diff --git a/test_diff_fitting.py b/diff_fitting.py
similarity index 100%
rename from test_diff_fitting.py
rename to diff_fitting.py
diff --git a/gext/buffer.py b/gext/buffer.py
index 373eba7efbf022398efea258df3fadc1314ef9da..39dd6bbaf7dc31e48760cb9d33ede54220cdbf84 100644
--- a/gext/buffer.py
+++ b/gext/buffer.py
@@ -8,15 +8,19 @@ class CircularBuffer:
"""Circular buffer to store the last `n` matrices."""
- def __init__(self, n: int, shape: Tuple[int, ...]):
+ def __init__(self, n: int):
self.n = n
- self.shape = shape
- self.buffer = [np.zeros(shape, dtype=np.float64) for _ in range(n)]
+ self.buffer = None
self.index = 0
self.count = 0
+ def _allocate_buffer(self, shape: Tuple[int, ...]):
+ self.buffer = [np.zeros(shape, dtype=np.float64) for _ in range(self.n)]
+
def push(self, data):
"""Add a new matrix to the buffer."""
+ if self.buffer is None:
+ self._allocate_buffer(data.shape)
self.buffer[self.index] = data.copy()
self.index = (self.index + 1) % self.n
if self.count < self.n:
diff --git a/gext/descriptors.py b/gext/descriptors.py
index e7813e40626fb3e6dffc7dc05e2a666a68f4b93e..2163412b5ae80260712883ef2270c950dde2e981 100644
--- a/gext/descriptors.py
+++ b/gext/descriptors.py
@@ -3,9 +3,7 @@
import numpy as np
from scipy.spatial.distance import pdist
-class Distance:
-
- """Distance matrix descriptors."""
+class BaseFitting:
supported_options = {}
@@ -18,6 +16,10 @@ class Distance:
if len(kwargs) > 0:
raise ValueError("Invalid arguments given to the descriptor class.")
+class Distance(BaseFitting):
+
+ """Distance matrix descriptors."""
+
def compute(self, coords: np.ndarray) -> np.ndarray:
"""Compute the distance matric as a descriptor."""
return pdist(coords, metric="euclidean")
@@ -31,3 +33,16 @@ class Coulomb(Distance):
def compute(self, coords: np.ndarray) -> np.ndarray:
"""Compute the Coulomb matrix as a descriptor."""
return 1.0/super().compute(coords)
+
+class FlattenMatrix(BaseFitting):
+
+ """Use the quantity as it is, just flatten it."""
+
+ supported_options = {}
+
+ def __init__(self, **kwargs):
+ pass
+
+ def compute(self, matrix: np.ndarray) -> np.ndarray:
+ """Compute the descriptor by flattening the matrix."""
+ return matrix.flatten()
diff --git a/gext/main.py b/gext/main.py
index 680a24d4328e9313d2d10a91339b3f4167a63ffc..61124b7debd4b6d02398532563f6e20920083aa6 100644
--- a/gext/main.py
+++ b/gext/main.py
@@ -5,7 +5,7 @@ import numpy as np
from . import grassmann
from .fitting import LeastSquare, QuasiTimeReversible,DiffFitting
-from .descriptors import Distance, Coulomb
+from .descriptors import Distance, Coulomb, FlattenMatrix
from .buffer import CircularBuffer
class Extrapolator:
@@ -35,19 +35,15 @@ class Extrapolator:
self.natoms = natoms
self.set_options(**kwargs)
- self.gammas = CircularBuffer(self.options["nsteps"],
- (self.nelectrons//2, self.nbasis))
- self.descriptors = CircularBuffer(self.options["nsteps"],
- ((self.natoms - 1)*self.natoms//2, ))
+ self.gammas = CircularBuffer(self.options["nsteps"])
+ self.descriptors = CircularBuffer(self.options["nsteps"])
if self.options["store_overlap"]:
- self.overlaps = CircularBuffer(self.options["nsteps"],
- (self.nbasis, self.nbasis))
+ self.overlaps = CircularBuffer(self.options["nsteps"])
if self.options["tangent"]=="one_before_last":
- self.coeffs=CircularBuffer(self.options["nsteps"],
- (self.nelectrons//2, self.nbasis))
+ self.coeffs = CircularBuffer(self.options["nsteps"])
self.tangent: Optional[np.ndarray] = None
- self.H_cores=CircularBuffer(self.options["nsteps"],
- (self.nbasis, self.nbasis))
+ self.H_cores = CircularBuffer(self.options["nsteps"])
