Diagrams.Color.HSV:lerp from diagrams-contrib-1.3.0.5

Percentage Accurate: 98.1% → 100.0%

Time: 8.2s

Alternatives: 5

Speedup: 1.7×

• 21.1% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

\[\begin{array}{l} \\ \left(1 - x\right) \cdot y + x \cdot z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* (- 1.0 x) y) (* x z)))

C

double code(double x, double y, double z) {
	return ((1.0 - x) * y) + (x * z);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((1.0d0 - x) * y) + (x * z)
end function

Java

public static double code(double x, double y, double z) {
	return ((1.0 - x) * y) + (x * z);
}

Python

def code(x, y, z):
	return ((1.0 - x) * y) + (x * z)

Julia

function code(x, y, z)
	return Float64(Float64(Float64(1.0 - x) * y) + Float64(x * z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((1.0 - x) * y) + (x * z);
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision]

Initial Program: 98.1% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(1 - x\right) \cdot y + x \cdot z \end{array} \]

FPCore

(FPCore (x y z) :precision binary64 (+ (* (- 1.0 x) y) (* x z)))

C

double code(double x, double y, double z) {
	return ((1.0 - x) * y) + (x * z);
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    code = ((1.0d0 - x) * y) + (x * z)
end function

Java

public static double code(double x, double y, double z) {
	return ((1.0 - x) * y) + (x * z);
}

Python

def code(x, y, z):
	return ((1.0 - x) * y) + (x * z)

Julia

function code(x, y, z)
	return Float64(Float64(Float64(1.0 - x) * y) + Float64(x * z))
end

MATLAB

function tmp = code(x, y, z)
	tmp = ((1.0 - x) * y) + (x * z);
end

Wolfram

code[x_, y_, z_] := N[(N[(N[(1.0 - x), $MachinePrecision] * y), $MachinePrecision] + N[(x * z), $MachinePrecision]), $MachinePrecision]

Alternative 1: 100.0% accurate, 1.7× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 98.0%

   \[\left(1 - x\right) \cdot y + x \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{y + x \cdot \left(z + -1 \cdot y\right)} \]
4. Step-by-step derivation

   1. +-commutative N/A

      \[\leadsto y + x \cdot \color{blue}{\left(-1 \cdot y + z\right)} \]

   2. distribute-lft-in N/A

      \[\leadsto y + \color{blue}{\left(x \cdot \left(-1 \cdot y\right) + x \cdot z\right)} \]

   3. mul-1-neg N/A

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

   4. distribute-rgt-neg-in N/A

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

   5. mul-1-neg N/A

      \[\leadsto y + \left(\color{blue}{-1 \cdot \left(x \cdot y\right)} + x \cdot z\right) \]

   6. associate-+r+ N/A

      \[\leadsto \color{blue}{\left(y + -1 \cdot \left(x \cdot y\right)\right) + x \cdot z} \]

   7. mul-1-neg N/A

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

   8. unsub-neg N/A

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

   9. associate-+l- N/A

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

   10. distribute-lft-out-- N/A

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

   11. unsub-neg N/A

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

   12. mul-1-neg N/A

       \[\leadsto y - x \cdot \left(y + \color{blue}{-1 \cdot z}\right) \]

   13. sub-neg N/A

       \[\leadsto \color{blue}{y + \left(\mathsf{neg}\left(x \cdot \left(y + -1 \cdot z\right)\right)\right)} \]

   14. mul-1-neg N/A

       \[\leadsto y + \color{blue}{-1 \cdot \left(x \cdot \left(y + -1 \cdot z\right)\right)} \]

   15. +-commutative N/A

       \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(y + -1 \cdot z\right)\right) + y} \]

   16. mul-1-neg N/A

       \[\leadsto \color{blue}{\left(\mathsf{neg}\left(x \cdot \left(y + -1 \cdot z\right)\right)\right)} + y \]

   17. distribute-rgt-neg-in N/A

       \[\leadsto \color{blue}{x \cdot \left(\mathsf{neg}\left(\left(y + -1 \cdot z\right)\right)\right)} + y \]

