machine_learning/naive_bayes.cpp

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machine_learning/naive_bayes.cpp
/*******************************************************
* Copyright (c) 2014, ArrayFire
* All rights reserved.
*
* This file is distributed under 3-clause BSD license.
* The complete license agreement can be obtained at:
* http://arrayfire.com/licenses/BSD-3-Clause
********************************************************/
#include <arrayfire.h>
#include <stdio.h>
#include <vector>
#include <string>
#include <af/util.h>
#include <math.h>
#include "mnist_common.h"
using namespace af;
// Get accuracy of the predicted results
float accuracy(const array& predicted, const array& target)
{
return 100 * count<float>(predicted == target) / target. elements();
}
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();
// Get mean and variance from trianing data
mu = constant(0, feat_len, num_classes);
sig2 = constant(0, feat_len, num_classes);
for (int ii = 0; ii < num_classes; ii++) {
array idx = where(train_classes == ii);
array train_feats_ii = lookup(train_feats, idx, 1);
mu(span, ii) = mean(train_feats_ii, 1);
// Some pixels are always 0. Add a small variance.
sig2(span,ii) = var(train_feats_ii, 0, 1) + 0.01;
// Calculate priors
priors[ii] = (float)idx.elements() / (float)num_samples;
}
mu. eval();
sig2.eval();
}
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);
// Predict the probabilities for testing data
// Using log of probabilities to reduce rounding errors
array log_probs = constant(1, num_test, num_classes);
for (int ii = 0; ii < num_classes; ii++) {
// Tile the current mean and variance to the testing data size
array Mu = tile(mu (span, ii), 1, num_test);
array Sig2 = tile(sig2(span, ii), 1, num_test);
// This is the same as log of the CDF of the normal distribution
array Df = test_feats - Mu;
array log_P = (-(Df * Df) / (2 * Sig2)) - log(sqrt(2 * af::Pi * Sig2));
// Accumulate the probabilities, multiply with priors (add log of priors)
log_probs(span, ii) = log(priors[ii]) + sum(log_P).T();
}
// Get the location of the maximum value
array val, idx;
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)
{
array mu, sig2;
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);
}
af::sync();
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);
}
af::sync();
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;
// Load mnist data
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 );
// Get training parameters
array mu, sig2;
float *priors = new float[num_classes];
naive_bayes_train(priors, mu, sig2, train_feats, train_labels, num_classes);
// Predict the classes
array res_labels = naive_bayes_predict(priors, mu, sig2, test_feats, num_classes);
delete[] priors;
// Results
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();
// FIXME: Crashing in mnist_common.h::classify
//display_results<false>(test_images, res_labels, test_labels , 20);
}
}
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 {
af::setDevice(device);
af::info();
naive_bayes_demo(console, perc);
} catch (af::exception &ae) {
std::cerr << ae. what() << std::endl;
}
return 0;
}