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Description:

jijcimod 1.6.2

cimod : C++ header-only library for a binary quadratic model

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How to use
You should only include a header src/binary_quadratic_model.hpp in your project.
Example
C++
#include "src/binary_quadratic_model.hpp"

using namespace cimod;
int main()
{
// Set linear biases and quadratic biases
Linear<uint32_t, double> linear{ {1, 1.0}, {2, 2.0}, {3, 3.0}, {4, 4.0} };
Quadratic<uint32_t, double> quadratic
{
{std::make_pair(1, 2), 12.0}, {std::make_pair(1, 3), 13.0}, {std::make_pair(1, 4), 14.0},
{std::make_pair(2, 3), 23.0}, {std::make_pair(2, 4), 24.0},
{std::make_pair(3, 4), 34.0}
};

// Set offset
double offset = 0.0;

// Set variable type
Vartype vartype = Vartype::BINARY;
// Create a BinaryQuadraticModel instance
BinaryQuadraticModel<uint32_t, double, cimod::Dense> bqm(linear, quadratic, offset, vartype);

//linear terms -> bqm.get_linear()
//quadratic terms -> bqm.get_quadratic()

return 0;
}

Python
import cimod
import dimod

# Set linear biases and quadratic biases
linear = {1:1.0, 2:2.0, 3:3.0, 4:4.0}
quadratic = {(1,2):12.0, (1,3):13.0, (1,4):14.0, (2,3):23.0, (2,4):24.0, (3,4):34.0}

# Set offset
offset = 0.0

# Set variable type
vartype = dimod.BINARY

# Create a BinaryQuadraticModel instance
bqm = cimod.BinaryQuadraticModel(linear, quadratic, offset, vartype)

print(bqm.linear)
print(bqm.quadratic)

For Contributor
Use pre-commit for auto chech before git commit.
.pre-commit-config.yaml
# pipx install pre-commit
# or
# pip install pre-commit
pre-commit install

Install
via this directory
$ python -m pip install -vvv .

via pip
# Binary
$ pip install jij-cimod
# From Source
$ pip install --no-binary=jij-cimod jij-cimod

Test
Python
$ python -m venv .venv
$ pip install pip-tools
$ pip-compile setup.cfg
$ pip-compile dev-requirements.in
$ pip-sync requirements.txt dev-requirements.txt
$ source .venv/bin/activate
$ export CMAKE_BUILD_TYPE=Debug
$ python setup.py --force-cmake install --build-type Debug -G Ninja
$ python setup.py --build-type Debug test
$ python -m coverage html

C++
$ mkdir build
$ cmake -DCMAKE_BUILD_TYPE=Debug -S . -B build
$ cmake --build build --parallel
$ cd build
$ ./tests/cimod_test
# Alternatively Use CTest
$ ctest --extra-verbose --parallel --schedule-random

Needs: CMake > 3.22, C++17

Format

$ pip-compile format-requirements.in
$ pip-sync format-requirements.txt

$ python -m isort
$ python -m black

Aggressive Format

$ python -m isort --force-single-line-imports --verbose ./cimod
$ python -m autoflake --in-place --recursive --remove-all-unused-imports --ignore-init-module-imports --remove-unused-variables ./cimod
$ python -m autopep8 --in-place --aggressive --aggressive --recursive ./cimod
$ python -m isort ./cimod
$ python -m black ./cimod

Lint

$ pip-compile setup.cfg
$ pip-compile dev-requirements.in
$ pip-compile lint-requirements.in
$ pip-sync requirements.txt dev-requirements.txt lint-requirements.txt

$ python -m flake8
$ python -m mypy
$ python -m pyright

Benchmark
Benchmark code
import dimod
import cimod
import time

fil = open("benchmark", "w")
fil.write("N t_dimod t_cimod\n")

def benchmark(N, test_fw):
linear = {}
quadratic = {}

spin = {}

# interactions

for i in range(N):
spin[i] = 1

for elem in range(N):
linear[elem] = 2.0*elem;

for i in range(N):
for j in range(i+1, N):
if i != j:
quadratic[(i,j)] = (i+j)/(N)

t1 = time.time()

# initialize
a = test_fw.BinaryQuadraticModel(linear, quadratic, 0, test_fw.BINARY)
a.change_vartype(test_fw.SPIN)

# calculate energy for 50 times.
for _ in range(50):
print(a.energy(spin))

t2 = time.time()

return t2-t1

d_arr = []
c_arr = []

for N in [25, 50, 100, 200, 300, 400, 600, 800,1000, 1600, 2000, 3200, 5000]:
print("N {}".format(N))
d = benchmark(N, dimod)
c = benchmark(N, cimod)
print("{} {} {}".format(N, d, c))
fil.write("{} {} {}\n".format(N, d, c))

Software versions

Package
Version

cimod
1.0.3

dimod
0.9.2

Result

Licences
Copyright 2022 Jij Inc.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.