Java Code Examples for org.apache.commons.math3.util.MathArrays#checkNonNegative()
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org.apache.commons.math3.util.MathArrays#checkNonNegative() .
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Example 1
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Checks to make sure that the input long[][] array is rectangular,
* has at least 2 rows and 2 columns, and has all non-negative entries.
*
* @param in input 2-way table to check
* @throws NullArgumentException if the array is null
* @throws DimensionMismatchException if the array is not valid
* @throws NotPositiveException if the array contains any negative entries
*/
private void checkArray(final long[][] in)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException {
if (in.length < 2) {
throw new DimensionMismatchException(in.length, 2);
}
if (in[0].length < 2) {
throw new DimensionMismatchException(in[0].length, 2);
}
MathArrays.checkRectangular(in);
MathArrays.checkNonNegative(in);
}
Example 2
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Checks to make sure that the input long[][] array is rectangular,
* has at least 2 rows and 2 columns, and has all non-negative entries.
*
* @param in input 2-way table to check
* @throws NullArgumentException if the array is null
* @throws DimensionMismatchException if the array is not valid
* @throws NotPositiveException if the array contains any negative entries
*/
private void checkArray(final long[][] in)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException {
if (in.length < 2) {
throw new DimensionMismatchException(in.length, 2);
}
if (in[0].length < 2) {
throw new DimensionMismatchException(in[0].length, 2);
}
MathArrays.checkRectangular(in);
MathArrays.checkNonNegative(in);
}
Example 3
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Checks to make sure that the input long[][] array is rectangular,
* has at least 2 rows and 2 columns, and has all non-negative entries.
*
* @param in input 2-way table to check
* @throws NullArgumentException if the array is null
* @throws DimensionMismatchException if the array is not valid
* @throws NotPositiveException if the array contains any negative entries
*/
private void checkArray(final long[][] in)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException {
if (in.length < 2) {
throw new DimensionMismatchException(in.length, 2);
}
if (in[0].length < 2) {
throw new DimensionMismatchException(in[0].length, 2);
}
MathArrays.checkRectangular(in);
MathArrays.checkNonNegative(in);
}
Example 4
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Checks to make sure that the input long[][] array is rectangular,
* has at least 2 rows and 2 columns, and has all non-negative entries.
*
* @param in input 2-way table to check
* @throws NullArgumentException if the array is null
* @throws DimensionMismatchException if the array is not valid
* @throws NotPositiveException if the array contains any negative entries
*/
private void checkArray(final long[][] in)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException {
if (in.length < 2) {
throw new DimensionMismatchException(in.length, 2);
}
if (in[0].length < 2) {
throw new DimensionMismatchException(in[0].length, 2);
}
MathArrays.checkRectangular(in);
MathArrays.checkNonNegative(in);
}
Example 5
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 6 votes |
|
/**
* Checks to make sure that the input long[][] array is rectangular,
* has at least 2 rows and 2 columns, and has all non-negative entries.
*
* @param in input 2-way table to check
* @throws NullArgumentException if the array is null
* @throws DimensionMismatchException if the array is not valid
* @throws NotPositiveException if the array contains any negative entries
*/
private void checkArray(final long[][] in)
throws NullArgumentException, DimensionMismatchException,
NotPositiveException {
if (in.length < 2) {
throw new DimensionMismatchException(in.length, 2);
}
if (in[0].length < 2) {
throw new DimensionMismatchException(in[0].length, 2);
}
MathArrays.checkRectangular(in);
MathArrays.checkNonNegative(in);
}
Example 6
| Source File: GTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Computes the <a href="http://en.wikipedia.org/wiki/G-test">G statistic
* for Goodness of Fit</a> comparing {@code observed} and {@code expected}
* frequency counts.
*
* <p>This statistic can be used to perform a G test (Log-Likelihood Ratio
* Test) evaluating the null hypothesis that the observed counts follow the
* expected distribution.</p>
*
* <p><strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive. </li>
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the same length and their
* common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* <p><strong>Note:</strong>This implementation rescales the
* {@code expected} array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return G-Test statistic
* @throws NotPositiveException if {@code observed} has negative entries
* @throws NotStrictlyPositiveException if {@code expected} has entries that
* are not strictly positive
* @throws DimensionMismatchException if the array lengths do not match or
* are less than 2.
