https://github.com/subhash3/naive-bayes-classifier
Naive Bayes Classifier in Typescript
https://github.com/subhash3/naive-bayes-classifier
machine-learning naive-bayes-classifier typescript
Last synced: 11 months ago
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Naive Bayes Classifier in Typescript
- Host: GitHub
- URL: https://github.com/subhash3/naive-bayes-classifier
- Owner: Subhash3
- Created: 2021-05-13T06:17:53.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-05-13T16:43:04.000Z (about 5 years ago)
- Last Synced: 2025-04-25T20:02:54.894Z (about 1 year ago)
- Topics: machine-learning, naive-bayes-classifier, typescript
- Language: TypeScript
- Homepage:
- Size: 109 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Naive Bayes Classifer
Just trying to "learn naive bayes classifer"
### API
```ts
/* Loads the dataset from a json file of data samples*/
loadDatasetFromFile(file: string);
/* Assign the dataset to be an array of pre-loaded samples*/
loadDataset(data: IFC_Iris_Data_Sample[]);
/* Computes the mean and standard deviation of different features of each category */
train();
/* Computes the probability of each class given a new data sample by using bayes theorem */
predict(newSample: IFC_Iris_Data_Sample, returnProbabilities: boolean = false);
/* Loads the dataset from an array(array(strings))
Something like this
[
["1.0", "3.2", "6.9", "4.20"],
["1.0", "3.2", "6.9", "4.20"],
["1.0", "3.2", "6.9", "4.20"],
<-- Features --> <- Category ->
]
*/
loadDatasetFromArr(data: (string[])[]);
```
### How to run?
```bash
# Install npm dependencies
git clone https://github.com/Subhash3/Naive-Bayes-Classifier
cd Naive-Bayes-Classifier
npm install
# Start
ts-node source/index.js
# Build: Compile ts to generate js files
npm run build # this creates a build/ folder with all the compiled (js) code
```
### Dataset format
* A dataset is a list of data samples of the following interface
```ts
interface IFC_Iris_Data_Sample {
features: number[],
category: string
}
Eg:
dataset = [
{
"features": [
3.393533211,
2.331273381
],
"category": 0
},
{
"features": [
3.110073483,
1.781539638
],
"category": 0
}
]
```
### TODO
- [x] Loss Computation.
- [ ] Apply log to bayes chain to get rid of overflow errors.
- [ ] Document the code nicely in tsdoc format.
- [ ] Use a matrix of size mxn where m is the no.of categories and n is the number of features such that the location [i][j] contains the mean and standard deviation of ith feature, given class = j.
- [ ] Extend the classifier to text classification.
#### Feel free to contribute and open any issues.