FilesExpand file tree

v1.20.0

/

randUtils.ts

Copy path

Blame

More file actions

Blame

More file actions

Latest commit

 

History

History

History

174 lines (154 loc) · 4.38 KB

v1.20.0

/

randUtils.ts

Copy path

Top

File metadata and controls

• Code
• Blame

174 lines (154 loc) · 4.38 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

/**

* @license

* Copyright 2021, JsData. All rights reserved.

*

* This source code is licensed under the MIT license found in the

* LICENSE file in the root directory of this source tree.

* Unless required by applicable law or agreed to in writing, software

* distributed under the License is distributed on an "AS IS" BASIS,

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

* See the License for the specific language governing permissions and

* limitations under the License.

* ==========================================================================

*/

export function _prng_restore(prng: any, xg: any, opts: any) {

let state = opts && opts.state

if (state) {

if (typeof state == 'object') xg.copy(state, xg)

prng.state = () => xg.copy(xg, {})

}

}

export function alea(seed?: any, opts?: any) {

let xg = new AleaGen(seed)

let prng = () => xg.next()

_prng_restore(prng, xg, opts)

return prng

}

class AleaGen {

c: number

s0: number

s1: number

s2: number

constructor(seed: any) {

if (seed == null) seed = +new Date()

let n = 0xefc8249d

// Apply the seeding algorithm from Baagoe.

this.c = 1

this.s0 = mash(' ')

this.s1 = mash(' ')

this.s2 = mash(' ')

this.s0 -= mash(seed)

if (this.s0 < 0) {

this.s0 += 1

}

this.s1 -= mash(seed)

if (this.s1 < 0) {

this.s1 += 1

}

this.s2 -= mash(seed)

if (this.s2 < 0) {

this.s2 += 1

}

function mash(data: any) {

data = String(data)

for (let i = 0; i < data.length; i++) {

n += data.charCodeAt(i)

let h = 0.02519603282416938 * n

n = h >>> 0

h -= n

h *= n

n = h >>> 0

h -= n

n += h * 0x100000000 // 2^32

}

return (n >>> 0) * 2.3283064365386963e-10 // 2^-32

}

}

next() {

let { c, s0, s1, s2 } = this

let t = 2091639 * s0 + c * 2.3283064365386963e-10 // 2^-32

this.s0 = s1

this.s1 = s2

return (this.s2 = t - (this.c = t | 0))

}

copy(f: any, t: any) {

t.c = f.c

t.s0 = f.s0

t.s1 = f.s1

t.s2 = f.s2

return t

}

}

export type int = number

/**

* Creates a new random number generator, optionally using the given seed.

*

* @param seed (Optional) the seed to be used.

* @returns A new RNG method which returns floats from range `[0,1)`.

*/

export const createRng = (seed?: number) =>

alea(seed?.toString()) as () => number

/**

* Take a uniform [0,1) random number generator (RNG) function and turns it into an

* integer RNG function.

*

* @param rand An RNG in the range of [0,1).

* @returns An RNG that takes an integer range (from, until) as arguments and returns

* a random integer in the range [from, until).

*/

export const randInt = (rand: () => number) => (from: int, until: int) => {

if (from >= until) {

throw new Error('randInt(rng)(from,until): from must be less than until.')

}

const result = Math.floor(from + (until - from) * rand())

if (result === until) {

return result - 1

}

return result

}

/**

* Generates a random latin hypercube sampling, i.e. unique samples whose

* features are evenly distributed over the range `[0,1)`. Latin hypercube

* sampling is useful to create random non-duplicate samples that are

* somewhat evenly distribute in the `[0,1)**nFeatures` hypercube.

*

* @see {@link https://en.wikipedia.org/wiki/Latin_hypercube_sampling}

*/

export const lhs = (rand: () => number) => (nSamples: int, nFeatures: int) => {

const _randInt = randInt(rand)

const result: Float64Array[] = []

// init columns to linspace(0,1, nSamples)

const normalizeDiv = nSamples - 1 || 1

for (let i = nSamples - 1; i >= 0; i--) {

const row = new Float64Array(nFeatures)

row.fill(i / normalizeDiv)

result.push(row)

}

// shuffle columns independently

for (let i = nSamples - 1; i >= 1; i--) {

let n = i + 1

const ri = result[i]

for (let j = nFeatures - 1; j >= 0; j--) {

const k = _randInt(0, n)

const rij = ri[j]

ri[j] = result[k][j]

result[k][j] = rij

}

}

return result

}

interface Indexable<T> {

[i: number]: T

readonly length: number

}

export const shuffle =

(rand: () => number) =>

<T>(array: Indexable<T>) => {

const _randInt = randInt(rand)

for (let i = array.length; i > 1; i--) {

const j = _randInt(0, i)

const tmp = array[i - 1]

array[i - 1] = array[j]

array[j] = tmp

}

}