# Make sure features exist to fit a knn object
if features is None:
    raise ValueError(
        "Both knn and features arguments cannot be None at the same time. Not enough information to compute outlier scores."
    )

# Select a good value for the number of neighbors
if k is None:
    k = DEFAULT_K  # use default when knn and k are both None
N, M = features.shape
if k > N:  # Ensure number of neighbors less than number of examples
    raise ValueError(
        f"Number of nearest neighbors k={k} cannot exceed the number of examples N={len(features)} passed into the estimator (knn)."
    )

# Select a decent distance metric
if M > 3:  # use euclidean distance for lower dimensional spaces
    metric = "cosine"
elif N > 100:  # Use efficient implementation (numerically unstable in edge cases)
    metric = "euclidean"
else:  # Use scipy implementation for precise results
    metric = euclidean

# Construct the knn object
knn = NearestNeighbors(n_neighbors=k, metric=metric).fit(features)

knn: NearestNeighbors = features_to_knn(features, n_neighbors=k)  # Optionally, specify a metric