cRegression 0.1.0

Fast C++ regression library with Python bindings

cRegression

cRegression is a fast and easy-to-use Python library for linear regression, powered by a C++ backend using pybind11.
It allows you to fit simple linear regression models, make predictions, and evaluate model performance efficiently with NumPy arrays.

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Fast C++ regression library with Python bindings.

cRegression is a high-performance linear regression library written in C++ with seamless Python integration using pybind11 and numpy. It provides a simple API for fitting models and making predictions with the speed of C++.

Installation

You can install cRegression using pip:

pip install cRegression

Usage

import numpy as np
from cRegression import LinearRegression

2. Prepare your data

# Example data
x = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

3. Create a LinearRegression object

lr = LinearRegression(x, y)

4. Access model parameters

print("Slope (b):", lr.slope())
print("Intercept (a):", lr.intercept())

5. Make predictions

new_x = [6, 7]
predictions = lr.predict(new_x)
print("Predicted y:", predictions)

6. View detailed regression statistics

lr.summary()

This prints a complete summary including:

• Mean(X) and Mean(Y)
• Pearson correlation
• Std(X) and Std(Y)
• Slope and Intercept
• RMSE and MAE
• R²
• Residual standard error
• Slope standard error
• t-value of slope

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API Reference

LinearRegression(x, y)

• Parameters:
  • x – 1D NumPy array of independent variable values.
  • y – 1D NumPy array of dependent variable values.
• Description: Initializes the linear regression model by fitting y = a + b*x.

Method	Description
slope()	Returns the slope (b) of the regression line.
intercept()	Returns the y-intercept (a) of the regression line.
predict(values)	Returns predicted y values for given x values.
predict_single(value)	Returns the predicted y for a single x value.
rmse()	Returns Root Mean Squared Error.
mae()	Returns Mean Absolute Error.
r_squared()	Returns coefficient of determination R².
residual_std_error()	Returns residual standard error.
slope_standard_error()	Returns standard error of the slope.
t_value_slope()	Returns t-value for slope significance.
summary()	Prints detailed regression statistics and error metrics.

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Notes

• Input arrays x and y must have the same length.
• Optimized with C++ for speed on large datasets.
• Simple and lightweight, focusing on core linear regression functionality.