I'm playing around integrating differential forms in maxima, and this is my program:
init_cartan([x,y]);
omega:1/(sqrt(x-y))*dx~dy;
phi:[u^2+v^2,2*u*v];
diff(phi,u);
diff(phi,v);
scalar:diff(phi,v)|(diff(phi,u)|omega);
integrand:subst(u^2+v^2,x,subst(2*u*v,y,scalar));
integrand:radcan(integrand);
integrate(integrate(integrand,u,0,1),v,0,1);
In the subst bit I have to give the relations between u,v and x,y again. This seems redundant. Is there a more natural expression?
Consider this two examples:
load("cartan");
init_cartan([x,y]);
omega:1/(sqrt(x-y))*dx~dy;
/* declare functional dependencies ... */
depends([x, y], [u, v]);
/* this definition can be used latter */
def_xy: [x=u^2+v^2, y=2*u*v];
phi: [x, y];
scalar: diff(phi,v)|(diff(phi,u)|omega);
/* substitute ... */
scalar: subst(def_xy, scalar);
/* and force re-evalution of 'diff */
scalar: ev(scalar, diff);
integrand:radcan(scalar);
integrate(integrate(integrand,u,0,1),v,0,1);
and
load("cartan");
init_cartan([x,y]);
omega:1/(sqrt(x-y))*dx~dy;
/* declare functional dependencies ... */
depends([x, y], [u, v]);
/* give "numeric values" */
numerval(x, u^2+v^2, y, 2*u*v);
phi: [x, y];
scalar: diff(phi,v)|(diff(phi,u)|omega);
/* evaluate to numeric values (also switches `float' to true so 1/2 evaluates to 0.5) ... */
scalar: ev(scalar, numer=true);
/* and force re-evalution of 'diff */
scalar: ev(scalar, diff);
integrand:radcan(scalar);
integrate(integrate(integrand,u,0,1),v,0,1);
load(cartan);
init_cartan([x,y]);
omega:1/sqrt(x-y)*dx~dy;
def_xy:[x=u^2+v^2,y=2*u*v];
phi:subst(def_xy,[x,y]);
scalar:subst(def_xy,(diff(phi,u)|(diff(phi,v)|omega)));
integrand:radcan(scalar);
integrate(integrate(integrand,v,0,u),u,0,1);
Related
No documentation is generated for any of the functions for my C repo
My configuration:
# Difference with default Doxyfile 1.9.1
PROJECT_NAME = WLib
OUTPUT_DIRECTORY = doxy
OPTIMIZE_OUTPUT_FOR_C = YES
EXTRACT_ALL = YES
CASE_SENSE_NAMES = NO
HIDE_SCOPE_NAMES = YES
INPUT = .
FILE_PATTERNS = *.c \
*.h
RECURSIVE = YES
Edit the code:
/** \fn Array Fill
* \param sa size of the array A in bytes
* \param a the array A
* \param sb size of the array B in bytes
* \param b the array B
* \brief Takes two arrays and their sizes. Fills the array A with as many
* instances of array B as the size of array A can handle.
* \return The array A
* Method:
* -# If /e a = NULL, then the array of size /e sa will be allocated
* -# If /e b = NULL and /e sb = 0, then array will be filled with zeros
* -# If /e sb = 0, the function does nothing and returns NULL
* -# Declares a variable /e i, this is be the pointer offset
* -# Assignes array /e b to array /e a offsetted by /e i, and incriments
* /e i by /e sb. This step is repeated until less than sb bytes are
* left untreated
* -# Assignes the remaining part of array /e a with whatever piece of
* array /e b fits
*/
VO* afl(register const U16 sa,
register VO* a,
register U8 sb,
register const VO* b ) {
...
}
The supplied code gives directly an answer when running it, it gives the warning:
warning: documented symbol 'Array Fill' was not declared or defined.
when looking at the code we see here:
\fn Array Fill
but from the documentation we learn:
\fn (function declaration)
Indicates that a comment block contains documentation for a function
(either global or as a member of a class). This command is only needed
if a comment block is not placed in front (or behind) the function
declaration or definition.
