def compute_norm(self, field=None, **defaults_config):
'''
Proves ‖a ⊗ b ⊗ ... ⊗ y ⊗ z‖ = ‖a‖·‖b‖·...·‖y‖·‖z‖.
'''
from proveit.logic import EvaluationError
from . import norm_of_tensor_prod, norm_preserving_tensor_prod
_a = self.operands
_i = _a.num_elements()
_K = VecSpaces.get_field(field)
vec_spaces = VecSpaces.known_vec_spaces(self.operands, field=_K)
# See if all of the operand normalizations evaluate to one.
all_norm_one = True
try:
for operand in self.operands:
if isinstance(operand, ExprRange):
with defaults.temporary() as tmp_defaults:
tmp_defaults.assumptions = defaults.assumptions + (
operand.parameter_condition(),)
body_norm = operand.body.compute_norm()
if body_norm.rhs.evaluated() != one:
all_norm_one = False
break
else:
if operand.compute_norm().rhs.evaluated() != one:
all_norm_one = False
break
except (EvaluationError, NotImplementedError):
all_norm_one = False
pass
if all_norm_one:
thm = norm_preserving_tensor_prod
else:
thm = norm_of_tensor_prod
return thm.instantiate({K: _K, i: _i, V: vec_spaces, a: _a})
def double_scaling_reduction(self, **defaults_config):
from . import doubly_scaled_as_singly_scaled
if not isinstance(self.scaled, ScalarMult):
raise ValueError("'double_scaling_reduction' is only applicable "
"for a doubly nested ScalarMult")
# Reduce doubly-nested ScalarMult
_x = self.scaled.scaled
# _V = VecSpaces.known_vec_space(_x)
# the following is a little klunky, but trying to avoid the
# use of a default field=Real if we're actually dealing with
# complex scalars somewhere in the vector
from proveit import free_vars
if any([InSet(elem, Complex).proven() for elem in free_vars(self)]):
_V = VecSpaces.known_vec_space(self, field=Complex)
else:
_V = VecSpaces.known_vec_space(self)
_K = VecSpaces.known_field(_V)
_alpha = self.scalar
_beta = self.scaled.scalar
return doubly_scaled_as_singly_scaled.instantiate({
K: _K,
V: _V,
x: _x,
alpha: _alpha,
beta: _beta
})
def shallow_simplification(self, *, must_evaluate=False,
**defaults_config):
'''
Returns a proven simplification equation for this TensorProd
expression assuming the operands have been simplified.
Currently deals only with:
(1) simplifying a TensorProd(x) (i.e. a TensorProd with a
single operand x) to x itself. For example,
TensorProd(x) = x.
(2) Ungrouping nested tensor products.
(3) Factoring out scalars.
'''
if self.operands.is_single():
from . import unary_tensor_prod_def
_V = VecSpaces.known_vec_space(self.operand)
_K = VecSpaces.known_field(_V)
return unary_tensor_prod_def.instantiate(
{K:_K, V:_V, A:self.operands[0]}, preserve_all=True)
# for convenience updating our equation:
expr = self
eq = TransRelUpdater(expr)
if TensorProd._simplification_directives_.ungroup:
# ungroup the expression (disassociate nested additions).
_n = 0
length = expr.operands.num_entries() - 1
# loop through all operands
while _n < length:
operand = expr.operands[_n]
if isinstance(operand, TensorProd):
# if it is grouped, ungroup it
expr = eq.update(expr.disassociation(
_n, preserve_all=True))
length = expr.operands.num_entries()
_n += 1
if TensorProd._simplification_directives_.factor_scalars:
# Next, pull out scalar factors
try:
VecSpaces.known_vec_space(self)
except ValueError:
raise UnsatisfiedPrerequisites(
"No known vector space for %s"%self)
for _k, operand in enumerate(expr.operands):
if isinstance(operand, ScalarMult):
# Just pull out the first one we see and let
# recursive simplifications take care of any more.
# To make sure this happens we turn auto_simplify
# on, and those simpiflications should all be fair
# game as part of shallow_simplification.
expr = eq.update(expr.scalar_factorization(
_k, auto_simplify=True))
break
# Future processing possible here.
return eq.relation
def compute_norm(self, **defaults_config):
'''
Proves ‖a v‖ = |a| ‖v‖.
'''
from proveit.linear_algebra.inner_products import scaled_norm
vec_space = VecSpaces.known_vec_space(self.scaled)
field = VecSpaces.known_field(vec_space)
return scaled_norm.instantiate({
K: field,
H: vec_space,
alpha: self.scalar,
x: self.scaled
})
def disassociation(self, idx, *, field=None,
**defaults_config):
'''
Given vector operands, or all CartExp operands, deduce that
this expression is equal to a form in which operand at index
idx is no longer grouped together.
For example, calling
(a ⊗ b ⊗ ... (l ⊗ ... ⊗ m) ... ⊗ y ⊗ z).association(l-1)
would return
|- (a ⊗ b ⊗ ... (l ⊗ ... ⊗ m) ... ⊗ y ⊗ z) =
(a ⊗ b ⊗ ... ⊗ l ⊗ ... ⊗ m ⊗ ... ⊗ y ⊗ z)
Or calling (R3 ⊗ (R3 ⊗ R3)).disassociate(1) would return
|- (R3 ⊗ (R3 ⊗ R3)) = (R3 ⊗ R3 ⊗ R3)
For this to work in the vectors case, the vector operands must
be known to be in vector spaces of a common field. If the
field is not specified, then VecSpaces.default_field is used.
For this to work in the case of CartExp operands, all operands
must be (recursively) CartExps and each must be known to be
a vector space.
