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

Percentage Accurate: 100.0% → 100.0%

Time: 3.2s

Alternatives: 6

Speedup: 1.0×

• 21.2% 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

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

Alternative 2: 60.4% accurate, 0.3× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} t_0 := x \cdot \left(-z\right)\\ \mathbf{if}\;z \leq -3 \cdot 10^{+256}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -1.65 \cdot 10^{+227}:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq -6.2 \cdot 10^{+189}:\\ \;\;\;\;t_0\\ \mathbf{elif}\;z \leq -3.2:\\ \;\;\;\;y \cdot z\\ \mathbf{elif}\;z \leq 5.1 \cdot 10^{-20}:\\ \;\;\;\;x\\ \mathbf{elif}\;z \leq 6 \cdot 10^{+43} \lor \neg \left(z \leq 3.75 \cdot 10^{+121} \lor \neg \left(z \leq 2.6 \cdot 10^{+214}\right) \land z \leq 4.8 \cdot 10^{+241}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;t_0\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (let* ((t_0 (* x (- z))))
   (if (<= z -3e+256)
     t_0
     (if (<= z -1.65e+227)
       (* y z)
       (if (<= z -6.2e+189)
         t_0
         (if (<= z -3.2)
           (* y z)
           (if (<= z 5.1e-20)
             x
             (if (or (<= z 6e+43)
                     (not
                      (or (<= z 3.75e+121)
                          (and (not (<= z 2.6e+214)) (<= z 4.8e+241)))))
               (* y z)
               t_0))))))))

C

double code(double x, double y, double z) {
	double t_0 = x * -z;
	double tmp;
	if (z <= -3e+256) {
		tmp = t_0;
	} else if (z <= -1.65e+227) {
		tmp = y * z;
	} else if (z <= -6.2e+189) {
		tmp = t_0;
	} else if (z <= -3.2) {
		tmp = y * z;
	} else if (z <= 5.1e-20) {
		tmp = x;
	} else if ((z <= 6e+43) || !((z <= 3.75e+121) || (!(z <= 2.6e+214) && (z <= 4.8e+241)))) {
		tmp = y * 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 * -z
    if (z <= (-3d+256)) then
        tmp = t_0
    else if (z <= (-1.65d+227)) then
        tmp = y * z
    else if (z <= (-6.2d+189)) then
        tmp = t_0
    else if (z <= (-3.2d0)) then
        tmp = y * z
    else if (z <= 5.1d-20) then
        tmp = x
    else if ((z <= 6d+43) .or. (.not. (z <= 3.75d+121) .or. (.not. (z <= 2.6d+214)) .and. (z <= 4.8d+241))) then
        tmp = y * 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 * -z;
	double tmp;
	if (z <= -3e+256) {
		tmp = t_0;
	} else if (z <= -1.65e+227) {
		tmp = y * z;
	} else if (z <= -6.2e+189) {
		tmp = t_0;
	} else if (z <= -3.2) {
		tmp = y * z;
	} else if (z <= 5.1e-20) {
		tmp = x;
	} else if ((z <= 6e+43) || !((z <= 3.75e+121) || (!(z <= 2.6e+214) && (z <= 4.8e+241)))) {
		tmp = y * z;
	} else {
		tmp = t_0;
	}
	return tmp;
}

Python

def code(x, y, z):
	t_0 = x * -z
	tmp = 0
	if z <= -3e+256:
		tmp = t_0
	elif z <= -1.65e+227:
		tmp = y * z
	elif z <= -6.2e+189:
		tmp = t_0
	elif z <= -3.2:
		tmp = y * z
	elif z <= 5.1e-20:
		tmp = x
	elif (z <= 6e+43) or not ((z <= 3.75e+121) or (not (z <= 2.6e+214) and (z <= 4.8e+241))):
		tmp = y * z
	else:
		tmp = t_0
	return tmp

