// sincos iterates a sin or cos Taylor series.
func sincos(name string, c Context, index int, x *big.Float, z *big.Float, exp uint64, factorial *big.Float) *big.Float {
term := newFloat(c).Set(floatOne)
for j := 0; j < index; j++ {
term.Mul(term, x)
}
xN := newFloat(c).Set(term)
x2 := newFloat(c).Mul(x, x)
n := newFloat(c)
for loop := newLoop(c.Config(), name, x, 4); ; {
// Invariant: factorial holds -1ⁿ*exponent!.
factorial.Neg(factorial)
term.Quo(term, factorial)
z.Add(z, term)
if loop.done(z) {
break
}
// Advance x**index (multiply by x²).
term.Mul(xN, x2)
xN.Set(term)
// Advance factorial.
factorial.Mul(factorial, n.SetUint64(exp+1))
factorial.Mul(factorial, n.SetUint64(exp+2))
exp += 2
}
return z
}
// Sqrt returns the square root n.
func Sqrt(n *big.Float) *big.Float {
prec := n.Prec()
x := new(big.Float).SetPrec(prec).SetInt64(1)
z := new(big.Float).SetPrec(prec).SetInt64(1)
half := new(big.Float).SetPrec(prec).SetFloat64(0.5)
t := new(big.Float).SetPrec(prec)
for {
z.Copy(x)
t.Mul(x, x)
t.Sub(t, n)
t.Quo(t, x)
t.Mul(t, half)
x.Sub(x, t)
if x.Cmp(z) == 0 {
break
}
}
return x
}
func renderFloat(img *image.RGBA) {
var yminF, ymaxMinF, heightF big.Float
yminF.SetInt64(ymin)
ymaxMinF.SetInt64(ymax - ymin)
heightF.SetInt64(height)
var xminF, xmaxMinF, widthF big.Float
xminF.SetInt64(xmin)
xmaxMinF.SetInt64(xmax - xmin)
widthF.SetInt64(width)
var y, x big.Float
for py := int64(0); py < height; py++ {
// y := float64(py)/height*(ymax-ymin) + ymin
y.SetInt64(py)
y.Quo(&y, &heightF)
y.Mul(&y, &ymaxMinF)
y.Add(&y, &yminF)
for px := int64(0); px < width; px++ {
// x := float64(px)/width*(xmax-xmin) + xmin
x.SetInt64(px)
x.Quo(&x, &widthF)
x.Mul(&x, &xmaxMinF)
x.Add(&x, &xminF)
c := mandelbrotFloat(&x, &y)
if c == nil {
c = color.Black
}
img.Set(int(px), int(py), c)
}
}
}
// sincos iterates a sin or cos Taylor series.
func sincos(name string, index int, x, z, exponent, factorial *big.Float) *big.Float {
plus := false
term := newF().Set(floatOne)
for j := 0; j < index; j++ {
term.Mul(term, x)
}
xN := newF().Set(term)
x2 := newF().Mul(x, x)
loop := newLoop(name, x, 4)
for {
// Invariant: factorial holds exponent!.
term.Quo(term, factorial)
if plus {
z.Add(z, term)
} else {
z.Sub(z, term)
}
plus = !plus
if loop.terminate(z) {
break
}
// Advance x**index (multiply by x²).
term.Mul(xN, x2)
xN.Set(term)
// Advance exponent and factorial.
exponent.Add(exponent, floatOne)
factorial.Mul(factorial, exponent)
exponent.Add(exponent, floatOne)
factorial.Mul(factorial, exponent)
}
return z
}
// Compute the square root of n using Newton's Method. We start with
// an initial estimate for sqrt(n), and then iterate
// x_{i+1} = 1/2 * ( x_i + (n / x_i) )
// Result is returned in x
func (e *Pslq) Sqrt(n, x *big.Float) {
if n == x {
panic("need distinct input and output")
}
if n.Sign() == 0 {
x.Set(n)
return
} else if n.Sign() < 0 {
panic("Sqrt of negative number")
}
prec := n.Prec()
// Use the floating point square root as initial estimate
nFloat64, _ := n.Float64()
x.SetPrec(prec).SetFloat64(math.Sqrt(nFloat64))
// We use t as a temporary variable. There's no need to set its precision
// since big.Float values with unset (== 0) precision automatically assume
// the largest precision of the arguments when used as the result (receiver)
// of a big.Float operation.
var t big.Float
// Iterate.
for {
t.Quo(n, x) // t = n / x_i
t.Add(x, &t) // t = x_i + (n / x_i)
t.Mul(&e.half, &t) // x_{i+1} = 0.5 * t
if x.Cmp(&t) == 0 {
// Exit loop if no change to result
break
}
x.Set(&t)
}
}
// Multiply
func (a Scalar) Mul_S(b S) S {
var x, y big.Float
x = big.Float(a)
y = big.Float(b.(Scalar))
z := x.Mul(&x, &y)
return (Scalar)(*z)
}
func abs(x *complexFloat) *big.Float {
r := new(big.Float).SetPrec(prec)
i := new(big.Float).SetPrec(prec)
r.Copy(x.r)
i.Copy(x.i)
r.Mul(r, x.r) // r^2
i.Mul(i, x.i) // i^2
r.Add(r, i) // r^2 + i^2
return sqrtFloat(r) // sqrt(r^2 + i^2)
}
// Sqrt returns a big.Float representation of the square root of
// z. Precision is the same as the one of the argument. The function
// panics if z is negative, returns ±0 when z = ±0, and +Inf when z =
// +Inf.
