Object-oriented programming in Lua
Although Lua does not have a built-in concept of classes, object-oriented programming can be achieved using two language features: first-class functions and tables. By placing functions and related data into a table, an object is formed. Inheritance (both single and multiple) can be implemented using the metatable mechanism, telling the object to look up nonexistent methods and fields in parent object(s).
There is no such concept as "class" with these techniques; rather, prototypes are used, similar to Self or #JavaScript. New objects are created either with a factory method (that constructs new objects from scratch) or by cloning an existing object.
Lua provides some syntactic sugar to facilitate object orientation. To declare member functions inside a prototype table, one can use function
#Lua
Although Lua does not have a built-in concept of classes, object-oriented programming can be achieved using two language features: first-class functions and tables. By placing functions and related data into a table, an object is formed. Inheritance (both single and multiple) can be implemented using the metatable mechanism, telling the object to look up nonexistent methods and fields in parent object(s).
There is no such concept as "class" with these techniques; rather, prototypes are used, similar to Self or #JavaScript. New objects are created either with a factory method (that constructs new objects from scratch) or by cloning an existing object.
Lua provides some syntactic sugar to facilitate object orientation. To declare member functions inside a prototype table, one can use function
table:func(args), which is equivalent to function table.func(self, args). Calling class methods also makes use of the colon: object:func(args) is equivalent to object.func(object, args). #Lua
#TalkingSemicolon
Tools for natural language generation
Either Keras/TensorFlow if going with #Python or TorchNN if going with #Lua.
Nothing else really works.
Tools for natural language generation
Either Keras/TensorFlow if going with #Python or TorchNN if going with #Lua.
Nothing else really works.
Data layout
- is how multidimensional arrays are stored in a linear storage such as RAM. It is critical for:
• correctly passing arrays between programs written in different programming languages
• performance when traversing an array because modern CPUs, due to caching, process sequential data more efficiently than non-sequential data
• contiguous access makes it possible to use SIMD instructions that operate on vectors of data
Row-major versus column-major order
The difference between the orders lies in which elements of an array are contiguous in memory. In a row-major order, the consecutive elements of a row reside next to each other, whereas the same holds true for consecutive elements of a column in a column-major order. While the terms allude to the rows and columns of a two-dimensional array, the orders can be generalized to arrays of any dimension.
Transposition
As exchanging the indices of an array is the essence of array transposition, an array stored as row-major but read as column-major (or vice versa) will appear transposed. As actually performing this rearrangement in memory is typically an expensive operation, some systems provide options to specify individual matrices as being stored transposed.
Languages support
• Row-major: #C/C++/Objective-C (for C-style arrays), PL/I, #Pascal, Speakeasy, SAS, and Rasdaman
• Column-major: #Fortran, #MATLAB, GNU Octave, S-Plus, #R, #Julia, and Scilab.
• Neither (for less dense arrays):
• Iliffe vectors: #Java, #Scala, #Swift. #Ruby, #Perl, #PHP, #JavaScript, Visual Basic .NET
• Lists of lists: #Python, Wolfram Language of Wolfram Mathematica
• Tables of tables: #Lua
- is how multidimensional arrays are stored in a linear storage such as RAM. It is critical for:
• correctly passing arrays between programs written in different programming languages
• performance when traversing an array because modern CPUs, due to caching, process sequential data more efficiently than non-sequential data
• contiguous access makes it possible to use SIMD instructions that operate on vectors of data
Row-major versus column-major order
The difference between the orders lies in which elements of an array are contiguous in memory. In a row-major order, the consecutive elements of a row reside next to each other, whereas the same holds true for consecutive elements of a column in a column-major order. While the terms allude to the rows and columns of a two-dimensional array, the orders can be generalized to arrays of any dimension.
Transposition
As exchanging the indices of an array is the essence of array transposition, an array stored as row-major but read as column-major (or vice versa) will appear transposed. As actually performing this rearrangement in memory is typically an expensive operation, some systems provide options to specify individual matrices as being stored transposed.
Languages support
• Row-major: #C/C++/Objective-C (for C-style arrays), PL/I, #Pascal, Speakeasy, SAS, and Rasdaman
• Column-major: #Fortran, #MATLAB, GNU Octave, S-Plus, #R, #Julia, and Scilab.
• Neither (for less dense arrays):
• Iliffe vectors: #Java, #Scala, #Swift. #Ruby, #Perl, #PHP, #JavaScript, Visual Basic .NET
• Lists of lists: #Python, Wolfram Language of Wolfram Mathematica
• Tables of tables: #Lua