If your comment block is in front of the function declaration or
definition this command can (and to avoid redundancy should) be
omitted.
A full function declaration including arguments should be specified
after the \fn command on a single line, since the argument ends at the
end of the line!
and furthermore in the given example in the documentaion:
const char *member(char,int) throw(std::out_of_range);
and
/*! \fn const char *Fn_Test::member(char c,int n)
* \brief A member function.
* \param c a character.
* \param n an integer.
* \exception std::out_of_range parameter is out of range.
* \return a character pointer.
*/
in other words the syntax for the \fn command should be in this case:
\fn VO* afl(register const U16 sa, register VO* a, register U8 sb, register const VO* b)
Note:
the \fn command is typically for the usage when the documentation of a function is not directly in font of the function. In the case of the issue the \fn command is not necessary.
It looks like that the \fn here is used in the context of the \brief command and the used \brief command in the context of the \details command.
In my .h file of my C program I have a function:
/** Calculates foo using algorithm A1.
* \param[in] v1 first input
* \param[in] v2 second input
* \param[in] s1 first string
* \param[in] s2 second string
* \return foo(v1,v2)
*/
int foo( int v1, int v2, char *s1, char *s2);
Now in the beginning of the file I want to discuss the function. So I say:
/** To facilitate such operations I have:
* #li int foo( int v1, int v2, char *s1, char *s2)
*
* which allows me to blah.
*/
Now in my doxygen file, the word foo is not linked to the function definition section at the end. However, if I say:
/** To facilitate such operations I have:
* #li int foo()
*
* which allows me to blah.
*/
The autolinking works fine. Including the arguments breaks the autolinking function and I can't for the life of me figure out why. This happens to all my functions which have > 1 input arguments. foo() and foo(int i) work but foo(int i, int j, int k, int m) does not work. Does anyone have any idea why this occurs?
I'm not quite seeing the issue I'm having, and I've tried suggestions I've seen in other forum posts. I am trying to write a (fortran) mex file, per request of my boss. However, I was getting warnings when passing a matrix to my computational routine. If I ignored the warning, my MATLAB shut down. So now I'm trying a simpler program, an inner product. However, I am still getting the warning: "Expected a procedure at (1)" where (1) is at 'call innerProd(x,y,c)' underneath the x. I'm not sure what that means... I've included my code.
#include "fintrf.h"
C======================================================================
#if 0
C
C innerProd.F
C .F file needs to be preprocessed to generate .for equivalent
C
#endif
C
C innerProd.F
C calculates the inner product
C This is a MEX file for MATLAB.
C Copyright 1984-2011 The MathWorks, Inc.
C $Revision: 1.12.2.9 $
C======================================================================
C Gateway routine
subroutine mexFunction(nlhs, plhs, nrhs, prhs)
C Declarations
implicit none
C mexFunction arguments:
mwPointer:: plhs(*), prhs(*)
integer:: nlhs, nrhs
C Function declarations:
mwPointer:: mxCreateDoubleMatrix, mxGetPr,mxGetM, mxGetData
integer:: mxIsNumeric
C Pointers to input/output mxArrays:
mwPointer:: x_ptr, y_ptr, c_ptr
C Array information:
mwSize:: m
C Arguments for computational routine:
real*8:: x,y,c
C----------------------------------------------------------------------
C Check for proper number of arguments.
if (nrhs .ne. 2) then
call mexErrMsgTxt('Error.')
elseif (nlhs .ne. 1) then
call mexErrMsgTxt('One output required.')
endif
C Check to see if inputs are numeric.
if (mxIsNumeric(prhs(1)) .ne. 1 ) then
call mexErrMsgTxt('Input # 1 is not a numeric array.')
elseif (mxIsNumeric(prhs(2)) .ne. 1) then
call mexErrMsgTxt('Input #2 is not a numeric array.')