*/
public double g(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
if (expected.length < 2) {
throw new DimensionMismatchException(expected.length, 2);
}
if (expected.length != observed.length) {
throw new DimensionMismatchException(expected.length, observed.length);
}
MathArrays.checkPositive(expected);
MathArrays.checkNonNegative(observed);
double sumExpected = 0d;
double sumObserved = 0d;
for (int i = 0; i < observed.length; i++) {
sumExpected += expected[i];
sumObserved += observed[i];
}
double ratio = 1d;
boolean rescale = false;
if (Math.abs(sumExpected - sumObserved) > 10E-6) {
ratio = sumObserved / sumExpected;
rescale = true;
}
double sum = 0d;
for (int i = 0; i < observed.length; i++) {
final double dev = rescale ?
FastMath.log((double) observed[i] / (ratio * expected[i])) :
FastMath.log((double) observed[i] / expected[i]);
sum += ((double) observed[i]) * dev;
}
return 2d * sum;
}
Example 7
| Source File: GTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Computes the <a href="http://en.wikipedia.org/wiki/G-test">G statistic
* for Goodness of Fit</a> comparing {@code observed} and {@code expected}
* frequency counts.
*
* <p>This statistic can be used to perform a G test (Log-Likelihood Ratio
* Test) evaluating the null hypothesis that the observed counts follow the
* expected distribution.</p>
*
* <p><strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive. </li>
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the same length and their
* common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* <p><strong>Note:</strong>This implementation rescales the
* {@code expected} array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return G-Test statistic
* @throws NotPositiveException if {@code observed} has negative entries
* @throws NotStrictlyPositiveException if {@code expected} has entries that
* are not strictly positive
* @throws DimensionMismatchException if the array lengths do not match or
* are less than 2.
*/
public double g(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
if (expected.length < 2) {
throw new DimensionMismatchException(expected.length, 2);
}
if (expected.length != observed.length) {
throw new DimensionMismatchException(expected.length, observed.length);
}
MathArrays.checkPositive(expected);
MathArrays.checkNonNegative(observed);
double sumExpected = 0d;
double sumObserved = 0d;
for (int i = 0; i < observed.length; i++) {
sumExpected += expected[i];
sumObserved += observed[i];
}
double ratio = 1d;
boolean rescale = false;
if (FastMath.abs(sumExpected - sumObserved) > 10E-6) {
ratio = sumObserved / sumExpected;
rescale = true;
}
double sum = 0d;
for (int i = 0; i < observed.length; i++) {
final double dev = rescale ?
FastMath.log((double) observed[i] / (ratio * expected[i])) :
FastMath.log((double) observed[i] / expected[i]);
sum += ((double) observed[i]) * dev;
}
return 2d * sum;
}
Example 8
| Source File: GTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Computes the <a href="http://en.wikipedia.org/wiki/G-test">G statistic
* for Goodness of Fit</a> comparing {@code observed} and {@code expected}
* frequency counts.
*
* <p>This statistic can be used to perform a G test (Log-Likelihood Ratio
* Test) evaluating the null hypothesis that the observed counts follow the
* expected distribution.</p>
*
* <p><strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive. </li>
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the same length and their
* common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* <p><strong>Note:</strong>This implementation rescales the
* {@code expected} array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return G-Test statistic
* @throws NotPositiveException if {@code observed} has negative entries
* @throws NotStrictlyPositiveException if {@code expected} has entries that
* are not strictly positive
* @throws DimensionMismatchException if the array lengths do not match or
* are less than 2.
*/
public double g(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
if (expected.length < 2) {
throw new DimensionMismatchException(expected.length, 2);
}
if (expected.length != observed.length) {
throw new DimensionMismatchException(expected.length, observed.length);
}
MathArrays.checkPositive(expected);
MathArrays.checkNonNegative(observed);
double sumExpected = 0d;
double sumObserved = 0d;
for (int i = 0; i < observed.length; i++) {
sumExpected += expected[i];
sumObserved += observed[i];
}
double ratio = 1d;
boolean rescale = false;
if (Math.abs(sumExpected - sumObserved) > 10E-6) {
ratio = sumObserved / sumExpected;
rescale = true;
}
double sum = 0d;
for (int i = 0; i < observed.length; i++) {
final double dev = rescale ?
