Unexpected Errors in Naive Bayes Classifier
0
I was working on my project on a Naive Bayes implementation when I started running into some unexpected issues. Any help?
`
#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <vector>
#include <algorithm>
#include <ctime>
#include <cstdio>
#include <cctype>
#include <cstdlib>
using namespace std;
struct Person
{
int age;
string workclass;
int fnlwgt;
string sex;
int capitalGain;
int capitalLoss;
int hoursPerWeek;
string salary;
void printPerson()
{
cout << age << endl << workclass << endl << fnlwgt;
cout << endl << sex << endl;
cout << capitalGain << endl << capitalLoss;
cout << endl << hoursPerWeek << endl << salary << endl;
}
void getPerson()
{
cout << "nEnter age: ";
cin >> age;
cout << "nEnter work class: ";
getline(cin, workclass);
cout << "nEnter final weight: ";
cin >> fnlwgt;
cout << "nEnter sex: ";
getline(cin, sex);
cout << "nEnter capital gain: ";
cin >> capitalGain;
cout << "nEnter capital loss: ";
cin >> capitalLoss;
cout << "nEnter salary: ";
getline(cin, salary);
}
};
void writeToData()
{
ofstream writer;
writer.open("Dataset.dat", ios::binary | ios::app);
if (!writer)
{
cout << "nError in file open!!";
return;
}
Person p;
while(1)
{
char yn;
p.getPerson();
writer.write((char*)&p, sizeof(p));
cout << "nAre there more records? ";
cin >> yn;
if (yn == 'n' || yn == 'N')
{
break;
}
}
}
string convertInt(int x){
string result;
ostringstream convert;
convert << x;
result = convert.str();
return result;
}
int randNumGenerator(int max){
int num = (rand() % max);
return num;
}
template <typename Predicate>
vector<Person> filter( vector<Person>& testData, Predicate pred) {
vector<Person> out;
copy_if(testData.begin(), testData.end(), back_inserter(out), pred);
return out;
}
vector<Person> positive = filter(testData, [=](const Person& p)
return p.salary == ">50K"
void setData(Person person, string line){
line.erase(remove(line.begin(), line.end(), ','), line.end());
stringstream s(line);
string str;
string workclass;
string sex;
string salary;
int age;
string ageStr;
int fnlwgt;
string fnlwgtStr;
int capitalGain;
string capitalGainStr;
int capitalLoss;
string capitalLossStr;
int hoursPerWeek;
string hoursPerWeekStr;
if(s >> age >> workclass >> fnlwgt >> sex >> capitalGain >> capitalLoss >> hoursPerWeek >> salary)
{
ageStr = convertInt(age);
fnlwgtStr = convertInt(fnlwgt);
capitalGainStr = convertInt(capitalGain);
capitalLossStr = convertInt(capitalLoss);
hoursPerWeekStr = convertInt(hoursPerWeek);
if(ageStr == "?" || workclass == "?" || fnlwgtStr == "?" || sex == "?" || capitalGainStr == "?" || capitalLossStr == "?" || hoursPerWeekStr == "?")
{
}
else{
person.age = age;
person.workclass = workclass;
person.fnlwgt = fnlwgt;
person.sex = sex;
person.capitalGain = capitalGain;
person.capitalLoss = capitalLoss;
person.hoursPerWeek = hoursPerWeek;
person.salary = salary;
testData.push_back(person);
}
}
}
vector<Person> sample(vector<Person> wholeDataSet, int percentage, vector<Person> &testingSet){
int wholeDataSize = wholeDataSet.size();
vector<Person> stratifiedSet;
int limit = (wholeDataSize * percentage) / 100;
int randNum= 0;
vector<bool> numsUsedAlready(wholeDataSize);
for(int i = 0; i < limit; i++){
randNum = randNumGenerator(wholeDataSize);
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(wholeDataSize);
}
numsUsedAlready[randNum] = true;
stratifiedSet.push_back(wholeDataSet[randNum]);
}
for(int i = 0; i < numsUsedAlready.size(); i++){
if(!numsUsedAlready[i]){
testingSet.push_back(wholeDataSet[i]);
}
}
return stratifiedSet;
}
vector<Person> concatVectors(vector<Person> a, vector<Person> b){
vector<Person> ab;
ab.reserve(a.size() + b.size());
ab.insert(ab.end(), a.begin(), a.end());
ab.insert(ab.end(), b.begin(), b.end());
return ab;
}
void compareAttributeInt(int sample, int trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
void compareAttributeStr(string sample, string trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
class Count
{
public:
int pos = 0;
int neg = 0;
void increment(bool flag) {
if (flag) {
pos++;
}
else {
neg++;
}
}
};
float naiveBayesian(vector<Person> trainingSet, vector<Person> testingSet){
float accuracy = 0;
int accuracyCount = 0;
int randNum = 0;
vector<Person> sampleSet;
vector<bool> numsUsedAlready(testingSet.size());
for(int i = 0; i < 20; i++){
randNum = randNumGenerator(testingSet.size());
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(testingSet.size());
}
numsUsedAlready[randNum] = true;
sampleSet.push_back(testingSet[randNum]);
}
int numOver50k = 0;
int numUnder50k = 0;
for(int i = 0; i < trainingSet.size(); i++){
if(trainingSet[i].salary == ">50K"){
numOver50k++;
}
else{
numUnder50k++;
}
}
float probOver50k = (float)numOver50k / trainingSet.size();
float probUnder50k = (float)numUnder50k / trainingSet.size();
float probYes = 0;
float probNo = 0;
float yes= 0;
float no = 0;
template <typename T>
Count compute_count(const Person& sample, const vector<Person>& trainingSet, T Person::*member)
{
Count res;
for (const Person& training : trainingSet) {
if ((sample.*member) == (training.*member)) {
res.increment(training.salary == ">50K");
}
}
return res;
}
for (const Person& sample : sampleSet)
{
vector<Count> counts;