endif
C Find dimensions of mxArrays
m=mxGetM(prhs(1))
C create Fortran arrays from the input arguments
x_ptr=mxGetData(prhs(1))
call mxCopyPtrToReal8(x_ptr,x,m)
y_ptr= mxGetData(prhs(2))
call mxCopyPtrToReal8(y_ptr,y,m)
C create matrix for the return argument
plhs(1) =mxCreateDoubleMatrix(1,1,0)
c_ptr= mxGetPr(plhs(1))
C Call the computational subroutine.
call innerProd(x,y,c)
C Load the output into a MATLAB array.
call mxCopyReal8ToPtr(c, c_ptr, 1)
return
end subroutine mexFunction
C----------------------------------------------------------------------
C Computational routine
subroutine innerProd(x,y,c)
implicit none
real*8:: x,y,temp,c
integer:: i,m
do i=1,m
temp=temp+x(i)*y(i)
end do
c = temp
return
end subroutine innerProd
I'm just learning this for the first time, and I would appreciate any suggestions. Even if it is where to look for solutions. I've looked through MATLAB mex Fortran aids online. There isn't any help there. I can't get the function to run, so I can't use print statements which is a good way to debug. I think mex has a print function, I will try to get that to work.
Thank you!
The main problem is that you haven't anywhere declared the arguments of innerProd as arrays. That holds for the actual arguments x and y in the subroutine mexFunction and the dummy arguments x and y in innerProd itself.
So, in innerProd the expression x(i) isn't referencing the i-th element of the real*8 array x, but the real*8 result of the function x with argument i. As the x you've passed isn't a function (procedure), this is an error.
There are ways to solve this, but all involve declaring the dummy arguments as arrays. Which brings up another point.
You have in innerProd
integer:: i,m
do i=1,m
temp=temp+x(i)*y(i)
end do
where m is not defined. Crucially, from mexfunction you're expecting the compiler to know that m is the size of the arrays x and y: this isn't true. m is a variable local to innerProd. Instead you may want to pass it as an argument to the subroutine and use that to dimension the arrays:
subroutine innerProd(x,y,c,m)
implicit none
integer :: m
real*8:: x(m),y(m),temp,c
...
end subroutine
[You could, of course, use assumed-shape arrays (and the SIZE intrinsic), but that's an additional complication requiring more substantial changes.] You also need to think about how to declare arrays appropriately in mexfunction, noting that the call to mxCopyPtrToReal8 also requires an array argument.
I couldn't get the Fortran code to work for innerProd, but I did get C code to work. I recommend, if you are having issues with Fortran, to use C. It seems that Matlab is more flexible when it comes to mex files and C. Here is the code:
#include "mex.h"
/*
* innerProd.c
*
*Computational function that takes the inner product of two vectors.
*
*Mex-file for MATLAB.
*/
void innerProd(double *x, double *y,double *c,int m){
double temp;
int i;
for(i=0; i < m; i++){
temp = temp + x[i]*y[i];
}
*c=temp;
}
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
double *x, *y, *c;
size_t m;
/*check for proper number of arguments.*/
if(nrhs != 2){
mexErrMsgIdAndTxt("MATLAB:innerProd:invalidNumInputs","Two input required.");
}
/*The input must be a noncomplex double vector.*/
m = mxGetM(prhs[0]);
if(!mxIsDouble(prhs[0]) || mxIsComplex(prhs[0]) || m==1){
mexErrMsgIdAndTxt("MATLAB:innerProd:inputNotRealDouble","Input must be noncomplex double vector");
}
/*create return argument */
plhs[0] = mxCreateDoubleMatrix(1,1,0);
c=mxGetPr(plhs[0]);
/*Assign pointers to each input and output. */
x = mxGetPr(prhs[0]);
y=mxGetPr(prhs[1]);
/*call subroutine*/
innerProd(x,y,c,m);
}
I will still take suggestions on the Fortran code above, though. I'd like to know how to get it to work. Thanks!
