def calc_IDR(theta, r1, r2):
"""
idr <- 1 - e.z
o <- order(idr)
idr.o <- idr[o]
idr.rank <- rank(idr.o, ties.method = "max")
top.mean <- function(index, x) {
mean(x[1:index])
}
IDR.o <- sapply(idr.rank, top.mean, idr.o)
IDR <- idr
IDR[o] <- IDR.o
"""
mu, sigma, rho, p = theta
z1 = compute_pseudo_values(r1, mu, sigma, p, EPS=1e-12)
z2 = compute_pseudo_values(r2, mu, sigma, p, EPS=1e-12)
localIDR = 1-calc_post_membership_prbs(numpy.array(theta), z1, z2)
if idr.FILTER_PEAKS_BELOW_NOISE_MEAN:
localIDR[z1 + z2 < 0] = 1
# it doesn't make sense for the IDR values to be smaller than the
# optimization tolerance
localIDR = numpy.clip(localIDR, idr.CONVERGENCE_EPS_DEFAULT, 1)
local_idr_order = localIDR.argsort()
ordered_local_idr = localIDR[local_idr_order]
ordered_local_idr_ranks = rankdata( ordered_local_idr, method='max' )
IDR = []
for i, rank in enumerate(ordered_local_idr_ranks):
IDR.append(ordered_local_idr[:rank].mean())
IDR = numpy.array(IDR)[local_idr_order.argsort()]
return localIDR, IDR
def calc_IDR(theta, r1, r2):
"""
idr <- 1 - e.z
o <- order(idr)
idr.o <- idr[o]
idr.rank <- rank(idr.o, ties.method = "max")
top.mean <- function(index, x) {
mean(x[1:index])
}
IDR.o <- sapply(idr.rank, top.mean, idr.o)
IDR <- idr
IDR[o] <- IDR.o
"""
mu, sigma, rho, p = theta
z1 = compute_pseudo_values(r1, mu, sigma, p, EPS=1e-12)
z2 = compute_pseudo_values(r2, mu, sigma, p, EPS=1e-12)
localIDR = 1 - calc_post_membership_prbs(numpy.array(theta), z1, z2)
if idr.FILTER_PEAKS_BELOW_NOISE_MEAN:
localIDR[z1 + z2 < 0] = 1
# it doesn't make sense for the IDR values to be smaller than the
# optimization tolerance
localIDR = numpy.clip(localIDR, idr.CONVERGENCE_EPS_DEFAULT, 1)
local_idr_order = localIDR.argsort()
ordered_local_idr = localIDR[local_idr_order]
ordered_local_idr_ranks = rankdata(ordered_local_idr, method='max')
IDR = []
for i, rank in enumerate(ordered_local_idr_ranks):
IDR.append(ordered_local_idr[:rank].mean())
IDR = numpy.array(IDR)[local_idr_order.argsort()]
return localIDR, IDR
def EM_step(z1, z2, starting_point):
i_mu, i_sigma, i_rho, i_p = starting_point
ez = calc_post_membership_prbs(starting_point, z1, z2)
# just a small optimization
ez_sum = ez.sum()
mu_1 = (ez*z1).sum()/(ez_sum)
mu_2 = (ez*z2).sum()/(ez_sum)
mu = (mu_1 + mu_2)/2
weighted_sum_sqs_1 = (ez*((z1-mu)**2)).sum()
weighted_sum_sqs_2 = (ez*((z2-mu)**2)).sum()
weighted_sum_prod = (ez*(z2-mu)*(z1-mu)).sum()
sigma = math.sqrt((weighted_sum_sqs_1+weighted_sum_sqs_2)/(2*ez_sum))
rho = 2*(ez*(z1-mu)*(z2-mu)).sum()/(
weighted_sum_sqs_1 + weighted_sum_sqs_2)
p = ez_sum/len(ez)
return numpy.array([mu, sigma, rho, p])
