fixed fast coefficient computation (#243)

* fixed fast coefficient computation

* updated pandas version dependency for tests

* removed Python 3.8 from CI

* removed Python 3.9 from mpi CI

.github/workflows/develop-test.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
         test-group: [short, optimize, multivariate, optimize-experimental]
     steps:
       - uses: actions/checkout@v2
@@ -60,7 +60,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}
@@ -80,7 +80,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.9", "3.10"]
+        python-version: ["3.10"]
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}
@@ -102,7 +102,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10"]
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}
@@ -125,7 +125,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8"]
+        python-version: ["3.10"]
 
     steps:
       - uses: actions/checkout@v2

pyproject.toml

@@ -54,7 +54,7 @@ tracker = "https://github.com/LLNL/MuyGPyS/issues"
 tests = [
     "absl-py>=0.13.0",
     "matplotlib>=3.2.1",
-    "pandas==1.5.2",
+    "pandas>=2.2.2",
 ]
 dev = [
     "black>=21.1.0",

src/MuyGPyS/_src/gp/muygps/numpy.py

@@ -90,4 +90,6 @@ def _muygps_fast_posterior_mean_precompute(
     train_nn_targets_fast: np.ndarray,
     **kwargs,
 ) -> np.ndarray:
-    return np.linalg.solve(Kin, train_nn_targets_fast)
+    if train_nn_targets_fast.ndim == 2:
+        train_nn_targets_fast = train_nn_targets_fast[:, :, None]
+    return np.squeeze(np.linalg.solve(Kin, train_nn_targets_fast))