AI-ML
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# This file was automatically generated by Dist::Zilla::Plugin::ModuleBuild v6.010.
use strict;
use warnings;
use Module::Build 0.28;
use lib qw{inc}; use MyBuilder;
my %module_build_args = (
"build_requires" => {
"Module::Build" => "0.28"
},
"configure_requires" => {
"Module::Build" => "0.28"
},
"dist_abstract" => "Perl interface to ML",
"dist_author" => [
"Rui Meira <ruimiguelcm96\@gmail.com>"
],
"dist_name" => "AI-ML",
"dist_version" => "0.001",
"license" => "perl",
"module_name" => "AI::ML",
"recursive_test_files" => 1,
"test_requires" => {
"ExtUtils::MakeMaker" => 0,
"File::Spec" => 0,
"Test2::V0" => "0.000060",
"Test::More" => 0
}
);
my %fallback_build_requires = (
"ExtUtils::MakeMaker" => 0,
"File::Spec" => 0,
"Module::Build" => "0.28",
"Test2::V0" => "0.000060",
"Test::More" => 0
);
unless ( eval { Module::Build->VERSION(0.4004) } ) {
delete $module_build_args{test_requires};
$module_build_args{build_requires} = \%fallback_build_requires;
}
my $build = MyBuilder->new(%module_build_args);
$build->create_build_script;
#ifndef __ML_CONFIG_H__
#define __ML_CONFIG_H__
#ifdef USE_REAL
# define REAL double
#else
# define REAL float
#endif
#endif
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include "C/nn.h"
#define NEW_MATRIX(t,m,r,c) m = (Matrix*)malloc(sizeof(Matrix));\
m->rows = r; m->columns = c;\
m->values = (t*)malloc(r*c*sizeof(t));
REAL sigmoid(REAL a, void* v){
#ifdef USE_REAL
return (1 / (1 + exp(-a)));
#else
return (1 / (1 + expf(-a)));
#endif
}
REAL d_sigmoid(REAL a, void* v){
REAL s = sigmoid(a, NULL);
return s * (1 - s);
}
REAL hyperbolic_tan(REAL a, void* v){
#ifdef USE_REAL
return tanh(a);
#else
return tanhf(a);
#endif
}
REAL d_hyperbolic_tan(REAL a, void* v){
#ifdef USE_REAL
return (1 - pow(tanh(a),2));
#else
return (1 - powf(tanhf(a),2));
#endif
}
Matrix *matrix_sigmoid(Matrix *m){
return element_wise(m, sigmoid, NULL);
}
Matrix *matrix_ReLU(Matrix *m){
return element_wise(m, ReLU, NULL);
}
Matrix *matrix_d_ReLU(Matrix *m){
return element_wise(m, d_ReLU, NULL);
}
Matrix *matrix_LReLU(Matrix *m, REAL v){
return element_wise(m, LReLU, &v);
}
Matrix *matrix_d_LReLU(Matrix *m, REAL v){
return element_wise(m, d_LReLU, &v);
}
REAL ReLU(REAL a, void* v){
if(a >= 0) return a;
return 0;
}
REAL d_ReLU(REAL a, void *v){
if(a < 0) return 0;
return 1;
}
REAL LReLU(REAL a, void *v){
if(a >= 0) return a;
return a * * ((REAL*)v);
}
REAL d_LReLU(REAL a, void *v){
if(a >= 0) return 1;
return *((REAL*)v);
}
Matrix *matrix_softmax(Matrix *m){
Matrix *sm = matrix_sum(m, -get_max(m));
Matrix *em = matrix_exp(sm);
Matrix *vm;
if(m->rows == 1 && m->columns > 1){
vm = sum(em, HORIZONTAL);
}
else if(m->columns == 1 && m->rows > 1){
vm = sum(em, VERTICAL);
}
Matrix *sf = div_matrices(em, vm);
destroy(sm);
destroy(vm);
destroy(em);
return sf;
}
Matrix *matrix_d_softmax(Matrix *m){
Matrix *exp = matrix_exp(m);
Matrix *s = sum(exp, VERTICAL);
return div_matrices( mul_matrices(exp, sub_matrices(s, exp)) ,s);
}
Matrix *matrix_d_sigmoid(Matrix *m){
return element_wise(m, d_sigmoid, NULL);
}
Matrix *matrix_tanh(Matrix *m){
return element_wise(m, hyperbolic_tan, NULL);
}
Matrix *matrix_d_tanh(Matrix *m){
return element_wise(m, d_hyperbolic_tan, NULL);
}
Matrix *matrix_exp(Matrix *m){
return element_wise(m, exponential, NULL);
}
REAL sigmoid_cost(Matrix *X, Matrix *Y, Matrix *weights){
Matrix *h;
int m, i, size;
m = Y->rows;
h = matrix_sigmoid(dot(X,weights,0,0));
//class1 = matrix_mul(mul_matrices(transpose(Y), matrix_log(h)),-1);
//class2 = mul_matrices(matrix_sum(matrix_mul(Y, -1), 1), matrix_sum(matrix_mul(h, -1), 1));
size = Y->rows * Y->columns;
# ifdef USE_REAL
double cost = 0;
for(i = 0; i < size; i++){
cost += -Y->values[i]*log(h->values[i]) - ( 1 - Y->values[i]) * log(1 - h->values[i]);
}
# else
float cost = 0;
for(i = 0; i < size; i++){
cost += -Y->values[i]*logf(h->values[i]) - ( 1 - Y->values[i]) * logf(1 - h->values[i]);
}
# endif
return cost/m;
}
Matrix *mini_batch(Matrix *m, int start, int size, int axis){
Matrix *r;
int end;
if(start < 0){
fprintf(stderr, "start index need to be bigger or equal index 0\n");
exit(1);
}
end = start + size - 1;
if(axis == 0) // every training example is a column
{
if(end >= m->columns){
fprintf(stderr, "Out of index of columns\n");
exit(1);
}
r = slice(m, -1, -1, start, end);
}
else if(axis == 1){
if(end >= m->rows){
fprintf(stderr, "Out of index of rows\n");
exit(1);
}
r = slice(m, start, end, -1, -1);
}
else{
fprintf(stderr, "Invalid axis\n");
exit(1);
}
return r;
}
Matrix *predict_binary_classification(Matrix *m, REAL threshold){
Matrix *yatt;
int i, rows, cols, size;
rows = m->rows;
cols = m->columns;
size = rows * cols;
NEW_MATRIX(REAL, yatt, rows, cols);
for( i = 0; i < size; i++ ){
//fprintf(stderr, "%f\n", m->values[i]);
if(m->values[i] >= threshold){
yatt->values[i] = 1;
}
else{
yatt->values[i] = 0;
}
//fprintf(stderr, "%f -> %f\n", m->values[i], yatt->values[i]);
}
return yatt;
}
double accuracy(Matrix *y, Matrix *yatt){
double total = 0;
int size = y->columns * y->rows;
for ( int i = 0; i < size; i++ ){
if(y->values[i] == yatt->values[i]) total++;
}
return (total /size);
}
double precision(Matrix *y, Matrix *yatt){
double tp = 0, fp = 0;
int size = y->columns * y->rows;
for ( int i = 0; i < size; i++ ){
// Total predicted positive
if(yatt->values[i] == 1){
// True positive
if(y->values[i] == 1) tp++;
else fp++; // False positive
}
}
return (tp / (tp + fp));
}
double recall(Matrix *y, Matrix *yatt){
double tp = 0, fn = 0;
int size = y->columns * y->rows;
for ( int i = 0; i < size; i++ ){
//Total actual positive
if(y->values[i] == 1){
// True Positive
if(yatt->values[i] == 1) tp++;
else fn++;
}
}
return (tp / (tp + fn));
}
double f1(Matrix *y, Matrix *yatt){
double prec = precision(y, yatt);
double rec = recall(y, yatt);
double v = 2* ( ( prec * rec ) / ( prec + rec ) );
return v;
}
#include "C/config.h"
typedef struct s_matrix{
int columns;
int rows;
REAL *values;
}Matrix;
typedef enum _axis{
HORIZONTAL = 0,
VERTICAL = 1
} Axis;
Matrix *sub_matrices(Matrix *A, Matrix *B);
Matrix *mul_matrices(Matrix *A, Matrix *B);
Matrix *div_matrices(Matrix *A, Matrix *B);
Matrix *dot(Matrix *A, Matrix *B, int A_t, int B_t);
Matrix *slice(Matrix *m, int x0, int x1, int y0, int y1);
Matrix *mini_batch(Matrix *m, int start, int size, int axis);
Matrix *sum(Matrix *m, Axis axis);
Matrix *div_matrices(Matrix *A, Matrix *B);
Matrix *broadcasting(Matrix *A, Matrix *B, Axis axis, REAL f(REAL, REAL));
REAL real_mul(REAL a, REAL b);
REAL get_max(Matrix*);
Matrix* matrix_sum(Matrix*, REAL);
REAL real_sub(REAL a, REAL b);
REAL real_sum(REAL a, REAL b);
REAL real_div(REAL a, REAL b);
REAL sigmoid(REAL a, void* v);
REAL ReLU(REAL a, void* v);
REAL d_ReLU(REAL a, void* v);
REAL LReLU(REAL a, void *v);
REAL d_LReLU(REAL a, void *v);
REAL d_sigmoid(REAL a, void* v);
REAL exponential(REAL a, void* v);
Matrix *element_wise(Matrix *m, REAL f(REAL, void*), void* data);
Matrix *matrix_sigmoid(Matrix *m);
Matrix *matrix_ReLU(Matrix *m);
Matrix *matrix_d_ReLU(Matrix *m);
Matrix *matrix_LReLU(Matrix *m, REAL v);
Matrix *matrix_d_LReLU(Matrix *m, REAL v);
Matrix *matrix_softmax(Matrix *m);
Matrix *matrix_d_softmax(Matrix *m);
Matrix *matrix_d_sigmoid(Matrix *m);
Matrix *matrix_tanh(Matrix *m);
Matrix *matrix_d_tanh(Matrix *m);
Matrix *matrix_exp(Matrix *m);
void destroy(Matrix *m);
REAL sigmoid_cost(Matrix *X, Matrix *Y, Matrix *weights);
Matrix *predict_binary_classification(Matrix *m, REAL threshold);
double accuracy(Matrix *y, Matrix *yatt);
double precision(Matrix *y, Matrix *yatt);
double recall(Matrix *y, Matrix *yatt);
double f1(Matrix *y, Matrix *yatt);
#ifdef USE_REAL
void dgemm_ (char*, char*, int*, int*, int*, double*, double*, int*, double*, int*, double*, double*, int*);
#else
void sgemm_ (char*, char*, int*, int*, int*, float*, float*, int*, float*, int*, float*, float*, int*);
#endif
0.001 2019-08-31
- First Version. Complete useless.
