diff options
| author | Stanislaw Halik <sthalik@misaki.pl> | 2019-01-16 11:45:13 +0100 |
|---|---|---|
| committer | Stanislaw Halik <sthalik@misaki.pl> | 2019-01-16 11:45:13 +0100 |
| commit | bbdfe42628cc324904a49d472230c8cbbfd9e1d5 (patch) | |
| tree | 0ae6a380649af4a854c88245abb1c9fa3a571cc4 /eigen/unsupported | |
| parent | 3e07e568a1ae478b89812d91438d75179c94ab35 (diff) | |
update eigen
Diffstat (limited to 'eigen/unsupported')
39 files changed, 290 insertions, 286 deletions
diff --git a/eigen/unsupported/Eigen/CXX11/Tensor b/eigen/unsupported/Eigen/CXX11/Tensor index 7ecb4c7..bb6523d 100644 --- a/eigen/unsupported/Eigen/CXX11/Tensor +++ b/eigen/unsupported/Eigen/CXX11/Tensor @@ -39,6 +39,8 @@ * \code * #include <Eigen/CXX11/Tensor> * \endcode + * + * Much of the documentation can be found \ref eigen_tensors "here". */ #include <cmath> diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/README.md b/eigen/unsupported/Eigen/CXX11/src/Tensor/README.md index 98e8381..da70fa2 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/README.md +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/README.md @@ -1,4 +1,4 @@ -# Eigen Tensors +# Eigen Tensors {#eigen_tensors} Tensors are multidimensional arrays of elements. Elements are typically scalars, but more complex types such as strings are also supported. @@ -8,7 +8,7 @@ but more complex types such as strings are also supported. ## Tensor Classes You can manipulate a tensor with one of the following classes. They all are in -the namespace ```::Eigen.``` +the namespace `::Eigen.` ### Class Tensor<data_type, rank> @@ -21,10 +21,10 @@ matrix. Tensors of this class are resizable. For example, if you assign a tensor of a different size to a Tensor, that tensor is resized to match its new value. -#### Constructor Tensor<data_type, rank>(size0, size1, ...) +#### Constructor `Tensor<data_type, rank>(size0, size1, ...)` -Constructor for a Tensor. The constructor must be passed ```rank``` integers -indicating the sizes of the instance along each of the the ```rank``` +Constructor for a Tensor. The constructor must be passed `rank` integers +indicating the sizes of the instance along each of the the `rank` dimensions. // Create a tensor of rank 3 of sizes 2, 3, 4. This tensor owns @@ -34,18 +34,18 @@ dimensions. // Resize t_3d by assigning a tensor of different sizes, but same rank. t_3d = Tensor<float, 3>(3, 4, 3); -#### Constructor Tensor<data_type, rank>(size_array) +#### Constructor `Tensor<data_type, rank>(size_array)` Constructor where the sizes for the constructor are specified as an array of values instead of an explicitly list of parameters. The array type to use is -```Eigen::array<Eigen::Index>```. The array can be constructed automatically +`Eigen::array<Eigen::Index>`. The array can be constructed automatically from an initializer list. // Create a tensor of strings of rank 2 with sizes 5, 7. Tensor<string, 2> t_2d({5, 7}); -### Class TensorFixedSize<data_type, Sizes<size0, size1, ...>> +### Class `TensorFixedSize<data_type, Sizes<size0, size1, ...>>` Class to use for tensors of fixed size, where the size is known at compile time. Fixed sized tensors can provide very fast computations because all their @@ -57,7 +57,7 @@ tensor data is held onto the stack and does not cause heap allocation and free. // Create a 4 x 3 tensor of floats. TensorFixedSize<float, Sizes<4, 3>> t_4x3; -### Class TensorMap<Tensor<data_type, rank>> +### Class `TensorMap<Tensor<data_type, rank>>` This is the class to use to create a tensor on top of memory allocated and owned by another part of your code. It allows to view any piece of allocated @@ -67,7 +67,7 @@ data are stored. A TensorMap is not resizable because it does not own the memory where its data are stored. -#### Constructor TensorMap<Tensor<data_type, rank>>(data, size0, size1, ...) +#### Constructor `TensorMap<Tensor<data_type, rank>>(data, size0, size1, ...)` Constructor for a Tensor. The constructor must be passed a pointer to the storage for the data, and "rank" size attributes. The storage has to be @@ -83,20 +83,20 @@ large enough to hold all the data. // You can also map fixed-size tensors. Here we get a 1d view of // the 2d fixed-size tensor. - Tensor<float, Sizes<4, 5>> t_4x3; - TensorMap<Tensor<float, 1>> t_12(t_4x3, 12); + TensorFixedSize<float, Sizes<4, 5>> t_4x3; + TensorMap<Tensor<float, 1>> t_12(t_4x3.data(), 12); -#### Class TensorRef +#### Class `TensorRef` See Assigning to a TensorRef below. ## Accessing Tensor Elements -#### <data_type> tensor(index0, index1...) +#### `<data_type> tensor(index0, index1...)` -Return the element at position ```(index0, index1...)``` in tensor -```tensor```. You must pass as many parameters as the rank of ```tensor```. +Return the element at position `(index0, index1...)` in tensor +`tensor`. You must pass as many parameters as the rank of `tensor`. The expression can be used as an l-value to set the value of the element at the specified position. The value returned is of the datatype of the tensor. @@ -121,8 +121,8 @@ specified position. The value returned is of the datatype of the tensor. ## TensorLayout -The tensor library supports 2 layouts: ```ColMajor``` (the default) and -```RowMajor```. Only the default column major layout is currently fully +The tensor library supports 2 layouts: `ColMajor` (the default) and +`RowMajor`. Only the default column major layout is currently fully supported, and it is therefore not recommended to attempt to use the row major layout at the moment. @@ -136,7 +136,7 @@ All the arguments to an expression must use the same layout. Attempting to mix different layouts will result in a compilation error. It is possible to change the layout of a tensor or an expression using the -```swap_layout()``` method. Note that this will also reverse the order of the +`swap_layout()` method. Note that this will also reverse the order of the dimensions. Tensor<float, 2, ColMajor> col_major(2, 4); @@ -173,35 +173,35 @@ the following code computes the elementwise addition of two tensors: Tensor<float, 3> t3 = t1 + t2; While the code above looks easy enough, it is important to understand that the -expression ```t1 + t2``` is not actually adding the values of the tensors. The +expression `t1 + t2` is not actually adding the values of the tensors. The expression instead constructs a "tensor operator" object of the class TensorCwiseBinaryOp<scalar_sum>, which has references to the tensors -```t1``` and ```t2```. This is a small C++ object that knows how to add -```t1``` and ```t2```. It is only when the value of the expression is assigned -to the tensor ```t3``` that the addition is actually performed. Technically, -this happens through the overloading of ```operator=()``` in the Tensor class. +`t1` and `t2`. This is a small C++ object that knows how to add +`t1` and `t2`. It is only when the value of the expression is assigned +to the tensor `t3` that the addition is actually performed. Technically, +this happens through the overloading of `operator=()` in the Tensor class. This mechanism for computing tensor expressions allows for lazy evaluation and optimizations which are what make the tensor library very fast. -Of course, the tensor operators do nest, and the expression ```t1 + t2 * -0.3f``` is actually represented with the (approximate) tree of operators: +Of course, the tensor operators do nest, and the expression `t1 + t2 * 0.3f` +is actually represented with the (approximate) tree of operators: TensorCwiseBinaryOp<scalar_sum>(t1, TensorCwiseUnaryOp<scalar_mul>(t2, 0.3f)) ### Tensor Operations and C++ "auto" -Because Tensor operations create tensor operators, the C++ ```auto``` keyword +Because Tensor operations create tensor operators, the C++ `auto` keyword does not have its intuitive meaning. Consider these 2 lines of code: Tensor<float, 3> t3 = t1 + t2; auto t4 = t1 + t2; -In the first line we allocate the tensor ```t3``` and it will contain the -result of the addition of ```t1``` and ```t2```. In the second line, ```t4``` +In the first line we allocate the tensor `t3` and it will contain the +result of the addition of `t1` and `t2`. In the second line, `t4` is actually the tree of tensor operators that will compute the addition of -```t1``` and ```t2```. In fact, ```t4``` is *not* a tensor and you cannot get +`t1` and `t2`. In fact, `t4` is *not* a tensor and you cannot get the values of its elements: Tensor<float, 3> t3 = t1 + t2; @@ -210,8 +210,8 @@ the values of its elements: auto t4 = t1 + t2; cout << t4(0, 0, 0); // Compilation error! -When you use ```auto``` you do not get a Tensor as a result but instead a -non-evaluated expression. So only use ```auto``` to delay evaluation. +When you use `auto` you do not get a Tensor as a result but instead a +non-evaluated expression. So only use `auto` to delay evaluation. Unfortunately, there is no single underlying concrete type for holding non-evaluated expressions, hence you have to use auto in the case when you do @@ -257,9 +257,9 @@ There are several ways to control when expressions are evaluated: #### Assigning to a Tensor, TensorFixedSize, or TensorMap. The most common way to evaluate an expression is to assign it to a Tensor. In -the example below, the ```auto``` declarations make the intermediate values +the example below, the `auto` declarations make the intermediate values "Operations", not Tensors, and do not cause the expressions to be evaluated. -The assignment to the Tensor ```result``` causes the evaluation of all the +The assignment to the Tensor `result` causes the evaluation of all the operations. auto t3 = t1 + t2; // t3 is an Operation. @@ -272,17 +272,17 @@ Operation to a TensorFixedSize instead of a Tensor, which is a bit more efficient. // We know that the result is a 4x4x2 tensor! - TensorFixedSize<float, 4, 4, 2> result = t5; + TensorFixedSize<float, Sizes<4, 4, 2>> result = t5; Simiarly, assigning an expression to a TensorMap causes its evaluation. Like tensors of type TensorFixedSize, TensorMaps cannot be resized so they have