private void getnegphase() {
/*
* It does the negative phase of unsupervised RBM training algorithm
*
* For details, please refer to Dr. Hinton's paper:
* Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
*/
// start calculate the negative phase
// calculate the curved value of v1,h1
// find the vector of v1
Matrix negdata = poshidstates.times(vishid.transpose());
// (1 * numhid) * (numhid * numdims) = (1 * numdims)
negdata.plusEquals(visbiases);
// poshidstates*vishid' + visbiases
double[][] tmp1 = negdata.getArray();
int i1 = 0;
while (i1 < numdims) {
tmp1[0][i1] = 1 / (1 + Math.exp(-tmp1[0][i1]));
i1++;
}
// find the vector of h1
neghidprobs = negdata.times(vishid);
// (1 * numdims) * (numdims * numhid) = (1 * numhid)
neghidprobs.plusEquals(hidbiases);
double[][] tmp2 = neghidprobs.getArray();
int i2 = 0;
while (i2 < numhid) {
tmp2[0][i2] = 1 / (1 + Math.exp(-tmp2[0][i2]));
i2++;
}
negprods = negdata.transpose().times(neghidprobs);
// (numdims * 1) *(1 * numhid) = (numdims * numhid)
}
/**
* Calculate how many maps to run. Number of maps is bounded by a minimum of the cumulative size
* of the copy / (distcp.bytes.per.map, default BYTES_PER_MAP or -m on the command line) and at
* most (distcp.max.map.tasks, default MAX_MAPS_PER_NODE * nodes in the cluster).
*
* @param totalBytes Count of total bytes for job
* @param job The job to configure
* @return Count of maps to run.
*/
private static void setMapCount(long totalBytes, JobConf job) throws IOException {
int numMaps = (int) (totalBytes / job.getLong(BYTES_PER_MAP_LABEL, BYTES_PER_MAP));
numMaps =
Math.min(
numMaps,
job.getInt(
MAX_MAPS_LABEL,
MAX_MAPS_PER_NODE * new JobClient(job).getClusterStatus().getTaskTrackers()));
job.setNumMapTasks(Math.max(numMaps, 1));
}
private static void analyzeResult(FileSystem fs, int testType, long execTime, String resFileName)
throws IOException {
Path reduceFile;
if (testType == TEST_TYPE_WRITE) reduceFile = new Path(WRITE_DIR, "part-00000");
else reduceFile = new Path(READ_DIR, "part-00000");
DataInputStream in;
in = new DataInputStream(fs.open(reduceFile));
BufferedReader lines;
lines = new BufferedReader(new InputStreamReader(in));
long tasks = 0;
long size = 0;
long time = 0;
float rate = 0;
float sqrate = 0;
String line;
while ((line = lines.readLine()) != null) {
StringTokenizer tokens = new StringTokenizer(line, " \t\n\r\f%");
String attr = tokens.nextToken();
if (attr.endsWith(":tasks")) tasks = Long.parseLong(tokens.nextToken());
else if (attr.endsWith(":size")) size = Long.parseLong(tokens.nextToken());
else if (attr.endsWith(":time")) time = Long.parseLong(tokens.nextToken());
else if (attr.endsWith(":rate")) rate = Float.parseFloat(tokens.nextToken());
else if (attr.endsWith(":sqrate")) sqrate = Float.parseFloat(tokens.nextToken());
}
double med = rate / 1000 / tasks;
double stdDev = Math.sqrt(Math.abs(sqrate / 1000 / tasks - med * med));
String resultLines[] = {
"----- DFSCIOTest ----- : "
+ ((testType == TEST_TYPE_WRITE)
? "write"
: (testType == TEST_TYPE_READ) ? "read" : "unknown"),
" Date & time: " + new Date(System.currentTimeMillis()),
" Number of files: " + tasks,
"Total MBytes processed: " + size / MEGA,
" Throughput mb/sec: " + size * 1000.0 / (time * MEGA),
"Average IO rate mb/sec: " + med,
" Std IO rate deviation: " + stdDev,
" Test exec time sec: " + (float) execTime / 1000,