This software is copyright (c) 2018 by Rui Meira.
This is free software; you can redistribute it and/or modify it under
the same terms as the Perl 5 programming language system itself.
Terms of the Perl programming language system itself
a) the GNU General Public License as published by the Free
Software Foundation; either version 1, or (at your option) any
later version, or
b) the "Artistic License"
--- The GNU General Public License, Version 1, February 1989 ---
This software is Copyright (c) 2018 by Rui Meira.
This is free software, licensed under:
The GNU General Public License, Version 1, February 1989
GNU GENERAL PUBLIC LICENSE
Version 1, February 1989
Copyright (C) 1989 Free Software Foundation, Inc.
51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
Everyone is permitted to copy and distribute verbatim copies
of this license document, but changing it is not allowed.
Preamble
The license agreements of most software companies try to keep users
at the mercy of those companies. By contrast, our General Public
License is intended to guarantee your freedom to share and change free
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General Public License applies to the Free Software Foundation's
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When we speak of free software, we are referring to freedom, not
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To protect your rights, we need to make restrictions that forbid
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TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
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a) cause the modified files to carry prominent notices stating that
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Mere aggregation of another independent work with the Program (or its
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END OF TERMS AND CONDITIONS
Appendix: How to Apply These Terms to Your New Programs
If you develop a new program, and you want it to be of the greatest
possible use to humanity, the best way to achieve this is to make it
free software which everyone can redistribute and change under these
terms.
To do so, attach the following notices to the program. It is safest to
attach them to the start of each source file to most effectively convey
the exclusion of warranty; and each file should have at least the
"copyright" line and a pointer to where the full notice is found.
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) 19yy <name of author>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 1, or (at your option)
any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
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GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston MA 02110-1301 USA
Also add information on how to contact you by electronic and paper mail.
If the program is interactive, make it output a short notice like this
when it starts in an interactive mode:
Gnomovision version 69, Copyright (C) 19xx name of author
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
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The hypothetical commands `show w' and `show c' should show the
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You should also get your employer (if you work as a programmer) or your
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necessary. Here a sample; alter the names:
Yoyodyne, Inc., hereby disclaims all copyright interest in the
program `Gnomovision' (a program to direct compilers to make passes
at assemblers) written by James Hacker.
<signature of Ty Coon>, 1 April 1989
Ty Coon, President of Vice
That's all there is to it!
--- The Artistic License 1.0 ---
This software is Copyright (c) 2018 by Rui Meira.
This is free software, licensed under:
The Artistic License 1.0
The Artistic License
Preamble
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may be copied, such that the Copyright Holder maintains some semblance of
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the package the right to use and distribute the Package in a more-or-less
customary fashion, plus the right to make reasonable modifications.
Definitions:
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2. You may apply bug fixes, portability fixes and other modifications derived
from the Public Domain or from the Copyright Holder. A Package modified in such
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3. You may otherwise modify your copy of this Package in any way, provided that
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form, provided that you do at least ONE of the following:
a) distribute a Standard Version of the executables and library files,
together with instructions (in the manual page or equivalent) on where to
get the Standard Version.
b) accompany the distribution with the machine-readable source of the Package
with your modifications.
c) accompany any non-standard executables with their corresponding Standard
Version executables, giving the non-standard executables non-standard
names, and clearly documenting the differences in manual pages (or
equivalent), together with instructions on where to get the Standard
Version.
d) make other distribution arrangements with the Copyright Holder.
5. You may charge a reasonable copying fee for any distribution of this
Package. You may charge any fee you choose for support of this Package. You
may not charge a fee for this Package itself. However, you may distribute this
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from the programs of this Package do not automatically fall under the copyright
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be considered part of this Package.
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products derived from this software without specific prior written permission.
9. THIS PACKAGE IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR IMPLIED
WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF
MERCHANTIBILITY AND FITNESS FOR A PARTICULAR PURPOSE.
The End
# This file was automatically generated by Dist::Zilla::Plugin::Manifest v6.010.
Build.PL
C/config.h
C/nn.c
C/nn.h
Changes
LICENSE
MANIFEST
META.json
META.yml
README
README.md
XS/ML.xs.inc
dist.ini
inc/MyBuilder.pm
lib/AI/ML.pm
lib/AI/ML/Expr.pm
lib/AI/ML/LinearRegression.pm
lib/AI/ML/LogisticRegression.pm
lib/AI/ML/NeuralNetwork.pm
run.sh
scripts/load_data.c
scripts/mnist.pl
t/00-report-prereqs.dd
t/00-report-prereqs.t
t/01-activation-funcs.t
t/02-cost_functions.t
t/03-mini-batch.t
t/04-linear-regression.t
t/05-logistic-regression.t
t/06-accuracy-precision-recall-f1.t
t/07-neural-network.t
t/08-gradient-checking.t_
t/W1_grad_check.csv
t/W2_grad_check.csv
t/W3_grad_check.csv
t/b1_grad_check.csv
t/b2_grad_check.csv
t/b3_grad_check.csv
t/dW1_grad_check.csv
t/dW2_grad_check.csv
t/dW3_grad_check.csv
t/dataset_adjetivos.csv
t/db1_grad_check.csv
t/db2_grad_check.csv
t/db3_grad_check.csv
t/logistic.csv
t/pred-nn.csv
t/w1.csv
t/w2.csv
t/x.csv
t/x_grad_check.csv
t/y.csv
t/y_grad_check.csv
xt/author/00-compile.t
xt/author/pod-syntax.t
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---
abstract: 'Perl interface to ML'
author:
- 'Rui Meira <ruimiguelcm96@gmail.com>'
build_requires:
ExtUtils::MakeMaker: '0'
File::Spec: '0'
Module::Build: '0.28'
Test2::V0: '0.000060'
Test::More: '0'
configure_requires:
Module::Build: '0.28'
dynamic_config: 0
generated_by: 'Dist::Zilla version 6.010, CPAN::Meta::Converter version 2.150010'
license: perl
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version: '1.4'
name: AI-ML
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- eg
- examples
- inc
- share
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version: '0.001'
AI::ML::Expr:
file: lib/AI/ML/Expr.pm
version: '0.001'
AI::ML::LinearRegression:
file: lib/AI/ML/LinearRegression.pm
version: '0.001'
AI::ML::LogisticRegression:
file: lib/AI/ML/LogisticRegression.pm
version: '0.001'
AI::ML::NeuralNetwork:
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This Perl model, AI::ML, is a module with some machine learning algorithms.
XS/ML.xs.inc view on Meta::CPAN
#include "EXTERN.h"
#include "perl.h"
#include "XSUB.h"
#include "C/nn.h"
MODULE = AI::ML PACKAGE = AI::ML::Expr
SV *
_sigmoid(a)
unsigned long a;
PREINIT:
Matrix *m;
CODE:
m = matrix_sigmoid((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_tanh(a)
unsigned long a;
PREINIT:
Matrix *m;
CODE:
m = matrix_tanh((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_d_tanh(a)
unsigned long a;
PREINIT:
Matrix *m;
CODE:
m = matrix_d_tanh((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_relu(a)
unsigned long a;
PREINIT:
Matrix* m;
CODE:
m = matrix_ReLU((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_d_relu(a)
unsigned long a;
PREINIT:
Matrix* m;
CODE:
m = matrix_d_ReLU((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_lrelu(a, v)
unsigned long a;
REAL v;
PREINIT:
Matrix* m;
CODE:
m = matrix_LReLU((Matrix*)a, v);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_d_lrelu(a, v)
unsigned long a;
REAL v;
PREINIT:
Matrix* m;
CODE:
m = matrix_d_LReLU((Matrix*)a, v);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_predict_binary_classification(a, v)
unsigned long a;
REAL v;
PREINIT:
Matrix* m;
CODE:
m = predict_binary_classification((Matrix*)a, v);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_softmax(a)
unsigned long a;
PREINIT:
Matrix* m;
CODE:
m = matrix_softmax((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_d_softmax(a)
unsigned long a;
PREINIT:
Matrix* m;
CODE:
m = matrix_d_softmax((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_d_sigmoid(a)
unsigned long a;
PREINIT:
Matrix* m;
CODE:
m = matrix_d_sigmoid((Matrix*)a);
RETVAL = newSVuv((unsigned long)m);
OUTPUT:
RETVAL
SV *
_sigmoid_cost(x, y, w)
unsigned long x;
unsigned long y;
unsigned long w;
CODE:
RETVAL = newSVnv(sigmoid_cost((Matrix*)x, (Matrix*)y, (Matrix*)w));
OUTPUT:
RETVAL
SV *
_mini_batch(m, start, s, axis)
unsigned long m;
int start;
int s;
int axis;
PREINIT:
Matrix* r;
CODE:
r = mini_batch((Matrix*)m, start, s, axis);
RETVAL = newSVnv((unsigned long)r);
OUTPUT:
RETVAL
SV *
_accuracy(y, yatt)
unsigned long y;
unsigned long yatt;
CODE:
RETVAL = newSVnv(accuracy((Matrix*)y, (Matrix*)yatt));
OUTPUT:
RETVAL
SV *
_precision(y, yatt)
unsigned long y;
unsigned long yatt;
CODE:
RETVAL = newSVnv(precision((Matrix*)y, (Matrix*)yatt));
OUTPUT:
RETVAL
SV *
_recall(y, yatt)
unsigned long y;
unsigned long yatt;
CODE:
RETVAL = newSVnv(recall((Matrix*)y, (Matrix*)yatt));
OUTPUT:
RETVAL
SV *
_f1(y, yatt)
unsigned long y;
unsigned long yatt;
CODE:
RETVAL = newSVnv(f1((Matrix*)y, (Matrix*)yatt));
OUTPUT:
RETVAL
name = AI-ML
author = Rui Meira <ruimiguelcm96@gmail.com>
license = Perl_5
copyright_holder = Rui Meira
copyright_year = 2018
version = 0.001
[@Starter]
-remove = MakeMaker
-remove = GatherDir
;[PodCoverageTests]
[ModuleBuild]
mb_class = MyBuilder
[Prereqs / TestRequires]
Test2::V0 = 0.000060
[Git::GatherDir]
exclude_mathc = ^_
inc/MyBuilder.pm view on Meta::CPAN
package MyBuilder;
use base 'Module::Build';
use warnings;
use strict;
use Config;
use ExtUtils::ParseXS;
use ExtUtils::Mkbootstrap;
use Path::Tiny;
my $EXTRA_O_FLAGS = "";
my $EXTRA_FLAGS = "-lblas -llapack";
sub ACTION_code {
my $self = shift;
$EXTRA_O_FLAGS .= " -DUSE_REAL" unless exists $self->args->{'with-float'};
$self->update_XS("XS/ML.xs.inc");
$self->dispatch("create_objects");
$self->dispatch("compile_xs");
$self->SUPER::ACTION_code;
}
sub update_XS {
my ($self, $file) = @_;
my $output = $file;
$output =~ s/\.inc$//;
open my $i_fh, "<", $file or die "$!";
open my $o_fh, ">", $output or die "$!";
while (<$i_fh>) {
s/REAL/float/g;
print {$o_fh} $_;
}
close $o_fh;
close $i_fh;
}
sub ACTION_create_objects {
my $self = shift;
my $cbuilder = $self->cbuilder;
my $c_progs = $self->rscan_dir("C", qr/\.c$/);
for my $file (@$c_progs) {
my $object = $file;
$object =~ s/\.c$/.o/;
next if $self->up_to_date($file, $object);
$cbuilder->compile(
object_file => $object,
extra_compiler_flags => $EXTRA_O_FLAGS,
source => $file,
include_dirs => ["."]
