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Posted to commits@commons.apache.org by ah...@apache.org on 2021/11/25 16:17:43 UTC

[commons-statistics] branch master updated: Update normal distribution pdf to compute directly.

This is an automated email from the ASF dual-hosted git repository.

aherbert pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/commons-statistics.git


The following commit(s) were added to refs/heads/master by this push:
     new d731ea4  Update normal distribution pdf to compute directly.
d731ea4 is described below

commit d731ea4530feb70cf66801aa84e4b813493cecc7
Author: aherbert <ah...@apache.org>
AuthorDate: Thu Nov 25 16:16:28 2021 +0000

    Update normal distribution pdf to compute directly.
    
    Use of exp(logDensity) can suffer errors as x increases in magnitude for
    the standard normal distribution (mu=0, sd=1).
    
    Approximate errors:
    x   ulp
    3   3
    6   10
    12  >40
    
    Added extended precision test data for the standard normal distribution
    computed using maxima. ULP errors are below 2 for x <= 12 when computing
    the PDF directly.
    
    Added extended precision computation of x * sqrt(2 * pi) for use in the
    PDF computation for normal distributions with a mean other than 1.
    
    This computes more accurately than x * Math.sqrt(2 * Math.PI), or x *
    SQRT2PI if using a pre-computed constant.
---
 .../statistics/distribution/ExtendedPrecision.java |   68 +
 .../distribution/NormalDistribution.java           |   10 +-
 .../distribution/ExtendedPrecisionTest.java        |   57 +-
 .../distribution/NormalDistributionTest.java       |   19 +-
 .../commons/statistics/distribution/normpdf.csv    | 1564 ++++++++++++++++++++
 .../commons/statistics/distribution/xsqrt2pi.csv   |  527 +++++++
 6 files changed, 2233 insertions(+), 12 deletions(-)

diff --git a/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/ExtendedPrecision.java b/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/ExtendedPrecision.java
index 7fa3c93..c12e7d0 100644
--- a/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/ExtendedPrecision.java
+++ b/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/ExtendedPrecision.java
@@ -16,6 +16,8 @@
  */
 package org.apache.commons.statistics.distribution;
 
+import java.math.BigDecimal;
+
 /**
  * Computes extended precision floating-point operations.
  *
@@ -27,6 +29,8 @@ package org.apache.commons.statistics.distribution;
  * <p>Adapted from {@code org.apache.commons.numbers.core.ExtendedPrecion}.
  */
 final class ExtendedPrecision {
+    /** sqrt(2 pi). Computed to 64-digits. */
+    private static final String SQRT_TWO_PI = "2.506628274631000502415765284811045253006986740609938316629923576";
     /**
      * The multiplier used to split the double value into high and low parts. From
      * Dekker (1971): "The constant should be chosen equal to 2^(p - p/2) + 1,
@@ -42,11 +46,75 @@ final class ExtendedPrecision {
     private static final double SCALE_UP = 0x1.0p600;
     /** Scale down by 2^600. */
     private static final double SCALE_DOWN = 0x1.0p-600;
+    /** sqrt(2 pi) as a double. */
+    private static final double SQRT2PI;
+    /** Upper bits of sqrt(2 pi). */
+    private static final double SQRT2PI_H;
+    /** Lower bits of sqrt(2 pi). */
+    private static final double SQRT2PI_L;
+    /** Round-off from sqrt(2 pi) as a double. */
+    private static final double SQRT2PI_R;
+
+    static {
+        // Initialise constants
+        final BigDecimal sqrt2pi = new BigDecimal(SQRT_TWO_PI);
+
+        // Use a 106-bit number as:
+        // (SQRT2PI, SQRT2PI_R)
+        SQRT2PI = sqrt2pi.doubleValue();
+        SQRT2PI_R = sqrt2pi.subtract(new BigDecimal(SQRT2PI)).doubleValue();
+
+        // Split the upper 53-bits for extended precision multiplication
+        SQRT2PI_H = highPartUnscaled(SQRT2PI);
+        SQRT2PI_L = SQRT2PI - SQRT2PI_H;
+    }
 
