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Some methods of some distributions will not support array shape parameters." } ] } ] } ], "title": [], "level": 0, "target": null }, "Other Parameters": { "__type": "Section", "__tag": 4015, "children": [], "title": [], "level": 0, "target": null } }, "_ordered_sections": [ "Summary", "Extended Summary", "Parameters", "Attributes", "Methods", "Returns", "Yields", "Receives", "Other Parameters", "Raises", "Warns", "Warnings", "Notes" ], "item_file": "build-install/usr/lib/python3.14/site-packages/scipy/stats/_distribution_infrastructure.py", "item_line": 3853, "item_type": "function", "aliases": [ "scipy.stats.make_distribution" ], "example_section_data": { "__type": "Section", "__tag": 4015, "children": [ { "__type": "Code", "__tag": 4050, "value": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import stats, special\n", "execution_status": "success", "out": "" }, { "__type": "Text", "__tag": 4046, "value": "\nCreate a `ContinuousDistribution` from `scipy.stats.loguniform`.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "LogUniform = stats.make_distribution(stats.loguniform)\nX = LogUniform(a=1.0, b=3.0)\nnp.isclose((X + 0.25).median(), stats.loguniform.ppf(0.5, 1, 3, loc=0.25))\n", "execution_status": "success", "out": "np.True_\n" }, { "__type": "Code", "__tag": 4050, "value": "X.plot()\n", "execution_status": "failure", "out": "\n" }, { "__type": "Code", "__tag": 4050, "value": "sample = X.sample(10000, rng=np.random.default_rng())\n", "execution_status": "success", "out": "" }, { "__type": "Code", "__tag": 4050, "value": "plt.hist(sample, density=True, bins=30)\nplt.legend(('pdf', 'histogram'))\n", "execution_status": "failure", "out": "\n" }, { "__type": "Code", "__tag": 4050, "value": "plt.show()\n", "execution_status": "success", "out": "" }, { "__type": "Figure", "__tag": 4024, "value": { "__type": "RefInfo", "__tag": 4000, "module": "scipy", "version": "2.0.0.dev0+git20260907.41eeb59", "kind": "assets", "path": "fig-bbbe27cb8fdd96f7.png" } }, { "__type": "Text", "__tag": 4046, "value": "\nCreate a custom continuous distribution.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "class MyLogUniform:\n @property\n def __make_distribution_version__(self):\n return \"1.16.0\"\n\n @property\n def parameters(self):\n return {'a': {'endpoints': (0, np.inf),\n 'inclusive': (False, False)},\n 'b': {'endpoints': ('a', np.inf),\n 'inclusive': (False, False)}}\n\n @property\n def support(self):\n return {'endpoints': ('a', 'b'), 'inclusive': (True, True)}\n\n def pdf(self, x, a, b):\n return 1 / (x * (np.log(b)- np.log(a)))\nMyLogUniform = stats.make_distribution(MyLogUniform())\nY = MyLogUniform(a=1.0, b=3.0)\nnp.isclose(Y.cdf(2.), X.cdf(2.))\n", "execution_status": "success", "out": "np.True_\n" }, { "__type": "Text", "__tag": 4046, "value": "\nCreate a custom distribution with variable support.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "class MyUniformCube:\n @property\n def __make_distribution_version__(self):\n return \"1.16.0\"\n\n @property\n def parameters(self):\n return {\"a\": (-np.inf, np.inf),\n \"b\": {'endpoints':('a', np.inf), 'inclusive':(True, False)}}\n\n @property\n def support(self):\n def left(*, a, b):\n return a**3\n\n def right(*, a, b):\n return b**3\n return (left, right)\n\n def pdf(self, x, *, a, b):\n return 1 / (3*(b - a)*np.cbrt(x)**2)\n\n def cdf(self, x, *, a, b):\n return (np.cbrt(x) - a) / (b - a)\nMyUniformCube = stats.make_distribution(MyUniformCube())\nX = MyUniformCube(a=-2, b=2)\nY = stats.Uniform(a=-2, b=2)**3\n", "execution_status": "success", "out": "" }, { "__type": "Code", "__tag": 4050, "value": "X.support()\n", "execution_status": "failure", "out": "(np.float64(-8.0), np.float64(8.0))\n" }, { "__type": "Code", "__tag": 4050, "value": "np.isclose(X.cdf(2.1), Y.cdf(2.1))\n", "execution_status": "success", "out": "np.True_\n" }, { "__type": "Text", "__tag": 4046, "value": "\nCreate a custom distribution with multiple parameterizations. Here we create a\ncustom version of the beta distribution that has an alternative parameterization\nin terms of the mean ``mu`` and a dispersion parameter ``nu``.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "class MyBeta:\n @property\n def __make_distribution_version__(self):\n return \"1.16.0\"\n\n @property\n def parameters(self):\n return ({\"a\": (0, np.inf), \"b\": (0, np.inf)},\n {\"mu\": (0, 1), \"nu\": (0, np.inf)})\n\n def process_parameters(self, a=None, b=None, mu=None, nu=None):\n if a is not None and b is not None:\n nu = a + b\n mu = a / nu\n else:\n a = mu * nu\n b = nu - a\n return dict(a=a, b=b, mu=mu, nu=nu)\n\n @property\n def support(self):\n return {'endpoints': (0, 1)}\n\n def pdf(self, x, a, b, mu, nu):\n return special._ufuncs._beta_pdf(x, a, b)\n\n def cdf(self, x, a, b, mu, nu):\n return special.betainc(a, b, x)\nMyBeta = stats.make_distribution(MyBeta())\nX = MyBeta(a=2.0, b=2.0)\nY = MyBeta(mu=0.5, nu=4.0)\nnp.isclose(X.pdf(0.3), Y.pdf(0.3))\n", "execution_status": "success", "out": "np.True_\n" }, { "__type": "Text", "__tag": 4046, "value": "\nCreate a custom discrete distribution with one continuous shape parameter and one\ndiscrete shape parameter.\n\n" }, { "__type": "Code", "__tag": 4050, "value": "class MyBinomial:\n @property\n def __make_distribution_version__(self):\n return \"1.16.0\"\n\n @property\n def parameters(self):\n return {'n': {'domain_type': 'discrete',\n 'endpoints': (0., np.inf),\n 'inclusive': (True, False)},\n 'p': {'domain_type': 'continuous',\n 'endpoints': (0., 1.),\n 'inclusive': (True, True)}}\n\n @property\n def support(self):\n return {'endpoints': (0, 'n'), 'inclusive': (True, True)}\n\n def pmf(self, x, n, p):\n return special.binom(n, x) * p**x * (1 - p)**(n-x)\nMyBinomial = stats.make_distribution(MyBinomial())\nX = stats.Binomial(n=10, p=0.4)\nY = MyBinomial(n=10, p=0.4)\nnp.isclose(Y.cdf(8.), X.cdf(8.))\n", "execution_status": "success", "out": "np.True_\n" } ], "title": [], "level": 0, "target": null }, "see_also": [], "signature": { "__type": "SignatureNode", "__tag": 4029, "kind": "function", "parameters": [ { "__type": "SigParam", "__tag": 4030, "name": "dist", "annotation": { "__type": "Empty", "__tag": 4031 }, "kind": "POSITIONAL_OR_KEYWORD", "default": { "__type": "Empty", "__tag": 4031 } } ], "return_annotation": { "__type": "Empty", "__tag": 4031 }, "target_name": "make_distribution" }, "references": null, "qa": "scipy.stats._distribution_infrastructure:make_distribution", "arbitrary": [], "local_refs": [ "CustomDistribution", "dist" ] }