);
}
}
sub ACTION_compile_xs {
my $self = shift;
my $cbuilder = $self->cbuilder;
my $archdir = path($self->blib, "arch", "auto", "AI", "ML");
$archdir->mkpath unless -d $archdir;
my $xs = path("XS", "ML.xs");
my $xs_c = path("XS", "ML.c");
if (!$self->up_to_date($xs, $xs_c)) {
ExtUtils::ParseXS::process_file(
filename => $xs->stringify,
prototypes => 0,
output => $xs_c->stringify
);
}
my $xs_o = path("XS", "ML.o");
if (!$self->up_to_date($xs_c, $xs_o)) {
$cbuilder->compile(
source => $xs_c,
extra_compiler_flags => $EXTRA_O_FLAGS,
include_dirs => ["."],
object_file => $xs_o
);
}
my $bs_file = path( $archdir, "ML.bs");
if (!$self->up_to_date($xs_o, $bs_file) ) {
ExtUtils::Mkbootstrap::Mkbootstrap($bs_file);
if (!-f $bs_file) {
$bs_file->touch;
}
}
my $objects = $self->rscan_dir("C", qr/\.o$/);
push @$objects, $xs_o;
my $lib_file = path($archdir, "ML.$Config{dlext}");
if (!$self->up_to_date( $objects, $lib_file )) {
$cbuilder->link(
module_name => 'AI::ML',
extra_linker_flags => $EXTRA_FLAGS,
objects => $objects,
lib_file => $lib_file,
);
}
}
1;
lib/AI/ML.pm view on Meta::CPAN
# ABSTRACT: Perl interface to ML
use strict;
use warnings;
package AI::ML;
use parent 'DynaLoader';
use Math::Lapack;
bootstrap AI::ML;
#sub dl_load_flags { 1 }
1;
lib/AI/ML/Expr.pm view on Meta::CPAN
# ABSTRACT: turns baubles into trinkets
package AI::ML::Expr;
use strict;
use warnings;
use Chart::Gnuplot;
use Scalar::Util 'blessed';
use AI::ML;
use Math::Lapack;
use aliased 'Math::Lapack::Matrix' => 'M';
use parent 'Exporter';
use parent 'Math::Lapack::Expr';
our @EXPORT = qw(mini_batch tanh sigmoid relu lrelu d_sigmoid d_relu d_lrelu d_tanh softmax sigmoid_cost plot plot_cost);
use Math::Lapack::Expr;
sub _bless {
my $matrix = shift;
return bless { _matrix => $matrix, type => 'matrix' } => "Math::Lapack::Matrix";
}
=head2 sigmoid
Allow apply the function sigmoid to every element of the matrix.
$m = $m->sigmoid();
$m = sigmoid($m);
=cut
sub sigmoid {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'sigmoid', args => [$self] } => __PACKAGE__
}
sub eval_sigmoid {
my $tree = shift;
if (blessed($tree) && $tree->isa("Math::Lapack::Matrix")) {
return _bless _sigmoid($tree->matrix_id);
}
die "Sigmoid for non matrix: " . ref($tree);
}
=head2 relu
Allows apply the function relu to every element of the matrix.
$m = $m->relu();
$m = relu($m);
=cut
sub relu {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'relu', args => [$self] } => __PACKAGE__;
}
sub eval_relu {
my $tree = shift;
if (ref($tree) eq "Math::Lapack::Matrix") {
return _bless _relu($tree->matrix_id);
}
die "ReLU for non matrix";
}
=head2 d_relu
Allows apply the function d_relu to every element of the matrix.
$m = $m->d_relu();
$m = d_relu($m);
=cut
sub d_relu {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'd_relu', args => [$self] } => __PACKAGE__;
}
sub eval_d_relu {
my $tree = shift;
if (ref($tree) eq "Math::Lapack::Matrix") {
return _bless _d_relu($tree->matrix_id);
}
die "ReLU for non matrix";
}
=head2 lrelu
Allows apply the function lrelu to every element of the matrix.
$th::Lapack::Matrixref(1)m = lrelu($m, 0.0001);
$m = m->lrelu(0.1);
=cut
sub lrelu {
my ($self, $v) = @_;
return bless { package => __PACKAGE__, type => 'lrelu', args => [$self, $v] } => __PACKAGE__;
}
sub eval_lrelu {
my ($tree, $v) = @_;
if (ref($tree) eq "Math::Lapack::Matrix") {
return _bless _lrelu($tree->matrix_id, $v);
}
die "lReLU for non matrix";
}
=head2 d_lrelu
Allows apply the function d_lrelu to every element of the matrix.
$th::Lapack::Matrixref(1)m = lrelu($m, 0.0001);
$m = m->lrelu(0.1);
=cut
sub d_lrelu {
my ($self, $v) = @_;
return bless { package => __PACKAGE__, type => 'd_lrelu', args => [$self, $v] } => __PACKAGE__;
}
sub eval_d_lrelu {
my ($tree, $v) = @_;
if (ref($tree) eq "Math::Lapack::Matrix") {
return _bless _d_lrelu($tree->matrix_id, $v);
}
die "lReLU for non matrix";
}
=head2 softmax
Allows apply the function softmax to every element of the matrix.
$m = softmax($m);
$m = $m->softmax();
=cut
sub softmax {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'softmax', args => [$self] } => __PACKAGE__;
}
sub eval_softmax {
my $tree = shift;
if (ref($tree) eq "Math::Lapack::Matrix") {
my $s = $tree->max();
my $e_x = exp( $tree - $s );
my $div = sum( $e_x, 1 );
return $e_x / $div;
#use Data::Dumper;
#print STDERR Dumper $matrix;
# return _bless _softmax($tree->matrix_id);
}
die "softmax for non matrix";
}
=head2 d_softmax
Allows apply the function d_softmax to every element of the matrix.
$m = d_softmax($m);
$m = $m->d_softmax();
=cut
sub d_softmax {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'd_softmax', args => [$self] } => __PACKAGE__;
}
sub eval_d_softmax {
my $tree = shift;
if (ref($tree) eq "Math::Lapack::Matrix") {
return _bless _d_softmax($tree->matrix_id);
}
die "d_softmax for non matrix";
}
=head2 tanh
Allows apply the function tanh to every element of the matrix.
$m = tanh($m);
$m = $m->tanh();
=cut
sub tanh {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'tanh', args => [$self] } => __PACKAGE__;
}
sub eval_tanh {
my $tree = shift;
if( ref($tree) eq "Math::Lapack::Matrix"){
return _bless _tanh($tree->matrix_id);
}
die "tanh for non matrix";
}
=head2 d_tanh
Allows apply the function d_tanh to every element of the matrix.
$m = d_tanh($m);
$m = $m->d_tanh();
=cut
sub d_tanh {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'd_tanh', args => [$self] } => __PACKAGE__;
}
sub eval_d_tanh {
my $tree = shift;
if( ref($tree) eq "Math::Lapack::Matrix"){
return _bless _d_tanh($tree->matrix_id);
}
die "d_tanh for non matrix";
}
=head2 d_sigmoid
Allow apply the derivate of function sigmoid to every element of the matrix.
$m = $m->d_sigmoid();
$m = d_sigmoid($m);
=cut
sub d_sigmoid {
my ($self) = @_;
return bless { package => __PACKAGE__, type => 'd_sigmoid', args => [$self] } => __PACKAGE__;
}
sub eval_d_sigmoid {
my $tree = shift;
if( ref($tree) eq "Math::Lapack::Matrix"){
return _bless _d_sigmoid($tree->matrix_id);
}
return "d_sigmoid for non matrix";
}
=head2 sigmoid_cost
Allows get the value of the cost of sigmoid function.
