bayes

An implementation of Naive Bayes classifier. More details are in docs.

Usage

This package allows to classify a new entity into one or another category (class) according to features of the entity. The algorithm uses known data to calculate a weight of each feature for each category.

func Example() {
	// there are two jars of cookies, they are our training set.
	// Cookies have be round or star-shaped.
	// There are plain or chocolate chips cookies.
	jar1 := ft.Class("Jar1")
	jar2 := ft.Class("Jar2")

	// Every preclassified feature-set provides data for one cookie. It tells
	// what jar has the cookie, what its kind and shape.
	cookie1 := ft.ClassFeatures{
		Class: jar1,
		Features: []ft.Feature{
			{Name: "kind", Value: "plain"},
			{Name: "shape", Value: "round"},
		},
	}
	cookie2 := ft.ClassFeatures{
		Class: jar1,
		Features: []ft.Feature{
			{Name: "kind", Value: "plain"},
			{Name: "shape", Value: "star"},
		},
	}
	cookie3 := ft.ClassFeatures{
		Class: jar1,
		Features: []ft.Feature{
			{Name: "kind", Value: "chocolate"},
			{Name: "shape", Value: "star"},
		},
	}
	cookie4 := ft.ClassFeatures{
		Class: jar1,
		Features: []ft.Feature{
			{Name: "kind", Value: "plain"},
			{Name: "shape", Value: "round"},
		},
	}
	cookie5 := ft.ClassFeatures{
		Class: jar1,
		Features: []ft.Feature{
			{Name: "kind", Value: "plain"},
			{Name: "shape", Value: "round"},
		},
	}
	cookie6 := ft.ClassFeatures{
		Class: jar2,
		Features: []ft.Feature{
			{Name: "kind", Value: "chocolate"},
			{Name: "shape", Value: "star"},
		},
	}
	cookie7 := ft.ClassFeatures{
		Class: jar2,
		Features: []ft.Feature{
			{Name: "kind", Value: "chocolate"},
			{Name: "shape", Value: "star"},
		},
	}
	cookie8 := ft.ClassFeatures{
		Class: jar2,
		Features: []ft.Feature{
			{Name: "kind", Value: "chocolate"},
			{Name: "shape", Value: "star"},
		},
	}

	lfs := []ft.ClassFeatures{
		cookie1, cookie2, cookie3, cookie4, cookie5, cookie6, cookie7, cookie8,
	}

	nb := bayes.New()
	nb.Train(lfs)
	oddsPrior, err := nb.PriorOdds(jar1)
	if err != nil {
		log.Println(err)
	}

	// If we got a chocolate star-shaped cookie, which jar it came from most
	// likely?
	aCookie := []ft.Feature{
		{Name: ft.Name("kind"), Value: ft.Value("chocolate")},
		{Name: ft.Name("shape"), Value: ft.Value("star")},
	}

	res, err := nb.PosteriorOdds(aCookie)
	if err != nil {
		fmt.Println(err)
	}

	// it is more likely to that a random cookie comes from Jar1, but
	// for chocolate and star-shaped cookie it is more likely to come from
	// Jar2.
	fmt.Printf("Prior odds for Jar1 are %0.2f\n", oddsPrior)
	fmt.Printf("The cookie came from %s, with odds %0.2f\n", res.MaxClass, res.MaxOdds)
	// Output:
	// Prior odds for Jar1 are 1.67
	// The cookie came from Jar2, with odds 7.50
}

Development

Testing

go test

Other implementations:

Go, Java, Python, R, Ruby