Diagrams.ThreeD.Transform:aboutX from diagrams-lib-1.3.0.3, B

Average Accuracy: 99.8% → 99.8%

Time: 8.2s

Precision: binary64

Cost: 13248

Math

\[x \cdot \sin y + z \cdot \cos y \]

↓

\[z \cdot \cos y + x \cdot \sin y \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* x (sin y)) (* z (cos y))))

↓

(FPCore (x y z) :precision binary64 (+ (* z (cos y)) (* x (sin y))))

C

double code(double x, double y, double z) {
	return (x * sin(y)) + (z * cos(y));
}

↓

double code(double x, double y, double z) {
	return (z * cos(y)) + (x * sin(y));
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x * sin(y)) + (z * cos(y))
end function

↓

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (z * cos(y)) + (x * sin(y))
end function

Java

public static double code(double x, double y, double z) {
	return (x * Math.sin(y)) + (z * Math.cos(y));
}

↓

public static double code(double x, double y, double z) {
	return (z * Math.cos(y)) + (x * Math.sin(y));
}

Python

def code(x, y, z):
	return (x * math.sin(y)) + (z * math.cos(y))

↓

def code(x, y, z):
	return (z * math.cos(y)) + (x * math.sin(y))

Julia

function code(x, y, z)
	return Float64(Float64(x * sin(y)) + Float64(z * cos(y)))
end

↓

function code(x, y, z)
	return Float64(Float64(z * cos(y)) + Float64(x * sin(y)))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * sin(y)) + (z * cos(y));
end

↓

function tmp = code(x, y, z)
	tmp = (z * cos(y)) + (x * sin(y));
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[Sin[y], $MachinePrecision]), $MachinePrecision] + N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

↓

code[x_, y_, z_] := N[(N[(z * N[Cos[y], $MachinePrecision]), $MachinePrecision] + N[(x * N[Sin[y], $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 99.8%

   \[x \cdot \sin y + z \cdot \cos y \]

2. Final simplification 99.8%

   \[\leadsto z \cdot \cos y + x \cdot \sin y \]

Alternatives

Alternative 1
Accuracy	74.6%
Cost	7386

\[\begin{array}{l} t_0 := x \cdot \sin y\\ \mathbf{if}\;y \leq -4 \cdot 10^{-6}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;y \leq 0.0058:\\ \;\;\;\;z + x \cdot y\\ \mathbf{elif}\;y \leq 4.3 \cdot 10^{+52} \lor \neg \left(y \leq 1.55 \cdot 10^{+104} \lor \neg \left(y \leq 1.35 \cdot 10^{+165}\right) \land y \leq 4 \cdot 10^{+273}\right):\\ \;\;\;\;z \cdot \cos y\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \]

Alternative 2
Accuracy	85.1%
Cost	7250

\[\begin{array}{l} \mathbf{if}\;x \leq -9.6 \cdot 10^{-148} \lor \neg \left(x \leq 3.8 \cdot 10^{-78} \lor \neg \left(x \leq 10^{+17}\right) \land x \leq 7.4 \cdot 10^{+55}\right):\\ \;\;\;\;z + x \cdot \sin y\\ \mathbf{else}:\\ \;\;\;\;z \cdot \cos y\\ \end{array} \]

Alternative 3
Accuracy	74.8%
Cost	6857

\[\begin{array}{l} \mathbf{if}\;y \leq -480 \lor \neg \left(y \leq 0.004\right):\\ \;\;\;\;z \cdot \cos y\\ \mathbf{else}:\\ \;\;\;\;z + x \cdot y\\ \end{array} \]

Alternative 4
Accuracy	41.5%
Cost	456

\[\begin{array}{l} \mathbf{if}\;z \leq -1.55 \cdot 10^{-252}:\\ \;\;\;\;z\\ \mathbf{elif}\;z \leq 1.02 \cdot 10^{-149}:\\ \;\;\;\;x \cdot y\\ \mathbf{else}:\\ \;\;\;\;z\\ \end{array} \]

Alternative 5
Accuracy	52.2%
Cost	320

\[z + x \cdot y \]

Alternative 6
Accuracy	38.9%
Cost	64

\[z \]

Reproduce

herbie shell --seed 2023147 
(FPCore (x y z)
  :name "Diagrams.ThreeD.Transform:aboutX from diagrams-lib-1.3.0.3, B"
  :precision binary64
  (+ (* x (sin y)) (* z (cos y))))