[erlang-questions] How to calculate series expansion e^x
Guido Rumi
guido.rumi@REDACTED
Wed Mar 4 00:32:05 CET 2015
I would change
fact(N) ->
case N of
0 -> 1;
1 -> 1;
_ -> N * fact(N - 1)
end.
to
fact(N) ->
fact_tr(N, 1).
fact_tr(N, Acc) ->
case N of
0 -> Acc;
1 -> Acc;
_ -> fact_tr (N - 1, N * Acc)
end.
To make it more efficient
http://stackoverflow.com/questions/33923/what-is-tail-recursion
On Tue, Mar 3, 2015 at 6:32 PM, Harit Himanshu <
harit.subscriptions@REDACTED> wrote:
> Richard and Alex
>
> Thanks a lot for your help. I got help up with my day job and could not
> try it out. Thanks a lot for your help on this problem.
> I tried a different version and it looks like following. Let me know if
> you see any issues with that
>
> -module(solution).
> -export([main/0]).
>
> main() ->
> {ok, [N]} = io:fread("", "~d"),
> Data = read_input([], N),
> [io:format("~p~n", [e_x(X)]) || X <- Data].
>
> read_input(Input, N) ->
> case N of
> 0 -> lists:reverse(Input);
> _ -> {ok, [Num]} = io:fread("", "~s"),
> {NumFloat, _} = string:to_float(Num),
> read_input([NumFloat | Input], N - 1)
> end.
>
> e_x(X) ->
> 1 + lists:sum([math:pow(X, N) / fact(N) || N <- lists:seq(1, 9)]).
>
> fact(N) ->
> case N of
> 0 -> 1;
> 1 -> 1;
> _ -> N * fact(N - 1)
> end.
>
> On Wed, Feb 25, 2015 at 4:38 PM, Alex Alvarez <eajam@REDACTED> wrote:
>
>> Or just simply...
>>
>>
>> -module(e_fun).
>>
>> -export ([start/2]).
>>
>> start (N, X) ->
>> 1 + step (N - 1, 1, X, 1.0, 0).
>>
>> step (0, _, _, _, S) -> S;
>> step (N, NN, X, Z, S) ->
>> ZZ = Z * X/NN,
>> step (N - 1, NN + 1, X, ZZ, S + ZZ).
>>
>>
>> Here I'm just reusing the each previous term (ZZ) of the Taylor series to
>> compute the next term and then add it up to the sum (S).
>>
>> Cheers,
>> Alex
>>
>>
>>
>> On 02/24/2015 10:27 PM, Richard A. O'Keefe wrote:
>>
>> On 25/02/2015, at 11:00 am, Harit Himanshu <harit.subscriptions@REDACTED> <harit.subscriptions@REDACTED> wrote:
>>
>>
>> I am trying to learn Functional Programming and Erlang by practicing problems available online.
>>
>> One question where I don't know about solving it is
>>
>> The series expansion of ex is given by:
>>
>> 1 + x + x2/2! + x3/3! + x4/4! + .......
>>
>> Evaluate e^x for given values of x, by using the above expansion for the first 10 terms.
>>
>>
>> The problem statement could be found here
>>
>> Can someone guide me how this could be achieved in Erlang?
>>
>> The first 10 terms of exp(X) are
>> X X^2 X^3 X^9
>> 1 + --- + --- + --- + ... + ---
>> 1 2 6 9!
>> This is a polynomial. We can evaluate it cheaply using
>> Horner's Rule. Any time you have a polynomial to
>> evaluate, you should think about using Horner's Rule.
>>
>> X / X / X / X \ \ \
>> 1 + --- * | 1 + --- * | 1 + --- * ... | 1 + --- | ...| |
>> 1 \ 2 \ 3 \ 9 / / /
>>
>> We can see a building block here: 1 + (X/n)*Rest.
>>
>> So let's make a function for the building block:
>>
>> step(X, N, Rest)
>> when is_float(X), is_float(N), is_float(Rest) ->
>> 1.0 + (X/N)*Rest.
>>
>> There are 9 steps, which is small enough to do by hand:
>> (NINE steps to get TEN terms. I hope that's clear.)
>>
>> exp(X)
>> when is_float(X) ->
>> step(X, 1.0, step(X, 2.0, step(X, 3.0,
>> step(X, 4.0, step(X, 5.0, step(X, 6.0,
>> step(X, 7.0, step(X, 8.0, step(X, 9.0, 1.0))))))))).
>>
>>
>> I should point out that this is a lousy way to compute
>> exp(X) for abs(X) "large". Waving hands vaguely, you
>> want X^10/10! "small". The relative error for X = 1.0
>> is -1.1e-7, for X = 2.0 is -4.6e-5, and for X = 4.0 is
>> a scary -0.0081.
>>
>> One technique that's used in cases like this is
>> RANGE REDUCTION. e^x = 2^(1.442695... * x).
>> To begin, scale x by log of e to the base 2,
>> and separate that into an integer part N and a fraction
>> part F. e^x is then 2^(N+F) = 2^N*2^F. Since F is
>> "small", we can use something like this polynomial.
>> And then we can use the equivalent of C's ldexp() to
>> handle the 2^N* part. Or we could if Erlang _had_ an
>> equivalent of ldexp().
>>
>> You don't really need the is_float/1 tests; I put them in
>> so that the compiler could generate better native code.
>>
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