public void map(Object key, Text value, OutputCollector<Text, Text> output, Reporter reporter)
throws IOException {
String[] _allCols = StringUtils.splitPreserveAllTokens(value.toString(), splitChar);
StringBuffer stringBuffer = new StringBuffer();
for (int i = 0; i < _allCols.length; i++) {
stringBuffer.append(i).append('=').append(_allCols[i]).append("\t");
}
output.collect(new Text(_allCols[0]), new Text(stringBuffer.toString()));
}
public void map(
LongWritable key,
Text value,
OutputCollector<IntWritable, ClusterWritable> output,
Reporter reporter)
throws IOException {
String movieIdStr = new String();
String reviewStr = new String();
String userIdStr = new String();
String reviews = new String();
String line = new String();
String tok = new String("");
long movieId;
int review, userId, p, q, r, rater, rating, movieIndex;
int clusterId = 0;
int[] n = new int[maxClusters];
float[] sq_a = new float[maxClusters];
float[] sq_b = new float[maxClusters];
float[] numer = new float[maxClusters];
float[] denom = new float[maxClusters];
float max_similarity = 0.0f;
float similarity = 0.0f;
Cluster movie = new Cluster();
ClusterWritable movies_arrl = new ClusterWritable();
StringBuffer sb = new StringBuffer();
line = ((Text) value).toString();
movieIndex = line.indexOf(":");
for (r = 0; r < maxClusters; r++) {
numer[r] = 0.0f;
denom[r] = 0.0f;
sq_a[r] = 0.0f;
sq_b[r] = 0.0f;
n[r] = 0;
}
if (movieIndex > 0) {
movieIdStr = line.substring(0, movieIndex);
sb.append(movieIdStr).append(":");
movieId = Long.parseLong(movieIdStr);
movie.movie_id = movieId;
reviews = line.substring(movieIndex + 1);
StringTokenizer token = new StringTokenizer(reviews, ",");
int attrCnt = 0;
// while (token.hasMoreTokens()) { Leo
while (token.hasMoreTokens() && attrCnt < attrNum) {
tok = token.nextToken();
int reviewIndex = tok.indexOf("_");
// userIdStr = tok.substring(0, reviewIndex); //Leo
userIdStr = String.valueOf(attrCnt);
reviewStr = tok.substring(reviewIndex + 1);
if (attrCnt > 0) {
sb.append(",");
}
sb.append(String.valueOf(attrCnt)).append("_").append(reviewStr);
userId = Integer.parseInt(userIdStr);
review = Integer.parseInt(reviewStr);
for (r = 0; r < totalClusters; r++) {
/*for (q = 0; q < centroids_ref[r].total; q++) {
rater = centroids_ref[r].reviews.get(q).rater_id;
rating = (int) centroids_ref[r].reviews.get(q).rating;
if (userId == rater) {
numer[r] += (float) (review * rating);
sq_a[r] += (float) (review * review);
sq_b[r] += (float) (rating * rating);
n[r]++; // counter
break; // to avoid multiple ratings by the same reviewer
}
}*/
// Leo
rating = (int) centroids_ref[r].reviews.get(attrCnt).rating;
numer[r] += (float) ((review - rating) * (review - rating));
n[r]++; // counter
}
attrCnt++;
}
for (p = 0; p < totalClusters; p++) {
/*denom[p] = (float) ((Math.sqrt((double) sq_a[p])) * (Math
.sqrt((double) sq_b[p])));
if (denom[p] > 0) {
similarity = numer[p] / denom[p];
if (similarity > max_similarity) {
max_similarity = similarity;
clusterId = p;
}
}*/
// Leo
similarity = 250 - numer[p];
if (similarity > max_similarity) {
max_similarity = similarity;
clusterId = p;
}
}
// movies_arrl.movies.add(line);//Leo
movies_arrl.movies.add(sb.toString());
movies_arrl.similarities.add(max_similarity);
movies_arrl.similarity = max_similarity;
output.collect(new IntWritable(clusterId), movies_arrl);
reporter.incrCounter(Counter.WORDS, 1);
}
}