例
The basic process is to define parameters, supply training data to generate a model on, then make predictions based on the model. There are a default set of parameters that should get some results with most any input, so we'll start by looking at the data.
Data is supplied in either a file, a stream, or as an array. If supplied in a file or a stream, it must contain one line per training example, which must be formatted as an integer class (usually 1 and -1) followed by a series of feature/value pairs, in increasing feature order. The features are integers, the values floats, usually scaled 0-1. For example:
-1 1:0.43 3:0.12 9284:0.2
In a document classification problem, say a spam checker, each line would represent a document. There would be two classes, -1 for spam, 1 for ham. Each feature would represent some word, and the value would represent that importance of that word to the document (perhaps the frequency count, with the total scaled to unit length). Features that were 0 (e.g. the word did not appear in the document at all) would simply not be included.
In array mode, the data must be passed as an array of arrays. Each sub-array must have the class as the first element, then key => value sets for the feature values pairs.
This data is passed to the SVM class's train function, which will return an SVM model is successful.
Once a model has been generated, it can be used to make predictions about previously unseen data. This can be passed as an array to the model's predict function, in the same format as before, but without the label. The response will be the class.
Models can be saved and restored as required, using the save and load functions, which both take a file location.
例1 Train from array
<?php
$data = array(
array(-1, 1 => 0.43, 3 => 0.12, 9284 => 0.2),
array(1, 1 => 0.22, 5 => 0.01, 94 => 0.11),
);
$svm = new SVM();
$model = $svm->train($data);
$data = array(1 => 0.43, 3 => 0.12, 9284 => 0.2);
$result = $model->predict($data);
var_dump($result);
$model->save('model.svm');
?>上の例の出力は、 たとえば以下のようになります。
int(-1)
例2 Train from a file
<?php
$svm = new SVM();
$model = $svm->train("traindata.txt");
?>User Contributed Notes 5 notes
the example rated negative rated by the guy sign "6765419 at qq dot com" also works too!
<?php
$data = array(
array(-1, 1 =>170, 2 => 60),//-1 表示男生，key 1表示身高，key 2表示体重=Represents a boy, key 1 represents height, key 2 represents weight
array(-1, 1 =>180, 2 => 70),
array(1, 1 => 160, 2 => 46),//1 表示女生，key 1表示身高，key 2表示体重=Represents a girl, key 1 represents height, key 2 represents weight
array(1, 1 => 155, 2 => 40),
);
$svm = new SVM();
$model = $svm->train($data);
$data = array( 1 => 165, 2 =>60);//测试数据 =Test Data
$result = $model->predict($data);
echo var_dump($result);//echo var_export($result);
//return;
?>
so i got :
float(-1)ok i did more tests..
getting the source
https://github.com/ianbarber/php-svm/blob/master/tests/002_predict.phpt modified ..
<?php
$svm = new svmmodel();
//$result = $svm->load(dirname(__FILE__) . '/australian.model');
$result = $svm->load('australian.model');
if($result) {
$data = array(
"1" => 1,
2 => -0.731729,
3 => -0.886786,
4 => -1,
5 => 0.230769,
"6" => -0.25,
7 => -0.783509,
8 => 1,
9 => 1,
10 => "-0.820896",
11 => -1,
13 => -0.92,
"14" => "-1"
);
$result = $svm->predict($data);
if($result > 0) {
echo "ok";
print_r($result);
} else {
echo "predict failed: $result";
}
} else {
echo "loading failed";
}
?>
with additional https://github.com/ianbarber/php-svm/blob/master/tests/australian.scale dropped inside the test folder where .php file is located i am able after running to get the result:
================================
ok1
so it's worki forgot a detail!
the installation folders if you think to install it manually in windows xampp should be c:\xampp\php\lib\libsvm-3.1 (for the files i described in the first post) and extension in c:\xampp\php\ext (php_svm.dll)
works.good luckfrom pecl.php.net i download svm php_svm-0.2.3-8.1-ts-vs16-x64.zip so i read in README.md ..
=====================================================
Data is supplied in either a file, a stream, or as an an array. If supplied in a file or a stream, it must contain one line per training example, which must be formatted as an integer class (usually 1 and -1) followed by a series of feature/value pairs, in increasing feature order. The features are integers, the values floats, usually scaled 0-1. For example:
-1 1:0.43 3:0.12 9284:0.2
=====================================================
so creating traindata.txt with the content -1 1:0.43 3:0.12 9284:0.2 leads me to use it in the second example:
<?php
$svm = new SVM();
$model = $svm->train("traindata.txt");
$model->save('model2.svm');
?>
and running and editing the model2.svm i got the content:
-------------------------------------------------------------------
svm_type c_svc
kernel_type rbf
gamma 0.00010771219302024989
nr_class 1
total_sv 0
rho
label -1
nr_sv 0
SV
--------------------------------------------------------------------
so yes i think it's work, how i said i need to do more tests to get control with main functions to think to other more complicatedpremises:php 8.1 ,windows 64
----------------------------------
install (for beginners)
--------
after i visit https://github.com/ianbarber/php-svm
and i got from url found on page(install script)
.. http://www.csie.ntu.edu.tw/~cjlin/cgi-bin/libsvm.cgi?+http://www.csie.ntu.edu.tw/~cjlin/libsvm+tar.gz
and manual install it:
1.php.ini
(after the main group extension=... about 12 pieces)
...
extension=svm
...
2.I put manually inside php a folder called libsvm-3.1 then i unzip there libsvm.dll , libsvmread.mexw64 ,libsvmwrite.mexw64 , svmpredict.mexw64 , svm-predict.exe, svm-scale.exe , svm-toy.exe , svmtrain.mexw64, svm-train.exe !
running
<?php
$data = array(
array(-1, 1 => 0.43, 3 => 0.12, 9284 => 0.2),
array(1, 1 => 0.22, 5 => 0.01, 94 => 0.11),
);
$svm = new SVM();
$model = $svm->train($data);
$data = array(1 => 0.43, 3 => 0.12, 9284 => 0.2);
$result = $model->predict($data);
var_dump($result);
$model->save('model.svm');
?>
via server(apache ,php,mariadb;even custom or xampp) now i got results:
i got model.svm with the content
================================
svm_type c_svc
kernel_type rbf
gamma 0.00010771219302024989
nr_class 2
total_sv 2
rho 0
label 1 -1
nr_sv 1 1
SV
1 1:0.22 5:0.01 94:0.11
-1 1:0.43 3:0.12 9284:0.2
=================================
so i think is very cool ..for a startup.
i will look around phpt files from github to understand why in yesterday's tests i got errors with some function witch require 2 parameters and not one like in the manual