0.1.00 ( 4.11)
Copyright © 2013 Luca Tringali
Abstract
Kartesio is a program for calculating best fit curves with experimental points using regression algorithms or neural networks.
Table of Contents
Kartesio is a program for calculating best fit curves with experimental points using regression algorithms or neural networks. It is free and licensed under the Public License.
Kartesio has a simple interface that allows you to plot points and a curve. The curve is usually calculated by the program, but you can also write it by yourself.
As soon as you open Kartesio, you will get a blank table and a blank plot. This is also the same screen you can get in every moment just clicking on ( → ). You can try to best fit your experimental points with a regression algorithm or a neural network, using the tools in the appropriate tab. Please note that regression algorithm needs maxima to be installed on your computer, while the neural network method needs ZorbaNeural.
You should start adding your points to the table. Obviously, a bidimensional point is identified with two coordinates (on X and Y axis).
In the Regression tab, you can write a generic function that will be used by Kartesio as a model for the fitting curve. The function must be written with the variables x and y, and with variable coefficients (represented by letters). Obviously, you can also write numeric coefficients. Please take note that the function must be biuniqe.
Clicking the button, Kartesio will start to calculate coefficients for the function you wrote, trying to best fit the experimental points. The final function will appear the edit box close to the bottom edge of the window.
After the fit operation, points and the curve will be automatically plotted. Anyway, you may prefer to change to plotted area to see better the image. This can be done using the four spinboxes: Xmin is the minimum value of X axis, and Xmax is the maximum value. For example, if you write respectively 0 and 1, then the plot will start from 0 and end to 1. The same logic works for Y axis.
If you change plot limits, you may need to change also the resolution: if the resolution of the plot is too little, you will see every curve as a single line. If the resolution is too high you will waste a lot of CPU time to draw the plot.
The neural network method works like the regression one, but you can not write you own function: you must choose it from a list. This list, anyway, contains practically every kind of function you may want (periodic functions are not usable with a neural network).
Usually, back propagation training is just what you need. For this reason it is checked by default. Just modify the number of iterations (it should not be too high, or the process may end up with way too strange value) and then press the button. Please take note that the neural network, exactly as a human brain, may give you different results: if you press the button more than once and you will find out that the network calculates every time a different best fitting curve.
If you are not satisfied by the back propagation training result, you could also use the genetic algorithm training. This can be done simply checking the appropriate checkbox. Genetic algorithm training takes a lot more CPU resources, so you better use a very low iterations number (not more than 500).
Sometimes it is useful to redraw the plot. For example, it is if you have manually changed the best fitting curve or if you edited some points and you don't want to recalculate the fitting function. Just use the button.
To know how much the fitting curve is different from your experimental points, you can look at the root mean square error. To add it to the plot, it is needed to check the checkbox Show RMS error. Then press the button to redraw the plot: it should contain a red label with the RMS error.
6.1. | Will I ever have to pay for Kartesio? |
No, never. Kartesio is licensed under the GPL, so you will never have to pay for this program. |
Kartesio
Program Copyright, 2010-2011 Luca Tringali TRINGALINVENT@libero.it
Table of Contents
Kartesio itself can be found on The Kartesio home page and is part of the KDE-Edu project