#!/usr/bin/env python # ------------------------------------------------------------------------- # Name: kdetools # Purpose: # # Author: pab # # Created: 01.11.2008 # Copyright: (c) pab 2008 # Licence: LGPL # ------------------------------------------------------------------------- from __future__ import absolute_import, division import copy import numpy as np import scipy import warnings from itertools import product from scipy import interpolate, linalg, optimize, sparse, special, stats from scipy.special import gamma from numpy import pi, sqrt, atleast_2d, exp, newaxis # @UnresolvedImport from wafo.misc import meshgrid, nextpow2, tranproc # , trangood from wafo.containers import PlotData from wafo.dctpack import dct, dctn, idctn from wafo.plotbackend import plotbackend as plt try: from wafo import fig except ImportError: warnings.warn('fig import only supported on Windows') def _invnorm(q): return special.ndtri(q) _stats_epan = (1. / 5, 3. / 5, np.inf) _stats_biwe = (1. / 7, 5. / 7, 45. / 2) _stats_triw = (1. / 9, 350. / 429, np.inf) _stats_rect = (1. / 3, 1. / 2, np.inf) _stats_tria = (1. / 6, 2. / 3, np.inf) _stats_lapl = (2, 1. / 4, np.inf) _stats_logi = (pi ** 2 / 3, 1. / 6, 1 / 42) _stats_gaus = (1, 1. / (2 * sqrt(pi)), 3. / (8 * sqrt(pi))) __all__ = ['sphere_volume', 'TKDE', 'KDE', 'Kernel', 'accum', 'qlevels', 'iqrange', 'gridcount', 'kde_demo1', 'kde_demo2', 'test_docstrings'] def sphere_volume(d, r=1.0): """ Returns volume of d-dimensional sphere with radius r Parameters ---------- d : scalar or array_like dimension of sphere r : scalar or array_like radius of sphere (default 1) Example ------- >>> sphere_volume(2., r=2.) 12.566370614359172 >>> sphere_volume(2., r=1.) 3.1415926535897931 Reference --------- Wand,M.P. and Jones, M.C. (1995) 'Kernel smoothing' Chapman and Hall, pp 105 """ return (r ** d) * 2.0 * pi ** (d / 2.0) / (d * gamma(d / 2.0)) class KDEgauss(object): """ Kernel-Density Estimator base class. Parameters ---------- data : (# of dims, # of data)-array datapoints to estimate from hs : array-like (optional) smooting parameter vector/matrix. (default compute from data using kernel.get_smoothing function) alpha : real scalar (optional) sensitivity parameter (default 0 regular KDE) A good choice might be alpha = 0.5 ( or 1/D) alpha = 0 Regular KDE (hs is constant) 0 < alpha <= 1 Adaptive KDE (Make hs change) Members ------- d : int number of dimensions n : int number of datapoints Methods ------- kde.eval_grid_fast(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde(x0, x1,..., xd) : array same as kde.eval_grid_fast(x0, x1,..., xd) """ def __init__(self, data, hs=None, kernel=None, alpha=0.0, xmin=None, xmax=None, inc=512): self.dataset = atleast_2d(data) self.hs = hs self.kernel = kernel if kernel else Kernel('gauss') self.alpha = alpha self.xmin = xmin self.xmax = xmax self.inc = inc self.initialize() def initialize(self): self.d, self.n = self.dataset.shape self._set_xlimits() self._initialize() def _initialize(self): self._compute_smoothing() def _compute_smoothing(self): """Computes the smoothing matrix.""" get_smoothing = self.kernel.get_smoothing h = self.hs if h is None: h = get_smoothing(self.dataset) h = np.atleast_1d(h) hsiz = h.shape if (len(hsiz) == 1) or (self.d == 1): if max(hsiz) == 1: h = h * np.ones(self.d) else: h.shape = (self.d,) # make sure it has the correct dimension # If h negative calculate automatic values ind, = np.where(h <= 0) for i in ind.tolist(): h[i] = get_smoothing(self.dataset[i]) deth = h.prod() self.inv_hs = np.diag(1.0 / h) else: # fully general smoothing matrix deth = linalg.det(h) if deth <= 0: raise ValueError( 'bandwidth matrix h must be positive definit!') self.inv_hs = linalg.inv(h) self.hs = h self._norm_factor = deth * self.n def _set_xlimits(self): amin = self.dataset.min(axis=-1) amax = self.dataset.max(axis=-1) iqr = iqrange(self.dataset, axis=-1) sigma = np.minimum(np.std(self.dataset, axis=-1, ddof=1), iqr / 1.34) # xyzrange = amax - amin # offset = xyzrange / 4.0 offset = 2 * sigma if self.xmin is None: self.xmin = amin - offset else: self.xmin = self.xmin * np.ones((self.d, 1)) if self.xmax is None: self.xmax = amax + offset else: self.xmax = self.xmax * np.ones((self.d, 1)) def eval_grid_fast(self, *args, **kwds): """Evaluate the estimated pdf on a grid. Parameters ---------- arg_0,arg_1,... arg_d-1 : vectors Alternatively, if no vectors is passed in then arg_i = linspace(self.xmin[i], self.xmax[i], self.inc) output : string optional 'value' if value output 'data' if object output Returns ------- values : array-like The values evaluated at meshgrid(*args). """ if len(args) == 0: args = [] for i in range(self.d): args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc)) self.args = args return self._eval_grid_fun(self._eval_grid_fast, *args, **kwds) def _eval_grid_fast(self, *args, **kwds): X = np.vstack(args) d, inc = X.shape # dx = X[:, 1] - X[:, 0] R = X.max(axis=-1) - X.min(axis=-1) t_star = (self.hs / R) ** 2 I = (np.asfarray(np.arange(0, inc)) * pi) ** 2 In = [] for i in range(d): In.append(I * t_star[i] * 0.5) Inc = meshgrid(*In) if d > 1 else In kw = np.zeros((inc,) * d) for i in range(d): kw += exp(-Inc[i]) y = kwds.get('y', 1.0) d, n = self.dataset.shape # Find the binned kernel weights, c. c = gridcount(self.dataset, X, y=y) / n # Perform the convolution. at = dctn(c) * kw z = idctn(at) * at.size / np.prod(R) return z * (z > 0.0) def _eval_grid_fun(self, eval_grd, *args, **kwds): output = kwds.pop('output', 'value') f = eval_grd(*args, **kwds) if output == 'value': return f else: titlestr = 'Kernel density estimate (%s)' % self.kernel.name kwds2 = dict(title=titlestr) kwds2['plot_kwds'] = dict(plotflag=1) kwds2.update(**kwds) args = self.args if self.d == 1: args = args[0] wdata = PlotData(f, args, **kwds2) if self.d > 1: PL = np.r_[10:90:20, 95, 99, 99.9] try: ql = qlevels(f, p=PL) wdata.clevels = ql wdata.plevels = PL except: pass return wdata def _check_shape(self, points): points = atleast_2d(points) d, m = points.shape if d != self.d: if d == 1 and m == self.d: # points was passed in as a row vector points = np.reshape(points, (self.d, 1)) else: msg = "points have dimension %s, dataset has dimension %s" raise ValueError(msg % (d, self.d)) return points def eval_points(self, points, **kwds): """Evaluate the estimated pdf on a set of points. Parameters ---------- points : (# of dimensions, # of points)-array Alternatively, a (# of dimensions,) vector can be passed in and treated as a single point. Returns ------- values : (# of points,)-array The values at each point. Raises ------ ValueError if the dimensionality of the input points is different than the dimensionality of the KDE. """ points = self._check_shape(points) return self._eval_points(points, **kwds) def _eval_points(self, points, **kwds): pass __call__ = eval_grid_fast class _KDE(object): """ Kernel-Density Estimator base class. Parameters ---------- data : (# of dims, # of data)-array datapoints to estimate from hs : array-like (optional) smooting parameter vector/matrix. (default compute from data using kernel.get_smoothing function) kernel : kernel function object. kernel must have get_smoothing method alpha : real scalar (optional) sensitivity parameter (default 0 regular KDE) A good choice might be alpha = 0.5 ( or 1/D) alpha = 0 Regular KDE (hs is constant) 0 < alpha <= 1 Adaptive KDE (Make hs change) Members ------- d : int number of dimensions n : int number of datapoints Methods ------- kde.eval_grid_fast(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_grid(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_points(points) : array evaluate the estimated pdf on a provided set of points kde(x0, x1,..., xd) : array same as kde.eval_grid(x0, x1,..., xd) """ def __init__(self, data, hs=None, kernel=None, alpha=0.0, xmin=None, xmax=None, inc=512): self.dataset = atleast_2d(data) self.hs = hs self.kernel = kernel if kernel else Kernel('gauss') self.alpha = alpha self.xmin = xmin self.xmax = xmax self.inc = inc self.initialize() def initialize(self): self.d, self.n = self.dataset.shape if self.n > 1: self._set_xlimits() self._initialize() def _initialize(self): pass def _set_xlimits(self): amin = self.dataset.min(axis=-1) amax = self.dataset.max(axis=-1) iqr = iqrange(self.dataset, axis=-1) self._sigma = np.minimum( np.std(self.dataset, axis=-1, ddof=1), iqr / 1.34) # xyzrange = amax - amin # offset = xyzrange / 4.0 offset = self._sigma if self.xmin is None: self.xmin = amin - offset else: self.xmin = self.xmin * np.ones((self.d, 1)) if self.xmax is None: self.xmax = amax + offset else: self.xmax = self.xmax * np.ones((self.d, 1)) def get_args(self, xmin=None, xmax=None): if xmin is None: xmin = self.xmin else: xmin = [min(i, j) for i, j in zip(xmin, self.xmin)] if xmax is None: xmax = self.xmax else: xmax = [max(i, j) for i, j in zip(xmax, self.xmax)] args = [] for i in range(self.d): args.append(np.linspace(xmin[i], xmax[i], self.inc)) return args def eval_grid_fast(self, *args, **kwds): """Evaluate the estimated pdf on a grid. Parameters ---------- arg_0,arg_1,... arg_d-1 : vectors Alternatively, if no vectors is passed in then arg_i = linspace(self.xmin[i], self.xmax[i], self.inc) output : string optional 'value' if value output 'data' if object output Returns ------- values : array-like The values evaluated at meshgrid(*args). """ if len(args) == 0: args = self.get_args() self.args = args return self._eval_grid_fun(self._eval_grid_fast, *args, **kwds) def _eval_grid_fast(self, *args, **kwds): pass def eval_grid(self, *args, **kwds): """Evaluate the estimated pdf on a grid. Parameters ---------- arg_0,arg_1,... arg_d-1 : vectors Alternatively, if no vectors is passed in then arg_i = linspace(self.xmin[i], self.xmax[i], self.inc) output : string optional 'value' if value output 'data' if object output Returns ------- values : array-like The values evaluated at meshgrid(*args). """ if len(args) == 0: args = [] for i in range(self.d): args.append(np.linspace(self.xmin[i], self.xmax[i], self.inc)) self.args = args return self._eval_grid_fun(self._eval_grid, *args, **kwds) def _eval_grid(self, *args): pass def _eval_grid_fun(self, eval_grd, *args, **kwds): output = kwds.pop('output', 'value') f = eval_grd(*args, **kwds) if output == 'value': return f else: titlestr = 'Kernel density estimate (%s)' % self.kernel.name kwds2 = dict(title=titlestr) kwds2['plot_kwds'] = kwds.pop('plot_kwds', dict(plotflag=1)) kwds2.update(**kwds) args = self.args if self.d == 1: args = args[0] wdata = PlotData(f, args, **kwds2) if self.d > 1: PL = np.r_[10:90:20, 95, 99, 99.9] try: ql = qlevels(f, p=PL) wdata.clevels = ql wdata.plevels = PL except: pass return wdata def _check_shape(self, points): points = atleast_2d(points) d, m = points.shape if d != self.d: if d == 1 and m == self.d: # points was passed in as a row vector points = np.reshape(points, (self.d, 1)) else: msg = "points have dimension %s, dataset has dimension %s" raise ValueError(msg % (d, self.d)) return points def eval_points(self, points, **kwds): """Evaluate the estimated pdf on a set of points. Parameters ---------- points : (# of dimensions, # of points)-array Alternatively, a (# of dimensions,) vector can be passed in and treated as a single point. Returns ------- values : (# of points,)-array The values at each point. Raises ------ ValueError if the dimensionality of the input points is different than the dimensionality of the KDE. """ points = self._check_shape(points) return self._eval_points(points, **kwds) def _eval_points(self, points, **kwds): pass __call__ = eval_grid class TKDE(_KDE): """ Transformation Kernel-Density Estimator. Parameters ---------- dataset : (# of dims, # of data)-array datapoints to estimate from hs : array-like (optional) smooting parameter vector/matrix. (default compute from data using kernel.get_smoothing function) kernel : kernel function object. kernel must have get_smoothing method alpha : real scalar (optional) sensitivity parameter (default 0 regular KDE) A good choice might be alpha = 0.5 ( or 1/D) alpha = 0 Regular KDE (hs is constant) 0 < alpha <= 1 Adaptive KDE (Make hs change) xmin, xmax : vectors specifying the default argument range for the kde.eval_grid methods. For the kde.eval_grid_fast methods the values must cover the range of the data. (default min(data)-range(data)/4, max(data)-range(data)/4) If a single value of xmin or xmax is given then the boundary is the is the same for all dimensions. inc : scalar integer defining the default dimension of the output from kde.eval_grid methods (default 512) (For kde.eval_grid_fast: A value below 50 is very fast to compute but may give some inaccuracies. Values between 100 and 500 give very accurate results) L2 : array-like vector of transformation parameters (default 1 no transformation) t(xi;L2) = xi^L2*sign(L2) for L2(i) ~= 0 t(xi;L2) = log(xi) for L2(i) == 0 If single value of L2 is given then the transformation is the same in all directions. Members ------- d : int number of dimensions n : int number of datapoints Methods ------- kde.eval_grid_fast(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_grid(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_points(points) : array evaluate the estimated pdf on a provided set of points kde(x0, x1,..., xd) : array same as kde.eval_grid(x0, x1,..., xd) Example ------- N = 20 data = np.random.rayleigh(1, size=(N,)) >>> data = np.array([ ... 