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#! /usr/bin/env python
# Last Change: Thu Jul 12 05:00 PM 2007 J
# TODO:
# - having "fake tests" to check that all mode (scalar, diag and full) are
# executables
# - having a dataset to check against
import sys
from numpy.testing import *
import numpy as N
set_package_path()
from pyem.densities import gauss_den
import pyem.densities
restore_path()
#Optional:
set_local_path()
# import modules that are located in the same directory as this file.
from testcommon import DEF_DEC
restore_path()
class TestDensities(NumpyTestCase):
def _generate_test_data_1d(self):
self.va = 2.0
self.mu = 1.0
self.X = N.linspace(-2, 2, 10)[:, N.newaxis]
self.Yt = N.array([0.02973257230591, 0.05512079811082,
0.09257745306945, 0.14086453882683, 0.19418015562214,
0.24250166773127, 0.27436665745048, 0.28122547107069,
0.26114678964743, 0.21969564473386])
def _generate_test_data_2d_diag(self):
#============================
# Small test in 2d (diagonal)
#============================
self.mu = N.atleast_2d([-1.0, 2.0])
self.va = N.atleast_2d([2.0, 3.0])
self.X = N.zeros((10, 2))
self.X[:,0] = N.linspace(-2, 2, 10)
self.X[:,1] = N.linspace(-1, 3, 10)
self.Yt = N.array([0.01129091565384, 0.02025416837152,
0.03081845516786, 0.03977576221540, 0.04354490552910,
0.04043592581117, 0.03184994053539, 0.02127948225225,
0.01205937178755, 0.00579694938623 ])
def _generate_test_data_2d_full(self):
#============================
# Small test in 2d (full mat)
#============================
self.mu = N.array([[0.2, -1.0]])
self.va = N.array([[1.2, 0.1], [0.1, 0.5]])
X1 = N.linspace(-2, 2, 10)[:, N.newaxis]
X2 = N.linspace(-3, 3, 10)[:, N.newaxis]
self.X = N.concatenate(([X1, X2]), 1)
self.Yt = N.array([0.00096157109751, 0.01368908714856,
0.07380823191162, 0.15072050533842,
0.11656739937861, 0.03414436965525,
0.00378789836599, 0.00015915297541,
0.00000253261067, 0.00000001526368])
#=====================
# Basic accuracy tests
#=====================
class test_py_implementation(TestDensities):
def _test(self, level, decimal = DEF_DEC):
Y = gauss_den(self.X, self.mu, self.va)
assert_array_almost_equal(Y, self.Yt, decimal)
def _test_log(self, level, decimal = DEF_DEC):
Y = gauss_den(self.X, self.mu, self.va, log = True)
assert_array_almost_equal(N.exp(Y), self.Yt, decimal)
def test_2d_diag(self, level = 0):
self._generate_test_data_2d_diag()
self._test(level)
def test_2d_full(self, level = 0):
self._generate_test_data_2d_full()
self._test(level)
def test_1d(self, level = 0):
self._generate_test_data_1d()
self._test(level)
def test_2d_diag_log(self, level = 0):
self._generate_test_data_2d_diag()
self._test_log(level)
def test_2d_full_log(self, level = 0):
self._generate_test_data_2d_full()
self._test_log(level)
def test_1d_log(self, level = 0):
self._generate_test_data_1d()
self._test_log(level)
#=====================
# Basic speed tests
#=====================
class test_speed(NumpyTestCase):
def __init__(self, *args, **kw):
NumpyTestCase.__init__(self, *args, **kw)
import sys
import re
try:
a = open('/proc/cpuinfo').readlines()
b = re.compile('cpu MHz')
c = [i for i in a if b.match(i)]
fcpu = float(c[0].split(':')[1])
self.fcpu = fcpu * 1e6
self.hascpu = True
except:
print "Could not read cpu frequency"
self.hascpu = False
self.fcpu = 0.
