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#p(Pass|Bad, A, High) = p(Pass) * p(Bad|Pass) * p(A|Pass) * p(High|Pass)
#p(Fail|Bad, A, High) = p(Fail) * p(Bad|Fail) * p(A|Fail) * p(High|Fail)
dataset = [
{"Assignment": "Good", "Project": "A", "Exam": "High", "Label": "Pass"},
{"Assignment": "Good", "Project": "B", "Exam": "High", "Label": "Pass"},
{"Assignment": "Bad", "Project": "B", "Exam": "Low", "Label": "Fail"},
{"Assignment": "Bad", "Project": "C", "Exam": "High", "Label": "Fail"},
{"Assignment": "Good", "Project": "C", "Exam": "Low", "Label": "Fail"},
{"Assignment": "Good", "Project": "C", "Exam": "High", "Label": "Pass"},
{"Assignment": "Bad", "Project": "B", "Exam": "High", "Label": "Pass"},
{"Assignment": "Good", "Project": "A", "Exam": "Low", "Label": "Pass"},
{"Assignment": "Bad", "Project": "A", "Exam": "Low", "Label": "Fail"},
{"Assignment": "Good", "Project": "B", "Exam": "Low", "Label": "Pass"}
]
#P(c=ci)
def prior(c,ci):
total = len(dataset)
count = 0.0
for student in dataset:
if(student[c] is not None and student[c] == ci):
count+=1
return count/total
#P(f=fi|c=ci)
def likelihood(f, fi, c, ci):
c_count = 0.0
f_count = 0.0
for student in dataset:
if(student[c] is not None and student[c] == ci):
if(student[f] is not None and student[f] == fi):
f_count+=1
c_count+=1
if(c_count > 0.0):
return f_count/c_count
return None
#P(C=ci|f1,f2,f3,...fn) = p(ci) * p(f1|ci) * p(f2|ci) * ... * p(fn|ci)
def posterior(c,ci, feature_dictonary):
p_c = prior(c, ci)
for key in feature_dictonary.keys():
p_c *= likelihood(key,feature_dictonary[key],c,ci)
return p_c
print("Probability for passing:{0}".format(posterior("Label","Pass",{"Assignment": "Bad", "Project": "A", "Exam": "High"})))
print("Probability for failing:{0}".format(posterior("Label","Fail",{"Assignment": "Bad", "Project": "A", "Exam": "High"}))) |