Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, C

Percentage Accurate: 99.9% → 99.9%

Time: 1.5s

Alternatives: 8

Speedup: 1.0×

Specification

Math

\[x \cdot \left(y + z\right) + z \cdot 5 \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (+ (* x (+ y z)) (* z 5.0)))

C

double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Fortran

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x * (y + z)) + (z * 5.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Python

def code(x, y, z):
	return (x * (y + z)) + (z * 5.0)

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(y + z)) + Float64(z * 5.0))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (y + z)) + (z * 5.0);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] + N[(z * 5.0), $MachinePrecision]), $MachinePrecision]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	(x * (y + z)) + (z * (5))
END code

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[x \cdot \left(y + z\right) + z \cdot 5 \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (+ (* x (+ y z)) (* z 5.0)))

C

double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Fortran

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = (x * (y + z)) + (z * 5.0d0)
end function

Java

public static double code(double x, double y, double z) {
	return (x * (y + z)) + (z * 5.0);
}

Python

def code(x, y, z):
	return (x * (y + z)) + (z * 5.0)

Julia

function code(x, y, z)
	return Float64(Float64(x * Float64(y + z)) + Float64(z * 5.0))
end

MATLAB

function tmp = code(x, y, z)
	tmp = (x * (y + z)) + (z * 5.0);
end

Wolfram

code[x_, y_, z_] := N[(N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision] + N[(z * 5.0), $MachinePrecision]), $MachinePrecision]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	(x * (y + z)) + (z * (5))
END code

Alternative 1: 98.9% accurate, 1.1× speedup

Math

\[\mathsf{fma}\left(z, x - -5, y \cdot x\right) \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (fma z (- x -5.0) (* y x)))

C

double code(double x, double y, double z) {
	return fma(z, (x - -5.0), (y * x));
}

Julia

function code(x, y, z)
	return fma(z, Float64(x - -5.0), Float64(y * x))
end

Wolfram

code[x_, y_, z_] := N[(z * N[(x - -5.0), $MachinePrecision] + N[(y * x), $MachinePrecision]), $MachinePrecision]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	(z * (x - (-5))) + (y * x)
END code

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]
2. Step-by-step derivation

3. Applied rewrites 98.8%

   \[\leadsto \mathsf{fma}\left(z, x - -5, y \cdot x\right) \]
4. Add Preprocessing

Alternative 2: 98.8% accurate, 1.1× speedup

Math

\[\mathsf{fma}\left(x, y, z \cdot \left(5 + x\right)\right) \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (fma x y (* z (+ 5.0 x))))

C

double code(double x, double y, double z) {
	return fma(x, y, (z * (5.0 + x)));
}

Julia

function code(x, y, z)
	return fma(x, y, Float64(z * Float64(5.0 + x)))
end

Wolfram

code[x_, y_, z_] := N[(x * y + N[(z * N[(5.0 + x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	(x * y) + (z * ((5) + x))
END code

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]

2. Taylor expanded in z around 0

   \[\leadsto x \cdot y + z \cdot \left(5 + x\right) \]
3. Step-by-step derivation

4. Applied rewrites 98.9%

   \[\leadsto \mathsf{fma}\left(x, y, z \cdot \left(5 + x\right)\right) \]
5. Add Preprocessing

