diff --git a/.gitignore b/.gitignore
index c5b2148a43663314ad0317ed1d16a12c0627085e..d240bbd11c52c593b0012a6b5b07c5c81004d909 100644
--- a/.gitignore
+++ b/.gitignore
@@ -116,6 +116,7 @@ TODO.txt
b_asic/_version.py
docs_sphinx/_build/
docs_sphinx/examples
+docs_sphinx/sg_execution_times.rst
result_images/
.coverage
Digraph.gv
diff --git a/b_asic/core_operations.py b/b_asic/core_operations.py
index df0868ee53b0b2d8b0d74706e8f3b31cad813892..3a1474cc7513955e8bd7536e7bf4350fe61d94cd 100644
--- a/b_asic/core_operations.py
+++ b/b_asic/core_operations.py
@@ -1099,6 +1099,108 @@ class MAD(AbstractOperation):
p._index = i
+class MADS(AbstractOperation):
+ __slots__ = (
+ "_is_add",
+ "_override_zero_on_src0",
+ "_src0",
+ "_src1",
+ "_src2",
+ "_name",
+ "_latency",
+ "_latency_offsets",
+ "_execution_time",
+ )
+ _is_add: Optional[bool]
+ _override_zero_on_src0: Optional[bool]
+ _src0: Optional[SignalSourceProvider]
+ _src1: Optional[SignalSourceProvider]
+ _src2: Optional[SignalSourceProvider]
+ _name: Name
+ _latency: Optional[int]
+ _latency_offsets: Optional[Dict[str, int]]
+ _execution_time: Optional[int]
+
+ is_swappable = True
+
+ def __init__(
+ self,
+ is_add: Optional[bool] = True,
+ override_zero_on_src0: Optional[bool] = False,
+ src0: Optional[SignalSourceProvider] = None,
+ src1: Optional[SignalSourceProvider] = None,
+ src2: Optional[SignalSourceProvider] = None,
+ name: Name = Name(""),
+ latency: Optional[int] = None,
+ latency_offsets: Optional[Dict[str, int]] = None,
+ execution_time: Optional[int] = None,
+ ):
+ """Construct a MADS operation."""
+ super().__init__(
+ input_count=3,
+ output_count=1,
+ name=Name(name),
+ input_sources=[src0, src1, src2],
+ latency=latency,
+ latency_offsets=latency_offsets,
+ execution_time=execution_time,
+ )
+ self.set_param("is_add", is_add)
+ self.set_param("override_zero_on_src0", override_zero_on_src0)
+
+ @classmethod
+ def type_name(cls) -> TypeName:
+ return TypeName("mads")
+
+ def evaluate(self, a, b, c):
+ if self.is_add:
+ if self.override_zero_on_src0:
+ return b * c
+ else:
+ return a + b * c
+ else:
+ if self.override_zero_on_src0:
+ return -b * c
+ else:
+ return a - b * c
+
+ @property
+ def is_add(self) -> bool:
+ """Get if operation is an addition."""
+ return self.param("is_add")
+
+ @is_add.setter
+ def is_add(self, is_add: bool) -> None:
+ """Set if operation is an addition."""
+ self.set_param("is_add", is_add)
+
+ @property
+ def override_zero_on_src0(self) -> bool:
+ """Get if operation is overriding a zero on port src0."""
+ return self.param("override_zero_on_src0")
+
+ @override_zero_on_src0.setter
+ def override_zero_on_src0(self, override_zero_on_src0: bool) -> None:
+ """Set if operation is overriding a zero on port src0."""
+ self.set_param("override_zero_on_src0", override_zero_on_src0)
+
+ @property
+ def is_linear(self) -> bool:
+ return (
+ self.input(1).connected_source.operation.is_constant
+ or self.input(2).connected_source.operation.is_constant
+ )
+
+ def swap_io(self) -> None:
+ self._input_ports = [
+ self._input_ports[0],
+ self._input_ports[2],
+ self._input_ports[1],
+ ]
+ for i, p in enumerate(self._input_ports):
+ p._index = i
+
+
class SymmetricTwoportAdaptor(AbstractOperation):
r"""
Wave digital filter symmetric twoport-adaptor operation.
