Unroll loops and data within toFunction()

lib/neuralnetwork.js

@@ -370,27 +370,42 @@ NeuralNetwork.prototype = {
     return this;
   },
 
-   toFunction: function() {
-    var json = this.toJSON();
+  toFunction: function() {
     // return standalone function that mimics run()
-    return new Function("input",
-'  var net = ' + JSON.stringify(json) + ';\n\n\
-  for (var i = 1; i < net.layers.length; i++) {\n\
-    var layer = net.layers[i];\n\
-    var output = {};\n\
-    \n\
-    for (var id in layer) {\n\
-      var node = layer[id];\n\
-      var sum = node.bias;\n\
-      \n\
-      for (var iid in node.weights) {\n\
-        sum += node.weights[iid] * input[iid];\n\
-      }\n\
-      output[id] = (1 / (1 + Math.exp(-sum)));\n\
-    }\n\
-    input = output;\n\
-  }\n\
-  return output;');
+
+    var layers = this.toJSON().layers;
+    // we start with 'input', but will use each layer as the next input
+    var input = 'input';
+
+    // NOTE: keys are sorted so as to guarantee a consistent order and matching
+    //   key-to-index, as indexes are used to generate variable names
+    return new Function(input, [].concat(
+      _.map(_.keys(layers[0]).sort(), function(iid, i) {
+        // collect our inputs as variables
+        // e.g. var input_0 = input['x'];
+        return 'var ' + input + '_' + i + ' = ' + input + '[' + JSON.stringify(iid) + '];';
+      }),
+      _.map(layers.slice(1), function(layer, i) {
+        var output = 'output' + i;
+        var result = _.map(_.keys(layer).sort(), function(id, j) {
+          var node = layer[id];
+          // e.g. bias + (weight_0 * inputX_0) + (weight_1 * inputX_1) + ...
+          var sum = [
+            JSON.stringify(node.bias)
+          ].concat(_.map(_.keys(node.weights).sort(), function(iid, k) {
+            return JSON.stringify(node.weights[iid]) + ' * ' + input + '_' + k;
+          })).join(' + ');
+          return 'var ' + output + '_' + j + ' = (1 / (1 + Math.exp(-(' + sum + '))));';
+        }).join('\n');
+        input = output;
+        return result;
+      }), [
+        // finally, return the resultant object, which consists of the values of our last layer
+        // e.g. return {a: outputX_0, b: outputX_1, c: outputX_2};
+        'return {' + _.map(_.keys(layers[layers.length - 1]).sort(), function(id, j) {
+          return JSON.stringify(id) + ': ' + input + '_' + j;
+        }).join(', ') + '};'
+      ]).join('\n'));
   },
 
   // This will create a TrainStream (WriteStream)