x1 = np.sort(25 * np.random.rand(1, N) -
25 * np.random.rand(1, N)).reshape(N, 1)
x2 = np.sort(25 * np.random.rand(1, N) -
25 * np.random.rand(1, N)).reshape(N, 1)
x1s = np.linspace(-24, 24, num=300).reshape(300, 1)
x2s = np.linspace(-24, 24, num=300).reshape(300, 1)
x = np.hstack((x1, x2))
xs = np.hstack((x1s, x2s))
y = x1**2 - 10 * x1 * (np.sin(x2))**3 + np.random.normal(scale=10,
size=(N, 1))
ys = x1s**2 - 10 * x1s * (np.sin(x2s))**3
hyp = np.array([[-3.0], [-4.0], [6.0]])
# Perform standard Guassian Process
GPR = GP.GPRegression(x, y, noise=True)
GPR.SetKernel('Gaussian')
GPR.SetHyp(hyp)
start = time.time()
GPR.OptimizeHyp(maxnumlinesearch=20, random_starts=4)
end = time.time()
print('Standard GP done in %.8f seconds' % (end - start))
gp_opttime = end - start
GPR.GPR()
GPR.Predict(xs)
# Perform a Structured Kernel Interpolation regression
KISSGP = GP.GPRegression(x, y, noise=True)
KISSGP.GenerateGrid([m, m])
KISSGP.Interpolate(scheme='cubic')
KISSGP.SetKernel('Gaussian')
'''
x1 = np.sort(np.random.normal(scale=10,size=(1,N))).reshape(N,1)
x2 = np.sort(np.random.normal(scale=10,size=(1,N))).reshape(N,1)
'''
x1 = np.sort(25 * np.random.rand(1, N) -
25 * np.random.rand(1, N)).reshape(N, 1)
x2 = np.sort(25 * np.random.rand(1, N) -
25 * np.random.rand(1, N)).reshape(N, 1)
x1s = np.linspace(-28, 28, num=300).reshape(300, 1)
x2s = np.linspace(-28, 28, num=300).reshape(300, 1)
x = np.hstack((x1, x2))
xs = np.hstack((x1s, x2s))
y = x1**2 - 10 * x1 * (np.sin(x2))**3 + np.random.normal(scale=10,
size=(N, 1))
Model1 = GP.GPRegression(x, y, noise=True)
Model1.GenerateGrid([m, m])
Model1.Interpolate(scheme='cubic')
Model1.SetKernel('Gaussian')
start = time.time()
#Model1.OptimizeHyp(maxnumlinesearch=40,random_starts=1)
end = time.time()
Model1.KISSGP()
Model1.Predict(xs)
print('Kiss-GP done in %.8f seconds' % (end - start))
K = Model1.Kuu
W = Model1.W
D = 2
