ArrayFire: machine_learning/geneticalgorithm.cpp (original) (raw)
#include
#include
#include
#include
using namespace af;
static const float DefaultTopFittest = 0.5;
const array& sampleY, const int n) {
return searchSpace(sampleY * n + sampleX);
}
array selectFittest(const array& sampleZ, const int nSamples,
const float topFit = DefaultTopFittest) {
array indices, values;
sort(values, indices, sampleZ);
int topFitElem = topFit * nSamples;
return (n > topFitElem) ? indices(seq(n - topFitElem, n - 1)) : indices;
}
void reproduce(array& searchSpace, array& sampleX, array& sampleY,
array& sampleZ, const int nSamples, const int n) {
array selection = selectFittest(sampleZ, nSamples);
array parentsX = sampleX(selection);
array parentsY = sampleY(selection);
int bits = (int)log2(n);
array lowermask = (1 << crossover) - 1;
array uppermask = INT_MAX - lowermask;
array childrenX1 = (parentsX1 & uppermask) + (parentsX2 & lowermask);
array childrenY1 = (parentsY1 & uppermask) + (parentsY2 & lowermask);
array childrenX2 = (parentsX2 & uppermask) + (parentsX1 & lowermask);
array childrenY2 = (parentsY2 & uppermask) + (parentsY1 & lowermask);
sampleX = join(0, childrenX1, childrenX2);
sampleY = join(0, childrenY1, childrenY2);
array mutantX = sampleX;
array mutantY = sampleY;
mutantX = mutantX ^ (1 << (randu(nSamples / 2, u32) % bits));
mutantY = mutantY ^ (1 << (randu(nSamples / 2, u32) % bits));
sampleX = join(0, sampleX, mutantX);
sampleY = join(0, sampleY, mutantY);
sampleZ = update(searchSpace, sampleX, sampleY, n);
}
void initSamples(array& searchSpace, array& sampleX, array& sampleY,
array& sampleZ, const int nSamples, const int n) {
sampleX = randu(nSamples, u32) % n;
sampleY = randu(nSamples, u32) % n;
sampleZ = update(searchSpace, sampleX, sampleY, n);
}
void init(array& searchSpace, array& searchSpaceXDisplay,
array& sampleZ, const int nSamples, const int n) {
searchSpace = range(dim4(n / 2, n / 2), 0) + range(dim4(n / 2, n / 2), 1);
searchSpace = join(0, searchSpace, flip(searchSpace, 0));
searchSpace = join(1, searchSpace, flip(searchSpace, 1));
searchSpaceXDisplay = iota(dim4(n, 1), dim4(1, n));
searchSpaceYDisplay = iota(dim4(1, n), dim4(n, 1));
initSamples(searchSpace, sampleX, sampleY, sampleZ, nSamples, n);
}
void reproducePrint(float& currentMax, array& searchSpace, array& sampleX,
array& sampleY, array& sampleZ, const float trueMax,
const int nSamples, const int n) {
if (currentMax < trueMax * 0.99) {
float maximum = max(sampleZ);
array whereM = where(sampleZ == maximum);
if (maximum < trueMax * 0.99) {
printf("Current max at ");
} else {
printf("\nMax found at ");
}
printf("(%d,%d): %f (trueMax %f)\n",
sampleX(whereM).scalar(),
sampleY(whereM).scalar(), maximum, trueMax);
currentMax = maximum;
reproduce(searchSpace, sampleX, sampleY, sampleZ, nSamples, n);
}
}
void geneticSearch(bool console, const int nSamples, const int n) {
array searchSpaceXDisplay;
array searchSpaceYDisplay;
init(searchSpace, searchSpaceXDisplay, searchSpaceYDisplay, sampleX,
sampleY, sampleZ, nSamples, n);
float trueMax = max(searchSpace);
float maximum = -trueMax;
if (!console) {
af::Window win(1600, 800, "Arrayfire Genetic Algorithm Search Demo");
win.grid(1, 2);
do {
reproducePrint(maximum, searchSpace, sampleX, sampleY, sampleZ,
trueMax, nSamples, n);
win(0, 0).setAxesTitles("IdX", "IdY", "Search Space");
win(0, 1).setAxesTitles("IdX", "IdY", "Search Space");
win(0, 0).surface(searchSpaceXDisplay, searchSpaceYDisplay,
searchSpace);
win(0, 1).scatter(sampleX.as(f32), sampleY.as(f32), sampleZ.as(f32),
win.show();
} while (!win.close());
} else {
do {
reproducePrint(maximum, searchSpace, sampleX, sampleY, sampleZ,
trueMax, nSamples, n);
} while (maximum < trueMax * 0.99);
}
}
int main(int argc, char** argv) {
bool console = false;
const int n = 32;
const int nSamples = 16;
if (argc > 2 || (argc == 2 && strcmp(argv[1], "-"))) {
printf("usage: %s [-]\n", argv[0]);
return -1;
} else if (argc == 2 && argv[1][0] == '-') {
console = true;
}
try {
printf(
"** ArrayFire Genetic Algorithm Search Demo **\n\n"
"Search for trueMax in a search space where the objective "
"function is defined as :\n\n"
"SS(x ,y) = min(x, n - (x + 1)) + min(y, n - (y + 1))\n\n"
"(x, y) belongs to RxR; R = [0, n); n = %d\n\n",
n);
if (!console) {
printf(
"The left figure shows the objective function.\n"
"The right figure shows current generation's "
"parameters and function values.\n\n");
}
geneticSearch(console, nSamples, n);
return 0;
}
Window object to render af::arrays.
A multi dimensional data container.
const array as(dtype type) const
Casts the array into another data type.
dim_t elements() const
Get the total number of elements across all dimensions of the array.
Generic object that represents size and shape.
An ArrayFire exception class.
virtual const char * what() const
Returns an error message for the exception in a string format.
seq is used to create sequences for indexing af::array
@ u32
32-bit unsigned integral values
@ f32
32-bit floating point values
array::array_proxy rows(int first, int last)
Returns a reference to sequence of rows.
AFAPI array iota(const dim4 &dims, const dim4 &tile_dims=dim4(1), const dtype ty=f32)
C++ Interface to generate an array with [0, n-1] values modified to specified dimensions and tiling.
AFAPI array range(const dim4 &dims, const int seq_dim=-1, const dtype ty=f32)
C++ Interface to generate an array with [0, n-1] values along the seq_dim dimension and tiled across ...
AFAPI array flip(const array &in, const unsigned dim)
C++ Interface to flip an array.
AFAPI array join(const int dim, const array &first, const array &second)
C++ Interface to join 2 arrays along a dimension.
AFAPI array randu(const dim4 &dims, const dtype ty, randomEngine &r)
C++ Interface to create an array of random numbers uniformly distributed.
AFAPI void setSeed(const unsigned long long seed)
C++ Interface to set the seed of the default random number generator.
AFAPI array where(const array &in)
C++ Interface to locate the indices of the non-zero values in an array.
AFAPI array log2(const array &in)
C++ Interface to evaluate the base 2 logarithm.