Created
May 17, 2018 22:05
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#!/usr/bin/env perl6 | |
# inspired by https://narimiran.github.io/2018/05/10/python-numpy-nim.html | |
use nqp; | |
constant N = 10_000; | |
constant sigma = 0.1e0; | |
constant f = (2 / N).Num; | |
constant mu = 0.001e0; | |
constant nEpochs = 1_000; | |
sub normdist ($m, $σ) { | |
my $r = sqrt -2 * log rand; | |
my $Θ = τ * rand; | |
$r * cos($Θ) * $σ + $m; | |
} | |
sub gradientDescent(num @x, num @d, $in_mu, $in_nEpochs) { | |
my num $start = now.Num; | |
my num @y; | |
my num $err; | |
my num $w0 = 0e0; | |
my num $w1 = 0e0; | |
my num $mu = $in_mu; | |
my int $nEpochs = $in_nEpochs; | |
@y[N - 1] = 0e0; | |
for ^$nEpochs { | |
my num $grad0 = 0e0; | |
my num $grad1 = 0e0; | |
for ^N -> $i { | |
$err = nqp::mul_n(f, nqp::sub_n(@d.AT-POS($i), @y.AT-POS($i))); | |
$grad0 = nqp::add_n($grad0, $err); | |
$grad1 = nqp::add_n($grad1, nqp::mul_n($err, @x.AT-POS($i))); | |
} | |
$w0 += nqp::mul_n(mu, $grad0); | |
$w1 += nqp::mul_n(mu, $grad1); | |
for ^N -> $i { | |
@y[$i] = nqp::add_n($w0, nqp::mul_n($w1, @x[$i])); | |
} | |
} | |
($w0, $w1); | |
} | |
my num @input_x; | |
my num @input_d; | |
@input_x[N - 1] = @input_d[N - 1] = 0e0; | |
note "generating values"; | |
for ^N { | |
my num $ival = $_.Num; | |
@input_x[$_] = f * $ival; | |
@input_d[$_] = 3e0 + 2e0 * $ival + normdist(0, 0.1); | |
} | |
note "done"; | |
my $start = now; | |
say gradientDescent(@input_x, @input_d, mu, nEpochs); | |
say "Perl6 time: { now - $start }"; |
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