# Linear

Linearly interpolates a given set of points.

Controller: **CodeCogs**

## Interface

C++

HTML

## Class Linear

Linear interpolation is a process employed in mathematics, and numerous applications thereof including computer graphics. It is a very simple form of interpolation. In numerical analysis a linear interpolation of certain points that are in reality values of some function f is typically used to approximate the function f. Linear interpolation can be regarded as a trivial example of polynomial interpolation. The error of this approximation is defined as where p denotes the linear interpolation polynomial defined as follows It can be proven using Rolle's theorem that if f has two continuous derivatives, the error is bounded by As you see, the approximation between two points on a given function gets worse with the second derivative of the function that is approximated. This is intuitively correct as well: the "curvier" the function is, the worse is the approximations made with simple linear interpolation. Below you will find the interpolation graphs for a set of points obtained by evaluating the function , displayed in light blue, at particular abscissas. The linear interpolating function, displayed in red, has been calculated using this class. In the first graph there had been chosen a number of 12 points, while in the second 36 points were considered. You may notice the root mean squared error in each of the cases.## References:

Wikipedia, http://en.wikipedia.org/wiki/Linear_interpolation### Example 1

- The following example displays 20 interpolated 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/approximation/interpolation/linear.h> #include <cmath> #include <iostream> #include <iomanip> using namespace std; #define PI 3.1415 #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 linear interpolation routine with known data points Maths::Interpolation::Linear A(N, x, y); // Interrogate linear fitting curve to find interpolated values int N_out = 20; 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(13) << A.getValue(xx) << endl; } return 0; }

**Output:**x = 3.1415 y = -5.89868e-005 x = 3.63753 y = 0.0765858 x = 4.13355 y = 0.153231 x = 4.62958 y = 0.0678533 x = 5.12561 y = -0.0879685 x = 5.62163 y = -0.137135 x = 6.11766 y = -0.022215 x = 6.61368 y = 0.0804548 x = 7.10971 y = 0.060627 x = 7.60574 y = 0.0407992 x = 8.10176 y = -0.0110834 x = 8.59779 y = -0.0715961 x = 9.09382 y = -0.0619804 x = 9.58984 y = 0.0221467 x = 10.0859 y = 0.081803 x = 10.5819 y = 0.0313408 x = 11.0779 y = -0.0191214 x = 11.5739 y = -0.0324255 x = 12.07 y = -0.0406044 x = 12.566 y = -0.0146181

## See Also

Also consider the regression methods: Regression/Discrete, Regression/Forsythe, Regression/Orthogonal, Regression/Stiefel### Authors

*Lucian Bentea (August 2005)*

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## Members of Linear

#### Linear

Initializes the necessary data for following evaluations of the fitting lines.Linear( int `n`double* `x`double* `y`) *[constructor]*n The number of initial points x The x-coordinates for the initial points y The y-coordinates for the initial points

#### GetValue

Returns the approximated ordinate at the given abscissa.doublegetValue( double `x`) ### Note

- This function is not designed to provide extrapolation points, thus you need to keep the value of x in the interval from X[0] to X[N - 1].

x The abscissa of the interpolation point

## Linear Once

doubleLinear_once( | int | N | |

double* | x | ||

double* | y | ||

double | a | ) |

This function implements the Linear class for one off calculations, thereby avoid the need to instantiate the Linear class yourself.

### Example 2

- The following graph is constructed from interpolating the following 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 x The x-coordinates for the initial points (evenly spaced!) 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

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