Added some python tests.

bindings/python/cconfigspace/base.py

@@ -239,9 +239,9 @@ class ccs_numeric(ct.Union):
 
   def get_value(self, t):
     if t == ccs_numeric_type.NUM_INTEGER:
-      return self.f
+      return self.i
     elif t == ccs_numeric_type.NUM_FLOAT:
-      return self.v
+      return self.f
     else:
       raise Error(ccs_error(ccs_error.INVALID_VALUE))
 

bindings/python/cconfigspace/distribution.py

 import ctypes as ct
 from . import libcconfigspace
-from .base import Object, Error, ccs_error, ccs_int, ccs_float, ccs_bool, ccs_result, ccs_rng, ccs_distribution, ccs_numeric_type, ccs_numeric, CEnumeration, NUM_FLOAT, NUM_INTEGER, _ccs_get_function
+from .base import Object, Error, ccs_error, ccs_int, ccs_float, ccs_bool, ccs_result, ccs_rng, ccs_distribution, ccs_numeric_type, ccs_numeric, CEnumeration, NUM_FLOAT, NUM_INTEGER, _ccs_get_function, ccs_false, ccs_true
 from .interval import ccs_interval
 
 class ccs_distribution_type(CEnumeration):
@@ -81,6 +81,8 @@ class Distribution(Object):
     self._scale_type = v.value
     return self._scale_type
 
+  scale = scale_type
+
   @property
   def quantization(self):
     if hasattr(self, "_quantization"):
@@ -219,6 +221,14 @@ class NormalDistribution(Distribution):
     else:
       super().__init__(handle = handle, retain = retain)
 
+  @classmethod
+  def int(cls, mu, sigma, scale = ccs_scale_type.LINEAR, quantization = 0):
+    return cls(data_type = NUM_INTEGER, mu = mu, sigma = sigma, scale = scale, quantization = quantization)
+
+  @classmethod
+  def float(cls, mu, sigma, scale = ccs_scale_type.LINEAR, quantization = 0.0):
+    return cls(data_type = NUM_FLOAT, mu = mu, sigma = sigma, scale = scale, quantization = quantization)
+
   @property
   def mu(self):
     if hasattr(self, "_mu"):

bindings/python/cconfigspace/interval.py

@@ -9,11 +9,30 @@ class ccs_interval(ct.Structure):
               ('_lower_included', ccs_bool),
               ('_upper_included', ccs_bool)]
 
+  def __init__(self, t = ccs_numeric_type.NUM_FLOAT, lower = 0.0, upper = 1.0, lower_included = True, upper_included = False):
+    if t == ccs_numeric_type.NUM_INTEGER:
+      self._lower.i = lower
+      self._upper.i = upper
+    elif t == ccs_numeric_type.NUM_FLOAT:
+      self._lower.f = lower
+      self._upper.f = upper
+    else:
+      raise Error(ccs_error(ccs_error.INVALID_VALUE))
+    self._type.value = t
+    if lower_included:
+      self._lower_included = ccs_true
+    else:
+      self._lower_included = ccs_false
+    if upper_included:
+      self._upper_included = ccs_true
+    else:
+      self._upper_included = ccs_false
+
   @property
   def type(self):
     return self._type.value
 
-  @property
+  @type.setter
   def type(self, v):
     self._type.value = v
 
@@ -79,6 +98,7 @@ class ccs_interval(ct.Structure):
     else:
       self._upper_included = ccs_false
 
+  @property
   def empty(self):
     v = ccs_bool(0)
     res = ccs_interval_empty(ct.byref(self), ct.byref(v))

bindings/python/cconfigspace/rng.py

@@ -23,10 +23,12 @@ class Rng(Object):
   def from_handle(cls, handle):
     return cls(handle, retain = True)
 
-  def set_seed(self, value):
-    res = ccs_rng_set_seed(self.handle, value)
-    Error.check(res)
-    return self
+  def __setattr__(self, name, value):
+    if name == 'seed':
+      res = ccs_rng_set_seed(self.handle, value)
+      Error.check(res)
+      return None
+    super().__setattr__(name, value)
 
   def get(self):
     v = ct.c_ulong(0)
@@ -40,12 +42,14 @@ class Rng(Object):
     Error.check(res)
     return v.value
 
