def viterbi(self, s: list) -> list:
"""
viterbi calculates the most probable state sequence for a set of observed symbols.
PARAMETERS
----------
s : list
A set of observed symbols.
RETURNS
-------
list
The most probable state sequence as an {@link ArrayList}.
"""
result = []
sequenceLength = len(s)
gamma = Matrix(sequenceLength, self.stateCount * self.stateCount)
phi = Matrix(sequenceLength, self.stateCount * self.stateCount)
qs = Vector(sequenceLength, 0)
emission1 = s[0]
emission2 = s[1]
for i in range(self.stateCount):
for j in range(self.stateCount):
observationLikelihood = self.states[i].getEmitProb(
emission1) * self.states[j].getEmitProb(emission2)
gamma.setValue(
1, i * self.stateCount + j,
self.safeLog(self.__pi.getValue(i, j)) +
self.safeLog(observationLikelihood))
for t in range(2, sequenceLength):
emission = s[t]
for j in range(self.stateCount * self.stateCount):
current = self.__logOfColumn(j)
previous = gamma.getRowVector(t - 1).skipVector(
self.stateCount, j // self.stateCount)
current.addVector(previous)
maxIndex = current.maxIndex()
observationLikelihood = self.states[
j % self.stateCount].getEmitProb(emission)
gamma.setValue(
t, j,
current.getValue(maxIndex) +
self.safeLog(observationLikelihood))
phi.setValue(t, j,
maxIndex * self.stateCount + j // self.stateCount)
qs.setValue(sequenceLength - 1,
gamma.getRowVector(sequenceLength - 1).maxIndex())
result.insert(
0, self.states[int(qs.getValue(sequenceLength - 1)) %
self.stateCount].getState())
for i in range(sequenceLength - 2, 0, -1):
qs.setValue(i, phi.getValue(i + 1, int(qs.getValue(i + 1))))
result.insert(
0,
self.states[int(qs.getValue(i)) % self.stateCount].getState())
result.insert(
0, self.states[int(qs.getValue(1)) // self.stateCount].getState())
return result
def viterbi(self, s: list) -> list:
"""
viterbi calculates the most probable state sequence for a set of observed symbols.
PARAMETERS
----------
s : list
A set of observed symbols.
RETURNS
-------
list
The most probable state sequence as an {@link ArrayList}.
"""
result = []
sequenceLength = len(s)
gamma = Matrix(sequenceLength, self.stateCount)
phi = Matrix(sequenceLength, self.stateCount)
qs = Vector(sequenceLength, 0)
emission = s[0]
for i in range(self.stateCount):
observationLikelihood = self.states[i].getEmitProb(emission)
gamma.setValue(0, i, self.safeLog(self.__pi.getValue(i)) + self.safeLog(observationLikelihood))
for t in range(1, sequenceLength):
emission = s[t]
for j in range(self.stateCount):
tempArray = self.__logOfColumn(j)
tempArray.addVector(gamma.getRowVector(t - 1))
maxIndex = tempArray.maxIndex()
observationLikelihood = self.states[j].getEmitProb(emission)
gamma.setValue(t, j, tempArray.getValue(maxIndex) + self.safeLog(observationLikelihood))
phi.setValue(t, j, maxIndex)
qs.setValue(sequenceLength - 1, gamma.getRowVector(sequenceLength - 1).maxIndex())
result.insert(0, self.states[int(qs.getValue(sequenceLength - 1))].getState())
for i in range(sequenceLength - 2, -1, -1):
qs.setValue(i, phi.getValue(i + 1, int(qs.getValue(i + 1))))
result.insert(0, self.states[int(qs.getValue(i))].getState())
return result
class MatrixTest(unittest.TestCase):
def setUp(self):
self.small = Matrix(3, 3)
for i in range(3):
for j in range(3):
self.small.setValue(i, j, 1.0)
self.v = Vector(3, 1.0)
self.large = Matrix(1000, 1000)
for i in range(1000):
for j in range(1000):
self.large.setValue(i, j, 1.0)
self.medium = Matrix(100, 100)
for i in range(100):
for j in range(100):
self.medium.setValue(i, j, 1.0)
self.V = Vector(1000, 1.0)
self.vr = Vector(100, 1.0)
self.random = Matrix(100, 100, 1, 10, 1)
self.originalSum = self.random.sumOfElements()
self.identity = Matrix(100)
def test_ColumnWiseNormalize(self):
mClone = self.small.clone()
mClone.columnWiseNormalize()
self.assertEqual(3, mClone.sumOfElements())
MClone = self.large.clone()
MClone.columnWiseNormalize()
self.assertAlmostEqual(1000, MClone.sumOfElements(), 3)
self.identity.columnWiseNormalize()
self.assertEqual(100, self.identity.sumOfElements())
def test_MultiplyWithConstant(self):
self.small.multiplyWithConstant(4)
self.assertEqual(36, self.small.sumOfElements())
self.small.divideByConstant(4)
self.large.multiplyWithConstant(1.001)
self.assertAlmostEqual(1001000, self.large.sumOfElements(), 3)
self.large.divideByConstant(1.001)
self.random.multiplyWithConstant(3.6)
self.assertAlmostEqual(self.originalSum * 3.6,
self.random.sumOfElements(), 4)
self.random.divideByConstant(3.6)
def test_DivideByConstant(self):
self.small.divideByConstant(4)
self.assertEqual(2.25, self.small.sumOfElements())
