def load(fn_ins, simplify=False, corner=None, rm_zero=False):
zero_names = OrderedSet()
Ads, b = loadc_Ads_b(fn_ins, corner=corner, ico=True)
if rm_zero:
zero_names = rm_zero_cols(Ads)
if simplify:
print('Simplifying corner %s' % (corner, ))
Ads, b = simplify_rows(Ads, b, remove_zd=False, corner=corner)
return State(Ads, zero_names=zero_names)
def run(fout, fns_in, corner, verbose=0):
Ads, b = loadc_Ads_b(fns_in, corner, ico=True)
Ads, b = simplify_rows(Ads, b, corner=corner)
fout.write('ico,fast_max fast_min slow_max slow_min,rows...\n')
for row_b, row_ds in zip(b, Ads):
# write in same format, but just stick to this corner
out_b = [str(row_b) for _i in range(4)]
ico = '1'
items = [ico, ' '.join(out_b)]
for k, v in sorted(row_ds.items()):
items.append('%u %s' % (v, k))
fout.write(','.join(items) + '\n')
def run(fns_in, sub_json=None, verbose=False):
assert len(fns_in) > 0
# arbitrary corner...data is thrown away
Ads, b = loadc_Ads_b(fns_in, "slow_max")
if sub_json:
print('Subbing JSON %u rows' % len(Ads))
#pds(Ads, 'Orig')
names_old = index_names(Ads)
run_sub_json(Ads, sub_json, verbose=verbose)
names_new = index_names(Ads)
print("Sub: %u => %u names" % (len(names_old), len(names_new)))
print(names_new)
print('Subbed JSON %u rows' % len(Ads))
names = names_new
#pds(Ads, 'Sub')
else:
names = index_names(Ads)
# Squash into a matrix
# A_ub2, b_ub2 = Adi2matrix_random(A_ubd, b, names)
Amat, _bmat = Ads2matrix_linear(Ads, b)
#pmatrix(Amat, 'Matrix')
'''
The matrix must be fully ranked to even be considered reasonable
Even then, floating point error *possibly* could make it fully ranked, although probably not since we have whole numbers
Hence the slogdet check
'''
print
# https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.linalg.matrix_rank.html
rank = np.linalg.matrix_rank(Amat)
print('rank: %s / %d col' % (rank, len(names)))
# doesn't work on non-square matrices
if 0:
# https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.linalg.slogdet.html
sign, logdet = np.linalg.slogdet(Amat)
# If the determinant is zero, then sign will be 0 and logdet will be -Inf
if sign == 0 and logdet == float('-inf'):
print('slogdet :( : 0')
else:
print('slogdet :) : %s, %s' % (sign, logdet))
if rank != len(names):
raise Exception("Matrix not fully ranked w/ %u / %u" %
(rank, len(names)))
def run(fns_in, corner, bounds_csv, verbose=False):
print('Loading data')
Ads, borig = loadc_Ads_b(fns_in, corner, ico=True)
bounds = load_bounds(bounds_csv, corner)
# verify is flattened
for k in bounds.keys():
assert 'GROUP_' not in k, 'Must operate on flattened bounds'
# compute our timing model delay at the given corner
bgots = []
for row_ds in Ads:
delays = [n * bounds[x] for x, n in row_ds.items()]
bgots.append(sum(delays))
ses = (np.asarray(bgots) - np.asarray(borig))**2
mse = (ses).mean(axis=None)
print('MSE aggregate: %0.1f' % mse)
print('Min SE: %0.1f' % min(ses))
print('Max SE: %0.1f' % max(ses))
def load(fn_ins, simplify=False, corner=None):
Ads, b = loadc_Ads_b(fn_ins, corner=corner, ico=True)
if simplify:
print('Simplifying corner %s' % (corner, ))
Ads, b = simplify_rows(Ads, b, remove_zd=False, corner=corner)
return State(Ads)
def run(fns_in,
corner,
run_corner,
sub_json=None,
bounds_csv=None,
dedup=True,
massage=False,
outfn=None,
verbose=False,
**kwargs):
print('Loading data')
Ads, b = loadc_Ads_b(fns_in, corner, ico=True)
# Remove duplicate rows
# is this necessary?
# maybe better to just add them into the matrix directly
if dedup:
oldn = len(Ads)
iold = instances(Ads)
Ads, b = simplify_rows(Ads, b, corner=corner)
print('Simplify %u => %u rows' % (oldn, len(Ads)))
print('Simplify %u => %u instances' % (iold, instances(Ads)))
if sub_json:
print('Sub: %u rows' % len(Ads))
iold = instances(Ads)
names_old = index_names(Ads)
run_sub_json(Ads, sub_json, verbose=verbose)
names = index_names(Ads)
print("Sub: %u => %u names" % (len(names_old), len(names)))
print('Sub: %u => %u instances' % (iold, instances(Ads)))
else:
names = index_names(Ads)
'''
Substitution .csv
Special .csv containing one variable per line
Used primarily for multiple optimization passes, such as different algorithms or additional constraints
'''
if bounds_csv:
Ads2, b2 = loadc_Ads_b([bounds_csv], corner, ico=True)
bounds = Ads2bounds(Ads2, b2)
assert len(bounds), 'Failed to load bounds'
rows_old = len(Ads)
Ads, b = filter_bounds(Ads, b, bounds, corner)
print('Filter bounds: %s => %s + %s rows' %
(rows_old, len(Ads), len(Ads2)))
Ads = Ads + Ads2
b = b + b2
assert len(Ads) or allow_zero_eqns()
assert len(Ads) == len(b), 'Ads, b length mismatch'
if verbose:
print
print_eqns(Ads, b, verbose=verbose)
#print
#col_dist(A_ubd, 'final', names)
if massage:
try:
Ads, b = massage_equations(Ads, b, corner=corner)
except SimplifiedToZero:
if not allow_zero_eqns():
raise
print('WARNING: simplified to zero equations')
Ads = []
b = []
print('Converting to numpy...')
names, Anp = A_ds2np(Ads)
run_corner(Anp,
np.asarray(b),
names,
corner,
outfn=outfn,
verbose=verbose,
**kwargs)