     /** No instances. */
     private ExtendedPrecision() {}
 
     /**
+     * Multiply the term by sqrt(2 pi).
+     *
+     * @param x Value (assumed to be positive)
+     * @return x * sqrt(2 pi)
+     */
+    static double xsqrt2pi(double x) {
+        // Note: Do not convert x to absolute for this use case
+        if (x > BIG) {
+            if (x == Double.POSITIVE_INFINITY) {
+                return Double.POSITIVE_INFINITY;
+            }
+            return computeXsqrt2pi(x * SCALE_DOWN) * SCALE_UP;
+        } else if (x < SMALL) {
+            // Note: Ignore possible zero for this use case
+            return computeXsqrt2pi(x * SCALE_UP) * SCALE_DOWN;
+        } else {
+            return computeXsqrt2pi(x);
+        }
+    }
+
+    /**
+     * Compute {@code a * sqrt(2 * pi)}.
+     *
+     * @param a Value
+     * @return the result
+     */
+    private static double computeXsqrt2pi(double a) {
+        // Split the number
+        final double ha = highPartUnscaled(a);
+        final double la = a - ha;
+
+        // Extended precision product with sqrt(2 * pi)
+        final double x = a * SQRT2PI;
+        final double xx = productLow(ha, la, SQRT2PI_H, SQRT2PI_L, x);
+
+        // Add the term a multiplied by the round-off from sqrt(2 * pi)
+        // result = a * (SQRT2PI + SQRT2PI_R)
+        // Sum from small to high
+        return a * SQRT2PI_R + xx + x;
+    }
+
+    /**
      * Compute {@code sqrt(2 * x * x)}.
      *
      * <p>The result is computed using a high precision computation of
diff --git a/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/NormalDistribution.java b/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/NormalDistribution.java
index fd0be84..2848dd4 100644
--- a/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/NormalDistribution.java
+++ b/commons-statistics-distribution/src/main/java/org/apache/commons/statistics/distribution/NormalDistribution.java
@@ -49,6 +49,10 @@ public final class NormalDistribution extends AbstractContinuousDistribution {
      * differences as the error function computes close to 0 in the extreme tail.
      */
     private final double sdSqrt2;
+    /**
+     * Standard deviation multiplied by sqrt(2 pi). Computed to high precision.
+     */
+    private final double sdSqrt2pi;
 
     /**
      * @param mean Mean for this distribution.
@@ -62,6 +66,8 @@ public final class NormalDistribution extends AbstractContinuousDistribution {
         // Minimise rounding error by computing sqrt(2 * sd * sd) exactly.
         // Compute using extended precision with care to avoid over/underflow.
         sdSqrt2 = ExtendedPrecision.sqrt2xx(sd);
+        // Compute sd * sqrt(2 * pi)
+        sdSqrt2pi = ExtendedPrecision.xsqrt2pi(sd);
     }
 
     /**
@@ -93,7 +99,9 @@ public final class NormalDistribution extends AbstractContinuousDistribution {
     /** {@inheritDoc} */
     @Override
     public double density(double x) {
-        return Math.exp(logDensity(x));
+        final double x0 = x - mean;
+        final double x1 = x0 / standardDeviation;
+        return Math.exp(-0.5 * x1 * x1) / sdSqrt2pi;
     }
 
     /** {@inheritDoc} */
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExtendedPrecisionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExtendedPrecisionTest.java
index 3c6398a..9378cdf 100644
--- a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExtendedPrecisionTest.java
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/ExtendedPrecisionTest.java
@@ -25,6 +25,7 @@ import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.TestMethodOrder;
 import org.junit.jupiter.params.ParameterizedTest;
 import org.junit.jupiter.params.provider.CsvFileSource;
+import org.junit.jupiter.params.provider.ValueSource;
 