counts.push_back(compute_count(sample, trainingSet, &Person::age));
counts.push_back(compute_count(sample, trainingSet, &Person::workclass));
counts.push_back(compute_count(sample, trainingSet, &Person::fnlwgt));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalGain));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalLoss));
counts.push_back(compute_count(sample, trainingSet, &Person::hoursPerWeek));
}
bool salaryGreaterThan50k = (probYes * probOver50k) > (probNo * probUnder50k);
double probYes = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.pos / numOver50k;
});
double probNo = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.neg / numUnder50k;
});
if ((salaryGreaterThan50k && (sample.salary == ">50K")) || (!salaryGreaterThan50k && !(sample.salary == ">50K")))
++accuracyCount;
accuracy = (float)accuracyCount / 20;
return accuracy;
}
void stratifiedSample(){
srand(time(NULL));
vector<Person> positiveSamples;
vector<Person> negativeSamples;
positiveSamples = setPositive(positiveSamples);
negativeSamples = setNegative(negativeSamples);
vector<Person> posTestingSet10 = positiveSamples;
vector<Person> posTestingSet30 = positiveSamples;
vector<Person> posTestingSet50 = positiveSamples;
vector<Person> posTestingSet70 = positiveSamples;
vector<Person> posTestingSet90 = positiveSamples;
vector<Person> negTestingSet10 = negativeSamples;
vector<Person> negTestingSet30 = negativeSamples;
vector<Person> negTestingSet50 = negativeSamples;
vector<Person> negTestingSet70 = negativeSamples;
vector<Person> negTestingSet90 = negativeSamples;
vector<Person> posStratifiedSet_10;
vector<Person> posTesting_10;
vector<Person> negStratifiedSet_10;
vector<Person> negTesting_10;
vector<Person> posStratifiedSet_30;
vector<Person> posTesting_30;
vector<Person> negStratifiedSet_30;
vector<Person> negTesting_30;
vector<Person> posStratifiedSet_50;
vector<Person> posTesting_50;
vector<Person> negStratifiedSet_50;
vector<Person> negTesting_50;
vector<Person> posStratifiedSet_70;
vector<Person> posTesting_70;
vector<Person> negStratifiedSet_70;
vector<Person> negTesting_70;
vector<Person> posStratifiedSet_90;
vector<Person> posTesting_90;
vector<Person> negStratifiedSet_90;
vector<Person> negTesting_90;
vector<Person> stratifiedSet_10;
vector<Person> stratifiedSet_30;
vector<Person> stratifiedSet_50;
vector<Person> stratifiedSet_70;
vector<Person> stratifiedSet_90;
vector<Person> testingSet_10;
vector<Person> testingSet_30;
vector<Person> testingSet_50;
vector<Person> testingSet_70;
vector<Person> testingSet_90;
posStratifiedSet_10 = sample(posTestingSet10, 10, posTesting_10);
negStratifiedSet_10 = sample(negTestingSet10, 10, negTesting_10);
posStratifiedSet_30 = sample(posTestingSet30, 30, posTesting_30);
negStratifiedSet_30 = sample(negTestingSet30, 30, negTesting_30);
posStratifiedSet_50 = sample(posTestingSet50, 50, posTesting_50);
negStratifiedSet_50 = sample(negTestingSet50, 50, negTesting_50);
posStratifiedSet_70 = sample(posTestingSet70, 70, posTesting_70);
negStratifiedSet_70 = sample(negTestingSet70, 70, negTesting_70);
posStratifiedSet_90 = sample(posTestingSet90, 90, posTesting_90);
negStratifiedSet_90 = sample(negTestingSet90, 90, negTesting_90);
stratifiedSet_10 = concatVectors(posStratifiedSet_10, negStratifiedSet_10);
stratifiedSet_30 = concatVectors(posStratifiedSet_30, negStratifiedSet_30);
stratifiedSet_50 = concatVectors(posStratifiedSet_50, negStratifiedSet_50);
stratifiedSet_70 = concatVectors(posStratifiedSet_70, negStratifiedSet_70);
stratifiedSet_90 = concatVectors(posStratifiedSet_90, negStratifiedSet_90);
testingSet_10 = concatVectors(posTesting_10, negTesting_10);
testingSet_30 = concatVectors(posTesting_30, negTesting_30);
testingSet_50 = concatVectors(posTesting_50, negTesting_50);
testingSet_70 = concatVectors(posTesting_70, negTesting_70);
testingSet_90 = concatVectors(posTesting_90, negTesting_90);
float accuracy10 = 0;
float accuracy30 = 0;
float accuracy50 = 0;
float accuracy70 = 0;
float accuracy90 = 0;
accuracy10 = naiveBayesian(stratifiedSet_10, testingSet_10);
cout << "accuracy for 10%: " << accuracy10 << endl;
accuracy30 = naiveBayesian(stratifiedSet_30, testingSet_30);
cout << "accuracy for 30%: " << accuracy30 << endl;
accuracy50 = naiveBayesian(stratifiedSet_50, testingSet_50);
cout << "accuracy for 50%: " << accuracy50 << endl;
accuracy70 = naiveBayesian(stratifiedSet_70, testingSet_70);
cout << "accuracy for 70%: " << accuracy70 << endl;
accuracy90 = naiveBayesian(stratifiedSet_90, testingSet_90);
cout << "accuracy for 90%: " << accuracy90 << endl;
}
void readInputFile()
{
ifstream reader;
reader.open("Dataset.dat" , ios::binary);
if(!reader)
{
cout << "nError in open";
return;
}
Person p;
reader.read((char*) &p, sizeof(p));
}
int main(int argc, char const *argv)
{
system("CLS");
readInputFile();
stratifiedSample();
return 0;
}
`
I work on the inbuilt gcc compiler for mac and the errors it throws during compilation are:
What have I been doing wrong?
c++ error-handling
asked 3 mins ago
S-A117
1
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
0
I was working on my project on a Naive Bayes implementation when I started running into some unexpected issues. Any help?
