Added Limited Epoch Event Buffer
for better frontend performance when using big max epoch number
This commit is contained in:
@@ -8,6 +8,7 @@ use App\Models\SimpleBinaryPerceptronTraining;
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use App\Services\DataSetReader;
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use App\Services\DataSetReader;
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use App\Services\ISynapticWeightsProvider;
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use App\Services\ISynapticWeightsProvider;
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use App\Services\PerceptronIterationEventBuffer;
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use App\Services\PerceptronIterationEventBuffer;
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use App\Services\PerceptronLimitedEpochEventBuffer;
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use App\Services\ZeroSynapticWeights;
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use App\Services\ZeroSynapticWeights;
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use Illuminate\Http\Request;
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use Illuminate\Http\Request;
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@@ -91,8 +92,15 @@ class PerceptronController extends Controller
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case 'simple':
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case 'simple':
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$dataset['defaultLearningRate'] = 0.015;
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$dataset['defaultLearningRate'] = 0.015;
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break;
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break;
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case 'gradientdescent':
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$dataset['defaultLearningRate'] = 0.001;
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$dataset['defaultMinError'] = 2.0;
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break;
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}
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}
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break;
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break;
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case 'table_2_11':
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$dataset['defaultMinError'] = 1.0;
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break;
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}
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}
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$datasets[] = $dataset;
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$datasets[] = $dataset;
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}
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}
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@@ -121,9 +129,14 @@ class PerceptronController extends Controller
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$synapticWeightsProvider = new ZeroSynapticWeights();
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$synapticWeightsProvider = new ZeroSynapticWeights();
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}
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}
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$iterationEventBuffer = new PerceptronIterationEventBuffer($sessionId, $trainingId);
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if ($maxIterations > config('perceptron.limited_broadcast_iterations')) {
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$iterationsInterval = (int)($maxIterations / config('perceptron.limited_broadcast_iterations'));
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$iterationEventBuffer = new PerceptronLimitedEpochEventBuffer($sessionId, $trainingId, $iterationsInterval);
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}
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$dataSetReader = $this->getDataSetReader($dataSet);
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$dataSetReader = $this->getDataSetReader($dataSet);
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$iterationEventBuffer = new PerceptronIterationEventBuffer($sessionId, $trainingId);
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$networkTraining = match ($perceptronType) {
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$networkTraining = match ($perceptronType) {
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'simple' => new SimpleBinaryPerceptronTraining($dataSetReader, $learningRate, $maxIterations, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId),
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'simple' => new SimpleBinaryPerceptronTraining($dataSetReader, $learningRate, $maxIterations, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId),
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@@ -51,6 +51,16 @@ class DataSetReader {
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return $randomLine;
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return $randomLine;
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}
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}
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public function getNextLine(): array | null {
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if (!isset($this->currentLines[0])) {
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return null; // No more lines to read
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}
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$this->lastReadLineIndex = array_search($this->currentLines[0], $this->lines, true);
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return array_shift($this->currentLines);
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}
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public function getInputSize(): int
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public function getInputSize(): int
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{
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{
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return count($this->lines[0]) - 1; // Don't count the label
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return count($this->lines[0]) - 1; // Don't count the label
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10
app/Services/IPerceptronIterationEventBuffer.php
Normal file
10
app/Services/IPerceptronIterationEventBuffer.php
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@@ -0,0 +1,10 @@
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<?php
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namespace App\Services;
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interface IPerceptronIterationEventBuffer {
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public function flush(): void ;
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public function addIteration(int $iteration, int $exampleIndex, float $error, array $synaptic_weights): void ;
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}
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@@ -2,15 +2,11 @@
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namespace App\Services;
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namespace App\Services;
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use Illuminate\Support\Facades\Log;
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class PerceptronIterationEventBuffer implements IPerceptronIterationEventBuffer {
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class PerceptronIterationEventBuffer {
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private $data;
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private $data;
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private int $nextSizeIncreaseThreshold;
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private int $nextSizeIncreaseThreshold;
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private int $underSizeIncreaseCount = 0;
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private int $underSizeIncreaseCount = 0;
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private int $MAX_SIZE = 50;
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public function __construct(
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public function __construct(
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private string $sessionId,
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private string $sessionId,
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private string $trainingId,
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private string $trainingId,
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@@ -26,9 +22,9 @@ class PerceptronIterationEventBuffer {
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$this->data = [];
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$this->data = [];
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}
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}
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public function addIteration(int $iteration, int $exampleIndex, float $error, array $synaptic_weights): void {
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public function addIteration(int $epoch, int $exampleIndex, float $error, array $synaptic_weights): void {
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$this->data[] = [
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$this->data[] = [
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"iteration" => $iteration,
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"epoch" => $epoch,
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"exampleIndex" => $exampleIndex,
