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

Percentage Accurate: 97.8% → 100.0%

Time: 5.4s

Alternatives: 8

Speedup: 1.3×

• 20.6% 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: 97.8% 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, 0.1× 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

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 2: 56.5% accurate, 0.4× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(-y\right)\\ \mathbf{if}\;x \leq -8.5 \cdot 10^{+98}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq -3.4 \cdot 10^{+45}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1.55 \cdot 10^{-124}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 3.3 \cdot 10^{-191}:\\ \;\;\;\;y\\ \mathbf{elif}\;x \leq 3 \cdot 10^{-63}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 1.32 \cdot 10^{-13}:\\ \;\;\;\;y\\ \mathbf{elif}\;x \leq 7400000000000 \lor \neg \left(x \leq 2.3 \cdot 10^{+80}\right) \land x \leq 1.45 \cdot 10^{+174}:\\ \;\;\;\;x \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- y))))
   (if (<= x -8.5e+98)
     (* x z)
     (if (<= x -3.4e+45)
       t_0
       (if (<= x -1.55e-124)
         (* x z)
         (if (<= x 3.3e-191)
           y
           (if (<= x 3e-63)
             (* x z)
             (if (<= x 1.32e-13)
               y
               (if (or (<= x 7400000000000.0)
                       (and (not (<= x 2.3e+80)) (<= x 1.45e+174)))
                 (* x z)
                 t_0)))))))))

