Distributed least squares approximation (dlsa) implemented with Apache Spark
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Updated
Nov 9, 2023 - Python
Distributed least squares approximation (dlsa) implemented with Apache Spark
Numerical analysis methods implemented in Python.
Python Tools to Practically Model and Solve the Problem of High Speed Rotor Balancing.
RANSAC (RANdom SAmple Consensus) Algorithm Implementation
**curve_fit_utils** is a Python module containing useful tools for curve fitting
Robust Regression for arbitrary non-linear functions
A fast and low memory requirement version of PointHop and PointHop++, which is built upon Apache Spark.
Some useful algorithms implemented in Python
DLTReconvolution - A Python based software for the analysis of lifetime spectra using the iterative least-square reconvolution method
Robust locally weighted multiple regression in Python
This is the implementation of the five regression methods Least Square (LS), Regularized Least Square (RLS), LASSO, Robust Regression (RR) and Bayesian Regression (BR).
Several numerical methods are provided here written in Python.
Homework 1 for the course ENPM667: Perception for Autonomous Robots
WLS, weighted linear regression, weighted least squares in pure Python w/o any dependencies.
Machine learning library for symbolic fitting: the unknown system/function is described via NARMAX algebraic expressions being linear combinations of arbitrary non-linear terms provided by the user (like 0.2x²+0.7sin(x) or x[k-1]*y[k-4]^2).
This repository consists of the codes that I wrote for implementing various pattern recognition algorithms
a collection of numerical methods written in python language.
Python machine learning program to calculate, predict and visualize using Least Squares method.
Algorithms related to clustering such as k-Medians, DBSCAN as well as vector quantization.
This python application takes the information from the spread of COVID-19 in the US and determines the effectiveness of the Stay At Home Orders for each state. To analyze the effectiveness, I used a cubic least square polynomial and the SIR model and compared these two models before and after date the stay at home orders were issued.
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