我正在制作一个应用程序，该应用程序使用多种不同的算法(代理)分析一个或多个系列的数据.我想到这些代理中的每一个都可以实现为单独的 Python 脚本，我在我的应用程序中使用 Python C API 或 Boost.Python 运行这些脚本.
I'm making an application that analyses one or more series of data using several different algorithms (agents). I came to the idea that each of these agents could be implemented as separate Python scripts which I run using either the Python C API or Boost.Python in my app.
我有点担心运行时开销 TBH，因为我正在进行一些非常繁重的数据处理，我不想每次模拟都需要等待几分钟.我通常会进行数十万(如果不是数百万)次调用外部代理"的迭代；我是更好地对应用程序中的所有内容进行硬编码，还是性能下降可以容忍?
I'm a little worried about runtime overhead TBH, as I'm doing some pretty heavy duty data processing and I don't want to have to wait several minutes for each simulation. I will typically be making hundreds of thousands, if not millions, of iterations in which I invoke the external "agents"; am I better of just hardcoding everything in the app, or will the performance drop be tolerable?
另外，除了 Python 之外，我还能使用其他任何解释性语言吗?
Also, are there any other interpreted languages I can use other than Python?
是的，吨.Lua 和 Python 似乎是最受欢迎的:
Yes, tons. Lua and Python seems to be the most popular:
嵌入 Lua
嵌入 Python
嵌入 Tcl
嵌入 Ruby
嵌入 Perl
嵌入 JavaScript
JavaScript 引擎有几十种，这只是一个例子.他们中的一些人的速度也快得惊人.
There are dozens of JavaScript engines around, this is just an example. Some of them are also frighteningly quick.
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