`
#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <vector>
#include <algorithm>
#include <ctime>
#include <cstdio>
#include <cctype>
#include <cstdlib>
using namespace std;
struct Person
{
int age;
string workclass;
int fnlwgt;
string sex;
int capitalGain;
int capitalLoss;
int hoursPerWeek;
string salary;
void printPerson()
{
cout << age << endl << workclass << endl << fnlwgt;
cout << endl << sex << endl;
cout << capitalGain << endl << capitalLoss;
cout << endl << hoursPerWeek << endl << salary << endl;
}
void getPerson()
{
cout << "nEnter age: ";
cin >> age;
cout << "nEnter work class: ";
getline(cin, workclass);
cout << "nEnter final weight: ";
cin >> fnlwgt;
cout << "nEnter sex: ";
getline(cin, sex);
cout << "nEnter capital gain: ";
cin >> capitalGain;
cout << "nEnter capital loss: ";
cin >> capitalLoss;
cout << "nEnter salary: ";
getline(cin, salary);
}
};
void writeToData()
{
ofstream writer;
writer.open("Dataset.dat", ios::binary | ios::app);
if (!writer)
{
cout << "nError in file open!!";
return;
}
Person p;
while(1)
{
char yn;
p.getPerson();
writer.write((char*)&p, sizeof(p));
cout << "nAre there more records? ";
cin >> yn;
if (yn == 'n' || yn == 'N')
{
break;
}
}
}
string convertInt(int x){
string result;
ostringstream convert;
convert << x;
result = convert.str();
return result;
}
int randNumGenerator(int max){
int num = (rand() % max);
return num;
}
template <typename Predicate>
vector<Person> filter( vector<Person>& testData, Predicate pred) {
vector<Person> out;
copy_if(testData.begin(), testData.end(), back_inserter(out), pred);
return out;
}
vector<Person> positive = filter(testData, [=](const Person& p)
return p.salary == ">50K"
void setData(Person person, string line){
line.erase(remove(line.begin(), line.end(), ','), line.end());
stringstream s(line);
string str;
string workclass;
string sex;
string salary;
int age;
string ageStr;
int fnlwgt;
string fnlwgtStr;
int capitalGain;
string capitalGainStr;
int capitalLoss;
string capitalLossStr;
int hoursPerWeek;
string hoursPerWeekStr;
if(s >> age >> workclass >> fnlwgt >> sex >> capitalGain >> capitalLoss >> hoursPerWeek >> salary)
{
ageStr = convertInt(age);
fnlwgtStr = convertInt(fnlwgt);
capitalGainStr = convertInt(capitalGain);
capitalLossStr = convertInt(capitalLoss);
hoursPerWeekStr = convertInt(hoursPerWeek);
if(ageStr == "?" || workclass == "?" || fnlwgtStr == "?" || sex == "?" || capitalGainStr == "?" || capitalLossStr == "?" || hoursPerWeekStr == "?")
{
}
else{
person.age = age;
person.workclass = workclass;
person.fnlwgt = fnlwgt;
person.sex = sex;
person.capitalGain = capitalGain;
person.capitalLoss = capitalLoss;
person.hoursPerWeek = hoursPerWeek;
person.salary = salary;
testData.push_back(person);
}
}
}
vector<Person> sample(vector<Person> wholeDataSet, int percentage, vector<Person> &testingSet){
int wholeDataSize = wholeDataSet.size();
vector<Person> stratifiedSet;
int limit = (wholeDataSize * percentage) / 100;
int randNum= 0;
vector<bool> numsUsedAlready(wholeDataSize);
for(int i = 0; i < limit; i++){
randNum = randNumGenerator(wholeDataSize);
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(wholeDataSize);
}
numsUsedAlready[randNum] = true;
stratifiedSet.push_back(wholeDataSet[randNum]);
}
for(int i = 0; i < numsUsedAlready.size(); i++){
if(!numsUsedAlready[i]){
testingSet.push_back(wholeDataSet[i]);
}
}
return stratifiedSet;
}
vector<Person> concatVectors(vector<Person> a, vector<Person> b){
vector<Person> ab;
ab.reserve(a.size() + b.size());
ab.insert(ab.end(), a.begin(), a.end());
ab.insert(ab.end(), b.begin(), b.end());
return ab;
}
void compareAttributeInt(int sample, int trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
void compareAttributeStr(string sample, string trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
class Count
{
public:
int pos = 0;
int neg = 0;
void increment(bool flag) {
if (flag) {
pos++;
}
else {
neg++;
}
}
};
float naiveBayesian(vector<Person> trainingSet, vector<Person> testingSet){
float accuracy = 0;
int accuracyCount = 0;
int randNum = 0;
vector<Person> sampleSet;
vector<bool> numsUsedAlready(testingSet.size());
for(int i = 0; i < 20; i++){
randNum = randNumGenerator(testingSet.size());
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(testingSet.size());
}
numsUsedAlready[randNum] = true;
sampleSet.push_back(testingSet[randNum]);
}
int numOver50k = 0;
int numUnder50k = 0;
for(int i = 0; i < trainingSet.size(); i++){
if(trainingSet[i].salary == ">50K"){
numOver50k++;
}
else{
numUnder50k++;
}
}
float probOver50k = (float)numOver50k / trainingSet.size();
float probUnder50k = (float)numUnder50k / trainingSet.size();
float probYes = 0;
float probNo = 0;
float yes= 0;
float no = 0;
template <typename T>
Count compute_count(const Person& sample, const vector<Person>& trainingSet, T Person::*member)
{
Count res;
for (const Person& training : trainingSet) {
if ((sample.*member) == (training.*member)) {
res.increment(training.salary == ">50K");
}
}
return res;
}
for (const Person& sample : sampleSet)
{
vector<Count> counts;
counts.push_back(compute_count(sample, trainingSet, &Person::age));
