From 8069bfc3e3f09faffd48724432aef5cb4d0eb6ee Mon Sep 17 00:00:00 2001
From: Michele Nottoli <michele.nottoli@gmail.com>
Date: Tue, 7 Nov 2023 14:32:28 +0100
Subject: [PATCH] Lint.
---
gext/fitting.py | 12 ++++++------
1 file changed, 6 insertions(+), 6 deletions(-)
diff --git a/gext/fitting.py b/gext/fitting.py
index 9422b15..c726142 100644
--- a/gext/fitting.py
+++ b/gext/fitting.py
@@ -61,11 +61,11 @@ class LeastSquare(AbstractFitting):
"""Given a set of vectors and a target return the fitting
coefficients."""
matrix = np.array(vectors).T
- A = matrix.T @ matrix
+ 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)
+ a += np.identity(len(b))*self.options["regularization"]
+ coefficients = np.linalg.solve(a, b)
return np.array(coefficients, dtype=np.float64)
class QuasiTimeReversible(AbstractFitting):
@@ -98,12 +98,12 @@ class QuasiTimeReversible(AbstractFitting):
else:
time_reversible_matrix = matrix[:, :q//2+1] + matrix[:, :q//2-1:-1]
- A = time_reversible_matrix.T @ time_reversible_matrix
+ a = time_reversible_matrix.T @ time_reversible_matrix
b = time_reversible_matrix.T @ (target + past_target)
if self.options["regularization"] > 0.0:
- A += np.identity(len(b))*self.options["regularization"]
- coefficients = np.linalg.solve(A, b)
+ a += np.identity(len(b))*self.options["regularization"]
+ coefficients = np.linalg.solve(a, b)
if q == 1:
full_coefficients = np.concatenate(([-1.0], coefficients))
--
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