from sklearn.cluster import DBSCAN

def my_distance(point_a, point_b):
    """Custom distance between two points."""
    # point_a and point_b are 1D arrays
    return some_calculation(point_a, point_b)

db = DBSCAN(eps=5, min_samples=3, metric=my_distance)

def create_weighted_distance(weight_x, weight_y):
    """Create a distance function with specific weights."""
    def distance(a, b):
        dx = a[0] - b[0]
        dy = a[1] - b[1]
        return np.sqrt((weight_x * dx)**2 + (weight_y * dy)**2)
    return distance

# Create distances with different weights
dist_equal = create_weighted_distance(1.0, 1.0)
dist_x_heavy = create_weighted_distance(2.0, 0.5)

# Use with DBSCAN
db = DBSCAN(eps=10, min_samples=3, metric=dist_x_heavy)

def create_manhattan_distance(scale=1.0):
    """
    Manhattan distance with optional scaling.
    Measures distance as sum of absolute differences.
    This is just one example - you can design custom metrics for your specific needs.
    """
    def distance(a, b):
        return scale * (abs(a[0] - b[0]) + abs(a[1] - b[1]))
    return distance

# Use with DBSCAN
manhattan_metric = create_manhattan_distance(scale=1.5)
db = DBSCAN(eps=10, min_samples=3, metric=manhattan_metric)

from scipy.spatial.distance import cdist, pdist, squareform

# Custom distance for cdist
def custom_metric(u, v):
    return np.sqrt(np.sum((u - v)**2))

# Distance matrix between two sets of points
dist_matrix = cdist(points_a, points_b, metric=custom_metric)

# Pairwise distances within one set
pairwise = pdist(points, metric=custom_metric)
dist_matrix = squareform(pairwise)