   18. accelerator-lowering-fma.f64 N/A

       \[\leadsto \color{blue}{\mathsf{fma}\left(x, \mathsf{neg}\left(\left(y + -1 \cdot z\right)\right), y\right)} \]

5. Simplified 100.0%

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

Alternative 2: 84.9% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := \mathsf{fma}\left(0 - y, x, y\right)\\ \mathbf{if}\;y \leq -1.35 \cdot 10^{-40}:\\ \;\;\;\;t\_0\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-68}:\\ \;\;\;\;\mathsf{fma}\left(z, x, y\right)\\ \mathbf{else}:\\ \;\;\;\;t\_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (fma (- 0.0 y) x y)))
   (if (<= y -1.35e-40) t_0 (if (<= y 4e-68) (fma z x y) t_0))))

C

double code(double x, double y, double z) {
	double t_0 = fma((0.0 - y), x, y);
	double tmp;
	if (y <= -1.35e-40) {
		tmp = t_0;
	} else if (y <= 4e-68) {
		tmp = fma(z, x, y);
	} else {
		tmp = t_0;
	}
	return tmp;
}

Julia

function code(x, y, z)
	t_0 = fma(Float64(0.0 - y), x, y)
	tmp = 0.0
	if (y <= -1.35e-40)
		tmp = t_0;
	elseif (y <= 4e-68)
		tmp = fma(z, x, y);
	else
		tmp = t_0;
	end
	return tmp
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(N[(0.0 - y), $MachinePrecision] * x + y), $MachinePrecision]}, If[LessEqual[y, -1.35e-40], t$95$0, If[LessEqual[y, 4e-68], N[(z * x + y), $MachinePrecision], t$95$0]]]

Derivation

1. Split input into 2 regimes

2. if y < -1.35e-40 or 4.00000000000000027e-68 < y

   1. Initial program 96.6%

      \[\left(1 - x\right) \cdot y + x \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{y + x \cdot \left(z + -1 \cdot y\right)} \]
   4. Step-by-step derivation

      1. +-commutative N/A

         \[\leadsto y + x \cdot \color{blue}{\left(-1 \cdot y + z\right)} \]

      2. distribute-lft-in N/A

         \[\leadsto y + \color{blue}{\left(x \cdot \left(-1 \cdot y\right) + x \cdot z\right)} \]

      3. mul-1-neg N/A

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

      4. distribute-rgt-neg-in N/A

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

      5. mul-1-neg N/A

         \[\leadsto y + \left(\color{blue}{-1 \cdot \left(x \cdot y\right)} + x \cdot z\right) \]

      6. associate-+r+ N/A

         \[\leadsto \color{blue}{\left(y + -1 \cdot \left(x \cdot y\right)\right) + x \cdot z} \]

      7. mul-1-neg N/A

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

      8. unsub-neg N/A

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

      9. associate-+l- N/A

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

      10. distribute-lft-out-- N/A

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

      11. unsub-neg N/A

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

      12. mul-1-neg N/A

          \[\leadsto y - x \cdot \left(y + \color{blue}{-1 \cdot z}\right) \]

      13. sub-neg N/A

          \[\leadsto \color{blue}{y + \left(\mathsf{neg}\left(x \cdot \left(y + -1 \cdot z\right)\right)\right)} \]

      14. mul-1-neg N/A

          \[\leadsto y + \color{blue}{-1 \cdot \left(x \cdot \left(y + -1 \cdot z\right)\right)} \]

      15. +-commutative N/A

          \[\leadsto \color{blue}{-1 \cdot \left(x \cdot \left(y + -1 \cdot z\right)\right) + y} \]

      16. mul-1-neg N/A

          \[\leadsto \color{blue}{\left(\mathsf{neg}\left(x \cdot \left(y + -1 \cdot z\right)\right)\right)} + y \]

      17. distribute-rgt-neg-in N/A

          \[\leadsto \color{blue}{x \cdot \left(\mathsf{neg}\left(\left(y + -1 \cdot z\right)\right)\right)} + y \]

      18. accelerator-lowering-fma.f64 N/A

          \[\leadsto \color{blue}{\mathsf{fma}\left(x, \mathsf{neg}\left(\left(y + -1 \cdot z\right)\right), y\right)} \]

