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#! /usr/bin/env python
# Last Change: Thu Nov 09 05:00 PM 2006 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
restore_path()
#Optional:
set_local_path()
# import modules that are located in the same directory as this file.
restore_path()
DEF_DEC = 12
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])
class test_py_implementation(TestDensities):
def _check(self, level, decimal = DEF_DEC):
Y = gauss_den(self.X, self.mu, self.va)
assert_array_almost_equal(Y, self.Yt, decimal)
def check_2d_diag(self, level = 0):
self._generate_test_data_2d_diag()
self._check(level)
def check_2d_full(self, level = 0):
self._generate_test_data_2d_full()
self._check(level)
def check_py_1d(self, level = 0):
self._generate_test_data_1d()
self._check(level)
class test_c_implementation(TestDensities):
def _check(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 ImportError, inst:
print "Error while importing C implementation, not tested"
print " -> (Import error was %s)" % inst
def check_1d(self, level = 0):
self._generate_test_data_1d()
self._check(level)
def check_2d_diag(self, level = 0):
self._generate_test_data_2d_diag()
self._check(level)
def check_2d_full(self, level = 0):
self._generate_test_data_2d_full()
self._check(level)
if __name__ == "__main__":
NumpyTest().run()
# def generate_test_data(n, d, mode = 'diag', file='test.dat'):
# """Generate a set of data of dimension d, with n frames,
# that is input data, mean, var and output of gden, so that
# other implementations can be tested against"""
# mu = randn(1, d)
# if mode == 'diag':
# va = abs(randn(1, d))
# elif mode == 'full':
# va = randn(d, d)
# va = dot(va, va.transpose())
#
# input = randn(n, d)
# output = gauss_den(input, mu, va)
#
# import tables
# h5file = tables.openFile(file, "w")
#
# h5file.createArray(h5file.root, 'input', input)
# h5file.createArray(h5file.root, 'mu', mu)
# h5file.createArray(h5file.root, 'va', va)
# h5file.createArray(h5file.root, 'output', output)
#
# h5file.close()
#
# def test_gauss_den():
# """"""
# # import tables
# # import numpy as N
# #
# # filename = 'dendata.h5'
#
# # # # Dimension 1
# # # d = 1
# # # mu = 1.0
# # # va = 2.0
#
# # # X = randn(1e3, 1)
#
# # # Y = gauss_den(X, mu, va)
#
# # # h5file = tables.openFile(filename, "w")
#
# # # h5file.createArray(h5file.root, 'X', X)
# # # h5file.createArray(h5file.root, 'mu', mu)
# # # h5file.createArray(h5file.root, 'va', va)
# # # h5file.createArray(h5file.root, 'Y', Y)
#
# # # h5file.close()
#
# # # # Dimension 2, diag
# # # d = 2
# # # mu = N.array([1.0, -2.0])
# # # va = N.array([1.0, 2.0])
#
# # # X = randn(1e3, 2)
#
# # # Y = gauss_den(X, mu, va)
#
# # # h5file = tables.openFile(filename, "w")
#
# # # h5file.createArray(h5file.root, 'X', X)
# # # h5file.createArray(h5file.root, 'mu', mu)
# # # h5file.createArray(h5file.root, 'va', va)
# # # h5file.createArray(h5file.root, 'Y', Y)
#
# # # Dimension 2, full
# # d = 2
# # mu = N.array([[0.2, -1.0]])
# # va = N.array([[1.2, 0.1], [0.1, 0.5]])
#
# # X = randn(1e3, 2)
#
# # Y = gauss_den(X, mu, va)
#
# # h5file = tables.openFile(filename, "w")
#
# # h5file.createArray(h5file.root, 'X', X)
# # h5file.createArray(h5file.root, 'mu', mu)
# # h5file.createArray(h5file.root, 'va', va)
# # h5file.createArray(h5file.root, 'Y', Y)
#
# # h5file.close()
#
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