""
};
PrintStream res = new PrintStream(new FileOutputStream(new File(resFileName), true));
for (int i = 0; i < resultLines.length; i++) {
LOG.info(resultLines[i]);
res.println(resultLines[i]);
}
}
protected void waitForOpenSlot(int maxProcessesOnNode, Reporter reporter)
throws IOException, InterruptedException {
while (true) {
// sleep for a random length of time between 0 and 60 seconds
long sleepTime = (long) (Math.random() * 1000 * 60);
logger.info("sleeping for " + sleepTime);
Thread.sleep(sleepTime);
int numRunningMappers = getNumRunningMappers();
logger.info("num running mappers: " + numRunningMappers);
if (numRunningMappers < maxProcessesOnNode) return;
reporter.progress();
}
}
public void map(
LongWritable key,
Text value,
OutputCollector<DoubleWritable, DoubleWritable> output,
Reporter reporter)
throws IOException {
String line = value.toString();
DoubleWritable clave = new DoubleWritable();
DoubleWritable valor = new DoubleWritable();
clave.set(Double.parseDouble(line));
valor.set(Math.sqrt(Double.parseDouble(line)));
output.collect(clave, valor);
}
@Override
public void reduce(
Text key, /*[*/
Iterator /*]*/<IntWritable> values,
/*[*/ OutputCollector<Text, IntWritable> output,
Reporter reporter /*]*/)
throws IOException {
int maxValue = Integer.MIN_VALUE;
while (
/*[*/ values.hasNext() /*]*/) {
maxValue = Math.max(maxValue, /*[*/ values.next().get() /*]*/);
}
/*[*/ output.collect /*]*/(key, new IntWritable(maxValue));
}
private void prop2nextLayer() {
/*
* It computes the forward propagation algorithm.
*/
poshidprobs = data.times(vishid);
// (1 * numdims) * (numdims * numhid)
poshidprobs.plusEquals(hidbiases);
// data*vishid + hidbiases
double[][] product_tmp2 = poshidprobs.getArray();
for (int i2 = 0; i2 < numhid; i2++) {
/*
* compute the updated input, and write them to newinput
*/
product_tmp2[0][i2] = 1 / (1 + Math.exp(-product_tmp2[0][i2]));
newinput[i2] = (int) (product_tmp2[0][i2] * 255.0);
}
}
public void map(
Text key, LongWritable value, OutputCollector<K, LongWritable> collector, Reporter reporter)
throws IOException {
String name = key.toString();
long size = value.get();
long seed = Long.parseLong(name);
if (size == 0) return;
reporter.setStatus("opening " + name);
FSDataInputStream in = fs.open(new Path(DATA_DIR, name));
try {
for (int i = 0; i < SEEKS_PER_FILE; i++) {
// generate a random position
long position = Math.abs(random.nextLong()) % size;
// seek file to that position
reporter.setStatus("seeking " + name);
in.seek(position);
byte b = in.readByte();
// check that byte matches
byte checkByte = 0;
// advance random state to that position
random.setSeed(seed);
for (int p = 0; p <= position; p += check.length) {
reporter.setStatus("generating data for " + name);
if (fastCheck) {
checkByte = (byte) random.nextInt(Byte.MAX_VALUE);
} else {
random.nextBytes(check);
checkByte = check[(int) (position % check.length)];
}
}
assertEquals(b, checkByte);
}
} finally {
in.close();
}
}
private void getposphase() {
/*
* It does the positive phase of unsupervised RBM training algorithm
*
* For details, please refer to Dr. Hinton's paper:
* Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
*/
// Start calculate the positive phase
// calculate the cured value of h0
poshidprobs = data.times(vishid);
// (1 * numdims) * (numdims * numhid)