func Sqrt(z *big.Float) *big.Float {
// panic on negative z
if z.Sign() == -1 {
panic("Sqrt: argument is negative")
}
// √±0 = ±0
if z.Sign() == 0 {
return big.NewFloat(float64(z.Sign()))
}
// √+Inf = +Inf
if z.IsInf() {
return big.NewFloat(math.Inf(+1))
}
// Compute √(a·2**b) as
// √(a)·2**b/2 if b is even
// √(2a)·2**b/2 if b > 0 is odd
// √(0.5a)·2**b/2 if b < 0 is odd
//
// The difference in the odd exponent case is due to the fact that
// exp/2 is rounded in different directions when exp is negative.
mant := new(big.Float)
exp := z.MantExp(mant)
switch exp % 2 {
case 1:
mant.Mul(big.NewFloat(2), mant)
case -1:
mant.Mul(big.NewFloat(0.5), mant)
}
// Solving x² - z = 0 directly requires a Quo call, but it's
// faster for small precisions.
//
// Solving 1/x² - z = 0 avoids the Quo call and is much faster for
// high precisions.
//
// Use sqrtDirect for prec <= 128 and sqrtInverse for prec > 128.
var x *big.Float
if z.Prec() <= 128 {
x = sqrtDirect(mant)
} else {
x = sqrtInverse(mant)
}
// re-attach the exponent and return
return x.SetMantExp(x, exp/2)
}
// sqrt for big.Float
func sqrt(given *big.Float) *big.Float {
const prec = 200
steps := int(math.Log2(prec))
given.SetPrec(prec)
half := new(big.Float).SetPrec(prec).SetFloat64(0.5)
x := new(big.Float).SetPrec(prec).SetInt64(1)
t := new(big.Float)
for i := 0; i <= steps; i++ {
t.Quo(given, x)
t.Add(x, t)
t.Mul(half, t)
}
return x
}
func mandelbrotFloat(a, b *big.Float) color.Color {
var x, y, nx, ny, x2, y2, f2, f4, r2, tmp big.Float
f2.SetInt64(2)
f4.SetInt64(4)
x.SetInt64(0)
y.SetInt64(0)
defer func() { recover() }()
for n := uint8(0); n < iterations; n++ {
// Not update x2 and y2
// because they are already updated in the previous loop
nx.Sub(&x2, &y2)
nx.Add(&nx, a)
tmp.Mul(&x, &y)
ny.Mul(&f2, &tmp)
ny.Add(&ny, b)
x.Set(&nx)
y.Set(&ny)
x2.Mul(&x, &x)
y2.Mul(&y, &y)
r2.Add(&x2, &y2)
if r2.Cmp(&f4) > 0 {
return color.Gray{255 - contrast*n}
}
}
return color.Black
}
// compute √z using newton to solve
// t² - z = 0 for t
func sqrtDirect(z *big.Float) *big.Float {
// f(t)/f'(t) = 0.5(t² - z)/t
half := big.NewFloat(0.5)
f := func(t *big.Float) *big.Float {
x := new(big.Float).Mul(t, t) // x = t²
x.Sub(x, z) // x = t² - z
x.Mul(half, x) // x = 0.5(t² - z)
return x.Quo(x, t) // return x = 0.5(t² - z)/t
}
// initial guess
zf, _ := z.Float64()
guess := big.NewFloat(math.Sqrt(zf))
return newton(f, guess, z.Prec())
}
// hypot for big.Float
func hypot(p, q *big.Float) *big.Float {
// special cases
switch {
case p.IsInf() || q.IsInf():
return big.NewFloat(math.Inf(1))
}
p = p.Abs(p)
q = q.Abs(q)
if p.Cmp(p) < 0 {
p, q = q, p
}
if p.Cmp(big.NewFloat(0)) == 0 {
return big.NewFloat(0)
}
q = q.Quo(q, p)
return sqrt(q.Mul(q, q).Add(q, big.NewFloat(1))).Mul(q, p)
}
func sqrtFloat(x *big.Float) *big.Float {
t1 := new(big.Float).SetPrec(prec)
t2 := new(big.Float).SetPrec(prec)
t1.Copy(x)
// Iterate.
// x{n} = (x{n-1}+x{0}/x{n-1}) / 2
for i := 0; i <= steps; i++ {
if t1.Cmp(zero) == 0 || t1.IsInf() {
return t1
}
t2.Quo(x, t1)
t2.Add(t2, t1)
t1.Mul(half, t2)
}
return t1
}
// Exp returns a big.Float representation of exp(z). Precision is
// the same as the one of the argument. The function returns +Inf
// when z = +Inf, and 0 when z = -Inf.
func Exp(z *big.Float) *big.Float {
// exp(0) == 1
if z.Sign() == 0 {
return big.NewFloat(1).SetPrec(z.Prec())
}
// Exp(+Inf) = +Inf
if z.IsInf() && z.Sign() > 0 {
return big.NewFloat(math.Inf(+1)).SetPrec(z.Prec())
}
// Exp(-Inf) = 0
if z.IsInf() && z.Sign() < 0 {
return big.NewFloat(0).SetPrec(z.Prec())
}
guess := new(big.Float)
// try to get initial estimate using IEEE-754 math
zf, _ := z.Float64()
if zfs := math.Exp(zf); zfs == math.Inf(+1) || zfs == 0 {
// too big or too small for IEEE-754 math,
// perform argument reduction using
// e^{2z} = (e^z)²
halfZ := new(big.Float).Mul(z, big.NewFloat(0.5))
halfExp := Exp(halfZ.SetPrec(z.Prec() + 64))
return new(big.Float).Mul(halfExp, halfExp).SetPrec(z.Prec())
} else {
// we got a nice IEEE-754 estimate
guess.SetFloat64(zfs)
}
// f(t)/f'(t) = t*(log(t) - z)
f := func(t *big.Float) *big.Float {
x := new(big.Float)
x.Sub(Log(t), z)
return x.Mul(x, t)
}
x := newton(f, guess, z.Prec())
return x
}
// This example shows how to use big.Float to compute the square root of 2 with
// a precision of 200 bits, and how to print the result as a decimal number.
