def writeWithHarvester():
from BinnedSpikeTrain import BinnedSpikeTrain
from InitBox import initialize5
from Simulator import Path, OUSinusoidalParams
import time
N_phi = 20;
print 'N_phi = ', N_phi
phis = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
# D = DataHarvester('test2')
D = DataHarvester('test2', 'test3')
base_name = 'sinusoidal_spike_train_T='
for regime_name, T_sim, T_thresh in zip(['superT', 'subT', 'crit', 'superSin'],
[5000 , 20000, 5000, 5000],
[4., 32, 16., 16.]):
regime_label = base_name + str(T_sim)+ '_' + regime_name
for sample_id in xrange(3,4):
file_name = regime_label + '_' + str(sample_id) + '.path'
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phis)
ps = binnedTrain._Path._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
D.setRegime(regime_name,abg_true, T_sim)
phi_omit = None
binnedTrain.pruneBins(phi_omit, N_thresh = 64, T_thresh=T_thresh)
Tf = binnedTrain.getTf()
D.addSample(sample_id, Tf, binnedTrain.getBinCount(), binnedTrain.getSpikeCount())
start = time.clock()
abg_init = initialize5(binnedTrain)
finish = time.clock()
D.addEstimate(sample_id, 'Initializer', abg_init, finish-start)
abg_est = abg_init
start = time.clock()
# abg_est = NMEstimator(S, binnedTrain, abg_init)
time.sleep(rand())
finish = time.clock()
D.addEstimate(sample_id, 'Nelder-Mead', abg_est, finish-start)
start = time.clock()
# abg_est = BFGSEstimator(S, binnedTrain, abg_init)
time.sleep(rand())
finish = time.clock()
D.addEstimate(sample_id, 'BFGS', abg_est, finish-start)
start = time.clock()
# abg_est = FortetEstimator(binnedTrain, abg_init)
time.sleep(rand())
finish = time.clock()
D.addEstimate(sample_id, 'Fortet', abg_est, finish-start)
def GradedDriver():
from scipy.optimize import fmin_bfgs
N_phi = 10;
print 'N_phi = ', N_phi
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
print 'GradedEstimator'
for file_name in ['sinusoidal_spike_train_T=20000_subT_3.path',
'sinusoidal_spike_train_T=20000_subT_8.path',
'sinusoidal_spike_train_T=20000_subT_13.path']:
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
binnedTrain.pruneBins(None, N_thresh = 32, T_thresh = 32.)
abg_est = abs( initialize5(binnedTrain))
print 'abg_init = ',abg_est
theta = binnedTrain.theta
for T_thresh, N_thresh, max_iters in zip([32/8., 32/4., 32/2., 32.],
[128, 128, 64, 32],
[50,50,100,None]):
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
binnedTrain.pruneBins(None, N_thresh, T_thresh)
print 'N_bins = ', len(binnedTrain.bins.keys())
Tf = binnedTrain.getTf()
print 'Tf = ', Tf
dx = .02; dt = FPMultiPhiSolver.calculate_dt(dx, 4., 10.)
phis = binnedTrain.bins.keys();
S = FPMultiPhiSolver(theta, phis, dx, dt,
Tf, X_MIN = -.5)
from scipy.optimize import fmin
def func(abg):
'Solve it:'
Fs = S.solve(abg, visualize=False)[:,:,-1]
Ss = S.transformSurvivorData(binnedTrain)
Ls = Fs - Ss
'Return'
G = .5*sum(Ls*Ls)*S._dt
return G
abg_est = fmin(func, abg_est, ftol = 1e-2*T_thresh, maxiter=max_iters)
print 'current_estimate = ', abg_est
print 'final estimate = ', abg_est
def NelderMeadSubTEstimator():
N_phi = 20;
print 'N_phi = ', N_phi
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
batch_start = time.clock()
base_name = 'sinusoidal_spike_train_T='
D = DataHarvester('SubT_NMx16_refined_sim_dt')
for regime_name, T_sim, T_thresh in zip(['subT'],
[20000],
[32.]):
regime_label = base_name + str(T_sim)+ '_' + regime_name
for sample_id in xrange(1,17):
file_name = regime_label + '_' + str(sample_id) + '.path'
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
ps = binnedTrain._Train._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
D.setRegime(regime_name,abg_true, T_sim)
phi_omit = None
binnedTrain.pruneBins(phi_omit, N_thresh = 64, T_thresh=T_thresh)
Tf = binnedTrain.getTf()
D.addSample(sample_id, Tf, binnedTrain.getBinCount(), binnedTrain.getSpikeCount())
dx = .04; dt = FPMultiPhiSolver.calculate_dt(dx, 4., 2.)