poshidprobs.plusEquals(hidbiases);
// data*vishid + hidbiases
double[][] product_tmp2 = poshidprobs.getArray();
int i2 = 0;
while (i2 < numhid) {
product_tmp2[0][i2] = 1 / (1 + Math.exp(-product_tmp2[0][i2]));
i2++;
}
posprods = data.transpose().times(poshidprobs);
// (numdims * 1) * (1 * numhid)
// end of the positive phase calculation, find the binary presentation of h0
int i3 = 0;
double[][] tmp1 = poshidprobs.getArray();
double[][] tmp2 = new double[1][numhid];
Random randomgenerator = new Random();
while (i3 < numhid) {
/*
* a sampling according to possiblity given by poshidprobs
*/
if (tmp1[0][i3] > randomgenerator.nextDouble()) tmp2[0][i3] = 1;
else tmp2[0][i3] = 0;
i3++;
}
// poshidstates is a binary sampling according to possiblity given by poshidprobs
poshidstates = new Matrix(tmp2);
}
public static boolean stopIteration(Configuration conf) throws IOException {
FileSystem fs = FileSystem.get(conf);
Path preFile = new Path("preX/Result");
Path curFile = new Path("curX/part-00000");
if (!(fs.exists(preFile) && fs.exists(curFile))) {
System.exit(1);
}
boolean stop = true;
String line1, line2;
FSDataInputStream in1 = fs.open(preFile);
FSDataInputStream in2 = fs.open(curFile);
InputStreamReader isr1 = new InputStreamReader(in1);
InputStreamReader isr2 = new InputStreamReader(in2);
BufferedReader br1 = new BufferedReader(isr1);
BufferedReader br2 = new BufferedReader(isr2);
while ((line1 = br1.readLine()) != null && (line2 = br2.readLine()) != null) {
String[] str1 = line1.split("\\s+");
String[] str2 = line2.split("\\s+");
double preElem = Double.parseDouble(str1[1]);
double curElem = Double.parseDouble(str2[1]);
if (Math.abs(preElem - curElem) > eps) {
stop = false;
break;
}
}
if (stop == false) {
fs.delete(preFile, true);
if (fs.rename(curFile, preFile) == false) {
System.exit(1);
}
}
return stop;
}
@Override
public void reduce(
IntWritable key,
Iterator<ClusterWritable> values,
OutputCollector<IntWritable, Text> output,
Reporter reporter)
throws IOException {
float sumSimilarity = 0.0f;
int numMovies = 0;
float avgSimilarity = 0.0f;
float similarity = 0.0f;
int s = 0;
int count;
float diff = 0.0f;
float minDiff = 1.0f;
int candidate = 0;
String data = new String("");
String shortline = new String("");
ArrayList<String> arrl = new ArrayList<String>();
ArrayList<Float> simArrl = new ArrayList<Float>();
String oneElm = new String();
int indexShort, index2;
Text val = new Text();
while (values.hasNext()) {
ClusterWritable cr = (ClusterWritable) values.next();
similarity = cr.similarity;
simArrl.addAll(cr.similarities);
for (int i = 0; i < cr.movies.size(); i++) {
oneElm = cr.movies.get(i);
indexShort =
oneElm.indexOf(
",",
1000); // to avoid memory error caused by long arrays; it will results less
// accurate
if (indexShort == -1) {
shortline = new String(oneElm);
} else {
shortline = new String(oneElm.substring(0, indexShort));
}
arrl.add(shortline);
output.collect(key, new Text(oneElm));
}
numMovies += cr.movies.size();
sumSimilarity += similarity;
}
if (numMovies > 0) {
avgSimilarity = sumSimilarity / (float) numMovies;
}
diff = 0.0f;
minDiff = 1.0f;
for (s = 0; s < numMovies; s++) {
diff = (float) Math.abs(avgSimilarity - simArrl.get(s));
if (diff < minDiff) {
minDiff = diff;
candidate = s;
}
}
data = arrl.get(candidate);
index2 = data.indexOf(":");
