List of usage examples for org.apache.commons.math3.stat.descriptive DescriptiveStatistics getStandardDeviation
public double getStandardDeviation()
From source file:com.fpuna.preproceso.PreprocesoTS.java
private static void calculoFeatures(Registro[] muestras, String activity) { DescriptiveStatistics stats_x = new DescriptiveStatistics(); DescriptiveStatistics stats_y = new DescriptiveStatistics(); DescriptiveStatistics stats_z = new DescriptiveStatistics(); //DescriptiveStatistics stats_m1 = new DescriptiveStatistics(); //DescriptiveStatistics stats_m2 = new DescriptiveStatistics(); double[] fft_x; double[] fft_y; double[] fft_z; double[] AR_4; for (int i = 0; i < muestras.length; i++) { stats_x.addValue(muestras[i].getValor_x()); stats_y.addValue(muestras[i].getValor_y()); stats_z.addValue(muestras[i].getValor_z()); }/*w w w.j a va 2 s . com*/ //********* FFT ********* fft_x = Util.transform(stats_x.getValues()); fft_y = Util.transform(stats_y.getValues()); fft_z = Util.transform(stats_z.getValues()); //******************* Eje X *******************// //mean(s) - Arithmetic mean System.out.print(stats_x.getMean() + ","); //std(s) - Standard deviation System.out.print(stats_x.getStandardDeviation() + ","); //mad(s) - Median absolute deviation // //max(s) - Largest values in array System.out.print(stats_x.getMax() + ","); //min(s) - Smallest value in array System.out.print(stats_x.getMin() + ","); //skewness(s) - Frequency signal Skewness System.out.print(stats_x.getSkewness() + ","); //kurtosis(s) - Frequency signal Kurtosis System.out.print(stats_x.getKurtosis() + ","); //energy(s) - Average sum of the squares System.out.print(stats_x.getSumsq() / stats_x.getN() + ","); //entropy(s) - Signal Entropy System.out.print(Util.calculateShannonEntropy(fft_x) + ","); //iqr (s) Interquartile range System.out.print(stats_x.getPercentile(75) - stats_x.getPercentile(25) + ","); try { //autoregression (s) -4th order Burg Autoregression coefficients AR_4 = AutoRegression.calculateARCoefficients(stats_x.getValues(), 4, true); System.out.print(AR_4[0] + ","); System.out.print(AR_4[1] + ","); System.out.print(AR_4[2] + ","); System.out.print(AR_4[3] + ","); } catch (Exception ex) { Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex); } //meanFreq(s) - Frequency signal weighted average System.out.print(Util.meanFreq(fft_x, stats_x.getValues()) + ","); //******************* Eje Y *******************// //mean(s) - Arithmetic mean System.out.print(stats_y.getMean() + ","); //std(s) - Standard deviation System.out.print(stats_y.getStandardDeviation() + ","); //mad(s) - Median absolute deviation // //max(s) - Largest values in array System.out.print(stats_y.getMax() + ","); //min(s) - Smallest value in array System.out.print(stats_y.getMin() + ","); //skewness(s) - Frequency signal Skewness System.out.print(stats_y.getSkewness() + ","); //kurtosis(s) - Frequency signal Kurtosis System.out.print(stats_y.getKurtosis() + ","); //energy(s) - Average sum of the squares System.out.print(stats_y.getSumsq() / stats_y.getN() + ","); //entropy(s) - Signal Entropy System.out.print(Util.calculateShannonEntropy(fft_y) + ","); //iqr (s) Interquartile range System.out.print(stats_y.getPercentile(75) - stats_y.getPercentile(25) + ","); try { //autoregression (s) -4th order Burg Autoregression coefficients AR_4 = AutoRegression.calculateARCoefficients(stats_y.getValues(), 