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Logistic Regression in C

This project provides a basic implementation of logistic regression in C. Logistic regression is a linear model commonly used for binary classification tasks.

Overview

• Logistic_Regression.c: The main C source file containing the implementation of logistic regression.
• data.csv: Sample dataset in CSV format for training the logistic regression model.

Usage

1. Compile the code:

   gcc LogisticRegression.c -o LogisticRegression -lm

2. Run the executable:

   ./LogisticRegression

Data Format

The input data should be in CSV format with the last column containing the class labels. Each row represents a data point, where the first n_in columns are the features and the last column is the class label. For example:

x1,x2,x3,x4,x5,x6,y
1,2,3,4,5,6,0
7,8,9,10,11,12,1
...

Parameters

• learning_rate: The learning rate determines the step size taken during optimization.
• n_epochs: The number of epochs defines how many times the algorithm will iterate over the entire dataset during training.
• n_in: The number of input features in the dataset.
• n_out: The number of output classes (2 for binary classification).

Output

The code outputs the accuracy of the model on the training dataset for each epoch. The accuracy is calculated as the percentage of correctly predicted labels.

Implementation Details

• The LR struct represents the logistic regression model, containing the weights W and biases b.
• The LR_construct function initializes the model with random weights and biases.
• The LR_train function performs gradient descent to update the weights and biases based on the training data.
• The LR_predict function predicts the class label for a given input using the trained model.
• The LR_softmax function applies the softmax activation function to convert model outputs into class probabilities.

Data Set

The current dataset is extracted from UCI Machine Learning Repository. It consists of Multivariate Gait Data and contains Bilateral (left, right) joint angle (ankle, knee, hip) times series data collected from 10 healthy subjects under 3 walking conditions (unbraced, knee braced, ankle braced). For each condition, each subject’s data consists of 10 consecutive gait cycles.

• Instances: 181800

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Made by

• Madhav Kataria
• Roll no - B23CH1025