Increase max request time

Dockerfile

@@ -24,6 +24,8 @@ RUN npm run build
 # Final PHP image
 FROM dunglas/frankenphp:1.12.2-php8.3-alpine AS final
 
+COPY docker/php.ini-production "$PHP_INI_DIR/php.ini"
+
 # Install system dependencies
 RUN apk add --no-cache \
     bash \

app/Exceptions/TrainingCancelledException.php

@@ -0,0 +1,9 @@
+<?php
+
+namespace App\Exceptions;
+
+use RuntimeException;
+
+class TrainingCancelledException extends RuntimeException
+{
+}

app/Http/Controllers/PerceptronController.php

@@ -3,6 +3,7 @@
 namespace App\Http\Controllers;
 
 use App\Events\PerceptronInitialization;
+use App\Exceptions\TrainingCancelledException;
 use App\Http\Requests\RunPerceptronRequest;
 use App\Models\NetworksTraining\ADALINEPerceptronTraining;
 use App\Models\NetworksTraining\GradientDescentPerceptronTraining;
@@ -18,15 +19,31 @@ use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 use App\Services\SynapticWeightsProvider\RandomSynapticWeights;
 use App\Services\SynapticWeightsProvider\ZeroSynapticWeights;
 use Illuminate\Http\Request;
+use Illuminate\Support\Facades\Cache;
 
 class PerceptronController extends Controller
 {
+    private function cancellationKey(string $trainingId): string
+    {
+        return "perceptron-training-cancelled:{$trainingId}";
+    }
+
+    public function cancel(Request $request)
+    {
+        $trainingId = $request->validate([
+            'training_id' => ['required', 'string', 'max:100'],
+        ])['training_id'];
+
+        Cache::put($this->cancellationKey($trainingId), true, now()->addHour());
+
+        return response()->noContent();
+    }
     /**
      * Display the specified resource.
      */
     public function index(Request $request)
     {
-        $perceptronType = $request->query('type');
+        $perceptronType = $request->query('type', 'simple'); ;
 
         $learningRate = 0.01;
         $maxIterations = 200;
@@ -220,6 +237,8 @@ class PerceptronController extends Controller
         $sessionId = $request->input('session_id', session()->getId());
         $trainingId = $request->input('training_id');
 
+        Cache::forget($this->cancellationKey($trainingId));
+
         // Zero initialization prevents hidden layers from receiving a gradient.
         if ($perceptronType === 'multilayer' && $weightInitMethod === 'zeros') {
             $synapticWeightsProvider = new RandomSynapticWeights;
@@ -236,17 +255,22 @@ class PerceptronController extends Controller
         $datasetReader = $this->getDataSetReader($dataSet);
 
         $networkTraining = match ($perceptronType) {
-            'simple' => new SimpleBinaryPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId),
+            'simple' => new SimpleBinaryPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'gradientdescent' => new GradientDescentPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'gradientdescent' => new GradientDescentPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'adaline' => new ADALINEPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'adaline' => new ADALINEPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'monolayer' => new MonoLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'monolayer' => new MonoLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
-            'multilayer' => new MultiLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $hiddenLayers, $hiddenLayersNeurons, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError),
+            'multilayer' => new MultiLayerPerceptronTraining($datasetReader, $learningRate, $maxEpochs, $hiddenLayers, $hiddenLayersNeurons, $synapticWeightsProvider, $iterationEventBuffer, $sessionId, $trainingId, $minError, fn (): bool => connection_aborted() || Cache::has($this->cancellationKey($trainingId))),
             default => null,
         };
 
         event(new PerceptronInitialization($datasetReader->lines, $networkTraining->activationFunction, $sessionId, $trainingId));
 
+        try {
             $networkTraining->start();
+        } catch (TrainingCancelledException) {
+            $networkTraining->cancel();
+            Cache::forget($this->cancellationKey($trainingId));
+        }
 
         return back()->with('success', [
             'message' => 'Training completed',

app/Models/NetworksTraining/ADALINEPerceptronTraining.php

@@ -8,6 +8,7 @@ use App\Models\Perceptrons\Perceptron;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+use Closure;
 
 class ADALINEPerceptronTraining extends NetworkTraining
 {
@@ -26,8 +27,9 @@ class ADALINEPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->perceptron = new GradientDescentPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
     }
 

app/Models/NetworksTraining/GradientDescentPerceptronTraining.php

@@ -8,6 +8,7 @@ use App\Models\Perceptrons\Perceptron;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+use Closure;
 
 class GradientDescentPerceptronTraining extends NetworkTraining
 {
@@ -26,8 +27,9 @@ class GradientDescentPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->perceptron = new GradientDescentPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
     }
 