C

double code(double x, double y, double z) {
	double t_0 = x * -y;
	double tmp;
	if (x <= -8.5e+98) {
		tmp = x * z;
	} else if (x <= -3.4e+45) {
		tmp = t_0;
	} else if (x <= -1.55e-124) {
		tmp = x * z;
	} else if (x <= 3.3e-191) {
		tmp = y;
	} else if (x <= 3e-63) {
		tmp = x * z;
	} else if (x <= 1.32e-13) {
		tmp = y;
	} else if ((x <= 7400000000000.0) || (!(x <= 2.3e+80) && (x <= 1.45e+174))) {
		tmp = x * z;
	} else {
		tmp = t_0;
	}
	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) :: t_0
    real(8) :: tmp
    t_0 = x * -y
    if (x <= (-8.5d+98)) then
        tmp = x * z
    else if (x <= (-3.4d+45)) then
        tmp = t_0
    else if (x <= (-1.55d-124)) then
        tmp = x * z
    else if (x <= 3.3d-191) then
        tmp = y
    else if (x <= 3d-63) then
        tmp = x * z
    else if (x <= 1.32d-13) then
        tmp = y
    else if ((x <= 7400000000000.0d0) .or. (.not. (x <= 2.3d+80)) .and. (x <= 1.45d+174)) then
        tmp = x * 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;
	double tmp;
	if (x <= -8.5e+98) {
		tmp = x * z;
	} else if (x <= -3.4e+45) {
		tmp = t_0;
	} else if (x <= -1.55e-124) {
		tmp = x * z;
	} else if (x <= 3.3e-191) {
		tmp = y;
	} else if (x <= 3e-63) {
		tmp = x * z;
	} else if (x <= 1.32e-13) {
		tmp = y;
	} else if ((x <= 7400000000000.0) || (!(x <= 2.3e+80) && (x <= 1.45e+174))) {
		tmp = x * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * -y
	tmp = 0
	if x <= -8.5e+98:
		tmp = x * z
	elif x <= -3.4e+45:
		tmp = t_0
	elif x <= -1.55e-124:
		tmp = x * z
	elif x <= 3.3e-191:
		tmp = y
	elif x <= 3e-63:
		tmp = x * z
	elif x <= 1.32e-13:
		tmp = y
	elif (x <= 7400000000000.0) or (not (x <= 2.3e+80) and (x <= 1.45e+174)):
		tmp = x * z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(-y))
	tmp = 0.0
	if (x <= -8.5e+98)
		tmp = Float64(x * z);
	elseif (x <= -3.4e+45)
		tmp = t_0;
	elseif (x <= -1.55e-124)
		tmp = Float64(x * z);
	elseif (x <= 3.3e-191)
		tmp = y;
	elseif (x <= 3e-63)
		tmp = Float64(x * z);
	elseif (x <= 1.32e-13)
		tmp = y;
	elseif ((x <= 7400000000000.0) || (!(x <= 2.3e+80) && (x <= 1.45e+174)))
		tmp = Float64(x * z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * -y;
	tmp = 0.0;
	if (x <= -8.5e+98)
		tmp = x * z;
	elseif (x <= -3.4e+45)
		tmp = t_0;
	elseif (x <= -1.55e-124)
		tmp = x * z;
	elseif (x <= 3.3e-191)
		tmp = y;
	elseif (x <= 3e-63)
		tmp = x * z;
	elseif (x <= 1.32e-13)
		tmp = y;
	elseif ((x <= 7400000000000.0) || (~((x <= 2.3e+80)) && (x <= 1.45e+174)))
		tmp = x * z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * (-y)), $MachinePrecision]}, If[LessEqual[x, -8.5e+98], N[(x * z), $MachinePrecision], If[LessEqual[x, -3.4e+45], t$95$0, If[LessEqual[x, -1.55e-124], N[(x * z), $MachinePrecision], If[LessEqual[x, 3.3e-191], y, If[LessEqual[x, 3e-63], N[(x * z), $MachinePrecision], If[LessEqual[x, 1.32e-13], y, If[Or[LessEqual[x, 7400000000000.0], And[N[Not[LessEqual[x, 2.3e+80]], $MachinePrecision], LessEqual[x, 