to have the rank and sizes of the expression that are assigned to them. -#### Calling eval(). +#### Calling `eval()`. When you compute large composite expressions, you sometimes want to tell Eigen that an intermediate value in the expression tree is worth evaluating ahead of -time. This is done by inserting a call to the ```eval()``` method of the +time. This is done by inserting a call to the `eval()` method of the expression Operation. // The previous example could have been written: @@ -291,15 +291,15 @@ expression Operation. // If you want to compute (t1 + t2) once ahead of time you can write: Tensor<float, 3> result = ((t1 + t2).eval() * 0.2f).exp(); -Semantically, calling ```eval()``` is equivalent to materializing the value of +Semantically, calling `eval()` is equivalent to materializing the value of the expression in a temporary Tensor of the right size. The code above in effect does: // .eval() knows the size! - TensorFixedSize<float, 4, 4, 2> tmp = t1 + t2; + TensorFixedSize<float, Sizes<4, 4, 2>> tmp = t1 + t2; Tensor<float, 3> result = (tmp * 0.2f).exp(); -Note that the return value of ```eval()``` is itself an Operation, so the +Note that the return value of `eval()` is itself an Operation, so the following code does not do what you may think: // Here t3 is an evaluation Operation. t3 has not been evaluated yet. @@ -312,24 +312,24 @@ following code does not do what you may think: // an intermediate tensor to represent t3.x Tensor<float, 3> result = t4; -While in the examples above calling ```eval()``` does not make a difference in +While in the examples above calling `eval()` does not make a difference in performance, in other cases it can make a huge difference. In the expression -below the ```broadcast()``` expression causes the ```X.maximum()``` expression +below the `broadcast()` expression causes the `X.maximum()` expression to be evaluated many times: Tensor<...> X ...; Tensor<...> Y = ((X - X.maximum(depth_dim).reshape(dims2d).broadcast(bcast)) * beta).exp(); -Inserting a call to ```eval()``` between the ```maximum()``` and -```reshape()``` calls guarantees that maximum() is only computed once and +Inserting a call to `eval()` between the `maximum()` and +`reshape()` calls guarantees that maximum() is only computed once and greatly speeds-up execution: Tensor<...> Y = ((X - X.maximum(depth_dim).eval().reshape(dims2d).broadcast(bcast)) * beta).exp(); -In the other example below, the tensor ```Y``` is both used in the expression +In the other example below, the tensor `Y` is both used in the expression and its assignment. This is an aliasing problem and if the evaluation is not done in the right order Y will be updated incrementally during the evaluation resulting in bogus results: @@ -337,8 +337,8 @@ resulting in bogus results: Tensor<...> Y ...; Y = Y / (Y.sum(depth_dim).reshape(dims2d).broadcast(bcast)); -Inserting a call to ```eval()``` between the ```sum()``` and ```reshape()``` -expressions ensures that the sum is computed before any updates to ```Y``` are +Inserting a call to `eval()` between the `sum()` and `reshape()` +expressions ensures that the sum is computed before any updates to `Y` are done. Y = Y / (Y.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast)); @@ -347,21 +347,21 @@ Note that an eval around the full right hand side expression is not needed because the generated has to compute the i-th value of the right hand side before assigning it to the left hand side. -However, if you were assigning the expression value to a shuffle of ```Y``` -then you would need to force an eval for correctness by adding an ```eval()``` +However, if you were assigning the expression value to a shuffle of `Y` +then you would need to force an eval for correctness by adding an `eval()` call for the right hand side: Y.shuffle(...) = (Y / (Y.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast))).eval(); -#### Assigning to a TensorRef. +#### Assigning to a `TensorRef`. If you need to access only a few elements from the value of an expression you can avoid materializing the value in a full tensor by using a TensorRef. A TensorRef is a small wrapper class for any Eigen Operation. It provides -overloads for the ```()``` operator that let you access individual values in +overloads for the `()` operator that let you access individual values in the expression. TensorRef is convenient, because the Operation themselves do not provide a way to access individual elements. @@ -390,7 +390,7 @@ such as contractions and convolutions. The implementations are optimized for different environments: single threaded on CPU, multi threaded on CPU, or on a GPU using cuda. Additional implementations may be added later. -You can choose which implementation to use with the ```device()``` call. If +You can choose which implementation to use with the `device()` call. If you do not choose an implementation explicitly the default implementation that uses a single thread on the CPU is used. @@ -406,7 +406,7 @@ single-threaded CPU implementation: Tensor<float, 2> b(30, 40); Tensor<float, 2> c = a + b; -To choose a different implementation you have to insert a ```device()``` call +To choose a different implementation you have to insert a `device()` call before the assignment of the result. For technical C++ reasons this requires that the Tensor for the result be declared on its own. This means that you have to know the size of the result. @@ -414,16 +414,16 @@ have to know the size of the result. Eigen::Tensor<float, 2> c(30, 40); c.device(...) = a + b; -The call to ```device()``` must be the last call on the left of the operator=. +The call to `device()` must be the last call on the left of the operator=. -You must pass to the ```device()``` call an Eigen device object. There are +You must pass to the `device()` call an Eigen device object. There are presently three devices you can use: DefaultDevice, ThreadPoolDevice and GpuDevice. #### Evaluating With the DefaultDevice -This is exactly the same as not inserting a ```device()``` call. +This is exactly the same as not inserting a `device()` call. DefaultDevice my_device; c.device(my_device) = a + b; @@ -452,24 +452,24 @@ memory for tensors with cuda. In the documentation of the tensor methods and Operation we mention datatypes that are tensor-type specific: -#### <Tensor-Type>::Dimensions +#### `<Tensor-Type>::``Dimensions` -Acts like an array of ints. Has an ```int size``` attribute, and can be +Acts like an array of ints. Has an `int size` attribute, and can be indexed like an array to access individual values. Used to represent the -dimensions of a tensor. See ```dimensions()```. +dimensions of a tensor. See `dimensions()`. -#### <Tensor-Type>::Index +#### `<Tensor-Type>::``Index` -Acts like an ```int```. Used for indexing tensors along their dimensions. See -```operator()```, ```dimension()```, and ```size()```. +Acts like an `int`. Used for indexing tensors along their dimensions. See +`operator()`, `dimension()`, and `size()`. -#### <Tensor-Type>::Scalar +#### `<Tensor-Type>::``Scalar` Represents the datatype of individual tensor elements. For example, for a -```Tensor<float>```, ```Scalar``` is the type ```float```. See -```setConstant()```. +`Tensor<float>`, `Scalar` is the type `float`. See +`setConstant()`. -#### <Operation> +#### `<Operation>` We use this pseudo type to indicate that a tensor Operation is returned by a method. We indicate in the text the type and dimensions of the tensor that the @@ -489,7 +489,7 @@ Tensor, TensorFixedSize, and TensorMap. ## Metadata -### int NumDimensions +### `int NumDimensions` Constant value indicating the number of dimensions of a Tensor. This is also known as the tensor "rank". @@ -498,10 +498,10 @@ known as the tensor "rank". cout << "Dims " << a.NumDimensions; => Dims 2 -### Dimensions dimensions() +### `Dimensions dimensions()` Returns an array-like object representing the dimensions of the tensor. -The actual type of the dimensions() result is <Tensor-Type>::Dimensions. +The actual type of the `dimensions()` result is `<Tensor-Type>::``Dimensions`. Eigen::Tensor<float, 2> a(3, 4); const Eigen::Tensor<float, 2>::Dimensions& d = a.dimensions(); @@ -509,17 +509,17 @@ The actual type of the dimensions() result is <Tensor-Type>::Dimensions. << ", dim 1: " << d[1]; => Dim size: 2, dim 0: 3, dim 1: 4 -If you use a C++11 compiler, you can use ```auto``` to simplify the code: +If you use a C++11 compiler, you can use `auto` to simplify the code: const auto& d = a.dimensions(); cout << "Dim size: " << d.size << ", dim 0: " << d[0] << ", dim 1: " << d[1]; => Dim size: 2, dim 0: 3, dim 1: 4 -### Index dimension(Index n) +### `Index dimension(Index n)` Returns the n-th dimension of the tensor. The actual type of the -```dimension()``` result is ```<Tensor-Type>::Index```, but you can +`dimension()` result is `<Tensor-Type>::``Index`, but you can always use it like an int. Eigen::Tensor<float, 2> a(3, 4); @@ -527,11 +527,11 @@ always use it like an int. cout << "Dim 1: " << dim1; => Dim 1: 4 -### Index size() +### `Index size()` Returns the total number of elements in the tensor. This is the product of all -the tensor dimensions. The actual type of the ```size()``` result is -```<Tensor-Type>::Index```, but you can always use it like an int. +the tensor dimensions. The actual type of the `size()` result is +`<Tensor-Type>::``Index`, but you can always use it like an int. Eigen::Tensor<float, 2> a(3, 4); cout << "Size: " << a.size(); @@ -540,11 +540,11 @@ the tensor dimensions. The actual type of the ```size()``` result is ### Getting Dimensions From An Operation -A few operations provide ```dimensions()``` directly, -e.g. ```TensorReslicingOp```. Most operations defer calculating dimensions +A few operations provide `dimensions()` directly, +e.g. `TensorReslicingOp`. Most operations defer calculating dimensions until the operation is being evaluated. If you need access to the dimensions of a deferred operation, you can wrap it in a TensorRef (see Assigning to a -TensorRef above), which