counts.push_back(compute_count(sample, trainingSet, &Person::workclass));
counts.push_back(compute_count(sample, trainingSet, &Person::fnlwgt));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalGain));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalLoss));
counts.push_back(compute_count(sample, trainingSet, &Person::hoursPerWeek));
}
bool salaryGreaterThan50k = (probYes * probOver50k) > (probNo * probUnder50k);
double probYes = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.pos / numOver50k;
});
double probNo = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.neg / numUnder50k;
});
if ((salaryGreaterThan50k && (sample.salary == ">50K")) || (!salaryGreaterThan50k && !(sample.salary == ">50K")))
++accuracyCount;
accuracy = (float)accuracyCount / 20;
return accuracy;
}
void stratifiedSample(){
srand(time(NULL));
vector<Person> positiveSamples;
vector<Person> negativeSamples;
positiveSamples = setPositive(positiveSamples);
negativeSamples = setNegative(negativeSamples);
vector<Person> posTestingSet10 = positiveSamples;
vector<Person> posTestingSet30 = positiveSamples;
vector<Person> posTestingSet50 = positiveSamples;
vector<Person> posTestingSet70 = positiveSamples;
vector<Person> posTestingSet90 = positiveSamples;
vector<Person> negTestingSet10 = negativeSamples;
vector<Person> negTestingSet30 = negativeSamples;
vector<Person> negTestingSet50 = negativeSamples;
vector<Person> negTestingSet70 = negativeSamples;
vector<Person> negTestingSet90 = negativeSamples;
vector<Person> posStratifiedSet_10;
vector<Person> posTesting_10;
vector<Person> negStratifiedSet_10;
vector<Person> negTesting_10;
vector<Person> posStratifiedSet_30;
vector<Person> posTesting_30;
vector<Person> negStratifiedSet_30;
vector<Person> negTesting_30;
vector<Person> posStratifiedSet_50;
vector<Person> posTesting_50;
vector<Person> negStratifiedSet_50;
vector<Person> negTesting_50;
vector<Person> posStratifiedSet_70;
vector<Person> posTesting_70;
vector<Person> negStratifiedSet_70;
vector<Person> negTesting_70;
vector<Person> posStratifiedSet_90;
vector<Person> posTesting_90;
vector<Person> negStratifiedSet_90;
vector<Person> negTesting_90;
vector<Person> stratifiedSet_10;
vector<Person> stratifiedSet_30;
vector<Person> stratifiedSet_50;
vector<Person> stratifiedSet_70;
vector<Person> stratifiedSet_90;
vector<Person> testingSet_10;
vector<Person> testingSet_30;
vector<Person> testingSet_50;
vector<Person> testingSet_70;
vector<Person> testingSet_90;
posStratifiedSet_10 = sample(posTestingSet10, 10, posTesting_10);
negStratifiedSet_10 = sample(negTestingSet10, 10, negTesting_10);
posStratifiedSet_30 = sample(posTestingSet30, 30, posTesting_30);
negStratifiedSet_30 = sample(negTestingSet30, 30, negTesting_30);
posStratifiedSet_50 = sample(posTestingSet50, 50, posTesting_50);
negStratifiedSet_50 = sample(negTestingSet50, 50, negTesting_50);
posStratifiedSet_70 = sample(posTestingSet70, 70, posTesting_70);
negStratifiedSet_70 = sample(negTestingSet70, 70, negTesting_70);
posStratifiedSet_90 = sample(posTestingSet90, 90, posTesting_90);
negStratifiedSet_90 = sample(negTestingSet90, 90, negTesting_90);
stratifiedSet_10 = concatVectors(posStratifiedSet_10, negStratifiedSet_10);
stratifiedSet_30 = concatVectors(posStratifiedSet_30, negStratifiedSet_30);
stratifiedSet_50 = concatVectors(posStratifiedSet_50, negStratifiedSet_50);
stratifiedSet_70 = concatVectors(posStratifiedSet_70, negStratifiedSet_70);
stratifiedSet_90 = concatVectors(posStratifiedSet_90, negStratifiedSet_90);
testingSet_10 = concatVectors(posTesting_10, negTesting_10);
testingSet_30 = concatVectors(posTesting_30, negTesting_30);
testingSet_50 = concatVectors(posTesting_50, negTesting_50);
testingSet_70 = concatVectors(posTesting_70, negTesting_70);
testingSet_90 = concatVectors(posTesting_90, negTesting_90);
float accuracy10 = 0;
float accuracy30 = 0;
float accuracy50 = 0;
float accuracy70 = 0;
float accuracy90 = 0;
accuracy10 = naiveBayesian(stratifiedSet_10, testingSet_10);
cout << "accuracy for 10%: " << accuracy10 << endl;
accuracy30 = naiveBayesian(stratifiedSet_30, testingSet_30);
cout << "accuracy for 30%: " << accuracy30 << endl;
accuracy50 = naiveBayesian(stratifiedSet_50, testingSet_50);
cout << "accuracy for 50%: " << accuracy50 << endl;
accuracy70 = naiveBayesian(stratifiedSet_70, testingSet_70);
cout << "accuracy for 70%: " << accuracy70 << endl;
accuracy90 = naiveBayesian(stratifiedSet_90, testingSet_90);
cout << "accuracy for 90%: " << accuracy90 << endl;
}
void readInputFile()
{
ifstream reader;
reader.open("Dataset.dat" , ios::binary);
if(!reader)
{
cout << "nError in open";
return;
}
Person p;
reader.read((char*) &p, sizeof(p));
}
int main(int argc, char const *argv)
{
system("CLS");
readInputFile();
stratifiedSample();
return 0;
}
`
I work on the inbuilt gcc compiler for mac and the errors it throws during compilation are:
What have I been doing wrong?
c++ error-handling
asked 3 mins ago
S-A117
1
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
0
0
0
I was working on my project on a Naive Bayes implementation when I started running into some unexpected issues. Any help?
