diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml index f1885bc3..2476dd9d 100644 --- a/.github/workflows/run_tests.yaml +++ b/.github/workflows/run_tests.yaml @@ -37,14 +37,46 @@ jobs: - name: Pixi install run: | - pixi install + pixi install --locked - name: Build package run: | pixi run mojo package numojo cp numojo.mojopkg tests/ - - name: Run tests + - name: Run Core Tests run: | - pixi run mojo test tests -I . - pixi run mojo test tests/core/test_matrix.mojo -I . -D F_CONTIGUOUS + echo "Testing core modules..." + pixi run mojo run -I tests/ tests/core/test_array_indexing_and_slicing.mojo + pixi run mojo run -I tests/ tests/core/test_array_methods.mojo + pixi run mojo run -I tests/ tests/core/test_bool_masks.mojo + pixi run mojo run -I tests/ tests/core/test_complexArray.mojo + pixi run mojo run -I tests/ tests/core/test_complexSIMD.mojo + pixi run mojo run -I tests/ tests/core/test_matrix.mojo + pixi run mojo run -I tests/ -D F_CONTIGUOUS tests/core/test_matrix.mojo + pixi run mojo run -I tests/ tests/core/test_shape_strides_item.mojo + + - name: Run Routine Tests + run: | + echo "Testing routines..." + pixi run mojo run -I tests/ tests/routines/test_creation.mojo + pixi run mojo run -I tests/ tests/routines/test_functional.mojo + pixi run mojo run -I tests/ tests/routines/test_indexing.mojo + pixi run mojo run -I tests/ tests/routines/test_io.mojo + pixi run mojo run -I tests/ tests/routines/test_linalg.mojo + pixi run mojo run -I tests/ tests/routines/test_manipulation.mojo + pixi run mojo run -I tests/ tests/routines/test_math.mojo + pixi run mojo run -I tests/ tests/routines/test_random.mojo + pixi run mojo run -I tests/ tests/routines/test_statistics.mojo + pixi run mojo run -I tests/ tests/routines/test_sorting.mojo + pixi run mojo run -I tests/ tests/routines/test_searching.mojo + + - name: Run Science Tests + run: | + echo "Testing science modules..." + pixi run mojo run -I tests/ tests/science/test_signal.mojo + + - name: Cleanup + if: always() + run: | + rm -f tests/numojo.mojopkg diff --git a/.gitignore b/.gitignore index 84d1b604..b4c52fb5 100644 --- a/.gitignore +++ b/.gitignore @@ -12,7 +12,6 @@ # pixi environments .pixi -pixi.lock /venv @@ -28,6 +27,7 @@ numojo.mojopkg /bench.mojo /test*.mojo /test*.ipynb +bench_*.mojo /tempCodeRunnerFile.mojo kgen.trace.* diff --git a/assets/matrix_test.mojo b/assets/matrix_test.mojo new file mode 100644 index 00000000..a9ceec04 --- /dev/null +++ b/assets/matrix_test.mojo @@ -0,0 +1,1811 @@ +""" +`numojo.Matrix` provides: + +- `Matrix` type (2DArray). +- `_MatrixIter` type (for iteration). +- Dunder methods for initialization, indexing, slicing, and arithmetics. +- Auxiliary functions. +""" + +from algorithm import parallelize, vectorize +from memory import UnsafePointer, memcpy, memset_zero +from random import random_float64 +from sys import simd_width_of +from python import PythonObject, Python + +from numojo.core.flags import Flags +from numojo.core.ndarray import NDArray +from numojo.core.data_container import DataContainer +from numojo.core.utility import _get_offset +from numojo.routines.manipulation import broadcast_to, reorder_layout +from numojo.routines.linalg.misc import issymmetric + + +# ===----------------------------------------------------------------------===# +# Matrix struct +# ===----------------------------------------------------------------------===# + + +struct Matrix[dtype: DType = DType.float64]( + ImplicitlyCopyable, Movable, Sized, Stringable, Writable +): + # TODO: Add buffer_type in the parameters. + """ + `Matrix` is a special case of `NDArray` (2DArray) but has some targeted + optimization since the number of dimensions is known at the compile time. + It has simpler indexing and slicing methods, which is very useful when users + only want to work with 2-dimensional arrays. + + NuMojo's `Matrix` is `NDArray` with fixed `ndim` known at compile time. + It may be different in some behaviors compared to `numpy.matrix`. + + - For `__getitem__`, passing in two `Int` returns a scalar, + and passing in one `Int` or two `Slice` returns a `Matrix`. + - We do not need auxiliary types `NDArrayShape` and `NDArrayStrides` + as the shape and strides information is fixed in length `Tuple[Int,Int]`. + + Parameters: + dtype: Type of item in NDArray. Default type is DType.float64. + + The matrix can be uniquely defined by the following features: + 1. The data buffer of all items. + 2. The shape of the matrix. + 3. The data type of the elements (compile-time known). + + Attributes: + - _buf (saved as row-majored, C-type) + - shape + - size (shape[0] * shape[1]) + - strides (shape[1], 1) + + Default constructor: + - [dtype], shape + - [dtype], data + + [checklist] CORE METHODS that have been implemented: + - [x] `Matrix.any` and `mat.logic.all` + - [x] `Matrix.any` and `mat.logic.any` + - [x] `Matrix.argmax` and `mat.sorting.argmax` + - [x] `Matrix.argmin` and `mat.sorting.argmin` + - [x] `Matrix.argsort` and `mat.sorting.argsort` + - [x] `Matrix.astype` + - [x] `Matrix.cumprod` and `mat.mathematics.cumprod` + - [x] `Matrix.cumsum` and `mat.mathematics.cumsum` + - [x] `Matrix.fill` and `mat.creation.full` + - [x] `Matrix.flatten` + - [x] `Matrix.inv` and `mat.linalg.inv` + - [x] `Matrix.max` and `mat.sorting.max` + - [x] `Matrix.mean` and `mat.statistics.mean` + - [x] `Matrix.min` and `mat.sorting.min` + - [x] `Matrix.prod` and `mat.mathematics.prod` + - [x] `Matrix.reshape` + - [x] `Matrix.resize` + - [x] `Matrix.round` and `mat.mathematics.round` (TODO: Check this after next Mojo update) + - [x] `Matrix.std` and `mat.statistics.std` + - [x] `Matrix.sum` and `mat.mathematics.sum` + - [x] `Matrix.trace` and `mat.linalg.trace` + - [x] `Matrix.transpose` and `mat.linalg.transpose` (also `Matrix.T`) + - [x] `Matrix.variance` and `mat.statistics.variance` (`var` is primitive) + """ + + alias width: Int = simd_width_of[dtype]() # + """Vector size of the data type.""" + + # var _buf: DataContainer[dtype] + var _buf: UnsafePointer[Scalar[dtype], **_] + """Data buffer of the items in the NDArray.""" + + var shape: Tuple[Int, Int] + """Shape of Matrix.""" + + var size: Int + """Size of Matrix.""" + + var strides: Tuple[Int, Int] + """Strides of matrix.""" + + var flags: Flags + "Information about the memory layout of the array." + + # ===-------------------------------------------------------------------===# + # Life cycle methods + # ===-------------------------------------------------------------------===# + + @always_inline("nodebug") + fn __init__( + out self, + shape: Tuple[Int, Int], + order: String = "C", + ): + """ + Create a new matrix of the given shape,without initializing data. + + Args: + shape: Tuple representing (rows, columns). + order: Use "C" for row-major (C-style) layout or "F" for column-major + (Fortran-style) layout. Defaults to "C". + """ + + self.shape = (shape[0], shape[1]) + if order == "C": + self.strides = (shape[1], 1) + else: + self.strides = (1, shape[0]) + self.size = shape[0] * shape[1] + # self._buf = DataContainer[dtype](size=self.size) + self._buf = UnsafePointer[ + Scalar[dtype], mut=True, origin = MutableOrigin.empty + ].alloc(self.size) + self.flags = Flags( + self.shape, self.strides, owndata=True, writeable=True + ) + + # * Should we take var ref and transfer ownership or take a read ref and copy it? + # @always_inline("nodebug") + # fn __init__( + # out self, + # var data: Self, + # ): + # """ + # Construct a matrix from matrix. + # """ + + # self = data^ + + # @always_inline("nodebug") + # fn __init__( + # out self, + # data: NDArray[dtype], + # ) raises: + # """ + # Construct a matrix from array. + # """ + + # if data.ndim == 1: + # self.shape = (1, data.shape[0]) + # self.strides = (data.shape[0], 1) + # self.size = data.shape[0] + # elif data.ndim == 2: + # self.shape = (data.shape[0], data.shape[1]) + # self.strides = (data.shape[1], 1) + # self.size = data.shape[0] * data.shape[1] + # else: + # raise Error(String("Shape too large to be a matrix.")) + + # self._buf = DataContainer[dtype](self.size) + + # self.flags = Flags( + # self.shape, self.strides, owndata=True, writeable=True + # ) + + # if data.flags["C_CONTIGUOUS"]: + # for i in range(data.shape[0]): + # memcpy( + # self._buf.ptr.offset(i * self.shape[0]), + # data._buf.ptr.offset(i * data.shape[0]), + # self.shape[0], + # ) + # else: + # for i in range(data.shape[0]): + # for j in range(data.shape[1]): + # self._store(i, j, data._getitem(i, j)) + + @always_inline("nodebug") + fn __init__( + out self, + shape: Tuple[Int, Int], + strides: Tuple[Int, Int], + offset: Int, + ptr: UnsafePointer[Scalar[dtype], *_], + ): + """ + Initialize Matrix that does not own the data. + The data is owned by another Matrix. + + Args: + shape: Shape of the view. + strides: Strides of the view. + offset: Offset in pointer of the data buffer. + ptr: Pointer to the data buffer of the original array. + """ + self.shape = shape + self.strides = strides + self.size = shape[0] * shape[1] + # self._buf = DataContainer(ptr=ptr.offset(offset)) + self._buf = ptr.offset(offset) + self.flags = Flags( + self.shape, self.strides, owndata=False, writeable=False + ) + + @always_inline("nodebug") + fn __copyinit__(out self, other: Self): + """ + Copy other into self. + """ + self.shape = (other.shape[0], other.shape[1]) + self.strides = (other.strides[0], other.strides[1]) + self.size = other.size + # self._buf = DataContainer[dtype](other.size) + # memcpy(self._buf.ptr, other._buf.ptr, other.size) + self._buf = UnsafePointer[ + Scalar[dtype], mut=True, origin = MutableOrigin.empty + ].alloc(other.size) + memcpy(self._buf, other._buf, other.size) + self.flags = other.flags + + @always_inline("nodebug") + fn __moveinit__(out self, deinit other: Self): + """ + Move other into self. + """ + self.shape = other.shape^ + self.strides = other.strides^ + self.size = other.size + self._buf = other._buf + self.flags = other.flags^ + + @always_inline("nodebug") + fn __del__(deinit self): + var owndata: Bool = self.flags.OWNDATA + if owndata: + print("Matrix __del__ called", self.size, self.flags.OWNDATA) + self._buf.free() + + # ===-------------------------------------------------------------------===# + # Slicing and indexing methods + # ===-------------------------------------------------------------------===# + + fn __getitem__(self, var x: Int, var y: Int) raises -> Scalar[dtype]: + """ + Return the scalar at the index. + + Args: + x: The row number. + y: The column number. + + Returns: + A scalar matching the dtype of the array. + """ + + if x < 0: + x = self.shape[0] + x + + if y < 0: + y = self.shape[1] + y + + if (x >= self.shape[0]) or (y >= self.shape[1]): + raise Error( + String( + "Index ({}, {}) exceed the matrix shape ({}, {})" + ).format(x, y, self.shape[0], self.shape[1]) + ) + + # return self._buf.load(x * self.strides[0] + y * self.strides[1]) + return self._buf.load(x * self.strides[0] + y * self.strides[1]) + + fn __getitem__(ref self: Self, var x: Int) -> Matrix[dtype]: + """ + Return the corresponding row at the index. + + Args: + x: The row number. + """ + print("_getitem__ called") + # var new_ptr = self._buf.origin_cast[ + # target_mut = True, + # target_origin=MutableOrigin.cast_from[__origin_of(self)], + # ]() + var new_ptr = self._buf.origin_cast[ + Origin(__origin_of(self)).mut, __origin_of(self) + ]() + return Matrix[dtype]( + shape=(1, self.shape[1]), + strides=(self.strides[0], self.strides[1]), + offset=x * self.strides[0], + ptr=new_ptr, + # ptr = self._buf.get_ptr() + ) + + fn _store[ + width: Int = 1 + ](mut self, var x: Int, simd: SIMD[dtype, width]) raises: + """ + `__setitem__` for row with width. + Unsafe: No boundary check! + """ + self._buf.store(x, simd) + + # fn __getitem__(self, var x: Int) raises -> Self: + # """ + # Return the corresponding row at the index. + + # Args: + # x: The row number. + # """ + + # if x < 0: + # x = self.shape[0] + x + + # if x >= self.shape[0]: + # raise Error( + # String("Index {} exceed the row number {}").format( + # x, self.shape[0] + # ) + # ) + + # var res = Self(shape=(1, self.shape[1]), order=self.order()) + + # if self.flags.C_CONTIGUOUS: + # var ptr = self._buf.ptr.offset(x * self.strides[0]) + # memcpy(res._buf.ptr, ptr, self.shape[1]) + # else: + # for j in range(self.shape[1]): + # res[0, j] = self[x, j] + + # return res^ + + fn __getitem__(self, x: Slice, y: Slice) -> Self: + """ + Get item from two slices. + """ + var start_x: Int + var end_x: Int + var step_x: Int + var start_y: Int + var end_y: Int + var step_y: Int + start_x, end_x, step_x = x.indices(self.shape[0]) + start_y, end_y, step_y = y.indices(self.shape[1]) + var range_x = range(start_x, end_x, step_x) + var range_y = range(start_y, end_y, step_y) + + # The new matrix with the corresponding shape + var B = Matrix[dtype]( + shape=(len(range_x), len(range_y)), order=self.order() + ) + + # Fill in the values at the corresponding index + var row = 0 + for i in range_x: + var col = 0 + for j in range_y: + B._store(row, col, self._load(i, j)) + col += 1 + row += 1 + + return B^ + + # fn __getitem__(self, x: Slice, var y: Int) -> Self: + # """ + # Get item from one slice and one int. + # """ + # if y < 0: + # y = self.shape[1] + y + + # var start_x: Int + # var end_x: Int + # var step_x: Int + # start_x, end_x, step_x = x.indices(self.shape[0]) + # var