Haskell Data.Decimal for rounding issue? - haskell

I would like to create a list of all the real numbers between 1.0 and 2.0 in two-decimal-place increments. This
dList = [1.00,1.01..2.00]
however, creates float run-on problems
dList = [1.0,1.01,1.02,1.03,1.04,1.05,1.06,1.07,1.08,1.09,1.1,1.11,1.12,
1.1300000000000001,1.1400000000000001,1.1500000000000001, ...
To remedy this I found what I thought was a function in Data.Decimal namely, roundTo. I was hoping to eventually run this
map (roundTo 2) [1.1,1.2..2.0]
and get rid of the float run-on, but it produces a giant error message. This page is for this beginner undecipherable. And so I'm trying to do this with an hs file loaded at a ghci REPL. This is the code
import Data.Decimal
dList :: [Decimal]
dList = [1.00,1.01..2.0]
main = print dList
and it produces
Could not find module ‘Data.Decimal’
Lost, I am....

Note
This answer is an FYI for all of you beginners simply trying to follow along in a beginner Haskell book that has you typing code into a text editor, firing up the ghci REPL and doing :load my-haskell-code.hs.
YMMV Solution
As can be gleaned above, Data.Decimal is not a standard included Prelude sort of package. It must be loaded independently -- and no, it doesn't work to simply put import Data.Decimal at the top of your code. As leftaroundabout said in a comment above, the very simplest way for Haskell beginners not yet doing projects is to start ghci thusly
stack ghci --package Decimal
Of course YMMV depending on how you installed Haskell. I installed Haskell through the stack project management, hence, the stack before ghci --package Decimal. Another unique thing about my setup is I'm using Emacs org-mode's Babel code blocks, which is largely the same as the basic type-and-load way, i.e., non-project. I did try to alter Emacs's haskell-process-args-stack-ghci which is in haskell-customize.el by just adding --package Decimal but it didn't work. Instead, I simply went to my bash command line and put in stack ghci --package Decimal, then I restarted a separate org-mode Babel ghci and it worked. Now,
dList :: [Decimal]
dList = [1.00,1.01..2.00]
> dList
[1,1.01,1.02,1.03,1.04,1.05,1.06,1.07,1.08,1.09,1.10,1.11,1.12,1.13,1.14,1.15,1.16,1.17,1.18,1.19,1.20,1.21,1.22,1.23,1.24,1.25,1.26,1.27,1.28,1.29,1.30,1.31,1.32,1.33,1.34,1.35,1.36,1.37,1.38,1.39,1.40,1.41,1.42,1.43,1.44,1.45,1.46,1.47,1.48,1.49,1.50,1.51,1.52,1.53,1.54,1.55,1.56,1.57,1.58,1.59,1.60,1.61,1.62,1.63,1.64,1.65,1.66,1.67,1.68,1.69,1.70,1.71,1.72,1.73,1.74,1.75,1.76,1.77,1.78,1.79,1.80,1.81,1.82,1.83,1.84,1.85,1.86,1.87,1.88,1.89,1.90,1.91,1.92,1.93,1.94,1.95,1.96,1.97,1.98,1.99,2.00]
no muss, no fuss. I killed the ghci and loaded it without the --package Decimal and it still knew about Decimal, so this change is recorded quasi-permanently in my ~/.stack directory somewhere? Oddly, the bash ghci session doesn't know about the *haskell* ghci session in org-mode. Also, when just using the Emacs haskell mode stand-alone for type-and-load, its ghci session also doesn't play well with the org-mode ghci. I've gone with the minimalist Emacs org-mode babel because it seems better than just lhs literal Haskell. If anyone knows how to make literal Haskell sing like org-mode, I'd like to know.
Postmortem
I guess I insisted on figuring out Decimal because in researching the whole rounding issue I started seeing a wild divergence of suggested solutions (not necessarily here, but other places) and scary technical arguments. Decimal seemed the simplest in a wild storm of competing rounding strategies. Rounding should be simple, but in Haskell it turned into a time-sucking trip down multiple rabbit holes.

