meta command
meta
Command providing access to other commands.
meta alea
genetic
brush
network
meta alea genetic(id)
Returns the reproducible probabilities of crossover and mutation of
genetic id.
meta alea genetic(id)=croi,muta
Changes these probabilities.
Options:
croi,muta,muta0,dmuta: forces the value muta0+randf(dmuta) to muta.
meta alea brush(id)
Returns the bounds of random colors (reproducible) of the brush id.
meta alea brush=r1,g1,b1,r2,g2,b2
Changes these bounds.
Note: the pixels of the brush id
are random colors (reproducible) in the interval [r1,g1,b1] * [r2,g2,b2].
meta alea network(id)
Returns the coefficients a1, a2, a, a0 of adaptive learning of the
network id.
meta alea network(ida1,a2,a,a0
Changes these coefficinets.
Note:
When the error remains above 50% over a images (a1 to a2), the matrix of the network id
is reset randomly, which can change a network trouble initialized.
meta axis
meta axis vol(id)
Returns the meta axis property of volume id.
meta axis vol(id)=v1,si1,si2,r1, v2,s21,s22,r2,...
Changes this property.
Note:
The points of volume id will be outside cylinders axis vertices
si1,si2 of volume vi and radius ri.
meta axis force
meta axis force vol(id)
Returns the meta axis force property of volume id.
meta axis force vol(id)=v1,si1,si2,r1,k1, v2,s21,s22,r2,k2,...
Changes this property.
Note:
The points of volume id will be outside cylinders axis vertices
si1,si2 of volume vi and radius ri by forces coefficient ki.
meta axis vol vol
meta axis vol vol(id)
Returns the meta axis vol property of volume id.
meta axis vol vol(id)=d,v1,v2,...
Changes this property.
Note:
The points of volume id will be outside cylinders axis CG
volumes vi and radius d.
meta axis vol force
meta axis vol force vol(id)
Returns the meta axis vol force property of volume id.
meta axis vol force vol(id)=d,k,v1,v2,...
Changes this property.
Note:
The points of volume id will be outside cylinders axis CG
volumes vi and radius d by forces coefficient k.
meta axis validate
meta axis validate vol(id)
Returns the meta axis validate property of volume id.
meta axis vol force vol(id)=v
Changes this property.
Note:
if v==0 inhibits properties meta axis vol
meta axis vol force of volume id.
meta box vol
meta box vol(id)
Returns the bos of volume num, which is tehe first mta box.
meta(n)box vol(num)
Returns the last box of volume num.
See also:
generate meta box vol.
meta coe network
meta coe(1)network(id)
Returns the parameters of the lower bound of the constant learning of the
neural network id.
meta coe(2)network(id)
Returns the parameters of the luppr bound of the constant learning of the neural network id.
meta coe(1)network(id)=c1_1,c1_2,c1,c1_0, dc1_1,dc1_2,dc1,dc1_0, k,k_min,k_max,k,cpt
Changes these parameters of the of the lower bound
c1 (varying between entre c1_1 and c1_2, with decrement dc1).
meta coe(2)network(id)=c2_1,c2_2,c2,c2_0, dc2_1,dc2_2,dc2,dc2_0
Changes these parameters of the of theupper bound
c2 (varying between c2_1 and c2_2, with decrement dc2).
Variation of ethe learnin constant
The learning constant
of network id will vary between c1 (genening) to c2 (end).
If k and cpt_k present:
k=k_min,k_max,k,cpt
cpt_k=cpt_min,kcpt_max,cpt_k,cpt
c1 and c2 are multiplied by k every cpt_k when
moy=moyenne() < 0.5
The command
interaction displ network error
displays 3 scales allowing to choose c1 between c1_min and c1_max,
c2 between c2_min and c2_max,
and k between k_min and k_max.
meta displ
meta displ
Returns the character allowing to hide the displays.
meta displ("c")
Change this character.
Notes:
1)in interaction mode, when typing the key c,
the menus, the messages and the
texts are not displayed (only volumes are displayed).
2) if c is . (dot) this feature is not active any more.
meta generate
meta generate vol(id)
Returne property meta generate of volume id
Note:
flags initialy set to nul and set to 1 when normals are computed.
meta genetic
meta genetic(id)
Returns the average notes of genetic id if generate genetic adjust.
meta law
meta law brush(id)
Returns the transformed output (law) of the motif captured by the neural brush (of type luminance),
allowing it to be displayed.
meta matrix network
meta matrix(m)
Returns the determinant of the square matrix m size 1, 4 or 9.
meta matrix network(id)
Returns the learning parameters of
neural network id.
meta matrix network(num)=Max_noise,max_noise,d_noise
Changes these parameters.
Notes:
Max_noise: maximum duration resets.
max_noise: current duration resets.
d_noise: noisy coefficient.
During learning, if the error is greater than 0.25, the constants learning are multiplied by 0.75 each max_noise step,
and if, moreover, the error remains greater than 0.25 and if its rate of change is less than 0001,
the matrix and the learning constant are reseted.
meta motif
meta motif brush(id)
Returns the motif captured by a neural brush (of type luminance),
allowing it to be displayed.
meta move
meta move vol(id)
Returns internal property interaction move
of vol id: list of flags = 1 (if attached), 0 (if free).
meta move coe vol(id)
Returns (number of object attached) / (number of objects) in [0,1].
meta normal
meta normal vol(id)=v
With commands vol vertex vol(id)=v and
allows illuminate the volume id by light vi according to the
normal at the corresponding vertex of volume v.
meta NP obj
meta NP obj(id)
Returns the blocks number of properties of the object
identifier id.
meta number
meta number traj(id)
Returns the numbers n1,n2 of images of trajectory id type name.
meta number traj(id)n1,n2
Changes these numbers.
meta name traj(id)
Changes this name.
meta brush
For a neural brush id and behavioural
this property stores the return law of the command motif network(idr) where motif network(idr)
is the network associated with the brush id.
meta rand genetic(id)
Returns the non reproducible probabilities of crossover and mutation of
genetic id.
meta rand genetic(id)=croi,muta
Changes these non reproducible probabilities croi of crossover and muta of mutation of
genetic id.
Options:
croi,muta,muta0,dmuta: forces value muta0+randf(dmuta) as muta.
meta rand brush(id)
Returns the bounds of non reproductible random colors of the brush id.
meta rand brush=r1,g1,b1,r2,g2,b2
Changes thes bounds.
Note: the pixels of the brush id
are non reproducible random colors in the interval [r1,g1,b1] * [r2,g2,b2].
meta name
meta name traj(id)
Returns the name of the files read by the trajectory id type name.
meta name traj(id)
Changes this name.
meta network
meta alea network(id)
Returns the coefficients (a1,a2,a,a0) of random deformations of
neural network id.
meta alea network(id)=a1,a2,a,a0
Changes these coefficients (0,1000,200,200 default).
meta coe network(id)
Returns the coefficients (d1,d2,d,d0) of variation of the
learning constant
of network id.
meta coe network(id)=d1,d2,d,d0
Changes these coefficients (0,25,0,25 default).
meta coe(n) network(id)
n=1: returns the variation coefficients (c1_1,c1_2,c1,c1_0) of the learning constant c1 of network id.
n=2: returns the variation coefficients (c2_1,c2_2,c2,c2_0) of the learning constant c2 of network id.
meta coe(n) network(id)=c1_1,c1_2,c1,c1_0
Changes these coefficiejnts (c1,c2,c,c0=1,10,2,2 default).
meta error network(id)
Returns the error curve dimensioned 32 by default, to change it to for example
m=calloc(100,1);meta error network(1)=m;
The command
displ network error
display this curve.
It is necessary to execute it each training.
meta error network(id)=[1,n]
Changes the dimension n of the error curve of network id.
meta validate network(id)
Returns the validation coefficients (val_cpt,val_err,val_stat,val_nb) of network id.
meta validate network(id)=val_cpt,val_err,val_stat,val_nb
Changes these coefficients.
meta var(n) network(id)
n=1: returns the variation coefficients (dc1_1,dc1_2,dc1,dc1_0) of the learning constant
c1 of network id.
n=2: returns the variation coefficients (dc2_1,dc2_2,dc2,dc2_0) of the learning constant
c2 (dc1,dc2,dc,dc0=0,1,.01,.01 default).
meta var(n) network(id)=dc2,dc2,dc,dc2
Changes these coefficients.
meta put
meta put vol(id1)
Returns property meta put of volume id1.
meta put vol(id)=id2,v1,v2
Changes this property.
Note:
vertices v1 of volume id1 are forced on vertices v2 of volume id2.
meta put
meta put vol(id1)
Returns the property meta put of volume id1.
meta put vol(id)=id2,v1,v2
Changes this property.
Note:
Vertices v1 of volume id1 are forced on sur vertices
v2 of volume id2.
meta roll
meta roll light(id)
Returns the meta roll property of light id.
meta roll light(id)=a,b
Changes this property.
Note:
roll=a/(foc^b) is automatically calculated when foc is modified (a=3, b=3 are good values).
meta sphere
meta sphere force
meta sphere force vol(id)
Returns the meta sphere force property of volume id.
meta sphere force vol(id)=v1,d1,k1, v2,d2,k2,...
Changes this property.
Note:
The points of volume id will be outside spheres center CG of volumes,
vi, radius di by forces coefficients ki.
meta sphere validate
meta sphere validate vol(id)
Returns the meta sphere validate property of volume id.
meta sphere validate vol(id)=v
Changes this property.
Note:
if v==0 inhibits properties meta sphere vol
meta sphere force of volume id.
meta sphere vol(id)
Returns the meta sphere property of volume id.
meta sphere vol(id)=v1,r1, v2,r2,...
Changes this property.
Note:
The points of volume id will be outside of the spheres centered at the CG of
volumes vi and radius ri.
meta spring
meta spring poi vertex(s) vol(id)
Returns the spring poi vertex property of vertices s of volume id.
meta spring poi vertex(s) vol(id)=raid,visc,x1,y1,z1,x2,y2,z2,...
Changes this property.
Note:
In animation mode, if yes dynamic is active, vertices s of volume id
are related to points (x1,y1,z1), (x2,y2,z2), ... by springs of stiffness raid and viscosity raid visc.
meta spring vertex(s)vol(id)
Returns these coefficients.
meta spring vertex(s)vol(id)=r1,v1,f1,s1,r2,v2,f2,s2,...
Changes these coefficients.
Notes:
1) The vertex s of volume id will experience a force due to the biasing spring through the vertices
s1, s2, ... of volumes f1, f2, ... with stiffness r1, r2, .. and viscosities v1, v2, ...
2) if fi=0 then fi=id.
meta transf
meta T0 T vol(id0)
Returns the property meta T0 T of volume id0.
meta(0)T0 T vol(id0)=n,c
Adds such a property.
Note
T0 T are dilx dily dilz rota rotx roty rotz
does: T0 matrix vol(id0)=c * (T matrix vol(n))
yes meta must be active.
Options:
meta(0)T0 T1 T2 vol(id0)=n1,c1:
does: T0 matrix vol(id0)=c1 * (T1 matrix vol(n1)) + c1 * (T1 matrix vol(n1))
meta(0)T0 T follow vol(id0)=c:
does recursvly, for each follower of volume id0:
Ti matrix vol(ni)=c * (T0 matrix vol(id2))
Examples:
meta(0)roty rota rota vol(1)=2,1,3,1;
The roty matrix of volume 1 will be the sum of rota matrix
of volumes 2 and 3.
meta(0)rota rota follow vol(1)=.75;
The rota matrix of each follower of volume id0 will be
.75 * (rota matrix) vol(id0) (usefull, for example, to automaticalt animate
the fingers of an hand).
meta transf sin vol(id)
Returns the property meta transf sin of vol id.
meta transf sin vol(id)=c1,c2, w,t
Changes this property.
Note:
If volume id is type particle with
property vol vol(id)=id2, the vertices of volume
id2 will be transformed by transf(c) (c = sin[c1,c2]) t incremented
every vertex.
Options:
c1,c2, w,t, t,dt: t incremented by dt every instance of volume id.
meta validate network
meta validate axis
meta validate axis limit vol(id)
Returns the property n,x1,y1,z1,x2,y2,z2 of volume id.
meta validate axis limit vol(id)=n,x1,y1,z1,x2,y2,z2
Changes those parameters.
Notes:
meta validate axis limit vol(id)=0: starts the process.
the parameter n returned is the number of images when
axis matrix vol(id) is in the gap [x1,y1,z1,x2,y2,z2].
usefull in some applications of genetic algorithmes as a way to notate an axis limited in an interval.
Options:
line: the parameter n returned is linerally interpolated such as:
0 if ax < ax1 or ax > ax2
1 if ax < ax1 or ax > ax2
by default thresholde: 0 if (ax < ax1 or ax > ax2) else 1.
force
meta validate force
meta validate force axis vol(id)
Returns the property n,an1,an2,x1,y1,z1,x2,y2,z2 of volume id.
meta validate force axis vol(id)=n,an1,an2,x1,y1,z1,x2,y2,z2
Changes those parameters.
Notes:
meta validate force axis limit vol(id)=0: starts the process.
the parameter n returned is the number of images when
axis matrix vol(id) is in the gap [x1,y1,z1,x2,y2,z2].
meta validate force rota vol(id)
Returns the property n,an1,an2 of volume id.
meta validate force axis vol(id)=n,an1,an2
Changes those parameters.
Notes:
meta validate force rota limit vol(id)=0: starts the process.
the parameter n returned is the number of images when
rota matrix vol(id) is in the gap [an1,an2].
meta validate network(id)
Returns n,e,stat,nb of the learning network id:
n=number of calls already made.
e=error
stat=1 the end of learning.
nb=maximum calls, changing on scale np
in displ network error.
meta validate rota
meta validate rota limit vol(id)
Returns the property cpt,a1,a2 of volume id.
meta validate rota limit vol(id)=n,a1,a2
Changes those parameters.
Notes:
meta validate rota limit vol(id)=0: starts the process.
the parameter n returned is the number of images when
rota matrix vol(id) is in the gap [a1,a2].
usefull in some applications of genetic algorithmes as a way to notate an angle limited in an interval..
Options:
line: the parameter n returned is linerally interpolated such as:
0 if a < a1 or a > a2
1 if a < a1 or a > a2
by default thresholde: 0 if (a < a1 or a > a2) else 1.
meta validate vol(id)
Returns nb,cpt:
n=number of images before the volume id will be invisible.
cpt=meter
meta var
meta var network
meta var network(id)=min_noise,max_noise,cpt_noise,cpt
The matrix is reset when each cpt_noise when moy > 0.5.
The command
displ network error
displays a scale allowing to choose cpt_noise between min_noise and max_noise.
See also
no meta
yes meta