func Example_sqrt2() {
// We'll do computations with 200 bits of precision in the mantissa.
const prec = 200
// Compute the square root of 2 using Newton's Method. We start with
// an initial estimate for sqrt(2), and then iterate:
// x_{n+1} = 1/2 * ( x_n + (2.0 / x_n) )
// Since Newton's Method doubles the number of correct digits at each
// iteration, we need at least log_2(prec) steps.
steps := int(math.Log2(prec))
// Initialize values we need for the computation.
two := new(big.Float).SetPrec(prec).SetInt64(2)
half := new(big.Float).SetPrec(prec).SetFloat64(0.5)
// Use 1 as the initial estimate.
x := new(big.Float).SetPrec(prec).SetInt64(1)
// We use t as a temporary variable. There's no need to set its precision
// since big.Float values with unset (== 0) precision automatically assume
// the largest precision of the arguments when used as the result (receiver)
// of a big.Float operation.
t := new(big.Float)
// Iterate.
for i := 0; i <= steps; i++ {
t.Quo(two, x) // t = 2.0 / x_n
t.Add(x, t) // t = x_n + (2.0 / x_n)
x.Mul(half, t) // x_{n+1} = 0.5 * t
}
// We can use the usual fmt.Printf verbs since big.Float implements fmt.Formatter
fmt.Printf("sqrt(2) = %.50f\n", x)
// Print the error between 2 and x*x.
t.Mul(x, x) // t = x*x
fmt.Printf("error = %e\n", t.Sub(two, t))
// Output:
// sqrt(2) = 1.41421356237309504880168872420969807856967187537695
// error = 0.000000e+00
}
// return: x^y
func Pow(x *big.Float, n int64) *big.Float {
res := new(big.Float).Copy(x)
if n < 0 {
res = res.Quo(big.NewFloat(1), res)
n = -n
} else if n == 0 {
return big.NewFloat(1)
}
y := big.NewFloat(1)
for i := n; i > 1; {
if i%2 == 0 {
i /= 2
} else {
y = y.Mul(res, y)
i = (i - 1) / 2
}
res = res.Mul(res, res)
}
return res.Mul(res, y)
}
// Estimates the Variance based on the data set
// If the data set is relatively small (< 1000 examples), then remove 1 from the total
func (ad *AnomalyDetection) estimateVariance() *big.Float {
// this means that the mean was never calculated before, therefore do it now
// the means is needed for the cimputation of the deviation
if ad.mean.Cmp(zero) == 0 {
ad.estimateMean()
}
// initialize the total to zero
totalVariance := big.NewFloat(0)
totalDeviation := big.NewFloat(0)
var deviation big.Float
var deviationCopy big.Float
var singleVariance big.Float
// Loop while a is smaller than 1e100.
for _, element := range ad.dataSet {
// first calculate the deviation for each element, by subtracting the mean, take the absolute value
deviation.Sub(&element, &ad.mean).Abs(&deviation)
// add it to the total
totalDeviation.Add(totalDeviation, &deviation)
// calculate the variance by squaring it
singleVariance = *deviationCopy.Mul(&deviation, &deviation) // ^2
// the calculate the variance
totalVariance.Add(totalVariance, &singleVariance)
}
// calculate the variance
// assign the variance to the anomaly detection object
ad.variance = *totalVariance.Quo(totalVariance, &ad.totalSamples)
// calculate the deviation
ad.deviation = *totalDeviation.Quo(totalDeviation, &ad.totalSamples)
return &ad.variance
}
func mul(x, y *complexFloat) *complexFloat {
t1 := new(big.Float).SetPrec(prec)
t2 := new(big.Float).SetPrec(prec)
z := newComplexFloat()
t1.Mul(x.r, y.r)
t2.Mul(x.i, y.i)
t2.Neg(t2)
z.r.Add(t1, t2)
t1.Mul(x.r, y.i)
t2.Mul(x.i, y.r)
z.i.Add(t1, t2)
return z
}
// Pow returns a big.Float representation of z**w. Precision is the same as the one
// of the first argument. The function panics when z is negative.
