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Small modifications

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per.andreas.brodtkorb 14 years ago
parent 1484f9128e
commit 85792549d1
1 changed files with 64 additions and 86 deletions
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pywafo/src/wafo/kdetools.py
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@ -5,14 +5,14 @@
# Author: pab
#
# Created: 01.11.2008
# Copyright: (c) pab2 2008
# Copyright: (c) pab 2008
# Licence: LGPL
#-------------------------------------------------------------------------------
#!/usr/bin/env python
from __future__ import division
import warnings
import numpy as np
from numpy import pi, sqrt, atleast_2d, exp, newaxis #@UnresolvedImport
from numpy import pi, sqrt, atleast_2d, exp, newaxis, array #@UnresolvedImport
import scipy
from scipy import linalg
from scipy.special import gamma
@ -20,12 +20,12 @@ from misc import tranproc, trangood
from itertools import product
_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_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)))
@ -103,6 +103,7 @@ class TKDE(object):
... 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)
@ -239,11 +240,12 @@ class KDE(object):
... 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([ 0.0541248 , 0.16555235, 0.33084399, 0.45293325, 0.48345808,
0.48345808, 0.45293325, 0.33084399, 0.16555235, 0.0541248 ])
array([ 0.17252055, 0.41014271, 0.61349072, 0.57023834, 0.37198073,
0.21409279, 0.12738463, 0.07460326, 0.03956191, 0.01887164])
import pylab as plb
h1 = plb.plot(x, f) # 1D probability density plot
@ -340,7 +342,6 @@ class KDE(object):
if m >= 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 += self.kernel(tdiff) / self._lambda[i] ** d
@ -507,8 +508,8 @@ class Kernel(object):
(0.1111111111111111, 0.81585081585081587, inf)
>>> triweight(np.linspace(-1,1,11))
array([ 0. , 0.05103, 0.28672, 0.64827, 0.96768, 1.09375,
0.96768, 0.64827, 0.28672, 0.05103, 0. ])
array([ 0. , 0.046656, 0.262144, 0.592704, 0.884736, 1. ,
0.884736, 0.592704, 0.262144, 0.046656, 0. ])
>>> triweight.hns(np.random.normal(size=100))
See also
@ -556,13 +557,18 @@ class Kernel(object):
def hns(self, data):
'''
HNS Normal Scale Estimate of Smoothing Parameter.
Returns Normal Scale Estimate of Smoothing Parameter.
CALL: h = hns(data,kernel)
Parameter
---------
data : 2D array
shape d x n (d = # dimensions )
h = one dimensional optimal value for smoothing parameter
given the data and kernel. size 1 x D
data = data matrix, size N x D (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
@ -579,7 +585,9 @@ class Kernel(object):
data = rndnorm(0, 1,20,1)
h = hns(data,'epan');
See also hste, hbcv, hboot, hos, hldpi, hlscv, hscv, hstt, kde
See also:
---------
hste, hbcv, hboot, hos, hldpi, hlscv, hscv, hstt, kde
Reference:
---------
@ -606,7 +614,7 @@ class Kernel(object):
return np.where(iqr > 0, np.minimum(stdA, iqr / 1.349), stdA) * AMISEconstant
def hos(self, data):
''' Return Oversmoothing Parameter.
''' Returns Oversmoothing Parameter.
@ -817,15 +825,15 @@ def accum(accmap, a, func=None, size=None, fill_value=0, dtype=None):
>>> # 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]],
])
... [[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.],
@ -880,6 +888,7 @@ def bitget(int_type, offset):
'''
mask = (1 << offset)
return (int_type & mask) != 0
def gridcount(data, X):
'''
GRIDCOUNT D-dimensional histogram using linear binning.
@ -922,8 +931,10 @@ def gridcount(data, X):
>>> c = wk.gridcount(data,x)
>>> h = plb.plot(x,c,'.') # 1D histogram
>>> h1 = plb.plot(x,c/dx/N) # 1D probability density plot
>>> np.trapz(x,c/dx/N)
>>> pdf = c/dx/N
>>> h1 = plb.plot(x, pdf) # 1D probability density plot
>>> np.trapz(pdf, x)
0.99999999999999956
See also
--------
@ -1005,45 +1016,12 @@ def gridcount(data, X):
c = c.transpose(1, 0, 2)
return c
def test_kde():
import numpy as np
import wafo.kdetools as wk
import pylab as plb
N = 500;
data = np.random.rayleigh(1, size=(1, N))
kde = wk.KDE(data)
x = np.linspace(0, max(data.ravel()) + 1, 10)
#X,Y = np.meshgrid(x, x)
f = kde(x)
#plb.hist(data.ravel())
plb.plot(x, f)
plb.show()
def test_gridcount():
import numpy as np
import wafo.kdetools as wk
import pylab as plb
N = 500;
data = np.random.rayleigh(1, size=(2, N))
x = np.linspace(0, max(data.ravel()) + 1, 10)
X = np.vstack((x, x))
dx = x[1] - x[0]
c = wk.gridcount(data, X)
h = plb.contourf(x, x, c)
plb.show()
h = plb.plot(x, c, '.') # 1D histogram
h1 = plb.plot(x, c / dx / N) # 1D probability density plot
t = np.trapz(x, c / dx / N)
print(t)
def main():
import doctest
doctest.testmod()
if __name__ == '__main__':
#main()
#test_gridcount()
test_kde()
main()

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