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* Stream-based runner Additionally, restricted the possible Monad instances that can be used with the `repeat` function and also generalized `repeat` to work for both `Step` and `StepS` structures. * Allow for custom measurements Measurements that are collected from a collection and then saved as a case class, will automatically have a parquet schema derived and as a result will be able to be saved to disk in parquet format. * Force string identifiers for algorithm and problem The user is now forced to define a name for the algorithm and the problem so that they can be identified in the result output. It's not the best strategy, but it is a valid one until a better scheme is devised. * Disallow empty algorithm and problem names Move empty literals for algorithm and problem names to compile time errors. * Adjust the collection after environment changes Missed acutally using the `onChange` handler in the interpretter.
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@@ -4,8 +4,6 @@ package eda | |
import scalaz._ | ||
import Scalaz._ | ||
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import spire.implicits._ | ||
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object EDA { | ||
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def eda[M, S, A]( | ||
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package cilib | ||
package exec | ||
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import eu.timepit.refined.api.Refined | ||
import eu.timepit.refined.auto._ | ||
import eu.timepit.refined.numeric._ | ||
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sealed abstract class Env | ||
final case object Unchanged extends Env | ||
final case object Change extends Env | ||
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object Env { | ||
def constant: Stream[Env] = | ||
Stream(Unchanged) #::: constant | ||
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def frequency[A](n: Int Refined Positive): Stream[Env] = | ||
(constant.take(n - 1) #::: Stream[Env](Change)) #::: frequency(n) | ||
} |
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package cilib | ||
package exec | ||
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import com.sksamuel.avro4s._ | ||
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final case class Measurement[A: SchemaFor](alg: String, | ||
prob: String, | ||
iteration: Int, | ||
env: Env, | ||
seed: Long, | ||
data: A) |
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package cilib | ||
package exec | ||
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import scalaz.Monad | ||
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abstract class MonadStep[M[_]: Monad] { | ||
def pointR[A](r: RVar[A]): M[A] | ||
} |
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