#include <math.h>
#include <stdio.h>
#include <string>
#include <vector>
#include "mnist_common.h"
float accuracy(
const array &predicted,
const array &target) {
}
void naive_bayes_train(
float *priors,
array &mu,
array &sig2,
const array &train_feats,
const array &train_classes,
int num_classes) {
const int feat_len = train_feats.
dims(0);
const int num_samples = train_classes.
elements();
mu =
constant(0, feat_len, num_classes);
sig2 =
constant(0, feat_len, num_classes);
for (int ii = 0; ii < num_classes; ii++) {
priors[ii] = (float)idx.
elements() / (float)num_samples;
}
}
array naive_bayes_predict(
float *priors,
const array &mu,
const array &sig2,
const array &test_feats,
int num_classes) {
int num_test = test_feats.
dims(1);
for (int ii = 0; ii < num_classes; ii++) {
array Df = test_feats - Mu;
log_probs(
span, ii) =
log(priors[ii]) +
sum(log_P).T();
}
max(val, idx, log_probs, 1);
return idx;
}
void benchmark_nb(
const array &train_feats,
const array test_feats,
const array &train_labels,
int num_classes) {
int iter = 25;
float *priors = new float[num_classes];
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);
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) {
array train_images, train_labels;
array 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);
int feature_length = train_images.
elements() / num_train;
array train_feats =
moddims(train_images, feature_length, num_train);
array test_feats =
moddims(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.
T();
test_labels = test_labels.
T();
}
}
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.
array log(const array &in)
C++ Interface to evaluate the natural logarithm.
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.
void setDevice(const int device)
Sets the current device.
void sync(const int device=-1)
Blocks until the device is finished processing.
array lookup(const array &in, const array &idx, const int dim=-1)
Lookup the values of an input array by indexing with another array.
array moddims(const array &in, const dim4 &dims)
C++ Interface to modify the dimensions of an input array to a specified shape.
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.
array count(const array &in, const int dim=-1)
C++ Interface to count non-zero values in an array along a given dimension.
array max(const array &in, const int dim=-1)
C++ Interface to return the maximum along a given dimension.
array sum(const array &in, const int dim=-1)
C++ Interface to sum array elements over a given dimension.
array where(const array &in)
C++ Interface to locate the indices of the non-zero values in an array.
array mean(const array &in, const dim_t dim=-1)
C++ Interface for mean.
array var(const array &in, const bool isbiased=false, const dim_t dim=-1)
C++ Interface for variance.
seq span
A special value representing the entire axis of an af::array.