Now we have all the pieces. Let's look at how to put it all together:

1. We first ingest the dataset and then split the data out into training and cross validation sets. The dataset is split into ten parts for a k-fold cross-validation. We won't do that. Instead, we'll do a single fold cross-validation by holding out 30% of the data for cross-validation:

  typ := "bare"
  examples, err := ingest(typ)
  log.Printf("errs %v", err)
  log.Printf("Examples loaded: %d", len(examples))
  shuffle(examples)
  cvStart := len(examples) - len(examples)/3
  cv := examples[cvStart:]
  examples = examples[:cvStart]

2. We then train the classifier and then check to see whether the classifier can predict its own dataset well:

  c := New()
  c.Train(examples)

  var corrects, totals float64
  for _, ex := range examples {
    // log.Printf...