Answer to your specific float problem
By far the simplest option in this case is
[n/100 | n<-[0..200]]
or variations on the same idea:
map (/100) [0..200]
(*1e-2) . fromIntegral <$> [0 .. 200 :: Int] -- (*) is more efficient than (/),
-- but will actually introduce rounding
-- errors (non-accumulating) again
No need for any special decimal library or rational numbers.
The reason this works better than the [x₀, x₁ .. xe] approach is that integers below 252 can be expressed exactly in floating point (whereas decimal fractions cannot). Therefore, the range [0..200] is exactly what you want it to be. Then at the end, dividing each of these numbers by 100 will still not give you exact representations of the hundredths you want to get – because such representations don't exist – but you will for each element get the closest possible approximation. And that closest possible approximation is in fact printed in x.yz form, even with the standard print function. By comparison, in [1.00,1.01..2.0] you keep adding up already-approximate values, thereby compounding the errors.
You could also use your original range calculation in exact rationals, and only then convert them to float★ – this still doesn't require a decimal library
map fromRational [0, 0.01 .. 2]
Rational arithmetic is often an easy fix for similar problems – but I tend to advise against this, because it usually scales badly. Algorithms that have rounding problems in floating-point will more often than not run into memory problems in rational arithmetic, because you need to carry an ever-growing range of precision through the entire calculation. The better solution is to avoid needing the precision in the first place, like with the n/100 suggestion.
Also, arguably the [x₀, x₁ .. xe] syntax is a broken design anyway; integer ranges have far clearer semantics.
Note also that the float errors in your original attempt aren't necessarily a problem at all. An error of 10-9 in a real-world measured quantity is for all meaningful purposes neglectable. If you need something to be truely exact, you probably shouldn't be using fractional values at all, but straight integers. So, conside Carl's suggestion of just living with the float-deviations, but simply printing them in a suitably rounded form, by way of showFFloat, printf, or Text.Show.Pragmatic.print.
★Actually, in this specific case both solutions are almost equivalent, because converting Rational to float involves floating-point dividing the numerator by the denominator.
Answer to your module-loading problem
In cases where you do need the Decimal library (or some other library), you need to depend on it.
The easiest way to do this is to use Stack and add Decimal to your global project. Then, you can load the file with stack ghci and it'll know where to look for Data.Decimal.
Alternatively, and IMO preferrably, you should create a project package yourself and only make that depend on Decimal. This can be done with either Stack or Cabal-install.
$ mkdir my-decimal-project
$ cd my-decimal-project
$ cabal init
Now you're being asked some questions about the name etc. of your project, you can mostly answer with the defaults. Say your project defines a library (you can add an executable later on if you want.)
cabal init creates amongst other things a my-decimal-project.cabal file. In that file, add Decimal to the dependency, and your own source file to exposed-modules.
Then you need to (still in your project directory) cabal install --dependencies-only to fetch the Decimal library, and then cabal repl to load your module.

Related

Unclear why functions from Data.Ratio are not exposed and how to work around

I am implementing an algorithm using Data.Ratio (convergents of continued fractions).
However, I encounter two obstacles:
The algorithm starts with the fraction 1%0 - but this throws a zero denominator exception.
I would like to pattern match the constructor a :% b
I was exploring on hackage. An in particular the source seems to be using exactly these features (e.g. defining infinity = 1 :% 0, or pattern matching for numerator).
As beginner, I am also confused where it is determined that (%), numerator and such are exposed to me, but not infinity and (:%).
I have already made a dirty workaround using a tuple of integers, but it seems silly to reinvent the wheel about something so trivial.
Also would be nice to learn how read the source which functions are exposed.
They aren't exported precisely to prevent people from doing stuff like this. See, the type
data Ratio a = a:%a
contains too many values. In particular, e.g. 2/6 and 3/9 are actually the same number in ℚ and both represented by 1:%3. Thus, 2:%6 is in fact an illegal value, and so is, sure enough, 1:%0. Or it might be legal but all functions know how to treat them so 2:%6 is for all observable means equal to 1:%3 – I don't in fact know which of these options GHC chooses, but at any rate it's an implementation detail and could change in future releases without notice.
If the library authors themselves use such values for e.g. optimisation tricks that's one thing – they have after all full control over any algorithmic details and any undefined behaviour that could arise. But if users got to construct such values, it would result in brittle code.
So – if you find yourself starting an algorithm with 1/0, then you should indeed not use Ratio at all there but simply store numerator and denominator in a plain tuple, which has no such issues, and only make the final result a Ratio with %.

How to produce infinity in Haskell?

