This question already has answers here:
Why are floating point numbers inaccurate?
(5 answers)
Closed 2 years ago.
This question is a follow on from a previous question I asked. The answers I received suggested that I make use of the Go math.Big library. In this question I use the library but unfortunately to little effect.
I am trying to using the Binet formula to calculate fib(100). I am using
Go's Big.Float but without success. I get accuracy to about 10 decimal
places. Please advise.
I am trying to avoid loops/recursion as I think these approaches will
not scale well. Hence my attempt to leverage Binet's formula
// currently produces inaccurate results as the input increases.
package main
import (
"fmt"
"math/big"
"math"
"strconv"
)
func fib(n int) float64 {
var sroot5 = new(big.Float).SetPrec(200).SetFloat64(2.236067977499789696409173668731276235440618359611525724270897)
var phi = new(big.Float).SetPrec(200).SetFloat64(1.61803398874989484820458683436563811772030917980576286213544862)
var minusPhi = new(big.Float).SetPrec(200).SetFloat64(-0.61803398874989484820458683436563811772030917980576)
var fltP float64;
fltP, _ = phi.Float64()
var fltN float64;
fltN, _ = minusPhi.Float64()
var denom float64
denom, _ = sroot5.Float64()
// Magic fib formula (Binet) is:
// (Phi ^ n - (-phi ^ n)) / sqrt(5)
z := (math.Pow(fltP, float64(n)) - math.Pow(fltN, float64(n))) / denom
return math.Ceil(z)
}
func main() {
fib(100)
fmt.Println(strconv.FormatFloat(fib(100), 'f', 0, 64))
fmt.Println("true answer of fib(100) should be -> 354224848179261915075")
}
You are using IEEE 754 64-bit floating point.
In Go, to calculate fib(100) accurately you could simply say:
package main
import (
"fmt"
"math/big"
)
func fib(n int) *big.Int {
f := big.NewInt(0)
a, b := big.NewInt(0), big.NewInt(1)
for i := 0; i <= n; i++ {
f.Set(a)
a.Set(b)
b.Add(f, b)
}
return f
}
func main() {
fmt.Println(fib(100))
}
Output:
354224848179261915075
Related
This question already has answers here:
Can you declare multiple variables at once in Go?
(8 answers)
Closed 5 years ago.
when i write:
package main
import (
"fmt"
)
func Sqrt(x float64) float64 {
var ret float64
for z := 1.0, n := 0;n < 10;n++ {
ret = z - (z*z - x) / 2*z
}
return ret
}
func main() {
fmt.Println(Sqrt(2))
}
syntax error: z := 1.0, n used as value.
and bring
z := 1.0
out of the for block below
package main
import (
"fmt"
)
func Sqrt(x float64) float64 {
var ret float64
z := 1.0
for n := 0;n < 10;n++ {
ret = z - (z*z - x) / 2*z
}
return ret
}
func main() {
fmt.Println(Sqrt(2))
}
it's ok so how can i define two variables in init of the for block?
A For clause has an Init Statement which is a Simple Statement, including only one Assigment
So in your case, you cannot declare multiple variable with different type/values. You could use a tuple assignment though
for z, n := 1.0, 0; n < 10; n++ {
(playground)
When printing out some values from a map of structs. I see certain float64 values with alternative notation. The test passes but how do you read this notation (4e-06). Is this value indeed the same as "0.000004"?
package main
import (
"fmt"
"strconv"
"testing"
)
func TestXxx(t *testing.T) {
num := fmt.Sprintf("%f", float64(1.225788)-float64(1.225784)) // 0.000004
f, _ := strconv.ParseFloat(num, 64)
if f == 0.000004 {
t.Log("Success")
} else {
t.Error("Not Equal", num)
}
if getFloat(f) == 0.000004 {
t.Log("Success")
}else{
t.Error("Fail", getFloat(f))
}
}
func getFloat(f float64) float64 {
fmt.Println("My Float:",f) // 4e-06
return f
}
The notation is called Scientific notation, and it is a convenient way to print very small or very large numbers in compact, short form.
It has the form of
m × 10n
(m times ten raised to the power of n)
In programming languages it is written / printed as:
men
See Spec: Floating-point literals.
Your number: 4e-06, where m=4 and n=-6, which means 4*10-6 which equals to 0.000004.
In order to print your floats in a regular way you can do something like this example:
package main
import (
"fmt"
"strconv"
)
func main() {
a, _ := strconv.ParseFloat("0.000004", 64)
b, _ := strconv.ParseFloat("0.0000000004", 64)
c := fmt.Sprintf("10.0004")
cc, _ := strconv.ParseFloat(c, 64)
fmt.Printf("%.6f\n", a) // 6 numbers after the point
fmt.Printf("%.10f\n", b) // 10 numbers afer the point
fmt.Printf("%.4f\n", cc) // 4 numbers after the point
}
Output:
0.000004
0.0000000004
10.0004
It is the same number. You can use fmt.Printf("My Float: %.6f\n",f) if you don't like the scientific notation. (This format requests that 6 digits will be printed after the decimal point.)
