C:/Musimathics_local/Musimat/MusimatChapter9/C091406b.cpp File Reference

#include "MusimatChapter9.h"

Go to the source code of this file.

Functions
	MusimatChapter9Section (C091406b)
Integer	getIndex (RealList L, Real R)
Static Void	para1 ()

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Function Documentation

Integer getIndex	(	RealList	L,
		Real	R
	)

Definition at line 15 of file C091406b.cpp.

                                    {
        Integer i;
        For (i = Length(L) - 1; i >= 0; i = i - 1){
                If (R > L[i]){
                        Return(i + 1);
                }
        }
        Return( 0 );
}

MusimatChapter9Section

(

C091406b

)

Definition at line 2 of file C091406b.cpp.

References para1().

                                 {
Print("*** Traversing the Cumulative Distribution Function ***");
/*****************************************************************************

Traversing the Cumulative Distribution Function

To automate this, we start at the top end of the cumulative distribution function and work 
down. As we go, we compare the value of R to the current step size. We’ve gone one step too far 
when the value of R exceeds the step size, so we return the previous step as the answer, and stop.
*****************************************************************************/
        para1(); // Step into this function to continue.)
}

Static Void para1

(

)

Definition at line 25 of file C091406b.cpp.

References accumulate(), Chapter9::f, getIndex(), n(), normalize(), Random(), and sum().

                    {
Print("*** Invoking getIndex() ***");
/*****************************************************************************
We can invoke getIndex() as follows:
*****************************************************************************/

Real R = Random();
Integer p = getIndex(f, R); // where f was defined previously
Print(p);

/*****************************************************************************
If R is 0.1, then p prints 0. Now let’s bring all the pieces together. Here is a program that creates 
a melody of 25 pitches favoring pitches that are at the low end of the chromatic scale:
*****************************************************************************/
Print("*** Melody of 25 low pitches ***");
RealList f(12.0, 11.0, 10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0);
StringList n("C", "Cs", "D", "Ds", "E", "F", "Fs", "G", "Gs", "A", "As", "B", "c");
f = normalize(f, sum(f));       // replace f with its normalized form
f = accumulate(f);      // calculate cumulative distribution function
StringList s;   // a place to put the result

For (Integer i = 0; i < 25; i = i + 1){
        Integer p = getIndex(f, Random());
        s[i] = n[p];
}

Print("Melody: ", s);                                                   // print the melody

/*****************************************************************************
Running this program will generate something like figure 9.23, depending upon the values pro-
duced by Random(). As we see, lower pitches are favored in approximately the proportions we 
specified. The longer the sample melody, the more likely the pitch choices would conform on aver-
age to the distribution function.
*****************************************************************************/
}}

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