📦 custom-distance-metrics — 自定义距离度量

Define custom distance/similarity metrics for clustering and ML algorithms.

Usage

This skill provides utilities for defining and using custom distance metrics.

from scipy.spatial.distance import cdist
# Define custom metric
def custom_distance(x, y):
    return sum((a - b) ** 2 for a, b in zip(x, y))
# Use with sklearn
from sklearn.cluster import DBSCAN
clustering = DBSCAN(eps=0.5, min_samples=5, metric=custom_distance)

Examples

See the skill documentation for more examples.