FastMath.log((double) observed[i] / (ratio * expected[i])) :
FastMath.log((double) observed[i] / expected[i]);
sum += ((double) observed[i]) * dev;
}
return 2d * sum;
}
Example 9
| Source File: GTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Computes the <a href="http://en.wikipedia.org/wiki/G-test">G statistic
* for Goodness of Fit</a> comparing {@code observed} and {@code expected}
* frequency counts.
*
* <p>This statistic can be used to perform a G test (Log-Likelihood Ratio
* Test) evaluating the null hypothesis that the observed counts follow the
* expected distribution.</p>
*
* <p><strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive. </li>
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the same length and their
* common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* <p><strong>Note:</strong>This implementation rescales the
* {@code expected} array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return G-Test statistic
* @throws NotPositiveException if {@code observed} has negative entries
* @throws NotStrictlyPositiveException if {@code expected} has entries that
* are not strictly positive
* @throws DimensionMismatchException if the array lengths do not match or
* are less than 2.
*/
public double g(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
if (expected.length < 2) {
throw new DimensionMismatchException(expected.length, 2);
}
if (expected.length != observed.length) {
throw new DimensionMismatchException(expected.length, observed.length);
}
MathArrays.checkPositive(expected);
MathArrays.checkNonNegative(observed);
double sumExpected = 0d;
double sumObserved = 0d;
for (int i = 0; i < observed.length; i++) {
sumExpected += expected[i];
sumObserved += observed[i];
}
double ratio = 1d;
boolean rescale = false;
if (FastMath.abs(sumExpected - sumObserved) > 10E-6) {
ratio = sumObserved / sumExpected;
rescale = true;
}
double sum = 0d;
for (int i = 0; i < observed.length; i++) {
final double dev = rescale ?
FastMath.log((double) observed[i] / (ratio * expected[i])) :
FastMath.log((double) observed[i] / expected[i]);
sum += ((double) observed[i]) * dev;
}
return 2d * sum;
}
Example 10
| Source File: GTest.java From astor with GNU General Public License v2.0 | 5 votes |
|
/**
* Computes the <a href="http://en.wikipedia.org/wiki/G-test">G statistic
* for Goodness of Fit</a> comparing {@code observed} and {@code expected}
* frequency counts.
*
* <p>This statistic can be used to perform a G test (Log-Likelihood Ratio
* Test) evaluating the null hypothesis that the observed counts follow the
* expected distribution.</p>
*
* <p><strong>Preconditions</strong>: <ul>
* <li>Expected counts must all be positive. </li>
* <li>Observed counts must all be ≥ 0. </li>
* <li>The observed and expected arrays must have the same length and their
* common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* <p><strong>Note:</strong>This implementation rescales the
* {@code expected} array if necessary to ensure that the sum of the
* expected and observed counts are equal.</p>
*
* @param observed array of observed frequency counts
* @param expected array of expected frequency counts
* @return G-Test statistic
* @throws NotPositiveException if {@code observed} has negative entries
* @throws NotStrictlyPositiveException if {@code expected} has entries that
* are not strictly positive
* @throws DimensionMismatchException if the array lengths do not match or
* are less than 2.
*/
public double g(final double[] expected, final long[] observed)
throws NotPositiveException, NotStrictlyPositiveException,
DimensionMismatchException {
if (expected.length < 2) {
throw new DimensionMismatchException(expected.length, 2);
}
if (expected.length != observed.length) {
throw new DimensionMismatchException(expected.length, observed.length);
}
MathArrays.checkPositive(expected);
MathArrays.checkNonNegative(observed);
double sumExpected = 0d;
double sumObserved = 0d;
for (int i = 0; i < observed.length; i++) {
sumExpected += expected[i];
sumObserved += observed[i];
}
double ratio = 1d;
boolean rescale = false;
if (Math.abs(sumExpected - sumObserved) > 10E-6) {
ratio = sumObserved / sumExpected;
rescale = true;
}
double sum = 0d;
for (int i = 0; i < observed.length; i++) {
final double dev = rescale ?
