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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 import numpy as np
from numpy import pi, sqrt, exp, percentile from numpy import pi, sqrt, exp, percentile
from numpy.fft import fft, ifft from numpy.fft import fft, ifft
from scipy import optimize, linalg from scipy import optimize
from scipy.special import gamma from scipy.special import gamma
from scipy.linalg import sqrtm
from wafo.misc import tranproc # , trangood from wafo.misc import tranproc # , trangood
from wafo.kdetools.gridding import gridcount from wafo.kdetools.gridding import gridcount
from wafo.dctpack import dct from wafo.dctpack import dct
@ -289,8 +290,8 @@ class _Kernel(object):
def kernel(self, x): def kernel(self, x):
return self._kernel(np.atleast_2d(x)) return self._kernel(np.atleast_2d(x))
def deriv4_6_8_10(self, t, numout=4): # def deriv4_6_8_10(self, t, numout=4):
raise NotImplementedError('Method not implemented for this kernel!') # raise NotImplementedError('Method not implemented for this kernel!')
def get_ste_constant(self, n): def get_ste_constant(self, n):
mu2, R = self.stats[:2] 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.kernel = _MKERNEL_DICT[name[:4]]
self.get_smoothing = getattr(self, fun) self.get_smoothing = getattr(self, fun)
@ -681,7 +682,7 @@ class Kernel(object):
""" """
return self.hns(data) / 0.93 return self.hns(data) / 0.93
def _hmns_scale(self, d): def _hmns_scale(self, n, d):
name = self.name name = self.name
short_name = name[:4].lower() short_name = name[:4].lower()
scale_dict = dict(epan=(8.0 * (d + 4.0) * (2 * sqrt(pi)) ** d / 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']: if d > 2 and short_name in ['biwe', 'triw']:
raise NotImplementedError('Not implemented for d>2 and ' raise NotImplementedError('Not implemented for d>2 and '
'kernel {}'.format(name)) 'kernel {}'.format(name))
return scale_dict[short_name] return scale_dict[short_name] * n ** (-1. / (d + 4))
def hmns(self, data): def hmns(self, data):
"""Returns Multivariate Normal Scale Estimate of Smoothing Parameter. """Returns Multivariate Normal Scale Estimate of Smoothing Parameter.
@ -742,9 +743,7 @@ class Kernel(object):
d, n = a.shape d, n = a.shape
if d == 1: if d == 1:
return self.hns(data) return self.hns(data)
scale = self._hmns_scale(d) return self._hmns_scale(n, d) * np.real(sqrtm(np.cov(a)))
cov_a = np.cov(a)
return scale * linalg.sqrtm(cov_a).real * n ** (-1. / (d + 4))
@staticmethod @staticmethod
def _get_g(k_order_2, mu2, psi_order, n, order): def _get_g(k_order_2, mu2, psi_order, n, order):
@ -1069,7 +1068,7 @@ class Kernel(object):
True True
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
plt.plot(hvec,score) plt.plot(hvec, score)
See also: See also:
hste, hbcv, hboot, hos, hldpi, hlscv, hstt, kde, kdefun 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] dx = x[1] - x[0]
c = wkg.gridcount(data, x) c = wkg.gridcount(data, x)
assert_allclose(c.sum(), len(data))
assert_allclose(c, assert_allclose(c,
[0.1430937435034, 5.864465648665, 9.418694957317207, [0.1430937435034, 5.864465648665, 9.418694957317207,
2.9154367000439, 0.6583089504704, 0.0, 2.9154367000439, 0.6583089504704, 0.0,
@ -34,6 +35,7 @@ class TestKdeTools(unittest.TestCase):
dx = x[1] - x[0] dx = x[1] - x[0]
X = np.vstack((x, x)) X = np.vstack((x, x))
