There are 0 repository under median topic.
calculate_all method for aggregate functions in Active Record
MQL5 header file for 'Median and Turbo renko indicator bundle' available for MT5 via MQL5 Market. The file lets you easily create a Renko EA in MT5 using the Median Renko indicator.
High-dimensional medians (medoid, geometric median, etc.). Fast implementations in Python.
Sensor for Home Assistant that gets reset at midnight
Shiny App for comparison of samples
Arduino library to determine the running median by means of a circular buffer.
Fast and differentiable geometric median, a multivariate median analogue. Install with `pip install geom-median`
High performance, native (C) implementations of various statistical functions
An implementation of an efficient O(n) median filter in Rust.
A small library that does the statistics for your numbers.
URL to App Conversion
This repository contains a statistic library for the C programming language which prepare useful functions for dealing with average, standard deviation etc.
Free WordPress Plugin: The Mean, Median, Mode, and Range calculator helps you find these statistics quickly and conveniently. Learn how to use this calculator's output by reading this article. www.calculator.io/mean-median-mode-range-calculator/
Fast and simple statistics on the command line.
This repository is on different types of data, types of missing values and how to handle missing value
Online statistics
State estimation for output with outlier (journal article matlab code) observer, Kalman-filter, Control
Image steganography is implemented by replacing image pixels with cipher pixels to hide the existence of the message. To enhance the security, cryptographic algorithm, RSA, has been modified into symmetric keying system to encrypt and decrypt the message into cipher text and vice versa solely for this purpose. The sender will encrypt the message with a key and then embed the cipher text into an image and send the image object via any transmission medium like internet along with the key to the receiver, so that the receiver can extract the cipher text from the image and then decrypt by the key that the sender have sent. The result was satisfactory and fulfilled the purpose but there arose many major issues like, visibility of the cipher pixel in case for low resolution image; data corruption of the cipher text due to noise; limitation of grayscale image only; sender have to send a couple of image for comparison of the pixel data etc. However, noise problem have been challenged with the help of median filter but still there remains many scope to establish this algorithm into a better efficient and optimized one.
Geometric median (GM) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0.5. However, its computational complexity makes it infeasible for robustifying stochastic gradient descent (SGD) for high-dimensional optimization problems. In this paper, we show that by applying Gm to only a judiciously chosen block of coordinates at a time and using a memory mechanism, one can retain the breakdown point of 0.5 for smooth non-convex problems, with non-asymptotic convergence rates comparable to the SGD with GM.
A collection of simple Python Programs for Applied Statistics.
LIDAR Filter will provide you with useful functions to reduce noise in the data coming from a LIDAR sensor attached to your robot. LIDAR filter provides two filters entitled as range and temporal median.
TEC, foF2, and DST Index plotting
Finding The Median In Large Sets Of Numbers Split Across N Servers using zeromq and nodejs (experimental)
Stochastic tools, distrubution, analysis
Free WordPress Plugin: Calculator for mean, median, and mode in statistics. Use this calculator to get the mean, median, mode, range, and the average for any data set. www.calculator.io/mean-median-mode-calculator/
Compute the Kruskal-Wallis test for equal medians.
Wilcoxon signed rank test.
An agnostic pagination package for JavaScript.
This project uses statistical analysis to detect fraudulent credit card transactions by examining patterns and anomalies in a dataset of 10,000 transactions, calculating averages, medians, frequencies, and identifying outliers to distinguish between legitimate and fraudulent activities.
Central tendency, distribution, skewness and kurtosis
Mean, median, mode, range, deviation and more