0.75355792, 0.72779194, 0.94149169, 0.07841119,2.32291887, ... 1.10419995, 0.77055114, 0.60288273, 1.36883635, 1.74754326, ... 1.09547561, 1.01671133, 0.73211143, 0.61891719, 0.75903487, ... 1.8919469 , 0.72433808, 1.92973094, 0.44749838, 1.36508452]) >>> import wafo.kdetools as wk >>> x = np.linspace(0.01, max(data.ravel()) + 1, 10) >>> kde = wk.TKDE(data, hs=0.5, L2=0.5) >>> f = kde(x) >>> f array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318, 0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882]) >>> kde.eval_grid(x) array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318, 0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882]) >>> kde.eval_grid_fast(x) array([ 1.04018924, 0.45838973, 0.39514689, 0.32860532, 0.26433301, 0.20717976, 0.15907697, 0.1201077 , 0.08941129, 0.06574899]) import pylab as plb h1 = plb.plot(x, f) # 1D probability density plot t = np.trapz(f, x) """ def __init__(self, data, hs=None, kernel=None, alpha=0.0, xmin=None, xmax=None, inc=512, L2=None): self.L2 = L2 super(TKDE, self).__init__(data, hs, kernel, alpha, xmin, xmax, inc) def _initialize(self): self._check_xmin() tdataset = self._dat2gaus(self.dataset) xmin = self.xmin if xmin is not None: xmin = self._dat2gaus(np.reshape(xmin, (-1, 1))) xmax = self.xmax if xmax is not None: xmax = self._dat2gaus(np.reshape(xmax, (-1, 1))) self.tkde = KDE(tdataset, self.hs, self.kernel, self.alpha, xmin, xmax, self.inc) if self.inc is None: self.inc = self.tkde.inc def _check_xmin(self): if self.L2 is not None: amin = self.dataset.min(axis=-1) # default no transformation L2 = np.atleast_1d(self.L2) * np.ones(self.d) self.xmin = np.where(L2 != 1, np.maximum( self.xmin, amin / 100.0), self.xmin).reshape((-1, 1)) def _dat2gaus(self, points): if self.L2 is None: return points # default no transformation # default no transformation L2 = np.atleast_1d(self.L2) * np.ones(self.d) tpoints = copy.copy(points) for i, v2 in enumerate(L2.tolist()): tpoints[i] = np.log(points[i]) if v2 == 0 else points[i] ** v2 return tpoints def _gaus2dat(self, tpoints): if self.L2 is None: return tpoints # default no transformation # default no transformation L2 = np.atleast_1d(self.L2) * np.ones(self.d) points = copy.copy(tpoints) for i, v2 in enumerate(L2.tolist()): points[i] = np.exp( tpoints[i]) if v2 == 0 else tpoints[i] ** (1.0 / v2) return points def _scale_pdf(self, pdf, points): if self.L2 is None: return pdf # default no transformation L2 = np.atleast_1d(self.L2) * np.ones(self.d) for i, v2 in enumerate(L2.tolist()): factor = v2 * np.sign(v2) if v2 else 1 pdf *= np.where(v2 == 1, 1, points[i] ** (v2 - 1) * factor) if (np.abs(np.diff(pdf)).max() > 10).any(): msg = ''' Numerical problems may have occured due to the power transformation. Check the KDE for spurious spikes''' warnings.warn(msg) return pdf def eval_grid_fast2(self, *args, **kwds): """Evaluate the estimated pdf on a grid. Parameters ---------- arg_0,arg_1,... arg_d-1 : vectors Alternatively, if no vectors is passed in then arg_i = gauss2dat(linspace(dat2gauss(self.xmin[i]), dat2gauss(self.xmax[i]), self.inc)) output : string optional 'value' if value output 'data' if object output Returns ------- values : array-like The values evaluated at meshgrid(*args). """ return self._eval_grid_fun(self._eval_grid_fast, *args, **kwds) def _eval_grid_fast(self, *args, **kwds): if self.L2 is None: f = self.tkde.eval_grid_fast(*args, **kwds) self.args = self.tkde.args return f targs = [] if len(args): targs0 = self._dat2gaus(list(args)) xmin = [min(t) for t in targs0] xmax = [max(t) for t in targs0] targs = self.tkde.get_args(xmin, xmax) tf = self.tkde.eval_grid_fast(*targs) self.args = self._gaus2dat(list(self.tkde.args)) points = meshgrid(*self.args) if self.d > 1 else self.args f = self._scale_pdf(tf, points) if len(args): ipoints = meshgrid(*args) if self.d > 1 else args # shape0 = points[0].shape # shape0i = ipoints[0].shape for i in range(self.d): points[i].shape = (-1,) # ipoints[i].shape = (-1,) points = np.asarray(points).T # ipoints = np.asarray(ipoints).T fi = interpolate.griddata(points, f.ravel(), tuple(ipoints), method='linear', fill_value=0.0) # fi.shape = shape0i self.args = args r = kwds.get('r', 0) if r == 0: return fi * (fi > 0) else: return fi return f def _eval_grid(self, *args, **kwds): if self.L2 is None: return self.tkde.eval_grid(*args, **kwds) targs = self._dat2gaus(list(args)) tf = self.tkde.eval_grid(*targs, **kwds) points = meshgrid(*args) if self.d > 1 else self.args f = self._scale_pdf(tf, points) return f def _eval_points(self, points): """Evaluate the estimated pdf on a set of points. Parameters ---------- points : (# of dimensions, # of points)-array Alternatively, a (# of dimensions,) vector can be passed in and treated as a single point. Returns ------- values : (# of points,)-array The values at each point. Raises ------ ValueError if the dimensionality of the input points is different than the dimensionality of the KDE. """ if self.L2 is None: return self.tkde.eval_points(points) tpoints = self._dat2gaus(points) tf = self.tkde.eval_points(tpoints) f = self._scale_pdf(tf, points) return f class KDE(_KDE): """ Kernel-Density Estimator. Parameters ---------- data : (# of dims, # of data)-array datapoints to estimate from hs : array-like (optional) smooting parameter vector/matrix. (default compute from data using kernel.get_smoothing function) kernel : kernel function object. kernel must have get_smoothing method alpha : real scalar (optional) sensitivity parameter (default 0 regular KDE) A good choice might be alpha = 0.5 ( or 1/D) alpha = 0 Regular KDE (hs is constant) 0 < alpha <= 1 Adaptive KDE (Make hs change) xmin, xmax : vectors specifying the default argument range for the kde.eval_grid methods. For the kde.eval_grid_fast methods the values must cover the range of the data. (default min(data)-range(data)/4, max(data)-range(data)/4) If a single value of xmin or xmax is given then the boundary is the is the same for all dimensions. inc : scalar integer (default 512) defining the default dimension of the output from kde.eval_grid methods (For kde.eval_grid_fast: A value below 50 is very fast to compute but may give some inaccuracies. Values between 100 and 500 give very accurate results) Members ------- d : int number of dimensions n : int number of datapoints Methods ------- kde.eval_grid_fast(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_grid(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_points(points) : array evaluate the estimated pdf on a provided set of points kde(x0, x1,..., xd) : array same as kde.eval_grid(x0, x1,..., xd) Example ------- N = 20 data = np.random.rayleigh(1, size=(N,)) >>> data = np.array([ ... 0.75355792, 0.72779194, 0.94149169, 0.07841119, 2.32291887, ... 1.10419995, 0.77055114, 0.60288273, 1.36883635, 1.74754326, ... 1.09547561, 1.01671133, 0.73211143, 0.61891719, 0.75903487, ... 1.8919469 , 0.72433808, 1.92973094, 0.44749838, 1.36508452]) >>> x = np.linspace(0, max(data.ravel()) + 1, 10) >>> import wafo.kdetools as wk >>> kde = wk.KDE(data, hs=0.5, alpha=0.5) >>> f = kde(x) >>> f array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073, 0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164]) >>> kde.eval_grid(x) array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073, 0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164]) >>> kde0 = wk.KDE(data, hs=0.5, alpha=0.0) >>> kde0.eval_points(x) array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 , 0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821]) >>> kde0.eval_grid(x) array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 , 0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821]) >>> f = kde0.eval_grid(x, output='plotobj') >>> f.data array([ 0.2039735 , 0.40252503, 0.54595078, 0.52219649, 0.3906213 , 0.26381501, 0.16407362, 0.08270612, 0.02991145, 0.00720821]) >>> f = kde0.eval_grid_fast() >>> np.allclose(np.interp(x, kde0.args[0], f), ... [ 0.20397743, 0.40252228, 0.54594119, 0.52219025, 0.39062189, ... 0.2638171 , 0.16407487, 0.08270755, 0.04784434, 0.04784434]) True >>> f1 = kde0.eval_grid_fast(output='plot') >>> np.allclose(np.interp(x, f1.args, f1.data), ... [ 0.20397743, 0.40252228, 0.54594119, 0.52219025, 0.39062189, ... 0.2638171 , 0.16407487, 0.08270755, 0.04784434, 0.04784434]) True h = f1.plot() import pylab as plb h1 = plb.plot(x, f) # 1D probability density plot t = np.trapz(f, x) """ def __init__(self, data, hs=None, kernel=None, alpha=0.0, xmin=None, xmax=None, inc=512): super(KDE, self).__init__(data, hs, kernel, alpha, xmin, xmax, inc) def _initialize(self): self._compute_smoothing() self._lambda = np.ones(self.n) if self.alpha > 0: # pilt = KDE(self.dataset, hs=self.hs, kernel=self.kernel, alpha=0) # f = pilt.eval_points(self.dataset) # get a pilot estimate by # regular KDE (alpha=0) f = self.eval_points(self.dataset) # pilot estimate g = np.exp(np.mean(np.log(f))) self._lambda = (f / g) ** (-self.alpha) if self.inc is None: unused_tau, tau = self.kernel.effective_support() xyzrange = 8 * self._sigma L1 = 10 self.inc = 2 ** nextpow2( max(48, (L1 * xyzrange / (tau * self.hs)).max())) pass def _compute_smoothing(self): """Computes the smoothing matrix.""" get_smoothing = self.kernel.get_smoothing h = self.hs if h is None: h = get_smoothing(self.dataset) h = np.atleast_1d(h) hsiz = h.shape if (len(hsiz) == 1) or (self.d == 1): if max(hsiz) == 1: h = h * np.ones(self.d) else: h.shape = (self.d,) # make sure it has the correct dimension # If h negative calculate automatic values ind, = np.where(h <= 0) for i in ind.tolist(): h[i] = get_smoothing(self.dataset[i]) deth = h.prod() self.inv_hs = np.diag(1.0 / h) else: # fully general smoothing matrix deth = linalg.det(h) if deth <= 0: raise ValueError( 'bandwidth matrix h must be positive definit!') self.inv_hs = linalg.inv(h) self.hs = h self._norm_factor = deth * self.n def _eval_grid_fast(self, *args, **kwds): X = np.vstack(args) d, inc = X.shape dx = X[:, 1] - X[:, 0] Xn = [] nfft0 = 2 * inc nfft = (nfft0,) * d x0 = np.linspace(-inc, inc, nfft0 + 1) for i in range(d): Xn.append(x0[:-1] * dx[i]) Xnc = meshgrid(*Xn) if d > 1 else Xn shape0 = Xnc[0].shape for i in range(d): Xnc[i].shape = (-1,) Xn = np.dot(self.inv_hs, np.vstack(Xnc)) # Obtain the kernel weights. kw = self.kernel(Xn) # plt.plot(kw) # plt.draw() # plt.show() norm_fact0 = (kw.sum() * dx.prod() * self.n) norm_fact = (self._norm_factor * self.kernel.norm_factor(d, self.n)) if np.abs(norm_fact0 - norm_fact) > 0.05 * norm_fact: warnings.warn( 'Numerical inaccuracy due to too low discretization. ' + 'Increase the discretization of the evaluation grid ' + '(inc=%d)!' % inc) norm_fact = norm_fact0 kw = kw / norm_fact r = kwds.get('r', 0) if r != 0: kw *= np.vstack(Xnc) ** r if d > 1 else Xnc[0] kw.shape = shape0 kw = np.fft.ifftshift(kw) fftn = np.fft.fftn ifftn = np.fft.ifftn y = kwds.get('y', 1.0) # if self.alpha>0: # y = y / self._lambda**d # Find the binned kernel weights, c. c = gridcount(self.dataset, X, y=y) # Perform the convolution. z = np.real(ifftn(fftn(c, s=nfft) * fftn(kw))) ix = (slice(0, inc),) * d if r == 0: return z[ix] * (z[ix] > 0.0) else: return z[ix] def _eval_grid(self, *args, **kwds): grd = meshgrid(*args) if len(args) > 1 else list(args) shape0 = grd[0].shape d = len(grd) for i in range(d): grd[i] = grd[i].ravel() f = self.eval_points(np.vstack(grd), **kwds) return f.reshape(shape0) def _eval_points(self, points, **kwds): """Evaluate the estimated pdf on a set of points. Parameters ---------- points : (# of dimensions, # of points)-array Alternatively, a (# of dimensions,) vector can be passed in and treated as a single point. Returns ------- values : (# of points,)-array The values at each point. Raises ------ ValueError if the dimensionality of the input points is different than the dimensionality of the KDE. """ d, m = points.shape result = np.zeros((m,)) r = kwds.get('r', 0) if r == 0: def fun(xi): return 1 else: def fun(xi): return (xi ** r).sum(axis=0) if m >= self.n: y = kwds.get('y', np.ones(self.n)) # there are more points than data, so loop over data for i in range(self.n): diff = self.dataset[:, i, np.newaxis] - points tdiff = np.dot(self.inv_hs / self._lambda[i], diff) result += y[i] * \ fun(diff) * self.kernel(tdiff) / self._lambda[i] ** d else: y = kwds.get('y', 1) # loop over points for i in range(m): diff = self.dataset - points[:, i, np.newaxis] tdiff = np.dot(self.inv_hs, diff / self._lambda[np.newaxis, :]) tmp = y * fun(diff) * self.kernel(tdiff) / self._lambda ** d result[i] = tmp.sum(axis=-1) result /= (self._norm_factor * self.kernel.norm_factor(d, self.n)) return result class KRegression(_KDE): """ Kernel-Regression Parameters ---------- data : (# of dims, # of data)-array datapoints to estimate from y : # of data - array response variable p : scalar integer (0 or 1) Nadaraya-Watson estimator if p=0, local linear estimator if p=1. hs : array-like (optional) smooting parameter vector/matrix. (default compute from data using kernel.get_smoothing function) kernel : kernel function object. kernel must have get_smoothing method alpha : real scalar (optional) sensitivity parameter (default 0 regular KDE) A good choice might be alpha = 0.5 ( or 1/D) alpha = 0 Regular KDE (hs is constant) 0 < alpha <= 1 Adaptive KDE (Make hs change) xmin, xmax : vectors specifying the default argument range for the kde.eval_grid methods. For the kde.eval_grid_fast methods the