Before starting the optimization, it is important to initialize the model. This notebook quickly takes you over the available initial designs available in GPyOpt.
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%pylab inline
import numpy as np
import GPyOpt
import GPy
from GPyOpt.experiment_design import initial_design
import matplotlib.pyplot as plt
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func = GPyOpt.objective_examples.experimentsNd.alpine1(input_dim=2)
mixed_domain =[{'name': 'var1_2', 'type': 'continuous', 'domain': (-10,10),'dimensionality': 1},
{'name': 'var5', 'type': 'continuous', 'domain': (-1,5)}]
space = GPyOpt.Design_space(mixed_domain)
data_init = 500
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### --- Grid design
X = initial_design('grid',space,data_init)
plt.plot(X[:,0],X[:,1],'b.')
plt.title('Grid design')
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### --- Random initial design
X = initial_design('random',space,data_init)
plt.plot(X[:,0],X[:,1],'b.')
plt.title('Random design')
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### --- Latin design
X = initial_design('latin',space,data_init)
plt.plot(X[:,0],X[:,1],'b.')
plt.title('Latin design')
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### --- Sobol design
X = initial_design('sobol',space,data_init)
plt.plot(X[:,0],X[:,1],'b.')
plt.title('Sobol design')