provides ```dimensions()``` and ```dimension()``` as +TensorRef above), which provides `dimensions()` and `dimension()` as above. TensorRef can also wrap the plain Tensor types, so this is a useful idiom in @@ -567,11 +567,11 @@ to the rank of the tensor. The content of the tensor is not initialized. ### TensorFixedSize -Creates a tensor of the specified size. The number of arguments in the Size<> +Creates a tensor of the specified size. The number of arguments in the Sizes<> template parameter determines the rank of the tensor. The content of the tensor is not initialized. - Eigen::TensorFixedSize<float, Size<3, 4>> a; + Eigen::TensorFixedSize<float, Sizes<3, 4>> a; cout << "Rank: " << a.rank() << endl; => Rank: 2 cout << "NumRows: " << a.dimension(0) << " NumCols: " << a.dimension(1) << endl; @@ -581,14 +581,14 @@ is not initialized. Creates a tensor mapping an existing array of data. The data must not be freed until the TensorMap is discarded, and the size of the data must be large enough -to accomodate of the coefficients of the tensor. +to accommodate the coefficients of the tensor. float data[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}; - Eigen::TensorMap<float, 2> a(data, 3, 4); + Eigen::TensorMap<Tensor<float, 2>> a(data, 3, 4); cout << "NumRows: " << a.dimension(0) << " NumCols: " << a.dimension(1) << endl; => NumRows: 3 NumCols: 4 cout << "a(1, 2): " << a(1, 2) << endl; - => a(1, 2): 9 + => a(1, 2): 7 ## Contents Initialization @@ -602,9 +602,9 @@ You can use one of the methods below to initialize the tensor memory. These have an immediate effect on the tensor and return the tensor itself as a result. These are not tensor Operations which delay evaluation. -### <Tensor-Type> setConstant(const Scalar& val) +### `<Tensor-Type> setConstant(const Scalar& val)` -Sets all elements of the tensor to the constant value ```val```. ```Scalar``` +Sets all elements of the tensor to the constant value `val`. `Scalar` is the type of data stored in the tensor. You can pass any value that is convertible to that type. @@ -618,8 +618,8 @@ Returns the tensor itself in case you want to chain another call. 12.3 12.3 12.3 12.3 12.3 12.3 12.3 12.3 -Note that ```setConstant()``` can be used on any tensor where the element type -has a copy constructor and an ```operator=()```: +Note that `setConstant()` can be used on any tensor where the element type +has a copy constructor and an `operator=()`: Eigen::Tensor<string, 2> a(2, 3); a.setConstant("yolo"); @@ -630,9 +630,9 @@ has a copy constructor and an ```operator=()```: yolo yolo yolo -### <Tensor-Type> setZero() +### `<Tensor-Type> setZero()` -Fills the tensor with zeros. Equivalent to ```setConstant(Scalar(0))```. +Fills the tensor with zeros. Equivalent to `setConstant(Scalar(0))`. Returns the tensor itself in case you want to chain another call. a.setZero(); @@ -644,7 +644,7 @@ Returns the tensor itself in case you want to chain another call. 0 0 0 0 -### <Tensor-Type> setValues({..initializer_list}) +### `<Tensor-Type> setValues({..initializer_list})` Fills the tensor with explicit values specified in a std::initializer_list. The type of the initializer list depends on the type and rank of the tensor. @@ -653,10 +653,10 @@ If the tensor has rank N, the initializer list must be nested N times. The most deeply nested lists must contains P scalars of the Tensor type where P is the size of the last dimension of the Tensor. -For example, for a ```TensorFixedSize<float, 2, 3>``` the initializer list must +For example, for a `TensorFixedSize<float, 2, 3>` the initializer list must contains 2 lists of 3 floats each. -```setValues()``` returns the tensor itself in case you want to chain another +`setValues()` returns the tensor itself in case you want to chain another call. Eigen::Tensor<float, 2> a(2, 3); @@ -680,7 +680,7 @@ code only sets the values of the first row of the tensor. 10 20 30 1000 1000 1000 -### <Tensor-Type> setRandom() +### `<Tensor-Type> setRandom()` Fills the tensor with random values. Returns the tensor itself in case you want to chain another call. @@ -693,16 +693,16 @@ want to chain another call. -0.211234 0.823295 0.536459 -0.0452059 0.566198 -0.604897 -0.444451 0.257742 -You can customize ```setRandom()``` by providing your own random number +You can customize `setRandom()` by providing your own random number generator as a template argument: a.setRandom<MyRandomGenerator>(); -Here, ```MyRandomGenerator``` must be a struct with the following member -functions, where Scalar and Index are the same as ```<Tensor-Type>::Scalar``` -and ```<Tensor-Type>::Index```. +Here, `MyRandomGenerator` must be a struct with the following member +functions, where Scalar and Index are the same as `<Tensor-Type>::``Scalar` +and `<Tensor-Type>::``Index`. -See ```struct UniformRandomGenerator``` in TensorFunctors.h for an example. +See `struct UniformRandomGenerator` in TensorFunctors.h for an example. // Custom number generator for use with setRandom(). struct MyRandomGenerator { @@ -747,7 +747,7 @@ values of a tensor expression, the expression must either be evaluated or wrapped in a TensorRef. -### Scalar* data() and const Scalar* data() const +### `Scalar* data()` and `const Scalar* data() const` Returns a pointer to the storage for the tensor. The pointer is const if the tensor was const. This allows direct access to the data. The layout of the @@ -767,7 +767,7 @@ Scalar is the type of data stored in the tensor. ## Tensor Operations -All the methods documented below return non evaluated tensor ```Operations```. +All the methods documented below return non evaluated tensor `Operations`. These can be chained: you can apply another Tensor Operation to the value returned by the method. @@ -775,10 +775,10 @@ The chain of Operation is evaluated lazily, typically when it is assigned to a tensor. See "Controlling when Expression are Evaluated" for more details about their evaluation. -### <Operation> constant(const Scalar& val) +### `<Operation> constant(const Scalar& val)` Returns a tensor of the same type and dimensions as the original tensor but -where all elements have the value ```val```. +where all elements have the value `val`. This is useful, for example, when you want to add or subtract a constant from a tensor, or multiply every element of a tensor by a scalar. @@ -803,14 +803,14 @@ tensor, or multiply every element of a tensor by a scalar. 0.6 0.6 0.6 0.6 0.6 0.6 -### <Operation> random() +### `<Operation> random()` Returns a tensor of the same type and dimensions as the current tensor but where all elements have random values. This is for example useful to add random values to an existing tensor. The generation of random values can be customized in the same manner -as for ```setRandom()```. +as for `setRandom()`. Eigen::Tensor<float, 2> a(2, 3); a.setConstant(1.0f); @@ -833,7 +833,7 @@ All these operations take a single input tensor as argument and return a tensor of the same type and dimensions as the tensor to which they are applied. The requested operations are applied to each element independently. -### <Operation> operator-() +### `<Operation> operator-()` Returns a tensor of the same type and dimensions as the original tensor containing the opposite values of the original tensor. @@ -852,42 +852,42 @@ containing the opposite values of the original tensor. -1 -1 -1 -1 -1 -1 -### <Operation> sqrt() +### `<Operation> sqrt()` Returns a tensor of the same type and dimensions as the original tensor containing the square roots of the original tensor. -### <Operation> rsqrt() +### `<Operation> rsqrt()` Returns a tensor of the same type and dimensions as the original tensor containing the inverse square roots of the original tensor. -### <Operation> square() +### `<Operation> square()` Returns a tensor of the same type and dimensions as the original tensor containing the squares of the original tensor values. -### <Operation> inverse() +### `<Operation> inverse()` Returns a tensor of the same type and dimensions as the original tensor containing the inverse of the original tensor values. -### <Operation> exp() +### `<Operation> exp()` Returns a tensor of the same type and dimensions as the original tensor containing the exponential of the original tensor. -### <Operation> log() +### `<Operation> log()` Returns a tensor of the same type and dimensions as the original tensor containing the natural logarithms of the original tensor. -### <Operation> abs() +### `<Operation> abs()` Returns a tensor of the same type and dimensions as the original tensor containing the absolute values of the original tensor. -### <Operation> pow(Scalar exponent) +### `<Operation> pow(Scalar exponent)` Returns a tensor of the same type and dimensions as the original tensor containing the coefficients of the original tensor to the power of the @@ -914,17 +914,17 @@ cubic roots of an int Tensor: 0 1 2 3 4 5 -### <Operation> operator * (Scalar scale) +### `<Operation> operator * (Scalar scale)` Multiplies all the coefficients of the input tensor by the provided scale. -### <Operation> cwiseMax(Scalar threshold) +### `<Operation> cwiseMax(Scalar threshold)` TODO -### <Operation> cwiseMin(Scalar threshold) +### `<Operation> cwiseMin(Scalar threshold)` TODO -### <Operation> unaryExpr(const CustomUnaryOp& func) +### `<Operation> unaryExpr(const CustomUnaryOp& func)` TODO @@ -936,39 +936,39 @@ dimensions as the tensors to which they are applied, and unless otherwise specified it is also of the same type. The requested operations are applied to each pair of elements independently. -### <Operation> operator+(const OtherDerived& other) +### `<Operation> operator+(const OtherDerived& other)` Returns a tensor of the same type and dimensions as the input tensors containing the coefficient wise sums of the inputs. -### <Operation> operator-(const OtherDerived& other) +### `<Operation> operator-(const OtherDerived& other)` Returns a tensor of the same type and dimensions as the input tensors containing the coefficient wise differences of the