@@ -1519,6 +1621,51 @@ class Shift(AbstractOperation):
self.set_param("value", value)
+class DontCare(AbstractOperation):
+ r"""
+ Dont-care operation
+
+ Used for ignoring the input to another operation and thus avoiding dangling input nodes.
+
+ Parameters
+ ----------
+ name : Name, optional
+ Operation name.
+
+ """
+
+ __slots__ = "_name"
+ _name: Name
+
+ is_linear = True
+
+ def __init__(self, name: Name = ""):
+ """Construct a DontCare operation."""
+ super().__init__(
+ input_count=0,
+ output_count=1,
+ name=name,
+ latency_offsets={"out0": 0},
+ )
+
+ @classmethod
+ def type_name(cls) -> TypeName:
+ return TypeName("dontcare")
+
+ def evaluate(self):
+ return 0
+
+ @property
+ def latency(self) -> int:
+ return self.latency_offsets["out0"]
+
+ def __repr__(self) -> str:
+ return "DontCare()"
+
+ def __str__(self) -> str:
+ return "dontcare"
+
+
class Sink(AbstractOperation):
r"""
Sink operation.
diff --git a/b_asic/sfg_generators.py b/b_asic/sfg_generators.py
index 737501f1650c0b4756f218fb45675a64b4ea0bcb..3e76f159706a72d349d8006c27c894372f5a1ce3 100644
--- a/b_asic/sfg_generators.py
+++ b/b_asic/sfg_generators.py
@@ -9,10 +9,13 @@ from typing import TYPE_CHECKING, Dict, Optional, Sequence, Union
import numpy as np
from b_asic.core_operations import (
+ MADS,
Addition,
Butterfly,
ConstantMultiplication,
+ DontCare,
Name,
+ Reciprocal,
SymmetricTwoportAdaptor,
)
from b_asic.signal import Signal
@@ -432,6 +435,82 @@ def radix_2_dif_fft(points: int) -> SFG:
return SFG(inputs=inputs, outputs=outputs)
+def ldlt_matrix_inverse(N: int) -> SFG:
+ """Generates an SFG for the LDLT matrix inverse algorithm.
+
+ Parameters
+ ----------
+ N : int
+ Dimension of the square input matrix.
+
+ Returns
+ -------
+ SFG
+ Signal Flow Graph
+ """
+ inputs = []
+ A = [[None for _ in range(N)] for _ in range(N)]
+ for i in range(N):
+ for j in range(i, N):
+ in_op = Input()
+ A[i][j] = in_op
+ inputs.append(in_op)
+
+ D = [None for _ in range(N)]
+ for i in range(N):
+ D[i] = A[i][i]
+
+ D_inv = [None for _ in range(N)]
+
+ R = [[None for _ in range(N)] for _ in range(N)]
+ M = [[None for _ in range(N)] for _ in range(N)]
+
+ # R*di*R^T factorization
+ for i in range(N):
+ for k in range(i):
+ D[i] = MADS(False, False, D[i], M[k][i], R[k][i])
+
+ D_inv[i] = Reciprocal(D[i])
+
+ for j in range(i + 1, N):
+ R[i][j] = A[i][j]
+
+ for k in range(i):
+ R[i][j] = MADS(False, False, R[i][j], M[k][i], R[k][j])
+
+ # if is_complex:
+ # M[i][j] = ComplexConjugate(R[i][j])
+ # else:
+ M[i][j] = R[i][j]
+
+ R[i][j] = MADS(True, True, DontCare(), R[i][j], D_inv[i])
+
+ # back substitution
+ A_inv = [[None for _ in range(N)] for _ in range(N)]
+ for i in reversed(range(N)):
+ A_inv[i][i] = D_inv[i]
+ for j in reversed(range(i + 1)):
+ for k in reversed(range(j + 1, N)):
+ if k == N - 1 and i != j:
+ A_inv[j][i] = MADS(False, True, DontCare(), R[j][k], A_inv[i][k])
+ else:
+ if A_inv[i][k]:
+ A_inv[j][i] = MADS(
