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massively improved printing

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This commit is contained in:
Max Zwiessele 2013-06-28 11:00:09 +01:00
parent 66a6bde715
commit 08a902ed6c
1 changed files with 29 additions and 12 deletions
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41
GPy/inference/scg.py
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@ -26,11 +26,14 @@ import numpy as np
import sys
def print_out(len_maxiters, display, fnow, current_grad, beta, iteration):
if display:
print '\r',
print '{0:>0{mi}g} {1:> 12e} {2:> 12e} {3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len_maxiters), # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
sys.stdout.flush()
def print_out(len_maxiters, fnow, current_grad, beta, iteration):
print '\r',
print '{0:>0{mi}g} {1:> 12e} {2:> 12e} {3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len_maxiters), # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
sys.stdout.flush()
def exponents(fnow, current_grad):
exps = [np.abs(fnow), current_grad]
return np.sign(exps) * np.log10(exps).astype(int)
def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xtol=None, ftol=None, gtol=None):
"""
@ -52,6 +55,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
ftol = 1e-6
if gtol is None:
gtol = 1e-5
sigma0 = 1.0e-8
fold = f(x, *optargs) # Initial function value.
function_eval = 1
@ -74,6 +78,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
len_maxiters = len(str(maxiters))
if display:
print ' {0:{mi}s} {1:11s} {2:11s} {3:11s}'.format("I", "F", "Scale", "|g|", mi=len_maxiters)
exps = exponents(fnow, current_grad)
p_iter = iteration
# Main optimization loop.
while iteration < maxiters:
@ -104,7 +110,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
function_eval += 1
if function_eval >= max_f_eval:
status = "Maximum number of function evaluations exceeded"
status = "maximum number of function evaluations exceeded"
break
# return x, flog, function_eval, status
@ -122,15 +128,24 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
flog.append(fnow) # Current function value
iteration += 1
print_out(len_maxiters, display, fnow, current_grad, beta, iteration)
if display:
n_exps = exponents(fnow, current_grad)
if iteration - p_iter >= 6 and ((iteration >= p_iter * 2.78) or np.any(n_exps < exps)):
exps = n_exps
p_iter = iteration
print ''
print_out(len_maxiters, fnow, current_grad, beta, iteration)
if success:
# Test for termination
if (np.max(np.abs(alpha * d)) < xtol) or (np.abs(fnew - fold) < ftol):
status = 'converged'
if (np.abs(fnew - fold) < ftol):
status = 'converged - relative reduction in objective'
break
# return x, flog, function_eval, status
elif (np.max(np.abs(alpha * d)) < xtol):
status = 'converged - relative stepsize'
break
else:
# Update variables for new position
gradnew = gradf(x, *optargs)
@ -139,7 +154,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
fold = fnew
# If the gradient is zero then we are done.
if current_grad <= gtol:
status = 'converged'
status = 'converged - relative reduction in gradient'
break
# return x, flog, function_eval, status
@ -164,6 +179,8 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=500, display=True, xto
status = "maxiter exceeded"
if display:
print_out(len_maxiters, display, fnow, current_grad, beta, iteration)
print ""
print_out(len_maxiters, fnow, current_grad, beta, iteration)
print ""
print status
return x, flog, function_eval, status