inputs. -### <Operation> operator*(const OtherDerived& other) +### `<Operation> operator*(const OtherDerived& other)` Returns a tensor of the same type and dimensions as the input tensors containing the coefficient wise products of the inputs. -### <Operation> operator/(const OtherDerived& other) +### `<Operation> operator/(const OtherDerived& other)` Returns a tensor of the same type and dimensions as the input tensors containing the coefficient wise quotients of the inputs. This operator is not supported for integer types. -### <Operation> cwiseMax(const OtherDerived& other) +### `<Operation> cwiseMax(const OtherDerived& other)` Returns a tensor of the same type and dimensions as the input tensors containing the coefficient wise maximums of the inputs. -### <Operation> cwiseMin(const OtherDerived& other) +### `<Operation> cwiseMin(const OtherDerived& other)` Returns a tensor of the same type and dimensions as the input tensors containing the coefficient wise mimimums of the inputs. -### <Operation> Logical operators +### `<Operation> Logical operators` The following logical operators are supported as well: @@ -1013,16 +1013,23 @@ multidimensional case. Eigen::Tensor<int, 2> a(2, 3); a.setValues({{1, 2, 3}, {6, 5, 4}}); Eigen::Tensor<int, 2> b(3, 2); - a.setValues({{1, 2}, {4, 5}, {5, 6}}); + b.setValues({{1, 2}, {4, 5}, {5, 6}}); // Compute the traditional matrix product - array<IndexPair<int>, 1> product_dims = { IndexPair(1, 0) }; + Eigen::array<Eigen::IndexPair<int>, 1> product_dims = { Eigen::IndexPair<int>(1, 0) }; Eigen::Tensor<int, 2> AB = a.contract(b, product_dims); // Compute the product of the transpose of the matrices - array<IndexPair<int>, 1> transpose_product_dims = { IndexPair(0, 1) }; + Eigen::array<Eigen::IndexPair<int>, 1> transposed_product_dims = { Eigen::IndexPair<int>(0, 1) }; Eigen::Tensor<int, 2> AtBt = a.contract(b, transposed_product_dims); + // Contraction to scalar value using a double contraction. + // First coordinate of both tensors are contracted as well as both second coordinates, i.e., this computes the sum of the squares of the elements. + Eigen::array<Eigen::IndexPair<int>, 2> double_contraction_product_dims = { Eigen::IndexPair<int>(0, 0), Eigen::IndexPair<int>(1, 1) }; + Eigen::Tensor<int, 0> AdoubleContractedA = a.contract(a, double_contraction_product_dims); + + // Extracting the scalar value of the tensor contraction for further usage + int value = AdoubleContractedA(0); ## Reduction Operations @@ -1032,13 +1039,13 @@ original tensor. The values in the returned tensor are computed by applying a the dimensions along which the slices are made. The Eigen Tensor library provides a set of predefined reduction operators such -as ```maximum()``` and ```sum()``` and lets you define additional operators by +as `maximum()` and `sum()` and lets you define additional operators by implementing a few methods from a reductor template. ### Reduction Dimensions All reduction operations take a single parameter of type -```<TensorType>::Dimensions``` which can always be specified as an array of +`<TensorType>::``Dimensions` which can always be specified as an array of ints. These are called the "reduction dimensions." The values are the indices of the dimensions of the input tensor over which the reduction is done. The parameter can have at most as many element as the rank of the input tensor; @@ -1119,52 +1126,52 @@ scalar, represented as a zero-dimension tensor. 276 -### <Operation> sum(const Dimensions& new_dims) -### <Operation> sum() +### `<Operation> sum(const Dimensions& new_dims)` +### `<Operation> sum()` Reduce a tensor using the sum() operator. The resulting values are the sum of the reduced values. -### <Operation> mean(const Dimensions& new_dims) -### <Operation> mean() +### `<Operation> mean(const Dimensions& new_dims)` +### `<Operation> mean()` Reduce a tensor using the mean() operator. The resulting values are the mean of the reduced values. -### <Operation> maximum(const Dimensions& new_dims) -### <Operation> maximum() +### `<Operation> maximum(const Dimensions& new_dims)` +### `<Operation> maximum()` Reduce a tensor using the maximum() operator. The resulting values are the largest of the reduced values. -### <Operation> minimum(const Dimensions& new_dims) -### <Operation> minimum() +### `<Operation> minimum(const Dimensions& new_dims)` +### `<Operation> minimum()` Reduce a tensor using the minimum() operator. The resulting values are the smallest of the reduced values. -### <Operation> prod(const Dimensions& new_dims) -### <Operation> prod() +### `<Operation> prod(const Dimensions& new_dims)` +### `<Operation> prod()` Reduce a tensor using the prod() operator. The resulting values are the product of the reduced values. -### <Operation> all(const Dimensions& new_dims) -### <Operation> all() +### `<Operation> all(const Dimensions& new_dims)` +### `<Operation> all()` Reduce a tensor using the all() operator. Casts tensor to bool and then checks whether all elements are true. Runs through all elements rather than short-circuiting, so may be significantly inefficient. -### <Operation> any(const Dimensions& new_dims) -### <Operation> any() +### `<Operation> any(const Dimensions& new_dims)` +### `<Operation> any()` Reduce a tensor using the any() operator. Casts tensor to bool and then checks whether any element is true. Runs through all elements rather than short-circuiting, so may be significantly inefficient. -### <Operation> reduce(const Dimensions& new_dims, const Reducer& reducer) +### `<Operation> reduce(const Dimensions& new_dims, const Reducer& reducer)` -Reduce a tensor using a user-defined reduction operator. See ```SumReducer``` +Reduce a tensor using a user-defined reduction operator. See `SumReducer` in TensorFunctors.h for information on how to implement a reduction operator. @@ -1191,24 +1198,24 @@ dd a comment to this line => a 1 2 3 - 6 5 4 + 4 5 6 b 1 3 6 4 9 15 -### <Operation> cumsum(const Index& axis) +### `<Operation> cumsum(const Index& axis)` Perform a scan by summing consecutive entries. -### <Operation> cumprod(const Index& axis) +### `<Operation> cumprod(const Index& axis)` Perform a scan by multiplying consecutive entries. ## Convolutions -### <Operation> convolve(const Kernel& kernel, const Dimensions& dims) +### `<Operation> convolve(const Kernel& kernel, const Dimensions& dims)` Returns a tensor that is the output of the convolution of the input tensor with the kernel, along the specified dimensions of the input tensor. The dimension size for dimensions of the output tensor @@ -1251,7 +1258,7 @@ These operations return a Tensor with different dimensions than the original Tensor. They can be used to access slices of tensors, see them with different dimensions, or pad tensors with additional data. -### <Operation> reshape(const Dimensions& new_dims) +### `<Operation> reshape(const Dimensions& new_dims)` Returns a view of the input tensor that has been reshaped to the specified new dimensions. The argument new_dims is an array of Index values. The @@ -1273,7 +1280,7 @@ the number of elements in the input tensor. This operation does not move any data in the input tensor, so the resulting contents of a reshaped Tensor depend on the data layout of the original Tensor. -For example this is what happens when you ```reshape()``` a 2D ColMajor tensor +For example this is what happens when you `reshape()` a 2D ColMajor tensor to one dimension: Eigen::Tensor<float, 2, Eigen::ColMajor> a(2, 3); @@ -1314,7 +1321,7 @@ The previous example can be rewritten as follow: Eigen::Tensor<float, 2, Eigen::ColMajor> a(2, 3); a.setValues({{0.0f, 100.0f, 200.0f}, {300.0f, 400.0f, 500.0f}}); Eigen::array<Eigen::DenseIndex, 2> two_dim({2, 3}); - Eigen::Tensor<float, 1, Eigen::ColMajor> b; + Eigen::Tensor<float, 1, Eigen::ColMajor> b(6); b.reshape(two_dim) = a; cout << "b" << endl << b << endl; => @@ -1330,7 +1337,7 @@ Note that "b" itself was not reshaped but that instead the assignment is done to the reshape view of b. -### <Operation> shuffle(const Shuffle& shuffle) +### `<Operation> shuffle(const Shuffle& shuffle)` Returns a copy of the input tensor whose dimensions have been reordered according to the specified permutation. The argument shuffle @@ -1371,14 +1378,14 @@ Let's rewrite the previous example to take advantage of this feature: output.shuffle({2, 0, 1}) = input; -### <Operation> stride(const Strides& strides) +### `<Operation> stride(const Strides& strides)` Returns a view of the input tensor that strides (skips stride-1 elements) along each of the dimensions. The argument strides is an array of Index values. The dimensions of the resulting tensor are ceil(input_dimensions[i] / strides[i]). -For example this is what happens when you ```stride()``` a 2D tensor: +For example this is what happens when you `stride()` a 2D tensor: Eigen::Tensor<int, 2> a(4, 3); a.setValues({{0, 100, 200}, {300, 400, 500}, {600, 700, 800}, {900, 1000, 1100}}); @@ -1397,7 +1404,7 @@ It is possible to assign a tensor to a stride: output.stride({2, 3, 4}) = input; -### <Operation> slice(const StartIndices& offsets, const Sizes& extents) +### `<Operation> slice(const StartIndices& offsets, const Sizes& extents)` Returns a sub-tensor of the given tensor. For each dimension i, the slice is made of the coefficients stored between offset[i] and offset[i] + extents[i] in @@ -1423,7 +1430,7 @@ the input tensor. 