U md`@sdZddlZddZddZddZd d Zd d d ZGdddZddd ZGdddeZ ded<GdddeZ GdddeZ GdddeZ dS)z|Examples of non-linear functions for non-parametric regression Created on Sat Jan 05 20:21:22 2013 Author: Josef Perktold NcCs|dtd|dS)z(Fan and Gijbels example function 1 npexpxr g/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/statsmodels/sandbox/nonparametric/dgp_examples.pyfg1 sr cCs|dtd|ddS)z:Eubank similar to Fan and Gijbels example function 1 ?irrrr r r fg1eusr cCs$td|dtd|dS)z(Fan and Gijbels example function 2 rr)rsinrrr r r fg2srcCs&t|d|d|d|dS)z&made up example with sin, square @?r)rrrr r r func1srzuBase Class for Univariate non-linear example Does not work on it's own. needs additional at least self.func ) descriptionrefc@s$eZdZdZd ddZd ddZdS) _UnivariateFunctiona%(description)s Parameters ---------- nobs : int number of observations to simulate x : None or 1d array If x is given then it is used for the exogenous variable instead of creating a random sample distr_x : None or distribution instance Only used if x is None. The rvs method is used to create a random sample of the exogenous (explanatory) variable. distr_noise : None or distribution instance The rvs method is used to create a random sample of the errors. Attributes ---------- x : ndarray, 1-D exogenous or explanatory variable. x is sorted. y : ndarray, 1-D endogenous or response variable y_true : ndarray, 1-D expected values of endogenous or response variable, i.e. values of y without noise func : callable underlying function (defined by subclass) %(ref)s NcCs|dkr:|dkr&tjjd|j|d}n |j|d}|||_|dkr^tjjd|j|d}n |j|d}t|dr|| |j9}| ||_ }|||_ dS)Nr)locscalesize)r het_scale) rrandomnormals_xZrvssortrs_noisehasattrrfuncy_truey)selfnobsrdistr_x distr_noisenoiser$r r r __init__Ps   z_UnivariateFunction.__init__TcCs~|dkr*ddlm}|}|ddd}|rD|j|j|jdddt|j |j d}|j|| |dd d d |jS) a plot the mean function and optionally the scatter of the sample Parameters ---------- scatter : bool If true, then add scatterpoints of sample to plot. ax : None or matplotlib axis instance If None, then a matplotlib.pyplot figure is created, otherwise the given axis, ax, is used. Returns ------- Figure This is either the created figure instance or the one associated with ax if ax is given. Nror )alphadrbzdgp mean)Zlwcolorlabel) Zmatplotlib.pyplotZpyplotZfigureZ add_subplotplotrr%rZlinspaceminmaxr#)r&ZscatterZaxZpltZfigZxxr r r r3hs z_UnivariateFunction.plot)rNNN)TN)__name__ __module__ __qualname____doc__r+r3r r r r r/s rz=Fan and Gijbels example function 1 linear trend plus a hump z References ---------- Fan, Jianqing, and Irene Gijbels. 1992. "Variable Bandwidth and Local Linear Regression Smoothers." The Annals of Statistics 20 (4) (December): 2008-2036. doi:10.2307/2242378. cs(eZdZejeZdfdd ZZS)UnivariateFanGijbels1rNcs0d|_d|_t|_t|j|j||||ddS)Nrgffffff?r'rr(r))rr!r r#super __class__r+r&r'rr(r)r=r r r+szUnivariateFanGijbels1.__init__)rNNNr6r7r8rr9docr+ __classcell__r r r?r r:s r:z4Fan and Gijbels example function 2 sin plus a hump rcs(eZdZejeZdfdd ZZS)UnivariateFanGijbels2rNcs0d|_d|_t|_t|j|j||||ddS)Nrr r;)rr!rr#r<r=r+r>r?r r r+szUnivariateFanGijbels2.__init__)rNNNr@r r r?r rCs rCcs"eZdZdZdfdd ZZS)UnivariateFanGijbels1EUz Eubank p.179f 2NcsD|dkrddlm}|j}d|_t|_t|j|j||||ddS)Nrstatsg333333?r;) scipyrGuniformr!r r#r<r=r+r&r'rr(r)rGr?r r r+s z UnivariateFanGijbels1EU.__init__)rENNN)r6r7r8r9r+rBr r r?r rDsrDcs*eZdZdZdfdd ZddZZS) UnivariateFunc1z0 made up, with sin and quadratic trend rNcs\|dkr*|dkr*ddlm}|dd}n |jd}d|_t|_tt|j ||||ddS)NrrFrr;) rHrGrIshaper!rr#r<rKr+rJr?r r r+s  zUnivariateFunc1.__init__cCsttd|S)N)rsqrtabs)r&rr r r rszUnivariateFunc1.het_scale)rNNN)r6r7r8r9r+rrBr r r?r rKs rK) r9numpyrr r rrrArr:rCrDrKr r r r s$ X