Skip to main content

C++ library for a binary quadratic model

Project description

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

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)

Install

via this directory

$ python -m pip install .

via pip

$ pip install jij-cimod 

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

benchmark

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

jij_cimod-1.3.4.tar.gz (202.4 kB view hashes)

Uploaded Source

Built Distributions

jij_cimod-1.3.4-cp310-cp310-win_amd64.whl (884.9 kB view hashes)

Uploaded CPython 3.10 Windows x86-64

jij_cimod-1.3.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view hashes)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

jij_cimod-1.3.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.1 MB view hashes)

Uploaded CPython 3.10 manylinux: glibc 2.17+ ARM64

jij_cimod-1.3.4-cp310-cp310-macosx_11_0_arm64.whl (1.0 MB view hashes)

Uploaded CPython 3.10 macOS 11.0+ ARM64

jij_cimod-1.3.4-cp310-cp310-macosx_10_9_x86_64.whl (1.2 MB view hashes)

Uploaded CPython 3.10 macOS 10.9+ x86-64

jij_cimod-1.3.4-cp39-cp39-win_amd64.whl (884.6 kB view hashes)

Uploaded CPython 3.9 Windows x86-64

jij_cimod-1.3.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view hashes)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

jij_cimod-1.3.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.1 MB view hashes)

Uploaded CPython 3.9 manylinux: glibc 2.17+ ARM64

jij_cimod-1.3.4-cp39-cp39-macosx_11_0_arm64.whl (1.0 MB view hashes)

Uploaded CPython 3.9 macOS 11.0+ ARM64

jij_cimod-1.3.4-cp39-cp39-macosx_10_9_x86_64.whl (1.2 MB view hashes)

Uploaded CPython 3.9 macOS 10.9+ x86-64

jij_cimod-1.3.4-cp38-cp38-win_amd64.whl (884.6 kB view hashes)

Uploaded CPython 3.8 Windows x86-64

jij_cimod-1.3.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view hashes)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

jij_cimod-1.3.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.1 MB view hashes)

Uploaded CPython 3.8 manylinux: glibc 2.17+ ARM64

jij_cimod-1.3.4-cp38-cp38-macosx_11_0_arm64.whl (1.0 MB view hashes)

Uploaded CPython 3.8 macOS 11.0+ ARM64

jij_cimod-1.3.4-cp38-cp38-macosx_10_9_x86_64.whl (1.2 MB view hashes)

Uploaded CPython 3.8 macOS 10.9+ x86-64

jij_cimod-1.3.4-cp37-cp37m-win_amd64.whl (891.3 kB view hashes)

Uploaded CPython 3.7m Windows x86-64

jij_cimod-1.3.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB view hashes)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

jij_cimod-1.3.4-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.1 MB view hashes)

Uploaded CPython 3.7m manylinux: glibc 2.17+ ARM64

jij_cimod-1.3.4-cp37-cp37m-macosx_10_9_x86_64.whl (1.1 MB view hashes)

Uploaded CPython 3.7m macOS 10.9+ x86-64

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page