c = wkg.gridcount(data, X) c = wkg.gridcount(data, X)
assert_allclose(c.sum(), N)
assert_allclose(c, assert_allclose(c,
[[0.38922806, 0.8987982, 0.34676493, 0.21042807, 0.], [[0.38922806, 0.8987982, 0.34676493, 0.21042807, 0.],
[1.15012203, 5.16513541, 3.19250588, 0.55420752, 0.], [1.15012203, 5.16513541, 3.19250588, 0.55420752, 0.],
@ -52,6 +54,7 @@ class TestKdeTools(unittest.TestCase):
dx = x[1] - x[0] dx = x[1] - x[0]
X = np.vstack((x, x, x)) X = np.vstack((x, x, x))
c = wkg.gridcount(data, X) c = wkg.gridcount(data, X)
assert_allclose(c.sum(), N)
assert_allclose(c, assert_allclose(c,
[[[8.74229894e-01, 1.27910940e+00, 1.42033973e-01], [[[8.74229894e-01, 1.27910940e+00, 1.42033973e-01],
[1.94778915e+00, 2.59536282e+00, 3.28213680e-01], [1.94778915e+00, 2.59536282e+00, 3.28213680e-01],
@ -69,43 +72,44 @@ class TestKdeTools(unittest.TestCase):
@staticmethod @staticmethod
def test_gridcount_4d(): def test_gridcount_4d():
N = 20 N = 10
data = np.reshape(DATA2D, (4, -1)) data = np.reshape(DATA2D, (4, N))
x = np.linspace(0, max(np.ravel(data)) + 1, 3) x = np.linspace(0, max(np.ravel(data)) + 1, 3)
dx = x[1] - x[0] dx = x[1] - x[0]
X = np.vstack((x, x, x, x)) X = np.vstack((x, x, x, x))
c = wkg.gridcount(data, X) c = wkg.gridcount(data, X)
assert_allclose(c, truth = [[[[1.77163904e-01, 1.87720108e-01, 0.0],
[[[[1.77163904e-01, 1.87720108e-01, 0.0], [5.72573585e-01, 6.09557834e-01, 0.0],
[5.72573585e-01, 6.09557834e-01, 0.0], [3.48549923e-03, 4.05931870e-02, 0.0]],
[3.48549923e-03, 4.05931870e-02, 0.0]], [[1.83770124e-01, 2.56357594e-01, 0.0],
[[1.83770124e-01, 2.56357594e-01, 0.0], [4.35845892e-01, 6.14958970e-01, 0.0],
[4.35845892e-01, 6.14958970e-01, 0.0], [3.07662204e-03, 3.58312786e-02, 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],
[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],
[[[3.41883175e-01, 5.97977973e-01, 0.0], [5.72071865e-01, 8.58566538e-01, 0.0],
[5.72071865e-01, 8.58566538e-01, 0.0], [3.46939323e-03, 4.04056116e-02, 0.0]],
[3.46939323e-03, 4.04056116e-02, 0.0]], [[3.58861043e-01, 6.28962785e-01, 0.0],
[[3.58861043e-01, 6.28962785e-01, 0.0], [8.80697705e-01, 1.47373158e+00, 0.0],
[8.80697705e-01, 1.47373158e+00, 0.0], [2.22868504e-01, 1.18008528e-01, 0.0]],
[2.22868504e-01, 1.18008528e-01, 0.0]], [[2.91835067e-03, 2.60268355e-02, 0.0],
[[2.91835067e-03, 2.60268355e-02, 0.0], [3.63686503e-02, 1.07959459e-01, 0.0],
[3.63686503e-02, 1.07959459e-01, 0.0], [1.88555613e-02, 7.06358976e-03, 0.0]]],
[1.88555613e-02, 7.06358976e-03, 0.0]]], [[[3.13810608e-03, 2.11731327e-02, 0.0],
[[[3.13810608e-03, 2.11731327e-02, 0.0], [6.71606255e-03, 4.53139824e-02, 0.0],
[6.71606255e-03, 4.53139824e-02, 0.0], [0.0, 0.0, 0.0]],
[0.0, 0.0, 0.0]], [[7.05946179e-03, 5.44614852e-02, 0.0],
[[7.05946179e-03, 5.44614852e-02, 0.0], [1.09099593e-01, 1.95935584e-01, 0.0],
[1.09099593e-01, 1.95935584e-01, 0.0], [6.61257395e-02, 2.47717418e-02, 0.0]],
[6.61257395e-02, 2.47717418e-02, 0.0]], [[6.38695629e-04, 5.69610302e-03, 0.0],
[[6.38695629e-04, 5.69610302e-03, 0.0], [1.00358265e-02, 2.44053065e-02, 0.0],
[1.00358265e-02, 2.44053065e-02, 0.0], [5.67244468e-03, 2.12498697e-03, 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) 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__": if __name__ == "__main__":

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