def run_interpolation_with_various_betas(points,
kernel=RBF(10, (1e-2, 1e2)) * C(1)):
"""
Runs Interpolation with number of picked points(beta) from 1 to all points-1 picked and using uniform distribution in
the picking in 1 Dimesnsion
"""
rmse_data = []
for i in range(
1, len(points)
): # runs through all number of picked points starting at 1 and ending with all points picked-1
sum_rmse = 0
for j in range(
0, 3
): # runs every interpolation with a certain beta 5 times and averages the results
picked_points = pick_uniform_random_points(points, i)
interpolated_points = interpolate_unknown_points(
picked_points, points, kernel)
sum_rmse = sum_rmse + root_mean_square_error(interpolated_points)
rmse_data.append(sum_rmse / 5)
plot_numbers(rmse_data, range(1, len(points)))
return rmse_data
def run_interpolations_with_random_betas_1d():
"""
Runs Interpolation with random picking of the value of Beta
"""
rmse_values = []
picked_points_values = []
number_of_times = 100 # this will be the number of points displayed on the graph
random = np.random.default_rng()
for i in range(0, number_of_times):
test_points = create_points_with_random_pollution_1d(100, 100, 10)
test_picked_points = pick_uniform_random_points(
test_points,
random.integers(1, 100)) # picks a random number of known points
test_interpolated_points = interpolate_unknown_points(
test_picked_points, test_points)
test_rmse = root_mean_square_error(test_interpolated_points)
rmse_values.append(test_rmse)
picked_points_values.append(len(test_picked_points))
plot_numbers(rmse_values, picked_points_values)