I'm newbie in Golan, this should be an easy question for experienced golang devs. I try to do the same test from Spotify to see how fast we can go in Golang :)
The usual bit-twiddling C solutions translate immediately to Go.
package main
import "fmt"
func BitReverse32(x uint32) uint32 {
x = (x&0x55555555)<<1 | (x&0xAAAAAAAA)>>1
x = (x&0x33333333)<<2 | (x&0xCCCCCCCC)>>2
x = (x&0x0F0F0F0F)<<4 | (x&0xF0F0F0F0)>>4
x = (x&0x00FF00FF)<<8 | (x&0xFF00FF00)>>8
return (x&0x0000FFFF)<<16 | (x&0xFFFF0000)>>16
}
func main() {
cases := []uint32{0x1, 0x100, 0x1000, 0x1000000, 0x10000000, 0x80000000, 0x89abcdef}
for _, c := range cases {
fmt.Printf("%08x -> %08x\n", c, BitReverse32(c))
}
}
Note: since 2013, you now have a dedicate math/bits package with Go 1.9 (August 2017).
And it does come with a collection of Reverse() and ReverseBytes() functions: no need to implement one anymore.
Plus, on most architectures, functions in this package are additionally recognized by the compiler and treated as intrinsics for additional performance.
The most straight-forward solution would be converting the bits into a number with strconv and then reversing the number by shifting the bits. I'm not sure how fast it would be, but it should work.
package main
import "fmt"
import "strconv"
func main() {
bits := "10100001"
bits_number := 8
number, _ := strconv.ParseUint(bits, 2, bits_number)
r_number := number - number // reserve type
for i := 0; i < bits_number; i++ {
r_number <<= 1
r_number |= number & 1
number >>= 1
}
fmt.Printf("%s [%d]\n", strconv.FormatUint(r_number, 2), r_number)
}
http://play.golang.org/p/YLS5wkY-iv
I'm writing a cube root function in Google Go using Newton's method. I want to check the results using math/cmplx.Pow(), but for the life of me, I can't figure out how. How do I do this?
Have you tried myCubicRootOfx = Pow(x, 1.0/3) ?
edited: thanks to Jason McCreary comment:
We cannot use 1/3 as the 2nd parameter to Pow as this is a integer division and hence doesn't produce the expected 1/3 value. By using 1.0/3 or 1/3.0 etc. we effectively produce a float with the 0.333333... value.
I wrote the cube root function using Newton's method as part of the Go Tour Exercise 47. Perhaps the two functions below (Cbrt1 and Cbrt) are helpful.
package main
import (
"fmt"
"math/cmplx"
)
// Newton's method cube root function that hopes for
// convergence within 20 iterations
func Cbrt1(x complex128) complex128 {
var z complex128 = x
for i:= 0; i < 20; i++ {
z = z - ((z*z*z - x) / (3.0*z*z))
}
return z
}
// Newton's method cube root function that runs until stable
func Cbrt(x complex128) complex128 {
var z, z0 complex128 = x, x
for {
z = z - ((z*z*z - x) / (3.0*z*z))
if cmplx.Abs(z - z0) < 1e-10 {
break
}
z0 = z
}
return z
}
func main() {
fmt.Println(Cbrt(2.0) , "should match" , cmplx.Pow(2, 1.0/3.0))
}
As you're using Newton's method, I suppose you're starting with a positive real number.
So you don't need complex numbers.