   5. Simplified 100.0%

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

      1. sub-neg N/A

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

      2. distribute-lft-in N/A

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

      3. distribute-rgt-neg-in N/A

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

      4. distribute-lft-neg-out N/A

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

      5. sub0-neg N/A

         \[\leadsto \left(x \cdot z + \color{blue}{\left(0 - x\right)} \cdot y\right) + y \]

      6. *-commutative N/A

         \[\leadsto \left(x \cdot z + \color{blue}{y \cdot \left(0 - x\right)}\right) + y \]

      7. +-commutative N/A

         \[\leadsto \color{blue}{\left(y \cdot \left(0 - x\right) + x \cdot z\right)} + y \]

      8. associate-+l+ N/A

         \[\leadsto \color{blue}{y \cdot \left(0 - x\right) + \left(x \cdot z + y\right)} \]

      9. +-commutative N/A

         \[\leadsto y \cdot \left(0 - x\right) + \color{blue}{\left(y + x \cdot z\right)} \]

      10. sub0-neg N/A

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

      11. distribute-rgt-neg-out N/A

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

      12. distribute-lft-neg-in N/A

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

      13. accelerator-lowering-fma.f64 N/A

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

      14. neg-sub0 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{0 - y}, x, y + x \cdot z\right) \]

      15. --lowering--.f64 N/A

          \[\leadsto \mathsf{fma}\left(\color{blue}{0 - y}, x, y + x \cdot z\right) \]

      16. +-commutative N/A

          \[\leadsto \mathsf{fma}\left(0 - y, x, \color{blue}{x \cdot z + y}\right) \]

      17. accelerator-lowering-fma.f64 97.9

          \[\leadsto \mathsf{fma}\left(0 - y, x, \color{blue}{\mathsf{fma}\left(x, z, y\right)}\right) \]

   7. Applied egg-rr 97.9%

      \[\leadsto \color{blue}{\mathsf{fma}\left(0 - y, x, \mathsf{fma}\left(x, z, y\right)\right)} \]
   8. Step-by-step derivation

      1. sub0-neg N/A

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

      2. neg-lowering-neg.f64 97.9

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

   9. Applied egg-rr 97.9%

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

   10. Taylor expanded in x around 0

       \[\leadsto \mathsf{fma}\left(\mathsf{neg}\left(y\right), x, \color{blue}{y}\right) \]
   11. Step-by-step derivation

   12. Simplified 88.2%

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

   if -1.35e-40 < y < 4.00000000000000027e-68

   1. Initial program 100.0%

      \[\left(1 - x\right) \cdot y + x \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{y} + x \cdot z \]
   4. Step-by-step derivation

   5. Simplified 88.5%

      \[\leadsto \color{blue}{y} + x \cdot z \]
   6. Step-by-step derivation

      1. +-commutative N/A

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

      2. *-commutative N/A

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

      3. accelerator-lowering-fma.f64 88.5

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

   7. Applied egg-rr 88.5%

      \[\leadsto \color{blue}{\mathsf{fma}\left(z, x, y\right)} \]
3. Recombined 2 regimes into one program.

4. Final simplification 88.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;y \leq -1.35 \cdot 10^{-40}:\\ \;\;\;\;\mathsf{fma}\left(0 - y, x, y\right)\\ \mathbf{elif}\;y \leq 4 \cdot 10^{-68}:\\ \;\;\;\;\mathsf{fma}\left(z, x, y\right)\\ \mathbf{else}:\\ \;\;\;\;\mathsf{fma}\left(0 - y, x, y\right)\\ \end{array} \]
5. Add Preprocessing

Alternative 3: 61.3% accurate, 0.9× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -4.8 \cdot 10^{-53}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 5.6 \cdot 10^{-29}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= x -4.8e-53) (* x z) (if (<= x 5.6e-29) y (* x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if (x <= -4.8e-53) {
		tmp = x * z;
	} else if (x <= 5.6e-29) {
		tmp = y;
	} else {
		tmp = x * z;
	}
	return tmp;
}

