Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23

Percentage Accurate: 99.9% → 99.9%

Time: 10.5s

Alternatives: 6

Speedup: 1.0×

• 30.2% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(x \cdot y + z\right) \cdot y + t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(x \cdot y + z\right) \cdot y + t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (+ (* (+ (* x y) z) y) t))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 99.9% accurate, 0.0× speedup

Math

\[\begin{array}{l} \\ \mathsf{fma}\left(\mathsf{fma}\left(x, y, z\right), y, t\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (fma (fma x y z) y t))

C

double code(double x, double y, double z, double t) {
	return fma(fma(x, y, z), y, t);
}

Julia

function code(x, y, z, t)
	return fma(fma(x, y, z), y, t)
end

Wolfram

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

Derivation

1. Initial program 99.9%

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

   1. fma-define 99.9%

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

   2. fma-define 99.9%

      \[\leadsto \mathsf{fma}\left(\color{blue}{\mathsf{fma}\left(x, y, z\right)}, y, t\right) \]

3. Simplified 99.9%

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

Alternative 2: 77.5% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{+30} \lor \neg \left(y \leq -15500\right) \land \left(y \leq -15000 \lor \neg \left(y \leq 4 \cdot 10^{+47} \lor \neg \left(y \leq 8 \cdot 10^{+69}\right) \land y \leq 5.2 \cdot 10^{+160}\right)\right):\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -9.2e+30)
         (and (not (<= y -15500.0))
              (or (<= y -15000.0)
                  (not
                   (or (<= y 4e+47)
                       (and (not (<= y 8e+69)) (<= y 5.2e+160)))))))
   (* x (* y y))
   (+ t (* y z))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9.2e+30) || (!(y <= -15500.0) && ((y <= -15000.0) || !((y <= 4e+47) || (!(y <= 8e+69) && (y <= 5.2e+160)))))) {
		tmp = x * (y * y);
	} else {
		tmp = t + (y * z);
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-9.2d+30)) .or. (.not. (y <= (-15500.0d0))) .and. (y <= (-15000.0d0)) .or. (.not. (y <= 4d+47) .or. (.not. (y <= 8d+69)) .and. (y <= 5.2d+160))) then
        tmp = x * (y * y)
    else
        tmp = t + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9.2e+30) || (!(y <= -15500.0) && ((y <= -15000.0) || !((y <= 4e+47) || (!(y <= 8e+69) && (y <= 5.2e+160)))))) {
		tmp = x * (y * y);
	} else {
		tmp = t + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -9.2e+30) or (not (y <= -15500.0) and ((y <= -15000.0) or not ((y <= 4e+47) or (not (y <= 8e+69) and (y <= 5.2e+160))))):
		tmp = x * (y * y)
	else:
		tmp = t + (y * z)
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -9.2e+30) || (!(y <= -15500.0) && ((y <= -15000.0) || !((y <= 4e+47) || (!(y <= 8e+69) && (y <= 5.2e+160))))))
		tmp = Float64(x * Float64(y * y));
	else
		tmp = Float64(t + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -9.2e+30) || (~((y <= -15500.0)) && ((y <= -15000.0) || ~(((y <= 4e+47) || (~((y <= 8e+69)) && (y <= 5.2e+160)))))))
		tmp = x * (y * y);
	else
		tmp = t + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -9.2e+30], And[N[Not[LessEqual[y, -15500.0]], $MachinePrecision], Or[LessEqual[y, -15000.0], N[Not[Or[LessEqual[y, 4e+47], And[N[Not[LessEqual[y, 8e+69]], $MachinePrecision], LessEqual[y, 5.2e+160]]]], $MachinePrecision]]]], N[(x * N[(y * y), $MachinePrecision]), $MachinePrecision], N[(t + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if y < -9.2e30 or -15500 < y < -15000 or 4.0000000000000002e47 < y < 8.0000000000000006e69 or 5.2000000000000001e160 < y

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 71.2%

      \[\leadsto \color{blue}{x \cdot \left(\frac{y \cdot z}{x} + {y}^{2}\right)} + t \]
   4. Step-by-step derivation

      1. +-commutative 71.2%

         \[\leadsto x \cdot \color{blue}{\left({y}^{2} + \frac{y \cdot z}{x}\right)} + t \]

      2. distribute-lft-in 71.2%

         \[\leadsto \color{blue}{\left(x \cdot {y}^{2} + x \cdot \frac{y \cdot z}{x}\right)} + t \]

      3. unpow2 71.2%

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

      4. associate-*l* 81.2%

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

      5. associate-/l* 81.2%

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

      6. associate-*r* 72.3%

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

      7. distribute-lft-out 99.0%

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

      8. *-commutative 99.0%

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

   5. Simplified 99.0%

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

   6. Taylor expanded in t around 0 84.3%

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

   7. Taylor expanded in y around inf 75.3%

      \[\leadsto x \cdot \left(y \cdot \color{blue}{y}\right) \]

   if -9.2e30 < y < -15500 or -15000 < y < 4.0000000000000002e47 or 8.0000000000000006e69 < y < 5.2000000000000001e160

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 82.6%

      \[\leadsto \color{blue}{z} \cdot y + t \]
3. Recombined 2 regimes into one program.