'''
# ORIGINAL BELOW before augmenting for CartExp cases
# from . import tensor_prod_disassociation
# _V = VecSpaces.known_vec_space(self, field=field)
# _K = VecSpaces.known_field(_V)
# eq = apply_disassociation_thm(
# self, idx, tensor_prod_disassociation,
# repl_map_extras={K:_K, V:_V}).derive_consequent()
# return eq.with_wrapping_at()
if not TensorProd.all_ops_are_cart_exp(self):
from . import tensor_prod_disassociation
_V = VecSpaces.known_vec_space(self, field=field)
_K = VecSpaces.known_field(_V)
eq = apply_disassociation_thm(
self, idx, tensor_prod_disassociation,
repl_map_extras={K:_K, V:_V}).derive_consequent()
return eq.with_wrapping_at()
else:
from . import tensor_prod_vec_space_disassociation
if field is None:
_K = VecSpaces.known_field(self.operands[0])
else:
_K = field
eq = apply_disassociation_thm(
self, idx, tensor_prod_vec_space_disassociation,
repl_map_extras={K:_K})
return eq.with_wrapping_at()
def distribution(self, idx, *, field=None,
**defaults_config):
'''
Given a TensorProd operand at the (0-based) index location
'idx' that is a vector sum or summation, prove the distribution
over that TensorProd factor and return an equality to the
original TensorProd. For example, we could take the TensorProd
tens_prod = TensorProd(a, b+c, d)
and call tens_prod.distribution(1) to obtain:
|- TensorProd(a, b+c, d) =
TensorProd(a, b, d) + TensorProd(a, c, d)
'''
from . import (tensor_prod_distribution_over_add,
tensor_prod_distribution_over_summation)
_V = VecSpaces.known_vec_space(self, field=field)
_K = VecSpaces.known_field(_V)
sum_factor = self.operands[idx]
_a = self.operands[:idx]
_c = self.operands[idx+1:]
_i = _a.num_elements()
_k = _c.num_elements()
if isinstance(sum_factor, VecAdd):
_b = sum_factor.operands
_V = VecSpaces.known_vec_space(self, field=field)
_j = _b.num_elements()
# use preserve_all=True in the following instantiation
# because the instantiation is an intermediate step;
# otherwise auto_simplification can over-do things
impl = tensor_prod_distribution_over_add.instantiate(
{K:_K, i:_i, j:_j, k:_k, V:_V, a:_a, b:_b, c:_c},
preserve_all=True)
return impl.derive_consequent().with_wrapping_at()
elif isinstance(sum_factor, VecSum):
_b = sum_factor.indices
_j = _b.num_elements()
_f = Lambda(sum_factor.indices, sum_factor.summand)
_Q = Lambda(sum_factor.indices, sum_factor.condition)
# use preserve_all=True in the following instantiation
# because the instantiation is an intermediate step;
# otherwise auto_simplification can over-do things
impl = tensor_prod_distribution_over_summation.instantiate(
{K:_K, f:_f, Q:_Q, i:_i, j:_j, k:_k,
V:_V, a:_a, b:_b, c:_c}, preserve_all=True)
return impl.derive_consequent().with_wrapping_at()
else:
raise ValueError(
"Don't know how to distribute tensor product over " +
str(sum_factor.__class__) + " factor")
def scalar_one_elimination(self, **defaults_config):
'''
Equivalence method that derives a simplification in which
a single scalar multiplier of 1 is eliminated.
For example, letting v denote a vector element of some VecSpace,
then ScalarMult(one, v).scalar_one_elimination() would return
|- ScalarMult(one, v) = v
Will need to know that v is in VecSpaces(K) and that the
multiplicative identity 1 is in the field K. This might require
assumptions or pre-proving of a VecSpace that contains the
vector appearing in the ScalarMult expression.
'''
from proveit.numbers import one
# from . import elim_one_left, elim_one_right
from . import one_as_scalar_mult_id
# The following seems silly -- if the scalar is not 1, just
# return with no change instead?
if self.scalar != one:
raise ValueError(
"For ScalarMult.scalar_one_elimination(), the scalar "
"multiplier in {0} was expected to be 1 but instead "
"was {1}.".format(self, self.scalar))
# obtain the instance params:
# _K is the field; _V is the vector space over field _K;
# and _v is the vector in _V being scaled
_K, _v, _V = one_as_scalar_mult_id.all_instance_params()
# isolate the vector portion
_v_sub = self.scaled
# Find a containing vector space V in the theorem.
# This may fail!
_V_sub = list(VecSpaces.yield_known_vec_spaces(_v_sub))[0]
# Find a vec space field, hopefully one that contains the mult
# identity 1. This may fail!
_K_sub = list(VecSpaces.yield_known_fields(_V_sub))[0]
# If we made it this far, can probably instantiate the theorem
# (although it could still fail if 1 is not in the field _K_sub)
return one_as_scalar_mult_id.instantiate({
_K: _K_sub,
_v: _v_sub,
_V: _V_sub
})
def deduce_in_vec_space(self, vec_space=None, *, field,
**defaults_config):
'''
Deduce that the tensor product of vectors is in a vector space
which is the tensor product of corresponding vectors spaces.
'''
from . import tensor_prod_is_in_tensor_prod_space
_a = self.operands
_i = _a.num_elements()
_K = VecSpaces.get_field(field)
vec_spaces = VecSpaces.known_vec_spaces(self.operands, field=_K)
membership = tensor_prod_is_in_tensor_prod_space.instantiate(
{K: _K, i: _i, V: vec_spaces, a: _a})
if vec_space is not None and membership.domain != vec_space:
sub_rel = SubsetEq(membership.domain, vec_space)
return sub_rel.derive_superset_membership(self)
return membership
def scalar_factorization(self, idx=None, *, field=None,
**defaults_config):
'''
Prove the factorization of a scalar from one of the tensor
product operands and return the original tensor product equal
to the factored version. If idx is provided, it will specify
the (0-based) index location of the ScalarMult operand with the
multiplier to factor out. If no idx is provided, the first
ScalarMult operand will be targeted.
For example,
TensorProd(a, ScalarMult(c, b), d).factorization(1)
returns
|- TensorProd(a, ScalarMult(c, b), d) =
c TensorProd(a, b, d)
As a prerequisite, the operands must be known to be vectors in
vector spaces over a common field which contains the scalar
multiplier being factored. If the field is not specified,
then VecSpaces.default_field is used.
'''
from . import factor_scalar_from_tensor_prod
if idx is None:
for _k, operand in enumerate(self.operands):
if isinstance(operand, ScalarMult):
idx = _k
break
elif idx < 0:
# use wrap-around indexing
idx = self.operand.num_entries() + idx
if not isinstance(self.operands[idx], ScalarMult):
raise TypeError("Expected the 'operand' and 'operand_idx' to be "
"a ScalarMult")
_V = VecSpaces.known_vec_space(self, field=field)
_K = VecSpaces.known_field(_V)
_alpha = self.operands[idx].scalar
_a = self.operands[:idx]
_b = self.operands[idx].scaled
_c = self.operands[idx+1:]
_i = _a.num_elements()
_k = _c.num_elements()
impl = factor_scalar_from_tensor_prod.instantiate(
{K:_K, alpha:_alpha, i:_i, k:_k, V:_V, a:_a, b:_b, c:_c})
return impl.derive_consequent().with_wrapping_at()
def deduce_in_vec_space(self, vec_space=None, *, field, **defaults_config):
'''
Prove that this scaled vector is in a vector space. The vector
space may be specified or inferred via known memberships.
