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https://github.com/austinTalbot7241993/LocallyAdaptiveFactorProcess

Blazing-Fast Implementation of Locally Adaptive Factor Processes
https://github.com/austinTalbot7241993/LocallyAdaptiveFactorProcess

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Blazing-Fast Implementation of Locally Adaptive Factor Processes

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# Locally Adaptive Factor Process



This repository implements the model described in the paper

"Locally adaptive factor processes for multivariate time series" located at



https://www.jmlr.org/papers/volume15/durante14a/durante14a.pdf.



This project is implemented in C using the GSL library to provide an 

incredibly efficient and quick implementation. This code is approximately

80x faster than implementations in Julia and about 200x faster than Numpy

implementations. 



## Why is my code so much faster



Since most people are likely skeptical of the latter claim, as those 

packages wrap the same Fortran code that I use, I will now explain why 

my implementation is so much faster. Memory allocation is incredibly painful

and requires keeping track of allocations and frees. Higher order languages

such as Python and Julia "free" us from these inconveniences by 

automatically allocating and freeing memory as needed. This introduces 

overhead, but this is generally ignored as implementing methods in these 

higher order languages is so much quicker than the explicit memory 

management required by C. And it is assumed that memory allocation is 

insignificant compared to the time performing the actual computation of 

interest.



I avoid all overhead with allocations by explicitly managing the memory

in this code. At the beginning of the script, all variables needed at 

every stage of computation are allocated. These addresses are then reused 

by the sampler at every stage. At the end of sampling the memory is freed.

During the entire period of sampling no allocations or frees are 

performed. So if the sampler were run for 1 iteration we would observe 0 

performance gain. However, with many iterations we asymptotically ignore 

any time spent on allocation, resulting in the incredible speedups claimed.



## Coding style



This implementation of C uses a very distinctive coding style. All error 

handling is done via GOTOs, which are the only acceptable usage of GOTOs 

in my opinion. Every method returns a 0 if succesful and any outputs of 

the method are passed via reference on the inputs. If an error is detected

the script goes to ERROR at the end of the function and returns a 1. An 

example function is shown below.



```

int exampleFunction(inputs,*outputs){

    // What the code does

    if(error_checking)  GOTO ERROR;



    return(0);

ERROR:

    printf("(Error in exampleFunction)\n");

    return(1);

}

```



Then when I want to use this function by any other function I can do this 

as follows



```

int outerFunction(inputs2,*outputs2){

    if(exampleFunction(inputs,*outputs)) GOTO ERROR;



ERROR:

    printf("(Error in outerFunction)\n");

    return(1);

}

```



As any expression within an if statement is evaluated, in a single line I 

can call the function and conveniently check for errors. Since C is so low

level, this if statement is basically a single FLOP and insignificant 

computation. So if I have an error in any function I get a stack of 

messages 

```

(Error in function1)

(Error in function2)

(Error in function3)

(Error in function4)

(Error in function5)

```

which makes debugging very easy. In the code this is not readily apparent 

because I use macros so that any errors return (1) the function name, (2)

a unique error code, and (3) the line number of the error. But the principle

is the same.



Unfortunately, when I coded this I believed the code was self-documenting.

I was clearly mistaken, and I will hopefully soon return to comment the 

code. If you are planning to use this code and are confused, please 

contact me at firstname . lastname 1993 @ gmail.