# encoding: utf-8 # module scipy.stats._stats # from C:\Programs\Python\Python313\Lib\site-packages\scipy\stats\_stats.cp313-win_amd64.pyd # by generator 1.147 # no doc # imports import builtins as __builtins__ # import warnings as warnings # C:\Programs\Python\Python313\Lib\warnings.py import numpy as np # C:\Programs\Python\Python313\Lib\site-packages\numpy\__init__.py import scipy as scipy # C:\Programs\Python\Python313\Lib\site-packages\scipy\__init__.py # functions def gaussian_kernel_estimate(*args, **kwargs): # real signature unknown """ Evaluate a multivariate Gaussian kernel estimate. Parameters ---------- points : array_like with shape (n, d) Data points to estimate from in d dimensions. values : real[:, :] with shape (n, p) Multivariate values associated with the data points. xi : array_like with shape (m, d) Coordinates to evaluate the estimate at in d dimensions. cho_cov : array_like with shape (d, d) (Lower) Cholesky factor of the covariance. Returns ------- estimate : double[:, :] with shape (m, p) Multivariate Gaussian kernel estimate evaluated at the input coordinates. """ pass def gaussian_kernel_estimate_log(points, real, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ """ def gaussian_kernel_estimate_log(points, real[:, :] values, xi, cho_cov) Evaluate the log of the estimated pdf on a provided set of points. Parameters ---------- points : array_like with shape (n, d) Data points to estimate from in ``d`` dimensions. values : real[:, :] with shape (n, p) Multivariate values associated with the data points. xi : array_like with shape (m, d) Coordinates to evaluate the estimate at in ``d`` dimensions. cho_cov : array_like with shape (d, d) (Lower) Cholesky factor of the covariance. Returns ------- estimate : double[:, :] with shape (m, p) The log of the multivariate Gaussian kernel estimate evaluated at the input coordinates. """ pass def genhyperbolic_logpdf(*args, **kwargs): # real signature unknown pass def genhyperbolic_pdf(*args, **kwargs): # real signature unknown pass def geninvgauss_logpdf(*args, **kwargs): # real signature unknown pass def solve_triangular(a, b, trans=0, lower=False, unit_diagonal=False, overwrite_b=False, check_finite=True): # reliably restored by inspect """ Solve the equation ``a x = b`` for `x`, assuming a is a triangular matrix. Parameters ---------- a : (M, M) array_like A triangular matrix b : (M,) or (M, N) array_like Right-hand side matrix in ``a x = b`` lower : bool, optional Use only data contained in the lower triangle of `a`. Default is to use upper triangle. trans : {0, 1, 2, 'N', 'T', 'C'}, optional Type of system to solve: ======== ========= trans system ======== ========= 0 or 'N' a x = b 1 or 'T' a^T x = b 2 or 'C' a^H x = b ======== ========= unit_diagonal : bool, optional If True, diagonal elements of `a` are assumed to be 1 and will not be referenced. overwrite_b : bool, optional Allow overwriting data in `b` (may enhance performance) check_finite : bool, optional Whether to check that the input matrices contain only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Returns ------- x : (M,) or (M, N) ndarray Solution to the system ``a x = b``. Shape of return matches `b`. Raises ------ LinAlgError If `a` is singular Notes ----- .. versionadded:: 0.9.0 Examples -------- Solve the lower triangular system a x = b, where:: [3 0 0 0] [4] a = [2 1 0 0] b = [2] [1 0 1 0] [4] [1 1 1 1] [2] >>> import numpy as np >>> from scipy.linalg import solve_triangular >>> a = np.array([[3, 0, 0, 0], [2, 1, 0, 0], [1, 0, 1, 0], [1, 1, 1, 1]]) >>> b = np.array([4, 2, 4, 2]) >>> x = solve_triangular(a, b, lower=True) >>> x array([ 1.33333333, -0.66666667, 2.66666667, -1.33333333]) >>> a.dot(x) # Check the result array([ 4., 2., 4., 2.]) """ pass def von_mises_cdf(*args, **kwargs): # real signature unknown pass def _center_distance_matrix(*args, **kwargs): # real signature unknown pass def _kendall_dis(*args, **kwargs): # real signature unknown pass def _local_correlations(*args, **kwargs): # real signature unknown pass def _local_covariance(*args, **kwargs): # real signature unknown pass def _rank_distance_matrix(*args, **kwargs): # real signature unknown pass def _studentized_range_cdf_logconst(*args, **kwargs): # real signature unknown """ Evaluates log of constant terms in the cdf integrand """ pass def _studentized_range_pdf_logconst(*args, **kwargs): # real signature unknown """ Evaluates log of constant terms in the pdf integrand """ pass def _toint64(*args, **kwargs): # real signature unknown pass def _transform_distance_matrix(*args, **kwargs): # real signature unknown pass def _weightedrankedtau(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_Enum(*args, **kwargs): # real signature unknown pass # no classes # variables with complex values __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x00000274F7E1CFB0>' __pyx_capi__ = { '_genhyperbolic_logpdf': None, # (!) real value is '' '_genhyperbolic_pdf': None, # (!) real value is '' '_geninvgauss_pdf': None, # (!) real value is '' '_studentized_range_cdf': None, # (!) real value is '' '_studentized_range_cdf_asymptotic': None, # (!) real value is '' '_studentized_range_moment': None, # (!) real value is '' '_studentized_range_pdf': None, # (!) real value is '' '_studentized_range_pdf_asymptotic': None, # (!) real value is '' } __spec__ = None # (!) real value is "ModuleSpec(name='scipy.stats._stats', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x00000274F7E1CFB0>, origin='C:\\\\Programs\\\\Python\\\\Python313\\\\Lib\\\\site-packages\\\\scipy\\\\stats\\\\_stats.cp313-win_amd64.pyd')" __test__ = {}