A field for the vector space must be specified.
'''
from . import scalar_mult_closure
if vec_space is None:
# No vector space given, so we'll have to look for
# a known membership of 'scaled' in a vector space.
# This may be arbitrarily chosen.
vec_space = VecSpaces.known_vec_space(self.scaled, field=field)
field = VecSpaces.known_field(vec_space)
return scalar_mult_closure.instantiate({
K: field,
V: vec_space,
a: self.scalar,
x: self.scaled
})
def derive_cartexp_membership(self, **defaults_config):
'''
Derive membership in the CartExp which is a superset of the
TensorExp.
'''
from . import tensor_exp_inclusion, tensor_exp_of_cart_exp_inclusion
if isinstance(self.domain.base, CartExp):
_V = self.domain.base
_K = VecSpaces.known_field(_V)
_m = self.domain.base.exponent
_n = self.domain.exponent
inclusion = tensor_exp_of_cart_exp_inclusion.instantiate(
{V:_V, K:_K, m:_m, n:_n})
else:
_V = self.domain.base
_K = VecSpaces.known_field(_V)
_n = self.domain.exponent
inclusion = tensor_exp_inclusion.instantiate(
{V:_V, K:_K, n:_n})
return inclusion.derive_superset_membership(self.element)
def shallow_simplification(self, *, must_evaluate=False,
**defaults_config):
'''
Simplify via inner product linearity:
<a x, y> = a <x, y>
<x, y> = <x, a y>
<x + y, z> = <x, z> + <y, z>
<x, y + z> = <x, y> + <x, z>
'''
from proveit.linear_algebra import VecSpaces, ScalarMult, VecAdd
from proveit.linear_algebra.inner_products import (
inner_prod_scalar_mult_left, inner_prod_scalar_mult_right,
inner_prod_vec_add_left, inner_prod_vec_add_right)
_u, _v = self.operands
try:
vec_space = VecSpaces.common_known_vec_space((_u, _v))
except UnsatisfiedPrerequisites:
# No known common vectors space for the operands, so
# we have no specific shallow_simplication we can do here.
return Operation.shallow_simplification(
self, must_evaluate=must_evaluate)
field = VecSpaces.known_field(vec_space)
simp = None
if isinstance(_u, ScalarMult):
simp = inner_prod_scalar_mult_left.instantiate(
{K:field, H:vec_space, a:_u.scalar, x:_u.scaled, y:_v})
if isinstance(_v, ScalarMult):
simp = inner_prod_scalar_mult_right.instantiate(
{K:field, H:vec_space, a:_v.scalar, x:_u, y:_v.scaled})
if isinstance(_u, VecAdd):
simp = inner_prod_vec_add_left.instantiate(
{K:field, H:vec_space, x:_u.terms[0], y:_u.terms[1], z:_v})
if isinstance(_v, VecAdd):
simp = inner_prod_vec_add_right.instantiate(
{K:field, H:vec_space, x:_u, y:_v.terms[0], z:_v.terms[1]})
if simp is None:
return Operation.shallow_simplification(
self, must_evaluate=must_evaluate)
if must_evaluate and not is_irreducible_value(simp.rhs):
return simp.inner_expr().rhs.evaluate()
return simp
def insert_vec_on_both_sides_of_equals(tensor_equality, idx, vec,
rhs_idx = None, *,
field = None,
**defaults_config):
'''
From an equality with tensor products of vectors on
both sides, derive a similar equality but with a vector
operand inserted at the particular given zero-based index (idx).
A different index may be specified for the right side as the
left side by setting rhs_idx (i.e., if entries don't line up
due to differences of ExprRange entries), but the default will
be to use the same.
'''
from . import insert_vec_on_both_sides_of_equality
# First check various characteristics of the tensor_equality
tensor_equality = TensorProd._check_tensor_equality(
tensor_equality, allow_unary=True)
if idx < 0:
# use wrap-around indexing
idx = tensor_equality.num_entries() + idx
if rhs_idx is None:
rhs_idx = idx # use the same index on both sides by default
_a = tensor_equality.lhs.operands[:idx]
_b = vec
_c = tensor_equality.lhs.operands[idx:]
_d = tensor_equality.rhs.operands[:rhs_idx]
_e = tensor_equality.rhs.operands[rhs_idx:]
_i = _a.num_elements()
_k = _c.num_elements()
vec_space = VecSpaces.known_vec_space(tensor_equality.lhs,
field=field)
_K = VecSpaces.known_field(vec_space)
_U = VecSpaces.known_vec_spaces(_a, field=_K)
_V = VecSpaces.known_vec_space(_b, field=_K)
_W = VecSpaces.known_vec_spaces(_c, field=_K)
impl = insert_vec_on_both_sides_of_equality.instantiate(
{K:_K, i:_i, k:_k, U:_U, V:_V, W:_W,
a:_a, b:_b, c:_c, d:_d, e:_e})
return impl.derive_consequent().with_mimicked_style(tensor_equality)
def compute_norm(self, **defaults_config):
'''
Proves ‖x + y‖ = sqrt(‖x‖^2 + ‖y‖^2) if the inner product
of x and y is zero.
'''
from proveit.linear_algebra import InnerProd, ScalarMult
from . import norm_of_sum_of_orthogonal_pair
if self.operands.is_double():
_a, _b = self.operands
_x = _a.scaled if isinstance(_a, ScalarMult) else _a
_y = _b.scaled if isinstance(_b, ScalarMult) else _b
if Equals(InnerProd(_x, _y), zero).proven():
vec_space = VecSpaces.known_vec_space(_a)
field = VecSpaces.known_field(vec_space)
return norm_of_sum_of_orthogonal_pair.instantiate({
K: field,
V: vec_space,
a: _a,
b: _b
})
raise NotImplementedError(
"VecAdd.compute_norm is only implemented for an "
"orthogonal pair of vectors")
def deduce_in_vec_space(self, vec_space=None, *, field=None,
**defaults_config):
'''
Prove that this vector summation is in a vector space.
'''
from . import summation_closure
if vec_space is None:
with defaults.temporary() as tmp_defaults:
tmp_defaults.assumptions = (defaults.assumptions +
self.conditions.entries)
vec_space = VecSpaces.known_vec_space(self.summand,
field=field)
_V = vec_space
_K = VecSpaces.known_field(_V)
_b = self.indices
_j = _b.num_elements()
_f = Lambda(self.indices, self.summand)
if not hasattr(self, 'condition'):
print(self)
_Q = Lambda(self.indices, self.condition)
return summation_closure.instantiate(
{j:_j, K:_K, f:_f, Q:_Q, V:_V, b:_b}).derive_consequent()
def deduce_in_vec_space(self, vec_space=None, *, field, **defaults_config):
'''
Prove that this Qmult is in a vector space (e.g., if it is
a ket).