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"exampleIndex" => $exampleIndex,
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"error" => $error,
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"error" => $error,
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"weights" => $synaptic_weights,
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"weights" => $synaptic_weights,
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@@ -42,8 +38,8 @@ class PerceptronIterationEventBuffer {
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$this->flush();
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$this->flush();
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$this->nextSizeIncreaseThreshold *= $this->sizeIncreaseFactor;
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$this->nextSizeIncreaseThreshold *= $this->sizeIncreaseFactor;
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if ($this->nextSizeIncreaseThreshold > $this->MAX_SIZE) {
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if ($this->nextSizeIncreaseThreshold > config('perceptron.broadcast_iteration_size')) {
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$this->nextSizeIncreaseThreshold = $this->MAX_SIZE; // Cap the threshold to the maximum size
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$this->nextSizeIncreaseThreshold = config('perceptron.broadcast_iteration_size'); // Cap the threshold to the maximum size
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}
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}
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}
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}
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}
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}
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51
app/Services/PerceptronLimitedEpochEventBuffer.php
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51
app/Services/PerceptronLimitedEpochEventBuffer.php
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@@ -0,0 +1,51 @@
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<?php
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namespace App\Services;
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class PerceptronLimitedEpochEventBuffer implements IPerceptronIterationEventBuffer {
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private array $data;
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private int $underSizeIncreaseCount = 0;
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public function __construct(
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private string $sessionId,
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private string $trainingId,
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private int $epochInterval,
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private int $sizeIncreaseStart = 10,
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) {
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$this->data = [];
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}
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public function flush(): void {
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event(new \App\Events\PerceptronTrainingIteration($this->data, $this->sessionId, $this->trainingId));
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$this->data = [];
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}
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public function addIteration(int $epoch, int $exampleIndex, float $error, array $synaptic_weights): void {
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$newData = [
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"epoch" => $epoch,
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"exampleIndex" => $exampleIndex,
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"error" => $error,
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"weights" => $synaptic_weights,
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];
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if ($this->underSizeIncreaseCount <= $this->sizeIncreaseStart) { // Special case where we need to send each iteration separately
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$this->underSizeIncreaseCount++;
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$this->data[] = $newData;
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$this->flush();
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return;
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}
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$lastEpoch = $this->data[0]['epoch'] ?? null;
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if ($this->data && $lastEpoch !== $epoch) { // Current Epoch has changed from the last one
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if ($lastEpoch % $this->epochInterval === 0) { // The last epoch need to be sent
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$this->flush(); // Flush all data from the previous epoch
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}
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else {
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$this->data = [];
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}
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$lastEpoch = $epoch;
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}
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$this->data[] = $newData;
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}
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}
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19
config/perceptron.php
Normal file
19
config/perceptron.php
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@@ -0,0 +1,19 @@
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<?php
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return [
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/**
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* Minimum number of iterations for which the broadcast of the training progress is allowed in full.
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* Beyond this number of iterations, the broadcast will be splitted every x iterations,
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* x is limited_broadcast_number
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*/
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'limited_broadcast_iterations' => 200,
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/**
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* How much broadcasts is sent when in limmited broadcast mode
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*/
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'limited_broadcast_number' => 200,
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'broadcast_iteration_size' => 75,
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];
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4
public/data_sets/logic_or.csv
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4
public/data_sets/logic_or.csv
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@@ -0,0 +1,4 @@
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0, 0, -1
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0, 1, 1
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1, 0, 1
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1, 1, 1
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@@ -15,14 +15,29 @@ const allWeightPerIteration: ComputedRef<number[][]> = computed(() => {
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return iteration.weights.flat(2);
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return iteration.weights.flat(2);
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});
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});
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});
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});
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const rowBgDark = computed(() => {
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let isEven = false;
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return props.iterations.map((iteration, index, arr) => {
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if (index > 0 && arr[index - 1].epoch !== iteration.epoch) {
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isEven = !isEven;
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}
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return isEven;
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});
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});
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</script>
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</script>
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<template>
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<template>
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<table class="table w-full border-collapse border border-gray-300">
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<table class="table w-full border-collapse border border-gray-300">
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<tr class="text-left" v-if="props.iterations.length > 0">
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<tr class="text-left" v-if="props.iterations.length > 0">
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<th>Itération</th>
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<th>Époch</th>
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<th>Exemple</th>
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<th>Exemple</th>
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<th v-for="(weight, index) in allWeightPerIteration[allWeightPerIteration.length - 1]" v-bind:key="index">
