Selling Icecreams: Difference between revisions

From Artificial Neural Network for PHP
 
Line 64: Line 64:


<source lang="php">
<source lang="php">
<?php

require_once 'ANN/Loader.php';
require_once '../ANN/Loader.php';


use ANN\Network;
use ANN\Network;
Line 72: Line 72:
use ANN\Values;
use ANN\Values;


try
try {
$objNetwork = Network::loadFromFile('icecreams.dat');
{
} catch (Exception $e) {
$objNetwork = Network::loadFromFile('icecreams.dat');
die('Network not found');
}
catch(Exception $e)
{
die('Network not found');
}
}


try
try {
$objTemperature = InputValue::loadFromFile('input_temperature.dat'); // Temperature in Celsius
{
$objTemperature = InputValue::loadFromFile('input_temperature.dat'); // Temperature in Celsius


$objHumidity = InputValue::loadFromFile('input_humidity.dat'); // Humidity percentage
$objHumidity = InputValue::loadFromFile('input_humidity.dat'); // Humidity percentage


$objIcecream = OutputValue::loadFromFile('output_quantity.dat'); // Quantity of sold ice-creams
$objIcecream = OutputValue::loadFromFile('output_quantity.dat'); // Quantity of sold ice-creams
} catch (Exception $e) {
}
die('Error loading value objects');
catch(Exception $e)
{
die('Error loading value objects');
}
}


try
try {
$objValues = Values::loadFromFile('values_icecreams.dat');
{
} catch (Exception $e) {
$objValues = Values::loadFromFile('values_icecreams.dat');
die('Loading of values failed');
}
catch(Exception $e)
{
die('Loading of values failed');
}
}


$objValues->input( // input values appending the loaded ones
$objValues->input( // input values appending the loaded ones
$objTemperature->getInputValue(17),
$objTemperature->getInputValue(17), $objHumidity->getInputValue(12))
$objHumidity->getInputValue(12)
->input($objTemperature->getInputValue(31), $objHumidity->getInputValue(42))
->input($objTemperature->getInputValue(31), $objHumidity->getInputValue(34))
)
->input($objTemperature->getInputValue(34), $objHumidity->getInputValue(21));
->input(
$objTemperature->getInputValue(31),
$objHumidity->getInputValue(42)
)
->input(
$objTemperature->getInputValue(31),
$objHumidity->getInputValue(34)
)
->input(
$objTemperature->getInputValue(34),
$objHumidity->getInputValue(21)
);


$objNetwork->setValues($objValues);
$objNetwork->setValues($objValues);
Line 124: Line 104:
$arrOutputs = $objNetwork->getOutputs();
$arrOutputs = $objNetwork->getOutputs();


foreach($arrOutputs as $arrOutput)
foreach ($arrOutputs as $arrOutput)
foreach($arrOutput as $floatOutput)
foreach ($arrOutput as $floatOutput)
print $objIcecream->getRealOutputValue($floatOutput). '<br />';
print $objIcecream->getRealOutputValue($floatOutput) . "\n";

</source>
</source>

Latest revision as of 07:59, 6 October 2025

FAQ

For information about dat-files have a view to the FAQ page.

Training

<?php
require_once '../ANN/Loader.php';

use ANN\Network;
use ANN\InputValue;
use ANN\OutputValue;
use ANN\Values;

try {
    $objNetwork = Network::loadFromFile('icecreams.dat');
} catch (Exception $e) {
    $objNetwork = new Network(2, 5, 1);

    $objTemperature = new InputValue(- 15, 50); // Temperature in Celsius

    $objTemperature->saveToFile('input_temperature.dat');

    $objHumidity = new InputValue(0, 100); // Humidity percentage

    $objHumidity->saveToFile('input_humidity.dat');

    $objIcecream = new OutputValue(0, 300); // Quantity of sold ice-creams

    $objIcecream->saveToFile('output_quantity.dat');

    $objValues = new Values();

    $objValues->train()
        ->input($objTemperature->getInputValue(20), $objHumidity->getInputValue(10))
        ->output($objIcecream->getOutputValue(20))
        ->input($objTemperature->getInputValue(30), $objHumidity->getInputValue(40))
        ->output($objIcecream->getOutputValue(90))
        ->input($objTemperature->getInputValue(32), $objHumidity->getInputValue(30))
        ->output($objIcecream->getOutputValue(70))
        ->input($objTemperature->getInputValue(33), $objHumidity->getInputValue(20))
        ->output($objIcecream->getOutputValue(75));

    $objValues->saveToFile('values_icecreams.dat');
}

try {
    $objValues = Values::loadFromFile('values_icecreams.dat');
} catch (Exception $e) {
    die('Loading of values failed');
}

$objNetwork->setValues($objValues);

$boolTrained = $objNetwork->train();

print ($boolTrained) ? 'Network trained' : 'Network not trained completely. Please re-run the script';

$objNetwork->saveToFile('icecreams.dat');

Using trained network

<?php
require_once '../ANN/Loader.php';

use ANN\Network;
use ANN\InputValue;
use ANN\OutputValue;
use ANN\Values;

try {
    $objNetwork = Network::loadFromFile('icecreams.dat');
} catch (Exception $e) {
    die('Network not found');
}

try {
    $objTemperature = InputValue::loadFromFile('input_temperature.dat'); // Temperature in Celsius

    $objHumidity = InputValue::loadFromFile('input_humidity.dat'); // Humidity percentage

    $objIcecream = OutputValue::loadFromFile('output_quantity.dat'); // Quantity of sold ice-creams
} catch (Exception $e) {
    die('Error loading value objects');
}

try {
    $objValues = Values::loadFromFile('values_icecreams.dat');
} catch (Exception $e) {
    die('Loading of values failed');
}

$objValues->input( // input values appending the loaded ones
$objTemperature->getInputValue(17), $objHumidity->getInputValue(12))
    ->input($objTemperature->getInputValue(31), $objHumidity->getInputValue(42))
    ->input($objTemperature->getInputValue(31), $objHumidity->getInputValue(34))
    ->input($objTemperature->getInputValue(34), $objHumidity->getInputValue(21));

$objNetwork->setValues($objValues);

$arrOutputs = $objNetwork->getOutputs();

foreach ($arrOutputs as $arrOutput)
    foreach ($arrOutput as $floatOutput)
        print $objIcecream->getRealOutputValue($floatOutput) . "\n";