Similarily to How to produce a NaN in Haskell ...
In C, there is the INFINITY macro, defined by math.h.
Again, in http://hackage.haskell.org/package/ClassyPrelude-0.1/docs/Prelude-Math.html I can see falicities to test for infnity, but not to produce one.
Therefore, is my only choice something like 1/0?
The iee754 package has functions and constants specific to that floating point format.
In particular, it has the Numeric.IEEE.infinity constant for members for the IEEE class (which float and double belong to). It is pretty much just implemented as 1/0 though, so your call if you want the package dependency for a prettier name.

CLPFD for real numbers

CLP(FD) allows user to set the domain for every wannabe-integer variable, so it's able to solve equations.
So far so good.
However you can't do the same in CLP(R) or similar languages (where you can do only simple inferences). And it's not hard to understand why: the fractional part of a number may have an almost infinite region, putted down by an implementation limit. This mean the search space will be too large to make any practical use for a solver which deals with floats like with integers. So it's the user task to write generator in CLP(R) and set constraint guards where needed to get variables instantiated with numbers (if simple inference is not possible).
So my question here: is there any CLP(FD)-like language over reals? I think it could be implemented by use of number rounding, searching and following incremental approximation.
There are at least some major CLP(FD) solvers that support real (decision) variables:
Gecode
JaCoP
ECLiPSe CLP (ic library)
Choco (using Ibex)
(The first three also support var float in MiniZinc.)
The answser to your question is yes. There is Constraint-based Solvers dedicated for floating numbers. I do not have a list of solvers but I know that that ibex http://www.ibex-lib.org is a library allowing the use of floats. You should also have a look at SMT-Solvers implementing the Real-Theory (http://smtlib.cs.uiowa.edu/solvers.shtml).

A good way to avoid "sharing"?

Suppose that someone would translate this simple Python code to Haskell:
def important_astrological_calculation(digits):
# Get the first 1000000 digits of Pi!
lucky_numbers = calculate_first_digits_of_pi(1000000)
return digits in lucky_numbers
Haskell version:
importantAstrologicalCalculation digits =
isInfixOf digits luckyNumbers
where
luckyNumbers = calculateFirstDigitsOfPi 1000000
After working with the Haskell version, the programmer is astonished to discover that his Haskell version "leaks" memory - after the first time his function is called, luckyNumbers never gets freed. That is troubling as the program includes some more similar functions and the memory consumed by all of them is significant.
Is there an easy and elegant way to make the program "forget" luckyNumbers?
In this case, your pidigits list is a constant (or "constant applicative form
), and GHC will probably float it out, calculate it once, and share amongst uses. If there are no references to the CAF, it will be garbage collected.
Now, in general, if you want something to be recalculated, turn it into a function (e.g. by adding a dummy () parameter) and enable -fno-full-laziness. Examples in the linked question on CAFs: How to make a CAF not a CAF in Haskell?
Three ways to solve this (based on this blog post)
Using INLINE pragmas
Add {-# INLINE luckyNumbers #-} and another for importantAstrologicalCalculation.
This will make separate calls be independent from each other, each using their own copy of the luckyNumbers which is iterated once and is immediately collected by the GC.
Pros:
Require minimal changes to our code
Cons:
Fragile? kuribas wrote wrote that "INLINE doen’t guarantee inlining, and it depends on optimization flags"
Machine code duplication. May create larger and potentially less efficient executables
Using the -fno-full-laziness GHC flag
Wrap luckyNumbers with a dummy lambda and use -fno-full-laziness:
{-# OPTIONS -fno-full-laziness #-}
luckyNumbers _ = calculateFirstDigitsOfPi 1000000
Without the flag, GHC may notice that the expression in luckyNumbers doesn't use its parameter and so it may float it out and share it.
Pros:
No machine code duplication: the implementation of fibs is shared without the resulting list being shared!
Cons:
Fragile? I fear that this solution might break if another module uses fibs and GHC decides to inline it, and this second module didn't enable -fno-full-laziness
Relies on GHC flags. These might change more easily than the language standard does
Requires modification to our code including in all of fibs's call sites
Functionalization
Alonzo Church famously discovered that data can be encoded in functions, and we can use it to avoid creating data structures that could be shared.
luckyNumbers can be made to a function folding over the digits of pi rather than a data structure.
Pros:
Solid. Little doubt that this will resume working in the face of various compiler optimization
Cons:
More verbose
Non-standard. We're not using standard lists anymore, and those have a wealth of standard library functions supporting them which we may need to re-implement

What programming languages support arbitrary precision arithmetic?