values must cover the range of the data. (default min(data)-range(data)/4, max(data)-range(data)/4) If a single value of xmin or xmax is given then the boundary is the is the same for all dimensions. inc : scalar integer (default 128) defining the default dimension of the output from kde.eval_grid methods (For kde.eval_grid_fast: A value below 50 is very fast to compute but may give some inaccuracies. Values between 100 and 500 give very accurate results) Members ------- d : int number of dimensions n : int number of datapoints Methods ------- kde.eval_grid_fast(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_grid(x0, x1,..., xd) : array evaluate the estimated pdf on meshgrid(x0, x1,..., xd) kde.eval_points(points) : array evaluate the estimated pdf on a provided set of points kde(x0, x1,..., xd) : array same as kde.eval_grid(x0, x1,..., xd) Example ------- >>> import wafo.kdetools as wk >>> N = 100 >>> x = np.linspace(0, 1, N) >>> ei = np.random.normal(loc=0, scale=0.075, size=(N,)) >>> ei = np.sqrt(0.075) * np.sin(100*x) >>> y = 2*np.exp(-x**2/(2*0.3**2))+3*np.exp(-(x-1)**2/(2*0.7**2)) + ei >>> kreg = wk.KRegression(x, y) >>> f = kreg(output='plotobj', title='Kernel regression', plotflag=1) >>> np.allclose(f.data[:5], ... [ 3.18381052, 3.18362269, 3.18343648, 3.1832536 , 3.1830757 ]) True h = f.plot(label='p=0') """ def __init__(self, data, y, p=0, hs=None, kernel=None, alpha=0.0, xmin=None, xmax=None, inc=128, L2=None): self.tkde = TKDE(data, hs=hs, kernel=kernel, alpha=alpha, xmin=xmin, xmax=xmax, inc=inc, L2=L2) self.y = y self.p = p def eval_grid_fast(self, *args, **kwds): self._grdfun = self.tkde.eval_grid_fast return self.tkde._eval_grid_fun(self._eval_gridfun, *args, **kwds) def eval_grid(self, *args, **kwds): self._grdfun = self.tkde.eval_grid return self.tkde._eval_grid_fun(self._eval_gridfun, *args, **kwds) def _eval_gridfun(self, *args, **kwds): grdfun = self._grdfun s0 = grdfun(*args, r=0) t0 = grdfun(*args, r=0, y=self.y) if self.p == 0: return (t0 / (s0 + _TINY)).clip(min=-_REALMAX, max=_REALMAX) elif self.p == 1: s1 = grdfun(*args, r=1) s2 = grdfun(*args, r=2) t1 = grdfun(*args, r=1, y=self.y) return ((s2 * t0 - s1 * t1) / (s2 * s0 - s1 ** 2)).clip(min=-_REALMAX, max=_REALMAX) __call__ = eval_grid_fast class BKRegression(object): ''' Kernel-Regression on binomial data method : {'beta', 'wilson'} method is one of the following 'beta', return Bayesian Credible interval using beta-distribution. 'wilson', return Wilson score interval a, b : scalars parameters of the beta distribution defining the apriori distribution of p, i.e., the Bayes estimator for p: p = (y+a)/(n+a+b). Setting a=b=0.5 gives Jeffreys interval. ''' def __init__(self, *args, **kwds): self.method = kwds.pop('method', 'beta') self.a = max(kwds.pop('a', 0.5), _TINY) self.b = max(kwds.pop('b', 0.5), _TINY) self.kreg = KRegression(*args, **kwds) # defines bin width (i.e. smoothing) in empirical estimate self.hs_e = None # self.x = self.kreg.tkde.dataset # self.y = self.kreg.y def _set_smoothing(self, hs): self.kreg.tkde.hs = hs self.kreg.tkde.initialize() x = property(fget=lambda cls: cls.kreg.tkde.dataset.squeeze()) y = property(fget=lambda cls: cls.kreg.y) kernel = property(fget=lambda cls: cls.kreg.tkde.kernel) hs = property(fset=_set_smoothing, fget=lambda cls: cls.kreg.tkde.hs) def _get_max_smoothing(self, fun=None): """Return maximum value for smoothing parameter.""" x = self.x y = self.y if fun is None: get_smoothing = self.kernel.get_smoothing else: get_smoothing = getattr(self.kernel, fun) hs1 = get_smoothing(x) # hx = np.median(np.abs(x-np.median(x)))/0.6745*(4.0/(3*n))**0.2 if (y == 1).any(): hs2 = get_smoothing(x[y == 1]) # hy = np.median(np.abs(y-np.mean(y)))/0.6745*(4.0/(3*n))**0.2 else: hs2 = 4 * hs1 # hy = 4*hx hopt = sqrt(hs1 * hs2) return hopt, hs1, hs2 def get_grid(self, hs_e=None): if hs_e is None: if self.hs_e is None: hs1 = self._get_max_smoothing('hste')[0] hs2 = self._get_max_smoothing('hos')[0] self.hs_e = sqrt(hs1 * hs2) hs_e = self.hs_e x = self.x xmin, xmax = x.min(), x.max() ni = max(2 * int((xmax - xmin) / hs_e) + 3, 5) sml = hs_e # *0.1 xi = np.linspace(xmin - sml, xmax + sml, ni) return xi def prb_ci(self, n, p, alpha=0.05, **kwds): """Return Confidence Interval for the binomial probability p. Parameters ---------- n : array-like number of Bernoulli trials p : array-like estimated probability of success in each trial alpha : scalar confidence level method : {'beta', 'wilson'} method is one of the following 'beta', return Bayesian Credible interval using beta-distribution. 'wilson', return Wilson score interval a, b : scalars parameters of the beta distribution defining the apriori distribution of p, i.e., the Bayes estimator for p: p = (y+a)/(n+a+b). Setting a=b=0.5 gives Jeffreys interval. """ if self.method.startswith('w'): # Wilson score z0 = -_invnorm(alpha / 2) den = 1 + (z0 ** 2. / n) xc = (p + (z0 ** 2) / (2 * n)) / den halfwidth = (z0 * sqrt((p * (1 - p) / n) + (z0 ** 2 / (4 * (n ** 2))))) / den plo = (xc - halfwidth).clip(min=0) # wilson score pup = (xc + halfwidth).clip(max=1.0) # wilson score else: # Jeffreys intervall a=b=0.5 # st.beta.isf(alpha/2, y+a, n-y+b) y = n*p, n-y = n*(1-p) a = self.a b = self.b st = stats pup = np.where( p == 1, 1, st.beta.isf(alpha / 2, n * p + a, n * (1 - p) + b)) plo = np.where(p == 0, 0, st.beta.isf(1 - alpha / 2, n * p + a, n * (1 - p) + b)) return plo, pup def prb_empirical(self, xi=None, hs_e=None, alpha=0.05, color='r', **kwds): """Returns empirical binomial probabiltity. Parameters ---------- x : ndarray position vector y : ndarray binomial response variable (zeros and ones) alpha : scalar confidence level color: used in plot Returns ------- P(x) : PlotData object empirical probability """ if xi is None: xi = self.get_grid(hs_e) x = self.x y = self.y c = gridcount(x, xi) # + self.a + self.b # count data if (y == 1).any(): c0 = gridcount(x[y == 1], xi) # + self.a # count success else: c0 = np.zeros(xi.shape) prb = np.where(c == 0, 0, c0 / (c + _TINY)) # assume prb==0 for c==0 CI = np.vstack(self.prb_ci(c, prb, alpha, **kwds)) prb_e = PlotData(prb, xi, plotmethod='plot', plot_args=['.'], plot_kwds=dict(markersize=6, color=color, picker=5)) prb_e.dataCI = CI.T prb_e.count = c return prb_e def prb_smoothed(self, prb_e, hs, alpha=0.05, color='r', label=''): """Return smoothed binomial probability. Parameters ---------- prb_e : PlotData object with empirical binomial probabilites hs : smoothing parameter alpha : confidence level color : color of plot object label : label for plot object """ x_e = prb_e.args n_e = len(x_e) dx_e = x_e[1] - x_e[0] n = self.x.size x_s = np.linspace(x_e[0], x_e[-1], 10 * n_e + 1) self.hs = hs prb_s = self.kreg(x_s, output='plotobj', title='', plot_kwds=dict( color=color, linewidth=2)) # dict(plotflag=7)) m_nan = np.isnan(prb_s.data) if m_nan.any(): # assume 0/0 division prb_s.data[m_nan] = 0.0 # prb_s.data[np.isnan(prb_s.data)] = 0 # expected number of data in each bin c_s = self.kreg.tkde.eval_grid_fast(x_s) * dx_e * n plo, pup = self.prb_ci(c_s, prb_s.data, alpha) prb_s.dataCI = np.vstack((plo, pup)).T prb_s.prediction_error_avg = np.trapz( pup - plo, x_s) / (x_s[-1] - x_s[0]) if label: prb_s.plot_kwds['label'] = label prb_s.children = [PlotData([plo, pup], x_s, plotmethod='fill_between', plot_kwds=dict(alpha=0.2, color=color)), prb_e] # empirical oversmooths the data # p_s = prb_s.eval_points(self.x) # dp_s = np.diff(prb_s.data) # k = (dp_s[:-1]*dp_s[1:]<0).sum() # numpeaks # p_e = self.y # n_s = interpolate.interp1d(x_s, c_s)(self.x) # plo, pup = self.prb_ci(n_s, p_s, alpha) # sigmai = (pup-plo) # aicc = (((p_e-p_s)/sigmai)**2).sum()+ 2*k*(k+1)/np.maximum(n-k+1,1) p_e = prb_e.eval_points(x_s) p_s = prb_s.data dp_s = np.sign(np.diff(p_s)) k = (dp_s[:-1] != dp_s[1:]).sum() # numpeaks # sigmai = (pup-plo)+_EPS # aicc = (((p_e-p_s)/sigmai)**2).sum()+ 2*k*(k+1)/np.maximum(n_e-k+1,1) # + np.abs((p_e-pup).clip(min=0)-(p_e-plo).clip(max=0)).sum() sigmai = _logit(pup) - _logit(plo) + _EPS aicc = ((((_logit(p_e) - _logit(p_s)) / sigmai) ** 2).sum() + 2 * k * (k + 1) / np.maximum(n_e - k + 1, 1) + np.abs((p_e - pup).clip(min=0) - (p_e - plo).clip(max=0)).sum()) prb_s.aicc = aicc # prb_s.labels.title = '' # prb_s.labels.title='perr=%1.3f,aicc=%1.3f, n=%d, hs=%1.3f' % # (prb_s.prediction_error_avg,aicc,n,hs) return prb_s def prb_search_best(self, prb_e=None, hsvec=None, hsfun='hste', alpha=0.05, color='r', label=''): """Return best smoothed binomial probability. Parameters ---------- prb_e : PlotData object with empirical binomial probabilites hsvec : arraylike (default np.linspace(hsmax*0.1,hsmax,55)) vector smoothing parameters hsfun : method for calculating hsmax """ if prb_e is None: prb_e = self.prb_empirical( hs_e=self.hs_e, alpha=alpha, color=color) if hsvec is None: hsmax = self._get_max_smoothing(hsfun)[0] # @UnusedVariable hsmax = max(hsmax, self.hs_e) hsvec = np.linspace(hsmax * 0.2, hsmax, 55) hs_best = hsvec[-1] + 0.1 prb_best = self.prb_smoothed(prb_e, hs_best, alpha, color, label) aicc = np.zeros(np.size(hsvec)) for i, hi in enumerate(hsvec): f = self.prb_smoothed(prb_e, hi, alpha, color, label) aicc[i] = f.aicc if f.aicc <= prb_best.aicc: prb_best = f hs_best = hi prb_best.score = PlotData(aicc, hsvec) prb_best.hs = hs_best self._set_smoothing(hs_best) return prb_best class _Kernel(object): def __init__(self, r=1.0, stats=None): self.r = r # radius of kernel self.stats = stats def norm_factor(self, d=1, n=None): return 1.0 def norm_kernel(self, x): X = np.atleast_2d(x) return self._kernel(X) / self.norm_factor(*X.shape) def kernel(self, x): return self._kernel(np.atleast_2d(x)) def deriv4_6_8_10(self, t, numout=4): raise Exception('Method not implemented for this kernel!') def effective_support(self): """Return the effective support of kernel. The kernel must be symmetric and compactly supported on [-tau tau] if the kernel has infinite support then the kernel must have the effective support in [-tau tau], i.e., be negligible outside the range """ return self._effective_support() def _effective_support(self): return - self.r, self.r __call__ = kernel class _KernelMulti(_Kernel): # p=0; %Sphere = rect for 1D # p=1; %Multivariate Epanechnikov kernel. # p=2; %Multivariate Bi-weight Kernel # p=3; %Multi variate Tri-weight Kernel # p=4; %Multi variate Four-weight Kernel def __init__(self, r=1.0, p=1, stats=None): self.r = r self.p = p self.stats = stats def norm_factor(self, d=1, n=None): r = self.r p = self.p c = 2 ** p * np.prod(np.r_[1:p + 1]) * sphere_volume(d, r) / np.prod( np.r_[(d + 2):(2 * p + d + 1):2]) # normalizing constant return c def _kernel(self, x): r = self.r p = self.p x2 = x ** 2 return ((1.0 - x2.sum(axis=0) / r ** 2).clip(min=0.0)) ** p mkernel_epanechnikov = _KernelMulti(p=1, stats=_stats_epan) mkernel_biweight = _KernelMulti(p=2, stats=_stats_biwe) mkernel_triweight = _KernelMulti(p=3, stats=_stats_triw) class _KernelProduct(_KernelMulti): # p=0; %rectangular # p=1; %1D product Epanechnikov kernel. # p=2; %1D product Bi-weight Kernel # p=3; %1D product Tri-weight Kernel # p=4; %1D product Four-weight Kernel def norm_factor(self, d=1, n=None): r = self.r p = self.p c = (2 ** p * np.prod(np.r_[1:p + 1]) * sphere_volume(1, r) / np.prod(np.r_[(1 + 2):(2 * p + 2):2])) return c ** d def _kernel(self, x): r = self.r # radius pdf = (1 - (x / r) ** 2).clip(min=0.0) return pdf.prod(axis=0) mkernel_p1epanechnikov = _KernelProduct(p=1, stats=_stats_epan) mkernel_p1biweight = _KernelProduct(p=2, stats=_stats_biwe) mkernel_p1triweight = _KernelProduct(p=3, stats=_stats_triw) class _KernelRectangular(_Kernel): def _kernel(self, x): return np.where(np.all(np.abs(x) <= self.r, axis=0), 1, 0.0) def norm_factor(self, d=1, n=None): r = self.r return (2 * r) ** d mkernel_rectangular = _KernelRectangular(stats=_stats_rect) class _KernelTriangular(_Kernel): def _kernel(self, x): pdf = (1 - np.abs(x)).clip(min=0.0) return pdf.prod(axis=0) mkernel_triangular = _KernelTriangular(stats=_stats_tria) class _KernelGaussian(_Kernel): def _kernel(self, x): sigma = self.r / 4.0 x2 = (x / sigma) ** 2 return exp(-0.5 * x2.sum(axis=0)) def norm_factor(self, d=1, n=None): sigma = self.r / 4.0 return (2 * pi * sigma) ** (d / 2.0) def deriv4_6_8_10(self, t, numout=4): """Returns 4th, 6th, 8th and 10th derivatives of the kernel function.""" phi0 = exp(-0.5 * t ** 2) / sqrt(2 * pi) p4 = [1, 0, -6, 0, +3] p4val = np.polyval(p4, t) * phi0 if numout == 1: return p4val out = [p4val] pn = p4 for unusedix in range(numout - 1): pnp1 = np.polyadd(-np.r_[pn, 0], np.polyder(pn)) pnp2 = np.polyadd(-np.r_[pnp1, 0], np.polyder(pnp1)) out.append(np.polyval(pnp2, t) * phi0) pn = pnp2 return out mkernel_gaussian = _KernelGaussian(r=4.0, stats=_stats_gaus) # def mkernel_gaussian(X): # x2 = X ** 2 # d = X.shape[0] # return (2 * pi) ** (-d / 2) * exp(-0.5 * x2.sum(axis=0)) class _KernelLaplace(_Kernel): def _kernel(self, x): absX = np.abs(x) return exp(-absX.sum(axis=0)) def norm_factor(self, d=1, n=None): return 2 ** d mkernel_laplace = _KernelLaplace(r=7.0, stats=_stats_lapl) class _KernelLogistic(_Kernel): def _kernel(self, x): s = exp(-x) return np.prod(1.0 / (s + 1) ** 2, axis=0) mkernel_logistic = _KernelLogistic(r=7.0, stats=_stats_logi) _MKERNEL_DICT = dict( epan=mkernel_epanechnikov, biwe=mkernel_biweight, triw=mkernel_triweight, p1ep=mkernel_p1epanechnikov, p1bi=mkernel_p1biweight, p1tr=mkernel_p1triweight, rect=mkernel_rectangular, tria=mkernel_triangular, lapl=mkernel_laplace, logi=mkernel_logistic, gaus=mkernel_gaussian ) _KERNEL_EXPONENT_DICT = dict( re=0, sp=0, ep=1, bi=2, tr=3, fo=4, fi=5, si=6, se=7) class Kernel(object): """Multivariate kernel. Parameters ---------- name : string defining the kernel. Valid options are: 'epanechnikov' - Epanechnikov kernel. 