def test_pi1_pi1adjoint():
lc_1 = parse_concatenation('12-21')
assert pi1(lc_1, 2) == lc_1
lc_1 = parse_concatenation('12')
assert pi1(lc_1, 2) == pi1(pi1(lc_1, 2), 2)
lc_1 = parse_concatenation('123')
lc.assert_almost_equal(pi1(lc_1, 3), pi1(pi1(lc_1, 3), 3))
dim = 2
n = 3
for w in itertools.product(range(1, dim + 1), repeat=n):
left = lc.lift(ShuffleWord(w))
for v in itertools.product(range(1, dim + 1), repeat=n):
right = lc.lift(ConcatenationWord(v))
assert inner_product_sw_cw(left,
pi1(right, n)) == inner_product_sw_cw(
pi1adjoint(left, n), right)
# pi1adjoint does _not_ depend on the realization of an element in the dual of Lie inside of T(\R^d).
assert pi1adjoint(parse_shuffle('12'),
2) == pi1adjoint(parse_shuffle('[0.5] 12 - [0.5] 21'), 2)
# For everything orthogonal on Lie, the result should be zero.
zero = lc.LinearCombination()
assert pi1adjoint(parse_shuffle('11'), 2) == zero
assert pi1adjoint(parse_shuffle('22'), 2) == zero
lc.assert_almost_equal(
pi1adjoint(parse_shuffle('12') * parse_shuffle('21'), 4), zero)
def permutation_invariants(dim,level):
"""Basis for the permutation invariants."""
partitions = set_partitions( range(level), dim )
invs = []
for p in partitions:
inv = dict()
for draw in possible_draws_without_repetition( range(1,dim+1), len(p) ):
inv[ words.ShuffleWord(_partition_draw_to_word(level,p,draw)) ] = 1
invs.append(lc.LinearCombination(inv))
return invs
def test_shuffle_word():
t = lambda x, y: lc.Tensor((x, y))
def sw(*args):
return ShuffleWord(args)
lc_1 = lc.lift(sw(1, 2))
lc_2 = lc.lift(sw(3, ))
assert lc.LinearCombination( {sw( 1,2,3 ):1, sw( 1,3,2 ):1, sw( 3,1,2 ):1} ) \
== lc_1 * lc_2
assert lc.LinearCombination( {t( sw(), sw(1,2) ):1, t(sw(1),sw(2)):1, t( sw(1,2), sw() ):1} ) \
== lc_1.apply_linear_function( ShuffleWord.coproduct )
# Condition on product vs coproduct: \Delta( \tau \tau' ) = \Delta(\tau) \Delta(\tau').
lc_1 = lc.lift(sw(1, 2))
lc_2 = lc.lift(sw())
assert (lc_1 * lc_2).apply_linear_function( ShuffleWord.coproduct ) \
== lc_1.apply_linear_function( ShuffleWord.coproduct ) \
* lc_2.apply_linear_function( ShuffleWord.coproduct )
lc_1 = lc.lift(sw(1, 2))
lc_2 = lc.lift(sw(3, ))
assert (lc_1 * lc_2).apply_linear_function( ShuffleWord.coproduct ) \
== lc_1.apply_linear_function( ShuffleWord.coproduct ) \
* lc_2.apply_linear_function( ShuffleWord.coproduct )
# Condition on antipode: \Nabla (A \otimes id) \Delta = \eta \vareps.
assert lc.LinearCombination() \
== lc_1.apply_linear_function(ShuffleWord.coproduct)\
.apply_linear_function( lc.Tensor.fn_otimes_linear(lc.id,ShuffleWord.antipode) )\
.apply_linear_function( lc.Tensor.m12 )
assert lc.lift(sw()) \
== lc.lift(sw())\
.apply_linear_function(ShuffleWord.coproduct)\
.apply_linear_function( lc.Tensor.fn_otimes_linear(lc.id,ShuffleWord.antipode) )\
.apply_linear_function( lc.Tensor.m12 )
# Unit.
assert lc_1 == ShuffleWord.unit() * lc_1
# Counit.
assert lc_1 == lc_1\
.apply_linear_function( ShuffleWord.coproduct )\
.apply_linear_function( lc.Tensor.id_otimes_fn( ShuffleWord.counit) )
def restricted_permutation_invariants_for_partition(dim,level,p):
"""Put 1 in the first box; fill other boxes; permute all."""
