| system_defaults = {} | |
| config_paths = [ | |
| Path.home()/'.myconfig', | |
| ] | |
| env_config = config_filter(os.environ) | |
| user_config = get_config(*config_paths) | |
| config = d_merge(env_config, user_config, system_defaults) |
| DROP VIEW IF EXISTS flat_links; | |
| CREATE TEMPORARY VIEW flat_links AS | |
| SELECT | |
| l.id as link_id, title, snippet, link, domain, rank, | |
| link_type, search_engine_name, page_number, | |
| requested_at, num_results_for_query, | |
| num_results, q | |
| FROM link l | |
| LEFT JOIN ( | |
| SELECT |
| #!/usr/bin/env ruby | |
| require 'json' | |
| require 'yaml' | |
| class AutoSuggest | |
| def self.call(*args) | |
| new(*args).call | |
| end |
s = Sample.new(5,8) # Uses a default 95% confidence level, drawing 5,000 samples
s.call # Returns a histogram of 5 entries
s.mean # Returns the calculated mean between 5 and 8
s.sample_mean # Returns the mean of the sample data
s.sigma # Returns the standard deviation, calculated from the confidence interval
s1 = Sample.new(5, 8, confidence: 0.8) # Changes the confidence level to 80%
Sample.call(5,8) # Cuts to the chase, just returns the histogram
Try something twice, with a probability of failure at 10%, record wether the cummulative result was failure. Do that 1 million times, and form a posterior belief about this situation.
TruthSampling.call(n_samples: 1_000_000, trials: 2, probability: 0.1)
Or, here's a basketball example. If I've got a 1/6 free throw average, and I take 4 free throws, what is the chance I make at least one of those 4?
TruthSampling.call(trials: 4, probability: 1/6.0)
{false=>0.48256, true=>0.51744}
This gives me about a 52% chance of making at least one basket.