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JohanMabilleJohanMabille
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Merge pull request #2088 from zhujun98/improve_histogram_performance_with_equal_bins
Improve histogram performance with equal bin sizes
2 parents d03043b + d99144d commit 591fbb5

1 file changed

Lines changed: 93 additions & 51 deletions

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‎include/xtensor/xhistogram.hpp‎

Lines changed: 93 additions & 51 deletions
Original file line numberDiff line numberDiff line change
@@ -44,6 +44,79 @@ namespace xt
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return xt::searchsorted(std::forward<E2>(bin_edges), std::forward<E1>(data), right);
4545
}
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namespace detail
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{
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template <class R = double, class E1, class E2, class E3>
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inline auto histogram_imp(E1&& data, E2&& bin_edges, E3&& weights, bool density, bool equal_bins)
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{
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using size_type = common_size_type_t<std::decay_t<E1>, std::decay_t<E2>, std::decay_t<E3>>;
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using value_type = typename std::decay_t<E3>::value_type;
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XTENSOR_ASSERT(data.dimension() == 1);
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XTENSOR_ASSERT(weights.dimension() == 1);
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XTENSOR_ASSERT(bin_edges.dimension() == 1);
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XTENSOR_ASSERT(weights.size() == data.size());
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XTENSOR_ASSERT(bin_edges.size() >= 2);
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XTENSOR_ASSERT(std::is_sorted(bin_edges.cbegin(), bin_edges.cend()));
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XTENSOR_ASSERT(xt::amin(data)[0] >= bin_edges[0]);
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XTENSOR_ASSERT(xt::amax(data)[0] <= bin_edges[bin_edges.size() - 1]);
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size_t n_bins = bin_edges.size() - 1;
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xt::xtensor<value_type, 1> count = xt::zeros<value_type>({ n_bins });
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if (equal_bins)
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{
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std::array<typename std::decay_t<E2>::value_type, 2> bounds = xt::minmax(bin_edges)();
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auto left = static_cast<double>(bounds[0]);
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auto right = static_cast<double>(bounds[1]);
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double norm = 1. / (right - left);
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for (size_t i = 0; i < data.size(); ++i)
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{
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auto v = static_cast<double>(data(i));
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// left and right are not bounds of data
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if ( v >= left & v < right )
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{
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auto i_bin = static_cast<size_t>(static_cast<double>(n_bins) * (v - left) * norm);
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count(i_bin) += weights(i);
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}
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else if ( v == right )
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{
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count(n_bins - 1) += weights(i);
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}
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}
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}
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else
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{
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auto sorter = xt::argsort(data);
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size_type ibin = 0;
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for (auto& idx : sorter)
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{
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while (data[idx] >= bin_edges[ibin + 1] && ibin < bin_edges.size() - 2)
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{
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++ibin;
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}
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count[ibin] += weights[idx];
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}
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}
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xt::xtensor<R, 1> prob = xt::cast<R>(count);
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if (density)
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{
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R n = static_cast<R>(data.size());
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for (size_type i = 0; i < bin_edges.size() - 1; ++i)
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{
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prob[i] /= (static_cast<R>(bin_edges[i + 1] - bin_edges[i]) * n);
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}
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}
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return prob;
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}
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118+
} //detail
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47120
/**
48121
* @ingroup histogram
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* @brief Compute the histogram of a set of data.
@@ -57,45 +130,11 @@ namespace xt
57130
template <class R = double, class E1, class E2, class E3>
58131
inline auto histogram(E1&& data, E2&& bin_edges, E3&& weights, bool density = false)
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{
60-
using size_type = common_size_type_t<std::decay_t<E1>, std::decay_t<E2>, std::decay_t<E3>>;
61-
using value_type = typename std::decay_t<E3>::value_type;
62-
63-
XTENSOR_ASSERT(data.dimension() == 1);
64-
XTENSOR_ASSERT(weights.dimension() == 1);
65-
XTENSOR_ASSERT(bin_edges.dimension() == 1);
66-
XTENSOR_ASSERT(weights.size() == data.size());
67-
XTENSOR_ASSERT(bin_edges.size() >= 2);
68-
XTENSOR_ASSERT(std::is_sorted(bin_edges.cbegin(), bin_edges.cend()));
69-
XTENSOR_ASSERT(xt::amin(data)[0] >= bin_edges[0]);
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XTENSOR_ASSERT(xt::amax(data)[0] <= bin_edges[bin_edges.size() - 1]);
71-
72-
xt::xtensor<value_type, 1> count = xt::zeros<value_type>({ bin_edges.size() - 1 });
73-
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auto sorter = xt::argsort(data);
75-
76-
size_type ibin = 0;
77-
78-
for (auto& idx : sorter)
79-
{
80-
while (data[idx] >= bin_edges[ibin + 1] && ibin < bin_edges.size() - 2)
81-
{
82-
++ibin;
83-
}
84-
count[ibin] += weights[idx];
85-
}
86-
87-
xt::xtensor<R, 1> prob = xt::cast<R>(count);
88-
89-
if (density)
90-
{
91-
R n = static_cast<R>(data.size());
92-
for (size_type i = 0; i < bin_edges.size() - 1; ++i)
93-
{
94-
prob[i] /= (static_cast<R>(bin_edges[i + 1] - bin_edges[i]) * n);
95-
}
96-
}
97-
98-
return prob;
133+
return detail::histogram_imp<R>(std::forward<E1>(data),
134+
std::forward<E2>(bin_edges),
135+
std::forward<E3>(weights),
136+
density,
137+
false);
99138
}
100139

101140
/**
@@ -114,10 +153,11 @@ namespace xt
114153

115154
auto n = data.size();
116155

117-
return histogram(std::forward<E1>(data),
118-
std::forward<E2>(bin_edges),
119-
xt::ones<value_type>({ n }),
120-
density);
156+
return detail::histogram_imp(std::forward<E1>(data),
157+
std::forward<E2>(bin_edges),
158+
xt::ones<value_type>({ n }),
159+
density,
160+
false);
121161
}
122162

123163
/**
@@ -138,10 +178,11 @@ namespace xt
138178

139179
auto bin_edges = histogram_bin_edges(data, xt::ones<value_type>({ n }), bins);
140180

141-
return histogram(std::forward<E1>(data),
142-
std::forward<E1>(bin_edges),
143-
xt::ones<value_type>({ n }),
144-
density);
181+
return detail::histogram_imp(std::forward<E1>(data),
182+
std::forward<E1>(bin_edges),
183+
xt::ones<value_type>({ n }),
184+
density,
185+
true);
145186
}
146187

147188
/**
@@ -159,10 +200,11 @@ namespace xt
159200
{
160201
auto bin_edges = histogram_bin_edges(data, weights, bins);
161202

162-
return histogram(std::forward<E1>(data),
163-
std::forward<E1>(bin_edges),
164-
std::forward<E2>(weights),
165-
density);
203+
return detail::histogram_imp(std::forward<E1>(data),
204+
std::forward<E1>(bin_edges),
205+
std::forward<E2>(weights),
206+
density,
207+
true);
166208
}
167209

168210
/**

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