FastMath.log((double) observed[i] / (ratio * expected[i])) :
FastMath.log((double) observed[i] / expected[i]);
sum += ((double) observed[i]) * dev;
}
return 2d * sum;
}
Example 11
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a
* <a href="http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm">
* Chi-Square two sample test statistic</a> comparing bin frequency counts
* in <code>observed1</code> and <code>observed2</code>. The
* sums of frequency counts in the two samples are not required to be the
* same. The formula used to compute the test statistic is</p>
* <code>
* ∑[(K * observed1[i] - observed2[i]/K)<sup>2</sup> / (observed1[i] + observed2[i])]
* </code> where
* <br/><code>K = &sqrt;[&sum(observed2 / ∑(observed1)]</code>
* </p>
* <p>This statistic can be used to perform a Chi-Square test evaluating the
* null hypothesis that both observed counts follow the same distribution.</p>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative.
* </li>
* <li>Observed counts for a specific bin must not both be zero.
* </li>
* <li>Observed counts for a specific sample must not all be 0.
* </li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> must have
* the same length and their common length must be at least 2.
* </li></ul></p><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @return chiSquare test statistic
* @throws DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws ZeroException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at some index is zero
* for both arrays
* @since 1.2
*/
public double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
boolean unequalCounts = false;
double weight = 0.0;
for (int i = 0; i < observed1.length; i++) {
countSum1 += observed1[i];
countSum2 += observed2[i];
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
// Compare and compute weight only if different
unequalCounts = countSum1 != countSum2;
if (unequalCounts) {
weight = FastMath.sqrt((double) countSum1 / (double) countSum2);
}
// Compute ChiSquare statistic
double sumSq = 0.0d;
double dev = 0.0d;
double obs1 = 0.0d;
double obs2 = 0.0d;
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
obs1 = observed1[i];
obs2 = observed2[i];
if (unequalCounts) { // apply weights
dev = obs1/weight - obs2 * weight;
} else {
dev = obs1 - obs2;
}
sumSq += (dev * dev) / (obs1 + obs2);
}
}
return sumSq;
}
Example 12
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a
* <a href="http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm">
* Chi-Square two sample test statistic</a> comparing bin frequency counts
* in <code>observed1</code> and <code>observed2</code>. The
* sums of frequency counts in the two samples are not required to be the
* same. The formula used to compute the test statistic is</p>
* <code>
* ∑[(K * observed1[i] - observed2[i]/K)<sup>2</sup> / (observed1[i] + observed2[i])]
* </code> where
* <br/><code>K = &sqrt;[&sum(observed2 / ∑(observed1)]</code>
* </p>
* <p>This statistic can be used to perform a Chi-Square test evaluating the
* null hypothesis that both observed counts follow the same distribution.</p>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative.
* </li>
* <li>Observed counts for a specific bin must not both be zero.
* </li>
* <li>Observed counts for a specific sample must not all be 0.
* </li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> must have
* the same length and their common length must be at least 2.
* </li></ul></p><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @return chiSquare test statistic
* @throws DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws ZeroException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at some index is zero
* for both arrays
* @since 1.2
*/
public double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
boolean unequalCounts = false;
double weight = 0.0;
for (int i = 0; i < observed1.length; i++) {
countSum1 += observed1[i];
countSum2 += observed2[i];
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
// Compare and compute weight only if different
unequalCounts = countSum1 != countSum2;
if (unequalCounts) {
weight = FastMath.sqrt((double) countSum1 / (double) countSum2);
}
// Compute ChiSquare statistic
double sumSq = 0.0d;
double dev = 0.0d;
double obs1 = 0.0d;
double obs2 = 0.0d;
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
obs1 = observed1[i];
obs2 = observed2[i];
if (unequalCounts) { // apply weights
dev = obs1/weight - obs2 * weight;
} else {
dev = obs1 - obs2;
}
sumSq += (dev * dev) / (obs1 + obs2);
}
}
return sumSq;
}
Example 13
| Source File: GTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a G (Log-Likelihood Ratio) two sample test statistic for
* independence comparing frequency counts in
* {@code observed1} and {@code observed2}. The sums of frequency
* counts in the two samples are not required to be the same. The formula
* used to compute the test statistic is </p>
*
* <p>{@code 2 * totalSum * [H(rowSums) + H(colSums) - H(k)]}</p>
*
* <p> where {@code H} is the
* <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">
* Shannon Entropy</a> of the random variable formed by viewing the elements
* of the argument array as incidence counts; <br/>
* {@code k} is a matrix with rows {@code [observed1, observed2]}; <br/>
* {@code rowSums, colSums} are the row/col sums of {@code k}; <br>
* and {@code totalSum} is the overall sum of all entries in {@code k}.</p>
*
* <p>This statistic can be used to perform a G test evaluating the null
* hypothesis that both observed counts are independent </p>
*
* <p> <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative. </li>
* <li>Observed counts for a specific bin must not both be zero. </li>
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must have
* the same length and their common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data
* set
* @return G-Test statistic
* @throws DimensionMismatchException the the lengths of the arrays do not
* match or their common length is less than 2
* @throws NotPositiveException if any entry in {@code observed1} or
* {@code observed2} is negative
* @throws ZeroException if either all counts of
* {@code observed1} or {@code observed2} are zero, or if the count
* at the same index is zero for both arrays.
