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In Lua, "table" is a fundamental type that can be used either as an array (numerical index, fast) or as an associative array. The keys and values can be of any type, except nil. The following focuses on non-numerical indexes. A table literal is written as { value, key = value, [index] = value, ["non id string"] = value }. For example:
It will also work on arrays,-- but for arrays it is more efficient to use the # operator.-----function p. size (t) checkType ('size', 1, t, 'table') local i = 0 for _ in pairs (t) do i = i + 1 end return i end local function defaultKeySort (item1, item2)-- "number" < "string", so numbers will be sorted before strings. local type1, type2 = type ...
The torch.class(classname, parentclass) function can be used to create object factories . When the constructor is called, torch initializes and sets a Lua table with the user-defined metatable, which makes the table an object.
This module includes a number of set operations for Lua tables. It currently has union, intersection and complement functions for both key/value pairs and for values only. . It is a meta-module, meant to be called from other Lua modules, and should not be called directly from #invo
A table is a sequence, optionally supplemented by named keys: digit["two"]="2". Several table functions like table.concat will only work with the numbered values and ignore named keys. The metatable offers a large, optional set of methods for altering table behavior. For example, you can define a table to be callable like a function.
Marshalling data between C and Lua functions is also done using the stack. To call a Lua function, arguments are pushed onto the stack, and then the lua_call is used to call the actual function. When writing a C function to be directly called from Lua, the arguments are read from the stack. Here is an example of calling a Lua function from C:
Support for multi-dimensional arrays may also be provided by external libraries, which may even support arbitrary orderings, where each dimension has a stride value, and row-major or column-major are just two possible resulting interpretations. Row-major order is the default in NumPy [19] (for Python).
Functional programming – uses evaluation of mathematical functions and avoids state and mutable data Generic programming – uses algorithms written in terms of to-be-specified-later types that are then instantiated as needed for specific types provided as parameters