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timfuz leastsq: iteration print 

Signed-off-by: John McMaster <johndmcmaster@gmail.com>
This commit is contained in:
John McMaster 2018-08-29 13:37:20 -07:00
parent 9e63d9ee55
commit 4fee67322f
1 changed files with 24 additions and 3 deletions
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27
experiments/timfuz/solve_leastsq.py
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@ -65,7 +65,23 @@ def run_corner(Anp, b, names, verbose=False, opts={}, meta={}, outfn=None):
if rows < cols: if rows < cols:
raise Exception("rows must be >= cols") raise Exception("rows must be >= cols")
tlast = [None]
iters = [0]
printn = [0]
def progress_print():
iters[0] += 1
if tlast[0] is None:
tlast[0]= time.time()
if time.time() - tlast[0] > 1.0:
sys.stdout.write('I:%d ' % iters[0])
tlast[0] = time.time()
printn[0] += 1
if printn[0] % 10 == 0:
sys.stdout.write('\n')
sys.stdout.flush()
def func(params): def func(params):
progress_print()
return (b - np.dot(Anp, params)) return (b - np.dot(Anp, params))
print('') print('')
@ -75,8 +91,10 @@ def run_corner(Anp, b, names, verbose=False, opts={}, meta={}, outfn=None):
# starting at 0 completes quicky, but gives a solution near 0 with terrible results # starting at 0 completes quicky, but gives a solution near 0 with terrible results
# maybe give a starting estimate to the smallest net delay with the indicated variable # maybe give a starting estimate to the smallest net delay with the indicated variable
#x0 = np.array([1000.0 for _x in range(cols)]) #x0 = np.array([1000.0 for _x in range(cols)])
print('Creating x0 estimate')
x0 = mkestimate(Anp, b) x0 = mkestimate(Anp, b)
#print('x0', x0) #print('x0', x0)
if 0: if 0:
x, cov_x, infodict, mesg, ier = optimize.leastsq(func, x0, args=(), full_output=True) x, cov_x, infodict, mesg, ier = optimize.leastsq(func, x0, args=(), full_output=True)
print('x', x) print('x', x)
@ -86,10 +104,13 @@ def run_corner(Anp, b, names, verbose=False, opts={}, meta={}, outfn=None):
print('ier', ier) print('ier', ier)
print(' Solution found: %s' % (ier in (1, 2, 3, 4))) print(' Solution found: %s' % (ier in (1, 2, 3, 4)))
else: else:
print('Solving')
res = least_squares(func, x0, bounds=(0, float('inf'))) res = least_squares(func, x0, bounds=(0, float('inf')))
print(res) if 0:
print('') print(res)
print(res.x) print('')
print(res.x)
print('Done')
def main(): def main():
import argparse import argparse