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a49db27 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | import numpy as np
def remove_outlier_points(points_tuples, k_nearest=2, threshold=2.0):
"""
Robust outlier detection for list of (x,y) tuples.
Only requires numpy.
Args:
points_tuples: list of (x,y) tuples
k_nearest: number of neighbors to consider
threshold: multiplier for median distance
Returns:
list: filtered list of (x,y) tuples with outliers removed
list: list of booleans indicating which points were kept (True = kept)
"""
points = np.array(points_tuples)
n_points = len(points)
# Calculate pairwise distances manually
dist_matrix = np.zeros((n_points, n_points))
for i in range(n_points):
for j in range(i + 1, n_points):
# Euclidean distance between points i and j
dist = np.sqrt(np.sum((points[i] - points[j]) ** 2))
dist_matrix[i, j] = dist
dist_matrix[j, i] = dist
# Get k nearest neighbors' distances
k = min(k_nearest, n_points - 1)
neighbor_distances = np.partition(dist_matrix, k, axis=1)[:, :k]
avg_neighbor_dist = np.mean(neighbor_distances, axis=1)
# Calculate mask using median distance
median_dist = np.median(avg_neighbor_dist)
mask = avg_neighbor_dist <= threshold * median_dist
# Return filtered tuples and mask
filtered_tuples = [t for t, m in zip(points_tuples, mask) if m]
return filtered_tuples
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