600 700 -### <Operation> chip(const Index offset, const Index dim) +### `<Operation> chip(const Index offset, const Index dim)` A chip is a special kind of slice. It is the subtensor at the given offset in the dimension dim. The returned tensor has one fewer dimension than the input @@ -1474,7 +1481,7 @@ lvalue. For example: 0 0 0 -### <Operation> reverse(const ReverseDimensions& reverse) +### `<Operation> reverse(const ReverseDimensions& reverse)` Returns a view of the input tensor that reverses the order of the coefficients along a subset of the dimensions. The argument reverse is an array of boolean @@ -1482,7 +1489,7 @@ values that indicates whether or not the order of the coefficients should be reversed along each of the dimensions. This operation preserves the dimensions of the input tensor. -For example this is what happens when you ```reverse()``` the first dimension +For example this is what happens when you `reverse()` the first dimension of a 2D tensor: Eigen::Tensor<int, 2> a(4, 3); @@ -1504,7 +1511,7 @@ of a 2D tensor: 0 100 200 -### <Operation> broadcast(const Broadcast& broadcast) +### `<Operation> broadcast(const Broadcast& broadcast)` Returns a view of the input tensor in which the input is replicated one to many times. @@ -1528,11 +1535,11 @@ made in each of the dimensions. 0 100 200 0 100 200 300 400 500 300 400 500 -### <Operation> concatenate(const OtherDerived& other, Axis axis) +### `<Operation> concatenate(const OtherDerived& other, Axis axis)` TODO -### <Operation> pad(const PaddingDimensions& padding) +### `<Operation> pad(const PaddingDimensions& padding)` Returns a view of the input tensor in which the input is padded with zeros. @@ -1557,7 +1564,7 @@ Returns a view of the input tensor in which the input is padded with zeros. 0 0 0 0 -### <Operation> extract_patches(const PatchDims& patch_dims) +### `<Operation> extract_patches(const PatchDims& patch_dims)` Returns a tensor of coefficient patches extracted from the input tensor, where each patch is of dimension specified by 'patch_dims'. The returned tensor has @@ -1644,9 +1651,7 @@ patch index: 5 6 7 10 11 -### <Operation> extract_image_patches(const Index patch_rows, const Index patch_cols, - const Index row_stride, const Index col_stride, - const PaddingType padding_type) +### `<Operation> extract_image_patches(const Index patch_rows, const Index patch_cols, const Index row_stride, const Index col_stride, const PaddingType padding_type)` Returns a tensor of coefficient image patches extracted from the input tensor, which is expected to have dimensions ordered as follows (depending on the data @@ -1701,7 +1706,7 @@ sizes: ## Special Operations -### <Operation> cast<T>() +### `<Operation> cast<T>()` Returns a tensor of type T with the same dimensions as the original tensor. The returned tensor contains the values of the original tensor converted to @@ -1730,18 +1735,16 @@ but you can easily cast the tensors to floats to do the division: 1 2 2 -### <Operation> eval() +### `<Operation> eval()` TODO ## Representation of scalar values -Scalar values are often represented by tensors of size 1 and rank 1. It would be -more logical and user friendly to use tensors of rank 0 instead. For example -Tensor<T, N>::maximum() currently returns a Tensor<T, 1>. Similarly, the inner -product of 2 1d tensors (through contractions) returns a 1d tensor. In the -future these operations might be updated to return 0d tensors instead. +Scalar values are often represented by tensors of size 1 and rank 0.For example +Tensor<T, N>::maximum() currently returns a Tensor<T, 0>. Similarly, the inner +product of 2 1d tensors (through contractions) returns a 0d tensor. ## Limitations diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/Tensor.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/Tensor.h index 1940a96..00295a2 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/Tensor.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/Tensor.h @@ -23,12 +23,12 @@ namespace Eigen { * The %Tensor class encompasses only dynamic-size objects so far. * * The first two template parameters are required: - * \tparam Scalar_ \anchor tensor_tparam_scalar Numeric type, e.g. float, double, int or std::complex<float>. + * \tparam Scalar_ Numeric type, e.g. float, double, int or `std::complex<float>`. * User defined scalar types are supported as well (see \ref user_defined_scalars "here"). * \tparam NumIndices_ Number of indices (i.e. rank of the tensor) * * The remaining template parameters are optional -- in most cases you don't have to worry about them. - * \tparam Options_ \anchor tensor_tparam_options A combination of either \b #RowMajor or \b #ColMajor, and of either + * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of either * \b #AutoAlign or \b #DontAlign. * The former controls \ref TopicStorageOrders "storage order", and defaults to column-major. The latter controls alignment, which is required * for vectorization. It defaults to aligning tensors. Note that tensors currently do not support any operations that profit from vectorization. @@ -42,13 +42,13 @@ namespace Eigen { * \endcode * * This class can be extended with the help of the plugin mechanism described on the page - * \ref TopicCustomizingEigen by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN. + * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_TENSOR_PLUGIN. * * <i><b>Some notes:</b></i> * * <dl> * <dt><b>Relation to other parts of Eigen:</b></dt> - * <dd>The midterm developement goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that + * <dd>The midterm development goal for this class is to have a similar hierarchy as Eigen uses for matrices, so that * taking blocks or using tensors in expressions is easily possible, including an interface with the vector/matrix code * by providing .asMatrix() and .asVector() (or similar) methods for rank 2 and 1 tensors. However, currently, the %Tensor * class does not provide any of these features and is only available as a stand-alone class that just allows for diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h index 7a45a5c..f573608 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorBase.h @@ -22,7 +22,9 @@ namespace Eigen { * This class is the common parent of the Tensor and TensorMap class, thus * making it possible to use either class interchangably in expressions. */ - +#ifndef EIGEN_PARSED_BY_DOXYGEN +// FIXME Doxygen does not like the inheritance with different template parameters +// Since there is no doxygen documentation inside, we disable it for now template<typename Derived> class TensorBase<Derived, ReadOnlyAccessors> { @@ -1004,7 +1006,7 @@ class TensorBase : public TensorBase<Derived, ReadOnlyAccessors> { EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Derived& derived() const { return *static_cast<const Derived*>(this); } }; - +#endif // EIGEN_PARSED_BY_DOXYGEN } // end namespace Eigen #endif // EIGEN_CXX11_TENSOR_TENSOR_BASE_H diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h index ee16cde..c70dea0 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h @@ -116,15 +116,6 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment> void evalProduct(Scalar* buffer) const { - typedef - typename internal::remove_const<typename EvalLeftArgType::Scalar>::type - LhsScalar; - typedef - typename internal::remove_const<typename EvalRightArgType::Scalar>::type - RhsScalar; - typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits; - typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator; - typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator; typedef internal::TensorContractionInputMapper< LhsScalar, Index, internal::Lhs, LeftEvaluator, left_nocontract_t, contract_t, internal::packet_traits<LhsScalar>::size, diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h index b24cdeb..451940d 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorDimensions.h @@ -192,7 +192,7 @@ template <std::size_t V1=0, std::size_t V2=0, std::size_t V3=0, std::size_t V4=0 } #endif - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE DenseIndex operator[] (const int index) const { + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Index operator[] (const Index index) const { switch (index) { case 0: return internal::get<0, Base>::value; @@ -206,7 +206,7 @@ template <std::size_t V1=0, std::size_t V2=0, std::size_t V3=0, std::size_t V4=0 return internal::get<4, Base>::value; default: eigen_assert(false && "index overflow"); - return static_cast<DenseIndex>(-1); + return static_cast<Index>(-1); } } diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h index bbd5eb3..8bece4e 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorForcedEval.h @@ -12,19 +12,6 @@ namespace Eigen { -/** \class TensorForcedEval - * \ingroup CXX11_Tensor_Module - * - * \brief Tensor reshaping class. - * - * - */ -/// template <class> class MakePointer_ is added to convert the host pointer to the device pointer. -/// It is added due to the fact that for our device compiler T* is not allowed. -/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer T. -/// This is done through our MakePointer_ class. By default the Type in the MakePointer_<T> is T* . -/// Therefore, by adding the default value, we managed to convert the type and it does not break any -/// existing code as its default value is T*. namespace internal { template<typename XprType, template <class> class MakePointer_> struct traits<TensorForcedEvalOp<XprType, MakePointer_> > @@ -65,6 +52,21 @@ struct nested<TensorForcedEvalOp<XprType, MakePointer_>, 1, typename eval<Tensor +// FIXME use proper doxygen documentation (e.g. \tparam MakePointer_) + +/** \class TensorForcedEvalOp + * \ingroup CXX11_Tensor_Module + * + * \brief Tensor reshaping class. + * + * + */ +/// `template <class> class MakePointer_` is added to convert the host pointer to the device pointer. +/// It is added due to the fact that for our device compiler `T*` is not allowed. +/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer `T`. +/// This is done through our `MakePointer_` class. By default the Type in the `MakePointer_<T>` is `T*` . +/// Therefore, by adding the default value, we managed to convert the type and it does not break any +/// existing code as its default value is `T*`. template<typename XprType, template <class> class MakePointer_> class TensorForcedEvalOp : public TensorBase<TensorForcedEvalOp<XprType, MakePointer_>, ReadOnlyAccessors> { diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h index eb1d493..e27753b 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorGenerator.h @@ -12,7 +12,7 @@ namespace Eigen { -/** \class TensorGenerator +/** \class TensorGeneratorOp * \ingroup