Julia

function code(x, y, z)
	t_0 = Float64(x * Float64(-z))
	tmp = 0.0
	if (z <= -3e+256)
		tmp = t_0;
	elseif (z <= -1.65e+227)
		tmp = Float64(y * z);
	elseif (z <= -6.2e+189)
		tmp = t_0;
	elseif (z <= -3.2)
		tmp = Float64(y * z);
	elseif (z <= 5.1e-20)
		tmp = x;
	elseif ((z <= 6e+43) || !((z <= 3.75e+121) || (!(z <= 2.6e+214) && (z <= 4.8e+241))))
		tmp = Float64(y * z);
	else
		tmp = t_0;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	t_0 = x * -z;
	tmp = 0.0;
	if (z <= -3e+256)
		tmp = t_0;
	elseif (z <= -1.65e+227)
		tmp = y * z;
	elseif (z <= -6.2e+189)
		tmp = t_0;
	elseif (z <= -3.2)
		tmp = y * z;
	elseif (z <= 5.1e-20)
		tmp = x;
	elseif ((z <= 6e+43) || ~(((z <= 3.75e+121) || (~((z <= 2.6e+214)) && (z <= 4.8e+241)))))
		tmp = y * z;
	else
		tmp = t_0;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := Block[{t$95$0 = N[(x * (-z)), $MachinePrecision]}, If[LessEqual[z, -3e+256], t$95$0, If[LessEqual[z, -1.65e+227], N[(y * z), $MachinePrecision], If[LessEqual[z, -6.2e+189], t$95$0, If[LessEqual[z, -3.2], N[(y * z), $MachinePrecision], If[LessEqual[z, 5.1e-20], x, If[Or[LessEqual[z, 6e+43], N[Not[Or[LessEqual[z, 3.75e+121], And[N[Not[LessEqual[z, 2.6e+214]], $MachinePrecision], LessEqual[z, 4.8e+241]]]], $MachinePrecision]], N[(y * z), $MachinePrecision], t$95$0]]]]]]]

Derivation

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

Alternative 3: 84.3% accurate, 0.8× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;x \leq -5.5 \cdot 10^{+83} \lor \neg \left(x \leq 7.8 \cdot 10^{-82}\right):\\ \;\;\;\;x - x \cdot z\\ \mathbf{else}:\\ \;\;\;\;x + y \cdot z\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= x -5.5e+83) (not (<= x 7.8e-82))) (- x (* x z)) (+ x (* y z))))

C

double code(double x, double y, double z) {
	double tmp;
	if ((x <= -5.5e+83) || !(x <= 7.8e-82)) {
		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 <= (-5.5d+83)) .or. (.not. (x <= 7.8d-82))) 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 <= -5.5e+83) || !(x <= 7.8e-82)) {
		tmp = x - (x * z);
	} else {
		tmp = x + (y * z);
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (x <= -5.5e+83) or not (x <= 7.8e-82):
		tmp = x - (x * z)
	else:
		tmp = x + (y * z)
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((x <= -5.5e+83) || !(x <= 7.8e-82))
		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 <= -5.5e+83) || ~((x <= 7.8e-82)))
		tmp = x - (x * z);
	else
		tmp = x + (y * z);
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[x, -5.5e+83], N[Not[LessEqual[x, 7.8e-82]], $MachinePrecision]], N[(x - N[(x * z), $MachinePrecision]), $MachinePrecision], N[(x + N[(y * z), $MachinePrecision]), $MachinePrecision]]

Derivation

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

Alternative 4: 60.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \begin{array}{l} \mathbf{if}\;z \leq -3.2 \lor \neg \left(z \leq 3.2 \cdot 10^{-21}\right):\\ \;\;\;\;y \cdot z\\ \mathbf{else}:\\ \;\;\;\;x\\ \end{array} \end{array} \]

FPCore

(FPCore (x y z)
 :precision binary64
 (if (or (<= z -3.2) (not (<= z 3.2e-21))) (* y z) x))

C

double code(double x, double y, double z) {
	double tmp;
	if ((z <= -3.2) || !(z <= 3.2e-21)) {
		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 <= (-3.2d0)) .or. (.not. (z <= 3.2d-21))) 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 <= -3.2) || !(z <= 3.2e-21)) {
		tmp = y * z;
	} else {
		tmp = x;
	}
	return tmp;
}

Python

def code(x, y, z):
	tmp = 0
	if (z <= -3.2) or not (z <= 3.2e-21):
		tmp = y * z
	else:
		tmp = x
	return tmp

Julia

function code(x, y, z)
	tmp = 0.0
	if ((z <= -3.2) || !(z <= 3.2e-21))
		tmp = Float64(y * z);
	else
		tmp = x;
	end
	return tmp
end

MATLAB

function tmp_2 = code(x, y, z)
	tmp = 0.0;
	if ((z <= -3.2) || ~((z <= 3.2e-21)))
		tmp = y * z;
	else
		tmp = x;
	end
	tmp_2 = tmp;
end

Wolfram

code[x_, y_, z_] := If[Or[LessEqual[z, -3.2], N[Not[LessEqual[z, 3.2e-21]], $MachinePrecision]], N[(y * z), $MachinePrecision], x]

Derivation

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

Alternative 5: 75.1% accurate, 1.4× speedup

Math

\[\begin{array}{l} \\ x + y \cdot z \end{array} \]

FPCore

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

C

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

Java

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

Python

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

Julia

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

MATLAB

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

Wolfram

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

Derivation

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

Alternative 6: 35.8% 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

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

Reproduce

herbie shell --seed 2023343 
(FPCore (x y z)
  :name "Diagrams.ThreeD.Shapes:frustum from diagrams-lib-1.3.0.3, B"
  :precision binary64
  (+ x (* (- y x) z)))