func Pow(z *big.Float, w *big.Float) *big.Float {
if z.Sign() < 0 {
panic("Pow: negative base")
}
// Pow(z, 0) = 1.0
if w.Sign() == 0 {
return big.NewFloat(1).SetPrec(z.Prec())
}
// Pow(z, 1) = z
// Pow(+Inf, n) = +Inf
if w.Cmp(big.NewFloat(1)) == 0 || z.IsInf() {
return new(big.Float).Copy(z)
}
// Pow(z, -w) = 1 / Pow(z, w)
if w.Sign() < 0 {
x := new(big.Float)
zExt := new(big.Float).Copy(z).SetPrec(z.Prec() + 64)
wNeg := new(big.Float).Neg(w)
return x.Quo(big.NewFloat(1), Pow(zExt, wNeg)).SetPrec(z.Prec())
}
// w integer fast path
if w.IsInt() {
wi, _ := w.Int64()
return powInt(z, int(wi))
}
// compute w**z as exp(z log(w))
x := new(big.Float).SetPrec(z.Prec() + 64)
logZ := Log(new(big.Float).Copy(z).SetPrec(z.Prec() + 64))
x.Mul(w, logZ)
x = Exp(x)
return x.SetPrec(z.Prec())
}
// Evaluates a BBP term
//
// sum(k=0->inf)(1/base**k * (1/a*k + b))
func bbp(prec uint, base, a, b int64, result *big.Float) {
var term, power, aFp, bFp, _1, k, _base, oldresult big.Float
power.SetPrec(prec).SetInt64(1)
result.SetPrec(prec).SetInt64(0)
aFp.SetPrec(prec).SetInt64(a)
bFp.SetPrec(prec).SetInt64(b)
_1.SetPrec(prec).SetInt64(1)
k.SetPrec(prec).SetInt64(0)
_base.SetPrec(prec).SetInt64(base)
for {
oldresult.Set(result)
term.Mul(&aFp, &k)
term.Add(&term, &bFp)
term.Quo(&_1, &term)
term.Mul(&term, &power)
result.Add(result, &term)
if oldresult.Cmp(result) == 0 {
break
}
power.Quo(&power, &_base)
k.Add(&k, &_1)
}
}
// fast path for z**w when w is an integer
func powInt(z *big.Float, w int) *big.Float {
// get mantissa and exponent of z
mant := new(big.Float)
exp := z.MantExp(mant)
// result's exponent
exp = exp * w
// result's mantissa
x := big.NewFloat(1).SetPrec(z.Prec())
// Classic right-to-left binary exponentiation
for w > 0 {
if w%2 == 1 {
x.Mul(x, mant)
}
w >>= 1
mant.Mul(mant, mant)
}
return new(big.Float).SetMantExp(x, exp)
}
// compute √z using newton to solve
// 1/t² - z = 0 for x and then inverting.
func sqrtInverse(z *big.Float) *big.Float {
// f(t)/f'(t) = -0.5t(1 - zt²)
nhalf := big.NewFloat(-0.5)
one := big.NewFloat(1)
f := func(t *big.Float) *big.Float {
u := new(big.Float)
u.Mul(t, t) // u = t²
u.Mul(u, z) // u = zt²
u.Sub(one, u) // u = 1 - zt²
u.Mul(u, nhalf) // u = -0.5(1 - zt²)
return new(big.Float).Mul(t, u) // x = -0.5t(1 - zt²)
}
// initial guess
zf, _ := z.Float64()
guess := big.NewFloat(1 / math.Sqrt(zf))
// There's another operation after newton,
// so we need to force it to return at least
// a few guard digits. Use 32.
x := newton(f, guess, z.Prec()+32)
return x.Mul(z, x).SetPrec(z.Prec())
}
func bbpTerm(terms chan<- *big.Float, k int64) {
fourTimesK := new(big.Float).SetInt64(k)
fourTimesK.Mul(fourTimesK, four)
// (-1)^k / 4^k
multiplicand := new(big.Float).SetPrec(precision).Quo(new(big.Float).SetFloat64(math.Pow(-1, float64(k))), new(big.Float).SetFloat64(math.Pow(4, float64(k))))
// 2 / (4*k+1)
firstSubTerm := new(big.Float).SetPrec(precision).Quo(two, new(big.Float).Add(fourTimesK, one))
// 2 / (4*k+2)
secondSubTerm := new(big.Float).SetPrec(precision).Quo(two, new(big.Float).Add(fourTimesK, two))
// 1 / (4*k+3)
thirdSubTerm := new(big.Float).SetPrec(precision).Quo(one, new(big.Float).Add(fourTimesK, three))
// (2 / (4*k+1) + 2 / (4*k+2) + 1 / (4*k+3))
parenthesis := new(big.Float).SetPrec(precision).Add(firstSubTerm, secondSubTerm)
parenthesis.Add(parenthesis, thirdSubTerm)
// fmt.Printf("k = %d: multiplicand = %s; firstSubTerm = %s; secondSubTerm = %s; thirdSubTerm = %s; parenthesis = %s\n", k, multiplicand.Text('f', 5), firstSubTerm.Text('f', 5), secondSubTerm.Text('f', 5), thirdSubTerm.Text('f', 5), parenthesis.Text('f', 5))
// ((-1)^k / 4^k) * (2 / (4*k+1) + 2 / (4*k+2) + 1 / (4*k+3))
terms <- new(big.Float).SetPrec(precision).Mul(multiplicand, parenthesis)
}
// Returns acot(x) in result
func acot(prec uint, x int64, result *big.Float) {
var term, power, _x, _kp, x2, oldresult big.Float
_x.SetPrec(prec).SetInt64(x)
power.SetPrec(prec).SetInt64(1)
power.Quo(&power, &_x) // 1/x
x2.Mul(&_x, &_x)
result.SetPrec(prec).SetInt64(0)
positive := true
for k := int64(1); ; k += 2 {
oldresult.Set(result)
kp := k
if !positive {
kp = -k
}
positive = !positive
_kp.SetPrec(prec).SetInt64(kp)
term.Quo(&power, &_kp)
result.Add(result, &term)
if oldresult.Cmp(result) == 0 {
break
}
power.Quo(&power, &x2)
}
}
// Log returns a big.Float representation of the natural logarithm of
// z. Precision is the same as the one of the argument. The function
// panics if z is negative, returns -Inf when z = 0, and +Inf when z =
// +Inf
func Log(z *big.Float) *big.Float {
// panic on negative z
if z.Sign() == -1 {
panic("Log: argument is negative")
}
// Log(0) = -Inf
if z.Sign() == 0 {
return big.NewFloat(math.Inf(-1)).SetPrec(z.Prec())
}
prec := z.Prec() + 64 // guard digits
one := big.NewFloat(1).SetPrec(prec)
two := big.NewFloat(2).SetPrec(prec)
four := big.NewFloat(4).SetPrec(prec)
// Log(1) = 0
if z.Cmp(one) == 0 {
return big.NewFloat(0).SetPrec(z.Prec())
}
// Log(+Inf) = +Inf
if z.IsInf() {
return big.NewFloat(math.Inf(+1)).SetPrec(z.Prec())
}
x := new(big.Float).SetPrec(prec)
// if 0 < z < 1 we compute log(z) as -log(1/z)
var neg bool
if z.Cmp(one) < 0 {
x.Quo(one, z)
neg = true
} else {
x.Set(z)
}
// We scale up x until x >= 2**(prec/2), and then we'll be allowed
// to use the AGM formula for Log(x).
