def main():
assert len(argv) == 1, "give me no arguments"
path = "kmer_histograms"
sampleId = "mixedB"
fitter = EnrichedHapDipFitter(path + "/" + sampleId + ".mixed.kmer_dist")
paramNames = fitter.paramNames
defaultParams = {
"zp.copy.y": 3.000,
"zp.copy.hom": 3.000,
"zp.copy.het": 3.000,
"p.e": 0.942,
"shape.e": 3.000,
"scale.e": 1.000,
"p.y": 0.900,
"u.y": 64.000,
"sd.y": 14.826,
"shape.y": 0.000,
"p.hom": 0.800,
"u.hom": 5.120,
"sd.hom": 1.186,
"var.het": 1.407,
}
goodParams = {
"zp.copy.y": 2.042,
"zp.copy.hom": 3.157,
"zp.copy.het": 17.795,
"p.e": 0.935,
"shape.e": 0.096,
"scale.e": 0.465,
"p.y": 0.621,
"u.y": 68.084,
"sd.y": 8.626,
"shape.y": 0.057,
"p.hom": 0.853,
"u.hom": 11.101,
"sd.hom": 3.600,
"var.het": 10.916,
}
numSteps = 10
for (paramIx, name) in enumerate(paramNames):
if paramIx != 0:
print
for step in xrange(1, numSteps + 1):
print '=== param %d of %s ("%s") step %d of %s ===' % (1 + paramIx, len(paramNames), name, step, numSteps)
initParams = dict(goodParams)
initParams[name] += step * (defaultParams[name] - goodParams[name]) / numSteps
fitter.set_params(initParams)
fitParams = fitter.fit()
if fitParams == None:
print params_to_text(paramNames, initParams, prefix="init:")
print "(failure or non-convergence)"
print "... return code ..."
print fitter.retCode
print "... stdout ..."
print fitter.stdout
print "... stderr ..."
print fitter.stderr
continue
print params_to_text(paramNames, initParams, fitParams, prefix="init:", prefix2="cvrg:")
fitParams = params_to_float(fitParams)
distance = vector_distance(fitParams, goodParams)
print "dGood: %.8f" % distance
def main():
assert (len(argv) == 1), "give me no arguments"
numTrials = 1000
random_seed("acorn")
explainFailure = False
path = "kmer_histograms"
#sampleId = "mixedB"
#defaultParams = {"zp.copy.y" : 3.000,
# "zp.copy.hom" : 3.000,
# "zp.copy.het" : 3.000,
# "p.e" : 0.942,
# "shape.e" : 3.000,
# "scale.e" : 1.000,
# "p.y" : 0.900,
# "u.y" : 64.000,
# "sd.y" : 14.826,
# "shape.y" : 0.000,
# "p.hom" : 0.800,
# "u.hom" : 5.120,
# "sd.hom" : 1.186,
# "var.het" : 1.407}
#goodParams = {"zp.copy.y" : 2.042,
# "zp.copy.hom" : 3.157,
# "zp.copy.het" : 17.795,
# "p.e" : 0.935,
# "shape.e" : 0.096,
# "scale.e" : 0.465,
# "p.y" : 0.621,
# "u.y" : 68.084,
# "sd.y" : 8.626,
# "shape.y" : 0.057,
# "p.hom" : 0.853,
# "u.hom" : 11.101,
# "sd.hom" : 3.600,
# "var.het" : 10.916}
sampleId = "apple_E12_L150_D80_K25"
defaultParams = {"zp.copy.y" : 3.000,
"zp.copy.hom" : 3.000,
"zp.copy.het" : 3.000,
"p.e" : 0.940,
"shape.e" : 3.000,
"scale.e" : 1.000,
"p.y" : 0.900,
"u.y" : 62.000,
"sd.y" : 16.309,
"shape.y" : 0.000,
"p.hom" : 0.800,
"u.hom" : 4.960,
"sd.hom" : 1.305,
"var.het" : 1.702}
goodParams = {"zp.copy.y" : 2.047,
"zp.copy.hom" : 3.390,
"zp.copy.het" : 1.137,
"p.e" : 0.937,
"shape.e" : 0.114,
"scale.e" : 0.452,
"p.y" : 0.630,
"u.y" : 65.974,
"sd.y" : 8.666,
"shape.y" : 0.228,
"p.hom" : 0.818,
"u.hom" : 13.622,
"sd.hom" : 4.086,
"var.het" : 15.274}
fitter = EnrichedHapDipFitter(path+"/"+sampleId+".mixed.kmer_dist")
paramNames = fitter.paramNames
convergenceCount = 0
for trialNumber in xrange(numTrials):
print "=== trial %d of %d ===" \
% (1+trialNumber,numTrials)
# choose initial params as a random point in hypercube between "good"
# and "bad"
initParams = dict(goodParams)
norm2Init = 0.0
for (paramIx,name) in enumerate(paramNames):
step = unit_random()
initParams[name] += step*(defaultParams[name]-goodParams[name])
norm2Init += step*step
normInit = sqrt(norm2Init) / len(paramNames)
fitter.set_params(initParams)
fitParams = fitter.fit()
if (fitParams == None):
print params_to_text(paramNames,initParams,prefix="init-[%d]:" % trialNumber)
print "normInit: %.8f" % normInit
print "(failure or non-convergence)"
if (explainFailure):
print "... return code ..."
