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Merge branch 'devel' of github.com:SheffieldML/GPy into devel

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Zhenwen Dai 2014-11-03 12:05:52 +00:00
commit 6625e27fec
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1
GPy/inference/mcmc/__init__.py
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from hmc import HMC

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GPy/inference/optimization/BayesOpt.py
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import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
####### Preliminar BO with standad acquisition functions ###############################
# Types of BO
# MM: Maximum (or minimum) mean
# MPI: Maximum posterior improvement
# MUI: Maximum upper interval
def BOacquisition(X,Y,model,type_bo="MPI",type_objective="max",par_mpi = 0,z_mui=1.96,plot=True,n_eval = 500):
# Only works in dimension 1
# Grid where the GP will be evaluated
X_star = np.linspace(min(X)-10,max(X)+10,n_eval)
X_star = X_star[:,None]
# Posterior GP evaluated on the grid
fest = model.predict(X_star)
# Calculate the acquisition function
## IF Maximize
if type_objective == "max":
if type_bo == "MPI": # add others here
acqu = norm.cdf((fest[0]-(1+par_mpi)*max(fest[0])) / fest[1])
acqu = acqu/(2*max(acqu))
if type_bo == "MM":
acqu = fest[0]/max(fest[0])
acqu = acqu/(2*max(acqu))
if type_bo == "MUI":
acqu = fest[0]+z_mui*np.sqrt(fest[1])
acqu = acqu/(2*max(acqu))
optimal_loc = np.argmax(acqu)
x_new = X_star[optimal_loc]
## IF Minimize
if type_objective == "min":
if type_bo == "MPI": # add others here
acqu = 1-norm.cdf((fest[0]-(1+par_mpi)*min(fest[0])) / fest[1])
acqu = acqu/(2*max(acqu))
if type_bo == "MM":
acqu = 1-fest[0]/max(fest[0])
acqu = acqu/(2*max(acqu))
if type_bo == "MUI":
acqu = -fest[0]+z_mui*np.sqrt(fest[1])
acqu = acqu/(2*max(acqu))
optimal_loc = np.argmax(acqu)
x_new = X_star[optimal_loc]
# Plot GP posterior, collected data and the acquisition function
if plot:
plt.plot(X,Y , 'p')
plt.title('Acquisition function')
model.plot()
plt.plot(X_star, acqu, 'r--')
# Return the point where we shoould take the new sample
return x_new
###############################################################

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GPy/inference/optimization/__init__.py
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from scg import SCG
from optimization import *
from hmc import HMC,HMC_shortcut