/* Function for calculating laplacePerTerm for individual query terms */
public static double laplacePerTerm(
String termFreq, double docLen, double avgLen, long vocabSize, int queryWords) {
// System.out.println("Query Words:::");
String[] termFs = termFreq.split(" ");
// System.out.println("TermFs::: "+ termFreq);
double log_p_lap = 0.0;
/*
* if(termFs.length <= queryWords){
*
* for(int j=0;j<(queryWords-termFs.length); j++){ log_p_lap +=
* Math.log(((0 + 1) / (docLen + vocabSize)); } log_p_lap+= (Math.log((0
* + 1) / (docLen + vocabSize))) * (queryWords - termFs.length);
*
* }
*/
if (termFs.length < queryWords) {
log_p_lap += (Math.log((0 + 1) / (docLen + vocabSize))) * (queryWords - termFs.length);
}
for (int i = 0; i < termFs.length; i++) {
log_p_lap += Math.log(((Integer.parseInt(termFs[i])) + 1) / (docLen + vocabSize));
}
return log_p_lap;
}
public static String humanReadableByteCount(long bytes, boolean si) {
int unit = si ? 1000 : 1024;
if (bytes < unit) return bytes + " B";
int exp = (int) (Math.log(bytes) / Math.log(unit));
String pre = (si ? "kMGTPE" : "KMGTPE").charAt(exp - 1) + (si ? "" : "i");
return String.format("%.1f %sB", bytes / Math.pow(unit, exp), pre);
}
public Double eval(Double lmbda) {
double c1 = 0.0;
double c3 = -1.0 / (double) Math.sqrt(2.0 * variance);
double sum = 0.0;
double cpart = -0.5 * Math.log(Math.sqrt(2.0 * Math.PI * variance));
for (int i = 0; i < data.size(); i++) {
for (int k = 0; k < channels.length; k++) {
c1 += cpart;
double pi = 0.0;
for (Integer t : transcripts.keySet()) {
if (transcripts.get(t).contains(i)) {
double dit = delta(i, t);
double gammai = gammas[t][k];
double pit = gammai * Math.exp(-lmbda * dit);
pi += pit;
}
}
double zi = Math.log(pi);
double err = data.values(i)[channels[k]] - zi;
sum += (err * err);
}
}
return c1 + c3 * sum;
}
public Double eval(Double gamma) {
double c1 = 0.0;
double c3 = -1.0 / (double) Math.sqrt(2.0 * variance);
double sum = 0.0;
double cpart = -0.5 * Math.log(Math.sqrt(2.0 * Math.PI * variance));
for (Integer i : transcripts.get(gammat)) {
c1 += cpart;
double pi = 0.0;
for (int t = 0; t < fiveprime.length; t++) {
if (transcripts.get(t).contains(i)) {
double gammai = gammat == t ? gamma : gammas[t][gammak];
double dit = delta(i, t);
double pit = gammai * Math.exp(-lambda * dit);
pi += pit;
}
}
double zi = Math.log(pi);
double err = data.values(i)[channels[gammak]] - zi;
sum += (err * err);
}
return c1 + c3 * sum;
}
private static JointClassification jointJointClassify(ssd.Document doc) {
double[][] classprobs = new double[PartsOfSpeech.length][CATEGORIES.length];
double[] jointprobs = new double[CATEGORIES.length];
double[] ranks = new double[CATEGORIES.length];
double[] classif_variances = new double[PartsOfSpeech.length];
for (double d : jointprobs) d = 0;
for (int i = 0; i < CATEGORIES.length; i++) ranks[i] = 0;
JointClassification jc = new JointClassification(CATEGORIES, jointprobs);
for (int i = 0; i < PartsOfSpeech.length; i++) {
String pos = PartsOfSpeech[i];
if (doc.mPosTexts.get(pos) != null) {
JointClassification jc1 = compiledClassifiers.get(pos).classify(doc.mPosTexts.get(pos));
for (int j = 0; j < CATEGORIES.length; j++)
classprobs[i][j] = getCategoryConditionalProbability(CATEGORIES[j], jc1);
}
}
for (int i = 0; i < classif_variances.length; i++) {
classif_variances[i] =
variance(classprobs[i]); // get the variance of each classifier into classif_variances
}
double average_variance = 0, max_classif_variance = -1, sum_variance = 0;
