I added tests to core/impl/tensor_impl.hpp #3
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| Tensor<T> t1(s1); | ||
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| REQUIRE(t1.shape == s1); | ||
| REQUIRE(t1.data == AlignedPtr<T>(s1.total())); |
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I don't think the content of the pointer is guaranteed to match between allocations. It would be better to test that the length of data == s1.total()
| TEST_CASE_TEMPLATE("Tensor(Shape&, PtrType&)", T, test_data_types) | ||
| { | ||
| Shape s1; | ||
| AlignedPtr<T> ptr1; |
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This is an empty pointer and empty shape. The test would be better if you set the shape and filled the pointer with some values. For example-
Shape s1 = Shape{2, 2};
AlignedPtr<T> ptr1 = {1, 2, 3, 4};
Then you can explicitly test if tensor shape == {2, 2} and aligned ptr data = {1, 2, 3, 4}
…al projection function that returns vector projected onto line L spanned by vector s.
| inline Tensor<DataType> gaussian_elim(Tensor<DataType>& T1) | ||
| { | ||
| static_assert(std::is_floating_point<DataType>::value, | ||
| "eigenvalues() requires signed data"); |
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I think you forgot to change eigenvalues() :)
| } | ||
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| template <typename DataType> | ||
| inline Tensor<DataType> gaussian_elim(Tensor<DataType>& T1, Tensor<DataType>& V1) |
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Can you add documentation for the 2 tensor case
| TNT_ASSERT(T1.shape.num_axes() == 2, InvalidParameterException("tnt::gaussian_elim()", | ||
| __FILE__, __LINE__, "Gaussian elimination requires 2D tensors")) | ||
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| return detail::OptimizedGaussianElimination<DataType>::eval(T1); |
| } | ||
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| if (ptr[lead] == 1) { | ||
| clearColumn(ptr, i, (lead - (num_cols * i)), num_rows, num_cols, vct); |
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super nitpicky but I like functions to be lowercase_underscore instead of camel case
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| static void divideVector(DataType* vct, int row, DataType value) | ||
| { | ||
| vct[row] = vct[row] - value; |
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Is it expected that vct is updated in place? Meaning if I pass in 2 tensors should I expect both to change?
| DataType* s = V2.data.data; | ||
| int num_rows = V1.shape.axes[ROW_INDEX]; | ||
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| scalar_multiply(s, (dotProduct(v, s, num_rows) / dotProduct(s, s, num_rows)), num_rows); |
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You can just do V2 * dotProduct(...). Scalar multiplication is a method for tensors and should be faster then your version (it uses SIMD)
| template <typename DataType, typename Enable = void> | ||
| struct OptimizedOrthogonalProjection | ||
| { | ||
| static Tensor<DataType> eval(const Tensor<DataType>&, const Tensor<DataType>&); |
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The inputs are const here but not in your *_impl.hpp file. I think they should be const in both places
| /// \requires V1 and V2 shall be one dimensional | ||
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| template <typename DataType> | ||
| inline Tensor<DataType> project(Tensor<DataType>& V1, Tensor<DataType> V2) |
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I think the inputs should be const here as well
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