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diff --git a/third_party/eigen3/Eigen/src/SparseCore/AmbiVector.h b/third_party/eigen3/Eigen/src/SparseCore/AmbiVector.h
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+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_AMBIVECTOR_H
+#define EIGEN_AMBIVECTOR_H
+
+namespace Eigen {
+
+namespace internal {
+
+/** \internal
+ * Hybrid sparse/dense vector class designed for intensive read-write operations.
+ *
+ * See BasicSparseLLT and SparseProduct for usage examples.
+ */
+template<typename _Scalar, typename _Index>
+class AmbiVector
+{
+ public:
+ typedef _Scalar Scalar;
+ typedef _Index Index;
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+
+ AmbiVector(Index size)
+ : m_buffer(0), m_zero(0), m_size(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
+ {
+ resize(size);
+ }
+
+ void init(double estimatedDensity);
+ void init(int mode);
+
+ Index nonZeros() const;
+
+ /** Specifies a sub-vector to work on */
+ void setBounds(Index start, Index end) { m_start = start; m_end = end; }
+
+ void setZero();
+
+ void restart();
+ Scalar& coeffRef(Index i);
+ Scalar& coeff(Index i);
+
+ class Iterator;
+
+ ~AmbiVector() { delete[] m_buffer; }
+
+ void resize(Index size)
+ {
+ if (m_allocatedSize < size)
+ reallocate(size);
+ m_size = size;
+ }
+
+ Index size() const { return m_size; }
+
+ protected:
+
+ void reallocate(Index size)
+ {
+ // if the size of the matrix is not too large, let's allocate a bit more than needed such
+ // that we can handle dense vector even in sparse mode.
+ delete[] m_buffer;
+ if (size<1000)
+ {
+ Index allocSize = (size * sizeof(ListEl))/sizeof(Scalar);
+ m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl);
+ m_buffer = new Scalar[allocSize];
+ }
+ else
+ {
+ m_allocatedElements = (size*sizeof(Scalar))/sizeof(ListEl);
+ m_buffer = new Scalar[size];
+ }
+ m_size = size;
+ m_start = 0;
+ m_end = m_size;
+ }
+
+ void reallocateSparse()
+ {
+ Index copyElements = m_allocatedElements;
+ m_allocatedElements = (std::min)(Index(m_allocatedElements*1.5),m_size);
+ Index allocSize = m_allocatedElements * sizeof(ListEl);
+ allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
+ Scalar* newBuffer = new Scalar[allocSize];
+ memcpy(newBuffer, m_buffer, copyElements * sizeof(ListEl));
+ delete[] m_buffer;
+ m_buffer = newBuffer;
+ }
+
+ protected:
+ // element type of the linked list
+ struct ListEl
+ {
+ Index next;
+ Index index;
+ Scalar value;
+ };
+
+ // used to store data in both mode
+ Scalar* m_buffer;
+ Scalar m_zero;
+ Index m_size;
+ Index m_start;
+ Index m_end;
+ Index m_allocatedSize;
+ Index m_allocatedElements;
+ Index m_mode;
+
+ // linked list mode
+ Index m_llStart;
+ Index m_llCurrent;
+ Index m_llSize;
+};
+
+/** \returns the number of non zeros in the current sub vector */
+template<typename _Scalar,typename _Index>
+_Index AmbiVector<_Scalar,_Index>::nonZeros() const
+{
+ if (m_mode==IsSparse)
+ return m_llSize;
+ else
+ return m_end - m_start;
+}
+
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::init(double estimatedDensity)
+{
+ if (estimatedDensity>0.1)
+ init(IsDense);
+ else
+ init(IsSparse);
+}
+
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::init(int mode)
+{
+ m_mode = mode;
+ if (m_mode==IsSparse)
+ {
+ m_llSize = 0;
+ m_llStart = -1;
+ }
+}
+
+/** Must be called whenever we might perform a write access
+ * with an index smaller than the previous one.
+ *
+ * Don't worry, this function is extremely cheap.
+ */
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::restart()
+{
+ m_llCurrent = m_llStart;
+}
+
+/** Set all coefficients of current subvector to zero */
+template<typename _Scalar,typename _Index>
+void AmbiVector<_Scalar,_Index>::setZero()
+{
+ if (m_mode==IsDense)
+ {
+ for (Index i=m_start; i<m_end; ++i)
+ m_buffer[i] = Scalar(0);
+ }
+ else
+ {
+ eigen_assert(m_mode==IsSparse);
+ m_llSize = 0;
+ m_llStart = -1;
+ }
+}
+
+template<typename _Scalar,typename _Index>
+_Scalar& AmbiVector<_Scalar,_Index>::coeffRef(_Index i)
+{
+ if (m_mode==IsDense)
+ return m_buffer[i];
+ else
+ {
+ ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+ // TODO factorize the following code to reduce code generation
+ eigen_assert(m_mode==IsSparse);
+ if (m_llSize==0)
+ {
+ // this is the first element
+ m_llStart = 0;
+ m_llCurrent = 0;
+ ++m_llSize;
+ llElements[0].value = Scalar(0);
+ llElements[0].index = i;
+ llElements[0].next = -1;
+ return llElements[0].value;
+ }
+ else if (i<llElements[m_llStart].index)
+ {
+ // this is going to be the new first element of the list
+ ListEl& el = llElements[m_llSize];
+ el.value = Scalar(0);
+ el.index = i;
+ el.next = m_llStart;
+ m_llStart = m_llSize;
+ ++m_llSize;
+ m_llCurrent = m_llStart;
+ return el.value;
+ }
+ else
+ {
+ Index nextel = llElements[m_llCurrent].next;
+ eigen_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
+ while (nextel >= 0 && llElements[nextel].index<=i)
+ {
+ m_llCurrent = nextel;
+ nextel = llElements[nextel].next;
+ }
+
+ if (llElements[m_llCurrent].index==i)
+ {
+ // the coefficient already exists and we found it !
