#include <stdio.h>
#include <vector>
#include <string>
#include <math.h>
#include "mnist_common.h"
float accuracy(
const array& predicted,
const array& target)
{
return 100 * count<float>(predicted == target) / target.
elements();
}
void naive_bayes_train(float *priors,
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++) {
sig2(
span,ii) =
var(train_feats_ii, 0, 1) + 0.01;
priors[ii] = (float)idx.
elements() / (float)num_samples;
}
}
array naive_bayes_predict(
float *priors,
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);
array res_labels = 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);
std::cerr << ae.
what() << std::endl;
}
return 0;
}