+  @property
   def min(self):
     v = ct.c_ulong(0)
     res = ccs_rng_min(self.handle, ct.byref(v))
     Error.check(res)
     return v.value
 
+  @property
   def max(self):
     v = ct.c_ulong(0)
     res = ccs_rng_max(self.handle, ct.byref(v))

bindings/python/test/test_base.py

+import unittest
+import sys
+sys.path.insert(1, '.')
+sys.path.insert(1, '..')
+import cconfigspace as ccs
+from math import sin
+
+
+class TestBase(unittest.TestCase):
+
+  def test_version(self):
+    ver = ccs.ccs_get_version()
+    self.assertIsInstance(ver, ccs.ccs_version)
+
+  def test_datum_value(self):
+    d = ccs.ccs_datum()
+    d.value = None
+    self.assertEqual( ccs.NONE, d.type )
+    self.assertIsNone( d.value )
+    d.value = ccs.ccs_inactive
+    self.assertEqual( ccs.INACTIVE, d.type )
+    self.assertEqual( ccs.ccs_inactive, d.value )
+    d.value = False
+    self.assertEqual( ccs.BOOLEAN, d.type )
+    self.assertFalse( d.value )
+    d.value = True
+    self.assertEqual( ccs.BOOLEAN, d.type )
+    self.assertTrue( d.value )
+    d.value = 1.5
+    self.assertEqual( ccs.FLOAT, d.type )
+    self.assertEqual( 1.5, d.value )
+    d.value = 2
+    self.assertEqual( ccs.INTEGER, d.type )
+    self.assertEqual( 2, d.value )
+    d.value = "foo"
+    self.assertEqual( ccs.STRING, d.type )
+    self.assertEqual( "foo", d.value )
+    d.value = ccs.Rng()
+    self.assertEqual( ccs.OBJECT, d.type )
+    self.assertIsInstance( d.value, ccs.Rng )
+    self.assertEqual( ccs.RNG, d.value.object_type )
+
+  def test_numeric(self):
+    n = ccs.ccs_numeric()
+    n.set_value(2)
+    self.assertEqual( 2, n.get_value(ccs.NUM_INTEGER))
+    n.set_value(1.5)
+    self.assertEqual( 1.5, n.get_value(ccs.NUM_FLOAT))
+
+if __name__ == '__main__':
+    unittest.main()

