def test_isRedundat_onlyOneHyperplane_3d(self):
d = 3
h = Hyperplane([1.0, 1.0, 0.0], 5)
r = Region([h], d)
resul = h.isRedundant(r)
self.assertFalse(resul,
"no puede ser redundante si no hay otro hiperplanos")
pass
def test_classify_twoSquarePoliedrom(self):
d = 2
k = 2
t0 = "blue"
t1 = "red"
sample_blue1 = Sample((8.0, 8.0))
sample_blue2 = Sample((2.0, 2.0))
sample_red = Sample((5.0, 5.0))
h1 = Hyperplane({0: 1, 1: 0}, 4)
h2 = Hyperplane({0: -1, 1: 0}, 0)
h3 = Hyperplane({0: 0, 1: 1}, 4)
h4 = Hyperplane({0: 1, 1: -1}, 0)
h5 = Hyperplane({0: 1, 1: 0}, 10)
h6 = Hyperplane({0: -1, 1: 0}, -6)
h7 = Hyperplane({0: 0, 1: 1}, 10)
h8 = Hyperplane({0: 0, 1: -1}, -6)
classifier = Classifier([], [], t0, t1, d, k)
classifier.regions = [
Region([h1, h2, h3, h4], d),
Region([h5, h6, h7, h8], d)
]
self.assertEquals(t1, classifier.classify(sample_blue1),
"la muestra debe ser azul")
self.assertEquals(t1, classifier.classify(sample_blue2),
"la muestra debe ser azul")
self.assertEquals(t0, classifier.classify(sample_red),
"la muestra debe ser roja")
def defineHyperplane(group,cluster):
def getVals(model,dic):
ret = {}
for key,value in dic.items():
ret[key] = model.getVal(value)
return ret
dimension = cluster.getDimension()
################## model #########################################
model = Model()
model.hideOutput()
piVars = {}
for f in range(dimension):
piVars[f] = model.addVar(vtype="CONTINUOUS", name="pi[%s]" % (f),lb=None)
alfaVar = model.addVar(vtype="CONTINUOUS", name="alfa",lb=None)
objective = model.addVar(vtype="CONTINUOUS", name="objective",lb=None)
model.addCons(quicksum(piVars[f]*piVars[f] for f in range(dimension)) <= objective , "objective_function" )
for sample in cluster.getSamples():
model.addCons(quicksum(piVars[f]*sample.getFeature(f) for f in range(dimension))>= alfaVar + 1, "r1%s" % (sample))
for sample in group.getSamples():
model.addCons(quicksum(piVars[f]*sample.getFeature(f) for f in range(dimension)) <= alfaVar - 1,"f2%s" % (sample))
model.setObjective(objective,sense="minimize")
model.optimize()
return Hyperplane(getVals(model,piVars), model.getVal(alfaVar))
def test_isRedundant_thereIsNotRedundant_3d(self):
d = 3
h1 = Hyperplane([1.0, 1.0, 0.0], 5.5)
h2 = Hyperplane([2.0, 1.0, 0.0], 6.0)
h3 = Hyperplane([3.0, 1.0, 0.0], 7.0)
r = Region([h1, h2, h3], d)
self.assertFalse(h1.isRedundant(r),
"el hiperplano x + y = 5.5 no es redundante")
self.assertFalse(h2.isRedundant(r),
"el hiperplano 2x + y = 6 no es redundante")
self.assertFalse(h3.isRedundant(r),
"el hiperplano 3x + y = 7 no es redundante")
def test_classify_Polytope(self):
d = 2
k = 2
t0 = "blue"
t1 = "red"
sample_blue = Sample((-5.0, -5.0))
sample_red = Sample((5.0, 5.0))
h1 = Hyperplane({0: 1, 1: 2}, 5)
h2 = Hyperplane({0: 1, 1: 0.5}, 5)
classifier = Classifier([], [], t0, t1, d, k)
classifier.regions = [Region([h1, h2], d)]