String movieStr = data.substring(0, index2);
String reviews = data.substring(index2 + 1);
StringTokenizer token = new StringTokenizer(reviews, ",");
count = 0;
while (token.hasMoreTokens()) {
token.nextToken();
count++;
}
System.out.println(
"The key = "
+ key.toString()
+ " has members = "
+ numMovies
+ " simil = "
+ simArrl.get(candidate));
val = new Text(simArrl.get(candidate) + " " + movieStr + " " + count + " " + reviews);
output.collect(key, val);
reporter.incrCounter(Counter.VALUES, 1);
}
@Override
public int getPartition(IntPair key, NullWritable value, int numPartitions) {
return Math.abs(key.getFirst() * 127) % numPartitions;
}
public static boolean isSameTime(String begin, String end) {
long s = Math.abs(DateStringUtils.second(begin, end, DATETIME_STYLE));
return s <= 1800;
}
public static double calAlpha(double theta, double dec) {
if (Math.abs(dec) + theta > 89.9) return 180;
return (double)
Math.toDegrees(
Math.abs(
Math.atan(
Math.sin(Math.toRadians(theta))
/ Math.sqrt(
Math.cos(Math.toRadians(dec - theta))
* Math.cos(Math.toRadians(dec + theta))))));
}
public class NeighborSearch {
public static final int numZones = 180;
public static final int numBlocks = 360;
public static double theta = 1.0 / 60.0;
public static double blockWidth = 360.0 / numBlocks;
public static double zoneHeight = 180.0 / numZones;
private static double blockRanges[][] = new double[numBlocks][2];
private static double zoneRanges[][] = new double[numZones][2];
private static double maxAlphas[] = new double[numZones];
private static double costheta = Math.cos(Math.toRadians(theta));
public static double calAlpha(double theta, double dec) {
if (Math.abs(dec) + theta > 89.9) return 180;
return (double)
Math.toDegrees(
Math.abs(
Math.atan(
Math.sin(Math.toRadians(theta))
/ Math.sqrt(
Math.cos(Math.toRadians(dec - theta))
* Math.cos(Math.toRadians(dec + theta))))));
}
public static void init() {
zoneRanges[0][0] = -90;
zoneRanges[0][1] = -90 + zoneHeight;
for (int i = 1; i < zoneRanges.length; i++) {
zoneRanges[i][0] = zoneRanges[i - 1][1];
zoneRanges[i][1] = zoneRanges[i][0] + zoneHeight;
}
blockRanges[0][0] = 0;
blockRanges[0][1] = blockWidth;
for (int i = 1; i < blockRanges.length; i++) {
blockRanges[i][0] = blockRanges[i - 1][1];
blockRanges[i][1] = blockRanges[i][0] + blockWidth;
}
for (int i = 0; i < maxAlphas.length; i++) {
double maxDec = zoneRanges[i][1];
if (maxDec <= 0) maxDec = zoneRanges[i][0];
maxAlphas[i] = calAlpha(theta, maxDec);
}
}
public static class Map extends MapReduceBase
implements Mapper<LongWritable, Star, BlockIDWritable, PairWritable> {
/* it seems it's very costly to create an object in Java.
* reuse these objects in every map invocation. */
private BlockIDWritable loc = new BlockIDWritable();
private PairWritable p = new PairWritable();
BlockIDWritable loc1 = new BlockIDWritable();
public Map() {
init();
}
public void map(
LongWritable key,
Star value,
OutputCollector<BlockIDWritable, PairWritable> output,
Reporter reporter)
throws IOException {
loc.set(value.ra, value.dec);
int zoneNum = loc.zoneNum;
int raNum = loc.raNum;
p.set(value, null);
/*
* When the block size increases (> theta), only part of a block
* needs to be copied to its neighbor.
*/
output.collect(loc, p);
/*
* only replicate objects in the border of a block. I expect most of
* objects don't need to be copied.