//
// Double x until the condition is met, and keep track of the
// number of doubling we did (needed to scale back later).
lim := new(big.Float)
lim.SetMantExp(two, int(prec/2))
k := 0
for x.Cmp(lim) < 0 {
x.Mul(x, x)
k++
}
// Compute the natural log of x using the fact that
// log(x) = π / (2 * AGM(1, 4/x))
// if
// x >= 2**(prec/2),
// where prec is the desired precision (in bits)
pi := pi(prec)
agm := agm(one, x.Quo(four, x)) // agm = AGM(1, 4/x)
x.Quo(pi, x.Mul(two, agm)) // reuse x, we don't need it
if neg {
x.Neg(x)
}
// scale the result back multiplying by 2**-k
// reuse lim to reduce allocations.
x.Mul(x, lim.SetMantExp(one, -k))
return x.SetPrec(z.Prec())
}
func MultiplyBigFloatWithoutNew(bigFloatStartFloat, bigFloatFactor *big.Float) (calculated *big.Float) {
calculated = bigFloatStartFloat.Mul(bigFloatStartFloat, bigFloatFactor)
return
}
// Given a vector of real numbers x = [x_0, x_1, ..., x_n], this
// uses the PSLQ algorithm to find a list of integers
// [c_0, c_1, ..., c_n] such that
//
// |c_1 * x_1 + c_2 * x_2 + ... + c_n * x_n| < tolerance
//
// and such that max |c_k| < maxcoeff. If no such vector exists, Pslq
// returns one of the errors in this package depending on whether it
// has run out of iterations, precision or explored up to the
// maxcoeff. The tolerance defaults to 3/4 of the precision.
//
// This is a fairly direct translation of the pseudocode given by
// David Bailey, "The PSLQ Integer Relation Algorithm":
// http://www.cecm.sfu.ca/organics/papers/bailey/paper/html/node3.html
//
// If a result is returned, the first non-zero element will be positive
func (e *Pslq) Run(x []big.Float) ([]big.Int, error) {
n := len(x)
if n <= 1 {
return nil, ErrorBadArguments
}
// At too low precision, the algorithm becomes meaningless
if e.prec < 64 {
return nil, ErrorPrecisionTooLow
}
if e.verbose && int(e.prec)/max(2, int(n)) < 5 {
log.Printf("Warning: precision for PSLQ may be too low")
}
if e.verbose {
log.Printf("PSLQ using prec %d and tol %g", e.prec, e.tol)
}
if e.tol.Sign() == 0 {
return nil, ErrorToleranceRoundsToZero
}
// Temporary variables
tmp0 := new(big.Float).SetPrec(e.prec)
tmp1 := new(big.Float).SetPrec(e.prec)
bigTmp := new(big.Int)
// Convert to use 1-based indexing to allow us to be
// consistent with Bailey's indexing.
xNew := make([]big.Float, len(x)+1)
minx := new(big.Float).SetPrec(e.prec)
minxFirst := true
for i, xk := range x {
p := &xNew[i+1]
p.Set(&xk)
tmp0.Abs(p)
if minxFirst || tmp0.Cmp(minx) < 0 {
minxFirst = false
minx.Set(tmp0)
}
}
x = xNew
if debug {
printVector("x", x)
}
// Sanity check on magnitudes
if minx.Sign() == 0 {
return nil, ErrorZeroArguments
}
tmp1.SetInt64(128)
tmp0.Quo(&e.tol, tmp1)
if minx.Cmp(tmp0) < 0 { // minx < tol/128
return nil, ErrorArgumentTooSmall
}
tmp0.SetInt64(4)
tmp1.SetInt64(3)
tmp0.Quo(tmp0, tmp1)
var γ big.Float
e.Sqrt(tmp0, &γ) // sqrt(4<<prec)/3)
if debug {
fmt.Printf("γ = %f\n", &γ)
}
A := newBigIntMatrix(n+1, n+1)
B := newBigIntMatrix(n+1, n+1)
H := newMatrix(n+1, n+1)
// Initialization Step 1
//
// Set the n×n matrices A and B to the identity.
for i := 1; i <= n; i++ {
for j := 1; j <= n; j++ {
if i == j {
A[i][j].SetInt64(1)
B[i][j].SetInt64(1)
} else {
A[i][j].SetInt64(0)
B[i][j].SetInt64(0)
}
H[i][j].SetInt64(0)
}
}
if debug {
printBigIntMatrix("A", A)
printBigIntMatrix("B", B)
printMatrix("H", H)
}
// Initialization Step 2
//
// For k := 1 to n
// compute s_k := sqrt( sum_j=k^n x_j^2 )
// endfor.
// Set t = 1/s1.
