{"id":23084113,"url":"https://github.com/bethgelab/decompose","last_synced_at":"2025-08-16T01:31:52.070Z","repository":{"id":38679982,"uuid":"126986342","full_name":"bethgelab/decompose","owner":"bethgelab","description":"Blind source separation based on the probabilistic tensor factorisation framework","archived":false,"fork":false,"pushed_at":"2018-08-02T08:29:48.000Z","size":275,"stargazers_count":43,"open_issues_count":0,"forks_count":11,"subscribers_count":12,"default_branch":"master","last_synced_at":"2024-11-15T07:42:21.546Z","etag":null,"topics":["blind-source-separation","decompose","tensor-factorization","tensorflow"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bethgelab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-03-27T12:56:28.000Z","updated_at":"2024-01-04T16:21:48.000Z","dependencies_parsed_at":"2022-08-25T03:42:12.808Z","dependency_job_id":null,"html_url":"https://github.com/bethgelab/decompose","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bethgelab%2Fdecompose","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bethgelab%2Fdecompose/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bethgelab%2Fdecompose/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bethgelab%2Fdecompose/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bethgelab","download_url":"https://codeload.github.com/bethgelab/decompose/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":229881197,"owners_count":18138643,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["blind-source-separation","decompose","tensor-factorization","tensorflow"],"created_at":"2024-12-16T15:48:58.887Z","updated_at":"2024-12-16T15:48:59.611Z","avatar_url":"https://github.com/bethgelab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Decompose\nBlind source separation based on the probabilistic tensor factorisation framework\n\n## Installation\nDecompose demands python 3.6 and tensorflow 1.7. The newest github code of decompose can be installed using pip:\n```bash\npip3 install git+https://github.com/bethgelab/decompose\n```\n\n## Quick start\nDecompose provides an interface that is similar to the interface of scikit-learn:\n\n```python\nimport numpy as np\nfrom sklearn.datasets import make_low_rank_matrix\n\nfrom decompose.sklearn import DECOMPOSE\nfrom decompose.distributions.cenNormal import CenNormal\n\n\n# create a numpy array containing a synthetic low rank dataset\nX = make_low_rank_matrix(n_samples=1000, n_features=1000,\n                         effective_rank=3, tail_strength=0.5)\n\n# create an instance of a decompose model\nmodel = DECOMPOSE(modelDirectory=\"/tmp/myNewModel\",\n                  priors=[CenNormal(), CenNormal()],\n                  n_components=3)\n\n# train the model and transform the training data\nU0 = model.fit_transform(X)\n\n# learned filter bank\nU1 = model.components_\n\n# variance ratio of the sources\nvarianceRatio = model.variance_ratio_\n\n# reconstruction of the data\nXHat = np.dot(U0.T, U1)\n```\n\n## Publication\nThe publication linked to this repository is available on arXiv:\n[[1803.08882] Trace your sources in large-scale data: one ring to find them all](http://arxiv.org/abs/1803.08882)\n\n## Version\nThe repository is still in alpha stage and does not yet contain all the code used in the publication above. In the next days documentation and features will be added.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbethgelab%2Fdecompose","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbethgelab%2Fdecompose","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbethgelab%2Fdecompose/lists"}