'''
from proveit.physics.quantum import QmultCodomain
# In the process of proving that 'self' is in QmultCodomain,
# it will prove it is a vector in a Hilbert space if
# appropriate.
QmultCodomain.membership_object(self).conclude()
if vec_space is not None:
return InSet(self, vec_space).prove()
return InSet(self, VecSpaces.known_vec_space(self,
field=field)).prove()
def yield_known_hilbert_spaces(vec):
'''
Given a vector expression, vec, yield any Hilbert spaces
known to contain vec.
'''
for vec_space in VecSpaces.yield_known_vec_spaces(vec, field=Complex):
if vec_space in HilbertSpacesLiteral.known_spaces_memberships:
# This vector space is already known to be an inner
# product space.
yield vec_space
else:
try:
deduce_as_hilbert_space(vec_space)
yield vec_space
except NotImplementedError:
# Not known you to prove 'vec_space' is an inner
# product space.
pass
def vec_sum_elimination(self, field=None, **defaults_config):
'''
For a VecSum in which the summand does not depend on the
summation index, return an equality between this VecSum and
the equivalent expression in which the VecSum is eliminated.
For example, suppose self = VecSum(i, v, Interval(2, 4)).
Then self.vec_sum_elimination() would return
|- self = 3*v
where the 3*v is actually ScalarMult(3, v).
The method works only for a VecSum over a single summation
index, and simply returns self = self if the VecSum elimination
is not possible due to the summand being dependent on the
index of summation.
'''
expr = self
summation_index = expr.index
eq = TransRelUpdater(expr)
if summation_index not in free_vars(expr.summand):
vec_space_membership = expr.summand.deduce_in_vec_space(
field=field,
assumptions = defaults.assumptions + expr.conditions.entries)
_V_sub = vec_space_membership.domain
_K_sub = VecSpaces.known_field(_V_sub)
_j_sub = expr.condition.domain.lower_bound
_k_sub = expr.condition.domain.upper_bound
_v_sub = expr.summand
from proveit.linear_algebra.addition import vec_sum_of_constant_vec
eq.update(vec_sum_of_constant_vec.instantiate(
{V: _V_sub, K: _K_sub, j: _j_sub, k: _k_sub, v: _v_sub}))
else:
print("VecSum cannot be eliminated. The summand {0} appears "
"to depend on the index of summation {1}".
format(expr.summand, summation_index))
return eq.relation
def tensor_prod_factoring(self, idx=None, idx_beg=None, idx_end=None,
field=None, **defaults_config):
'''
For a VecSum with a TensorProd summand or ScalarMult summand
with a scaled attribute being a TensorProd, factor out from
the VecSum the TensorProd vectors other than the ones indicated
by the (0-based) idx, or idx_beg and idx_end pair and return
an equality between the original VecSum and the new TensorProd.
For example, we could take the VecSum defined by
vec_sum = VecSum(TensorProd(x, f(i), y, z))
and call vec_sum.tensor_prod_factoring(idx_beg=1, idx_end=2)
to obtain:
|- VecSum(TensorProd(x, f(i), y, z)) =
TensorProd(x, VecSum(TensorProd(f(i), y)), z)
This method should work even if the summand is a nested
ScalarMult. Note that any vectors inside the TensorProd that
depend on the index of summation cannot be pulled out of the
VecSum and thus will cause the method to fail if not chosen
to remain inside the VecSum. If all idx args are 'None',
method will factor out all possible vector factors, including
the case where all factors could be removed and the VecSum
eliminated entirely.
Note that this method only works when self has a single
index of summation.
'''
expr = self
the_summand = self.summand
eq = TransRelUpdater(expr)
# Check that
# (1) the_summand is a TensorProd
# or (2) the_summand is a ScalarMult;
# otherwise, this method does not apply
from proveit.linear_algebra import ScalarMult, TensorProd
if isinstance(the_summand, ScalarMult):
# try shallow simplification first to remove nested
# ScalarMults and multiplicative identities
expr = eq.update(expr.inner_expr().summand.shallow_simplification())
the_summand = expr.summand
if isinstance(the_summand, TensorProd):
tensor_prod_expr = the_summand
tensor_prod_summand = True
tensor_prod_factors_list = list(
the_summand.operands.entries)
elif (isinstance(the_summand, ScalarMult)
and isinstance(the_summand.scaled, TensorProd)):
tensor_prod_expr = the_summand.scaled
tensor_prod_summand = False
tensor_prod_factors_list = list(
the_summand.scaled.operands.entries)
else:
raise ValueError(
"tensor_prod_factoring() requires the VecSum summand "
"to be a TensorProd or a ScalarMult (with its 'scaled' "
"attribute a TensorProd); instead the "
"summand is {}".format(self.instance_expr))
if idx is None and idx_beg is None and idx_end is None:
# prepare to take out all possible factors, including
# the complete elimination of the VecSum if possible
if expr.index not in free_vars(expr.summand):
# summand does not depend on index of summation
# so we can eliminate the VecSum entirely
return expr.vec_sum_elimination(field=field)
if expr.index in free_vars(tensor_prod_expr):
# identify the extractable vs. non-extractable
# TensorProd factors (and there must be at least
# one such non-extractable factor)
idx_beg = -1
idx_end = -1
for i in range(len(expr.summand.operands.entries)):
if expr.index in free_vars(tensor_prod_expr.operands[i]):
if idx_beg == -1:
idx_beg = i
idx_end = idx_beg
else:
idx_end = i
else:
# The alternative is that the summand is
# a ScalarMult with the scalar (but not the scaled)
# being dependent on the index of summation. It's not
# obvious what's best to do in this case, but we set
# things up to factor out all but the last of the
# TensorProd factors (so we'll factor out at least
# 1 factor)
idx_beg = len(tensor_prod_expr.operands.entries) - 1
idx_end = idx_beg
# Check that the provided idxs are within bounds
# (it should refer to an actual TensorProd operand)
num_vec_factors = len(tensor_prod_factors_list)
if idx is not None and idx >= num_vec_factors:
raise ValueError(
"idx value {0} provided for tensor_prod_factoring() "
"method is out-of-bounds; the TensorProd summand has "
"{1} factors: {2}, and thus possibly indices 0-{3}".