'biweight' - Bi-weight kernel. 'triweight' - Tri-weight kernel. 'p1epanechnikov' - product of 1D Epanechnikov kernel. 'p1biweight' - product of 1D Bi-weight kernel. 'p1triweight' - product of 1D Tri-weight kernel. 'triangular' - Triangular kernel. 'gaussian' - Gaussian kernel 'rectangular' - Rectangular kernel. 'laplace' - Laplace kernel. 'logistic' - Logistic kernel. Note that only the first 4 letters of the kernel name is needed. Examples -------- N = 20 data = np.random.rayleigh(1, size=(N,)) >>> data = np.array([ ... 0.75355792, 0.72779194, 0.94149169, 0.07841119, 2.32291887, ... 1.10419995, 0.77055114, 0.60288273, 1.36883635, 1.74754326, ... 1.09547561, 1.01671133, 0.73211143, 0.61891719, 0.75903487, ... 1.8919469 , 0.72433808, 1.92973094, 0.44749838, 1.36508452]) >>> import wafo.kdetools as wk >>> gauss = wk.Kernel('gaussian') >>> gauss.stats() (1, 0.28209479177387814, 0.21157109383040862) >>> np.allclose(gauss.hscv(data), 0.21779575) True >>> np.allclose(gauss.hstt(data), 0.16341135) True >>> np.allclose(gauss.hste(data), 0.19179399) True >>> np.allclose(gauss.hldpi(data), 0.22502733) True >>> wk.Kernel('laplace').stats() (2, 0.25, inf) >>> triweight = wk.Kernel('triweight') >>> np.allclose(triweight.stats(), ... (0.1111111111111111, 0.81585081585081587, np.inf)) True >>> np.allclose(triweight(np.linspace(-1,1,11)), ... [ 0., 0.046656, 0.262144, 0.592704, 0.884736, 1., ... 0.884736, 0.592704, 0.262144, 0.046656, 0.]) True >>> np.allclose(triweight.hns(data), 0.82, rtol=1e-2) True >>> np.allclose(triweight.hos(data), 0.88, rtol=1e-2) True >>> np.allclose(triweight.hste(data), 0.57, rtol=1e-2) True >>> np.allclose(triweight.hscv(data), 0.648, rtol=1e-2) True See also -------- mkernel References ---------- B. W. Silverman (1986) 'Density estimation for statistics and data analysis' Chapman and Hall, pp. 43, 76 Wand, M. P. and Jones, M. C. (1995) 'Density estimation for statistics and data analysis' Chapman and Hall, pp 31, 103, 175 """ def __init__(self, name, fun='hste'): # 'hns'): self.kernel = _MKERNEL_DICT[name[:4]] # self.name = self.kernel.__name__.replace('mkernel_', '').title() try: self.get_smoothing = getattr(self, fun) except: self.get_smoothing = self.hste def _get_name(self): return self.kernel.__class__.__name__.replace('_Kernel', '').title() name = property(_get_name) def get_smoothing(self, *args, **kwds): pass def stats(self): """Return some 1D statistics of the kernel. Returns ------- mu2 : real scalar 2'nd order moment, i.e.,int(x^2*kernel(x)) R : real scalar integral of squared kernel, i.e., int(kernel(x)^2) Rdd : real scalar integral of squared double derivative of kernel, i.e., int( (kernel''(x))^2 ). Reference --------- Wand,M.P. and Jones, M.C. (1995) 'Kernel smoothing' Chapman and Hall, pp 176. """ return self.kernel.stats def deriv4_6_8_10(self, t, numout=4): return self.kernel.deriv4_6_8_10(t, numout) def effective_support(self): return self.kernel.effective_support() def hns(self, data): """Returns Normal Scale Estimate of Smoothing Parameter. Parameter --------- data : 2D array shape d x n (d = # dimensions ) Returns ------- h : array-like one dimensional optimal value for smoothing parameter given the data and kernel. size D HNS only gives an optimal value with respect to mean integrated square error, when the true underlying distribution is Gaussian. This works reasonably well if the data resembles a Gaussian distribution. However if the distribution is asymmetric, multimodal or have long tails then HNS may return a to large smoothing parameter, i.e., the KDE may be oversmoothed and mask important features of the data. (=> large bias). One way to remedy this is to reduce H by multiplying with a constant factor, e.g., 0.85. Another is to try different values for H and make a visual check by eye. Example: data = rndnorm(0, 1,20,1) h = hns(data,'epan') See also: --------- hste, hbcv, hboot, hos, hldpi, hlscv, hscv, hstt, kde Reference: --------- B. W. Silverman (1986) 'Density estimation for statistics and data analysis' Chapman and Hall, pp 43-48 Wand,M.P. and Jones, M.C. (1995) 'Kernel smoothing' Chapman and Hall, pp 60--63 """ A = np.atleast_2d(data) n = A.shape[1] # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() AMISEconstant = (8 * sqrt(pi) * R / (3 * mu2 ** 2 * n)) ** (1. / 5) iqr = iqrange(A, axis=1) # interquartile range stdA = np.std(A, axis=1, ddof=1) # use of interquartile range guards against outliers. # the use of interquartile range is better if # the distribution is skew or have heavy tails # This lessen the chance of oversmoothing. return np.where(iqr > 0, np.minimum(stdA, iqr / 1.349), stdA) * AMISEconstant def hos(self, data): """Returns Oversmoothing Parameter. Parameter --------- data = data matrix, size N x D (D = # dimensions ) Returns ------- h : vector size 1 x D one dimensional maximum smoothing value for smoothing parameter given the data and kernel. The oversmoothing or maximal smoothing principle relies on the fact that there is a simple upper bound for the AMISE-optimal bandwidth for estimation of densities with a fixed value of a particular scale measure. While HOS will give too large bandwidth for optimal estimation of a general density it provides an excellent starting point for subjective choice of bandwidth. A sensible strategy is to plot an estimate with bandwidth HOS and then sucessively look at plots based on convenient fractions of HOS to see what features are present in the data for various amount of smoothing. The relation to HNS is given by: HOS = HNS/0.93 Example: -------- data = rndnorm(0, 1,20,1) h = hos(data,'epan'); See also hste, hbcv, hboot, hldpi, hlscv, hscv, hstt, kde, kdefun Reference --------- B. W. Silverman (1986) 'Density estimation for statistics and data analysis' Chapman and Hall, pp 43-48 Wand,M.P. and Jones, M.C. (1986) 'Kernel smoothing' Chapman and Hall, pp 60--63 """ return self.hns(data) / 0.93 def hmns(self, data): """Returns Multivariate Normal Scale Estimate of Smoothing Parameter. CALL: h = hmns(data,kernel) h = M dimensional optimal value for smoothing parameter given the data and kernel. size D x D data = data matrix, size D x N (D = # dimensions ) kernel = 'epanechnikov' - Epanechnikov kernel. 'biweight' - Bi-weight kernel. 'triweight' - Tri-weight kernel. 'gaussian' - Gaussian kernel Note that only the first 4 letters of the kernel name is needed. HMNS only gives a optimal value with respect to mean integrated square error, when the true underlying distribution is Multivariate Gaussian. This works reasonably well if the data resembles a Multivariate Gaussian distribution. However if the distribution is asymmetric, multimodal or have long tails then HNS is maybe more appropriate. Example: data = rndnorm(0, 1,20,2) h = hmns(data,'epan') See also -------- hns, hste, hbcv, hboot, hos, hldpi, hlscv, hscv, hstt Reference ---------- B. W. Silverman (1986) 'Density estimation for statistics and data analysis' Chapman and Hall, pp 43-48, 87 Wand,M.P. and Jones, M.C. (1995) 'Kernel smoothing' Chapman and Hall, pp 60--63, 86--88 """ # TODO: implement more kernels A = np.atleast_2d(data) d, n = A.shape if d == 1: return self.hns(data) name = self.name[:4].lower() if name == 'epan': # Epanechnikov kernel a = (8.0 * (d + 4.0) * (2 * sqrt(pi)) ** d / sphere_volume(d)) ** (1. / (4.0 + d)) elif name == 'biwe': # Bi-weight kernel a = 2.7779 if d > 2: raise ValueError('not implemented for d>2') elif name == 'triw': # Triweight a = 3.12 if d > 2: raise ValueError('not implemented for d>2') elif name == 'gaus': # Gaussian kernel a = (4.0 / (d + 2.0)) ** (1. / (d + 4.0)) else: raise ValueError('Unknown kernel.') covA = scipy.cov(A) return a * linalg.sqrtm(covA).real * n ** (-1. / (d + 4)) def hste(self, data, h0=None, inc=128, maxit=100, releps=0.01, abseps=0.0): '''HSTE 2-Stage Solve the Equation estimate of smoothing parameter. CALL: hs = hste(data,kernel,h0) hs = one dimensional value for smoothing parameter given the data and kernel. size 1 x D data = data matrix, size N x D (D = # dimensions ) kernel = 'gaussian' - Gaussian kernel (default) ( currently the only supported kernel) h0 = initial starting guess for hs (default h0=hns(A,kernel)) Example: x = rndnorm(0,1,50,1); hs = hste(x,'gauss'); See also hbcv, hboot, hos, hldpi, hlscv, hscv, hstt, kde, kdefun Reference --------- B. W. Silverman (1986) 'Density estimation for statistics and data analysis' Chapman and Hall, pp 57--61 Wand,M.P. and Jones, M.C. (1986) 'Kernel smoothing' Chapman and Hall, pp 74--75 ''' # TODO: NB: this routine can be made faster: # TODO: replace the iteration in the end with a Newton Raphson scheme A = np.atleast_2d(data) d, n = A.shape # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() AMISEconstant = (8 * sqrt(pi) * R / (3 * mu2 ** 2 * n)) ** (1. / 5) STEconstant = R / (mu2 ** (2) * n) sigmaA = self.hns(A) / AMISEconstant if h0 is None: h0 = sigmaA * AMISEconstant h = np.asarray(h0, dtype=float) nfft = inc * 2 amin = A.min(axis=1) # Find the minimum value of A. amax = A.max(axis=1) # Find the maximum value of A. arange = amax - amin # Find the range of A. # xa holds the x 'axis' vector, defining a grid of x values where # the k.d. function will be evaluated. ax1 = amin - arange / 8.0 bx1 = amax + arange / 8.0 kernel2 = Kernel('gauss') mu2, R, unusedRdd = kernel2.stats() STEconstant2 = R / (mu2 ** (2) * n) fft = np.fft.fft ifft = np.fft.ifft for dim in range(d): s = sigmaA[dim] ax = ax1[dim] bx = bx1[dim] xa = np.linspace(ax, bx, inc) xn = np.linspace(0, bx - ax, inc) c = gridcount(A[dim], xa) # Step 1 psi6NS = -15 / (16 * sqrt(pi) * s ** 7) psi8NS = 105 / (32 * sqrt(pi) * s ** 9) # Step 2 k40, k60 = kernel2.deriv4_6_8_10(0, numout=2) g1 = (-2 * k40 / (mu2 * psi6NS * n)) ** (1.0 / 7) g2 = (-2 * k60 / (mu2 * psi8NS * n)) ** (1.0 / 9) # Estimate psi6 given g2. # kernel weights. kw4, kw6 = kernel2.deriv4_6_8_10(xn / g2, numout=2) # Apply fftshift to kw. kw = np.r_[kw6, 0, kw6[-1:0:-1]] z = np.real(ifft(fft(c, nfft) * fft(kw))) # convolution. psi6 = np.sum(c * z[:inc]) / (n * (n - 1) * g2 ** 7) # Estimate psi4 given g1. kw4 = kernel2.deriv4_6_8_10(xn / g1, numout=1) # kernel weights. kw = np.r_[kw4, 0, kw4[-1:0:-1]] # Apply 'fftshift' to kw. z = np.real(ifft(fft(c, nfft) * fft(kw))) # convolution. psi4 = np.sum(c * z[:inc]) / (n * (n - 1) * g1 ** 5) h1 = h[dim] h_old = 0 count = 0 while ((abs(h_old - h1) > max(releps * h1, abseps)) and (count < maxit)): count += 1 h_old = h1 # Step 3 gamma = ((2 * k40 * mu2 * psi4 * h1 ** 5) / (-psi6 * R)) ** (1.0 / 7) # Now estimate psi4 given gamma. # kernel weights. kw4 = kernel2.deriv4_6_8_10(xn / gamma, numout=1) kw = np.r_[kw4, 0, kw4[-1:0:-1]] # Apply 'fftshift' to kw. z = np.real(ifft(fft(c, nfft) * fft(kw))) # convolution. psi4Gamma = np.sum(c * z[:inc]) / (n * (n - 1) * gamma ** 5) # Step 4 h1 = (STEconstant2 / psi4Gamma) ** (1.0 / 5) # Kernel other than Gaussian scale bandwidth h1 = h1 * (STEconstant / STEconstant2) ** (1.0 / 5) if count >= maxit: warnings.warn('The obtained value did not converge.') h[dim] = h1 # end for dim loop return h def hisj(self, data, inc=512, L=7): ''' HISJ Improved Sheather-Jones estimate of smoothing parameter. Unlike many other implementations, this one is immune to problems caused by multimodal densities with widely separated modes. The estimation does not deteriorate for multimodal densities, because it do not assume a parametric model for the data. Parameters ---------- data - a vector of data from which the density estimate is constructed inc - the number of mesh points used in the uniform discretization Returns ------- bandwidth - the optimal bandwidth Reference --------- Kernel density estimation via diffusion Z. I. Botev, J. F. Grotowski, and D. P. Kroese (2010) Annals of Statistics, Volume 38, Number 5, pages 2916-2957. ''' A = np.atleast_2d(data) d, n = A.shape # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() STEconstant = R / (n * mu2 ** 2) amin = A.min(axis=1) # Find the minimum value of A. amax = A.max(axis=1) # Find the maximum value of A. arange = amax - amin # Find the range of A. # xa holds the x 'axis' vector, defining a grid of x values where # the k.d. function will be evaluated. ax1 = amin - arange / 8.0 bx1 = amax + arange / 8.0 kernel2 = Kernel('gauss') mu2, R, unusedRdd = kernel2.stats() STEconstant2 = R / (mu2 ** (2) * n) def fixed_point(t, N, I, a2): ''' this implements the function t-zeta*gamma^[L](t)''' prod = np.prod # L = 7 logI = np.log(I) f = 2 * pi ** (2 * L) * \ (a2 * exp(L * logI - I * pi ** 2 * t)).sum() for s in range(L - 1, 1, -1): K0 = prod(np.r_[1:2 * s:2]) / sqrt(2 * pi) const = (1 + (1. / 2) ** (s + 1. / 2)) / 3 time = (2 * const * K0 / N / f) ** (2. / (3 + 2 * s)) f = 2 * pi ** (2 * s) * \ (a2 * exp(s * logI - I * pi ** 2 * time)).sum() return t - (2 * N * sqrt(pi) * f) ** (-2. / 5) h = np.empty(d) for dim in range(d): ax = ax1[dim] bx = bx1[dim] xa = np.linspace(ax, bx, inc) R = bx - ax c = gridcount(A[dim], xa) N = len(set(A[dim])) # a = dct(c/c.sum(), norm=None) a = dct(c / len(A[dim]), norm=None) # now compute the optimal bandwidth^2 using the referenced method I = np.asfarray(np.arange(1, inc)) ** 2 a2 = (a[1:] / 2) ** 2 def fun(t): return fixed_point(t, N, I, a2) x = np.linspace(0, 0.1, 150) ai = x[0] f0 = fun(ai) for bi in x[1:]: f1 = fun(bi) if f1 * f0 <= 0: # print('ai = %g, bi = %g' % (ai,bi)) break else: ai = bi # y = np.asarray([fun(j) for j in x]) # plt.figure(1) # plt.plot(x,y) # plt.show() # use fzero to solve the equation t=zeta*gamma^[5](t) try: t_star = optimize.brentq(fun, a=ai, b=bi) except: t_star = 0.28 * N ** (-2. / 5) warnings.warn('Failure in obtaining smoothing parameter') # smooth the discrete cosine transform of initial data using t_star # a_t = a*exp(-np.arange(inc)**2*pi**2*t_star/2) # now apply the inverse discrete cosine transform # density = idct(a_t)/R; # take the rescaling of the data into account bandwidth = sqrt(t_star) * R # Kernel other than Gaussian scale bandwidth h[dim] = bandwidth * (STEconstant / STEconstant2) ** (1.0 / 5) # end for dim loop return h def hstt(self, data, h0=None, inc=128, maxit=100, releps=0.01, abseps=0.0): '''HSTT Scott-Tapia-Thompson estimate of smoothing parameter. CALL: hs = hstt(data,kernel) hs = one dimensional value for smoothing parameter given the data and kernel. size 1 x D data = data matrix, size N x D (D = # dimensions ) kernel = 'epanechnikov' - Epanechnikov kernel. (default) 'biweight' - Bi-weight kernel. 