84 lines
2.6 KiB
C++
84 lines
2.6 KiB
C++
//=============================================================================
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// MusE Reader
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// Music Score Reader
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// $Id$
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//
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// Copyright (C) 2010 Werner Schweer
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//
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// This program is free software; you can redistribute it and/or modify
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// it under the terms of the GNU General Public License version 2.
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//
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// This program is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with this program; if not, write to the Free Software
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// Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
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//=============================================================================
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#include "utils.h"
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#include "omr.h"
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char Omr::bitsSetTable[256];
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//---------------------------------------------------------
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// initUtils
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//---------------------------------------------------------
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void Omr::initUtils()
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{
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static bool initialized = false;
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if (initialized)
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return;
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initialized = true;
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//
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// populate the bitsSetTable
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bitsSetTable[0] = 0;
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for (int i = 1; i < 256; i++)
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bitsSetTable[i] = (i & 1) + bitsSetTable[i/2];
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}
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//---------------------------------------------------------
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// mean
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// Compute the arithmetic mean of a dataset using the
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// recurrence relation
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// mean_(n) = mean(n-1) + (data[n] - mean(n-1))/(n+1)
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//---------------------------------------------------------
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double mean(const double data[], int size)
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{
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long double mean = 0;
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for (int i = 0; i < size; i++)
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mean += (data[i] - mean) / (i + 1);
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return mean;
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}
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//---------------------------------------------------------
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// covariance
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//---------------------------------------------------------
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double covariance(const double data1[],
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const double data2[], int n, double mean1, double mean2)
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{
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long double covariance = 0.0;
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/* find the sum of the squares */
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for (size_t i = 0; i < (size_t)n; i++) {
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const long double delta1 = (data1[i] - mean1);
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const long double delta2 = (data2[i] - mean2);
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covariance += (delta1 * delta2 - covariance) / (i + 1);
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}
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return covariance;
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}
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double covariance(const double data1[], const double data2[], int n)
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{
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double mean1 = mean(data1, n);
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double mean2 = mean(data2, n);
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return covariance(data1, data2, n, mean1, mean2);
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}
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