semvec/pypi-packages huggingface dataset:
ntarp 0.0.2
The NTarp clustering algorithm

# NTarp
A Scikit-Learn implementation of the NTarp clustering algorithm, originally introduced [here](https://arxiv.org/pdf/1806.05297.pdf) and developed further [here](https://arxiv.org/abs/2008.09579). 

The objective function of NTarp is the minimum normalized within-ss, and so this also necessarily includes an efficient way of computing this. The method used here is comparable to [CKmeans.1d.dp](https://github.com/AldenMB/Ckmeans.1d.dp) in terms of algorithmic efficiency, but roughly twice as fast in practice (at least in my testing).

# Installation

```
pip install ntarp
```

# Usage

The main interface is the `ntarp.NTarp` object, which uses the sklearn cluster interface, through the `fit` and `predict` methods. In addition the withinss function is available as `ntarp.separability.w`.
TFilterPy 0.1
A short description of your module

## Karman Filter
In this implementation, we define a class called ParticleFilter which takes in the number of particles, state and observation dimensions, as well as the transition and observation models as arguments.

The initialize method initializes the particles and their weights based on the initial state.

The predict method propagates the particles forward in time using the transition model and adds some noise to the resulting state.

The update method computes the likelihood of the observations given each particle's state, and updates the particle weights accordingly.

The resample method resamples particles based on their weights to ensure that the particle distribution is not degenerate.

The step method performs one iteration of the particle filter, calling predict, update, and resample as necessary. It returns the current estimate of the state.