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Alternative to std::stod

big leigh bowski (8)
Hi,

After painstakingly coding this up I have been informed that std::stod from the <string> library won't work for this assignment as the default compiler at my school does not support c++11 ISO standard. What are my alternatives in this case?

I needed it to read things like this:

center_1[i-1] += stod(train_1.data[j][i])

I've provided the full code for context. Let me know if anything else will help. Thank you.

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#include "Matrix615.h"
#include <cstdlib>
#include <algorithm>
#include <vector>
#include <iomanip>
#include <string>

using namespace std;

double simpleClassifier(const char* train_dat_filename, const char* test_dat_filename)
{
    Matrix615<string> train_dat;
    Matrix615<string> test_dat;

    train_dat.readFromFile(train_dat_filename);
    test_dat.readFromFile(test_dat_filename);

    Matrix615<string> train_0;
    Matrix615<string> train_1;

    for(int i = 1; i < train_dat.colNums(); i++)
    {
    	if (train_dat.data[i][0].compare("1") == 0)
    	{
    		train_1.data.push_back(train_dat.data[i]);
    	}
    	else
    	{
    		train_0.data.push_back(train_dat.data[i]);
    	}
    }
  	
	vector<double> center_1(train_1.colNums()-1);
	vector<double> center_0(train_0.colNums()-1);
    
	for(int i=1; i < train_1.colNums(); i++)
	{
		for(int j=0; j < train_1.rowNums(); j++)
		{
			center_1[i-1] += stod(train_1.data[j][i]);
		}
		center_1[i-1] = center_1[i-1]/train_1.rowNums();
	}

	for(int i = 1; i < train_0.colNums(); i++)
	{
		for(int j = 0; j < train_0.rowNums(); j++)
		{
			center_0[i-1] += stod(train_0.data[j][i]);
		}
		center_0[i-1] = center_0[i-1]/train_0.rowNums();
	}

	vector<double> s_1(center_1.size());
	vector<double> s_0(center_0.size());

	for(int i=1; i < train_1.colNums(); i++)
	{
		for(int j=0; j < train_1.rowNums(); j++)
		{
			double average = center_1[i - 1];
			double reading = stod(train_1.data[j][i]);
			double tosquare = reading - average;
			s_1[i-1] += (tosquare*tosquare);
		}
		s_1[i-1] = s_1[i-1]/(train_1.rowNums()-1);
	}

	for(int i=1; i < train_0.colNums(); i++)
	{
		for(int j=0; j < train_0.rowNums(); j++)
		{
			double average = center_0[i - 1];
			double reading = stod(train_0.data[j][i]);
			double tosquare = reading - average;
			s_0[i-1] += (tosquare*tosquare);
		}
		s_0[i-1] = s_0[i-1]/(train_0.rowNums()-1);
	}

	vector <vector <double> > stretchedc_1;
	for(int i=0;i<train_dat.rowNums();i++)
	{
		stretchedc_1.push_back(center_1);
	}

	vector <vector <double> > stretchedc_0;
	for(int i = 0; i < train_dat.rowNums(); i++)
	{
		stretchedc_0.push_back(center_0);
	}

	vector <vector <double> > stretcheds_1;
	for(int i = 0; i < train_dat.rowNums(); i++)
	{
		stretcheds_1.push_back(s_1);
	}

	vector <vector <double> > stretcheds_0;
	for(int i = 0; i < train_dat.rowNums(); i++)
	{
		stretcheds_0.push_back(s_0);
	}

	vector < vector<double> > test_minus_stretch1;
	vector <double> nextRow1(test_dat.colNums()-1);
	vector <double> rowSum1(test_dat.rowNums()-1);

	for(int i= 1; i < test_dat.rowNums(); i++)
	{
		for(int j = 1; j < test_dat.colNums(); j++) 
		{

			nextRow1[j-1] = stod(test_dat.data[i][j]) - stretchedc_1[i-1][j-1];
			nextRow1[j-1] *= nextRow1[j-1];
			nextRow1[j-1] /= stretcheds_1[i-1][j-1];
			rowSum1[i-1] += nextRow1[j-1];
		}
		test_minus_stretch1.push_back(nextRow1);
	}

	vector < vector<double> > test_minus_stretch0;
	vector <double> nextRow0(test_dat.colNums()-1);
	vector <double> rowSum0(test_dat.rowNums()-1);

	for(int i= 1; i < test_dat.rowNums(); i++)
	{
		for(int j = 1; j < test_dat.colNums(); j++) 
		{

			nextRow0[j-1] = stod(test_dat.data[i][j]) - stretchedc_0[i-1][j-1];
			nextRow0[j-1] *= nextRow0[j-1];
			nextRow0[j-1] /= stretcheds_0[i-1][j-1];
			rowSum0[i-1] += nextRow0[j-1];
		}
		test_minus_stretch0.push_back(nextRow0);

	}

	vector <int> y_pred(rowSum1.size());
	for(int i = 0; i < rowSum1.size(); i++)
	{
		if (rowSum1[i] <= rowSum0[i])
		{
			y_pred[i] = 1;
		}
		else y_pred[i] = 0;
	}	

	vector <double> result(rowSum0.size());
	double pred = 0;
	for(int i=0;i<rowSum0.size();i++)
	{
		result[i] = (y_pred[i] == stoi(test_dat.data[i+1][0]));
		pred += result[i]/rowSum0.size();
	}
	cout << setprecision(4) << pred << endl;

	return 0.0;
}

int main(int argc, char** argv)
{
    simpleClassifier(argv[1], argv[2]);
    return 0;
}
jlb (4973)
What are my alternatives in this case?

I'd suggest stringstreams. The stringstreams are available in every standard version of C++.

Duthomhas (13145)
Either use a stringstream (#include <sstream>) or strtod() (#include <cstdlib>).
big leigh bowski (8)
Thank you both. I was able to use boost::lexical_cast, would stringstream or strtod be anymore efficient?
jlb (4973)
Since you can't use C++11 are you allowed to use boost?

I'd recommend the stringstream, since it is standard. You could even make your own function named stod() that use a stringstream internally.


Last edited on
Duthomhas (13145)
The stod() function actually uses strtod() internally.
big leigh bowski (8)
Strangely enough we are able to use the boost library. I haven't looked into stod boost alternatives.
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