'triweight' - Tri-weight kernel. 'triangular' - Triangular kernel. 'gaussian' - Gaussian kernel 'rectangular' - Rectangular kernel. 'laplace' - Laplace kernel. 'logistic' - Logistic kernel. HSTT returns Scott-Tapia-Thompson (STT) estimate of smoothing parameter. This is a Solve-The-Equation rule (STE). Simulation studies shows that the STT estimate of HS is a good choice under a variety of models. A comparison with likelihood cross-validation (LCV) indicates that LCV performs slightly better for short tailed densities. However, STT method in contrast to LCV is insensitive to outliers. Example ------- x = rndnorm(0,1,50,1); hs = hstt(x,'gauss'); See also -------- hste, hbcv, hboot, hos, hldpi, hlscv, hscv, kde, kdebin Reference --------- B. W. Silverman (1986) 'Density estimation for statistics and data analysis' Chapman and Hall, pp 57--61 ''' A = np.atleast_2d(data) d, n = A.shape # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() AMISEconstant = (8 * sqrt(pi) * R / (3 * mu2 ** 2 * n)) ** (1. / 5) STEconstant = R / (mu2 ** (2) * n) sigmaA = self.hns(A) / AMISEconstant if h0 is None: h0 = sigmaA * AMISEconstant h = np.asarray(h0, dtype=float) nfft = inc * 2 amin = A.min(axis=1) # Find the minimum value of A. amax = A.max(axis=1) # Find the maximum value of A. arange = amax - amin # Find the range of A. # xa holds the x 'axis' vector, defining a grid of x values where # the k.d. function will be evaluated. ax1 = amin - arange / 8.0 bx1 = amax + arange / 8.0 fft = np.fft.fft ifft = np.fft.ifft for dim in range(d): s = sigmaA[dim] datan = A[dim] / s ax = ax1[dim] / s bx = bx1[dim] / s xa = np.linspace(ax, bx, inc) xn = np.linspace(0, bx - ax, inc) c = gridcount(datan, xa) count = 1 h_old = 0 h1 = h[dim] / s delta = (bx - ax) / (inc - 1) while ((abs(h_old - h1) > max(releps * h1, abseps)) and (count < maxit)): count += 1 h_old = h1 kw4 = self.kernel(xn / h1) / (n * h1 * self.norm_factor(d=1)) kw = np.r_[kw4, 0, kw4[-1:0:-1]] # Apply 'fftshift' to kw. f = np.real(ifft(fft(c, nfft) * fft(kw))) # convolution. # Estimate psi4=R(f'') using simple finite differences and # quadrature. ix = np.arange(1, inc - 1) z = ((f[ix + 1] - 2 * f[ix] + f[ix - 1]) / delta ** 2) ** 2 psi4 = delta * z.sum() h1 = (STEconstant / psi4) ** (1. / 5) if count >= maxit: warnings.warn('The obtained value did not converge.') h[dim] = h1 * s # end % for dim loop return h def hscv(self, data, hvec=None, inc=128, maxit=100, fulloutput=False): ''' HSCV Smoothed cross-validation estimate of smoothing parameter. CALL: [hs,hvec,score] = hscv(data,kernel,hvec) hs = smoothing parameter hvec = vector defining possible values of hs (default linspace(0.25*h0,h0,100), h0=0.62) score = score vector data = data vector kernel = 'gaussian' - Gaussian kernel the only supported Note that only the first 4 letters of the kernel name is needed. Example: data = rndnorm(0,1,20,1) [hs hvec score] = hscv(data,'epan'); plot(hvec,score) See also hste, hbcv, hboot, hos, hldpi, hlscv, hstt, kde, kdefun Wand,M.P. and Jones, M.C. (1986) 'Kernel smoothing' Chapman and Hall, pp 75--79 ''' # TODO: Add support for other kernels than Gaussian A = np.atleast_2d(data) d, n = A.shape # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() AMISEconstant = (8 * sqrt(pi) * R / (3 * mu2 ** 2 * n)) ** (1. / 5) STEconstant = R / (mu2 ** (2) * n) sigmaA = self.hns(A) / AMISEconstant if hvec is None: H = AMISEconstant / 0.93 hvec = np.linspace(0.25 * H, H, maxit) hvec = np.asarray(hvec, dtype=float) steps = len(hvec) score = np.zeros(steps) nfft = inc * 2 amin = A.min(axis=1) # Find the minimum value of A. amax = A.max(axis=1) # Find the maximum value of A. arange = amax - amin # Find the range of A. # xa holds the x 'axis' vector, defining a grid of x values where # the k.d. function will be evaluated. ax1 = amin - arange / 8.0 bx1 = amax + arange / 8.0 kernel2 = Kernel('gauss') mu2, R, unusedRdd = kernel2.stats() STEconstant2 = R / (mu2 ** (2) * n) fft = np.fft.fft ifft = np.fft.ifft h = np.zeros(d) hvec = hvec * (STEconstant2 / STEconstant) ** (1. / 5.) k40, k60, k80, k100 = kernel2.deriv4_6_8_10(0, numout=4) psi8 = 105 / (32 * sqrt(pi)) psi12 = 3465. / (512 * sqrt(pi)) g1 = (-2. * k60 / (mu2 * psi8 * n)) ** (1. / 9.) g2 = (-2. * k100 / (mu2 * psi12 * n)) ** (1. / 13.) for dim in range(d): s = sigmaA[dim] ax = ax1[dim] / s bx = bx1[dim] / s datan = A[dim] / s xa = np.linspace(ax, bx, inc) xn = np.linspace(0, bx - ax, inc) c = gridcount(datan, xa) kw4, kw6 = kernel2.deriv4_6_8_10(xn / g1, numout=2) kw = np.r_[kw6, 0, kw6[-1:0:-1]] z = np.real(ifft(fft(c, nfft) * fft(kw))) psi6 = np.sum(c * z[:inc]) / (n ** 2 * g1 ** 7) kw4, kw6, kw8, kw10 = kernel2.deriv4_6_8_10(xn / g2, numout=4) kw = np.r_[kw10, 0, kw10[-1:0:-1]] z = np.real(ifft(fft(c, nfft) * fft(kw))) psi10 = np.sum(c * z[:inc]) / (n ** 2 * g2 ** 11) g3 = (-2. * k40 / (mu2 * psi6 * n)) ** (1. / 7.) g4 = (-2. * k80 / (mu2 * psi10 * n)) ** (1. / 11.) kw4 = kernel2.deriv4_6_8_10(xn / g3, numout=1) kw = np.r_[kw4, 0, kw4[-1:0:-1]] z = np.real(ifft(fft(c, nfft) * fft(kw))) psi4 = np.sum(c * z[:inc]) / (n ** 2 * g3 ** 5) kw4, kw6, kw8 = kernel2.deriv4_6_8_10(xn / g3, numout=3) kw = np.r_[kw8, 0, kw8[-1:0:-1]] z = np.real(ifft(fft(c, nfft) * fft(kw))) psi8 = np.sum(c * z[:inc]) / (n ** 2 * g4 ** 9) const = (441. / (64 * pi)) ** (1. / 18.) * \ (4 * pi) ** (-1. / 5.) * \ psi4 ** (-2. / 5.) * psi8 ** (-1. / 9.) M = np.atleast_2d(datan) Y = (M - M.T).ravel() for i in range(steps): g = const * n ** (-23. / 45) * hvec[i] ** (-2) sig1 = sqrt(2 * hvec[i] ** 2 + 2 * g ** 2) sig2 = sqrt(hvec[i] ** 2 + 2 * g ** 2) sig3 = sqrt(2 * g ** 2) term2 = np.sum(kernel2(Y / sig1) / sig1 - 2 * kernel2( Y / sig2) / sig2 + kernel2(Y / sig3) / sig3) score[i] = 1. / (n * hvec[i] * 2. * sqrt(pi)) + term2 / n ** 2 idx = score.argmin() # Kernel other than Gaussian scale bandwidth h[dim] = hvec[idx] * (STEconstant / STEconstant2) ** (1 / 5) if idx == 0: warnings.warn( 'Optimum is probably lower than hs=%g for dim=%d' % (h[dim] * s, dim)) elif idx == maxit - 1: warnings.warn( 'Optimum is probably higher than hs=%g for dim=%d' % (h[dim] * s, dim)) hvec = hvec * (STEconstant / STEconstant2) ** (1 / 5) if fulloutput: return h * sigmaA, score, hvec, sigmaA else: return h * sigmaA def hldpi(self, data, L=2, inc=128): '''HLDPI L-stage Direct Plug-In estimate of smoothing parameter. CALL: hs = hldpi(data,kernel,L) hs = one dimensional value for smoothing parameter given the data and kernel. size 1 x D data = data matrix, size N x D (D = # dimensions ) kernel = 'epanechnikov' - Epanechnikov kernel. 'biweight' - Bi-weight kernel. 'triweight' - Tri-weight kernel. 'triangluar' - Triangular kernel. 'gaussian' - Gaussian kernel 'rectangular' - Rectanguler kernel. 'laplace' - Laplace kernel. 'logistic' - Logistic kernel. L = 0,1,2,3,... (default 2) Note that only the first 4 letters of the kernel name is needed. Example: x = rndnorm(0,1,50,1); hs = hldpi(x,'gauss',1); See also hste, hbcv, hboot, hos, hlscv, hscv, hstt, kde, kdefun Wand,M.P. and Jones, M.C. (1995) 'Kernel smoothing' Chapman and Hall, pp 67--74 ''' A = np.atleast_2d(data) d, n = A.shape # R= int(mkernel(x)^2), mu2= int(x^2*mkernel(x)) mu2, R, unusedRdd = self.stats() AMISEconstant = (8 * sqrt(pi) * R / (3 * n * mu2 ** 2)) ** (1. / 5) STEconstant = R / (n * mu2 ** 2) sigmaA = self.hns(A) / AMISEconstant nfft = inc * 2 amin = A.min(axis=1) # Find the minimum value of A. amax = A.max(axis=1) # Find the maximum value of A. arange = amax - amin # Find the range of A. # xa holds the x 'axis' vector, defining a grid of x values where # the k.d. function will be evaluated. ax1 = amin - arange / 8.0 bx1 = amax + arange / 8.0 kernel2 = Kernel('gauss') mu2, unusedR, unusedRdd = kernel2.stats() fft = np.fft.fft ifft = np.fft.ifft h = np.zeros(d) for dim in range(d): s = sigmaA[dim] datan = A[dim] # / s ax = ax1[dim] # / s bx = bx1[dim] # / s xa = np.linspace(ax, bx, inc) xn = np.linspace(0, bx - ax, inc) c = gridcount(datan, xa) r = 2 * L + 4 rd2 = L + 2 # Eq. 3.7 in Wand and Jones (1995) PSI_r = (-1) ** (rd2) * np.prod( np.r_[rd2 + 1:r + 1]) / (sqrt(pi) * (2 * s) ** (r + 1)) PSI = PSI_r if L > 0: # High order derivatives of the Gaussian kernel Kd = kernel2.deriv4_6_8_10(0, numout=L) # L-stage iterations to estimate PSI_4 for ix in range(L, 0, -1): gi = (-2 * Kd[ix - 1] / (mu2 * PSI * n)) ** (1. / (2 * ix + 5)) # Obtain the kernel weights. KW0 = kernel2.deriv4_6_8_10(xn / gi, numout=ix) if ix > 1: KW0 = KW0[-1] # Apply 'fftshift' to kw. kw = np.r_[KW0, 0, KW0[inc - 1:0:-1]] # Perform the convolution. z = np.real(ifft(fft(c, nfft) * fft(kw))) PSI = np.sum(c * z[:inc]) / (n ** 2 * gi ** (2 * ix + 3)) # end # end h[dim] = (STEconstant / PSI) ** (1. / 5) return h def norm_factor(self, d=1, n=None): return self.kernel.norm_factor(d, n) def eval_points(self, points): return self.kernel(np.atleast_2d(points)) __call__ = eval_points def mkernel(X, kernel): """MKERNEL Multivariate Kernel Function. Paramaters ---------- X : array-like matrix size d x n (d = # dimensions, n = # evaluation points) kernel : string defining kernel 'epanechnikov' - Epanechnikov kernel. 'biweight' - Bi-weight kernel. 'triweight' - Tri-weight kernel. 'p1epanechnikov' - product of 1D Epanechnikov kernel. 'p1biweight' - product of 1D Bi-weight kernel. 'p1triweight' - product of 1D Tri-weight kernel. 'triangular' - Triangular kernel. 'gaussian' - Gaussian kernel 'rectangular' - Rectangular kernel. 'laplace' - Laplace kernel. 'logistic' - Logistic kernel. Note that only the first 4 letters of the kernel name is needed. Returns ------- z : ndarray kernel function values evaluated at X See also -------- kde, kdefun, kdebin References ---------- B. W. Silverman (1986) 'Density estimation for statistics and data analysis' Chapman and Hall, pp. 43, 76 Wand, M. P. and Jones, M. C. (1995) 'Density estimation for statistics and data analysis' Chapman and Hall, pp 31, 103, 175 """ fun = _MKERNEL_DICT[kernel[:4]] return fun(np.atleast_2d(X)) def accumsum(accmap, a, size, dtype=None): if dtype is None: dtype = a.dtype size = np.atleast_1d(size) if len(size) > 1: binx = accmap[:, 0] biny = accmap[:, 1] out = sparse.coo_matrix( (a.ravel(), (binx, biny)), shape=size, dtype=dtype).tocsr() else: binx = accmap.ravel() zero = np.zeros(len(binx)) out = sparse.coo_matrix( (a.ravel(), (binx, zero)), shape=(size, 1), dtype=dtype).tocsr() return out def accumsum2(accmap, a, size): return np.bincount(accmap.ravel(), a.ravel(), np.array(size).max()) def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None): """An accumulation function similar to Matlab's `accumarray` function. Parameters ---------- accmap : ndarray This is the "accumulation map". It maps input (i.e. indices into `a`) to their destination in the output array. The first `a.ndim` dimensions of `accmap` must be the same as `a.shape`. That is, `accmap.shape[:a.ndim]` must equal `a.shape`. For example, if `a` has shape (15,4), then `accmap.shape[:2]` must equal (15,4). In this case `accmap[i,j]` gives the index into the output array where element (i,j) of `a` is to be accumulated. If the output is, say, a 2D, then `accmap` must have shape (15,4,2). The value in the last dimension give indices into the output array. If the output is 1D, then the shape of `accmap` can be either (15,4) or (15,4,1) a : ndarray The input data to be accumulated. func : callable or None The accumulation function. The function will be passed a list of values from `a` to be accumulated. If None, numpy.sum is assumed. size : ndarray or None The size of the output array. If None, the size will be determined from `accmap`. fill_value : scalar The default value for elements of the output array. dtype : numpy data type, or None The data type of the output array. If None, the data type of `a` is used. Returns ------- out : ndarray The accumulated results. The shape of `out` is `size` if `size` is given. Otherwise the shape is determined by the (lexicographically) largest indices of the output found in `accmap`. Examples -------- >>> from numpy import array, prod >>> a = array([[1,2,3],[4,-1,6],[-1,8,9]]) >>> a array([[ 1, 2, 3], [ 4, -1, 