| import numpy as np |
| |
| # Uses least squares regression to find the solution |
| # when there is one unknown variable. |
| def print_lstsq_solution(A, B): |
| A_inv = np.linalg.pinv(A) |
| x = np.matmul(A_inv, B) |
| print("least squares solution:", x[0][0]) |
| |
| # Uses the pseudoinverse matrix to find the solution |
| # when there are two unknown variables. |
| def print_pinv_solution(A, mv, B): |
| new_A = np.concatenate((A, mv), axis=1) |
| new_A_inv = np.linalg.pinv(new_A) |
| new_x = np.matmul(new_A_inv, B) |
| print("pinv solution:", new_x[0][0], new_x[1][0]) |
| |
| # Traverses the data and prints out one value for |
| # each update type. |
| def print_solutions(file_path): |
| data = np.genfromtxt(file_path, delimiter="\t") |
| |
| prev_update = 0 |
| split_list_indices = list() |
| for i, val in enumerate(data): |
| if prev_update != val[3]: |
| split_list_indices.append(i) |
| prev_update = val[3] |
| |
| split = np.split(data, split_list_indices) |
| |
| for array in split: |
| A, mv, B, update = np.hsplit(array, 4) |
| print("update type:", update[0][0]) |
| print_lstsq_solution(A, B) |
| print_pinv_solution(A, mv, B) |
| print() |
| |
| if __name__ == "__main__": |
| print_solutions("data/lowres_64f_target150_data.txt") |