What programming languages support arbitrary precision arithmetic and could you give a short example of how to print an arbitrary number of digits?
Some languages have this support built in. For example, take a look at java.math.BigDecimal in Java, or decimal.Decimal in Python.
Other languages frequently have a library available to provide this feature. For example, in C you could use GMP or other options.
The "Arbitrary-precision software" section of this article gives a good rundown of your options.
Mathematica.
N[Pi, 100]
3.141592653589793238462643383279502884197169399375105820974944592307816406286208998628034825342117068
Not only does mathematica have arbitrary precision but by default it has infinite precision. It keeps things like 1/3 as rationals and even expressions involving things like Sqrt[2] it maintains symbolically until you ask for a numeric approximation, which you can have to any number of decimal places.
In Common Lisp,
(format t "~D~%" (expt 7 77))
"~D~%" in printf format would be "%d\n". Arbitrary precision arithmetic is built into Common Lisp.
Smalltalk supports arbitrary precision Integers and Fractions from the beginning.
Note that gnu Smalltalk implementation does use GMP under the hood.
I'm also developping ArbitraryPrecisionFloat for various dialects (Squeak/Pharo Visualworks and Dolphin), see http://www.squeaksource.com/ArbitraryPrecisionFl.html
Python has such ability. There is an excellent example here.
From the article:
from math import log as _flog
from decimal import getcontext, Decimal
def log(x):
if x < 0:
return Decimal("NaN")
if x == 0:
return Decimal("-inf")
getcontext().prec += 3
eps = Decimal("10")**(-getcontext().prec+2)
# A good initial estimate is needed
r = Decimal(repr(_flog(float(x))))
while 1:
r2 = r - 1 + x/exp(r)
if abs(r2-r) < eps:
break
else:
r = r2
getcontext().prec -= 3
return +r
Also, the python quick start tutorial discusses the arbitrary precision: http://docs.python.org/lib/decimal-tutorial.html
and describes getcontext:
the getcontext() function accesses the
current context and allows the
settings to be changed.
Edit: Added clarification on getcontext.
Many people recommended Python's decimal module, but I would recommend using mpmath over decimal for any serious numeric uses.
COBOL
77 VALUE PIC S9(4)V9(4).
a signed variable witch 4 decimals.
PL/1
DCL VALUE DEC FIXED (4,4);
:-) I can't remember the other old stuff...
Jokes apart, as my example show, I think you shouldn't choose a programming language depending on a single feature. Virtually all decent and recent language support fixed precision in some dedicated classes.
Scheme (a variation of lisp) has a capability called 'bignum'. there are many good scheme implementations available both full language environments and embeddable scripting options.
a few I can vouch for
MitScheme (also referred to as gnu scheme)
PLTScheme
Chezscheme
Guile (also a gnu project)
Scheme 48
Ruby whole numbers and floating point numbers (mathematically speaking: rational numbers) are by default not strictly tied to the classical CPU related limits. In Ruby the integers and floats are automatically, transparently, switched to some "bignum types", if the size exceeds the maximum of the classical sizes.
One probably wants to use some reasonably optimized and "complete", multifarious, math library that uses the "bignums". This is where the Mathematica-like software truly shines with its capabilities.
As of 2011 the Mathematica is extremely expensive and terribly restricted from hacking and reshipping point of view, specially, if one wants to ship the math software as a component of a small, low price end, web application or an open source project. If one needs to do only raw number crunching, where visualizations are not required, then there exists a very viable alternative to the Mathematica and Maple. The alternative is the REDUCE Computer Algebra System, which is Lisp based, open source and mature (for decades) and under active development (in 2011). Like Mathematica, the REDUCE uses symbolic calculation.
For the recognition of the Mathematica I say that as of 2011 it seems to me that the Mathematica is the best at interactive visualizations, but I think that from programming point of view there are more convenient alternatives even if Mathematica were an open source project. To me it seems that the Mahtematica is also a bit slow and not suitable for working with huge data sets. It seems to me that the niche of the Mathematica is theoretical math, not real-life number crunching. On the other hand the publisher of the Mathematica, the Wolfram Research, is hosting and maintaining one of the most high quality, if not THE most high quality, free to use, math reference sites on planet Earth: the http://mathworld.wolfram.com/
The online documentation system that comes bundled with the Mathematica is also truly good.
