Programming language detector algorithm.

Key feature: the algorithm is aimed on classifying short and possibly incomplete code snippets, without any prior knowledge of file extension, context, keywords, etc.

Currently recognized languages: c, c++, c#, clojure, coffeescript, css, erlang, f#, go, html, java, javascript, lua, matlab, objective c, pascal, perl, php, python, r, ruby, scala, shell, sql, swift, typescript, visual basic.

BigInteger value = bd.unscaledValue();
char[] buffer = value.abs().toString().toCharArray();
int numDigitsLeft = buffer.length - 1;
int endPosition = numDigitsLeft % 2;
int length = (precision + 2) / 2;

var _a = this, source = _a.source, count = _a.count;
if (count === 0) {
    return _super.prototype.error.call(this, err);
}

unsigned int mw = 0;
int support = -1;
if (hnvml) {
	support = nvml_get_power_usage(hnvml, gpu->gpu_id, &mw);
}

    BOOL ret;
    DWORD err;

    if (!PyArg_ParseTuple(args, F_HANDLE "O", &handle, &bufobj))
      return NULL;

    self.path = base_path
    self.target = spec['target_name']
    self.type = spec['type']
    self.toolset = spec['toolset']

    {
        $adapter = new Ftp($this->options + ['systemType' => 'unknown']);
        $adapter->listContents();
    }

Clearly, not all languages can be distinguished within a short snippet, see for example C and C++, that's why the model outputs a probability distribution. The larger the snippet, the more confident the model is.

Apache 2.0