put examples
=cut
sub sigmoid_cost {
my ($x, $y, $weights) = @_;
return _sigmoid_cost($x->matrix_id, $y->matrix_id, $weights->matrix_id);
}
=head2 mini-batch
=cut
sub mini_batch {
my ($self, $start, $size, $axis) = @_;
$axis = 0 unless defined $axis; #default
return _bless _mini_batch($self->matrix_id, $start, $size, $axis);
}
=head2 prediction
=cut
sub prediction {
my ($self, %opts) = @_;
my $t = exists $opts{threshold} ? $opts{threshold} : 0.50;
return _bless _predict_binary_classification($self->matrix_id, $t);
}
=head2 precision
=cut
sub precision {
my ($y, $yatt) = @_;
return _precision($y->matrix_id, $yatt->matrix_id);
}
=head2 accuracy
=cut
sub accuracy {
my ($y, $yatt) = @_;
return _accuracy($y->matrix_id, $yatt->matrix_id);
}
=head2 recall
=cut
sub recall {
my ($y, $yatt) = @_;
return _recall($y->matrix_id, $yatt->matrix_id);
}
=head2 f1
=cut
sub f1 {
my ($y, $yatt) = @_;
return _f1($y->matrix_id, $yatt->matrix_id);
}
=head2 plot
=cut
sub plot {
my ($x, $y, $theta, $file) = @_;
my @xdata = $x->vector_to_list();
my @ydata = $y->vector_to_list();
my @thetas = $theta->vector_to_list();
my $f = $thetas[0] . "+" . $thetas[1] . "*x";
#print STDERR "$_\n" for(@xdata);
#rint STDERR "$_\n" for(@ydata);
#print STDERR "$f\n";
#print STDERR "\n\nFILE == $file\n\n";
my $chart = Chart::Gnuplot->new(
output => $file,
title => "Nice one",
xlabel => "x",
ylabel => "y"
);
my $points = Chart::Gnuplot::DataSet->new(
xdata => \@xdata,
ydata => \@ydata,
style => "points"
);
my $func = Chart::Gnuplot::DataSet->new(
func => $f
);
$chart->plot2d($points, $func);
}
=head2 plot_cost
=cut
sub plot_cost{
my ($file, @costs) = @_;
my @iters = (1 .. scalar(@costs));
my $chart = Chart::Gnuplot->new(
output => $file,
title => "Cost",
xlabel => "Iter",
ylabel => "Cost"
);
$chart->png;
my $data = Chart::Gnuplot::DataSet->new(
xdata => \@iters,
ydata => \@costs,
style => "linespoints"
);
$chart->plot2d($data);
}
1;
lib/AI/ML/LinearRegression.pm view on Meta::CPAN
# ABSTRACT: turns baubles into trinkets
package AI::ML::LinearRegression;
use strict;
use warnings;
use Scalar::Util 'blessed';
use aliased 'Math::Lapack::Matrix' => 'M';
use Math::Lapack::Expr;
use parent 'AI::ML::Expr';
use Data::Dumper;
=head2 new
=cut
sub new {
my ($self, %opts) = @_;
$self = bless {} => 'AI::ML::LinearRegression';
$self->{grad} = $opts{gradient} if exists $opts{gradient};
$self->{reg} = $opts{lambda} if exists $opts{lambda};
$self->{cost} = $opts{cost} if exists $opts{cost};
$self->{plot} = $opts{plot} if exists $opts{plot};
$self->{n} = exists $opts{n} ? $opts{n} : 100;
$self->{alpha} = exists $opts{alpha} ? $opts{alpha} : 0.1;
return $self;
}
=head2 linear_regression
considerando X com as dimensoes(m,n) e theta com as dimensoes (n,1)
#Default is normal equation
#Option
#gradient => not use normal equation
#plot => plot data and linear
#cost => plot cost
#alpha
#n => number of iterations
=cut
sub train {
my ($self, $x, $y) = @_;
my ($thetas, $iters, $alpha, $lambda);
if( exists $self->{grad} ) {
$iters = $self->{n};
$alpha = $self->{alpha};
my ($cost, $grads, $reg_thetas);
my $x = Math::Lapack::Matrix::concatenate(
M->ones($x->rows, 1),
$x
);
my ($m, $n) = $x->shape();
$thetas = M->random($n,1);
my @cost_values = ();
if(defined $self->{reg}){
$lambda = $self->{reg};
for my $i (1..$iters){
$cost = sum( ( ($x x $thetas) - $y) ** 2) / (2 * $m) + $lambda * sum( $thetas->slice(x0 => 1) );
push @cost_values, $cost->get_element(0,0) if defined $self->{cost};
$grads = ($x->T x (($x x $thetas)-$y)) / $m;
$reg_thetas = ($lambda / $m) * $thetas;
# do not regularize theta 0
$reg_thetas->set_element(0,0,0);
$thetas = $thetas - $alpha * ( $grads + $reg_thetas );
}
}
else{
for my $i (1..$iters)
{
if( exists $self->{cost} ) {
$cost = sum( ( ($x x $thetas) - $y) ** 2) / (2 * $m);
push @cost_values,$cost->get_element(0,0);
}
$grads = ($x->T x (($x x $thetas)-$y)) / $m;
$thetas = $thetas - $alpha * $grads;
}
}
AI::ML::Expr::plot($x->slice(col => 1), $y, $thetas, $self->{plot}) if defined $self->{plot};
AI::ML::Expr::plot_cost($self->{cost}, @cost_values) if exists $self->{cost};
}
else{
$thetas = normal_eq($x, $y);
AI::ML::Expr::plot($x, $y, $thetas, $self->{plot}) if defined $self->{plot};
}
$self->{thetas} = $thetas;
}
=head2 normal_eq
=cut
sub normal_eq {
my ($x, $y) = @_;
#adiciona coluna de uns a matrix X
$x = Math::Lapack::Matrix::concatenate(
M->ones($x->rows, 1),
$x
);
return ((($x->T x $x)->inverse) x $x->T) x $y;
}
=head2 linear_regression_pred
devolve o valor previsto
considerando X com as dimensoes(m,n) e theta com as dimensoes (n,1)
=cut
sub linear_regression_pred {
my ($x, $thetas) = @_;
return $x x $thetas;
}
1;
lib/AI/ML/LogisticRegression.pm view on Meta::CPAN
# ABSTRACT: turns baubles into trinketsA
package AI::ML::LogisticRegression;
use strict;
use warnings;
use Scalar::Util 'blessed';
use aliased 'Math::Lapack::Matrix' => 'M';
use Math::Lapack::Expr;
use AI::ML::Expr;
use parent 'AI::ML::Expr';
use Data::Dumper;
=head2 new
=cut
sub new {
my ($self, %opts) = @_;
$self = bless {} => 'AI::ML::LogisticRegression';
$self->{reg} = $opts{reg} if exists $opts{reg};
$self->{cost} = $opts{cost} if exists $opts{cost};
$self->{plot} = $opts{plot} if exists $opts{plot};
$self->{n} = exists $opts{n} ? $opts{n} : 100;
$self->{alpha} = exists $opts{alpha} ? $opts{alpha} : 0.1;
return $self;
}
=head2 logistic_regression
considerando x [m,n]
considerando y [m,1]
=cut
sub train {
my ($self, $x, $y) = @_;
my ($lambda, $thetas, $h, $cost, $reg, $reg_thetas, $grad);
my $iters = $self->{n};
my $alpha = $self->{alpha};
#my $cost_file = exists $opts{cost} ? $opts{cost} : undef;
$x = Math::Lapack::Matrix::concatenate(
M->ones($x->rows,1),
$x
);
my($m, $n) = $x->shape;
$thetas = M->random($n,1);
my @cost_values=();
if ( exists $self->{reg} ) {
$lambda = $self->{reg};
for my $i (1 .. $iters) {
$h = sigmoid($x x $thetas);
$reg = ($lambda / (2 * $m)) * sum( $thetas->slice(x0 => 1) ** 2 );
$cost = (-1 / $m) * sum($y * log($h) + (1 - $y) * log(1-$h)) + $reg;
push @cost_values, $cost->get_element(0,0) if exists $self->{cost};
$reg_thetas = ($lambda / $m) * $thetas;
$reg_thetas->set_element(0,0,0);
$grad = ($x->T x ($h - $y)) / $m;
$thetas = $thetas - $alpha * ( $grad + $reg_thetas );
}
}
else {
for my $i (1 .. $iters) {
$h = sigmoid($x x $thetas);
$cost = (-1 / $m)*sum(($y * log($h)) + ((1-$y) * log(1-$h)));
push @cost_values, $cost->get_element(0,0) if exists $self->{cost};
$grad = ($x->T x ($h - $y)) / $m;
$thetas = $thetas - $alpha * $grad;
}
}
AI::ML::Expr::plot_cost($self->{cost}, @cost_values) if exists $self->{cost};
$self->{thetas} = $thetas;
}
=head2 classification
=cut
sub classification {
my ($self, $x) = @_;
$x = (M->ones($x->rows,1))->append($x);
$self->{classification} = sigmoid($x x $self->{thetas});
}
=head2 prediction
=cut
sub prediction {
my ($self, $x, %opts) = @_;
$x = Math::Lapack::Matrix::concatenate(
M->ones($x->rows,1),
$x
);
my $h = sigmoid($x x $self->{thetas});
$self->{yatt} = AI::ML::Expr::prediction($h, %opts);
}
=head2 accuracy
=cut
sub accuracy {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::accuracy($y, $self->{yatt});
}
=head2 precision
=cut
sub precision {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::precision($y, $self->{yatt});
}
=head2 recall
=cut
sub recall {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::recall($y, $self->{yatt});
}
=head2 f1
=cut
sub f1 {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::f1($y, $self->{yatt});
}
1;
lib/AI/ML/NeuralNetwork.pm view on Meta::CPAN
# ABSTRACT: turns baubles into trinkets
package AI::ML::NeuralNetwork;
use strict;
use warnings;
use Scalar::Util 'blessed';
use Math::Lapack::Matrix;
use Math::Lapack::Expr;
use parent 'AI::ML::Expr';
my $functions = {
sigmoid => \&AI::ML::Expr::sigmoid,
relu => \&AI::ML::Expr::relu,
lrelu => \&AI::ML::Expr::lrelu,
softmax => \&AI::ML::Expr::softmax,
tanh => \&AI::ML::Expr::tanh,
dsigmoid => \&AI::ML::Expr::d_sigmoid,
drelu => \&AI::ML::Expr::d_relu,
dlrelu => \&AI::ML::Expr::d_lrelu,
dtanh => \&AI::ML::Expr::d_tanh
};
=head2 new
=cut
sub new {
my ($self, $layers, %opts) = @_;
$self = bless {} => 'AI::ML::NeuralNetwork';
my $i = 0;
for my $href ( @$layers ) {
if( $i == 0 ){
$self->{"l$i"} = { units => $href };
}
else {
if( $href =~ qw.^\d+$. ){
$self->{"l$i"} = { units => $href, func => "sigmoid", dfunc => "dsigmoid" };
}
elsif( ref($href) eq "HASH" ) {
if ( exists $href->{func} ) {
if ( exists $functions->{$href->{func}} )
{
$self->{"l$i"}{func} = $href->{func};
$self->{"l$i"}{dfunc} = 'd' . $href->{func};
}
else
{
die "Invalid activation function for layer $i: $href->{func}\n";
}
}
else {
$self->{"l$i"}{func} = "sigmoid";
}
if( exists($href->{units}) && $href->{units} =~ qw. ^\d+$ . ) {
$self->{"l$i"}{units} = $href->{units};
} else{
die "undefined number of units in layer $i\n";
}
}
}
$i++;
}
$self->load_weights_bias();
$self->{n} = exists $opts{n} ? $opts{n} : 100;
$self->{alpha} = exists $opts{alpha} ? $opts{alpha} : 0.1;