I have found a really tricky problem, which I can not seem to fix easily. In short, I would like to return from a mex file an array, which has been passed as mex function input. You could trivially do this:
void mexFunction(int nargout, mxArray *pargout [ ], int nargin, const mxArray *pargin[])
{
pargout[0] = pargin[0];
}
But this is not what I need. I would like to get the raw pointer from pargin[0], process it internally, and return a freshly created mex array by setting the corresponding data pointer. Like that:
#include <mex.h>
void mexFunction(int nargout, mxArray *pargout [ ], int nargin, const mxArray *pargin[])
{
mxArray *outp;
double *data;
int m, n;
/* get input array */
data = mxGetData(pargin[0]);
m = mxGetM(pargin[0]);
n = mxGetN(pargin[0]);
/* copy pointer to output array */
outp = mxCreateNumericMatrix(0,0,mxDOUBLE_CLASS,mxREAL);
mxSetM(outp, m);
mxSetN(outp, n);
mxSetData(outp, data);
/* segfaults with or without the below line */
mexMakeMemoryPersistent(data);
pargout[0] = outp;
}
It doesn't work. I get a segfault, if not immediately, then after a few calls. I believe nothing is said about such scenario in the documentation. The only requirement is hat the data pointer has been allocated using mxCalloc, which it obviously has. Hence, I would assume this code is legal.
I need to do this, because I am parsing a complicated MATLAB structure into my internal C data structures. I process the data, some of the data gets re-allocated, some doesn't. I would like to transparently return the output structure, without thinking when I have to simply copy an mxArray (first code snippet), and when I actually have to create it.
Please help!
EDIT
After further looking and discussing with Amro, it seems that even my first code snippet is unsupported and can cause MATLAB crashes in certain situations, e.g., when passing structure fields or cell elements to such mex function:
>> a.field = [1 2 3];
>> b = pargin_to_pargout(a.field); % ok - works and assigns [1 2 3] to b
>> pargin_to_pargout(a.field); % bad - segfault
It seems I will have to go down the 'undocumented MATLAB' road and use mxCreateSharedDataCopy and mxUnshareArray.
You should use mxDuplicateArray, thats the documented way:
#include "mex.h"
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[])
{
plhs[0] = mxDuplicateArray(prhs[0]);
}
While undocumented, the MEX API function mxCreateSharedDataCopy iswas given as a solution by MathWorks, now apparently disavowed, for creating a shared-data copy of an mxArray. MathWorks even provides an example in their solution, mxsharedcopy.c.
As described in that removed MathWorks Solution (1-6NU359), the function can be used to clone the mxArray header. However, the difference between doing plhs[0] = prhs[0]; and plhs[0] = mxCreateSharedDataCopy(prhs[0]); is that the first version just copies the mxArray* (a pointer) and hence does not create a new mxArray container (at least not until the mexFunction returns and MATLAB works it's magic), which would increment the data's reference count in both mxArrays.
Why might this be a problem? If you use plhs[0] = prhs[0]; and make no further modification to plhs[0] before returning from mexFunction, all is well and you will have a shared data copy thanks to MATLAB. However, if after the above assignment you modify plhs[0] in the MEX function, the change be seen in prhs[0] as well since it refers to the same data buffer. On the other hand, when explicitly generating a shared copy (with mxCreateSharedDataCopy) there are two different mxArray objects and a change to one array's data will trigger a copy operation resulting in two completely independent arrays. Also, direct assignment can cause segmentation faults in some cases.
Modified MathWorks Example
Start with an example using a modified mxsharedcopy.c from the MathWorks solution referenced above. The first important step is to provide the prototype for the mxCreateSharedDataCopy function:
/* Add this declaration because it does not exist in the "mex.h" header */
extern mxArray *mxCreateSharedDataCopy(const mxArray *pr);
As the comment states, this is not in mex.h, so you have to declare this yourself.
