// license that can be found in the LICENSE file.

package issue9510b

/*
static double csquare(double a, double b) {
	__complex__ double d;
	__real__ d = a;
	__imag__ d = b;
	return __real__ (d * d);
}
*/
import "C"

func F(a, b float64) float64 {
	return float64(C.csquare(C.double(a), C.double(b)))

// license that can be found in the LICENSE file.

package issue9510a

/*
static double csquare(double a, double b) {
	__complex__ double d;
	__real__ d = a;
	__imag__ d = b;
	return __real__ (d * d);
}
*/
import "C"

func F(a, b float64) float64 {
	return float64(C.csquare(C.double(a), C.double(b)))

icon-square.png...

logo-square.png...

logo-square.png...

	z2.Square(z)             // 2
	t.Square(&z2)            // 4
	t.Square(&t)             // 8
	z9.Multiply(&t, z)       // 9
	z11.Multiply(&z9, &z2)   // 11
	t.Square(&z11)           // 22
	z2_5_0.Multiply(&t, &z9) // 31 = 2^5 - 2^0

	t.Square(&z2_5_0) // 2^6 - 2^1
	for i := 0; i < 4; i++ {
		t.Square(&t) // 2^10 - 2^5
	}
	z2_10_0.Multiply(&t, &z2_5_0) // 2^10 - 2^0

339E          ; mapped                 ; 006B 006D     # 1.1  SQUARE KM
339F          ; mapped                 ; 006D 006D 0032 #1.1  SQUARE MM SQUARED
33A0          ; mapped                 ; 0063 006D 0032 #1.1  SQUARE CM SQUARED
33A1          ; mapped                 ; 006D 0032     # 1.1  SQUARE M SQUARED
33A2          ; mapped                 ; 006B 006D 0032 #1.1  SQUARE KM SQUARED

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}
   * values (i.e. it is the square root of the mean of the squares of the vertical distances between

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}
   * values (i.e. it is the square root of the mean of the squares of the vertical distances between

   *
   * <p>This fit minimizes the root-mean-square error in {@code y} as a function of {@code x}. This
   * error is defined as the square root of the mean of the squares of the differences between the
   * actual {@code y} values of the data and the values predicted by the fit for the {@code x}
   * values (i.e. it is the square root of the mean of the squares of the vertical distances between