 /**
  * Test for {@link ExtendedPrecision}.
@@ -33,10 +34,14 @@ import org.junit.jupiter.params.provider.CsvFileSource;
 class ExtendedPrecisionTest {
     /** sqrt(2). */
     private static final double ROOT2 = Math.sqrt(2.0);
-    /** The sum of the squared ULP error for the first standard computation. */
-    private static final RMS RMS1 = new RMS();
-    /** The sum of the squared ULP error for the second standard computation. */
-    private static final RMS RMS2 = new RMS();
+    /** sqrt(2 * pi) to 64 digits. This is 1 ULP different from Math.sqrt(2 * Math.PI). */
+    private static final double ROOT2PI = 2.506628274631000502415765284811045253006986740609938316629923576;
+    /** The sum of the squared ULP error for the first standard computation for sqrt(2 * x * x). */
+    private static final RMS SQRT2XX_RMS1 = new RMS();
+    /** The sum of the squared ULP error for the second standard computation for sqrt(2 * x * x). */
+    private static final RMS SQRT2XX_RMS2 = new RMS();
+    /** The sum of the squared ULP error for the first computation for x * sqrt(2 pi). */
+    private static final RMS XSQRT2PI_RMS = new RMS();
 
     /**
      * Class to compute the root mean squared error (RMS).
@@ -120,7 +125,7 @@ class ExtendedPrecisionTest {
 
     /**
      * Test the extended precision {@code sqrt(2 * x * x)}. The expected result
-     * is an exact computation. For comparison ulp errors are collected for
+     * is an extended precision computation. For comparison ulp errors are collected for
      * two standard precision computations.
      *
      * @param x Value x
@@ -133,23 +138,55 @@ class ExtendedPrecisionTest {
         final double e = expected.doubleValue();
         Assertions.assertEquals(e, ExtendedPrecision.sqrt2xx(x));
         // Compute error for the standard computations
-        addError(Math.sqrt(2 * x * x), expected, e, RMS1);
-        addError(x * ROOT2, expected, e, RMS2);
+        addError(Math.sqrt(2 * x * x), expected, e, SQRT2XX_RMS1);
+        addError(x * ROOT2, expected, e, SQRT2XX_RMS2);
     }
 
     @Test
     void testSqrt2xxStandardPrecision1() {
         // Typical result:   max   0.7780  rms   0.2144
-        assertSqrt2xxStandardPrecision(RMS1, 0.9, 0.3);
+        assertPrecision(SQRT2XX_RMS1, 0.9, 0.3);
     }
 
     @Test
     void testSqrt2xxStandardPrecision2() {
         // Typical result:   max   1.0598  rms   0.4781
-        assertSqrt2xxStandardPrecision(RMS2, 1.3, 0.6);
+        assertPrecision(SQRT2XX_RMS2, 1.3, 0.6);
     }
 
-    private static void assertSqrt2xxStandardPrecision(RMS rms, double maxError, double rmsError) {
+    @ParameterizedTest
+    @ValueSource(doubles = {0, 1, Double.MAX_VALUE, Double.POSITIVE_INFINITY, Double.NaN})
+    void testXsqrt2piEdgeCases(double x) {
+        final double expected = x * ROOT2PI;
+        final double actual = ExtendedPrecision.xsqrt2pi(x);
+        Assertions.assertEquals(expected, actual, 1e-15);
+    }
+
+    /**
+     * Test the extended precision {@code x * sqrt(2 * pi)}. The expected result
+     * is an extended precision computation. For comparison ulp errors are collected for
+     * a standard computation.
+     *
+     * @param x Value x
+     * @param expected Expected result of {@code x * sqrt(2 * pi)}.
+     */
+    @ParameterizedTest
+    @Order(1)
+    @CsvFileSource(resources = "xsqrt2pi.csv")
+    void testXsqrt2pi(double x, BigDecimal expected) {
+        final double e = expected.doubleValue();
+        Assertions.assertEquals(e, ExtendedPrecision.xsqrt2pi(x));
+        // Compute error for the standard computation
+        addError(x * ROOT2PI, expected, e, XSQRT2PI_RMS);
+    }
+
+    @Test
+    void testXsqrt2piPrecision() {
+        // Typical result:   max   1.1397  rms   0.5368
+        assertPrecision(XSQRT2PI_RMS, 1.2, 0.6);
+    }
+
+    private static void assertPrecision(RMS rms, double maxError, double rmsError) {
         Assertions.assertTrue(rms.getMax() < maxError, () -> "max error: " + rms.getMax());
         Assertions.assertTrue(rms.getRMS() < rmsError, () -> "rms error: " + rms.getRMS());
     }
diff --git a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java
index fe16072..4f8e42b 100644
--- a/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java
+++ b/commons-statistics-distribution/src/test/java/org/apache/commons/statistics/distribution/NormalDistributionTest.java
@@ -17,14 +17,21 @@
 
 package org.apache.commons.statistics.distribution;
 