Alternative 3: 98.8% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_0 := x \cdot \left(y + z\right)\\ \mathbf{if}\;x \leq -14298.649587223466:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 0.00053302600741813:\\ \;\;\;\;\mathsf{fma}\left(x, y, z \cdot 5\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (let* ((t_0 (* x (+ y z))))
  (if (<= x -14298.649587223466)
    t_0
    (if (<= x 0.00053302600741813) (fma x y (* z 5.0)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (y + z);
	double tmp;
	if (x <= -14298.649587223466) {
		tmp = t_0;
	} else if (x <= 0.00053302600741813) {
		tmp = fma(x, y, (z * 5.0));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(y + z))
	tmp = 0.0
	if (x <= -14298.649587223466)
		tmp = t_0;
	elseif (x <= 0.00053302600741813)
		tmp = fma(x, y, Float64(z * 5.0));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -14298.649587223466], t$95$0, If[LessEqual[x, 0.00053302600741813], N[(x * y + N[(z * 5.0), $MachinePrecision]), $MachinePrecision], t$95$0]]]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	LET t_0 = (x * (y + z)) IN
		LET tmp_1 = IF (x <= (533026007418130050134752462298592945444397628307342529296875e-63)) THEN ((x * y) + (z * (5))) ELSE t_0 ENDIF IN
		LET tmp = IF (x <= (-14298649587223466369323432445526123046875e-36)) THEN t_0 ELSE tmp_1 ENDIF IN
	tmp
END code

Derivation

1. Split input into 2 regimes

2. if x < -14298.649587223466 or 5.3302600741813005e-4 < x

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 88.5%

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + z\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 64.7%

      \[\leadsto x \cdot \left(y + z\right) \]

   if -14298.649587223466 < x < 5.3302600741813005e-4

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in z around 0

      \[\leadsto x \cdot y + z \cdot \left(5 + x\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 98.9%

      \[\leadsto \mathsf{fma}\left(x, y, z \cdot \left(5 + x\right)\right) \]

   5. Taylor expanded in x around 0

      \[\leadsto \mathsf{fma}\left(x, y, z \cdot 5\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 75.7%

      \[\leadsto \mathsf{fma}\left(x, y, z \cdot 5\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 4: 84.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} t_0 := x \cdot \left(y + z\right)\\ \mathbf{if}\;x \leq -2.8368855981903414 \cdot 10^{-51}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 8858021122195.69:\\ \;\;\;\;\mathsf{fma}\left(5, z, x \cdot z\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (let* ((t_0 (* x (+ y z))))
  (if (<= x -2.8368855981903414e-51)
    t_0
    (if (<= x 8858021122195.69) (fma 5.0 z (* x z)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (y + z);
	double tmp;
	if (x <= -2.8368855981903414e-51) {
		tmp = t_0;
	} else if (x <= 8858021122195.69) {
		tmp = fma(5.0, z, (x * z));
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(y + z))
	tmp = 0.0
	if (x <= -2.8368855981903414e-51)
		tmp = t_0;
	elseif (x <= 8858021122195.69)
		tmp = fma(5.0, z, Float64(x * z));
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.8368855981903414e-51], t$95$0, If[LessEqual[x, 8858021122195.69], N[(5.0 * z + N[(x * z), $MachinePrecision]), $MachinePrecision], t$95$0]]]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	LET t_0 = (x * (y + z)) IN
		LET tmp_1 = IF (x <= (8858021122195689453125e-9)) THEN (((5) * z) + (x * z)) ELSE t_0 ENDIF IN
		LET tmp = IF (x <= (-28368855981903414102920337465896913142028897505741484614367286972627935745057502704794255345263793652131194254318143559929623156480593682005064692930318415164947509765625e-220)) THEN t_0 ELSE tmp_1 ENDIF IN
	tmp
END code

Derivation

1. Split input into 2 regimes

2. if x < -2.8368855981903414e-51 or 8858021122195.6895 < x

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 88.5%

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + z\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 64.7%

      \[\leadsto x \cdot \left(y + z\right) \]