for (double d : classif_variances) {
sum_variance += d;
if (d > max_classif_variance) max_classif_variance = d;
}
average_variance = sum_variance / classif_variances.length;
// say we cheat and let the average be the same as the 'all' partofspeech
for (int i = 0; i < PartsOfSpeech.length; i++)
if (PartsOfSpeech[i].equals("all")) average_variance = classif_variances[i];
// average_variance=max_classif_variance;
/*switch this all back, write the paper, finish the presentation. WRAP THE F**K UP */
/* No! this method achieves 67% accuracy on 20news! */
for (int i = 0; i < CATEGORIES.length; i++)
for (int j = 0; j < classif_variances.length; j++)
ranks[i] += classif_variances[j] * classprobs[j][i] / sum_variance;
double ranksum = 0;
for (double r : ranks) ranksum += r;
for (int i = 0; i < CATEGORIES.length; i++) ranks[i] = ranks[i] / ranksum;
for (int i = 0; i < CATEGORIES.length; i++) ranks[i] = Math.log(ranks[i]) / Math.log(2);
ranks = sort_with_categories(ranks);
// the new transformed matrix is a matrix of log2 of probabilities of belonging calculated from
// ranks
jc = new JointClassification(CATEGORIES, ranks);
return jc;
}
public static double getEntropy(double[] P) {
double entropy = 0;
for (double p : P) if (1 / (p * Math.log(1 / p)) != 0) entropy += -p * Math.log(p);
double psum = 0;
for (double p : P) psum += p;
System.err.println("Sum of probs: " + psum + "[ ");
for (double p : P) System.err.printf("%1.2f,", p);
System.err.println("Entropy: " + entropy);
return entropy;
}
// Query based on term
public static HashMap<String, Double> queryTF(
Client client,
String qb,
String index,
String type,
HashMap<String, Double> result,
HashMap<String, Double> lengthmap,
HashMap<String, Double> staticmap)
throws IOException, JSONException {
HashMap parammap = new HashMap();
parammap.put("field", "text");
parammap.put("term", qb);
SearchResponse responsesearch =
client
.prepareSearch(index)
.setQuery(
new FunctionScoreQueryBuilder(QueryBuilders.matchQuery("text", qb))
.boostMode("replace")
.add(ScoreFunctionBuilders.scriptFunction("getTF").params(parammap)))
.setSize(100000)
.setNoFields()
.execute()
.actionGet();
JSONObject obj = new JSONObject(responsesearch);
JSONObject obj2 = obj.getJSONObject("hits");
JSONArray hits = obj2.getJSONArray("hits");
Iterator itrnext = staticmap.keySet().iterator();
while (itrnext.hasNext()) {
String id = itrnext.next().toString();
double oldtf = staticmap.get(id);
double v = 178081;
double len = lengthmap.get(id);
double newtf = 1 / (len + v);
double lap = Math.log(newtf);
staticmap.put(id, oldtf + lap);
}
for (int i = 0; i < hits.length(); i++) {
JSONObject newobj = hits.getJSONObject(i);
String id = newobj.getString("id");
Double tf = newobj.getDouble("score");
double len = lengthmap.get(id);
double v = 178081;
double lap = (tf + 1) / (len + v);
double inc = 1 / (len + v);
lap = Math.log(lap);
inc = Math.log(inc);
if (staticmap.containsKey(id)) {
double oldtf = staticmap.get(id);
staticmap.put(id, oldtf + lap - inc);
} else staticmap.put(id, lap);
}
return staticmap;
}
public TopicScores getTokenDocumentDiscrepancies() {
TopicScores scores = new TopicScores("token-doc-diff", numTopics, numTopWords);
scores.wordScoresDefined = true;
for (int topic = 0; topic < numTopics; topic++) {
int[][] matrix = topicCodocumentMatrices[topic];
TreeSet<IDSorter> sortedWords = topicSortedWords.get(topic);
double topicScore = 0.0;
double[] wordDistribution = new double[numTopWords];
