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()