDATA[阿_旭-优快云博客]]></description><category></category></item><item><title><![CDATA[240903python 操作excel-读取head和内容]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141874872</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141874872</guid><author>aa_moon</author><pubDate>Wed, 04 Sep 2024 00:02:55 +0800</pubDate><description><![CDATA[import pandas as pdCSV_PATH = 'magic.csv'
df = pd.read_csv(CSV_PATH)
print(df.columns)
print(df.T)]]></description><category></category></item><item><title><![CDATA[240729 递归神经网络]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141608101</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141608101</guid><author>aa_moon</author><pubDate>Tue, 27 Aug 2024 17:23:20 +0800</pubDate><description><![CDATA[在多个尺度上进行测试。# 创建2层的神经网络。# 训练递归神经网络。# 使用网格画出输出结果。]]></description><category></category></item><item><title><![CDATA[240727 深度神经网络]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141607871</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141607871</guid><author>aa_moon</author><pubDate>Tue, 27 Aug 2024 17:15:26 +0800</pubDate><description><![CDATA[定义深度神经网络 每个隐藏层有10个神经元。红色是实际数据，绿色是预测的点。# 设置训练算法为梯度下降。]]></description><category></category></item><item><title><![CDATA[240726 sift特征点检测]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141605706</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141605706</guid><author>aa_moon</author><pubDate>Tue, 27 Aug 2024 16:40:01 +0800</pubDate><description><![CDATA[import sysimport cv2# 加载数据# 提取关键点# 画出特征点# 展示。]]></description><category></category></item><item><title><![CDATA[240725 显示棱角点]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141605506</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141605506</guid><author>aa_moon</author><pubDate>Tue, 27 Aug 2024 16:07:47 +0800</pubDate><description><![CDATA[Harris 角点检测。]]></description><category></category></item><item><title><![CDATA[240724 均衡化直方图]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141604901</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141604901</guid><author>aa_moon</author><pubDate>Tue, 27 Aug 2024 15:50:32 +0800</pubDate><description><![CDATA[U 和 V 表示色度（Chrominance、缩写Chroma），即为色调和饱和度。# y通道 - Y 表示亮度（Luminance、缩写Luma），即为灰度值。]]></description><category></category></item><item><title><![CDATA[240723 边缘检测]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141599585</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141599585</guid><author>aa_moon</author><pubDate>Tue, 27 Aug 2024 12:16:14 +0800</pubDate><description><![CDATA[索贝尔边缘检测-水平。# 索贝尔边缘检测-垂直。# canny边缘检测。拉普拉斯和canny。# 拉普拉斯边缘检测。]]></description><category></category></item><item><title><![CDATA[240826 计算电影推荐]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141573077</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141573077</guid><author>aa_moon</author><pubDate>Mon, 26 Aug 2024 20:55:10 +0800</pubDate><description><![CDATA[通过前面几天的knn相关知识，来计算电影推荐。# 根据皮尔逊进行排序。# 生成电影评分列表。# 为给定用户生成电影推荐。# 打印推荐电影列表。]]></description><category></category></item><item><title><![CDATA[240723 knn电影推荐]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141573007</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141573007</guid><author>aa_moon</author><pubDate>Mon, 26 Aug 2024 20:49:08 +0800</pubDate><description><![CDATA[通过前面两篇，所以对某一个人A推荐电影，就是找到这个人最类似的人B已经看过的电影，然后将电影推荐A。# 直接加载得分数据。]]></description><category></category></item><item><title><![CDATA[240722 knn 使用皮尔逊找到相似用户]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141572938</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141572938</guid><author>aa_moon</author><pubDate>Mon, 26 Aug 2024 20:45:23 +0800</pubDate><description><![CDATA[计算所有相同评分电影的评分的平方和。# 提取两个用户都评分过的电影。# 计算相同评分的平方值和。# 都没有评分则是 0。# 计算数据集乘积之和。# 计算皮尔逊相关度。]]></description><category></category></item><item><title><![CDATA[240721 knn 计算用户1和用户2之间的评分-相关度]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141572791</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141572791</guid><author>aa_moon</author><pubDate>Mon, 26 Aug 2024 20:38:10 +0800</pubDate><description><![CDATA[knn通过计算电影相关度，计算用户1和用户2的评分。# 提取用户1和用户2的评论过的电影。# 如果没有评分，得分为0。# 计算欧式距离分数。]]></description><category></category></item><item><title><![CDATA[240720 knn 最近邻]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141572599</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141572599</guid><author>aa_moon</author><pubDate>Mon, 26 Aug 2024 20:27:18 +0800</pubDate><description><![CDATA[用输入数据的10倍设置网格。其中虚线就是拟合后的模型。]]></description><category></category></item><item><title><![CDATA[240719 聚类算法的评价]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141497305</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141497305</guid><author>aa_moon</author><pubDate>Sat, 24 Aug 2024 11:42:57 +0800</pubDate><description><![CDATA[评价kmean在集群的最佳数量， 我们迭代一系列的值， 找出其中的峰值的 性能。度量聚类算法的一个好方法是观察集群被分离的离散程度。]]></description><category></category></item><item><title><![CDATA[240718 均值漂移聚类]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141496347</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141496347</guid><author>aa_moon</author><pubDate>Sat, 24 Aug 2024 11:09:54 +0800</pubDate><description><![CDATA[均值漂移]]></description><category></category></item><item><title><![CDATA[240717 聚类 kmean]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141496121</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141496121</guid><author>aa_moon</author><pubDate>Sat, 24 Aug 2024 10:37:20 +0800</pubDate><description><![CDATA[k-means算法是最流行的聚类算法之一。这个算法常常利用数据的不同属性将输入数据划分。分组是使用最优化的技术实现的， 即让各组内的数据点与该组中心点的距离平方和最小。无监督学习是一种对不含标记的数据建立模型的机器学习范式。]]></description><category></category></item><item><title><![CDATA[240716 svm找到最优参数]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141440849</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141440849</guid><author>aa_moon</author><pubDate>Fri, 23 Aug 2024 00:18:20 +0800</pubDate><description><![CDATA[结果代码# 加载数据# 切分出测试数据# 设置方法#打印结果# 评价分数。]]></description><category></category></item><item><title><![CDATA[240715 svm设置置信度]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141440823</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141440823</guid><author>aa_moon</author><pubDate>Fri, 23 Aug 2024 00:14:42 +0800</pubDate><description><![CDATA[print "\n打印边界距离:"print "\n设置置信度:"]]></description><category></category></item><item><title><![CDATA[240715 分类器- 逻辑回归分类]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141405553</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141405553</guid><author>aa_moon</author><pubDate>Thu, 22 Aug 2024 00:13:23 +0800</pubDate><description><![CDATA[定义绘制图形的范围。]]></description><category></category></item><item><title><![CDATA[240714 分类器 - 贝叶斯分类]]></title><link>https://blog.youkuaiyun.com/aa_moon/article/details/141405523</link><guid>https://blog.youkuaiyun.com/aa_moon/article/details/141405523</guid><author>aa_moon</author><pubDate>Thu, 22 Aug 2024 00:09:42 +0800</pubDate><description><![CDATA[结果代码X = []y = []# 计算训练# 分割测试数据# 计算指标# 画图。]]></description><category></category></item></channel></rss>