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<th
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v-for="(weight, index) in allWeightPerIteration[
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allWeightPerIteration.length - 1
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]"
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v-bind:key="index"
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>
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X<sub>{{ index }}</sub>
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X<sub>{{ index }}</sub>
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</th>
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</th>
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<th>Erreur</th>
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<th>Erreur</th>
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@@ -31,12 +46,15 @@ const allWeightPerIteration: ComputedRef<number[][]> = computed(() => {
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v-for="(iteration, index) in props.iterations"
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v-for="(iteration, index) in props.iterations"
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v-bind:key="index"
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v-bind:key="index"
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:class="{
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:class="{
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'bg-gray-900': iteration.iteration % 2 === 0,
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'bg-gray-900': rowBgDark[index],
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}"
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}"
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>
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>
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<td>{{ iteration.iteration }}</td>
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<td>{{ iteration.epoch }}</td>
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<td>{{ iteration.exampleIndex }}</td>
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<td>{{ iteration.exampleIndex }}</td>
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<td v-for="(weight, index) in allWeightPerIteration[index]" v-bind:key="index">
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<td
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v-for="(weight, index) in allWeightPerIteration[index]"
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v-bind:key="index"
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>
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{{ weight.toFixed(2) }}
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{{ weight.toFixed(2) }}
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</td>
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</td>
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<td>{{ iteration.error.toFixed(2) }}</td>
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<td>{{ iteration.error.toFixed(2) }}</td>
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@@ -46,7 +46,7 @@ function getPerceptronDecisionBoundaryDataset(
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networkWeights[0].length == 1 &&
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networkWeights[0].length == 1 &&
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networkWeights[0][0].length == 3
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networkWeights[0][0].length == 3
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) { // Unique, 3 weights perceptron
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) { // Unique, 3 weights perceptron
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const perceptronWeights = networkWeights[0][0]; // We take the unique
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const perceptronWeights = networkWeights[0][0]; // We take the unique perceptron
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function perceptronLine(x: number): number {
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function perceptronLine(x: number): number {
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// w0 + w1*x + w2*y = 0 => y = -(w1/w2)*x - w0/w2
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// w0 + w1*x + w2*y = 0 => y = -(w1/w2)*x - w0/w2
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@@ -161,7 +161,7 @@ function getPerceptronDecisionBoundaryDataset(
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color: function (context) {
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color: function (context) {
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if (context.tick.value == 0) {
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if (context.tick.value == 0) {
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return gridColorBold;
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return gridColorBold;
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}
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}
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return gridColor;
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return gridColor;
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},
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},
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@@ -174,7 +174,7 @@ function getPerceptronDecisionBoundaryDataset(
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color: function (context) {
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color: function (context) {
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if (context.tick.value == 0) {
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if (context.tick.value == 0) {
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return gridColorBold;
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return gridColorBold;
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}
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}
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return gridColor;
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return gridColor;
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},
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},
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@@ -47,11 +47,17 @@ watch(selectedDatasetCopy, (newvalue) => {
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(dataset) => dataset.label === newvalue
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(dataset) => dataset.label === newvalue
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) || null;
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) || null;
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let defaultLearningRate = props.defaultLearningRate;
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// LearningRate
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learningRate.value = props.defaultLearningRate;
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if (selectedDatasetCopy && selectedDatasetCopy.defaultLearningRate !== undefined) {
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if (selectedDatasetCopy && selectedDatasetCopy.defaultLearningRate !== undefined) {
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defaultLearningRate = selectedDatasetCopy.defaultLearningRate;
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learningRate.value = selectedDatasetCopy.defaultLearningRate;
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}
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}
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learningRate.value = defaultLearningRate;
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// MinError
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minError.value = props.minError;
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if (selectedDatasetCopy && selectedDatasetCopy.defaultMinError !== undefined) {
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minError.value = selectedDatasetCopy.defaultMinError;
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}
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// MaxIterations
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maxIterations.value = props.defaultMaxIterations;
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maxIterations.value = props.defaultMaxIterations;
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})
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})
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@@ -196,7 +202,7 @@ watch(selectedDatasetCopy, (newValue) => {
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<!-- MAX ITERATIONS -->
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<!-- MAX ITERATIONS -->
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<FormField name="max_iterations">
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<FormField name="max_iterations">
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<FormItem>
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<FormItem>
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<FormLabel>Nombre maximum d'itérations</FormLabel>
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<FormLabel>Nombre maximum d'epoch</FormLabel>
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<FormControl>
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<FormControl>
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<Input
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<Input
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type="number"
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type="number"
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Reference in New Issue
Block a user