def sample_Carls_kernel():
kernel = fk.Carls_Mauna_kernel()
X = np.linspace(0,50,500)
# Todo: set random seed.
sample = gpml.sample_from_gp_prior(kernel, X)
pylab.figure()
pylab.plot(X, sample)
pylab.title('Carl''s kernel');
def sample_Carls_kernel():
kernel = fk.Carls_Mauna_kernel()
X = np.linspace(0, 50, 500)
# Todo: set random seed.
sample = gpml.sample_from_gp_prior(kernel, X)
pylab.figure()
pylab.plot(X, sample)
pylab.title('Carl'
's kernel')
def sample_mauna_best():
# This kernel was chosen from a run of Mauna datapoints.
kernel = ( 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) )
X = np.linspace(0,50,500)
# Todo: set random seed.
sample = gpml.sample_from_gp_prior(kernel, X)
pylab.figure()
pylab.plot(X, sample)
pylab.title('( SqExp(ell=-0.7, sf=-1.3) + SqExp(ell=4.8, sf=2.3) ) \n x ( SqExp(ell=3.0, sf=0.5) + Periodic(ell=0.4, p=-0.0, sf=-0.9) )')
def sample_mauna_best():
# This kernel was chosen from a run of Mauna datapoints.
kernel = ( 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) )
X = np.linspace(0, 50, 500)
# Todo: set random seed.
sample = gpml.sample_from_gp_prior(kernel, X)
pylab.figure()
pylab.plot(X, sample)
pylab.title(
'( SqExp(ell=-0.7, sf=-1.3) + SqExp(ell=4.8, sf=2.3) ) \n x ( SqExp(ell=3.0, sf=0.5) + Periodic(ell=0.4, p=-0.0, sf=-0.9) )'
)
def plot_gef_load_Z01():
# This kernel was chosen from a run of gef_load datapoints.
# kernel = eval(ProductKernel([ covMask(ndim=12, active_dimension=0, base_kernel=RQKernel(lengthscale=0.268353, output_variance=-0.104149, alpha=-2.105742)), covMask(ndim=12, active_dimension=9, base_kernel=SqExpKernel(lengthscale=1.160242, output_variance=0.004344)), SumKernel([ covMask(ndim=12, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=-0.823413, period=0.000198, output_variance=-0.917064)), covMask(ndim=12, active_dimension=0, base_kernel=RQKernel(lengthscale=-0.459219, output_variance=-0.077250, alpha=-2.212718)) ]) ]))
X, y, D = fear_load_mat('../data/gef_load_full_Xy.mat', 1)
kernel = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
(fk.MaskKernel(D, 0, fk.SqExpPeriodicKernel(-0.823413, 0.000198, -0.917064)) + fk.MaskKernel(D, 0, fk.RQKernel(-0.459219, -0.077250, -2.212718)))
# Todo: set random seed.
sample = gpml.sample_from_gp_prior(kernel, X[0:499, :])
pylab.figure()
pylab.plot(X[0:499, 0], y[0:499])
pylab.title('GEFCom2012 Z01 and T09 - first 500 data points')
pylab.xlabel('Time')
pylab.ylabel('Load')
def plot_gef_load_Z01():
# This kernel was chosen from a run of gef_load datapoints.
# kernel = eval(ProductKernel([ covMask(ndim=12, active_dimension=0, base_kernel=RQKernel(lengthscale=0.268353, output_variance=-0.104149, alpha=-2.105742)), covMask(ndim=12, active_dimension=9, base_kernel=SqExpKernel(lengthscale=1.160242, output_variance=0.004344)), SumKernel([ covMask(ndim=12, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=-0.823413, period=0.000198, output_variance=-0.917064)), covMask(ndim=12, active_dimension=0, base_kernel=RQKernel(lengthscale=-0.459219, output_variance=-0.077250, alpha=-2.212718)) ]) ]))
X, y, D = fear_load_mat('../data/gef_load_full_Xy.mat', 1)
kernel = fk.MaskKernel(D, 0, fk.RQKernel(0.268353, -0.104149, -2.105742)) * fk.MaskKernel(D, 9, fk.SqExpKernel(1.160242, 0.004344)) * \
(fk.MaskKernel(D, 0, fk.SqExpPeriodicKernel(-0.823413, 0.000198, -0.917064)) + fk.MaskKernel(D, 0, fk.RQKernel(-0.459219, -0.077250, -2.212718)))
# Todo: set random seed.
sample = gpml.sample_from_gp_prior(kernel, X[0:499,:])
pylab.figure()
pylab.plot(X[0:499,0], y[0:499])
pylab.title('GEFCom2012 Z01 and T09 - first 500 data points')
pylab.xlabel('Time')
pylab.ylabel('Load')