double[] docDistribution = new double[numTopWords];
double wordSum = 0.0;
double docSum = 0.0;
int position = 0;
Iterator<IDSorter> iterator = sortedWords.iterator();
while (iterator.hasNext() && position < numTopWords) {
IDSorter info = iterator.next();
wordDistribution[position] = info.getWeight();
docDistribution[position] = matrix[position][position];
wordSum += wordDistribution[position];
docSum += docDistribution[position];
position++;
}
for (position = 0; position < numTopWords; position++) {
double p = wordDistribution[position] / wordSum;
double q = docDistribution[position] / docSum;
double meanProb = 0.5 * (p + q);
double score = 0.0;
if (p > 0) {
score += 0.5 * p * Math.log(p / meanProb);
}
if (q > 0) {
score += 0.5 * q * Math.log(q / meanProb);
}
scores.setTopicWordScore(topic, position, score);
topicScore += score;
}
scores.setTopicScore(topic, topicScore);
}
return scores;
}
// Entropy(S) = - sum(1..n) [ p(i)*log(pi)}
private double computeEntropy() {
double sum = 0.0;
int numTags = myTags.size();
for (int i = 0; i < numTags; i++) {
String tag = (String) myTags.elementAt(i);
if (!tag.equals(boundaryTag)) {
double probTag = getUnigramProb(tag);
double logProbTag = Math.log(probTag) / Math.log(2.0); // Math.log is natural log, base e
sum += probTag * logProbTag;
}
}
return -sum;
}
public static double betainv(double x, double p, double q) {
// ALGORITHM AS 63 APPL. STATIST. VOL.32, NO.1
// Computes P(Beta>x)
double beta = Maths.logBeta(p, q), acu = 1E-14;
double cx, psq, pp, qq, x2, term, ai, betain, ns, rx, temp;
boolean indx;
if (p <= 0 || q <= 0) return (-1.0);
if (x <= 0 || x >= 1) return (-1.0);
psq = p + q;
cx = 1 - x;
if (p < psq * x) {
x2 = cx;
cx = x;
pp = q;
qq = p;
indx = true;
} else {
x2 = x;
pp = p;
qq = q;
indx = false;
}
term = 1;
ai = 1;
betain = 1;
ns = qq + cx * psq;
rx = x2 / cx;
temp = qq - ai;
if (ns == 0) rx = x2;
while (temp > acu && temp > acu * betain) {
term = term * temp * rx / (pp + ai);
betain = betain + term;
temp = Math.abs(term);
if (temp > acu && temp > acu * betain) {
ai++;
ns--;
if (ns >= 0) {
temp = qq - ai;
if (ns == 0) rx = x2;
} else {
temp = psq;
psq += 1;
}
}
}
betain *= Math.exp(pp * Math.log(x2) + (qq - 1) * Math.log(cx) - beta) / pp;
if (indx) betain = 1 - betain;
return (betain);
}
public double findEntropy(Collection<String[]> data) {
double p_good = ((double) count(true, data)) / data.size();
double p_bad = ((double) count(false, data)) / data.size();
double entropy;
if (p_good > 0 && p_bad > 0)
entropy = -1.0 / Math.log(2) * (p_good * Math.log(p_good) + p_bad * Math.log(p_bad));
else entropy = 0;
// System.out.println("p_good (" + p_good + ") p_bad (" + p_bad +")" + " entropy (" + entropy
// + ")");
return entropy;
}
public Double eval(Double lmbda) {
double c = 1.0 / (double) Math.sqrt(variance);
double sum = 0.0;
for (int i = 0; i < data.size(); i++) {
for (int k = 0; k < channels.length; k++) {
double pi = 0.0;
double pdi = 0.0;
for (Integer t : transcripts.keySet()) {
if (transcripts.get(t).contains(i)) {
double gammai = gammas[t][k];
double dit = delta(i, t);
double falloff = Math.exp(-lmbda * dit);
double pit = gammai * falloff;
double pdit = pit * dit;
pi += pit;
pdi += pdit;
}
}
double zi = Math.log(pi);
double err = data.values(i)[channels[k]] - zi;
double ratio = pdi / pi;
double termi = (err * ratio);
sum += termi;
}
}
return c * sum;
}
public Double eval(Double gamma) {
double c = 1.0 / (double) Math.sqrt(variance);
double sum = 0.0;
