Programming language detector algorithm
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.