`
#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <vector>
#include <algorithm>
#include <ctime>
#include <cstdio>
#include <cctype>
#include <cstdlib>
using namespace std;
struct Person
{
int age;
string workclass;
int fnlwgt;
string sex;
int capitalGain;
int capitalLoss;
int hoursPerWeek;
string salary;
void printPerson()
{
cout << age << endl << workclass << endl << fnlwgt;
cout << endl << sex << endl;
cout << capitalGain << endl << capitalLoss;
cout << endl << hoursPerWeek << endl << salary << endl;
}
void getPerson()
{
cout << "nEnter age: ";
cin >> age;
cout << "nEnter work class: ";
getline(cin, workclass);
cout << "nEnter final weight: ";
cin >> fnlwgt;
cout << "nEnter sex: ";
getline(cin, sex);
cout << "nEnter capital gain: ";
cin >> capitalGain;
cout << "nEnter capital loss: ";
cin >> capitalLoss;
cout << "nEnter salary: ";
getline(cin, salary);
}
};
void writeToData()
{
ofstream writer;
writer.open("Dataset.dat", ios::binary | ios::app);
if (!writer)
{
cout << "nError in file open!!";
return;
}
Person p;
while(1)
{
char yn;
p.getPerson();
writer.write((char*)&p, sizeof(p));
cout << "nAre there more records? ";
cin >> yn;
if (yn == 'n' || yn == 'N')
{
break;
}
}
}
string convertInt(int x){
string result;
ostringstream convert;
convert << x;
result = convert.str();
return result;
}
int randNumGenerator(int max){
int num = (rand() % max);
return num;
}
template <typename Predicate>
vector<Person> filter( vector<Person>& testData, Predicate pred) {
vector<Person> out;
copy_if(testData.begin(), testData.end(), back_inserter(out), pred);
return out;
}
vector<Person> positive = filter(testData, [=](const Person& p)
return p.salary == ">50K"
void setData(Person person, string line){
line.erase(remove(line.begin(), line.end(), ','), line.end());
stringstream s(line);
string str;
string workclass;
string sex;
string salary;
int age;
string ageStr;
int fnlwgt;
string fnlwgtStr;
int capitalGain;
string capitalGainStr;
int capitalLoss;
string capitalLossStr;
int hoursPerWeek;
string hoursPerWeekStr;
if(s >> age >> workclass >> fnlwgt >> sex >> capitalGain >> capitalLoss >> hoursPerWeek >> salary)
{
ageStr = convertInt(age);
fnlwgtStr = convertInt(fnlwgt);
capitalGainStr = convertInt(capitalGain);
capitalLossStr = convertInt(capitalLoss);
hoursPerWeekStr = convertInt(hoursPerWeek);
if(ageStr == "?" || workclass == "?" || fnlwgtStr == "?" || sex == "?" || capitalGainStr == "?" || capitalLossStr == "?" || hoursPerWeekStr == "?")
{
}
else{
person.age = age;
person.workclass = workclass;
person.fnlwgt = fnlwgt;
person.sex = sex;
person.capitalGain = capitalGain;
person.capitalLoss = capitalLoss;
person.hoursPerWeek = hoursPerWeek;
person.salary = salary;
testData.push_back(person);
}
}
}
vector<Person> sample(vector<Person> wholeDataSet, int percentage, vector<Person> &testingSet){
int wholeDataSize = wholeDataSet.size();
vector<Person> stratifiedSet;
int limit = (wholeDataSize * percentage) / 100;
int randNum= 0;
vector<bool> numsUsedAlready(wholeDataSize);
for(int i = 0; i < limit; i++){
randNum = randNumGenerator(wholeDataSize);
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(wholeDataSize);
}
numsUsedAlready[randNum] = true;
stratifiedSet.push_back(wholeDataSet[randNum]);
}
for(int i = 0; i < numsUsedAlready.size(); i++){
if(!numsUsedAlready[i]){
testingSet.push_back(wholeDataSet[i]);
}
}
return stratifiedSet;
}
vector<Person> concatVectors(vector<Person> a, vector<Person> b){
vector<Person> ab;
ab.reserve(a.size() + b.size());
ab.insert(ab.end(), a.begin(), a.end());
ab.insert(ab.end(), b.begin(), b.end());
return ab;
}
void compareAttributeInt(int sample, int trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
void compareAttributeStr(string sample, string trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
class Count
{
public:
int pos = 0;
int neg = 0;
void increment(bool flag) {
if (flag) {
pos++;
}
else {
neg++;
}
}
};
float naiveBayesian(vector<Person> trainingSet, vector<Person> testingSet){
float accuracy = 0;
int accuracyCount = 0;
int randNum = 0;
vector<Person> sampleSet;
vector<bool> numsUsedAlready(testingSet.size());
for(int i = 0; i < 20; i++){
randNum = randNumGenerator(testingSet.size());
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(testingSet.size());
}
numsUsedAlready[randNum] = true;
sampleSet.push_back(testingSet[randNum]);
}
int numOver50k = 0;
int numUnder50k = 0;
for(int i = 0; i < trainingSet.size(); i++){
if(trainingSet[i].salary == ">50K"){
numOver50k++;
}
else{
numUnder50k++;
}
}
float probOver50k = (float)numOver50k / trainingSet.size();
float probUnder50k = (float)numUnder50k / trainingSet.size();
float probYes = 0;
float probNo = 0;
float yes= 0;
float no = 0;
template <typename T>
Count compute_count(const Person& sample, const vector<Person>& trainingSet, T Person::*member)
{
Count res;
for (const Person& training : trainingSet) {
if ((sample.*member) == (training.*member)) {
res.increment(training.salary == ">50K");
}
}
return res;
}
for (const Person& sample : sampleSet)
{
vector<Count> counts;
counts.push_back(compute_count(sample, trainingSet, &Person::age));
