/* * Copyright (c) 2016 Google Inc. All Rights Reserved. * * Licensed 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. */ package com.android.vts.util; import com.android.vts.proto.VtsReportMessage.VtsProfilingRegressionMode; /** Helper object for storing statistics. */ public class StatSummary { private String label; private double min; private double max; private double mean; private double sumSq; private int n; private VtsProfilingRegressionMode regression_mode; /** * Create a statistical summary. * *

Sets the label, min, max, mean, sum of squared error, n, and mode as provided. * * @param label The (String) label to assign to the summary. * @param min The minimum observed value. * @param max The maximum observed value. * @param mean The average observed value. * @param sumSq The sum of squared error. * @param n The number of values observed. * @param mode The VtsProfilingRegressionMode to use when analyzing performance. */ public StatSummary( String label, double min, double max, double mean, double sumSq, int n, VtsProfilingRegressionMode mode) { this.label = label; this.min = min; this.max = max; this.mean = mean; this.sumSq = sumSq; this.n = n; this.regression_mode = mode; } /** * Initializes the statistical summary. * *

Sets the label as provided. Initializes the mean, variance, and n (number of values seen) * to 0. * * @param label The (String) label to assign to the summary. * @param mode The VtsProfilingRegressionMode to use when analyzing performance. */ public StatSummary(String label, VtsProfilingRegressionMode mode) { this(label, Double.MAX_VALUE, Double.MIN_VALUE, 0, 0, 0, mode); } /** * Update the mean and variance using Welford's single-pass method. * * @param value The observed value in the stream. */ public void updateStats(double value) { n += 1; double oldMean = mean; mean = oldMean + (value - oldMean) / n; sumSq = sumSq + (value - mean) * (value - oldMean); if (value < min) min = value; if (value > max) max = value; } /** * Combine the mean and variance with another StatSummary. * * @param stat The StatSummary to combine with. */ public void merge(StatSummary stat) { double delta = stat.getMean() - mean; int newN = n + stat.getCount(); sumSq = sumSq + stat.getSumSq() + delta / newN * delta * n * stat.getCount(); double recipN = 1.0 / newN; mean = n * recipN * mean + stat.getCount() * recipN * stat.getMean(); n = newN; } /** * Gets the best case of the stream. * * @return The min or max. */ public double getBestCase() { switch (regression_mode) { case VTS_REGRESSION_MODE_DECREASING: return getMax(); default: return getMin(); } } /** * Gets the calculated min of the stream. * * @return The min. */ public double getMin() { return min; } /** * Gets the calculated max of the stream. * * @return The max. */ public double getMax() { return max; } /** * Gets the calculated mean of the stream. * * @return The mean. */ public double getMean() { return mean; } /** * Gets the calculated sum of squared error of the stream. * * @return The sum of squared error. */ public double getSumSq() { return sumSq; } /** * Gets the calculated standard deviation of the stream. * * @return The standard deviation. */ public double getStd() { return Math.sqrt(sumSq / (n - 1)); } /** * Gets the number of elements that have passed through the stream. * * @return Number of elements. */ public int getCount() { return n; } /** * Gets the label for the summarized statistics. * * @return The (string) label. */ public String getLabel() { return label; } /** * Gets the regression mode. * * @return The VtsProfilingRegressionMode value. */ public VtsProfilingRegressionMode getRegressionMode() { return regression_mode; } }