range_x = range(start_x, end_x, step_x) + + # # The new matrix with the corresponding shape + # var B = Matrix[dtype](shape=(len(range_x), 1), order=self.order()) + + # # Fill in the values at the corresponding index + # var row = 0 + # for i in range_x: + # B._store(row, 0, self._load(i, y)) + # row += 1 + + # return B^ + + # fn __getitem__(self, var x: Int, y: Slice) -> Self: + # """ + # Get item from one int and one slice. + # """ + # if x < 0: + # x = self.shape[0] + x + + # var start_y: Int + # var end_y: Int + # var step_y: Int + # start_y, end_y, step_y = y.indices(self.shape[1]) + # var range_y = range(start_y, end_y, step_y) + + # # The new matrix with the corresponding shape + # var B = Matrix[dtype](shape=(1, len(range_y)), order=self.order()) + + # # Fill in the values at the corresponding index + # var col = 0 + # for j in range_y: + # B._store(0, col, self._load(x, j)) + # col += 1 + + # return B^ + + # fn __getitem__(self, indices: List[Int]) raises -> Self: + # """ + # Get item by a list of integers. + # """ + + # var ncol = self.shape[1] + # var nrow = len(indices) + # var res = Matrix.zeros[dtype](shape=(nrow, ncol)) + # for i in range(nrow): + # res[i] = self[indices[i]] + # return res^ + + fn _load[width: Int = 1](self, x: Int, y: Int) -> SIMD[dtype, width]: + """ + `__getitem__` with width. + Unsafe: No boundary check! + """ + return self._buf.load[width=width]( + x * self.strides[0] + y * self.strides[1] + ) + + fn __setitem__(self, x: Int, y: Int, value: Scalar[dtype]) raises: + """ + Return the scalar at the index. + + Args: + x: The row number. + y: The column number. + value: The value to be set. + """ + + if (x >= self.shape[0]) or (y >= self.shape[1]): + raise Error( + String( + "Index ({}, {}) exceed the matrix shape ({}, {})" + ).format(x, y, self.shape[0], self.shape[1]) + ) + + self._buf.store(x * self.strides[0] + y * self.strides[1], value) + + fn __setitem__(self, var x: Int, value: Self) raises: + """ + Set the corresponding row at the index with the given matrix. + + Args: + x: The row number. + value: Matrix (row vector). + """ + + if x < 0: + x = self.shape[0] + x + + if x >= self.shape[0]: + raise Error( + String( + "Error: Elements of `index` ({}) \n" + "exceed the matrix shape ({})." + ).format(x, self.shape[0]) + ) + + if value.shape[0] != 1: + raise Error( + String( + "Error: The value should has only 1 row, " + "but it has {} rows." + ).format(value.shape[0]) + ) + + if self.shape[1] != value.shape[1]: + raise Error( + String( + "Error: Matrix has {} columns, " + "but the value has {} columns." + ).format(self.shape[1], value.shape[1]) + ) + + var ptr = self._buf.offset(x * self.shape[1]) + memcpy(ptr, value._buf, value.size) + + fn _store[ + width: Int = 1 + ](mut self, x: Int, y: Int, simd: SIMD[dtype, width]): + """ + `__setitem__` with width. + Unsafe: No boundary check! + """ + self._buf.store(x * self.strides[0] + y * self.strides[1], simd) + + # ===-------------------------------------------------------------------===# + # Other dunders and auxiliary methods + # ===-------------------------------------------------------------------===# + + fn __iter__(self) raises -> _MatrixIter[__origin_of(self), dtype]: + """Iterate over elements of the Matrix, returning copied value. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((4,4)) + for i in A: + print(i) + ``` + + Returns: + An iterator of Matrix elements. + """ + + return _MatrixIter[__origin_of(self), dtype]( + matrix=self, + length=self.shape[0], + ) + + fn __len__(self) -> Int: + """ + Returns length of 0-th dimension. + """ + return self.shape[0] + + fn __reversed__( + self, + ) raises -> _MatrixIter[__origin_of(self), dtype, forward=False]: + """Iterate backwards over elements of the Matrix, returning + copied value. + + Returns: + A reversed iterator of Matrix elements. + """ + + return _MatrixIter[__origin_of(self), dtype, forward=False]( + matrix=self, + length=self.shape[0], + ) + + fn __str__(self) -> String: + return String.write(self) + + fn write_to[W: Writer](self, mut writer: W): + fn print_row(self: Self, i: Int, sep: String) raises -> String: + var result: String = String("[") + var number_of_sep: Int = 1 + if self.shape[1] <= 6: + for j in range(self.shape[1]): + if j == self.shape[1] - 1: + number_of_sep = 0 + result += String(self[i, j]) + sep * number_of_sep + else: + for j in range(3): + result += String(self[i, j]) + sep + result += String("...") + sep + for j in range(self.shape[1] - 3, self.shape[1]): + if j == self.shape[1] - 1: + number_of_sep = 0 + result += String(self[i, j]) + sep * number_of_sep + result += String("]") + return result + + var sep: String = String("\t") + var newline: String = String("\n ") + var number_of_newline: Int = 1 + var result: String = "[" + + try: + if self.shape[0] <= 6: + for i in range(self.shape[0]): + if i == self.shape[0] - 1: + number_of_newline = 0 + result += ( + print_row(self, i, sep) + newline * number_of_newline + ) + else: + for i in range(3): + result += print_row(self, i, sep) + newline + result += String("...") + newline + for i in range(self.shape[0] - 3, self.shape[0]): + if i == self.shape[0] - 1: + number_of_newline = 0 + result += ( + print_row(self, i, sep) + newline * number_of_newline + ) + result += String("]") + writer.write( + result + + "\nDType: " + + String(self.dtype) + + " Shape: " + + String(self.shape[0]) + + "x" + + String(self.shape[1]) + + " Strides: " + + String(self.strides[0]) + + "," + + String(self.strides[1]) + + " C: " + + String(self.flags["C_CONTIGUOUS"]) + + " F: " + + String(self.flags["F_CONTIGUOUS"]) + + " Own: " + + String(self.flags["OWNDATA"]) + ) + except e: + print("Cannot transfer matrix to string!", e) + + # ===-------------------------------------------------------------------===# + # Arithmetic dunder methods + # ===-------------------------------------------------------------------===# + + fn __add__( + read self: Matrix[dtype, *_], read other: Matrix[dtype, *_] + ) raises -> Matrix[dtype, *_]: + # if (self.shape[0] == other.shape[0]) and ( + # self.shape[1] == other.shape[1] + # ): + return _arithmetic_func_matrix_matrix_to_matrix[dtype, SIMD.__add__]( + self, other + ) + + # fn __add__(self, other: Self) raises -> Self: + # if (self.shape[0] == other.shape[0]) and ( + # self.shape[1] == other.shape[1] + # ): + # return _arithmetic_func_matrix_matrix_to_matrix[ + # dtype, SIMD.__add__ + # ](self, other) + # elif (self.shape[0] < other.shape[0]) or ( + # self.shape[1] < other.shape[1] + # ): + # return _arithmetic_func_matrix_matrix_to_matrix[ + # dtype, SIMD.__add__ + # ](broadcast_to(self.copy(), other.shape, self.order()), other) + # else: + # return _arithmetic_func_matrix_matrix_to_matrix[ + # dtype, SIMD.__add__ + # ](self, broadcast_to(other.copy(), self.shape, self.order())) + + # fn __add__(self, other: Scalar[dtype]) raises -> Self: + # """Add matrix to scalar. + + # ```mojo + # from numojo import Matrix + # var A = Matrix.ones(shape=(4, 4)) + # print(A + 2) + # ``` + # """ + # return self + broadcast_to[dtype](other, self.shape, self.order()) + + fn __radd__(self, other: Scalar[dtype]) raises -> Self: + """ + Right-add. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(2 + A) + ``` + """ + return broadcast_to[dtype](other, self.shape, self.order()) + self + + fn __sub__(self, other: Self) raises -> Self: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__sub__ + ](self, other) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__sub__ + ](broadcast_to(self.copy(), other.shape, self.order()), other) + else: + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__sub__ + ](self, broadcast_to(other.copy(), self.shape, self.order())) + + fn __sub__(self, other: Scalar[dtype]) raises -> Self: + """Subtract matrix by scalar. + + ```mojo + from numojo import Matrix + A = Matrix(shape=(4, 4)) + print(A - 2) + ``` + """ + return self - broadcast_to[dtype](other, self.shape, self.order()) + + fn __rsub__(self, other: Scalar[dtype]) raises -> Self: + """ + Right-sub. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(2 - A) + ``` + """ + return broadcast_to[dtype](other, self.shape, self.order()) - self + + fn __mul__(self, other: Self) raises -> Self: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__mul__ + ](self, other) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__mul__ + ](broadcast_to(self.copy(), other.shape, self.order()), other) + else: + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__mul__ + ](self, broadcast_to(other.copy(), self.shape, self.order())) + + fn __mul__(self, other: Scalar[dtype]) raises -> Self: + """Mutiply matrix by scalar. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A * 2) + ``` + """ + return self * broadcast_to[dtype](other, self.shape, self.order()) + + fn __rmul__(self, other: Scalar[dtype]) raises -> Self: + """ + Right-mul. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(2 * A) + ``` + """ + return broadcast_to[dtype](other, self.shape, self.order()) * self + + fn __truediv__(self, other: Self) raises -> Self: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__truediv__ + ](self, other) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__truediv__ + ](broadcast_to(self.copy(), other.shape, self.order()), other) + else: + return _arithmetic_func_matrix_matrix_to_matrix[ + dtype, SIMD.__truediv__ + ](self, broadcast_to(other.copy(), self.shape, self.order())) + + fn __truediv__(self, other: Scalar[dtype]) raises -> Self: + """Divide matrix by scalar.""" + return self / broadcast_to[dtype](other, self.shape, order=self.order()) + + # Shouldn't we do the operation inplace? + fn __pow__(self, rhs: Scalar[dtype]) raises -> Self: + """Power of items.""" + var result: Self = self.copy() + for i in range(self.size): + result._buf.ptr[i] = self._buf.ptr[i].__pow__(rhs) + return result^ + + fn __lt__(self, other: Self) raises -> Matrix[DType.bool]: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.lt]( + self, other + ) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.lt]( + broadcast_to(self.copy(), other.shape, self.order()), other + ) + else: + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.lt]( + self, broadcast_to(other.copy(), self.shape, self.order()) + ) + + fn __lt__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: + """Matrix less than scalar. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A < 2) + ``` + """ + return self < broadcast_to[dtype](other, self.shape, self.order()) + + fn __le__(self, other: Self) raises -> Matrix[DType.bool]: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.le]( + self, other + ) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.le]( + broadcast_to(self.copy(), other.shape, self.order()), other + ) + else: + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.le]( + self, broadcast_to(other.copy(), self.shape, self.order()) + ) + + fn __le__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: + """Matrix less than and equal to scalar. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A <= 2) + ``` + """ + return self <= broadcast_to[dtype](other, self.shape, self.order()) + + fn __gt__(self, other: Self) raises -> Matrix[DType.bool]: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.gt]( + self, other + ) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.gt]( + broadcast_to(self.copy(), other.shape, self.order()), other + ) + else: + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.gt]( + self, broadcast_to(other.copy(), self.shape, self.order()) + ) + + fn __gt__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: + """Matrix greater than scalar. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A > 2) + ``` + """ + return self > broadcast_to[dtype](other, self.shape, self.order()) + + fn __ge__(self, other: Self) raises -> Matrix[DType.bool]: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ge]( + self, other + ) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ge]( + broadcast_to(self.copy(), other.shape, self.order()), other + ) + else: + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ge]( + self, broadcast_to(other.copy(), self.shape, self.order()) + ) + + fn __ge__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: + """Matrix greater than and equal to scalar. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A >= 2) + ``` + """ + return self >= broadcast_to[dtype](other, self.shape, self.order()) + + fn __eq__(self, other: Self) raises -> Matrix[DType.bool]: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.eq]( + self, other + ) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.eq]( + broadcast_to(self.copy(), other.shape, self.order()), other + ) + else: + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.eq]( + self, broadcast_to(other.copy(), self.shape, self.order()) + ) + + fn __eq__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: + """Matrix less than and equal to scalar. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A == 2) + ``` + """ + return self == broadcast_to[dtype](other, self.shape, self.order()) + + fn __ne__(self, other: Self) raises -> Matrix[DType.bool]: + if (self.shape[0] == other.shape[0]) and ( + self.shape[1] == other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ne]( + self, other + ) + elif (self.shape[0] < other.shape[0]) or ( + self.shape[1] < other.shape[1] + ): + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ne]( + broadcast_to(self.copy(), other.shape, self.order()), other + ) + else: + return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ne]( + self, broadcast_to(other.copy(), self.shape, self.order()) + ) + + fn __ne__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: + """Matrix less than and equal to scalar. + + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A != 2) + ``` + """ + return self != broadcast_to[dtype](other, self.shape, self.order()) + + fn __matmul__(self, other: Self) raises -> Self: + return numojo.linalg.matmul(self, other) + + # ===-------------------------------------------------------------------===# + # Core methods + # ===-------------------------------------------------------------------===# + + fn all(self) -> Scalar[dtype]: + """ + Test whether all array elements evaluate to True. + """ + return numojo.logic.all(self) + + fn all(self, axis: Int) raises -> Self: + """ + Test whether all array elements evaluate to True along axis. + """ + return numojo.logic.all(self, axis=axis) + + fn any(self) -> Scalar[dtype]: + """ + Test whether any array elements evaluate to True. + """ + return numojo.logic.any(self) + + fn any(self, axis: Int) raises -> Self: + """ + Test whether any array elements evaluate to True along axis. + """ + return numojo.logic.any(self, axis=axis) + + fn argmax(self) raises -> Scalar[DType.int]: + """ + Index of the max. It is first flattened before sorting. + """ + return numojo.math.argmax(self) + + fn argmax(self, axis: Int) raises -> Matrix[DType.int]: + """ + Index of the max along the given axis. + """ + return numojo.math.argmax(self, axis=axis) + + fn argmin(self) raises -> Scalar[DType.int]: + """ + Index of the min. It is first flattened before sorting. + """ + return numojo.math.argmin(self) + + fn argmin(self, axis: Int) raises -> Matrix[DType.int]: + """ + Index of the min along the given axis. + """ + return numojo.math.argmin(self, axis=axis) + + fn argsort(self) raises -> Matrix[DType.int]: + """ + Argsort the Matrix. It is first flattened before sorting. + """ + return numojo.math.argsort(self) + + fn argsort(self, axis: Int) raises -> Matrix[DType.int]: + """ + Argsort the Matrix along the given axis. + """ + return numojo.math.argsort(self.copy(), axis=axis) + + fn astype[asdtype: DType](self) -> Matrix[asdtype]: + """ + Copy of the matrix, cast to a specified type. + """ + var res = Matrix[asdtype]( + shape=(self.shape[0], self.shape[1]), order=self.order() + ) + for i in range(self.size): + res._buf.ptr[i] = self._buf.ptr[i].cast[asdtype]() + return res^ + + fn cumprod(self) raises -> Matrix[dtype]: + """ + Cumprod of flattened matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.cumprod()) + ``` + """ + return numojo.math.cumprod(self.copy()) + + fn cumprod(self, axis: Int) raises -> Matrix[dtype]: + """ + Cumprod of Matrix along the axis. + + Args: + axis: 0 or 1. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.cumprod(axis=0)) + print(A.cumprod(axis=1)) + ``` + """ + return numojo.math.cumprod(self.copy(), axis=axis) + + fn cumsum(self) raises -> Matrix[dtype]: + return numojo.math.cumsum(self.copy()) + + fn cumsum(self, axis: Int) raises -> Matrix[dtype]: + return numojo.math.cumsum(self.copy(), axis=axis) + + fn fill(self, fill_value: Scalar[dtype]): + """ + Fill the matrix with value. + + See also function `mat.creation.full`. + """ + for i in range(self.size): + self._buf.ptr[i] = fill_value + + fn flatten(self) -> Self: + """ + Return a flattened copy of the matrix. + """ + var res = Self(shape=(1, self.size), order=self.order()) + memcpy(res._buf.ptr, self._buf.ptr, res.size) + return res^ + + fn inv(self) raises -> Self: + """ + Inverse of matrix. + """ + return numojo.linalg.inv(self) + + fn order(self) -> String: + """ + Returns the order. + """ + var order: String = "F" + if self.flags.C_CONTIGUOUS: + order = "C" + return order + + fn max(self) raises -> Scalar[dtype]: + """ + Find max item. It is first flattened before sorting. + """ + return numojo.math.extrema.max(self) + + fn max(self, axis: Int) raises -> Self: + """ + Find max item along the given axis. + """ + return numojo.math.extrema.max(self, axis=axis) + + fn mean[ + returned_dtype: DType = DType.float64 + ](self) raises -> Scalar[returned_dtype]: + """ + Calculate the arithmetic average of all items in the Matrix. + """ + return numojo.statistics.mean[returned_dtype](self) + + fn mean[ + returned_dtype: DType = DType.float64 + ](self, axis: Int) raises -> Matrix[returned_dtype]: + """ + Calculate the arithmetic average of a Matrix along the axis. + + Args: + axis: 0 or 1. + """ + return numojo.statistics.mean[returned_dtype](self, axis=axis) + + fn min(self) raises -> Scalar[dtype]: + """ + Find min item. It is first flattened before sorting. + """ + return numojo.math.extrema.min(self) + + fn min(self, axis: Int) raises -> Self: + """ + Find min item along the given axis. + """ + return numojo.math.extrema.min(self, axis=axis) + + fn prod(self) -> Scalar[dtype]: + """ + Product of all items in the Matrix. + """ + return numojo.math.prod(self) + + fn prod(self, axis: Int) raises -> Self: + """ + Product of items in a Matrix along the axis. + + Args: + axis: 0 or 1. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.prod(axis=0)) + print(A.prod(axis=1)) + ``` + """ + return numojo.math.prod(self, axis=axis) + + fn reshape(self, shape: Tuple[Int, Int]) raises -> Self: + """ + Change shape and size of matrix and return a new matrix. + """ + if shape[0] * shape[1] != self.size: + raise Error( + String( + "Cannot reshape matrix of size {} into shape ({}, {})." + ).format(self.size, shape[0], shape[1]) + ) + var res = Self(shape=shape, order="C") + if self.flags.F_CONTIGUOUS: + var temp = self.reorder_layout() + memcpy(res._buf.ptr, temp._buf.ptr, res.size) + res = res.reorder_layout() + else: + memcpy(res._buf.ptr, self._buf.ptr, res.size) + return res^ + + fn resize(mut self, shape: Tuple[Int, Int]) raises: + """ + Change shape and size of matrix in-place. + """ + if shape[0] * shape[1] > self.size: + var other = Self(shape=shape) + if self.flags.C_CONTIGUOUS: + memcpy(other._buf.ptr, self._buf.ptr, self.size) + for i in range(self.size, other.size): + other._buf.ptr[i] = 0 + else: + var idx = 0 + for i in range(other.size): + other._buf.ptr.store(i, 0.0) + if idx < self.size: + other._buf.ptr[i] = self._buf.ptr[ + (i % self.shape[1]) * self.shape[0] + + (i // self.shape[1]) + ] + idx += 1 + other = other.reorder_layout() + self = other^ + else: + self.shape[0] = shape[0] + self.shape[1] = shape[1] + self.size = shape[0] * shape[1] + + if self.flags.C_CONTIGUOUS: + self.strides[0] = shape[1] + else: + self.strides[1] = shape[0] + + fn round(self, decimals: Int) raises -> Self: + return numojo.math.rounding.round(self.copy(), decimals=decimals) + + fn std[ + returned_dtype: DType = DType.float64 + ](self, ddof: Int = 0) raises -> Scalar[returned_dtype]: + """ + Compute the standard deviation. + + Args: + ddof: Delta degree of freedom. + """ + return numojo.statistics.std[returned_dtype](self, ddof=ddof) + + fn std[ + returned_dtype: DType = DType.float64 + ](self, axis: Int, ddof: Int = 0) raises -> Matrix[returned_dtype]: + """ + Compute the standard deviation along axis. + + Args: + axis: 0 or 1. + ddof: Delta degree of freedom. + """ + return numojo.statistics.std[returned_dtype](self, axis=axis, ddof=ddof) + + fn sum(self) -> Scalar[dtype]: + """ + Sum up all items in the Matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.sum()) + ``` + """ + return numojo.math.sum(self) + + fn sum(self, axis: Int) raises -> Self: + """ + Sum up the items in a Matrix along the axis. + + Args: + axis: 0 or 1. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.sum(axis=0)) + print(A.sum(axis=1)) + ``` + """ + return numojo.math.sum(self, axis=axis) + + fn trace(self) raises -> Scalar[dtype]: + """ + Trace of matrix. + """ + return numojo.linalg.trace(self) + + fn issymmetric(self) -> Bool: + """ + Transpose of matrix. + """ + return issymmetric(self) + + fn transpose(self) -> Self: + """ + Transpose of matrix. + """ + return transpose(self) + + fn reorder_layout(self) raises -> Self: + """ + Reorder_layout matrix. + """ + return reorder_layout(self) + + fn T(self) -> Self: + return transpose(self) + + fn variance[ + returned_dtype: DType = DType.float64 + ](self, ddof: Int = 0) raises -> Scalar[returned_dtype]: + """ + Compute the variance. + + Args: + ddof: Delta degree of freedom. + """ + return numojo.statistics.variance[returned_dtype](self, ddof=ddof) + + fn variance[ + returned_dtype: DType = DType.float64 + ](self, axis: Int, ddof: Int = 0) raises -> Matrix[returned_dtype]: + """ + Compute the variance along axis. + + Args: + axis: 0 or 1. + ddof: Delta degree of freedom. + """ + return numojo.statistics.variance[returned_dtype]( + self, axis=axis, ddof=ddof + ) + + # ===-------------------------------------------------------------------===# + # To other data types + # ===-------------------------------------------------------------------===# + + fn to_ndarray(self) raises -> NDArray[dtype]: + """Create `NDArray` from `Matrix`. + + It makes a copy of the buffer of the matrix. + """ + + var ndarray: NDArray[dtype] = NDArray[dtype]( + shape=List[Int](self.shape[0], self.shape[1]), order="C" + ) + memcpy(ndarray._buf.ptr, self._buf.ptr, ndarray.size) + + return ndarray^ + + fn to_numpy(self) raises -> PythonObject: + """See `numojo.core.utility.to_numpy`.""" + try: + var np = Python.import_module("numpy") + + var np_arr_dim = Python.list() + np_arr_dim.append(self.shape[0]) + np_arr_dim.append(self.shape[1]) + + np.set_printoptions(4) + + # Implement a dictionary for this later + var numpyarray: PythonObject + var np_dtype = np.float64 + if dtype == DType.float16: + np_dtype = np.float16 + elif dtype == DType.float32: + np_dtype = np.float32 + elif dtype == DType.int64: + np_dtype = np.int64 + elif dtype == DType.int32: + np_dtype = np.int32 + elif dtype == DType.int16: + np_dtype = np.int16 + elif dtype == DType.int8: + np_dtype = np.int8 + elif dtype == DType.uint64: + np_dtype = np.uint64 + elif dtype == DType.uint32: + np_dtype = np.uint32 + elif dtype == DType.uint16: + np_dtype = np.uint16 + elif dtype == DType.uint8: + np_dtype = np.uint8 + elif dtype == DType.bool: + np_dtype = np.bool_ + elif dtype == DType.int: + np_dtype = np.int64 + + var order = "C" if self.flags.C_CONTIGUOUS else "F" + numpyarray = np.empty(np_arr_dim, dtype=np_dtype, order=order) + var pointer_d = numpyarray.__array_interface__["data"][ + 0 + ].unsafe_get_as_pointer[dtype]() + memcpy(pointer_d, self._buf.ptr, self.size) + + return numpyarray^ + + except e: + print("Error in converting to numpy", e) + return PythonObject() + + # ===-----------------------------------------------------------------------===# + # Static methods to construct matrix + # ===-----------------------------------------------------------------------===# + + @staticmethod + fn full[ + dtype: DType = DType.float64 + ]( + shape: Tuple[Int, Int], + fill_value: Scalar[dtype] = 0, + order: String = "C", + ) -> Matrix[dtype]: + """Return a matrix with given shape and filled value. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.full(shape=(10, 10), fill_value=100) + ``` + """ + + var matrix = Matrix[dtype](shape, order) + for i in range(shape[0] * shape[1]): + matrix._buf.ptr.store(i, fill_value) + + return matrix^ + + @staticmethod + fn zeros[ + dtype: DType = DType.float64 + ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[dtype]: + """Return a matrix with given shape and filled with zeros. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(10, 10)) + ``` + """ + + var M = Matrix[dtype](shape, order) + memset_zero(M._buf.ptr, M.size) + return M^ + + @staticmethod + fn ones[ + dtype: DType = DType.float64 + ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[dtype]: + """Return a matrix with given shape and filled with ones. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(10, 10)) + ``` + """ + + return Matrix.full[dtype](shape=shape, fill_value=1) + + @staticmethod + fn identity[ + dtype: DType = DType.float64 + ](len: Int, order: String = "C") -> Matrix[dtype]: + """Return an identity matrix with given size. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.identity(12) + ``` + """ + var matrix = Matrix.zeros[dtype]((len, len), order) + for i in range(len): + matrix._buf.ptr.store( + i * matrix.strides[0] + i * matrix.strides[1], 1 + ) + return matrix^ + + @staticmethod + fn rand[ + dtype: DType = DType.float64 + ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[dtype]: + """Return a matrix with random values uniformed distributed between 0 and 1. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((12, 12)) + ``` + + Parameters: + dtype: The data type of the NDArray elements. + + Args: + shape: The shape of the Matrix. + order: The order of the Matrix. "C" or "F". + """ + var result = Matrix[dtype](shape, order) + for i in range(result.size): + result._buf.ptr.store(i, random_float64(0, 1).cast[dtype]()) + return result^ + + @staticmethod + fn fromlist[ + dtype: DType + ]( + object: List[Scalar[dtype]], + shape: Tuple[Int, Int] = (0, 0), + order: String = "C", + ) raises -> Matrix[dtype]: + """Create a matrix from a 1-dimensional list into given shape. + + If no shape is passed, the return matrix will be a row vector. + + Example: + ```mojo + from numojo import Matrix + fn main() raises: + print(Matrix.fromlist(List[Float64](1, 2, 3, 4, 5), (5, 1))) + ``` + """ + + if (shape[0] == 0) and (shape[1] == 0): + var M = Matrix[dtype](shape=(1, len(object))) + memcpy(M._buf.ptr, object.unsafe_ptr(), M.size) + return M^ + + if shape[0] * shape[1] != len(object): + var message = String( + "The input has {} elements, but the target has the shape {}x{}" + ).format(len(object), shape[0], shape[1]) + raise Error(message) + var M = Matrix[dtype](shape=shape, order="C") + memcpy(M._buf.ptr, object.unsafe_ptr(), M.size) + if order == "F": + M = M.reorder_layout() + return M^ + + @staticmethod + fn fromstring[ + dtype: DType = DType.float64 + ]( + text: String, shape: Tuple[Int, Int] = (0, 0), order: String = "C" + ) raises -> Matrix[dtype]: + """Matrix initialization from string representation of an matrix. + + Comma, right brackets, and whitespace are treated as seperators of numbers. + Digits, underscores, and minus signs are treated as a part of the numbers. + + If now shape is passed, the return matrix will be a row vector. + + Example: + ```mojo + from numojo.prelude import * + from numojo import Matrix + fn main() raises: + var A = Matrix.fromstring[f32]( + "1 2 .3 4 5 6.5 7 1_323.12 9 10, 11.12, 12 13 14 15 16", (4, 4)) + ``` + ```console + [[1.0 2.0 0.30000001192092896 4.0] + [5.0 6.5 7.0 1323.1199951171875] + [9.0 10.0 11.119999885559082 12.0] + [13.0 14.0 15.0 16.0]] + Size: 4x4 DType: float32 + ``` + + Args: + text: String representation of a matrix. + shape: Shape of the matrix. + order: Order of the matrix. "C" or "F". + """ + + var data = List[Scalar[dtype]]() + var bytes = text.as_bytes() + var number_as_str: String = "" + var size = shape[0] * shape[1] + + for i in range(len(bytes)): + var b = bytes[i] + if ( + chr(Int(b)).isdigit() + or (chr(Int(b)) == ".") + or (chr(Int(b)) == "-") + ): + number_as_str = number_as_str + chr(Int(b)) + if i == len(bytes) - 1: # Last byte + var number = atof(number_as_str).cast[dtype]() + data.append(number) # Add the number to the data buffer + number_as_str = "" # Clean the number cache + if ( + (chr(Int(b)) == ",") + or (chr(Int(b)) == "]") + or (chr(Int(b)) == " ") + ): + if number_as_str != "": + var number = atof(number_as_str).cast[dtype]() + data.append(number) # Add the number to the data buffer + number_as_str = "" # Clean the number cache + + if (shape[0] == 0) and (shape[1] == 0): + return Matrix.fromlist(data) + + if size != len(data): + var message = String( + "The number of items in the string is {}, which does not match" + " the given shape {}x{}." + ).format(len(data), shape[0], shape[1]) + raise Error(message) + + var result = Matrix[dtype](shape=shape) + for i in range(len(data)): + result._buf.ptr[i] = data[i] + return result^ + + +# ===-----------------------------------------------------------------------===# +# MatrixIter struct +# ===-----------------------------------------------------------------------===# + + +# ! Should the iterator be mutable or not? +struct _MatrixIter[ + is_mutable: Bool, //, + lifetime: Origin[is_mutable], + dtype: DType, + forward: Bool = True, +](Copyable, Movable): + """Iterator for Matrix. + + Parameters: + is_mutable: Whether the iterator is mutable. + lifetime: The lifetime of the underlying Matrix data. + dtype: The data type of the item. + forward: The iteration direction. `False` is backwards. + """ + + var index: Int + var matrix: Matrix[dtype] + var length: Int + + fn __init__( + out self, + matrix: Matrix[dtype], + length: Int, + ): + self.index = 0 if forward else length + self.length = length + self.matrix = matrix.copy() + + fn __iter__(self) -> Self: + return self.copy() + + fn __next__(mut self) raises -> Matrix[dtype]: + @parameter + if forward: + var current_index = self.index + self.index += 1 + return self.matrix[current_index] + else: + var current_index = self.index + self.index -= 1 + return self.matrix[current_index] + + @always_inline + fn __has_next__(self) -> Bool: + @parameter + if forward: + return self.index < self.length + else: + return self.index > 0 + + fn __len__(self) -> Int: + @parameter + if forward: + return self.length - self.index + else: + return self.index + + +# ===-----------------------------------------------------------------------===# +# Backend fucntions using SMID functions +# ===-----------------------------------------------------------------------===# + + +fn _arithmetic_func_matrix_matrix_to_matrix[ + dtype: DType, + simd_func: fn[type: DType, simd_width: Int] ( + SIMD[type, simd_width], SIMD[type, simd_width] + ) -> SIMD[type, simd_width], +](A: Matrix[dtype], B: Matrix[dtype]) raises -> Matrix[dtype]: + """ + Matrix[dtype] & Matrix[dtype] -> Matrix[dtype] + + For example: `__add__`, `__sub__`, etc. + """ + alias simd_width = simd_width_of[dtype]() + if A.order() != B.order(): + raise Error( + String("Matrix order {} does not match {}.").format( + A.order(), B.order() + ) + ) + + if (A.shape[0] != B.shape[0]) or (A.shape[1] != B.shape[1]): + raise Error( + String("Shape {}x{} does not match {}x{}.").format( + A.shape[0], A.shape[1], B.shape[0], B.shape[1] + ) + ) + + var C = Matrix[dtype](shape=A.shape, order=A.order()) + + @parameter + fn vec_func[simd_width: Int](i: Int): + C._buf.store( + i, + simd_func( + A._buf.load[width=simd_width](i), + B._buf.load[width=simd_width](i), + ), + ) + + vectorize[vec_func, simd_width](A.size) + + return C^ + + +fn _arithmetic_func_matrix_to_matrix[ + dtype: DType, + simd_func: fn[type: DType, simd_width: Int] ( + SIMD[type, simd_width] + ) -> SIMD[type, simd_width], +](A: Matrix[dtype]) -> Matrix[dtype]: + """ + Matrix[dtype] -> Matrix[dtype] + + For example: `sin`, `cos`, etc. + """ + alias simd_width: Int = simd_width_of[dtype]() + + var C: Matrix[dtype] = Matrix[dtype](shape=A.shape, order=A.order()) + + @parameter + fn vec_func[simd_width: Int](i: Int): + C._buf.ptr.store(i, simd_func(A._buf.ptr.load[width=simd_width](i))) + + vectorize[vec_func, simd_width](A.size) + + return C^ + + +fn _logic_func_matrix_matrix_to_matrix[ + dtype: DType, + simd_func: fn[type: DType, simd_width: Int] ( + SIMD[type, simd_width], SIMD[type, simd_width] + ) -> SIMD[DType.bool, simd_width], +](A: Matrix[dtype], B: Matrix[dtype]) raises -> Matrix[DType.bool]: + """ + Matrix[dtype] & Matrix[dtype] -> Matrix[bool] + """ + alias width = simd_width_of[dtype]() + + if A.order() != B.order(): + raise Error( + String("Matrix order {} does not match {}.").format( + A.order(), B.order() + ) + ) + + if (A.shape[0] != B.shape[0]) or (A.shape[1] != B.shape[1]): + raise Error( + String("Shape {}x{} does not match {}x{}.").format( + A.shape[0], A.shape[1], B.shape[0], B.shape[1] + ) + ) + + var t0 = A.shape[0] + var t1 = A.shape[1] + var C = Matrix[DType.bool](shape=A.shape, order=A.order()) + + @parameter + fn calculate_CC(m: Int): + @parameter + fn vec_func[simd_width: Int](n: Int): + C._store[simd_width]( + m, + n, + simd_func(A._load[simd_width](m, n), B._load[simd_width](m, n)), + ) + + vectorize[vec_func, width](t1) + + parallelize[calculate_CC](t0, t0) + + var _t0 = t0 + var _t1 = t1 + var _A = ( + A.copy() + ) # ! perhaps remove this explicit copy if we don't need to extend it's lifetime. + var _B = B.copy() + + return C^ diff --git a/numojo/__init__.mojo b/numojo/__init__.mojo index 50a16eee..9488b874 100644 --- a/numojo/__init__.mojo +++ b/numojo/__init__.mojo @@ -208,7 +208,7 @@ from numojo.routines.creation import ( ) from numojo.routines import indexing -from numojo.routines.indexing import where, compress, take_along_axis +from numojo.routines.indexing import `where`, compress, take_along_axis from numojo.routines.functional import apply_along_axis diff --git a/numojo/core/__init__.mojo b/numojo/core/__init__.mojo index e90a56d7..b8cf4ec5 100644 --- a/numojo/core/__init__.mojo +++ b/numojo/core/__init__.mojo @@ -5,6 +5,8 @@ from .ndarray import NDArray from .item import Item from .ndshape import NDArrayShape from .ndstrides import NDArrayStrides +from .own_data import OwnData +from .ref_data import RefData from .complex import ( ComplexSIMD, diff --git a/numojo/core/complex/complex_ndarray.mojo b/numojo/core/complex/complex_ndarray.mojo index cf207431..5d509303 100644 --- a/numojo/core/complex/complex_ndarray.mojo +++ b/numojo/core/complex/complex_ndarray.mojo @@ -40,7 +40,8 @@ import builtin.math as builtin_math from builtin.type_aliases import Origin from collections.optional import Optional from math import log10, sqrt -from memory import UnsafePointer, memset_zero, memcpy +from memory import memset_zero, memcpy +from memory import LegacyUnsafePointer as UnsafePointer from python import PythonObject from sys import simd_width_of from utils import Variant @@ -393,7 +394,7 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( # fn __getitem__(self, *slices: Variant[Slice, Int]) raises -> Self # Get by mix of slices/ints # # 4. Advanced Indexing - # fn __getitem__(self, indices: NDArray[DType.index]) raises -> Self # Get by index array + # fn __getitem__(self, indices: NDArray[DType.int]) raises -> Self # Get by index array # fn __getitem__(self, indices: List[Int]) raises -> Self # Get by list of indices # fn __getitem__(self, mask: NDArray[DType.bool]) raises -> Self # Get by boolean mask # fn __getitem__(self, mask: List[Bool]) raises -> Self # Get by boolean list @@ -646,13 +647,21 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( # Fast path for C-contiguous if self.flags.C_CONTIGUOUS: var block = self.size // self.shape[0] - memcpy(result._re._buf.ptr, self._re._buf.ptr + norm * block, block) - memcpy(result._im._buf.ptr, self._im._buf.ptr + norm * block, block) + memcpy( + dest=result._re._buf.ptr, + src=self._re._buf.ptr + norm * block, + count=block, + ) + memcpy( + dest=result._im._buf.ptr, + src=self._im._buf.ptr + norm * block, + count=block, + ) return result^ # F layout - self._re._copy_first_axis_slice[Self.dtype](self._re, norm, result._re) - self._im._copy_first_axis_slice[Self.dtype](self._im, norm, result._im) + self[Self.dtype]._re._copy_first_axis_slice(self._re, norm, result._re) + self[Self.dtype]._im._copy_first_axis_slice(self._im, norm, result._im) return result^ fn __getitem__(self, var *slices: Slice) raises -> Self: @@ -927,7 +936,7 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( narr = self.__getitem__(slice_list^) return narr^ - fn __getitem__(self, indices: NDArray[DType.index]) raises -> Self: + fn __getitem__(self, indices: NDArray[DType.int]) raises -> Self: """ Get items from 0-th dimension of a ComplexNDArray of indices. If the original array is of shape (i,j,k) and @@ -969,14 +978,14 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( ) ) memcpy( - result._re._buf.ptr + i * size_per_item, - self._re._buf.ptr + indices.item(i) * size_per_item, - size_per_item, + dest=result._re._buf.ptr + i * size_per_item, + src=self._re._buf.ptr + indices.item(i) * size_per_item, + count=size_per_item, ) memcpy( - result._im._buf.ptr + i * size_per_item, - self._im._buf.ptr + indices.item(i) * size_per_item, - size_per_item, + dest=result._im._buf.ptr + i * size_per_item, + src=self._im._buf.ptr + indices.item(i) * size_per_item, + count=size_per_item, ) return result^ @@ -985,7 +994,7 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( """ Get items from 0-th dimension of a ComplexNDArray of indices. It is an overload of - `__getitem__(self, indices: NDArray[DType.index]) raises -> Self`. + `__getitem__(self, indices: NDArray[DType.int]) raises -> Self`. Args: indices: A list of Int. @@ -998,7 +1007,7 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( """ - var indices_array = NDArray[DType.index](shape=Shape(len(indices))) + var indices_array = NDArray[DType.int](shape=Shape(len(indices))) for i in range(len(indices)): (indices_array._buf.ptr + i).init_pointee_copy(indices[i]) @@ -1108,14 +1117,14 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( for i in range(mask.size): if mask.item(i): memcpy( - result._re._buf.ptr + offset * size_per_item, - self._re._buf.ptr + i * size_per_item, - size_per_item, + dest=result._re._buf.ptr + offset * size_per_item, + src=self._re._buf.ptr + i * size_per_item, + count=size_per_item, ) memcpy( - result._im._buf.ptr + offset * size_per_item, - self._im._buf.ptr + i * size_per_item, - size_per_item, + dest=result._im._buf.ptr + offset * size_per_item, + src=self._im._buf.ptr + i * size_per_item, + count=size_per_item, ) offset += 1 @@ -1657,13 +1666,21 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( ), ) ) - memcpy(self._re._buf.ptr + norm * block, val._re._buf.ptr, block) - memcpy(self._im._buf.ptr + norm * block, val._im._buf.ptr, block) + memcpy( + dest=self._re._buf.ptr + norm * block, + src=val._re._buf.ptr, + count=block, + ) + memcpy( + dest=self._im._buf.ptr + norm * block, + src=val._im._buf.ptr, + count=block, + ) return # F order - self._re._write_first_axis_slice[Self.dtype](self._re, norm, val._re) - self._im._write_first_axis_slice[Self.dtype](self._im, norm, val._im) + self[Self.dtype]._re._write_first_axis_slice(self._re, norm, val._re) + self[Self.dtype]._im._write_first_axis_slice(self._im, norm, val._im) fn __setitem__(mut self, index: Item, val: ComplexSIMD[cdtype]) raises: """ @@ -1854,7 +1871,7 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( # self.