Fortran

real(8) function code(x, y, z)
    real(8), intent (in) :: x
    real(8), intent (in) :: y
    real(8), intent (in) :: z
    real(8) :: tmp
    if (x <= (-4.8d-53)) then
        tmp = x * z
    else if (x <= 5.6d-29) then
        tmp = y
    else
        tmp = x * z
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (x <= -4.8e-53) {
		tmp = x * z;
	} else if (x <= 5.6e-29) {
		tmp = y;
	} else {
		tmp = x * z;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if x <= -4.8e-53:
		tmp = x * z
	elif x <= 5.6e-29:
		tmp = y
	else:
		tmp = x * z
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (x <= -4.8e-53)
		tmp = Float64(x * z);
	elseif (x <= 5.6e-29)
		tmp = y;
	else
		tmp = Float64(x * z);
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (x <= -4.8e-53)
		tmp = x * z;
	elseif (x <= 5.6e-29)
		tmp = y;
	else
		tmp = x * z;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[x, -4.8e-53], N[(x * z), $MachinePrecision], If[LessEqual[x, 5.6e-29], y, N[(x * z), $MachinePrecision]]]

Derivation

1. Split input into 2 regimes

2. if x < -4.80000000000000015e-53 or 5.6000000000000005e-29 < x

   1. Initial program 97.0%

      \[\left(1 - x\right) \cdot y + x \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in y around 0

      \[\leadsto \color{blue}{x \cdot z} \]
   4. Step-by-step derivation

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{x \cdot z + 0} \]

      2. accelerator-lowering-fma.f64 50.8

         \[\leadsto \color{blue}{\mathsf{fma}\left(x, z, 0\right)} \]

   5. Simplified 50.8%

      \[\leadsto \color{blue}{\mathsf{fma}\left(x, z, 0\right)} \]
   6. Step-by-step derivation

      1. +-rgt-identity N/A

         \[\leadsto \color{blue}{x \cdot z} \]

      2. *-commutative N/A

         \[\leadsto \color{blue}{z \cdot x} \]

      3. *-lowering-*.f64 50.8

         \[\leadsto \color{blue}{z \cdot x} \]

   7. Applied egg-rr 50.8%

      \[\leadsto \color{blue}{z \cdot x} \]

   if -4.80000000000000015e-53 < x < 5.6000000000000005e-29

   1. Initial program 100.0%

      \[\left(1 - x\right) \cdot y + x \cdot z \]
   2. Add Preprocessing

   3. Taylor expanded in x around 0

      \[\leadsto \color{blue}{y} \]
   4. Step-by-step derivation

   5. Simplified 82.4%

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

4. Final simplification 62.1%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -4.8 \cdot 10^{-53}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 5.6 \cdot 10^{-29}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;x \cdot z\\ \end{array} \]
5. Add Preprocessing

Alternative 4: 75.8% accurate, 2.4× speedup

Math

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

FPCore

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

C

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

Julia

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

Wolfram

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

Derivation

1. Initial program 98.0%

   \[\left(1 - x\right) \cdot y + x \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{y} + x \cdot z \]
4. Step-by-step derivation

5. Simplified 70.5%

   \[\leadsto \color{blue}{y} + x \cdot z \]
6. Step-by-step derivation

   1. +-commutative N/A

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

   2. *-commutative N/A

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

   3. accelerator-lowering-fma.f64 70.5

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

7. Applied egg-rr 70.5%

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

Alternative 5: 35.9% accurate, 17.0× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return 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 = y
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := y

Derivation

1. Initial program 98.0%

   \[\left(1 - x\right) \cdot y + x \cdot z \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{y} \]
4. Step-by-step derivation

5. Simplified 33.3%

   \[\leadsto \color{blue}{y} \]
6. Add Preprocessing

Developer Target 1: 100.0% accurate, 1.4× speedup

Math

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

FPCore

(FPCore (x y z) :precision binary64 (- y (* x (- y z))))

C

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

Fortran

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Reproduce

herbie shell --seed 2024195 
(FPCore (x y z)
  :name "Diagrams.Color.HSV:lerp  from diagrams-contrib-1.3.0.5"
  :precision binary64

  :alt
  (! :herbie-platform default (- y (* x (- y z))))

  (+ (* (- 1.0 x) y) (* x z)))