ml-classifier.wppl

var classifierNet = stack([
  function(x) { T.get(x, 0); },       // Extract scalar output from vector.
  sigmoid,                            // Squish to [0,1], representing class probability of input.
  linear(1, 'net1', modelParamL2(1)), // L2 prior on parameters.
  function(x) { Vector([x]); }        // Map input from scalar to vector.
]);

var data = [
  {x: -2, label: false},
  {x: -3, label: false},
  {x: -2, label: false},
  {x: 1, label: true},
  {x: 2, label: true},
  {x: 1.5, label: true},
  {x: 2.5, label: true}];

var model = function() {
  mapData({data}, function(datum) {
    var p = classifierNet(datum.x);
    observe(Bernoulli({p}), datum.label);
  });
};

var params = Optimize({
  model,
  steps: 1000,
  optMethod: {adam: {stepSize: .1}}});

// Here we create a function that runs the net using the optimized
// parameters.
var optimizedClassifier = withParams(params, classifierNet);

// Inspect the optimized function by passing some values through it.
map(function(x) {
  return {x, prob: optimizedClassifier(x)};
}, _.range(-3, 4));

// Suggestion: Try changing the strength of the prior on the network
// parameters.