print fitter.retCode
print "... stdout ..."
print fitter.stdout
print "... stderr ..."
print fitter.stderr
continue
print params_to_text(paramNames,initParams,fitParams,
prefix="init+[%d]:" % trialNumber,
prefix2="cvrg[%d]:" % trialNumber)
fitParams = params_to_float(fitParams)
dGood = vector_distance(fitParams,goodParams)
print "normInit: %.8f" % normInit
print "dGood: %.8f" % dGood
convergenceCount += 1
print "%d of %d trials converged" % (convergenceCount,numTrials)
def main():
assert (len(argv) == 3), "need the sampleID and number of trials, and nothing else"
sampleId = argv[1]
numTrials = int(argv[2])
random_seed("acorn")
explainFailure = False
path = "kmer_histograms"
# ask the curve fitter what the default paramters are
fitter = EnrichedHapDipFitter(path+"/"+sampleId+".mixed.kmer_dist")
paramNames = fitter.paramNames
defaultParams = fitter.default_params()
if (defaultParams == None):
print "(failed to get default params)"
if (explainFailure):
print "... return code ..."
print hdFitter.retCode
print "... stdout ..."
print hdFitter.stdout
print "... stderr ..."
print hdFitter.stderr
assert (False)
defaultParams = params_to_float(defaultParams)
# read the "good" parameters (usually produced by explore3_hap_dip)
fitFilename = path+"/"+sampleId+".mixed.fit"
f = file(fitFilename,"rt")
goodParams = params_from_text([line for line in f])
f.close()
for name in defaultParams:
assert (name in goodParams), \
"parameter \"%s\" missing from %s" % (name,fitFilename)
for name in goodParams:
assert (name in defaultParams), \
"extra parameter \"%s\" in %s" % (name,fitFilename)
goodParams = params_to_float(goodParams)
print params_to_text(paramNames,goodParams,defaultParams,
prefix="good:",prefix2="dflt:")
# run the convergence trials
convergenceCount = 0
for trialNumber in xrange(numTrials):
print "=== trial %d of %d ===" \
% (1+trialNumber,numTrials)
# choose initial params as a random point in hypercube between "good"
# and "bad"
initParams = dict(goodParams)
norm2Init = 0.0
for (paramIx,name) in enumerate(paramNames):
step = unit_random()
initParams[name] += step*(defaultParams[name]-goodParams[name])
norm2Init += step*step
normInit = sqrt(norm2Init) / len(paramNames)
fitter.set_params(initParams)
fitParams = fitter.fit()
if (fitParams == None):
print params_to_text(paramNames,initParams,prefix="init-[%d]:" % trialNumber)
print "normInit: %.8f" % normInit
print "(failure or non-convergence)"
if (explainFailure):
print "... return code ..."
print fitter.retCode
print "... stdout ..."
print fitter.stdout
print "... stderr ..."
print fitter.stderr
continue
print params_to_text(paramNames,initParams,fitParams,
prefix="init+[%d]:" % trialNumber,
prefix2="cvrg[%d]:" % trialNumber)
fitParams = params_to_float(fitParams)
dGood = vector_distance(fitParams,goodParams)
print "normInit: %.8f" % normInit
print "dGood: %.8f" % dGood
convergenceCount += 1
print "%d of %d trials converged" % (convergenceCount,numTrials)