The next part of the mxsharedcopy.c creates new mxArrays in the following ways:
A deep copy via mxDuplicateArray:
copy1 = mxDuplicateArray(prhs[0]);
A shared copy via mxCreateSharedDataCopy:
copy2 = mxCreateSharedDataCopy(copy1);
Direct copy of the mxArray*, added by me:
copy0 = prhs[0]; // OK, but don't modify copy0 inside mexFunction!
Then it prints the address of the data buffer (pr) for each mxArray and their first values. Here is the output of the modified mxsharedcopy(x) for x=ones(1e3);:
prhs[0] = 72145590, mxGetPr = 18F90060, value = 1.000000
copy0 = 72145590, mxGetPr = 18F90060, value = 1.000000
copy1 = 721BF120, mxGetPr = 19740060, value = 1.000000
copy2 = 721BD4B0, mxGetPr = 19740060, value = 1.000000
What happened:
As expected, comparing prhs[0] and copy0 we have not created anything new except another pointer to the same mxArray.
Comparing prhs[0] and copy1, notice that mxDuplicateArray created a new mxArray at address 721BF120, and copied the data into a new buffer at 19740060.
copy2 has a different address (mxArray*) from copy1, meaning it is also a different mxArray not just the same one pointed to by different variables, but they both share the same data at address 19740060.
The question reduces to: Is it safe to return in plhs[0] either of copy0 or copy2 (from simple pointer copy or mxCreateSharedDataCopy, respectively) or is it necessary to use mxDuplicateArray, which actually copies the data? We can show that mxCreateSharedDataCopy would work by destroying copy1 and verifying that copy2 is still valid:
mxDestroyArray(copy1);
copy2val0 = *mxGetPr(copy2); % no crash!
Applying Shared-Data Copy to Input
Back to the question. Take this a step further than the MathWorks example and return a share-data copy of the input. Just do:
if (nlhs>0) plhs[0] = mxCreateSharedDataCopy(prhs[0]);
Hold your breath!
>> format debug
>> x=ones(1,2)
x =
Structure address = 9aff820 % mxArray*
m = 1
n = 2
pr = 2bcc8500 % double*
pi = 0
1 1
>> xDup = mxsharedcopy(x)
xDup =
Structure address = 9afe2b0 % mxArray* (different)
m = 1
n = 2
pr = 2bcc8500 % double* (same)
pi = 0
1 1
>> clear x
>> xDup % hold your breath!
xDup =
Structure address = 9afe2b0
m = 1
n = 2
pr = 2bcc8500 % double* (still same!)
pi = 0
1 1
Now for a temporary input (without format debug):
>> tempDup = mxsharedcopy(2*ones(1e3));
>> tempDup(1)
ans =
2
Interestingly, if I test without mxCreateSharedDataCopy (i.e. with just plhs[0] = prhs[0];), MATLAB doesn't crash but the output variable never materializes:
>> tempDup = mxsharedcopy(2*ones(1e3)) % no semi-colon
>> whos tempDup
>> tempDup(1)
Undefined function 'tempDup' for input arguments of type 'double'.
R2013b, Windows, 64-bit.
