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Small updates.

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master
Per A Brodtkorb 8 years ago
parent 77cf6a6b6f
commit 5a948ffbbc
2 changed files with 44 additions and 41 deletions
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19
wafo/kdetools/kernels.py
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@ -9,8 +9,9 @@ import warnings
import numpy as np
from numpy import pi, sqrt, exp, percentile
from numpy.fft import fft, ifft
from scipy import optimize, linalg
from scipy import optimize
from scipy.special import gamma
from scipy.linalg import sqrtm
from wafo.misc import tranproc # , trangood
from wafo.kdetools.gridding import gridcount
from wafo.dctpack import dct
@ -289,8 +290,8 @@ class _Kernel(object):
def kernel(self, x):
return self._kernel(np.atleast_2d(x))
def deriv4_6_8_10(self, t, numout=4):
raise NotImplementedError('Method not implemented for this kernel!')
# def deriv4_6_8_10(self, t, numout=4):
# raise NotImplementedError('Method not implemented for this kernel!')
def get_ste_constant(self, n):
mu2, R = self.stats[:2]
@ -542,7 +543,7 @@ class Kernel(object):
"""
def __init__(self, name, fun='hste'): # 'hns'):
def __init__(self, name, fun='hste'):
self.kernel = _MKERNEL_DICT[name[:4]]
self.get_smoothing = getattr(self, fun)
@ -681,7 +682,7 @@ class Kernel(object):
"""
return self.hns(data) / 0.93
def _hmns_scale(self, d):
def _hmns_scale(self, n, d):
name = self.name
short_name = name[:4].lower()
scale_dict = dict(epan=(8.0 * (d + 4.0) * (2 * sqrt(pi)) ** d /
@ -694,7 +695,7 @@ class Kernel(object):
if d > 2 and short_name in ['biwe', 'triw']:
raise NotImplementedError('Not implemented for d>2 and '
'kernel {}'.format(name))
return scale_dict[short_name]
return scale_dict[short_name] * n ** (-1. / (d + 4))
def hmns(self, data):
"""Returns Multivariate Normal Scale Estimate of Smoothing Parameter.
@ -742,9 +743,7 @@ class Kernel(object):
d, n = a.shape
if d == 1:
return self.hns(data)
scale = self._hmns_scale(d)
cov_a = np.cov(a)
return scale * linalg.sqrtm(cov_a).real * n ** (-1. / (d + 4))
return self._hmns_scale(n, d) * np.real(sqrtm(np.cov(a)))
@staticmethod
def _get_g(k_order_2, mu2, psi_order, n, order):
@ -1069,7 +1068,7 @@ class Kernel(object):
True
import matplotlib.pyplot as plt
plt.plot(hvec,score)
plt.plot(hvec, score)
See also:
hste, hbcv, hboot, hos, hldpi, hlscv, hstt, kde, kdefun