for (Integer i : transcripts.get(gammat)) {
double pi = 0.0;
for (int t = 0; t < fiveprime.length; t++) {
if (transcripts.get(t).contains(i)) {
double gammai = gammat == t ? gamma : gammas[t][gammak];
double dit = delta(i, t);
double pit = gammai * Math.exp(-lambda * dit);
pi += pit;
}
}
double zi = Math.log(pi);
double err = data.values(i)[channels[gammak]] - zi;
double ratio = (Math.exp(-lambda * delta(i, gammat))) / pi;
double termi = (err * ratio);
sum += termi;
}
return c * sum;
}
public double error() {
double sum = 0.0;
for (int i = 0; i < data.size(); i++) {
for (int k = 0; k < channels.length; k++) {
double pi = 0.0;
for (Integer t : transcripts.keySet()) {
if (transcripts.get(t).contains(i)) {
double gammai = gammas[t][k];
double dit = delta(i, t);
double pit = gammai * Math.exp(-lambda * dit);
pi += pit;
}
}
double zi = Math.log(pi);
double err = Math.abs(data.values(i)[channels[k]] - zi);
sum += err;
}
}
return sum;
}
/**
* Apply this operator (function) to the supplied argument
*
* @param value the argument
* @return the result
*/
protected double applyFunction(double value) {
switch (m_operator) {
case 'l':
return Math.log(value);
case 'b':
return Math.abs(value);
case 'c':
return Math.cos(value);
case 'e':
return Math.exp(value);
case 's':
return Math.sqrt(value);
case 'f':
return Math.floor(value);
case 'h':
return Math.ceil(value);
case 'r':
return Math.rint(value);
case 't':
return Math.tan(value);
case 'n':
return Math.sin(value);
}
return Double.NaN;
}
/**
* @param placeNameServiceSet the set of PlaceNameService objects that PlaceNameLayer will render.
* @throws IllegalArgumentException if {@link
* gov.nasa.worldwind.layers.placename.PlaceNameServiceSet} is null
*/
public PlaceNameLayer(PlaceNameServiceSet placeNameServiceSet) {
if (placeNameServiceSet == null) {
String message = Logging.getMessage("nullValue.PlaceNameServiceSetIsNull");
Logging.logger().fine(message);
throw new IllegalArgumentException(message);
}
//
this.placeNameServiceSet = placeNameServiceSet.deepCopy();
for (int i = 0; i < this.placeNameServiceSet.getServiceCount(); i++) {
// todo do this for long as well and pick min
int calc1 =
(int)
(PlaceNameService.TILING_SECTOR.getDeltaLatDegrees()
/ this.placeNameServiceSet
.getService(i)
.getTileDelta()
.getLatitude()
.getDegrees());
int numLevels = (int) Math.log(calc1);
navTiles.add(
new NavigationTile(
this.placeNameServiceSet.getService(i),
PlaceNameService.TILING_SECTOR,
numLevels,
"top"));
}
if (!WorldWind.getMemoryCacheSet().containsCache(Tile.class.getName())) {
long size = Configuration.getLongValue(AVKey.PLACENAME_LAYER_CACHE_SIZE, 2000000L);
MemoryCache cache = new BasicMemoryCache((long) (0.85 * size), size);
cache.setName("Placename Tiles");
WorldWind.getMemoryCacheSet().addCache(Tile.class.getName(), cache);
}
}
public TopicScores getCoherence() {
TopicScores scores = new TopicScores("coherence", numTopics, numTopWords);
scores.wordScoresDefined = true;
for (int topic = 0; topic < numTopics; topic++) {
int[][] matrix = topicCodocumentMatrices[topic];
double topicScore = 0.0;
for (int row = 0; row < numTopWords; row++) {
double rowScore = 0.0;
double minScore = 0.0;
for (int col = 0; col < row; col++) {
double score =
Math.log((matrix[row][col] + model.beta) / (matrix[col][col] + model.beta));
rowScore += score;
if (score < minScore) {
minScore = score;
}
}
topicScore += rowScore;
scores.setTopicWordScore(topic, row, minScore);
}
scores.setTopicScore(topic, topicScore);