bindings/python/test/test_distribution.py

+import unittest
+import sys
+sys.path.insert(1, '.')
+sys.path.insert(1, '..')
+import cconfigspace as ccs
+
+class TestDistribution(unittest.TestCase):
+
+  def test_from_handle_Roulette(self):
+    d = ccs.RouletteDistribution(areas = [ 1.0, 2.0, 1.0, 0.5 ])
+    d2 = ccs.Object.from_handle(d.handle)
+    self.assertEqual( d.__class__, d2.__class__)
+    self.assertEqual( d.handle.value, d2.handle.value)
+
+  def test_create_roulette(self):
+    areas = [ 1.0, 2.0, 1.0, 0.5 ]
+    s = sum(areas)
+    d = ccs.RouletteDistribution(areas = areas)
+    self.assertEqual( ccs.DISTRIBUTION, d.object_type )
+    self.assertEqual( ccs.ROULETTE, d.type )
+    self.assertEqual( ccs.NUM_INTEGER, d.data_type )
+    self.assertEqual( ccs.LINEAR, d.scale )
+    self.assertEqual( 1, d.dimension )
+    a = d.areas
+    self.assertTrue(  sum(a) > 0.999 )
+    self.assertTrue(  sum(a) < 1.001 )
+    for i in range(len(a)):
+      self.assertTrue( a[i] < areas[i] * 1.001 / s )
+      self.assertTrue( a[i] > areas[i] * 0.999 / s )
+    i = d.bounds
+    self.assertEqual( ccs.NUM_INTEGER, i.type)
+    self.assertEqual( 0, i.lower )
+    self.assertEqual( 4, i.upper )
+    self.assertTrue( i.lower_included )
+    self.assertFalse( i.upper_included )
+
+  def test_from_handle_normal(self):
+    d = ccs.NormalDistribution()
+    d2 = ccs.Object.from_handle(d.handle)
+    self.assertEqual( d.__class__, d2.__class__)
+    self.assertEqual( d.handle.value, d2.handle.value)
+
+  def test_create_normal(self):
+    d = ccs.NormalDistribution()
+    self.assertEqual( ccs.DISTRIBUTION, d.object_type )
+    self.assertEqual( ccs.NORMAL, d.type )
+    self.assertEqual( ccs.NUM_FLOAT, d.data_type )
+    self.assertEqual( ccs.LINEAR, d.scale )
+    self.assertEqual( 1, d.dimension )
+    self.assertEqual( 0.0, d.mu )
+    self.assertEqual( 1.0, d.sigma )
+    i = d.bounds
+    self.assertEqual( ccs.NUM_FLOAT, i.type )
+    self.assertEqual( float('-inf'), i.lower )
+    self.assertEqual( float('inf'), i.upper )
+    self.assertFalse( i.lower_included )
+    self.assertFalse( i.upper_included )
+
+  def test_create_normal_float(self):
+    d = ccs.NormalDistribution.float(mu = 2.0, sigma = 6.0)
+    self.assertEqual( ccs.DISTRIBUTION, d.object_type )
+    self.assertEqual( ccs.NORMAL, d.type )
+    self.assertEqual( ccs.NUM_FLOAT, d.data_type )
+    self.assertEqual( ccs.LINEAR, d.scale )
+    self.assertEqual( 1, d.dimension )
+    self.assertEqual( 2.0, d.mu )
+    self.assertEqual( 6.0, d.sigma )
+
+  def test_create_normal_int(self):
+    d = ccs.NormalDistribution.int(mu = 2.0, sigma = 6.0)
+    self.assertEqual( ccs.DISTRIBUTION, d.object_type )
+    self.assertEqual( ccs.NORMAL, d.type )
+    self.assertEqual( ccs.NUM_INTEGER, d.data_type )
+    self.assertEqual( ccs.LINEAR, d.scale )
+    self.assertEqual( 1, d.dimension )
+    self.assertEqual( 2.0, d.mu )
+    self.assertEqual( 6.0, d.sigma )
+
+  def test_sample_normal(self):
+    rng = ccs.Rng()
+    d = ccs.NormalDistribution()
+    i = d.bounds
+    v = d.sample(rng)
+    self.assertTrue( i.include(v) )
+    a = d.samples(rng, 100)
+    self.assertEqual( 100, len(a) )
+    for v in a:
+      self.assertTrue( i.include(v) )
+
+  def test_from_handle_uniform(self):
+    d = ccs.UniformDistribution()