app/Models/NetworksTraining/MonoLayerPerceptronTraining.php

@@ -10,6 +10,7 @@ use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 use App\Services\SynapticWeightsProvider\SimpleNetworkWeightsProvider;
+use Closure;
 use Illuminate\Support\Arr;
 
 class MonoLayerPerceptronTraining extends NetworkTraining
@@ -35,8 +36,9 @@ class MonoLayerPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->isRegression = $datasetReader->getInputSize() === 1;
         $networkWeightsProvider = new SimpleNetworkWeightsProvider($synapticWeightsProvider);
         $this->network = new NetworkPerceptron(

app/Models/NetworksTraining/MultiLayerPerceptronTraining.php

@@ -11,6 +11,7 @@ use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
 use App\Services\SynapticWeightsProvider\SimpleNetworkWeightsProvider;
+use Closure;
 use Illuminate\Support\Arr;
 
 class MultiLayerPerceptronTraining extends NetworkTraining
@@ -37,8 +38,9 @@ class MultiLayerPerceptronTraining extends NetworkTraining
         string $sessionId,
         string $trainingId,
         private float $minError,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->labels = $datasetReader->getLabels();
         $this->isRegression = $datasetReader->getOutputSize() === 1
             || ($datasetReader->getOutputSize() > 2

app/Models/NetworksTraining/NetworkTraining.php

@@ -3,9 +3,11 @@
 namespace App\Models\NetworksTraining;
 
 use App\Events\PerceptronTrainingEnded;
+use App\Exceptions\TrainingCancelledException;
 use App\Models\ActivationsFunctions;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
+use Closure;
 
 abstract class NetworkTraining
 {
@@ -24,6 +26,7 @@ abstract class NetworkTraining
         protected IPerceptronIterationEventBuffer $iterationEventBuffer,
         protected string $sessionId,
         protected string $trainingId,
+        protected ?Closure $isCancelled = null,
     ) {}
 
     abstract public function start(): void;
@@ -50,9 +53,18 @@ abstract class NetworkTraining
 
     protected function addIterationToBuffer(float $error, array $synapticWeights)
     {
+        if ($this->isCancelled !== null && ($this->isCancelled)()) {
+            throw new TrainingCancelledException;
+        }
+
         $this->iterationEventBuffer->addIteration($this->epoch, $this->datasetReader->getLastReadLineIndex(), $error, $synapticWeights);
     }
 
+    public function cancel(): void
+    {
+        $this->broadcastTrainingEnded('Entraînement annulé');
+    }
+
     public function getEpoch(): int
     {
         return $this->epoch;

app/Models/NetworksTraining/SimpleBinaryPerceptronTraining.php

@@ -9,6 +9,7 @@ use App\Models\Perceptrons\SimpleBinaryPerceptron;
 use App\Services\DatasetReader\IDataSetReader;
 use App\Services\IterationEventBuffer\IPerceptronIterationEventBuffer;
 use App\Services\SynapticWeightsProvider\ISynapticWeightsProvider;
+use Closure;
 
 class SimpleBinaryPerceptronTraining extends NetworkTraining
 {
@@ -28,8 +29,9 @@ class SimpleBinaryPerceptronTraining extends NetworkTraining
         IPerceptronIterationEventBuffer $iterationEventBuffer,
         string $sessionId,
         string $trainingId,
+        ?Closure $isCancelled = null,
     ) {
-        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId);
+        parent::__construct($datasetReader, $maxEpochs, $iterationEventBuffer, $sessionId, $trainingId, $isCancelled);
         $this->perceptron = new SimpleBinaryPerceptron($synapticWeightsProvider->generate($datasetReader->getInputSize()));
     }
 

config/perceptron.php

@@ -22,7 +22,7 @@ return [
     /**
      * Minimum time between training progress broadcasts, in milliseconds.
      */
-    'broadcast_minimum_interval_ms' => 0,
+    'broadcast_minimum_interval_ms' => 100,
 
     /**
      * Maximum number of weights for which all iteration weights are broadcast

public/helptext/gradientdescent.md

@@ -59,7 +59,7 @@ Reprenons le dataset oblique afin de pouvoir comparer les résultats, et diminuo
 
 Puis cliquez sur "Lancer" tout en laissant les autres paramètres intacts.
 