+import java.math.BigDecimal;
 import org.junit.jupiter.api.Assertions;
 import org.junit.jupiter.api.Test;
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.CsvFileSource;
 
 /**
  * Test cases for {@link NormalDistribution}.
  * Extends {@link BaseContinuousDistributionTest}. See javadoc of that class for details.
  */
 class NormalDistributionTest extends BaseContinuousDistributionTest {
+    /** A standard normal distribution used for calculations.
+     * This is immutable and thread-safe and can be used across instances. */
+    private static final NormalDistribution STANDARD_NORMAL = NormalDistribution.of(0, 1);
+
     @Override
     ContinuousDistribution makeDistribution(Object... parameters) {
         final double mean = (Double) parameters[0];
@@ -56,7 +63,7 @@ class NormalDistributionTest extends BaseContinuousDistributionTest {
     protected double getHighPrecisionRelativeTolerance() {
         // Tests are limited by the survival probability.
         // Tolerance is 1.6653345369377348E-14.
-        // This is lowest achieved with various implementations of the
+        // This is the lowest achieved with various implementations of the
         // survival function against high precision reference data.
         // It requires computing the factor sqrt(2 * sd * sd) exactly.
         return 75 * RELATIVE_EPS;
@@ -169,4 +176,14 @@ class NormalDistributionTest extends BaseContinuousDistributionTest {
             Assertions.assertEquals(x, x0, Math.abs(x) * 1e-11, () -> "CDF = " + cdf);
         }
     }
+
+    @ParameterizedTest
+    @CsvFileSource(resources = "normpdf.csv")
+    void testPDF(double x, BigDecimal expected) {
+        final double e = expected.doubleValue();
+        final double a = STANDARD_NORMAL.density(x);
+        // Require high precision. Currently this does not work at 1 ULP.
+        Assertions.assertEquals(e, a, Math.ulp(e) * 2,
+            () -> "ULP error: " + expected.subtract(new BigDecimal(a)).doubleValue() / Math.ulp(e));
+    }
 }
diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/normpdf.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/normpdf.csv
new file mode 100644
index 0000000..d012c32
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/normpdf.csv
@@ -0,0 +1,1564 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# High-precision test data for the normal distribution PDF
+# Computed using the following maxima script:
+#
+# fpprec : 128;
+# sqrt2pi : bfloat(2.506628274631000502415765284811045253006986740609938316629923576342293654607841974946595838378057266116009972665203879644866323618);
+# f(x) := exp(-0.5 * bfloat(x) * bfloat(x)) / sqrt2pi;
+# str(x) := ssubst("e","b",string(x));
+#
+# out : openw("normpdf.csv");
+# x : float(makelist(i / 128, i, 1, 128 * 12));
+# for i in x do printf(out, "~f, ~a~%", i, str(f(i))), fpprintprec : 30;
+# close(out);
+
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diff --git a/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/xsqrt2pi.csv b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/xsqrt2pi.csv
new file mode 100644
index 0000000..9f3807f
--- /dev/null
+++ b/commons-statistics-distribution/src/test/resources/org/apache/commons/statistics/distribution/xsqrt2pi.csv
@@ -0,0 +1,527 @@
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# High-precision test data for: x * sqrt(2 * pi)
+# Computed from uniform double in the range [1, 2) using:
+#
+# BigDecimal SQRT2PI = new BigDecimal("2.506628274631000502415765284811045253006986740609938316629923576342293654607841974946595838378057266116009972665203879644866323618");
+# MathContext mc = new MathContext(25);
+# for (int i = 0; i < 500; i++) {
+#      // Combine an unbiased exponent of 0 with random 52 bit mantissa
+#      double u = Double.longBitsToDouble(
+#          (1023L << 52) | (ThreadLocalRandom.current().nextLong() >>> 12));
+#      System.out.printf("%s,%s%n", u, new BigDecimal(u).multiply(SQRT2PI, mc));
+# }
+
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