https://github.com/maximilianfeldthusen/linearregression
https://github.com/maximilianfeldthusen/linearregression
cpp dlib linear-regression
Last synced: 6 months ago
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- Host: GitHub
- URL: https://github.com/maximilianfeldthusen/linearregression
- Owner: maximilianfeldthusen
- License: bsd-3-clause
- Created: 2025-03-26T16:29:52.000Z (6 months ago)
- Default Branch: TFD
- Last Pushed: 2025-03-26T16:34:19.000Z (6 months ago)
- Last Synced: 2025-03-26T17:37:41.780Z (6 months ago)
- Topics: cpp, dlib, linear-regression
- Language: C++
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Documentation
### LinearRegression
This C++ code demonstrates how to perform linear regression using the Dlib library. Let's break it down step by step to understand its components and functionality.
### Includes and Namespaces
```cpp
#include
#include
#include
#include
#include
#include
using namespace std;
using namespace dlib;
```
- The code includes necessary headers from the C++ standard library (`` and ``) and the Dlib library for matrix operations, statistics, support vector machines (SVM), and optimization functions.
- It uses `std` and `dlib` namespaces to avoid prefixing standard and Dlib functions with their respective namespaces.
### Data Structure
```cpp
struct DataPoint {
double feature1;
double feature2;
double label;
};
```
- A simple struct `DataPoint` is defined to hold a single data point, consisting of two features (`feature1` and `feature2`) and a corresponding label (`label`).
### Loading Data
```cpp
void load_data(vector& data) {
data.push_back({1.0, 2.0, 3.0});
data.push_back({2.0, 3.0, 5.0});
data.push_back({3.0, 4.0, 7.0});
data.push_back({4.0, 5.0, 9.0});
data.push_back({5.0, 6.0, 11.0});
}
```
- The `load_data` function fills a `vector` with predefined data points. In a real application, this data might be loaded from files or databases, but here it's hardcoded for simplicity.
### Performing Linear Regression
```cpp
void perform_linear_regression(const vector& data) {
matrix X(data.size(), 2); // 2 features
matrix y(data.size(), 1); // 1 output
for (size_t i = 0; i < data.size(); i++) {
X(i, 0) = data[i].feature1;
X(i, 1) = data[i].feature2;
y(i, 0) = data[i].label;
}
```
- The function `perform_linear_regression` takes the data as input and prepares two matrices:
- `X`: A matrix of size `(number of data points) x (number of features)`, where each row corresponds to a data point and each column corresponds to a feature.
- `y`: A matrix of size `(number of data points) x 1`, which contains the labels of the data points.
```cpp
matrix weights;
weights = pinv(X) * y; // Pseudo-inverse method for linear regression
```
- The weights (coefficients) for the linear regression model are calculated using the pseudo-inverse method (`pinv(X)`), which is a common technique for solving linear regression problems.
```cpp
cout << "Weights: " << trans(weights) << endl;
```
- The calculated weights are printed to the console. The `trans(weights)` function transposes the weights matrix for better readability.
```cpp
for (const auto& point : data) {
matrix input(2, 1);
input(0, 0) = point.feature1;
input(1, 0) = point.feature2;
matrix prediction = trans(weights) * input; // Transpose weights for correct multiplication
cout << "Predicted label for (" << point.feature1 << ", " << point.feature2 << ") is " << prediction(0, 0) << endl;
}
}
```
- A loop iterates through each data point, constructs an input matrix from the features, and calculates the predicted label using the formula \( \text{prediction} = \text{weights}^T \cdot \text{input} \), where `weights` is transposed to match the dimensions for matrix multiplication.
- Each predicted label is printed to the console.
### Main Function
```cpp
int main() {
vector data;
load_data(data);
perform_linear_regression(data);
return 0;
}
```
- The `main` function initializes a vector to hold the data points, calls `load_data` to populate it, and then calls `perform_linear_regression` to compute and display the results.
### Summary
This code snippet implements a basic linear regression model using Dlib for matrix operations. It loads a small dataset, computes weights for the linear model, and makes predictions based on those weights. The implementation is straightforward and serves as an introductory example of applying linear regression with matrix mathematics in C++.