+ False, False, A_inv[j][i], R[j][k], A_inv[i][k]
+ )
+ else:
+ A_inv[j][i] = MADS(
+ False, False, A_inv[j][i], R[j][k], A_inv[k][i]
+ )
+
+ outputs = []
+ for i in range(N):
+ for j in range(i, N):
+ outputs.append(Output(A_inv[i][j]))
+
+ return SFG(inputs, outputs)
+
+
def _construct_dif_fft_stage(
ports_from_previous_stage: list["OutputPort"],
number_of_stages: int,
diff --git a/test/test_core_operations.py b/test/test_core_operations.py
index 1a5f367b07bfb9d9a3bf6d3d70f7b1a53901db90..4b34ac58fa029c5093a6c8674cc484aca5586724 100644
--- a/test/test_core_operations.py
+++ b/test/test_core_operations.py
@@ -3,6 +3,9 @@
import pytest
from b_asic import (
+ MAD,
+ MADS,
+ SFG,
Absolute,
Addition,
AddSub,
@@ -11,10 +14,13 @@ from b_asic import (
Constant,
ConstantMultiplication,
Division,
+ DontCare,
+ Input,
LeftShift,
Max,
Min,
Multiplication,
+ Output,
Reciprocal,
RightShift,
Shift,
@@ -47,6 +53,14 @@ class TestConstant:
test_operation.value = 4
assert test_operation.value == 4
+ def test_constant_repr(self):
+ test_operation = Constant(3)
+ assert repr(test_operation) == "Constant(3)"
+
+ def test_constant_str(self):
+ test_operation = Constant(3)
+ assert str(test_operation) == "3"
+
class TestAddition:
"""Tests for Addition class."""
@@ -83,16 +97,16 @@ class TestSubtraction:
class TestAddSub:
"""Tests for AddSub class."""
- def test_addition_positive(self):
+ def test_addsub_positive(self):
test_operation = AddSub(is_add=True)
assert test_operation.evaluate_output(0, [3, 5]) == 8
- def test_addition_negative(self):
+ def test_addsub_negative(self):
test_operation = AddSub(is_add=True)
assert test_operation.evaluate_output(0, [-3, -5]) == -8
assert test_operation.is_add
- def test_addition_complex(self):
+ def test_addsub_complex(self):
test_operation = AddSub(is_add=True)
assert test_operation.evaluate_output(0, [3 + 5j, 4 + 6j]) == 7 + 11j
@@ -116,6 +130,22 @@ class TestAddSub:
test_operation = AddSub(is_add=True)
assert test_operation.is_swappable
+ def test_addsub_is_add_getter(self):
+ test_operation = AddSub(is_add=False)
+ assert not test_operation.is_add
+
+ test_operation = AddSub(is_add=True)
+ assert test_operation.is_add
+
+ def test_addsub_is_add_setter(self):
+ test_operation = AddSub(is_add=False)
+ test_operation.is_add = True
+ assert test_operation.is_add
+
+ test_operation = AddSub(is_add=True)
+ test_operation.is_add = False
+ assert not test_operation.is_add
+
class TestMultiplication:
"""Tests for Multiplication class."""
@@ -148,6 +178,13 @@ class TestDivision:
test_operation = Division()
assert test_operation.evaluate_output(0, [60 + 40j, 10 + 20j]) == 2.8 - 1.6j
+ def test_mads_is_linear(self):
+ test_operation = Division(Constant(3), Addition(Input(), Constant(3)))
+ assert not test_operation.is_linear
+
+ test_operation = Division(Addition(Input(), Constant(3)), Constant(3))
+ assert test_operation.is_linear
+
class TestSquareRoot:
"""Tests for SquareRoot class."""
