Unused classes (in "src/test").
This commit is contained in:
Gilles Sadowski
parent
9cfd17601b
commit
53cb2cce5f
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@ -1,159 +0,0 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math4.legacy.stat.descriptive;
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import java.io.Serializable;
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import java.util.ArrayList;
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import java.util.List;
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import org.apache.commons.math4.legacy.exception.MathIllegalArgumentException;
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import org.apache.commons.math4.legacy.util.FastMath;
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/**
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*/
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public class ListUnivariateImpl extends DescriptiveStatistics implements Serializable {
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/** Serializable version identifier */
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private static final long serialVersionUID = -8837442489133392138L;
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/**
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* Holds a reference to a list - GENERICs are going to make
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* our lives easier here as we could only accept List<Number>
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*/
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protected List<Double> list = new ArrayList<>();
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/**
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* Construct a ListUnivariate with a specific List.
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* @param list The list that will back this DescriptiveStatistics
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*/
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public ListUnivariateImpl(List<Double> list) {
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this.list = list;
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}
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/**
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* Default constructor
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*/
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public ListUnivariateImpl() {
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}
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/** {@inheritDoc} */
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@Override
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public double[] getValues() {
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int length = list.size();
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// If the window size is not INFINITE_WINDOW AND
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// the current list is larger that the window size, we need to
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// take into account only the last n elements of the list
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// as defined by windowSize
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final int wSize = getWindowSize();
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if (wSize != DescriptiveStatistics.INFINITE_WINDOW && wSize < list.size()) {
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length = list.size() - FastMath.max(0, list.size() - wSize);
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}
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// Create an array to hold all values
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double[] copiedArray = new double[length];
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for (int i = 0; i < copiedArray.length; i++) {
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copiedArray[i] = getElement(i);
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}
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return copiedArray;
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}
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/** {@inheritDoc} */
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@Override
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public double getElement(int index) {
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double value = Double.NaN;
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int calcIndex = index;
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final int wSize = getWindowSize();
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if (wSize != DescriptiveStatistics.INFINITE_WINDOW && wSize < list.size()) {
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calcIndex = (list.size() - wSize) + index;
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}
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try {
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value = list.get(calcIndex);
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} catch (MathIllegalArgumentException e) {
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e.printStackTrace();
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}
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return value;
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}
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/** {@inheritDoc} */
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@Override
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public long getN() {
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int n = 0;
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final int wSize = getWindowSize();
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if (wSize != DescriptiveStatistics.INFINITE_WINDOW) {
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if (list.size() > wSize) {
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n = wSize;
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} else {
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n = list.size();
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}
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} else {
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n = list.size();
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}
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return n;
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}
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/** {@inheritDoc} */
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@Override
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public void addValue(double v) {
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list.add(v);
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}
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/**
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* Clears all statistics.
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* <p>
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* <strong>N.B.: </strong> This method has the side effect of clearing the underlying list.
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*/
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@Override
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public void clear() {
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list.clear();
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}
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/**
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* Apply the given statistic to this univariate collection.
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* @param stat the statistic to apply
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* @return the computed value of the statistic.
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*/
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@Override
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public double apply(UnivariateStatistic stat) {
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final double[] v = this.getValues();
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if (v != null) {
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return stat.evaluate(v, 0, v.length);
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}
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return Double.NaN;
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}
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/** {@inheritDoc} */
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@Override
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public void setWindowSize(int windowSize) {
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super.setWindowSize(windowSize);
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// Discard elements from the front of the list if "windowSize"
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// is less than the size of the list.
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final int extra = list.size() - windowSize;
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if (extra > 0) {
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list.subList(0, extra).clear();
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}
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}
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}
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@ -1,157 +0,0 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math4.legacy.stat.descriptive;
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import java.util.ArrayList;
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import java.util.List;
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import org.apache.commons.math4.legacy.TestUtils;
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import org.apache.commons.math4.legacy.util.FastMath;
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import org.junit.Assert;
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import org.junit.Test;
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/**
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* Test cases for the {@link ListUnivariateImpl} class.
