“KNN COMPUTE_DISTANCES_NO_LOOP” Réponses codées

KNN COMPUTE_DISTANCES_NO_LOOP

def compute_distances_no_loops(self, X):
    """
    Compute the distance between each test point in X and each training point
    in self.X_train using no explicit loops.

    Input / Output: Same as compute_distances_two_loops
    """
    num_test = X.shape[0]
    num_train = self.X_train.shape[0]

    # Expand ||x - y||**2 = ||x||**2 - 2 x.T ⋅y + ||y||**2,
    # where ||x||**2 = sum(x**2) (element-wise on matrix rows)
    # The final result is a (num_test, num_train) matrix
    # so the x**2 and y**2 intermediates must be reshaped appropriately
    x2 = np.sum(X**2, axis=1).reshape((num_test, 1))
    y2 = np.sum(self.X_train**2, axis=1).reshape((1, num_train))
    xy = -2*np.matmul(X, self.X_train.T)
    dists = np.sqrt(x2 + xy + y2)

    return dists

Difference was: 0.000000
Good! The distance matrices are the same