Calculating information theory quantities since 2017
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Updated
Jun 8, 2019 - C++
Calculating information theory quantities since 2017
Developing Variety Of Methods For Identifying Anomalies And Making Decisions According To The Majority Vote
PAQ8PX compression archiver
calculates information entropy, and information gains.
My Java Codes
Python binding for Finite State Entropy
Supervised-ML-Decision-Tree-C5.0-Entropy-Iris-Flower-Using Entropy Criteria - Classification Model. Import Libraries and data set, EDA, Apply Label Encoding, Model Building - Building/Training Decision Tree Classifier (C5.0) using Entropy Criteria. Validation and Testing Decision Tree Classifier (C5.0) Model
Context aware Move To Front Transform based compressor
Implementation of classic machine learning concepts and algorithms from scratch and math behind their implementation.Written in Jupiter Notebook Python
Visualisation for id3 classification algorithm.
Command line utility to generate secure random passwords
Extracts low speed segments from spatiotemporal trajectories using moving median of speed. Fast and robust. Adaptively determines the parameters from the data, instead of setting objective, arbitrary parameters. Each trajectory in a set of trajectories will have unique subjective parameters.
Poisson distribution entropy.
Lognormal distribution differential entropy.
Minimum entropy estimation tool based on NIST SP 800-90B. Written in C++ by using proven libraries.
Implementation and evaluation of FPGA RNG via oscillating rings against NIST 800-22 Tests
This repository contains an implementation of the Decision Tree algorithm from scratch using various impurity methods such as Gini index, entropy, misclassification error, etc.
Implementation of algorithm for foreground-background separation in low quality patrimonial document images.
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