def compare_kernels_experiment():
kernel1 = fk.Carls_Mauna_kernel()
kernel2 = ( fk.SqExpKernel(-0.7, -1.3) + fk.SqExpKernel(4.8, 2.3) ) * \
( fk.SqExpKernel(3.0, 0.5) + fk.SqExpPeriodicKernel(0.4, -0.0, -0.9) )
#kernel2 = ( SqExp(ell=-0.8, sf=-1.4) + Periodic(ell=0.5, p=-0.3, sf=-1.1) + RQ(ell=1.9, sf=1.6, a=0.2) + ( SqExp(ell=4.5, sf=1.0) x Periodic(ell=0.6, p=-0.0, sf=0.1) ) )
X, y = load_mauna_original()
N_orig = X.shape[0] # subsample data.
X = X[:N_orig // 5, :]
y = y[:N_orig // 5, :]
print "Carl's kernel"
print kernel1.pretty_print()
kernel_hypers1, nll1 = gpml.optimize_params(kernel1.gpml_kernel_expression(), kernel1.param_vector(), \
X, y, noise=np.log(0.19), iters=100 )
k1_opt = kernel1.family().from_param_vector(kernel_hypers1)
print k1_opt.pretty_print()
print "Carl's NLL =", nll1
print "Our kernel"
print kernel2.pretty_print()
kernel_hypers2, nll2 = gpml.optimize_params(kernel2.gpml_kernel_expression(), kernel2.param_vector(), \
X, y, noise=np.log(0.19), iters=100)
k2_opt = kernel2.family().from_param_vector(kernel_hypers2)
print k2_opt.pretty_print()
print "Our NLL =", nll2
def compare_kernels_experiment():
kernel1 = fk.Carls_Mauna_kernel()
kernel2 = ( fk.SqExpKernel(-0.7, -1.3) + fk.SqExpKernel(4.8, 2.3) ) * \
( fk.SqExpKernel(3.0, 0.5) + fk.SqExpPeriodicKernel(0.4, -0.0, -0.9) )
#kernel2 = ( SqExp(ell=-0.8, sf=-1.4) + Periodic(ell=0.5, p=-0.3, sf=-1.1) + RQ(ell=1.9, sf=1.6, a=0.2) + ( SqExp(ell=4.5, sf=1.0) x Periodic(ell=0.6, p=-0.0, sf=0.1) ) )
X, y = load_mauna_original()
N_orig = X.shape[0] # subsample data.
X = X[:N_orig//5, :]
y = y[:N_orig//5, :]
print "Carl's kernel"
print kernel1.pretty_print()
kernel_hypers1, nll1 = gpml.optimize_params(kernel1.gpml_kernel_expression(), kernel1.param_vector(), \
X, y, noise=np.log(0.19), iters=100 )
k1_opt = kernel1.family().from_param_vector(kernel_hypers1)
print k1_opt.pretty_print()
print "Carl's NLL =", nll1
print "Our kernel"
print kernel2.pretty_print()
kernel_hypers2, nll2 = gpml.optimize_params(kernel2.gpml_kernel_expression(), kernel2.param_vector(), \
X, y, noise=np.log(0.19), iters=100)
k2_opt = kernel2.family().from_param_vector(kernel_hypers2)
print k2_opt.pretty_print()
print "Our NLL =", nll2
def not_tet_num_params():
'''The number of hyper-parameters passed in to gpml should be the same as the number returned.'''
k = fk.SqExpKernel(0, 0)
init_params = k.param_vector()
X, y = load_mauna()
optimized_hypers, _ = gpml.optimize_params(k.gpml_kernel_expression(), init_params, X, y)
assert optimized_hypers.size == init_params.size
def not_tet_num_params():
'''The number of hyper-parameters passed in to gpml should be the same as the number returned.'''
k = fk.SqExpKernel(0, 0)
init_params = k.param_vector()
X, y = load_mauna()
optimized_hypers, _ = gpml.optimize_params(k.gpml_kernel_expression(),
init_params, X, y)
assert optimized_hypers.size == init_params.size
def call_gpml_test():
np.random.seed(0)
k = fk.SumKernel([fk.SqExpKernel(0, 0), fk.SqExpKernel(0, 0)])
print k.gpml_kernel_expression()
print k.pretty_print()
print '[%s]' % k.param_vector()
X, y = load_mauna()
N_orig = X.shape[0]
X = X[:N_orig // 3, :]
y = y[:N_orig // 3, :]
results = []
pylab.figure()
for i in range(15):
init_params = np.random.normal(size=k.param_vector().size)
#kernel_hypers, nll, nlls = gpml.optimize_params(k.gpml_kernel_expression(), k.param_vector(), X, y, return_all=True)
kernel_hypers, nll, nlls = gpml.optimize_params(
k.gpml_kernel_expression(), init_params, X, y, return_all=True)
print "kernel_hypers =", kernel_hypers
print "nll =", nll
k_opt = k.family().from_param_vector(kernel_hypers)
print k_opt.gpml_kernel_expression()
print k_opt.pretty_print()
print '[%s]' % k_opt.param_vector()
pylab.semilogx(range(1, nlls.size + 1), nlls)
results.append((kernel_hypers, nll))
pylab.draw()
print
print
results = sorted(results, key=lambda p: p[1])
for kernel_hypers, nll in results:
print nll, kernel_hypers
print "done"
def call_gpml_test():
np.random.seed(0)
k = fk.SumKernel([fk.SqExpKernel(0, 0), fk.SqExpKernel(0, 0)])
print k.gpml_kernel_expression()
print k.pretty_print()
print '[%s]' % k.param_vector()
X, y = load_mauna()
N_orig = X.shape[0]
X = X[:N_orig//3, :]
y = y[:N_orig//3, :]
results = []
pylab.figure()
for i in range(15):
init_params = np.random.normal(size=k.param_vector().size)
#kernel_hypers, nll, nlls = gpml.optimize_params(k.gpml_kernel_expression(), k.param_vector(), X, y, return_all=True)
kernel_hypers, nll, nlls = gpml.optimize_params(k.gpml_kernel_expression(), init_params, X, y, return_all=True)
print "kernel_hypers =", kernel_hypers
print "nll =", nll
k_opt = k.family().from_param_vector(kernel_hypers)
print k_opt.gpml_kernel_expression()
print k_opt.pretty_print()
print '[%s]' % k_opt.param_vector()
pylab.semilogx(range(1, nlls.size+1), nlls)
results.append((kernel_hypers, nll))
pylab.draw()
print
print
results = sorted(results, key=lambda p: p[1])
for kernel_hypers, nll in results:
print nll, kernel_hypers
print "done"