format(idx, len(tensor_prod_factors_list),
tensor_prod_factors_list,
len(tensor_prod_factors_list)-1))
if idx_beg is not None and idx_end is not None:
if (idx_end < idx_beg or idx_beg >= num_vec_factors or
idx_end >= num_vec_factors):
raise ValueError(
"idx_beg value {0} or idx_end value {1} (or both) "
"provided for tensor_prod_factoring() "
"method is/are out-of-bounds; the TensorProd summand "
"has {2} factors: {3}, and thus possibly indices 0-{3}".
format(idx_beg, idx_end, num_vec_factors,
tensor_prod_factors_list,num_vec_factors-1))
if idx is not None:
# take single idx as the default
idx_beg = idx
idx_end = idx
# Check that the TensorProd factors to be factored out do not
# rely on the VecSum index of summation
summation_index = expr.index
for i in range(num_vec_factors):
if i < idx_beg or i > idx_end:
the_factor = tensor_prod_factors_list[i]
if summation_index in free_vars(the_factor):
raise ValueError(
"TensorProd factor {0} cannot be factored "
"out of the given VecSum summation because "
"it is a function of the summation index {1}.".
format(the_factor, summation_index))
# Everything checks out as best we can tell, so prepare to
# import and instantiate the appropriate theorem,
# depending on whether:
# (1) the_summand is a TensorProd, or
# (2) the_summand is a ScalarMult (with a TensorProd 'scaled')
if tensor_prod_summand:
from proveit.linear_algebra.tensors import (
tensor_prod_distribution_over_summation)
else:
from proveit.linear_algebra.tensors import (
tensor_prod_distribution_over_summation_with_scalar_mult)
if idx_beg != idx_end:
# need to associate the elements and change idx value
# but process is slightly different in the two cases
if tensor_prod_summand:
expr = eq.update(expr.inner_expr().summand.association(
idx_beg, idx_end-idx_beg+1))
tensor_prod_expr = expr.summand
else:
expr = eq.update(expr.inner_expr().summand.scaled.association(
idx_beg, idx_end-idx_beg+1))
tensor_prod_expr = expr.summand.scaled
idx = idx_beg
from proveit import K, f, Q, i, j, k, V, a, b, c, s
# actually, maybe it doesn't matter and we can deduce the
# vector space regardless: (Adding this temp 12/26/21)
vec_space_membership = expr.summand.deduce_in_vec_space(
field=field,
assumptions = defaults.assumptions + expr.conditions.entries)
_V_sub = vec_space_membership.domain
# Substitutions regardless of Case
_K_sub = VecSpaces.known_field(_V_sub)
_b_sub = expr.indices
_j_sub = _b_sub.num_elements()
_Q_sub = Lambda(expr.indices, expr.condition)
# Case-specific substitutions, using updated tensor_prod_expr:
_a_sub = tensor_prod_expr.operands[:idx]
_c_sub = tensor_prod_expr.operands[idx+1:]
_f_sub = Lambda(expr.indices, tensor_prod_expr.operands[idx])
if not tensor_prod_summand:
_s_sub = Lambda(expr.indices, expr.summand.scalar)
# Case-dependent substitutions:
_i_sub = _a_sub.num_elements()
_k_sub = _c_sub.num_elements()
if tensor_prod_summand:
impl = tensor_prod_distribution_over_summation.instantiate(
{K:_K_sub, f:_f_sub, Q:_Q_sub, i:_i_sub, j:_j_sub,
k:_k_sub, V:_V_sub, a:_a_sub, b:_b_sub, c:_c_sub},
preserve_expr=expr)
else:
impl = (tensor_prod_distribution_over_summation_with_scalar_mult.
instantiate(
{K:_K_sub, f:_f_sub, Q:_Q_sub, i:_i_sub, j:_j_sub,
k:_k_sub, V:_V_sub, a:_a_sub, b:_b_sub, c:_c_sub,
s: _s_sub}, preserve_expr=expr))
expr = eq.update(impl.derive_consequent(
assumptions = defaults.assumptions + expr.conditions.entries).
derive_reversed())
return eq.relation
def conclude(self, **defaults_config):
'''
Called on self = [elem in (A x B x ...)] (where x denotes
a tensor product and elem = a x b x ...) and knowing or
assuming that (a in A) and (b in B) and ..., derive and
return self.
'''
if isinstance(self.element, TensorProd):
from . import tensor_prod_is_in_tensor_prod_space
from proveit.linear_algebra import VecSpaces
# we will need the domain acknowledged as a VecSpace
# so we can later get its underlying field
self.domain.deduce_as_vec_space()
_a_sub = self.element.operands
_i_sub = _a_sub.num_elements()
_K_sub = VecSpaces.known_field(self.domain)
vec_spaces = self.domain.operands
return tensor_prod_is_in_tensor_prod_space.instantiate({
a:
_a_sub,
i:
_i_sub,
K:
_K_sub,
V:
vec_spaces
})
if isinstance(self.element, ScalarMult):
from proveit.linear_algebra.scalar_multiplication import (
scalar_mult_closure)
from proveit.linear_algebra import VecSpaces
self.domain.deduce_as_vec_space()
_V_sub = VecSpaces.known_vec_space(self.element.scaled, field=None)
_K_sub = VecSpaces.known_field(_V_sub)
_a_sub = self.element.scalar
_x_sub = self.element.scaled
return scalar_mult_closure.instantiate({
V: _V_sub,
K: _K_sub,
a: _a_sub,
x: _x_sub
})
if isinstance(self.element, VecSum):
from proveit.linear_algebra.addition import summation_closure
from proveit.linear_algebra import VecSpaces
self.domain.deduce_as_vec_space()
# might want to change the following to use
# vec_space_membership = self.element.summand.deduce_in_vec_space()
# then _V_sub = vec_space_membership.domain
_V_sub = VecSpaces.known_vec_space(self.element.summand)
_K_sub = VecSpaces.known_field(_V_sub)
_b_sub = self.element.indices
_j_sub = _b_sub.num_elements()
_f_sub = Lambda(self.element.indices, self.element.summand)
_Q_sub = Lambda(self.element.indices, self.element.condition)
imp = summation_closure.instantiate({
V: _V_sub,
K: _K_sub,
b: _b_sub,
j: _j_sub,
f: _f_sub,
Q: _Q_sub
})
return imp.derive_consequent()
raise ProofFailure(
self, defaults.assumptions, "Element {0} is neither a TensorProd "
"nor a ScalarMult.".format(self.element))
def factors_extraction(self, field=None, **defaults_config):
'''
Derive an equality between this VecSum and the result
when all possible leading scalar factors have been extracted
and moved to the front of the VecSum (for example, in the
case where the summand of the VecSum is a ScalarMult) and
all possible tensor product factors have been moved outside
the VecSum (in front if possible, or afterward if necessary).