}
return scores;
}
@Override
public void trainMostSimilar(List<EnsembleSim> simList) {
if (simList.isEmpty()) {
throw new IllegalStateException("no examples to train on!");
}
mostSimilarInterpolator.trainMostSimilar(simList);
// Remove things that have no observed metrics
List<EnsembleSim> pruned = new ArrayList<EnsembleSim>();
for (EnsembleSim es : simList) {
if (es != null && es.getNumMetricsWithScore() > 0) {
pruned.add(es);
}
}
double[][] X = new double[pruned.size()][numMetrics * 2];
double[] Y = new double[pruned.size()];
for (int i = 0; i < pruned.size(); i++) {
Y[i] = pruned.get(i).knownSim.similarity;
EnsembleSim es = mostSimilarInterpolator.interpolate(pruned.get(i));
for (int j = 0; j < numMetrics; j++) {
X[i][2 * j] = es.getScores().get(j);
X[i][2 * j + 1] = Math.log(es.getRanks().get(j) + 1);
}
}
OLSMultipleLinearRegression regression = new OLSMultipleLinearRegression();
regression.newSampleData(Y, X);
mostSimilarCoefficients = new TDoubleArrayList(regression.estimateRegressionParameters());
double pearson = Math.sqrt(regression.calculateRSquared());
LOG.info("coefficients are " + mostSimilarCoefficients.toString());
LOG.info("pearson for multiple regression is " + pearson);
}
public TopicScores getDistanceFromUniform() {
int[] tokensPerTopic = model.tokensPerTopic;
TopicScores scores = new TopicScores("uniform_dist", numTopics, numTopWords);
scores.wordScoresDefined = true;
int numTypes = alphabet.size();
for (int topic = 0; topic < numTopics; topic++) {
double topicScore = 0.0;
int position = 0;
TreeSet<IDSorter> sortedWords = topicSortedWords.get(topic);
for (IDSorter info : sortedWords) {
int type = info.getID();
double count = info.getWeight();
double score =
(count / tokensPerTopic[topic]) * Math.log((count * numTypes) / tokensPerTopic[topic]);
if (position < numTopWords) {
scores.setTopicWordScore(topic, position, score);
}
topicScore += score;
position++;
}
scores.setTopicScore(topic, topicScore);
}
return scores;
}
public double getChi2(Graph g) {
double chi2 = Double.MAX_VALUE;
for (int i = 0; i < components.length; i++) {
chi2 = Math.min(chi2, -2 * Math.log(weights[i]) + components[i].getChi2(g));
}
return chi2;
}
public static Complex log(double x_re, double x_im) {
double h;
/* #ifdef JAVA5 */
// h = Math.hypot(x_re, x_im);
/* #else */
h = DComplex.hypot(x_re, x_im);
/* #endif */
return make(Math.log(h), Math.atan2(x_im, x_re));
}
/*Generates a random exponential distribution random time difference vs. timeIn based on the frequency lambda */
private double nextRandomTime(double lambda) {
double timeLag = 0;
/*System.out.println("I am computing nextRandomTime");
System.out.println("When I am getting timeIn of " +timeIn+" and lambda of "+lambda);*/
double random = Math.random();
timeLag = -Math.log(random) / (lambda / (LENGTHOFADAY * 1000));
/*System.out.println("I am returning a time difference of "+timeLag);*/
return timeLag;
}
public boolean play() {
try {
if (playState != STOPPED) playStop();
if (audioBytes == null) return false;
DataLine.Info info = new DataLine.Info(Clip.class, format);
clip = (Clip) AudioSystem.getLine(info);
clip.addLineListener(new ClipListener());
long clipStart = (long) (audioBytes.length * getStartTime() / (getDuration() * 1000.0));
long clipEnd = (long) (audioBytes.length * getEndTime() / (getDuration() * 1000.0));
if ((clipEnd - clipStart) > MAX_CLIP_LENGTH) clipEnd = clipStart + MAX_CLIP_LENGTH;
byte[] clipBytes = new byte[(int) (clipEnd - clipStart)];
