def query_model(self):
if flip(0.9):
self.a = uniform(0, 1)
else:
self.a = 0.7
self.b = flip(self.a)
def query_model(self):
self.breast_cancer = flip(0.01)
if self.breast_cancer:
self.positive_mammogram = flip(0.8)
else:
self.positive_mammogram = flip(0.096)
def query_model(self):
if flip(0.9):
self.a = beta(1, 5)
else:
self.a = 0.7
self.b = flip(self.a)
def query_model(self):
if flip(0.9):
self.a = uniform(0, 1)
else:
self.a = 0.7
self.b = flip(self.a)
def query_model(self):
self.breast_cancer = flip(0.01)
if self.breast_cancer:
self.positive_mammogram = flip(0.8)
else:
self.positive_mammogram = flip(0.096)
def query_model(self):
if flip(0.9):
self.a = beta(1, 5)
else:
self.a = 0.7
self.b = flip(self.a)
def query_model(self):
if flip(0.7):
self.proc = self.proc1
else:
self.proc = self.proc2
self.samp = self.proc(1)
def query_model(self):
if flip(0.9):
self.a = exponential(2.0)
else:
self.a = 0.7
self.b = gaussian(self.a, 1.0)
def query_model(self):
if flip(0.7):
self.proc = self.proc1
else:
self.proc = self.proc2
self.samp = self.proc(1)
def query_model(self):
if flip(0.9):
self.a = exponential(2.0)
else:
self.a = 0.7
self.b = gaussian(self.a, 1.0)
def query_model(self):
if flip():
self.hypothesis = BIG
else:
self.hypothesis = SMALL
self.observations = [uniform_draw(self.hypothesis_set(self.hypothesis)) for i in xrange(len(self.data))]
self.observations.sort()
def query_model(self):
if flip():
self.hypothesis = BIG
else:
self.hypothesis = SMALL
self.observations = [
uniform_draw(self.hypothesis_set(self.hypothesis))
for i in xrange(len(self.data))
]
self.observations.sort()
def query_model(self):
self.lung_cancer = flip(0.01)
self.TB = flip(0.005)
self.cold = flip(0.2)
self.stomach_flu = flip(0.1)
self.other = flip(0.1)
self.cough = (self.cold and flip(0.5)) or \
(self.lung_cancer and flip(0.3)) or \
(self.TB and flip(0.7)) or \
(self.other and flip(0.01))
self.fever = (self.cold and flip(0.3)) or \
(self.stomach_flu and flip(0.5)) or \
(self.TB and flip(0.2)) or \
(self.other and flip(0.01))
self.chest_pain = (self.lung_cancer and flip(0.4)) or \
(self.TB and flip(0.5)) or \
(self.other and flip(0.01))
self.shortness_of_breath = (self.lung_cancer and flip(0.4)) or \
(self.TB and flip(0.5)) or \
(self.other and flip(0.01))
def query_model(self):
self.hyp = flip(0.7)
def proc2(self, x):
return flip(0.8)
def query_model(self):
if flip(0.7):
weight = 0.2
else:
weight = 0.8
self.val = flip(weight)
import pystoch
from pystoch import flip
result = []
for sample in xrange(SAMPLES):
result.append(int(flip(0.7)))
exresult = 0.7
def bit_flip(self, fidelity, x):
if x:
return flip(fidelity)
else:
return flip(1 - fidelity)
def sample(self):
if flip(0.7):
weight = 0.2
else:
weight = 0.8
return flip(weight)
def query_model(self):
self.a = flip(0.7)
def query_model(self):
self.val = flip()
def coin(self, weight):
if flip(weight):
return 'H'
return 'T'
def query_model(self):
self.hyp = flip(0.7)
self.cond = self.bit_flip(0.8, self.hyp)
def sample(self):
return int(flip(0.7))
def query_model(self):
self.lung_cancer = flip(0.01)
self.TB = flip(0.005)
self.cold = flip(0.2)
self.stomach_flu = flip(0.1)
self.other = flip(0.1)
self.cough = (
(self.cold and flip(0.5))
or (self.lung_cancer and flip(0.3))
or (self.TB and flip(0.7))
or (self.other and flip(0.01))
)
self.fever = (
(self.cold and flip(0.3))
or (self.stomach_flu and flip(0.5))
or (self.TB and flip(0.2))
or (self.other and flip(0.01))
)
self.chest_pain = (self.lung_cancer and flip(0.4)) or (self.TB and flip(0.5)) or (self.other and flip(0.01))
self.shortness_of_breath = (
(self.lung_cancer and flip(0.4)) or (self.TB and flip(0.5)) or (self.other and flip(0.01))
)
def query_model(self):
if flip(0.7):
weight = 0.2
else:
weight = 0.8
self.val = flip(weight)
def proc1(self, x):
return flip(0.2)
def query_model(self):
self.A = int(flip(self.baserate))
self.B = int(flip(self.baserate))
self.C = int(flip(self.baserate))
self.D = self.A + self.B + self.C
def query_model(self):
self.a = flip(0.7)
def sample(self):
return int(flip(0.7))
def coin(self, weight):
if flip(weight):
return 'H'
return 'T'
def condition(self):
return flip(self.a)
def power_law(self, prob, x):
if flip(prob):
return x
else:
return self.power_law(prob, x + 1)
def query_model(self):
self.val = flip()
def query_model(self):
if flip(0.7):
self.val = flip(0.2)
else:
self.val = flip(0.8)
def proc2(self, x):
return flip(0.8)
def query_model(self):
self.a = flip()
self.b = flip()
def proc1(self, x):
return flip(0.2)
def bit_flip(self, fidelity, x):
if x:
return flip(fidelity)
else:
return flip(1 - fidelity)
def query_model(self):
self.hyp = flip(0.7)
def sample(self):
if flip(0.7):
return flip(0.2)
else:
return flip(0.8)
def query_model(self):
self.val = int(flip(0.7))
def query_model(self):
self.a = flip()
self.b = flip()
def condition(self):
return flip(self.a)
def power_law(self, prob, x):
if flip(prob):
return x
else:
return self.power_law(prob, x + 1)
def query_model(self):
if flip(0.7):
self.val = flip(0.2)
else:
self.val = flip(0.8)
def sample(self):
if flip(0.7):
return flip(0.2)
else:
return flip(0.8)
def sample(self):
if flip(0.7):
weight = 0.2
else:
weight = 0.8
return flip(weight)