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 Post subject: Python optimize.leastsq to alglib miml_d_v
PostPosted: Wed Jul 01, 2015 5:38 pm 
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Joined: Wed Jul 01, 2015 5:26 pm
Posts: 1
I am new with alglib and I am trying to use miml_d_v in c# as a replacement for a function I am using in Python called optimize.leastsq of the library scify, however I am having some troubles with it.

The original code produce some data that I reproduce in the following code in python, and I would like to get the same results with alglib in C#.

Is there anyone that could help?

Python Code:
import scipy
from scipy import optimize

p0=[4873728.540127272, -785466.155700958, 4025286.807390065, 0.00093518512201935322]

ret=[[-32.434757861495015, -0.065348023336239361, -0.1034045151670188, -0.1717015531728977, -0.59427038934081844, -0.18318382750730977, -0.16254255832172945, -32.449899899214508],
[-32.434757861495015, -0.065348023336239361, -0.1034045151670188, -0.1717015531728977, -0.59427038934081844, -0.18318382750730977, -0.16254255832172945, -32.449899899214508],
[-32.434757861495015, -0.065348023336239361, -0.1034045151670188, -0.1717015531728977, -0.59427038934081844, -0.18318382750730977, -0.16254255832172945, -32.449899899214508],
[-32.448173513263463, -0.065421742527807275, -0.10338040240063293, -0.17192136949161083, -0.5947541311848914, -0.18321835632668823, -0.16264121994609027, -32.479343549162145],
[-32.431583742052318, -0.065339026320759358, -0.10338103946896396, -0.17171039677923561, -0.59437672263011387, -0.18315894732950272, -0.1625546041526833, -32.447159752249718],
[-32.464888094365598, -0.065291705030001104, -0.10345637464263159, -0.17169058705912918, -0.59459532373584856, -0.183318230090663, -0.16269966895924895, -32.472408668696879],
[-32.437264488637446, -0.065354632646321215, -0.10341374467471283, -0.17171460852259784, -0.59432261073961912, -0.18319688285700994, -0.16255561367142959, -32.452406526356931],
[-7.0108473300933832e-05, 0.0087499652417667717, 0.0097119369629178887, -0.019899209938012082, 0.0027980973944067978, -0.015756631374824807, -0.0017866643145680444, 0.00013757720589637756],
[-0.013485751301050185, 0.0086762461091344639, 0.0097360496634637268, -0.020119026070460694, 0.0023143558762967606, -0.01579116019420327, -0.0018853258807212132, -0.029306043684482572],
[0.0031039953231811524, 0.008758962215991848, 0.0097354125292926311, -0.019908053486142326, 0.0026917647104710363, -0.015731751336716129, -0.0017987100756727175, 0.0028777107596397399],
[-0.030200292170047757, 0.0088062834419209337, 0.0096600775366851403, -0.019888243882451223, 0.0024731633253395578, -0.01589103376027198, -0.0019437747308984417, -0.022371158748865128],
[-0.0025763265788555146, 0.0087433570102065337, 0.0097027089613150336, -0.019912263157311844, 0.0027458845172077442, -0.015769684594124568, -0.0017997175338678078, -0.002368640899658203],
[-6.4688175916671748e-05, 0.0087499773766082697, 0.0097119587312846679, -0.019899188692215849, 0.0027981874998658919, -0.015756601968314514, -0.0017866392154246584, 0.00014257058501243591]]

i=-1
def positionErrorFunction(p, data):
global i
i=i+1
return ret[i]

p1, ier = optimize.leastsq(positionErrorFunction, p0[:], args=(0))
print("p1: %s" %p1)
print("ier: %s" %ier)

#solution
# p1: [ 4.87372425e+06 -7.85462104e+05 4.02528066e+06 9.35032471e-04]
#ier: 2


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