4. Final simplification 79.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.2 \cdot 10^{+30} \lor \neg \left(y \leq -15500\right) \land \left(y \leq -15000 \lor \neg \left(y \leq 4 \cdot 10^{+47} \lor \neg \left(y \leq 8 \cdot 10^{+69}\right) \land y \leq 5.2 \cdot 10^{+160}\right)\right):\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t + y \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 87.9% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -6.8 \cdot 10^{+120} \lor \neg \left(z \leq 4.8 \cdot 10^{+45}\right) \land \left(z \leq 2.4 \cdot 10^{+116} \lor \neg \left(z \leq 1.4 \cdot 10^{+139}\right)\right):\\ \;\;\;\;t + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;t + y \cdot \left(x \cdot y\right)\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= z -6.8e+120)
         (and (not (<= z 4.8e+45)) (or (<= z 2.4e+116) (not (<= z 1.4e+139)))))
   (+ t (* y z))
   (+ t (* y (* x y)))))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -6.8e+120) || (!(z <= 4.8e+45) && ((z <= 2.4e+116) || !(z <= 1.4e+139)))) {
		tmp = t + (y * z);
	} else {
		tmp = t + (y * (x * y));
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((z <= (-6.8d+120)) .or. (.not. (z <= 4.8d+45)) .and. (z <= 2.4d+116) .or. (.not. (z <= 1.4d+139))) then
        tmp = t + (y * z)
    else
        tmp = t + (y * (x * y))
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((z <= -6.8e+120) || (!(z <= 4.8e+45) && ((z <= 2.4e+116) || !(z <= 1.4e+139)))) {
		tmp = t + (y * z);
	} else {
		tmp = t + (y * (x * y));
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (z <= -6.8e+120) or (not (z <= 4.8e+45) and ((z <= 2.4e+116) or not (z <= 1.4e+139))):
		tmp = t + (y * z)
	else:
		tmp = t + (y * (x * y))
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((z <= -6.8e+120) || (!(z <= 4.8e+45) && ((z <= 2.4e+116) || !(z <= 1.4e+139))))
		tmp = Float64(t + Float64(y * z));
	else
		tmp = Float64(t + Float64(y * Float64(x * y)));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((z <= -6.8e+120) || (~((z <= 4.8e+45)) && ((z <= 2.4e+116) || ~((z <= 1.4e+139)))))
		tmp = t + (y * z);
	else
		tmp = t + (y * (x * y));
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[z, -6.8e+120], And[N[Not[LessEqual[z, 4.8e+45]], $MachinePrecision], Or[LessEqual[z, 2.4e+116], N[Not[LessEqual[z, 1.4e+139]], $MachinePrecision]]]], N[(t + N[(y * z), $MachinePrecision]), $MachinePrecision], N[(t + N[(y * N[(x * y), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if z < -6.79999999999999998e120 or 4.79999999999999979e45 < z < 2.4e116 or 1.3999999999999999e139 < z

   1. Initial program 100.0%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0 84.7%

      \[\leadsto \color{blue}{z} \cdot y + t \]

   if -6.79999999999999998e120 < z < 4.79999999999999979e45 or 2.4e116 < z < 1.3999999999999999e139

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 90.1%

      \[\leadsto \color{blue}{\left(x \cdot y\right)} \cdot y + t \]
   4. Step-by-step derivation

      1. *-commutative 90.1%

         \[\leadsto \color{blue}{\left(y \cdot x\right)} \cdot y + t \]

   5. Simplified 90.1%

      \[\leadsto \color{blue}{\left(y \cdot x\right)} \cdot y + t \]
3. Recombined 2 regimes into one program.

