def plot_gef_load_Z01_split_mean_temp():
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)))
kernel_1 = 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))
posterior_mean_1 = gpml.posterior_mean(kernel,
kernel_1,
X[0:499, :],
y[0:499],
iters=10)
kernel_2 = 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.RQKernel(-0.459219, -0.077250, -2.212718))
posterior_mean_2 = gpml.posterior_mean(kernel,
kernel_2,
X[0:499, :],
y[0:499],
iters=10)
plt.figure()
host = host_subplot(111, axes_class=AA.Axes)
plt.subplots_adjust(right=0.85)
par1 = host.twinx()
# host.set_xlim(0, 2)
# host.set_ylim(0, 2)
host.set_xlabel("Temperature (T09)")
# par1.set_ylabel("Periodic component")
plt.title('Posterior mean function')
host.set_ylabel("Load posterior mean")
p2, = host.plot(X[0:499, 9], y[0:499], 'o', alpha=0.5)
p1, = host.plot(X[0:499, 9], posterior_mean_2, 'o')
# par1.set_ylim(0, 4)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
# par1.axis["right"].label.set_color(p2.get_color())
plt.draw()
plt.show()
def plot_gef_load_Z01_smooth_2d_mean():
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)))
kernel_1 = 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))
kernel_2 = 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.RQKernel(-0.459219, -0.077250, -2.212718))
min_T = -3.0
max_T = 1.0
N_T = 10
temps = np.repeat(np.linspace(min_T, max_T, N_T), 499)
input = np.tile(X[0:499, :], (N_T, 1))
input[:, 9] = temps
posterior_mean = gpml.posterior_mean(kernel,
kernel_2,
X[0:499, :],
y[0:499],
input,
iters=300)
X_plt = X[0:499, 0]
Y_plt = np.linspace(min_T, max_T, N_T)
Z_plt = np.reshape(posterior_mean, (N_T, 499), 'A')
data = {'X': X_plt, 'Y': Y_plt, 'Z': Z_plt, 'post_mean': posterior_mean}
scipy.io.savemat('temp_data.mat', data)
def plot_gef_load_Z01_smooth_2d_mean():
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)))
kernel_1 = 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))
kernel_2 = 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.RQKernel(-0.459219, -0.077250, -2.212718))
min_T = -3.0
max_T = 1.0
N_T = 10
temps = np.repeat(np.linspace(min_T, max_T, N_T), 499)
input = np.tile(X[0:499,:], (N_T, 1))
input[:,9] = temps
posterior_mean = gpml.posterior_mean(kernel, kernel_2, X[0:499,:], y[0:499], input, iters=300)
X_plt = X[0:499,0]
Y_plt = np.linspace(min_T, max_T, N_T)
Z_plt = np.reshape(posterior_mean, (N_T, 499), 'A')
data = {'X': X_plt, 'Y': Y_plt, 'Z': Z_plt, 'post_mean': posterior_mean}
scipy.io.savemat('temp_data.mat', data)
def plot_gef_load_Z01_split_mean_temp():
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)))
kernel_1 = 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))
posterior_mean_1 = gpml.posterior_mean(kernel, kernel_1, X[0:499,:], y[0:499], iters=10)
kernel_2 = 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.RQKernel(-0.459219, -0.077250, -2.212718))
posterior_mean_2 = gpml.posterior_mean(kernel, kernel_2, X[0:499,:], y[0:499], iters=10)
plt.figure()
host = host_subplot(111, axes_class=AA.Axes)
plt.subplots_adjust(right=0.85)
par1 = host.twinx()
# host.set_xlim(0, 2)
# host.set_ylim(0, 2)
host.set_xlabel("Temperature (T09)")
# par1.set_ylabel("Periodic component")
plt.title('Posterior mean function')
host.set_ylabel("Load posterior mean")
p2, = host.plot(X[0:499,9], y[0:499], 'o', alpha=0.5)
p1, = host.plot(X[0:499,9], posterior_mean_2, 'o')
# par1.set_ylim(0, 4)
host.legend()
host.axis["left"].label.set_color(p1.get_color())
# par1.axis["right"].label.set_color(p2.get_color())
plt.draw()
plt.show()