def test_single(self):
self.assertEqual(classify_normal({'a': 100}, {'A': [{'a': 100}]}), 'A')
self.assertEqual(
classify_normal({
'a': 100,
'b': 0
}, {'A': [{
'a': 100,
'b': 0
}]}), 'A')
self.assertEqual(
classify_normal({
'a': 100,
'b': 0
}, {
'A': [{
'a': 100,
'b': 10
}],
'B': [{
'a': 50,
'b': 100
}]
}), None)
def test_single(self):
self.assertEqual(classify_normal({'a': 100}, {'A': [{'a': 100}]}), 'A')
self.assertEqual(classify_normal({'a': 100, 'b': 0},
{'A': [{'a': 100, 'b': 0}]}), 'A')
self.assertEqual(classify_normal({'a': 100, 'b': 0},
{'A': [{'a': 100, 'b': 10}],
'B': [{'a': 50, 'b': 100}]}), None)
def test_sample(self):
instance = {'height': 6, 'weight': 130, 'foot size': 8}
training = {'male': [{'height': 6, 'weight': 180, 'foot size': 12},
{'height': 5.92, 'weight': 190, 'foot size': 11},
{'height': 5.58, 'weight': 170, 'foot size': 12},
{'height': 5.92, 'weight': 165, 'foot size': 10}],
'female': [{'height': 5, 'weight': 100, 'foot size': 6},
{'height': 5.5, 'weight': 150, 'foot size': 8},
{'height': 5.42, 'weight': 130, 'foot size': 7},
{'height': 5.75, 'weight': 150, 'foot size': 9}]}
self.assertEqual(classify_normal(instance, training), 'female')
def test_basic(self):
self.assertEqual(
classify_normal({
'a': 100,
'b': 0
}, {
'A': [{
'a': 100,
'b': 10
}, {
'a': 99,
'b': -10
}],
'B': [{
'a': 50,
'b': 100
}, {
'a': 70,
'b': 90
}]
}), 'A')
def test_sample(self):
instance = {'height': 6, 'weight': 130, 'foot size': 8}
training = {
'male': [{
'height': 6,
'weight': 180,
'foot size': 12
}, {
'height': 5.92,
'weight': 190,
'foot size': 11
}, {
'height': 5.58,
'weight': 170,
'foot size': 12
}, {
'height': 5.92,
'weight': 165,
'foot size': 10
}],
'female': [{
'height': 5,
'weight': 100,
'foot size': 6
}, {
'height': 5.5,
'weight': 150,
'foot size': 8
}, {
'height': 5.42,
'weight': 130,
'foot size': 7
}, {
'height': 5.75,
'weight': 150,
'foot size': 9
}]
}
self.assertEqual(classify_normal(instance, training), 'female')
classify_normal({
'height': 6,
'weight': 130,
'foot size': 8
}, {
'male': [{
'height': 6,
'weight': 180,
'foot size': 12
}, {
'height': 5.92,
'weight': 190,
'foot size': 11
}, {
'height': 5.58,
'weight': 170,
'foot size': 12
}, {
'height': 5.92,
'weight': 165,
'foot size': 10
}],
'female': [{
'height': 5,
'weight': 100,
'foot size': 6
}, {
'height': 5.5,
'weight': 150,
'foot size': 8
}, {
'height': 5.42,
'weight': 130,
'foot size': 7
}, {
'height': 5.75,
'weight': 150,
'foot size': 9
}]
}))
def test_basic(self):
self.assertEqual(classify_normal({'a': 100, 'b': 0},
{'A': [{'a': 100, 'b': 10},
{'a': 99, 'b': -10}],
'B': [{'a': 50, 'b': 100},
{'a': 70, 'b':90}]}), 'A')
import sys
sys.path.append('../')
from bayesian import Bayes, classify_normal, classify
print(' == High Level Functions == ')
print(' -- Gender Classification -- ')
# Decides if the person with those measures is male or female.
print(classify_normal({'height': 6, 'weight': 130, 'foot size': 8},
{'male': [{'height': 6, 'weight': 180, 'foot size': 12},
{'height': 5.92, 'weight': 190, 'foot size': 11},
{'height': 5.58, 'weight': 170, 'foot size': 12},
{'height': 5.92, 'weight': 165, 'foot size': 10}],
'female': [{'height': 5, 'weight': 100, 'foot size': 6},
{'height': 5.5, 'weight': 150, 'foot size': 8},
{'height': 5.42, 'weight': 130, 'foot size': 7},
{'height': 5.75, 'weight': 150, 'foot size': 9}]}))
print('')
print(' -- Spam Detection With `Classify` -- ')
spams = ["buy viagra", "dear recipient", "meet sexy singles"] # etc
genuines = ["let's meet tomorrow", "remember to buy milk"]
message = "remember the meeting tomorrow"
# Classify as "genuine" because of the words "remember" and "tomorrow".
print(classify(message, {'spam': spams, 'genuine': genuines}))
# Classifies "unknown_file" as either a Python or Java file, considering
# you have directories with examples of each language.
#print classify_file("unknown_file", ["java_files", "python_files"])
