Simple Plotting Library for C++
This is a simple plotting library that I wrote mostly for myself in C++. The Purpose of it is to be a light header-only library to allow easy plotting in C++.
The Code has been tested on Linux Ubuntu 16.04 with C++11. You should be able to replicate the results yourself just by pulling the repository and compiling the plot.cpp file.
Note: The images are all saved as .ppm files.
Example plots:
Example Code:
int main(){ setHeight(1024); setWidth(1024); vector< vector<int> > testLine = {{0,0},{1,2},{2,4},{4,8},{8,16}}; // y = x*2 vector< vector<int> > testLineWeird = {{0,0},{1,2},{2,4},{4,3},{8,16}}; vector< vector<int> > testPoints = {{0,0}, {5,5}, {1,6}, {3,2}, {1,2}, {3,4}, {2,3}, {2,2}, {3,3}, {5,2}, {7,8}}; vector< vector<int> > parabola; for(int i = 0; i <= 100; i++)parabola.push_back({i, i*i}); // In order to make a plot we need to put each one of our parameters in a vector. So that we can plot multiple // point groups with different colors as well as get a bit more flexibility // Here are some quick examples plotPoints("selectionDotWithGrid.ppm", vector< vector< vector<int> > >{testPoints}, 0, 1, 7, vector<Color>{Color(60,10,255)}, vector<string>{"*"}); plotPoints("selectionCrossWithGrid.ppm", vector< vector< vector<int> > >{testPoints}, 0, 1, 7, vector<Color>{Color(60,10,255)}, vector<string>{"+"}); plotLine("testLinePlotParabola.ppm", vector< vector< vector<int> > >{parabola}, 0, 1, 1, vector<Color>{Color(60,10,255)}); plotPoints("groupPointPlot.ppm", vector< vector< vector<int> > >{testPoints, testLineWeird}, 0, 1, 6, vector<Color>{Color(60,10,255), Color(255,10,60)}, vector<string>{"*","*"}); }Plotting the Iris Dataset:
#include <bits/stdc++.h> #include "plot.h" using namespace std; /* 1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4. petal width in cm 5. class: Iris-setosa --> 0 Iris-versicolor --> 1 Iris-virginica --> 2 */ int main(){ // 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); }Panagiotis Petridis, High School Student
Greece
If you have an opening in your team be sure to shoot me an email!