TextClassificationSerial/TextClassificationSerial.cpp at master · mremad/TextClassificationSerial · GitHub

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// TextClassificationSerial.cpp : Defines the entry point for the console application.
//

#include<stdio.h>
#include"EM.h"
#include"FeatureConstructor.h"
#include"FileReader.h"
#include"LabelFactory.h"
#include"TestResults.h"

#defineDATA_PATH_EMAD"/Users/Mohamed/Desktop/TextClassificationSerial/TrainingData.txt"
#defineDATA_PATH_MOUMEN""
#defineDATA_PATH_SHAABAN"TrainingData.txt"

#defineTEST_PATH_EMAD"/Users/Mohamed/Desktop/TextClassificationSerial/TestData.txt"
#defineTEST_PATH_MOUMEN""
#defineTEST_PATH_SHAABAN"TestData.txt"

intmain(int argc, char* argv[])
{

int document_size = 11290; // max 11290
int desired_labeled = 5500; // max 5500
int num_labels = 20;

int test_documents = 7500;

	FileReader fr = FileReader(document_size,DATA_PATH_EMAD);
 fr.read_files();
 fr.read_files_per_label(desired_labeled/num_labels, num_labels);

	FeatureConstructor fc = FeatureConstructor(fr.documents_size,desired_labeled);
 fc.extract_vocab(fr.data_list, fr.documents_size, desired_labeled);
 fc.construct_feature_vectors(fr.data_list, fr.documents_size, desired_labeled);

	fr.deallocate();
//LabelFactory lf = LabelFactory();
//lf.select_labeled_docs(fc.feature_vector, fc.NUM_OF_DOCUMENTS, desired_labeled, fc.NUM_OF_LABELS);

	NaiveBayesClassifier nc = NaiveBayesClassifier(fc.NUM_OF_LABELS,fc.NUM_OF_UNIQUE_WORDS);

 nc.calculate_likelihood(fc.feature_vector, fc.NUM_OF_UNIQUE_WORDS, desired_labeled, fc.NUM_OF_LABELS);

 nc.calculate_prior(fc.feature_vector, desired_labeled, fc.NUM_OF_LABELS);

//ConsolePrint::print_2d_float(fc.NUM_OF_UNIQUE_WORDS, 20, nc.get_likelihood());

/*EM em = EM();
 em.run_em(&nc, fc.feature_vector , lf.labeled_fv, lf.unlabeled_fv, fc.NUM_OF_UNIQUE_WORDS, fc.NUM_OF_DOCUMENTS-desired_labeled, desired_labeled, fc.NUM_OF_LABELS);*/

 TestResults tr = TestResults(TEST_PATH_EMAD, test_documents, &fc, &nc);
 tr.start_test();

//int label = nc.classify_unlabeled_document(fc.feature_vector[0],fc.NUM_OF_UNIQUE_WORDS,fc.NUM_OF_LABELS);
//printf("Number of labels: %d Chosen Label: %d\n",fc.NUM_OF_LABELS,label);

//label = nc.classify_unlabeled_document(fc.feature_vector[499],fc.NUM_OF_UNIQUE_WORDS,fc.NUM_OF_LABELS);
//printf("Number of labels: %d Chosen Label: %d\n",fc.NUM_OF_LABELS,label);

/*label = nc.classify_unlabeled_document(fc.feature_vector[1000],fc.NUM_OF_UNIQUE_WORDS,fc.NUM_OF_LABELS);
 printf("Number of labels: %d Chosen Label: %d\n",fc.NUM_OF_LABELS,label);

 label = nc.classify_unlabeled_document(fc.feature_vector[1499],fc.NUM_OF_UNIQUE_WORDS,fc.NUM_OF_LABELS);
 printf("Number of labels: %d Chosen Label: %d\n",fc.NUM_OF_LABELS,label);

 label = nc.classify_unlabeled_document(fc.feature_vector[2000],fc.NUM_OF_UNIQUE_WORDS,fc.NUM_OF_LABELS);
 printf("Number of labels: %d Chosen Label: %d\n",fc.NUM_OF_LABELS,label);

 label = nc.classify_unlabeled_document(fc.feature_vector[2500],fc.NUM_OF_UNIQUE_WORDS,fc.NUM_OF_LABELS);
 printf("Number of labels: %d Chosen Label: %d\n",fc.NUM_OF_LABELS,label);

 label = nc.classify_unlabeled_document(fc.feature_vector[2999],fc.NUM_OF_UNIQUE_WORDS,fc.NUM_OF_LABELS);
 printf("Number of labels: %d Chosen Label: %d\n",fc.NUM_OF_LABELS,label);*/


printf("Program terminated safely\n");
getchar();
return0;
}