def main(G=5000000,iterations=50000,init_matrix=None,init_mu=None,verbose=True):
"""Test case for FD-inference"""
print "generating genome"
genome = random_site(G)
print "generating eps"
eps = score_genome_np(TRUE_ENERGY_MATRIX,genome)
min_mu,max_mu = -40,0
mu = bisect_interval(lambda mu:np.sum(fd_solve_np(eps,mu))-q,min_mu,max_mu,verbose=True,tolerance=1e-1)
print "computing ps"
true_ps = fd_solve_np(eps,mu)
print "true q:",np.sum(true_ps)
print "generating chip dataset"
mapped_reads = np.array(map_reads_np(chip_ps_np(true_ps,MEAN_FRAGMENT_LENGTH,NUM_CELLS_ORIGINAL),G))
print "finished chip dataset"
if init_matrix is None:
init_matrix = random_energy_matrix(w)
if init_mu is None:
init_mu = -20#random.random()*40 - 20
init_scores = score_genome_np(init_matrix,genome)
init_state = ((init_matrix,init_mu),init_scores)
logf = lambda state:complete_log_likelihood(state,mapped_reads)
print "true mu:",mu
print "true log_likelihood:",logf(((TRUE_ENERGY_MATRIX,mu),eps))
rprop = lambda state:complete_rprop(state,genome)
print "hitting mh loop"
matrix_chain = mh(logf,proposal=rprop,x0=init_state,dprop=log_dprop,capture_state=capture_state,verbose=verbose,use_log=True,iterations=iterations,modulus=100)
return matrix_chain,genome,mapped_reads
def test_update_scores_np():
w = 10
matrix = random_energy_matrix(w)
i = random.randrange(w)
j = random.randrange(4)
dw = random.random()
print i,j,dw
new_matrix = [row[:] for row in matrix]
new_matrix[i][j] += dw
genome = random_site(100000)
fwd_scores,rev_scores = score_genome_np(matrix,genome,both_strands=True)
fwd_scores_ref,rev_scores_ref = score_genome_np(new_matrix,genome,both_strands=True)
fwd_scores_test,rev_scores_test = update_scores_np(fwd_scores,rev_scores,i,j,dw,w,genome)
fwd_scores_ref2 = update_scores_np_ref(fwd_scores,i,j,dw,genome,w)
print l2_np(fwd_scores_ref,fwd_scores_test),l2_np(fwd_scores_ref,fwd_scores_ref2)
print l2_np(rev_scores_ref,rev_scores_test)
