gpmp::ml::BayesBernoulli Class Reference

#include <bayes_clf.hpp>

Public Member Functions
	BayesBernoulli (double alpha_param=1.0)
	Constructor for BayesBernoulli class. More...
	~BayesBernoulli ()
	Destructor for BayesBernoulli class. More...
void	train (const std::vector< std::vector< size_t >> &data, const std::vector< std::string > &labels)
	Train the classifier with a set of labeled data. More...
std::string	predict (const std::vector< size_t > &newData) const
	Predict the class of a new data point. More...
void	display () const
	Display the learned probabilities. More...

Public Attributes
std::unordered_map< std::string, double >	class_probs
std::unordered_map< std::string, std::unordered_map< size_t, double > >	feat_probs
double	alpha

Detailed Description

Bernoulli Naive Bayes is a part of the Naive Bayes family. It is based on the Bernoulli Distribution and accepts only binary values, i.e., 0 or 1. If the features of the dataset are binary, then we can assume that Bernoulli Naive Bayes is the algorithm to be used.

Definition at line 123 of file bayes_clf.hpp.

Constructor & Destructor Documentation

BayesBernoulli()

gpmp::ml::BayesBernoulli::BayesBernoulli ( double  alpha_param = 1.0 )

inline

Constructor for BayesBernoulli class.

Parameters

alpha

Additive (Laplace/Lidstone) smoothing parameter

Definition at line 134 of file bayes_clf.hpp.

                                             : alpha(alpha_param) {
    }

~BayesBernoulli()

gpmp::ml::BayesBernoulli::~BayesBernoulli ( )

inline

Destructor for BayesBernoulli class.

Definition at line 140 of file bayes_clf.hpp.

                      {
    }

Member Function Documentation

display()

void gpmp::ml::BayesBernoulli::display

(

)

const

Display the learned probabilities.

Note

This method is for debugging purposes

Definition at line 212 of file bayes_clf.cpp.

                                         {
    std::cout << "Class Probabilities:\n";
    for (const auto &entry : class_probs) {
        std::cout << entry.first << ": " << entry.second << "\n";
    }
 
    std::cout << "\nFeature Probabilities:\n";
    for (const auto &class_entry : feat_probs) {
        std::cout << class_entry.first << ":\n";
        for (const auto &feat_entry : class_entry.second) {
            std::cout << "  Feature " << feat_entry.first << ": "
                      << feat_entry.second << "\n";
        }
    }
}

Referenced by main().

predict()

std::string gpmp::ml::BayesBernoulli::predict

(

const std::vector< size_t > &

newData

)

const

Predict the class of a new data point.

Parameters

newData

A vector of size_t representing the features of the new data point

Returns

The predicted class label as a string

Definition at line 191 of file bayes_clf.cpp.

                                                                     {
    double max_prob = -std::numeric_limits<double>::infinity();
    std::string predicted_class;
 
    for (const auto &class_entry : class_probs) {
        double probability = log(class_entry.second);
 
        for (size_t i = 0; i < new_data.size(); ++i) {
            probability +=
                new_data[i] * log(feat_probs.at(class_entry.first).at(i));
        }
 
        if (probability > max_prob) {
            max_prob = probability;
            predicted_class = class_entry.first;
        }
    }
 
    return predicted_class;
}

Referenced by main().

train()

void gpmp::ml::BayesBernoulli::train	(	const std::vector< std::vector< size_t >> &	data,
		const std::vector< std::string > &	labels
	)

Train the classifier with a set of labeled data.

Parameters

data	A vector of vectors representing the training instances
labels	A vector of strings representing the corresponding class labels

Definition at line 155 of file bayes_clf.cpp.

                                        {
    size_t numInstances = data.size();
    size_t num_feats = data[0].size();
 
    for (size_t i = 0; i < numInstances; ++i) {
        std::string classLabel = labels[i];
 
        // update class probabilities
        class_probs[classLabel] += 1.0;
 
        // update feature probabilities
        for (size_t j = 0; j < num_feats; ++j) {
            feat_probs[classLabel][j] += data[i][j];
        }
    }
 
    // laplace smoothing
    double smoothing_factor = alpha * 2.0;
    for (auto &entry : class_probs) {
        entry.second =
            (entry.second + alpha) / (numInstances + smoothing_factor);
    }
 
    for (auto &class_entry : feat_probs) {
        for (auto &feat_entry : class_entry.second) {
            feat_entry.second =
                (feat_entry.second + alpha) /
                (class_probs[class_entry.first] + smoothing_factor);
        }
    }
}

Referenced by main().

Member Data Documentation

alpha

double gpmp::ml::BayesBernoulli::alpha

Definition at line 128 of file bayes_clf.hpp.

class_probs

std::unordered_map<std::string, double> gpmp::ml::BayesBernoulli::class_probs

Definition at line 125 of file bayes_clf.hpp.

feat_probs

std::unordered_map<std::string, std::unordered_map<size_t, double> > gpmp::ml::BayesBernoulli::feat_probs

Definition at line 127 of file bayes_clf.hpp.

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The documentation for this class was generated from the following files:

• include/openGPMP/ml/bayes_clf.hpp
• modules/ml/bayes_clf.cpp