def obtainRenewalDensities(phis, abg, theta, Tf):
xmin = FPMultiPhiSolver.calculate_xmin(Tf, abg)
dx = .025; dt = FPMultiPhiSolver.calculate_dt(dx, abg, xmin, factor = 5.)
S = FPMultiPhiSolver(theta, phis,
dx, dt, Tf, xmin);
Fs = S.solve(abg)
ts = S._ts;
iG = squeeze(Fs[:, :,-1])
g = -diff(iG)/ S._dt;
t_mids = (S._ts[1:]+S._ts[:-1])/2.0
return g, S._ts ,t_mids
def MixedEstimator(abg_init, binnedTrain, dp_tol = 1e-2):
phis = binnedTrain.bins.keys();
theta = binnedTrain.theta
dp = dp_tol*2.0;
abg = abg_init
while dp > dp_tol:
Tf = binnedTrain.getTf()
xmin = FPMultiPhiSolver.calculate_xmin(Tf, abg)
dx = FPMultiPhiSolver.calculate_dx(abg, xmin)
dt = FPMultiPhiSolver.calculate_dt(dx, abg, xmin, factor = 8.)
S = FPMultiPhiSolver(theta, phis,
dx, dt, Tf, xmin)
Fs = S.solve(abg, visualize=False)
Ss = S.transformSurvivorData(binnedTrain)
Ls = Fs[:,:,-1] - Ss
Nus = S.solveAdjoint(abg, Ls)
dGdp = S.estimateParameterGradient(abg, Fs, Nus)
from numpy.linalg.linalg import norm
dG_normalized = dGdp/ norm(dGdp)
dp = FortetLineEstimator(binnedTrain, abg, dG_normalized, dp_tol)
abg = abg - dp*dG_normalized
print 'dG = ', dG_normalized
print 'dp = ', dp
print 'abg = (%.3g, %.3g, %.3g)'%(abg[0],abg[1],abg[2])
print '-'
return abg
