Selling Icecreams: Difference between revisions
From Artificial Neural Network for PHP
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<source lang="php"> |
<source lang="php"> |
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<?php |
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require_once 'ANN/Loader.php'; |
require_once '../ANN/Loader.php'; |
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use ANN\Network; |
use ANN\Network; |
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use ANN\Values; |
use ANN\Values; |
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try |
try { |
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$objNetwork = Network::loadFromFile('icecreams.dat'); |
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{ |
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} catch (Exception $e) { |
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$objNetwork = Network::loadFromFile('icecreams.dat'); |
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$objNetwork = new Network(2, 5, 1); |
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} |
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catch(Exception $e) |
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{ |
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print 'Creating a new one...'; |
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$objNetwork = new Network(2, 5, 1); |
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$objTemperature = new InputValue(- 15, 50); // Temperature in Celsius |
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$objTemperature |
$objTemperature->saveToFile('input_temperature.dat'); |
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$objHumidity = new InputValue(0, 100); // Humidity percentage |
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$objTemperature->saveToFile('input_temperature.dat'); |
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$objHumidity |
$objHumidity->saveToFile('input_humidity.dat'); |
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$objIcecream = new OutputValue(0, 300); // Quantity of sold ice-creams |
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$objHumidity->saveToFile('input_humidity.dat'); |
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$objIcecream->saveToFile('output_quantity.dat'); |
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$objValues = new Values(); |
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$objIcecream = new OutputValue(0, 300); // Quantity of sold ice-creams |
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$objValues->train() |
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$objIcecream->saveToFile('output_quantity.dat'); |
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->input($objTemperature->getInputValue(20), $objHumidity->getInputValue(10)) |
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->output($objIcecream->getOutputValue(20)) |
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->input($objTemperature->getInputValue(30), $objHumidity->getInputValue(40)) |
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->output($objIcecream->getOutputValue(90)) |
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->input($objTemperature->getInputValue(32), $objHumidity->getInputValue(30)) |
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->output($objIcecream->getOutputValue(70)) |
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->input($objTemperature->getInputValue(33), $objHumidity->getInputValue(20)) |
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->output($objIcecream->getOutputValue(75)); |
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$objValues->saveToFile('values_icecreams.dat'); |
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$objValues = new Values; |
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$objValues->train() |
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->input( |
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$objTemperature->getInputValue(20), |
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$objHumidity->getInputValue(10) |
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) |
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->output( |
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$objIcecream->getOutputValue(20) |
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) |
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->input( |
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$objTemperature->getInputValue(30), |
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$objHumidity->getInputValue(40) |
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) |
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->output( |
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$objIcecream->getOutputValue(90) |
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) |
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->input( |
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$objTemperature->getInputValue(32), |
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$objHumidity->getInputValue(30) |
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) |
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->output( |
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$objIcecream->getOutputValue(70) |
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) |
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->input( |
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$objTemperature->getInputValue(33), |
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$objHumidity->getInputValue(20) |
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) |
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->output( |
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$objIcecream->getOutputValue(75) |
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); |
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$objValues->saveToFile('values_icecreams.dat'); |
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unset($objValues); |
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unset($objTemperature); |
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unset($objHumidity); |
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unset($objIcecream); |
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} |
} |
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try |
try { |
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$objValues = Values::loadFromFile('values_icecreams.dat'); |
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{ |
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} catch (Exception $e) { |
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$objTemperature = InputValue::loadFromFile('input_temperature.dat'); // Temperature in Celsius |
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die('Loading of values failed'); |
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$objHumidity = InputValue::loadFromFile('input_humidity.dat'); // Humidity percentage |
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$objIcecream = OutputValue::loadFromFile('output_quantity.dat'); // Quantity of sold ice-creams |
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} |
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catch(Exception $e) |
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{ |
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die('Error loading value objects'); |
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} |
} |
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$objNetwork->setValues($objValues); |
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try |
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{ |
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$objValues = Values::loadFromFile('values_icecreams.dat'); |
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} |
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catch(Exception $e) |
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{ |
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die('Loading of values failed'); |
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} |
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$objNetwork->setValues($objValues); // to be called as of version 2.0.6 |
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$boolTrained = $objNetwork->train(); |
$boolTrained = $objNetwork->train(); |
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print ($boolTrained) ? 'Network trained' : 'Network not trained completely. Please re-run the script'; |
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print ($boolTrained) |
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? 'Network trained' |
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: 'Network not trained completely. Please re-run the script'; |
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$objNetwork->saveToFile('icecreams.dat'); |
$objNetwork->saveToFile('icecreams.dat'); |
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$objNetwork->printNetwork(); |
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</source> |
</source> |
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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
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). '<br />';