@@ -200,6 +237,13 @@ class TestMin:
test_operation = Min()
assert test_operation.evaluate_output(0, [-30, -5]) == -30
+ def test_min_complex(self):
+ test_operation = Min()
+ with pytest.raises(
+ ValueError, match="core_operations.Min does not support complex numbers."
+ ):
+ test_operation.evaluate_output(0, [-1 - 1j, 2 + 2j])
+
class TestAbsolute:
"""Tests for Absolute class."""
@@ -216,6 +260,13 @@ class TestAbsolute:
test_operation = Absolute()
assert test_operation.evaluate_output(0, [3 + 4j]) == 5.0
+ def test_max_complex(self):
+ test_operation = Max()
+ with pytest.raises(
+ ValueError, match="core_operations.Max does not support complex numbers."
+ ):
+ test_operation.evaluate_output(0, [-1 - 1j, 2 + 2j])
+
class TestConstantMultiplication:
"""Tests for ConstantMultiplication class."""
@@ -234,6 +285,136 @@ class TestConstantMultiplication:
assert test_operation.evaluate_output(0, [3 + 4j]) == 1 + 18j
+class TestMAD:
+ def test_mad_positive(self):
+ test_operation = MAD()
+ assert test_operation.evaluate_output(0, [1, 2, 3]) == 5
+
+ def test_mad_negative(self):
+ test_operation = MAD()
+ assert test_operation.evaluate_output(0, [-3, -5, -8]) == 7
+
+ def test_mad_complex(self):
+ test_operation = MAD()
+ assert test_operation.evaluate_output(0, [3 + 6j, 2 + 6j, 1 + 1j]) == -29 + 31j
+
+ def test_mad_is_linear(self):
+ test_operation = MAD(
+ Constant(3), Addition(Input(), Constant(3)), Addition(Input(), Constant(3))
+ )
+ assert test_operation.is_linear
+
+ test_operation = MAD(
+ Addition(Input(), Constant(3)), Constant(3), Addition(Input(), Constant(3))
+ )
+ assert test_operation.is_linear
+
+ test_operation = MAD(
+ Addition(Input(), Constant(3)), Addition(Input(), Constant(3)), Constant(3)
+ )
+ assert not test_operation.is_linear
+
+ def test_mad_swap_io(self):
+ test_operation = MAD()
+ assert test_operation.evaluate_output(0, [1, 2, 3]) == 5
+ test_operation.swap_io()
+ assert test_operation.evaluate_output(0, [1, 2, 3]) == 5
+
+
+class TestMADS:
+ def test_mads_positive(self):
+ test_operation = MADS(is_add=False)
+ assert test_operation.evaluate_output(0, [1, 2, 3]) == -5
+
+ def test_mads_negative(self):
+ test_operation = MADS(is_add=False)
+ assert test_operation.evaluate_output(0, [-3, -5, -8]) == -43
+
+ def test_mads_complex(self):
+ test_operation = MADS(is_add=False)
+ assert test_operation.evaluate_output(0, [3 + 6j, 2 + 6j, 1 + 1j]) == 7 - 2j
+
+ def test_mads_positive_add(self):
+ test_operation = MADS(is_add=True)
+ assert test_operation.evaluate_output(0, [1, 2, 3]) == 7
+
+ def test_mads_negative_add(self):
+ test_operation = MADS(is_add=True)
+ assert test_operation.evaluate_output(0, [-3, -5, -8]) == 37
+
+ def test_mads_complex_add(self):
+ test_operation = MADS(is_add=True)
+ assert test_operation.evaluate_output(0, [3 + 6j, 2 + 6j, 1 + 1j]) == -1 + 14j
+
+ def test_mads_zero_override(self):
+ test_operation = MADS(is_add=True, override_zero_on_src0=True)
