--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/hotspot/src/share/vm/utilities/numberSeq.cpp Thu Jun 05 15:57:56 2008 -0700
@@ -0,0 +1,243 @@
+/*
+ * Copyright 2001-2007 Sun Microsystems, Inc. All Rights Reserved.
+ * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
+ *
+ * This code is free software; you can redistribute it and/or modify it
+ * under the terms of the GNU General Public License version 2 only, as
+ * published by the Free Software Foundation.
+ *
+ * This code 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
+ * version 2 for more details (a copy is included in the LICENSE file that
+ * accompanied this code).
+ *
+ * You should have received a copy of the GNU General Public License version
+ * 2 along with this work; if not, write to the Free Software Foundation,
+ * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
+ *
+ * Please contact Sun Microsystems, Inc., 4150 Network Circle, Santa Clara,
+ * CA 95054 USA or visit www.sun.com if you need additional information or
+ * have any questions.
+ *
+ */
+
+# include "incls/_precompiled.incl"
+# include "incls/_numberSeq.cpp.incl"
+
+AbsSeq::AbsSeq(double alpha) :
+ _num(0), _sum(0.0), _sum_of_squares(0.0),
+ _davg(0.0), _dvariance(0.0), _alpha(alpha) {
+}
+
+void AbsSeq::add(double val) {
+ if (_num == 0) {
+ // if the sequence is empty, the davg is the same as the value
+ _davg = val;
+ // and the variance is 0
+ _dvariance = 0.0;
+ } else {
+ // otherwise, calculate both
+ _davg = (1.0 - _alpha) * val + _alpha * _davg;
+ double diff = val - _davg;
+ _dvariance = (1.0 - _alpha) * diff * diff + _alpha * _dvariance;
+ }
+}
+
+double AbsSeq::avg() const {
+ if (_num == 0)
+ return 0.0;
+ else
+ return _sum / total();
+}
+
+double AbsSeq::variance() const {
+ if (_num <= 1)
+ return 0.0;
+
+ double x_bar = avg();
+ double result = _sum_of_squares / total() - x_bar * x_bar;
+ if (result < 0.0) {
+ // due to loss-of-precision errors, the variance might be negative
+ // by a small bit
+
+ // guarantee(-0.1 < result && result < 0.0,
+ // "if variance is negative, it should be very small");
+ result = 0.0;
+ }
+ return result;
+}
+
+double AbsSeq::sd() const {
+ double var = variance();
+ guarantee( var >= 0.0, "variance should not be negative" );
+ return sqrt(var);
+}
+
+double AbsSeq::davg() const {
+ return _davg;
+}
+
+double AbsSeq::dvariance() const {
+ if (_num <= 1)
+ return 0.0;
+
+ double result = _dvariance;
+ if (result < 0.0) {
+ // due to loss-of-precision errors, the variance might be negative
+ // by a small bit
+
+ guarantee(-0.1 < result && result < 0.0,
+ "if variance is negative, it should be very small");
+ result = 0.0;
+ }
+ return result;
+}
+
+double AbsSeq::dsd() const {
+ double var = dvariance();
+ guarantee( var >= 0.0, "variance should not be negative" );
+ return sqrt(var);
+}
+
+NumberSeq::NumberSeq(double alpha) :
+ AbsSeq(alpha), _maximum(0.0), _last(0.0) {
+}
+
+bool NumberSeq::check_nums(NumberSeq *total, int n, NumberSeq **parts) {
+ for (int i = 0; i < n; ++i) {
+ if (parts[i] != NULL && total->num() != parts[i]->num())
+ return false;
+ }
+ return true;
+}
+
+NumberSeq::NumberSeq(NumberSeq *total, int n, NumberSeq **parts) {
+ guarantee(check_nums(total, n, parts), "all seq lengths should match");
+ double sum = total->sum();
+ for (int i = 0; i < n; ++i) {
+ if (parts[i] != NULL)
+ sum -= parts[i]->sum();
+ }
+
+ _num = total->num();
+ _sum = sum;
+
+ // we do not calculate these...
+ _sum_of_squares = -1.0;
+ _maximum = -1.0;
+ _davg = -1.0;
+ _dvariance = -1.0;
+}
+
+void NumberSeq::add(double val) {
+ AbsSeq::add(val);
+
+ _last = val;
+ if (_num == 0) {
+ _maximum = val;
+ } else {
+ if (val > _maximum)
+ _maximum = val;
+ }
+ _sum += val;
+ _sum_of_squares += val * val;
+ ++_num;
+}
+
+
+TruncatedSeq::TruncatedSeq(int length, double alpha):
+ AbsSeq(alpha), _length(length), _next(0) {
+ _sequence = NEW_C_HEAP_ARRAY(double, _length);
+ for (int i = 0; i < _length; ++i)
+ _sequence[i] = 0.0;
+}
+
+void TruncatedSeq::add(double val) {
+ AbsSeq::add(val);
+
+ // get the oldest value in the sequence...
+ double old_val = _sequence[_next];
+ // ...remove it from the sum and sum of squares
+ _sum -= old_val;
+ _sum_of_squares -= old_val * old_val;
+
+ // ...and update them with the new value
+ _sum += val;
+ _sum_of_squares += val * val;
+
+ // now replace the old value with the new one
+ _sequence[_next] = val;
+ _next = (_next + 1) % _length;
+
+ // only increase it if the buffer is not full
+ if (_num < _length)
+ ++_num;
+
+ guarantee( variance() > -1.0, "variance should be >= 0" );
+}
+
+// can't easily keep track of this incrementally...
+double TruncatedSeq::maximum() const {
+ if (_num == 0)
+ return 0.0;
+ double ret = _sequence[0];
+ for (int i = 1; i < _num; ++i) {
+ double val = _sequence[i];
+ if (val > ret)
+ ret = val;
+ }
+ return ret;
+}
+
+double TruncatedSeq::last() const {
+ if (_num == 0)
+ return 0.0;
+ unsigned last_index = (_next + _length - 1) % _length;
+ return _sequence[last_index];
+}
+
+double TruncatedSeq::oldest() const {
+ if (_num == 0)
+ return 0.0;
+ else if (_num < _length)
+ // index 0 always oldest value until the array is full
+ return _sequence[0];
+ else {
+ // since the array is full, _next is over the oldest value
+ return _sequence[_next];
+ }
+}
+
+double TruncatedSeq::predict_next() const {
+ if (_num == 0)
+ return 0.0;
+
+ double num = (double) _num;
+ double x_squared_sum = 0.0;
+ double x_sum = 0.0;
+ double y_sum = 0.0;
+ double xy_sum = 0.0;
+ double x_avg = 0.0;
+ double y_avg = 0.0;
+
+ int first = (_next + _length - _num) % _length;
+ for (int i = 0; i < _num; ++i) {
+ double x = (double) i;
+ double y = _sequence[(first + i) % _length];
+
+ x_squared_sum += x * x;
+ x_sum += x;
+ y_sum += y;
+ xy_sum += x * y;
+ }
+ x_avg = x_sum / num;
+ y_avg = y_sum / num;
+
+ double Sxx = x_squared_sum - x_sum * x_sum / num;
+ double Sxy = xy_sum - x_sum * y_sum / num;
+ double b1 = Sxy / Sxx;
+ double b0 = y_avg - b1 * x_avg;
+
+ return b0 + b1 * num;
+}