When talking about speed, then it's worth to mention that REDUCE is said to run even on a Linux router. The REDUCE itself is written in Lisp, but it comes with 2 of its very own, specific, Lisp implementations. One of the Lisps is implemented in Java and the other is implemented in C. Both of them work decently, at least from math point of view. The REDUCE has 2 modes: the traditional "math mode" and a "programmers mode" that allows full access to all of the internals by the language that the REDUCE is self written in: Lisp.
So, my opinion is that if one looks at the amount of work that it takes to write math routines, not to mention all of the symbolic calculations that are all MATURE in the REDUCE, then one can save enormous amount of time (decades, literally) by doing most of the math part in REDUCE, specially given that it has been tested and debugged by professional mathematicians over a long period of time, used for doing symbolic calculations on old-era supercomputers for real professional tasks and works wonderfully, truly fast, on modern low end computers. Neither has it crashed on me, unlike at least one commercial package that I don't want to name here.
http://www.reduce-algebra.com/
To illustrate, where the symbolic calculation is essential in practice, I bring an example of solving a system of linear equations by matrix inversion. To invert a matrix, one needs to find determinants. The rounding that takes place with the directly CPU supported floating point types, can render a matrix that theoretically has an inverse, to a matrix that does not have an inverse. This in turn introduces a situation, where most of the time the software might work just fine, but if the data is a bit "unfortunate" then the application crashes, despite the fact that algorithmically there's nothing wrong in the software, other than the rounding of floating point numbers.
The absolute precision rational numbers do have a serious limitation. The more computations is performed with them, the more memory they consume. As of 2011 I don't know any solutions to that problem other than just being careful and keeping track of the number of operations that has been performed with the numbers and then rounding the numbers to save memory, but one has to do the rounding at a very precise stage of the calculations to avoid the aforementioned problems. If possible, then the rounding should be done at the very end of the calculations as the very last operation.
In PHP you have BCMath. You not need to load any dll or compile any module.
Supports numbers of any size and precision, represented as string
<?php
$a = '1.234';
$b = '5';
echo bcadd($a, $b); // 6
echo bcadd($a, $b, 4); // 6.2340
?>
Apparently Tcl also has them, from version 8.5, courtesy of LibTomMath:
http://wiki.tcl.tk/5193
http://www.tcl.tk/cgi-bin/tct/tip/237.html
http://math.libtomcrypt.com/
There are several Javascript libraries that handle arbitrary-precision arithmetic.
For example, using my big.js library:
Big.DP = 20; // Decimal Places
var pi = Big(355).div(113)
console.log( pi.toString() ); // '3.14159292035398230088'
In R you can use the Rmpfr package:
library(Rmpfr)
exp(mpfr(1, 120))
## 1 'mpfr' number of precision 120 bits
## [1] 2.7182818284590452353602874713526624979
You can find the vignette here: Arbitrarily Accurate Computation with R:
The Rmpfr Package
Java natively can do bignum operations with BigDecimal. GMP is the defacto standard library for bignum with C/C++.
If you want to work in the .NET world you can use still use the java.math.BigDecimal class. Just add a reference to vjslib (in the framework) and then you can use the java classes.
The great thing is, they can be used fron any .NET language. For example in C#:
using java.math;
namespace MyNamespace
{
class Program
{
static void Main(string[] args)
{
BigDecimal bd = new BigDecimal("12345678901234567890.1234567890123456789");
Console.WriteLine(bd.ToString());
}
}
}
The (free) basic program x11 basic ( http://x11-basic.sourceforge.net/ ) has arbitrary precision for integers. (and some useful commands as well, e.g. nextprime( abcd...pqrs))
IBM's interpreted scripting language Rexx, provides custom precision setting with Numeric. https://www.ibm.com/docs/en/zos/2.1.0?topic=instructions-numeric.
The language runs on mainframes and pc operating systems and has very powerful parsing and variable handling as well as extension packages. Object Rexx is the most recent implementation. Links from https://en.wikipedia.org/wiki/Rexx
Haskell has excellent support for arbitrary-precision arithmetic built in, and using it is the default behavior. At the REPL, with no imports or setup required:
Prelude> 2 ^ 2 ^ 12
