From de81f95bad2b7ea02448b7650d3ecac9dfbff160 Mon Sep 17 00:00:00 2001
From: James Nelson <ja94nelson@gmail.com>
Date: Tue, 28 Jul 2026 15:04:32 +0100
Subject: [PATCH] regression

---
 qkernel/model.py | 60 +++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 59 insertions(+), 1 deletion(-)

diff --git a/qkernel/model.py b/qkernel/model.py
index 06ff038..45ac625 100644
--- a/qkernel/model.py
+++ b/qkernel/model.py
@@ -2,7 +2,7 @@
 
 import numpy as np
 from sklearn.svm import SVC
-
+from sklearn.kernel_ridge import KernelRidge
 
 class Model(ABC):
     """
@@ -113,3 +113,61 @@ def predict(self, gram: np.ndarray) -> np.ndarray:
             Predicted labels.
         """
         return self.model.predict(gram)
+
+
+class KRR(Model):
+    """
+    Quantum-kernel-compatible Kernel Ridge Regression wrapper.
+
+    This class uses scikit-learn's KernelRidge with a precomputed kernel,
+    making it suitable for quantum kernel methods (QKRR-style pipelines).
+
+    Parameters
+    ----------
+    alpha : float, default=1.0
+        Regularization strength. Larger values mean stronger regularization.
+    **kwargs
+        Additional keyword arguments passed to sklearn.kernel_ridge.KernelRidge.
+    """
+
+    def __init__(self, alpha: float = 1.0, **kwargs) -> None:
+        """
+        Initialize the KRR model with a precomputed kernel.
+
+        Parameters
+        ----------
+        alpha : float, default=1.0
+            Regularization strength.
+        **kwargs
+            Keyword arguments forwarded to sklearn's KernelRidge.
+        """
+        self.model = KernelRidge(kernel="precomputed", alpha=alpha, **kwargs)
+
+    def train(self, gram_train: np.ndarray, y_train: np.ndarray) -> None:
+        """
+        Train the KRR model.
+
+        Parameters
+        ----------
+        gram_train : np.ndarray
+            Training Gram matrix of shape (n_train, n_train).
+        y_train : np.ndarray
+            Training targets.
+        """
+        self.model.fit(gram_train, y_train)
+
+    def predict(self, gram: np.ndarray) -> np.ndarray:
+        """
+        Predict target values.
+
+        Parameters
+        ----------
+        gram : np.ndarray
+            Gram matrix of shape (n_samples, n_train) for prediction.
+
+        Returns
+        -------
+        np.ndarray
+            Predicted values.
+        """
+        return self.model.predict(gram)