From 8b4a8c085a975758e81132b99b13252183d6ed70 Mon Sep 17 00:00:00 2001
From: Michele Nottoli <michele.nottoli@gmail.com>
Date: Thu, 13 Mar 2025 14:23:23 +0100
Subject: [PATCH] Some old update.
---
gext/fitting.py | 36 ++++++++++++++++++++++++++----------
1 file changed, 26 insertions(+), 10 deletions(-)
diff --git a/gext/fitting.py b/gext/fitting.py
index 958f9fd..b0a988f 100644
--- a/gext/fitting.py
+++ b/gext/fitting.py
@@ -160,6 +160,14 @@ class LeastSquare(AbstractFitting):
result += vector * c
return result
+ def dummy_extrapolate(self, _):
+ coefficients = np.zeros(len(self.A))
+ coefficients[-1] = 1.0
+ result = np.zeros(self.gammas[0].shape)
+ for c, vector in zip(coefficients, self.gammas):
+ result += vector * c
+ return result
+
class QuasiTimeReversible(AbstractFitting):
"""Quasi time reversible fitting scheme."""
@@ -167,7 +175,6 @@ class QuasiTimeReversible(AbstractFitting):
supported_options = {
"normalize": False,
"regularization": 1e-4,
- "full_second_order": False
}
def __init__(self, **kwargs):
@@ -248,6 +255,7 @@ class PolynomialRegression(AbstractFitting):
supported_options = {
"normalize": False,
"regularization": 1e-4,
+ "full_second_order": False,
"ref": -1,
"order": 1}
@@ -290,29 +298,31 @@ class PolynomialRegression(AbstractFitting):
gammas.append(
gamma_list[i].flatten() - self.gamma_ref)
- d = np.array(descriptors, dtype=np.float64).T
+ D = np.array(descriptors, dtype=np.float64).T
gammas = np.array(gammas, dtype=np.float64).T
else:
- d = np.array(descriptor_list, dtype=np.float64).T
+ D = np.array(descriptor_list, dtype=np.float64).T
gammas = np.reshape(gamma_list,
(len(gamma_list), self.gamma_shape[0]*self.gamma_shape[1])).T
norm = 1.0
if self.options["normalize"]:
- norm = np.linalg.norm(d)
+ norm = np.linalg.norm(D)
if self.options["order"] >= 2:
if self.options["full_second_order"]:
- outer = np.outer(d, d)
- d = np.vstack((d, outer.flatten()))
+ O = np.empty((D.shape[0]**2, D.shape[1]))
+ for i in range(D.shape[1]):
+ O[:, i] = np.outer(D[:, i], D[:, i]).flatten()
+ D = np.vstack((D, O))
else:
- d = np.vstack((d, d**2))
+ D = np.vstack((D, D**2))
- A = d.T @ d
+ A = D.T @ D
if self.options["regularization"] > 0.0:
A += np.identity(len(A))*self.options["regularization"]**2*norm**2
- self.matrix = d @ np.linalg.inv(A) @ gammas.T
+ self.matrix = D @ np.linalg.solve(A, gammas.T)
def extrapolate(self, descriptor):
"""Apply the matrix to the current descriptor."""
@@ -320,7 +330,13 @@ class PolynomialRegression(AbstractFitting):
descriptor -= self.descriptor_ref
descriptor = np.array([descriptor])
if self.options["order"] >= 2:
- descriptor = np.concatenate([descriptor, descriptor**2], axis=1)
+ if self.options["full_second_order"]:
+ descriptor = np.concatenate(
+ [descriptor,
+ np.outer(descriptor,descriptor).flatten()],
+ axis=1)
+ else:
+ descriptor = np.concatenate([descriptor, descriptor**2], axis=1)
gamma = descriptor @ self.matrix
if self.options["ref"]:
gamma += self.gamma_ref
--
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