mxsharedcopy.cpp (modified C++ version):
#include "mex.h"
/* Add this declaration because it does not exist in the "mex.h" header */
extern "C" mxArray *mxCreateSharedDataCopy(const mxArray *pr);
bool mxUnshareArray(const mxArray *pr, const bool noDeepCopy); // true if not successful
void mexFunction(int nlhs,mxArray *plhs[],int nrhs,const mxArray *prhs[])
{
mxArray *copy1(NULL), *copy2(NULL), *copy0(NULL);
//(void) plhs; /* Unused parameter */
/* Check for proper number of input and output arguments */
if (nrhs != 1)
mexErrMsgTxt("One input argument required.");
if (nlhs > 1)
mexErrMsgTxt("Too many output arguments.");
copy0 = const_cast<mxArray*>(prhs[0]); // ADDED
/* First make a regular deep copy of the input array */
copy1 = mxDuplicateArray(prhs[0]);
/* Then make a shared copy of the new array */
copy2 = mxCreateSharedDataCopy(copy1);
/* Print some information about the arrays */
// mexPrintf("Created shared data copy, and regular deep copy\n");
mexPrintf("prhs[0] = %X, mxGetPr = %X, value = %lf\n",prhs[0],mxGetPr(prhs[0]),*mxGetPr(prhs[0]));
mexPrintf("copy0 = %X, mxGetPr = %X, value = %lf\n",copy0,mxGetPr(copy0),*mxGetPr(copy0));
mexPrintf("copy1 = %X, mxGetPr = %X, value = %lf\n",copy1,mxGetPr(copy1),*mxGetPr(copy1));
mexPrintf("copy2 = %X, mxGetPr = %X, value = %lf\n",copy2,mxGetPr(copy2),*mxGetPr(copy2));
/* TEST: Destroy the first copy */
//mxDestroyArray(copy1);
//copy1 = NULL;
//mexPrintf("\nFreed copy1\n");
/* RESULT: copy2 will still be valid */
//mexPrintf("copy2 = %X, mxGetPr = %X, value = %lf\n",copy2,mxGetPr(copy2),*mxGetPr(copy2));
if (nlhs>0) plhs[0] = mxCreateSharedDataCopy(prhs[0]);
//if (nlhs>0) plhs[0] = const_cast<mxArray*>(prhs[0]);
}
I'm working on a verification-tool for some VHDL-Code in MATLAB/Octave. Therefore I need data types which generate "real" overflows:
intmax('int32') + 1
ans = -2147483648
Later on, it would be helpful if I can define the bit width of a variable, but that is not so important right now.
When I build a C-like example, where a variable gets increased until it's smaller than zero, it spins forever and ever:
test = int32(2^30);
while (test > 0)
test = test + int32(1);
end
Another approach I tried was a custom "overflow"-routine which was called every time after a number is changed. This approach was painfully slow, not practicable and not working in all cases at all. Any suggestions?
In MATLAB, one option you have is to overload the methods that handle arithmetic operations for integer data types, creating your own custom overflow behavior that will result in a "wrap-around" of the integer value. As stated in the documentation:
You can define or overload your own
methods for int* (as you can for any
object) by placing the appropriately
named method in an #int* folder within
a folder on your path. Type help
datatypes for the names of the methods
you can overload.
This page of the documentation lists the equivalent methods for the arithmetic operators. The binary addition operation A+B is actually handled by the function plus(A,B). Therefore, you can create a folder called #int32 (placed in another folder on your MATLAB path) and put a function plus.m in there that will be used instead of the built-in method for int32 data types.
Here's an example of how you could design your overloaded plus function in order to create the overflow/underflow behavior you want:
function C = plus(A,B)
%# NOTE: This code sample is designed to work for scalar values of
%# the inputs. If one or more of the inputs is non-scalar,
%# the code below will need to be vectorized to accommodate,
%# and error checking of the input sizes will be needed.
if (A > 0) && (B > (intmax-A)) %# An overflow condition
C = builtin('plus',intmin,...
B-(intmax-A)-1); %# Wraps around to negative
elseif (A < 0) && (B < (intmin-A)) %# An underflow condition
C = builtin('plus',intmax,...
B-(intmin-A-1)); %# Wraps around to positive
else
C = builtin('plus',A,B); %# No problems; call the built-in plus.m
end
end
Notice that I call the built-in plus method (using the BUILTIN function) to perform addition of int32 values that I know will not suffer overflow/underflow problems. If I were to instead perform the integer addition using the operation A+B it would result in a recursive call to my overloaded plus method, which could lead to additional computational overhead or (in the worst-case scenario where the last line was C = A+B;) infinite recursion.
Here's a test, showing the wrap-around overflow behavior in action:
>> A = int32(2147483642); %# A value close to INTMAX
>> for i = 1:10, A = A+1; disp(A); end
2147483643
2147483644
2147483645
2147483646
2147483647 %# INTMAX
-2147483648 %# INTMIN
-2147483647
-2147483646
-2147483645
-2147483644
If you want to get C style numeric operations, you can use a MEX function to call the C operators directly, and by definition they'll work like C data types.