*/
if (value.dec > zoneRanges[zoneNum][0] + theta
&& value.dec < zoneRanges[zoneNum][1] - theta
&& value.ra > blockRanges[raNum][0] + maxAlphas[zoneNum]
&& value.ra < blockRanges[raNum][1] - maxAlphas[zoneNum]) return;
/*
* the code below is to copy the star to some neighbors. We only
* need to copy an object to the bottom, left, left bottom, left top
* neighbors
*/
value.margin = true;
/*
* we should treat the entire zone 0 as a block, so we only needs to
* copy some objects at the corner to their neighbors
*/
if (loc.zoneNum == 0) {
/* copy the object to the right top neighbor */
if (value.ra >= blockRanges[raNum][1] - maxAlphas[zoneNum]
&& value.ra <= blockRanges[raNum][1]
&& value.dec >= zoneRanges[zoneNum][1] - theta
&& value.dec <= zoneRanges[zoneNum][1]) {
// BlockIDWritable loc1 = new BlockIDWritable();
/* raNum of objects in zone 0 is always 0,
* we need to recalculate it. */
// loc1.raNum = BlockIDWritable.ra2Num(value.ra) + 1;
// if (loc1.raNum == numBlocks) {
// loc1.raNum = 0;
// value.ra -= 360;
// }
// loc1.zoneNum = loc.zoneNum + 1;
/// output.collect(loc1, p);
}
return;
} else if (loc.zoneNum == numZones - 1) {
/* copy the object to the bottom neighbor */
if (value.dec >= zoneRanges[zoneNum][0] && value.dec <= zoneRanges[zoneNum][0] + theta) {
/* raNum of objects in zone zoneNum - 1 is always 0,
* we need to recalculate it. */
loc1.raNum = BlockIDWritable.ra2Num(value.ra);
loc1.zoneNum = loc.zoneNum - 1;
output.collect(loc1, p);
/* copy the object to the right bottom neighbor */
while (value.ra >= blockRanges[loc1.raNum][1] - maxAlphas[zoneNum]
&& value.ra <= blockRanges[loc1.raNum][1]) {
loc1.raNum++;
if (loc1.raNum == numBlocks) {
loc1.raNum = 0;
value.ra -= 360;
}
loc1.zoneNum = loc.zoneNum - 1;
output.collect(loc1, p);
}
}
return;
}
boolean wrap = false;
loc1.raNum = loc.raNum;
/* copy the object to the right neighbor */
while (value.ra >= blockRanges[loc1.raNum][1] - maxAlphas[zoneNum]
&& value.ra <= blockRanges[loc1.raNum][1]) {
loc1.raNum++;
loc1.zoneNum = loc.zoneNum;
/*
* when the object is copied to the right neighbor, we need to
* be careful. we need to convert ra and raNum if ra is close to
* 360.
*/
if (loc1.raNum == numBlocks) {
loc1.raNum = 0;
value.ra -= 360;
wrap = true;
}
output.collect(loc1, p);
/* copy the object to the right bottom neighbor */
if (value.dec >= zoneRanges[zoneNum][0] && value.dec <= zoneRanges[zoneNum][0] + theta) {
loc1.zoneNum = loc.zoneNum - 1;
output.collect(loc1, p);
}
/* copy the object to the right top neighbor */
if (value.dec >= zoneRanges[zoneNum][1] - theta && value.dec <= zoneRanges[zoneNum][1]) {
loc1.zoneNum = loc.zoneNum + 1;
output.collect(loc1, p);
}
}
if (wrap) {
value.ra += 360;
}
/* copy the object to the bottom neighbor */
if (value.dec >= zoneRanges[zoneNum][0] && value.dec <= zoneRanges[zoneNum][0] + theta) {
loc1.raNum = loc.raNum;
loc1.zoneNum = loc.zoneNum - 1;
if (loc1.zoneNum == 0) loc1.raNum = 0;
output.collect(loc1, p);
}
}
}
public static class Reduce extends MapReduceBase
implements Reducer<BlockIDWritable, PairWritable, BlockIDWritable, PairWritable> {
PairWritable p = new PairWritable();
public Reduce() {
init();
}
void search(
Vector<Star> v1,
Vector<Star> v2,
BlockIDWritable key,
OutputCollector<BlockIDWritable, PairWritable> output)
throws IOException {
for (int i = 0; i < v1.size(); i++) {
for (int j = 0; j < v2.size(); j++) {
Star star1 = v1.get(i);
Star star2 = v2.get(j);
// what is this margin about
if (star1.margin && star2.margin) continue;
