omega vs Deltax for the anatomical alpha and beta

The problem: This plot does not look nice for the case alpha=beta=1/29.3.

load datos_celegans_struct
pos_cuad=coste2pos_cuad(todas.A/29.3,todas.M/29.3+todas.S,todas.f);
omega=sum([todas.A/29.3 todas.M/29.3+todas.S],2);
desv=abs(todas.pos_real-pos_cuad)
plot(desv,omega,’.’)

It is not a problem for exponent 2, because the metric is bad also. But it does not look nice for exponent one, for which the metric is excelent:

>> [pos,omega_general,costes,x]=coste2pos_restofijas(todas.A/29.3,todas.M/23.3+todas.S,todas.f,todas.pos_real,1);
>> desv=abs(todas.pos_real-pos);
>> plot(desv,omega,’.’)

The reason: omega is not valid in this case. For example (blue is the actual cost, red is the cost approximated by omega):

>> n=4;clf; plot(x,costes(n,:)); hold on; plot(x,abs(x-pos(n))*omega(n),’r’); ejes=axis; plot(todas.pos_real(n)*[1 1],ejes(3:4),’k’);title(num2str(omega_general(n)))

>> n=27;clf; plot(x,costes(n,:)); hold on; plot(x,abs(x-pos(n))*omega(n),’r’); ejes=axis; plot(todas.pos_real(n)*[1 1],ejes(3:4),’k’);title(num2str(omega_general(n)))


>> n=54;clf; plot(x,costes(n,:)); hold on; plot(x,abs(x-pos(n))*omega(n),’r’); ejes=axis; plot(todas.pos_real(n)*[1 1],ejes(3:4),’k’);title(num2str(omega_general(n)))

And many more cases.

Solution: I define a “generalized omega”, which is simply the one that best fits the cost in the direction of the deviation. With this generalized omega the costs look like this:

>> n=4;clf; plot(x,costes(n,:)); hold on; plot(x,abs(x-pos(n))*omega_general(n),’r’); ejes=axis; plot(todas.pos_real(n)*[1 1],ejes(3:4),’k’);title(num2str(omega_general(n)))

>> n=27;clf; plot(x,costes(n,:)); hold on; plot(x,abs(x-pos(n))*omega_general(n),’r’); ejes=axis; plot(todas.pos_real(n)*[1 1],ejes(3:4),’k’);title(num2str(omega_general(n)))

>> n=54;clf; plot(x,costes(n,:)); hold on; plot(x,abs(x-pos(n))*omega_general(n),’r’); ejes=axis; plot(todas.pos_real(n)*[1 1],ejes(3:4),’k’);title(num2str(omega_general(n)))

With this generalized omega, the plot looks much better:

>> desv=abs(todas.pos_real-pos);
>> plot(desv,omega_general,’.’)

In fact, the two outliers are AVAL and AVAR:

>> find(desv>.3 & omega_general>3)
ans =
54
55

And for these the cost is not so well approximated (see above the cost for neuron 54).

Of course, it can still improve.

Exponent from the mean (removing the two outliers):

>> buenas=(desv<.3 | omega_general<3);
>> [xi,b]=omegadesv2bestfittingexponent_exp(desv(buenas),omega_general(buenas))
xi =
1.5765
b =
0.0995

Exponent from the envelope:
>> [desv_acum,omega_media]=omegadesv_acumuladas(desv(buenas),omega_general(buenas),1,4);

>> plot(log10(desv_acum),log10(omega_media),’.-‘)

Not a great fit, but again near the Bayesian parameter. If you ask me, the envelope is just fucking lucky.

 

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