From 7e0c78d9499fc9ce5cc1c0d812f799946b9386e4 Mon Sep 17 00:00:00 2001
From: NameSheep <71328881+liu2048@users.noreply.github.com>
Date: Fri, 3 Jan 2025 21:26:28 +0800
Subject: [PATCH] Fix ModuleNotFoundError for hp_ddc

Fixes #4

Add the missing `hp_ddc.py` file to resolve the `ModuleNotFoundError`.

* Implement the `HPDDC` class in `src/models/clustering_module/hp_ddc.py`.
* Import necessary modules.
* Define the `__init__` and `forward` methods for the `HPDDC` class.
---
 src/models/clustering_module/hp_ddc.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 src/models/clustering_module/hp_ddc.py

diff --git a/src/models/clustering_module/hp_ddc.py b/src/models/clustering_module/hp_ddc.py
new file mode 100644
index 0000000..2e4aa90
--- /dev/null
+++ b/src/models/clustering_module/hp_ddc.py
@@ -0,0 +1,19 @@
+import torch.nn as nn
+from models.clustering_module.kernel_width import get_kernel_width_module
+
+class HPDDC(nn.Module):
+    def __init__(self, cfg, input_size):
+        super().__init__()
+
+        hidden_layers = [nn.Linear(input_size[0], cfg.n_hidden), nn.ReLU()]
+        if cfg.use_bn:
+            hidden_layers.append(nn.BatchNorm1d(num_features=cfg.n_hidden))
+        self.hidden = nn.Sequential(*hidden_layers)
+        self.output = nn.Sequential(nn.Linear(cfg.n_hidden, cfg.n_clusters), nn.Softmax(dim=1))
+        
+        self.kernel_width = get_kernel_width_module(cfg.kernel_width_config, input_size=[cfg.n_hidden])
+        
+    def forward(self, x):
+        hidden = self.hidden(x)
+        output = self.output(hidden)
+        return hidden, output