introduction

gaussian process bayesian optimization visualization.ipynb

code descrition

• use less samples to find the maximum value in test function(Branin Rosenbrock)
• generate 20 points in x y dimension
• in order to plot the 3D function, use meshgrid to get the points
• means 20*20 = 400 samples are necessary to plot the function
• all data = 400 samples = design_domain + train_set
• use train_set -> GP to get a surrogate model
• use design_design -> BO to find to good point and add it to train_set
• (train_set + good point) + (design_domain - good point) = all_data
• if in some epochs, the good point change little, stop iteration

you can choose Branin or Rosenbrock by select the cell.

• the first pic is Branin and the second pic is Rosenbrock function
• the green points are train set
• the gray plane is the true function branin, use 400 points by meshgrid()
• the red plane is the prediction of GP on design_domain(all data train set)
• the red vertical line and the blue star point tell us the next point that should be added

• after some iterations , the predction is close to the true function

• the maximum of acquistion function, actually, iteration8 is the best.

draw_branin_function.ipynb

• how to draw the funciton by meshgrid

About author

var ihubo = {
  nickName  : "Duke",
  site : "https://github.com/WholeG/GPBO"
}