6], [-1, 8, 9]]) >>> # Sum the diagonals. >>> accmap = array([[0,1,2],[2,0,1],[1,2,0]]) >>> s = accum(accmap, a) >>> s array([ 9, 7, 15]) >>> # A 2D output, from sub-arrays with shapes and positions like this: >>> # [ (2,2) (2,1)] >>> # [ (1,2) (1,1)] >>> accmap = array([ ... [[0,0],[0,0],[0,1]], ... [[0,0],[0,0],[0,1]], ... [[1,0],[1,0],[1,1]]]) >>> # Accumulate using a product. >>> accum(accmap, a, func=prod, dtype=float) array([[ -8., 18.], [ -8., 9.]]) >>> # Same accmap, but create an array of lists of values. >>> accum(accmap, a, func=lambda x: x, dtype='O') array([[[1, 2, 4, -1], [3, 6]], [[-1, 8], [9]]], dtype=object) """ # Check for bad arguments and handle the defaults. if accmap.shape[:a.ndim] != a.shape: raise ValueError( "The initial dimensions of accmap must be the same as a.shape") if func is None: func = np.sum if dtype is None: dtype = a.dtype if accmap.shape == a.shape: accmap = np.expand_dims(accmap, -1) adims = tuple(range(a.ndim)) if size is None: size = 1 + np.squeeze(np.apply_over_axes(np.max, accmap, axes=adims)) size = np.atleast_1d(size) # Create an array of python lists of values. vals = np.empty(size, dtype='O') for s in product(*[range(k) for k in size]): vals[s] = [] for s in product(*[range(k) for k in a.shape]): indx = tuple(accmap[s]) val = a[s] vals[indx].append(val) # Create the output array. out = np.empty(size, dtype=dtype) for s in product(*[range(k) for k in size]): if vals[s] == []: out[s] = fill_value else: out[s] = func(vals[s]) return out def qlevels(pdf, p=(10, 30, 50, 70, 90, 95, 99, 99.9), x1=None, x2=None): """QLEVELS Calculates quantile levels which encloses P% of PDF. CALL: [ql PL] = qlevels(pdf,PL,x1,x2); ql = the discrete quantile levels. pdf = joint point density function matrix or vector PL = percent level (default [10:20:90 95 99 99.9]) x1,x2 = vectors of the spacing of the variables (Default unit spacing) QLEVELS numerically integrates PDF by decreasing height and find the quantile levels which encloses P% of the distribution. If X1 and (or) X2 is unspecified it is assumed that dX1 and dX2 is constant. NB! QLEVELS normalizes the integral of PDF to N/(N+0.001) before calculating QL in order to reflect the sampling of PDF is finite. Currently only able to handle 1D and 2D PDF's if dXi is not constant (i=1,2). Example ------- >>> import wafo.stats as ws >>> x = np.linspace(-8,8,2001); >>> PL = np.r_[10:90:20, 90, 95, 99, 99.9] >>> qlevels(ws.norm.pdf(x),p=PL, x1=x); array([ 0.39591707, 0.37058719, 0.31830968, 0.23402133, 0.10362052, 0.05862129, 0.01449505, 0.00178806]) # compared with the exact values >>> ws.norm.pdf(ws.norm.ppf((100-PL)/200)) array([ 0.39580488, 0.370399 , 0.31777657, 0.23315878, 0.10313564, 0.05844507, 0.01445974, 0.00177719]) See also -------- qlevels2, tranproc """ norm = 1 # normalize cdf to unity pdf = np.atleast_1d(pdf) if any(pdf.ravel() < 0): raise ValueError( 'This is not a pdf since one or more values of pdf is negative') fsiz = pdf.shape fsizmin = min(fsiz) if fsizmin == 0: return [] N = np.prod(fsiz) d = len(fsiz) if x1 is None or ((x2 is None) and d > 2): fdfi = pdf.ravel() else: if d == 1: # pdf in one dimension dx22 = np.ones(1) else: # % pdf in two dimensions dx2 = np.diff(x2.ravel()) * 0.5 dx22 = np.r_[0, dx2] + np.r_[dx2, 0] dx1 = np.diff(x1.ravel()) * 0.5 dx11 = np.r_[0, dx1] + np.r_[dx1, 0] dx1x2 = dx22[:, None] * dx11 fdfi = (pdf * dx1x2).ravel() p = np.atleast_1d(p) if np.any((p < 0) | (100 < p)): raise ValueError('PL must satisfy 0 <= PL <= 100') p2 = p / 100.0 ind = np.argsort(pdf.ravel()) # sort by height of pdf ind = ind[::-1] fi = pdf.flat[ind] # integration in the order of decreasing height of pdf Fi = np.cumsum(fdfi[ind]) if norm: # %normalize Fi to make sure int pdf dx1 dx2 approx 1 Fi = Fi / Fi[-1] * N / (N + 1.5e-8) maxFi = np.max(Fi) if maxFi > 1: warnings.warn('this is not a pdf since cdf>1! normalizing') Fi = Fi / Fi[-1] * N / (N + 1.5e-8) elif maxFi < .95: msg = '''The given pdf is too sparsely sampled since cdf<.95. Thus QL is questionable''' warnings.warn(msg) # make sure Fi is strictly increasing by not considering duplicate values ind, = np.where(np.diff(np.r_[Fi, 1]) > 0) # calculating the inverse of Fi to find the index ui = tranproc(Fi[ind], fi[ind], p2) # to the desired quantile level # ui=smooth(Fi(ind),fi(ind),1,p2(:),1) % alternative # res=ui-ui2 if np.any(ui >= max(pdf.ravel())): warnings.warn('The lowest percent level is too close to 0%') if np.any(ui <= min(pdf.ravel())): msg = '''The given pdf is too sparsely sampled or the highest percent level is too close to 100%''' warnings.warn(msg) ui[ui < 0] = 0.0 return ui def qlevels2(data, p=(10, 30, 50, 70, 90, 95, 99, 99.9), method=1): """QLEVELS2 Calculates quantile levels which encloses P% of data. CALL: [ql PL] = qlevels2(data,PL,method); ql = the discrete quantile levels, size D X Np Parameters ---------- data : data matrix, size D x N (D = # of dimensions) p : percent level vector, length Np (default [10:20:90 95 99 99.9]) method : integer 1 Interpolation so that F(X_(k)) == (k-0.5)/n. (default) 2 Interpolation so that F(X_(k)) == k/(n+1). 3 Based on the empirical distribution. Returns ------- QLEVELS2 sort the columns of data in ascending order and find the quantile levels for each column which encloses P% of the data. Examples : Finding quantile levels enclosing P% of data: -------- >>> import wafo.stats as ws >>> PL = np.r_[10:90:20, 90, 95, 99, 99.9] >>> xs = ws.norm.rvs(size=2500000) >>> np.allclose(qlevels2(ws.norm.pdf(xs), p=PL), ... [0.3958, 0.3704, 0.3179, 0.2331, 0.1031, 0.05841, 0.01451, 0.001751], ... rtol=1e-1) True # compared with the exact values >>> ws.norm.pdf(ws.norm.ppf((100-PL)/200)) array([ 0.39580488, 0.370399 , 0.31777657, 0.23315878, 0.10313564, 0.05844507, 0.01445974, 0.00177719]) # Finding the median of xs: >>> '%2.2f' % np.abs(qlevels2(xs,50)[0]) '0.00' See also -------- qlevels """ q = 100 - np.atleast_1d(p) return percentile(data, q, axis=-1, method=method) _PKDICT = {1: lambda k, w, n: (k - w) / (n - 1), 2: lambda k, w, n: (k - w / 2) / n, 3: lambda k, w, n: k / n, 4: lambda k, w, n: k / (n + 1), 5: lambda k, w, n: (k - w / 3) / (n + 1 / 3), 6: lambda k, w, n: (k - w * 3 / 8) / (n + 1 / 4)} def _compute_qth_weighted_percentile(a, q, axis, out, method, weights, overwrite_input): # normalise weight vector such that sum of the weight vector equals to n q = np.atleast_1d(q) / 100.0 if (q < 0).any() or (q > 1).any(): raise ValueError("percentile must be in the range [0,100]") shape0 = a.shape if axis is None: sorted_ = a.ravel() else: taxes = [i for i in range(a.ndim)] taxes[-1], taxes[axis] = taxes[axis], taxes[-1] sorted_ = np.transpose(a, taxes).reshape(-1, shape0[axis]) ind = sorted_.argsort(axis=-1) if overwrite_input: sorted_.sort(axis=-1) else: sorted_ = np.sort(sorted_, axis=-1) w = np.atleast_1d(weights) n = len(w) w = w * n / w.sum() # Work on each column separately because of weight vector m = sorted_.shape[0] nq = len(q) y = np.zeros((m, nq)) pk_fun = _PKDICT.get(method, 1) for i in range(m): sortedW = w[ind[i]] # rearrange the weight according to ind k = sortedW.cumsum() # cumulative weight # different algorithm to compute percentile pk = pk_fun(k, sortedW, n) # Interpolation between pk and sorted_ for given value of q y[i] = np.interp(q, pk, sorted_[i]) if axis is None: return np.squeeze(y) else: shape1 = list(shape0) shape1[axis], shape1[-1] = shape1[-1], nq return np.squeeze(np.transpose(y.reshape(shape1), taxes)) # method=1: p(k) = k/(n-1) # method=2: p(k) = (k+0.5)/n. # method=3: p(k) = (k+1)/n # method=4: p(k) = (k+1)/(n+1) # method=5: p(k) = (k+2/3)/(n+1/3) # method=6: p(k) = (k+5/8)/(n+1/4) _KDICT = {1: lambda p, n: p * (n - 1), 2: lambda p, n: p * n - 0.5, 3: lambda p, n: p * n - 1, 4: lambda p, n: p * (n + 1) - 1, 5: lambda p, n: p * (n + 1. / 3) - 2. / 3, 6: lambda p, n: p * (n + 1. / 4) - 5. / 8} def _compute_qth_percentile(sorted_, q, axis, out, method): if not np.isscalar(q): p = [_compute_qth_percentile(sorted_, qi, axis, None, method) for qi in q] if out is not None: out.flat = p return p q = q / 100.0 if (q < 0) or (q > 1): raise ValueError("percentile must be in the range [0,100]") indexer = [slice(None)] * sorted_.ndim Nx = sorted_.shape[axis] k_fun = _KDICT.get(method, 1) index = np.clip(k_fun(q, Nx), 0, Nx - 1) i = int(index) if i == index: indexer[axis] = slice(i, i + 1) weights1 = np.array(1) sumval = 1.0 else: indexer[axis] = slice(i, i + 2) j = i + 1 weights1 = np.array([(j - index), (index - i)], float) wshape = [1] * sorted_.ndim wshape[axis] = 2 weights1.shape = wshape sumval = weights1.sum() # Use add.reduce in both cases to coerce data type as well as # check and use out array. return np.add.reduce(sorted_[indexer] * weights1, axis=axis, out=out) / sumval def percentile(a, q, axis=None, out=None, overwrite_input=False, method=1, weights=None): """Compute the qth percentile of the data along the specified axis. Returns the qth percentile of the array elements. Parameters ---------- a : array_like Input array or object that can be converted to an array. q : float in range of [0,100] (or sequence of floats) percentile to compute which must be between 0 and 100 inclusive axis : {None, int}, optional Axis along which the percentiles are computed. The default (axis=None) is to compute the median along a flattened version of the array. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output, but the type (of the output) will be cast if necessary. overwrite_input : {False, True}, optional If True, then allow use of memory of input array (a) for calculations. The input array will be modified by the call to median. This will save memory when you do not need to preserve the contents of the input array. Treat the input as undefined, but it will probably be fully or partially sorted. Default is False. Note that, if `overwrite_input` is True and the input is not already an ndarray, an error will be raised. method : scalar integer defining the interpolation method. Valid options are 1 : p[k] = k/(n-1). In this case, p[k] = mode[F(x[k])]. This is used by S. (default) 2 : p[k] = (k+0.5)/n. That is a piecewise linear function where the knots are the values midway through the steps of the empirical cdf. This is popular amongst hydrologists. Matlab also uses this formula. 3 : p[k] = (k+1)/n. That is, linear interpolation of the empirical cdf. 4 : p[k] = (k+1)/(n+1). Thus p[k] = E[F(x[k])]. This is used by Minitab and by SPSS. 5 : p[k] = (k+2/3)/(n+1/3). Then p[k] =~ median[F(x[k])]. The resulting quantile estimates are approximately median-unbiased regardless of the distribution of x. 6 : p[k] = (k+5/8)/(n+1/4). The resulting quantile estimates are approximately unbiased for the expected order statistics if x is normally distributed. Returns ------- pcntile : ndarray A new array holding the result (unless `out` is specified, in which case that array is returned instead). If the input contains integers, or floats of smaller precision than 64, then the output data-type is float64. Otherwise, the output data-type is the same as that of the input. See Also -------- mean, median Notes ----- Given a vector V of length N, the qth percentile of V is the qth ranked value in a sorted copy of V. A weighted average of the two nearest neighbors is used if the normalized ranking does not match q exactly. The same as the median if q is 0.5; the same as the min if q is 0; and the same as the max if q is 1 Examples -------- >>> import wafo.kdetools as wk >>> a = np.array([[10, 7, 4], [3, 2, 1]]) >>> a array([[10, 7, 4], [ 3, 2, 1]]) >>> wk.percentile(a, 50) 3.5 >>> wk.percentile(a, 50, axis=0) array([ 6.5, 4.5, 2.5]) >>> wk.percentile(a, 50, axis=0, weights=np.ones(2)) array([ 6.5, 4.5, 2.5]) >>> wk.percentile(a, 50, axis=1) array([ 7., 2.]) >>> wk.percentile(a, 50, axis=1, weights=np.ones(3)) array([ 7., 2.]) >>> m = wk.percentile(a, 50, axis=0) >>> out = np.zeros_like(m) >>> wk.percentile(a, 50, axis=0, out=m) array([ 6.5, 4.5, 2.5]) >>> m array([ 6.5, 4.5, 2.5]) >>> b = a.copy() >>> wk.percentile(b, 50, axis=1, overwrite_input=True) array([ 7., 2.]) >>> assert not np.all(a==b) >>> b = a.copy() >>> wk.percentile(b, 50, axis=None, overwrite_input=True) 3.5 >>> np.all(a==b) True """ a = np.asarray(a) try: if q == 0: return a.min(axis=axis, out=out) elif q == 100: return a.max(axis=axis, out=out) except: pass if weights is not None: return _compute_qth_weighted_percentile(a, q, axis, out, method, weights, overwrite_input) elif overwrite_input: if axis is None: sorted_ = np.sort(a, axis=axis) else: a.sort(axis=axis) sorted_ = a else: sorted_ = np.sort(a, axis=axis) if axis is None: axis = 0 return _compute_qth_percentile(sorted_, q, axis, out, method) def iqrange(data, axis=None): """Returns the Inter Quartile Range of data. Parameters ---------- data : array-like Input array or object that can be converted to an array. axis : {None, int}, optional Axis along which the percentiles are computed. The default (axis=None) is to compute the median along a flattened version of the array. Returns ------- r : array-like abs(np.percentile(data, 75, axis)-np.percentile(data, 25, axis)) Notes ----- IQRANGE is a robust measure of spread. The use of interquartile range guards against outliers if the distribution have heavy tails. Example ------- >>> a = np.arange(101) >>> iqrange(a) 50.0 See also -------- np.std """ return np.abs(np.percentile(data, 75, axis=axis) - np.percentile(data, 25, axis=axis)) def bitget(int_type, offset): """Returns the value of the bit at the offset position in int_type. Example ------- >>> bitget(5, np.r_[0:4]) array([1, 0, 1, 0]) """ return np.bitwise_and(int_type, 1 << offset) >> offset def gridcount(data, X, y=1): ''' Returns D-dimensional histogram using linear binning. Parameters ---------- data = column vectors with D-dimensional data, shape D x Nd X = row vectors defining discretization, shape D x N Must include the range of the data. Returns ------- c = gridcount, shape N x N x ... x N GRIDCOUNT obtains the grid counts using linear binning. There are 2 strategies: simple- or linear- binning. Suppose that an observation occurs at x and that the nearest point below and above is y and z, respectively. Then simple binning strategy assigns a unit weight to either y or z, whichever is closer. Linear binning, on the other hand, assigns the grid point at y with the weight of (z-x)/(z-y) and the gridpoint at z a weight of (y-x)/(z-y). In terms of approximation error of using gridcounts as pdf-estimate, linear binning is significantly more accurate than simple binning. NOTE: The interval [min(X);max(X)] must include the range of the data. The order of C is permuted in the same order as meshgrid for D==2 or D==3. Example ------- >>> import numpy as np >>> import wafo.kdetools as wk >>> import pylab as plb >>> N = 20 >>> data = np.random.rayleigh(1,N) >>> data = np.array( ... [ 1.07855907, 1.51199717, 1.54382893, 1.54774808, 1.51913566, ... 