for draw in possible_draws_without_repetition( range(2,dim+1), len(p)-1 ):
#inv = fla.Elt([]) # XXX
inv = lc.LinearCombination()
draw = [1] + draw
word = _partition_draw_to_word( level, p, draw )
for perm in restricted_permutations( dim ):
permuted = map( lambda n: ((word[n]-1)^perm) +1, range(level) ) # XXX signature / words .. is 1-based but permutations are 0-based
inv += lc.LinearCombination.lift( words.ShuffleWord(permuted) ) # XXX slow #fla.word2Elt( tuple(permuted) )
yield inv
def _gl_invariant_for_identity(dim,level):
assert level % dim == 0
weight = level // dim
result = dict()
for taus in itertools.product(symmetric(dim),repeat=weight):
index = []
sign = 1
for tau in taus:
sign *= tau.signature()
for i in range(dim):
index.append( (i^tau) + 1)
result[ words.ShuffleWord(index) ] = sign
return lc.LinearCombination(result)
def _so_invariants_without_det(dim,level):
if level % 2 == 1:
yield from []
else:
for partitions in equal_size_partitions(range(0,level), 2):
inv = defaultdict(int)
for letters in itertools.product( range(1,dim+1), repeat=level//2 ):
current_word = np.zeros( level, dtype=np.int8 )
for i in range(level//2):
current_word[ partitions[i][0] ] = letters[i]
current_word[ partitions[i][1] ] = letters[i]
inv[ words.ShuffleWord(current_word) ] += 1
yield lc.LinearCombination(inv)
def hoffman_LOG_dual(psi):
"""p.58 in Hoffman"""
# XXX Slow hack.
def _LOG_dual_monomial(m):
_ret = lc.LinearCombination()
for C in finer(CompositionConcatenation((m, ))):
_ret += sympy.Rational((-1)**(len(C) - 1), len(C)) * lc.lift(C)
return _ret
ret = lc.LinearCombination()
for D, c in psi.items():
ret += c * reduce(operator.mul, map(_LOG_dual_monomial, D))
return ret
def gl_invariants(dim,level):
"""Basis for the GL invariants."""
if level % dim != 0:
return
else:
weight = level // dim
for_identity = _gl_invariant_for_identity(dim,level)
for yt in rectangular_standard_young_tableaux(dim, weight):
sigma = _tableau_to_permutation( yt )**(-1)
def permute(word):
letters = word
permuted = tuple( letters[i^sigma] for i in range(len(letters)) )
return words.ShuffleWord(permuted)
yield lc.LinearCombination( {permute(x):y for x,y in for_identity.items()} )
def signature(x, upto_level):
# TODO Using Chen might be faster.
ONE = dp.CompositionConcatenation()
if isinstance(x, list) or isinstance(x, tuple):
x = np.array(x)
if len(x.shape) == 1:
x = x[np.newaxis, :]
dim, N = x.shape
delta_x = np.diff(x, prepend=0, axis=1)
prevz = [ONE]
sig = lc.LinearCombination({ONE: np.ones(N)})
for level in range(1, upto_level + 1):
currentz = []
for ell in range(0, dim):
for prev in prevz:
if not prev == ONE:
concat_v = dp.CompositionConcatenation(
tuple(p for p in prev) + (dp.Monomial({ell: 1}), ))
concat_s = np.hstack(
(np.array([0.]),
np.cumsum(sig[prev][0:-1] * delta_x[ell][1:])))
currentz.append(concat_v)
sig[concat_v] = concat_s
if prev == ONE:
last_monomial = dp.Monomial()
else:
last_monomial = dp.Monomial(prev[-1])
dp.safe_add(last_monomial, ell, +1)
bullet_v = dp.CompositionConcatenation(
tuple(p for p in prev[0:-1]) + (last_monomial, ))
bullet_s = np.hstack(
(np.array([0.]),
np.cumsum(sig[prev][1:] * delta_x[ell][1:])))
if not prev == ONE:
bullet_s = bullet_s - concat_s
currentz.append(bullet_v)
sig[bullet_v] = bullet_s
prevz = currentz
return sig
def _so_invariants_with_one_det(dim,level):
# no determinant fits or, using one determinant, the other spots cannot be used for inner products
if level < dim or (level - dim) % 2 == 1:
yield from []
elif level == dim:
yield _gl_invariant_for_identity(dim, dim)
else:
for det_positions in itertools.combinations(range(level),dim): # positions for the determinant
rest = set(range(level))-set(det_positions)
for partitions in equal_size_partitions(rest, 2):
inv = defaultdict(int)
for tau in symmetric(dim):
for letters in itertools.product( range(1,dim+1), repeat=level//2 ):
current_word = np.zeros( level, dtype=np.int8 )
for i in range( (level-dim)//2 ):
current_word[ partitions[i][0] ] = letters[i]
current_word[ partitions[i][1] ] = letters[i]
det_letters = [ (i^tau) + 1 for i in range(dim) ]
for i in range(dim):
current_word[ det_positions[i] ] = det_letters[i]
sign = tau.signature()
inv[ words.ShuffleWord(current_word) ] += sign
yield lc.LinearCombination( inv )
def test_lead_lag():