For example, we could take the VecSum
vec_sum = VecSum(ScalarMult(a, TensorProd(x, f(i), y))),
where the index of summation is i, and call
vec_sum.factor_extraction() to obtain:
|- vec_sum =
ScalarMult(a, TensorProd(x, VecSum(f(i)), y))
Note that any factors inside the summand that depend on the
index of summation cannot be pulled out from inside the VecSum,
and thus pose limitations on the result.
Note that this method only works when self has a single
index of summation, and only when self has a summand that is
a ScalarMult or TensorProd.
Later versions of this method should provide mechanisms to
specify factors to extract from, and/or leave behind in, the
VecSum.
'''
expr = self
summation_index = expr.index
assumptions = defaults.assumptions + expr.conditions.entries
assumptions_with_conditions = (
defaults.assumptions + expr.conditions.entries)
# for convenience in updating our equation:
# this begins with eq.relation as expr = expr
eq = TransRelUpdater(expr)
# If the summand is a ScalarMult, perform a
# shallow_simplification(), which will remove nested
# ScalarMults and multiplicative identities. This is
# intended to simplify without changing too much the
# intent of the user. This might even transform the
# ScalarMult object into something else.
from proveit.linear_algebra import ScalarMult, TensorProd
if isinstance(expr.summand, ScalarMult):
expr = eq.update(
expr.inner_expr().summand.shallow_simplification())
if isinstance(expr.summand, ScalarMult):
# had to re-check, b/c the shallow_simplification might
# have transformed the ScalarMult into the scaled object
tensor_prod_summand = False # not clearly useful; review please
the_scalar = expr.summand.scalar
elif isinstance(expr.summand, TensorProd):
tensor_prod_summand = True # not clearly useful; review please
if isinstance(expr.summand, ScalarMult):
if summation_index not in free_vars(expr.summand.scalar):
# it doesn't matter what the scalar is; the whole thing
# can be pulled out in front of the VecSum
from proveit.linear_algebra.scalar_multiplication import (
distribution_over_vec_sum)
summand_in_vec_space = expr.summand.deduce_in_vec_space(
field=field, assumptions=assumptions_with_conditions)
_V_sub = summand_in_vec_space.domain
_K_sub = VecSpaces.known_field(_V_sub)
_b_sub = expr.indices
_j_sub = _b_sub.num_elements()
_f_sub = Lambda(expr.indices, expr.summand.scaled)
_Q_sub = Lambda(expr.indices, expr.condition)
_k_sub = expr.summand.scalar
imp = distribution_over_vec_sum.instantiate(
{V: _V_sub, K: _K_sub, b: _b_sub, j: _j_sub,
f: _f_sub, Q: _Q_sub, k: _k_sub},
assumptions=assumptions_with_conditions)
expr = eq.update(imp.derive_consequent(
assumptions=assumptions_with_conditions).derive_reversed())
else:
# The scalar portion is dependent on summation index.
# If the scalar itself is a Mult of things, go through
# and pull to the front of the Mult all individual
# factors that are not dependent on the summation index.
if isinstance(expr.summand.scalar, Mult):
# Repeatedly pull index-independent factors #
# to the front of the Mult factors #
# prepare to count the extractable and
# unextractable factors
_num_factored = 0
_num_unfactored = len(expr.summand.scalar.operands.entries)
# go through factors from back to front
for the_factor in reversed(
expr.summand.scalar.operands.entries):
if summation_index not in free_vars(the_factor):
expr = eq.update(
expr.inner_expr().summand.scalar.factorization(
the_factor,
assumptions=assumptions_with_conditions,
preserve_all=True))
_num_factored += 1
_num_unfactored -= 1
# group the factorable factors
if _num_factored > 0:
expr = eq.update(
expr.inner_expr().summand.scalar.association(
0, _num_factored,
assumptions=assumptions_with_conditions,
preserve_all=True))
# group the unfactorable factors
if _num_unfactored > 1:
expr = eq.update(
expr.inner_expr().summand.scalar.association(
1, _num_unfactored,
assumptions=assumptions_with_conditions,
preserve_all=True))
# finally, extract any factorable scalar factors
if _num_factored > 0:
from proveit.linear_algebra.scalar_multiplication import (
distribution_over_vec_sum_with_scalar_mult)
# Mult._simplification_directives_.ungroup = False
# _V_sub = VecSpaces.known_vec_space(expr, field=field)
summand_in_vec_space = (
expr.summand.deduce_in_vec_space(
field = field,
assumptions =
assumptions_with_conditions))
_V_sub = summand_in_vec_space.domain
_K_sub = VecSpaces.known_field(_V_sub)
_b_sub = expr.indices
_j_sub = _b_sub.num_elements()
_f_sub = Lambda(expr.indices, expr.summand.scaled)
_Q_sub = Lambda(expr.indices, expr.condition)
_c_sub = Lambda(expr.indices,
expr.summand.scalar.operands[1])
_k_sub = expr.summand.scalar.operands[0]
# when instantiating, we set preserve_expr=expr;
# otherwise auto_simplification disassociates inside
# the Mult.
impl = distribution_over_vec_sum_with_scalar_mult.instantiate(
{V:_V_sub, K:_K_sub, b: _b_sub, j: _j_sub,
f: _f_sub, Q: _Q_sub, c:_c_sub, k: _k_sub},
preserve_expr=expr,
assumptions=assumptions_with_conditions)
expr = eq.update(impl.derive_consequent(
assumptions=assumptions_with_conditions).
derive_reversed())
else:
# The scalar component is dependent on summation
# index but is not a Mult.
# Revert everything and return self = self.
print("Found summation index {0} in the scalar {1} "
"and the scalar is not a Mult object.".
format(summation_index, expr.summand.scalar))
eq = TransRelUpdater(self)
# ============================================================ #
# VECTOR FACTORS #
# ============================================================ #
# After the scalar factors (if any) have been dealt with,
# proceed with the vector factors in any remaining TensorProd
# in the summand.
# Notice that we are not guaranteed at this point that we even
# have a TensorProd to factor, and if we do have a TensorProd
# we have not identified the non-index-dependent factors to
# extract.
# After processing above for scalar factors, we might now have
# (1) expr = VecSum (we didn't find scalar factors to extract),
# inside of which we might have a ScalarMult or a TensorProd;
# or (2) expr = ScalarMult (we found some scalar factors to
# extract), with a VecSum as the scaled component.
if isinstance(expr, VecSum):
expr = eq.update(expr.tensor_prod_factoring())
elif isinstance(expr, ScalarMult) and isinstance(expr.scaled, VecSum):
expr = eq.update(expr.inner_expr().scaled.tensor_prod_factoring())
return eq.relation
def factorization(self,
the_factor,
pull="left",
group_factors=True,
field=None,
**defaults_config):
'''
Deduce an equality between this VecAdd expression and a
version in which either:
(1) the scalar factor the_factor has been factored out in
front (or possibly out behind) to produce a new ScalarMult;
OR
(2) the tensor product factor the_factor has been factored
out in front (or possible out behind) to produce a new
TensorProd.
