Merge pull request #576 from helmholtz-analytics/release/0.4.x

Release/0.4.x

CHANGELOG.md

@@ -1,5 +1,10 @@
 # Pending Additions
 
+- [#573](https://github.com/helmholtz-analytics/heat/pull/573) Bugfix: matmul fixes: early out for 2 vectors, remainders not added if inner block is 1 for split 10 case
+
+
+# v0.4.0
+
 - Update documentation theme to "Read the Docs"
 - [#429](https://github.com/helmholtz-analytics/heat/pull/429) Create submodule for Linear Algebra functions
 - [#429](https://github.com/helmholtz-analytics/heat/pull/429) Implemented QR
@@ -27,11 +32,11 @@
 - [#534](https://github.com/helmholtz-analytics/heat/pull/534) `eye()` supports all 2D split combinations and matrix configurations.
 - [#535](https://github.com/helmholtz-analytics/heat/pull/535) Introduction of BaseEstimator and clustering, classification and regression mixins.
 - [#536](https://github.com/helmholtz-analytics/heat/pull/536) Getting rid of the docs folder
+- [#541](https://github.com/helmholtz-analytics/heat/pull/541) Introduction of basic halo scheme for inter-rank operations
 - [#558](https://github.com/helmholtz-analytics/heat/pull/558) `sanitize_memory_layout` assumes default memory layout of the input tensor
 - [#558](https://github.com/helmholtz-analytics/heat/pull/558) Support for PyTorch 1.5.0 added
 - [#562](https://github.com/helmholtz-analytics/heat/pull/562) Bugfix: split semantics of ht.squeeze()
 - [#567](https://github.com/helmholtz-analytics/heat/pull/567) Bugfix: split differences for setitem are now assumed to be correctly given, error will come from torch upon the setting of the value
-- [#573](https://github.com/helmholtz-analytics/heat/pull/573) Bugfix: matmul fixes: early out for 2 vectors, remainders not added if inner block is 1 for split 10 case
 
 # v0.3.0
 

heat/cluster/tests/test_spectral.py

@@ -57,7 +57,7 @@ def test_fit_iris(self):
         self.assertIsInstance(spectral.labels_, ht.DNDarray)
 
         spectral = ht.cluster.Spectral(
-            metric="euclidean", laplacian="eNeighbour", theshold=0.5, boundary="upper", n_lanczos=m
+            metric="euclidean", laplacian="eNeighbour", threshold=0.5, boundary="upper", n_lanczos=m
         )
         labels = spectral.fit_predict(iris)
         self.assertIsInstance(labels, ht.DNDarray)
@@ -66,7 +66,7 @@ def test_fit_iris(self):
             gamma=0.1,
             metric="rbf",
             laplacian="eNeighbour",
-            theshold=0.5,
+            threshold=0.5,
             boundary="upper",
             n_lanczos=m,
         )

heat/core/version.py

@@ -1,5 +1,5 @@
 major = 0
-minor = 3
+minor = 4
 micro = 0
 extension = None