System.arraycopy(audioBytes, (int) clipStart, clipBytes, 0, clipBytes.length);
clip.open(format, clipBytes, 0, clipBytes.length);
FloatControl panControl = (FloatControl) clip.getControl(FloatControl.Type.PAN);
panControl.setValue((float) panSetting / 100.0f);
double value = (double) gainSetting;
FloatControl gainControl = (FloatControl) clip.getControl(FloatControl.Type.MASTER_GAIN);
float dB = (float) (Math.log(value == 0.0 ? 0.0001 : value) / Math.log(10.0) * 20.0);
gainControl.setValue(dB);
double playStartTime = (player.getSeekTime() / 100) * (playGetLength());
clip.setMicrosecondPosition((long) playStartTime);
clip.start();
playState = PLAYING;
return true;
} catch (Exception ex) {
ex.printStackTrace();
playState = STOPPED;
clip = null;
return false;
}
}
/**
* Convert data to probability co-occurrences (aka calculating the kernel)
*
* @param d the data to convert
* @param u the perplexity of the model
* @return the probabilities of co-occurrence
*/
public INDArray computeGaussianPerplexity(final INDArray d, double u) {
int n = d.rows();
final INDArray p = zeros(n, n);
final INDArray beta = ones(n, 1);
final double logU = Math.log(u);
log.info("Calculating probabilities of data similarities..");
for (int i = 0; i < n; i++) {
if (i % 500 == 0 && i > 0) log.info("Handled " + i + " records");
double betaMin = Double.NEGATIVE_INFINITY;
double betaMax = Double.POSITIVE_INFINITY;
int[] vals = Ints.concat(ArrayUtil.range(0, i), ArrayUtil.range(i + 1, d.columns()));
INDArrayIndex[] range = new INDArrayIndex[] {new NDArrayIndex(vals)};
INDArray row = d.slice(i).get(range);
Pair<INDArray, INDArray> pair = hBeta(row, beta.getDouble(i));
INDArray hDiff = pair.getFirst().sub(logU);
int tries = 0;
// while hdiff > tolerance
while (BooleanIndexing.and(abs(hDiff), Conditions.greaterThan(tolerance)) && tries < 50) {
// if hdiff > 0
if (BooleanIndexing.and(hDiff, Conditions.greaterThan(0))) {
if (Double.isInfinite(betaMax)) beta.putScalar(i, beta.getDouble(i) * 2.0);
else beta.putScalar(i, (beta.getDouble(i) + betaMax) / 2.0);
betaMin = beta.getDouble(i);
} else {
if (Double.isInfinite(betaMin)) beta.putScalar(i, beta.getDouble(i) / 2.0);
else beta.putScalar(i, (beta.getDouble(i) + betaMin) / 2.0);
betaMax = beta.getDouble(i);
}
pair = hBeta(row, beta.getDouble(i));
hDiff = pair.getFirst().subi(logU);
tries++;
}
p.slice(i).put(range, pair.getSecond());
}
// dont need data in memory after
log.info("Mean value of sigma " + sqrt(beta.rdiv(1)).mean(Integer.MAX_VALUE));
BooleanIndexing.applyWhere(p, Conditions.isNan(), new Value(realMin));
// set 0 along the diagonal
INDArray permute = p.transpose();
INDArray pOut = p.add(permute);
pOut.divi(pOut.sum(Integer.MAX_VALUE));
BooleanIndexing.applyWhere(
pOut, Conditions.lessThan(Nd4j.EPS_THRESHOLD), new Value(Nd4j.EPS_THRESHOLD));
// ensure no nans
return pOut;
}
/*
* Compute entropy value for an array of bytes.
*
* The returned number represents entropy per bit!
* For good long number seed (8bytes seed) it should be in range <2.75,3> (higher is better)
*
* For large set of bytes (>100) it should be almost 8 (means almost 8 random bits per byte).
*/
public static float entropy(byte[] f) {
int counts[] = new int[256];
float entropy = 0;
float total = f.length;
for (byte b : f) counts[b+128]++;
for (int c : counts) {
if (c == 0) continue;
float p = c / total;
/* Compute entropy per bit in byte.
*
* To compute entropy per byte compute log with base 256 = log(p)/log(256).
