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