# CurveFitting Crack [Win/Mac]

CurveFitting  is a Java based application designed to fit a curve, given by several points, by a cubic Spline, a Polynomal or a Fourier-series.

## CurveFitting Crack+ For PC [Updated]

This project provides you with a Java based application to Fit a curve, given by several points, by cubic Splines, Polynomial and Fourier Series.

In the user interface some parameters are available to fit the curve to. For example, if you want to fit a parabola, your coordinates will be the Y-axis coordinates and a very low tolerance will be good (otherwise a cubic curve will be superimposed to the parabola). In the upper left corner you can choose between linear or cubic spline.

Why should you use this project?

CurveFitting works fine when you have a lot of data, but it falls apart if there’s a gap. But in most cases it is still the best.
For example a stock chart is a one dimensional curve, so you can use this for an algo trading software.

You can fit a curve (spline, polynomial or fourier) to every part of your data. A spline is nice to fit out the ends and the middle of your data. With polynomial you can fit in the middle of your data and in the ends you can use cubic splines. But with polynomials you have to calculate the derivative first.

For the Fourier series you can fit one or multiple curves by inversing the x-axis.

How to install this project?

Well, CurveFitting is a Java based application and you have to have Java on your machine. Java can’t be downloaded due to the so called Java-setup. The version that ships with Debian is already outdated.

CurveFitting: What else should I install?

Most of the time you only need a simple program, like Excel or MS-Word. But there are cases in which you are required to have a whole application. In this case you’ll need to set up your data, e.g. by making a simple spreadsheet. The application then needs to fit the curve by the data from your spreadsheet.
There are more applications, e.g. the Open Source packages Selenium or Appium. But they cost money.

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—————————-
General Description:
– accepts an array of points as input
– cubic or polynomal spline will be used to fit the curve and output the
function
\(f(x)= a_0 +a_1x +a_2x^2 +a_3x^3\). The default algorithm to fit is the
cubic spline
– or simply input a function, by the user
– a plot window is displayed where the point, the function and the spline curve
are shown.
– Clicking on the points with a mouse will add an additional point to the curve
– right mouse click will give the coordinates of a point of the curve and the
function and plot window should show additional information
– Run ‘fit_known_curve’ to fit a cubic spline to a number of already given
points
– Run ‘fit_known_function’ to fit a polynomal function to a number of points
with a fixed number of variables
– Input a function and a number of variables to fit it
– Fits a Fourier series of a given number of variables
– Fits a polynomial (degree x) to a given number of points
– ‘fit_known_function’ is also available for fitting a polynomial (degree x) to
a number of points
———————————
Explanation of the input:
———————————
The points are given as an array of points.
– For fitting a cubic spline to N points, the points have to be arranged in
succession starting from the first one. The number of points in the input array
is divided by 3. The spline will be constructed for every triplet of points.
– For fitting a polynomial of degree x, the points have to be arranged in
succession in real space. x points are given to the function, which should
calculate x+1 points. The points are expressed in form (y,x).
– For fitting a polynomial, the points should start at x=0 with the first point
and should increase to x=1 with the last one. For using ‘fit_known_function’,
if the function is a quadratic polynomial (f(x)=ax^2+bx+c), then only
an array of size (2*x) is given to the function
– To fit
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## CurveFitting

– Works with Splines, Polynomials, and Fourier series to fit a curve, given by a finite number of sampled points in time, and time-intervals. This application can be used to model either periodic or noisy curves.
– The fit is performed using the open source solver FA
– The application uses the popular solver FA based on the Feng-Frost method. This method estimates the parameter that make a curve fits the data, thus minimizing the Mean Squared Error.
– The application has a graphical user interface (GUI) where points are sampled, a curve is plotted and this curve is then fit with the three types of model. A new window is then opened where some plot parameters, such as the points sampled, the type of curve sampled, and the number of terms in the Fourier series, can be changed.
– The application has a pipeline of different algorithms that can be applied to the curve and the parameters of the curve. This pipeline can be set by the user.
– The application is designed to fit curves sampled according to certain applications and has been particularly developed for modelling AR-chirps (Chirped pulses) emitted by fast rotating neutron stars.
– An example of a curve sampled according to an AR-chirp is in the figure below.

![Initial curve given by AR-chirp in time.](AR_Chirp_curve.png){width=”48.00000%”}

This application has been designed to accurately reproduce the AR-chirp and output is presented below.

![Curve fitting (red line) AR-chirp (green line) by Fourier series.](AR_Chirp_Fourier.png){width=”48.00000%”}

The data set used for the application is sampled at 2000 points using a time-interval of 0.01 seconds. Different models have been tested and the results are shown in the following plots. The red and green lines represent the curve as sampled and the curve fitted using a Fourier series, respectively. Below the applications, four panels, are shown. The first panel contains information about the points sampled, the second panel contains information about the data on the Y-axis of the first panel, the third panel displays the time-interval, the fourth and last one gives the error between the model and the curve. The application is explained in more detail in the following description.

![

## What’s New In?

A spline is a piecewise-linear function defined by a number of control points that have positions in one or more dimensions.
The spline is smoothed out at each point by a polynomial, the degree of which is user-defined. The number of polynomial coefficients is also user-defined.
The polynomial approximating the spline near the spline’s control points is user-defined. The degree of the polynomial is user-defined.
Degree description of the scheme used:

The current implementation is written in Java and can be downloaded from:

After installation, you have to run the main.jar file. The usage instructions can be found in doc/usage.txt.

Acknowledgements
================
This project is supported by the German Federal Ministry of Education and Research (BMBF) under contract number 01GQ0461
It was implemented using the Java programming language and some interesting math algorithms in the Mathematics Library JOMa

Authors
=======
Maintainer:
Matthias Lechner
Operation: Compassion

7/1/19

Op: Compassion

Moved to: Upstate New York

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## System Requirements For CurveFitting:

Android – 2.2.2 and up
iOS – 4.0 and up
Windows Phone – 7.5 and up
Minimum of 128 MB RAM and 8 GB storage space
Requires a dual-core 1 GHz or faster processor
Recommended: 1 GB RAM and 16 GB storage space