5553d2c014
PyObjectPlus::ProcessReplica() is now called when any of its subclasses are replicated. This is important because PyObjectPlus::ProcessReplica() NULL's the 'm_proxy' python pointer I added recently. Without this a replicated subclass of PyObjectPlus could have an invalid pointer (crashing the BGE). This change also means CValue::AddDataToReplica() can be moved into CValue::ProcessReplica() since ProcessReplica is always called.
671 lines
20 KiB
C++
671 lines
20 KiB
C++
/**
|
|
* Set random/camera stuff
|
|
*
|
|
* $Id$
|
|
*
|
|
* ***** BEGIN GPL LICENSE BLOCK *****
|
|
*
|
|
* This program is free software; you can redistribute it and/or
|
|
* modify it under the terms of the GNU General Public License
|
|
* as published by the Free Software Foundation; either version 2
|
|
* of the License, or (at your option) any later version.
|
|
*
|
|
* 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., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
|
|
*
|
|
* The Original Code is Copyright (C) 2001-2002 by NaN Holding BV.
|
|
* All rights reserved.
|
|
*
|
|
* The Original Code is: all of this file.
|
|
*
|
|
* Contributor(s): none yet.
|
|
*
|
|
* ***** END GPL LICENSE BLOCK *****
|
|
*/
|
|
|
|
#include "BoolValue.h"
|
|
#include "IntValue.h"
|
|
#include "FloatValue.h"
|
|
#include "SCA_IActuator.h"
|
|
#include "SCA_RandomActuator.h"
|
|
#include "math.h"
|
|
#include "MT_Transform.h"
|
|
|
|
#ifdef HAVE_CONFIG_H
|
|
#include <config.h>
|
|
#endif
|
|
|
|
/* ------------------------------------------------------------------------- */
|
|
/* Native functions */
|
|
/* ------------------------------------------------------------------------- */
|
|
|
|
SCA_RandomActuator::SCA_RandomActuator(SCA_IObject *gameobj,
|
|
long seed,
|
|
SCA_RandomActuator::KX_RANDOMACT_MODE mode,
|
|
float para1,
|
|
float para2,
|
|
const STR_String &propName,
|
|
PyTypeObject* T)
|
|
: SCA_IActuator(gameobj, T),
|
|
m_propname(propName),
|
|
m_parameter1(para1),
|
|
m_parameter2(para2),
|
|
m_distribution(mode)
|
|
{
|
|
// m_base is never deleted, probably a memory leak!
|
|
m_base = new SCA_RandomNumberGenerator(seed);
|
|
m_counter = 0;
|
|
enforceConstraints();
|
|
}
|
|
|
|
|
|
|
|
SCA_RandomActuator::~SCA_RandomActuator()
|
|
{
|
|
/* intentionally empty */
|
|
}
|
|
|
|
|
|
|
|
CValue* SCA_RandomActuator::GetReplica()
|
|
{
|
|
SCA_RandomActuator* replica = new SCA_RandomActuator(*this);
|
|
// replication just copy the m_base pointer => common random generator
|
|
replica->ProcessReplica();
|
|
return replica;
|
|
}
|
|
|
|
|
|
|
|
bool SCA_RandomActuator::Update()
|
|
{
|
|
//bool result = false; /*unused*/
|
|
bool bNegativeEvent = IsNegativeEvent();
|
|
|
|
RemoveAllEvents();
|
|
|
|
|
|
CValue *tmpval = NULL;
|
|
|
|
if (bNegativeEvent)
|
|
return false; // do nothing on negative events
|
|
|
|
switch (m_distribution) {
|
|
case KX_RANDOMACT_BOOL_CONST: {
|
|
/* un petit peu filthy */
|
|
bool res = !(m_parameter1 < 0.5);
|
|
tmpval = new CBoolValue(res);
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_BOOL_UNIFORM: {
|
|
/* flip a coin */
|
|
bool res;
|
|
if (m_counter > 31) {
|
|
m_previous = m_base->Draw();
|
|
res = ((m_previous & 0x1) == 0);
|
|
m_counter = 1;
|
|
} else {
|
|
res = (((m_previous >> m_counter) & 0x1) == 0);
|
|
m_counter++;
|
|
}
|
|
tmpval = new CBoolValue(res);
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_BOOL_BERNOUILLI: {
|
|
/* 'percentage' */
|
|
bool res;
|
|
res = (m_base->DrawFloat() < m_parameter1);
|
|
tmpval = new CBoolValue(res);
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_INT_CONST: {
|
|
/* constant */
|
|
tmpval = new CIntValue((int) floor(m_parameter1));
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_INT_UNIFORM: {
|
|
/* uniform (toss a die) */
|
|
int res;
|
|
/* The [0, 1] interval is projected onto the [min, max+1] domain, */
|
|
/* and then rounded. */
|
|
res = (int) floor( ((m_parameter2 - m_parameter1 + 1) * m_base->DrawFloat())
|
|
+ m_parameter1);
|
|
tmpval = new CIntValue(res);
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_INT_POISSON: {
|
|
/* poisson (queues) */
|
|
/* If x_1, x_2, ... is a sequence of random numbers with uniform */
|
|
/* distribution between zero and one, k is the first integer for */
|
|
/* which the product x_1*x_2*...*x_k < exp(-\lamba). */
|
|
float a = 0.0, b = 0.0;
|
|
int res = 0;
|
|
/* The - sign is important here! The number to test for, a, must be */
|
|
/* between 0 and 1. */
|
|
a = exp(-m_parameter1);
|
|
/* a quickly reaches 0.... so we guard explicitly for that. */
|
|
if (a < FLT_MIN) a = FLT_MIN;
|
|
b = m_base->DrawFloat();
|
|
while (b >= a) {
|
|
b = b * m_base->DrawFloat();
|
|
res++;
|
|
};
|
|
tmpval = new CIntValue(res);
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_FLOAT_CONST: {
|
|
/* constant */
|
|
tmpval = new CFloatValue(m_parameter1);
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_FLOAT_UNIFORM: {
|
|
float res = ((m_parameter2 - m_parameter1) * m_base->DrawFloat())
|
|
+ m_parameter1;
|
|
tmpval = new CFloatValue(res);
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_FLOAT_NORMAL: {
|
|
/* normal (big numbers): para1 = mean, para2 = std dev */
|
|
|
|
/*
|
|
|
|
070301 - nzc - Changed the termination condition. I think I
|
|
made a small mistake here, but it only affects distro's where
|
|
the seed equals 0. In that case, the algorithm locks. Let's
|
|
just guard that case separately.
|
|
|
|
*/
|
|
|
|
float x = 0.0, y = 0.0, s = 0.0, t = 0.0;
|
|
if (m_base->GetSeed() == 0) {
|
|
/*
|
|
|
|
070301 - nzc
|
|
Just taking the mean here seems reasonable.
|
|
|
|
*/
|
|
tmpval = new CFloatValue(m_parameter1);
|
|
} else {
|
|
/*
|
|
|
|
070301 - nzc
|
|
Now, with seed != 0, we will most assuredly get some
|
|
sensible values. The termination condition states two
|
|
things:
|
|
1. s >= 0 is not allowed: to prevent the distro from
|
|
getting a bias towards high values. This is a small
|
|
correction, really, and might also be left out.
|
|
2. s == 0 is not allowed: to prevent a division by zero
|
|
when renormalising the drawn value to the desired
|
|
distribution shape. As a side effect, the distro will
|
|
never yield the exact mean.
|
|
I am not sure whether this is consistent, since the error
|
|
cause by #2 is of the same magnitude as the one
|
|
prevented by #1. The error introduced into the SD will be
|
|
improved, though. By how much? Hard to say... If you like
|
|
the maths, feel free to analyse. Be aware that this is
|
|
one of the really old standard algorithms. I think the
|
|
original came in Fortran, was translated to Pascal, and
|
|
then someone came up with the C code. My guess it that
|
|
this will be quite sufficient here.
|
|
|
|
*/
|
|
do
|
|
{
|
|
x = 2.0 * m_base->DrawFloat() - 1.0;
|
|
y = 2.0 * m_base->DrawFloat() - 1.0;
|
|
s = x*x + y*y;
|
|
} while ( (s >= 1.0) || (s == 0.0) );
|
|
t = x * sqrt( (-2.0 * log(s)) / s);
|
|
tmpval = new CFloatValue(m_parameter1 + m_parameter2 * t);
|
|
}
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_FLOAT_NEGATIVE_EXPONENTIAL: {
|
|
/* 1st order fall-off. I am very partial to using the half-life as */
|
|
/* controlling parameter. Using the 'normal' exponent is not very */
|
|
/* intuitive... */
|
|
/* tmpval = new CFloatValue( (1.0 / m_parameter1) */
|
|
tmpval = new CFloatValue( (m_parameter1)
|
|
* (-log(1.0 - m_base->DrawFloat())) );
|
|
|
|
}
|
|
break;
|
|
default:
|
|
{
|
|
/* unknown distribution... */
|
|
static bool randomWarning = false;
|
|
if (!randomWarning) {
|
|
randomWarning = true;
|
|
std::cout << "RandomActuator '" << GetName() << "' has an unknown distribution." << std::endl;
|
|
}
|
|
return false;
|
|
}
|
|
}
|
|
|
|
/* Round up: assign it */
|
|
CValue *prop = GetParent()->GetProperty(m_propname);
|
|
if (prop) {
|
|
prop->SetValue(tmpval);
|
|
}
|
|
tmpval->Release();
|
|
|
|
return false;
|
|
}
|
|
|
|
void SCA_RandomActuator::enforceConstraints() {
|
|
/* The constraints that are checked here are the ones fundamental to */
|
|
/* the various distributions. Limitations of the algorithms are checked */
|
|
/* elsewhere (or they should be... ). */
|
|
switch (m_distribution) {
|
|
case KX_RANDOMACT_BOOL_CONST:
|
|
case KX_RANDOMACT_BOOL_UNIFORM:
|
|
case KX_RANDOMACT_INT_CONST:
|
|
case KX_RANDOMACT_INT_UNIFORM:
|
|
case KX_RANDOMACT_FLOAT_UNIFORM:
|
|
case KX_RANDOMACT_FLOAT_CONST:
|
|
; /* Nothing to be done here. We allow uniform distro's to have */
|
|
/* 'funny' domains, i.e. max < min. This does not give problems. */
|
|
break;
|
|
case KX_RANDOMACT_BOOL_BERNOUILLI:
|
|
/* clamp to [0, 1] */
|
|
if (m_parameter1 < 0.0) {
|
|
m_parameter1 = 0.0;
|
|
} else if (m_parameter1 > 1.0) {
|
|
m_parameter1 = 1.0;
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_INT_POISSON:
|
|
/* non-negative */
|
|
if (m_parameter1 < 0.0) {
|
|
m_parameter1 = 0.0;
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_FLOAT_NORMAL:
|
|
/* standard dev. is non-negative */
|
|
if (m_parameter2 < 0.0) {
|
|
m_parameter2 = 0.0;
|
|
}
|
|
break;
|
|
case KX_RANDOMACT_FLOAT_NEGATIVE_EXPONENTIAL:
|
|
/* halflife must be non-negative */
|
|
if (m_parameter1 < 0.0) {
|
|
m_parameter1 = 0.0;
|
|
}
|
|
break;
|
|
default:
|
|
; /* unknown distribution... */
|
|
}
|
|
}
|
|
|
|
/* ------------------------------------------------------------------------- */
|
|
/* Python functions */
|
|
/* ------------------------------------------------------------------------- */
|
|
|
|
/* Integration hooks ------------------------------------------------------- */
|
|
PyTypeObject SCA_RandomActuator::Type = {
|
|
PyObject_HEAD_INIT(NULL)
|
|
0,
|
|
"SCA_RandomActuator",
|
|
sizeof(PyObjectPlus_Proxy),
|
|
0,
|
|
py_base_dealloc,
|
|
0,
|
|
0,
|
|
0,
|
|
0,
|
|
py_base_repr,
|
|
0,0,0,0,0,0,
|
|
py_base_getattro,
|
|
py_base_setattro,
|
|
0,0,0,0,0,0,0,0,0,
|
|
Methods
|
|
};
|
|
|
|
PyParentObject SCA_RandomActuator::Parents[] = {
|
|
&SCA_RandomActuator::Type,
|
|
&SCA_IActuator::Type,
|
|
&SCA_ILogicBrick::Type,
|
|
&CValue::Type,
|
|
NULL
|
|
};
|
|
|
|
PyMethodDef SCA_RandomActuator::Methods[] = {
|
|
//Deprecated functions ------>
|
|
{"setSeed", (PyCFunction) SCA_RandomActuator::sPySetSeed, METH_VARARGS, (PY_METHODCHAR)SetSeed_doc},
|
|
{"getSeed", (PyCFunction) SCA_RandomActuator::sPyGetSeed, METH_NOARGS, (PY_METHODCHAR)GetSeed_doc},
|
|
{"getPara1", (PyCFunction) SCA_RandomActuator::sPyGetPara1, METH_NOARGS, (PY_METHODCHAR)GetPara1_doc},
|
|
{"getPara2", (PyCFunction) SCA_RandomActuator::sPyGetPara2, METH_NOARGS, (PY_METHODCHAR)GetPara2_doc},
|
|
{"getDistribution", (PyCFunction) SCA_RandomActuator::sPyGetDistribution, METH_NOARGS, (PY_METHODCHAR)GetDistribution_doc},
|
|
{"setProperty", (PyCFunction) SCA_RandomActuator::sPySetProperty, METH_VARARGS, (PY_METHODCHAR)SetProperty_doc},
|
|
{"getProperty", (PyCFunction) SCA_RandomActuator::sPyGetProperty, METH_NOARGS, (PY_METHODCHAR)GetProperty_doc},
|
|
//<----- Deprecated
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setBoolConst),
|
|
KX_PYMETHODTABLE_NOARGS(SCA_RandomActuator, setBoolUniform),
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setBoolBernouilli),
|
|
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setIntConst),
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setIntUniform),
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setIntPoisson),
|
|
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setFloatConst),
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setFloatUniform),
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setFloatNormal),
|
|
KX_PYMETHODTABLE(SCA_RandomActuator, setFloatNegativeExponential),
|
|
{NULL,NULL} //Sentinel
|
|
};
|
|
|
|
PyAttributeDef SCA_RandomActuator::Attributes[] = {
|
|
KX_PYATTRIBUTE_FLOAT_RO("para1",SCA_RandomActuator,m_parameter1),
|
|
KX_PYATTRIBUTE_FLOAT_RO("para2",SCA_RandomActuator,m_parameter2),
|
|
KX_PYATTRIBUTE_ENUM_RO("distribution",SCA_RandomActuator,m_distribution),
|
|
KX_PYATTRIBUTE_STRING_RW_CHECK("property",0,100,false,SCA_RandomActuator,m_propname,CheckProperty),
|
|
KX_PYATTRIBUTE_RW_FUNCTION("seed",SCA_RandomActuator,pyattr_get_seed,pyattr_set_seed),
|
|
{ NULL } //Sentinel
|
|
};
|
|
|
|
PyObject* SCA_RandomActuator::pyattr_get_seed(void *self, const struct KX_PYATTRIBUTE_DEF *attrdef)
|
|
{
|
|
SCA_RandomActuator* act = static_cast<SCA_RandomActuator*>(self);
|
|
return PyInt_FromLong(act->m_base->GetSeed());
|
|
}
|
|
|
|
int SCA_RandomActuator::pyattr_set_seed(void *self, const struct KX_PYATTRIBUTE_DEF *attrdef, PyObject *value)
|
|
{
|
|
SCA_RandomActuator* act = static_cast<SCA_RandomActuator*>(self);
|
|
if (PyInt_Check(value)) {
|
|
int ival = PyInt_AsLong(value);
|
|
act->m_base->SetSeed(ival);
|
|
return 0;
|
|
} else {
|
|
PyErr_SetString(PyExc_TypeError, "actuator.seed = int: Random Actuator, expected an integer");
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
PyObject* SCA_RandomActuator::py_getattro(PyObject *attr) {
|
|
py_getattro_up(SCA_IActuator);
|
|
}
|
|
|
|
PyObject* SCA_RandomActuator::py_getattro_dict() {
|
|
py_getattro_dict_up(SCA_IActuator);
|
|
}
|
|
|
|
int SCA_RandomActuator::py_setattro(PyObject *attr, PyObject *value)
|
|
{
|
|
py_setattro_up(SCA_IActuator);
|
|
}
|
|
|
|
/* 1. setSeed */
|
|
const char SCA_RandomActuator::SetSeed_doc[] =
|
|
"setSeed(seed)\n"
|
|
"\t- seed: integer\n"
|
|
"\tSet the initial seed of the generator. Equal seeds produce\n"
|
|
"\tequal series. If the seed is 0, the generator will produce\n"
|
|
"\tthe same value on every call.\n";
|
|
PyObject* SCA_RandomActuator::PySetSeed(PyObject* args) {
|
|
ShowDeprecationWarning("setSeed()", "the seed property");
|
|
long seedArg;
|
|
if(!PyArg_ParseTuple(args, "i:setSeed", &seedArg)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_base->SetSeed(seedArg);
|
|
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 2. getSeed */
|
|
const char SCA_RandomActuator::GetSeed_doc[] =
|
|
"getSeed()\n"
|
|
"\tReturns the initial seed of the generator. Equal seeds produce\n"
|
|
"\tequal series.\n";
|
|
PyObject* SCA_RandomActuator::PyGetSeed()
|
|
{
|
|
ShowDeprecationWarning("getSeed()", "the seed property");
|
|
return PyInt_FromLong(m_base->GetSeed());
|
|
}
|
|
|
|
/* 4. getPara1 */
|
|
const char SCA_RandomActuator::GetPara1_doc[] =
|
|
"getPara1()\n"
|
|
"\tReturns the first parameter of the active distribution. Refer\n"
|
|
"\tto the documentation of the generator types for the meaning\n"
|
|
"\tof this value.";
|
|
PyObject* SCA_RandomActuator::PyGetPara1()
|
|
{
|
|
ShowDeprecationWarning("getPara1()", "the para1 property");
|
|
return PyFloat_FromDouble(m_parameter1);
|
|
}
|
|
|
|
/* 6. getPara2 */
|
|
const char SCA_RandomActuator::GetPara2_doc[] =
|
|
"getPara2()\n"
|
|
"\tReturns the first parameter of the active distribution. Refer\n"
|
|
"\tto the documentation of the generator types for the meaning\n"
|
|
"\tof this value.";
|
|
PyObject* SCA_RandomActuator::PyGetPara2()
|
|
{
|
|
ShowDeprecationWarning("getPara2()", "the para2 property");
|
|
return PyFloat_FromDouble(m_parameter2);
|
|
}
|
|
|
|
/* 8. getDistribution */
|
|
const char SCA_RandomActuator::GetDistribution_doc[] =
|
|
"getDistribution()\n"
|
|
"\tReturns the type of the active distribution.\n";
|
|
PyObject* SCA_RandomActuator::PyGetDistribution()
|
|
{
|
|
ShowDeprecationWarning("getDistribution()", "the distribution property");
|
|
return PyInt_FromLong(m_distribution);
|
|
}
|
|
|
|
/* 9. setProperty */
|
|
const char SCA_RandomActuator::SetProperty_doc[] =
|
|
"setProperty(name)\n"
|
|
"\t- name: string\n"
|
|
"\tSet the property to which the random value is assigned. If the \n"
|
|
"\tgenerator and property types do not match, the assignment is ignored.\n";
|
|
PyObject* SCA_RandomActuator::PySetProperty(PyObject* args) {
|
|
ShowDeprecationWarning("setProperty()", "the 'property' property");
|
|
char *nameArg;
|
|
if (!PyArg_ParseTuple(args, "s:setProperty", &nameArg)) {
|
|
return NULL;
|
|
}
|
|
|
|
CValue* prop = GetParent()->FindIdentifier(nameArg);
|
|
|
|
if (!prop->IsError()) {
|
|
m_propname = nameArg;
|
|
} else {
|
|
; /* not found ... */
|
|
}
|
|
prop->Release();
|
|
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 10. getProperty */
|
|
const char SCA_RandomActuator::GetProperty_doc[] =
|
|
"getProperty(name)\n"
|
|
"\tReturn the property to which the random value is assigned. If the \n"
|
|
"\tgenerator and property types do not match, the assignment is ignored.\n";
|
|
PyObject* SCA_RandomActuator::PyGetProperty()
|
|
{
|
|
ShowDeprecationWarning("getProperty()", "the 'property' property");
|
|
return PyString_FromString(m_propname);
|
|
}
|
|
|
|
/* 11. setBoolConst */
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setBoolConst,
|
|
"setBoolConst(value)\n"
|
|
"\t- value: 0 or 1\n"
|
|
"\tSet this generator to produce a constant boolean value.\n")
|
|
{
|
|
int paraArg;
|
|
if(!PyArg_ParseTuple(args, "i:setBoolConst", ¶Arg)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_BOOL_CONST;
|
|
m_parameter1 = (paraArg) ? 1.0 : 0.0;
|
|
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 12. setBoolUniform, */
|
|
KX_PYMETHODDEF_DOC_NOARGS(SCA_RandomActuator, setBoolUniform,
|
|
"setBoolUniform()\n"
|
|
"\tSet this generator to produce true and false, each with 50%% chance of occuring\n")
|
|
{
|
|
/* no args */
|
|
m_distribution = KX_RANDOMACT_BOOL_UNIFORM;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 13. setBoolBernouilli, */
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setBoolBernouilli,
|
|
"setBoolBernouilli(value)\n"
|
|
"\t- value: a float between 0 and 1\n"
|
|
"\tReturn false value * 100%% of the time.\n")
|
|
{
|
|
float paraArg;
|
|
if(!PyArg_ParseTuple(args, "f:setBoolBernouilli", ¶Arg)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_BOOL_BERNOUILLI;
|
|
m_parameter1 = paraArg;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 14. setIntConst,*/
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setIntConst,
|
|
"setIntConst(value)\n"
|
|
"\t- value: integer\n"
|
|
"\tAlways return value\n")
|
|
{
|
|
int paraArg;
|
|
if(!PyArg_ParseTuple(args, "i:setIntConst", ¶Arg)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_INT_CONST;
|
|
m_parameter1 = paraArg;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 15. setIntUniform,*/
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setIntUniform,
|
|
"setIntUniform(lower_bound, upper_bound)\n"
|
|
"\t- lower_bound: integer\n"
|
|
"\t- upper_bound: integer\n"
|
|
"\tReturn a random integer between lower_bound and\n"
|
|
"\tupper_bound. The boundaries are included.\n")
|
|
{
|
|
int paraArg1, paraArg2;
|
|
if(!PyArg_ParseTuple(args, "ii:setIntUniform", ¶Arg1, ¶Arg2)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_INT_UNIFORM;
|
|
m_parameter1 = paraArg1;
|
|
m_parameter2 = paraArg2;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 16. setIntPoisson, */
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setIntPoisson,
|
|
"setIntPoisson(value)\n"
|
|
"\t- value: float\n"
|
|
"\tReturn a Poisson-distributed number. This performs a series\n"
|
|
"\tof Bernouilli tests with parameter value. It returns the\n"
|
|
"\tnumber of tries needed to achieve succes.\n")
|
|
{
|
|
float paraArg;
|
|
if(!PyArg_ParseTuple(args, "f:setIntPoisson", ¶Arg)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_INT_POISSON;
|
|
m_parameter1 = paraArg;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 17. setFloatConst */
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setFloatConst,
|
|
"setFloatConst(value)\n"
|
|
"\t- value: float\n"
|
|
"\tAlways return value\n")
|
|
{
|
|
float paraArg;
|
|
if(!PyArg_ParseTuple(args, "f:setFloatConst", ¶Arg)) {
|
|
return NULL;
|
|
}
|
|
|
|
m_distribution = KX_RANDOMACT_FLOAT_CONST;
|
|
m_parameter1 = paraArg;
|
|
enforceConstraints();
|
|
Py_RETURN_NONE;
|
|
}
|
|
/* 18. setFloatUniform, */
|
|
KX_PYMETHODDEF_DOC_VARARGS(SCA_RandomActuator, setFloatUniform,
|
|
"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;
|
|
}
|
|
|
|
/* eof */
|