   if -2.8368855981903414e-51 < x < 8858021122195.6895

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in y around 0

      \[\leadsto 5 \cdot z + x \cdot z \]
   3. Step-by-step derivation

   4. Applied rewrites 62.4%

      \[\leadsto \mathsf{fma}\left(5, z, x \cdot z\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 5: 84.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := x \cdot \left(y + z\right)\\ \mathbf{if}\;x \leq -2.8368855981903414 \cdot 10^{-51}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 8858021122195.69:\\ \;\;\;\;z \cdot \left(5 + x\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (let* ((t_0 (* x (+ y z))))
  (if (<= x -2.8368855981903414e-51)
    t_0
    (if (<= x 8858021122195.69) (* z (+ 5.0 x)) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (y + z);
	double tmp;
	if (x <= -2.8368855981903414e-51) {
		tmp = t_0;
	} else if (x <= 8858021122195.69) {
		tmp = z * (5.0 + x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x * (y + z)
    if (x <= (-2.8368855981903414d-51)) then
        tmp = t_0
    else if (x <= 8858021122195.69d0) then
        tmp = z * (5.0d0 + x)
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x * (y + z);
	double tmp;
	if (x <= -2.8368855981903414e-51) {
		tmp = t_0;
	} else if (x <= 8858021122195.69) {
		tmp = z * (5.0 + x);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (y + z)
	tmp = 0
	if x <= -2.8368855981903414e-51:
		tmp = t_0
	elif x <= 8858021122195.69:
		tmp = z * (5.0 + x)
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(y + z))
	tmp = 0.0
	if (x <= -2.8368855981903414e-51)
		tmp = t_0;
	elseif (x <= 8858021122195.69)
		tmp = Float64(z * Float64(5.0 + x));
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (y + z);
	tmp = 0.0;
	if (x <= -2.8368855981903414e-51)
		tmp = t_0;
	elseif (x <= 8858021122195.69)
		tmp = z * (5.0 + x);
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.8368855981903414e-51], t$95$0, If[LessEqual[x, 8858021122195.69], N[(z * N[(5.0 + x), $MachinePrecision]), $MachinePrecision], t$95$0]]]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	LET t_0 = (x * (y + z)) IN
		LET tmp_1 = IF (x <= (8858021122195689453125e-9)) THEN (z * ((5) + x)) ELSE t_0 ENDIF IN
		LET tmp = IF (x <= (-28368855981903414102920337465896913142028897505741484614367286972627935745057502704794255345263793652131194254318143559929623156480593682005064692930318415164947509765625e-220)) THEN t_0 ELSE tmp_1 ENDIF IN
	tmp
END code

Derivation

1. Split input into 2 regimes

2. if x < -2.8368855981903414e-51 or 8858021122195.6895 < x

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 88.5%

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + z\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 64.7%

      \[\leadsto x \cdot \left(y + z\right) \]

   if -2.8368855981903414e-51 < x < 8858021122195.6895

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in z around inf

      \[\leadsto z \cdot \left(5 + x\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 62.4%

      \[\leadsto z \cdot \left(5 + x\right) \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 6: 84.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} t_0 := x \cdot \left(y + z\right)\\ \mathbf{if}\;x \leq -2.8368855981903414 \cdot 10^{-51}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;x \leq 9.957315309628584 \cdot 10^{-34}:\\ \;\;\;\;5 \cdot z\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (let* ((t_0 (* x (+ y z))))
  (if (<= x -2.8368855981903414e-51)
    t_0
    (if (<= x 9.957315309628584e-34) (* 5.0 z) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = x * (y + z);
	double tmp;
	if (x <= -2.8368855981903414e-51) {
		tmp = t_0;
	} else if (x <= 9.957315309628584e-34) {
		tmp = 5.0 * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: t_0
    real(8) :: tmp
    t_0 = x * (y + z)
    if (x <= (-2.8368855981903414d-51)) then
        tmp = t_0
    else if (x <= 9.957315309628584d-34) then
        tmp = 5.0d0 * z
    else
        tmp = t_0
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double t_0 = x * (y + z);
	double tmp;
	if (x <= -2.8368855981903414e-51) {
		tmp = t_0;
	} else if (x <= 9.957315309628584e-34) {
		tmp = 5.0 * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (y + z)
	tmp = 0
	if x <= -2.8368855981903414e-51:
		tmp = t_0
	elif x <= 9.957315309628584e-34:
		tmp = 5.0 * z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(y + z))
	tmp = 0.0
	if (x <= -2.8368855981903414e-51)
		tmp = t_0;
	elseif (x <= 9.957315309628584e-34)
		tmp = Float64(5.0 * z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (y + z);
	tmp = 0.0;
	if (x <= -2.8368855981903414e-51)
		tmp = t_0;
	elseif (x <= 9.957315309628584e-34)
		tmp = 5.0 * z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(y + z), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.8368855981903414e-51], t$95$0, If[LessEqual[x, 9.957315309628584e-34], N[(5.0 * z), $MachinePrecision], t$95$0]]]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	LET t_0 = (x * (y + z)) IN
		LET tmp_1 = IF (x <= (995731530962858429324473884117268777133337992305983115666935440622549596510538661930608296535272216942757950164377689361572265625e-162)) THEN ((5) * z) ELSE t_0 ENDIF IN
		LET tmp = IF (x <= (-28368855981903414102920337465896913142028897505741484614367286972627935745057502704794255345263793652131194254318143559929623156480593682005064692930318415164947509765625e-220)) THEN t_0 ELSE tmp_1 ENDIF IN
	tmp
END code

