# python

## Parcticle Filter Explained With Python Code From Scratch

In the following code I have implemented a localization algorithm based on particle filter. I have used conda to run my code, you can run the following for installation of dependencies:

and the code:

## Kalman Filter Explained With Python Code From Scratch

This snippet shows tracking mouse cursor with Python code from scratch and comparing the result with OpenCV. The CSV file that has been used are being created with below c++ code. A sample could be downloaded from here 1, 2, 3. Python Kalman Filter

C++ and OpenCV Kalman Filter Rapidcsv has been downloaded from

## How to develop GUI Application with PyQt (python Qt)

There are two main methods for developing GUI application with qt: 1) Adding all widgets in your code (your cpp or python code) 2) Creating qt UI files, adding widgets there and load everything into your application. 1)Adding all widgets in your code Here is the snippet for adding all widgets and their slots in code:

2)

## Hierarchical Clustring in python

Hierarchical Clustering is a method of clustering which build a hierarchy of clusters. It could be Agglomerative or Divisive. Agglomerative: At the first step, every item is a cluster, then clusters based on their distances are merged and form bigger clusters till all data is in one cluster (Bottom Up). The complexity is \( O (n^2log(n) ) \). Divisive: At the beginning,

## Naive Bayes Classifier Example with Python Code

In the below example I implemented a “Naive Bayes classifier” in python and in the following I used “sklearn” package to solve it again: and the output is:

## Density-Based Spatial Clustering (DBSCAN) with Python Code

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a data clustering algorithm It is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes. It starts with an arbitrary starting point that has not been visited. This point’s epsilon-neighborhood is retrieved, and if it

## Kernel Density Estimation (KDE) for estimating probability distribution function

There are several approaches for estimating the probability distribution function of a given data: 1)Parametric 2)Semi-parametric 3)Non-parametric A parametric one is GMM via algorithm such as expectation maximization. Here is my other post for expectation maximization. Example of Non-parametric is the histogram, where data are assigned to only one bin and depending on the number bins that fall within

## Finding optimal number of Clusters by using Cluster validation

This module finds the optimal number of components (number of clusters) for a given dataset. In order to find the optimal number of components for, first we used k-means algorithm with a different number of clusters, starting from 1 to a fixed max number. Then we checked the cluster validity by deploying \( C-index \) algorithm and

## Sample based-optimisation-based planner with signed distance fields cost map

Rapidly-exploring random trees (RRT) and their variant are a very power solution for solving motion planning problem in robotics, but they suffer from finding an optimise solution and the generated path is usually jerky with redundant movements. Sample based-optimisation-based planners benefit the robustness of RRT and the possibility of imposing a cost function. Here in this work, I