Diagrams.ThreeD.Shapes:frustum from diagrams-lib-1.3.0.3, B

Percentage Accurate: 100.0% → 100.0%

Time: 2.9s

Alternatives: 6

Speedup: 1.0×

• 20.8% of points produce a very large (infinite) output. You may want to add a precondition.

Specification

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Initial Program: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Alternative 1: 100.0% accurate, 1.0× speedup

Math

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

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

1. Initial program 100.0%

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

2. Final simplification 100.0%

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

Alternative 2: 61.4% accurate, 0.6× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -7 \cdot 10^{+237}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq -8.2 \cdot 10^{+44}:\\ \;\;\;\;z \cdot \left(-x\right)\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{-16} \lor \neg \left(z \leq 3.1 \cdot 10^{-66}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -7e+237)
   (* y z)
   (if (<= z -8.2e+44)
     (* z (- x))
     (if (or (<= z -2.1e-16) (not (<= z 3.1e-66))) (* y z) x))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -7e+237) {
		tmp = y * z;
	} else if (z <= -8.2e+44) {
		tmp = z * -x;
	} else if ((z <= -2.1e-16) || !(z <= 3.1e-66)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	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 (z <= (-7d+237)) then
        tmp = y * z
    else if (z <= (-8.2d+44)) then
        tmp = z * -x
    else if ((z <= (-2.1d-16)) .or. (.not. (z <= 3.1d-66))) then
        tmp = y * z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -7e+237) {
		tmp = y * z;
	} else if (z <= -8.2e+44) {
		tmp = z * -x;
	} else if ((z <= -2.1e-16) || !(z <= 3.1e-66)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -7e+237:
		tmp = y * z
	elif z <= -8.2e+44:
		tmp = z * -x
	elif (z <= -2.1e-16) or not (z <= 3.1e-66):
		tmp = y * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -7e+237)
		tmp = Float64(y * z);
	elseif (z <= -8.2e+44)
		tmp = Float64(z * Float64(-x));
	elseif ((z <= -2.1e-16) || !(z <= 3.1e-66))
		tmp = Float64(y * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -7e+237)
		tmp = y * z;
	elseif (z <= -8.2e+44)
		tmp = z * -x;
	elseif ((z <= -2.1e-16) || ~((z <= 3.1e-66)))
		tmp = y * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -7e+237], N[(y * z), $MachinePrecision], If[LessEqual[z, -8.2e+44], N[(z * (-x)), $MachinePrecision], If[Or[LessEqual[z, -2.1e-16], N[Not[LessEqual[z, 3.1e-66]], $MachinePrecision]], N[(y * z), $MachinePrecision], x]]]

Derivation

1. Split input into 3 regimes

2. if z < -6.99999999999999976e237 or -8.1999999999999993e44 < z < -2.1000000000000001e-16 or 3.0999999999999997e-66 < z

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 73.2%

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

      1. *-commutative 73.2%

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

   4. Simplified 73.2%

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

   5. Taylor expanded in x around 0 70.3%

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

   if -6.99999999999999976e237 < z < -8.1999999999999993e44

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 63.8%

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

      1. mul-1-neg 63.8%

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

      2. distribute-lft-neg-out 63.8%

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

      3. *-commutative 63.8%

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

   4. Simplified 63.8%

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

   5. Taylor expanded in z around inf 63.8%

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

      1. mul-1-neg 63.8%

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

      2. *-commutative 63.8%

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

      3. distribute-rgt-neg-in 63.8%

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

   7. Simplified 63.8%

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

   if -2.1000000000000001e-16 < z < 3.0999999999999997e-66

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 100.0%

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

      1. *-commutative 100.0%

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

   4. Simplified 100.0%

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

   5. Taylor expanded in x around inf 73.1%

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

4. Final simplification 70.9%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -7 \cdot 10^{+237}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq -8.2 \cdot 10^{+44}:\\ \;\;\;\;z \cdot \left(-x\right)\\ \mathbf{elif}\;z \leq -2.1 \cdot 10^{-16} \lor \neg \left(z \leq 3.1 \cdot 10^{-66}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 3: 84.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{+83} \lor \neg \left(x \leq 9 \cdot 10^{+177}\right):\\ \;\;\;\;x - x \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -2e+83) (not (<= x 9e+177))) (- x (* x z)) (+ x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2e+83) || !(x <= 9e+177)) {
		tmp = x - (x * z);
	} else {
		tmp = x + (y * 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 <= (-2d+83)) .or. (.not. (x <= 9d+177))) then
        tmp = x - (x * z)
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((x <= -2e+83) || !(x <= 9e+177)) {
		tmp = x - (x * z);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -2e+83) or not (x <= 9e+177):
		tmp = x - (x * z)
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -2e+83) || !(x <= 9e+177))
		tmp = Float64(x - Float64(x * z));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((x <= -2e+83) || ~((x <= 9e+177)))
		tmp = x - (x * z);
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -2e+83], N[Not[LessEqual[x, 9e+177]], $MachinePrecision]], N[(x - N[(x * z), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

1. Split input into 2 regimes

2. if x < -2.00000000000000006e83 or 8.9999999999999994e177 < x

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 96.9%

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

      1. mul-1-neg 96.9%

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

      2. distribute-lft-neg-out 96.9%

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

      3. *-commutative 96.9%

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

   4. Simplified 96.9%

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

      1. distribute-rgt-neg-out 96.9%

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

      2. unsub-neg 96.9%

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

      3. *-commutative 96.9%

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

   6. Applied egg-rr 96.9%

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

   if -2.00000000000000006e83 < x < 8.9999999999999994e177

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 89.3%

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

      1. *-commutative 89.3%

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

   4. Simplified 89.3%

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

4. Final simplification 91.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;x \leq -2 \cdot 10^{+83} \lor \neg \left(x \leq 9 \cdot 10^{+177}\right):\\ \;\;\;\;x - x \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]

