#! /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() #