#################
# Map to lead-lag.
#################
def replace_multidim(m, _dim):
# [0,1,2]
# [0',0'',1',1'',2',2'']
if len(m) == 0:
fail
types = []
tmp = {}
for d in range(2 * _dim):
if m.get(d, 0) >= 1:
go_to = d // 2
if d % 2 == 0:
typ = 'left'
else:
typ = 'right'
types.append(typ)
tmp[go_to] = m[d]
if all(map(lambda s: s == 'left', types)) or all(
map(lambda s: s == 'right', types)):
return types[0], dp.Monomial(tmp)
else:
return None, None
def _ll_to_ds_multidim(_dim):
def __ll_to_ds_multidim(cqs):
if len(cqs) == 1:
t, rep = replace_multidim(cqs[0], _dim)
if not rep is None:
yield (dp.CompositionQuasiShuffle(
(dp.Monomial(rep), )), +1)
else:
last = cqs[-1]
t_last, rep_last = replace_multidim(last, _dim)
if rep_last == None:
return
second_to_last = cqs[-2]
t_second_to_last, rep_second_to_last = replace_multidim(
second_to_last, _dim)
if rep_second_to_last == None:
return
for x, c in __ll_to_ds_multidim(cqs[0:-1]):
yield (dp.CompositionQuasiShuffle(x + (rep_last, )), +1)
if t_second_to_last == 'left' and t_last == 'right':
merged = dp.Monomial(x[-1])
for _x, _p in rep_last.items():
dp.safe_add(merged, _x, _p)
yield (dp.CompositionQuasiShuffle(x[:-1] + (merged, )),
+1)
return __ll_to_ds_multidim
def ll_to_ds_multidim(phi, _dim):
return phi.apply_linear_function(_ll_to_ds_multidim(_dim))
z = np.random.randint(-100, 100, size=(2, 10))
doubles = []
for d in range(2):
double = []
for i in range(0, len(z[d])):
double.append(z[d, i])
double.append(z[d, i])
doubles.append(double)
xs = []
for d in range(2):
xs.append(doubles[d][1:])
xs.append(doubles[d][:-1])
x = np.vstack(xs)
DSx = iss.terminal_values(iss.signature(x, 4))
DSz = iss.terminal_values(iss.signature(z, 4))
# 1' \mapsto 1
assert get(M_qs({0: 1}), DSx) == get(M_qs({0: 1}), DSz)
assert ll_to_ds_multidim(M_qs({0: 1}), 2) == M_qs({0: 1})
# 1'' \mapsto 1
assert get(M_qs({1: 1}), DSx) == get(M_qs({0: 1}), DSz)
assert ll_to_ds_multidim(M_qs({1: 1}), 2) == M_qs({0: 1})
# [1'^2] \mapsto (1^2)
assert get(M_qs({0: 2}), DSx) == get(M_qs({0: 2}), DSz)
assert ll_to_ds_multidim(M_qs({0: 2}), 2) == M_qs({0: 2})
# [1''^2] \mapsto (1^2)
assert get(M_qs({1: 2}), DSx) == get(M_qs({0: 2}), DSz)
assert ll_to_ds_multidim(M_qs({1: 2}), 2) == M_qs({0: 2})
# [1'1''] \mapsto _
assert get(M_qs({0: 1, 1: 1}), DSx) == 0
assert ll_to_ds_multidim(M_qs({0: 1, 1: 1}), 2) == lc.LinearCombination()
# 1'1' \mapsto 11
assert get(M_qs({0: 1}, {0: 1}), DSx) == get(M_qs({0: 1}, {0: 1}), DSz)
assert ll_to_ds_multidim(M_qs({0: 1}, {0: 1}), 2) == M_qs({0: 1}, {0: 1})
# 1'1'' \mapsto 11 + [1^2]
assert get(M_qs({0: 1}, {1: 1}),
DSx) == get(M_qs({0: 1}, {0: 1}), DSz) + get(M_qs({0: 2}), DSz)
assert ll_to_ds_multidim(M_qs({0: 1}, {1: 1}),
2) == M_qs({0: 1}, {0: 1}) + M_qs({0: 2})
# 1''1' \mapsto 11
assert get(M_qs({1: 1}, {0: 1}), DSx) == get(M_qs({0: 1}, {0: 1}), DSz)
assert ll_to_ds_multidim(M_qs({1: 1}, {0: 1}), 2) == M_qs({0: 1}, {0: 1})
