def base_visualization(grid_losses, w0_list, w1_list, mean_x, std_x, height,
weight):
"""Base Visualization for both models."""
w0, w1 = np.meshgrid(w0_list, w1_list)
fig = plt.figure()
# plot contourf
ax1 = fig.add_subplot(1, 2, 1)
cp = ax1.contourf(w0, w1, grid_losses.T, cmap=plt.cm.jet)
fig.colorbar(cp, ax=ax1)
ax1.set_xlabel(r'$w_0$')
ax1.set_ylabel(r'$w_1$')
# put a marker at the minimum
loss_star, w0_star, w1_star = get_best_parameters(w0_list, w1_list,
grid_losses)
ax1.plot(w0_star, w1_star, marker='*', color='r', markersize=20)
# plot f(x)
ax2 = fig.add_subplot(1, 2, 2)
ax2.scatter(height, weight, marker=".", color='b', s=5)
ax2.set_xlabel("x")
ax2.set_ylabel("y")
ax2.grid()
return fig
def base_visualization(grid_losses, w0_list, w1_list,
mean_x, std_x, height, weight):
"""Base Visualization for both models."""
w0, w1 = np.meshgrid(w0_list, w1_list)
fig = plt.figure()
# plot contourf
ax1 = fig.add_subplot(1, 2, 1)
cp = ax1.contourf(w0, w1, grid_losses.T, cmap=plt.cm.jet)
fig.colorbar(cp, ax=ax1)
ax1.set_xlabel(r'$w_0$')
ax1.set_ylabel(r'$w_1$')
# put a marker at the minimum
loss_star, w0_star, w1_star = get_best_parameters(
w0_list, w1_list, grid_losses)
ax1.plot(w0_star, w1_star, marker='*', color='r', markersize=20)
# plot f(x)
ax2 = fig.add_subplot(1, 2, 2)
ax2.scatter(height, weight, marker=".", color='b', s=5)
ax2.set_xlabel("x")
ax2.set_ylabel("y")
ax2.grid()
return fig
def grid_visualization(grid_losses, w0_list, w1_list, mean_x, std_x, height,
weight):
"""Visualize how the trained model looks like under the grid search."""
fig = base_visualization(grid_losses, w0_list, w1_list, mean_x, std_x,
height, weight)
loss_star, w0_star, w1_star = get_best_parameters(w0_list, w1_list,
grid_losses)
# plot prediciton
x, f = prediction(w0_star, w1_star, mean_x, std_x)
ax2 = fig.get_axes()[2]
ax2.plot(x, f, 'r')
return fig
def grid_visualization(grid_losses, w0_list, w1_list,
mean_x, std_x, height, weight):
"""Visualize how the trained model looks like under the grid search."""
fig = base_visualization(
grid_losses, w0_list, w1_list, mean_x, std_x, height, weight)
loss_star, w0_star, w1_star = get_best_parameters(
w0_list, w1_list, grid_losses)
# plot prediciton
x, f = prediction(w0_star, w1_star, mean_x, std_x)
ax2 = fig.get_axes()[2]
ax2.plot(x, f, 'r')
return fig