*/
public double gDataSetsComparison(final long[] observed1, final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
// Compute and compare count sums
final long[] collSums = new long[observed1.length];
final long[][] k = new long[2][observed1.length];
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
countSum1 += observed1[i];
countSum2 += observed2[i];
collSums[i] = observed1[i] + observed2[i];
k[0][i] = observed1[i];
k[1][i] = observed2[i];
}
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
final long[] rowSums = {countSum1, countSum2};
final double sum = (double) countSum1 + (double) countSum2;
return 2 * sum * (entropy(rowSums) + entropy(collSums) - entropy(k));
}
Example 14
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a
* <a href="http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm">
* Chi-Square two sample test statistic</a> comparing bin frequency counts
* in <code>observed1</code> and <code>observed2</code>. The
* sums of frequency counts in the two samples are not required to be the
* same. The formula used to compute the test statistic is</p>
* <code>
* ∑[(K * observed1[i] - observed2[i]/K)<sup>2</sup> / (observed1[i] + observed2[i])]
* </code> where
* <br/><code>K = &sqrt;[&sum(observed2 / ∑(observed1)]</code>
* </p>
* <p>This statistic can be used to perform a Chi-Square test evaluating the
* null hypothesis that both observed counts follow the same distribution.</p>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative.
* </li>
* <li>Observed counts for a specific bin must not both be zero.
* </li>
* <li>Observed counts for a specific sample must not all be 0.
* </li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> must have
* the same length and their common length must be at least 2.
* </li></ul></p><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @return chiSquare test statistic
* @throws DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws ZeroException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at some index is zero
* for both arrays
* @since 1.2
*/
public double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
boolean unequalCounts = false;
double weight = 0.0;
for (int i = 0; i < observed1.length; i++) {
countSum1 += observed1[i];
countSum2 += observed2[i];
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
// Compare and compute weight only if different
unequalCounts = countSum1 != countSum2;
if (unequalCounts) {
weight = FastMath.sqrt((double) countSum1 / (double) countSum2);
}
// Compute ChiSquare statistic
double sumSq = 0.0d;
double dev = 0.0d;
double obs1 = 0.0d;
double obs2 = 0.0d;
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
obs1 = observed1[i];
obs2 = observed2[i];
if (unequalCounts) { // apply weights
dev = obs1/weight - obs2 * weight;
} else {
dev = obs1 - obs2;
}
sumSq += (dev * dev) / (obs1 + obs2);
}
}
return sumSq;
}
Example 15
| Source File: GTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a G (Log-Likelihood Ratio) two sample test statistic for
* independence comparing frequency counts in
* {@code observed1} and {@code observed2}. The sums of frequency
* counts in the two samples are not required to be the same. The formula
* used to compute the test statistic is </p>
*
* <p>{@code 2 * totalSum * [H(rowSums) + H(colSums) - H(k)]}</p>
*
* <p> where {@code H} is the
* <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">
* Shannon Entropy</a> of the random variable formed by viewing the elements
* of the argument array as incidence counts; <br/>
* {@code k} is a matrix with rows {@code [observed1, observed2]}; <br/>
* {@code rowSums, colSums} are the row/col sums of {@code k}; <br>
* and {@code totalSum} is the overall sum of all entries in {@code k}.</p>
*
* <p>This statistic can be used to perform a G test evaluating the null
* hypothesis that both observed counts are independent </p>
*
* <p> <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative. </li>
* <li>Observed counts for a specific bin must not both be zero. </li>
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must have
* the same length and their common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data
* set
* @return G-Test statistic
* @throws DimensionMismatchException the the lengths of the arrays do not
* match or their common length is less than 2
* @throws NotPositiveException if any entry in {@code observed1} or
* {@code observed2} is negative
* @throws ZeroException if either all counts of
* {@code observed1} or {@code observed2} are zero, or if the count
* at the same index is zero for both arrays.