$self->{reg} = exists $opts{reg} ? $opts{reg} : undef;
$self->{cost} = exists $opts{cost} ? $opts{cost} : undef;
$self->{plot} = exists $opts{plot} ? $opts{plot} : undef;
return $self;
}
=head2 load_weights_bias
=cut
sub load_weights_bias {
my ($self) = @_;
my $size = keys %$self;
$self->{layers} = $size;
for my $i ( 1 .. $size-1 ) {
my $j = $i - 1;
$self->{"l$i"}{w} = Math::Lapack::Matrix->random($self->{"l$i"}{units}, $self->{"l$j"}{units});
$self->{"l$i"}{b} = Math::Lapack::Matrix->zeros($self->{"l$i"}{units}, 1);
}
}
=head2 train
=cut
sub train {
my ($self, $x, $y, %opts) = @_;
my $m = $x->columns;
my $layers = $self->{layers};
die "Wrong number of units in input layer" if ( $x->rows != $self->{"l0"}{units} );
die "Wrong number of units in output layer" if ( $y->rows != $self->{"l".($layers-1)}{units} );
my $var = { A0 => $x };
my $iters = $self->{n};
my $alpha = $self->{alpha};
my ($rows, $cols, $cost);
for my $iter (1 .. $iters) {
my $aux;
# forward propagation
my ($i,$j);
for ( 1 .. $layers-1){
$i = $_;
$j = $i - 1;
$var->{"Z$i"} = $self->{"l$i"}{w} x $var->{"A$j"} + $self->{"l$i"}{b};
$var->{"A$i"} = $functions->{ $self->{"l$i"}{func} }->($var->{"Z$i"});
$i++;
}
$i--;
if ($iter % 1000 == 0){
$cost = (-1 / $m)*sum(($y * log($var->{"A$i"})) + ((1-$y) * log(1-$var->{"A$i"})));
$cost = $cost->get_element(0,0);
}
#
## back propagation
$var->{"dz$i"} = $var->{"A$i"} - $y;
$aux = $var->{"dz$i"};
#$aux->save_csv("/tmp/DZ$i.csv");
$var->{"dw$i"} = (1 / $m) * ( $var->{"dz$i"} x T($var->{"A$j"}) );
$var->{"db$i"} = (1 / $m) * sum( $var->{"dz$i"} , 0 );
$var->{"da$j"} = T($self->{"l$i"}{w}) x $var->{"dz$i"};
$self->{"l$i"}{w} = $self->{"l$i"}{w} - ( $alpha * $var->{"dw$i"} );
$self->{"l$i"}{b} = $self->{"l$i"}{b} - ( $alpha * $var->{"db$i"} );
$self->{"l$i"}{b}->get_element(0,0); #force eval
$self->{"l$i"}{w}->get_element(0,0);
if($iter == 100){
$aux = $var->{"dw$i"};
#$aux->save_csv("/tmp/DW$i.csv");
$aux = $var->{"db$i"};
#$aux->save_csv("/tmp/DB$i.csv");
$aux = $var->{"da$j"};
#$aux->save_csv("/tmp/da$j.m");
$aux = $self->{"l$i"}{w};
#$aux->save_csv("/tmp/W$i.csv");
$aux = $self->{"l$i"}{b};
#$aux->save_csv("/tmp/B$i.csv");
}
##print STDERR Dumper($self,$var);
##
$i--;$j--;
for(; $j >= 0; $i--, $j--) {
#print STDERR "Iter: $i\n";
$var->{"dz$i"} = $var->{"da$i"} * $functions->{ $self->{"l$i"}{dfunc} }->($var->{"Z$i"}) ;
$var->{"dw$i"} = (1 / $m) * ( $var->{"dz$i"} x T($var->{"A$j"}) );
$var->{"db$i"} = (1 / $m) * sum( $var->{"dz$i"} , 0 );
$var->{"da$j"} = T($self->{"l$i"}{w}) x $var->{"dz$i"} if $j >= 1;
$self->{"l$i"}{w} = $self->{"l$i"}{w} - ( $alpha * $var->{"dw$i"} );
$self->{"l$i"}{b} = $self->{"l$i"}{b} - ( $alpha * $var->{"db$i"} );
if($iter == 100){
$aux = $var->{"dz$i"};
#$aux->save_csv("/tmp/DZ$i.csv");
$aux = $var->{"dw$i"};
#$aux->save_csv("/tmp/DW$i.csv");
$aux = $var->{"db$i"};
#$aux->save_csv("/tmp/DB$i.csv");
#if ($j>=1){$aux = $var->{"da$j"};
#$aux->save_csv("/tmp/da$j.m");}
$aux = $self->{"l$i"}{w};
#$aux->save_csv("/tmp/W$i.csv");
$aux = $self->{"l$i"}{b};
#$aux->save_csv("/tmp/B$i.csv");
}
}
}
$self->{grads} = %$var if exists $opts{grads};
}
=head2 gradient_checking
=cut
sub gradient_checking {
my ($self, $x, $y) = @_;
my ($params, $grads, %dims) = $self->_get_params_grads();
#print STDERR Dumper($params);
#print STDERR Dumper($grads);
#print STDERR Dumper(%dims);
#my $n = $params->rows;
#my $m = $params->columns;
#print STDERR "elements:$n,$m\nParams vector\n";
#for my $i (0..$n-1){
# print STDERR "$i:" .$params->get_element($i,0)."\n";
#}
#print STDERR "Grads vector\n";
#for my $j (0..$n-1){
# print STDERR $params->get_element($j,0)."\n";
#}
#my $epsilon = 1e-7;
#my $J_plus = Math::Lapack::Matrix->zeros($n,1);
#my $J_minus = Math::Lapack::Matrix->zeros($n,1);
#my $grad_aprox = Math::Lapack::Matrix->zeros($n,1);
#for my $i (0..$n-1){
# $theta_plus = $params;
# $theta_plus->set_element($i,0) = $theta_plus->get_element($i,0) + $epsilon;
# $J_plus($i,0) = _forward_prop_n($x, $y, _vector_to_hash($theta_plus, $n, %dims));
#
# $theta_minus = $params;
# $theta_minus->set_element($i,0) = $theta_minus->get_element($i,0) - $epsilon;
# $J_minus($i,0) = _forward_prop_n($x, $y, _vector_to_hash($theta_minus, $n));
# $grad_aprox($i,0) = ($J_plus($i,0) - $j_minus($i,0)) / (2*$epsilon);
#}
}
=head2 _vector_to_hash
=cut
sub _vector_to_hash {
my ($vector, $n, %dims) = @_;
my $size = $vector->rows;
my $pos = 0;
my ($n_values, $weight, $bias);
my %hash = {};
for my $i (1..$n-1){
$n_values = $dims{"w$i"}{rows} * $dims{"w$i"}{cols};
$weight = $vector->slice( row_range => [$pos, $pos+$n_values-1] );
$hash{"l$i"}{w} = $weight->reshape($dims{"w$i"}{rows}, $dims{"w$i"}{cols});
$pos += $n_values;
$n_values = $dims{"b$i"}{rows} * $dims{"b$i"}{cols};
$bias = $vector->reshape( row_range => [$pos, $pos+$n_values-1]);
$hash{"l$i"}{b} = $bias->reshape($dims{"b$i"}{rows},$dims{"b$i"}{cols});
$pos += $n_values;
}
return %hash;
}
=head2 _get_params_grads
=cut
sub _get_params_grads {
my ($self) = @_;
my ($matrix, $params, $grads, $n, %dims);
my ($r, $c);
$n = $self->{layers};
$matrix = $self->{"l1"}{w};
$dims{"w1"}{rows} = $matrix->rows;
$dims{"w1"}{cols} = $matrix->columns;
($r, $c) = $matrix->shape;
print STDERR "New dimension shape: $r,$c\n";
$params = $matrix->reshape($matrix->rows * $matrix->columns, 1);
($r, $c) = $params->shape;
print STDERR "$r,$c\n";
$matrix = $self->{grads}{"dw1"};
$grads = $matrix->reshape($matrix->rows * $matrix->columns, 1);
for my $i (1..$n-1){
print STDERR "layer: $i\n";
if( $i > 1 ){
$matrix = $self->{"l$i"}{w};
$dims{"w$i"}{rows} = $matrix->rows;
$dims{"w$i"}{cols} = $matrix->columns;
$matrix = $matrix->reshape($matrix->rows* $matrix->columns, 1);
($r, $c) = $matrix->shape;
print STDERR "New dimension shape: $r,$c\n";
$params->append($matrix,1);
$matrix = $self->{grads}{"dw$i"};
$grads->append($matrix->reshape($matrix->rows*$matrix->columns, 1),0);
}
($r, $c) = $params->shape;
print STDERR "$r,$c\n";
$matrix = $self->{"l$i"}{b};
$dims{"b$i"}{rows} = $matrix->rows;
$dims{"b$i"}{cols} = $matrix->columns;
($r, $c) = $matrix->shape;
print STDERR "New dimension shape: $r,$c\n";
$params->append($matrix->reshape($matrix->rows *$matrix->columns,1), 0);
($r, $c) = $params->shape;
print STDERR "$r,$c\n";
$matrix = $self->{grads}{"db$i"};
$grads->append($matrix->reshape($matrix->rows *$matrix->columns,1), 0);
}
#print STDERR "cols: $c, rows: $r\n";
#print STDERR Dumper(%dims);
return ($params, $grads, %dims);
}
=head2 prediction
=cut
sub prediction {
my ($self, $x, %opts) = @_;
my $layers = $self->{layers};
my $var = { A0 => $x };
my ($i, $j);
for ( 1 .. $layers-1){
$i = $_;
$j = $i - 1;
$var->{"Z$i"} = $self->{"l$i"}{w} x $var->{"A$j"} + $self->{"l$i"}{b};
$var->{"A$i"} = $functions->{ $self->{"l$i"}{func} }->($var->{"Z$i"});
$i++;
}
$i--;
$self->{yatt} = AI::ML::Expr::prediction($var->{"A$i"}, %opts);
}
=head2 accuracy
=cut
sub accuracy {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::accuracy($y, $self->{yatt});
}
=head2 precision
=cut
sub precision {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::precision($y, $self->{yatt});
}
=head2 recall
=cut
sub recall {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::recall($y, $self->{yatt});
}
=head2 f1
=cut
sub f1 {
my ($self, $y) = @_;
unless( exists $self->{yatt} ) {
print STDERR "You should first predict the values!\n";
exit;
}
return AI::ML::Expr::f1($y, $self->{yatt});
}
1;
#!/bin/bash
dzil build;
cd AI-ML-0.001;
perl Build.PL --with-double;
./Build
./Build test
scripts/load_data.c view on Meta::CPAN
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>
#include <string.h>
//https://gist.github.com/spaghetti-source/5620288
typedef struct s_matrix {
int columns;
int rows;
double *values;
} Matrix;
#define NEW_MATRIX(m,r,c) m=(Matrix*)malloc(sizeof(Matrix));\
m->rows = r; m->columns = c;\
m->values = (double*) malloc (r * c * sizeof(double));
void endianSwap(unsigned int *x) {
*x = (*x>>24)|((*x<<8)&0x00FF0000)|((*x>>8)&0x0000FF00)|(*x<<24);
}
Matrix *read_csv(char *path){
FILE *fimage = fopen(path, "rb");
unsigned int magic, num, row, col;
int i, j;
unsigned char pixel;
if(fread(&magic, 4, 1, fimage) <= 0) exit(1);
assert(magic == 0x03080000);
if(fread(&num, 4, 1, fimage) <= 0) exit(1);
endianSwap(&num);
if(fread(&row, 4, 1, fimage) <= 0) exit(1);
endianSwap(&row);
if(fread(&col, 4, 1, fimage) <= 0) exit(1);
endianSwap(&col);
Matrix *matrices;
int size = row * col;
NEW_MATRIX(matrices, num, size);
for(i = 0; i < num * size; i++){
if(fread(&pixel, 1, 1, fimage) <= 0){
exit(1);
}
//printf("value: %f\n", (double)pixel);
matrices->values[i] = (double)pixel;
}
fclose(fimage);
return matrices;
}
/*Matrix *read_label_csv(char *path){
FILE *flabel = fopen(path, "rb");
unsigned int magic, num;
unsigned char value;
if( fread(&magic, 4, 1, flabel) <= 0) exit(1);
assert(magic = 0x01080000);
if( fread(&num, 4, 1, flabel) <= 0) exit(1);
endianSwap(&num);
Matrix *m;
NEW_MATRIX(m, num, 1);
int i;