For example, if
x = VecAdd(ScalarMult(a, v1), ScalarMult(a, v2))
then x.factorization(a) produces:
|- x = ScalarMult(a, VecAdd(v1, v2)).
Prove-It will need to know or be able to derive a vector space
in which the vectors live.
This method only works if the terms of the VecAdd are all
ScalarMult objects or all TensorProd objects.
In the case of all ScalarMult objects, any nested ScalarMult
objects are first flattened if possible.
Note: In the case of a VecAdd of all TensorProd objects,
the lack of commutativity for tensor products limits any
factorable tensor product factors to those occurring on the
far left or far right of each tensor product term. Thus, for
example, if
x = VecAdd(TensorProd(v1, v2, v3), TensorProd(v1, v4, v5))
we can call x.factorization(v1) to obtain
|- x =
TensorProd(v1, VecAdd(TensorProd(v2, v3), TensorProd(v4, v5))),
but we cannot factor v1 our of the expression
y = VecAdd(TensorProd(v2, v1, v3), TensorProd(v4, v1, v5))
'''
expr = self
eq = TransRelUpdater(expr)
replacements = list(defaults.replacements)
from proveit.linear_algebra import ScalarMult, TensorProd
from proveit.numbers import one, Mult
# Case (1) VecAdd(ScalarMult, ScalarMult, ..., ScalarMult)
if all(isinstance(op, ScalarMult) for op in self.operands):
# look for the_factor in each scalar;
# code based on Add.factorization()
_b = []
for _i in range(expr.terms.num_entries()):
# remove nesting of ScalarMults
term = expr.terms[_i].shallow_simplification().rhs
expr = eq.update(
expr.inner_expr().terms[_i].shallow_simplification())
# simplify the scalar part of the ScalarMult
term = term.inner_expr().scalar.shallow_simplification().rhs
expr = eq.update(expr.inner_expr().terms[_i].scalar.
shallow_simplification())
if hasattr(term.scalar, 'factorization'):
term_scalar_factorization = term.scalar.factorization(
the_factor,
pull,
group_factors=group_factors,
group_remainder=True,
preserve_all=True)
if not isinstance(term_scalar_factorization.rhs, Mult):
raise ValueError(
"Expecting right hand side of each factorization "
"to be a product. Instead obtained: {}".format(
term_scalar_factorization.rhs))
if pull == 'left':
# the grouped remainder on the right
_b.append(
ScalarMult(
term_scalar_factorization.rhs.operands[-1],
term.scaled))
else:
# the grouped remainder on the left
_b.append(
ScalarMult(
term_scalar_factorization.rhs.operands[0],
term.scaled))
# substitute in the factorized term
expr = eq.update(
term_scalar_factorization.substitution(
expr.inner_expr().terms[_i].scalar,
preserve_all=True))
else:
if term.scalar != the_factor:
raise ValueError(
"Factor, %s, is not present in the term at "
"index %d of %s!" % (the_factor, _i, self))
if pull == 'left':
replacements.append(
Mult(term.scalar, one).one_elimination(1))
else:
replacements.append(
Mult(one, term.scalar).one_elimination(0))
_b.append(ScalarMult(one, term.scaled))
if not group_factors and isinstance(the_factor, Mult):
factor_sub = the_factor.operands
else:
factor_sub = ExprTuple(the_factor)
# pull left/right not really relevant for the ScalarMult
# cases; this simplification step still seems relevant
if defaults.auto_simplify:
# Simplify the remainder of the factorization if
# auto-simplify is enabled.
replacements.append(VecAdd(*_b).simplification())
from proveit import K, i, k, V, a
# Perhaps here we could search through the operands to find
# an appropriate VecSpace? Or maybe it doesn't matter?
vec_space_membership = expr.operands[0].deduce_in_vec_space(
field=field)
_V_sub = vec_space_membership.domain
_K_sub = VecSpaces.known_field(_V_sub)
_i_sub = expr.operands.num_elements()
_k_sub = the_factor
_a_sub = ExprTuple(*_b)
from proveit.linear_algebra.scalar_multiplication import (
distribution_over_vectors)
distribution = distribution_over_vectors.instantiate(
{
V: _V_sub,
K: _K_sub,
i: _i_sub,
k: _k_sub,
a: _a_sub
},
replacements=replacements)
# need to connect the distributed version back to the
# original self, via a shallow_simplification() of
# each of the ScalarMult terms resulting in the distribution
for _i in range(len(distribution.rhs.operands.entries)):
distribution = (distribution.inner_expr().rhs.operands[_i].
shallow_simplify())
eq.update(distribution.derive_reversed())
# Case (2) VecAdd(TensorProd, TensorProd, ..., TensorProd)
elif all(isinstance(op, TensorProd) for op in self.operands):
# if hasattr(the_factor, 'operands'):
# print("the_factor has operands: {}".format(the_factor.operands))
# the_factor_tuple = the_factor.operands.entries
# else:
# print("the_factor does not have operands: {}".format(the_factor))
# the_factor_tuple = (the_factor,)
if isinstance(the_factor, TensorProd):
the_factor_tuple = the_factor.operands.entries
else:
the_factor_tuple = (the_factor, )
# Setting the default_field here because the field
# used manually in the association step somehow gets lost
VecSpaces.default_field = field
# look for the_factor in each TensorProd appearing in
# the VecAdd operands, looking at the left vs. right
# sides depending on the 'pull' direction specified
_b = [] # to hold factors left behind
for _i in range(expr.terms.num_entries()):
