2023-07-15 17:15:38 +00:00
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{ lib
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, buildPythonPackage
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, fetchFromGitHub
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, pythonOlder
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# build inputs
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, networkx
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, numpy
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, scipy
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, scikit-learn
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, pandas
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, pyparsing
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, torch
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, statsmodels
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, tqdm
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, joblib
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, opt-einsum
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# check inputs
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, pytestCheckHook
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, pytest-cov
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, coverage
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, mock
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, black
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}:
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let
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pname = "pgmpy";
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2024-04-21 15:54:59 +00:00
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version = "0.1.25";
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2023-07-15 17:15:38 +00:00
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# optional-dependencies = {
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# all = [ daft ];
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# };
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in
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buildPythonPackage {
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inherit pname version;
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format = "setuptools";
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disabled = pythonOlder "3.7";
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src = fetchFromGitHub {
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owner = "pgmpy";
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repo = pname;
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2023-10-19 13:55:26 +00:00
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rev = "refs/tags/v${version}";
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2024-04-21 15:54:59 +00:00
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hash = "sha256-d2TNcJQ82XxTWdetLgtKXRpFulAEEzrr+cyRewoA6YI=";
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2023-07-15 17:15:38 +00:00
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};
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propagatedBuildInputs = [
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networkx
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numpy
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scipy
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scikit-learn
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pandas
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pyparsing
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torch
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statsmodels
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tqdm
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joblib
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opt-einsum
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];
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disabledTests = [
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"test_to_daft" # requires optional dependency daft
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];
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nativeCheckInputs = [
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pytestCheckHook
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# xdoctest
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pytest-cov
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coverage
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mock
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black
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];
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meta = with lib; {
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description = "Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks";
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homepage = "https://github.com/pgmpy/pgmpy";
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changelog = "https://github.com/pgmpy/pgmpy/releases/tag/v${version}";
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license = licenses.mit;
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maintainers = with maintainers; [ happysalada ];
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};
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}
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