Installation
gem install glowfish
Setup
require "glowfish"
glower = Glowfish::API.new('<GLOWFISH_SID>', '<GLOWFISH_AUTH_TOKEN>')
Useage
Get ready for some simple machine learning...
Training
response = glower.train({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
}, { # the response set
'class': [4, 3, 5, 6, ...etc]
}, {...}) # config options
Predict It's important to note that predicting will throw an error if you have not trained against a data set first.
response = glower.predict({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
}, { # the response set
'class': [4, 3, 5, 6, ...etc]
}, {...}) # config options
Clustering
response = glower.cluster({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
}, {...}) # config options
Feature Selection
response = glower.feature_select({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
}, { # the response set
'class': [4, 3, 5, 6, ...etc]
}, {...}) # config options
Filter Train
response = glower.filter_train(
[1, 2, 3, 4, ...etc] #userids,
[1, 2, 3, 4, ...etc] #productids,
[1, 2, 3, 4, ...etc] #ratings
)
Filter Predict
response = glower.filter_predict(
[1, 2, 3, 4, ...etc] #userids,
[1, 2, 3, 4, ...etc] #productids,
[1, 2, 3, 4, ...etc] #ratings
)
The Response
<Response> {
@code = [200-500]
@message = "STRING MESSAGE FOR API RETURN"
@data = {...data...}
@errors = {...key value errors...}
@metrics = {...timing and counting metrics from the API...}
}
Further Documentation
Docs - http://glowfish.readme.io/
Registration - http://glowfi.sh/