4, true); System.out.print(AR_4[0] + ","); System.out.print(AR_4[1] + ","); System.out.print(AR_4[2] + ","); System.out.print(AR_4[3] + ","); } catch (Exception ex) { Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex); } //meanFreq(s) - Frequency signal weighted average System.out.print(Util.meanFreq(fft_y, stats_y.getValues()) + ","); //******************* Eje Z *******************// //mean(s) - Arithmetic mean System.out.print(stats_z.getMean() + ","); //std(s) - Standard deviation System.out.print(stats_z.getStandardDeviation() + ","); //mad(s) - Median absolute deviation // //max(s) - Largest values in array System.out.print(stats_z.getMax() + ","); //min(s) - Smallest value in array System.out.print(stats_z.getMin() + ","); //skewness(s) - Frequency signal Skewness System.out.print(stats_z.getSkewness() + ","); //kurtosis(s) - Frequency signal Kurtosis System.out.print(stats_z.getKurtosis() + ","); //energy(s) - Average sum of the squares System.out.print(stats_z.getSumsq() / stats_z.getN() + ","); //entropy(s) - Signal Entropy System.out.print(Util.calculateShannonEntropy(fft_z) + ","); //iqr (s) Interquartile range System.out.print(stats_z.getPercentile(75) - stats_z.getPercentile(25) + ","); try { //autoregression (s) -4th order Burg Autoregression coefficients AR_4 = AutoRegression.calculateARCoefficients(stats_z.getValues(), 4, true); System.out.print(AR_4[0] + ","); System.out.print(AR_4[1] + ","); System.out.print(AR_4[2] + ","); System.out.print(AR_4[3] + ","); } catch (Exception ex) { Logger.getLogger(PreprocesoTS.class.getName()).log(Level.SEVERE, null, ex); } //meanFreq(s) - Frequency signal weighted average System.out.print(Util.meanFreq(fft_z, stats_z.getValues()) + ","); //******************* Feature combinados *******************/ //sma(s1; s2; s3) - Signal magnitude area System.out.print(Util.sma(stats_x.getValues(), stats_y.getValues(), stats_z.getValues()) + ","); //correlation(s1; s2) - Pearson Correlation coefficient System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_y.getValues()) + ","); System.out.print(new PearsonsCorrelation().correlation(stats_x.getValues(), stats_z.getValues()) + ","); System.out.print(new PearsonsCorrelation().correlation(stats_y.getValues(), stats_z.getValues()) + ","); //******************* Actividad *******************/ System.out.print(activity); System.out.print("\n"); }
From source file:io.prestosql.operator.aggregation.AbstractTestApproximateCountDistinct.java
@Test(dataProvider = "provideStandardErrors") public void testMultiplePositions(double maxStandardError) { DescriptiveStatistics stats = new DescriptiveStatistics(); for (int i = 0; i < 500; ++i) { int uniques = ThreadLocalRandom.current().nextInt(getUniqueValuesCount()) + 1; List<Object> values = createRandomSample(uniques, (int) (uniques * 1.5)); long actual = estimateGroupByCount(values, maxStandardError); double error = (actual - uniques) * 1.0 / uniques; stats.addValue(error);/* www. ja va2s. c o m*/ } assertLessThan(stats.getMean(), 1.0e-2); assertLessThan(stats.getStandardDeviation(), 1.0e-2 + maxStandardError); }
From source file:edu.nyu.vida.data_polygamy.standard_techniques.CorrelationTechniquesReducer.java
private double[] normalize(double[] array) { DescriptiveStatistics stats = new DescriptiveStatistics(array); double mean = stats.getMean(); double stdDev = stats.getStandardDeviation(); for (int i = 0; i < array.length; i++) { array[i] = (array[i] - mean) / stdDev; }/*from w ww . j a v a 2 s .co m*/ return array; }