1.11386486, 1.49146216, 1.51127214, 2.61287913, 0.94793051, ... 2.08532731, 1.35510641, 0.56759888, 1.55766981, 0.77883602, ... 0.9135759 , 0.81177855, 1.02111483, 1.76334202, 0.07571454]) >>> x = np.linspace(0,max(data)+1,50) >>> dx = x[1]-x[0] >>> c = wk.gridcount(data, x) >>> np.allclose(c[:5], [ 0., 0.9731147, 0.0268853, 0., 0.]) True >>> pdf = c/dx/N >>> np.allclose(np.trapz(pdf, x), 1) True h = plb.plot(x,c,'.') # 1D histogram h1 = plb.plot(x, pdf) # 1D probability density plot See also -------- bincount, accum, kdebin Reference ---------- Wand,M.P. and Jones, M.C. (1995) 'Kernel smoothing' Chapman and Hall, pp 182-192 ''' dat = np.atleast_2d(data) x = np.atleast_2d(X) y = np.atleast_1d(y).ravel() d = dat.shape[0] d1, inc = x.shape if d != d1: raise ValueError('Dimension 0 of data and X do not match.') dx = np.diff(x[:, :2], axis=1) xlo = x[:, 0] xup = x[:, -1] datlo = dat.min(axis=1) datup = dat.max(axis=1) if ((datlo < xlo) | (xup < datup)).any(): raise ValueError('X does not include whole range of the data!') csiz = np.repeat(inc, d) use_sparse = False if use_sparse: acfun = accumsum # faster than accum else: acfun = accumsum2 # accum binx = np.asarray(np.floor((dat - xlo[:, newaxis]) / dx), dtype=int) w = dx.prod() abs = np.abs # @ReservedAssignment if d == 1: x.shape = (-1,) c = np.asarray((acfun(binx, (x[binx + 1] - dat) * y, size=(inc, )) + acfun(binx + 1, (dat - x[binx]) * y, size=(inc, ))) / w).ravel() else: # d>2 Nc = csiz.prod() c = np.zeros((Nc,)) fact2 = np.asarray(np.reshape(inc * np.arange(d), (d, -1)), dtype=int) fact1 = np.asarray( np.reshape(csiz.cumprod() / inc, (d, -1)), dtype=int) # fact1 = fact1(ones(n,1),:); bt0 = [0, 0] X1 = X.ravel() for ir in range(2 ** (d - 1)): bt0[0] = np.reshape(bitget(ir, np.arange(d)), (d, -1)) bt0[1] = 1 - bt0[0] for ix in range(2): one = np.mod(ix, 2) two = np.mod(ix + 1, 2) # Convert to linear index # linear index to c b1 = np.sum((binx + bt0[one]) * fact1, axis=0) bt2 = bt0[two] + fact2 b2 = binx + bt2 # linear index to X c += acfun( b1, abs(np.prod(X1[b2] - dat, axis=0)) * y, size=(Nc,)) c = np.reshape(c / w, csiz, order='F') T = [i for i in range(d)] T[1], T[0] = T[0], T[1] # make sure c is stored in the same way as meshgrid c = c.transpose(*T) return c def kde_demo1(): """KDEDEMO1 Demonstrate the smoothing parameter impact on KDE. KDEDEMO1 shows the true density (dotted) compared to KDE based on 7 observations (solid) and their individual kernels (dashed) for 3 different values of the smoothing parameter, hs. """ import scipy.stats as st x = np.linspace(-4, 4, 101) x0 = x / 2.0 data = np.random.normal(loc=0, scale=1.0, size=7) kernel = Kernel('gauss') hs = kernel.hns(data) hVec = [hs / 2, hs, 2 * hs] for ix, h in enumerate(hVec): plt.figure(ix) kde = KDE(data, hs=h, kernel=kernel) f2 = kde(x, output='plot', title='h_s = %2.2f' % h, ylab='Density') f2.plot('k-') plt.plot(x, st.norm.pdf(x, 0, 1), 'k:') n = len(data) plt.plot(data, np.zeros(data.shape), 'bx') y = kernel(x0) / (n * h * kernel.norm_factor(d=1, n=n)) for i in range(n): plt.plot(data[i] + x0 * h, y, 'b--') plt.plot([data[i], data[i]], [0, np.max(y)], 'b') plt.axis([x.min(), x.max(), 0, 0.5]) def kde_demo2(): '''Demonstrate the difference between transformation- and ordinary-KDE. KDEDEMO2 shows that the transformation KDE is a better estimate for Rayleigh distributed data around 0 than the ordinary KDE. ''' import scipy.stats as st data = st.rayleigh.rvs(scale=1, size=300) x = np.linspace(1.5e-2, 5, 55) kde = KDE(data) f = kde(output='plot', title='Ordinary KDE (hs=%g)' % kde.hs) plt.figure(0) f.plot() plt.plot(x, st.rayleigh.pdf(x, scale=1), ':') # plotnorm((data).^(L2)) % gives a straight line => L2 = 0.5 reasonable tkde = TKDE(data, L2=0.5) ft = tkde(x, output='plot', title='Transformation KDE (hs=%g)' % tkde.tkde.hs) plt.figure(1) ft.plot() plt.plot(x, st.rayleigh.pdf(x, scale=1), ':') plt.figure(0) def kde_demo3(): '''Demonstrate the difference between transformation and ordinary-KDE in 2D KDEDEMO3 shows that the transformation KDE is a better estimate for Rayleigh distributed data around 0 than the ordinary KDE. ''' import scipy.stats as st data = st.rayleigh.rvs(scale=1, size=(2, 300)) # x = np.linspace(1.5e-3, 5, 55) kde = KDE(data) f = kde(output='plot', title='Ordinary KDE', plotflag=1) plt.figure(0) f.plot() plt.plot(data[0], data[1], '.') # plotnorm((data).^(L2)) % gives a straight line => L2 = 0.5 reasonable tkde = TKDE(data, L2=0.5) ft = tkde.eval_grid_fast( output='plot', title='Transformation KDE', plotflag=1) plt.figure(1) ft.plot() plt.plot(data[0], data[1], '.') plt.figure(0) def kde_demo4(N=50): '''Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior for 1D multimodal distributions KDEDEMO4 shows that the improved Sheather-Jones plug-in smoothing is a better compared to normal reference rules (in this case the hns) ''' import scipy.stats as st data = np.hstack((st.norm.rvs(loc=5, scale=1, size=(N,)), st.norm.rvs(loc=-5, scale=1, size=(N,)))) # x = np.linspace(1.5e-3, 5, 55) kde = KDE(data, kernel=Kernel('gauss', 'hns')) f = kde(output='plot', title='Ordinary KDE', plotflag=1) kde1 = KDE(data, kernel=Kernel('gauss', 'hisj')) f1 = kde1(output='plot', label='Ordinary KDE', plotflag=1) plt.figure(0) f.plot('r', label='hns=%g' % kde.hs) # plt.figure(2) f1.plot('b', label='hisj=%g' % kde1.hs) x = np.linspace(-4, 4) for loc in [-5, 5]: plt.plot(x + loc, st.norm.pdf(x, 0, scale=1) / 2, 'k:', label='True density') plt.legend() def kde_demo5(N=500): '''Demonstrate that the improved Sheather-Jones plug-in (hisj) is superior for 2D multimodal distributions KDEDEMO5 shows that the improved Sheather-Jones plug-in smoothing is better compared to normal reference rules (in this case the hns) ''' import scipy.stats as st data = np.hstack((st.norm.rvs(loc=5, scale=1, size=(2, N,)), st.norm.rvs(loc=-5, scale=1, size=(2, N,)))) kde = KDE(data, kernel=Kernel('gauss', 'hns')) f = kde(output='plot', title='Ordinary KDE (hns=%g %g)' % tuple(kde.hs.tolist()), plotflag=1) kde1 = KDE(data, kernel=Kernel('gauss', 'hisj')) f1 = kde1(output='plot', title='Ordinary KDE (hisj=%g %g)' % tuple(kde1.hs.tolist()), plotflag=1) plt.figure(0) plt.clf() f.plot() plt.plot(data[0], data[1], '.') plt.figure(1) plt.clf() f1.plot() plt.plot(data[0], data[1], '.') def kreg_demo1(hs=None, fast=False, fun='hisj'): """""" N = 100 # ei = np.random.normal(loc=0, scale=0.075, size=(N,)) ei = np.array([ -0.08508516, 0.10462496, 0.07694448, -0.03080661, 0.05777525, 0.06096313, -0.16572389, 0.01838912, -0.06251845, -0.09186784, -0.04304887, -0.13365788, -0.0185279, -0.07289167, 0.02319097, 0.06887854, -0.08938374, -0.15181813, 0.03307712, 0.08523183, -0.0378058, -0.06312874, 0.01485772, 0.06307944, -0.0632959, 0.18963205, 0.0369126, -0.01485447, 0.04037722, 0.0085057, -0.06912903, 0.02073998, 0.1174351, 0.17599277, -0.06842139, 0.12587608, 0.07698113, -0.0032394, -0.12045792, -0.03132877, 0.05047314, 0.02013453, 0.04080741, 0.00158392, 0.10237899, -0.09069682, 0.09242174, -0.15445323, 0.09190278, 0.07138498, 0.03002497, 0.02495252, 0.01286942, 0.06449978, 0.03031802, 0.11754861, -0.02322272, 0.00455867, -0.02132251, 0.09119446, -0.03210086, -0.06509545, 0.07306443, 0.04330647, 0.078111, -0.04146907, 0.05705476, 0.02492201, -0.03200572, -0.02859788, -0.05893749, 0.00089538, 0.0432551, 0.04001474, 0.04888828, -0.17708392, 0.16478644, 0.1171006, 0.11664846, 0.01410477, -0.12458953, -0.11692081, 0.0413047, -0.09292439, -0.07042327, 0.14119701, -0.05114335, 0.04994696, -0.09520663, 0.04829406, -0.01603065, -0.1933216, 0.19352763, 0.11819496, 0.04567619, -0.08348306, 0.00812816, -0.00908206, 0.14528945, 0.02901065]) x = np.linspace(0, 1, N) y0 = 2 * np.exp(-x ** 2 / (2 * 0.3 ** 2)) + \ 3 * np.exp(-(x - 1) ** 2 / (2 * 0.7 ** 2)) y = y0 + ei kernel = Kernel('gauss', fun=fun) hopt = kernel.hisj(x) kreg = KRegression( x, y, p=0, hs=hs, kernel=kernel, xmin=-2 * hopt, xmax=1 + 2 * hopt) if fast: kreg.__call__ = kreg.eval_grid_fast f = kreg(output='plot', title='Kernel regression', plotflag=1) plt.figure(0) f.plot(label='p=0') kreg.p = 1 f1 = kreg(output='plot', title='Kernel regression', plotflag=1) f1.plot(label='p=1') # print(f1.data) plt.plot(x, y, '.', label='data') plt.plot(x, y0, 'k', label='True model') plt.legend() plt.show() print(kreg.tkde.tkde.inv_hs) print(kreg.tkde.tkde.hs) _TINY = np.finfo(float).machar.tiny _REALMIN = np.finfo(float).machar.xmin _REALMAX = np.finfo(float).machar.xmax _EPS = np.finfo(float).eps def _logit(p): pc = p.clip(min=0, max=1) return (np.log(pc) - np.log1p(-pc)).clip(min=-40, max=40) def _logitinv(x): return 1.0 / (np.exp(-x) + 1) def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0): import scipy.stats as st # from sg_filter import SavitzkyGolay dist = st.norm norm1 = scale2 * (dist.pdf(-loc1, loc=-loc1, scale=scale1) + dist.pdf(-loc1, loc=loc1, scale=scale1)) def fun1(x): return ((dist.pdf(x, loc=-loc1, scale=scale1) + dist.pdf(x, loc=loc1, scale=scale1)) / norm1).clip(max=1.0) x = np.sort(6 * np.random.rand(n, 1) - 3, axis=0) y = (fun1(x) > np.random.rand(n, 1)).ravel() # y = (np.cos(x)>2*np.random.rand(n, 1)-1).ravel() x = x.ravel() if symmetric: xi = np.hstack((x.ravel(), -x.ravel())) yi = np.hstack((y, y)) i = np.argsort(xi) x = xi[i] y = yi[i] return x, y, fun1 def kreg_demo2(n=100, hs=None, symmetric=False, fun='hisj', plotlog=False): x, y, fun1 = _get_data(n, symmetric) kreg_demo3(x, y, fun1, hs=None, fun='hisj', plotlog=False) def kreg_demo3(x, y, fun1, hs=None, fun='hisj', plotlog=False): st = stats alpha = 0.1 z0 = -_invnorm(alpha / 2) n = x.size hopt, hs1, hs2 = _get_regression_smooting(x, y, fun='hos') if hs is None: hs = hopt forward = _logit reverse = _logitinv # forward = np.log # reverse = np.exp xmin, xmax = x.min(), x.max() ni = max(2 * int((xmax - xmin) / hopt) + 3, 5) print(ni) print(xmin, xmax) sml = hopt * 0.1 xi = np.linspace(xmin - sml, xmax + sml, ni) xiii = np.linspace(xmin - sml, xmax + sml, 4 * ni + 1) c = gridcount(x, xi) if (y == 1).any(): c0 = gridcount(x[y == 1], xi) else: c0 = np.zeros(xi.shape) yi = np.where(c == 0, 0, c0 / c) kreg = KRegression(x, y, hs=hs, p=0) fiii = kreg(xiii) yiii = interpolate.interp1d(xi, yi)(xiii) fit = fun1(xiii).clip(max=1.0) df = np.diff(fiii) eerr = np.abs((yiii - fiii)).std() + 0.5 * (df[:-1] * df[1:] < 0).sum() / n err = (fiii - fit).std() f = kreg( xiii, output='plotobj', title='%s err=%1.3f,eerr=%1.3f, n=%d, hs=%1.3f, hs1=%1.3f, hs2=%1.3f' % (fun, err, eerr, n, hs, hs1, hs2), plotflag=1) # yi[yi==0] = 1.0/(c[c!=0].min()+4) # yi[yi==1] = 1-1.0/(c[c!