This method is a lot more work than gnovice's overrides, but it should integrate better into a large codebase and is safer than altering the definition for built-in types, so I think it should be mentioned for completeness.
Here's a MEX file which performs the C "+" operation on a Matlab array. Make one of these for each operator you want C-style behavior on.
/* c_plus.c - MEX function: C-style (not Matlab-style) "+" operation */
#include "mex.h"
#include "matrix.h"
#include <stdio.h>
void mexFunction(
int nlhs, mxArray *plhs[],
int nrhs, const mxArray *prhs[]
)
{
mxArray *out;
/* In production code, input/output type and bounds checks would go here. */
const mxArray *a = prhs[0];
const mxArray *b = prhs[1];
int i, n;
int *a_int32, *b_int32, *out_int32;
short *a_int16, *b_int16, *out_int16;
mxClassID datatype = mxGetClassID(a);
int n_a = mxGetNumberOfElements(a);
int n_b = mxGetNumberOfElements(b);
int a_is_scalar = n_a == 1;
int b_is_scalar = n_b == 1;
n = n_a >= n_b ? n_a : n_b;
out = mxCreateNumericArray(mxGetNumberOfDimensions(a), mxGetDimensions(a),
datatype, mxIsComplex(a));
switch (datatype) {
case mxINT32_CLASS:
a_int32 = (int*) mxGetData(a);
b_int32 = (int*) mxGetData(b);
out_int32 = (int*) mxGetData(out);
for (i=0; i<n; i++) {
if (a_is_scalar) {
out_int32[i] = a_int32[i] + b_int32[i];
} else if (b_is_scalar) {
out_int32[i] = a_int32[i] + b_int32[0];
} else {
out_int32[i] = a_int32[i] + b_int32[i];
}
}
break;
case mxINT16_CLASS:
a_int16 = (short*) mxGetData(a);
b_int16 = (short*) mxGetData(b);
out_int16 = (short*) mxGetData(out);
for (i=0; i<n; i++) {
if (a_is_scalar) {
out_int16[i] = a_int16[0] + b_int16[i];
} else if (b_is_scalar) {
out_int16[i] = a_int16[i] + b_int16[0];
} else {
out_int16[i] = a_int16[i] + b_int16[i];
}
}
break;
/* Yes, you'd have to add a separate case for every numeric mxClassID... */
/* In C++ you could do it with a template. */
default:
mexErrMsgTxt("Unsupported array type");
break;
}
plhs[0] = out;
}
Then you have to figure out how to invoke it from your Matlab code. If you're writing all the code, you could just call "c_plus(a, b)" instead of "a + b" everywhere. Alternately, you could create your own numeric wrapper class, e.g. #cnumeric, that holds a Matlab numeric array in its field and defines plus() and other operations that invoke the approprate C style MEX function.
classdef cnumeric
properties
x % the underlying Matlab numeric array
end
methods
function obj = cnumeric(x)
obj.x = x;
end
function out = plus(a,b)
[a,b] = promote(a, b); % for convenience, and to mimic Matlab implicit promotion
if ~isequal(class(a.x), class(b.x))
error('inputs must have same wrapped type');
end
out_x = c_plus(a.x, b.x);
out = cnumeric(out_x);
end
% You'd have to define the math operations that you want normal
% Matlab behavior on, too
function out = minus(a,b)
[a,b] = promote(a, b);
out = cnumeric(a.x - b.x);
end
function display(obj)
fprintf('%s = \ncnumeric: %s\n', inputname(1), num2str(obj.x));
end
function [a,b] = promote(a,b)
%PROMOTE Implicit promotion of numeric to cnumeric and doubles to int
if isnumeric(a); a = cnumeric(a); end
if isnumeric(b); b = cnumeric(b); end
if isinteger(a.x) && isa(b.x, 'double')
b.x = cast(b.x, class(a.x));
end
if isinteger(b.x) && isa(a.x, 'double')
a.x = cast(a.x, class(b.x));
end
end
end
end
Then wrap your numbers in the #cnumeric where you want C-style int behavior and do math with them.
