颜色分类leetcode-mlrose:用于实现许多机器学习、随机优化和搜索算法的Python包

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颜色分类leetcode mlrose:机器学习、随机优化和搜索 mlrose 是一个 Python 包,用于将一些最常见的随机优化和搜索算法应用于一系列不同的优化问题,包括离散值和连续值参数空间。 项目背景 mlrose 最初是为了支持佐治亚理工学院 OMSCS/OMSA 课程 CS 7641:机器学习的学生而开发的。 它包括本课程中教授的所有随机优化算法的实现,以及将这些算法应用于整数字符串优化问题的功能,例如 N-Queens 和背包问题; 连续值优化问题,如神经网络权重问题; 和旅游优化问题,例如旅行商问题。 它还具有解决用户定义的优化问题的灵活性。 在开发时,不存在将所有这些功能集中在一个位置的单个 Python 包。 主要特点 随机优化算法 实现:爬山、随机爬山、模拟退火、遗传算法和(离散)MIMIC; 解决最大化和最小化问题; 定义算法的初始状态或从随机状态开始; 定义您自己的模拟退火衰减计划或使用三种预定义的可自定义衰减计划之一:几何衰减、算术衰减或指数衰减。 问题类型 解决离散值(位串和整数串)、连续值和旅游优化(旅行销售员)问题; 定义您自己的适应度函数以进行优化或

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