def assertAlmostEqualVector(self, found, expected, precision = 1e-7):
# we can use the optimized version if the two arrays are 1D numpy float arrays
if isinstance(found, numpy.ndarray) and \
isinstance(expected, numpy.ndarray) and \
found.ndim == 1 and expected.ndim == 1 and \
found.dtype == expected.dtype and \
found.dtype == numpy.float32:
return self.assertTrue(almostEqualArray(found, expected, precision))
self.assertEqual(len(found), len(expected))
for val1, val2 in zip(found, expected):
self.assertAlmostEqual(val1, val2, precision)
def assertAlmostEqualVector(self, found, expected, precision = 1e-7):
# we can use the optimized version if the two arrays are 1D numpy float arrays
if isinstance(found, numpy.ndarray) and \
isinstance(expected, numpy.ndarray) and \
found.ndim == 1 and expected.ndim == 1 and \
found.dtype == expected.dtype and \
found.dtype == numpy.float32:
return self.assertTrue(almostEqualArray(found, expected, precision))
self.assertEqual(len(found), len(expected))
for val1, val2 in zip(found, expected):
self.assertAlmostEqual(val1, val2, precision)
def assertAlmostEqualMatrix(self, found, expected, precision = 1e-7):
# we can use the optimized version if the two arrays are 2D numpy float arrays
if isinstance(found, numpy.ndarray) and \
isinstance(expected, numpy.ndarray) and \
found.ndim == 2 and expected.ndim == 2 and \
found.dtype == expected.dtype and \
found.dtype == numpy.float32:
return self.assertTrue(almostEqualArray(found, expected, precision))
self.assertEqual(len(found), len(expected))
for v1, v2 in zip(found, expected):
self.assertEqual(len(v1), len(v2))
self.assertAlmostEqualVector(array(v1).flatten(), array(v2).flatten(), precision)
def assertAlmostEqualMatrix(self, found, expected, precision = 1e-7):
# we can use the optimized version if the two arrays are 2D numpy float arrays
if isinstance(found, numpy.ndarray) and \
isinstance(expected, numpy.ndarray) and \
found.ndim == 2 and expected.ndim == 2 and \
found.dtype == expected.dtype and \
found.dtype == numpy.float32:
return self.assertTrue(almostEqualArray(found, expected, precision))
self.assertEqual(len(found), len(expected))
count = 0
for v1, v2 in zip(found, expected):
self.assertEqual(len(v1), len(v2))
for val1, val2 in zip(v1, v2):
if (isinstance(val1, numpy.ndarray)):
self.assertAlmostEqual(val1.all(), val2.all(), precision)
else:
self.assertAlmostEqual(val1, val2, precision)
count += 1
def assertAlmostEqualMatrix(self, found, expected, precision = 1e-7):
# we can use the optimized version if the two arrays are 2D numpy float arrays
if isinstance(found, numpy.ndarray) and \
isinstance(expected, numpy.ndarray) and \
found.ndim == 2 and expected.ndim == 2 and \
found.dtype == expected.dtype and \
found.dtype == numpy.float32:
return self.assertTrue(almostEqualArray(found, expected, precision))
self.assertEqual(len(found), len(expected))
count = 0
for v1, v2 in zip(found, expected):
self.assertEqual(len(v1), len(v2))
for val1, val2 in zip(v1, v2):
if (isinstance(val1, numpy.ndarray)):
self.assertAlmostEqual(val1.all(), val2.all(), precision)
else:
self.assertAlmostEqual(val1, val2, precision)
count += 1