List of usage examples for java.lang Math log10
@HotSpotIntrinsicCandidate public static double log10(double a)
From source file:org.esa.nest.dat.views.polarview.Axis.java
private static double getStepValue(double thevalue, boolean up) { final boolean negative = thevalue < 0.0D; double val = thevalue; if (negative) val = -val; final int exponent = (int) Math.floor(Math.log10(val)); val *= FastMath.pow(10D, -exponent); int i;/*w w w. j a v a2s. c o m*/ for (i = stepValues.length - 1; i > 0; i--) { if (val > stepValues[i]) break; } if (up) val = stepValues[i + 1]; else val = stepValues[i]; val *= FastMath.pow(10D, exponent); if (negative) val = -val; return val; }
From source file:org.geowebcache.s3.S3KeyBuilder.java
public String tileKey(long x, long y, long z, String gridSetId, String parametersId, MimeType mimeType) { StringBuilder key = new StringBuilder(256); if (LOG.isDebugEnabled()) { LOG.debug(String.format("tileKey for %s %s %s %s %s", x, y, z, gridSetId, parametersId, mimeType.getMimeType())); }/* w w w . ja v a 2 s. c om*/ long shift = z / 2; long half = 2 << shift; int digits = 1; if (half > 10) { digits = (int) (Math.log10(half)) + 1; } long halfx = x / half; long halfy = y / half; String fileExtension = mimeType.getFileExtension(); appendDimension(key, 2 * digits, x, y); key.append(File.separatorChar); appendDimension(key, digits, halfx, halfy); key.append(File.separatorChar); appendGridsetZoomLevelDir(gridSetId, z, key); appendParameters(parametersId, key); key.append('.'); key.append(fileExtension); String keyString = key.toString(); if (LOG.isDebugEnabled()) { LOG.debug(keyString); } return keyString; }
From source file:de.tudarmstadt.ukp.similarity.experiments.coling2012.util.WordIdfValuesGenerator.java
@SuppressWarnings("unchecked") public static void computeIdfScores(Dataset dataset) throws Exception { File outputFile = new File(UTILS_DIR + "/word-idf/" + dataset.toString() + ".txt"); System.out.println("Computing word idf values"); if (outputFile.exists()) { System.out.println(" - skipping, already exists"); } else {// w ww . j a va 2 s . com System.out.println(" - this may take a while..."); CollectionReader reader = ColingUtils.getCollectionReader(dataset); // Tokenization AnalysisEngineDescription seg = createPrimitiveDescription(BreakIteratorSegmenter.class); AggregateBuilder builder = new AggregateBuilder(); builder.add(seg, CombinationReader.INITIAL_VIEW, CombinationReader.VIEW_1); builder.add(seg, CombinationReader.INITIAL_VIEW, CombinationReader.VIEW_2); AnalysisEngine aggr_seg = builder.createAggregate(); // POS Tagging AnalysisEngineDescription pos = createPrimitiveDescription(OpenNlpPosTagger.class, OpenNlpPosTagger.PARAM_LANGUAGE, "en"); builder = new AggregateBuilder(); builder.add(pos, CombinationReader.INITIAL_VIEW, CombinationReader.VIEW_1); builder.add(pos, CombinationReader.INITIAL_VIEW, CombinationReader.VIEW_2); AnalysisEngine aggr_pos = builder.createAggregate(); // Lemmatization AnalysisEngineDescription lem = createPrimitiveDescription(StanfordLemmatizer.class); builder = new AggregateBuilder(); builder.add(lem, CombinationReader.INITIAL_VIEW, CombinationReader.VIEW_1); builder.add(lem, CombinationReader.INITIAL_VIEW, CombinationReader.VIEW_2); AnalysisEngine aggr_lem = builder.createAggregate(); // Output Writer AnalysisEngine writer = createPrimitive(WordIdfValuesGeneratorWriter.class, WordIdfValuesGeneratorWriter.PARAM_OUTPUT_FILE, outputFile.getAbsolutePath()); SimplePipeline.runPipeline(reader, aggr_seg, aggr_pos, aggr_lem, writer); // Now we have the text format lemma1###lemma2###...