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Removed obsolete code

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master
Per A Brodtkorb 8 years ago
parent 4f6bce3b1b
commit e77eb3e7a8
2 changed files with 24 additions and 243 deletions
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214
wafo/kdetools/kdetools.py
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@ -34,7 +34,7 @@ except ImportError:
warnings.warn('fig import only supported on Windows')
__all__ = ['TKDE', 'KDE', 'kde_demo1', 'kde_demo2', 'test_docstrings',
'KRegression', 'KDEgauss']
'KRegression', 'BKRegression']
def _assert(cond, msg):
@ -439,6 +439,13 @@ class TKDE(_KDE):
self.L2 = L2
super(TKDE, self).__init__(data, hs, kernel, alpha, xmin, xmax, inc)
# @property
# def dataset(self):
# return self._dataset
#
# @dataset.setter
# def dataset(self, data):
def _initialize(self):
self._check_xmin()
tdataset = self._dat2gaus(self.dataset)
@ -761,7 +768,7 @@ class KDE(_KDE):
def _eval_grid(self, *args, **kwds):
grd = meshgrid(*args) if len(args) > 1 else list(args)
grd = meshgrid(*args)
shape0 = grd[0].shape
d = len(grd)
for i in range(d):
@ -838,82 +845,7 @@ class KDE(_KDE):
return self._loop_over_points(self.dataset, points, y, r)
class KDEgauss(KDE):
""" Kernel-Density Estimator base class.
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
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(x0, x1,..., xd) : array
same as kde.eval_grid_fast(x0, x1,..., xd)
"""
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)
r = kwds.get('r', 0)
fun = self._moment_fun(r)
Inc = meshgrid(*In) if d > 1 else In
kw = np.zeros((inc,) * d)
for i in range(d):
kw += exp(-Inc[i]) * fun(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)
# Perform the convolution.
at = dctn(c) * kw / n
z = idctn(at) * (at.size-1) / np.prod(R)
return z * (z > 0.0)
__call__ = _KDE.eval_grid_fast
class KRegression(_KDE):
class KRegression(object): # _KDE):
""" Kernel-Regression
@ -1325,17 +1257,17 @@ def kde_demo2():
x = np.linspace(1.5e-2, 5, 55)
kde = KDE(data)
f = kde(output='plot', title='Ordinary KDE (hs={0:g})'.format(kde.hs))
f = kde(output='plot', title='Ordinary KDE (hs={0:})'.format(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)
hs = Kernel('gauss').get_smoothing(data**0.5)
tkde = TKDE(data, hs=hs, L2=0.5)
ft = tkde(x, output='plot',
title='Transformation KDE (hs={0:g})'.format(tkde.tkde.hs))
title='Transformation KDE (hs={0:})'.format(tkde.tkde.hs))
plt.figure(1)
ft.plot()
@ -1507,7 +1439,6 @@ def _logitinv(x):
def _get_data(n=100, symmetric=False, loc1=1.1, scale1=0.6, scale2=1.0):
st = scipy.stats
# from sg_filter import SavitzkyGolay
dist = st.norm
norm1 = scale2 * (dist.pdf(-loc1, loc=-loc1, scale=scale1) +
@ -1575,8 +1506,8 @@ def kreg_demo3(x, y, fun1, hs=None, fun='hisj', plotlog=False):
df = np.diff(fiii)
eerr = np.abs((yiii - fiii)).std() + 0.5 * (df[:-1] * df[1:] < 0).sum() / n
err = (fiii - fit).std()
msg = '{} err={1:1.3f},eerr={2:1.3f}, n={:d}, hs={:1.3f}, hs1={:1.3f}, '\
'hs2={:1.3f}'
msg = '{0} err={1:1.3f},eerr={2:1.3f}, n={3:d}, hs={4:}, hs1={5:}, '\
'hs2={6:}'
title = (msg.format(fun, err, eerr, n, hs, hs1, hs2))
f = kreg(xiii, output='plotobj', title=title, plotflag=1)
@ -1684,8 +1615,8 @@ def kreg_demo3(x, y, fun1, hs=None, fun='hisj', plotlog=False):
label='{:d} CI2'.format(int(100 * (1 - alpha))))
plt.plot(xiii, fun1(xiii), 'r', label='True model')
plt.scatter(xi, yi, label='data')
print('maxp = {:g}'.format(np.nanmax(f.data)))
print('hs = {:g}'.format(kreg.tkde.tkde.hs))
print('maxp = {}'.format(np.nanmax(f.data)))
print('hs = {}'.format(kreg.tkde.tkde.hs))
plt.legend()
h = plt.gca()
if plotlog:
@ -1875,7 +1806,7 @@ def check_bkregression():
fig.tile(range(0, k))
plt.ioff()
plt.show()
plt.show('hold')
def _get_regression_smooting(x, y, fun='hste'):
@ -1999,8 +1930,8 @@ def smoothed_bin_prb(x, y, hs, hopt, alpha=0.05, color='r', label='',
f.aicc = aicc
f.fun = kreg
f.labels.title = ('perr={:1.3f},aicc={:1.3f}, n={:d}, '
'hs={:1.3f}'.format(f.prediction_error_avg, aicc, n, hs))
ttl = "perr={0:1.3f}, aicc={1:1.3f}, n={2:d}, hs={3}"
f.labels.title = ttl.format(f.prediction_error_avg, aicc, n, hs)
return f
@ -2033,114 +1964,17 @@ def regressionbin(x, y, alpha=0.05, color='r', label=''):
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 = scipy.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', label='KDE hste'),
dict(color='green', label='TKDE hisj'),
dict(color='black', label='KDEgauss hste')]
else:
plot_options = [dict(colors='red'), dict(colors='green'),
dict(colors='black')]
plt.figure(1)
t0 = time.time()
kde0 = KDE(data, kernel=Kernel('gauss', 'hste'))
f0 = kde0.eval_grid_fast(output='plot', ylab='Density', r=0)
t1 = time.time()
total1 = t1-t0
f0.plot('.', **plot_options[0])
if dist.name != 'norm':
kde1 = TKDE(data, kernel=Kernel('gauss', 'hisj'), L2=.5)
f1 = kde1.eval_grid_fast(output='plot', ylab='Density', r=0)
f1.plot(**plot_options[1])
else:
kde1 = kde0
f1 = f0
t1 = time.time()
kde2 = KDEgauss(data)
f2 = kde2(output='plot', ylab='Density', r=0)
t2 = time.time()
total2 = t2-t1
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:', label='True pdf')
plt.axis([x.min(), x.max(), 0, fmax])
plt.legend()
plt.show()
print(fmax / f2.data.max())
try:
print('hs0={:s} hs1={:s} hs2={:s}'.format(str(kde0.hs.tolist()),
str(kde1.tkde.hs.tolist()),
str(kde2.hs.tolist())))
except:
pass
print('inc0 = {:d}, inc1 = {:d}, inc2 = {:d}'.format(kde0.inc, kde1.inc,
kde2.inc))
print(np.trapz(f0.data, f0.args), np.trapz(f2.data, f2.args))
print(total1, total2)
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 + 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. ])
if __name__ == '__main__':
if True:
if False:
test_docstrings(__file__)
else:
# test_kde()
kde_demo2()
# check_bkregression()
# check_regression_bin()
# check_kreg_demo3()
# check_kreg_demo4()
# kde_demo2()
# kreg_demo1(fast=True)
kde_gauss_demo(n=50)
# kreg_demo2(n=120,symmetric=True,fun='hste', plotlog=True)
plt.show('hold')