// For k := 1 to n:
// y_k := t * x_k
// s_k := t * s_k
// endfor.
s := make([]big.Float, n+1)
for i := 1; i <= n; i++ {
s[i].SetInt64(0)
}
for k := 1; k <= n; k++ {
var t big.Float
t.SetInt64(0)
for j := k; j <= n; j++ {
tmp0.Mul(&x[j], &x[j])
t.Add(&t, tmp0)
}
e.Sqrt(&t, &s[k])
}
if debug {
fmt.Println("Init Step 2")
printVector("s", s)
}
var t big.Float
t.Set(&s[1])
y := make([]big.Float, len(x))
copy(y, x)
for k := 1; k <= n; k++ {
// y[k] = (x[k] << prec) / t
y[k].Quo(&x[k], &t)
// s[k] = (s[k] << prec) / t
s[k].Quo(&s[k], &t)
}
if debug {
printVector("y", y)
printVector("s", s)
}
// Init Step 3
//
// Compute the n×(n−1) matrix H as follows:
// For i := 1 to n:
// for j := i + 1 to n − 1:
// set Hij := 0
// endfor
// if i ≤ n − 1 then set Hii := s_(i+1)/s_i
// for j := 1 to i−1:
// set Hij := −y_i * y_j / (s_j * s_(j+1))
// endfor
// endfor
for i := 1; i <= n; i++ {
for j := i + 1; j < n; j++ {
H[i][j].SetInt64(0)
}
if i <= n-1 {
if s[i].Sign() == 0 {
// Precision probably exhausted
return nil, ErrorPrecisionExhausted
}
// H[i][i] = (s[i+1] << prec) / s[i]
H[i][i].Quo(&s[i+1], &s[i])
}
for j := 1; j < i; j++ {
var sjj1 big.Float
sjj1.Mul(&s[j], &s[j+1])
if debug {
fmt.Printf("sjj1 = %f\n", &sjj1)
}
if sjj1.Sign() == 0 {
// Precision probably exhausted
return nil, ErrorPrecisionExhausted
}
// H[i][j] = ((-y[i] * y[j]) << prec) / sjj1
tmp0.Mul(&y[i], &y[j])
tmp0.Neg(tmp0)
H[i][j].Quo(tmp0, &sjj1)
}
}
if debug {
fmt.Println("Init Step 3")
printMatrix("H", H)
}
// Init Step 4
//
// Perform full reduction on H, simultaneously updating y, A and B:
//
// For i := 2 to n:
// for j := i−1 to 1 step−1:
// t := nint(Hij/Hjj)
// y_j := y_j + t * y_i
// for k := 1 to j:
// Hik := Hik − t * Hjk
// endfor
// for k := 1 to n:
// Aik := Aik − t * Ajk
// Bkj := Bkj + t * Bki
// endfor
// endfor
// endfor
for i := 2; i <= n; i++ {
for j := i - 1; j > 0; j-- {
//t = floor(H[i][j]/H[j,j] + 0.5)
var t big.Int
var tFloat big.Float
if H[j][j].Sign() == 0 {
// Precision probably exhausted
return nil, ErrorPrecisionExhausted
}
tmp0.Quo(&H[i][j], &H[j][j])
e.NearestInt(tmp0, &t)
tFloat.SetInt(&t).SetPrec(e.prec)
if debug {
fmt.Printf("H[i][j]=%f\n", &H[i][j])
fmt.Printf("H[j][j]=%f\n", &H[j][j])
fmt.Printf("tmp=%f\n", tmp0)
fmt.Printf("t=%d\n", &t)
}
// y[j] = y[j] + (t * y[i] >> prec)
tmp0.Mul(&y[i], &tFloat)
y[j].Add(&y[j], tmp0)
for k := 1; k <= j; k++ {
// H[i][k] = H[i][k] - (t * H[j][k] >> prec)
tmp0.Mul(&H[j][k], &tFloat)
H[i][k].Sub(&H[i][k], tmp0)
}
for k := 1; k <= n; k++ {
bigTmp.Mul(&t, &A[j][k])
A[i][k].Sub(&A[i][k], bigTmp)
bigTmp.Mul(&t, &B[k][i])
B[k][j].Add(&B[k][j], bigTmp)
}
}
}
if debug {
fmt.Println("Init Step 4")
printBigIntMatrix("A", A)
printBigIntMatrix("B", B)
printMatrix("H", H)
}
// Main algorithm
var REP int
var norm big.Int
vec := make([]big.Int, n)
for REP = 0; REP < e.maxsteps; REP++ {
// Step 1
//
// Select m such that γ^i * |Hii| is maximal when i = m.
m := -1
var szmax big.Float
szmax.SetInt64(-1)
var γPower big.Float
γPower.Set(&γ)
for i := 1; i < n; i++ {
var absH big.Float
absH.Abs(&H[i][i])
var sz big.Float
sz.Mul(&γPower, &absH)
// sz := (g**i * abs(h)) >> (prec * (i - 1))
if sz.Cmp(&szmax) > 0 {
m = i
szmax.Set(&sz)
}
γPower.Mul(&γPower, &γ)
}
if debug {
fmt.Println("Step 1")
fmt.Printf("szmax=%f\n", &szmax)
fmt.Printf("m=%d\n", m)
}
// Step 2
//
// Exchange entries m and m+1 of y, corresponding rows
// of A and H, and corresponding columns of B.
y[m], y[m+1] = y[m+1], y[m]
for i := 1; i < n+1; i++ {
H[m][i], H[m+1][i] = H[m+1][i], H[m][i]
}
for i := 1; i < n+1; i++ {
A[m][i], A[m+1][i] = A[m+1][i], A[m][i]
}
for i := 1; i < n+1; i++ {
B[i][m], B[i][m+1] = B[i][m+1], B[i][m]
}
if debug {
fmt.Println("Step 2")
printVector("y", y)
printBigIntMatrix("A", A)
printBigIntMatrix("B", B)
printMatrix("H", H)
}
// Step 3
//
// If m ≤ n−2 then update H as follows:
//
// t0 := sqrt( Hmm^2 + H(m,m+1)^2 )
// t1 := Hmm/t0
// t2 := H(m,m+1)/t0.