double dist = star1.x * star2.x + star1.y * star2.y + star1.z * star2.z;
if (dist > costheta) {
p.set(star1, star2, dist);
output.collect(key, p);
p.set(star2, star1, dist);
output.collect(key, p);
// num += 2;
}
}
} // end for i,j
}
public void reduce(
BlockIDWritable key,
Iterator<PairWritable> values,
OutputCollector<BlockIDWritable, PairWritable> output,
Reporter reporter)
throws IOException {
// Vector<Star> starV = new Vector<Star>();
int buketsizeX = 0;
int buketsizeY = 0;
double bwidth = maxAlphas[key.zoneNum]; // ra ,x
double bheight = theta; // dec ,y
/* add 10 more in each dimension to make sure there is no overflow. */
Vector<Star>[][] arrstarV =
new Vector[((int) (zoneHeight / bheight)) + 10]
[((int) (blockWidth / bwidth)) + 10]; // create bucket vector[Y][X]
int num = 0;
while (values.hasNext()) {
num++;
Star s = values.next().get(0);
// participant
double posx = (s.ra - blockRanges[key.raNum][0]) / bwidth;
int x = (int) posx + 1; // shit by 1 in case star comes from other block
double posy = (s.dec - zoneRanges[key.zoneNum][0]) / bheight;
int y = (int) posy + 1;
// set bucket size as max
if (buketsizeX < x) buketsizeX = x;
if (buketsizeY < y) buketsizeY = y;
// create according bucket
if (arrstarV[y][x] == null)
// TODO avaoid creating vectors here.
arrstarV[y][x] = new Vector<Star>();
// put star into bucket
arrstarV[y][x].add(s);
}
// start reducer
int i, j, row, col;
// for each bucket
for (row = 0; row <= buketsizeY; row++) {
for (col = 0; col <= buketsizeX; col++) {
// starV.clear();
// construct a new vector to do compare
// TODO we need to avoid searching objects in the border.
if (arrstarV[row][col] != null) {
// old method to generate output
for (i = 0; i < arrstarV[row][col].size(); i++) {
for (j = i + 1; j < arrstarV[row][col].size(); j++) {
Star star1 = arrstarV[row][col].get(i);
Star star2 = arrstarV[row][col].get(j);
// what is this margin about
if (star1.margin && star2.margin) continue;
double dist = star1.x * star2.x + star1.y * star2.y + star1.z * star2.z;
if (dist > costheta) {
p.set(star1, star2, dist);
output.collect(key, p);
p.set(star2, star1, dist);
output.collect(key, p);
// num += 2;
}
}
} // end for i,j
} // end if
else {
continue;
}
// 4 more neighbors
// right upper arrstarV[row-1][col+1] vs arrstarV[row][col]
if (row != 0 && arrstarV[row - 1][col + 1] != null) {
search(arrstarV[row][col], arrstarV[row - 1][col + 1], key, output);
}
// right arrstarV[row][col+1] vs arrstarV[row][col]
if (arrstarV[row][col + 1] != null) {
search(arrstarV[row][col], arrstarV[row][col + 1], key, output);
}
// right lower
if (arrstarV[row + 1][col + 1] != null) {
search(arrstarV[row][col], arrstarV[row + 1][col + 1], key, output);
}
// lower
if (arrstarV[row + 1][col] != null) {
search(arrstarV[row][col], arrstarV[row + 1][col], key, output);
} // end if
} // end colum
} // end row
}
}
public static void main(String[] args) throws Exception {
JobConf conf = new JobConf(NeighborSearch.class);
conf.setJobName("star searching");
conf.setOutputKeyClass(BlockIDWritable.class);
conf.setOutputValueClass(PairWritable.class);
conf.setMapperClass(Map.class);
// conf.setCombinerClass(Reduce.class);
conf.setReducerClass(Reduce.class);
// conf.setPartitionerClass(BlockPartitioner.class);
// conf.setFloat("mapred.reduce.slowstart.completed.maps", (float) 1.0);
conf.setInputFormat(StarInputFormat.class);
conf.setOutputFormat(StarOutputFormat.class);
FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf);
}
}