+ assert test_operation.evaluate_output(0, [1, 1, 1]) == 1
+
+ def test_mads_sub_zero_override(self):
+ test_operation = MADS(is_add=False, override_zero_on_src0=True)
+ assert test_operation.evaluate_output(0, [1, 1, 1]) == -1
+
+ def test_mads_is_linear(self):
+ test_operation = MADS(
+ src0=Constant(3),
+ src1=Addition(Input(), Constant(3)),
+ src2=Addition(Input(), Constant(3)),
+ )
+ assert not test_operation.is_linear
+
+ test_operation = MADS(
+ src0=Addition(Input(), Constant(3)),
+ src1=Constant(3),
+ src2=Addition(Input(), Constant(3)),
+ )
+ assert test_operation.is_linear
+
+ test_operation = MADS(
+ src0=Addition(Input(), Constant(3)),
+ src1=Addition(Input(), Constant(3)),
+ src2=Constant(3),
+ )
+ assert test_operation.is_linear
+
+ def test_mads_swap_io(self):
+ test_operation = MADS(is_add=False)
+ assert test_operation.evaluate_output(0, [1, 2, 3]) == -5
+ test_operation.swap_io()
+ assert test_operation.evaluate_output(0, [1, 2, 3]) == -5
+
+ def test_mads_is_add_getter(self):
+ test_operation = MADS(is_add=False)
+ assert not test_operation.is_add
+
+ test_operation = MADS(is_add=True)
+ assert test_operation.is_add
+
+ def test_mads_is_add_setter(self):
+ test_operation = MADS(is_add=False)
+ test_operation.is_add = True
+ assert test_operation.is_add
+
+ test_operation = MADS(is_add=True)
+ test_operation.is_add = False
+ assert not test_operation.is_add
+
+ def test_mads_override_zero_on_src0_getter(self):
+ test_operation = MADS(override_zero_on_src0=False)
+ assert not test_operation.override_zero_on_src0
+
+ test_operation = MADS(override_zero_on_src0=True)
+ assert test_operation.override_zero_on_src0
+
+ def test_mads_override_zero_on_src0_setter(self):
+ test_operation = MADS(override_zero_on_src0=False)
+ test_operation.override_zero_on_src0 = True
+ assert test_operation.override_zero_on_src0
+
+ test_operation = MADS(override_zero_on_src0=True)
+ test_operation.override_zero_on_src0 = False
+ assert not test_operation.override_zero_on_src0
+
+
class TestRightShift:
"""Tests for RightShift class."""
@@ -408,6 +589,33 @@ class TestDepends:
assert set(bfly1.inputs_required_for_output(1)) == {0, 1}
+class TestDontCare:
+ def test_create_sfg_with_dontcare(self):
+ i1 = Input()
+ dc = DontCare()
+ a = Addition(i1, dc)
+ o = Output(a)
+ sfg = SFG([i1], [o])
+
+ assert sfg.output_count == 1
+ assert sfg.input_count == 1
+
+ assert sfg.evaluate_output(0, [0]) == 0
+ assert sfg.evaluate_output(0, [1]) == 1
+
+ def test_dontcare_latency_getter(self):
+ test_operation = DontCare()
+ assert test_operation.latency == 0
+
+ def test_dontcare_repr(self):
+ test_operation = DontCare()
+ assert repr(test_operation) == "DontCare()"
+
+ def test_dontcare_str(self):
+ test_operation = DontCare()
+ assert str(test_operation) == "dontcare"
+
+
class TestSink:
def test_create_sfg_with_sink(self):
bfly = Butterfly()
@@ -420,3 +628,15 @@ class TestSink:
assert sfg1.input_count == 2
