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randoms.take(5)
 // res19: List[Double] = List(
-//   0.06322474558935709,
-//   0.15609402869150868,
-//   0.048563819853992274,
-//   0.9964774224655545,
-//   0.05273481156233717
+//   0.9246483520671808,
+//   0.8429475077324543,
+//   0.5051365750397849,
+//   0.7439270457282824,
+//   0.8704369061893246
 // )
 randoms.take(5)
 // res20: List[Double] = List(
-//   0.31109055084494464,
-//   0.02944367942097581,
-//   0.9797134903454171,
-//   0.3377926423507678,
-//   0.970895232668675
+//   0.9549341688492303,
+//   0.5209021277635721,
+//   0.39921990512104844,
+//   0.09972557476179833,
+//   0.8137684300964437
 // )

Now let’s define the same stream in a call by need style, using lazy val.

@@ -9939,19 +9939,19 @@

randomsByNeed.take(5)
 // res21: List[Double] = List(
-//   0.1202904356773008,
-//   0.1202904356773008,
-//   0.1202904356773008,
-//   0.1202904356773008,
-//   0.1202904356773008
+//   0.39814040025905306,
+//   0.39814040025905306,
+//   0.39814040025905306,
+//   0.39814040025905306,
+//   0.39814040025905306
 // )
 randomsByNeed.take(5)
 // res22: List[Double] = List(
-//   0.1202904356773008,
-//   0.1202904356773008,
-//   0.1202904356773008,
-//   0.1202904356773008,
-//   0.1202904356773008
+//   0.39814040025905306,
+//   0.39814040025905306,
+//   0.39814040025905306,
+//   0.39814040025905306,
+//   0.39814040025905306
 // )

If we wanted a stream that had a different random number for each element but those numbers were constant, we could redefine @@ -9971,19 +9971,19 @@

randomsByNeed2.take(5)
 // res23: List[Double] = List(
-//   0.9632679540189799,
-//   0.6893150570552151,
-//   0.5876089342430793,
-//   0.04994820622475149,
-//   0.6924043170615442
+//   0.06483073722643784,
+//   0.44608565266616895,
+//   0.07548156383902804,
+//   0.23826389198640185,
+//   0.4155611089292368
 // )
 randomsByNeed2.take(5)
 // res24: List[Double] = List(
-//   0.9632679540189799,
-//   0.6893150570552151,
-//   0.5876089342430793,
-//   0.04994820622475149,
-//   0.6924043170615442
+//   0.06483073722643784,
+//   0.44608565266616895,
+//   0.07548156383902804,
+//   0.23826389198640185,
+//   0.4155611089292368
 // )

These subtleties are one of the reasons that functional programmers try to avoid using state as far as possible.

@@ -11100,7 +11100,7 @@

summon[JsonWriter[String]]
-// res6: JsonWriter[String] = repl.MdocSession$MdocApp3$JsonWriter$$anon$7@197fb774
+// res6: JsonWriter[String] = repl.MdocSession$MdocApp3$JsonWriter$$anon$7@2cf6d2e4

Most type classes in Cats provide other means to summon instances. However, summon is a good fallback for debugging purposes. We can insert a call to summon @@ -13125,7 +13125,7 @@

def show(date: Date): String = s"${date.getTime}ms since the epoch."
new Date().show
-// res2: String = "1734701336290ms since the epoch."
+// res2: String = "1734703803682ms since the epoch."

However, Cats also provides a couple of convenient methods to simplify the process. There are two construction methods on the companion object of Show that we can use to define @@ -13295,7 +13295,7 @@

x === x
 // res12: Boolean = true
 x === y
-// res13: Boolean = true
+// res13: Boolean = false

6.3.4.1 Exercise: Equality, Liberty, and Felinity

Implement an instance of Eq for our running @@ -14453,10 +14453,10 @@

val func = (x: Int) => x + 1
-// func: Function1[Int, Int] = repl.MdocSession$MdocApp0$$$Lambda$19794/0x000000080510a840@3645fd90
+// func: Function1[Int, Int] = repl.MdocSession$MdocApp0$$$Lambda$19831/0x0000000805120840@5009efa9
 
 val liftedFunc = Functor[Option].lift(func)
-// liftedFunc: Function1[Option[Int], Option[Int]] = cats.Functor$$Lambda$19795/0x000000080510b040@eda63d
+// liftedFunc: Function1[Option[Int], Option[Int]] = cats.Functor$$Lambda$19832/0x0000000805130040@42480809
 
 liftedFunc(Option(1))
 // res1: Option[Int] = Some(value = 2)
@@ -15159,7 +15159,7 @@

val func1 = (x: Int) => x.toDouble val func2 = (y: Double) => y * 2
val func3 = func1.map(func2)
-// func3: Function1[Int, Double] = cats.instances.Function1Instances0$$anon$11$$Lambda$19800/0x000000080510e840@42dc7cfe
+// func3: Function1[Int, Double] = cats.instances.Function1Instances0$$anon$11$$Lambda$19837/0x0000000805136840@5bfae2c3

Function1 has two type parameters (the function argument and the result type):

trait Function1[-A, +B] {
@@ -15325,7 +15325,7 @@ 

val func2b: Double <= Double = func2

val func3c = func2b.contramap(func1)
-// func3c: Function1[Int, Double] = scala.Function1$$Lambda$19902/0x0000000805119040@5271faf5
+// func3c: Function1[Int, Double] = scala.Function1$$Lambda$19915/0x0000000805183040@4336b67f

The difference between func2 and func2b is purely syntactic—both refer to the same value and the type aliases are otherwise completely compatible. Incredibly, however, @@ -15852,7 +15852,7 @@

import scala.concurrent.ExecutionContext.Implicits.global
val fm = Monad[Future]
-// fm: Monad[[T >: Nothing <: Any] => Future[T]] = cats.instances.FutureInstances$$anon$1@70600a32
+// fm: Monad[[T >: Nothing <: Any] => Future[T]] = cats.instances.FutureInstances$$anon$1@1b6a4744

The Monad instance uses the captured ExecutionContext for subsequent calls to pure and flatMap:

@@ -16493,12 +16493,12 @@

math.random() } // Computing X -// x: Double = 0.5046522842998294 +// x: Double = 0.6878660826864387 x // first access -// res0: Double = 0.5046522842998294 +// res0: Double = 0.6878660826864387 x // second access -// res1: Double = 0.5046522842998294 +// res1: Double = 0.6878660826864387

This is an example of call-by-value evaluation: