forked from bartvdbraak/blender
557 lines
16 KiB
C++
557 lines
16 KiB
C++
/*
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* Set random/camera stuff
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*
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*
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* ***** BEGIN GPL LICENSE BLOCK *****
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*
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* This program is free software; you can redistribute it and/or
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* modify it under the terms of the GNU General Public License
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* as published by the Free Software Foundation; either version 2
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* of the License, or (at your option) any later version.
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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 Foundation,
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* Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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*
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* The Original Code is Copyright (C) 2001-2002 by NaN Holding BV.
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* All rights reserved.
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*
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* The Original Code is: all of this file.
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*
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* Contributor(s): none yet.
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*
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* ***** END GPL LICENSE BLOCK *****
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*/
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/** \file gameengine/GameLogic/SCA_RandomActuator.cpp
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* \ingroup gamelogic
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*/
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#include <stddef.h>
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#include "BoolValue.h"
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#include "IntValue.h"
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#include "FloatValue.h"
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#include "SCA_IActuator.h"
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#include "SCA_RandomActuator.h"
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#include "math.h"
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#include "MT_Transform.h"
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/* ------------------------------------------------------------------------- */
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/* Native functions */
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/* ------------------------------------------------------------------------- */
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SCA_RandomActuator::SCA_RandomActuator(SCA_IObject *gameobj,
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long seed,
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SCA_RandomActuator::KX_RANDOMACT_MODE mode,
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float para1,
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float para2,
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const STR_String &propName)
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: SCA_IActuator(gameobj, KX_ACT_RANDOM),
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m_propname(propName),
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m_parameter1(para1),
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m_parameter2(para2),
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m_distribution(mode)
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{
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m_base = new SCA_RandomNumberGenerator(seed);
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m_counter = 0;
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enforceConstraints();
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}
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SCA_RandomActuator::~SCA_RandomActuator()
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{
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m_base->Release();
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}
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CValue* SCA_RandomActuator::GetReplica()
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{
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SCA_RandomActuator* replica = new SCA_RandomActuator(*this);
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// replication just copy the m_base pointer => common random generator
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replica->ProcessReplica();
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return replica;
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}
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void SCA_RandomActuator::ProcessReplica()
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{
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SCA_IActuator::ProcessReplica();
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// increment reference count so that we can release the generator at the end
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m_base->AddRef();
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}
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bool SCA_RandomActuator::Update()
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{
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//bool result = false; /*unused*/
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bool bNegativeEvent = IsNegativeEvent();
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RemoveAllEvents();
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CValue *tmpval = NULL;
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if (bNegativeEvent)
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return false; // do nothing on negative events
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switch (m_distribution) {
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case KX_RANDOMACT_BOOL_CONST: {
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/* un petit peu filthy */
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bool res = !(m_parameter1 < 0.5);
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tmpval = new CBoolValue(res);
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}
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break;
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case KX_RANDOMACT_BOOL_UNIFORM: {
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/* flip a coin */
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bool res;
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if (m_counter > 31) {
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m_previous = m_base->Draw();
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res = ((m_previous & 0x1) == 0);
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m_counter = 1;
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} else {
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res = (((m_previous >> m_counter) & 0x1) == 0);
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m_counter++;
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}
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tmpval = new CBoolValue(res);
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}
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break;
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case KX_RANDOMACT_BOOL_BERNOUILLI: {
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/* 'percentage' */
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bool res;
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res = (m_base->DrawFloat() < m_parameter1);
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tmpval = new CBoolValue(res);
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}
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break;
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case KX_RANDOMACT_INT_CONST: {
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/* constant */
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tmpval = new CIntValue((int) floor(m_parameter1));
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}
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break;
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case KX_RANDOMACT_INT_UNIFORM: {
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/* uniform (toss a die) */
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int res;
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/* The [0, 1] interval is projected onto the [min, max+1] domain, */
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/* and then rounded. */
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res = (int) floor( ((m_parameter2 - m_parameter1 + 1) * m_base->DrawFloat())
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+ m_parameter1);
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tmpval = new CIntValue(res);
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}
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break;
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case KX_RANDOMACT_INT_POISSON: {
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/* poisson (queues) */
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/* If x_1, x_2, ... is a sequence of random numbers with uniform */
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/* distribution between zero and one, k is the first integer for */
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/* which the product x_1*x_2*...*x_k < exp(-\lamba). */
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float a, b;
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int res = 0;
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/* The - sign is important here! The number to test for, a, must be */
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/* between 0 and 1. */
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a = exp(-m_parameter1);
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/* a quickly reaches 0.... so we guard explicitly for that. */
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if (a < FLT_MIN) a = FLT_MIN;
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b = m_base->DrawFloat();
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while (b >= a) {
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b = b * m_base->DrawFloat();
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res++;
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};
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tmpval = new CIntValue(res);
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}
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break;
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case KX_RANDOMACT_FLOAT_CONST: {
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/* constant */
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tmpval = new CFloatValue(m_parameter1);
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}
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break;
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case KX_RANDOMACT_FLOAT_UNIFORM: {
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float res = ((m_parameter2 - m_parameter1) * m_base->DrawFloat())
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+ m_parameter1;
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tmpval = new CFloatValue(res);
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}
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break;
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case KX_RANDOMACT_FLOAT_NORMAL: {
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/* normal (big numbers): para1 = mean, para2 = std dev */
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/*
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070301 - nzc - Changed the termination condition. I think I
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made a small mistake here, but it only affects distro's where
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the seed equals 0. In that case, the algorithm locks. Let's
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just guard that case separately.
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*/
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float x = 0.0, y = 0.0, s = 0.0, t = 0.0;
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if (m_base->GetSeed() == 0) {
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/*
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070301 - nzc
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Just taking the mean here seems reasonable.
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*/
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tmpval = new CFloatValue(m_parameter1);
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} else {
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/*
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070301 - nzc
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Now, with seed != 0, we will most assuredly get some
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sensible values. The termination condition states two
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things:
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1. s >= 0 is not allowed: to prevent the distro from
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getting a bias towards high values. This is a small
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correction, really, and might also be left out.
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2. s == 0 is not allowed: to prevent a division by zero
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when renormalising the drawn value to the desired
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distribution shape. As a side effect, the distro will
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never yield the exact mean.
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I am not sure whether this is consistent, since the error
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cause by #2 is of the same magnitude as the one
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prevented by #1. The error introduced into the SD will be
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improved, though. By how much? Hard to say... If you like
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the maths, feel free to analyse. Be aware that this is
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one of the really old standard algorithms. I think the
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original came in Fortran, was translated to Pascal, and
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then someone came up with the C code. My guess it that
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this will be quite sufficient here.
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*/
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do
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{
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x = 2.0 * m_base->DrawFloat() - 1.0;
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y = 2.0 * m_base->DrawFloat() - 1.0;
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s = x*x + y*y;
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} while ( (s >= 1.0) || (s == 0.0) );
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t = x * sqrt( (-2.0 * log(s)) / s);
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tmpval = new CFloatValue(m_parameter1 + m_parameter2 * t);
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}
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}
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break;
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case KX_RANDOMACT_FLOAT_NEGATIVE_EXPONENTIAL: {
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/* 1st order fall-off. I am very partial to using the half-life as */
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/* controlling parameter. Using the 'normal' exponent is not very */
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/* intuitive... */
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/* tmpval = new CFloatValue( (1.0 / m_parameter1) */
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tmpval = new CFloatValue( (m_parameter1)
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* (-log(1.0 - m_base->DrawFloat())) );
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}
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break;
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default:
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{
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/* unknown distribution... */
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static bool randomWarning = false;
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if (!randomWarning) {
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randomWarning = true;
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std::cout << "RandomActuator '" << GetName() << "' has an unknown distribution." << std::endl;
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}
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return false;
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}
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}
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/* Round up: assign it */
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CValue *prop = GetParent()->GetProperty(m_propname);
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if (prop) {
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prop->SetValue(tmpval);
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}
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tmpval->Release();
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return false;
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}
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void SCA_RandomActuator::enforceConstraints() {
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/* The constraints that are checked here are the ones fundamental to */
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/* the various distributions. Limitations of the algorithms are checked */
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/* elsewhere (or they should be... ). */
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switch (m_distribution) {
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case KX_RANDOMACT_BOOL_CONST:
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case KX_RANDOMACT_BOOL_UNIFORM:
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case KX_RANDOMACT_INT_CONST:
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case KX_RANDOMACT_INT_UNIFORM:
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case KX_RANDOMACT_FLOAT_UNIFORM:
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case KX_RANDOMACT_FLOAT_CONST:
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; /* Nothing to be done here. We allow uniform distro's to have */
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/* 'funny' domains, i.e. max < min. This does not give problems. */
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break;
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case KX_RANDOMACT_BOOL_BERNOUILLI:
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/* clamp to [0, 1] */
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if (m_parameter1 < 0.0) {
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m_parameter1 = 0.0;
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} else if (m_parameter1 > 1.0) {
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m_parameter1 = 1.0;
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}
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break;
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case KX_RANDOMACT_INT_POISSON:
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/* non-negative */
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if (m_parameter1 < 0.0) {
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m_parameter1 = 0.0;
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}
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break;
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case KX_RANDOMACT_FLOAT_NORMAL:
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/* standard dev. is non-negative */
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if (m_parameter2 < 0.0) {
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m_parameter2 = 0.0;
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}
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break;
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case KX_RANDOMACT_FLOAT_NEGATIVE_EXPONENTIAL:
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/* halflife must be non-negative */
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if (m_parameter1 < 0.0) {
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m_parameter1 = 0.0;
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}
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break;
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default:
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; /* unknown distribution... */
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}
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}
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#ifdef WITH_PYTHON
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/* ------------------------------------------------------------------------- */
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/* Python functions */
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/* ------------------------------------------------------------------------- */
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/* Integration hooks ------------------------------------------------------- */
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PyTypeObject SCA_RandomActuator::Type = {
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PyVarObject_HEAD_INIT(NULL, 0)
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"SCA_RandomActuator",
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sizeof(PyObjectPlus_Proxy),
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0,
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py_base_dealloc,
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0,
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0,
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0,
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0,
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py_base_repr,
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0,0,0,0,0,0,0,0,0,
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Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,
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0,0,0,0,0,0,0,
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Methods,
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0,
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0,
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&SCA_IActuator::Type,
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0,0,0,0,0,0,
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py_base_new
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};
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PyMethodDef SCA_RandomActuator::Methods[] = {
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KX_PYMETHODTABLE(SCA_RandomActuator, setBoolConst),
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KX_PYMETHODTABLE_NOARGS(SCA_RandomActuator, setBoolUniform),
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KX_PYMETHODTABLE(SCA_RandomActuator, setBoolBernouilli),
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KX_PYMETHODTABLE(SCA_RandomActuator, setIntConst),
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KX_PYMETHODTABLE(SCA_RandomActuator, setIntUniform),
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KX_PYMETHODTABLE(SCA_RandomActuator, setIntPoisson),
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KX_PYMETHODTABLE(SCA_RandomActuator, setFloatConst),
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KX_PYMETHODTABLE(SCA_RandomActuator, setFloatUniform),
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KX_PYMETHODTABLE(SCA_RandomActuator, setFloatNormal),
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KX_PYMETHODTABLE(SCA_RandomActuator, setFloatNegativeExponential),
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{NULL,NULL} //Sentinel
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};
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PyAttributeDef SCA_RandomActuator::Attributes[] = {
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KX_PYATTRIBUTE_FLOAT_RO("para1",SCA_RandomActuator,m_parameter1),
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KX_PYATTRIBUTE_FLOAT_RO("para2",SCA_RandomActuator,m_parameter2),
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KX_PYATTRIBUTE_ENUM_RO("distribution",SCA_RandomActuator,m_distribution),
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KX_PYATTRIBUTE_STRING_RW_CHECK("propName",0,100,false,SCA_RandomActuator,m_propname,CheckProperty),
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KX_PYATTRIBUTE_RW_FUNCTION("seed",SCA_RandomActuator,pyattr_get_seed,pyattr_set_seed),
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{ NULL } //Sentinel
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};
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PyObject* SCA_RandomActuator::pyattr_get_seed(void *self, const struct KX_PYATTRIBUTE_DEF *attrdef)
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{
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SCA_RandomActuator* act = static_cast<SCA_RandomActuator*>(self);
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return PyLong_FromSsize_t(act->m_base->GetSeed());
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}
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int SCA_RandomActuator::pyattr_set_seed(void *self, const struct KX_PYATTRIBUTE_DEF *attrdef, PyObject *value)
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{
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SCA_RandomActuator* act = static_cast<SCA_RandomActuator*>(self);
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if (PyLong_Check(value)) {
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int ival = PyLong_AsSsize_t(value);
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act->m_base->SetSeed(ival);
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return PY_SET_ATTR_SUCCESS;
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} else {
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PyErr_SetString(PyExc_TypeError, "actuator.seed = int: Random Actuator, expected an integer");
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return PY_SET_ATTR_FAIL;
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}
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}
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/* 11. setBoolConst */
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KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setBoolConst,
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"setBoolConst(value)\n"
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"\t- value: 0 or 1\n"
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"\tSet this generator to produce a constant boolean value.\n")
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{
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int paraArg;
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if(!PyArg_ParseTuple(args, "i:setBoolConst", ¶Arg)) {
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return NULL;
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}
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m_distribution = KX_RANDOMACT_BOOL_CONST;
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m_parameter1 = (paraArg) ? 1.0 : 0.0;
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Py_RETURN_NONE;
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}
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/* 12. setBoolUniform, */
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KX_PYMETHODDEF_DOC_NOARGS(SCA_RandomActuator, setBoolUniform,
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"setBoolUniform()\n"
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"\tSet this generator to produce true and false, each with 50%% chance of occuring\n")
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{
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/* no args */
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m_distribution = KX_RANDOMACT_BOOL_UNIFORM;
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enforceConstraints();
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Py_RETURN_NONE;
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}
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/* 13. setBoolBernouilli, */
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KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setBoolBernouilli,
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"setBoolBernouilli(value)\n"
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"\t- value: a float between 0 and 1\n"
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"\tReturn false value * 100%% of the time.\n")
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{
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float paraArg;
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if(!PyArg_ParseTuple(args, "f:setBoolBernouilli", ¶Arg)) {
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return NULL;
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}
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m_distribution = KX_RANDOMACT_BOOL_BERNOUILLI;
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m_parameter1 = paraArg;
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enforceConstraints();
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Py_RETURN_NONE;
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}
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/* 14. setIntConst,*/
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KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setIntConst,
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"setIntConst(value)\n"
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"\t- value: integer\n"
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"\tAlways return value\n")
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{
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int paraArg;
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if(!PyArg_ParseTuple(args, "i:setIntConst", ¶Arg)) {
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return NULL;
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}
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m_distribution = KX_RANDOMACT_INT_CONST;
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m_parameter1 = paraArg;
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enforceConstraints();
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Py_RETURN_NONE;
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}
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/* 15. setIntUniform,*/
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KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setIntUniform,
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"setIntUniform(lower_bound, upper_bound)\n"
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"\t- lower_bound: integer\n"
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"\t- upper_bound: integer\n"
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"\tReturn a random integer between lower_bound and\n"
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"\tupper_bound. The boundaries are included.\n")
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{
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int paraArg1, paraArg2;
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if(!PyArg_ParseTuple(args, "ii:setIntUniform", ¶Arg1, ¶Arg2)) {
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return NULL;
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}
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m_distribution = KX_RANDOMACT_INT_UNIFORM;
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m_parameter1 = paraArg1;
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m_parameter2 = paraArg2;
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enforceConstraints();
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Py_RETURN_NONE;
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}
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/* 16. setIntPoisson, */
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KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setIntPoisson,
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"setIntPoisson(value)\n"
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"\t- value: float\n"
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"\tReturn a Poisson-distributed number. This performs a series\n"
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"\tof Bernouilli tests with parameter value. It returns the\n"
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"\tnumber of tries needed to achieve succes.\n")
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{
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float paraArg;
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if(!PyArg_ParseTuple(args, "f:setIntPoisson", ¶Arg)) {
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return NULL;
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}
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m_distribution = KX_RANDOMACT_INT_POISSON;
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m_parameter1 = paraArg;
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enforceConstraints();
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Py_RETURN_NONE;
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}
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/* 17. setFloatConst */
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KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setFloatConst,
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"setFloatConst(value)\n"
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"\t- value: float\n"
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"\tAlways return value\n")
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{
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float paraArg;
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if(!PyArg_ParseTuple(args, "f:setFloatConst", ¶Arg)) {
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return NULL;
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}
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m_distribution = KX_RANDOMACT_FLOAT_CONST;
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m_parameter1 = paraArg;
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enforceConstraints();
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Py_RETURN_NONE;
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}
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/* 18. setFloatUniform, */
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KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setFloatUniform,
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"setFloatUniform(lower_bound, upper_bound)\n"
|
|
"\t- lower_bound: float\n"
|
|
"\t- upper_bound: float\n"
|
|
"\tReturn a random integer between lower_bound and\n"
|
|
"\tupper_bound.\n")
|
|
{
|
|
float paraArg1, paraArg2;
|
|
if(!PyArg_ParseTuple(args, "ff:setFloatUniform", ¶Arg1, ¶Arg2)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_FLOAT_UNIFORM;
|
|
m_parameter1 = paraArg1;
|
|
m_parameter2 = paraArg2;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 19. setFloatNormal, */
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setFloatNormal,
|
|
"setFloatNormal(mean, standard_deviation)\n"
|
|
"\t- mean: float\n"
|
|
"\t- standard_deviation: float\n"
|
|
"\tReturn normal-distributed numbers. The average is mean, and the\n"
|
|
"\tdeviation from the mean is characterized by standard_deviation.\n")
|
|
{
|
|
float paraArg1, paraArg2;
|
|
if(!PyArg_ParseTuple(args, "ff:setFloatNormal", ¶Arg1, ¶Arg2)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_FLOAT_NORMAL;
|
|
m_parameter1 = paraArg1;
|
|
m_parameter2 = paraArg2;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 20. setFloatNegativeExponential, */
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setFloatNegativeExponential,
|
|
"setFloatNegativeExponential(half_life)\n"
|
|
"\t- half_life: float\n"
|
|
"\tReturn negative-exponentially distributed numbers. The half-life 'time'\n"
|
|
"\tis characterized by half_life.\n")
|
|
{
|
|
float paraArg;
|
|
if(!PyArg_ParseTuple(args, "f:setFloatNegativeExponential", ¶Arg)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_FLOAT_NEGATIVE_EXPONENTIAL;
|
|
m_parameter1 = paraArg;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
|
|
#endif
|
|
|
|
/* eof */
|