phis = binnedTrain.bins.keys();
theta = binnedTrain.theta
S = FPMultiPhiSolver(theta, phis,
dx, dt,
Tf, X_MIN = -.5)
start = time.clock()
abg_init = initialize5(binnedTrain)
finish = time.clock()
D.addEstimate(sample_id, 'Initializer', abg_init, finish-start)
abg_init = abs(abg_init)
start = time.clock()
abg_est = NMEstimator(S, binnedTrain, abg_init)
finish = time.clock()
D.addEstimate(sample_id, 'Nelder-Mead', abg_est, finish-start)
D.closeFile()
print 'batch time = ', (time.clock() - batch_start) / 3600.0, ' hrs'
def BFGSGradedEstimator():
from scipy.optimize import fmin_bfgs
print 'GradedEstimator'
N_phi = 10;
print 'N_phi = ', N_phi
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
phi_omit = None
for file_name in ['sinusoidal_spike_train_T=20000_subT_4.path',
'sinusoidal_spike_train_T=20000_subT_7.path',
'sinusoidal_spike_train_T=20000_subT_13.path']:
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
binnedTrain.pruneBins(phi_omit, N_thresh = 32, T_thresh = 32.)
abg_est = abs( initialize5(binnedTrain))
print 'abg_init = ',abg_est
for T_thresh, N_thresh, max_iters in zip([32/8., 32/4., 32/2., 32.],
[128, 128, 64, 32],
[32,24,16,8]):
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phis)
binnedTrain.pruneBins(phi_omit, N_thresh, T_thresh)
Tf = binnedTrain.getTf()
print 'Tf = ', Tf
print 'N_bins = ', len(binnedTrain.bins.keys())
solver_phis = binnedTrain.bins.keys();
theta = binnedTrain.theta
x_min = -.5;
S = FPMultiPhiSolver(theta, solver_phis,
.1, .1,
Tf, X_MIN = x_min)
lE = Estimator(S, binnedTrain, verbose = True)
# abg_est = fmin_bfgs(lE.func, abg_init, lE.dfunc, gtol = 1e-6*binnedTrain.getTf(), maxiter= 128, full_output = 0)
abg_est, fopt, gopt, Bopt, func_calls, grad_calls, warnflag = fmin_bfgs(lE.func, abg_est,
lE.dfunc, gtol = 1e-08*binnedTrain.getTf(), maxiter=max_iters, full_output = 1)
print 'estimate gradient =', gopt
print 'current_estimate = ', abg_est
print 'final estimate = ', abg_est
def BatchGradedNMEstimator():
N_phi = 20;
print 'N_phi = ', N_phi
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
batch_start = time.clock()
base_name = 'sinusoidal_spike_train_T='
D = DataHarvester('GradedNMx16', 'GradedNM_SubTx16')
N_thresh = 32
for regime_name, T_sim, T_thresh in zip(['subT'],
[20000],
[32.]):
regime_label = base_name + str(T_sim)+ '_' + regime_name
for sample_id in xrange(4,17):
file_name = regime_label + '_' + str(sample_id) + '.path'
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
ps = binnedTrain._Path._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
D.setRegime(regime_name,abg_true, T_sim)
phi_omit = None
binnedTrain.pruneBins(phi_omit, N_thresh = N_thresh, T_thresh=T_thresh)
Tf = binnedTrain.getTf()
D.addSample(sample_id, Tf, binnedTrain.getBinCount(), binnedTrain.getSpikeCount())
start = time.clock()
abg_init = initialize5(binnedTrain)
finish = time.clock()
D.addEstimate(sample_id, 'Initializer', abg_init, finish-start)
start = time.clock()
abg_est = GradedNMEstimator(file_name, phi_norms, abg_init, T_thresh, N_thresh)
finish = time.clock()
D.addEstimate(sample_id, 'Graded_Nelder-Mead', abg_est, finish-start)
start = time.clock()
abg_est = FortetEstimator(binnedTrain, abg_init)
finish = time.clock()
D.addEstimate(sample_id, 'Fortet', abg_est, finish-start)
D.closeFile()
print 'batch time = ', (time.clock() - batch_start) / 3600.0, ' hrs'
def writeManual():
from BinnedSpikeTrain import BinnedSpikeTrain
from InitBox import initialize5
import time
N_phi = 20;
print 'N_phi = ', N_phi
phis = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
h5file = openFile("manual_write.h5", mode = "w", title = "Manually Write Estimate Data")
grp = h5file.createGroup("/", 'Estimates', "Estimates INformation")
base_name = 'sinusoidal_spike_train_T='
for regime_name, T_sim, T_thresh in zip(['superT', 'subT', 'crit', 'superSin'],
[5000 , 20000, 5000, 5000],
[4., 32, 16., 16.]):
estTable = h5file.createTable(grp, regime_name, Estimate , "Regime Estimate")
# sampleTbl = h5file.createTable(grp, regime_name, Estimate , "Regime Estimate")
regime_label = base_name + str(T_sim)+ '_' + regime_name
for sample_id in xrange(1,3):
file_name = regime_label + '_' + str(sample_id) + '.path'
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phis)
