From f60da40e47c009c72944a82aeeb4cd7671fb1b38 Mon Sep 17 00:00:00 2001
From: Michele Nottoli <michele.nottoli@gmail.com>
Date: Mon, 6 Nov 2023 10:33:43 +0100
Subject: [PATCH] Reshape.
---
gext/fitting.py | 42 +++++++++++++++++++++++++++++-------------
gext/main.py | 9 ++++++---
2 files changed, 35 insertions(+), 16 deletions(-)
diff --git a/gext/fitting.py b/gext/fitting.py
index 94f4e87..1d9fe93 100644
--- a/gext/fitting.py
+++ b/gext/fitting.py
@@ -9,11 +9,23 @@ class AbstractFitting(abc.ABC):
"""Base class for fitting schemes."""
def __init__(self, **kwargs):
+ self.supported_options = {}
self.set_options(**kwargs)
@abc.abstractmethod
def set_options(self, **kwargs):
"""Base method for setting options."""
+ self.options = {}
+ for key, value in kwargs.items():
+ if key in self.supported_options:
+ self.options[key] = value
+ else:
+ raise ValueError(f"Unsupported option: {key}")
+
+ for option, default_value in self.supported_options.items():
+ if option not in self.options:
+ self.options[option] = default_value
+
@abc.abstractmethod
def fit(self, vectors: List[np.ndarray], target:np.ndarray):
@@ -33,22 +45,15 @@ class LeastSquare(AbstractFitting):
"""Simple least square minimization fitting."""
- supported_options = {
- "regularization": 0.0,
- }
+ def __init__(self, **kwargs):
+ self.supported_options = {
+ "regularization": 0.0,
+ }
+ super().__init__(**kwargs)
def set_options(self, **kwargs):
"""Set options for least square minimization"""
- self.options = {}
- for key, value in kwargs.items():
- if key in self.supported_options:
- self.options[key] = value
- else:
- raise ValueError(f"Unsupported option: {key}")
-
- for option, default_value in self.supported_options.items():
- if option not in self.options:
- self.options[option] = default_value
+ super().set_options(**kwargs)
if self.options["regularization"] < 0 \
or self.options["regularization"] > 100:
@@ -65,8 +70,19 @@ class QuasiTimeReversible(AbstractFitting):
"""Quasi time reversible fitting scheme. Not yet implemented."""
+ def __init__(self, **kwargs):
+ self.supported_options = {
+ "regularization": 0.0,
+ }
+ super().__init__(**kwargs)
+
def set_options(self, **kwargs):
"""Set options for quasi time reversible fitting"""
+ 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):
"""Time reversible least square minimization fitting."""
diff --git a/gext/main.py b/gext/main.py
index e87e37e..b48594a 100644
--- a/gext/main.py
+++ b/gext/main.py
@@ -12,9 +12,7 @@ class Extrapolator:
"""Class for performing Grassmann extrapolations. On initialization
it requires the number of electrons, the number of basis functions
- and the number of atoms of the molecule. The number of previous
- steps used by the extrapolator is an optional argument with default
- value of 6."""
+ and the number of atoms of the molecule."""
def __init__(self, nelectrons: int, nbasis: int, natoms: int, **kwargs):
@@ -83,12 +81,17 @@ class Extrapolator:
def load_data(self, coords: np.ndarray, coeff: np.ndarray,
overlap: np.ndarray):
"""Load a new data point in the extrapolator."""
+
+ # Crop the coefficient matrix up to the number of electron
+ # pairs, then apply S^1/2
coeff = self._crop_coeff(coeff)
coeff = self._normalize(coeff, overlap)
+ # if it is the first time we load data, set the tangent point
if self.tangent is None:
self._set_tangent(coeff)
+ # push the new data to the corresponding vectors
self.gammas.push(self._grassmann_log(coeff))
self.descriptors.push(self._compute_descriptor(coords))
self.overlaps.push(overlap)
--
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