for( i = 0; i < num; i++){
if( fread(&value, 1 , 1, flabel) <= 0 ) exit(1);
m->values[i] = (float)value;
//printf("%d\n",value);
}
fclose(flabel);
return m;
}*/
Matrix *read_label_csv(char *path){
FILE *flabel = fopen(path, "rb");
unsigned int magic, num;
unsigned char value;
if( fread(&magic, 4, 1, flabel) <= 0) exit(1);
assert(magic = 0x01080000);
if( fread(&num, 4, 1, flabel) <= 0) exit(1);
endianSwap(&num);
Matrix *m;
NEW_MATRIX(m, num, 10);
int i;
for( i = 0; i < num; i++){
if( fread(&value, 1 , 1, flabel) <= 0 ) {exit(1);}
if(i < 10) fprintf(stderr, "%d\n",value);
m->values[i * m->columns + value] = 1;
//printf("%d\n",pos);
}
fclose(flabel);
return m;
}
void save(Matrix *m, char *path){
FILE *f;
f = fopen(path, "w");
if(f == NULL) {
fprintf(stderr, "save: can't create file\n");
exit(1);
}
int row, col;
for( row = 0; row < m->rows; row++ ) {
for( col = 0; col < m->columns; col++ ) {
if( col == 0 ) {
fprintf(f, "%f", (double)m->values[row * m->columns + col]);
} else {
fprintf(f, ",%f", (double)m->values[row * m->columns + col]);
}
}
if( row < m->rows ) fprintf(f, "\n");
}
fclose(f);
}
void destroy(Matrix *m){
free(m->values);
free(m);
}
void print_values(Matrix *m){
int rows = 10;
int cols = m->columns;
for(int i = 0; i < rows; i++){
for(int j = 0; j < cols; j++){
fprintf(stderr, "%f\t", m->values[i * cols + j]);
}
fprintf(stderr, "\n");
}
}
int main(int argc, char *argv[]){
if (argc != 4) {
fprintf(stderr, "Usage: load <type> input-ubyte output-txt\n");
fprintf(stderr, "\ttype can be:\n");
fprintf(stderr, "\t\timages\n");
fprintf(stderr, "\t\tlabels\n");
exit(1);
}
char *path = argv[2];
Matrix *m;
if (strcmp(argv[1], "images") == 0) {
m = read_csv(path);
} else if (strcmp(argv[1], "labels") == 0) {
m = read_label_csv(path);
} else {
fprintf(stderr, "Unknown file type: %s\n", argv[1]);
}
save(m, argv[3]);
destroy(m);
return 0;
}
scripts/mnist.pl view on Meta::CPAN
#!/usr/bin/env perl
use warnings;
use strict;
use HTTP::Tiny;
use Data::Dumper;
use File::Fetch;
use Math::Lapack;
use Math::Lapack::Matrix;
use AI::ML;
use AI::ML::NeuralNetwork;
my %opt = (
"train-images" => "train-images-idx3-ubyte",
"train-labels" => "train-labels-idx1-ubyte",
"test-images" => "t10k-images-idx3-ubyte",
"test-labels" => "t10k-labels-idx1-ubyte"
);
_load_data();
sub _load_data {
_download_data();
# compile c file
system("gcc load_data.c -o load");
my @matrices;
for my $key ( keys %opt ) {
my (undef, $type) = split /-/, $key;
system("gunzip $opt{$key}.gz");
system("./load $type $opt{$key} $key.csv");
}
}
sub _download_data{
my $http = HTTP::Tiny->new();
my $url = "http://yann.lecun.com/exdb/mnist";
my $res;
for my $key ( keys %opt ) {
my $file = "$url/$opt{$key}.gz";
my $ff = File::Fetch->new(uri => $file);
my $aux = $ff->fetch() or die $ff->error;
#print "$file\n";
#$res = $http->get("$file");
#my $content = $res->{content};
# # $res = $http->get("$route/".$opt{$key});
#print STDERR Dumper $content;
}
}
t/00-report-prereqs.dd view on Meta::CPAN
do { my $x = {
'build' => {
'requires' => {
'Module::Build' => '0.28'
}
},
'configure' => {
'requires' => {
'Module::Build' => '0.28'
}
},
'develop' => {
'requires' => {
'File::Spec' => '0',
'IO::Handle' => '0',
'IPC::Open3' => '0',
'Test::More' => '0',
'Test::Pod' => '1.41'
}
},
'test' => {
'recommends' => {
'CPAN::Meta' => '2.120900'
},
'requires' => {
'ExtUtils::MakeMaker' => '0',
'File::Spec' => '0',
'Test2::V0' => '0.000060',
'Test::More' => '0'
}
}
};
$x;
}
t/00-report-prereqs.t view on Meta::CPAN
#!perl
use strict;
use warnings;
# This test was generated by Dist::Zilla::Plugin::Test::ReportPrereqs 0.027
use Test::More tests => 1;
use ExtUtils::MakeMaker;
use File::Spec;
# from $version::LAX
my $lax_version_re =
qr/(?: undef | (?: (?:[0-9]+) (?: \. | (?:\.[0-9]+) (?:_[0-9]+)? )?
|
(?:\.[0-9]+) (?:_[0-9]+)?
) | (?:
v (?:[0-9]+) (?: (?:\.[0-9]+)+ (?:_[0-9]+)? )?
|
(?:[0-9]+)? (?:\.[0-9]+){2,} (?:_[0-9]+)?
)
)/x;
# hide optional CPAN::Meta modules from prereq scanner
# and check if they are available
my $cpan_meta = "CPAN::Meta";
my $cpan_meta_pre = "CPAN::Meta::Prereqs";
my $HAS_CPAN_META = eval "require $cpan_meta; $cpan_meta->VERSION('2.120900')" && eval "require $cpan_meta_pre"; ## no critic
# Verify requirements?
my $DO_VERIFY_PREREQS = 1;
sub _max {
my $max = shift;
$max = ( $_ > $max ) ? $_ : $max for @_;
return $max;
}
sub _merge_prereqs {
my ($collector, $prereqs) = @_;
# CPAN::Meta::Prereqs object
if (ref $collector eq $cpan_meta_pre) {
return $collector->with_merged_prereqs(
CPAN::Meta::Prereqs->new( $prereqs )
);
}
# Raw hashrefs
for my $phase ( keys %$prereqs ) {
for my $type ( keys %{ $prereqs->{$phase} } ) {
for my $module ( keys %{ $prereqs->{$phase}{$type} } ) {
$collector->{$phase}{$type}{$module} = $prereqs->{$phase}{$type}{$module};
}
}
}
return $collector;
}
my @include = qw(
);
my @exclude = qw(
);
# Add static prereqs to the included modules list
my $static_prereqs = do './t/00-report-prereqs.dd';
# Merge all prereqs (either with ::Prereqs or a hashref)
my $full_prereqs = _merge_prereqs(
( $HAS_CPAN_META ? $cpan_meta_pre->new : {} ),
$static_prereqs
);
# Add dynamic prereqs to the included modules list (if we can)
my ($source) = grep { -f } 'MYMETA.json', 'MYMETA.yml';
my $cpan_meta_error;
if ( $source && $HAS_CPAN_META
&& (my $meta = eval { CPAN::Meta->load_file($source) } )
) {
$full_prereqs = _merge_prereqs($full_prereqs, $meta->prereqs);
}
else {
$cpan_meta_error = $@; # capture error from CPAN::Meta->load_file($source)
$source = 'static metadata';
}
my @full_reports;
my @dep_errors;
my $req_hash = $HAS_CPAN_META ? $full_prereqs->as_string_hash : $full_prereqs;
# Add static includes into a fake section
for my $mod (@include) {
$req_hash->{other}{modules}{$mod} = 0;
}
for my $phase ( qw(configure build test runtime develop other) ) {
next unless $req_hash->{$phase};
next if ($phase eq 'develop' and not $ENV{AUTHOR_TESTING});
for my $type ( qw(requires recommends suggests conflicts modules) ) {
next unless $req_hash->{$phase}{$type};
my $title = ucfirst($phase).' '.ucfirst($type);
my @reports = [qw/Module Want Have/];
for my $mod ( sort keys %{ $req_hash->{$phase}{$type} } ) {
next if $mod eq 'perl';
next if grep { $_ eq $mod } @exclude;
my $file = $mod;
$file =~ s{::}{/}g;
$file .= ".pm";
my ($prefix) = grep { -e File::Spec->catfile($_, $file) } @INC;
my $want = $req_hash->{$phase}{$type}{$mod};
$want = "undef" unless defined $want;
$want = "any" if !$want && $want == 0;
my $req_string = $want eq 'any' ? 'any version required' : "version '$want' required";
if ($prefix) {
my $have = MM->parse_version( File::Spec->catfile($prefix, $file) );
$have = "undef" unless defined $have;
push @reports, [$mod, $want, $have];
if ( $DO_VERIFY_PREREQS && $HAS_CPAN_META && $type eq 'requires' ) {
if ( $have !~ /\A$lax_version_re\z/ ) {
push @dep_errors, "$mod version '$have' cannot be parsed ($req_string)";
}
elsif ( ! $full_prereqs->requirements_for( $phase, $type )->accepts_module( $mod => $have ) ) {
push @dep_errors, "$mod version '$have' is not in required range '$want'";
}
}
}
else {
push @reports, [$mod, $want, "missing"];
if ( $DO_VERIFY_PREREQS && $type eq 'requires' ) {
push @dep_errors, "$mod is not installed ($req_string)";
}
}
}
if ( @reports ) {
push @full_reports, "=== $title ===\n\n";
my $ml = _max( map { length $_->[0] } @reports );
my $wl = _max( map { length $_->[1] } @reports );
my $hl = _max( map { length $_->[2] } @reports );
if ($type eq 'modules') {
splice @reports, 1, 0, ["-" x $ml, "", "-" x $hl];
push @full_reports, map { sprintf(" %*s %*s\n", -$ml, $_->[0], $hl, $_->[2]) } @reports;
}
else {
splice @reports, 1, 0, ["-" x $ml, "-" x $wl, "-" x $hl];
push @full_reports, map { sprintf(" %*s %*s %*s\n", -$ml, $_->[0], $wl, $_->[1], $hl, $_->[2]) } @reports;
}
push @full_reports, "\n";
}
}
}
if ( @full_reports ) {
diag "\nVersions for all modules listed in $source (including optional ones):\n\n", @full_reports;
}
if ( $cpan_meta_error || @dep_errors ) {
diag "\n*** WARNING WARNING WARNING WARNING WARNING WARNING WARNING WARNING ***\n";
}
if ( $cpan_meta_error ) {
my ($orig_source) = grep { -f } 'MYMETA.json', 'MYMETA.yml';
diag "\nCPAN::Meta->load_file('$orig_source') failed with: $cpan_meta_error\n";
}
if ( @dep_errors ) {
diag join("\n",
"\nThe following REQUIRED prerequisites were not satisfied:\n",
@dep_errors,
"\n"
);
}
pass;
# vim: ts=4 sts=4 sw=4 et:
t/01-activation-funcs.t view on Meta::CPAN
#!perl
use Test2::V0 qw'is float done_testing';
use Math::Lapack::Matrix;
use Math::Lapack::Expr;
use AI::ML::Expr;
use Data::Dumper;
my $m = Math::Lapack::Matrix->new(
[
[0, 0.002, 3, 4, 7.333, -.00008, -2.03456, 9, 100.3456, -300]
]
);
is($m->rows, 1, "Right number of rows");
is($m->columns, 10, "Right number of columns");
#Test Sigmoid
my $a = sigmoid($m);
_float($a->get_element(0,0), 0.5, "Element correct at 0,0");
_float($a->get_element(0,1), 5.005e-1, "Element correct at 0,1");
_float($a->get_element(0,2), 9.52574127e-001, "Element correct at 0,2");
