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This was part of the lecture "object oriented programming of heuristics" at the KU Ingolstadt.

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Regression_JavaClass

General:

This code solves a regression problem. Two methods are implemented:

  • for linear regression (KQS)
  • for quadratic functions, transformed as a linear problem It returns the m, t (or quadratic: a, lambda) parameter and the residuals (y - y_hat).

Structure:

  • datapoints = build object where a datapoint consist of a week and product price
  • DataPoint = a simple datapoint with week and price
  • DatePointOverview = a abstract class to have datapoints relating to each other via DataPoint class and x and y array seperated (for calculating reasons)
  • mathfunctions = interface, which contracts the functions to implement (for global usage)
  • Regression = the interface to contract classes which want to interact
  • regression = two methods implemented in Java
  • LinearModel = implementation of the linear problem (KQS)
  • QuadraticFunction = implementation of the quadratic problem, with log transformation and then processed like a linear problem

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This was part of the lecture "object oriented programming of heuristics" at the KU Ingolstadt.

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