Bump version: 0.4.2 → 0.4.3

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.2
+current_version = 0.4.3
 commit = True
 tag = True
 parse = (?P<major>\d+)\.(?P<minor>\d+)\.(?P<patch>\d+)

docs/source/conf.py

@@ -23,7 +23,7 @@ author = "Jensun Ravichandran"
 
 # The full version, including alpha/beta/rc tags
 #
-release = "0.4.2"
+release = "0.4.3"
 
 # -- General configuration ---------------------------------------------------
 

prototorch/__init__.py

@@ -1,7 +1,7 @@
 """ProtoTorch package."""
 
 # Core Setup
-__version__ = "0.4.2"
+__version__ = "0.4.3"
 
 __all_core__ = [
     "datasets",

prototorch/components/components.py

@@ -67,8 +67,9 @@ class LabeledComponents(Components):
                  *,
                  initialized_components=None):
         if initialized_components is not None:
-            super().__init__(initialized_components=initialized_components[0])
+            components, component_labels = initialized_components
-            self._labels = initialized_components[1]
+            super().__init__(initialized_components=components)
+            self._labels = component_labels
         else:
             self._initialize_labels(distribution)
             super().__init__(number_of_components=len(self._labels),

prototorch/datasets/__init__.py

@@ -1,11 +1,6 @@
 """ProtoTorch datasets."""
 
 from .abstract import NumpyDataset
+from .iris import Iris
 from .spiral import Spiral
 from .tecator import Tecator
-
-__all__ = [
-    "NumpyDataset",
-    "Spiral",
-    "Tecator",
-]

prototorch/functions/competitions.py

@@ -3,7 +3,6 @@
 import torch
 
 
-# @torch.jit.script
 def stratified_min(distances, labels):
     clabels = torch.unique(labels, dim=0)
     nclasses = clabels.size()[0]
@@ -31,15 +30,14 @@ def stratified_min(distances, labels):
     return winning_distances.T  # return with `batch_size` first
 
 
-# @torch.jit.script
 def wtac(distances, labels):
     winning_indices = torch.min(distances, dim=1).indices
     winning_labels = labels[winning_indices].squeeze()
     return winning_labels
 
 
-# @torch.jit.script
+def knnc(distances, labels, k=1):
-def knnc(distances, labels, k):
+    winning_indices = torch.topk(-distances, k=k, dim=1).indices
-    winning_indices = torch.topk(-distances, k=k.item(), dim=1).indices
+    winning_labels = torch.mode(labels[winning_indices].squeeze(),
-    winning_labels = labels[winning_indices].squeeze()
+                                dim=1).values
     return winning_labels

setup.py

@@ -23,6 +23,7 @@ INSTALL_REQUIRES = [
 ]
 DATASETS = [
     "requests",
+    "sklearn",
     "tqdm",
 ]
 DEV = ["bumpversion"]
@@ -42,7 +43,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.4.2",
+    version="0.4.3",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",