CXX11_Tensor_Module * * \brief Tensor generator class. diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h index a8e5575..e4fc86a 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorMap.h @@ -12,18 +12,20 @@ namespace Eigen { +// FIXME use proper doxygen documentation (e.g. \tparam MakePointer_) + /** \class TensorMap * \ingroup CXX11_Tensor_Module * * \brief A tensor expression mapping an existing array of data. * */ -/// template <class> class MakePointer_ is added to convert the host pointer to the device pointer. -/// It is added due to the fact that for our device compiler T* is not allowed. -/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer T. -/// This is done through our MakePointer_ class. By default the Type in the MakePointer_<T> is T* . +/// `template <class> class MakePointer_` is added to convert the host pointer to the device pointer. +/// It is added due to the fact that for our device compiler `T*` is not allowed. +/// If we wanted to use the same Evaluator functions we have to convert that type to our pointer `T`. +/// This is done through our `MakePointer_` class. By default the Type in the `MakePointer_<T>` is `T*` . /// Therefore, by adding the default value, we managed to convert the type and it does not break any -/// existing code as its default value is T*. +/// existing code as its default value is `T*`. template<typename PlainObjectType, int Options_, template <class> class MakePointer_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_, MakePointer_> > { public: diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h index 7ed3a3a..983f631 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExprConstructor.h @@ -33,7 +33,7 @@ struct EvalToLHSConstructor { EvalToLHSConstructor(const utility::tuple::Tuple<Params...> &t): expr((&(*(utility::tuple::get<N>(t).get_pointer())))) {} }; -/// \struct ExprConstructor is used to reconstruct the expression on the device and +/// struct ExprConstructor is used to reconstruct the expression on the device and /// recreate the expression with MakeGlobalPointer containing the device address /// space for the TensorMap pointers used in eval function. /// It receives the original expression type, the functor of the node, the tuple diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h index b1da685..cc18fcd 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractAccessor.h @@ -35,7 +35,7 @@ namespace Eigen { namespace TensorSycl { namespace internal { -/// \struct ExtractAccessor: Extract Accessor Class is used to extract the +/// struct ExtractAccessor: Extract Accessor Class is used to extract the /// accessor from a buffer. /// Depending on the type of the leaf node we can get a read accessor or a /// read_write accessor diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h index 4271253..9edd38e 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclExtractFunctors.h @@ -25,7 +25,7 @@ namespace Eigen { namespace TensorSycl { namespace internal { -/// \struct FunctorExtractor: This struct is used to extract the functors +/// struct FunctorExtractor: This struct is used to extract the functors /// constructed on /// the host-side, to pack them and reuse them in reconstruction of the /// expression on the device. diff --git a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h index 063b027..83915f3 100644 --- a/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h +++ b/eigen/unsupported/Eigen/CXX11/src/Tensor/TensorSyclTuple.h @@ -34,7 +34,7 @@ struct StaticIf<true, T> { /// \struct Tuple /// \brief is a fixed-size collection of heterogeneous values -/// \ztparam Ts... - the types of the elements that the tuple stores. +/// \tparam Ts... - the types of the elements that the tuple stores. /// Empty list is supported. template <class... Ts> struct Tuple {}; @@ -147,6 +147,8 @@ struct IndexList {}; template <size_t MIN, size_t N, size_t... Is> struct RangeBuilder; +// FIXME Doxygen has problems with recursive inheritance +#ifndef EIGEN_PARSED_BY_DOXYGEN /// \brief base Step: Specialisation of the \ref RangeBuilder when the /// MIN==MAX. In this case the Is... is [0 to sizeof...(tuple elements)) /// \tparam MIN is the starting index of the tuple @@ -164,6 +166,7 @@ struct RangeBuilder<MIN, MIN, Is...> { /// \tparam Is... are the list of generated index so far template <size_t MIN, size_t N, size_t... Is> struct RangeBuilder : public RangeBuilder<MIN, N - 1, N - 1, Is...> {}; +#endif // EIGEN_PARSED_BY_DOXYGEN /// \brief IndexRange that returns a [MIN, MAX) index range /// \tparam MIN is the starting index in the tuple diff --git a/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h b/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h index 0fe0b7c..5e97d07 100644 --- a/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h +++ b/eigen/unsupported/Eigen/CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h @@ -17,7 +17,7 @@ namespace internal { namespace group_theory { /** \internal - * \file CXX11/Tensor/util/TemplateGroupTheory.h + * \file CXX11/src/TensorSymmetry/util/TemplateGroupTheory.h * This file contains C++ templates that implement group theory algorithms. * * The algorithms allow for a compile-time analysis of finite groups. @@ -167,7 +167,9 @@ template< typename elements, bool dont_add_current_element // = false > -struct dimino_first_step_elements_helper : +struct dimino_first_step_elements_helper +#ifndef EIGEN_PARSED_BY_DOXYGEN + : // recursive inheritance is too difficult for Doxygen public dimino_first_step_elements_helper< Multiply, Equality, @@ -187,6 +189,7 @@ template< typename elements > struct dimino_first_step_elements_helper<Multiply, Equality, id, g, current_element, elements, true> +#endif // EIGEN_PARSED_BY_DOXYGEN { typedef elements type; constexpr static int global_flags = Equality<current_element, id>::global_flags; diff --git a/eigen/unsupported/Eigen/CXX11/src/util/EmulateCXX11Meta.h b/eigen/unsupported/Eigen/CXX11/src/util/EmulateCXX11Meta.h index f3aa1b1..8a536fa 100644 --- a/eigen/unsupported/Eigen/CXX11/src/util/EmulateCXX11Meta.h +++ b/eigen/unsupported/Eigen/CXX11/src/util/EmulateCXX11Meta.h @@ -188,7 +188,7 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const array<t, n>& a) { } template<typename t> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE t array_prod(const array<t, 0>& /*a*/) { - return 0; + return 1; } template<typename t> diff --git a/eigen/unsupported/Eigen/FFT b/eigen/unsupported/Eigen/FFT index 2c45b39..d8cf3e6 100644 --- a/eigen/unsupported/Eigen/FFT +++ b/eigen/unsupported/Eigen/FFT @@ -289,6 +289,7 @@ class FFT void inv( MatrixBase<OutputDerived> & dst, const MatrixBase<ComplexDerived> & src, Index nfft=-1) { typedef typename ComplexDerived::Scalar src_type; + typedef typename ComplexDerived::RealScalar real_type; typedef typename OutputDerived::Scalar dst_type; const bool realfft= (NumTraits<dst_type>::IsComplex == 0); EIGEN_STATIC_ASSERT_VECTOR_ONLY(OutputDerived) @@ -329,9 +330,9 @@ class FFT tmp.head(nhead) = src.head(nhead); tmp.tail(ntail) = src.tail(ntail); if (resize_input<0) { //shrinking -- create the Nyquist bin as the average of the two bins that fold into it - tmp(nhead) = ( src(nfft/2) + src( src.size() - nfft/2 ) )*src_type(.5); + tmp(nhead) = ( src(nfft/2) + src( src.size() - nfft/2 ) )*real_type(.5); }else{ // expanding -- split the old Nyquist bin into two halves - tmp(nhead) = src(nhead) * src_type(.5); + tmp(nhead) = src(nhead) * real_type(.5); tmp(tmp.size()-nhead) = tmp(nhead); } } diff --git a/eigen/unsupported/Eigen/MatrixFunctions b/eigen/unsupported/Eigen/MatrixFunctions index 0320606..60dc0a6 100644 --- a/eigen/unsupported/Eigen/MatrixFunctions +++ b/eigen/unsupported/Eigen/MatrixFunctions @@ -161,8 +161,8 @@ the z-axis. \include MatrixExponential.cpp Output: \verbinclude MatrixExponential.out -\note \p M has to be a matrix of \c float, \c double, \c long double -\c complex<float>, \c complex<double>, or \c complex<long double> . +\note \p M has to be a matrix of \c float, \c double, `long double` +\c complex<float>, \c complex<double>, or `complex<long double>` . \subsection matrixbase_log MatrixBase::log() @@ -219,9 +219,8 @@ documentation of \ref matrixbase_exp "exp()". \include MatrixLogarithm.cpp Output: \verbinclude MatrixLogarithm.out -\note \p M has to be a matrix of \c float, \c double, <tt>long -double</tt>, \c complex<float>, \c complex<double>, or \c complex<long -double> . +\note \p M has to be a matrix of \c float, \c double, `long +double`, \c complex<float>, \c complex<double>, or `complex<long double>`. \sa MatrixBase::exp(), MatrixBase::matrixFunction(), class MatrixLogarithmAtomic, MatrixBase::sqrt(). @@ -326,9 +325,9 @@ Example: \include MatrixPower_optimal.cpp Output: \verbinclude MatrixPower_optimal.out -\note \p M has to be a matrix of \c float, \c double, <tt>long -double</tt>, \c complex<float>, \c complex<double>, or \c complex<long -double> . +\note \p M has to be a matrix of \c float, \c double, `long +double`, \c complex<float>, \c complex<double>, or +\c complex<long double> . \sa MatrixBase::exp(), MatrixBase::log(), class MatrixPower. diff --git a/eigen/unsupported/Eigen/OpenGLSupport b/eigen/unsupported/Eigen/OpenGLSupport index 87f5094..085325c 100644 --- a/eigen/unsupported/Eigen/OpenGLSupport +++ b/eigen/unsupported/Eigen/OpenGLSupport @@ -184,7 +184,7 @@ inline void glRotate(const Rotation2D<float>& rot) } inline void glRotate(const Rotation2D<double>& rot) { - glRotated(rot.angle()*180.0/EIGEN_PI, 0.0, 0.0, 1.0); + glRotated(rot.angle()*180.0/double(EIGEN_PI), 0.0, 0.0, 1.0); } template<typename Derived> void glRotate(const RotationBase<Derived,3>& rot) diff --git a/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h b/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h index 279fe5c..2f50e99 100644 --- a/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h +++ b/eigen/unsupported/Eigen/src/AutoDiff/AutoDiffScalar.h @@ -534,7 +534,8 @@ struct ScalarBinaryOpTraits<typename DerType::Scalar,AutoDiffScalar<DerType>, Bi