ps = binnedTrain._Path._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
if 1 == sample_id:
print 'abg_true = ', abg_true
phi_omit = None
binnedTrain.pruneBins(phi_omit, N_thresh = 64, T_thresh=T_thresh)
print 'N_bins = ', len(binnedTrain.bins.keys())
Tf = binnedTrain.getTf()
print 'Tf = ', Tf
#Estimate:
estimate = estTable.row
start = time.clock()
abg_init = initialize5(binnedTrain)
finish = time.clock()
estimate['method'] = 'Initializer'
estimate['sample_id'] = sample_id
estimate['alpha'] = abg_init[0]
estimate['beta'] = abg_init[1]
estimate['gamma'] = abg_init[2]
estimate['walltime'] = finish - start
estimate.append()
abg_est = abg_init
start = time.clock()
# abg_est = NMEstimator(S, binnedTrain, abg_init)
time.sleep(rand())
print 'Est. time = ', time.clock() - start
print 'abg_est = ', abg_est
start = time.clock()
# abg_est = FortetEstimator(binnedTrain, abg_init)
time.sleep(2*rand())
print 'Est. time = ', time.clock() - start
print 'abg_est = ', abg_est
estTable.flush()
print '#'*44
print h5file
h5file.close()
def AdjointEstimator():
N_phi = 20;
print 'N_phi = ', N_phi
phis = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
file_name = 'sinusoidal_spike_train_N=1000_crit_1'
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phis)
phi_omit = None
binnedTrain.pruneBins(phi_omit, N_thresh = 100, T_thresh = 10.0)
print 'N_bins = ', len(binnedTrain.bins.keys())
Tf = binnedTrain.getTf()
print 'Tf = ', Tf
phis = binnedTrain.bins.keys();
theta = binnedTrain.theta
ps = binnedTrain._Train._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
print 'abg_true = ', abg_true
abg = abg_true
xmin = FPMultiPhiSolver.calculate_xmin(Tf, abg)
dx = FPMultiPhiSolver.calculate_dx(abg, xmin)
dt = FPMultiPhiSolver.calculate_dt(dx, abg, xmin, factor = 8.)
print 'xmin, dx, dt = ', xmin, dx, dt
S = FPMultiPhiSolver(theta, phis,
dx, dt, Tf, xmin)
abg_init = initialize5(binnedTrain)
print 'abg_init = ', abg_init
# start = time.clock()
# abg_est = TNCEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
# start = time.clock()
# abg_est = NMEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
#
# start = time.clock()
# abg_est = COBYLAEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
# start = time.clock()
# abg_est = CGEstimator(S, binnedTrain, abg_init)
# print 'Est. time = ', time.clock() - start
# print 'abg_est = ', abg_est
start = time.clock()
abg_est = BFGSEstimator(S, binnedTrain, abg_init)
print 'Est. time = ', time.clock() - start
print 'abg_est = ', abg_est
def BFGSItersComparison():
N_phi = 20;
print 'N_phi = ', N_phi
phi_norms = linspace(1/(2.*N_phi), 1. - 1/ (2.*N_phi), N_phi)
batch_start = time.clock()
base_name = 'sinusoidal_spike_train_T='
D = DataHarvester('BFGS_Iters')
for regime_name, T_sim, T_thresh in zip(['crit', 'superSin'],
[5000, 5000],
[16., 16.]):
regime_label = base_name + str(T_sim)+ '_' + regime_name
for sample_id in xrange(1,4):
file_name = regime_label + '_' + str(sample_id) + '.path'
print file_name
binnedTrain = BinnedSpikeTrain.initFromFile(file_name, phi_norms)
ps = binnedTrain._Train._params
abg_true = array((ps._alpha, ps._beta, ps._gamma))
D.setRegime(regime_name,abg_true, T_sim)
phi_omit = None
binnedTrain.pruneBins(phi_omit, N_thresh = 64, T_thresh=T_thresh)
Tf = binnedTrain.getTf()
D.addSample(sample_id, Tf, binnedTrain.getBinCount(), binnedTrain.getSpikeCount())
dx = .025; dt = FPMultiPhiSolver.calculate_dt(dx, 5., 2.)
phis = binnedTrain.bins.keys();
theta = binnedTrain.theta
S = FPMultiPhiSolver(theta, phis,
dx, dt,
Tf, X_MIN = -2.0)
start = time.clock()
abg_init = initialize5(binnedTrain)
finish = time.clock()
D.addEstimate(sample_id, 'Initializer', abg_init, finish-start)
start = time.clock()
abg_est = BFGSEstimator(S, binnedTrain, abg_init, max_iters = 8)
finish = time.clock()
D.addEstimate(sample_id, 'BFGS_8', abg_est, finish-start)
start = time.clock()
abg_est = BFGSEstimator(S, binnedTrain, abg_est,max_iters = 8)
finish = time.clock()
D.addEstimate(sample_id, 'BFGS_16', abg_est, finish-start)
start = time.clock()
abg_est = BFGSEstimator(S, binnedTrain, abg_est,max_iters = 8)
finish = time.clock()
D.addEstimate(sample_id, 'BFGS_24', abg_est, finish-start)
D.closeFile()
print 'batch time = ', (time.clock() - batch_start) / 3600.0, ' hrs'