+ return llElements[m_llCurrent].value;
+ }
+ else
+ {
+ if (m_llSize>=m_allocatedElements)
+ {
+ reallocateSparse();
+ llElements = reinterpret_cast<ListEl*>(m_buffer);
+ }
+ eigen_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
+ // let's insert a new coefficient
+ ListEl& el = llElements[m_llSize];
+ el.value = Scalar(0);
+ el.index = i;
+ el.next = llElements[m_llCurrent].next;
+ llElements[m_llCurrent].next = m_llSize;
+ ++m_llSize;
+ return el.value;
+ }
+ }
+ }
+}
+
+template<typename _Scalar,typename _Index>
+_Scalar& AmbiVector<_Scalar,_Index>::coeff(_Index i)
+{
+ if (m_mode==IsDense)
+ return m_buffer[i];
+ else
+ {
+ ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
+ eigen_assert(m_mode==IsSparse);
+ if ((m_llSize==0) || (i<llElements[m_llStart].index))
+ {
+ return m_zero;
+ }
+ else
+ {
+ Index elid = m_llStart;
+ while (elid >= 0 && llElements[elid].index<i)
+ elid = llElements[elid].next;
+
+ if (llElements[elid].index==i)
+ return llElements[m_llCurrent].value;
+ else
+ return m_zero;
+ }
+ }
+}
+
+/** Iterator over the nonzero coefficients */
+template<typename _Scalar,typename _Index>
+class AmbiVector<_Scalar,_Index>::Iterator
+{
+ public:
+ typedef _Scalar Scalar;
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+
+ /** Default constructor
+ * \param vec the vector on which we iterate
+ * \param epsilon the minimal value used to prune zero coefficients.
+ * In practice, all coefficients having a magnitude smaller than \a epsilon
+ * are skipped.
+ */
+ Iterator(const AmbiVector& vec, const RealScalar& epsilon = 0)
+ : m_vector(vec)
+ {
+ using std::abs;
+ m_epsilon = epsilon;
+ m_isDense = m_vector.m_mode==IsDense;
+ if (m_isDense)
+ {
+ m_currentEl = 0; // this is to avoid a compilation warning
+ m_cachedValue = 0; // this is to avoid a compilation warning
+ m_cachedIndex = m_vector.m_start-1;
+ ++(*this);
+ }
+ else
+ {
+ ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+ m_currentEl = m_vector.m_llStart;
+ while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<=m_epsilon)
+ m_currentEl = llElements[m_currentEl].next;
+ if (m_currentEl<0)
+ {
+ m_cachedValue = 0; // this is to avoid a compilation warning
+ m_cachedIndex = -1;
+ }
+ else
+ {
+ m_cachedIndex = llElements[m_currentEl].index;
+ m_cachedValue = llElements[m_currentEl].value;
+ }
+ }
+ }
+
+ Index index() const { return m_cachedIndex; }
+ Scalar value() const { return m_cachedValue; }
+
+ operator bool() const { return m_cachedIndex>=0; }
+
+ Iterator& operator++()
+ {
+ using std::abs;
+ if (m_isDense)
+ {
+ do {
+ ++m_cachedIndex;
+ } while (m_cachedIndex<m_vector.m_end && abs(m_vector.m_buffer[m_cachedIndex])<m_epsilon);
+ if (m_cachedIndex<m_vector.m_end)
+ m_cachedValue = m_vector.m_buffer[m_cachedIndex];
+ else
+ m_cachedIndex=-1;
+ }
+ else
+ {
+ ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
+ do {
+ m_currentEl = llElements[m_currentEl].next;
+ } while (m_currentEl>=0 && abs(llElements[m_currentEl].value)<m_epsilon);
+ if (m_currentEl<0)
+ {
+ m_cachedIndex = -1;
+ }
+ else
+ {
+ m_cachedIndex = llElements[m_currentEl].index;
+ m_cachedValue = llElements[m_currentEl].value;
+ }
+ }
+ return *this;
+ }
+
+ protected:
+ const AmbiVector& m_vector; // the target vector
+ Index m_currentEl; // the current element in sparse/linked-list mode
+ RealScalar m_epsilon; // epsilon used to prune zero coefficients
+ Index m_cachedIndex; // current coordinate
+ Scalar m_cachedValue; // current value
+ bool m_isDense; // mode of the vector
+};
+
+} // end namespace internal
+
+} // end namespace Eigen
+
+#endif // EIGEN_AMBIVECTOR_H