###lemman List<String> lines = FileUtils.readLines(outputFile); Map<String, Double> idfValues = new HashMap<String, Double>(); // Build up token representations of texts Set<List<String>> docs = new HashSet<List<String>>(); for (String line : lines) { List<String> doc = CollectionUtils.arrayToList(line.split("###")); docs.add(doc); } // Get the shared token list Set<String> tokens = new HashSet<String>(); for (List<String> doc : docs) tokens.addAll(doc); // Get the idf numbers for (String token : tokens) { double count = 0; for (List<String> doc : docs) { if (doc.contains(token)) count++; } idfValues.put(token, count); } // Compute the idf for (String lemma : idfValues.keySet()) { double idf = Math.log10(lines.size() / idfValues.get(lemma)); idfValues.put(lemma, idf); } // Store persistently StringBuilder sb = new StringBuilder(); for (String key : idfValues.keySet()) { sb.append(key + "\t" + idfValues.get(key) + LF); } FileUtils.writeStringToFile(outputFile, sb.toString()); System.out.println(" - done"); } }
From source file:uk.ac.diamond.scisoft.ncd.core.data.plots.LogLogPlotData.java
@Override public double getDataValue(int idx, IDataset axis, IDataset data) { return Math.log10(data.getDouble(idx)); }
From source file:gate.termraider.util.Utilities.java
public static double log2(double input) { /* log_a x = log_b x * log_a b * //from w ww. jav a 2s.co m * log_b x = log_a x / log_a b */ return Math.log10(input) / log10of2; }
From source file:dsp.unige.figures.ChannelHelper.java
/** * returns the freespace loss given a station and a satellite * // w w w. jav a 2s . c om * @param station * @param satellite * @return The freespace loss, in dB */ public static double getFreeSpaceLoss(Station station, Satellite satellite) { return 20 * Math.log10(Orbits.getDistance(satellite.ORBIT_TYPE)) + 20 * Math.log10(station.frequency) + 92.44; }
From source file:uk.ac.diamond.scisoft.ncd.core.data.plots.LogLogPlotData.java
@Override public double getAxisValue(int idx, IDataset axis) { return Math.log10(axis.getDouble(idx)); }
From source file:org.apache.hadoop.mapred.TestSequenceFileInputFilter.java
public void testRegexFilter() throws Exception { // set the filter class LOG.info("Testing Regex Filter with patter: \\A10*"); SequenceFileInputFilter.setFilterClass(job, SequenceFileInputFilter.RegexFilter.class); SequenceFileInputFilter.RegexFilter.setPattern(job, "\\A10*"); // clean input dir fs.delete(inDir, true);//from w w w . java2s.c o m // for a variety of lengths for (int length = 1; length < MAX_LENGTH; length += random.nextInt(MAX_LENGTH / 10) + 1) { LOG.info("******Number of records: " + length); createSequenceFile(length); int count = countRecords(0); assertEquals(count, length == 0 ? 0 : (int) Math.log10(length) + 1); } // clean up fs.delete(inDir, true); }
From source file:picard.analysis.TheoreticalSensitivity.java
/** * @param depthDistribution the probability of depth n is depthDistribution[n] for n = 0, 1. . . N - 1 * @param qualityDistribution the probability of quality q is qualityDistribution[q] for q = 0, 1. . . Q * @param sampleSize sample size is the number of random sums of quality scores for each m * @param logOddsThreshold is the log_10 of the likelihood ratio required to call a SNP, * for example 5 if the variant likelihood must be 10^5 times greater. * @param withLogging true to output log messages, false otherwise. *//*from w w w. j a v a 2s. com*/ public static double hetSNPSensitivity(final double[] depthDistribution, final double[] qualityDistribution, final int sampleSize, final double logOddsThreshold, final boolean withLogging) { final int N = Math.min(depthDistribution.length, MAX_CONSIDERED_DEPTH_HET_SENS + 1); if (withLogging) log.info("Creating Roulette Wheel"); final RouletteWheel qualitySampler = new RouletteWheel(qualityDistribution); //qualitySums[m] is a random sample of sums of m quality scores, for m = 0, 1, N - 1 if (withLogging) log.info("Calculating quality sums from quality sampler"); final List<ArrayList<Integer>> qualitySums = qualitySampler.sampleCumulativeSums(N, sampleSize, withLogging); //if