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Fix some spelling errors (mostly in comments).

This commit is contained in:
Florian Pigorsch 2018-10-20 17:07:41 +02:00
parent 880ce12e2d
commit 326bf401b9
121 changed files with 205 additions and 205 deletions

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@ -615,7 +615,7 @@ namespace datalog {
void ensure_engine();
// auxilary functions for SMT2 pretty-printer.
// auxiliary functions for SMT2 pretty-printer.
void declare_vars(expr_ref_vector& rules, mk_fresh_name& mk_fresh, std::ostream& out);
//undefined and private copy constructor and operator=

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@ -110,7 +110,7 @@ namespace datalog {
/**
\brief Manager for the \c rule class
\remark \c rule_manager objects are interchangable as long as they
\remark \c rule_manager objects are interchangeable as long as they
contain the same \c ast_manager object.
*/
class rule_manager

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@ -97,7 +97,7 @@ namespace datalog {
\brief Auxiliary function used to create a tail based on \c pred for a new rule.
The variables in \c pred are re-assigned using \c next_idx and \c varidx2var.
A variable is considered non-local to the rule if it is in the set \c non_local_vars.
Non-local variables are coppied to new_rule_args, and their sorts to \c new_rule_domain.
Non-local variables are copied to new_rule_args, and their sorts to \c new_rule_domain.
The new predicate is stored in \c new_pred.
*/
void mk_new_rule_tail(ast_manager & m, app * pred,

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@ -143,8 +143,8 @@ def_module_params('fp',
('spacer.native_mbp', BOOL, True, "Use native mbp of Z3"),
('spacer.eq_prop', BOOL, True, "Enable equality and bound propagation in arithmetic"),
('spacer.weak_abs', BOOL, True, "Weak abstraction"),
('spacer.restarts', BOOL, False, "Enable reseting obligation queue"),
('spacer.restart_initial_threshold', UINT, 10, "Intial threshold for restarts"),
('spacer.restarts', BOOL, False, "Enable resetting obligation queue"),
('spacer.restart_initial_threshold', UINT, 10, "Initial threshold for restarts"),
('spacer.random_seed', UINT, 0, "Random seed to be used by SMT solver"),
('spacer.mbqi', BOOL, True, 'Enable mbqi'),

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@ -11,7 +11,7 @@ Copyright (c) 2015 Microsoft Corporation
Abstract:
Horn normal form convertion.
Horn normal form conversion.
Author:

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@ -332,7 +332,7 @@ namespace datalog {
void internalize() {
// populate maps (should be bit-sets) of decendants.
// populate maps (should be bit-sets) of descendants.
if (m_internalized) {
return;
}

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@ -120,7 +120,7 @@ public:
This operation invalidates the line previously retrieved.
This operatio can be called only if we are not at the end of file.
This operation can be called only if we are not at the end of file.
User is free to modify the content of the returned array until the terminating NULL character.
*/
@ -876,7 +876,7 @@ protected:
/**
\brief Parse predicate arguments. If \c f==0, they are arguments of a predicate declaration.
If parsing a declaration, argumens names are pushed to the \c arg_names vector.
If parsing a declaration, argument names are pushed to the \c arg_names vector.
*/
dtoken parse_args(dtoken tok, func_decl* f, expr_ref_vector& args, svector<symbol> & arg_names) {
if (tok != TK_LP) {

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@ -295,7 +295,7 @@ namespace datalog {
Precondition: &orig.get_plugin()==this
*/
virtual base_object * mk_empty(const signature & s, family_id kind) {
SASSERT(kind==get_kind()); //if plugin uses multiple kinds, this function needs to be overriden
SASSERT(kind==get_kind()); //if plugin uses multiple kinds, this function needs to be overridden
return mk_empty(s);
}

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@ -1319,7 +1319,7 @@ namespace datalog {
if(!m_table_cond_columns.empty()) {
//We will keep the table variables that appear in the condition together
//with the index column and then iterate throught the tuples, evaluating
//with the index column and then iterate through the tuples, evaluating
//the rest of the condition on the inner relations.
unsigned_vector removed_cols;
unsigned table_data_col_cnt = r.m_table_sig.size()-1;

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@ -640,7 +640,7 @@ namespace datalog {
reg_idx m_src;
reg_idx m_tgt;
reg_idx m_delta;
bool m_widen; //if true, widening is performed intead of an union
bool m_widen; //if true, widening is performed instead of an union
public:
instr_union(reg_idx src, reg_idx tgt, reg_idx delta, bool widen)
: m_src(src), m_tgt(tgt), m_delta(delta), m_widen(widen) {}

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@ -253,7 +253,7 @@ namespace datalog {
\brief Return functor that transforms a table into one that lacks columns listed in
\c removed_cols array.
The \c removed_cols cotains columns of table \c t in strictly ascending order.
The \c removed_cols contains columns of table \c t in strictly ascending order.
*/
relation_transformer_fn * mk_project_fn(const relation_base & t, unsigned col_cnt,
const unsigned * removed_cols);
@ -420,7 +420,7 @@ namespace datalog {
\brief Return functor that transforms a table into one that lacks columns listed in
\c removed_cols array.
The \c removed_cols cotains columns of table \c t in strictly ascending order.
The \c removed_cols contains columns of table \c t in strictly ascending order.
If a project operation removes a non-functional column, all functional columns become
non-functional (so that none of the values in functional columns are lost)