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*
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*/
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public final class ListUnivariateImplTest {
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private double one = 1;
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private float two = 2;
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private int three = 3;
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private double mean = 2;
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private double sumSq = 18;
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private double sum = 8;
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private double var = 0.666666666666666666667;
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private double std = FastMath.sqrt(var);
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private double n = 4;
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private double min = 1;
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private double max = 3;
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private double tolerance = 10E-15;
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/** test stats */
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@Test
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public void testStats() {
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List<Double> externalList = new ArrayList<>();
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DescriptiveStatistics u = new ListUnivariateImpl( externalList );
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Assert.assertEquals("total count",0,u.getN(),tolerance);
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u.addValue(one);
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u.addValue(two);
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u.addValue(two);
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u.addValue(three);
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Assert.assertEquals("N",n,u.getN(),tolerance);
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Assert.assertEquals("sum",sum,u.getSum(),tolerance);
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Assert.assertEquals("sumsq",sumSq,u.getSumsq(),tolerance);
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Assert.assertEquals("var",var,u.getVariance(),tolerance);
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Assert.assertEquals("std",std,u.getStandardDeviation(),tolerance);
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Assert.assertEquals("mean",mean,u.getMean(),tolerance);
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Assert.assertEquals("min",min,u.getMin(),tolerance);
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Assert.assertEquals("max",max,u.getMax(),tolerance);
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u.clear();
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Assert.assertEquals("total count",0,u.getN(),tolerance);
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}
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@Test
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public void testN0andN1Conditions() {
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List<Double> list = new ArrayList<>();
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DescriptiveStatistics u = new ListUnivariateImpl(list);
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Assert.assertTrue("Mean of n = 0 set should be NaN", Double.isNaN( u.getMean() ) );
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Assert.assertTrue("Standard Deviation of n = 0 set should be NaN", Double.isNaN( u.getStandardDeviation() ) );
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Assert.assertTrue("Variance of n = 0 set should be NaN", Double.isNaN(u.getVariance() ) );
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list.add( Double.valueOf(one));
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Assert.assertTrue( "Mean of n = 1 set should be value of single item n1", u.getMean() == one);
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Assert.assertTrue( "StdDev of n = 1 set should be zero, instead it is: " + u.getStandardDeviation(), u.getStandardDeviation() == 0);
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Assert.assertTrue( "Variance of n = 1 set should be zero", u.getVariance() == 0);
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}
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@Test
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public void testSkewAndKurtosis() {
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DescriptiveStatistics u = new DescriptiveStatistics();
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double[] testArray = { 12.5, 12, 11.8, 14.2, 14.9, 14.5, 21, 8.2, 10.3, 11.3, 14.1,
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9.9, 12.2, 12, 12.1, 11, 19.8, 11, 10, 8.8, 9, 12.3 };
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for( int i = 0; i < testArray.length; i++) {
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u.addValue( testArray[i]);
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}
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Assert.assertEquals("mean", 12.40455, u.getMean(), 0.0001);
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Assert.assertEquals("variance", 10.00236, u.getVariance(), 0.0001);
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Assert.assertEquals("skewness", 1.437424, u.getSkewness(), 0.0001);
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Assert.assertEquals("kurtosis", 2.37719, u.getKurtosis(), 0.0001);
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}
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@Test
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public void testProductAndGeometricMean() {
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ListUnivariateImpl u = new ListUnivariateImpl(new ArrayList<>());
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u.setWindowSize(10);
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u.addValue( 1.0 );
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u.addValue( 2.0 );
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u.addValue( 3.0 );
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u.addValue( 4.0 );
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Assert.assertEquals( "Geometric mean not expected", 2.213364, u.getGeometricMean(), 0.00001 );
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// Now test rolling - StorelessDescriptiveStatistics should discount the contribution
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// of a discarded element
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for( int i = 0; i < 10; i++ ) {
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u.addValue( i + 2 );
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}
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// Values should be (2,3,4,5,6,7,8,9,10,11)
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Assert.assertEquals( "Geometric mean not expected", 5.755931, u.getGeometricMean(), 0.00001 );
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}
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/** test stats */
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@Test
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public void testSerialization() {
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DescriptiveStatistics u = new ListUnivariateImpl(new ArrayList<>());
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Assert.assertEquals("total count",0,u.getN(),tolerance);
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u.addValue(one);
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u.addValue(two);
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DescriptiveStatistics u2 = (DescriptiveStatistics)TestUtils.serializeAndRecover(u);
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u2.addValue(two);
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u2.addValue(three);
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Assert.assertEquals("N",n,u2.getN(),tolerance);
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Assert.assertEquals("sum",sum,u2.getSum(),tolerance);
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Assert.assertEquals("sumsq",sumSq,u2.getSumsq(),tolerance);
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Assert.assertEquals("var",var,u2.getVariance(),tolerance);
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Assert.assertEquals("std",std,u2.getStandardDeviation(),tolerance);
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Assert.assertEquals("mean",mean,u2.getMean(),tolerance);
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Assert.assertEquals("min",min,u2.getMin(),tolerance);
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Assert.assertEquals("max",max,u2.getMax(),tolerance);
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u2.clear();
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Assert.assertEquals("total count",0,u2.getN(),tolerance);
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}
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}
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@ -1,167 +0,0 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.commons.math4.legacy.stat.descriptive;
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import org.apache.commons.math4.legacy.util.FastMath;
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import org.junit.Assert;
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import org.junit.Test;
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import java.util.ArrayList;
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import java.util.List;
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/**
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* Test cases for the {@link ListUnivariateImpl} class.