_float($a->get_element(0,3), 9.82013790e-001, "Element correct at 0,3");
_float($a->get_element(0,4), 9.99346817e-001, "Element correct at 0,4");
_float($a->get_element(0,5), 4.99980000e-001, "Element correct at 0,5");
_float($a->get_element(0,6), 1.15621829e-001, "Element correct at 0,6");
_float($a->get_element(0,7), 9.99876605e-001, "Element correct at 0,7");
_float($a->get_element(0,8), 1, "Element correct at 0,8");
_float($a->get_element(0,9), 5.14820022e-131, "Element correct at 0,9");
#Test Derivative of Sigmoid
$a = d_sigmoid($m);
_float($a->get_element(0,0), 2.50000000e-001, "Element correct at 0,0");
_float($a->get_element(0,1), 2.49999750e-001, "Element correct at 0,1");
_float($a->get_element(0,2), 4.51766597e-002, "Element correct at 0,2");
_float($a->get_element(0,3), 1.76627062e-002, "Element correct at 0,3");
_float($a->get_element(0,4), 6.52756239e-004, "Element correct at 0,4");
_float($a->get_element(0,5), 2.50000000e-001, "Element correct at 0,5");
_float($a->get_element(0,6), 1.02253421e-001, "Element correct at 0,6");
_float($a->get_element(0,7), 1.23379350e-004, "Element correct at 0,7");
_float($a->get_element(0,8), 0, "Element correct at 0,8");
_float($a->get_element(0,9), 5.14820022e-131, "Element correct at 0,9");
#Test Relu
my $b = relu($m);
_float($b->get_element(0,0), 0, "Element correct at 0,0");
_float($b->get_element(0,1), 2.000000e-03, "Element correct at 0,1");
_float($b->get_element(0,2), 3, "Element correct at 0,2");
_float($b->get_element(0,3), 4, "Element correct at 0,3");
_float($b->get_element(0,4), 7.333000e+00, "Element correct at 0,4");
_float($b->get_element(0,5), 0, "Element correct at 0,5");
_float($b->get_element(0,6), 0, "Element correct at 0,6");
_float($b->get_element(0,7), 9, "Element correct at 0,7");
_float($b->get_element(0,8), 1.003456e+02, "Element correct at 0,8");
_float($b->get_element(0,9), 0, "Element correct at 0,9");
#Test derivative of Relu
$b = d_relu($m);
_float($b->get_element(0,0), 1, "Element correct at 0,0");
_float($b->get_element(0,1), 1, "Element correct at 0,1");
_float($b->get_element(0,2), 1, "Element correct at 0,2");
_float($b->get_element(0,3), 1, "Element correct at 0,3");
_float($b->get_element(0,4), 1, "Element correct at 0,4");
_float($b->get_element(0,5), 0, "Element correct at 0,5");
_float($b->get_element(0,6), 0, "Element correct at 0,6");
_float($b->get_element(0,7), 1, "Element correct at 0,7");
_float($b->get_element(0,8), 1, "Element correct at 0,8");
_float($b->get_element(0,9), 0, "Element correct at 0,9");
#Test leaky Relu
my $c = lrelu($m, .001);
_float($c->get_element(0,0), 0, "Element correct at 0,0");
_float($c->get_element(0,1), 2.000000e-03, "Element correct at 0,1");
_float($c->get_element(0,2), 3.000000e+00, "Element correct at 0,2");
_float($c->get_element(0,3), 4.000000e+00, "Element correct at 0,3");
_float($c->get_element(0,4), 7.333000e+00, "Element correct at 0,4");
_float($c->get_element(0,5), -8.000000e-08, "Element correct at 0,5");
_float($c->get_element(0,6), -2.034560e-03, "Element correct at 0,6");
_float($c->get_element(0,7), 9.000000e+00, "Element correct at 0,7");
_float($c->get_element(0,8), 1.003456e+02, "Element correct at 0,8");
_float($c->get_element(0,9), -3.000000e-01, "Element correct at 0,9");
#Test derivative of leaky Relu
$c = d_lrelu($m, .001);
_float($c->get_element(0,0), 1, "Element correct at 0,0");
_float($c->get_element(0,1), 1, "Element correct at 0,1");
_float($c->get_element(0,2), 1, "Element correct at 0,2");
_float($c->get_element(0,3), 1, "Element correct at 0,3");
_float($c->get_element(0,4), 1, "Element correct at 0,4");
_float($c->get_element(0,5), .001, "Element correct at 0,5");
_float($c->get_element(0,6), .001, "Element correct at 0,6");
_float($c->get_element(0,7), 1, "Element correct at 0,7");
_float($c->get_element(0,8), 1, "Element correct at 0,8");
_float($c->get_element(0,9), .001, "Element correct at 0,9");
# Test tanh
my $d = tanh($m);
_float($d->get_element(0,0), 0, "Element correct at 0,0");
_float($d->get_element(0,1), 1.99999733e-03, "Element correct at 0,1");
_float($d->get_element(0,2), 9.95054754e-01, "Element correct at 0,2");
_float($d->get_element(0,3), 9.99329300e-01, "Element correct at 0,3");
_float($d->get_element(0,4), 9.99999146e-01, "Element correct at 0,4");
_float($d->get_element(0,5), -7.99999998e-05, "Element correct at 0,5");
_float($d->get_element(0,6), -9.66389636e-01, "Element correct at 0,6");
_float($d->get_element(0,7), 9.99999970e-01, "Element correct at 0,7");
_float($d->get_element(0,8), 1, "Element correct at 0,8");
_float($d->get_element(0,9), -1, "Element correct at 0,9");
#Test derivative of tanh
$d = d_tanh($m);
_float($d->get_element(0,0), 1.00000000e+00, "Element correct at 0,0");
_float($d->get_element(0,1), 9.99996000e-01, "Element correct at 0,1");
_float($d->get_element(0,2), 9.86603717e-03, "Element correct at 0,2");
_float($d->get_element(0,3), 1.34095068e-03, "Element correct at 0,3");
_float($d->get_element(0,4), 1.70882169e-06, "Element correct at 0,4");
_float($d->get_element(0,5), 9.99999994e-01, "Element correct at 0,5");
_float($d->get_element(0,6), 6.60910712e-02, "Element correct at 0,6");
_float($d->get_element(0,7), 6.09199171e-08, "Element correct at 0,7");
_float($d->get_element(0,8), 0, "Element correct at 0,8");
_float($d->get_element(0,9), 0, "Element correct at 0,9");
# Test softmax
my $e = Math::Lapack::Matrix->new(
[
[1, 2, 1], # sample 1
[2, 4, 2], # sample 1
[3, 5, 3], # sample 2
[6, 6, 6]
]);
my $soft = softmax($e);
is($e->rows, 4, "Right number of rows - softmax");
is($e->columns, 3, "Right number of cols - softmax");
# prob of first col
_float($soft->get_element(0,0), 0.00626879, "Element correct at 0,0");
_float($soft->get_element(1,0), 0.01704033, "Element correct at 1,0");
_float($soft->get_element(2,0), 0.04632042, "Element correct at 2,0");
_float($soft->get_element(3,0), 0.93037047, "Element correct at 3,0");
# prob of second col
_float($soft->get_element(0,1), 0.01203764, "Element correct at 0,1");
_float($soft->get_element(1,1), 0.08894682, "Element correct at 1,1");
_float($soft->get_element(2,1), 0.24178252, "Element correct at 2,1");
_float($soft->get_element(3,1), 0.65723302, "Element correct at 3,1");
#prob of third col
_float($soft->get_element(0,2), 0.00626879, "Element correct at 0,2");
_float($soft->get_element(1,2), 0.01704033, "Element correct at 1,2");
_float($soft->get_element(2,2), 0.04632042, "Element correct at 2,2");
_float($soft->get_element(3,2), 0.93037047, "Element correct at 3,2");
done_testing;
sub _float {
my ($a, $b, $c) = @_;
is($a, float($b, tolerance => 0.00001), $c);
}
t/02-cost_functions.t view on Meta::CPAN
#!perl
use Math::Lapack::Matrix;
use Math::Lapack::Expr;
use AI::ML::Expr;
my $m_1 = Math::Lapack::Matrix->new([ [-2, 1, 2, 3] ]);
my $nr_tests = 0;
#Test d_relu
my $a = d_relu($m_1);
float($a->get_element(0,0), 0, "Element correct at 0,0");
float($a->get_element(0,1), 1, "Element correct at 0,1");
float($a->get_element(0,2), 1, "Element correct at 0,2");
float($a->get_element(0,3), 1, "Element correct at 0,3");
my $b = $a x $m_1->transpose;
float($b->get_element(0,0), 6, "Element correct at 0,0");
print "1..$nr_tests\n";
sub float {
$nr_tests++;
my ($a, $b, $explanation) = @_;
if (abs($a-$b) > 0.000001){
print "not ";
$explanation .= " ($a vs $b)";
}
print "ok $nr_tests - $explanation\n";
}
sub is {
$nr_tests++;
my ($a, $b, $explanation) = @_;
if ($a != $b){
print "not ";
$explanation .= " ($a vs $b)";
}
print "ok $nr_tests - $explanation\n";
}
t/03-mini-batch.t view on Meta::CPAN
#!perl
use Math::Lapack::Matrix;
use Math::Lapack::Expr;
use AI::ML::Expr;
my $m = Math::Lapack::Matrix->random(50,1000);
my $nr_tests = 0;
my $axis = 0;
my $size = 200;
my $start = 0;
for my $v (0..4){
my $a = mini_batch($m, $start, $size, $axis);
is($a->rows, 50, "Right number of rows\n");
is($a->columns, 200, "Right number of columns\n");
$start += $size;
}
my $m_1 = Math::Lapack::Matrix->random(1000,20);
$start = 0;
$axis = 1;
for my $i (0..4){
my $b = mini_batch($m_1, $start, $size, $axis);
is($b->rows, 200, "Right number of rows\n");
is($b->columns, 20, "Right number of columns\n");
$start += $size;
}
print "1..$nr_tests\n";
sub float {
$nr_tests++;
my ($a, $b, $explanation) = @_;
if (abs($a-$b) > 0.000001){
print "not ";
$explanation .= " ($a vs $b)";
}
print "ok $nr_tests - $explanation\n";
}
sub is {
$nr_tests++;
my ($a, $b, $explanation) = @_;
if ($a != $b){
print "not ";
$explanation .= " ($a vs $b)";
}
print "ok $nr_tests - $explanation\n";
}
t/04-linear-regression.t view on Meta::CPAN
#!perl
use Test2::V0;
use Math::Lapack::Matrix;
use AI::ML::LinearRegression;
my $x = Math::Lapack::Matrix->new([[12.39999962],[14.30000019],[14.5],[14.89999962],[16.10000038],[16.89999962],[16.5],[15.39999962],[17],[17.89999962],[18.79999924],[20.29999924],[22.39999962],[19.39999962],[15.5],[16.70000076],[17.29999924],[18.399...