66
wafo/kdetools/tests/test_gridding.py
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@ -19,6 +19,7 @@ class TestKdeTools(unittest.TestCase):
dx = x[1] - x[0]
c = wkg.gridcount(data, x)
assert_allclose(c.sum(), len(data))
assert_allclose(c,
[0.1430937435034, 5.864465648665, 9.418694957317207,
2.9154367000439, 0.6583089504704, 0.0,
@ -34,6 +35,7 @@ class TestKdeTools(unittest.TestCase):
dx = x[1] - x[0]
X = np.vstack((x, x))
c = wkg.gridcount(data, X)
assert_allclose(c.sum(), N)
assert_allclose(c,
[[0.38922806, 0.8987982, 0.34676493, 0.21042807, 0.],
[1.15012203, 5.16513541, 3.19250588, 0.55420752, 0.],
@ -52,6 +54,7 @@ class TestKdeTools(unittest.TestCase):
dx = x[1] - x[0]
X = np.vstack((x, x, x))
c = wkg.gridcount(data, X)
assert_allclose(c.sum(), N)
assert_allclose(c,
[[[8.74229894e-01, 1.27910940e+00, 1.42033973e-01],
[1.94778915e+00, 2.59536282e+00, 3.28213680e-01],
@ -69,43 +72,44 @@ class TestKdeTools(unittest.TestCase):
@staticmethod
def test_gridcount_4d():
N = 20
data = np.reshape(DATA2D, (4, -1))
N = 10
data = np.reshape(DATA2D, (4, N))
x = np.linspace(0, max(np.ravel(data)) + 1, 3)
dx = x[1] - x[0]
X = np.vstack((x, x, x, x))
c = wkg.gridcount(data, X)
assert_allclose(c,
[[[[1.77163904e-01, 1.87720108e-01, 0.0],
[5.72573585e-01, 6.09557834e-01, 0.0],
[3.48549923e-03, 4.05931870e-02, 0.0]],
[[1.83770124e-01, 2.56357594e-01, 0.0],
[4.35845892e-01, 6.14958970e-01, 0.0],
[3.07662204e-03, 3.58312786e-02, 0.0]],
[[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]],
[[[3.41883175e-01, 5.97977973e-01, 0.0],
[5.72071865e-01, 8.58566538e-01, 0.0],
[3.46939323e-03, 4.04056116e-02, 0.0]],
[[3.58861043e-01, 6.28962785e-01, 0.0],
[8.80697705e-01, 1.47373158e+00, 0.0],
[2.22868504e-01, 1.18008528e-01, 0.0]],
[[2.91835067e-03, 2.60268355e-02, 0.0],
[3.63686503e-02, 1.07959459e-01, 0.0],
[1.88555613e-02, 7.06358976e-03, 0.0]]],
[[[3.13810608e-03, 2.11731327e-02, 0.0],
[6.71606255e-03, 4.53139824e-02, 0.0],
[0.0, 0.0, 0.0]],
[[7.05946179e-03, 5.44614852e-02, 0.0],
[1.09099593e-01, 1.95935584e-01, 0.0],
[6.61257395e-02, 2.47717418e-02, 0.0]],
[[6.38695629e-04, 5.69610302e-03, 0.0],
[1.00358265e-02, 2.44053065e-02, 0.0],
[5.67244468e-03, 2.12498697e-03, 0.0]]]])
truth = [[[[1.77163904e-01, 1.87720108e-01, 0.0],
[5.72573585e-01, 6.09557834e-01, 0.0],
[3.48549923e-03, 4.05931870e-02, 0.0]],
[[1.83770124e-01, 2.56357594e-01, 0.0],
[4.35845892e-01, 6.14958970e-01, 0.0],
[3.07662204e-03, 3.58312786e-02, 0.0]],
[[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0],
[0.0, 0.0, 0.0]]],
[[[3.41883175e-01, 5.97977973e-01, 0.0],
[5.72071865e-01, 8.58566538e-01, 0.0],
[3.46939323e-03, 4.04056116e-02, 0.0]],
[[3.58861043e-01, 6.28962785e-01, 0.0],
[8.80697705e-01, 1.47373158e+00, 0.0],
[2.22868504e-01, 1.18008528e-01, 0.0]],
[[2.91835067e-03, 2.60268355e-02, 0.0],
[3.63686503e-02, 1.07959459e-01, 0.0],
[1.88555613e-02, 7.06358976e-03, 0.0]]],
[[[3.13810608e-03, 2.11731327e-02, 0.0],
[6.71606255e-03, 4.53139824e-02, 0.0],
[0.0, 0.0, 0.0]],
[[7.05946179e-03, 5.44614852e-02, 0.0],
[1.09099593e-01, 1.95935584e-01, 0.0],
[6.61257395e-02, 2.47717418e-02, 0.0]],
[[6.38695629e-04, 5.69610302e-03, 0.0],
[1.00358265e-02, 2.44053065e-02, 0.0],
[5.67244468e-03, 2.12498697e-03, 0.0]]]]
assert_allclose(c.sum(), N)
assert_allclose(c, truth)
t = np.trapz(np.trapz(np.trapz(np.trapz(c / dx**4 / N, x), x), x), x)
assert_allclose(t, 0.21183518274521254)
assert_allclose(t, 0.4236703654904251)
if __name__ == "__main__":

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