__setitem__(slices=slice_list, val=val) - fn __setitem__(self, index: NDArray[DType.index], val: Self) raises: + fn __setitem__(self, index: NDArray[DType.int], val: Self) raises: """ Returns the items of the ComplexNDArray from an array of indices. @@ -3141,7 +3158,7 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( fn __iter__( self, - ) raises -> _ComplexNDArrayIter[__origin_of(self._re), cdtype]: + ) raises -> _ComplexNDArrayIter[origin_of(self._re), cdtype]: """ Iterates over elements of the ComplexNDArray and return sub-arrays as view. @@ -3149,16 +3166,14 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( An iterator of ComplexNDArray elements. """ - return _ComplexNDArrayIter[__origin_of(self._re), cdtype]( + return _ComplexNDArrayIter[origin_of(self._re), cdtype]( self, dimension=0, ) fn __reversed__( self, - ) raises -> _ComplexNDArrayIter[ - __origin_of(self._re), cdtype, forward=False - ]: + ) raises -> _ComplexNDArrayIter[origin_of(self._re), cdtype, forward=False]: """ Iterates backwards over elements of the ComplexNDArray, returning copied value. @@ -3167,9 +3182,7 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( A reversed iterator of NDArray elements. """ - return _ComplexNDArrayIter[ - __origin_of(self._re), cdtype, forward=False - ]( + return _ComplexNDArrayIter[origin_of(self._re), cdtype, forward=False]( self, dimension=0, ) @@ -3272,13 +3285,13 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( var result: NDArray[dtype = Self.dtype] = NDArray[ dtype = Self.dtype ](self.shape) - memcpy(result._buf.ptr, self._re._buf.ptr, self.size) + memcpy(dest=result._buf.ptr, src=self._re._buf.ptr, count=self.size) return result^ elif type == "im": var result: NDArray[dtype = Self.dtype] = NDArray[ dtype = Self.dtype ](self.shape) - memcpy(result._buf.ptr, self._im._buf.ptr, self.size) + memcpy(dest=result._buf.ptr, src=self._im._buf.ptr, count=self.size) return result^ else: raise Error( @@ -3939,8 +3952,8 @@ struct ComplexNDArray[cdtype: ComplexDType = ComplexDType.float64]( ) ) - var diag_re = self._re.diagonal[Self.dtype](offset) - var diag_im = self._im.diagonal[Self.dtype](offset) + var diag_re = self[Self.dtype]._re.diagonal(offset) + var diag_im = self[Self.dtype]._im.diagonal(offset) return Self(diag_re^, diag_im^) fn trace(self) raises -> ComplexSIMD[cdtype]: diff --git a/numojo/core/data_container.mojo b/numojo/core/data_container.mojo index dbab9d19..06a8444d 100644 --- a/numojo/core/data_container.mojo +++ b/numojo/core/data_container.mojo @@ -6,11 +6,12 @@ # var ptr: UnsafePointer[Scalar[dtype]] # ===----------------------------------------------------------------------=== -from memory import UnsafePointer +from memory import UnsafePointer, LegacyUnsafePointer -struct DataContainer[dtype: DType](): - var ptr: UnsafePointer[Scalar[dtype]] +# temporary DataContainer to support transition from LegacyUnsafePointer to UnsafePointer. +struct DataContainerNew[dtype: DType, origin: MutOrigin](ImplicitlyCopyable): + var ptr: UnsafePointer[Scalar[dtype], origin] fn __init__(out self, size: Int): """ @@ -21,9 +22,123 @@ struct DataContainer[dtype: DType](): `ndarray.flags['OWN_DATA']` should be set as True. The memory should be freed by `__del__`. """ - self.ptr = UnsafePointer[Scalar[dtype]]().alloc(size) + self.ptr: UnsafePointer[Scalar[dtype], origin] = alloc[Scalar[dtype]]( + size + ).unsafe_origin_cast[origin]() - fn __init__(out self, ptr: UnsafePointer[Scalar[dtype]]): + fn __init__(out self, ptr: UnsafePointer[Scalar[dtype], origin]): + """ + Do not use this if you know what it means. + If the pointer is associated with another array, it might cause + dangling pointer problem. + + Notes: + `ndarray.flags['OWN_DATA']` should be set as False. + The memory should not be freed by `__del__`. + """ + self.ptr = ptr + + fn __moveinit__(out self, deinit other: Self): + """ + Move-initializes this DataContainerNew from another instance. + + Transfers ownership of the pointer from `other` to `self`. + After this operation, `other` should not be used. + """ + self.ptr = other.ptr + + fn get_ptr( + self, + ) -> ref [origin_of(self.ptr)] UnsafePointer[Scalar[dtype], origin]: + """ + Returns the internal pointer to the data buffer. + + Returns: + UnsafePointer[Scalar[dtype], origin]: The pointer to the underlying data. + """ + return self.ptr + + fn __str__(self) -> String: + """ + Returns a string representation of the DataContainerNew. + + Returns: + String: A string describing the container and its pointer. + """ + return "DatContainer with ptr: " + String(self.ptr) + + fn __getitem__(self, idx: Int) -> Scalar[dtype]: + """ + Gets the value at the specified index in the data buffer. + + Args: + idx: Index of the element to retrieve. + + Returns: + Scalar[dtype]: The value at the given index. + """ + return self.ptr[idx] + + fn __setitem__(mut self, idx: Int, val: Scalar[dtype]): + """ + Sets the value at the specified index in the data buffer. + + Args: + idx: Index of the element to set. + val: Value to assign. + """ + self.ptr[idx] = val + + fn offset(self, offset: Int) -> UnsafePointer[Scalar[dtype], origin]: + """ + Returns a pointer offset by the given number of elements. + + Args: + offset: Number of elements to offset the pointer. + + Returns: + UnsafePointer[Scalar[dtype], origin]: The offset pointer. + """ + return self.ptr.offset(offset) + + fn load[width: Int](self, offset: Int) -> SIMD[dtype, width]: + """ + Loads a value from the data buffer at the specified offset. + + Args: + offset: Offset from the start of the buffer. + + Returns: + Scalar[dtype]: The loaded value. + """ + return self.ptr.load[width=width](offset) + + fn store[width: Int](mut self, offset: Int, value: SIMD[dtype, width]): + """ + Stores a value into the data buffer at the specified offset. + + Args: + offset: Offset from the start of the buffer. + value: Value to store. + """ + self.ptr.store[width=width](offset, value) + + +struct DataContainer[dtype: DType](ImplicitlyCopyable): + var ptr: LegacyUnsafePointer[Scalar[dtype]] + + fn __init__(out self, size: Int): + """ + Allocate given space on memory. + The bytes allocated is `size` * `byte size of dtype`. + + Notes: + `ndarray.flags['OWN_DATA']` should be set as True. + The memory should be freed by `__del__`. + """ + self.ptr = LegacyUnsafePointer[Scalar[dtype]]().alloc(size) + + fn __init__(out self, ptr: LegacyUnsafePointer[Scalar[dtype]]): """ Do not use this if you know what it means. If the pointer is associated with another array, it might cause @@ -38,5 +153,5 @@ struct DataContainer[dtype: DType](): fn __moveinit__(out self, deinit other: Self): self.ptr = other.ptr - fn get_ptr(self) -> UnsafePointer[Scalar[dtype]]: + fn get_ptr(self) -> LegacyUnsafePointer[Scalar[dtype]]: return self.ptr diff --git a/numojo/core/item.mojo b/numojo/core/item.mojo index 9d3d8f9a..57a33d42 100644 --- a/numojo/core/item.mojo +++ b/numojo/core/item.mojo @@ -6,7 +6,8 @@ Implements Item type. from builtin.type_aliases import Origin from builtin.int import index as index_int -from memory import UnsafePointer, memset_zero, memcpy +from memory import memset_zero, memcpy +from memory import LegacyUnsafePointer as UnsafePointer from memory import memcmp from os import abort from sys import simd_width_of @@ -131,7 +132,7 @@ struct Item( """ self.ndim = other.ndim self._buf = UnsafePointer[Scalar[Self._type]]().alloc(self.ndim) - memcpy(self._buf, other._buf, self.ndim) + memcpy(dest=self._buf, src=other._buf, count=self.ndim) @always_inline("nodebug") fn __del__(deinit self): @@ -357,7 +358,7 @@ struct Item( A new Item with the same values. """ var res = Self(ndim=self.ndim, initialized=False) - memcpy(res._buf, self._buf, self.ndim) + memcpy(dest=res._buf, src=self._buf, count=self.ndim) return res^ fn swapaxes(self, axis1: Int, axis2: Int) raises -> Self: @@ -529,7 +530,7 @@ struct Item( fn _compute_slice_params( self, slice_index: Slice - ) raises -> (Int, Int, Int): + ) raises -> Tuple[Int, Int, Int]: """ Compute normalized slice parameters (start, step, length). @@ -725,7 +726,7 @@ struct _ItemIter[ else: return self.index > 0 - fn __next__(mut self) raises -> Scalar[DType.index]: + fn __next__(mut self) raises -> Scalar[DType.int]: @parameter if forward: var current_index = self.index diff --git a/numojo/core/matrix.mojo b/numojo/core/matrix.mojo index 7c8594dd..4e76807f 100644 --- a/numojo/core/matrix.mojo +++ b/numojo/core/matrix.mojo @@ -1,10 +1,16 @@ """ -`numojo.Matrix` provides: +NuMojo Matrix Module -- `Matrix` type (2DArray). -- `_MatrixIter` type (for iteration). -- Dunder methods for initialization, indexing, slicing, and arithmetics. -- Auxiliary functions. +This file implements the core 2D matrix type for the NuMojo numerical computing library. It provides efficient, flexible, and memory-safe matrix operations for scientific and engineering applications. + +Features: +- `Matrix`: The primary 2D array type for owning matrix data. +- `MatrixView`: Lightweight, non-owning views for fast slicing and submatrix access. +- Iterators for traversing matrix elements. +- Comprehensive dunder methods for initialization, indexing, slicing, and arithmetic. +- Utility functions for broadcasting, memory layout, and linear algebra routines. + +Use this module to create, manipulate, and analyze matrices with high performance and safety guarantees. """ from algorithm import parallelize, vectorize @@ -12,51 +18,150 @@ from memory import UnsafePointer, memcpy, memset_zero from random import random_float64 from sys import simd_width_of from python import PythonObject, Python +from math import ceil from numojo.core.flags import Flags from numojo.core.ndarray import NDArray -from numojo.core.data_container import DataContainer +from numojo.core.data_container import DataContainerNew as DataContainer +from numojo.core.traits.buffered import Buffered +from numojo.core.own_data import OwnData +from numojo.core.ref_data import RefData from numojo.core.utility import _get_offset from numojo.routines.manipulation import broadcast_to, reorder_layout from numojo.routines.linalg.misc import issymmetric +# TODO: currently a lot of the __getitem__ and __setitem__ methods raises if the index is out of bounds. An alternative is to clamp the indices to be within bounds, this will remove a lot of if conditions and improve performance I guess. Need to decide which behavior is preferred. # ===----------------------------------------------------------------------===# # Matrix struct # ===----------------------------------------------------------------------===# -struct Matrix[dtype: DType = DType.float64]( - Copyable, Movable, Sized, Stringable, Writable -): - # TODO: Add buffer_type in the parameters. +alias Matrix = MatrixImpl[_, own_data=True, origin = MutOrigin.external] +""" +Primary Matrix type for creating and manipulating 2D matrices in NuMojo. + +This is the main user-facing type alias for working with matrices. It represents +a matrix that owns and manages its underlying memory buffer. The data type parameter +is inferred from context or can be explicitly specified. + +The `Matrix` type is designed for standard matrix operations where full ownership +and control of the data is required. It allocates its own memory and is responsible +for cleanup when it goes out of scope. + +Type Parameters: + dtype: The data type of matrix elements. + +Usage: + ```mojo + from numojo.prelude import * + + # Create a matrix with explicit type + var mat = Matrix.zeros[nm.f32](shape=Tuple(3, 4)) + + # Create with default type DType.float64 + var mat2 = Matrix.zeros(shape=Tuple(2, 3)) + ``` + +Notes: + - This matrix owns its data and manages memory allocation/deallocation. + - For non-owning views into existing data, use methods like `get()`, `view()` which return `MatrixView`. + - Direct instantiation of `MatrixImpl` should be avoided; always use this alias. +""" + +alias MatrixView[dtype: DType, origin: MutOrigin] = MatrixImpl[ + dtype, own_data=False, origin=origin +] +""" +Non-owning view into matrix data for efficient memory access without copying. + +`MatrixView` represents a lightweight reference to matrix data that is owned by +another `Matrix` instance. It does not allocate or manage its own memory, instead +pointing to a subset or reinterpretation of existing matrix data. This enables +efficient slicing, row/column access, and memory sharing without data duplication. + +**IMPORTANT**: This type is for internal use and should not be directly instantiated by users. Views are created automatically by matrix operations like indexing, slicing, through the `get()` method. A full view of the matrix can be obtained via `view()` method. + +Type Parameters: + dtype: The data type of the matrix elements being viewed. + origin: Tracks the lifetime and mutability of the referenced data, ensuring + the view doesn't outlive the original data or violate mutability constraints. + +Key Characteristics: + - Does not own the underlying data buffer. + - Cannot be copied (to prevent dangling references) (Will be relaxed in future). + - Lifetime is tied to the owning Matrix instance. + - May have different shape/strides than the original matrix (e.g., for slices). + - Changes to the view affect the original matrix by default. + +Common