Regarding this question; seems that a library which implements most of Sparse Coding algorithms is SPAMS http://spams-devel.gforge.inria.fr/ which now offers Python support
That's a pretty broad question. Among sparse coding, dictionary learning, and nonnegative factorization, there are plenty of methods, many of which are still so new that there does not yet exist a dominant piece of Python software that is universally accepted.
These research areas have been my recent focus, and I primarily use Python. I just decided to write my own simple versions of matching pursuit, NMF (and nonnegative LS), KSVD, and more. I might recommend doing the same for your specific purposes; they are not difficult to implement and modify. Just get used to Numpy, Scipy, and numpy.linalg.
That said, there are publicly available Python tools, but you will need to search more specifically. For instance, "matching pursuit python" yields a few results.
For those with convex optimization formulations, you could use CVX which is really easy to use, but you may not find it fast enough.
I use MPTK ("the Matching Pursuit Toolkit") - http://gforge.inria.fr/projects/mptk/ - there are some python wrappers in the code, but I don't know how stable they are. Personally I just use Python wrappers around the commandline utilities, because in my case the data is usually on disk rather than in memory so there's no big drawback to the loose coupling.
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关于这个问题；
似乎实现大多数稀疏编码算法的库是 SPAMS
http://spams-devel.gforge.inria.fr/
现在提供 Python 支持
Regarding this question;
seems that a library which implements most of Sparse Coding algorithms is SPAMS
http://spams-devel.gforge.inria.fr/
which now offers Python support
这是一个相当广泛的问题。在稀疏编码、字典学习和非负因式分解中，有很多方法，其中许多方法仍然很新，以至于还没有一个被普遍接受的占主导地位的 Python 软件。
这些研究领域是我最近关注的重点，我主要使用Python。我只是决定编写自己的简单版本的匹配追踪、NMF（和非负 LS）、KSVD 等。我可能会建议您为特定目的做同样的事情；它们并不难实施和修改。只需习惯 Numpy、Scipy 和 numpy.linalg 即可。
也就是说，有公开可用的 Python 工具，但您需要更具体地搜索。例如，“匹配追踪 python”会产生一些结果。
对于那些使用凸优化公式的人，您可以使用 CVX，它确实很容易使用，但您可能会发现它不够快。
That's a pretty broad question. Among sparse coding, dictionary learning, and nonnegative factorization, there are plenty of methods, many of which are still so new that there does not yet exist a dominant piece of Python software that is universally accepted.
These research areas have been my recent focus, and I primarily use Python. I just decided to write my own simple versions of matching pursuit, NMF (and nonnegative LS), KSVD, and more. I might recommend doing the same for your specific purposes; they are not difficult to implement and modify. Just get used to Numpy, Scipy, and
numpy.linalg.That said, there are publicly available Python tools, but you will need to search more specifically. For instance, "matching pursuit python" yields a few results.
For those with convex optimization formulations, you could use CVX which is really easy to use, but you may not find it fast enough.
我想如果（a）你没有找到它们并且（b）它们没有任何问题，那么这些就是答案。
http://www.eecs.berkeley.edu/~jiayq/sparsecoding.html
http://www.google-melange.com/gsoc/project/google/gsoc2011/vladn/11001
I guess these are answers if (a) you didn't find them and (b) there's nothing wrong with them.
http://www.eecs.berkeley.edu/~jiayq/sparsecoding.html
http://www.google-melange.com/gsoc/project/google/gsoc2011/vladn/11001
我使用 MPTK（“匹配追踪工具包”） - http://gforge.inria.fr/projects/ mptk/ - 代码中有一些Python包装器，但我不知道它们有多稳定。就我个人而言，我只是在命令行实用程序周围使用 Python 包装器，因为在我的例子中，数据通常位于磁盘上而不是内存中，因此松散耦合没有太大的缺点。
I use MPTK ("the Matching Pursuit Toolkit") - http://gforge.inria.fr/projects/mptk/ - there are some python wrappers in the code, but I don't know how stable they are. Personally I just use Python wrappers around the commandline utilities, because in my case the data is usually on disk rather than in memory so there's no big drawback to the loose coupling.