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pysvm's Introduction

😄 Hi! Nai elen siluva lyenna.

In Quenya, "Nai elen siluva lyenna" means "May the stars shine on you".

Who am I?

Welt Xing, a 1st year graduate in the School of Artificial Intelligence, Nanjing University. In German, "welt" means the "world". My interests include:

  • Machine learning model and their implementation.
  • Deep learning method and framework.
  • Machine learning theory.
  • Graph learning.

In my blog, I record what I've learned:

1

My stat

Your stars, forks, and issues are most welcome and appreciated 🥳.

Some finished and updating work

  • libsvm-sc-reading: A Chinese version manual to explain how LIBSVM works.
  • PySVM: A NumPy implementation of SVM based on SMO algorithm.
  • PyDyNet: Deep learning framework implementation using Numpy based on Autodiff.
  • SAGOD: A library for anomaly detection on static attributed graph.

pysvm's People

Contributors

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Stargazers

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Watchers

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pysvm's Issues

保存模型

使用lambda表达式进行运算不利于模型的保存的。若使用joblib等工具保存模型,需要将lambda表达式重写为类成员方法。使用dill工具保存内核状态可以解决这一问题,但这是有风险的。

对比sklearn:gamma的取值问题

在sklearn里对gamma的描述是使用X.var()

gamma : {'scale', 'auto'} or float, default='scale'
        Kernel coefficient for 'rbf', 'poly' and 'sigmoid'.

        - if ``gamma='scale'`` (default) is passed then it uses
          1 / (n_features * X.var()) as value of gamma,
        - if 'auto', uses 1 / n_features.

但目前这里用的是X.std(),

kernel_func = self.register_kernel(X.std())

导致收敛速度很慢,但确实取得了更高的准确度,这是有什么考量吗?

对比大概是
sklearn 时间0.004s 准确度0.9035
X.var()+1阶 时间2.7s 准确度0.9035
X.var()+2阶 时间18s 准确度0.9035
X.std()+1阶 时间15s 准确度0.9649
X.std()+2阶 时间25s 准确度0.9649

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