# Notice we're not ready to deal with ExprRange
# versions of Add operands here!
# We are also implicitly assuming that each TensorProd
# has at least two operands
term = expr.terms[_i]
if hasattr(term, 'operands'):
term_tuple = term.operands.entries
else:
term_tuple = (term, )
if pull == 'left':
# look for factor at left-most-side
if the_factor_tuple != term_tuple[0:len(the_factor_tuple)]:
raise ValueError(
"VecAdd.factorization() expecting the_factor "
"{0} to appear at the leftmost side of each "
"addend, but {0} does not appear at the "
"leftmost side of the addend {1}.".format(
the_factor, term))
else:
# we're OK, so save away the remainder of
# factors from the rhs of the term,
# and group any multi-term factor on the left
if len(term_tuple[len(the_factor_tuple):]) == 1:
_b.append(term_tuple[-1])
else:
_b.append(
TensorProd(
*term_tuple[len(the_factor_tuple):]))
# then create an associated version of the
# expr to match the eventual thm instantiation
# ALSO NEED TO DO THIS FOR THE RIGHT CASE
expr = eq.update(
expr.inner_expr().operands[_i].association(
len(the_factor_tuple),
len(term_tuple) - len(the_factor_tuple),
preserve_all=True))
# perhaps we actually don't need the assoc step?
# if len(the_factor_tuple) != 1:
# expr = eq.update(expr.inner_expr().operands[_i].
# association(0, len(the_factor_tuple),
# preserve_all=True))
elif pull == 'right':
# look for factor at right-most-side
if the_factor_tuple != term_tuple[-(
len(the_factor_tuple)):]:
raise ValueError(
"VecAdd.factorization() expecting the_factor "
"{0} to appear at the rightmost side of each "
"addend, but {0} does not appear at the "
"rightmost side of the addend {1}.".format(
the_factor, term))
else:
# we're OK, so save away the remainder of
# factors from the lhs of the term,
# and group any multi-term factor on the right
if len(term_tuple[0:-(len(the_factor_tuple))]) == 1:
_b.append(term_tuple[0])
else:
_b.append(
TensorProd(
*term_tuple[0:-(len(the_factor_tuple))]))
# then create an associated version of the
# expr to match the eventual thm instantiation
expr = eq.update(
expr.inner_expr().operands[_i].association(
0,
len(term_tuple) - len(the_factor_tuple),
preserve_all=True))
# perhaps we actually don't need the assoc step?
# if len(the_factor_tuple) != 1:
# expr = eq.update(expr.inner_expr().operands[_i].
# association(
# len(term_tuple)-len(the_factor_tuple),
# len(the_factor_tuple),
# preserve_all=True))
else:
raise ValueError(
"VecAdd.factorization() requires 'pull' argument "
"to be specified as either 'left' or 'right'.")
# now ready to instantiate the TensorProd/VecAdd
# theorem: tensor_prod_distribution_over_add
# and derive it's reversed result
from proveit.linear_algebra.tensors import (
tensor_prod_distribution_over_add)
from proveit import a, b, c, i, j, k, K, V
from proveit.numbers import zero, one, num
# useful to get ahead of time the num of operands
# in the_factor and define the replacement
# if hasattr(the_factor, 'operands'):
# num_factor_entries = num(the_factor.operands.num_entries())
# factor_entries = the_factor.operands.entries
# else:
# num_factor_entries = one
# factor_entries = (the_factor,)
# useful to get ahead of time the num of operands
# in the_factor and define the replacement
if isinstance(the_factor, TensorProd):
num_factor_entries = num(the_factor.operands.num_entries())
factor_entries = the_factor.operands.entries
else:
num_factor_entries = one
factor_entries = (the_factor, )
# call deduce_in_vec_space() on the original self
# instead of the current expr, otherwise we can run into
# compications due to the associated sub-terms
vec_space_membership = self.operands[0].deduce_in_vec_space(
field=field)
_V_sub = vec_space_membership.domain
_K_sub = VecSpaces.known_field(_V_sub)
if pull == 'left':
# num of operands in left the_factor
_i_sub = num_factor_entries
# num of operands in right factor
_k_sub = zero
# the actual factor operands
_a_sub = factor_entries
# the other side is empty
_c_sub = ()
elif pull == 'right':
# left side is empty
_i_sub = zero
# right side has the factor
_k_sub = num_factor_entries
# left side is empty
_a_sub = ()
# right side has the factor
_c_sub = factor_entries
_j_sub = num(len(_b))
_b_sub = ExprTuple(*_b)
from proveit.linear_algebra.tensors import (
tensor_prod_distribution_over_add)
impl = tensor_prod_distribution_over_add.instantiate(
{
V: _V_sub,
K: _K_sub,
i: _i_sub,
j: _j_sub,
k: _k_sub,
a: _a_sub,
b: _b_sub,
c: _c_sub
},
preserve_all=True)
conseq = impl.derive_consequent()
eq.update(conseq.derive_reversed())
else:
print("Not yet an identified case. Sorry!")
return eq.relation
def deduce_in_vec_space(self, vec_space=None, *, field, **defaults_config):
'''
Prove that this linear combination of vectors is in a vector
space. The vector space may be specified or inferred via known
memberships. A field for the vector space must be specified.
'''
from proveit.linear_algebra import ScalarMult
terms = self.terms
if vec_space is None:
vec_space = VecSpaces.common_known_vec_space(terms, field=field)
field = VecSpaces.known_field(vec_space)
all_scaled = all((isinstance(term, ScalarMult) or (
isinstance(term, ExprRange) and isinstance(term.body, ScalarMult)))
for term in terms)
if all_scaled:
# Use a linear combination theorem since everything
# is scaled.
from proveit.linear_algebra.scalar_multiplication import (
binary_lin_comb_closure, lin_comb_closure)
if terms.is_double():
# Special linear combination binary case
_a, _b = terms[0].scalar, terms[1].scalar
_x, _y = terms[0].scaled, terms[1].scaled
return binary_lin_comb_closure.instantiate({
K: field,
V: vec_space,
a: _a,
b: _b,
x: _x,
y: _y
})
else:
# General linear combination case
_a = []
_x = []
for term in terms:
if isinstance(term, ExprRange):
_a.append(
ExprRange(term.parameter, term.body.scalar,
term.true_start_index,
term.true_end_index))
_x.append(
ExprRange(term.parameter, term.body.scaled,
term.true_start_index,
term.true_end_index))
else:
_a.append(term.scalar)
_x.append(term.scaled)
_n = terms.num_elements()
return lin_comb_closure.instantiate({
n: _n,
K: field,
V: vec_space,
a: _a,
x: _x
})
else:
# Use a vector addition closure theorem.
from . import binary_closure, closure
if terms.is_double():
# Special binary case
return binary_closure.instantiate({
K: field,
V: vec_space,
x: terms[0],
y: terms[1]
})
else:
# General case
_n = terms.num_elements()
return closure.instantiate({
n: _n,
K: field,
V: vec_space,
x: terms
})