You may simply do
package main
import (
"fmt"
"math"
)
func main() {
x := 100.0
root := math.Pow(x, 1.0/3.0)
fmt.Println(root)
}
For example,
package main
import (
"fmt"
"math/cmplx"
)
func main() {
var x complex128
x = -8
y := cmplx.Pow(x, 1.0/3.0)
fmt.Println(y)
x = -27i
y = cmplx.Pow(x, 1.0/3.0)
fmt.Println(y)
x = -8 - 27i
y = cmplx.Pow(x, 1.0/3.0)
fmt.Println(y)
x = complex(-8, -27)
y = cmplx.Pow(x, 1.0/3.0)
fmt.Println(y)
}
Output:
(1+1.732050807568877i)
(2.5980762113533156-1.4999999999999996i)
(2.4767967587776756-1.7667767800295509i)
(2.4767967587776756-1.7667767800295509i)
The Go Programming Language Specification
Package cmplx
Looks like go has changed more recently than some of the other answers, so I figured I would update - they built cuberoot right into math as Cbrt. It takes, and returns, a float64. A quick reference is at godoc math | grep Cbrt (if you've got godoc installed, which I highly recommend)
import "math"
func main() {
var x float64 = math.Cbrt(8)
fmt.Println(x) // prints 2
}
try something like this
package main
import(
"fmt"
"math/cmplx"
)
func Cbrt(x complex128) complex128 {
z := complex128(1)
for i:=0;i<100;i++ { // OR JUST for{ since you will outrun complex128 in worth case
last_z := z
z = z - ((z*z*z - x)/(3 *z*z))
if last_z == z{
return z
}
}
return z
}
func main() {
fmt.Println("good enough", Cbrt(9))
fmt.Println("builtin", cmplx.Pow(9, 1.0/3.0))
}
I am trying to generate random numbers (integers) in Go, to no avail. I found the rand package in crypto/rand, which seems to be what I want, but I can't tell from the documentation how to use it. This is what I'm trying right now:
b := []byte{}
something, err := rand.Read(b)
fmt.Printf("something = %v\n", something)
fmt.Printf("err = %v\n", err)
But unfortunately this always outputs:
something = 0
err = <nil>
Is there a way to fix this so that it actually generates random numbers? Alternatively, is there a way to set the upper bound on the random numbers this generates?
Depending on your use case, another option is the math/rand package. Don't do this if you're generating numbers that need to be completely unpredictable. It can be helpful if you need to get results that are reproducible, though -- just pass in the same seed you passed in the first time.
Here's the classic "seed the generator with the current time and generate a number" program:
package main
import (
"fmt"
"math/rand"
"time"
)
func main() {
rand.Seed(time.Now().Unix())
fmt.Println(rand.Int())
}
crypto/rand provides only binary stream of random data, but you can read integers from it using encoding/binary:
package main
import "encoding/binary"
import "crypto/rand"
func main() {
var n int32
binary.Read(rand.Reader, binary.LittleEndian, &n)
println(n)
}
As of 1 april 2012, after the release of the stable version of the lang, you can do the following:
package main
import "fmt"
import "time"
import "math/rand"
func main() {
rand.Seed(time.Now().UnixNano()) // takes the current time in nanoseconds as the seed
fmt.Println(rand.Intn(100)) // this gives you an int up to but not including 100
}
You can also develop your own random number generator, perhaps based upon a simple "desert island PRNG", a Linear Congruential Generator. Also, look up L'Ecuyer (1999), Mersenne Twister, or Tausworthe generator...
https://en.wikipedia.org/wiki/Pseudorandom_number_generator
(Avoid RANDU, it was popular in the 1960's, but the random numbers generated fall on 15 hyperplanes in 3-space).
package pmPRNG
import "errors"
const (
Mersenne31 = 2147483647 // = 2^31-1
Mersenne31Inv = 1.0 / 2147483647.0 // = 4.656612875e-10
// a = 16807
a = 48271
)
// Each stream gets own seed
type PRNGStream struct {
state int
}
func PRNGStreamNew(seed int) *PRNGStream {
prng := (&PRNGStream{})
prng.SetSeed(seed)
return prng
}
// enforce seed in [1, 2^31-1]
func (r*PRNGStream) SetSeed(seed int) error {
var err error
if seed < 1 || seed > Mersenne31 {
err = errors.New("Seed OOB")
}
if seed > Mersenne31 { seed = seed % Mersenne31 }
if seed < 1 { seed = 1 }
r.state = seed
return err
}
// Dig = Park-Miller DesertIslandGenerator
// integer seed in [1, 2^31-1]
func (r*PRNGStream) Dig(seed int) float32 {
xprev := r.state // x[i-1]
xnext := (a * xprev) % Mersenne31 // x[i] = (a*x[i-1])%m
r.state = xnext // x[i-1] = x[i]
Ri := float32(xnext) * Mersenne31Inv // convert Ui to Ri
return Ri
}
func (r*PRNGStream) Rand() float32 {
r.state = (uint64_t)*r.state * Multby % 0x7fffffff
return float32(r.state) * Mersenne31Inv
}
A few relevant links:
https://en.wikipedia.org/wiki/Lehmer_random_number_generator
You might use this function to update your x[i+1], instead of the one above,
val = ((state * 1103515245) + 12345) & 0x7fffffff
(basically, different values of a, c, m)
https://www.redhat.com/en/blog/understanding-random-number-generators-and-their-limitations-linux
https://www.iro.umontreal.ca/~lecuyer/myftp/papers/handstat.pdf
https://www.math.utah.edu/~alfeld/Random/Random.html
https://learn.microsoft.com/en-us/archive/msdn-magazine/2016/august/test-run-lightweight-random-number-generation