counts.push_back(compute_count(sample, trainingSet, &Person::workclass));
counts.push_back(compute_count(sample, trainingSet, &Person::fnlwgt));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalGain));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalLoss));
counts.push_back(compute_count(sample, trainingSet, &Person::hoursPerWeek));
}
bool salaryGreaterThan50k = (probYes * probOver50k) > (probNo * probUnder50k);
double probYes = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.pos / numOver50k;
});
double probNo = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.neg / numUnder50k;
});
if ((salaryGreaterThan50k && (sample.salary == ">50K")) || (!salaryGreaterThan50k && !(sample.salary == ">50K")))
++accuracyCount;
accuracy = (float)accuracyCount / 20;
return accuracy;
}
void stratifiedSample(){
srand(time(NULL));
vector<Person> positiveSamples;
vector<Person> negativeSamples;
positiveSamples = setPositive(positiveSamples);
negativeSamples = setNegative(negativeSamples);
vector<Person> posTestingSet10 = positiveSamples;
vector<Person> posTestingSet30 = positiveSamples;
vector<Person> posTestingSet50 = positiveSamples;
vector<Person> posTestingSet70 = positiveSamples;
vector<Person> posTestingSet90 = positiveSamples;
vector<Person> negTestingSet10 = negativeSamples;
vector<Person> negTestingSet30 = negativeSamples;
vector<Person> negTestingSet50 = negativeSamples;
vector<Person> negTestingSet70 = negativeSamples;
vector<Person> negTestingSet90 = negativeSamples;
vector<Person> posStratifiedSet_10;
vector<Person> posTesting_10;
vector<Person> negStratifiedSet_10;
vector<Person> negTesting_10;
vector<Person> posStratifiedSet_30;
vector<Person> posTesting_30;
vector<Person> negStratifiedSet_30;
vector<Person> negTesting_30;
vector<Person> posStratifiedSet_50;
vector<Person> posTesting_50;
vector<Person> negStratifiedSet_50;
vector<Person> negTesting_50;
vector<Person> posStratifiedSet_70;
vector<Person> posTesting_70;
vector<Person> negStratifiedSet_70;
vector<Person> negTesting_70;
vector<Person> posStratifiedSet_90;
vector<Person> posTesting_90;
vector<Person> negStratifiedSet_90;
vector<Person> negTesting_90;
vector<Person> stratifiedSet_10;
vector<Person> stratifiedSet_30;
vector<Person> stratifiedSet_50;
vector<Person> stratifiedSet_70;
vector<Person> stratifiedSet_90;
vector<Person> testingSet_10;
vector<Person> testingSet_30;
vector<Person> testingSet_50;
vector<Person> testingSet_70;
vector<Person> testingSet_90;
posStratifiedSet_10 = sample(posTestingSet10, 10, posTesting_10);
negStratifiedSet_10 = sample(negTestingSet10, 10, negTesting_10);
posStratifiedSet_30 = sample(posTestingSet30, 30, posTesting_30);
negStratifiedSet_30 = sample(negTestingSet30, 30, negTesting_30);
posStratifiedSet_50 = sample(posTestingSet50, 50, posTesting_50);
negStratifiedSet_50 = sample(negTestingSet50, 50, negTesting_50);
posStratifiedSet_70 = sample(posTestingSet70, 70, posTesting_70);
negStratifiedSet_70 = sample(negTestingSet70, 70, negTesting_70);
posStratifiedSet_90 = sample(posTestingSet90, 90, posTesting_90);
negStratifiedSet_90 = sample(negTestingSet90, 90, negTesting_90);
stratifiedSet_10 = concatVectors(posStratifiedSet_10, negStratifiedSet_10);
stratifiedSet_30 = concatVectors(posStratifiedSet_30, negStratifiedSet_30);
stratifiedSet_50 = concatVectors(posStratifiedSet_50, negStratifiedSet_50);
stratifiedSet_70 = concatVectors(posStratifiedSet_70, negStratifiedSet_70);
stratifiedSet_90 = concatVectors(posStratifiedSet_90, negStratifiedSet_90);
testingSet_10 = concatVectors(posTesting_10, negTesting_10);
testingSet_30 = concatVectors(posTesting_30, negTesting_30);
testingSet_50 = concatVectors(posTesting_50, negTesting_50);
testingSet_70 = concatVectors(posTesting_70, negTesting_70);
testingSet_90 = concatVectors(posTesting_90, negTesting_90);
float accuracy10 = 0;
float accuracy30 = 0;
float accuracy50 = 0;
float accuracy70 = 0;
float accuracy90 = 0;
accuracy10 = naiveBayesian(stratifiedSet_10, testingSet_10);
cout << "accuracy for 10%: " << accuracy10 << endl;
accuracy30 = naiveBayesian(stratifiedSet_30, testingSet_30);
cout << "accuracy for 30%: " << accuracy30 << endl;
accuracy50 = naiveBayesian(stratifiedSet_50, testingSet_50);
cout << "accuracy for 50%: " << accuracy50 << endl;
accuracy70 = naiveBayesian(stratifiedSet_70, testingSet_70);
cout << "accuracy for 70%: " << accuracy70 << endl;
accuracy90 = naiveBayesian(stratifiedSet_90, testingSet_90);
cout << "accuracy for 90%: " << accuracy90 << endl;
}
void readInputFile()
{
ifstream reader;
reader.open("Dataset.dat" , ios::binary);
if(!reader)
{
cout << "nError in open";
return;
}
Person p;
reader.read((char*) &p, sizeof(p));
}
int main(int argc, char const *argv)
{
system("CLS");
readInputFile();
stratifiedSample();
return 0;
}
`
I work on the inbuilt gcc compiler for mac and the errors it throws during compilation are:
What have I been doing wrong?
c++ error-handling
asked 3 mins ago
S-A117
1
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I was working on my project on a Naive Bayes implementation when I started running into some unexpected issues. Any help?
