python - 预测整数的简单 SVM 算法

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在我的项目中，我们应该使用基于 SVM 的算法。因此，为了了解 SVM 实现的基本概念，我们尝试实现一种算法，当输入 1000 个整数的数组时，其中前 95 个整数的值范围为 0-5，然后接下来的 5 个整数约为 10,000，然后再次95 个整数的值范围为 0-5，接下来的 5 个大约为 10,000，依此类推，将能够预测接下来的 100 个整数(第 1001 - 1100 个)，前 95 个整数大约为 0-5，最后 5 个整数大约为 10,000 ...

如何实现？首选编程语言是 python。那么有没有像 libsvm 这样的 svm 模块可以促进这一点呢？

我知道这可能是一个愚蠢的问题，但任何帮助将不胜感激!

请回复

Answer 1

以下是 Python wiki 中有关 AI(特别是 SVM)的一些资源:

Milk - Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems.

LibSVM - LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. A Python interface is available by by default.

Shogun - The machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM) . It provides a generic SVM object interfacing to several different SVM implementations, among them the state of the art OCAS, Liblinear, LibSVM, SVMLight, SVMLin and GPDT. Each of the SVMs can be combined with a variety of kernels. The toolbox not only provides efficient implementations of the most common kernels, like the Linear, Polynomial, Gaussian and Sigmoid Kernel but also comes with a number of recent string kernels. SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and Python and is proudly released as Machine Learning Open Source Software