-On peut voir en dessous du tableau que l'entraînement s'est arrêté car le nombre maximal d'époques a été atteint. On pourrait l'augmenter (max 5000 pour mon petit serveur), mais ce serait du temps perdu. La solution du réseau est suffisante et il n'y aurait pas beaucoup de gain, même pour le triple d'itérations maximales en plus. Pour le prouver, cliquez sur le bouton `Afficher uniquement l'erreur quadratique moyenne`, la ligne que l'on peut voir ressemble fortement à la fonction logarithme $y = log(x^{-1})$. La progression de la descente du gradient est donc logarithmique ; les plus gros changements se font dans les premières époques.
+On peut voir en dessous du tableau que l'entraînement s'est arrêté car le nombre maximal d'époques a été atteint. On pourrait l'augmenter (max 5000 pour mon petit serveur), mais ce serait du temps perdu. La solution du réseau est suffisante et il n'y aurait pas beaucoup de gain, même pour le triple d'itérations maximales en plus. Pour le prouver, cliquez sur le bouton `Afficher uniquement l'erreur quadratique moyenne`, la ligne que l'on peut voir ressemble fortement à la fonction logarithme $y = log(x^{-1})$ (attention à l'échelle qui est elle-même logarithmique). La progression de la descente du gradient est donc logarithmique ; les plus gros changements se font dans les premières époques.
 
 </div>
 

resources/js/app.ts

@@ -33,6 +33,7 @@ createInertiaApp({
                         'data-auto-track': 'true',
                         'data-performance': 'true',
                         'data-domains': 'perceptron.matthiasg.dev,matthiasg.dev,www.matthiasg.dev',
+                        'data-exclude-search': 'false',
                     }
                 }),
             )

resources/js/components/PerceptronIterationsErrorsGraph.vue

@@ -113,8 +113,8 @@ const datasets = computed<ErrorDataset[]>(() => {
                     min: 0,
                 },
                 y: {
+                    type: !epochErrorOnly ? 'linear' : 'logarithmic',
                     stacked: true,
-                    beginAtZero: !props.isRegression,
                     grid: {
                         color: function (context) {
                             if (context.tick.value == 0) {

resources/js/components/PerceptronSetup.vue

@@ -1,5 +1,6 @@
 <script setup lang="ts">
 import { useForm } from '@inertiajs/vue3';
+import { trackUmamiEvent } from '@jaseeey/vue-umami-plugin';
 import { ref, watch } from 'vue';
 import {
     Form,
@@ -12,6 +13,7 @@ import {
     NativeSelect,
     NativeSelectOption,
 } from '@/components/ui/native-select';
+import { cancel } from '@/routes/perceptron';
 import type {
     Dataset,
     InitializationMethod,
@@ -23,8 +25,9 @@ import Card from './ui/card/Card.vue';
 import CardContent from './ui/card/CardContent.vue';
 import CardHeader from './ui/card/CardHeader.vue';
 import CardTitle from './ui/card/CardTitle.vue';
-import Input from './ui/input/Input.vue';
 import FormError from './ui/form/FormError.vue';
+import Input from './ui/input/Input.vue';
+import Spinner from './ui/spinner/Spinner.vue';
 
 const props = defineProps<{
     type: PerceptronType;
@@ -98,43 +101,69 @@ watch(maxIterations, (value) => {
 watch(selectedDatasetCopy, (newvalue) => {
     form.clearErrors('dataset');
 
-    const selectedDatasetCopy = props.datasets.find(
+    const selectedDatasetCopy =
-        (dataset) => dataset.label === newvalue
+        props.datasets.find((dataset) => dataset.label === newvalue) || null;
-    ) || null;
 
     // LearningRate
     learningRate.value = props.defaultLearningRate;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultLearningRate !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultLearningRate !== undefined
+    ) {
         learningRate.value = selectedDatasetCopy.defaultLearningRate;
     }
     // MinError
     minError.value = props.minError;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultMinError !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultMinError !== undefined
+    ) {
         minError.value = selectedDatasetCopy.defaultMinError;
     }
     // MaxIterations
     maxIterations.value = props.defaultMaxIterations;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultMaxIterations !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultMaxIterations !== undefined
+    ) {
         maxIterations.value = selectedDatasetCopy.defaultMaxIterations;
     }
     // HiddenLayers
     hiddenLayers.value = props.hiddenLayers;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultHiddenLayers !== undefined) {
+    if (
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultHiddenLayers !== undefined
+    ) {
         hiddenLayers.value = selectedDatasetCopy.defaultHiddenLayers;
     }
     hiddenLayersNeurons.value = props.hiddenLayersNeurons;
-    if (selectedDatasetCopy && selectedDatasetCopy.defaultHiddenLayersNeurons !== undefined) {
+    if (
-        hiddenLayersNeurons.value = selectedDatasetCopy.defaultHiddenLayersNeurons;
+        selectedDatasetCopy &&
+        selectedDatasetCopy.defaultHiddenLayersNeurons !== undefined
+    ) {
+        hiddenLayersNeurons.value =
+            selectedDatasetCopy.defaultHiddenLayersNeurons;
     }
-})
+});
 