a quality sum of m qualities exceeds the quality sum threshold for n total reads, a SNP is called final ArrayList<Double> qualitySumThresholds = new ArrayList<>(N); final double LOG_10 = Math.log10(2); for (int n = 0; n < N; n++) qualitySumThresholds.add(10 * (n * LOG_10 + logOddsThreshold)); //probabilityToExceedThreshold[m][n] is the probability that the sum of m quality score //exceeds the nth quality sum threshold if (withLogging) log.info("Calculating theoretical het sensitivity"); final List<ArrayList<Double>> probabilityToExceedThreshold = proportionsAboveThresholds(qualitySums, qualitySumThresholds); final List<ArrayList<Double>> altDepthDistribution = hetAltDepthDistribution(N); double result = 0.0; for (int n = 0; n < N; n++) { for (int m = 0; m <= n; m++) { result += depthDistribution[n] * altDepthDistribution.get(n).get(m) * probabilityToExceedThreshold.get(m).get(n); } } return result; }
From source file:javatranslation.matlab.dfa.java
public static void dfafunction(double[] tau, double MinBox, double MaxBox, int DFAorder) { double[] incoef = null; double[] l = null; l = new double[50]; incoef = new double[50]; for (int i = 0; i < 50; i++) { l = logspace(MinBox, MaxBox);/*from w w w . jav a 2s . c o m*/ //System.out.println(l[i]); incoef[i] = Math.round(l[i]); } double xx = mean(tau); for (int i = 0; i < tau.length; i++) { tau[i] = tau[i] - xx; } double[] Y = cumsum(tau, dfa.mean(tau)); double maxnumseg = incoef.length; double[] winlen = null; winlen = new double[50]; for (int truta = 0; truta < 50; truta++) { winlen[truta] = incoef[truta]; } Arrays.sort(winlen); ArrayUtils.reverse(winlen); double Ylength = Y.length; double F[][] = new double[(int) maxnumseg][1]; for (int k = 0; k < maxnumseg; k++) { F[k][0] = 0; } double[] timevec = new double[50]; for (int kk = 0; kk < maxnumseg; kk++) { timevec[kk] = winlen[kk]; double numsegm = Math.floor(Ylength / winlen[kk]); double tempY[][] = new double[1][(int) numsegm]; for (int k = 0; k < numsegm; k++) { tempY[0][k] = 0; } for (int zz = 0; zz < numsegm; zz++) { double overflowtest = zz * winlen[kk]; if (overflowtest <= Ylength) { double[] tempvec = null; int ko = 0; for (double p = ((zz - 1) * winlen[kk] + 1); p <= (zz * winlen[kk]); p = ((zz - 1) * winlen[kk] + 1) + 1) { // there are some errors in this loop tempvec[ko] = Y[(int) p]; System.out.println(tempvec[(int) p]); ko++; } //double temppol = polyfit(timevec,tempvec,DFAorder); MultivariateVectorOptimizer optimizer; optimizer = null; PolynomialFitter x = new PolynomialFitter(optimizer); // error here too double[] temppol = x.fit(DFAorder, timevec); double[] temppol2 = x.fit(DFAorder, tempvec); double[] arrayOfCoefficients = new double[temppol2.length]; arrayOfCoefficients = temppol2; int len = arrayOfCoefficients.length; double retAnswer = 0; double ret = 0; for (int i = 0; i < len; i++) { retAnswer = retAnswer + Math.pow(arrayOfCoefficients[i], i); } for (int i = 0; i < tempvec.length; i++) { ret = tempvec[i] - (Math.pow(retAnswer, 2)); } tempY[0][zz] = ((ret) / winlen[kk]); } } int k; double[] kopp = new double[(int) maxnumseg]; for (k = 0; k < maxnumseg; k++) { kopp[k] = tempY[0][k]; } double nonzerotempY = dfa.sum2(kopp); F[kk][0] = Math.sqrt(dfa.sum(kopp) / nonzerotempY); } double[] LF; LF = new double[(int) maxnumseg]; double[] LN; LN = new double[(int) maxnumseg]; for (int i = 0; i < maxnumseg; i++) { LF[i] = Math.log10(F[i][0]); LN[i] = Math.log10(winlen[i]); } double[][] XLN = new double[LN.length][LN.length]; for (int i = 0; i < LN.length; i++) { XLN[i][0] = 1; XLN[i][1] = LN[i]; } SimpleRegression x; x = new SimpleRegression(); x.addObservations(XLN, LF); // and probably error in this one too RegressionResults b = x.regress(); // System.out.println(b.getParameterEstimates()); //double LF=Math.log10(F); % log fluctuations //double LN=Math.log10(winlen); }