*/
public double gDataSetsComparison(final long[] observed1, final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
// Compute and compare count sums
final long[] collSums = new long[observed1.length];
final long[][] k = new long[2][observed1.length];
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
countSum1 += observed1[i];
countSum2 += observed2[i];
collSums[i] = observed1[i] + observed2[i];
k[0][i] = observed1[i];
k[1][i] = observed2[i];
}
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
final long[] rowSums = {countSum1, countSum2};
final double sum = (double) countSum1 + (double) countSum2;
return 2 * sum * (entropy(rowSums) + entropy(collSums) - entropy(k));
}
Example 16
| Source File: GTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a G (Log-Likelihood Ratio) two sample test statistic for
* independence comparing frequency counts in
* {@code observed1} and {@code observed2}. The sums of frequency
* counts in the two samples are not required to be the same. The formula
* used to compute the test statistic is </p>
*
* <p>{@code 2 * totalSum * [H(rowSums) + H(colSums) - H(k)]}</p>
*
* <p> where {@code H} is the
* <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">
* Shannon Entropy</a> of the random variable formed by viewing the elements
* of the argument array as incidence counts; <br/>
* {@code k} is a matrix with rows {@code [observed1, observed2]}; <br/>
* {@code rowSums, colSums} are the row/col sums of {@code k}; <br>
* and {@code totalSum} is the overall sum of all entries in {@code k}.</p>
*
* <p>This statistic can be used to perform a G test evaluating the null
* hypothesis that both observed counts are independent </p>
*
* <p> <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative. </li>
* <li>Observed counts for a specific bin must not both be zero. </li>
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must have
* the same length and their common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data
* set
* @return G-Test statistic
* @throws DimensionMismatchException the the lengths of the arrays do not
* match or their common length is less than 2
* @throws NotPositiveException if any entry in {@code observed1} or
* {@code observed2} is negative
* @throws ZeroException if either all counts of
* {@code observed1} or {@code observed2} are zero, or if the count
* at the same index is zero for both arrays.
*/
public double gDataSetsComparison(final long[] observed1, final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
// Compute and compare count sums
final long[] collSums = new long[observed1.length];
final long[][] k = new long[2][observed1.length];
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
countSum1 += observed1[i];
countSum2 += observed2[i];
collSums[i] = observed1[i] + observed2[i];
k[0][i] = observed1[i];
k[1][i] = observed2[i];
}
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
final long[] rowSums = {countSum1, countSum2};
final double sum = (double) countSum1 + (double) countSum2;
return 2 * sum * (entropy(rowSums) + entropy(collSums) - entropy(k));
}
Example 17
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a
* <a href="http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm">
* Chi-Square two sample test statistic</a> comparing bin frequency counts
* in <code>observed1</code> and <code>observed2</code>. The
* sums of frequency counts in the two samples are not required to be the
* same. The formula used to compute the test statistic is</p>
* <code>
* ∑[(K * observed1[i] - observed2[i]/K)<sup>2</sup> / (observed1[i] + observed2[i])]
* </code> where
* <br/><code>K = &sqrt;[&sum(observed2 / ∑(observed1)]</code>
* </p>
* <p>This statistic can be used to perform a Chi-Square test evaluating the
* null hypothesis that both observed counts follow the same distribution.</p>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative.
* </li>
* <li>Observed counts for a specific bin must not both be zero.
* </li>
* <li>Observed counts for a specific sample must not all be 0.
* </li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> must have
* the same length and their common length must be at least 2.