self.assertEquals(t1, classifier.classify(sample_blue),
"la muestra debe ser azul")
self.assertEquals(t0, classifier.classify(sample_red),
"la muestra debe ser roja")
def test_Classify_squarePolyedrom(self):
d = 2
k = 1
t0 = "blue"
t1 = "red"
sample_blue = Sample((2.0, 2.0))
sample_red = Sample((6.0, 0.0))
h1 = Hyperplane({0: 1, 1: 0}, 4)
h2 = Hyperplane({0: -1, 1: 0}, 0)
h3 = Hyperplane({0: 0, 1: 1}, 4)
h4 = Hyperplane({0: 1, 1: -1}, 0)
classifier = Classifier([], [], t0, t1, d, k)
classifier.regions = [Region([h1, h2, h3, h4], d)]
self.assertEquals(t1, classifier.classify(sample_blue),
"la muestra debe ser azul")
self.assertEquals(t0, classifier.classify(sample_red),
"la muestra debe ser roja")
def test_Classify_irregularPolyhedron(self):
d = 2
k = 1
t0 = "blue"
t1 = "red"
sample_blue = Sample((0.0, 0.0))
sample_red = Sample((20.0, 20.0))
h1 = Hyperplane({0: 1, 1: 1}, 10)
h2 = Hyperplane({0: -10, 1: -1}, 10)
h3 = Hyperplane({0: 4, 1: -1}, 50)
h4 = Hyperplane({0: 1, 1: -1}, 10)
classifier = Classifier([], [], t0, t1, d, k)
classifier.regions = [Region([h1, h2, h3, h4], d)]
self.assertEquals(t1, classifier.classify(sample_blue),
"la muestra debe ser azul")
self.assertEquals(t0, classifier.classify(sample_red),
"la muestra debe ser roja")
def test_isRedundat_thereIsRedundant_3d(self):
d = 3
h1 = Hyperplane([1.0, 1.0, 0.0], 5.5)
h2 = Hyperplane([1.0, 1.0, 0.0], 6.0)
h3 = Hyperplane([1.0, 1.0, 0.0], 7.0)
r = Region([h1, h2, h3], d)
self.assertFalse(h1.isRedundant(r),
"el hiperplano x + y = 5.5 no es redundante")
self.assertTrue(
h2.isRedundant(r),
"el hiperplano x + y = 6 es redundante por el hiperplano x 'y = 5.5"
)
self.assertTrue(
h3.isRedundant(r),
"el hiperplano x + y = 7 es redundante por el hiperplano x 'y = 5.5"
)
pass
def test_classify_twoPolytope(self):
d = 2
k = 2
t0 = "blue"
t1 = "red"
sample_blue1 = Sample((-10.0, -5.0))
sample_blue2 = Sample((40.0, 20.0))
sample_red = Sample((5.0, 5.0))
h1 = Hyperplane({0: 1, 1: 2}, 5)
h2 = Hyperplane({0: 1, 1: 0.5}, 5)
h3 = Hyperplane({0: -1, 1: -2}, -30)
h4 = Hyperplane({0: -1, 1: -0.5}, -30)
classifier = Classifier([], [], t0, t1, d, k)
classifier.regions = [Region([h1, h2], d), Region([h3, h4], d)]
self.assertEquals(t1, classifier.classify(sample_blue1),
"la muestra debe ser azul")
self.assertEquals(t1, classifier.classify(sample_blue2),
"la muestra debe ser azul")
self.assertEquals(t0, classifier.classify(sample_red),
"la muestra debe ser roja")
def test_Classify_trivial(self):
d = 2
k = 1
t0 = "blue"
t1 = "red"
sample_blue = Sample((2.0, 0.0))
sample_red = Sample((6.0, 0.0))
hyperplane = Hyperplane({0: 4, 1: 0}, 16)
classifier = Classifier([], [], t0, t1, d, k)
classifier.regions = [Region([hyperplane], d)]
self.assertEquals(t1, classifier.classify(sample_blue),
"la muestra debe ser azul")
self.assertEquals(t0, classifier.classify(sample_red),
"la muestra debe ser roja")
