From 0c054fa47d325cdb78af827e83e90778c9044892 Mon Sep 17 00:00:00 2001
From: Zahra Askarpour <Zahra.Askarpour@mathematik.uni-stuttgart.de>
Date: Thu, 15 Feb 2024 11:36:48 +0100
Subject: [PATCH] DiffFitting
---
gext/fitting.py | 56 ++++++++++++++++++++++++++++++++++++++++++++++++-
gext/main.py | 9 +++++---
2 files changed, 61 insertions(+), 4 deletions(-)
diff --git a/gext/fitting.py b/gext/fitting.py
index efd09f0..84bac2d 100644
--- a/gext/fitting.py
+++ b/gext/fitting.py
@@ -41,6 +41,58 @@ class AbstractFitting(abc.ABC):
result += vector*coeff
return result
+
+class DiffFitting(AbstractFitting):
+
+ """Simple least square minimization fitting."""
+
+ supported_options = {
+ "regularization": 0.0,
+ }
+
+ def set_options(self, **kwargs):
+ """Set options for least square minimization"""
+ super().set_options(**kwargs)
+
+ if self.options["regularization"] < 0 \
+ or self.options["regularization"] > 100:
+ raise ValueError("Unsupported value for regularization")
+
+ def fit(self, vectors: List[np.ndarray], target: np.ndarray):
+ """Given a set of vectors and a target return the fitting
+ coefficients."""
+ 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)
+
+ 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."""
+ 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
+
class LeastSquare(AbstractFitting):
"""Simple least square minimization fitting."""
@@ -66,8 +118,9 @@ class LeastSquare(AbstractFitting):
if self.options["regularization"] > 0.0:
a += np.identity(len(b))*self.options["regularization"]
coefficients = np.linalg.solve(a, b)
+ print(coefficients)
return np.array(coefficients, dtype=np.float64)
-
+
class QuasiTimeReversible(AbstractFitting):
"""Quasi time reversible fitting scheme."""
@@ -115,3 +168,4 @@ class QuasiTimeReversible(AbstractFitting):
full_coefficients = np.concatenate(([-1.0], coefficients[:-1],
2.0*coefficients[-1:], coefficients[-2::-1]))
return np.array(full_coefficients, dtype=np.float64)
+
diff --git a/gext/main.py b/gext/main.py
index f1546f3..e09140c 100644
--- a/gext/main.py
+++ b/gext/main.py
@@ -4,7 +4,7 @@ from typing import Optional
import numpy as np
from . import grassmann
-from .fitting import LeastSquare, QuasiTimeReversible
+from .fitting import LeastSquare, QuasiTimeReversible,DiffFitting
from .descriptors import Distance, Coulomb
from .buffer import CircularBuffer
@@ -18,7 +18,7 @@ class Extrapolator:
"verbose": False,
"nsteps": 6,
"descriptor": "distance",
- "fitting": "leastsquare",
+ "fitting": "diff",
"allow_partially_filled": True,
"store_overlap": True,
}
@@ -84,6 +84,8 @@ class Extrapolator:
if self.options["fitting"] == "leastsquare":
self.fitting_calculator = LeastSquare()
+ elif self.options["fitting"] == "diff":
+ self.fitting_calculator = DiffFitting()
elif self.options["fitting"] == "qtr":
self.fitting_calculator = QuasiTimeReversible()
else:
@@ -135,12 +137,13 @@ class Extrapolator:
prev_descriptors = self.descriptors.get(n)
descriptor = self._compute_descriptor(coords)
fit_coefficients = self._fit(prev_descriptors, descriptor)
+ print(fit_coefficients)
# use the fitting coefficients and the previous gammas to
# extrapolate a new gamma
gammas = self.gammas.get(n)
gamma = self.fitting_calculator.linear_combination(gammas, fit_coefficients)
-
+
if self.options["verbose"]:
fit_descriptor = self.fitting_calculator.linear_combination(
prev_descriptors, fit_coefficients)
--
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