1044388881413152506691752710716624382579964249047383780384233483283953907971557456848826811934997558340890106714439262837987573438185793607263236087851365277945956976543709998340361590134383718314428070011855946226376318839397712745672334684344586617496807908705803704071284048740118609114467977783598029006686938976881787785946905630190260940599579453432823469303026696443059025015972399867714215541693835559885291486318237914434496734087811872639496475100189041349008417061675093668333850551032972088269550769983616369411933015213796825837188091833656751221318492846368125550225998300412344784862595674492194617023806505913245610825731835380087608622102834270197698202313169017678006675195485079921636419370285375124784014907159135459982790513399611551794271106831134090584272884279791554849782954323534517065223269061394905987693002122963395687782878948440616007412945674919823050571642377154816321380631045902916136926708342856440730447899971901781465763473223850267253059899795996090799469201774624817718449867455659250178329070473119433165550807568221846571746373296884912819520317457002440926616910874148385078411929804522981857338977648103126085903001302413467189726673216491511131602920781738033436090243804708340403154190336
(try this yourself at https://tryhaskell.org/)
If you're writing code stored in a file and you want to print a number, you have to convert it to a string first. The show function does that.
module Test where
main = do
let x = 2 ^ 2 ^ 12
let xStr = show x
putStrLn xStr
(try this yourself at code.world: https://www.code.world/haskell#Pb_gPCQuqY7r77v1IHH_vWg)
What's more, Haskell's Num abstraction lets you defer deciding what type to use as long as possible.
-- Define a function to make big numbers. The (inferred) type is generic.
Prelude> superbig n = 2 ^ 2 ^ n
-- We can call this function with different concrete types and get different results.
Prelude> superbig 5 :: Int
4294967296
Prelude> superbig 5 :: Float
4.2949673e9
-- The `Int` type is not arbitrary precision, and we might overflow.
Prelude> superbig 6 :: Int
0
-- `Double` can hold bigger numbers.
Prelude> superbig 6 :: Double
1.8446744073709552e19
Prelude> superbig 9 :: Double
1.3407807929942597e154
-- But it is also not arbitrary precision, and can still overflow.
Prelude> superbig 10 :: Double
Infinity
-- The Integer type is arbitrary-precision though, and can go as big as we have memory for and patience to wait for the result.
Prelude> superbig 12 :: Integer
1044388881413152506691752710716624382579964249047383780384233483283953907971557456848826811934997558340890106714439262837987573438185793607263236087851365277945956976543709998340361590134383718314428070011855946226376318839397712745672334684344586617496807908705803704071284048740118609114467977783598029006686938976881787785946905630190260940599579453432823469303026696443059025015972399867714215541693835559885291486318237914434496734087811872639496475100189041349008417061675093668333850551032972088269550769983616369411933015213796825837188091833656751221318492846368125550225998300412344784862595674492194617023806505913245610825731835380087608622102834270197698202313169017678006675195485079921636419370285375124784014907159135459982790513399611551794271106831134090584272884279791554849782954323534517065223269061394905987693002122963395687782878948440616007412945674919823050571642377154816321380631045902916136926708342856440730447899971901781465763473223850267253059899795996090799469201774624817718449867455659250178329070473119433165550807568221846571746373296884912819520317457002440926616910874148385078411929804522981857338977648103126085903001302413467189726673216491511131602920781738033436090243804708340403154190336
-- If we don't specify a type, Haskell will infer one with arbitrary precision.
Prelude> superbig 12
1044388881413152506691752710716624382579964249047383780384233483283953907971557456848826811934997558340890106714439262837987573438185793607263236087851365277945956976543709998340361590134383718314428070011855946226376318839397712745672334684344586617496807908705803704071284048740118609114467977783598029006686938976881787785946905630190260940599579453432823469303026696443059025015972399867714215541693835559885291486318237914434496734087811872639496475100189041349008417061675093668333850551032972088269550769983616369411933015213796825837188091833656751221318492846368125550225998300412344784862595674492194617023806505913245610825731835380087608622102834270197698202313169017678006675195485079921636419370285375124784014907159135459982790513399611551794271106831134090584272884279791554849782954323534517065223269061394905987693002122963395687782878948440616007412945674919823050571642377154816321380631045902916136926708342856440730447899971901781465763473223850267253059899795996090799469201774624817718449867455659250178329070473119433165550807568221846571746373296884912819520317457002440926616910874148385078411929804522981857338977648103126085903001302413467189726673216491511131602920781738033436090243804708340403154190336

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