4. Final simplification 88.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -6.8 \cdot 10^{+120} \lor \neg \left(z \leq 4.8 \cdot 10^{+45}\right) \land \left(z \leq 2.4 \cdot 10^{+116} \lor \neg \left(z \leq 1.4 \cdot 10^{+139}\right)\right):\\ \;\;\;\;t + y \cdot z\\ \mathbf{else}:\\ \;\;\;\;t + y \cdot \left(x \cdot y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 64.1% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{+28} \lor \neg \left(y \leq 2 \cdot 10^{-48}\right):\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z t)
 :precision binary64
 (if (or (<= y -9.5e+28) (not (<= y 2e-48))) (* x (* y y)) t))

C

double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9.5e+28) || !(y <= 2e-48)) {
		tmp = x * (y * y);
	} else {
		tmp = t;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    real(8) :: tmp
    if ((y <= (-9.5d+28)) .or. (.not. (y <= 2d-48))) then
        tmp = x * (y * y)
    else
        tmp = t
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z, double t) {
	double tmp;
	if ((y <= -9.5e+28) || !(y <= 2e-48)) {
		tmp = x * (y * y);
	} else {
		tmp = t;
	}
	return tmp;
}

Python

def code(x, y, z, t):
	tmp = 0
	if (y <= -9.5e+28) or not (y <= 2e-48):
		tmp = x * (y * y)
	else:
		tmp = t
	return tmp

Julia

function code(x, y, z, t)
	tmp = 0.0
	if ((y <= -9.5e+28) || !(y <= 2e-48))
		tmp = Float64(x * Float64(y * y));
	else
		tmp = t;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z, t)
	tmp = 0.0;
	if ((y <= -9.5e+28) || ~((y <= 2e-48)))
		tmp = x * (y * y);
	else
		tmp = t;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_, t_] := If[Or[LessEqual[y, -9.5e+28], N[Not[LessEqual[y, 2e-48]], $MachinePrecision]], N[(x * N[(y * y), $MachinePrecision]), $MachinePrecision], t]

Derivation

1. Split input into 2 regimes

2. if y < -9.49999999999999927e28 or 1.9999999999999999e-48 < y

   1. Initial program 99.9%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in x around inf 73.9%

      \[\leadsto \color{blue}{x \cdot \left(\frac{y \cdot z}{x} + {y}^{2}\right)} + t \]
   4. Step-by-step derivation

      1. +-commutative 73.9%

         \[\leadsto x \cdot \color{blue}{\left({y}^{2} + \frac{y \cdot z}{x}\right)} + t \]

      2. distribute-lft-in 73.3%

         \[\leadsto \color{blue}{\left(x \cdot {y}^{2} + x \cdot \frac{y \cdot z}{x}\right)} + t \]

      3. unpow2 73.3%

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

      4. associate-*l* 80.9%

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

      5. associate-/l* 80.9%

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

      6. associate-*r* 78.5%

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

      7. distribute-lft-out 97.9%

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

      8. *-commutative 97.9%

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

   5. Simplified 97.9%

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

   6. Taylor expanded in t around 0 77.3%

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

   7. Taylor expanded in y around inf 64.1%

      \[\leadsto x \cdot \left(y \cdot \color{blue}{y}\right) \]

   if -9.49999999999999927e28 < y < 1.9999999999999999e-48

   1. Initial program 100.0%

      \[\left(x \cdot y + z\right) \cdot y + t \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0 66.5%

      \[\leadsto \color{blue}{t} \]
3. Recombined 2 regimes into one program.

4. Final simplification 65.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -9.5 \cdot 10^{+28} \lor \neg \left(y \leq 2 \cdot 10^{-48}\right):\\ \;\;\;\;x \cdot \left(y \cdot y\right)\\ \mathbf{else}:\\ \;\;\;\;t\\ \end{array} \]
5. Add Preprocessing

Alternative 5: 99.9% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ t + y \cdot \left(z + x \cdot y\right) \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 (+ t (* y (+ z (* x y)))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 99.9%

   \[\left(x \cdot y + z\right) \cdot y + t \]
2. Add Preprocessing

3. Final simplification 99.9%

   \[\leadsto t + y \cdot \left(z + x \cdot y\right) \]
4. Add Preprocessing

Alternative 6: 37.9% accurate, 9.0× speedup

Math

\[\begin{array}{l} \\ t \end{array} \]

FPCore

(FPCore (x y z t) :precision binary64 t)

C

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

Fortran

real(8) function code(x, y, z, t)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8), intent (in) :: t
    code = t
end function

Java

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

Python

def code(x, y, z, t):
	return t

Julia

function code(x, y, z, t)
	return t
end

MATLAB

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

Wolfram

code[x_, y_, z_, t_] := t

Derivation

1. Initial program 99.9%

   \[\left(x \cdot y + z\right) \cdot y + t \]
2. Add Preprocessing

3. Taylor expanded in y around 0 34.4%

   \[\leadsto \color{blue}{t} \]
4. Add Preprocessing

Reproduce

herbie shell --seed 2024107 
(FPCore (x y z t)
  :name "Language.Haskell.HsColour.ColourHighlight:unbase from hscolour-1.23"
  :precision binary64
  (+ (* (+ (* x y) z) y) t))