my $y = Math::Lapack::Matrix->new([[11.19999981],[12.5],[12.69999981],[13.10000038],[14.10000038],[14.80000019],[14.39999962],[13.39999962],[14.89999962],[15.60000038],[16.39999962],[17.70000076],[19.60000038],[16.89999962],[14],[14.60000038],[15.100...
my $m = AI::ML::LinearRegression->new(plot => "../../plot.png");
$m->train($x, $y);
my $t = $m->{thetas};
is($t->rows, 2, "Right number of rows");
is($t->columns, 1, "Right number of columns");
_float($t->get_element(0,0), 0.43458449, "Normal Equation - Right value of theta 0,0");
_float($t->get_element(1,0), 0.85114404, "Normal Equation - Right value of theta 1,0");
my $m1 = AI::ML::LinearRegression->new(
cost => "../../cost1.png",
gradient => "foo",
plot => "../../plot1.png",
n => 50,
alpha => 0.001
);
$m1->train($x, $y);
is($m1->{thetas}->rows, 2, "Right number of rows");
is($m1->{thetas}->columns, 1, "Right number of columns");
_float($m1->{thetas}->get_element(0,0), 0.86412871, "Normal Equation - Right value of theta 0,0");
_float($m1->{thetas}->get_element(1,0), 0.8269897, "Normal Equation - Right value of theta 1,0");
my $n = AI::ML::LinearRegression->new(
lambda => 1,
cost => "../../cost2.png",
gradient => "foo",
plot => "../../plot2.png",
n => 50,
alpha => 0.001);
$n->train($x, $y);
is($n->{thetas}->rows, 2, "Right number of rows");
is($n->{thetas}->columns, 1, "Right number of columns");
_float($n->{thetas}->get_element(0,0), 0.78473628, "Normal Equation - Right value of theta 0,0");
_float($n->{thetas}->get_element(1,0), 0.83133813, "Normal Equation - Right value of theta 1,0");
### FIXME: if the tests generate files, you should test them.
## and delete them afterwads
done_testing();
sub _float {
my ($a, $b, $c) = @_;
is($a, float($b, tolerance => 0.000001), $c);
}
t/05-logistic-regression.t view on Meta::CPAN
#!perl
use Test2::V0 qw'is float done_testing';
use Math::Lapack::Matrix;
use Math::Lapack;
use AI::ML::LogisticRegression;
Math::Lapack->seed_rng(0);
my $x = Math::Lapack::Matrix::read_csv("t/logistic.csv", col_range =>[0,2]);
my $y = Math::Lapack::Matrix::read_csv("t/logistic.csv", col => 3);
is($x->rows, 306, "Right number of rows");
is($y->rows, 306, "Right number of rows");
is($x->columns, 3, "Right number of cols");
is($y->columns, 1, "Right number of cols");
is($x->get_element(0,0),30, "Right element 0,0 of x");
is($x->get_element(1,2),3, "Right element 1,2 of x");
is($x->get_element(3,1),59, "Right element 3,1 of x");
is($x->get_element(2,2),0, "Right element 2,2 of x");
is($y->get_element(3,0),1, "Right element 3,0 of y");
is($y->get_element(7,0),2, "Right element 7,0 of y");
$x->norm_std_deviation();
$y = $y - 1;
my $m = AI::ML::LogisticRegression->new(
n => 10000,
alpha => 0.5,
cost => "../../logistcost.png"
);
$m->train($x, $y);
my $thetas = $m->{thetas};
_float($thetas->get_element(0,0), -1.07661735,"Right vale of theta 0,0");
_float($thetas->get_element(1,0), 0.21463009,"Right vale of theta 1,0");
_float($thetas->get_element(2,0), -0.03173973,"Right vale of theta 2,0");
_float($thetas->get_element(3,0), 0.63483062,"Right vale of theta 3,0");
$m->prediction($x);
_float($m->accuracy($y), 0.7483660130718954, "Right value of accuracy");
_float($m->precision($y), 0.5833333333333334, "Right value of precision");
_float($m->recall($y), 0.1728395061728395, "Right value of recall");
_float($m->f1($y), 0.26666666666666666, "Right value of f1");
#print STDERR "Accuracy: $acc\n";
#print STDERR "Precison: $prec\n";
#print STDERR "Recall: $rec\n";
#print STDERR "F1: $f1\n";
my $n = AI::ML::LogisticRegression->new(
n => 10000,
alpha => 0.5,
cost => "../../logistcost_reg.png",
reg => 2
);
$n->train($x, $y);
$thetas = $n->{thetas};
_float($thetas->get_element(0,0), -1.07368839, "Right vale of theta 0,0");
_float($thetas->get_element(1,0), 0.204271, "Right vale of theta 1,0");
_float($thetas->get_element(2,0), -0.02933972, "Right vale of theta 2,0");
_float($thetas->get_element(3,0), 0.60950995, "Right vale of theta 3,0");
$n->prediction($x);
_float($n->accuracy($y), 0.7450980392156863, "Right value of accuracy");
_float($n->precision($y), 0.5652173913043478, "Right value of precision");
_float($n->recall($y), 0.16049382716049382, "Right value of recall");
_float($n->f1($y), .25, "Right value of f1");
done_testing;
sub _float {
my ($a, $b, $c) = @_;
is($a, float($b, tolerance => 0.01), $c);
}
t/06-accuracy-precision-recall-f1.t view on Meta::CPAN
#!perl
use Math::Lapack::Matrix;
use AI::ML::Expr;
my $nr_tests=0;
my $y = Math::Lapack::Matrix->new([
[1],[0],[1],[1],[0],[0],[1],[1],[1],[1],[0],[0],[1],[0],[0]
]);
my $yatt = Math::Lapack::Matrix->new([
[1],[0],[0],[0],[1],[1],[0],[1],[1],[1],[1],[0],[0],[0],[0]
]);
# True Positive = 4
# False Positive = 3
# False Negative = 4
my $acc = AI::ML::Expr::accuracy($y, $yatt);
float($acc, 0.5333333, "Right accuracy");
my $prec = AI::ML::Expr::precision($y, $yatt);
float($prec, 0.571428571, "Right Precision");
my $rec = AI::ML::Expr::recall($y, $yatt);
float($rec, 0.5, "Right recall");
my $f_1 = AI::ML::Expr::f1($y, $yatt);
float($f_1, 0.533333334, "Right f1");
print "1..$nr_tests\n";
sub float {
$nr_tests++;
my ($a, $b, $explanation) = @_;
if (abs($a-$b) > 0.000001){
print "not ";
$explanation .= " ($a vs $b)";
}
print "ok $nr_tests - $explanation\n";
}
sub is {
$nr_tests++;
my ($a, $b, $explanation) = @_;
if ($a != $b){
print "not ";
$explanation .= " ($a vs $b)";
}
print "ok $nr_tests - $explanation\n";
}
t/07-neural-network.t view on Meta::CPAN
#!perl
use Test2::V0 qw'is float done_testing';
use Math::Lapack::Matrix;
use AI::ML::NeuralNetwork;
use Math::Lapack;
use Data::Dumper;
Math::Lapack->seed_rng(0);
my $x = Math::Lapack::Matrix::read_csv("t/x.csv");
my $y = Math::Lapack::Matrix::read_csv("t/y.csv");
my $NN = AI::ML::NeuralNetwork->new(
[
2,
{func => "tanh", units => 3},
1
],
n => 5000,
alpha => 1.2
);
$NN->{"l1"}{w} = Math::Lapack::Matrix::read_csv("t/w1.csv");
$NN->{"l2"}{w} = Math::Lapack::Matrix::read_csv("t/w2.csv");
my $pred = Math::Lapack::Matrix::read_csv("t/pred-nn.csv");
$NN->train($x, $y);
$NN->prediction($x);
is($NN->{yatt}->rows, $pred->rows, "Right number of rows");
is($NN->{yatt}->columns, $pred->columns, "Right number of columns");
for (0..$NN->{yatt}->columns-1) {
is( $NN->{yatt}->get_element(0, $_),
$pred->get_element(0, $_),
"Right element on 0,$_"
);
}
_float($NN->accuracy($y), 0.98, "Right accuracy");
_float($NN->precision($y), 0.970588235294118, "Right precision");
_float($NN->recall($y), .99, "Right recall");
_float($NN->f1($y), 0.98019801980198, "Right F1");
done_testing();
sub _float {
my ($a, $b, $c) = @_;
is($a, float($b, tolerance => 0.1), $c);
}
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