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Naive Bayesian Classification for Golang.

License: Other

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bayesian's Issues

Allow classifier to initialise with only one class

Current code panics in case the classifier is initialised with just 1 class.
However I have an edge case where there might only be a single class.
So I was thinking maybe classifier could be allowed to initialise with just 1 class.
I made changes in the code and tested for my use case. It worked. However, the unit tests fail in this case.
Is there any specific reason to keep this limitation?
What would be a good way to solve this, if any?

Prior probability includes word frequencies?

This is rather a question than an actual issue, but anyway.

First, did I get it right that the prior probability P(C_j) of a class is the number of document within that class, divided by the total number of documents?

And if so, why does the getPriors() function set the prior prob. of a class C to the number of words in documents of that class (classData.Total) divided by the total number of words? I'd expect that for the prior prob, words don't play any role, yet.

Probably I have a problem in understanding, so please try to enlight me.

Request for a new function that will enable adding of new class to an existing classifier

Hi,

I found this library very useful.
I think since this has a supervised learning mechanism,
it would be good if we can add a new class for stuffs that can be learned
that can't be categorized from the existing classes.

Thanks

Seen() is always 0?

package main

import (
	"log"

	"github.com/jbrukh/bayesian"
)

const (
	Arabic  bayesian.Class = "Arabic"
	Malay   bayesian.Class = "Malay"
	Yiddish bayesian.Class = "Yiddish"
)

func main() {

	nbClassifier := bayesian.NewClassifier(Arabic, Malay, Yiddish)
	arabicStuff := []string{"algeria", "bahrain", "comoros"}
	malaysianStuff := []string{"malaysians", "bahasa"}
	yiddishStuff := []string{"jewish", "jews", "israel"}
	nbClassifier.Learn(arabicStuff, Arabic)
	nbClassifier.Learn(malaysianStuff, Malay)
	nbClassifier.Learn(yiddishStuff, Yiddish)

	log.Println(nbClassifier.Learned()) // 3
	log.Printf(`SEEN: %d`, nbClassifier.Seen()) // 0
}

request for a tag of an older commit

git tag -a 1.1 35eb93528ee -m "tag a specific older version that was built against"
git push --tags

In addition, it would be nice if current versions were tagged as well...

Release 1.0 is really old - make a new release

1.0 is still importing things like "gob" instead of "encoding/gob" etc. Can you make a new release? I can also help co-maintain the project is that helps.

Tools like dep will pick up release versions for most people and they will get code that won't work for newer versions of go.

Thanks!

Wrong package name in docs

Now it github.com/navossoc/bayesian instead of github.com/jbrukh/bayesian.
Was broken in PR #23

Add classes after classifier creation

Apologies in advance as my knowledge of Go is still somewhat limited, so this may be a naive question.

I want to expose the naive bayes classifying as a HTTP Web service, with both train and classify endpoints. I have no trouble with that, but I want the train endpoint to be able to accept new labels (labels that aren't currently in the classifier). Right now the labels are simply specified as consts and passed into the constructor. Can you think of the best way to add the ability to add labels at run-time?

Panic if underflow is detected in `SafeProbScores`

SafeProbScores ... If an underflow is detected, this method panics

Source

I am getting a bit confused by the comment in the method above according to the doc this method is suppose to panic but the code instead returns an error.

Am I missing something ?

Return bayesian.Class Instead of Index?

LogScores, ProbScores, and SafeProbScores all have a return parameter that is the index of the most likely class. I think if you're ever willing to break the current api it would be a ton more useful to return the actual bayesian.Class.

It would make simple usage as below much easier.

As it stands I have to know the index of which I passed it into my classifier. That's knowledge I'd rather not have to know. It's kind of a difficult way to do it.

const (
    Good bayesian.Class = "Good"
    Bad  bayesian.Class = "Bad"
    Ugly bayesian.Class = "Ugly"
)

classifier := bayesian.NewClassifier(Good, Bad, Ugly)

_, c, _, _ := classifier.SafeProbScores(wht)

if c == Ugly {
    fmt.Println("oh no")
}

what is good or bad?

Sorry, I didn't understand. After getting the result, how to know whether the result belongs to good or bad?

scores, likely, _ := classifier.LogScores(
                        []string{"tall", "girl"},
                     )

probs, likely, _ := classifier.ProbScores(
                        []string{"tall", "girl"},
                     )

likely == 1 is bad?
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