package edu.hawaii.jmotif.performance.digits;
import java.util.Arrays;
import java.util.Collection;
import java.util.HashMap;
import java.util.Map.Entry;
import java.util.concurrent.Callable;
import edu.hawaii.jmotif.algorithm.MatrixFactory;
import edu.hawaii.jmotif.sax.SAXFactory;
import edu.hawaii.jmotif.sax.alphabet.Alphabet;
import edu.hawaii.jmotif.sax.alphabet.NormalAlphabet;
import edu.hawaii.jmotif.text.SAXCollectionStrategy;
import edu.hawaii.jmotif.text.WordBag;
import edu.hawaii.jmotif.timeseries.TSException;
public class DigitConversionJob implements Callable<WordBag> {
protected final static int CLASSIC = 0;
protected final static int EXACT = 1;
protected final static int NOREDUCTION = 2;
private int[] params;
private Entry<String, double[]> entry;
public DigitConversionJob(Entry<String, double[]> e, int[] params) {
this.entry = e;
this.params = params;
}
@Override
public WordBag call() throws Exception {
SAXCollectionStrategy strategy = SAXCollectionStrategy.CLASSIC;
if (EXACT == this.params[3]) {
strategy = SAXCollectionStrategy.EXACT;
}
else if (NOREDUCTION == this.params[3]) {
strategy = SAXCollectionStrategy.NOREDUCTION;
}
return seriesToWordBag(this.entry.getKey(),
this.entry.getValue(), this.params, strategy);
}
/** The latin alphabet, lower case letters a-z. */
private static final char[] ALPHABET = { 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k',
'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z' };
private static final String COMMA = ",";
private WordBag seriesToWordBag(String label, double[] series, int[] params,
SAXCollectionStrategy strategy) throws IndexOutOfBoundsException, TSException {
Alphabet a = new NormalAlphabet();
WordBag resultBag = new WordBag(label);
int windowSize = params[0];
int paaSize = params[1];
int alphabetSize = params[2];
String oldStr = "";
for (int i = 0; i <= series.length - windowSize; i++) {
double[] paa = paa(zNormalize(subseries(series, i, windowSize)), paaSize);
char[] sax = ts2String(paa, a.getCuts(alphabetSize));
if (SAXCollectionStrategy.CLASSIC.equals(strategy)) {
if (oldStr.length() > 0 && SAXFactory.strDistance(sax, oldStr.toCharArray()) == 0) {
continue;
}
}
else if (SAXCollectionStrategy.EXACT.equals(strategy)) {
if (oldStr.equalsIgnoreCase(String.valueOf(sax))) {
continue;
}
}
oldStr = String.valueOf(sax);
resultBag.addWord(String.valueOf(sax));
}
return resultBag;
}
private double cosineSimilarity(WordBag testSample, HashMap<String, Double> weightVector) {
double res = 0;
for (Entry<String, Integer> entry : testSample.getWords().entrySet()) {
if (weightVector.containsKey(entry.getKey())) {
res = res + entry.getValue().doubleValue() * weightVector.get(entry.getKey()).doubleValue();
}
}
double m1 = magnitude(testSample.getWordsAsDoubles().values());
double m2 = magnitude(weightVector.values());
return res / (m1 * m2);
}
private double magnitude(Collection<Double> values) {
double res = 0.0D;
for (Double v : values) {
res = res + v * v;
}
return Math.sqrt(res);
}
/**
* Approximate the timeseries using PAA. If the timeseries has some NaN's they are handled as
* follows: 1) if all values of the piece are NaNs - the piece is approximated as NaN, 2) if there
* are some (more or equal one) values happened to be in the piece - algorithm will handle it as
* usual - getting the mean.
*
* @param ts The timeseries to approximate.
* @param paaSize The desired length of approximated timeseries.
* @return PAA-approximated timeseries.
* @throws TSException if error occurs.
*/
private double[] paa(double[] ts, int paaSize) throws TSException {
// fix the length
int len = ts.length;
// check for the trivial case
if (len == paaSize) {
return Arrays.copyOf(ts, ts.length);
}
else {
if (len % paaSize == 0) {
return MatrixFactory.colMeans(MatrixFactory.reshape(asMatrix(ts), len / paaSize, paaSize));
}
else {
// res = new double[len][paaSize];
// for (int j = 0; j < len; j++) {
// for (int i = 0; i < paaSize; i++) {
// int idx = j * paaSize + i;
// int row = idx % len;
// int col = idx / len;
// res[row][col] = ts[j];
// }
// }
double[] paa = new double[paaSize];
for (int i = 0; i < len * paaSize; i++) {
int idx = i / len; // the spot
int pos = i / paaSize; // the col spot
paa[idx] = paa[idx] + ts[pos];
}
for (int i = 0; i < paaSize; i++) {
paa[i] = paa[i] / (double) len;
}
return paa;
}
}
}
/**
* Mimics Matlab function for reshape: returns the m-by-n matrix B whose elements are taken
* column-wise from A. An error results if A does not have m*n elements.
