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# Biskit.mathUtils.SD

All Samples(12)  |  Call(11)  |  Derive(0)  |  Import(1)

slopes = [ M.linfit( range( l/n - last ), p )[0] for p in pm ]

mean, sd = N.average( slopes ), M.SD( slopes )

return [ r - mean < - z * sd for r in slopes ]

msDic[n]    = N.average(msLst)[j]
asDic[n]    = N.average(asLst)[j]
msDic_sd[n] = MAU.SD( msLst )[j]
asDic_sd[n] = MAU.SD( asLst )[j]
j += 1

-> float, standard dev of (v1 +/- v2)
"""
sd1 = MU.SD( v1 )
sd2 = MU.SD( v2 )
return sqrt( sd1**2 + sd2**2 )

def entropySD(self):
centropy = N.sum(-N.log(self.msm)*\
self.msm)/float(self.n_cluster)
return MU.SD(centropy)

def standardDeviation(self):
sd = MU.SD(self.msm)

avg = N.average( self.identities )
sd  = M.SD( self.identities ) or 1e-10  ## replace 0 standard deviation
z   = (self.identities - avg) / sd

"""
r = self.rmsList()
return N.average(r), mathUtils.SD(r)

## calculate rmsd and stdv
rmsd = N.sqrt(N.average(N.compress(mask, d)**2))
stdv = MU.SD(N.compress(mask, d))

## check conditions for convergence

p4 = []
for i in range( len(p) ) :
p_scale = (p[i] - N.average(p[i]) )/ math.SD(p[i])
p4 += [ N.resize( p_scale[N.argmax( N.array(p_scale) )] ,
N.shape( p[i] ) ) ]