`
#include <iostream>
#include <fstream>
#include <string>
#include <sstream>
#include <vector>
#include <algorithm>
#include <ctime>
#include <cstdio>
#include <cctype>
#include <cstdlib>
using namespace std;
struct Person
{
int age;
string workclass;
int fnlwgt;
string sex;
int capitalGain;
int capitalLoss;
int hoursPerWeek;
string salary;
void printPerson()
{
cout << age << endl << workclass << endl << fnlwgt;
cout << endl << sex << endl;
cout << capitalGain << endl << capitalLoss;
cout << endl << hoursPerWeek << endl << salary << endl;
}
void getPerson()
{
cout << "nEnter age: ";
cin >> age;
cout << "nEnter work class: ";
getline(cin, workclass);
cout << "nEnter final weight: ";
cin >> fnlwgt;
cout << "nEnter sex: ";
getline(cin, sex);
cout << "nEnter capital gain: ";
cin >> capitalGain;
cout << "nEnter capital loss: ";
cin >> capitalLoss;
cout << "nEnter salary: ";
getline(cin, salary);
}
};
void writeToData()
{
ofstream writer;
writer.open("Dataset.dat", ios::binary | ios::app);
if (!writer)
{
cout << "nError in file open!!";
return;
}
Person p;
while(1)
{
char yn;
p.getPerson();
writer.write((char*)&p, sizeof(p));
cout << "nAre there more records? ";
cin >> yn;
if (yn == 'n' || yn == 'N')
{
break;
}
}
}
string convertInt(int x){
string result;
ostringstream convert;
convert << x;
result = convert.str();
return result;
}
int randNumGenerator(int max){
int num = (rand() % max);
return num;
}
template <typename Predicate>
vector<Person> filter( vector<Person>& testData, Predicate pred) {
vector<Person> out;
copy_if(testData.begin(), testData.end(), back_inserter(out), pred);
return out;
}
vector<Person> positive = filter(testData, [=](const Person& p)
return p.salary == ">50K"
void setData(Person person, string line){
line.erase(remove(line.begin(), line.end(), ','), line.end());
stringstream s(line);
string str;
string workclass;
string sex;
string salary;
int age;
string ageStr;
int fnlwgt;
string fnlwgtStr;
int capitalGain;
string capitalGainStr;
int capitalLoss;
string capitalLossStr;
int hoursPerWeek;
string hoursPerWeekStr;
if(s >> age >> workclass >> fnlwgt >> sex >> capitalGain >> capitalLoss >> hoursPerWeek >> salary)
{
ageStr = convertInt(age);
fnlwgtStr = convertInt(fnlwgt);
capitalGainStr = convertInt(capitalGain);
capitalLossStr = convertInt(capitalLoss);
hoursPerWeekStr = convertInt(hoursPerWeek);
if(ageStr == "?" || workclass == "?" || fnlwgtStr == "?" || sex == "?" || capitalGainStr == "?" || capitalLossStr == "?" || hoursPerWeekStr == "?")
{
}
else{
person.age = age;
person.workclass = workclass;
person.fnlwgt = fnlwgt;
person.sex = sex;
person.capitalGain = capitalGain;
person.capitalLoss = capitalLoss;
person.hoursPerWeek = hoursPerWeek;
person.salary = salary;
testData.push_back(person);
}
}
}
vector<Person> sample(vector<Person> wholeDataSet, int percentage, vector<Person> &testingSet){
int wholeDataSize = wholeDataSet.size();
vector<Person> stratifiedSet;
int limit = (wholeDataSize * percentage) / 100;
int randNum= 0;
vector<bool> numsUsedAlready(wholeDataSize);
for(int i = 0; i < limit; i++){
randNum = randNumGenerator(wholeDataSize);
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(wholeDataSize);
}
numsUsedAlready[randNum] = true;
stratifiedSet.push_back(wholeDataSet[randNum]);
}
for(int i = 0; i < numsUsedAlready.size(); i++){
if(!numsUsedAlready[i]){
testingSet.push_back(wholeDataSet[i]);
}
}
return stratifiedSet;
}
vector<Person> concatVectors(vector<Person> a, vector<Person> b){
vector<Person> ab;
ab.reserve(a.size() + b.size());
ab.insert(ab.end(), a.begin(), a.end());
ab.insert(ab.end(), b.begin(), b.end());
return ab;
}
void compareAttributeInt(int sample, int trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
void compareAttributeStr(string sample, string trained, string salary, int &count, int posOrNeg){
if(sample == trained){
if(salary == ">50K" && posOrNeg == 1){
count++;
}
if(salary == "<=50K" && posOrNeg == 0){
count++;
}
}
}
class Count
{
public:
int pos = 0;
int neg = 0;
void increment(bool flag) {
if (flag) {
pos++;
}
else {
neg++;
}
}
};
float naiveBayesian(vector<Person> trainingSet, vector<Person> testingSet){
float accuracy = 0;
int accuracyCount = 0;
int randNum = 0;
vector<Person> sampleSet;
vector<bool> numsUsedAlready(testingSet.size());
for(int i = 0; i < 20; i++){
randNum = randNumGenerator(testingSet.size());
while(numsUsedAlready[randNum]){
randNum = randNumGenerator(testingSet.size());
}
numsUsedAlready[randNum] = true;
sampleSet.push_back(testingSet[randNum]);
}
int numOver50k = 0;
int numUnder50k = 0;
for(int i = 0; i < trainingSet.size(); i++){
if(trainingSet[i].salary == ">50K"){
numOver50k++;
}
else{
numUnder50k++;
}
}
float probOver50k = (float)numOver50k / trainingSet.size();
float probUnder50k = (float)numUnder50k / trainingSet.size();
float probYes = 0;
float probNo = 0;
float yes= 0;
float no = 0;
template <typename T>
Count compute_count(const Person& sample, const vector<Person>& trainingSet, T Person::*member)
{
Count res;
for (const Person& training : trainingSet) {
if ((sample.*member) == (training.*member)) {
res.increment(training.salary == ">50K");
}
}
return res;
}
for (const Person& sample : sampleSet)
{
vector<Count> counts;
counts.push_back(compute_count(sample, trainingSet, &Person::age));
counts.push_back(compute_count(sample, trainingSet, &Person::workclass));
counts.push_back(compute_count(sample, trainingSet, &Person::fnlwgt));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalGain));