53
wafo/tests/test_kdetools.py
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@ -80,59 +80,6 @@ class TestKdeTools(unittest.TestCase):
0.11072988772879173, 0.05992730870218242,
0.02687783924833738, 0.00974982785617795])
def skiptest0_KDEgauss_1D(self):
data, x = self.data, self.x
# kde = wk.KDE(data, hs=0.5, alpha=0.5)
kde0 = wk.KDEgauss(data, hs=0.5, alpha=0.0, inc=16)
fx = kde0.eval_grid(x)
assert_allclose(fx, [0.2039735, 0.40252503, 0.54595078,
0.52219649, 0.3906213, 0.26381501, 0.16407362,
0.08270612, 0.02991145, 0.00720821])
fx = kde0.eval_grid(x, r=1)
assert_allclose(-fx, [0.11911419724002906, 0.13440000694772541,
0.044400116190638696, -0.0677695267531197,
-0.09555596523854318, -0.07498819087690148,
-0.06167607128369182, -0.04678588231996062,
-0.024515979196411814, -0.008022010381009501])
fx = kde0.eval_grid(x, r=2)
assert_allclose(fx, [0.08728138131197069, 0.07558648034784508,
0.05093715852686607, 0.07908624791267539,
0.10495675573359599, 0.07916167222333347,
0.048168330179460386, 0.03438361415806721,
0.02197927811015286, 0.009222988165160621])
ffx = kde0.eval_grid_fast(x)
# print(ffx.tolist())
assert_allclose(ffx, [0.20729484, 0.39865044, 0.53716945, 0.5169322,
0.39060223, 0.26441126, 0.16388801, 0.08388527,
0.03227164, 0.00883579], 1e-6)
fx = kde0.eval_grid_fast(x, r=1)
assert_allclose(fx, [-0.11582450668441863, -0.12901768780183628,
-0.04402464127812092, 0.0636190549560749,
0.09345144501310157, 0.07573621607126926,
0.06149475587201987, 0.04550210608639078,
0.024427027615689087, 0.00885576504750473])
fx = kde0.eval_grid_fast(x, r=2)
assert_allclose(fx, [0.08499284131672676, 0.07572564161758065,
0.05329987919556978, 0.07849796347259348,
0.10232741197885842, 0.07869015379158453,
0.049431823916945394, 0.034527256372343613,
0.021517998409663567, 0.009527401063843402])
f = kde0.eval_grid_fast()
assert_allclose(f, [0.06807544, 0.12949095, 0.21985421, 0.33178031,
0.44334874, 0.52429234, 0.55140336, 0.52221323,
0.45500674, 0.3752208, 0.30046799, 0.235667,
0.17854402, 0.12721305, 0.08301993, 0.04862324])
assert_allclose(np.trapz(f, kde0.args), 0.96716261)
def test1_TKDE1D(self):
data = self.data
x = np.linspace(0.01, max(data) + 1, 10)

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