"Arc is designed for exploratory programming: the kind where you decide what to write by writing it. A good medium for exploratory programming is one that makes programs brief and malleable, so that's what we've aimed for. This is a medium for sketching software.Arc is unfinished. It's missing things you'd need to solve some types of problems. But it works well for basic web apps.
The first priority right now is the core language. We're trying to continue McCarthy's axiomatic approach all the way up to a complete language for day to day programming."
Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations.
The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code.
This makes Cython the ideal language for wrapping external C libraries, and for fast C modules that speed up the execution of Python code.
Monte - less comprehensive than Orange, written purely in Python (i.e. noSWIGed C++). Looks interesting (has several classifiers algorithms), but the APIs seems to be in an early phase (relatively new tool in version 0.1.0)
libsvm - Python API for most popular open source implementation of SVM.Note: libsvm is also included with Orange and PyML. (I used this tools during my PhD a few years ago)
RPy - not exactly a classification tool, but it is quite useful with a statistics tool when you are doing classification (it has a nice plotting capability, not unlike matlabs), check out the demo.
PyML - also less comprehensive than Orange (specialized towards classification and regression, it supports SVM/SMO, ANN and Ridge Regression), but it has a nice API. Example of use:
from PyML import multi, svm, datafunc
# read training data, last column has the class
mydataset = datafunc.SparseDataSet('iris.data', labelsColumn = -1)
myclassifier = multi.OneAgainstRest(svm.SVM())
print "cross-validation results", myclassifier.cv(mydataset)
My recommendation is to either go with Orange or with PyML.
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