counts.push_back(compute_count(sample, trainingSet, &Person::capitalLoss));
counts.push_back(compute_count(sample, trainingSet, &Person::hoursPerWeek));
}
bool salaryGreaterThan50k = (probYes * probOver50k) > (probNo * probUnder50k);
double probYes = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.pos / numOver50k;
});
double probNo = accumulate(counts.begin(), counts.end(), 1.0, [=](double init, const Count& count){
return init * count.neg / numUnder50k;
});
if ((salaryGreaterThan50k && (sample.salary == ">50K")) || (!salaryGreaterThan50k && !(sample.salary == ">50K")))
++accuracyCount;
accuracy = (float)accuracyCount / 20;
return accuracy;
}
void stratifiedSample(){
srand(time(NULL));
vector<Person> positiveSamples;
vector<Person> negativeSamples;
positiveSamples = setPositive(positiveSamples);
negativeSamples = setNegative(negativeSamples);
vector<Person> posTestingSet10 = positiveSamples;
vector<Person> posTestingSet30 = positiveSamples;
vector<Person> posTestingSet50 = positiveSamples;
vector<Person> posTestingSet70 = positiveSamples;
vector<Person> posTestingSet90 = positiveSamples;
vector<Person> negTestingSet10 = negativeSamples;
vector<Person> negTestingSet30 = negativeSamples;
vector<Person> negTestingSet50 = negativeSamples;
vector<Person> negTestingSet70 = negativeSamples;
vector<Person> negTestingSet90 = negativeSamples;
vector<Person> posStratifiedSet_10;
vector<Person> posTesting_10;
vector<Person> negStratifiedSet_10;
vector<Person> negTesting_10;
vector<Person> posStratifiedSet_30;
vector<Person> posTesting_30;
vector<Person> negStratifiedSet_30;
vector<Person> negTesting_30;
vector<Person> posStratifiedSet_50;
vector<Person> posTesting_50;
vector<Person> negStratifiedSet_50;
vector<Person> negTesting_50;
vector<Person> posStratifiedSet_70;
vector<Person> posTesting_70;
vector<Person> negStratifiedSet_70;
vector<Person> negTesting_70;
vector<Person> posStratifiedSet_90;
vector<Person> posTesting_90;
vector<Person> negStratifiedSet_90;
vector<Person> negTesting_90;
vector<Person> stratifiedSet_10;
vector<Person> stratifiedSet_30;
vector<Person> stratifiedSet_50;
vector<Person> stratifiedSet_70;
vector<Person> stratifiedSet_90;
vector<Person> testingSet_10;
vector<Person> testingSet_30;
vector<Person> testingSet_50;
vector<Person> testingSet_70;
vector<Person> testingSet_90;
posStratifiedSet_10 = sample(posTestingSet10, 10, posTesting_10);
negStratifiedSet_10 = sample(negTestingSet10, 10, negTesting_10);
posStratifiedSet_30 = sample(posTestingSet30, 30, posTesting_30);
negStratifiedSet_30 = sample(negTestingSet30, 30, negTesting_30);
posStratifiedSet_50 = sample(posTestingSet50, 50, posTesting_50);
negStratifiedSet_50 = sample(negTestingSet50, 50, negTesting_50);
posStratifiedSet_70 = sample(posTestingSet70, 70, posTesting_70);
negStratifiedSet_70 = sample(negTestingSet70, 70, negTesting_70);
posStratifiedSet_90 = sample(posTestingSet90, 90, posTesting_90);
negStratifiedSet_90 = sample(negTestingSet90, 90, negTesting_90);
stratifiedSet_10 = concatVectors(posStratifiedSet_10, negStratifiedSet_10);
stratifiedSet_30 = concatVectors(posStratifiedSet_30, negStratifiedSet_30);
stratifiedSet_50 = concatVectors(posStratifiedSet_50, negStratifiedSet_50);
stratifiedSet_70 = concatVectors(posStratifiedSet_70, negStratifiedSet_70);
stratifiedSet_90 = concatVectors(posStratifiedSet_90, negStratifiedSet_90);
testingSet_10 = concatVectors(posTesting_10, negTesting_10);
testingSet_30 = concatVectors(posTesting_30, negTesting_30);
testingSet_50 = concatVectors(posTesting_50, negTesting_50);
testingSet_70 = concatVectors(posTesting_70, negTesting_70);
testingSet_90 = concatVectors(posTesting_90, negTesting_90);
float accuracy10 = 0;
float accuracy30 = 0;
float accuracy50 = 0;
float accuracy70 = 0;
float accuracy90 = 0;
accuracy10 = naiveBayesian(stratifiedSet_10, testingSet_10);
cout << "accuracy for 10%: " << accuracy10 << endl;
accuracy30 = naiveBayesian(stratifiedSet_30, testingSet_30);
cout << "accuracy for 30%: " << accuracy30 << endl;
accuracy50 = naiveBayesian(stratifiedSet_50, testingSet_50);
cout << "accuracy for 50%: " << accuracy50 << endl;
accuracy70 = naiveBayesian(stratifiedSet_70, testingSet_70);
cout << "accuracy for 70%: " << accuracy70 << endl;
accuracy90 = naiveBayesian(stratifiedSet_90, testingSet_90);
cout << "accuracy for 90%: " << accuracy90 << endl;
}
void readInputFile()
{
ifstream reader;
reader.open("Dataset.dat" , ios::binary);
if(!reader)
{
cout << "nError in open";
return;
}
Person p;
reader.read((char*) &p, sizeof(p));
}
int main(int argc, char const *argv)
{
system("CLS");
readInputFile();
stratifiedSample();
return 0;
}
`
I work on the inbuilt gcc compiler for mac and the errors it throws during compilation are:
What have I been doing wrong?
c++ error-handling
c++ error-handling
asked 3 mins ago
S-A117
1
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
S-A117
1
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
S-A117
1
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 3 mins ago
S-A117
1
asked 3 mins ago
S-A117
1
1
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
S-A117 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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S-A117 is a new contributor. Be nice, and check out our Code of Conduct.
S-A117 is a new contributor. Be nice, and check out our Code of Conduct.
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