Added simple is better dataset and made minor dataset changes

Author: aboggust

index.html

@@ -170,8 +170,8 @@ <h4>
           <div class="dataset" title="Linear">
             <canvas class="data-thumbnail" data-dataset="linear"></canvas>
           </div>
-          <div class="dataset" title="Placeholder">
-            <canvas class="data-thumbnail" data-dataset="placeholder"></canvas>
+          <div class="dataset" title="Moon">
+            <canvas class="data-thumbnail" data-dataset="moon"></canvas>
           </div>
           <div class="dataset" title="Plane">
             <canvas class="data-thumbnail" data-regDataset="reg-plane"></canvas>

src/dataset.ts

@@ -228,15 +228,8 @@ export function classifyLinearTrainData(numSamples: number, noise: number):
 
   function genGauss(cx: number, cy: number, label: number) {
     for (let i = 0; i < numSamples / 2; i++) {
-      let x = Math.min(Math.max(normalRandom(cx, variance*5), -5), 5);
-      let y = Math.min(Math.max(normalRandom(cy, variance*5), -5), 5);
-      while (x + y > 0) { 
-        if (x >= -5) { 
-          x -= 1;
-        } else { 
-          y -= 1;
-        }
-      }
+      let x = Math.min(Math.max(normalRandom(cx, variance*4), -5), 5);
+      let y = Math.min(Math.max(normalRandom(cy, variance*4), -5), 5);
       points.push({x, y, label});
     }
   }
@@ -264,6 +257,41 @@ export function classifyLinearTestData(numSamples: number, noise: number):
   return points;
 }
 
+export function classifyMoonData(numSamples: number, noise: number):
+    Example2D[] {
+  let points: Example2D[] = [];
+  // let radius = 5;
+  // function getCircleLabel(p: Point, center: Point) {
+  //   return (dist(p, center) < (radius * 0.5)) ? 1 : -1;
+  // }
+
+  // Generate positive points inside the circle.
+  for (let i = 0; i < numSamples / 2; i++) {
+    let ind = randUniform(0, Math.PI);
+    let xnoise = randUniform(-0.5, 0.5)*(1 + noise*5);
+    let ynoise = randUniform(-0.5, 0.5)*(1 + noise*5);
+    let x = Math.cos(ind)*3 - 1.5 + xnoise;
+    let y = Math.sin(ind)*3 + 0.5 + ynoise;
+    let label = 1;
+    // let noiseX = randUniform(-Math.PI, 0) * noise;
+    // let noiseY = Math.cos(randUniform(-Math.PI, 0)) * noise;
+    // let label = getCircleLabel({x: x + noiseX, y: y + noiseY}, {x: 0, y: 0});
+    points.push({x, y, label});
+  }
+
+  // Generate negative points outside the circle.
+  for (let i = 0; i < numSamples / 2; i++) {
+    let ind = randUniform(0, Math.PI);
+    let xnoise = randUniform(-0.5, 0.5)*(1 + noise*5);
+    let ynoise = randUniform(-0.5, 0.5)*(1 + noise*5);
+    let x = (1 - Math.cos(ind))*3 - 1.5 + xnoise;
+    let y = (1 - Math.sin(ind))*3 - 3.5 + ynoise;
+    let label = -1;
+    points.push({x, y, label});
+  }
+  return points;
+}
+
 /**
  * Returns a sample from a uniform [a, b] distribution.
  * Uses the seedrandom library as the random generator.

src/state.ts

@@ -45,6 +45,7 @@ export let datasets: {[key: string]: dataset.DataGenerator} = {
   "gauss": dataset.classifyTwoGaussData,
   "spiral": dataset.classifySpiralData,
   "linear": dataset.classifyLinearTrainData,
+  "moon": dataset.classifyMoonData,
 };
 
 /** A map between dataset names and functions that generate regression data. */