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FDP聚类算法
说明： 一种无监督的聚类算法，基于密度聚类，名称为基于快速搜索与寻找密度峰值的聚类(Clustering by fast search and find of desity peaks)
- 2020-02-24 15:43:51下载
- 积分：1
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python-knn
主要利用Python软件，利用KNN算法对垃圾邮件进行分类(This paper mainly uses Python software to classify spam mail by using KNN algorithm)
- 2017-11-10 15:46:56下载
- 积分：1
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分位数回归
说明： 多种方法实现分位数回归,有完整原理解释，直接可用。(Multiple methods for quantile regression)
- 2020-03-03 14:37:14下载
- 积分：1
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jgthod
计算七类窗函数并给出归一化对数幅频曲线,同时也是利用窗函数法设计FIR滤波器的程序MDEFIR1所调用的子程序MWINDO(Seven kinds of window functions are calculated and normalized logarithmic amplitude-frequency curves are given. At the same time, it is also the subroutine MWINDO called by the program MDEFIR1 of designing FIR filter by using window function method.)
- 2018-09-06 05:12:15下载
- 积分：1
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Hive
bigdata hive use for hadoop
- 2018-04-01 16:13:56下载
- 积分：1
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1
说明： 深入理解Spark核心技术书籍，书籍对spark进行了深入讲解，并对spark源码进行了剖析(In-depth understanding of Spark core technology books, books on Spark in-depth explanation, and analysis of spark source code)
- 2020-06-18 10:40:02下载
- 积分：1
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Elaslic-finite-finite
弹性波数值模拟 时间域有限差分算法 双相介质(Numerical Simulation of Elastic Wave in time Domain finite difference algorithm for Dual-phase medium)
- 2018-09-11 22:51:07下载
- 积分：1
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StockPricePrediction-master
说明： python深度学习股票分析框架，就这么多了(python learning stock)
- 2019-06-18 12:19:59下载
- 积分：1
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boston_housing
说明： 采用机器学习预测房价.使用波士顿房屋信息数据来训练和测试一个模型，并对模型的性能和预测能力进行评估。(Using Machine Learning to Predict House Prices)
- 2019-10-04 11:48:44下载
- 积分：1
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Python-for-Finance-Second-Edition-master
Python是一种面向对象、解释型计算机程序设计语言，其应用领域非常广泛，包括数据分析、自然语言处理、机器学习、科学计算以及推荐系统构建等。 本书用Python语言来讲解算法的分析和设计。本书主要关注经典的算法，但同时会为读者理解基本算法问题和解决问题打下很好的基础。(Python is an object-oriented, interpretive computer programming language. It has a wide range of applications, including data analysis, natural language processing, machine learning, scientific computing and recommendation system construction. This book uses Python language to explain the analysis and design of algorithms. This book focuses on classical algorithms, but at the same time it will lay a good foundation for readers to understand basic algorithms and solve problems. The book consists of 11 chapters. The tree, graph, counting problem, inductive recursion, traversal, decomposition and merging, greedy algorithm, complex dependency, Dijkstra algorithm, matching and cutting problem, difficult problem and its dilution are introduced. The book has exercises and reference materials at the end of each chapter, which provides readers with more convenience for self-examination and further stu)
- 2018-11-24 15:20:49下载
- 积分：1
