def main():
from random import seed
from probability import genProbs
#### Unit test for training funciton
# Initiatializes seed for recurrent testing.
for _ in range(10):
dimen = (4, 3, 6, 5, 6)
classValues = 5
measures = MeasurementGenerator(dimen)
classes = ClassAssign(dimen, classValues)
conds = [list(genProbs(measures.range)) for _ in range(classValues)]
egain = [[2, 0, 0, 0, 1], [3, 4, 0, 2, 2], [2, 2, 5, 1, 1],
[2, 2, 3, 4, 1], [0, 1, -3, 2, 3]]
classifier = BayesClassifier(
None, conds, eGain=egain
) # Worries that supplying similar priors is affecting our results. Even though vFold updates.
y = train(classifier, 20, measures, classes, 6000, delta=.0005)
z = [y[i] - y[i - 1] for i in range(1, len(y))]
# Trying to figure out average negative error to see if this is floating point.
print(y)
print()
print(z)
q = [i for i in z if i < 0]
q = sum(q) / max(len(q), 1)
print(q)
print()
x = measures.genMeas(20)
p = classes.assign(x)
l = classifier.assign(x)
def __init__(self, dimen: Tuple[int]):
self.dimen = dimen
# Caclulates the size of space
self.range = 1
for N in self.dimen:
self.range *= N
# Generates probabilities for later measurements
self.cmlProbs = genCmlProbs(genProbs(self.range))
def genCCP(K: int, dimen: Tuple[int]) -> List[List[float]]: #### LINEAR ARRAY?
# Generates a randomly alotted class conditional probability array.
# Array is of size K, each index represents a corresponding class conditional probability list
# and each list is of size of measure space represented by dimen.
# Generates M space length.
N = 1
for n in dimen:
N *= n
# Generates K normalized probability tables; one for each class.
classProbs = [list(genProbs(N)) for _ in range(K)]
return classProbs
def __init__(self, dimen: Tuple[int], K: int):
## Dimen is representation of measurement space M
## K is amount of classes to be assigned
self.dimen = dimen
self.probs = genProbs(K) # For testing
self.cmlProbs = genCmlProbs(self.probs)
self.range = K
# Amount of possible measures is cardinality of M as represented by dimen
self.mSpaceSize = 1
for N in dimen:
self.mSpaceSize *= N
self.tags = None
self.genTags()
def main():
#### Unit tests
####### Tests for sampling function.
# Tests for distributions to be within range
for i in range(1, 1000):
sampSize = randint(1, 1000)
spaceRang = randint(3, 1000)
a = genProbs(spaceRang)
b = genCmlProbs(a)
c = genSamples(sampSize, b)
for j in c:
assert j <= (spaceRang - 1) and j >= 0
# Tests for suitable randomness so same samples aren't being generated.
for _ in range(1000):
sampSize = randint(2, 1000)
spaceRang = randint(3, 1000) # Provides sufficient space for variety
a = genProbs(spaceRang)
b = genCmlProbs(a)
c = genSamples(sampSize, b)
z = genSamples(sampSize, b)
assert c != z, c
#####Tests genMeas method
# Checks for measurement space to be of proper length
Z = randint(2, 1000)
dimen = genSpace(6)
size = randint(2, 1000)
generator = MeasurementGenerator(dimen)
x = generator.genMeas(size)
assert len(x) == size
# Checks for values to actually exist in measurement space
for _ in range(1, 1000):
maxValue = 1
for i in dimen:
maxValue *= i
for j in x:
assert j <= maxValue - 1, maxValue - j
###### Tests ClassAssign class
# Verifies no class is assigned outside class values
for _ in range(1000):
dimen = genSpace(5)
maxValue = 1
for i in dimen:
maxValue *= i
tagSize = randint(1, 100)
tagAssign = ClassAssign(dimen, tagSize)
for j in tagAssign.tags:
assert j <= tagSize - 1, (j, tagSize - 1)
# Verfies assignment funciton is of proper length.
assert len(tagAssign.tags) == maxValue