Derivation

1. Split input into 2 regimes

2. if x < -2.8368855981903414e-51 or 9.9573153096285843e-34 < x

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]
   3. Step-by-step derivation

   4. Applied rewrites 88.5%

      \[\leadsto x \cdot \left(y + \left(z + 5 \cdot \frac{z}{x}\right)\right) \]

   5. Taylor expanded in x around inf

      \[\leadsto x \cdot \left(y + z\right) \]
   6. Step-by-step derivation

   7. Applied rewrites 64.7%

      \[\leadsto x \cdot \left(y + z\right) \]

   if -2.8368855981903414e-51 < x < 9.9573153096285843e-34

   1. Initial program 99.9%

      \[x \cdot \left(y + z\right) + z \cdot 5 \]

   2. Taylor expanded in x around 0

      \[\leadsto 5 \cdot z \]
   3. Step-by-step derivation

   4. Applied rewrites 36.9%

      \[\leadsto 5 \cdot z \]
3. Recombined 2 regimes into one program.
4. Add Preprocessing

Alternative 7: 36.9% accurate, 3.0× speedup

Math

\[5 \cdot z \]

FPCore

(FPCore (x y z)
  :precision binary64
  :pre TRUE
  (* 5.0 z))

C

double code(double x, double y, double z) {
	return 5.0 * z;
}

Fortran

real(8) function code(x, y, z)
use fmin_fmax_functions
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = 5.0d0 * z
end function

Java

public static double code(double x, double y, double z) {
	return 5.0 * z;
}

Python

def code(x, y, z):
	return 5.0 * z

Julia

function code(x, y, z)
	return Float64(5.0 * z)
end

MATLAB

function tmp = code(x, y, z)
	tmp = 5.0 * z;
end

Wolfram

code[x_, y_, z_] := N[(5.0 * z), $MachinePrecision]

ReFlow

f(x, y, z):
	x in [-inf, +inf],
	y in [-inf, +inf],
	z in [-inf, +inf]
code: THEORY
BEGIN
f(x, y, z: real): real =
	(5) * z
END code

Derivation

1. Initial program 99.9%

   \[x \cdot \left(y + z\right) + z \cdot 5 \]

2. Taylor expanded in x around 0

   \[\leadsto 5 \cdot z \]
3. Step-by-step derivation

4. Applied rewrites 36.9%

   \[\leadsto 5 \cdot z \]
5. Add Preprocessing

Reproduce

herbie shell --seed 2026092 
(FPCore (x y z)
  :name "Graphics.Rendering.Plot.Render.Plot.Legend:renderLegendOutside from plot-0.2.3.4, C"
  :precision binary64
  (+ (* x (+ y z)) (* z 5.0)))