Text_Categorization/main2.cpp at master · andreaslin/Text_Categorization · GitHub

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#include <iostream>
#include <fstream>
#include <sstream>
#include <cstdlib>					// exit
#include "BN.h"						// Bayesian Network
#include <vector>					// vector
using namespace std;				// direct access to std

void readDataInput( ifstream *in, unsigned int **d ) {
	int in1, in2;
	unsigned int i, j;
	for ( ;; ) {
		*in >> in1;					// read an integer
		*in >> in2;					// read another integer
		i = (unsigned int)in1 - 1;
		j = (unsigned int)in2 - 1;
		d[i][j] = 1;
		if ( in->fail() ) break;			// failed ?
	} // for
}

void readLabelInput( ifstream *in, unsigned int *v ) {
	int in1;
	for ( unsigned int i = 0;; i += 1 ) {
		*in >> in1;					// read an integer
		v[i] = in1;
		if ( in->fail() ) break;			// failed ?
	} // for
}

bool convert( int &val, char *buffer ) {		// convert C string to integer
	std::stringstream ss( buffer );			// connect stream and buffer
	ss >> dec >> val;					// convert integer from buffer
	return ! ss.fail() &&				// conversion successful ?
		// characters after conversion all blank ?
		string( buffer ).find_first_not_of( " ", ss.tellg() ) == string::npos;
} // convert

void usage( char *argv[] ) {
	cerr << "Usage: " << argv[0]
	     << "Train-file Train-label Test-file Test-label" << endl;
	exit( EXIT_FAILURE );				// TERMINATE
} // usage

int main( int argc, char *argv[] ) {
	ifstream *train_file;				// train file
	ifstream *train_label_file;			// train label file
	ifstream *test_file;				// test file
	ifstream *test_label_file;			// test label file

	unsigned int **test_data;	// test data
	unsigned int *test_label;	// test label
	unsigned int **train_data;	// train data
	unsigned int *train_label;	// train label

	switch ( argc ) {
	case 5:
		test_label_file = new ifstream( argv[4] );
		if ( test_label_file->fail() ) {			// open failed ?
			cerr << "Error! Could not open output file \"" << argv[4] << "\"" << endl;
			usage( argv );
		} // if

		test_file = new ifstream( argv[3] );
		if ( test_file->fail() ) {					// open failed ?
			cerr << "Error! Could not open input file \"" << argv[3] << "\"" << endl;
			usage( argv );
		} // if

		train_label_file = new ifstream( argv[2] );
		if ( train_label_file->fail() ) {			// open failed ?
			cerr << "Error! Could not open input file \"" << argv[2] << "\"" << endl;
			usage( argv );
		} // if

		train_file = new ifstream( argv[1] );
		if ( train_file->fail() ) {					// open failed ?
			cerr << "Error! Could not open input file \"" << argv[1] << "\"" << endl;
			usage( argv );
		} // if
		break;
	default:						// wrong number of options
		usage( argv );
	} // switch

	// Read from file
	train_data = new unsigned int*[1061];
	train_label = new unsigned int[1061];
	test_data = new unsigned int*[707];
	test_label = new unsigned int[707];

	// Initialize training and testing data
	for( unsigned int i = 0; i < 1061; i += 1 ) {
		train_label[i] = 0;
		unsigned int *row;
		row = new unsigned int[3566];
		train_data[i] = row;
		for( unsigned int j = 0; j < 3566; j += 1 ) train_data[i][j] = 0;
	}

	for( unsigned int i = 0; i < 707; i += 1 ) {
		test_label[i] = 0;
		unsigned int *row;
		row = new unsigned int[3566];
		test_data[i] = row;
		for( unsigned int j = 0; j < 3566; j += 1 ) test_data[i][j] = 0;
	}

	readDataInput( train_file, train_data );
	readLabelInput( train_label_file, train_label );
	readDataInput( test_file, test_data );
	readLabelInput( test_label_file, test_label );

	// Initialize examples and attributes
	vector< unsigned int > train_examples;
	vector< unsigned int > attributes;
	for( unsigned int i = 0; i < 1061; i += 1 ) train_examples.push_back(i);
	for( unsigned int i = 0; i < 3566; i += 1 ) attributes.push_back(i);

	// Build the Bayes Network


	unsigned int train_correct = 0, test_correct = 0;
	for( unsigned int j = 0; j < 1061; j += 1 ) {}
		//		if ( dlt.getPrediction( j, dlt.root, true ) == train_label[j] ) train_correct += 1;
	for( unsigned int j = 0; j < 707; j += 1 ) {}
		//		if ( dlt.getPrediction( j, dlt.root, false ) == test_label[j] ) test_correct += 1;

	double train_percent = (double)train_correct/(double)1061;
	double test_percent = (double)test_correct/(double)707;

	cout << train_percent << endl;
	cout << test_percent << endl;

} // main