Merge pull request #32 from ml5js/format-documents

format all js documents

Author: shiffman

.prettierignore

@@ -1 +1,2 @@
 /dist
+/coverage

examples/Handpose/sketch.js

@@ -1,43 +1,43 @@
-let handpose;
-let video;
-let hands = [];
-
-function setup() {
-  createCanvas(640, 480);
-
-  // Create the video and hide it
-  video = createCapture(VIDEO);
-  video.size(width, height);
-  video.hide();
-
-  // Load the model and attach an event
-  handpose = ml5.handpose(video, modelReady);
-  handpose.on("hand", gotHands);
-}
-
-// Event for hand detection
-function gotHands(results) {
-  // Always save the latest output from the model in global variable "hands"
-  hands = results;
-}
-
-// Event for when model loaded
-function modelReady() {
-  console.log("Model ready!");
-}
-
-function draw() {
-  // Draw the video
-  image(video, 0, 0, width, height);
-
-  // Draw all the tracked hand points
-  for (let i = 0; i < hands.length; i++) {
-    let hand = hands[i];
-    for (let j = 0; j < hand.keypoints.length; j++) {
-      let keypoint = hand.keypoints[j];
-      fill(0, 255, 0);
-      noStroke();
-      circle(keypoint.x, keypoint.y, 10);
-    }
-  }
-}
+let handpose;
+let video;
+let hands = [];
+
+function setup() {
+  createCanvas(640, 480);
+
+  // Create the video and hide it
+  video = createCapture(VIDEO);
+  video.size(width, height);
+  video.hide();
+
+  // Load the model and attach an event
+  handpose = ml5.handpose(video, modelReady);
+  handpose.on("hand", gotHands);
+}
+
+// Event for hand detection
+function gotHands(results) {
+  // Always save the latest output from the model in global variable "hands"
+  hands = results;
+}
+
+// Event for when model loaded
+function modelReady() {
+  console.log("Model ready!");
+}
+
+function draw() {
+  // Draw the video
+  image(video, 0, 0, width, height);
+
+  // Draw all the tracked hand points
+  for (let i = 0; i < hands.length; i++) {
+    let hand = hands[i];
+    for (let j = 0; j < hand.keypoints.length; j++) {
+      let keypoint = hand.keypoints[j];
+      fill(0, 255, 0);
+      noStroke();
+      circle(keypoint.x, keypoint.y, 10);
+    }
+  }
+}