From source file:algorithms.quality.ColorDivergenceVariance.java
@Override public double getQuality(Colormap colormap) { DescriptiveStatistics stats = new DescriptiveStatistics(); Iterator<Point2D> ptIt = strategy.getPoints().iterator(); while (ptIt.hasNext()) { Point2D p1 = ptIt.next(); if (!ptIt.hasNext()) break; Point2D p2 = ptIt.next(); double dist = p1.distance(p2); Color colorA = colormap.getColor(p1.getX(), p1.getY()); Color colorB = colormap.getColor(p2.getX(), p2.getY()); // roughly 0-100 double cdist = MedianDivergenceComputer.colorDiff(colorA, colorB); double ratio = cdist / dist; stats.addValue(ratio);//ww w. ja v a2 s .c o m } return stats.getStandardDeviation(); }
From source file:com.caseystella.analytics.outlier.batch.rpca.RPCAOutlierAlgorithm.java
public double outlierScore(List<DataPoint> dataPoints, DataPoint value) { double[] inputData = new double[dataPoints.size() + 1]; int numNonZero = 0; if (scaling != ScalingFunctions.NONE) { int i = 0; final DescriptiveStatistics stats = new DescriptiveStatistics(); for (DataPoint dp : dataPoints) { inputData[i++] = dp.getValue(); stats.addValue(dp.getValue()); numNonZero += dp.getValue() > EPSILON ? 1 : 0; }//from w w w . j a v a 2 s . c o m inputData[i] = value.getValue(); GlobalStatistics globalStats = new GlobalStatistics() { { setMax(stats.getMax()); setMin(stats.getMin()); setMax(stats.getMean()); setStddev(stats.getStandardDeviation()); } }; for (i = 0; i < inputData.length; ++i) { inputData[i] = scaling.scale(inputData[i], globalStats); } } else { int i = 0; for (DataPoint dp : dataPoints) { inputData[i++] = dp.getValue(); numNonZero += dp.getValue() > EPSILON ? 1 : 0; } inputData[i] = value.getValue(); } int nCols = 1; int nRows = inputData.length; if (numNonZero > minRecords) { AugmentedDickeyFuller dickeyFullerTest = new AugmentedDickeyFuller(inputData); double[] inputArrayTransformed = inputData; if (!this.isForceDiff && dickeyFullerTest.isNeedsDiff()) { // Auto Diff inputArrayTransformed = dickeyFullerTest.getZeroPaddedDiff(); } else if (this.isForceDiff) { // Force Diff inputArrayTransformed = dickeyFullerTest.getZeroPaddedDiff(); } if (this.spenalty == null) { this.lpenalty = this.LPENALTY_DEFAULT; this.spenalty = this.SPENALTY_DEFAULT / Math.sqrt(Math.max(nCols, nRows)); } // Calc Mean double mean = 0; for (int n = 0; n < inputArrayTransformed.length; n++) { mean += inputArrayTransformed[n]; } mean /= inputArrayTransformed.length; // Calc STDEV double stdev = 0; for (int n = 0; n < inputArrayTransformed.length; n++) { stdev += Math.pow(inputArrayTransformed[n] - mean, 2); } stdev = Math.sqrt(stdev / (inputArrayTransformed.length - 1)); // Transformation: Zero Mean, Unit Variance for (int n = 0; n < inputArrayTransformed.length; n++) { inputArrayTransformed[n] = (inputArrayTransformed[n] - mean) / stdev; } // Read Input Data into Array // Read Input Data into Array double[][] input2DArray = new double[nRows][nCols]; input2DArray = VectorToMatrix(inputArrayTransformed, nRows, nCols); RPCA rSVD = new RPCA(input2DArray, this.lpenalty, this.spenalty); double[][] outputE = rSVD.getE().getData(); double[][] outputS = rSVD.getS().getData(); double[][] outputL = rSVD.getL().getData(); return outputS[nRows - 1][0]; } else { return Double.NaN; } }
From source file:mase.app.allocation.AllocationProblem.java
@Override public EvaluationResult[] evaluateSolution(GroupController gc, long seed) { AgentController[] acs = gc.getAgentControllers(numAgents); RealMatrix distanceMatrix = new Array2DRowRealMatrix(numAgents, types.length); for (int i = 0; i < numAgents; i++) { AllocationAgent aa = (AllocationAgent) acs[i]; for (int j = 0; j < types.length; j++) { distanceMatrix.setEntry(i, j, DIST.compute(aa.getLocation(), types[j])); }/* w w w .java 2 s .co m*/ } DescriptiveStatistics pd = pairDistances(distanceMatrix); // fitness FitnessResult fr = new FitnessResult(1 - pd.getMean() / FastMath.sqrt(dimensions)); // individual characterisation -- distance to each type List<EvaluationResult> vbrs = new ArrayList<>(); for (double[] dists : distanceMatrix.getData()) { vbrs.add(new VectorBehaviourResult(dists)); } CompoundEvaluationResult ser = new CompoundEvaluationResult(vbrs); // aux characterisation -- min, mean, max, sd pair distances VectorBehaviourResult aux = new VectorBehaviourResult(pd.getMin(), pd.getMean(), pd.getMax(), pd.getStandardDeviation()); return new EvaluationResult[] { fr, aux, ser }; }
From source file:classifiers.ComplexClassifier.java
/** * * @throws Exception/*w w w .j ava 2 s . c om*/ */ @Override public void BewertunginProzent() throws Exception { double count = 0; double[][] bestergeb = new double[1][2]; double[][] hilf; double[][] hilf2; for (int i = 0; i < anzahldurchlauf; i++) { Bootstrap(Modelmenge); train(this.traindaten); hilf = new double[1][2]; hilf2 = new double[1][2]; hilf = test(traindaten); this.trainergebnisse[0][0] = hilf[0][0]; this.trainergebnisse[0][1] = hilf[0][1]; //System.out.println("Fehlerquote Training:" + (double) (int) (this.trainergebnisse[0][0] * 100) / 100 + "%" + " " + "Dauer:" + " " + (int) (this.trainergebnisse[0][1]) + "ms"); hilf2 = test(testdaten); this.testergebnisse[0][0] = hilf2[0][0]; this.testergebnisse[0][1] = hilf2[0][1]; /* if(testergebnisse[i][0]<=max) { max=testergebnisse[i][0]; bestemodel=Model; bestergeb[0][0]=testergebnisse[i][0]; bestergeb[0][1]=testergebnisse[i][1]; }*/ //System.out.println("Validierung:"); //System.out.println("Validierungsngsdaten:"); // System.out.println("Fehlerquote Validierungs:" + " " + (double) (int) (this.testergebnisse[0][0] * 100) / 100 + "%" + " " + "Dauer:" + " " + (int) (this.testergebnisse[0][1]) + "ms"); // System.out.println("----------------------------------------------------------------------------------------"); } DescriptiveStatistics stats1 = new DescriptiveStatistics(); DescriptiveStatistics stat1 = new DescriptiveStatistics(); for (int i = 0; i < trainergebnisse.length; i++) { stats1.addValue(trainergebnisse[0][0]); stat1.addValue(trainergebnisse[0][1]); } double mean1 = stats1.getMean(); double std1 = stats1.getStandardDeviation(); double meanzeit1 = stat1.getMean(); double stdzeit1 = stat1.getStandardDeviation(); // System.out.println("Mittlere Felehrquote des Tainings:" + " " + (double) (int) (mean1 * 100) / 100 + "%" + "(" + (double) (int) ((std1 / Math.sqrt(anzahldurchlauf)) * 100) / 100 + "%)"); //System.out.println("Mittlere Dauer des trainings:" + " " + (int) (meanzeit1) + " " + "ms" + "(" + (int) ((stdzeit1 / Math.sqrt(anzahldurchlauf))) + "ms)"); //System.out.println("--------------------------------------------------------------------------------------"); DescriptiveStatistics stats = new DescriptiveStatistics(); DescriptiveStatistics stat = new DescriptiveStatistics(); // Add the data from the array for (int i = 0; i < testergebnisse.length; i++) { stats.addValue(testergebnisse[i][0]); stat.addValue(testergebnisse[i][1]); } this.Mittlerevalidierungsquote = stats.getMean(); this.stadartdeviationvalidierung = (int) (stats.getStandardDeviation() / Math.sqrt(anzahldurchlauf)); this.Mittlerezeit = stat.getMean(); this.standartdeviationtime = (int) (stat.getStandardDeviation() / Math.sqrt(anzahldurchlauf)); // System.out.println("Mittlere Fehlerquote der Validierungsmengen:" + " " + (double) (int) (Mittlerevalidierungsquote * 100) / 100 + "%" + "(" + (double) (int) ((stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) * 100) / 100 + "%)"); // System.out.println("Mittlere Dauer der Validierung :" + " " + (int) (Mittlerezeit) + " " + "ms" + "(" + (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf))) + "ms)"); erg[1] = (double) (int) (Mittlerevalidierungsquote * 100) / 100; erg[2] = Mittlerezeit; erg[3] = (double) (int) ((stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) * 100) / 100; erg[4] = (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf))); struct.setErgebnisse(erg); train(this.Modelmenge); hilf = test(Modelmenge); this.Modelergebnisse[0][0] = hilf[0][0]; this.Modelergebnisse[0][1] = hilf[0][1]; hilf = test(validierungsmenge); validierungsergebnisse[0][0] = hilf[0][0]; validierungsergebnisse[0][1] = hilf[0][1]; /* System.out.println("---------------------------------------------------------------------------------------");*/ // System.out.println("Fehlerquote der training auf dem Datensatz:" + " " + (double) (int) (Modelergebnisse[0][0] * 100) / 100 + "%"); //System.out.println("Zeit des trainings (Datensatz):" + " " + (int) (Modelergebnisse[0][1]) + " " + "ms"); //System.out.println("---------------------------------------------------------------------------------------"); //System.out.println("Fehlerquote der Test:" + " " + (double) (int) (validierungsergebnisse[0][0] * 100) / 100 + "%"); //System.out.println("Zeit der Test:" + " " + (int) (validierungsergebnisse[0][1]) + " " + "ms"); //System.out.println(); /* System.out.println("Beste Struktur:"+" "+"Validierung:"+" "+(int)bestergeb[0][0]+"%"+" "+"Zeit:"+" "+(int)bestergeb[0][1]+"ms"); System.out.println("---------------------------------------------------------------------------------------"); if(bestemodel!=null) bestemodel.ToString();*/ }
From source file:main.java.metric.Metric.java
public static void getServerStatistic(Cluster cluster) { DescriptiveStatistics _data = new DescriptiveStatistics(); DescriptiveStatistics _inflow = new DescriptiveStatistics(); DescriptiveStatistics _outflow = new DescriptiveStatistics(); DescriptiveStatistics _meDMV = new DescriptiveStatistics(); for (Server server : cluster.getServers()) { _data.addValue(server.getServer_total_data()); _inflow.addValue(server.getServer_inflow()); _outflow.addValue(server.getServer_outflow()); }//w w w .j a v a2s .c om // New - mutually exclusive server sets ArrayList<Integer> estimated_dmgr = new ArrayList<Integer>(); for (Entry<Pair<Integer, Integer>, Integer> entry : mutually_exclusive_serverSets.entrySet()) { _meDMV.addValue(entry.getValue()); estimated_dmgr.add(entry.getValue()); } // Sort the list containing data migration counts within individual server pairs Collections.sort(estimated_dmgr); // Sum up every floor(S/2) index values to get the estimated data migration time int estimated_mgr = 0; int j = 0; for (int i = 0; i < estimated_dmgr.size(); ++i) { j = (int) (i + Math.floor(Global.servers / 2)); estimated_mgr += estimated_dmgr.get(i); i = j; } mean_server_inflow.add(_inflow.getMean()); mean_server_outflow.add(_outflow.getMean()); mean_server_data.add(_data.getMean()); sd_server_data.add(_data.getStandardDeviation()); total_data.add((long) Global.global_dataCount); mean_pairwise_inter_server_data_mgr.add(_meDMV.getMean()); sd_pairwise_inter_server_data_mgr.add(_meDMV.getStandardDeviation()); estimated_data_mgr.add(estimated_mgr); }
From source file:com.intuit.tank.persistence.databases.BucketDataItemTest.java
/** * Run the DescriptiveStatistics getStats() method test. * //from w w w .j a v a 2 s . c om * @throws Exception * * @generatedBy CodePro at 9/10/14 10:32 AM */ @Test public void testGetStats_1() throws Exception { BucketDataItem fixture = new BucketDataItem(1, new Date(), new DescriptiveStatistics()); DescriptiveStatistics result = fixture.getStats(); assertNotNull(result); assertEquals( "DescriptiveStatistics:\nn: 0\nmin: NaN\nmax: NaN\nmean: NaN\nstd dev: NaN\nmedian: NaN\nskewness: NaN\nkurtosis: NaN\n", result.toString()); assertEquals(Double.NaN, result.getMax(), 1.0); assertEquals(Double.NaN, result.getVariance(), 1.0); assertEquals(Double.NaN, result.getMean(), 1.0); assertEquals(-1, result.getWindowSize()); assertEquals(0.0, result.getSumsq(), 1.0); assertEquals(Double.NaN, result.getKurtosis(), 1.0); assertEquals(0.0, result.getSum(), 1.0); assertEquals(Double.NaN, result.getSkewness(), 1.0); assertEquals(Double.NaN, result.getPopulationVariance(), 1.0); assertEquals(Double.NaN, result.getStandardDeviation(), 1.0); assertEquals(Double.NaN, result.getGeometricMean(), 1.0); assertEquals(0L, result.getN()); assertEquals(Double.NaN, result.getMin(), 1.0); }
From source file:classifiers.Simpleclassifier.java
@Override public void BewertunginProzent() throws Exception { System.out.println("Parameter:" + "Anzahldurchlauf:" + this.anzahldurchlauf); //System.out.println("----------------------------------------------------------------------------------------"); double count = 0; double max = 0; double[][] hilf = new double[1][2]; double[][] hilf2 = new double[1][2]; for (int i = 0; i < anzahldurchlauf; i++) { Bootstrap(Modelmenge);// w w w .ja va 2 s.c o m train(this.traindaten); // System.out.println("training NR" + " " + i + ":"); // System.out.println("trainingsdeaten:"); hilf = test(traindaten); this.trainergebnisse[i][0] = hilf[0][0]; this.trainergebnisse[i][1] = hilf[0][1]; //System.out.println("Fehlerquote TrainingNR" + " " + i + ":" + " " + (double)(int) (hilf[0][0]*100)/100 + "%" + " " + "Dauer:" + " " + (int) hilf[0][1] + "ms"); hilf2 = test(testdaten); this.testergebnisse[i][0] = hilf2[0][0]; this.testergebnisse[i][1] = hilf2[0][1]; // System.out.println("Validierung NR" + " " + i + ":"); //System.out.println("Validierungsngsdaten:"); // System.out.println("Fehlerquote Validierungs NR" + " " + i + ":" + " " + (double)(int) (hilf2[0][0]*100)/100 + "%" + " " + "Dauer:" + " " + (int) hilf2[0][1] + "ms"); //System.out.println("----------------------------------------------------------------------------------------"); } DescriptiveStatistics stats1 = new DescriptiveStatistics(); DescriptiveStatistics stat1 = new