*
* @param a the source matrix.
* @param n number of rows in the new matrix.
* @param m number of columns in the new matrix.
*
* @return reshaped matrix.
*/
private double[][] reshape(double[][] a, int n, int m) {
int cEl = 0;
int aRows = a.length;
double[][] res = new double[n][m];
for (int j = 0; j < m; j++) {
for (int i = 0; i < n; i++) {
res[i][j] = a[cEl % aRows][cEl / aRows];
cEl++;
}
}
return res;
}
/**
* Computes column means for the matrix.
*
* @param a the input matrix.
* @return result.
*/
private double[] colMeans(double[][] a) {
double[] res = new double[a[0].length];
for (int j = 0; j < a[0].length; j++) {
double sum = 0;
for (int i = 0; i < a.length; i++) {
sum += a[i][j];
}
// res[j] = sum / ((Integer) a.length).doubleValue();
res[j] = sum / ((double) a.length);
}
return res;
}
/**
* Converts the vector into one-row matrix.
*
* @param vector The vector.
* @return The matrix.
*/
private double[][] asMatrix(double[] vector) {
double[][] res = new double[1][vector.length];
for (int i = 0; i < vector.length; i++) {
res[0][i] = vector[i];
}
return res;
}
/**
* Z-Normalize timeseries to the mean zero and standard deviation of one.
*
* @param series The timeseries.
* @return Z-normalized time-series.
* @throws TSException if error occurs.
*/
private double[] zNormalize(double[] series) throws TSException {
// this is the resulting normalization
//
double[] res = new double[series.length];
// get mean and sdev, NaN's will be handled
//
double mean = mean(series);
double sd = stDev(series);
// another special case, where SD happens to be close to a zero, i.e. they all are the same for
// example
//
if (sd <= 0.001D) {
// here I assign another magic value - 0.001D which makes to middle band of the normal
// Alphabet
//
for (int i = 0; i < res.length; i++) {
if (Double.isInfinite(series[i]) || Double.isNaN(series[i])) {
res[i] = series[i];
}
else {
res[i] = 0.1D;
}
}
}
// normal case, everything seems to be fine
//
else {
// sd and mean here, - go-go-go
for (int i = 0; i < res.length; i++) {
res[i] = (series[i] - mean) / sd;
}
}
return res;
}
/**
* Computes the mean value of timeseries.
*
* @param series The timeseries.
* @return The mean value.
*/
private double mean(double[] series) {
double res = 0D;
int count = 0;
for (double tp : series) {
if (Double.isNaN(tp) || Double.isInfinite(tp)) {
continue;
}
else {
res += tp;
count += 1;
}
}
if (count > 0) {
return res / ((Integer) count).doubleValue();
}
return Double.NaN;
}
/**
* Computes the standard deviation of timeseries.
*
* @param series The timeseries.
* @return the standard deviation.
*/
private double stDev(double[] series) {
double num0 = 0D;
double sum = 0D;
int count = 0;
for (double tp : series) {
if (Double.isNaN(tp) || Double.isInfinite(tp)) {
continue;
}
else {
num0 = num0 + tp * tp;
sum = sum + tp;
count += 1;
}
}
if (count > 0) {
double len = ((Integer) count).doubleValue();
return Math.sqrt((len * num0 - sum * sum) / (len * (len - 1)));
}
return Double.NaN;
}
/**
* Extract subseries out of series.
*
* @param series The series array.
* @param start Start position
* @param length Length of subseries to extract.
* @return The subseries.
* @throws IndexOutOfBoundsException If error occurs.
*/
private double[] subseries(double[] series, int start, int length)
throws IndexOutOfBoundsException {
if (start + length > series.length) {
throw new IndexOutOfBoundsException("Unable to extract subseries, series length: "
+ series.length + ", start: " + start + ", subseries length: " + length);
}
double[] res = new double[length];
for (int i = 0; i < length; i++) {
res[i] = series[start + i];
}
return res;
}
/**
* Converts the timeseries into string using given cuts intervals. Useful for not-normal
* distribution cuts.
*
* @param vals The timeseries.
* @param cuts The cut intervals.
* @return The timeseries SAX representation.
*/
private char[] ts2String(double[] vals, double[] cuts) {
char[] res = new char[vals.length];
for (int i = 0; i < vals.length; i++) {
res[i] = num2char(vals[i], cuts);
}
return res;
}
/**
* Get mapping of a number to char.
*
* @param value the value to map.
* @param cuts the array of intervals.
* @return character corresponding to numeric value.
*/
private char num2char(double value, double[] cuts) {
int count = 0;
while ((count < cuts.length) && (cuts[count] <= value)) {
count++;
}
return ALPHABET[count];
}
}