 const trainingId = ref<string>('');
 
 function startTraining() {
+    trackUmamiEvent('perceptron-training-start', {
+        type: props.type,
+        dataset: selectedDatasetCopy.value,
+        weight_init_method: selectedMethod.value,
+        hidden_layers: hiddenLayers.value,
+        hidden_layers_neurons: hiddenLayersNeurons.value,
+        min_error: minError.value,
+        learning_rate: learningRate.value,
+        max_iterations: maxIterations.value,
+    });
+
     if (!selectedDatasetCopy.value) {
         form.setError(
             'dataset',
-            'Un dataset est nécessaire avant de lancer l\'entraînement.',
+            "Un dataset est nécessaire avant de lancer l'entraînement.",
         );
         console.log(form.errors);
         return;
@@ -156,19 +185,33 @@ function startTraining() {
         max_iterations: maxIterations.value,
     });
 
-    console.debug('[max_iterations] submitting', {
-        displayed: maxIterationsInput.value?.inputElement?.value,
-        refValue: maxIterations.value,
-        formValue: form.max_iterations,
-        limit: props.maxIterationsLimit,
-    });
-
     form.post('/perceptron/run', {
         preserveScroll: true,
     });
 }
 
 const emit = defineEmits(['update:selectedDataset', 'update:trainingId']);
+
+async function cancelTraining() {
+    trackUmamiEvent('perceptron-training-cancel', {
+        training_id: trainingId.value,
+    });
+
+    form.cancel();
+
+    await fetch(cancel().url, {
+        method: 'POST',
+        headers: {
+            Accept: 'application/json',
+            'Content-Type': 'application/json',
+        },
+        credentials: 'same-origin',
+        body: JSON.stringify({
+            training_id: trainingId.value,
+        }),
+    });
+}
+
 watch(selectedDatasetCopy, (newValue) => {
     emit('update:selectedDataset', newValue);
 });
@@ -182,6 +225,7 @@ watch(selectedDatasetCopy, (newValue) => {
         <CardContent>
             <Form
                 class="grid auto-cols-max grid-flow-row grid-cols-1 gap-4 space-y-6 md:grid-cols-2"
+                cancel-on-unmount
             >
                 <!-- DATASET -->
                 <FormField name="dataset">
@@ -206,7 +250,12 @@ watch(selectedDatasetCopy, (newValue) => {
                                 </NativeSelectOption>
                             </NativeSelect>
                         </FormControl>
-                        <FormError :error="form.errors.dataset || props.errors?.selectedDatasetCopy" />
+                        <FormError
+                            :error="
+                                form.errors.dataset ||
+                                props.errors?.selectedDatasetCopy
+                            "
+                        />
                     </FormItem>
                 </FormField>
 
@@ -224,7 +273,9 @@ watch(selectedDatasetCopy, (newValue) => {
                                 class="cursor-pointer"
                             >
                                 <NativeSelectOption
-                                    v-for="method in (props.type == 'multilayer' ? ['random'] : ['zeros', 'random'])"
+                                    v-for="method in props.type == 'multilayer'
+                                        ? ['random']
+                                        : ['zeros', 'random']"
                                     v-bind:key="method"
                                     :value="method"
                                 >
@@ -236,7 +287,10 @@ watch(selectedDatasetCopy, (newValue) => {
                 </FormField>
 
                 <!-- HIDDEN LAYERS -->
-                <FormField name="hidden_layers" v-if="props.type === 'multilayer'">
+                <FormField
+                    name="hidden_layers"
+                    v-if="props.type === 'multilayer'"
+                >
                     <FormItem>
                         <FormLabel>Nombre de couches cachées</FormLabel>
                         <FormControl>
@@ -255,9 +309,14 @@ watch(selectedDatasetCopy, (newValue) => {
                 </FormField>
 