Creation Patterns: + Views are typically created through: + - `matrix.get(row_idx)` - Get a view of a single row + - `matrix.get(row_slice, col_slice)` - Get a view of a submatrix + - `matrix.view()` - Get a view of the entire matrix + +Example: + ```mojo + from numojo.prelude import * + + var mat = Matrix.ones(shape=(4, 4)) + var row_view = mat.get(0) # Returns MatrixView of first row + # Modifying row_view would modify mat + ``` +""" + + +struct MatrixImpl[ + dtype: DType = DType.float64, + *, + own_data: Bool, + origin: MutOrigin, +](Copyable, Movable, Sized, Stringable, Writable): """ - `Matrix` is a special case of `NDArray` (2DArray) but has some targeted - optimization since the number of dimensions is known at the compile time. - It has simpler indexing and slicing methods, which is very useful when users - only want to work with 2-dimensional arrays. + Core implementation struct for 2D matrix operations with flexible ownership semantics. - NuMojo's `Matrix` is `NDArray` with fixed `ndim` known at compile time. - It may be different in some behaviors compared to `numpy.matrix`. + `MatrixImpl` is the underlying implementation for both owning matrices (`Matrix`) and non-owning matrix views (`MatrixView`). It provides a complete set of operations for 2D array manipulation with compile-time known dimensions, enabling optimizations not possible with generic N-dimensional arrays. - - For `__getitem__`, passing in two `Int` returns a scalar, - and passing in one `Int` or two `Slice` returns a `Matrix`. - - We do not need auxiliary types `NDArrayShape` and `NDArrayStrides` - as the shape and strides information is fixed in length `Tuple[Int,Int]`. + This struct represents a specialized case of `NDArray` optimized for 2D operations. The fixed dimensionality allows for simpler, more efficient indexing using direct `(row, col)` access patterns rather than generic coordinate tuples. This makes it particularly suitable for linear algebra, image processing, and other applications where 2D structure is fundamental. - Parameters: - dtype: Type of item in NDArray. Default type is DType.float64. + **Important**: Users should not instantiate `MatrixImpl` directly. Instead, use: + - `Matrix[dtype]` for matrices that own their data (standard usage) + - Methods like `get()`, `view()` that return `MatrixView` for non-owning views + + Direct instantiation of `MatrixImpl` may lead to undefined behavior related to + memory management and lifetime tracking. - The matrix can be uniquely defined by the following features: - 1. The data buffer of all items. - 2. The shape of the matrix. - 3. The data type of the elements (compile-time known). + Parameters: + dtype: The data type of matrix elements. Default is DType.float64. + own_data: Boolean flag indicating whether this instance owns and manages its underlying memory buffer. When True, the matrix allocates and frees its own memory. When False, it's a view into externally-owned data. + origin: Tracks the lifetime and mutability of the underlying data buffer, + enabling compile-time safety checks to prevent use-after-free and + other memory safety issues. Default is MutOrigin.external. + + Memory Layout: + Matrices can be stored in either: + - Row-major (C-style) layout: consecutive elements in a row are adjacent in memory + - Column-major (Fortran-style) layout: consecutive elements in a column are adjacent + + The layout affects cache efficiency for different access patterns and is tracked via the `strides` and `flags` attributes. + + Ownership Semantics: + **Owning matrices** (own_data=True): + - Allocate their own memory buffer during construction + - Responsible for freeing memory in destructor + - Can be copied (creates new independent matrix with copied data) + - Can be moved (transfers ownership efficiently) + + **View matrices** (own_data=False): + - Reference existing data from an owning matrix + - Do not allocate or free memory + - Cannot be copied currently. Attributes: - _buf (saved as row-majored, C-type) - shape - size (shape[0] * shape[1]) - - strides (shape[1], 1) + - strides Default constructor: - [dtype], shape @@ -88,11 +193,19 @@ struct Matrix[dtype: DType = DType.float64]( - [x] `Matrix.variance` and `mat.statistics.variance` (`var` is primitive) """ + comptime IteratorType[ + is_mutable: Bool, //, + matrix_origin: MutOrigin, + iterator_origin: Origin[is_mutable], + forward: Bool, + ] = _MatrixIter[dtype, matrix_origin, iterator_origin, forward] + """Iterator type for the Matrix.""" + alias width: Int = simd_width_of[dtype]() # """Vector size of the data type.""" - var _buf: DataContainer[dtype] - """Data buffer of the items in the NDArray.""" + var _buf: DataContainer[dtype, origin] + """Data buffer of the items in the Matrix.""" var shape: Tuple[Int, Int] """Shape of Matrix.""" @@ -115,93 +228,257 @@ struct Matrix[dtype: DType = DType.float64]( out self, shape: Tuple[Int, Int], order: String = "C", - ): + ) where own_data == True: """ - Create a new matrix of the given shape,without initializing data. + Initialize a new matrix with the specified shape and memory layout. + + This constructor creates a matrix of the given shape without initializing + its data. The memory layout can be specified as either row-major ("C") or + column-major ("F"). Args: - shape: Tuple representing (rows, columns). - order: Use "C" for row-major (C-style) layout or "F" for column-major - (Fortran-style) layout. Defaults to "C". - """ + shape: A tuple representing the dimensions of the matrix as (rows, columns). + order: A string specifying the memory layout. Use "C" for row-major + (C-style) layout or "F" for column-major (Fortran-style) layout. Defaults to "C". + Example: + ```mojo + from numojo.prelude import * + var mat_c = Matrix[f32](shape=(3, 4), order="C") # Row-major + var mat_f = Matrix[f32](shape=(3, 4), order="F") # Column-major + ``` + """ self.shape = (shape[0], shape[1]) if order == "C": self.strides = (shape[1], 1) else: self.strides = (1, shape[0]) self.size = shape[0] * shape[1] - self._buf = DataContainer[dtype](size=self.size) + self._buf = DataContainer[dtype, origin](size=self.size) self.flags = Flags( self.shape, self.strides, owndata=True, writeable=True ) - # * Should we take var ref and transfer ownership or take a read ref and copy it? + # * Should we take var ref and transfer ownership or take a read ref and copy the data? @always_inline("nodebug") fn __init__( out self, var data: Self, - ): - """ - Construct a matrix from matrix. + ) where own_data == True: """ + Initialize a new matrix by transferring ownership from another matrix. + + This constructor creates a new matrix instance by taking ownership of the + data from an existing matrix. The source matrix (`data`) will no longer + own its data after this operation. + Args: + data: The source matrix from which ownership of the data will be transferred. + + Notes: + - This operation is efficient as it avoids copying the data buffer. + - The source matrix (`data`) becomes invalid after the transfer and should not be used. + + Example: + ```mojo + from numojo.prelude import * + var mat1 = Matrix[f32](shape=(2, 3)) + # ... (initialize mat1 with data) ... + var mat2 = Matrix[f32](mat1^) # Transfer ownership from mat1 to mat2 + ``` + """ self = data^ @always_inline("nodebug") fn __init__( out self, - data: NDArray[dtype], - ) raises: + data: Self, + ) where own_data == True: + """ + Construct a new matrix by copying from another matrix. + + This initializer creates a new matrix instance by copying the data, shape and order from an existing matrix. The new matrix will have its own independent copy of the data. + + Args: + data: The source matrix to copy from. """ - Construct a matrix from array. + self = Self(data.shape, data.order()) + memcpy(dest=self._buf.ptr, src=data._buf.ptr, count=data.size) + + @always_inline("nodebug") + fn __init__( + out self, + data: NDArray[dtype], + ) raises where own_data == True: """ + Initialize a new matrix by copying data from an existing NDArray. + + This constructor creates a matrix instance with the same shape, data, and + memory layout as the provided NDArray. The data is copied into a new memory buffer owned by the matrix. + + Args: + data: An NDArray instance containing the data to initialize the matrix. + + Raises: + Error: If the provided NDArray has more than 2 dimensions, as it cannot be represented as a matrix. + Example: + ```mojo + from numojo.prelude import * + var arr = NDArray[f32](Shape(2, 3)) + # ... (initialize arr with data) ... + var mat = Matrix[f32](arr) # Create a matrix from the NDArray + ``` + """ if data.ndim == 1: self.shape = (1, data.shape[0]) self.strides = (data.shape[0], 1) self.size = data.shape[0] elif data.ndim == 2: self.shape = (data.shape[0], data.shape[1]) - self.strides = (data.shape[1], 1) + if data.flags["C_CONTIGUOUS"]: + self.strides = (data.shape[1], 1) + else: + self.strides = (1, data.shape[0]) self.size = data.shape[0] * data.shape[1] else: raise Error(String("Shape too large to be a matrix.")) - self._buf = DataContainer[dtype](self.size) - + self._buf = DataContainer[dtype, origin](self.size) self.flags = Flags( self.shape, self.strides, owndata=True, writeable=True ) + memcpy( + dest=self._buf.ptr, + src=data._buf.ptr, + count=self.size, + ) - if data.flags["C_CONTIGUOUS"]: - for i in range(data.shape[0]): - memcpy( - self._buf.ptr.offset(i * self.shape[0]), - data._buf.ptr.offset(i * data.shape[0]), - self.shape[0], - ) - else: - for i in range(data.shape[0]): - for j in range(data.shape[1]): - self._store(i, j, data._getitem(i, j)) + # to construct views + @always_inline("nodebug") + fn __init__( + out self, + shape: Tuple[Int, Int], + strides: Tuple[Int, Int], + data: DataContainer[dtype, origin], + ) where own_data == False: + """ + Initialize a non-owning `MatrixView`. + This constructor creates a Matrix instance that acts as a view into an + existing data buffer. The view does not allocate or manage memory; it + references data owned by another Matrix. It is an unsafe operation and should not be called by users directly. + + Args: + shape: A tuple representing the dimensions of the view as (rows, columns). + strides: A tuple representing the memory strides for accessing elements in the view. Strides determine how to traverse the data buffer to access elements in the matrix. + data: A DataContainer instance that holds the data buffer being referenced. + + Notes: + - This constructor is intended for internal use to create views into existing matrices! Users should not call this directly. + - The view does not own the data and relies on the lifetime of the + original data owner. + - Modifications to the view affect the original data by default. + """ + self.shape = shape + self.strides = strides + self.size = shape[0] * shape[1] + self._buf = data + self.flags = Flags( + self.shape, self.strides, owndata=False, writeable=False + ) + self.shape = shape + self.strides = strides + self.size = shape[0] * shape[1] + self._buf = data + self.flags = Flags( + self.shape, self.strides, owndata=False, writeable=False + ) + + # TODO: Prevent copying from views to views or views to owning matrices for now. + # FIXME: The `where` clause isn't functioning as expected currently, so we use `constrained` as a workaround. Once the `where` clause is stable, this logic should be moved there. + # FIXME: The current `__copyinit__` creates an instance with the same origin as the source. This should instead create an instance with an external origin. Update this behavior so that the default `.copy()` method can be used, and the `create_copy()` method can be removed. @always_inline("nodebug") fn __copyinit__(out self, other: Self): """ - Copy other into self. - """ + Initialize a new matrix by copying data from another matrix. + + This method creates a deep copy of the `other` matrix into `self`. It ensures that the copied matrix is independent of the source matrix, with its own memory allocation. + + Constraints: + - Copying is only allowed between matrices that own their data. + Views cannot be copied to ensure memory safety. + + Args: + other: The source matrix to copy from. Must be an owning matrix. + + Notes: + - This method uses the `constrained` mechanism to enforce the restriction that both the source and destination matrices must own their data. + - The copied matrix will have the same shape, strides, and data as the source matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat1 = Matrix[f32](shape=(2, 3)) + # ... (initialize mat1 with data) ... + var mat2 = mat1.copy() # Calls __copyinit__ to create a copy of mat1 + ``` + """ + constrained[ + other.own_data == True and own_data == True, + ( + "`.copy()` is only allowed for Matrices that own the data and" + " not views." + ), + ]() self.shape = (other.shape[0], other.shape[1]) self.strides = (other.strides[0], other.strides[1]) self.size = other.size - self._buf = DataContainer[dtype](other.size) - memcpy(self._buf.ptr, other._buf.ptr, other.size) - self.flags = other.flags + self._buf = DataContainer[dtype, origin](other.size) + memcpy(dest=self._buf.ptr, src=other._buf.ptr, count=other.size) + self.flags = Flags( + other.shape, other.strides, owndata=True, writeable=True + ) + + fn create_copy(self) -> Matrix[dtype]: + """ + Create a deep copy of the current matrix. + + This