ML_omega_distance: allow for 2 or more omegas and masks

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.7.5
+current_version = 0.7.6
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

.github/workflows/pythonapp.yml

@@ -13,10 +13,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v3
-    - name: Set up Python 3.10
+      - name: Set up Python 3.11
         uses: actions/setup-python@v4
         with:
-        python-version: "3.10"
+          python-version: "3.11"
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
@@ -27,15 +27,15 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.7", "3.8", "3.9", "3.10"]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
         os: [ubuntu-latest, windows-latest]
         exclude:
-        - os: windows-latest
-          python-version: "3.7"
           - os: windows-latest
             python-version: "3.8"
           - os: windows-latest
             python-version: "3.9"
+          - os: windows-latest
+            python-version: "3.10"
 
     runs-on: ${{ matrix.os }}
     steps:
@@ -60,7 +60,7 @@ jobs:
       - name: Set up Python 3.10
         uses: actions/setup-python@v4
         with:
-        python-version: "3.10"
+          python-version: "3.11"
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.7.5"
+release = "0.7.6"
 
 # -- General configuration ---------------------------------------------------
 

prototorch/__init__.py

@@ -17,7 +17,7 @@ from .core import similarities  # noqa: F401
 from .core import transforms  # noqa: F401
 
 # Core Setup
-__version__ = "0.7.5"
+__version__ = "0.7.6"
 
 __all_core__ = [
     "competitions",

prototorch/core/distances.py

@@ -73,6 +73,20 @@ def omega_distance(x, y, omega):
     return distances
 
 
+def ML_omega_distance(x, y, omegas, masks):
+    """Multi-Layer Omega distance."""
+    x, y = (arr.view(arr.size(0), -1) for arr in (x, y))
+    # omega = (omega_0 * mask_0) @ (omega_1 * mask_1)
+    omegas = [torch.mul(_omega, _mask) for _omega, _mask in zip(omegas, masks)]
+    omega = omegas[0] @ omegas[1]
+    for _omega in omegas[2:]:
+        omega = omega @ _omega
+    projected_x = x @ omega
+    projected_y = y @ omega
+    distances = squared_euclidean_distance(projected_x, projected_y)
+    return distances
+
+
 def lomega_distance(x, y, omegas):
     r"""Localized Omega distance.
 

setup.py

@@ -51,7 +51,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.7.5",
+    version="0.7.6",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",