Alternative 4: 75.4% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -1 \cdot 10^{+235}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq -1.4 \cdot 10^{+44}:\\ \;\;\;\;z \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (<= z -1e+235) (* y z) (if (<= z -1.4e+44) (* z (- x)) (+ x (* y z)))))

C

double code(double x, double y, double z) {
	double tmp;
	if (z <= -1e+235) {
		tmp = y * z;
	} else if (z <= -1.4e+44) {
		tmp = z * -x;
	} else {
		tmp = x + (y * 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 (z <= (-1d+235)) then
        tmp = y * z
    else if (z <= (-1.4d+44)) then
        tmp = z * -x
    else
        tmp = x + (y * z)
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if (z <= -1e+235) {
		tmp = y * z;
	} else if (z <= -1.4e+44) {
		tmp = z * -x;
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if z <= -1e+235:
		tmp = y * z
	elif z <= -1.4e+44:
		tmp = z * -x
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if (z <= -1e+235)
		tmp = Float64(y * z);
	elseif (z <= -1.4e+44)
		tmp = Float64(z * Float64(-x));
	else
		tmp = Float64(x + Float64(y * z));
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if (z <= -1e+235)
		tmp = y * z;
	elseif (z <= -1.4e+44)
		tmp = z * -x;
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[LessEqual[z, -1e+235], N[(y * z), $MachinePrecision], If[LessEqual[z, -1.4e+44], N[(z * (-x)), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]]

Derivation

1. Split input into 3 regimes

2. if z < -1.0000000000000001e235

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 88.9%

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

      1. *-commutative 88.9%

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

   4. Simplified 88.9%

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

   5. Taylor expanded in x around 0 88.9%

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

   if -1.0000000000000001e235 < z < -1.4e44

   1. Initial program 100.0%

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

   2. Taylor expanded in y around 0 63.8%

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

      1. mul-1-neg 63.8%

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

      2. distribute-lft-neg-out 63.8%

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

      3. *-commutative 63.8%

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

   4. Simplified 63.8%

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

   5. Taylor expanded in z around inf 63.8%

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

      1. mul-1-neg 63.8%

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

      2. *-commutative 63.8%

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

      3. distribute-rgt-neg-in 63.8%

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

   7. Simplified 63.8%

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

   if -1.4e44 < z

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 90.0%

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

      1. *-commutative 90.0%

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

   4. Simplified 90.0%

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

4. Final simplification 86.4%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -1 \cdot 10^{+235}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq -1.4 \cdot 10^{+44}:\\ \;\;\;\;z \cdot \left(-x\right)\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \]

Alternative 5: 61.4% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -2 \cdot 10^{-18} \lor \neg \left(z \leq 5.5 \cdot 10^{-68}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -2e-18) (not (<= z 5.5e-68))) (* y z) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2e-18) || !(z <= 5.5e-68)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	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 ((z <= (-2d-18)) .or. (.not. (z <= 5.5d-68))) then
        tmp = y * z
    else
        tmp = x
    end if
    code = tmp
end function

Java

public static double code(double x, double y, double z) {
	double tmp;
	if ((z <= -2e-18) || !(z <= 5.5e-68)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -2e-18) or not (z <= 5.5e-68):
		tmp = y * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -2e-18) || !(z <= 5.5e-68))
		tmp = Float64(y * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -2e-18) || ~((z <= 5.5e-68)))
		tmp = y * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -2e-18], N[Not[LessEqual[z, 5.5e-68]], $MachinePrecision]], N[(y * z), $MachinePrecision], x]

Derivation

1. Split input into 2 regimes

2. if z < -2.0000000000000001e-18 or 5.5000000000000003e-68 < z

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 63.6%

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

      1. *-commutative 63.6%

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

   4. Simplified 63.6%

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

   5. Taylor expanded in x around 0 61.2%

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

   if -2.0000000000000001e-18 < z < 5.5000000000000003e-68

   1. Initial program 100.0%

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

   2. Taylor expanded in y around inf 100.0%

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

      1. *-commutative 100.0%

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

   4. Simplified 100.0%

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

   5. Taylor expanded in x around inf 73.1%

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

4. Final simplification 67.6%

   \[\leadsto \begin{array}{l} \mathbf{if}\;z \leq -2 \cdot 10^{-18} \lor \neg \left(z \leq 5.5 \cdot 10^{-68}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \]

Alternative 6: 35.5% accurate, 7.0× speedup

Math

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

FPCore

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

C

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

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
end function

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

code[x_, y_, z_] := x

Derivation

1. Initial program 100.0%

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

2. Taylor expanded in y around inf 83.2%

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

   1. *-commutative 83.2%

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

4. Simplified 83.2%

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

5. Taylor expanded in x around inf 41.8%

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

6. Final simplification 41.8%

   \[\leadsto x \]

Reproduce

herbie shell --seed 2023331 
(FPCore (x y z)
  :name "Diagrams.ThreeD.Shapes:frustum from diagrams-lib-1.3.0.3, B"
  :precision binary64
  (+ x (* (- y x) z)))