Calculates euclidean distance between two data. :param input_a: ndarray of first vector. :param input_b: ndarray of second vector. :return: Euclidean distance of input_a and input_b. By using math.sqrt(), result will be float. >>> euclidean(np.array([0]), np.array(
(input_a: np.ndarray, input_b: np.ndarray)
| 17 | |
| 18 | |
| 19 | def euclidean(input_a: np.ndarray, input_b: np.ndarray) -> float: |
| 20 | """ |
| 21 | Calculates euclidean distance between two data. |
| 22 | :param input_a: ndarray of first vector. |
| 23 | :param input_b: ndarray of second vector. |
| 24 | :return: Euclidean distance of input_a and input_b. By using math.sqrt(), |
| 25 | result will be float. |
| 26 | |
| 27 | >>> euclidean(np.array([0]), np.array([1])) |
| 28 | 1.0 |
| 29 | >>> euclidean(np.array([0, 1]), np.array([1, 1])) |
| 30 | 1.0 |
| 31 | >>> euclidean(np.array([0, 0, 0]), np.array([0, 0, 1])) |
| 32 | 1.0 |
| 33 | """ |
| 34 | return math.sqrt(sum(pow(a - b, 2) for a, b in zip(input_a, input_b))) |
| 35 | |
| 36 | |
| 37 | def similarity_search( |