// for i := m to n:
// t3 := Him
// t4 := Hi,m+1
// Him := t1t3 +t2t4
// Hi,m+1 := −t2t3 +t1t4
// endfor.
if m <= n-2 {
tmp0.Mul(&H[m][m], &H[m][m])
tmp1.Mul(&H[m][m+1], &H[m][m+1])
tmp0.Add(tmp0, tmp1)
var t0 big.Float
e.Sqrt(tmp0, &t0)
// Precision probably exhausted
if t0.Sign() == 0 {
return nil, ErrorPrecisionExhausted
}
var t1, t2 big.Float
t1.Quo(&H[m][m], &t0)
t2.Quo(&H[m][m+1], &t0)
for i := m; i <= n; i++ {
var t3, t4 big.Float
t3.Set(&H[i][m])
t4.Set(&H[i][m+1])
// H[i][m] = (t1*t3 + t2*t4) >> prec
tmp0.Mul(&t1, &t3)
tmp1.Mul(&t2, &t4)
H[i][m].Add(tmp0, tmp1)
// H[i][m+1] = (-t2*t3 + t1*t4) >> prec
tmp0.Mul(&t2, &t3)
tmp1.Mul(&t1, &t4)
H[i][m+1].Sub(tmp1, tmp0)
}
}
if debug {
fmt.Println("Step 3")
printMatrix("H", H)
}
// Step 4
// Perform block reduction on H, simultaneously updating y, A and B:
//
// For i := m+1 to n:
// for j := min(i−1, m+1) to 1 step −1:
// t := nint(Hij/Hjj)
// yj := yj + t * yi
// for k := 1 to j:
// Hik := Hik − tHjk
// endfor
// for k := 1 to n:
// Aik := Aik −tAjk
// Bkj := Bkj +tBki
// endfor
// endfor
// endfor.
for i := m + 1; i <= n; i++ {
var t big.Int
var tFloat big.Float
for j := min(i-1, m+1); j > 0; j-- {
if H[j][j].Sign() == 0 {
// Precision probably exhausted
return nil, ErrorPrecisionExhausted
}
tmp0.Quo(&H[i][j], &H[j][j])
e.NearestInt(tmp0, &t)
tFloat.SetInt(&t).SetPrec(e.prec)
// y[j] = y[j] + ((t * y[i]) >> prec)
tmp0.Mul(&y[i], &tFloat)
y[j].Add(&y[j], tmp0)
for k := 1; k <= j; k++ {
// H[i][k] = H[i][k] - (t * H[j][k] >> prec)
tmp0.Mul(&H[j][k], &tFloat)
H[i][k].Sub(&H[i][k], tmp0)
}
for k := 1; k <= n; k++ {
bigTmp.Mul(&t, &A[j][k])
A[i][k].Sub(&A[i][k], bigTmp)
bigTmp.Mul(&t, &B[k][i])
B[k][j].Add(&B[k][j], bigTmp)
}
}
}
if debug {
fmt.Println("Step 4")
printBigIntMatrix("A", A)
printBigIntMatrix("B", B)
printMatrix("H", H)
}
// Step 6
//
// Termination test: If the largest entry of A exceeds
// the level of numeric precision used, then precision
// is exhausted. If the smallest entry of the y vector
// is less than the detection threshold, a relation
// has been detected and is given in the corresponding
// column of B.
//
// Until a relation is found, the error typically decreases
// slowly (e.g. a factor 1-10) with each step TODO: we could
// compare err from two successive iterations. If there is a
// large drop (several orders of magnitude), that indicates a
// "high quality" relation was detected. Reporting this to
// the user somehow might be useful.
maxAPrecision := 0
for i := 1; i <= n; i++ {
for j := 1; j <= n; j++ {
precision := A[i][j].BitLen()
if precision > maxAPrecision {
maxAPrecision = precision
}
}
}
if debug {
log.Printf("Max A precision = %d, precision = %d, tolerance %d, ratio = %.3f\n", maxAPrecision, e.prec, e.target, float64(maxAPrecision)/float64(e.target))
}
if float64(maxAPrecision)/float64(e.target) > 0.85 {
if e.verbose {
log.Printf("CANCELLING after step %d/%d.", REP, e.maxsteps)
}
return nil, ErrorPrecisionExhausted
}
var best_err big.Float
best_err.Set(&e.maxcoeff_fp)
for i := 1; i <= n; i++ {
var err big.Float
err.Abs(&y[i])
// Maybe we are done?
if err.Cmp(&e.tol) < 0 {
// We are done if the coefficients are acceptable
var maxc big.Int
for j := 1; j <= n; j++ {
if debug {
fmt.Printf("vec[%d]=%d\n", j-1, &B[j][i])
}
t := B[j][i]
if debug {
fmt.Printf("vec[%d]=%d\n", j-1, t)
}
vec[j-1] = t
if t.Sign() < 0 {
t.Neg(&t)
}
if t.Cmp(&maxc) > 0 {
maxc.Set(&t)
}
}
if debug {
fmt.Printf("maxc = %d, maxcoeff = %d\n", maxc, e.maxcoeff)
}
if maxc.Cmp(&e.maxcoeff) < 0 {
if e.verbose {
log.Printf("FOUND relation at iter %d/%d, error: %g", REP, e.maxsteps, &err)
}
// Find sign of first non zero item
sign := 0
for i := range vec {
sign = vec[i].Sign()
if sign != 0 {
break
}
}
// Normalise vec making first non-zero argument positive
if sign < 0 {
for i := range vec {
vec[i].Neg(&vec[i])
}
}
return vec, nil
}
}
if err.Cmp(&best_err) < 0 {
best_err = err
}
}
// Step 5
//
// Norm bound: Compute M := 1/maxj |Hj|, where Hj
// denotes the j-th row of H.
