ENH: Implement Gaussian Process

pyproject.toml

@@ -28,6 +28,7 @@ dependencies = [
     "numpy>=1.17.3",
     "nest-asyncio>=1.5.1",
     "scikit-image>=0.14.2",
+    "scikit_learn",
     "scipy>=1.8.0",
 ]
 dynamic = ["version"]

src/eddymotion/estimator.py

@@ -120,6 +120,7 @@ def estimate(
                 "avg",
                 "average",
                 "mean",
+                "gp",
             ) or model.lower().startswith("full")
 
             dwmodel = None

src/eddymotion/model/__init__.py

@@ -26,6 +26,7 @@
     AverageDWModel,
     DKIModel,
     DTIModel,
+    GPModel,
     ModelFactory,
     PETModel,
     TrivialB0Model,
@@ -36,6 +37,7 @@
     "AverageDWModel",
     "DKIModel",
     "DTIModel",
+    "GPModel",
     "TrivialB0Model",
     "PETModel",
 )

src/eddymotion/model/base.py

@@ -515,6 +515,13 @@ class DKIModel(BaseDWIModel):
     _model_class = "dipy.reconst.dki.DiffusionKurtosisModel"
 
 
+class GPModel(BaseDWIModel):
+    """A wrapper of :obj:`~eddymotion.model.dipy.GaussianProcessModel`."""
+
+    _modelargs = ("kernel_model",)
+    _model_class = "eddymotion.model.dipy.GaussianProcessModel"
+
+
 def _rasb2dipy(gradient):
     gradient = np.asanyarray(gradient)
     if gradient.ndim == 1: