Examples of org.apache.commons.math.stat.inference.ChiSquareTestImpl.chiSquare()

Package org.apache.commons.math.stat.inference

Class org.apache.commons.math.stat.inference.ChiSquareTestImpl

Examples of org.apache.commons.math.stat.inference.ChiSquareTestImpl.chiSquare()

org.apache.commons.math.stat.inference.ChiSquareTestImpl.chiSquare()
{@inheritDoc}
Note: This implementation rescales the expected array if necessary to ensure that the sum of the expected and observed counts are equal.
@param observed array of observed frequency counts @param expected array of expected frequency counts @return chi-square test statistic @throws IllegalArgumentException if preconditions are not metor length is less than 2

            msgBuffer.append("Chisquare test failed for mean = ");
            msgBuffer.append(mean);
            msgBuffer.append(" p-value = ");
            msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
            msgBuffer.append(" chisquare statistic = ");
            msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
            msgBuffer.append(". \n");
            msgBuffer.append("bin\t\texpected\tobserved\n");
            for (int i = 0; i < expected.length; i++) {
                msgBuffer.append("[");
                msgBuffer.append(i == 0 ? 1: binBounds.get(i - 1));

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        //score = cs;
        if(Double.isNaN(score)) score = 0.0;
        mcNemarScores.put(s, score);
      }

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        if(beforeCutOff && ((1.0 - csd.cumulativeProbability(mcNemarScores.get(s))) / foo) > 0.05) {
          System.out.println(count - 1);
          beforeCutOff = false;
          //break;
        }

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      if(observed > 0) g += 2 * observed * Math.log(observed/expected);
      if(observedElsewhere > 0) g += 2 * observedElsewhere * Math.log(observedElsewhere/expectedElsewhere); 
      
      long [] obsArray = new long[]{observed, observedElsewhere};
      double [] expectArray = new double[]{expected, expectedElsewhere};
      double cs = cst.chiSquare(expectArray, obsArray);
      
      double manualcs = 0.0;
      manualcs += Math.pow(observed - expected, 2.0) / expected;
      manualcs += Math.pow(observedElsewhere - expectedElsewhere, 2.0) / expectedElsewhere;

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        //score = cs;
        if(Double.isNaN(score)) score = 0.0;
        mcNemarScores.put(s, score);
      }

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        int f = falses.getCount(s);
        double et = (t + f) * ratio;
        double ef = (t + f) * (1.0 - ratio);
        long [] obsArray = new long[]{t, f};
        double [] expectArray = new double[]{et, ef};
        double cs = cst.chiSquare(expectArray, obsArray);
        if(beforeCutOff && ((1.0 - csd.cumulativeProbability(mcNemarScores.get(s))) / foo) > 0.05) {
          System.out.println(count - 1);
          beforeCutOff = false;
          //break;
        }

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            DecimalFormat df = new DecimalFormat("#.##");
            msgBuffer.append("Chisquare test failed");
            msgBuffer.append(" p-value = ");
            msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
            msgBuffer.append(" chisquare statistic = ");
            msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
            msgBuffer.append(". \n");
            msgBuffer.append("value\texpected\tobserved\n");
            for (int i = 0; i < expected.length; i++) {
                msgBuffer.append(valueLabels[i]);
                msgBuffer.append("\t");

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            msgBuffer.append("Chisquare test failed for mean = ");
            msgBuffer.append(mean);
            msgBuffer.append(" p-value = ");
            msgBuffer.append(chiSquareTest.chiSquareTest(expected, observed));
            msgBuffer.append(" chisquare statistic = ");
            msgBuffer.append(chiSquareTest.chiSquare(expected, observed));
            msgBuffer.append(". \n");
            msgBuffer.append("bin\t\texpected\tobserved\n");
            for (int i = 0; i < expected.length; i++) {
                msgBuffer.append("[");
                msgBuffer.append(i == 0 ? 1: binBounds.get(i - 1));

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            }


            ChiSquareTestImpl cs = new ChiSquareTestImpl();
            try {
                pValues.addValue(cs.chiSquareTest(expected, observed));
                xs.addValue(cs.chiSquare(expected, observed));
            } catch (Exception e) {
                e.printStackTrace();
            }


        }

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        double pValue = 0;
        double chiSq = 0;
        ChiSquareTestImpl cs = new ChiSquareTestImpl();
        try {
            pValue = cs.chiSquareTest(expected, observed);
            chiSq = cs.chiSquare(expected, observed);
        } catch (Exception e) {
            e.printStackTrace();
        }


        System.out.println("Hash Quality (Chi-Squared-Test): p-value = " + pValue + " , Chi-Squared-Statistic = "

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