Step-by-Step Process with Code

Step 1: Data Collection

Create a C++ program and input your dataset containing (x, y) points.

C++




#include <iostream>
#include <vector>
using namespace std;
 
int main()
{
    // Step 1: Data collection
    vector<pair<double, double> > dataPoints
        = { { 1, 2 }, { 2, 3 }, { 3, 1 }, { 4, 5 } };
    // Your code here.
    return 0;
}


Step 2: Parameterization

Assign parameters to data points. Let’s use equidistant parameterization.

C++




// Step 2: Chord-Length Parameterization
std::vector<double>
parameterization(const std::vector<Point>& data)
{
    std::vector<double> param;
    double total_distance = 0.0;
 
    for (size_t i = 0; i < data.size() - 1; ++i) {
        double dx = data[i + 1].x - data[i].x;
        double dy = data[i + 1].y - data[i].y;
        double distance = std::sqrt(dx * dx + dy * dy);
        total_distance += distance;
        param.push_back(total_distance);
    }
 
    // Normalize parameters to [0, 1]
    for (double& p : param) {
        p /= total_distance;
    }
 
    return param;
}


Step 3: Selecting Control Points

Choose endpoints and possibly a few interior points as control points.

C++




// Step 3: Selecting Control Points
std::vector<Point> control_points
    = { data[0], data[data.size() / 2], data.back() };


Step 4: Curve Fitting

Fit cubic polynomials between control points. We’ll use the equations for cubic splines.

C++




// Step 4: Curve Fitting
std::vector<CubicSpline> splines;
for (size_t i = 0; i < control_points.size() - 1; ++i) {
    CubicSpline spline;
    spline.fit(control_points[i], control_points[i + 1]);
    splines.push_back(spline);
}


Step 5: Solving for Coefficients

Calculate the coefficients of the cubic polynomials using C++.

C++




// Step 5: Constructing the Cubic Spline
double t; // Parameter in [0, 1]
Point interpolated_point;
 
// Interpolate at a specific parameter value t
interpolated_point = interpolate(splines, t);


Locating Sampling Points for Cubic Splines

In this article, we will investigate the complexities of locating sampling points for cubic splines. Cubic splines assume a critical part in PC designs, designing, and different logical fields, making it vital to excel at choosing the right focuses for building these smooth and precise bends. Here, we will characterize keywords, furnish bit-by-bit clarifications with code models, and address normal inquiries to extend your comprehension.

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Step-by-Step Process with Code

Step 1: Data Collection...

Example

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Conclusion

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Frequently Asked Questions

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