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

Average Accuracy: 100.0% → 100.0%

Time: 3.9s

Precision: binary64

Cost: 448

Math

\[\left(1 - x\right) \cdot y + x \cdot z \]

↓

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

FPCore

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

↓

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

C

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

↓

double code(double x, double y, double z) {
	return y + ((z - y) * 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 = ((1.0d0 - x) * y) + (x * z)
end function

↓

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

Java

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

↓

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

Python

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

↓

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

Julia

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

↓

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

MATLAB

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

↓

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

Wolfram

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

↓

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

Target

Original	100.0%
Target	100.0%
Herbie	100.0%

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

Derivation

1. Initial program 100.0%

   \[\left(1 - x\right) \cdot y + x \cdot z \]

2. Simplified 100.0%

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

   [Start] 100.0	\[ \left(1 - x\right) \cdot y + x \cdot z \]
   sub-neg [=>] 100.0	\[ \color{blue}{\left(1 + \left(-x\right)\right)} \cdot y + x \cdot z \]
   +-commutative [=>] 100.0	\[ \color{blue}{\left(\left(-x\right) + 1\right)} \cdot y + x \cdot z \]
   distribute-rgt1-in [<=] 100.0	\[ \color{blue}{\left(y + \left(-x\right) \cdot y\right)} + x \cdot z \]
   associate-+l+ [=>] 100.0	\[ \color{blue}{y + \left(\left(-x\right) \cdot y + x \cdot z\right)} \]
   +-commutative [=>] 100.0	\[ \color{blue}{\left(\left(-x\right) \cdot y + x \cdot z\right) + y} \]
   *-commutative [=>] 100.0	\[ \left(\color{blue}{y \cdot \left(-x\right)} + x \cdot z\right) + y \]
   neg-mul-1 [=>] 100.0	\[ \left(y \cdot \color{blue}{\left(-1 \cdot x\right)} + x \cdot z\right) + y \]
   associate-*r* [=>] 100.0	\[ \left(\color{blue}{\left(y \cdot -1\right) \cdot x} + x \cdot z\right) + y \]
   *-commutative [=>] 100.0	\[ \left(\left(y \cdot -1\right) \cdot x + \color{blue}{z \cdot x}\right) + y \]
   distribute-rgt-out [=>] 100.0	\[ \color{blue}{x \cdot \left(y \cdot -1 + z\right)} + y \]
   fma-def [=>] 100.0	\[ \color{blue}{\mathsf{fma}\left(x, y \cdot -1 + z, y\right)} \]
   +-commutative [=>] 100.0	\[ \mathsf{fma}\left(x, \color{blue}{z + y \cdot -1}, y\right) \]
   *-commutative [=>] 100.0	\[ \mathsf{fma}\left(x, z + \color{blue}{-1 \cdot y}, y\right) \]
   neg-mul-1 [<=] 100.0	\[ \mathsf{fma}\left(x, z + \color{blue}{\left(-y\right)}, y\right) \]
   unsub-neg [=>] 100.0	\[ \mathsf{fma}\left(x, \color{blue}{z - y}, y\right) \]

3. Taylor expanded in x around 0 100.0%

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

4. Final simplification 100.0%

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

Alternatives

Alternative 1
Accuracy	60.6%
Cost	1048

\[\begin{array}{l} t_0 := y \cdot \left(-x\right)\\ \mathbf{if}\;x \leq -1 \cdot 10^{+214}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq -14:\\ \;\;\;\;t_0\\ \mathbf{elif}\;x \leq -1.95 \cdot 10^{-88}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq 1.05 \cdot 10^{-78}:\\ \;\;\;\;y\\ \mathbf{elif}\;x \leq 3.2 \cdot 10^{+25}:\\ \;\;\;\;z \cdot x\\ \mathbf{elif}\;x \leq 7.8 \cdot 10^{+155}:\\ \;\;\;\;t_0\\ \mathbf{else}:\\ \;\;\;\;z \cdot x\\ \end{array} \]

Alternative 2
Accuracy	79.3%
Cost	585

\[\begin{array}{l} \mathbf{if}\;x \leq -6 \cdot 10^{-88} \lor \neg \left(x \leq 9 \cdot 10^{-79}\right):\\ \;\;\;\;\left(z - y\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]

Alternative 3
Accuracy	98.7%
Cost	585

\[\begin{array}{l} \mathbf{if}\;x \leq -1 \lor \neg \left(x \leq 0.31\right):\\ \;\;\;\;\left(z - y\right) \cdot x\\ \mathbf{else}:\\ \;\;\;\;y + z \cdot x\\ \end{array} \]

Alternative 4
Accuracy	62.2%
Cost	456

\[\begin{array}{l} \mathbf{if}\;y \leq -1.1 \cdot 10^{-34}:\\ \;\;\;\;y\\ \mathbf{elif}\;y \leq 5.6 \cdot 10^{-76}:\\ \;\;\;\;z \cdot x\\ \mathbf{else}:\\ \;\;\;\;y\\ \end{array} \]

Alternative 5
Accuracy	46.0%
Cost	64

\[y \]

Reproduce

herbie shell --seed 2023151 
(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)))