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Add .clang-format and apply to dimod/include/* #742

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merged 3 commits into from
Nov 19, 2020
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Add .clang-format and apply to dimod/include/* #742

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59528c3
Add .clang-format and apply to dimod/include/*
arcondello Oct 27, 2020
b6bb5ae
Scale indents in .clang-format
arcondello Oct 29, 2020
0a33771
Apply clang-format to tests
arcondello Oct 29, 2020
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13 changes: 13 additions & 0 deletions .clang-format
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Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
---
Language: Cpp
BasedOnStyle: Google

ColumnLimit: 80
NamespaceIndentation: Inner

# Scaled by a factor of 2 such that the base indent is 4
AccessModifierOffset: -3
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Why not -2?

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@arcondello arcondello Oct 28, 2020 •
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dwavesystems/dwave-ocean-sdk#103 (comment)

IndentWidth: 4
ContinuationIndentWidth: 8
ConstructorInitializerIndentWidth: 8
...
78 changes: 37 additions & 41 deletions dimod/include/dimod/adjarraybqm.h
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Expand Up @@ -23,7 +23,7 @@

namespace dimod {

template<class V, class B, class N = std::size_t>
template <class V, class B, class N = std::size_t>
class AdjArrayBQM {
public:
using bias_type = B;
Expand All @@ -32,7 +32,8 @@ class AdjArrayBQM {
using size_type = std::size_t;

using outvars_iterator = typename std::vector<std::pair<V, B>>::iterator;
using const_outvars_iterator = typename std::vector<std::pair<V, B>>::const_iterator;
using const_outvars_iterator =
typename std::vector<std::pair<V, B>>::const_iterator;

// in the future we'd probably like to make this protected
std::vector<std::pair<N, B>> invars;
Expand All @@ -41,10 +42,10 @@ class AdjArrayBQM {
AdjArrayBQM() {}

// can we specify this slightly better?
template<class BQM>
explicit AdjArrayBQM(const BQM &bqm) {
template <class BQM>
explicit AdjArrayBQM(const BQM& bqm) {
invars.reserve(bqm.num_variables());
outvars.reserve(2*bqm.num_interactions());
outvars.reserve(2 * bqm.num_interactions());

for (auto v = 0; v < bqm.num_variables(); ++v) {
invars.emplace_back(outvars.size(), bqm.linear(v));
Expand All @@ -59,9 +60,9 @@ class AdjArrayBQM {
*
* @param dense An array containing the biases. Assumed to contain
* `num_variables`^2 elements. The upper and lower triangle are summed.
* @param num_variables The number of variables.
* @param num_variables The number of variables.
*/
template<class B2>
template <class B2>
AdjArrayBQM(const B2 dense[], size_type num_variables,
bool ignore_diagonal = false) {
// we know how big our linear is going to be. We'd also like to
Expand All @@ -75,35 +76,30 @@ class AdjArrayBQM {
if (ignore_diagonal) {
invars.emplace_back(outvars.size(), 0);
} else {
invars.emplace_back(outvars.size(), dense[u*(num_variables+1)]);
invars.emplace_back(outvars.size(),
dense[u * (num_variables + 1)]);
}

for (size_type v = 0; v < num_variables; ++v) {
if (u == v) continue; // already did linear

qbias = dense[u*num_variables+v] + dense[v*num_variables+u];
qbias = dense[u * num_variables + v] +
dense[v * num_variables + u];

if (qbias != 0)
outvars.emplace_back(v, qbias);
if (qbias != 0) outvars.emplace_back(v, qbias);
}
}
}

size_type num_interactions() const {
return outvars.size() / 2;
}
size_type num_interactions() const { return outvars.size() / 2; }

size_type num_variables() const {
return invars.size();
}
size_type num_variables() const { return invars.size(); }

[[deprecated("Use AdjArrayBQM::linear(v)")]]
bias_type get_linear(variable_type v) const {
return linear(v);
}
[[deprecated("Use AdjArrayBQM::linear(v)")]] bias_type get_linear(
variable_type v) const { return linear(v); }

std::pair<bias_type, bool>
get_quadratic(variable_type u, variable_type v) const {
std::pair<bias_type, bool> get_quadratic(variable_type u,
variable_type v) const {
assert(u >= 0 && u < invars.size());
assert(v >= 0 && v < invars.size());
assert(u != v);
Expand All @@ -121,10 +117,10 @@ class AdjArrayBQM {
assert(v >= 0 && v < invars.size());

// need to check the case that v is the last variable
if ((unsigned) v == invars.size() - 1)
if ((unsigned)v == invars.size() - 1)
return outvars.size() - invars[v].first;

return invars[v+1].first - invars[v].first;
return invars[v + 1].first - invars[v].first;
}

bias_type& linear(variable_type v) {
Expand All @@ -137,54 +133,54 @@ class AdjArrayBQM {
return invars[v].second;
}

std::pair<outvars_iterator, outvars_iterator>
neighborhood(variable_type u) {
std::pair<outvars_iterator, outvars_iterator> neighborhood(
variable_type u) {
assert(u >= 0 && u < invars.size());

outvars_iterator end;
if ((unsigned) u == invars.size() - 1) {
if ((unsigned)u == invars.size() - 1) {
end = outvars.end();
} else {
end = outvars.begin() + invars[u+1].first;
end = outvars.begin() + invars[u + 1].first;
}

return std::make_pair(outvars.begin() + invars[u].first, end);
}

std::pair<const_outvars_iterator, const_outvars_iterator>
neighborhood(variable_type u) const {
std::pair<const_outvars_iterator, const_outvars_iterator> neighborhood(
variable_type u) const {
assert(u >= 0 && u < invars.size());

const_outvars_iterator end;
if ((unsigned) u == invars.size() - 1) {
if ((unsigned)u == invars.size() - 1) {
end = outvars.cend();
} else {
end = outvars.cbegin() + invars[u+1].first;
end = outvars.cbegin() + invars[u + 1].first;
}

return std::make_pair(outvars.cbegin() + invars[u].first, end);
}

/**
* The neighborhood of variable `v`.
*
*
* @param A variable `v`.
* @param The neighborhood will start with the first out variable that
* does not compare less than `start`.
*
* @returns A pair of iterators pointing to the start and end of the
* neighborhood.
*/
std::pair<const_outvars_iterator, const_outvars_iterator>
neighborhood(variable_type v, variable_type start) const {
std::pair<const_outvars_iterator, const_outvars_iterator> neighborhood(
variable_type v, variable_type start) const {
auto span = neighborhood(v);
auto low = std::lower_bound(span.first, span.second,
start, utils::comp_v<V, B>);
auto low = std::lower_bound(span.first, span.second, start,
utils::comp_v<V, B>);
return std::make_pair(low, span.second);
}
}

[[deprecated("Use AdjArrayBQM::linear(v)")]]
void set_linear(variable_type v, bias_type b) {
[[deprecated("Use AdjArrayBQM::linear(v)")]] void set_linear(
variable_type v, bias_type b) {
assert(v >= 0 && v < invars.size());
linear(v) = b;
}
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