Library and utility module for Bayesian reasoning

## Project description

bayesan is a small Python utility to reason about probabilities. It uses a Bayesian system to extract features, crunch belief updates and spew likelihoods back. You can use either the high-level functions to classify instances with supervised learning, or update beliefs manually with the Bayes class.

If you want to simply classify and move files into the most fitting folder, run this program from the command line passing the root folder path as parameter.

## High Level

```from bayesian import classify, classify_file

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})

# 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}]})

# 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"])

# Classifies every file under "folder" as either a Python or Java file,
# considering you have subdirectories with examples of each language.
print classify_folder("folder")
```

## Low Level

```from bayesian import Bayes

print ' -- Spam Filter --'
# Database with number of sightings of each words in (genuine, spam)
# emails.
words_odds = {'buy': (5, 100), 'viagra': (1, 1000), 'meeting': (15, 2)}
# Emails to be analyzed.
emails = [
"let's schedule a meeting for tomorrow", # 100% genuine (meeting)
"buy coffee for the meeting", # buy x meeting, should be genuine
]

for email in emails:
# Probabilities are normalized automatically.
b = Bayes([('genuine', 90), ('spam', 10)])
# Update probabilities, using the words in the emails as events and the
# database of chances to figure out the change.
b.update_from_events(email.split(), words_odds)
# Print the email and if it's likely spam or not.
print email[:15] + '...', b.most_likely()

print ''

print ' -- Spam Filter With Email Corpus -- '

# Email corpus. A hundred spam emails to buy products and with the word
# milk.
instances = {'spam': ["buy viagra", "buy cialis"] * 100 + ["meeting love"],
'genuine': ["meeting tomorrow", "buy milk"] * 100}

# Use str.split to extract features/events/words from the corpus and build
# the model.
model = bayesian.extract_events_odds(instances, str.split)
# Create a new Bayes instance with 10%/90% priors on emails being genuine.
b = Bayes({'spam': .9, 'genuine': .1})
# Update beliefs with features/events/words from an email.
# Print the email and if it's likely spam or not.
print "'buy coffee for meeting'", ':', b

print ''

print ' -- Classic Cancer Test Problem --'
# 1% chance of having cancer.
b = Bayes([('not cancer', 0.99), ('cancer', 0.01)])
# Test positive, 9.6% false positives and 80% true positives
b.update((9.6, 80))
print b
print 'Most likely:', b.most_likely()

print ''

print ' -- Are You Cheating? -- '
events_odds = {'heads': {'honest': .5, 'cheating': .9},
'tails': {'honest': .5, 'cheating': .1}}
b = Bayes({'cheating': .5, 'honest': .5})
b.update_from_events(results, events_odds)
print b

def b():
return Bayes((0.99, 0.01), labels=['not cancer', 'cancer'])

# Random equivalent examples, all achieve the same result.
b() * (9.6, 80)
(b() * (9.6, 80)).opposite().opposite()
b().update({'not cancer': 9.6, 'cancer': 80})
b().update((9.6, 80))
b().update_from_events(['pos'], {'pos': (9.6, 80)})
b().update_from_tests([True], [(9.6, 80)])
Bayes([('not cancer', 0.99), ('cancer', 0.01)]) * (9.6, 80)
Bayes({'not cancer': 0.99, 'cancer': 0.01}) * {'not cancer': 9.6,
'cancer': 80}
```

0.3.1

0.3.0

This version

0.2.1

0.2.0

0.1.0