Provides a series of implicit conversions to make the specification of regression models cleaner.
Each feature in the Regression model constructs an Iterable[(String, Double)]. Once each feature constructs
the iterable, the regression model maps this to a new one prefixed by the feature name. For instance, in the
example that follows, "intercept" would emit a value of type Long which would become a function of type
com.eharmony.aloha.semantics.func.GenAggFunc [A, Long]. This however doesn't match the expected
output type of com.eharmony.aloha.semantics.func.GenAggFunc [A, Iterable[(String, Double)] ].
Conversions are provide for {Byte, Short, Int, Long, Float, Double} and the Option equivalents so that can
produce specify the translate the JSON key-value pair "intercept": "1234L" to Iterable(("", 1234.0)), which
when prefixed will yield Iterable(("intercept", 1234.0))
Provides a series of implicit conversions to make the specification of regression models cleaner.
Each feature in the Regression model constructs an Iterable[(String, Double)]. Once each feature constructs the iterable, the regression model maps this to a new one prefixed by the feature name. For instance, in the example that follows, "intercept" would emit a value of type Long which would become a function of type com.eharmony.aloha.semantics.func.GenAggFunc [A, Long]. This however doesn't match the expected output type of com.eharmony.aloha.semantics.func.GenAggFunc [A, Iterable[(String, Double)] ]. Conversions are provide for {Byte, Short, Int, Long, Float, Double} and the Option equivalents so that can produce specify the translate the JSON key-value pair "intercept": "1234L" to Iterable(("", 1234.0)), which when prefixed will yield Iterable(("intercept", 1234.0))