DescriptiveStatistics(); // Add the data from the array for (int i = 0; i < trainergebnisse.length; i++) { stats1.addValue(trainergebnisse[i][0]); stat1.addValue(trainergebnisse[i][1]); } double mean1 = stats1.getMean(); double std1 = stats1.getStandardDeviation(); double meanzeit1 = stat1.getMean(); double stdzeit1 = stat1.getStandardDeviation(); // System.out.println("Mittlere Felehrquote des Tainings:" + " " + (double)(int) (mean1*100)/100 + "%" + "(" + (double)(int) ((std1 / Math.sqrt(anzahldurchlauf))*100)/100 + "%)"); //System.out.println("Mittlere Dauer des trainings:" + " " + (int) meanzeit1 + " " + "ms" + "(" + (int) ((stdzeit1 / Math.sqrt(anzahldurchlauf)) ) + "ms)"); //System.out.println("--------------------------------------------------------------------------------------"); DescriptiveStatistics stats = new DescriptiveStatistics(); DescriptiveStatistics stat = new DescriptiveStatistics(); // Add the data from the array for (int i = 0; i < testergebnisse.length; i++) { stats.addValue(testergebnisse[i][0]); stat.addValue(testergebnisse[i][1]); } this.Mittlerevalidierungsquote = stats.getMean(); this.stadartdeviationvalidierung = (int) (stats.getStandardDeviation() / Math.sqrt(anzahldurchlauf)); this.Mittlerezeit = stat.getMean(); this.standartdeviationtime = (int) (stat.getStandardDeviation() / Math.sqrt(anzahldurchlauf)); ergb[1] = Mittlerevalidierungsquote; ergb[2] = Mittlerezeit; ergb[3] = (double) (int) ((stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) * 100) / 100; ergb[4] = (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf))); struct.setErgebnisse(ergb); /* System.out.println("Durchnittliche Fehlerquote der Validierungsmengen:" + " " + (int) Mittlerevalidierungsquote + "%" + "(" + (int) (stadartdeviationvalidierung / Math.sqrt(anzahldurchlauf)) + "%)"); System.out.println("durchnittliche Dauer der Validierung :" + " " + (int) (Mittlerezeit) + " " + "ms" + "(" + (int) ((standartdeviationtime / Math.sqrt(anzahldurchlauf)) ) + "ms)");*/ train(this.Modelmenge); hilf = test(Modelmenge); this.Modelergebnisse[0][0] = hilf[0][0]; this.Modelergebnisse[0][1] = hilf[0][1]; hilf = test(validierungsmenge); validierungsergebnisse[0][0] = hilf[0][0]; validierungsergebnisse[0][1] = hilf[0][1]; /* System.out.println("---------------------------------------------------------------------------------------");*/ // System.out.println("Fehlerquote der training auf dem Datensatz:" + " " + (double)(int) (Modelergebnisse[0][0]*100)/100 + "%"); // System.out.println("Zeit des trainings (Datensatz):" + " " + (int) (Modelergebnisse[0][1] ) + " " + "ms"); // System.out.println("---------------------------------------------------------------------------------------"); // System.out.println("Fehlerquote der Test:" + " " + (double)(int) (validierungsergebnisse[0][0]*100)/100 + "%"); // System.out.println("Zeit der Test:" + " " + (int) (validierungsergebnisse[0][1] ) + " " + "ms"); /* Instances bestmodel=new Instances(Modelmenge,bestmodelindexen.length); double result; for(int i=0;i<bestmodelindexen.length;i++) { bestmodel.add(Modelmenge.instance(bestmodelindexen[i])); } train(bestmodel); result= test(validierungsmenge); System.out.println(); System.out.println("der Beste Model ist:"); System.out.println("-----------------------------------------"); System.out.println(bestmodel); System.out.println("mit eine Leistung von:"+" "+result+"%"); return result; }*/ }