linearReg.cpp

/*

▶ Linear Regression is a machine learning algorithm that finds a linear relation between a dependent and an independent feature.

▶ The equation can be represented as : y = mx + c (Predicted Line)
   y → Dependent feature
   x → Independent feature
   m → Line Slope
   c → Line Intercept

Steps
=====
⇒ Find mean of independent feature (x), xMean.
⇒ Find mean of dependent feature (y), yMean.
⇒ Find predicted line's slope (m), m = SUM[(x[i] - xMean)*(y[i] - yMean)] / SUM[(x[i] - xMean)].
⇒ Find predicted line's intercept (c), c = yMean - slope * xMean.
⇒ Use the obtained equation to predict result for test data.

*/

#include<iostream> // basic input output
#include<vector> // to store dataset in dynamic arrays (vectors)
#include<numeric> // for accumulate(), to find sum of vectors (dynamic arrays)
#include<cmath> // for sqrt(), to find square root used in slope calculation
using namespace std; // define standard namespace

// Predicted Line class
class PredLine {
public:
    float slope, intcpt, xMean, yMean; // class attributes
    float calcSlope(vector<int> , vector<int>); // member function to find line slope
} line;

float PredLine::calcSlope(vector<int> feat1, vector<int> feat2) {
    float slope, slopeNumerator = 0.0, slopeDenominator = 0.0;
    float f1Size = feat1.size(); // size of feature 1 vector

    // calculate numerator part in the slope formula
    for(int i = 0; i < f1Size; i++)
        slopeNumerator += (feat1[i] - line.xMean) * (feat2[i] - line.yMean);
    
    // calculate denominator part in the slope formula
    for(int i = 0; i < f1Size; i++)
        slopeDenominator += sqrt(feat1[i] - line.xMean);

    slope = slopeNumerator / slopeDenominator;
    return slope;
}

int main() {
    vector<int> feat1, feat2; // dynamic arrays for dependent and independent features
    char ch = 'y'; // continue choice variable
    int n; // no. of records
    float ele, x, y; // feature element, line equation variables
    
    cout << "======= LINEAR REGRESSION =======";

    while(ch == 'y') {
        cout << "\nEnter no. of records : ";
        cin >> n; // input no. of records
        
        // input data set
        cout << "\nEnter Independent Data (x) : ";
        // input independent feature
        for(int i = 0; i < n; i++) { cin >> ele; feat1.push_back(ele); }
        cout << "\nEnter Dependent Data (y) : ";
        // input dependent feature
        for(int i = 0; i < n; i++) { cin >> ele; feat2.push_back(ele); }
        // input test data
        cout << "\nEnter Test Data (x) : "; cin >> x;

        // find mean of x & y (dependent & independent features)
        line.xMean = accumulate(feat1.begin(), feat1.end(), 0) / n;
        line.yMean = accumulate(feat2.begin(), feat2.end(), 0) / n;

        // find slope & intercept of predicted line
        line.slope = line.calcSlope(feat1, feat2);
        line.intcpt = line.yMean - line.slope * line.xMean;

        // display predicted line, y = mx + c
        cout << "\nPredicted Line : " << "y = " << line.slope << "x + (" << line.intcpt << ")";

        // display predicted result
        cout << "\nPredicted Result (y) : " << line.slope * x + line.intcpt;

        cout << "\nTry Again ? (y/n) : ";
        cin >> ch; // input choice to try again
    }
    return 0;
}