examples/NeuralNetwork-color-classifier/sketch.js

@@ -1,84 +1,84 @@
-// Step 1: load data or create some data
-let data = [
-  { r: 255, g: 0, b: 0, color: "red-ish" },
-  { r: 254, g: 0, b: 0, color: "red-ish" },
-  { r: 253, g: 0, b: 0, color: "red-ish" },
-  { r: 0, g: 255, b: 0, color: "green-ish" },
-  { r: 0, g: 254, b: 0, color: "green-ish" },
-  { r: 0, g: 253, b: 0, color: "green-ish" },
-  { r: 0, g: 0, b: 255, color: "blue-ish" },
-  { r: 0, g: 0, b: 254, color: "blue-ish" },
-  { r: 0, g: 0, b: 253, color: "blue-ish" },
-];
-
-let classifer;
-let r = 255;
-let g = 0;
-let b = 0;
-let rSlider, gSlider, bSlider;
-let label = "training";
-
-function setup() {
-  createCanvas(640, 240);
-  rSlider = createSlider(0, 255, 255).position(10, 20);
-  gSlider = createSlider(0, 255, 0).position(10, 40);
-  bSlider = createSlider(0, 255, 0).position(10, 60);
-
-  // Step 2: set your neural network options
-  let options = {
-    task: "classification",
-    debug: true,
-  };
-
-  // Step 3: initialize your neural network
-  classifier = ml5.neuralNetwork(options);
-
-  // Step 4: add data to the neural network
-  for (let i = 0; i < data.length; i++) {
-    let item = data[i];
-    let inputs = [item.r, item.g, item.b];
-    let outputs = [item.color];
-    classifier.addData(inputs, outputs);
-  }
-
-  // Step 5: normalize your data;
-  classifier.normalizeData();
-
-  // Step 6: train your neural network
-  const trainingOptions = {
-    epochs: 32,
-    batchSize: 12,
-  };
-  classifier.train(trainingOptions, finishedTraining);
-}
-// Step 7: use the trained model
-function finishedTraining() {
-  classify();
-}
-
-// Step 8: make a classification
-function classify() {
-  const input = [r, g, b];
-  classifier.classify(input, handleResults);
-}
-
-function draw() {
-  r = rSlider.value();
-  g = gSlider.value();
-  b = bSlider.value();
-  background(r, g, b);
-  textAlign(CENTER, CENTER);
-  textSize(64);
-  text(label, width / 2, height / 2);
-}
-
-// Step 9: define a function to handle the results of your classification
-function handleResults(results, error) {
-  if (error) {
-    console.error(error);
-    return;
-  }
-  label = results[0].label;
-  // console.log(results); // {label: 'red', confidence: 0.8};
-  classify();
-}
+// Step 1: load data or create some data
+let data = [
+  { r: 255, g: 0, b: 0, color: "red-ish" },
+  { r: 254, g: 0, b: 0, color: "red-ish" },
+  { r: 253, g: 0, b: 0, color: "red-ish" },
+  { r: 0, g: 255, b: 0, color: "green-ish" },
+  { r: 0, g: 254, b: 0, color: "green-ish" },
+  { r: 0, g: 253, b: 0, color: "green-ish" },
+  { r: 0, g: 0, b: 255, color: "blue-ish" },
+  { r: 0, g: 0, b: 254, color: "blue-ish" },
+  { r: 0, g: 0, b: 253, color: "blue-ish" },
+];
+
+let classifer;
+let r = 255;
+let g = 0;
+let b = 0;
+let rSlider, gSlider, bSlider;
+let label = "training";
+
+function setup() {
+  createCanvas(640, 240);
+  rSlider = createSlider(0, 255, 255).position(10, 20);
+  gSlider = createSlider(0, 255, 0).position(10, 40);
+  bSlider = createSlider(0, 255, 0).position(10, 60);
+
+  // Step 2: set your neural network options
+  let options = {
+    task: "classification",
+    debug: true,
+  };
+
+  // Step 3: initialize your neural network
+  classifier = ml5.neuralNetwork(options);
+
+  // Step 4: add data to the neural network
+  for (let i = 0; i < data.length; i++) {
+    let item = data[i];
+    let inputs = [item.r, item.g, item.b];
+    let outputs = [item.color];
+    classifier.addData(inputs, outputs);
+  }
+
+  // Step 5: normalize your data;
+  classifier.normalizeData();
+
+  // Step 6: train your neural network
+  const trainingOptions = {
+    epochs: 32,
+    batchSize: 12,
+  };
+  classifier.train(trainingOptions, finishedTraining);
+}
+// Step 7: use the trained model
+function finishedTraining() {
+  classify();
+}
+
+// Step 8: make a classification
+function classify() {
+  const input = [r, g, b];
+  classifier.classify(input, handleResults);
+}
+
+function draw() {
+  r = rSlider.value();
+  g = gSlider.value();
+  b = bSlider.value();
+  background(r, g, b);
+  textAlign(CENTER, CENTER);
+  textSize(64);
+  text(label, width / 2, height / 2);
+}
+
+// Step 9: define a function to handle the results of your classification
+function handleResults(results, error) {
+  if (error) {
+    console.error(error);
+    return;
+  }
+  label = results[0].label;
+  // console.log(results); // {label: 'red', confidence: 0.8};
+  classify();
+}