//
// Then there can exist no relation vector whose
// Euclidean norm is less than M.
//
// Calculate a lower bound for the norm. We could do this
// more exactly (using the Euclidean norm) but there is probably
// no practical benefit.
var recnorm big.Float
recnorm.SetInt64(0)
for i := 1; i <= n; i++ {
for j := 1; j <= n; j++ {
tmp0.Abs(&H[i][j])
if tmp0.Cmp(&recnorm) > 0 {
recnorm.Set(tmp0)
}
}
}
norm.Set(&e.maxcoeff)
if recnorm.Sign() != 0 {
// norm = ((1 << (2 * prec)) / recnorm) >> prec
tmp0.Quo(&e.one, &recnorm)
tmp0.Int(&norm)
}
if e.verbose {
log.Printf("%2d/%2d: Error: %g Norm: %d", REP, e.maxsteps, &best_err, &norm)
}
if norm.Cmp(&e.maxcoeff) >= 0 {
if e.verbose {
log.Printf("CANCELLING after step %d/%d.", REP, e.maxsteps)
log.Printf("Could not find an integer relation. Norm bound: %d", &norm)
}
return nil, ErrorNoRelationFound
}
}
if e.verbose {
log.Printf("CANCELLING after step %d/%d.", REP, e.maxsteps)
log.Printf("Could not find an integer relation. Norm bound: %d", &norm)
}
return nil, ErrorIterationsExceeded
}
func quo(x, y *complexFloat) *complexFloat {
z := newComplexFloat()
denominator := new(big.Float).SetPrec(prec)
c2 := new(big.Float).SetPrec(prec)
d2 := new(big.Float).SetPrec(prec)
c2.Mul(y.r, y.r)
d2.Mul(y.i, y.i)
denominator.Add(c2, d2)
if denominator.Cmp(zero) == 0 || denominator.IsInf() {
return newComplexFloat()
}
ac := new(big.Float).SetPrec(prec)
bd := new(big.Float).SetPrec(prec)
ac.Mul(x.r, y.r)
bd.Mul(x.i, y.i)
bc := new(big.Float).SetPrec(prec)
ad := new(big.Float).SetPrec(prec)
bc.Mul(x.i, y.r)
ad.Mul(x.r, y.i)
z.r.Add(ac, bd)
z.r.Quo(z.r, denominator)
z.i.Add(bc, ad.Neg(ad))
z.i.Quo(z.i, denominator)
return z
}
func (f BigFloat) String() string {
var mant big.Float
exp := f.Float.MantExp(&mant)
positive := 1
if exp < 0 {
positive = 0
exp = -exp
}
verb, prec := byte('g'), 12
format := conf.Format()
if format != "" {
v, p, ok := conf.FloatFormat()
if ok {
verb, prec = v, p
}
}
// Printing huge floats can be very slow using
// big.Float's native methods; see issue #11068.
// For example 1e5000000 takes a minute of CPU time just
// to print. The code below is instantaneous, by rescaling
// first. It is however less feature-complete.
// (Big ints are problematic too, but if you print 1e50000000
// as an integer you probably won't be surprised it's slow.)
if fastFloatPrint && exp > 10000 {
// We always use %g to print the fraction, and it will
// never have an exponent, but if the format is %E we
// need to use a capital E.
eChar := 'e'
if verb == 'E' || verb == 'G' {
eChar = 'E'
}
fexp := newF().SetInt64(int64(exp))
fexp.Mul(fexp, floatLog2)
fexp.Quo(fexp, floatLog10)
// We now have a floating-point base 10 exponent.
// Break into the integer part and the fractional part.
// The integer part is what we will show.
// The 10**(fractional part) will be multiplied back in.
iexp, _ := fexp.Int(nil)
fraction := fexp.Sub(fexp, newF().SetInt(iexp))
// Now compute 10**(fractional part).
// Fraction is in base 10. Move it to base e.
fraction.Mul(fraction, floatLog10)
scale := exponential(fraction)
if positive > 0 {
mant.Mul(&mant, scale)
} else {
mant.Quo(&mant, scale)
}
ten := newF().SetInt64(10)
i64exp := iexp.Int64()
// For numbers not too far from one, print without the E notation.
// Shouldn't happen (exp must be large to get here) but just
// in case, we keep this around.
if -4 <= i64exp && i64exp <= 11 {
if i64exp > 0 {
for i := 0; i < int(i64exp); i++ {
mant.Mul(&mant, ten)
}
} else {
for i := 0; i < int(-i64exp); i++ {
mant.Quo(&mant, ten)
}
}
return fmt.Sprintf("%g\n", &mant)
} else {
sign := ""
if mant.Sign() < 0 {
sign = "-"
mant.Neg(&mant)
}
// If it has a leading zero, rescale.
digits := mant.Text('g', prec)
for digits[0] == '0' {
mant.Mul(&mant, ten)
if positive > 0 {
i64exp--
} else {
i64exp++
}
digits = mant.Text('g', prec)
}
return fmt.Sprintf("%s%s%c%c%d", sign, digits, eChar, "-+"[positive], i64exp)
}
}
return f.Float.Text(verb, prec)
}