self.small.multiplyWithConstant(4)
self.large.divideByConstant(10)
self.assertAlmostEqual(100000, self.large.sumOfElements(), 3)
self.large.multiplyWithConstant(10)
self.random.divideByConstant(3.6)
self.assertAlmostEqual(self.originalSum / 3.6,
self.random.sumOfElements(), 4)
self.random.multiplyWithConstant(3.6)
def test_Add(self):
self.random.add(self.identity)
self.assertAlmostEqual(self.originalSum + 100,
self.random.sumOfElements(), 4)
self.random.subtract(self.identity)
def test_AddVector(self):
self.large.addRowVector(4, self.V)
self.assertEqual(1001000, self.large.sumOfElements(), 0.0)
self.V.multiply(-1.0)
self.large.addRowVector(4, self.V)
self.V.multiply(-1.0)
def test_Subtract(self):
self.random.subtract(self.identity)
self.assertAlmostEqual(self.originalSum - 100,
self.random.sumOfElements(), 4)
self.random.add(self.identity)
def test_MultiplyWithVectorFromLeft(self):
result = self.small.multiplyWithVectorFromLeft(self.v)
self.assertEqual(9, result.sumOfElements())
result = self.large.multiplyWithVectorFromLeft(self.V)
self.assertEqual(1000000, result.sumOfElements())
result = self.random.multiplyWithVectorFromLeft(self.vr)
self.assertAlmostEqual(self.originalSum, result.sumOfElements(), 4)
def test_MultiplyWithVectorFromRight(self):
result = self.small.multiplyWithVectorFromRight(self.v)
self.assertEqual(9, result.sumOfElements())
result = self.large.multiplyWithVectorFromRight(self.V)
self.assertEqual(1000000, result.sumOfElements())
result = self.random.multiplyWithVectorFromRight(self.vr)
self.assertAlmostEqual(self.originalSum, result.sumOfElements(), 4)
def test_ColumnSum(self):
self.assertEqual(3, self.small.columnSum(randrange(3)))
self.assertEqual(1000, self.large.columnSum(randrange(1000)))
self.assertEqual(1, self.identity.columnSum(randrange(100)))
def test_SumOfRows(self):
self.assertEqual(9, self.small.sumOfRows().sumOfElements())
self.assertEqual(1000000, self.large.sumOfRows().sumOfElements())
self.assertEqual(100, self.identity.sumOfRows().sumOfElements())
self.assertAlmostEqual(self.originalSum,
self.random.sumOfRows().sumOfElements(), 3)
def test_RowSum(self):
self.assertEqual(3, self.small.rowSum(randrange(3)))
self.assertEqual(1000, self.large.rowSum(randrange(1000)))
self.assertEqual(1, self.identity.rowSum(randrange(100)))
def test_Multiply(self):
result = self.small.multiply(self.small)
self.assertEqual(27, result.sumOfElements())
result = self.medium.multiply(self.medium)
self.assertEqual(1000000.0, result.sumOfElements())
result = self.random.multiply(self.identity)
self.assertEqual(self.originalSum, result.sumOfElements())
result = self.identity.multiply(self.random)
self.assertEqual(self.originalSum, result.sumOfElements())
def test_ElementProduct(self):
result = self.small.elementProduct(self.small)
self.assertEqual(9, result.sumOfElements())
result = self.large.elementProduct(self.large)
self.assertEqual(1000000, result.sumOfElements())
result = self.random.elementProduct(self.identity)
self.assertEqual(result.trace(), result.sumOfElements())
def test_SumOfElements(self):
self.assertEqual(9, self.small.sumOfElements())
self.assertEqual(1000000, self.large.sumOfElements())
self.assertEqual(100, self.identity.sumOfElements())
self.assertEqual(self.originalSum, self.random.sumOfElements())
def test_Trace(self):
self.assertEqual(3, self.small.trace())
self.assertEqual(1000, self.large.trace())
self.assertEqual(100, self.identity.trace())
def test_Transpose(self):
self.assertEqual(9, self.small.transpose().sumOfElements())
self.assertEqual(1000000, self.large.transpose().sumOfElements())
self.assertEqual(100, self.identity.transpose().sumOfElements())
self.assertAlmostEqual(self.originalSum,
self.random.transpose().sumOfElements(), 3)
def test_IsSymmetric(self):
self.assertTrue(self.small.isSymmetric())
self.assertTrue(self.large.isSymmetric())
self.assertTrue(self.identity.isSymmetric())
self.assertFalse(self.random.isSymmetric())
def test_Determinant(self):
self.assertEqual(0, self.small.determinant())
self.assertEqual(0, self.large.determinant())
self.assertEqual(1, self.identity.determinant())
def test_Inverse(self):
self.identity.inverse()
self.assertEqual(100, self.identity.sumOfElements())
self.random.inverse()
self.random.inverse()
self.assertAlmostEqual(self.originalSum, self.random.sumOfElements(),
5)
def test_Characteristics(self):
vectors = self.small.characteristics()
self.assertEqual(2, len(vectors))
vectors = self.identity.characteristics()
self.assertEqual(100, len(vectors))
vectors = self.medium.characteristics()
self.assertEqual(46, len(vectors))
