Given a collection of observations it returns the most likely. An observation is anything that responds to to_a Array or that responds to to_h.
As an example imagine a person flashing at a distance a card displaying a number from 1 to 10. If five people are observing this remote scene and note their observations during 4 rounds we have
observer_1 = {first: 8, second: 6, third: 1, fourth: 4}
observer_2 = {first: 8, second: 6, third: 7, fourth: 4}
observer_3 = {first: 8, second: 9, third: 7, fourth: 4}
observer_4 = {first: 8, second: 9, third: 2, fourth: 8}
observer_5 = {first: 0, second: 2, third: 0, fourth: 1}The probable outcome seems to be 8-(6 or 9)-7-4. So the best observation
would either be 8-6-7-4 or 8-9-7-4.
Using Observance we determine the most likely observation(s) by doing:
observations = Observance.run(observer_1, observer_2, observer_3,
observer_4, observer_5)
observations[0].rating #=> 0.55
observations[0].object #=> observer_2
observations[1].rating #=> 0.55
observations[1].object #=> observer_3
observations[2].rating #=> 0.5
observations[3].rating #=> 0.4
observations[4].rating #=> 0.2As expected it gives us that observer_2 and observer_3 are equally the most likely to have happened.
Add gem 'observance' to your Gemfile or run gem install observance
This gem uses minitest. Just run bundle exec rake to test.
- Handle observations of different sizes (although makes less sense)
- Observations can be JSON documents since
Observancehandles nested hashes - Filter which set of keys to run
Observanceon
Observance needs as input collections of observations. An observation is anything
that responds to to_a or to_h.
For instance Hashes or Arrays:
o1 = {first_toss: 'head', second_toss: 'tail', third_toss: 'tail'}
o2 = {first_toss: 'head', second_toss: 'tail', third_toss: 'tail'}
o3 = {first_toss: 'tail', second_toss: 'tail', third_toss: 'tail'}
Observance.run(o1, o2, o3)
o1 = ['head', 'tail', 'tail']
o2 = ['head', 'tail', 'tail']
o3 = ['tail', 'tail', 'tail']
Observance.run(o1, o2, o3)Since a observation object is anything that responds to to_a or to_h, it
can also be a parsed JSON, or a slightly modified Struct as follows:
# Observation as a slightly modified Struct
Scores = Struct.new(:period_1, :period_2, :period_3, :period_4) do
def to_h
Hash[members.zip(to_a)]
end
end
tom_observations = Scores.new(5, 6, 7, 4)
jane_observations = Scores.new(5, 5, 7, 4)
bill_observations = Scores.new(5, 6, 6, 4)
results = Observance.run(tom_observations, jane_observations, bill_observations)
results.first #=> Tom sample is the most likely with a rating of 0.8333Running Observance, it returns a sorted Array of Observance::Observation objects. Each Observance::Observation contains the original observation object (usually a hash) along with a rating (a float number between 0 and 1).
The higher the rating the better the observation.
The results are sorted by rating - with the highest rating (most likely) being the first.
5 people observe a coin being tossed 5 times. Each sends their observations as an Array
o1 = ['head', 'tail', 'tail', 'head', 'tail']
o2 = ['tail', 'tail', 'tail', 'head', 'tail']
o3 = ['head', 'head', 'tail', 'head', 'tail']
o4 = ['head', 'head', 'head', 'head', 'tail']
o5 = ['head', 'tail', 'tail', 'tail', 'head']
results = Observance.run(o1, o2, o3, o4, o5)
results.class # => Array
puts results
#<struct Observance::Observation rating=0.76, object=["head", "tail", "tail", "head", "tail"], index=0>
#<struct Observance::Observation rating=0.72, object=["head", "head", "tail", "head", "tail"], index=2>
#<struct Observance::Observation rating=0.64, object=["tail", "tail", "tail", "head", "tail"], index=1>
#<struct Observance::Observation rating=0.6, object=["head", "head", "head", "head", "tail"], index=3>
#<struct Observance::Observation rating=0.52, object=["head", "tail", "tail", "tail", "head"], index=4>
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