+    d2 = ccs.Object.from_handle(d.handle)
+    self.assertEqual( d.__class__, d2.__class__)
+    self.assertEqual( d.handle.value, d2.handle.value)
+
+  def test_create_uniform(self):
+    d = ccs.UniformDistribution()
+    self.assertEqual( ccs.DISTRIBUTION, d.object_type )
+    self.assertEqual( ccs.UNIFORM, d.type )
+    self.assertEqual( ccs.NUM_FLOAT, d.data_type )
+    self.assertEqual( ccs.LINEAR, d.scale )
+    self.assertEqual( 1, d.dimension )
+    i = d.bounds
+    self.assertEqual( ccs.NUM_FLOAT, i.type )
+    self.assertEqual( 0.0, i.lower)
+    self.assertEqual( 1.0, i.upper)
+    self.assertEqual( 0.0, d.lower)
+    self.assertEqual( 1.0, d.upper)
+    self.assertTrue( i.lower_included )
+    self.assertFalse( i.upper_included )
+
+  def test_create_uniform_float(self):
+    d = ccs.UniformDistribution.float(lower = -1.0, upper = 1.0)
+    self.assertEqual( ccs.DISTRIBUTION, d.object_type )
+    self.assertEqual( ccs.UNIFORM, d.type )
+    self.assertEqual( ccs.NUM_FLOAT, d.data_type )
+    self.assertEqual( ccs.LINEAR, d.scale )
+    self.assertEqual( 1, d.dimension )
+    i = d.bounds
+    self.assertEqual( ccs.NUM_FLOAT, i.type )
+    self.assertEqual( -1.0, i.lower)
+    self.assertEqual(  1.0, i.upper)
+    self.assertEqual( -1.0, d.lower)
+    self.assertEqual(  1.0, d.upper)
+    self.assertTrue( i.lower_included )
+    self.assertFalse( i.upper_included )
+
+  def test_create_uniform_int(self):
+    d = ccs.UniformDistribution.int(lower = 0, upper = 100)
+    self.assertEqual( ccs.DISTRIBUTION, d.object_type )
+    self.assertEqual( ccs.UNIFORM, d.type )
+    self.assertEqual( ccs.NUM_INTEGER, d.data_type )
+    self.assertEqual( ccs.LINEAR, d.scale )
+    self.assertEqual( 1, d.dimension )
+    i = d.bounds
+    self.assertEqual( ccs.NUM_INTEGER, i.type )
+    self.assertEqual(  0,  i.lower)
+    self.assertEqual( 100, i.upper)
+    self.assertEqual(   0, d.lower)
+    self.assertEqual( 100, d.upper)
+    self.assertTrue( i.lower_included )
+    self.assertFalse( i.upper_included )
+
+  def test_oversampling_uniform_float(self):
+    d = ccs.UniformDistribution.float(lower = -1.0, upper = 1.0)
+    i = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -0.2, upper = 0.2)
+    self.assertTrue( d.oversampling(i) )
+    i = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -0.2, upper = 2.0)
+    self.assertTrue( d.oversampling(i) )
+    i = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -2.0, upper = 2.0)
+    self.assertFalse( d.oversampling(i) )
+
+  def test_oversampling_uniform_int(self):
+    d = ccs.UniformDistribution.int(lower = 0, upper = 100)
+    i = ccs.ccs_interval(t = ccs.NUM_INTEGER, lower = 5, upper = 50)
+    self.assertTrue( d.oversampling(i) )
+    i = ccs.ccs_interval(t = ccs.NUM_INTEGER, lower = 5, upper = 150)
+    self.assertTrue( d.oversampling(i) )
+    i = ccs.ccs_interval(t = ccs.NUM_INTEGER, lower = -5, upper = 150)
+    self.assertFalse( d.oversampling(i) )
+
+  def test_sample_uniform(self):
+    rng = ccs.Rng()
+    d = ccs.UniformDistribution()
+    i = d.bounds
+    v = d.sample(rng)
+    self.assertTrue( i.include(v) )
+    a = d.samples(rng, 100)
+    self.assertEqual( 100, len(a) )
+    for v in a:
+      self.assertTrue( i.include(v) )
+
+
+if __name__ == '__main__':
+    unittest.main()