1.45e+174]]], N[(x * z), $MachinePrecision], t$95$0]]]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 3: 79.6% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z - y\right)\\ \mathbf{if}\;x \leq -2.7 \cdot 10^{-124}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 5.5 \cdot 10^{-191}:\\ \;\;\;\;y\\ \mathbf{elif}\;x \leq 3 \cdot 10^{-63}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 2.1 \cdot 10^{-13}:\\ \;\;\;\;y\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- z y))))
   (if (<= x -2.7e-124)
     t_0
     (if (<= x 5.5e-191)
       y
       (if (<= x 3e-63) (* x z) (if (<= x 2.1e-13) y t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z - y);
	double tmp;
	if (x <= -2.7e-124) {
		tmp = t_0;
	} else if (x <= 5.5e-191) {
		tmp = y;
	} else if (x <= 3e-63) {
		tmp = x * z;
	} else if (x <= 2.1e-13) {
		tmp = y;
	} else {
		tmp = t_0;
	}
	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) :: t_0
    real(8) :: tmp
    t_0 = x * (z - y)
    if (x <= (-2.7d-124)) then
        tmp = t_0
    else if (x <= 5.5d-191) then
        tmp = y
    else if (x <= 3d-63) then
        tmp = x * z
    else if (x <= 2.1d-13) then
        tmp = y
    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 * (z - y);
	double tmp;
	if (x <= -2.7e-124) {
		tmp = t_0;
	} else if (x <= 5.5e-191) {
		tmp = y;
	} else if (x <= 3e-63) {
		tmp = x * z;
	} else if (x <= 2.1e-13) {
		tmp = y;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z - y)
	tmp = 0
	if x <= -2.7e-124:
		tmp = t_0
	elif x <= 5.5e-191:
		tmp = y
	elif x <= 3e-63:
		tmp = x * z
	elif x <= 2.1e-13:
		tmp = y
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z - y))
	tmp = 0.0
	if (x <= -2.7e-124)
		tmp = t_0;
	elseif (x <= 5.5e-191)
		tmp = y;
	elseif (x <= 3e-63)
		tmp = Float64(x * z);
	elseif (x <= 2.1e-13)
		tmp = y;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z - y);
	tmp = 0.0;
	if (x <= -2.7e-124)
		tmp = t_0;
	elseif (x <= 5.5e-191)
		tmp = y;
	elseif (x <= 3e-63)
		tmp = x * z;
	elseif (x <= 2.1e-13)
		tmp = y;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z - y), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -2.7e-124], t$95$0, If[LessEqual[x, 5.5e-191], y, If[LessEqual[x, 3e-63], N[(x * z), $MachinePrecision], If[LessEqual[x, 2.1e-13], y, t$95$0]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 4: 79.7% accurate, 0.7× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(z - y\right)\\ t_1 := y \cdot \left(1 - x\right)\\ \mathbf{if}\;x \leq -4.5 \cdot 10^{-124}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq 5.5 \cdot 10^{-191}:\\ \;\;\;\;t_1\\ \mathbf{elif}\;x \leq 3.2 \cdot 10^{-63}:\\ \;\;\;\;x \cdot z\\ \mathbf{elif}\;x \leq 1.75 \cdot 10^{-12}:\\ \;\;\;\;t_1\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- z y))) (t_1 (* y (- 1.0 x))))
   (if (<= x -4.5e-124)
     t_0
     (if (<= x 5.5e-191)
       t_1
       (if (<= x 3.2e-63) (* x z) (if (<= x 1.75e-12) t_1 t_0))))))

