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NaiveBayesClassifier.cpp
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NaiveBayesClassifier.cpp
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#include "NaiveBayesClassifier.h"
#define N_INF -1000000000
NaiveBayesClassifier::NaiveBayesClassifier(int number_labels,int number_unique_words)
{
first_parameter = (long double**)malloc(sizeof(long double*)*number_labels);
second_parameter = (long double*)malloc(sizeof(long double)*number_labels);
for(int i = 0;i<number_labels;i++)
first_parameter[i] = (long double*)malloc(sizeof(long double)*number_unique_words);
}
long double ** NaiveBayesClassifier::get_likelihood()
{
return first_parameter;
}
long double * NaiveBayesClassifier::get_prior()
{
return second_parameter;
}
void NaiveBayesClassifier::calculate_likelihood(int** feature_vectors,int number_unique_words, int number_documents, int number_labels)
{
printf("Calculating First parameter: \n");
for(int i = 0;i < number_labels;i++)
{
int all_label_occ = calculate_all_words_label_occurence(feature_vectors,number_unique_words,number_documents,i);
for(int j = 0; j < number_unique_words;j++)
{
first_parameter[i][j] = (long double)(calculate_single_word_label_occurence(feature_vectors,j,number_documents,i) + 1)/
(long double)(all_label_occ+number_unique_words);
//printf("%f ",first_parameter[i][j]);
}
//printf("\n");
}
//printf("\n");
}
int NaiveBayesClassifier::calculate_single_word_label_occurence(int ** feature_vectors, int word_index, int number_documents, int label)
{
int result = 0;
for(int i = 0;i<number_documents;i++)
{
if(label == feature_vectors[i][0])
result += feature_vectors[i][word_index+1];
}
return result;
}
int NaiveBayesClassifier::calculate_all_words_label_occurence(int ** feature_vectors, int number_unique_words, int number_documents, int label)
{
int result = 0;
for(int i = 0;i<number_unique_words;i++)
{
result += calculate_single_word_label_occurence(feature_vectors,i,number_documents,label);
}
return result;
}
void NaiveBayesClassifier::calculate_prior(int** feature_vectors, int number_documents, int number_labels)
{
printf("Calculating Second Parameter: \n");
int denom = number_documents+number_labels;
for(int i = 0;i<number_labels;i++)
{
second_parameter[i] = (long double)(1+calculate_label_occurance(feature_vectors,number_documents,i))/
(long double)denom;
//printf("%lf \n",second_parameter[i]);
}
//printf("\n");
}
int NaiveBayesClassifier::calculate_label_occurance(int ** feature_vectors, int number_documents, int label)
{
int result = 0;
for(int i = 0;i < number_documents; i++)
{
if(feature_vectors[i][0] == label)
result++;
}
return result;
}
int NaiveBayesClassifier::classify_unlabeled_document(int * unlabeled_feature_vector, int number_unique_words, int number_labels)
{
long double max_prob = N_INF;
int label = -1;
for(int i = 0;i < number_labels;i++)
{
long double prob = (long double)NaiveBayesClassifier::prob_document_label(unlabeled_feature_vector,number_unique_words,i);
if(prob > max_prob)
{
max_prob = prob;
label = i;
}
}
//unlabeled_feature_vector[0] = label;
return label;
}
long double NaiveBayesClassifier::prob_document_all_labels(int * feature_vector, int number_unique_words, int number_labels)
{
long double result = 0;
for(int i = 0;i<number_labels;i++)
{
result += NaiveBayesClassifier::prob_document_label(feature_vector,number_unique_words,i);
}
return result;
}
long double NaiveBayesClassifier::prob_document_label(int * feature_vector, int number_unique_words, int label)
{
long double result = log10(second_parameter[label]);
//printf("Label: %d Prior: %Lf \n",label,result);
for(int i = 0;i<number_unique_words;i++)
{
if(first_parameter[label][i] == 0)
continue;
result = result + (feature_vector[i+1]*log10(first_parameter[label][i]));
//printf("result: %f %d = %e \n",first_parameter[label][i], feature_vector[i] ,result);
}
//printf("Label: %d Final Prob: %Lf\n",label,result);
return result;
}