Selling Icecreams: Difference between revisions
From Artificial Neural Network for PHP
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== FAQ == |
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For information about dat-files have a view to the [[FAQ]] page. |
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== Training == |
== Training == |
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<source lang="php"> |
<source lang="php"> |
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require_once 'ANN/ |
require_once 'ANN/Loader.php'; |
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use ANN\Network; |
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use ANN\InputValue; |
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use ANN\OutputValue; |
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use ANN\Values; |
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try |
try |
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{ |
{ |
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$ |
$objNetwork = Network::loadFromFile('icecreams.dat'); |
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} |
} |
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catch(Exception $e) |
catch(Exception $e) |
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print 'Creating a new one...'; |
print 'Creating a new one...'; |
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$ |
$objNetwork = new Network(2, 5, 1); |
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$objTemperature = new InputValue(-15, 50); // Temperature in Celsius |
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$objTemperature->saveToFile('input_temperature.dat'); |
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$objHumidity = new InputValue(0, 100); // Humidity percentage |
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$objHumidity->saveToFile('input_humidity.dat'); |
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$objIcecream = new OutputValue(0, 300); // Quantity of sold ice-creams |
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$objIcecream->saveToFile('output_quantity.dat'); |
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$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 |
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$temperature = new ANN_InputValue(-15, 50); // Temperature |
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{ |
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$objTemperature = InputValue::loadFromFile('input_temperature.dat'); // Temperature in Celsius |
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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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$icecream = new ANN_OutputValue(0, 300); // Ice-Creams |
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} |
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catch(Exception $e) |
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{ |
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die('Error loading value objects'); |
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} |
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try |
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$inputs = array( |
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{ |
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array($temperature->getInputValue(20), $humidity->getInputValue(10)), |
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$objValues = Values::loadFromFile('values_icecreams.dat'); |
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array($temperature->getInputValue(30), $humidity->getInputValue(40)), |
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} |
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array($temperature->getInputValue(32), $humidity->getInputValue(30)), |
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catch(Exception $e) |
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array($temperature->getInputValue(33), $humidity->getInputValue(20)) |
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{ |
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); |
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die('Loading of values failed'); |
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} |
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$objNetwork->setValues($objValues); // to be called as of version 2.0.6 |
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$boolTrained = $objNetwork->train(); |
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print ($boolTrained) |
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? 'Network trained' |
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: 'Network not trained completely. Please re-run the script'; |
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$objNetwork->saveToFile('icecreams.dat'); |
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$objNetwork->printNetwork(); |
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</source> |
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== Using trained network == |
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<source lang="php"> |
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require_once 'ANN/Loader.php'; |
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use ANN\Network; |
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use ANN\InputValue; |
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use ANN\OutputValue; |
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use ANN\Values; |
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try |
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{ |
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$objNetwork = Network::loadFromFile('icecreams.dat'); |
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} |
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catch(Exception $e) |
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{ |
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die('Network not found'); |
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} |
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try |
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{ |
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$objTemperature = InputValue::loadFromFile('input_temperature.dat'); // Temperature in Celsius |
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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 $e) |
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{ |
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die('Error loading value objects'); |
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} |
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try |
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{ |
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$objValues = Values::loadFromFile('values_icecreams.dat'); |
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} |
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catch(Exception $e) |
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{ |
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die('Loading of values failed'); |
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} |
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$objValues->input( // input values appending the loaded ones |
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$outputs = array( |
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$objTemperature->getInputValue(17), |
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array($icecream->getOutputValue(20)), |
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$objHumidity->getInputValue(12) |
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array($icecream->getOutputValue(90)), |
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) |
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array($icecream->getOutputValue(70)), |
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->input( |
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array($icecream->getOutputValue(75)) |
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$objTemperature->getInputValue(31), |
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); |
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$objHumidity->getInputValue(42) |
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) |
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->input( |
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$objTemperature->getInputValue(31), |
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$objHumidity->getInputValue(34) |
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) |
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->input( |
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$objTemperature->getInputValue(34), |
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$objHumidity->getInputValue(21) |
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); |
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$objNetwork->setValues($objValues); |
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$network->setInputs($inputs); |
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$arrOutputs = $objNetwork->getOutputs(); |
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$network->setOutputs($outputs); |
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foreach($arrOutputs as $arrOutput) |
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$network->train(); |
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foreach($arrOutput as $floatOutput) |
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print $objIcecream->getRealOutputValue($floatOutput). '<br />'; |
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$network->saveToFile('icecreams.dat'); |
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</source> |
</source> |
Latest revision as of 11:33, 1 June 2011
FAQ
For information about dat-files have a view to the FAQ page.
Training
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)
{
print 'Creating a new one...';
$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)
);
$objValues->saveToFile('values_icecreams.dat');
unset($objValues);
unset($objTemperature);
unset($objHumidity);
unset($objIcecream);
}
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');
}
$objNetwork->setValues($objValues); // to be called as of version 2.0.6
$boolTrained = $objNetwork->train();
print ($boolTrained)
? 'Network trained'
: 'Network not trained completely. Please re-run the script';
$objNetwork->saveToFile('icecreams.dat');
$objNetwork->printNetwork();
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 />';