// Create containers to store each class

//50 samples for each class. Each one with 4 Features + class Label

vector< vector<double> > Iris_setosa(50, vector<double>(5));

vector< vector<double> > Iris_versicolor(50, vector<double>(5));

vector< vector<double> > Iris_virginica(50, vector<double>(5));

// Choose colors to represent each class

Color setosaColor(60,20,180);

Color versicolorColor(180,20,60);

Color virginicaColor(20,180,60);

// Read data to stdin

freopen("iris.data", "r", stdin); // Read data to stdin

// Fill each container

// 150 train samples

for(int sample = 0; sample < 150; sample++){

// 4 Features + Class Label

for(int feature = 0; feature < 5; feature++){

if(sample<50)cin >> Iris_setosa[sample][feature];

else if(sample<100)cin >> Iris_versicolor[sample%50][feature];

else if(sample<150)cin >> Iris_virginica[sample%100][feature];

}

}

// Set Width and height of plot

setHeight(512);

setWidth(512+256);

// In this tutorial we will be plotting the "petal length" against the "sepal length" for each sample

// First since we are creating a GroupPlot let's add our colors, symbols and data into containers

vector< vector< vector<double> > > plotData = {Iris_setosa, Iris_versicolor, Iris_virginica};

vector<Color> colors = {setosaColor, versicolorColor, virginicaColor};

vector<string> symbols = {"*","*","*"};

// Now let's plot the graph

// Filename, DataVector, xAxis, yAxis, dotSize, colorVector, Symbols

plotPoints("Iris.PetalLength.SepalLength.ppm", plotData, 0, 2, 4, colors, symbols);

}