def _prepare(self, n, d, mode):
niter = 10
x = 0.1 * N.random.randn(n, d)
mu = 0.1 * N.random.randn(d)
if mode == 'diag':
va = 0.1 * N.random.randn(d) ** 2
elif mode == 'full':
a = N.random.randn(d, d)
va = 0.1 * N.dot(a.T, a)
st = self.measure("gauss_den(x, mu, va)", niter)
return st / niter
def _bench(self, n, d, mode):
st = self._prepare(n, d, mode)
print "%d dimension, %d samples, %s mode: %8.2f " % (d, n, mode, st)
if self.hascpu:
print "Cost per frame is %f; cost per sample is %f" % \
(st * self.fcpu / n, st * self.fcpu / n / d)
def test1(self, level = 5):
cls = self.__class__
for n, d in [(1e5, 1), (1e5, 5), (1e5, 10), (1e5, 30), (1e4, 100)]:
self._bench(n, d, 'diag')
for n, d in [(1e4, 2), (1e4, 5), (1e4, 10), (5000, 40)]:
self._bench(n, d, 'full')
#================
# Logsumexp tests
#================
class test_py_logsumexp(TestDensities):
"""Class to compare logsumexp vs naive implementation."""
def test_underlow(self):
"""This function checks that logsumexp works as expected."""
# We check wether naive implementation would underflow, to be sure we
# are actually testing something here.
errst = N.seterr(under='raise')
try:
try:
a = N.array([[-1000]])
self.naive_logsumexp(a)
raise AssertionError("expected to catch underflow, we should"\
"not be here")
except FloatingPointError, e:
print "Catching underflow, as expected"
assert pyem.densities.logsumexp(a) == -1000.
try:
a = N.array([[-1000, -1000, -1000]])
self.naive_logsumexp(a)
raise AssertionError("expected to catch underflow, we should"\
"not be here")
except FloatingPointError, e:
print "Catching underflow, as expected"
assert_array_almost_equal(pyem.densities.logsumexp(a),
-998.90138771)
finally:
N.seterr(under=errst['under'])
def naive_logsumexp(self, data):
return N.log(N.sum(N.exp(data), 1))
def test_1d(self):
data = N.random.randn(1e1)[:, N.newaxis]
mu = N.array([[-5], [-6]])
va = N.array([[0.1], [0.1]])
y = pyem.densities.multiple_gauss_den(data, mu, va, log = True)
a1 = pyem.densities.logsumexp(y)
a2 = self.naive_logsumexp(y)
assert_array_equal(a1, a2)
def test_2d_full(self):
data = N.random.randn(1e1, 2)
mu = N.array([[-3, -1], [3, 3]])
va = N.array([[1.1, 0.4], [0.6, 0.8], [0.4, 0.2], [0.3, 0.9]])
y = pyem.densities.multiple_gauss_den(data, mu, va, log = True)
a1 = pyem.densities.logsumexp(y)
a2 = self.naive_logsumexp(y)
assert_array_almost_equal(a1, a2, DEF_DEC)
def test_2d_diag(self):
data = N.random.randn(1e1, 2)
mu = N.array([[-3, -1], [3, 3]])
va = N.array([[1.1, 0.4], [0.6, 0.8]])
y = pyem.densities.multiple_gauss_den(data, mu, va, log = True)
a1 = pyem.densities.logsumexp(y)
a2 = self.naive_logsumexp(y)
assert_array_almost_equal(a1, a2, DEF_DEC)
#=======================
# Test C implementation
#=======================
class test_c_implementation(TestDensities):
def _test(self, level, decimal = DEF_DEC):
try:
from pyem._c_densities import gauss_den as c_gauss_den
Y = c_gauss_den(self.X, self.mu, self.va)
assert_array_almost_equal(Y, self.Yt, decimal)
except Exception, inst:
print "Error while importing C implementation, not tested"
print " -> (Import error was %s)" % inst
def test_1d(self, level = 0):
self._generate_test_data_1d()
self._test(level)
def test_2d_diag(self, level = 0):
self._generate_test_data_2d_diag()
self._test(level)
def test_2d_full(self, level = 0):
self._generate_test_data_2d_full()
self._test(level)
class test_gauss_ell(NumpyTestCase):
def test_dim(self):
pyem.densities.gauss_ell([0, 1], [1, 2.], [0, 1])
try:
pyem.densities.gauss_ell([0, 1], [1, 2.], [0, 2])
raise AssertionError("this call should not succeed, bogus dim.")
except ValueError, e:
print "Call with bogus dim did not succeed, OK"
if __name__ == "__main__":
NumpyTest().run()
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