assert sfg.evaluate_output(1, [0, 1]) == sfg1.evaluate_output(0, [0, 1])
+
+ def test_sink_latency_getter(self):
+ test_operation = Sink()
+ assert test_operation.latency == 0
+
+ def test_sink_repr(self):
+ test_operation = Sink()
+ assert repr(test_operation) == "Sink()"
+
+ def test_sink_str(self):
+ test_operation = Sink()
+ assert str(test_operation) == "sink"
diff --git a/test/test_sfg_generators.py b/test/test_sfg_generators.py
index 7064658c0a0580b4521e6d0c58562565053b2c01..bbd8916ef3a47df39d49a6d0bfaaa96022dd44d5 100644
--- a/test/test_sfg_generators.py
+++ b/test/test_sfg_generators.py
@@ -3,15 +3,18 @@ import pytest
from scipy import signal
from b_asic.core_operations import (
+ MADS,
Addition,
Butterfly,
ConstantMultiplication,
+ Reciprocal,
SymmetricTwoportAdaptor,
)
from b_asic.sfg_generators import (
direct_form_1_iir,
direct_form_2_iir,
direct_form_fir,
+ ldlt_matrix_inverse,
radix_2_dif_fft,
transposed_direct_form_fir,
wdf_allpass,
@@ -637,3 +640,176 @@ class TestRadix2FFT:
POINTS = 5
with pytest.raises(ValueError, match="Points must be a power of two."):
radix_2_dif_fft(points=POINTS)
+
+
+class TestLdltMatrixInverse:
+ def test_1x1(self):
+ sfg = ldlt_matrix_inverse(N=1)
+
+ assert len(sfg.inputs) == 1
+ assert len(sfg.outputs) == 1
+
+ assert len(sfg.find_by_type_name(MADS.type_name())) == 0
+ assert len(sfg.find_by_type_name(Reciprocal.type_name())) == 1
+
+ A_input = [Constant(5)]
+
+ sim = Simulation(sfg, A_input)
+ sim.run_for(1)
+
+ res = sim.results
+ assert np.isclose(res["0"], 0.2)
+
+ def test_2x2_simple_spd(self):
+ sfg = ldlt_matrix_inverse(N=2)
+
+ assert len(sfg.inputs) == 3
+ assert len(sfg.outputs) == 3
+
+ assert len(sfg.find_by_type_name(MADS.type_name())) == 4
+ assert len(sfg.find_by_type_name(Reciprocal.type_name())) == 2
+
+ A = np.array([[1, 2], [2, 1]])
+ A_input = [Constant(1), Constant(2), Constant(1)]
+
+ A_inv = np.linalg.inv(A)
+
+ sim = Simulation(sfg, A_input)
+ sim.run_for(1)
+
+ res = sim.results
+ assert np.isclose(res["0"], A_inv[0, 0])
+ assert np.isclose(res["1"], A_inv[0, 1])
+ assert np.isclose(res["2"], A_inv[1, 1])
+
+ def test_3x3_simple_spd(self):
+ sfg = ldlt_matrix_inverse(N=3)
+
+ assert len(sfg.inputs) == 6
+ assert len(sfg.outputs) == 6
+
+ assert len(sfg.find_by_type_name(MADS.type_name())) == 15
+ assert len(sfg.find_by_type_name(Reciprocal.type_name())) == 3
+
+ A = np.array([[2, -1, 0], [-1, 3, -1], [0, -1, 2]])
+ A_input = [
+ Constant(2),
+ Constant(-1),
+ Constant(0),
+ Constant(3),
+ Constant(-1),
+ Constant(2),
+ ]
+
+ A_inv = np.linalg.inv(A)
+
+ sim = Simulation(sfg, A_input)
+ sim.run_for(1)
+
+ res = sim.results
+ assert np.isclose(res["0"], A_inv[0, 0])
+ assert np.isclose(res["1"], A_inv[0, 1])
+ assert np.isclose(res["2"], A_inv[0, 2])
+ assert np.isclose(res["3"], A_inv[1, 1])
+ assert np.isclose(res["4"], A_inv[1, 2])
+ assert np.isclose(res["5"], A_inv[2, 2])
+
+ def test_5x5_random_spd(self):
+ N = 5
+
+ sfg = ldlt_matrix_inverse(N=N)
+
