function test(NeuralNetwork $neuralNetwork, $withError = true)
{
$accuracy = [];
$errors = [];
foreach ($this->allTestData as $testData) {
$result = $neuralNetwork->calculate($testData->getData());
$resultBool = 0;
if ($result[0] < 0 && $result[1] > 0 && $testData->getExpectedResult() == [-1, 1]) {
$resultBool = 1;
}
if ($result[0] > 0 && $result[1] < 0 && $testData->getExpectedResult() == [1, -1]) {
$resultBool = 1;
}
$accuracy[] = $resultBool;
$errors[] = $neuralNetwork->getError($result, $testData->getExpectedResult());
}
echo '<div>Skuteczność <b>' . avg($accuracy) . '</b></div>';
echo '<div>Błąd <b>' . avg($errors) . '</b></div>';
// echo '<pre>'.print_r($neuralNetwork->toArray(), true).'</pre>';
?>
<div class="graphs">
<?php
if ($withError) {
?>
<div class="graphError" data-data="<?php
echo htmlspecialchars(json_encode($neuralNetwork->getHistoricErrors()));
?>
"></div>
<?php
}
?>
<div class="graphNetwork" data-data="<?php
echo htmlspecialchars(json_encode($neuralNetwork->toArray()), ENT_QUOTES, 'UTF-8');
?>
"></div>
</div>
<script>draw()</script>
<?php
flush();
ob_flush();
}
function test(NeuralNetwork $neuralNetwork)
{
$trainingDatas = prepareTrainingDatas();
foreach (NUMBERS as $number) {
?>
<div class="displays"><?php
?>
<div class="display"><?php
foreach ($number as $v) {
?>
<div class="display-pixel" style="opacity: <?php
echo ($v + 1) / 2;
?>
"></div><?php
}
?>
</div><?php
?>
<div class="display"><?php
foreach ($neuralNetwork->calculate($number) as $v) {
?>
<div class="display-pixel" style="opacity: <?php
echo ($v + 1) / 2;
?>
"></div><?php
}
?>
</div><?php
?>
</div><?php
}
?>
<div class="graphs">
<div class="graphNetwork" data-data="<?php
echo htmlspecialchars(json_encode($neuralNetwork->toArray()), ENT_QUOTES, 'UTF-8');
?>
"></div>
</div>
<script>draw()</script>
<?php
}
function test(NeuralNetwork $neuralNetwork)
{
$correctResult = [];
for ($x = MIN; $x <= MAX; $x += STEP) {
for ($y = MIN; $y <= MAX; $y += STEP) {
$result = func($x, $y);
if (is_infinite($result)) {
continue;
}
$correctResult[] = [$x, $y, func($x, $y)];
}
}
$networkResult = [];
for ($x = MIN; $x <= MAX; $x += STEP) {
for ($y = MIN; $y <= MAX; $y += STEP) {
$networkResult[] = [$x, $y, $neuralNetwork->calculate([$x, $y])[0]];
}
}
// echo '<pre>'.print_r($neuralNetwork->toArray(), true).'</pre>';
?>
<div class="graphs">
<div class="graph3d" data-data="<?php
echo json_encode($correctResult);
?>
"></div>
<div class="graph3d" data-data="<?php
echo json_encode($networkResult);
?>
"></div>
<div class="graphNetwork" data-data="<?php
echo htmlspecialchars(json_encode($neuralNetwork->toArray()), ENT_QUOTES, 'UTF-8');
?>
"></div>
</div>
<script>draw()</script>
<?php
flush();
ob_flush();
}