* </li></ul></p><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @return chiSquare test statistic
* @throws DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws ZeroException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at some index is zero
* for both arrays
* @since 1.2
*/
public double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
boolean unequalCounts = false;
double weight = 0.0;
for (int i = 0; i < observed1.length; i++) {
countSum1 += observed1[i];
countSum2 += observed2[i];
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
// Compare and compute weight only if different
unequalCounts = countSum1 != countSum2;
if (unequalCounts) {
weight = FastMath.sqrt((double) countSum1 / (double) countSum2);
}
// Compute ChiSquare statistic
double sumSq = 0.0d;
double dev = 0.0d;
double obs1 = 0.0d;
double obs2 = 0.0d;
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
obs1 = observed1[i];
obs2 = observed2[i];
if (unequalCounts) { // apply weights
dev = obs1/weight - obs2 * weight;
} else {
dev = obs1 - obs2;
}
sumSq += (dev * dev) / (obs1 + obs2);
}
}
return sumSq;
}
Example 18
| Source File: GTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a G (Log-Likelihood Ratio) two sample test statistic for
* independence comparing frequency counts in
* {@code observed1} and {@code observed2}. The sums of frequency
* counts in the two samples are not required to be the same. The formula
* used to compute the test statistic is </p>
*
* <p>{@code 2 * totalSum * [H(rowSums) + H(colSums) - H(k)]}</p>
*
* <p> where {@code H} is the
* <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">
* Shannon Entropy</a> of the random variable formed by viewing the elements
* of the argument array as incidence counts; <br/>
* {@code k} is a matrix with rows {@code [observed1, observed2]}; <br/>
* {@code rowSums, colSums} are the row/col sums of {@code k}; <br>
* and {@code totalSum} is the overall sum of all entries in {@code k}.</p>
*
* <p>This statistic can be used to perform a G test evaluating the null
* hypothesis that both observed counts are independent </p>
*
* <p> <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative. </li>
* <li>Observed counts for a specific bin must not both be zero. </li>
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must have
* the same length and their common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data
* set
* @return G-Test statistic
* @throws DimensionMismatchException the the lengths of the arrays do not
* match or their common length is less than 2
* @throws NotPositiveException if any entry in {@code observed1} or
* {@code observed2} is negative
* @throws ZeroException if either all counts of
* {@code observed1} or {@code observed2} are zero, or if the count
* at the same index is zero for both arrays.
*/
public double gDataSetsComparison(final long[] observed1, final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
// Compute and compare count sums
final long[] collSums = new long[observed1.length];
final long[][] k = new long[2][observed1.length];
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
countSum1 += observed1[i];
countSum2 += observed2[i];
collSums[i] = observed1[i] + observed2[i];
k[0][i] = observed1[i];
k[1][i] = observed2[i];
}
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
final long[] rowSums = {countSum1, countSum2};
final double sum = (double) countSum1 + (double) countSum2;
return 2 * sum * (entropy(rowSums) + entropy(collSums) - entropy(k));
}
Example 19
| Source File: ChiSquareTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a
* <a href="http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm">
* Chi-Square two sample test statistic</a> comparing bin frequency counts
* in <code>observed1</code> and <code>observed2</code>. The
* sums of frequency counts in the two samples are not required to be the
* same. The formula used to compute the test statistic is</p>
* <code>
* ∑[(K * observed1[i] - observed2[i]/K)<sup>2</sup> / (observed1[i] + observed2[i])]
* </code> where
* <br/><code>K = &sqrt;[&sum(observed2 / ∑(observed1)]</code>
* </p>
* <p>This statistic can be used to perform a Chi-Square test evaluating the
* null hypothesis that both observed counts follow the same distribution.</p>
* <p>
* <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative.
* </li>
* <li>Observed counts for a specific bin must not both be zero.
* </li>
* <li>Observed counts for a specific sample must not all be 0.
* </li>
* <li>The arrays <code>observed1</code> and <code>observed2</code> must have
* the same length and their common length must be at least 2.