+ assert len(sfg.inputs) == 15
+ assert len(sfg.outputs) == 15
+
+ assert len(sfg.find_by_type_name(MADS.type_name())) == 70
+ assert len(sfg.find_by_type_name(Reciprocal.type_name())) == N
+
+ A = self._generate_random_spd_matrix(N)
+
+ upper_tridiag = A[np.triu_indices_from(A)]
+
+ A_input = [Constant(num) for num in upper_tridiag]
+ A_inv = np.linalg.inv(A)
+
+ sim = Simulation(sfg, A_input)
+ sim.run_for(1)
+ res = sim.results
+
+ row_indices, col_indices = np.triu_indices(N)
+ expected_values = A_inv[row_indices, col_indices]
+ actual_values = [res[str(i)] for i in range(len(expected_values))]
+
+ for i in range(len(expected_values)):
+ assert np.isclose(actual_values[i], expected_values[i])
+
+ def test_20x20_random_spd(self):
+ N = 20
+
+ sfg = ldlt_matrix_inverse(N=N)
+
+ A = self._generate_random_spd_matrix(N)
+
+ assert len(sfg.inputs) == len(A[np.triu_indices_from(A)])
+ assert len(sfg.outputs) == len(A[np.triu_indices_from(A)])
+
+ assert len(sfg.find_by_type_name(Reciprocal.type_name())) == N
+
+ upper_tridiag = A[np.triu_indices_from(A)]
+
+ A_input = [Constant(num) for num in upper_tridiag]
+ A_inv = np.linalg.inv(A)
+
+ sim = Simulation(sfg, A_input)
+ sim.run_for(1)
+ res = sim.results
+
+ row_indices, col_indices = np.triu_indices(N)
+ expected_values = A_inv[row_indices, col_indices]
+ actual_values = [res[str(i)] for i in range(len(expected_values))]
+
+ for i in range(len(expected_values)):
+ assert np.isclose(actual_values[i], expected_values[i])
+
+ # def test_2x2_random_complex_spd(self):
+ # N = 2
+
+ # sfg = ldlt_matrix_inverse(N=N, is_complex=True)
+
+ # # A = self._generate_random_complex_spd_matrix(N)
+ # A = np.array([[2, 1+1j],[1-1j, 3]])
+
+ # assert len(sfg.inputs) == len(A[np.triu_indices_from(A)])
+ # assert len(sfg.outputs) == len(A[np.triu_indices_from(A)])
+
+ # assert len(sfg.find_by_type_name(Reciprocal.type_name())) == N
+
+ # upper_tridiag = A[np.triu_indices_from(A)]
+
+ # A_input = [Constant(num) for num in upper_tridiag]
+ # A_inv = np.linalg.inv(A)
+
+ # sim = Simulation(sfg, A_input)
+ # sim.run_for(1)
+ # res = sim.results
+
+ # row_indices, col_indices = np.triu_indices(N)
+ # expected_values = A_inv[row_indices, col_indices]
+ # actual_values = [res[str(i)] for i in range(len(expected_values))]
+
+ # for i in range(len(expected_values)):
+ # assert np.isclose(actual_values[i], expected_values[i])
+
+ def _generate_random_spd_matrix(self, N: int) -> np.ndarray:
+ A = np.random.rand(N, N)
+ A = (A + A.T) / 2 # ensure symmetric
+ min_eig = np.min(np.linalg.eigvals(A))
+ A += (np.abs(min_eig) + 0.1) * np.eye(N) # ensure positive definiteness
+ return A
+
+ # def _generate_random_complex_spd_matrix(self, N: int) -> np.ndarray:
+ # A = np.random.randn(N, N) + 1j * np.random.randn(N, N)
+ # A = (A + A.conj().T) / 2 # ensure symmetric
+ # min_eig = np.min(np.linalg.eigvals(A))
+ # A += (np.abs(min_eig) + 0.1) * np.eye(N) # ensure positive definiteness
+ # return A