*/
entropy -= p * Math.log(p)/Math.log(2);
}
return entropy;
}
public TopicScores getDocumentEntropy(int[] tokensPerTopic) {
TopicScores scores = new TopicScores("document_entropy", numTopics, numTopWords);
for (int topic = 0; topic < numTopics; topic++) {
scores.setTopicScore(
topic,
-sumCountTimesLogCount[topic] / tokensPerTopic[topic] + Math.log(tokensPerTopic[topic]));
}
return scores;
}
private static void playWav(String name, boolean loop, double volume)
throws FileNotFoundException, IOException, UnsupportedAudioFileException,
LineUnavailableException {
AudioInputStream ais = AudioSystem.getAudioInputStream(new File(path + name));
Clip clip = AudioSystem.getClip();
clip.open(ais);
if (loop) {
clip.loop(-1);
}
((FloatControl) clip.getControl(FloatControl.Type.MASTER_GAIN))
.setValue((float) (Math.log(volume) / Math.log(10.) * 20.));
clip.start();
wavMap.put(name, clip);
// // open the sound file as a Java input stream
// InputStream in = new FileInputStream(path + name);
// // create an audiostream from the inputstream
// AudioStream audioStream = new AudioStream(in);
// // play the audio clip with the audioplayer class
// AudioPlayer.player.start(audioStream);
// wavMap.put(name, audioStream);
}
public double pchisq(double q, double df) {
// Posten, H. (1989) American Statistician 43 p. 261-265
double df2 = df * .5;
double q2 = q * .5;
int n = 5, k;
double tk, CFL, CFU, prob;
if (q <= 0 || df <= 0)
throw new IllegalArgumentException("Illegal argument " + q + " or " + df + " for qnorm(p).");
if (q < df) {
tk = q2 * (1 - n - df2) / (df2 + 2 * n - 1 + n * q2 / (df2 + 2 * n));
for (k = n - 1; k > 1; k--)
tk = q2 * (1 - k - df2) / (df2 + 2 * k - 1 + k * q2 / (df2 + 2 * k + tk));
CFL = 1 - q2 / (df2 + 1 + q2 / (df2 + 2 + tk));
prob = Math.exp(df2 * Math.log(q2) - q2 - Maths.logGamma(df2 + 1) - Math.log(CFL));
} else {
tk = (n - df2) / (q2 + n);
for (k = n - 1; k > 1; k--) tk = (k - df2) / (q2 + k / (1 + tk));
CFU = 1 + (1 - df2) / (q2 + 1 / (1 + tk));
prob = 1 - Math.exp((df2 - 1) * Math.log(q2) - q2 - Maths.logGamma(df2) - Math.log(CFU));
}
return prob;
}
public static DComplex power(double x_re, double x_im, double y_re, double y_im) {
double h;
/* #ifdef JAVA5 */
// h = Math.hypot(x_re, x_im);
/* #else */
h = DComplex.hypot(x_re, x_im);
/* #endif */
double logr = Math.log(h);
double t = Math.atan2(x_im, x_re);
double r = Math.exp(logr * y_re - y_im * t);
t = y_im * logr + y_re * t;
return Complex.polar(r, t);
}
private Map<String, Double> getExpressionPvals(
Set<String> targets, boolean[] set1, boolean[] set2) {
Map<String, Double> map = new HashMap<String, Double>();
Histogram2D h = new Histogram2D(0.2);
System.out.println("targets = " + targets.size());
Progress prg = new Progress(targets.size());
for (String sym : targets) {
prg.tick();
if (expMan.getNonZeroRatio(sym) == 0) continue;
double pval = calcDiffPval(sym, set1, set2, true);
if (Double.isNaN(pval)) continue;
if (pval == 0) pval = 1E-11;
double pPerm = calcDiffPval(sym, set1, set2, false);
if (pPerm == 0) pPerm = 1E-5;
h.count(-Math.log(pval), -Math.log(pPerm));
// pval = 0 is not real and it is not compatible with fisher's combined probability.
// below is a better approximation.
// if (pval == 0)
// {
// pval = 1E-11;
// }
map.put(sym, pval);
}
Histogram2DPlot p = new Histogram2DPlot(h);
p.setLines(Arrays.asList(new double[] {1, 0}));
p.setVisible(true);
return map;
}