M/PhJ |dZddlZddlmZmZddlmZddlm Z ddl m Z m Z ddl mZdd lmZmZmZdd lmZeejejejejejejejejej Zd Z Gd dZ!dddddej" ej"fddfdZ#dddddej" ej"fddfdZ$dS)aa Univariate Kernel Density Estimators References ---------- Racine, Jeff. (2008) "Nonparametric Econometrics: A Primer," Foundation and Trends in Econometrics: Vol 3: No 1, pp1-88. http://dx.doi.org/10.1561/0800000009 https://en.wikipedia.org/wiki/Kernel_%28statistics%29 Silverman, B.W. Density Estimation for Statistics and Data Analysis. N) integratestats)kernels)cache_readonly) array_like float_like) bandwidths)forrtrevrtsilverman_transform) fast_linbin) gauepaunitribiwtriwcoscos2triccP |jdS#t$rtdwxYw)Nz!Call fit to fit the density first)density Exception ValueErrorselfs ]/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/statsmodels/nonparametric/kde.py _checkisfitr(s<> >>><===>s %c eZdZdZdZdddddddej ejffd Zed Z ed Z ed Z ed Z edZ dZdS) KDEUnivariatea Univariate Kernel Density Estimator. Parameters ---------- endog : array_like The variable for which the density estimate is desired. Notes ----- If cdf, sf, cumhazard, or entropy are computed, they are computed based on the definition of the kernel rather than the FFT approximation, even if the density is fit with FFT = True. `KDEUnivariate` is much faster than `KDEMultivariate`, due to its FFT-based implementation. It should be preferred for univariate, continuous data. `KDEMultivariate` also supports mixed data. See Also -------- KDEMultivariate kdensity, kdensityfft Examples -------- >>> import statsmodels.api as sm >>> import matplotlib.pyplot as plt >>> nobs = 300 >>> np.random.seed(1234) # Seed random generator >>> dens = sm.nonparametric.KDEUnivariate(np.random.normal(size=nobs)) >>> dens.fit() >>> plt.plot(dens.cdf) >>> plt.show() c6t|ddd|_dS)Nendogr T)ndim contiguous)rr#)rr#s r__init__zKDEUnivariate.__init__UswQ4HHH rnormal_referenceTNr c 8t|tr||_n&d|_t|st |d}|j} |rF|dkrd} t | |d} t | t| |||||||\} } }nt| |||||||\} } }| |_ | |_ ||_ t|||_ ||j _|'|j xj|zc_i|_|S) a  Attach the density estimate to the KDEUnivariate class. Parameters ---------- kernel : str The Kernel to be used. Choices are: - "biw" for biweight - "cos" for cosine - "epa" for Epanechnikov - "gau" for Gaussian. - "tri" for triangular - "triw" for triweight - "uni" for uniform bw : str, float, callable The bandwidth to use. Choices are: - "scott" - 1.059 * A * nobs ** (-1/5.), where A is `min(std(x),IQR/1.34)` - "silverman" - .9 * A * nobs ** (-1/5.), where A is `min(std(x),IQR/1.34)` - "normal_reference" - C * A * nobs ** (-1/5.), where C is calculated from the kernel. Equivalent (up to 2 dp) to the "scott" bandwidth for gaussian kernels. See bandwidths.py - If a float is given, its value is used as the bandwidth. - If a callable is given, it's return value is used. The callable should take exactly two parameters, i.e., fn(x, kern), and return a float, where: * x - the clipped input data * kern - the kernel instance used fft : bool Whether or not to use FFT. FFT implementation is more computationally efficient. However, only the Gaussian kernel is implemented. If FFT is False, then a 'nobs' x 'gridsize' intermediate array is created. gridsize : int If gridsize is None, max(len(x), 50) is used. cut : float Defines the length of the grid past the lowest and highest values of x so that the kernel goes to zero. The end points are ``min(x) - cut * adjust * bw`` and ``max(x) + cut * adjust * bw``. adjust : float An adjustment factor for the bw. Bandwidth becomes bw * adjust. Returns ------- KDEUnivariate The instance fit, z user-givenbwrz)Only gaussian kernel is available for fftNz#Weights are not implemented for fft)kernelr+adjustweightsgridsizeclipcut)h) isinstancestr bw_methodcallablerr#NotImplementedError kdensityfftkdensityrsupportr+ kernel_switchr,r.sum_cache) rr,r+fftr.r/r-r1r0r#msgrgrids rfitzKDEUnivariate.fitXs\@ b#   *DNN)DNB<< *D))   A)#...";)#... +! ! ! ! GT22!)! ! ! ! GT2  #F+b111 &    K  7;;== 0    r'c\t||jjtj tj}}n j\}}fd|jtj|ft}|jfdtd|D}tj |S)z Returns the cumulative distribution function evaluated at the support. Notes ----- Will not work if fit has not been called. NcTtj||S)N)npsqueezer)xskerns rfunczKDEUnivariate.cdf..funcs!:dll1a0011 1r'cjg|]/}tj|dz |d0S)r argsr)rquad).0ir#rIr:s r z%KDEUnivariate.cdf..sM    N4Q% H H H K   r'r ) rr,domainrDinfr:r_lenr#rangecumsum) rabr/probsr#rIrHr:s @@@@rcdfzKDEUnivariate.cdfs D{ ; F7BFqAA;DAq 2 2 2 2 2,%7 #w<<       1h''   yr'cTt|tj|j S)z Returns the hazard function evaluated at the support. Notes ----- Will not work if fit has not been called. )rrDlogsfrs r cumhazardzKDEUnivariate.cumhazards% Dtwr'c4t|d|jz S)z Returns the survival function evaluated at the support. Notes ----- Will not work if fit has not been called. r )rrZrs rr]zKDEUnivariate.sfs D48|r'ct|fd}|jj |j\}}ntj tj}}|j}t j||||fd S)z Returns the differential entropy evaluated at the support Notes ----- Will not work if fit has not been called. 1e-12 is added to each probability to ensure that log(0) is not called. cd||}|tj|dzzS)Ng-q=)rrDr\)rFrGpdfrHs rentrz#KDEUnivariate.entropy..entr s/,,q!$$Ce ,,, ,r'NrKr)rr,rQrDrRr#rrM)rrcrWrXr#rHs @rentropyzKDEUnivariate.entropys D - - - - -{ ; ";DAqqF7BFqA tQ999!<<rs  """"""""555555777777????????7777777777        >>>@6@6@6@6@6@6@6@6L    6'26  NNNNf    6'26  ]]]]]]r'