* </li></ul></p><p>
* If any of the preconditions are not met, an
* <code>IllegalArgumentException</code> is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data set
* @return chiSquare test statistic
* @throws DimensionMismatchException the the length of the arrays does not match
* @throws NotPositiveException if any entries in <code>observed1</code> or
* <code>observed2</code> are negative
* @throws ZeroException if either all counts of <code>observed1</code> or
* <code>observed2</code> are zero, or if the count at some index is zero
* for both arrays
* @since 1.2
*/
public double chiSquareDataSetsComparison(long[] observed1, long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
boolean unequalCounts = false;
double weight = 0.0;
for (int i = 0; i < observed1.length; i++) {
countSum1 += observed1[i];
countSum2 += observed2[i];
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
// Compare and compute weight only if different
unequalCounts = countSum1 != countSum2;
if (unequalCounts) {
weight = FastMath.sqrt((double) countSum1 / (double) countSum2);
}
// Compute ChiSquare statistic
double sumSq = 0.0d;
double dev = 0.0d;
double obs1 = 0.0d;
double obs2 = 0.0d;
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
obs1 = observed1[i];
obs2 = observed2[i];
if (unequalCounts) { // apply weights
dev = obs1/weight - obs2 * weight;
} else {
dev = obs1 - obs2;
}
sumSq += (dev * dev) / (obs1 + obs2);
}
}
return sumSq;
}
Example 20
| Source File: GTest.java From astor with GNU General Public License v2.0 | 4 votes |
|
/**
* <p>Computes a G (Log-Likelihood Ratio) two sample test statistic for
* independence comparing frequency counts in
* {@code observed1} and {@code observed2}. The sums of frequency
* counts in the two samples are not required to be the same. The formula
* used to compute the test statistic is </p>
*
* <p>{@code 2 * totalSum * [H(rowSums) + H(colSums) - H(k)]}</p>
*
* <p> where {@code H} is the
* <a href="http://en.wikipedia.org/wiki/Entropy_%28information_theory%29">
* Shannon Entropy</a> of the random variable formed by viewing the elements
* of the argument array as incidence counts; <br/>
* {@code k} is a matrix with rows {@code [observed1, observed2]}; <br/>
* {@code rowSums, colSums} are the row/col sums of {@code k}; <br>
* and {@code totalSum} is the overall sum of all entries in {@code k}.</p>
*
* <p>This statistic can be used to perform a G test evaluating the null
* hypothesis that both observed counts are independent </p>
*
* <p> <strong>Preconditions</strong>: <ul>
* <li>Observed counts must be non-negative. </li>
* <li>Observed counts for a specific bin must not both be zero. </li>
* <li>Observed counts for a specific sample must not all be 0. </li>
* <li>The arrays {@code observed1} and {@code observed2} must have
* the same length and their common length must be at least 2. </li></ul></p>
*
* <p>If any of the preconditions are not met, a
* {@code MathIllegalArgumentException} is thrown.</p>
*
* @param observed1 array of observed frequency counts of the first data set
* @param observed2 array of observed frequency counts of the second data
* set
* @return G-Test statistic
* @throws DimensionMismatchException the the lengths of the arrays do not
* match or their common length is less than 2
* @throws NotPositiveException if any entry in {@code observed1} or
* {@code observed2} is negative
* @throws ZeroException if either all counts of
* {@code observed1} or {@code observed2} are zero, or if the count
* at the same index is zero for both arrays.
*/
public double gDataSetsComparison(final long[] observed1, final long[] observed2)
throws DimensionMismatchException, NotPositiveException, ZeroException {
// Make sure lengths are same
if (observed1.length < 2) {
throw new DimensionMismatchException(observed1.length, 2);
}
if (observed1.length != observed2.length) {
throw new DimensionMismatchException(observed1.length, observed2.length);
}
// Ensure non-negative counts
MathArrays.checkNonNegative(observed1);
MathArrays.checkNonNegative(observed2);
// Compute and compare count sums
long countSum1 = 0;
long countSum2 = 0;
// Compute and compare count sums
final long[] collSums = new long[observed1.length];
final long[][] k = new long[2][observed1.length];
for (int i = 0; i < observed1.length; i++) {
if (observed1[i] == 0 && observed2[i] == 0) {
throw new ZeroException(LocalizedFormats.OBSERVED_COUNTS_BOTTH_ZERO_FOR_ENTRY, i);
} else {
countSum1 += observed1[i];
countSum2 += observed2[i];
collSums[i] = observed1[i] + observed2[i];
k[0][i] = observed1[i];
k[1][i] = observed2[i];
}
}
// Ensure neither sample is uniformly 0
if (countSum1 == 0 || countSum2 == 0) {
throw new ZeroException();
}
final long[] rowSums = {countSum1, countSum2};
final double sum = (double) countSum1 + (double) countSum2;
return 2 * sum * (entropy(rowSums) + entropy(collSums) - entropy(k));
}
