forked from ccf-ai-infra/GPUCodeForces
57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import torch
|
|
import torch.nn as nn
|
|
|
|
class Model(nn.Module):
|
|
"""
|
|
Bray-Curtis Distance implementation.
|
|
Computes the Bray-Curtis distance between two sets of vectors.
|
|
"""
|
|
def __init__(self):
|
|
super(Model, self).__init__()
|
|
|
|
def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Compute Bray-Curtis distance between x and y.
|
|
|
|
Args:
|
|
x (torch.Tensor): First set of vectors [batch_size, feature_dim]
|
|
y (torch.Tensor): Second set of vectors [batch_size, feature_dim]
|
|
|
|
Returns:
|
|
torch.Tensor: Bray-Curtis distances [batch_size]
|
|
"""
|
|
# Input validation
|
|
if x.shape != y.shape:
|
|
raise ValueError(f"Input tensors must have the same shape, got {x.shape} and {y.shape}")
|
|
|
|
if x.dim() != 2:
|
|
raise ValueError(f"Input tensors must be 2D, got {x.dim()}D")
|
|
|
|
# Compute Bray-Curtis distance: Σ|x_i - y_i| / Σ(|x_i| + |y_i|)
|
|
# Step 1: Compute absolute differences
|
|
diff = torch.abs(x - y)
|
|
|
|
# Step 2: Compute numerator (sum of absolute differences)
|
|
numerator = torch.sum(diff, dim=1)
|
|
|
|
# Step 3: Compute denominator (sum of absolute values)
|
|
denominator = torch.sum(torch.abs(x) + torch.abs(y), dim=1)
|
|
|
|
# Step 4: Handle division by zero
|
|
# When denominator is 0 (both vectors are all zeros), distance is 0
|
|
distance = torch.where(denominator > 0, numerator / denominator, torch.zeros_like(numerator))
|
|
|
|
return distance
|
|
|
|
batch_size = 512
|
|
feature_dim = 512
|
|
|
|
def get_inputs():
|
|
# Generate two sets of positive vectors (ecological data is typically non-negative)
|
|
x = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
|
|
y = torch.abs(torch.randn(batch_size, feature_dim)) + 0.1
|
|
return [x, y]
|
|
|
|
def get_init_inputs():
|
|
return [] # No special initialization inputs needed
|