+
def set_options(self, **kwargs):
"""Given an arbitrary amount of keyword arguments, parse them if
specified, set default values if not specified and raise an error
@@ -83,14 +79,14 @@ class Extrapolator:
elif self.options["descriptor"] == "coulomb":
self.descriptor_calculator = Coulomb()
elif self.options["descriptor"] == "H_core":
- pass
+ self.descriptor_calculator = FlattenMatrix()
else:
raise ValueError("Unsupported descriptor")
- if self.options["descriptor"] is not "H_core":
- self.descriptor_calculator.set_options(**descriptor_options)
+
+ self.descriptor_calculator.set_options(**descriptor_options)
if self.options["fitting"] == "leastsquare":
- eelf.fitting_calculator = LeastSquare()
+ self.fitting_calculator = LeastSquare()
elif self.options["fitting"] == "diff":
self.fitting_calculator = DiffFitting()
elif self.options["fitting"] == "qtr":
@@ -99,7 +95,7 @@ class Extrapolator:
raise ValueError("Unsupported fitting")
self.fitting_calculator.set_options(**fitting_options)
- def load_data(self, H_core: np.ndarray, coeff: np.ndarray, overlap):
+ def load_data(self, descriptor_input: np.ndarray, coeff: np.ndarray, overlap):
"""Load a new data point in the extrapolator."""
# Crop the coefficient matrix up to the number of electron
@@ -113,11 +109,8 @@ class Extrapolator:
# if it is the first time we load data, set the tangent point
if self.tangent is None and self.options["tangent"] is not "one_before_last":
self._set_tangent(coeff)
- if self.options["descriptor"]=="H_core":
- self.H_cores.push(H_core)
- else:
# push the new data to the corresponding vectors
- self.descriptors.push(self._compute_descriptor(coords))
+ self.descriptors.push(self._compute_descriptor(descriptor_input))
if self.options["store_overlap"]:
self.overlaps.push(overlap)
@@ -127,15 +120,12 @@ class Extrapolator:
c_guess = self.guess_coefficients(coords, overlap)
return c_guess @ c_guess.T
- def guess_coefficients(self, H_core: np.ndarray, overlap=None) -> np.ndarray:
+ def guess_coefficients(self, descriptor_input: np.ndarray, overlap=None) -> np.ndarray:
"""Get a new coefficient matrix to be used as a guess."""
# check if we have enough data points to perform an extrapolation
-
- if self.options["descriptor"]=="H_core":
- count=self.H_cores.count
- else:
- count = self.descriptors.count
+ count = self.descriptors.count
+
if self.options["allow_partially_filled"]:
if count == 0:
raise ValueError("Not enough data loaded in the extrapolator")
@@ -149,14 +139,10 @@ class Extrapolator:
raise ValueError("Guessing without overlap requires `store_overlap` true.")
# use the descriptors to find the fitting coefficients
- if self.options["descriptor"]=="H_core":
- prev_H_cores = self.H_cores.get(n)
- fit_coefficients = self._fit(prev_H_cores, H_core)
- else:
- prev_descriptors= self.descriptors.get(n)
- descriptor = self._compute_descriptor(coords)
- fit_coefficients = self._fit(prev_descriptors, descriptor)
- print(fit_coefficients)
+ 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
diff --git a/tests/test_buffer.py b/tests/test_buffer.py
index 81f495daf62446c2400c258125d2f66c203ff0c4..e3e5b076d84fea875ae5e2314488da18c3ddebb3 100644
--- a/tests/test_buffer.py
+++ b/tests/test_buffer.py
@@ -12,7 +12,7 @@ def test_buffer():
buffer_size = 10
nframes = 20
- buffer = CircularBuffer(buffer_size, shape)
+ buffer = CircularBuffer(buffer_size)
# partial load
for i in range(buffer_size // 2):
@@ -59,7 +59,7 @@ def test_buffer():
def test_buffer_manual():
shape = (5, 5)
- buffer = CircularBuffer(6, shape)
+ buffer = CircularBuffer(6)
for i in range(6):
matrix = np.full(shape, i)