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# Parabolic

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Approximates an arbitrary function using parabolic least squares fitting.
Controller: CodeCogs

C++
Excel
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## Class Parabolic

This class approximates an arbitrary function using a polynomial of degree 2, which makes it more suitable for approximating parabola-shaped graphs.

The algorithm finds the coefficients a, b and c such that the following quadratic function fits the given set of points with a minimum error, in terms of leasts squares minimization

Below you will find the regression graph for a set of points obtained by evaluating the function , displayed in light blue, at particular abscissas. The regression quadratic, displayed in red, has been calculated using this class. The root mean squared error is also displayed.

## References:

• F.R. Ruckdeschel, "BASIC Scientific Subroutines", Vol. II, BYTE/McGRAWW-HILL, 1981

### Example 1

The following example displays 10 approximated values (you may change this amount through the N_out variable) for the given function with abscissas equally spaced in the interval. The X and Y coordinate arrays are initialized by evaluating this function for N = 12 points equally spaced in the domain from to .
#include <codecogs/maths/regression/parabolic.h>

#include <cmath>
#include <iostream>
#include <iomanip>
using namespace std;

#define PI  3.1415926535897932384626433832795
#define N   12

int main()
{
// Declare and initialize two arrays to hold the coordinates of the initial data points
double x[N], y[N];

// Generate the points
double xx = PI, step = 4 * PI / (N - 1);
for (int i = 0; i < N; ++i, xx += step) {
x[i] = xx;
y[i] = sin(2 * xx) * xx;
}

// Initialize the regression approximation routine with known data points
Maths::Regression::Parabolic A(N, x, y);

// Interrogate the regression function to find approximated values
int N_out = 10;
xx = PI, step = (3 * PI) / (N_out - 1);
for (int i = 0; i < N_out; ++i, xx += step) {
cout << "x = " << setw(7) << xx << "  y = ";
cout << setw(11) << A.getValue(xx) << endl;
}
return 0;
}
Output:
x = 3.14159  y =   0.0202138
x = 4.18879  y =    0.196758
x = 5.23599  y =    0.313613
x = 6.28319  y =    0.370778
x = 7.33038  y =    0.368253
x = 8.37758  y =    0.306039
x = 9.42478  y =    0.184135
x =  10.472  y =  0.00254086
x = 11.5192  y =   -0.238743
x = 12.5664  y =   -0.539716

### Authors

Lucian Bentea (August 2005)
##### Source Code

Source code is available when you agree to a GP Licence or buy a Commercial Licence.

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## Members of Parabolic

#### Parabolic

 Parabolic( int n double* x double* y )[constructor]
Initializes the necessary data for following evaluations of the quadratic.
 n The number of initial points in the arrays x and y x The x-coordinates for the initial points y The y-coordinates for the initial points

#### GetValue

 doublegetValue( double x )[inline]
Returns the approximated ordinate at the given abscissa.
 x The abscissa of the approximation point

## Parabolic Once

 doubleParabolic_once( int n double* x double* y double a )
This function implements the Parabolic class for one off calculations, thereby avoid the need to instantiate the Parabolic class yourself.

### Example 2

The following graph fits a parabola to these values:
x = 1  y = 0.22
x = 2  y = 0.04
x = 3  y = -0.13
x = 4  y = -0.17
x = 5  y = -0.04
x = 6  y = 0.09
x = 7  y = 0.11

### Parameters

 n The number of initial points in the arrays x and y x The x-coordinates for the initial points y The y-coordinates for the initial points a The x-coordinate for the output point

### Returns

the interpolated y-coordinate that corresponds to a
##### Source Code

Source code is available when you agree to a GP Licence or buy a Commercial Licence.

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