>> cnumeric(int32(intmax))
ans =
cnumeric: 2147483647
>> cnumeric(int32(intmax)) - 1
ans =
cnumeric: 2147483646
>> cnumeric(int32(intmax)) + 1
ans =
cnumeric: -2147483648
>> cnumeric(int16(intmax('int16')))
ans =
cnumeric: 32767
>> cnumeric(int16(intmax('int16'))) + 1
ans =
cnumeric: -32768
There's your C-style overflow behavior, isolated from breaking the primitive #int32 type. Plus, you can pass a #cnumeric object in to other functions that are expecting regular numerics and it'll "work" as long as they treat their inputs polymorphically.
Performance caveat: because this is an object, + will have the slower speed of a method dispatch instead of a builtin. If you have few calls on large arrays, this'll be fast, because the actual numeric operations are in C. Lots of calls on small arrays, could slow things down, because you're paying the per method call overhead a lot.
I ran the following snippet of code
test = int32(2^31-12);
for i = 1:24
test = test + int32(1)
end
with unexpected results. It seems that, for Matlab, intmax('int32')+1==intmax('int32'). I'm running 2010a on a 64-bit Mac OS X.
Not sure that this as an answer, more confirmation that Matlab behaves counterintuitively. However, the documentation for the intmax() function states:
Any value larger than the value returned by intmax saturates to the intmax value when cast to a 32-bit integer.
So I guess Matlab is behaving as documented.
Hm, yes...
Actually, I was able to solve the problem with my custom "overflow"-Subroutine... Now it runs painfully slow, but without unexpected behaviour! My mistake was a missing round(), since Matlab/Octave will introduce small errors.
But if someone knows a faster solution, I would be glad to try it!
function ret = overflow_sg(arg,bw)
% remove possible rounding errors, and prepare returnvalue (if number is inside boundaries, nothing will happen)
ret = round(arg);
argsize = size(ret);
for i = 1:argsize(1)
for j = 1:argsize(2)
ret(i,j) = flow_sg(ret(i,j),bw);
end
end
end%function
%---
function ret = flow_sg(arg,bw)
ret = arg;
while (ret < (-2^(bw-1)))
ret = ret + 2^bw;
end
% Check for overflows:
while (ret > (2^(bw-1)-1))
ret = ret - 2^bw;
end
end%function
If 64 bits is enough to not overflow, and you need a lot of these, perhaps do this:
function ret = overflow_sg(arg,bw)
mask = int64(0);
for i=1:round(bw)
mask = bitset(mask,i);
end
topbit = bitshift(int64(1),round(bw-1));
subfrom = double(bitshift(topbit,1))
ret = bitand( int64(arg) , mask );
i = (ret >= topbit);
ret(i) = int64(double(ret(i))-subfrom);
if (bw<=32)
ret = int32(ret);
end
end
Almost everything is done as a matrix calculation, and a lot is done with bits, and everything is done in one step (no while loops), so it should be pretty fast. If you're going to populate it with rand, subtract 0.5 since it assumes it should round to integer values (rather than truncate).
I'm not a Java expert, but underlying Java classes available in Matlab should allow handling of overflows like C would. One solution I found, works only for single value, but it converts a number to the int16 (Short) or int32 (Integer) representation. You must do your math using Matlab double, then convert to Java int16 or int32, then convert back to Matlab double. Unfortunately Java doesn't appear to support unsigned types in this way, only signed.
double(java.lang.Short(hex2dec('7FFF')))
<br>ans = 32767
double(java.lang.Short(hex2dec('7FFF')+1))
<br>ans = -32768
double(java.lang.Short(double(intmax('int16'))+1))
<br>ans = -32768
double(java.lang.Integer(hex2dec('7FFF')+1))
<br>ans = 32768
https://www.tutorialspoint.com/java/lang/java_lang_integer.htm