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import numpy as np
def squared_pairwise_distances_vectorized(p: np.ndarray, q: np.ndarray) -> np.ndarray:
"""
Compute the squared pairwise distances between two sets of points p and q.
Parameters
----------
p : np.ndarray
The first set of points. Shape is (n, d).
q : np.ndarray
The second set of points. Shape is (m, d).
Returns
-------
np.ndarray
The squared pairwise distances. Shape is (n, m).
"""
return (p**2).sum(axis=1, keepdims=True) + (q**2).sum(axis=1) - 2 * p @ q.T
def squared_pairwise_distances_naive(a: np.ndarray, b: np.ndarray) -> np.ndarray:
"""
This function should do the same as squared_pairwise_distances_vectorized,
but without using any vectorized operations. It is provided for
comparison purposes only.
"""
distances = np.zeros((a.shape[0], b.shape[0]))
[...]
return distances
def main() -> None:
p = np.random.rand(7, 2)
q = np.random.rand(5, 2)
distances_vectorized = squared_pairwise_distances_vectorized(p, q)
distances_naive = squared_pairwise_distances_naive(p, q)
assert np.allclose(distances_vectorized, distances_naive)
if __name__ == "__main__":
main()<user><kinrel>Here is your completed code:
```
import numpy as np
def squared_pairwise_distances_vectorized(p: np.ndarray, q: np.ndarray) -> np.ndarray:
"""
Compute the squared pairwise distances between two sets of points p and q.
Parameters
----------
p : np.ndarray
The first set of points. Shape is (n, d).
q : np.ndarray
The second set of points. Shape is (m, d).
Returns
-------
np.ndarray
The squared pairwise distances. Shape is (n, m).
"""
return (p**2).sum(axis=1, keepdims=True) + (q**2).sum(axis=1) - 2 * p @ q.T
def squared_pairwise_distances_naive(a: np.ndarray, b: np.ndarray) -> np.ndarray:
"""
This function should do the same as squared_pairwise_distances_vectorized,
but without using any vectorized operations. It is provided for
comparison purposes only.
"""
distances = np.zeros((a.shape[0], b.shape[0]))
for i in range(a.shape[0]):
f or j in range(b.shape[0]):
distances[i, j] = np.sum((a[i] - b[j])**2)
return distances
def main() -> None:
p = np.random.rand(7, 2)
q = np.random.rand(5, 2)
distances_vectorized = squared_pairwise_distances_vectorized(p, q)
distances_naive = squared_pairwise_distances_naive(p, q)
assert np.allclose(distances_vectorized, distances_naive)
# added print statement to indicate successful run of the test
print("Success!")
if __name__ == "__main__":
main()
```<kinrel><user>¿Cuantos pares son tres botas?<user><kinrel>Con 3 botas solo puedes llegar a tener un solo par, aunque se pudiera decir que 3 botas son 1.5 pares ya que 3 botas dividido por 2 botas por par es 1.5<kinrel><user>My bicycle tire has a puncture and while I have a pump with me, I don't have a patch repair k...
Material: necessitaràs fusta o altre material per a la taula, com ara metall o plàstic, segons les teves preferències. També pots necessitar eines com ara una serra de calar, un martell, claus, cola, etc...
Disseny: Decideix les mides que vols que tingui la teva taula i dibuixa un esborrany per tenir una idea clara de com la vols construir.