C

double code(double x, double y, double z) {
	double t_0 = x * (z - y);
	double t_1 = y * (1.0 - x);
	double tmp;
	if (x <= -4.5e-124) {
		tmp = t_0;
	} else if (x <= 5.5e-191) {
		tmp = t_1;
	} else if (x <= 3.2e-63) {
		tmp = x * z;
	} else if (x <= 1.75e-12) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	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) :: t_0
    real(8) :: t_1
    real(8) :: tmp
    t_0 = x * (z - y)
    t_1 = y * (1.0d0 - x)
    if (x <= (-4.5d-124)) then
        tmp = t_0
    else if (x <= 5.5d-191) then
        tmp = t_1
    else if (x <= 3.2d-63) then
        tmp = x * z
    else if (x <= 1.75d-12) then
        tmp = t_1
    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 * (z - y);
	double t_1 = y * (1.0 - x);
	double tmp;
	if (x <= -4.5e-124) {
		tmp = t_0;
	} else if (x <= 5.5e-191) {
		tmp = t_1;
	} else if (x <= 3.2e-63) {
		tmp = x * z;
	} else if (x <= 1.75e-12) {
		tmp = t_1;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * (z - y)
	t_1 = y * (1.0 - x)
	tmp = 0
	if x <= -4.5e-124:
		tmp = t_0
	elif x <= 5.5e-191:
		tmp = t_1
	elif x <= 3.2e-63:
		tmp = x * z
	elif x <= 1.75e-12:
		tmp = t_1
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(z - y))
	t_1 = Float64(y * Float64(1.0 - x))
	tmp = 0.0
	if (x <= -4.5e-124)
		tmp = t_0;
	elseif (x <= 5.5e-191)
		tmp = t_1;
	elseif (x <= 3.2e-63)
		tmp = Float64(x * z);
	elseif (x <= 1.75e-12)
		tmp = t_1;
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * (z - y);
	t_1 = y * (1.0 - x);
	tmp = 0.0;
	if (x <= -4.5e-124)
		tmp = t_0;
	elseif (x <= 5.5e-191)
		tmp = t_1;
	elseif (x <= 3.2e-63)
		tmp = x * z;
	elseif (x <= 1.75e-12)
		tmp = t_1;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * N[(z - y), $MachinePrecision]), $MachinePrecision]}, Block[{t$95$1 = N[(y * N[(1.0 - x), $MachinePrecision]), $MachinePrecision]}, If[LessEqual[x, -4.5e-124], t$95$0, If[LessEqual[x, 5.5e-191], t$95$1, If[LessEqual[x, 3.2e-63], N[(x * z), $MachinePrecision], If[LessEqual[x, 1.75e-12], t$95$1, t$95$0]]]]]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 5: 56.8% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -3.6 \cdot 10^{-124} \lor \neg \left(x \leq 3.3 \cdot 10^{-191} \lor \neg \left(x \leq 3.2 \cdot 10^{-63}\right) \land x \leq 1.32 \cdot 10^{-13}\right):\\ \;\;\;\;x \cdot z\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -3.6e-124)
         (not (or (<= x 3.3e-191) (and (not (<= x 3.2e-63)) (<= x 1.32e-13)))))
   (* x z)
   y))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.6e-124) || !((x <= 3.3e-191) || (!(x <= 3.2e-63) && (x <= 1.32e-13)))) {
		tmp = x * z;
	} else {
		tmp = y;
	}
	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 <= (-3.6d-124)) .or. (.not. (x <= 3.3d-191) .or. (.not. (x <= 3.2d-63)) .and. (x <= 1.32d-13))) then
        tmp = x * z
    else
        tmp = y
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -3.6e-124) || !((x <= 3.3e-191) || (!(x <= 3.2e-63) && (x <= 1.32e-13)))) {
		tmp = x * z;
	} else {
		tmp = y;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -3.6e-124) or not ((x <= 3.3e-191) or (not (x <= 3.2e-63) and (x <= 1.32e-13))):
		tmp = x * z
	else:
		tmp = y
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -3.6e-124) || !((x <= 3.3e-191) || (!(x <= 3.2e-63) && (x <= 1.32e-13))))
		tmp = Float64(x * z);
	else
		tmp = y;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -3.6e-124) || ~(((x <= 3.3e-191) || (~((x <= 3.2e-63)) && (x <= 1.32e-13)))))
		tmp = x * z;
	else
		tmp = y;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -3.6e-124], N[Not[Or[LessEqual[x, 3.3e-191], And[N[Not[LessEqual[x, 3.2e-63]], $MachinePrecision], LessEqual[x, 1.32e-13]]]], $MachinePrecision]], N[(x * z), $MachinePrecision], y]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 6: 98.7% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -80000000 \lor \neg \left(x \leq 4 \cdot 10^{-12}\right):\\ \;\;\;\;x \cdot \left(z - y\right)\\ \mathbf{else}:\\ \;\;\;\;y + x \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -80000000.0) (not (<= x 4e-12))) (* x (- z y)) (+ y (* x z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -80000000.0) || !(x <= 4e-12)) {
		tmp = x * (z - y);
	} else {
		tmp = y + (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 <= (-80000000.0d0)) .or. (.not. (x <= 4d-12))) then
        tmp = x * (z - y)
    else
        tmp = y + (x * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -80000000.0) || !(x <= 4e-12)) {
		tmp = x * (z - y);
	} else {
		tmp = y + (x * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -80000000.0) or not (x <= 4e-12):
		tmp = x * (z - y)
	else:
		tmp = y + (x * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -80000000.0) || !(x <= 4e-12))
		tmp = Float64(x * Float64(z - y));
	else
		tmp = Float64(y + Float64(x * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -80000000.0) || ~((x <= 4e-12)))
		tmp = x * (z - y);
	else
		tmp = y + (x * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -80000000.0], N[Not[LessEqual[x, 4e-12]], $MachinePrecision]], N[(x * N[(z - y), $MachinePrecision]), $MachinePrecision], N[(y + N[(x * z), $MachinePrecision]), $MachinePrecision]]

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 7: 100.0% accurate, 1.3× speedup

Math

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

FPCore

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

C

double code(double x, double y, double z) {
	return y + (x * (z - 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 + (x * (z - y))
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Alternative 8: 37.3% accurate, 9.0× 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

&prev;&pcontext;&pcontext2;&ctx;
1. Add Preprocessing

Developer target: 100.0% accurate, 1.3× 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 2024008 
(FPCore (x y z)
  :name "Diagrams.Color.HSV:lerp  from diagrams-contrib-1.3.0.5"
  :precision binary64

  :herbie-target
  (- y (* x (- y z)))

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