                 <!-- HIDDEN LAYERS NEURONS -->
-                <FormField name="hidden_layers_neurons" v-if="props.type === 'multilayer'">
+                <FormField
+                    name="hidden_layers_neurons"
+                    v-if="props.type === 'multilayer'"
+                >
                     <FormItem>
-                        <FormLabel>Nombre de neurones par couche cachée</FormLabel>
+                        <FormLabel
+                            >Nombre de neurones par couche cachée</FormLabel
+                        >
                         <FormControl>
                             <!-- TODO : MAX input -->
                             <Input
@@ -319,13 +378,39 @@ watch(selectedDatasetCopy, (newValue) => {
                                 @input="handleMaxIterationsInput"
                             />
                         </FormControl>
-                        <div v-if="form.errors.max_iterations || props.errors.max_iterations">
+                        <div
-                            {{ form.errors.max_iterations || props.errors.max_iterations }}
+                            v-if="
+                                form.errors.max_iterations ||
+                                props.errors.max_iterations
+                            "
+                        >
+                            {{
+                                form.errors.max_iterations ||
+                                props.errors.max_iterations
+                            }}
                         </div>
                     </FormItem>
                 </FormField>
             </Form>
-            <Button variant="outline" class="cursor-pointer mt-6" @click="startTraining">Lancer</Button>
+
+            <Transition name="fade">
+                <Button
+                    variant="outline"
+                    class="mt-6 cursor-pointer"
+                    :disabled="form.processing"
+                    @click="startTraining"
+                    >Lancer<Spinner v-if="form.processing" class="ml-1"
+                /></Button>
+            </Transition>
+            <Transition name="fade">
+                <Button
+                    variant="outline"
+                    class="mt-6 ml-4 cursor-pointer"
+                    @click="cancelTraining"
+                    v-if="form.processing"
+                    >Annuler</Button
+                >
+            </Transition>
         </CardContent>
     </Card>
 </template>

resources/js/pages/PerceptronViewer.vue

@@ -1,5 +1,7 @@
 <script setup lang="ts">
 import { Head } from '@inertiajs/vue3';
+import { trackUmamiEvent } from '@jaseeey/vue-umami-plugin';
+import { BookOpenText } from '@lucide/vue';
 import { useEventListener } from '@vueuse/core';
 import {
     Chart as ChartJS,
@@ -13,11 +15,21 @@ import {
     LineElement,
     ScatterController,
     LineController,
+    BarController,
+    LogarithmicScale,
+
 } from 'chart.js';
 import { ArrowDown, ArrowUp } from 'lucide-vue-next';
 import { computed, nextTick, ref, watch } from 'vue';
+import HelpText from '@/components/HelpText.vue';
 import LinkHeader from '@/components/LinkHeader.vue';
 import Button from '@/components/ui/button/Button.vue';
+import Drawer from '@/components/ui/drawer/Drawer.vue';
+import DrawerContent from '@/components/ui/drawer/DrawerContent.vue';
+import DrawerTitle from '@/components/ui/drawer/DrawerTitle.vue';
+import DrawerTrigger from '@/components/ui/drawer/DrawerTrigger.vue';
+import Kbd from '@/components/ui/kbd/Kbd.vue';
+import KbdGroup from '@/components/ui/kbd/KbdGroup.vue';
 import ScrollArea from '@/components/ui/scroll-area/ScrollArea.vue';
 import {
     Tooltip as UiTooltip,
@@ -31,14 +43,6 @@ import IterationTable from '../components/IterationTable.vue';
 import PerceptronDecisionGraph from '../components/PerceptronDecisionGraph.vue';
 import PerceptronIterationsErrorsGraph from '../components/PerceptronIterationsErrorsGraph.vue';
 import PerceptronSetup from '../components/PerceptronSetup.vue';
-import HelpText from '@/components/HelpText.vue';
-import { BookOpenText } from '@lucide/vue';
-import Drawer from '@/components/ui/drawer/Drawer.vue';
-import DrawerTrigger from '@/components/ui/drawer/DrawerTrigger.vue';
-import DrawerContent from '@/components/ui/drawer/DrawerContent.vue';
-import DrawerTitle from '@/components/ui/drawer/DrawerTitle.vue';
-import KbdGroup from '@/components/ui/kbd/KbdGroup.vue';
-import Kbd from '@/components/ui/kbd/Kbd.vue';
 
 ChartJS.register(
     Title,
@@ -51,6 +55,8 @@ ChartJS.register(
     LineElement,
     ScatterController,
     LineController,
+    BarController,
+    LogarithmicScale
 );
 ChartJS.defaults.font.size = 16;
 ChartJS.defaults.color = '#FFF';
@@ -141,6 +147,7 @@ const handleDrawerOpenChange = async (open: boolean) => {
 
     if (open) {
         await restoreHelpScroll();
+        trackUmamiEvent('wiki-opened', { perceptronType: props.type });
     }
 };
 

routes/api.php

@@ -1,2 +1,8 @@
 <?php
 
+use App\Http\Controllers\PerceptronController;
+use Illuminate\Support\Facades\Route;
+
+Route::post('perceptron/cancel', [PerceptronController::class, 'cancel'])
+	->name('perceptron.cancel');
+