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@ -568,7 +568,7 @@ namespace datalog {
}
/**
In this function we modify the content of table functional columns without reseting indexes.
In this function we modify the content of table functional columns without resetting indexes.
This is ok as long as we do not allow indexing on functional columns.
*/
void sparse_table::ensure_fact(const table_fact & f) {

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@ -85,7 +85,7 @@ namespace datalog {
/**
\brief Restrict the set of used predicates to \c res.
The function deallocates unsused relations, it does not deal with rules.
The function deallocates unused relations, it does not deal with rules.
*/
void restrict_predicates(func_decl_set const& predicates) override;

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@ -7,8 +7,8 @@ Module Name:
Abstract:
Proviced abstract interface and some inplementations of algorithms
for strenghtning lemmas
Provides abstract interface and some implementations of algorithms
for strenghtening lemmas
Author:
@ -161,7 +161,7 @@ bool farkas_learner::is_pure_expr(func_decl_set const& symbs, expr* e, ast_manag
in a clausal version.
NB: the routine is not interpolating, though an interpolating variant would
be preferrable because then we can also use it for model propagation.
be preferable because then we can also use it for model propagation.
We collect the unit derivable nodes from bs.
These are the weakenings of bs, besides the

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@ -186,7 +186,7 @@ void lemma_quantifier_generalizer::find_candidates(expr *e,
std::sort(candidates.c_ptr(), candidates.c_ptr() + candidates.size(),
index_lt_proc(m));
// keep actual select indecies in the order found at the back of
// keep actual select indices in the order found at the back of
// candidate list. There is no particular reason for this order
candidates.append(extra);
}
@ -199,24 +199,24 @@ bool lemma_quantifier_generalizer::match_sk_idx(expr *e, app_ref_vector const &z
contains_app has_zk(m, zks.get(0));
if (!contains_selects(e, m)) return false;
app_ref_vector indicies(m);
get_select_indices(e, indicies);
if (indicies.size() > 2) return false;
app_ref_vector indices(m);
get_select_indices(e, indices);
if (indices.size() > 2) return false;
unsigned i=0;
if (indicies.size() == 1) {
if (!has_zk(indicies.get(0))) return false;
if (indices.size() == 1) {
if (!has_zk(indices.get(0))) return false;
}
else {
if (has_zk(indicies.get(0)) && !has_zk(indicies.get(1)))
if (has_zk(indices.get(0)) && !has_zk(indices.get(1)))
i = 0;
else if (!has_zk(indicies.get(0)) && has_zk(indicies.get(1)))
else if (!has_zk(indices.get(0)) && has_zk(indices.get(1)))
i = 1;
else if (!has_zk(indicies.get(0)) && !has_zk(indicies.get(1)))
else if (!has_zk(indices.get(0)) && !has_zk(indices.get(1)))
return false;
}
idx = indicies.get(i);
idx = indices.get(i);
sk = zks.get(0);
return true;
}

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@ -124,7 +124,7 @@ namespace spacer {
* We can rewrite (E2) to rewrite (E1) to
* (BP*Fark(BP)) => (neg(A*Fark(A) + BNP*Fark(BNP) + (neg D)*Fark(D))) (E3)
* and since we can derive (A*Fark(A) + BNP*Fark(BNP) + (neg D)*Fark(D)) from
* A, BNP and D, we also know that it is inconsisent. Therefore
* A, BNP and D, we also know that it is inconsistent. Therefore
* neg(A*Fark(A) + BNP*Fark(BNP) + (neg D)*Fark(D)) is a solution.
*
* Finally we also need the following workaround:

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@ -1097,7 +1097,7 @@ namespace tb {
m_S1.apply(2, delta, expr_offset(src.get_constraint(), 1), tmp2);
constraint = m.mk_and(tmp, tmp2);
// perform trival quantifier-elimination:
// perform trivial quantifier-elimination:
uint_set index_set;
expr_free_vars fv;
fv(head);

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@ -280,7 +280,7 @@ namespace datalog {
}
}
// model convertion: identity function.
// model conversion: identity function.
if (instantiated) {
result->inherit_predicates(source);

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@ -45,7 +45,7 @@ namespace datalog {
unsigned pt_len = r->get_positive_tail_size();
if(pt_len != r->get_uninterpreted_tail_size()) {
// we dont' expect rules with negative tails to be total
// we don't expect rules with negative tails to be total
return false;
}
@ -97,7 +97,7 @@ namespace datalog {
void mk_subsumption_checker::scan_for_total_rules(const rule_set & rules) {
bool new_discovered;
//we cycle through the rules until we keep discovering new total relations
//(discovering a total relation migh reveal other total relations)
//(discovering a total relation might reveal other total relations)
do {
new_discovered = false;
rule_set::iterator rend = rules.end();