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object ClassicRunner { | |
def run() = { | |
val input = Array( | |
Array( 0.0, 0.0 ), | |
Array( 1.0, 0.0 ), | |
Array( 0.0, 1.0 ), | |
Array( 1.0, 1.0 ) | |
) | |
val ideal = Array( |
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def run() = { | |
val input = | |
( 0.0 | 0.0 ) \\ | |
( 1.0 | 0.0 ) \\ | |
( 0.0 | 1.0 ) \\ | |
( 1.0 | 1.0 ) | |
val ideal = | |
Tuple1(0.0) \\ |
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val b = (1.0 | 2.0 | 3.0) \\ | |
(4.0 | 5.0 | 6.0) \\ | |
(7.0 | 8.0 | 9.0) | |
//gives | |
// 1.0 2.0 3.0 | |
// 4.0 5.0 6.0 | |
// 7.0 8.0 9.0 |
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implicit class EncogArray1[T](val elem1: T) { | |
def |(v: T) = new EncogArray2[T](elem1, v) | |
} | |
case class EncogArray2[T](elem1: T, elem2: T) { | |
def |(v: T) = new EncogArray3[T](elem1, elem2, v) | |
} | |
case class EncogArray3[T](elem1: T, elem2: T, elem3: T) { | |
def |(v: T) = new EncogArray4[T](elem1, elem2, elem3, v) | |
} | |
case class EncogArray4[T](elem1: T, elem2: T, elem3: T, elem4: T) { |
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implicit class EncogArray1[T](val elem1: T) { | |
def |(v: T) = new EncogArray2[T](elem1, v) | |
} | |
case class EncogArray2[T](elem1: T, elem2: T) { | |
def |(v: T) = new EncogArray3[T](elem1, elem2, v) | |
} | |
case class EncogArray3[T](elem1: T, elem2: T, elem3: T) { | |
def |(v: T) = new EncogArray4[T](elem1, elem2, elem3, v) | |
} | |
case class EncogArray4[T](elem1: T, elem2: T, elem3: T, elem4: T) { |
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val network = | |
(InputLayer having bias having 2.layers) + | |
(ActivationSigmoid having bias having 3.layers) + | |
(ActivationSigmoid having 1.layers) |
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val procedure = input into network using (d => new ResilientPropagation(d.network, d.data)) until (_ < 0.01) giving ideal | |
val trainResult = procedure.get() |
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val a = Array ( | |
Array(1,2,3), | |
Array(4,5), | |
Array(6) | |
) | |
//gives | |
// 1 2 3 | |
// 4 5 | |
// 6 |
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def receive: PartialFunction[Any, Unit] |
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Total { | |
case Eat(food) ⇒ | |
println(s"Tasty $food!") | |
Stopped | |
case Wait() ⇒ | |
Same | |
} |
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