EIGEN_EXPR_BINARYOP_SCALAR_RETURN_TYPE(typename Eigen::internal::remove_all<DerType>::type, typename Eigen::internal::traits<typename Eigen::internal::remove_all<DerType>::type>::Scalar, product) > \ FUNC(const Eigen::AutoDiffScalar<DerType>& x) { \ using namespace Eigen; \ - EIGEN_UNUSED typedef typename Eigen::internal::traits<typename Eigen::internal::remove_all<DerType>::type>::Scalar Scalar; \ + typedef typename Eigen::internal::traits<typename Eigen::internal::remove_all<DerType>::type>::Scalar Scalar; \ + EIGEN_UNUSED_VARIABLE(sizeof(Scalar)); \ CODE; \ } diff --git a/eigen/unsupported/Eigen/src/BVH/KdBVH.h b/eigen/unsupported/Eigen/src/BVH/KdBVH.h index 1b8d758..5e39af2 100644 --- a/eigen/unsupported/Eigen/src/BVH/KdBVH.h +++ b/eigen/unsupported/Eigen/src/BVH/KdBVH.h @@ -35,6 +35,7 @@ struct get_boxes_helper { { outBoxes.insert(outBoxes.end(), boxBegin, boxEnd); eigen_assert(outBoxes.size() == objects.size()); + EIGEN_ONLY_USED_FOR_DEBUG(objects); } }; diff --git a/eigen/unsupported/Eigen/src/IterativeSolvers/DGMRES.h b/eigen/unsupported/Eigen/src/IterativeSolvers/DGMRES.h index eff2dc8..4079e23 100644 --- a/eigen/unsupported/Eigen/src/IterativeSolvers/DGMRES.h +++ b/eigen/unsupported/Eigen/src/IterativeSolvers/DGMRES.h @@ -173,7 +173,7 @@ class DGMRES : public IterativeSolverBase<DGMRES<_MatrixType,_Preconditioner> > /** * Set the restart value (default is 30) */ - Index set_restart(const Index restart) { m_restart=restart; } + void set_restart(const Index restart) { m_restart=restart; } /** * Set the number of eigenvalues to deflate at each restart @@ -392,7 +392,6 @@ inline typename DGMRES<_MatrixType, _Preconditioner>::ComplexVector DGMRES<_Matr template< typename _MatrixType, typename _Preconditioner> inline typename DGMRES<_MatrixType, _Preconditioner>::ComplexVector DGMRES<_MatrixType, _Preconditioner>::schurValues(const RealSchur<DenseMatrix>& schurofH) const { - typedef typename MatrixType::Index Index; const DenseMatrix& T = schurofH.matrixT(); Index it = T.rows(); ComplexVector eig(it); diff --git a/eigen/unsupported/Eigen/src/IterativeSolvers/GMRES.h b/eigen/unsupported/Eigen/src/IterativeSolvers/GMRES.h index 5a82b0d..92618b1 100644 --- a/eigen/unsupported/Eigen/src/IterativeSolvers/GMRES.h +++ b/eigen/unsupported/Eigen/src/IterativeSolvers/GMRES.h @@ -21,7 +21,7 @@ namespace internal { * * Parameters: * \param mat matrix of linear system of equations -* \param Rhs right hand side vector of linear system of equations +* \param rhs right hand side vector of linear system of equations * \param x on input: initial guess, on output: solution * \param precond preconditioner used * \param iters on input: maximum number of iterations to perform diff --git a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h index 85ab3d9..e5ebbcf 100644 --- a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h +++ b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixExponential.h @@ -234,12 +234,13 @@ struct matrix_exp_computeUV<MatrixType, float> template <typename MatrixType> struct matrix_exp_computeUV<MatrixType, double> { + typedef typename NumTraits<typename traits<MatrixType>::Scalar>::Real RealScalar; template <typename ArgType> static void run(const ArgType& arg, MatrixType& U, MatrixType& V, int& squarings) { using std::frexp; using std::pow; - const double l1norm = arg.cwiseAbs().colwise().sum().maxCoeff(); + const RealScalar l1norm = arg.cwiseAbs().colwise().sum().maxCoeff(); squarings = 0; if (l1norm < 1.495585217958292e-002) { matrix_exp_pade3(arg, U, V); @@ -250,10 +251,10 @@ struct matrix_exp_computeUV<MatrixType, double> } else if (l1norm < 2.097847961257068e+000) { matrix_exp_pade9(arg, U, V); } else { - const double maxnorm = 5.371920351148152; + const RealScalar maxnorm = 5.371920351148152; frexp(l1norm / maxnorm, &squarings); if (squarings < 0) squarings = 0; - MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<double>(squarings)); + MatrixType A = arg.unaryExpr(MatrixExponentialScalingOp<RealScalar>(squarings)); matrix_exp_pade13(A, U, V); } } diff --git a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h index 3f7d777..3df8239 100644 --- a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h +++ b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixFunction.h @@ -7,8 +7,8 @@ // 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_MATRIX_FUNCTION -#define EIGEN_MATRIX_FUNCTION +#ifndef EIGEN_MATRIX_FUNCTION_H +#define EIGEN_MATRIX_FUNCTION_H #include "StemFunction.h" @@ -577,4 +577,4 @@ const MatrixFunctionReturnValue<Derived> MatrixBase<Derived>::cosh() const } // end namespace Eigen -#endif // EIGEN_MATRIX_FUNCTION +#endif // EIGEN_MATRIX_FUNCTION_H diff --git a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h index ff8f6e7..cf5fffa 100644 --- a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h +++ b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixLogarithm.h @@ -324,7 +324,7 @@ public: /** \brief Compute the matrix logarithm. * - * \param[out] result Logarithm of \p A, where \A is as specified in the constructor. + * \param[out] result Logarithm of \c A, where \c A is as specified in the constructor. */ template <typename ResultType> inline void evalTo(ResultType& result) const diff --git a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h index ebc433d..a3273da 100644 --- a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h +++ b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h @@ -57,8 +57,8 @@ class MatrixPowerParenthesesReturnValue : public ReturnByValue< MatrixPowerParen * \param[out] result */ template<typename ResultType> - inline void evalTo(ResultType& res) const - { m_pow.compute(res, m_p); } + inline void evalTo(ResultType& result) const + { m_pow.compute(result, m_p); } Index rows() const { return m_pow.rows(); } Index cols() const { return m_pow.cols(); } @@ -618,8 +618,8 @@ class MatrixPowerReturnValue : public ReturnByValue< MatrixPowerReturnValue<Deri * constructor. */ template<typename ResultType> - inline void evalTo(ResultType& res) const - { MatrixPower<PlainObject>(m_A.eval()).compute(res, m_p); } + inline void evalTo(ResultType& result) const + { MatrixPower<PlainObject>(m_A.eval()).compute(result, m_p); } Index rows() const { return m_A.rows(); } Index cols() const { return m_A.cols(); } @@ -669,8 +669,8 @@ class MatrixComplexPowerReturnValue : public ReturnByValue< MatrixComplexPowerRe * constructor. */ template<typename ResultType> - inline void evalTo(ResultType& res) const - { res = (m_p * m_A.log()).exp(); } + inline void evalTo(ResultType& result) const + { result = (m_p * m_A.log()).exp(); } Index rows() const { return m_A.rows(); } Index cols() const { return m_A.cols(); } diff --git a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h index afd88ec..2e5abda 100644 --- a/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h +++ b/eigen/unsupported/Eigen/src/MatrixFunctions/MatrixSquareRoot.h @@ -120,7 +120,6 @@ template <typename MatrixType, typename ResultType> void matrix_sqrt_quasi_triangular_diagonal(const MatrixType& T, ResultType& sqrtT) { using std::sqrt; - typedef typename MatrixType::Index Index; const Index size = T.rows(); for (Index i = 0; i < size; i++) { if (i == size - 1 || T.coeff(i+1, i) == 0) { @@ -139,7 +138,6 @@ void matrix_sqrt_quasi_triangular_diagonal(const MatrixType& T, ResultType& sqrt template <typename MatrixType, typename ResultType> void matrix_sqrt_quasi_triangular_off_diagonal(const MatrixType& T, ResultType& sqrtT) { - typedef typename MatrixType::Index Index; const Index size = T.rows(); for (Index j = 1; j < size; j++) { if (T.coeff(j, j-1) != 0) // if T(j-1:j, j-1:j) is a 2-by-2 block @@ -206,7 +204,6 @@ template <typename MatrixType, typename ResultType> void matrix_sqrt_triangular(const MatrixType &arg, ResultType &result) { using std::sqrt; - typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; eigen_assert(arg.rows() == arg.cols()); @@ -318,7 +315,6 @@ template<typename Derived> class MatrixSquareRootReturnValue : public ReturnByValue<MatrixSquareRootReturnValue<Derived> > { protected: - typedef typename Derived::Index Index; typedef typename internal::ref_selector<Derived>::type DerivedNested; public: diff --git a/eigen/unsupported/Eigen/src/Polynomials/PolynomialUtils.h b/eigen/unsupported/Eigen/src/Polynomials/PolynomialUtils.h index 40ba65b..394e857 100644 --- a/eigen/unsupported/Eigen/src/Polynomials/PolynomialUtils.h +++ b/eigen/unsupported/Eigen/src/Polynomials/PolynomialUtils.h @@ -20,8 +20,8 @@ namespace Eigen { * e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$. * \param[in] x : the value to evaluate the polynomial at. * - * <i><b>Note for stability:</b></i> - * <dd> \f$ |x| \le 1 \f$ </dd> + * \note for stability: + * \f$ |x| \le 1 \f$ */ template <typename Polynomials, typename T> inline @@ -67,8 +67,8 @@ T poly_eval( const Polynomials& poly, const T& x ) * by degrees i.e. poly[i] is the coefficient of degree i of the polynomial * e.g. \f$ 1 + 3x^2 \f$ is stored as a vector \f$ [ 1, 0, 3 ] \f$. * - * <i><b>Precondition:</b></i> - * <dd> the leading coefficient of the input polynomial poly must be non zero </dd> + * \pre + * the leading coefficient of the input polynomial poly must be non zero */ template <typename Polynomial> inline diff --git a/eigen/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h b/eigen/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h index 0e8350a..536a0c3 100644 --- a/eigen/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h +++ b/eigen/unsupported/Eigen/src/SparseExtra/BlockSparseMatrix.h @@ -931,7 +931,7 @@ class BlockSparseMatrix : public SparseMatrixBase<BlockSparseMatrix<_Scalar,_Blo } /** - * \returns the starting position of the block <id> in the array of values + * \returns the starting position of the block \p id in the array of values */ Index blockPtr(Index id) const { diff --git a/eigen/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h b/eigen/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h index 037a13f..0ffbc43 100644 --- a/eigen/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h +++ b/eigen/unsupported/Eigen/src/SparseExtra/DynamicSparseMatrix.h @@ -228,6 +228,9 @@ template<typename _Scalar, int _Options, typename _StorageIndex> EIGEN_DEPRECATED inline DynamicSparseMatrix() : m_innerSize(0), m_data(0) { + #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + #endif eigen_assert(innerSize()==0 && outerSize()==0); } @@ -235,6 +238,9 @@ template<typename _Scalar, int _Options, typename _StorageIndex> EIGEN_DEPRECATED inline DynamicSparseMatrix(Index rows, Index cols) : m_innerSize(0) { + #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + #endif resize(rows, cols); } @@ -243,12 +249,18 @@ template<typename _Scalar, int _Options, typename _StorageIndex> EIGEN_DEPRECATED explicit inline DynamicSparseMatrix(const SparseMatrixBase<OtherDerived>& other) : m_innerSize(0) { - Base::operator=(other.derived()); + #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + #endif + Base::operator=(other.derived()); } inline DynamicSparseMatrix(const DynamicSparseMatrix& other) : Base(), m_innerSize(0) { + #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN + #endif *this = other.derived(); } diff --git a/eigen/unsupported/Eigen/src/SparseExtra/MarketIO.h b/eigen/unsupported/Eigen/src/SparseExtra/MarketIO.h index 41e4af4..04b7d69 100644 --- a/eigen/unsupported/Eigen/src/SparseExtra/MarketIO.h +++ b/eigen/unsupported/Eigen/src/SparseExtra/MarketIO.h @@ -17,8 +17,8 @@ namespace Eigen { namespace internal { - template <typename Scalar> - inline bool GetMarketLine (std::stringstream& line, Index& M, Index& N, Index& i, Index& j, Scalar& value) + template <typename Scalar,typename IndexType> + inline bool GetMarketLine (std::stringstream& line, IndexType& M, IndexType& N, IndexType& i, IndexType& j, Scalar& value) { line >> i >> j >> value; i--; @@ -30,8 +30,8 @@ namespace internal else return false; } - template <typename Scalar> - inline bool GetMarketLine (std::stringstream& line, Index& M, Index& N, Index& i, Index& j, std::complex<Scalar>& value) + template <typename Scalar,typename IndexType> + inline bool GetMarketLine (std::stringstream& line, IndexType& M, IndexType& N, IndexType& i, IndexType& j, std::complex<Scalar>& value) { Scalar valR, valI; line >> i >> j >> valR >> valI; @@ -134,7 +134,7 @@ template<typename SparseMatrixType> bool loadMarket(SparseMatrixType& mat, const std::string& filename) { typedef typename SparseMatrixType::Scalar Scalar; - typedef typename SparseMatrixType::Index Index; + typedef typename SparseMatrixType::StorageIndex StorageIndex; std::ifstream input(filename.c_str(),std::ios::in); if(!input) return false; @@ -144,11 +144,11 @@ bool loadMarket(SparseMatrixType& mat, const std::string& filename) bool readsizes = false; - typedef Triplet<Scalar,Index> T; + typedef Triplet<Scalar,StorageIndex> T; std::vector<T> elements; - Index M(-1), N(-1), NNZ(-1); - Index count = 0; + StorageIndex M(-1), N(-1), NNZ(-1); + StorageIndex count = 0; while(input.getline(buffer, maxBuffersize)) { // skip comments @@ -171,7 +171,7 @@ bool loadMarket(SparseMatrixType& mat, const std::string& filename) } else { - Index i(-1), j(-1); + StorageIndex i(-1), j(-1); Scalar value; if( internal::GetMarketLine(line, M, N, i, j, value) ) { diff --git a/eigen/unsupported/Eigen/src/Splines/Spline.h b/eigen/unsupported/Eigen/src/Splines/Spline.h index 627f6e4..57788c8 100644 --- a/eigen/unsupported/Eigen/src/Splines/Spline.h +++ b/eigen/unsupported/Eigen/src/Splines/Spline.h @@ -249,8 +249,6 @@ namespace Eigen DenseIndex degree, const typename Spline<_Scalar, _Dim, _Degree>::KnotVectorType& knots) { - typedef typename Spline<_Scalar, _Dim, _Degree>::BasisVectorType BasisVectorType; - const DenseIndex p = degree; const DenseIndex i = Spline::Span(u, degree, knots); @@ -380,9 +378,6 @@ namespace Eigen typedef Spline<_Scalar, _Dim, _Degree> SplineType; enum { Order = SplineTraits<SplineType>::OrderAtCompileTime }; - typedef typename SplineTraits<SplineType>::Scalar Scalar; - typedef typename SplineTraits<SplineType>::BasisVectorType BasisVectorType; - const DenseIndex span = SplineType::Span(u, p, U); const DenseIndex n = (std::min)(p, order); diff --git a/eigen/unsupported/doc/examples/FFT.cpp b/eigen/unsupported/doc/examples/FFT.cpp index fcbf812..85e8a02 100644 --- a/eigen/unsupported/doc/examples/FFT.cpp +++ b/eigen/unsupported/doc/examples/FFT.cpp @@ -61,14 +61,14 @@ template <typename T> void RandomFill(std::vector<T> & vec) { for (size_t k=0;k<vec.size();++k) - vec[k] = T( rand() )/T(RAND_MAX) - .5; + vec[k] = T( rand() )/T(RAND_MAX) - T(.5); } template <typename T> void RandomFill(std::vector<std::complex<T> > & vec) { for (size_t k=0;k<vec.size();++k) - vec[k] = std::complex<T> ( T( rand() )/T(RAND_MAX) - .5, T( rand() )/T(RAND_MAX) - .5); + vec[k] = std::complex<T> ( T( rand() )/T(RAND_MAX) - T(.5), T( rand() )/T(RAND_MAX) - T(.5)); } template <typename T_time,typename T_freq> @@ -85,7 +85,7 @@ void fwd_inv(size_t nfft) vector<T_time> timebuf2; fft.inv(timebuf2,freqbuf); - long double rmse = mag2(timebuf - timebuf2) / mag2(timebuf); + T_time rmse = mag2(timebuf - timebuf2) / mag2(timebuf); cout << "roundtrip rmse: " << rmse << endl; } diff --git a/eigen/unsupported/test/CMakeLists.txt b/eigen/unsupported/test/CMakeLists.txt index 80cccd8..3a8775a 100644 --- a/eigen/unsupported/test/CMakeLists.txt +++ b/eigen/unsupported/test/CMakeLists.txt @@ -68,7 +68,7 @@ ei_add_test(EulerAngles) find_package(MPFR 2.3.0) find_package(GMP) -if(MPFR_FOUND AND EIGEN_COMPILER_SUPPORT_CXX11) +if(MPFR_FOUND AND EIGEN_COMPILER_SUPPORT_CPP11) include_directories(${MPFR_INCLUDES} ./mpreal) ei_add_property(EIGEN_TESTED_BACKENDS "MPFR C++, ") set(EIGEN_MPFR_TEST_LIBRARIES ${MPFR_LIBRARIES} ${GMP_LIBRARIES}) @@ -154,7 +154,8 @@ if(EIGEN_TEST_CXX11) endif(EIGEN_TEST_SYCL) # It should be safe to always run these tests as there is some fallback code for # older compiler that don't support cxx11. - set(CMAKE_CXX_STANDARD 11) + # This is already set if EIGEN_TEST_CXX11 is enabled: + # set(CMAKE_CXX_STANDARD 11) ei_add_test(cxx11_eventcount "-pthread" "${CMAKE_THREAD_LIBS_INIT}") ei_add_test(cxx11_runqueue "-pthread" "${CMAKE_THREAD_LIBS_INIT}") diff --git a/eigen/unsupported/test/NonLinearOptimization.cpp b/eigen/unsupported/test/NonLinearOptimization.cpp index 1d682dd..f0c336c 100644 --- a/eigen/unsupported/test/NonLinearOptimization.cpp +++ b/eigen/unsupported/test/NonLinearOptimization.cpp @@ -565,7 +565,7 @@ void testLmdif1() // do the computation lmdif_functor functor; - DenseIndex nfev; + DenseIndex nfev = -1; // initialize to avoid maybe-uninitialized warning info = LevenbergMarquardt<lmdif_functor>::lmdif1(functor, x, &nfev); // check return value diff --git a/eigen/unsupported/test/matrix_function.cpp b/eigen/unsupported/test/matrix_function.cpp index 7c9b68a..6a2b219 100644 --- a/eigen/unsupported/test/matrix_function.cpp +++ b/eigen/unsupported/test/matrix_function.cpp @@ -25,7 +25,6 @@ inline bool test_isApprox_abs(const Type1& a, const Type2& b) template<typename MatrixType> MatrixType randomMatrixWithRealEivals(const typename MatrixType::Index size) { - typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; MatrixType diag = MatrixType::Zero(size, size); @@ -51,7 +50,6 @@ struct randomMatrixWithImagEivals<MatrixType, 0> { static MatrixType run(const typename MatrixType::Index size) { - typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; MatrixType diag = MatrixType::Zero(size, size); Index i = 0; @@ -79,7 +77,6 @@ struct randomMatrixWithImagEivals<MatrixType, 1> { static MatrixType run(const typename MatrixType::Index size) { - typedef typename MatrixType::Index Index; typedef typename MatrixType::Scalar Scalar; typedef typename MatrixType::RealScalar RealScalar; const Scalar imagUnit(0, 1); @@ -171,7 +168,6 @@ void testMatrixType(const MatrixType& m) { // Matrices with clustered eigenvalue lead to different code paths // in MatrixFunction.h and are thus useful for testing. - typedef typename MatrixType::Index Index; const Index size = m.rows(); for (int i = 0; i < g_repeat; i++) { diff --git a/eigen/unsupported/test/openglsupport.cpp b/eigen/unsupported/test/openglsupport.cpp index 706a816..5f63434 100644 --- a/eigen/unsupported/test/openglsupport.cpp +++ b/eigen/unsupported/test/openglsupport.cpp @@ -318,10 +318,6 @@ void test_openglsupport() GLint prg_id = createShader(vtx,frg); - typedef Vector2d Vector2d; - typedef Vector3d Vector3d; - typedef Vector4d Vector4d; - VERIFY_UNIFORM(dv,v2d, Vector2d); VERIFY_UNIFORM(dv,v3d, Vector3d); VERIFY_UNIFORM(dv,v4d, Vector4d); diff --git a/eigen/unsupported/test/polynomialsolver.cpp b/eigen/unsupported/test/polynomialsolver.cpp index 0c87478..4cfc46b 100644 --- a/eigen/unsupported/test/polynomialsolver.cpp +++ b/eigen/unsupported/test/polynomialsolver.cpp @@ -30,7 +30,6 @@ struct increment_if_fixed_size template<int Deg, typename POLYNOMIAL, typename SOLVER> bool aux_evalSolver( const POLYNOMIAL& pols, SOLVER& psolve ) { - typedef typename POLYNOMIAL::Index Index; typedef typename POLYNOMIAL::Scalar Scalar; typedef typename SOLVER::RootsType RootsType; @@ -107,7 +106,6 @@ void evalSolverSugarFunction( const POLYNOMIAL& pols, const ROOTS& roots, const // 1) the roots found are correct // 2) the roots have distinct moduli - typedef typename POLYNOMIAL::Scalar Scalar; typedef typename REAL_ROOTS::Scalar Real; //Test realRoots diff --git a/eigen/unsupported/test/sparse_extra.cpp b/eigen/unsupported/test/sparse_extra.cpp index a010ceb..7a049c8 100644 --- a/eigen/unsupported/test/sparse_extra.cpp +++ b/eigen/unsupported/test/sparse_extra.cpp @@ -8,10 +8,10 @@ // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -// import basic and product tests for deprectaed DynamicSparseMatrix +// import basic and product tests for deprecated DynamicSparseMatrix #define EIGEN_NO_DEPRECATED_WARNING -#include "sparse_basic.cpp" #include "sparse_product.cpp" +#include "sparse_basic.cpp" #include <Eigen/SparseExtra> template<typename SetterType,typename DenseType, typename Scalar, int Options> |