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Showing content from http://www.arrayfire.org/docs/machine_learning_2naive_bayes_8cpp-example.htm below:

ArrayFire: machine_learning/naive_bayes.cpp

#include <math.h>

#include <stdio.h>

#include <string>

#include <vector>

#include "mnist_common.h"

accuracy(

&predicted,

&target) {

100 * count<float>(predicted == target) / target.

();

}

naive_bayes_train(

*priors,

&mu,

&sig2,

&train_feats,

&train_classes,

int num_classes) {

feat_len = train_feats.

(0);

num_samples = train_classes.

();

mu = constant(0, feat_len, num_classes);

sig2 = constant(0, feat_len, num_classes);

for (int ii = 0; ii < num_classes; ii++) {

idx = where(train_classes == ii);

train_feats_ii = lookup(train_feats, idx, 1);

mu(span, ii) = mean(train_feats_ii, 1);

priors[ii] = (float)idx.

() / (float)num_samples;

}

}

naive_bayes_predict(

*priors,

&mu,

&sig2,

&test_feats,

num_classes) {

num_test = test_feats.

(1);

for (int ii = 0; ii < num_classes; ii++) {

Sig2 =

(sig2(span, ii), 1, num_test);

Df = test_feats - Mu;

log_probs(span, ii) =

(priors[ii]) +

(log_P).

();

}

(val, idx, log_probs, 1);

return idx;

}

benchmark_nb(

&train_feats,

test_feats,

&train_labels,

num_classes) {

int iter = 25;

float *priors = new float[num_classes];

timer::start();

for (int i = 0; i < iter; i++) {

naive_bayes_train(priors, mu, sig2, train_feats, train_labels,

num_classes);

}

printf("Training time: %4.4lf s\n", timer::stop() / iter);

timer::start();

for (int i = 0; i < iter; i++) {

naive_bayes_predict(priors, mu, sig2, test_feats, num_classes);

}

printf("Prediction time: %4.4lf s\n", timer::stop() / iter);

delete[] priors;

}

void naive_bayes_demo(bool console, int perc) {

train_images, train_labels;

test_images, test_labels;

int num_train, num_test, num_classes;

float frac = (float)(perc) / 100.0;

setup_mnist<false>(&num_classes, &num_train, &num_test, train_images,

test_images, train_labels, test_labels, frac);

feature_length = train_images.

() / num_train;

train_feats =

(train_images, feature_length, num_train);

test_feats =

(test_images, feature_length, num_test);

float *priors = new float[num_classes];

naive_bayes_train(priors, mu, sig2, train_feats, train_labels, num_classes);

naive_bayes_predict(priors, mu, sig2, test_feats, num_classes);

delete[] priors;

printf("Trainng samples: %4d, Testing samples: %4d\n", num_train, num_test);

printf("Accuracy on testing data: %2.2f\n",

accuracy(res_labels, test_labels));

benchmark_nb(train_feats, test_feats, train_labels, num_classes);

if (!console) {

test_images = test_images.

();

test_labels = test_labels.

();

}

}

int main(int argc, char **argv) {

int device = argc > 1 ? atoi(argv[1]) : 0;

bool console = argc > 2 ? argv[2][0] == '-' : false;

int perc = argc > 3 ? atoi(argv[3]) : 60;

try {

naive_bayes_demo(console, perc);

return 0;

}

A multi dimensional data container.

dim4 dims() const

Get dimensions of the array.

void eval() const

Evaluate any JIT expressions to generate data for the array.

array T() const

Get the transposed the array.

dim_t elements() const

Get the total number of elements across all dimensions of the array.

An ArrayFire exception class.

virtual const char * what() const

Returns an error message for the exception in a string format.

@ AF_VARIANCE_SAMPLE

Sample variance.

AFAPI array log(const array &in)

C++ Interface to evaluate the natural logarithm.

AFAPI array sqrt(const array &in)

C++ Interface to evaluate the square root.

array constant(T val, const dim4 &dims, const dtype ty=(af_dtype) dtype_traits< T >::ctype)

C++ Interface to generate an array with elements set to a specified value.

AFAPI void setDevice(const int device)

Sets the current device.

AFAPI void sync(const int device=-1)

Blocks until the device is finished processing.

AFAPI array moddims(const array &in, const dim4 &dims)

C++ Interface to modify the dimensions of an input array to a specified shape.

AFAPI array tile(const array &in, const unsigned x, const unsigned y=1, const unsigned z=1, const unsigned w=1)

C++ Interface to generate a tiled array.

AFAPI array max(const array &in, const int dim=-1)

C++ Interface to return the maximum along a given dimension.

AFAPI array sum(const array &in, const int dim=-1)

C++ Interface to sum array elements over a given dimension.

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