# 1'1''1' \mapsto 111 + (1^2)1
assert get(M_qs({0: 1}, {1: 1}, {0: 1}), DSx) == get(
M_qs({0: 1}, {0: 1}, {0: 1}), DSz) + get(M_qs({0: 2}, {0: 1}), DSz)
assert ll_to_ds_multidim(
M_qs({0: 1}, {1: 1},
{0: 1}), 2) == M_qs({0: 1}, {0: 1}, {0: 1}) + M_qs({0: 2}, {0: 1})
# 1''1'1'' \mapsto 111 + 1(1^2)
assert get(M_qs({1: 1}, {0: 1}, {1: 1}), DSx) == get(
M_qs({0: 1}, {0: 1}, {0: 1}), DSz) + get(M_qs({0: 1}, {0: 2}), DSz)
assert ll_to_ds_multidim(
M_qs({1: 1}, {0: 1},
{1: 1}), 2) == M_qs({0: 1}, {0: 1}, {0: 1}) + M_qs({0: 1}, {0: 2})
# 1''[1'^2] \mapsto 1[1^2]
assert get(M_qs({1: 1}, {0: 2}), DSx) == get(M_qs({0: 1}, {0: 2}), DSz)
assert ll_to_ds_multidim(M_qs({1: 1}, {0: 2}), 2) == M_qs({0: 1}, {0: 2})
# 1'[1''^2] \mapsto 1[1^2] + [1^3]
assert get(
M_qs({0: 1}, {1: 2}),
DSx) == get(M_qs({0: 1}, {0: 2}), DSz) + +get(M_qs({0: 3}), DSz)
assert ll_to_ds_multidim(M_qs({0: 1}, {1: 2}),
2) == M_qs({0: 1}, {0: 2}) + M_qs({0: 3})
# It does the job.
for C1 in dp.compositions(range(4), 4):
phi = M_qs(*C1)
assert get(ll_to_ds_multidim(phi, 2), DSz) == get(phi, DSx)
# It is a quasi-shuffle morphism.
assert ll_to_ds_multidim(M_qs({0: 1}, {1: 1}), 2) * ll_to_ds_multidim(
M_qs({2: 1}, {3: 1}), 2) == ll_to_ds_multidim(
M_qs({0: 1}, {1: 1}) * M_qs({2: 1}, {3: 1}), 2)
assert ll_to_ds_multidim(M_qs({0: 1}, {1: 1}), 2) * ll_to_ds_multidim(
M_qs({2: 1}, {2: 1}), 2) == ll_to_ds_multidim(
M_qs({0: 1}, {1: 1}) * M_qs({2: 1}, {2: 1}), 2)
assert ll_to_ds_multidim(M_qs({0: 1}, {1: 1}), 2) * ll_to_ds_multidim(
M_qs({3: 1}, {3: 1}), 2) == ll_to_ds_multidim(
M_qs({0: 1}, {1: 1}) * M_qs({3: 1}, {3: 1}), 2)
assert ll_to_ds_multidim(M_qs({0: 1}, {0: 1}), 2) * ll_to_ds_multidim(
M_qs({2: 1}, {2: 1}), 2) == ll_to_ds_multidim(
M_qs({0: 1}, {0: 1}) * M_qs({2: 1}, {2: 1}), 2)
comps = list(dp.compositions(range(4), 3))
for i in range(len(comps)):
for j in range(i + 1):
C1 = comps[i]
C2 = comps[j]
phi = M_qs(*C1)
psi = M_qs(*C2)
assert ll_to_ds_multidim(
phi * psi,
2) == ll_to_ds_multidim(phi, 2) * ll_to_ds_multidim(psi, 2)
def _LOG_dual_monomial(m):
_ret = lc.LinearCombination()
for C in finer(CompositionConcatenation((m, ))):
_ret += sympy.Rational((-1)**(len(C) - 1), len(C)) * lc.lift(C)
return _ret
def _EXP_dual_monomial(m):
_ret = lc.LinearCombination()
for C in finer(CompositionConcatenation((m, ))):
_ret += sympy.Rational(1, sympy.factorial(len(C))) * lc.lift(C)
return _ret
def test_concatentation_word():
def lcw(*args):
return lc.lift(ConcatenationWord(args))
assert lcw(1, 2, 3) + 77 * lcw(1, 9) == lc.LinearCombination.from_str(
"123 + [77] 19", ConcatenationWord)
t = lambda x, y: lc.Tensor((x, y))
def cw(*args):
return ConcatenationWord(args)
lc_1 = lc.lift(cw(1, 2))
lc_2 = lc.lift(cw(3))
assert lc.LinearCombination({ConcatenationWord((1, 2, 3)):
1}) == lc_1 * lc_2
o = lc.LinearCombination.otimes(lc_1, lc_1 + 7 * lc_2)