=0].min()+4) # yi[yi==0] = fi[yi==0] # yi[yi==0] = np.exp(stineman_interp(xi[yi==0], xi[yi>0],np.log(yi[yi>0]))) # yi[yi==0] = fun1(xi[yi==0]) try: yi[yi == 0] = yi[yi > 0].min() / sqrt(n) except: yi[yi == 0] = 1. / n yi[yi == 1] = 1 - (1 - yi[yi < 1].max()) / sqrt(n) logity = forward(yi) gkreg = KRegression(xi, logity, hs=hs, xmin=xmin - hopt, xmax=xmax + hopt) fg = gkreg.eval_grid( xi, output='plotobj', title='Kernel regression', plotflag=1) sa = (fg.data - logity).std() sa2 = iqrange(fg.data - logity) / 1.349 # print('sa=%g %g' % (sa, sa2)) sa = min(sa, sa2) # plt.figure(1) # plt.plot(xi, slogity-logity,'r.') # plt.plot(xi, logity-,'b.') # plt.plot(xi, fg.data-logity, 'b.') # plt.show() # return fg = gkreg.eval_grid( xiii, output='plotobj', title='Kernel regression', plotflag=1) pi = reverse(fg.data) dx = xi[1] - xi[0] ckreg = KDE(x, hs=hs) # ci = ckreg.eval_grid_fast(xi)*n*dx ciii = ckreg.eval_grid_fast(xiii) * dx * x.size # n*(1+symmetric) # sa1 = np.sqrt(1./(ciii*pi*(1-pi))) # plo3 = reverse(fg.data-z0*sa) # pup3 = reverse(fg.data+z0*sa) fg.data = pi pi = f.data # ref Casella and Berger (1990) "Statistical inference" pp444 # a = 2*pi + z0**2/(ciii+1e-16) # b = 2*(1+z0**2/(ciii+1e-16)) # plo2 = ((a-sqrt(a**2-2*pi**2*b))/b).clip(min=0,max=1) # pup2 = ((a+sqrt(a**2-2*pi**2*b))/b).clip(min=0,max=1) # Jeffreys intervall a=b=0.5 # st.beta.isf(alpha/2, x+a, n-x+b) ab = 0.07 # 0.055 pi1 = pi # fun1(xiii) pup2 = np.where(pi == 1, 1, st.beta.isf(alpha / 2, ciii * pi1 + ab, ciii * (1 - pi1) + ab)) plo2 = np.where(pi == 0, 0, st.beta.isf(1 - alpha / 2, ciii * pi1 + ab, ciii * (1 - pi1) + ab)) averr = np.trapz(pup2 - plo2, xiii) / \ (xiii[-1] - xiii[0]) + 0.5 * (df[:-1] * df[1:] < 0).sum() # f2 = kreg_demo4(x, y, hs, hopt) # Wilson score den = 1 + (z0 ** 2. / ciii) xc = (pi1 + (z0 ** 2) / (2 * ciii)) / den halfwidth = (z0 * sqrt((pi1 * (1 - pi1) / ciii) + (z0 ** 2 / (4 * (ciii ** 2))))) / den plo = (xc - halfwidth).clip(min=0) # wilson score pup = (xc + halfwidth).clip(max=1.0) # wilson score # pup = (pi + z0*np.sqrt(pi*(1-pi)/ciii)).clip(min=0,max=1) # dont use # plo = (pi - z0*np.sqrt(pi*(1-pi)/ciii)).clip(min=0,max=1) # mi = kreg.eval_grid(x) # sigma = (stineman_interp(x, xiii, pup)-stineman_interp(x, xiii, plo))/4 # aic = np.abs((y-mi)/sigma).std()+ 0.5*(df[:-1]*df[1:]<0).sum()/n # aic = np.abs((yiii-fiii)/(pup-plo)).std() + \ # 0.5*(df[:-1]*df[1:]<0).sum() + \ # ((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() k = (df[:-1] * df[1:] < 0).sum() # numpeaks sigmai = (pup - plo) aic = (((yiii - fiii) / sigmai) ** 2).sum() + \ 2 * k * (k + 1) / np.maximum(ni - k + 1, 1) + \ np.abs((yiii - pup).clip(min=0) - (yiii - plo).clip(max=0)).sum() # aic = (((yiii-fiii)/sigmai)**2).sum()+ 2*k*(k+1)/(ni-k+1) + \ # np.abs((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() # aic = averr + ((yiii-pup).clip(min=0)-(yiii-plo).clip(max=0)).sum() fg.plot(label='KReg grid aic=%2.3f' % (aic)) f.plot(label='KReg averr=%2.3f ' % (averr)) labtxt = '%d CI' % (int(100 * (1 - alpha))) plt.fill_between(xiii, pup, plo, alpha=0.20, color='r', linestyle='--', label=labtxt) plt.fill_between(xiii, pup2, plo2, alpha=0.20, color='b', linestyle=':', label='%d CI2' % (int(100 * (1 - alpha)))) plt.plot(xiii, fun1(xiii), 'r', label='True model') plt.scatter(xi, yi, label='data') print('maxp = %g' % (np.nanmax(f.data))) print('hs = %g' % (kreg.tkde.tkde.hs)) plt.legend() h = plt.gca() if plotlog: plt.setp(h, yscale='log') # plt.show() return hs1, hs2 def kreg_demo4(x, y, hs, hopt, alpha=0.05): st = stats n = x.size xmin, xmax = x.min(), x.max() ni = max(2 * int((xmax - xmin) / hopt) + 3, 5) sml = hopt * 0.1 xi = np.linspace(xmin - sml, xmax + sml, ni) xiii = np.linspace(xmin - sml, xmax + sml, 4 * ni + 1) kreg = KRegression(x, y, hs=hs, p=0) dx = xi[1] - xi[0] ciii = kreg.tkde.eval_grid_fast(xiii) * dx * x.size # ckreg = KDE(x,hs=hs) # ciiii = ckreg.eval_grid_fast(xiii)*dx* x.size #n*(1+symmetric) f = kreg(xiii, output='plotobj') # , plot_kwds=dict(plotflag=7)) pi = f.data # Jeffreys intervall a=b=0.5 # st.beta.isf(alpha/2, x+a, n-x+b) ab = 0.07 # 0.5 pi1 = pi pup = np.where(pi1 == 1, 1, st.beta.isf( alpha / 2, ciii * pi1 + ab, ciii * (1 - pi1) + ab)) plo = np.where(pi1 == 0, 0, st.beta.isf( 1 - alpha / 2, ciii * pi1 + ab, ciii * (1 - pi1) + ab)) # Wilson score # z0 = -_invnorm(alpha/2) # den = 1+(z0**2./ciii); # xc=(pi1+(z0**2)/(2*ciii))/den; # halfwidth=(z0*sqrt((pi1*(1-pi1)/ciii)+(z0**2/(4*(ciii**2)))))/den # plo2 = (xc-halfwidth).clip(min=0) # wilson score # pup2 = (xc+halfwidth).clip(max=1.0) # wilson score # f.dataCI = np.vstack((plo,pup)).T f.prediction_error_avg = np.trapz(pup - plo, xiii) / (xiii[-1] - xiii[0]) fiii = f.data c = gridcount(x, xi) if (y == 1).any(): c0 = gridcount(x[y == 1], xi) else: c0 = np.zeros(xi.shape) yi = np.where(c == 0, 0, c0 / c) f.children = [PlotData([plo, pup], xiii, plotmethod='fill_between', plot_kwds=dict(alpha=0.2, color='r')), PlotData(yi, xi, plotmethod='scatter', plot_kwds=dict(color='r', s=5))] yiii = interpolate.interp1d(xi, yi)(xiii) df = np.diff(fiii) k = (df[:-1] * df[1:] < 0).sum() # numpeaks sigmai = (pup - plo) aicc = (((yiii - fiii) / sigmai) ** 2).sum() + \ 2 * k * (k + 1) / np.maximum(ni - k + 1, 1) + \ np.abs((yiii - pup).clip(min=0) - (yiii - plo).clip(max=0)).sum() f.aicc = aicc f.labels.title = 'perr=%1.3f,aicc=%1.3f, n=%d, hs=%1.3f' % ( f.prediction_error_avg, aicc, n, hs) return f def check_kreg_demo3(): plt.ion() k = 0 for n in [50, 100, 300, 600, 4000]: x, y, fun1 = _get_data( n, symmetric=True, loc1=1.0, scale1=0.6, scale2=1.25) k0 = k for fun in ['hste', ]: hsmax, _hs1, _hs2 = _get_regression_smooting(x, y, fun=fun) for hi in np.linspace(hsmax * 0.25, hsmax, 9): plt.figure(k) k += 1 unused = kreg_demo3(x, y, fun1, hs=hi, fun=fun, plotlog=False) # kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False) fig.tile(range(k0, k)) plt.ioff() plt.show() def check_kreg_demo4(): plt.ion() # test_docstrings() # kde_demo2() # kreg_demo1(fast=True) # kde_gauss_demo() # kreg_demo2(n=120,symmetric=True,fun='hste', plotlog=True) k = 0 for _i, n in enumerate([100, 300, 600, 4000]): x, y, fun1 = _get_data( n, symmetric=True, loc1=0.1, scale1=0.6, scale2=0.75) # k0 = k hopt1, _h1, _h2 = _get_regression_smooting(x, y, fun='hos') hopt2, _h1, _h2 = _get_regression_smooting(x, y, fun='hste') hopt = sqrt(hopt1 * hopt2) # hopt = _get_regression_smooting(x,y,fun='hos')[0] for _j, fun in enumerate(['hste']): # , 'hisj', 'hns', 'hstt' hsmax, _hs1, _hs2 = _get_regression_smooting(x, y, fun=fun) fmax = kreg_demo4(x, y, hsmax + 0.1, hopt) for hi in np.linspace(hsmax * 0.1, hsmax, 55): f = kreg_demo4(x, y, hi, hopt) if f.aicc <= fmax.aicc: fmax = f plt.figure(k) k += 1 fmax.plot() plt.plot(x, fun1(x), 'r') # kreg_demo2(n=n,symmetric=True,fun='hste', plotlog=False) fig.tile(range(0, k)) plt.ioff() plt.show() def check_regression_bin(): plt.ion() # test_docstrings() # kde_demo2() # kreg_demo1(fast=True) # kde_gauss_demo() # kreg_demo2(n=120,symmetric=True,fun='hste', plotlog=True) k = 0 for _i, n in enumerate([100, 300, 600, 4000]): x, y, fun1 = _get_data( n, symmetric=True, loc1=0.1, scale1=0.6, scale2=0.75) fbest = regressionbin(x, y, alpha=0.05, color='g', label='Transit_D') figk = plt.figure(k) ax = figk.gca() k += 1 fbest.labels.title = 'N = %d' % n fbest.plot(axis=ax) ax.plot(x, fun1(x), 'r') ax.legend(frameon=False, markerscale=4) # ax = plt.gca() ax.set_yticklabels(ax.get_yticks() * 100.0) ax.grid(True) fig.tile(range(0, k)) plt.ioff() plt.show() def check_bkregression(): plt.ion() k = 0 for _i, n in enumerate([50, 100, 300, 600]): x, y, fun1 = _get_data( n, symmetric=True, loc1=0.1, scale1=0.6, scale2=0.75) bkreg = BKRegression(x, y) fbest = bkreg.prb_search_best( hsfun='hste', alpha=0.05, color='g', label='Transit_D') figk = plt.figure(k) ax = figk.gca() k += 1 # fbest.score.plot(axis=ax) # axsize = ax.axis() # ax.vlines(fbest.hs,axsize[2]+1,axsize[3]) # ax.set(yscale='log') fbest.labels.title = 'N = %d' % n fbest.plot(axis=ax) ax.plot(x, fun1(x), 'r') ax.legend(frameon=False, markerscale=4) # ax = plt.gca() ax.set_yticklabels(ax.get_yticks() * 100.0) ax.grid(True) fig.tile(range(0, k)) plt.ioff() plt.show() def _get_regression_smooting(x, y, fun='hste'): hs1 = Kernel('gauss', fun=fun).get_smoothing(x) # hx = np.median(np.abs(x-np.median(x)))/0.6745*(4.0/(3*n))**0.2 if (y == 1).any(): hs2 = Kernel('gauss', fun=fun).get_smoothing(x[y == 1]) # hy = np.median(np.abs(y-np.mean(y)))/0.6745*(4.0/(3*n))**0.2 else: hs2 = 4 * hs1 # hy = 4*hx # hy2 = Kernel('gauss', fun=fun).get_smoothing(y) # kernel = Kernel('gauss',fun=fun) # hopt = (hs1+2*hs2)/3 # hopt = (hs1+4*hs2)/5 #kernel.get_smoothing(x) # hopt = hs2 hopt = sqrt(hs1 * hs2) return hopt, hs1, hs2 def empirical_bin_prb(x, y, hopt, color='r'): """Returns empirical binomial probabiltity. Parameters ---------- x : ndarray position ve y : ndarray binomial response variable (zeros and ones) Returns ------- P(x) : PlotData object empirical probability """ xmin, xmax = x.min(), x.max() ni = max(2 * int((xmax - xmin) / hopt) + 3, 5) sml = hopt # *0.1 xi = np.linspace(xmin - sml, xmax + sml, ni) c = gridcount(x, xi) if (y == 1).any(): c0 = gridcount(x[y == 1], xi) else: c0 = np.zeros(xi.shape) yi = np.where(c == 0, 0, c0 / c) return PlotData(yi, xi, plotmethod='scatter', plot_kwds=dict(color=color, s=5)) def smoothed_bin_prb(x, y, hs, hopt, alpha=0.05, color='r', label='', bin_prb=None): ''' Parameters ---------- x,y hs : smoothing parameter hopt : spacing in empirical_bin_prb alpha : confidence level color : color of plot object bin_prb : PlotData object with empirical bin prb ''' if bin_prb is None: bin_prb = empirical_bin_prb(x, y, hopt, color) xi = bin_prb.args yi = bin_prb.data ni = len(xi) dxi = xi[1] - xi[0] n = x.size xiii = np.linspace(xi[0], xi[-1], 10 * ni + 1) kreg = KRegression(x, y, hs=hs, p=0) # expected number of data in each bin ciii = kreg.tkde.eval_grid_fast(xiii) * dxi * n f = kreg(xiii, output='plotobj') # , plot_kwds=dict(plotflag=7)) pi = f.data st = stats # Jeffreys intervall a=b=0.5 # st.beta.isf(alpha/2, x+a, n-x+b) ab = 0.07 # 0.5 pi1 = pi pup = np.where(pi1 == 1, 1, st.beta.isf( alpha / 2, ciii * pi1 + ab, ciii * (1 - pi1) + ab)) plo = np.where(pi1 == 0, 0, st.beta.isf( 1 - alpha / 2, ciii * pi1 + ab, ciii * (1 - pi1) + ab)) # Wilson score # z0 = -_invnorm(alpha/2) # den = 1+(z0**2./ciii); # xc=(pi1+(z0**2)/(2*ciii))/den; # halfwidth=(z0*sqrt((pi1*(1-pi1)/ciii)+(z0**2/(4*(ciii**2)))))/den # plo2 = (xc-halfwidth).clip(min=0) # wilson score # pup2 = (xc+halfwidth).clip(max=1.0) # wilson score # f.dataCI = np.vstack((plo,pup)).T f.prediction_error_avg = np.trapz(pup - plo, xiii) / (xiii[-1] - xiii[0]) fiii = f.data f.plot_kwds['color'] = color f.plot_kwds['linewidth'] = 2 if label: f.plot_kwds['label'] = label f.children = [PlotData([plo, pup], xiii, plotmethod='fill_between', plot_kwds=dict(alpha=0.2, color=color)), bin_prb] yiii = interpolate.interp1d(xi, yi)(xiii) df = np.diff(fiii) k = (df[:-1] * df[1:] < 0).sum() # numpeaks sigmai = (pup - plo) aicc = (((yiii - fiii) / sigmai) ** 2).sum() + \ 2 * k * (k + 1) / np.maximum(ni - k + 1, 1) + \ np.abs((yiii - pup).clip(min=0) - (yiii - plo).clip(max=0)).sum() f.aicc = aicc f.fun = kreg f.labels.title = 'perr=%1.3f,aicc=%1.3f, n=%d, hs=%1.3f' % ( f.prediction_error_avg, aicc, n, hs) return f def regressionbin(x, y, alpha=0.05, color='r', label=''): """Return kernel regression estimate for binomial data. Parameters ---------- x : arraylike positions y : arraylike of 0 and 1 """ hopt1, _h1, _h2 = _get_regression_smooting(x, y, fun='hos') hopt2, _h1, _h2 = _get_regression_smooting(x, y, fun='hste') hopt = sqrt(hopt1 * hopt2) fbest = smoothed_bin_prb(x, y, hopt2 + 0.1, hopt, alpha, color, label) bin_prb = fbest.children[-1] for fun in ['hste']: # , 'hisj', 'hns', 'hstt' hsmax, _hs1, _hs2 = _get_regression_smooting(x, y, fun=fun) for hi in np.linspace(hsmax * 0.1, hsmax, 55): f = smoothed_bin_prb(x, y, hi, hopt, alpha, color, label, bin_prb) if f.aicc <= fbest.aicc: fbest = f # hbest = hi return fbest def kde_gauss_demo(n=50): """KDEDEMO Demonstrate the KDEgauss. KDEDEMO1 shows the true density (dotted) compared to KDE based on 7 observations (solid) and their individual kernels (dashed) for 3 different values of the smoothing parameter, hs. """ st = stats # x = np.linspace(-4, 4, 101) # data = np.random.normal(loc=0, scale=1.0, size=n) # data = np.random.exponential(scale=1.0, size=n) # n1 = 128 # I = (np.arange(n1)*pi)**2 *0.01*0.5 # kw = exp(-I) # plt.plot(idctn(kw)) # return # dist = st.norm dist = st.expon data = dist.rvs(loc=0, scale=1.0, size=n) d, _N = np.atleast_2d(data).shape if d == 1: plot_options = [dict(color='red'), dict( color='green'), dict(color='black')] else: plot_options = [dict(colors='red'), dict(colors='green'), dict(colors='black')] plt.figure(1) kde0 = KDE(data, kernel=Kernel('gauss', 'hste')) f0 = kde0.eval_grid_fast(output='plot', ylab='Density') f0.plot(**plot_options[0]) kde1 = TKDE(data, kernel=Kernel('gauss', 'hisj'), L2=.5) f1 = kde1.eval_grid_fast(output='plot', ylab='Density') f1.plot(**plot_options[1]) kde2 = KDEgauss(data) f2 = kde2(output='plot', ylab='Density') x = f2.args f2.plot(**plot_options[2]) fmax = dist.pdf(x, 0, 1).max() if d == 1: plt.plot(x, dist.pdf(x, 0, 1), 'k:') plt.axis([x.min(), x.max(), 0, fmax]) plt.show() print(fmax / f2.data.max()) format_ = ''.join(('%g, ') * d) format_ = 'hs0=%s hs1=%s hs2=%s' % (format_, format_, format_) print(format_ % tuple(kde0.hs.tolist() + kde1.tkde.hs.tolist() + kde2.hs.tolist())) print('inc0 = %d, inc1 = %d, inc2 = %d' % (kde0.inc, kde1.inc, kde2.inc)) def test_kde(): data = np.array([ 0.75355792, 0.72779194, 0.94149169, 0.07841119, 2.32291887, 1.10419995, 0.77055114, 0.60288273, 1.36883635, 1.74754326, 1.09547561, 1.01671133, 0.73211143, 0.61891719, 0.75903487, 1.8919469, 0.72433808, 1.92973094, 0.44749838, 1.36508452]) x = np.linspace(0.01, max(data.ravel()) + 1, 10) kde = TKDE(data, hs=0.5, L2=0.5) _f = kde(x) # f = array([1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318, # 0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882]) _f1 = kde.eval_grid(x) # array([ 1.03982714, 0.45839018, 0.39514782, 0.32860602, 0.26433318, # 0.20717946, 0.15907684, 0.1201074 , 0.08941027, 0.06574882]) _f2 = kde.eval_grid_fast(x) # array([ 1.06437223, 0.46203314, 0.39593137, 0.32781899, 0.26276433, # 0.20532206, 0.15723498, 0.11843998, 0.08797755, 0. ]) def test_docstrings(): import doctest print('Testing docstrings in %s' % __file__) doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE) if __name__ == '__main__': test_docstrings() # test_kde() # check_bkregression() # check_regression_bin() # check_kreg_demo3() # check_kreg_demo4() # test_smoothn_1d() # test_smoothn_2d() # kde_demo2() # kreg_demo1(fast=True) # kde_gauss_demo() # kreg_demo2(n=120,symmetric=True,fun='hste', plotlog=True) # plt.show('hold')