added test example

ziyuan-linn · ziyuan-linn · commit 7dfb1a0cb67a · 2023-03-13T20:51:10.000-04:00
diff --git a/README.md b/README.md
@@ -19,7 +19,7 @@ npm run build
 This will create a build in the dist folder.
 
 
-Open `test/index.html` in the browser to see the build working.
+Open `test/index.html` in the browser to see the build working, running an example from the ml5 website.
 
 
 ## Process
diff --git a/test/index.html b/test/index.html
@@ -6,10 +6,10 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <title>Document</title>
     <script src="../package/dist/ml5.js"></script>
-    <script src="script.js"></script>
+    
     
 </head>
 <body>
-    
+    <script src="script.js"></script>
 </body>
 </html>
diff --git a/test/script.js b/test/script.js
@@ -1,2 +1,69 @@
-console.log(ml5);
+// Step 1: load data or create some data
+const 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" },
+];
 
+// Step 2: set your neural network options
+const options = {
+  task: "classification",
+  debug: true,
+};
+
+// Step 3: initialize your neural network
+const nn = ml5.neuralNetwork(options);
+
+// Step 4: add data to the neural network
+data.forEach((item) => {
+  const inputs = {
+    r: item.r,
+    g: item.g,
+    b: item.b,
+  };
+  const output = {
+    color: item.color,
+  };
+
+  nn.addData(inputs, output);
+});
+
+// Step 5: normalize your data;
+nn.normalizeData();
+
+// Step 6: train your neural network
+const trainingOptions = {
+  epochs: 32,
+  batchSize: 12,
+};
+nn.train(trainingOptions, finishedTraining);
+
+// Step 7: use the trained model
+function finishedTraining() {
+  classify();
+}
+
+// Step 8: make a classification
+function classify() {
+  const input = {
+    r: 255,
+    g: 0,
+    b: 0,
+  };
+  nn.classify(input, handleResults);
+}
+
+// Step 9: define a function to handle the results of your classification
+function handleResults(error, result) {
+  if (error) {
+    console.error(error);
+    return;
+  }
+  console.log(result); // {label: 'red', confidence: 0.8};
+}