assert lc.LinearCombination( { t(cw(1,2,1,2), cw(1,2,1,2)):1, t(cw(1,2,1,2),cw(1,2,3)):7, t(cw(1,2,1,2),cw(3,1,2)):7, t(cw(1,2,1,2),cw(3,3)):49} )\
== o * o
lc_1 = lc.lift(cw(1, 2))
lc_2 = lc.lift(cw(3, 4, 5))
def id_otimes_coproduct(t):
for x, c in ConcatenationWord.coproduct(t[1]):
yield (lc.Tensor((t[0], x[0], x[1])), c)
def coproduct_otimes_id(t):
for x, c in ConcatenationWord.coproduct(t[0]):
yield (lc.Tensor((x[0], x[1], t[1])), c)
# Coassociativity.
assert lc_1.apply_linear_function( ConcatenationWord.coproduct ).apply_linear_function( lc.Tensor.fn_otimes_linear( lc.id, ConcatenationWord.coproduct ) ) \
== lc_1.apply_linear_function( ConcatenationWord.coproduct ).apply_linear_function( lc.Tensor.fn_otimes_linear( ConcatenationWord.coproduct, lc.id ) )
assert lc_2.apply_linear_function( ConcatenationWord.coproduct ).apply_linear_function( lc.Tensor.fn_otimes_linear( lc.id, ConcatenationWord.coproduct ) ) \
== lc_2.apply_linear_function( ConcatenationWord.coproduct ).apply_linear_function( lc.Tensor.fn_otimes_linear( ConcatenationWord.coproduct, lc.id ) )
# Condition on product vs coproduct: \Delta( \tau \tau' ) = \Delta(\tau) \Delta(\tau').
assert (lc_1 * lc_2).apply_linear_function( ConcatenationWord.coproduct ) \
== lc_1.apply_linear_function( ConcatenationWord.coproduct ) \
* lc_2.apply_linear_function( ConcatenationWord.coproduct )
# Condition on antipode: \Nabla (A \otimes id) \Delta = \eta \vareps.
assert lc.LinearCombination() \
== lc_1.apply_linear_function(ConcatenationWord.coproduct)\
.apply_linear_function( lc.Tensor.fn_otimes_linear(lc.id,ConcatenationWord.antipode) )\
.apply_linear_function( lc.Tensor.m12 )
assert lc.lift(cw()) \
== lc.lift(cw())\
.apply_linear_function(ConcatenationWord.coproduct)\
.apply_linear_function( lc.Tensor.fn_otimes_linear(lc.id,ConcatenationWord.antipode) )\
.apply_linear_function( lc.Tensor.m12 )
# Unit.
assert lc_1 == ConcatenationWord.unit() * lc_1
# Counit.
#assert lc_1 == lc_1.apply_linear_function( ConcatenationWord.coproduct )\
# .apply_linear_function( lc.Tensor.fn_otimes_linear( lc.id, ConcatenationWord.counit ) )\
# .apply_linear_function( lc.Tensor.projection(0) )
assert lc_1 == lc_1.apply_linear_function( ConcatenationWord.coproduct )\
.apply_linear_function( lc.Tensor.id_otimes_fn( ConcatenationWord.counit ) )
# The inner product is a bit awkward:
# The natural dual of the concatenation algebra is the shuffle algebra.
# But the method LinearCombination.inner_product only works with vectors of the exact same type.
# Hence we need to transform one of them into the other.
assert 1 == lc.LinearCombination.inner_product(lc_1, lc_1)
lc_sw_1 = lc.lift(ShuffleWord((1, 2)))
assert 0 == lc.LinearCombination.inner_product(lc_sw_1, lc_1)
assert 1 == lc.LinearCombination.inner_product(
lc_sw_1.apply_linear_function(shuffle_word_to_concatenation_word),
lc_1)
def terminal_values(s):
return lc.LinearCombination({v: c[-1] for v, c in s.items()})
