Bump version: 0.4.3 → 0.4.4

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.3
+current_version = 0.4.4
 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 = "0.4.3"
+release = "0.4.4"
 
 # -- General configuration ---------------------------------------------------
 

prototorch/__init__.py

@@ -1,7 +1,7 @@
 """ProtoTorch package."""
 
 # Core Setup
-__version__ = "0.4.3"
+__version__ = "0.4.4"
 
 __all_core__ = [
     "datasets",

prototorch/datasets/iris.py

@@ -0,0 +1,15 @@
+"""Thin wrapper for the Iris classification dataset from sklearn.
+
+URL:
+    https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html
+
+"""
+
+from prototorch.datasets.abstract import NumpyDataset
+from sklearn.datasets import load_iris
+
+
+class Iris(NumpyDataset):
+    def __init__(self):
+        x, y = load_iris(return_X_y=True)
+        super().__init__(x, y)

prototorch/functions/competitions.py

@@ -38,6 +38,7 @@ def wtac(distances, labels):
 
 def knnc(distances, labels, k=1):
     winning_indices = torch.topk(-distances, k=k, dim=1).indices
-    winning_labels = torch.mode(labels[winning_indices].squeeze(),
-                                dim=1).values
+    # winning_labels = torch.mode(labels[winning_indices].squeeze(),
+    #                             dim=1).values
+    winning_labels = torch.mode(labels[winning_indices], dim=1).values
     return winning_labels

setup.py

@@ -43,7 +43,7 @@ ALL = DATASETS + DEV + DOCS + EXAMPLES + TESTS
 
 setup(
     name="prototorch",
-    version="0.4.3",
+    version="0.4.4",
     description="Highly extensible, GPU-supported "
     "Learning Vector Quantization (LVQ) toolbox "
     "built using PyTorch and its nn API.",

tests/test_functions.py

@@ -4,7 +4,6 @@ import unittest
 
 import numpy as np
 import torch
-
 from prototorch.functions import (activations, competitions, distances,
                                   initializers, losses)
 
@@ -139,7 +138,7 @@ class TestCompetitions(unittest.TestCase):
     def test_knnc_k1(self):
         d = torch.tensor([[2.0, 3.0, 1.99, 3.01], [2.0, 3.0, 2.01, 3.0]])
         labels = torch.tensor([0, 1, 2, 3])
-        actual = competitions.knnc(d, labels, k=torch.tensor([1]))
+        actual = competitions.knnc(d, labels, k=1)
         desired = torch.tensor([2, 0])
         mismatch = np.testing.assert_array_almost_equal(actual,
                                                         desired,