# public bjornbm /normaldistribution

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commit 84fa93d56e9c4c01a6998ebb4f18d5bd40239310 1 parent 9779cb4
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Showing 2 changed files with 5 additions and 5 deletions.

1. Data/Random/Normal.hs
2. normaldistribution.cabal
8  Data/Random/Normal.hs
 `@@ -39,7 +39,7 @@ Internally the library uses the Box-Muller method to generate` 39 39 ` normally distributed values from uniformly distributed random values.` 40 40 ` If more than one sample is needed taking samples off an infinite` 41 41 ` list (created by e.g. 'normals') will be roughly twice as efficient` 42 `-as repetedly generating individual samples with e.g. 'normal'.` 42 `+as repeatedly generating individual samples with e.g. 'normal'.` 43 43 ` ` 44 44 ` -}` 45 45 ` ` `@@ -91,20 +91,20 @@ boxMullers _ = []` 91 91 ` -- ===` 92 92 ` -- | Takes a random number generator g, and returns a random value` 93 93 ` -- normally distributed with mean 0 and standard deviation 1,` 94 `--- together with a new generator. This function is ananalogous to` 94 `+-- together with a new generator. This function is analogous to` 95 95 ` -- 'Random.random'.` 96 96 ` normal :: (RandomGen g, Random a, Floating a) => g -> (a,g)` 97 97 ` normal g0 = (fst \$ boxMuller u1 u2, g2)` 98 98 ` -- While The Haskell 98 report says "For fractional types, the` 99 99 ` -- range is normally the semi-closed interval [0,1)" we will` 100 `- -- specify the range explicitely just to be sure.` 100 `+ -- specify the range explicitly just to be sure.` 101 101 ` where` 102 102 ` (u1,g1) = randomR (0,1) g0` 103 103 ` (u2,g2) = randomR (0,1) g1` 104 104 ` ` 105 105 ` -- | Plural variant of 'normal', producing an infinite list of` 106 106 ` -- random values instead of returning a new generator. This function` 107 `--- is ananalogous to 'Random.randoms'.` 107 `+-- is analogous to 'Random.randoms'.` 108 108 ` normals :: (RandomGen g, Random a, Floating a) => g -> [a]` 109 109 ` normals = boxMullers . randoms` 110 110 ` `
2  normaldistribution.cabal
 `@@ -47,7 +47,7 @@ Description:` 47 47 ` normally distributed values from uniformly distributed random values.` 48 48 ` If more than one sample is needed taking samples off an infinite` 49 49 ` list (created by e.g. 'normals') will be roughly twice as efficient` 50 `- as repetedly generating individual samples with e.g. 'normal'.` 50 `+ as repeatedly generating individual samples with e.g. 'normal'.` 51 51 ` ` 52 52 ` Category: Math, Statistics` 53 53 ` Build-Type: Simple`