bindings/python/test/test_hyperparameter.py

+import unittest
+import sys
+sys.path.insert(1, '.')
+sys.path.insert(1, '..')
+import cconfigspace as ccs
+
+class TestHyperparameter(unittest.TestCase):
+
+  def test_from_handle_discrete(self):
+    values = [0, 1.5, 2, 7.2]
+    h = ccs.DiscreteHyperparameter(values = values)
+    h2 = ccs.Object.from_handle(h.handle)
+    self.assertEqual( h.__class__, h2.__class__ )
+    self.assertEqual( h.handle, h2.handle )
+
+  def test_discrete(self):
+    values = [0.2, 1.5, 2, 7.2]
+    h = ccs.DiscreteHyperparameter(values = values)
+    self.assertEqual( ccs.HYPERPARAMETER, h.object_type )
+    self.assertEqual( ccs.DISCRETE, h.type )
+    self.assertTrue( h.name[:5] == "param" )
+    self.assertIsNone( h.user_data.value )
+    self.assertEqual( 0.2, h.default_value )
+    self.assertFalse( h.check_value("foo") )
+    v = h.sample()
+    self.assertTrue( v in values )
+    vals = h.samples(100)
+    for v in vals:
+      self.assertTrue( v in values )
+
+  def test_ordinal_compare(self):
+    values = ["foo", 2, 3.0]
+    h = ccs.OrdinalHyperparameter(values = values)
+    self.assertEqual( 0, h.compare("foo", "foo") )
+    self.assertEqual( -1, h.compare("foo", 2) )
+    self.assertEqual( -1, h.compare("foo", 3.0) )
+    self.assertEqual( 1, h.compare(2, "foo") )
+    self.assertEqual( 0, h.compare(2, 2) )
+    self.assertEqual( -1, h.compare(2, 3.0) )
+    self.assertEqual( 1, h.compare(3.0, "foo") )
+    self.assertEqual( 1, h.compare(3.0, 2) )
+    self.assertEqual( 0, h.compare(3.0, 3.0) )
+    self.assertRaises( ccs.Error, h.compare, 4.0, "foo" )
+
+  def test_from_handle_ordinal(self):
+    values = ["foo", 2, 3.0]
+    h = ccs.OrdinalHyperparameter(values = values)
+    h2 = ccs.Object.from_handle(h.handle)
+    self.assertEqual( h.__class__, h2.__class__ )
+    self.assertEqual( h.handle, h2.handle )
+
+  def test_ordinal(self):
+    values = ["foo", 2, 3.0]
+    h = ccs.OrdinalHyperparameter(values = values)
+    self.assertEqual( ccs.HYPERPARAMETER, h.object_type )
+    self.assertEqual( ccs.ORDINAL, h.type )
+    self.assertTrue( h.name[:5] == "param" )
+    self.assertIsNone( h.user_data.value )
+    self.assertEqual( "foo", h.default_value )
+    self.assertEqual( ccs.UNIFORM, h.default_distribution.type )
+    self.assertEqual( values, h.values )
+    for v in values:
+      self.assertTrue( h.check_value(v) )
+    self.assertFalse( h.check_value("bar") )
+    v = h.sample()
+    self.assertTrue( v in values )
+    vals = h.samples(100)
+    for v in vals:
+      self.assertTrue( v in values )
+
+  def test_from_handle_categorical(self):
+    values = ["foo", 2, 3.0]
+    h = ccs.CategoricalHyperparameter(values = values)
+    h2 = ccs.Object.from_handle(h.handle)
+    self.assertEqual( h.__class__, h2.__class__ )
+    self.assertEqual( h.handle, h2.handle )
+
+  def test_categorical(self):
+    values = ["foo", 2, 3.0]
+    h = ccs.CategoricalHyperparameter(values = values)
+    self.assertEqual( ccs.HYPERPARAMETER, h.object_type )
+    self.assertEqual( ccs.CATEGORICAL, h.type )
+    self.assertTrue( h.name[:5] == "param" )
+    self.assertIsNone( h.user_data.value )
+    self.assertEqual( "foo", h.default_value )
+    self.assertEqual( ccs.UNIFORM, h.default_distribution.type )
+    self.assertEqual( values, h.values )
+    for v in values:
+      self.assertTrue( h.check_value(v) )
+    self.assertFalse( h.check_value("bar") )
+    v = h.sample()
+    self.assertTrue( v in values )
+    vals = h.samples(100)
+    for v in vals:
+      self.assertTrue( v in values )
+
+  def test_from_handle_numerical(self):
+    h = ccs.NumericalHyperparameter()
+    h2 = ccs.Object.from_handle(h.handle)
+    self.assertEqual( h.__class__, h2.__class__ )
+    self.assertEqual( h.handle, h2.handle )
+
+  def test_numerical(self):
+    h = ccs.NumericalHyperparameter()
+    self.assertEqual( ccs.HYPERPARAMETER, h.object_type )
+    self.assertEqual( ccs.NUMERICAL, h.type )
+    self.assertTrue( h.name[:5] == "param" )
+    self.assertIsNone( h.user_data.value )
+    self.assertEqual( 0.0, h.default_value )
+    self.assertEqual( ccs.UNIFORM, h.default_distribution.type )
+    self.assertEqual( ccs.NUM_FLOAT, h.data_type )
+    self.assertEqual( 0.0, h.lower )
+    self.assertEqual( 1.0, h.upper )
+    self.assertEqual( 0.0, h.quantization )
+    self.assertTrue( h.check_value(0.5) )
+    self.assertFalse( h.check_value(1.5) )
+    v = h.sample()
+    self.assertIsInstance( v, float )
+    self.assertTrue( v >= 0.0 and v < 1.0 )
+    vals = h.samples(100)
+    for v in vals:
+      self.assertIsInstance( v, float )
+      self.assertTrue( v >= 0.0 and v < 1.0 )
+
+  def test_numerical_float(self):
+    h = ccs.NumericalHyperparameter.float(lower = 0.0, upper = 1.0)
+    self.assertEqual( ccs.HYPERPARAMETER, h.object_type )
+    self.assertEqual( ccs.NUMERICAL, h.type )
+    self.assertTrue( h.name[:5] == "param" )
+    self.assertIsNone( h.user_data.value )
+    self.assertEqual( 0.0, h.default_value )
+    self.assertEqual( ccs.UNIFORM, h.default_distribution.type )
+    self.assertEqual( ccs.NUM_FLOAT, h.data_type )
+    self.assertEqual( 0.0, h.lower )
+    self.assertEqual( 1.0, h.upper )
+    self.assertEqual( 0.0, h.quantization )
+    self.assertTrue( h.check_value(0.5) )
+    self.assertFalse( h.check_value(1.5) )
+    v = h.sample()
+    self.assertIsInstance( v, float )
+    self.assertTrue( v >= 0.0 and v < 1.0 )
+    vals = h.samples(100)
+    for v in vals:
+      self.assertIsInstance( v, float )
+      self.assertTrue( v >= 0.0 and v < 1.0 )
+
+  def test_numerical_int(self):
+    h = ccs.NumericalHyperparameter.int(lower = 0, upper = 100)
+    self.assertEqual( ccs.HYPERPARAMETER, h.object_type )
+    self.assertEqual( ccs.NUMERICAL, h.type )
+    self.assertTrue( h.name[:5] == "param" )
+    self.assertIsNone( h.user_data.value )
+    self.assertEqual( 0, h.default_value )
+    self.assertEqual( ccs.UNIFORM, h.default_distribution.type )
+    self.assertEqual( ccs.NUM_INTEGER, h.data_type )
+    self.assertEqual( 0, h.lower )
+    self.assertEqual( 100, h.upper )
+    self.assertEqual( 0, h.quantization )
+    self.assertTrue( h.check_value(50) )
+    self.assertFalse( h.check_value(150) )
+    v = h.sample()
+    self.assertIsInstance( v, int )
+    self.assertTrue( v >= 0 and v < 100 )
+    vals = h.samples(100)
+    for v in vals:
+      self.assertIsInstance( v, int )
+      self.assertTrue( v >= 0 and v < 100 )
+
+if __name__ == '__main__':
+    unittest.main()

bindings/python/test/test_interval.py

+import unittest
+import sys
+sys.path.insert(1, '.')
+sys.path.insert(1, '..')
+import cconfigspace as ccs
+from math import sin
+
+
+class TestInterval(unittest.TestCase):
+
+  def test_create(self):
+    i = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 1.0)
+    self.assertEqual( ccs.NUM_FLOAT, i.type )
+    self.assertEqual( -1.0, i.lower )
+    self.assertEqual( 1.0, i.upper )
+    self.assertTrue( i.lower_included )
+    self.assertFalse( i.upper_included )
+
+  def test_empty(self):
+    i = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 1.0)
+    self.assertFalse( i.empty )
+    i = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = 1.0, upper = -1.0)
+    self.assertTrue( i.empty )
+
+  def test_equal(self):
+    i1 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 1.0)
+    i2 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 1.0)
+    self.assertEqual(i1, i2)
+    i2 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 1.0, upper_included = True)
+    self.assertNotEqual(i1, i2)
+
+  def test_intersect(self):
+    i1 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 1.0)
+    i2 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = 0.0, upper = 2.0)
+    i3 = i1.intersect(i2)
+    i4 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = 0.0, upper = 1.0)
+    self.assertEqual( i4, i3 )
+    i1 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 0.0)
+    i2 = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = 0.0, upper = 2.0)
+    i3 = i1.intersect(i2)
+    self.assertTrue( i3.empty )
+
+  def test_include(self):
+    i = ccs.ccs_interval(t = ccs.NUM_FLOAT, lower = -1.0, upper = 1.0)
+    self.assertTrue( i.include(0.0) )
+    self.assertFalse( i.include(2.0) )
+    i = ccs.ccs_interval(t = ccs.NUM_INTEGER, lower = -5, upper = 5)
+    self.assertTrue( i.include(0) )
+    self.assertFalse( i.include(6) )
+
+if __name__ == '__main__':