def part2a_0():
"""Check that you have all the features we expect"""
xs = exampleInput
phi = Counter({('-BEGIN-', '-FEAT-'): 1.0, ('-FEAT-', 'Beautiful'): 1.0, ('-FEAT-', 'PREV:-BEGIN-'): 1.0, ('-FEAT-', 'NEXT:2'): 1.0, ('-FEAT-', '-CAPITALIZED-'): 1.0, ('-FEAT-', '-POST-CAPITALIZED-'): 0.0})
phi_ = submission.nerFeatureFunction(0, '-BEGIN-', '-FEAT-', xs)
grader.requireIsTrue( Counters.approximateEquals(phi, phi_) )
phi = Counter({('-FEAT-', '-SIZE-'): 1.0, ('-SIZE-', 'PREV:Beautiful'): 1.0, ('-SIZE-', 'NEXT:bedroom'): 1.0, ('-SIZE-', '-PRE-CAPITALIZED-'): 1.0, ('-SIZE-', '2'): 1.0, ('-SIZE-', '-POST-CAPITALIZED-'): 0.0, ('-SIZE-', '-CAPITALIZED-'): 0.0})
phi_ = submission.nerFeatureFunction(1, '-FEAT-', '-SIZE-', xs)
grader.requireIsTrue( Counters.approximateEquals(phi, phi_) )
phi = Counter({('-SIZE-', '-SIZE-'): 1.0, ('-SIZE-', 'PREV:2'): 1.0, ('-SIZE-', 'bedroom'): 1.0, ('-SIZE-', 'NEXT:-END-'): 1.0, ('-SIZE-', '-CAPITALIZED-'): 0.0, ('-SIZE-', '-PRE-CAPITALIZED-'): 0.0})
phi_ = submission.nerFeatureFunction(2, '-SIZE-', '-SIZE-', xs)
grader.requireIsTrue( Counters.approximateEquals(phi, phi_) )
def part1b_3():
"""Check that values match our reference"""
xs = exampleInput
backward = [
Counter({'-SIZE-': 0.564, '-FEAT-': 0.435}),
Counter({'-SIZE-': 0.567, '-FEAT-': 0.432}),
Counter({'-FEAT-': 0.5, '-SIZE-': 0.5})]
backward_ = submission.computeBackward(simpleCRF, xs)
for vec, vec_ in zip( backward, backward_):
grader.requireIsTrue( Counters.approximateEquals( vec, vec_ ) )
def part1c_1():
"""Check that values match our reference"""
xs = exampleInput
T = [ Counter({('-BEGIN-', '-FEAT-'): 0.561, ('-BEGIN-', '-SIZE-'): 0.439}),
Counter({('-FEAT-', '-SIZE-'): 0.463, ('-SIZE-', '-SIZE-'): 0.343,
('-SIZE-', '-FEAT-'): 0.096, ('-FEAT-', '-FEAT-'): 0.096}),
Counter({('-SIZE-', '-SIZE-'): 0.590, ('-SIZE-', '-FEAT-'): 0.217,
('-FEAT-', '-SIZE-'): 0.151, ('-FEAT-', '-FEAT-'): 0.041})
]
T_ = submission.computeEdgeMarginals(simpleCRF, xs)
for t, t_ in zip(T, T_):
grader.requireIsTrue( Counters.approximateEquals(t, t_) )
def part2a_0():
"""Check that you have all the features we expect"""
xs = exampleInput
phi = Counter({
('-BEGIN-', '-FEAT-'): 1.0,
('-FEAT-', 'Beautiful'): 1.0,
('-FEAT-', 'PREV:-BEGIN-'): 1.0,
('-FEAT-', 'NEXT:2'): 1.0,
('-FEAT-', '-CAPITALIZED-'): 1.0,
('-FEAT-', '-POST-CAPITALIZED-'): 0.0
})
phi_ = submission.nerFeatureFunction(0, '-BEGIN-', '-FEAT-', xs)
grader.requireIsTrue(Counters.approximateEquals(phi, phi_))
phi = Counter({
('-FEAT-', '-SIZE-'): 1.0,
('-SIZE-', 'PREV:Beautiful'): 1.0,
('-SIZE-', 'NEXT:bedroom'): 1.0,
('-SIZE-', '-PRE-CAPITALIZED-'): 1.0,
('-SIZE-', '2'): 1.0,
('-SIZE-', '-POST-CAPITALIZED-'): 0.0,
('-SIZE-', '-CAPITALIZED-'): 0.0
})
phi_ = submission.nerFeatureFunction(1, '-FEAT-', '-SIZE-', xs)
grader.requireIsTrue(Counters.approximateEquals(phi, phi_))
phi = Counter({
('-SIZE-', '-SIZE-'): 1.0,
('-SIZE-', 'PREV:2'): 1.0,
('-SIZE-', 'bedroom'): 1.0,
('-SIZE-', 'NEXT:-END-'): 1.0,
('-SIZE-', '-CAPITALIZED-'): 0.0,
('-SIZE-', '-PRE-CAPITALIZED-'): 0.0
})
phi_ = submission.nerFeatureFunction(2, '-SIZE-', '-SIZE-', xs)
grader.requireIsTrue(Counters.approximateEquals(phi, phi_))
def part1b_3():
"""Check that values match our reference"""
xs = exampleInput
backward = [
Counter({
'-SIZE-': 0.564,
'-FEAT-': 0.435
}),
Counter({
'-SIZE-': 0.567,
'-FEAT-': 0.432
}),
Counter({
'-FEAT-': 0.5,
'-SIZE-': 0.5
})
]
backward_ = submission.computeBackward(simpleCRF, xs)
for vec, vec_ in zip(backward, backward_):
grader.requireIsTrue(Counters.approximateEquals(vec, vec_))
def part1c_1():
"""Check that values match our reference"""
xs = exampleInput
T = [
Counter({
('-BEGIN-', '-FEAT-'): 0.561,
('-BEGIN-', '-SIZE-'): 0.439
}),
Counter({
('-FEAT-', '-SIZE-'): 0.463,
('-SIZE-', '-SIZE-'): 0.343,
('-SIZE-', '-FEAT-'): 0.096,
('-FEAT-', '-FEAT-'): 0.096
}),
Counter({
('-SIZE-', '-SIZE-'): 0.590,
('-SIZE-', '-FEAT-'): 0.217,
('-FEAT-', '-SIZE-'): 0.151,
('-FEAT-', '-FEAT-'): 0.041
})
]
T_ = submission.computeEdgeMarginals(simpleCRF, xs)
for t, t_ in zip(T, T_):
grader.requireIsTrue(Counters.approximateEquals(t, t_))