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*/
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public final class MixedListUnivariateImplTest {
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private final double one = 1;
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private final float two = 2;
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private final int three = 3;
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private final double mean = 2;
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private final double sumSq = 18;
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private final double sum = 8;
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private final double var = 0.666666666666666666667;
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private final double std = FastMath.sqrt(var);
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private final double n = 4;
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private final double min = 1;
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private final double max = 3;
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private final double tolerance = 10E-15;
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public MixedListUnivariateImplTest() {
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}
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/** test stats */
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@Test
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public void testStats() {
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List<Double> externalList = new ArrayList<>();
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DescriptiveStatistics u = new ListUnivariateImpl(externalList);
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Assert.assertEquals("total count", 0, u.getN(), tolerance);
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u.addValue(one);
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u.addValue(two);
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u.addValue(two);
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u.addValue(three);
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Assert.assertEquals("N", n, u.getN(), tolerance);
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Assert.assertEquals("sum", sum, u.getSum(), tolerance);
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Assert.assertEquals("sumsq", sumSq, u.getSumsq(), tolerance);
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Assert.assertEquals("var", var, u.getVariance(), tolerance);
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Assert.assertEquals("std", std, u.getStandardDeviation(), tolerance);
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Assert.assertEquals("mean", mean, u.getMean(), tolerance);
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Assert.assertEquals("min", min, u.getMin(), tolerance);
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Assert.assertEquals("max", max, u.getMax(), tolerance);
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u.clear();
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Assert.assertEquals("total count", 0, u.getN(), tolerance);
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}
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@Test
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public void testN0andN1Conditions() {
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DescriptiveStatistics u = new ListUnivariateImpl(new ArrayList<>());
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Assert.assertTrue(
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"Mean of n = 0 set should be NaN",
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Double.isNaN(u.getMean()));
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Assert.assertTrue(
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"Standard Deviation of n = 0 set should be NaN",
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Double.isNaN(u.getStandardDeviation()));
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Assert.assertTrue(
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"Variance of n = 0 set should be NaN",
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Double.isNaN(u.getVariance()));
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u.addValue(one);
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Assert.assertTrue(
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"Mean of n = 1 set should be value of single item n1, instead it is " + u.getMean() ,
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u.getMean() == one);
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Assert.assertTrue(
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"StdDev of n = 1 set should be zero, instead it is: "
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+ u.getStandardDeviation(),
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u.getStandardDeviation() == 0);
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Assert.assertTrue(
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"Variance of n = 1 set should be zero",
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u.getVariance() == 0);
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}
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@Test
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public void testSkewAndKurtosis() {
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ListUnivariateImpl u =
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new ListUnivariateImpl(new ArrayList<>());
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u.addValue(12.5);
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u.addValue(12);
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u.addValue(11.8);
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u.addValue(14.2);
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u.addValue(14.5);
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u.addValue(14.9);
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u.addValue(12.0);
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u.addValue(21);
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u.addValue(8.2);
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u.addValue(10.3);
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u.addValue(11.3);
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u.addValue(14.1f);
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u.addValue(9.9);
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u.addValue(12.2);
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u.addValue(12.1);
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u.addValue(11);
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u.addValue(19.8);
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u.addValue(11);
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u.addValue(10);
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u.addValue(8.8);
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u.addValue(9);
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u.addValue(12.3);
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Assert.assertEquals("mean", 12.40455, u.getMean(), 0.0001);
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Assert.assertEquals("variance", 10.00236, u.getVariance(), 0.0001);
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Assert.assertEquals("skewness", 1.437424, u.getSkewness(), 0.0001);
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Assert.assertEquals("kurtosis", 2.37719, u.getKurtosis(), 0.0001);
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}
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@Test
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public void testProductAndGeometricMean() {
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ListUnivariateImpl u = new ListUnivariateImpl(new ArrayList<>());
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u.setWindowSize(10);
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u.addValue(1.0);
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u.addValue(2.0);
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u.addValue(3.0);
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u.addValue(4.0);
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Assert.assertEquals(
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"Geometric mean not expected",
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2.213364,
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u.getGeometricMean(),
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0.00001);
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// Now test rolling - StorelessDescriptiveStatistics should discount the contribution
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// of a discarded element
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for (int i = 0; i < 10; i++) {
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u.addValue(i + 2);
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}
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// Values should be (2,3,4,5,6,7,8,9,10,11)
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Assert.assertEquals(
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"Geometric mean not expected",
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5.755931,
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u.getGeometricMean(),
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0.00001);
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}
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}
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