test_fitting works

gext/fitting.py

@@ -61,37 +61,35 @@ class DiffFitting(AbstractFitting):
     def fit(self, vectors: List[np.ndarray], target: np.ndarray):
         """Given a set of vectors and a target return the fitting
         coefficients."""
+        if len(vectors) == 1:
+            raise ValueError("DiffFit does not work for one vector")
         target=target-vectors[-1]
         VECTORS=[]
-        print("lenvector", len(vectors))
-        if len(vectors)>1:
-            for i in range(2, len(vectors)+1):
-                print("lenvector", len(vectors))
-                VECTORS.append(vectors[i-2]-vectors[-1])
-                print(len(VECTORS))
-            matrix = np.array(VECTORS).T
-            a = matrix.T @ matrix
-            b = matrix.T @ target
-            if self.options["regularization"] > 0.0:
-                a += np.identity(len(b))*self.options["regularization"]
-            coefficients = np.linalg.solve(a, b)
-            print("coefficients", coefficients)
-            return np.array(coefficients, dtype=np.float64)
+        for i in range(0, len(vectors)+1):
+            VECTORS.append(vectors[i]-vectors[-1])
+        matrix = np.array(VECTORS).T
+        a = matrix.T @ matrix
+        b = matrix.T @ target
+        if self.options["regularization"] > 0.0:
+            a += np.identity(len(b))*self.options["regularization"]
+        coefficients = np.linalg.solve(a, b)
+        print("coefficients", coefficients)
+        return np.array(coefficients, dtype=np.float64)
 
     def linear_combination(self, vectors: List[np.ndarray],
             coefficients: np. ndarray) -> np.ndarray:
         """Given a set of vectors (or matrices) and the corresponding
         coefficients, build their linear combination."""
+        if len(vectors) == 1:
+            raise ValueError("DiffFit does not work for one vector")
         result = np.zeros(vectors[0].shape, dtype=np.float64)
         VECTORS_DiffFitting=[]
-        if len(vectors)>1:
-            for i in range(2,len(vectors)+1):
-                  VECTORS_DiffFitting.append(vectors[i-2]-vectors[-1])
-            for coeff, vector in zip(coefficients, vectors):
-                result += vector*coeff
-                result=result+vectors[-1]
-                print(result.shape)
-            return result
+        for i in range(0,len(vectors)+1):
+              VECTORS_DiffFitting.append(vectors[i]-vectors[-1])
+        for coeff, vector in zip(coefficients, VECTORS_DiffFitting):
+            result += vector*coeff
+        result=result+vectors[-1]
+        return result
 
 class LeastSquare(AbstractFitting):