method creates a new `Matrix` instance with the same shape, data, and + memory layout as the original matrix. The data is copied into a new memory + buffer owned by the new matrix, ensuring that the original and the copy are completely independent. + + Returns: + A new `Matrix` instance that is an exact copy of the + current matrix, including its shape and data. + + Example: + ```mojo + from numojo.prelude import * + var mat1 = Matrix[f32](shape=(2, 3)) + # ... (initialize mat1 with data) ... + var mat2 = mat1.create_copy() # Create a deep copy of mat1 + ``` + """ + var new_matrix = Matrix[dtype](shape=self.shape, order=self.order()) + memcpy(dest=new_matrix._buf.ptr, src=self._buf.ptr, count=self.size) + return new_matrix^ @always_inline("nodebug") fn __moveinit__(out self, deinit other: Self): """ - Move other into self. + Transfer ownership of resources from `other` to `self`. + + This method moves the data and metadata from the `other` matrix instance + into the current instance (`self`). After the move, the `other` instance + is left in an invalid state and should not be used. + + Args: + other: The source matrix instance whose resources will be moved. + + Notes: + - This operation is efficient as it avoids copying data. + - The `other` instance is deinitialized as part of this operation. """ self.shape = other.shape^ self.strides = other.strides^ @@ -211,78 +488,275 @@ struct Matrix[dtype: DType = DType.float64]( @always_inline("nodebug") fn __del__(deinit self): - var owndata: Bool - try: - owndata = self.flags["OWNDATA"] - except: - owndata = True - print("Invalid `OWNDATA` flag. Treat as `True`.") - if owndata: + """ + Destructor for the matrix instance. + + This method is called when the matrix instance is deinitialized. It ensures that resources owned by the matrix, such as its memory buffer, are properly released. + + Notes: + - This method only frees resources if the matrix owns its data. + - The `own_data` flag determines whether the memory buffer is freed. + """ + + @parameter + if own_data: self._buf.ptr.free() # ===-------------------------------------------------------------------===# # Slicing and indexing methods # ===-------------------------------------------------------------------===# - fn __getitem__(self, var x: Int, var y: Int) raises -> Scalar[dtype]: + @always_inline + fn index(self, row: Int, col: Int) -> Int: """ - Return the scalar at the index. + Calculate the linear index in the underlying data buffer for a given + 2D index (row, col) based on the matrix's strides. Args: - x: The row number. - y: The column number. + row: The row index. + col: The column index. Returns: - A scalar matching the dtype of the array. + The corresponding 1D index in the data buffer. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix[f32](shape=(3, 4)) + var idx = mat.index(1, 2) # Calculate linear index for (1, 2) + ``` + """ + return row * self.strides[0] + col * self.strides[1] + + @always_inline + fn normalize(self, idx: Int, dim: Int) -> Int: """ + Normalize a potentially negative index to its positive equivalent + within the bounds of the given dimension. - if x < 0: - x = self.shape[0] + x + Args: + idx: The index to normalize. Can be negative to indicate indexing + from the end (e.g., -1 refers to the last element). + dim: The size of the dimension to normalize against. - if y < 0: - y = self.shape[1] + y + Returns: + The normalized index as a non-negative integer. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix[f32](shape=(3, 4)) + var norm_idx = mat.normalize(-1, mat.shape[0]) # Normalize -1 to 2 + ``` + """ + var idx_norm = idx + if idx_norm < 0: + idx_norm = dim + idx_norm + return idx_norm - if (x >= self.shape[0]) or (y >= self.shape[1]): + fn __getitem__(self, x: Int, y: Int) raises -> Scalar[dtype]: + """ + Retrieve the scalar value at the specified row and column indices. + + Args: + x: The row index. Can be negative to index from the end. + y: The column index. Can be negative to index from the end. + + Returns: + The value at the specified (x, y) position in the matrix. + + Raises: + Error: If the provided indices are out of bounds for the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(3, 4)) + var value = mat[1, 2] # Retrieve value at row 1, column 2 + ``` + """ + if ( + x >= self.shape[0] + or x < -self.shape[0] + or y >= self.shape[1] + or y < -self.shape[1] + ): raise Error( String( "Index ({}, {}) exceed the matrix shape ({}, {})" ).format(x, y, self.shape[0], self.shape[1]) ) + var x_norm = self.normalize(x, self.shape[0]) + var y_norm = self.normalize(y, self.shape[1]) + return self._buf[self.index(x_norm, y_norm)] - return self._buf.ptr.load(x * self.strides[0] + y * self.strides[1]) - - fn __getitem__(self, var x: Int) raises -> Self: + # TODO: temporarily renaming all view returning functions to be `get` or `set` due to a Mojo bug with overloading `__getitem__` and `__setitem__` with different argument types. Created an issue in Mojo GitHub + fn get[ + is_mutable: Bool, //, view_origin: Origin[is_mutable] + ](ref [view_origin]self, x: Int) raises -> MatrixView[ + dtype, MutOrigin.cast_from[view_origin] + ]: """ - Return the corresponding row at the index. + Retrieve a view of the specified row in the matrix. + + This method returns a non-owning `MatrixView` that references the data of the specified row in the original matrix. The view does not allocate new memory and directly points to the existing data buffer of the matrix. + + Parameters: + is_mutable: An inferred boolean indicating whether the returned view should allow modifications to the underlying data. + view_origin: Tracks the mutability and lifetime of the data being viewed. Should not be specified directly by users as it can lead to unsafe behavior. Args: - x: The row number. - """ + x: The row index to retrieve. Negative indices are supported and follow Python conventions (e.g., -1 refers to the last row). - if x < 0: - x = self.shape[0] + x + Returns: + A `MatrixView` representing the specified row as a row vector. - if x >= self.shape[0]: + Raises: + Error: If the provided row index is out of bounds. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(3, 4)) + var row_view = mat.get(1) # Get a view of the second row + ``` + """ + constrained[ + Self.own_data == True, + ( + "Creating views from views is not supported currently to ensure" + " memory safety." + ), + ]() + if x >= self.shape[0] or x < -self.shape[0]: raise Error( String("Index {} exceed the row number {}").format( x, self.shape[0] ) ) - var res = Self(shape=(1, self.shape[1]), order=self.order()) + var x_norm = self.normalize(x, self.shape[0]) + var new_data = DataContainer[dtype, MutOrigin.cast_from[view_origin]]( + ptr=self._buf.get_ptr().unsafe_origin_cast[ + MutOrigin.cast_from[view_origin] + ]() + + x_norm * self.strides[0] + ) + var row_view = MatrixView[dtype, MutOrigin.cast_from[view_origin]]( + shape=(1, self.shape[1]), + strides=(self.strides[0], self.strides[1]), + data=new_data, + ) + return row_view^ + + # for creating a copy of the row. + fn __getitem__(self, var x: Int) raises -> Matrix[dtype]: + """ + Retrieve a copy of the specified row in the matrix. This method creates and returns a new `Matrix` instance that contains a copy of the data from the specified row of the original matrix. The returned matrix is a row vector with a shape of (1, number_of_columns). + + Args: + x: The row index to retrieve. Negative indices are supported and follow Python conventions (e.g., -1 refers to the last row). + + Returns: + A `Matrix` instance representing the specified row as a row vector. + + Raises: + Error: If the provided row index is out of bounds. + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(3, 4)) + var row_copy = mat[1] # Get a copy of the second row + ``` + """ + if x >= self.shape[0] or x < -self.shape[0]: + raise Error( + String("Index {} exceed the row size {}").format( + x, self.shape[0] + ) + ) + var x_norm = self.normalize(x, self.shape[0]) + var result = Matrix[dtype](shape=(1, self.shape[1]), order=self.order()) if self.flags.C_CONTIGUOUS: - var ptr = self._buf.ptr.offset(x * self.strides[0]) - memcpy(res._buf.ptr, ptr, self.shape[1]) + var ptr = self._buf.ptr.offset(x_norm * self.strides[0]) + memcpy(dest=result._buf.ptr, src=ptr, count=self.shape[1]) else: for j in range(self.shape[1]): - res[0, j] = self[x, j] + result[0, j] = self[x_norm, j] - return res^ + return result^ - fn __getitem__(self, x: Slice, y: Slice) -> Self: + fn get[ + is_mutable: Bool, //, view_origin: Origin[is_mutable] + ](ref [view_origin]self, x: Slice, y: Slice) -> MatrixView[ + dtype, MutOrigin.cast_from[view_origin] + ] where (own_data == True): """ - Get item from two slices. + Retrieve a view of the specified slice in the matrix. + + This method returns a non-owning `MatrixView` that references the data of the specified row in the original matrix. The view does not allocate new memory and directly points to the existing data buffer of the matrix. + + Parameters: + is_mutable: An inferred boolean indicating whether the returned view should allow modifications to the underlying data. + view_origin: Tracks the mutability and lifetime of the data being viewed. Should not be specified directly by users as it can lead to unsafe behavior. + + Args: + x: The row slice to retrieve. + y: The column slice to retrieve. + + Returns: + A `MatrixView` representing the specified slice of the matrix. + + Notes: + - Out of bounds indices are clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var slice_view = mat.get(Slice(1, 3), Slice(0, 2)) # Get a view of the submatrix + ``` + """ + start_x, end_x, step_x = x.indices(self.shape[0]) + start_y, end_y, step_y = y.indices(self.shape[1]) + + var new_data = DataContainer[dtype, MutOrigin.cast_from[view_origin]]( + ptr=self._buf.get_ptr() + .unsafe_origin_cast[MutOrigin.cast_from[view_origin]]() + .offset(start_x * self.strides[0] + start_y * self.strides[1]) + ) + var sliced_view = MatrixView[dtype, MutOrigin.cast_from[view_origin]]( + shape=( + Int(ceil((end_x - start_x) / step_x)), + Int(ceil((end_y - start_y) / step_y)), + ), + strides=(self.strides[0] * step_x, self.strides[1] * step_y), + data=new_data, + ) + return sliced_view^ + + # for creating a copy of the slice. + fn __getitem__(self, x: Slice, y: Slice) -> Matrix[dtype]: + """ + Retrieve a copy of the specified slice in the matrix. This method creates and returns a new `Matrix` instance that contains a copy of the data from the specified slice of the original matrix. The returned matrix will have the shape determined by the slice ranges. + + Args: + x: The row slice to retrieve. Supports Python slice syntax. + y: The column slice to retrieve. Supports Python slice syntax. + + Returns: + A `Matrix` instance representing the specified slice of the matrix. + + Notes: + - Out of bounds indices are clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var slice_copy = mat[1:3, 0:2] # Get a copy of the submatrix + ``` """ var start_x: Int var end_x: Int @@ -295,12 +769,9 @@ struct Matrix[dtype: DType = DType.float64]( var range_x = range(start_x, end_x, step_x) var range_y = range(start_y, end_y, step_y) - # The new matrix with the corresponding shape var B = Matrix[dtype]( shape=(len(range_x), len(range_y)), order=self.order() ) - - # Fill in the values at the corresponding index var row = 0 for i in range_x: var col = 0 @@ -311,9 +782,88 @@ struct Matrix[dtype: DType = DType.float64]( return B^ - fn __getitem__(self, x: Slice, var y: Int) -> Self: + fn get[ + is_mutable: Bool, //, view_origin: Origin[is_mutable] + ](ref [view_origin]self, x: Slice, var y: Int) raises -> MatrixView[ + dtype, MutOrigin.cast_from[view_origin] + ] where (own_data == True): + """ + Retrieve a view of a specific column slice in the matrix. This method returns a non-owning `MatrixView` that references the data of the specified column slice in the original matrix. The view does not allocate new memory and directly points to the existing data buffer of the matrix. + + Parameters: + is_mutable: An inferred boolean indicating whether the returned view should allow modifications to the underlying data. + view_origin: Tracks the mutability and lifetime of the data being viewed. Should not be specified directly by users as it can lead to unsafe behavior. + + Args: + x: The row slice to retrieve. This defines the range of rows to include in the view. + y: The column index to retrieve. This specifies the column to include in the view. + + Returns: + A `MatrixView` representing the specified column slice of the matrix. + + Raises: + Error: If the provided column index `y` is out of bounds. + + Notes: + - Out-of-bounds indices for `x` are clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var column_view = mat.get(Slice(0, 4), 2) # Get a view of the third column + ``` + """ + if y >= self.shape[1] or y < -self.shape[1]: + raise Error( + String("Index {} exceed the column number {}").format( + y, self.shape[1] + ) + ) + y = self.normalize(y, self.shape[1]) + var start_x: Int + var end_x: Int + var step_x: Int + start_x, end_x, step_x = x.indices(self.shape[0]) + + var new_data = DataContainer[dtype, MutOrigin.cast_from[view_origin]]( + ptr=self._buf.get_ptr() + .unsafe_origin_cast[MutOrigin.cast_from[view_origin]]() + .offset(start_x * self.strides[0] + y * self.strides[1]) + ) + var column_view = MatrixView[dtype, MutOrigin.cast_from[view_origin]]( + shape=( + Int(ceil((end_x - start_x) / step_x)), + 1, + ), + strides=(self.strides[0] * step_x, self.strides[1]), + data=new_data, + ) + + return column_view^ + + fn __getitem__(self, x: Slice, var y: Int) -> Matrix[dtype]: """ - Get item from one slice and one int. + Retrieve a copy of a specific column slice in the matrix. This method creates and returns a new `Matrix` instance that contains a copy + of the data from the specified and column slice of the original matrix. The returned matrix will have a shape determined by the row slice and a single column. + + Args: + x: The row slice to retrieve. This defines the range of rows to include in the copy. + y: The column index to retrieve. This specifies the column to include in the copy. + + Returns: + A `Matrix` instance representing the specified column slice of the matrix. + + Notes: + - Negative indices for `y` are normalized to their positive equivalent. + - Out-of-bounds indices for `x` are clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var column_copy = mat[0:4, 2] # Get a copy of the third column + ``` """ if y < 0: y = self.shape[1] + y @@ -323,35 +873,118 @@ struct Matrix[dtype: DType = DType.float64]( var step_x: Int start_x, end_x, step_x = x.indices(self.shape[0]) var range_x = range(start_x, end_x, step_x) - - # The new matrix with the corresponding shape - var B = Matrix[dtype](shape=(len(range_x), 1), order=self.order()) - - # Fill in the values at the corresponding index + var res = Matrix[dtype]( + shape=( + len(range_x), + 1, + ), + order=self.order(), + ) var row = 0 for i in range_x: - B._store(row, 0, self._load(i, y)) + res._store(row, 0, self._load(i, y)) row += 1 + return res^ - return B^ + fn get[ + is_mutable: Bool, //, view_origin: Origin[is_mutable] + ](ref [view_origin]self, var x: Int, y: Slice) raises -> MatrixView[ + dtype, MutOrigin.cast_from[view_origin] + ] where (own_data == True): + """ + Retrieve a view of a specific row slice in the matrix. This method returns a non-owning `MatrixView` that references the data of the specified row slice in the original matrix. The view does not allocate new memory and directly points to the existing data buffer of the matrix. + + Parameters: + is_mutable: An inferred boolean indicating whether the returned view should allow modifications to the underlying data. + view_origin: Tracks the mutability and lifetime of the data being viewed. Should not be specified directly by users as it can lead to unsafe behavior. + + Args: + x: The row index to retrieve. This specifies the row to include in the view. Negative indices are supported and follow Python conventions (e.g., -1 refers to the last row). + y: The column slice to retrieve. This defines the range of columns to include in the view. + + Returns: + A `MatrixView` representing the specified row slice of the matrix. + + Raises: + Error: If the provided row index `x` is out of bounds. - fn __getitem__(self, var x: Int, y: Slice) -> Self: + Notes: + - Out-of-bounds indices for `y` are clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var row_view = mat.get(1, Slice(0, 3)) # Get a view of the second row, columns 0 to 2 + ``` """ - Get item from one int and one slice. + if x >= self.shape[0] or x < -self.shape[0]: + raise Error( + String("Index {} exceed the row size {}").format( + x, self.shape[0] + ) + ) + x = self.normalize(x, self.shape[0]) + var start_y: Int + var end_y: Int + var step_y: Int + start_y, end_y, step_y = y.indices(self.shape[1]) + var new_data = DataContainer[dtype, MutOrigin.cast_from[view_origin]]( + ptr=self._buf.get_ptr() + .unsafe_origin_cast[MutOrigin.cast_from[view_origin]]() + .offset(x * self.strides[0] + start_y * self.strides[1]) + ) + var row_slice_view = MatrixView[ + dtype, MutOrigin.cast_from[view_origin] + ]( + shape=( + 1, + Int(ceil((end_y - start_y) / step_y)), + ), + strides=(self.strides[0], self.strides[1] * step_y), + data=new_data, + ) + return row_slice_view^ + + fn __getitem__(self, var x: Int, y: Slice) raises -> Matrix[dtype]: """ - if x < 0: - x = self.shape[0] + x + Retrieve a copy of a specific row slice in the matrix. This method creates and returns a new `Matrix` instance that contains a copy + of the data from the specified row and column slice of the original matrix. The returned matrix will have a shape of (1, number_of_columns_in_slice). + + Args: + x: The row index to retrieve. This specifies the row to include in the copy. Negative indices are supported and follow Python conventions (e.g., -1 refers to the last row). + y: The column slice to retrieve. This defines the range of columns to include in the copy. + + Returns: + A `Matrix` instance representing the specified row slice of the matrix. + Raises: + Error: If the provided row index `x` is out of bounds. + + Notes: + - Out-of-bounds indices for `y` are clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var row_copy = mat[1, 0:3] # Get a copy of the second row, columns 0 to 2 + ``` + """ + if x >= self.shape[0] or x < -self.shape[0]: + raise Error( + String("Index {} exceed the row size {}").format( + x, self.shape[0] + ) + ) + x = self.normalize(x, self.shape[0]) var start_y: Int var end_y: Int var step_y: Int start_y, end_y, step_y = y.indices(self.shape[1]) var range_y = range(start_y, end_y, step_y) - # The new matrix with the corresponding shape var B = Matrix[dtype](shape=(1, len(range_y)), order=self.order()) - - # Fill in the values at the corresponding index var col = 0 for j in range_y: B._store(0, col, self._load(x, j)) @@ -359,17 +992,71 @@ struct Matrix[dtype: DType = DType.float64]( return B^ - fn __getitem__(self, indices: List[Int]) raises -> Self: + fn __getitem__(self, indices: List[Int]) raises -> Matrix[dtype]: """ - Get item by a list of integers. + Retrieve a copy of specific rows in the matrix based on the provided indices. This method creates and returns a new `Matrix` instance that contains a copy of the data from the specified rows of the original matrix. The returned matrix will have a shape of (number_of_indices, number_of_columns). + + Args: + indices: A list of row indices to retrieve. Each index specifies a row to include in the resulting matrix. Negative indices are supported and follow Python conventions (e.g., -1 refers to the last row). + + Returns: + A `Matrix` instance containing the selected rows as a new matrix. + + Raises: + Error: If any of the provided indices are out of bounds. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var selected_rows = mat[List[Int](0, 1, 0)] # Get a copy of the + # first and second and first rows in a new matrix with shape (3, 4) + ``` + """ + var num_cols = self.shape[1] + var num_rows = len(indices) + var selected_rows = Matrix.zeros[dtype](shape=(num_rows, num_cols)) + for i in range(num_rows): + if indices[i] >= self.shape[0] or indices[i] < -self.shape[0]: + raise Error( + String("Index {} exceed the row size {}").format( + indices[i], self.shape[0] + ) + ) + selected_rows[i] = self[indices[i]] + return selected_rows^ + + fn load[width: Int = 1](self, idx: Int) raises -> SIMD[dtype, width]: """ + Load a SIMD element from the matrix at the specified linear index. - var ncol = self.shape[1] - var nrow = len(indices) - var res = Matrix.zeros[dtype](shape=(nrow, ncol)) - for i in range(nrow): - res[i] = self[indices[i]] - return res^ + Parameters: + width: The width of the SIMD element to load. Defaults to 1. + + Args: + idx: The linear index of the element to load. Negative indices are supported and follow Python conventions. + + Returns: + A SIMD element of the specified width containing the data at the given index. + + Raises: + Error: If the provided index is out of bounds. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.ones(shape=(4, 4)) + var simd_element = mat.load[4](2) # Load a SIMD element of width 4 from index 2 + ``` + """ + if idx >= self.size or idx < -self.size: + raise Error( + String("Index {} exceed the matrix size {}").format( + idx, self.size + ) + ) + var idx_norm = self.normalize(idx, self.size) + return self._buf.ptr.load[width=width](idx_norm) fn _load[width: Int = 1](self, x: Int, y: Int) -> SIMD[dtype, width]: """ @@ -380,38 +1067,150 @@ struct Matrix[dtype: DType = DType.float64]( x * self.strides[0] + y * self.strides[1] ) - fn __setitem__(self, x: Int, y: Int, value: Scalar[dtype]) raises: + fn _load[width: Int = 1](self, idx: Int) -> SIMD[dtype, width]: + """ + `__getitem__` with width. + Unsafe: No boundary check! + """ + return self._buf.ptr.load[width=width](idx) + + fn __setitem__(mut self, x: Int, y: Int, value: Scalar[dtype]) raises: """ - Return the scalar at the index. + Set the value at the specified row and column indices in the matrix. Args: - x: The row number. - y: The column number. - value: The value to be set. - """ + x: The row index. Can be negative to index from the end. + y: The column index. Can be negative to index from the end. + value: The value to set at the specified position. + + Raises: + Error: If the provided indices are out of bounds for the matrix. - if (x >= self.shape[0]) or (y >= self.shape[1]): + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(3, 4)) + mat[1, 2] = 5.0 # Set value at row 1, column 2 to 5.0 + ``` + """ + if ( + x >= self.shape[0] + or x < -self.shape[0] + or y >= self.shape[1] + or y < -self.shape[1] + ): raise Error( String( "Index ({}, {}) exceed the matrix shape ({}, {})" ).format(x, y, self.shape[0], self.shape[1]) ) + var x_norm: Int = self.normalize(x, self.shape[0]) + var y_norm: Int = self.normalize(y, self.shape[1]) - self._buf.ptr.store(x * self.strides[0] + y * self.strides[1], value) + self._buf.store(self.index(x_norm, y_norm), value) - fn __setitem__(self, var x: Int, value: Self) raises: + # FIXME: Setting with views is currently only supported through `.set()` method of the Matrix. Once Mojo resolve the symmetric getter setter issue, we can remove `.set()` methods. + fn __setitem__( + self, var x: Int, value: MatrixImpl[dtype, **_] + ) raises where Self.own_data == True and value.own_data == True: """ - Set the corresponding row at the index with the given matrix. + Assign a row in the matrix at the specified index with the given matrix. This method replaces the row at the specified index `x` with the data from + the provided `value` matrix. The `value` matrix must be a row vector with + the same number of columns as the target matrix. Args: - x: The row number. - value: Matrix (row vector). + x: The row index where the data will be assigned. Negative indices are + supported and follow Python conventions (e.g., -1 refers to the last row). + value: A `Matrix` instance representing the row vector to assign. + The `value` matrix can be in either C-contiguous or F-contiguous order. + + Raises: + Error: If the row index `x` is out of bounds. + Error: If the `value` matrix does not have exactly one row. + Error: If the number of columns in the `value` matrix does not match + the number of columns in the target matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(3, 4)) + var row_vector = Matrix.ones(shape=(1, 4)) + mat[1] = row_vector # Set the second row of mat to row_vector + ``` + """ + if x >= self.shape[0] or x < -self.shape[0]: + raise Error( + String( + "Error: Elements of `index` ({}) \n" + "exceed the matrix shape ({})." + ).format(x, self.shape[0]) + ) + + if value.shape[0] != 1: + raise Error( + String( + "Error: The value should have only 1 row, " + "but it has {} rows." + ).format(value.shape[0]) + ) + + if self.shape[1] != value.shape[1]: + raise Error( + String( + "Error: Matrix has {} columns, " + "but the value has {} columns." + ).format(self.shape[1], value.shape[1]) + ) + + if self.flags.C_CONTIGUOUS: + if value.flags.C_CONTIGUOUS: + var dest_ptr = self._buf.ptr.offset(x * self.strides[0]) + memcpy(dest=dest_ptr, src=value._buf.ptr, count=self.shape[1]) + else: + for j in range(self.shape[1]): + self._store(x, j, value._load(0, j)) + + # For F-contiguous + else: + if value.flags.F_CONTIGUOUS: + for j in range(self.shape[1]): + self._buf.ptr.offset(x + j * self.strides[1]).store( + value._buf.ptr.load(j * value.strides[1]) + ) + else: + for j in range(self.shape[1]): + self._store(x, j, value._load(0, j)) + + fn set(self, var x: Int, value: MatrixImpl[dtype, **_]) raises: """ + Assign a row in the matrix at the specified index with the given matrix. This method replaces the row at the specified index `x` with the data from + the provided `value` matrix. The `value` matrix must be a row vector with + the same number of columns as the target matrix. + + Args: + x: The row index where the data will be assigned. Negative indices are + supported and follow Python conventions (e.g., -1 refers to the last row). + value: A `Matrix` instance representing the row vector to assign. + The `value` matrix can be in either C-contiguous or F-contiguous order. - if x < 0: - x = self.shape[0] + x + Raises: + Error: If the row index `x` is out of bounds. + Error: If the `value` matrix does not have exactly one row. + Error: If the number of columns in the `value` matrix does not match + the number of columns in the target matrix. - if x >= self.shape[0]: + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(3, 4)) + var row_vector = Matrix.ones(shape=(1, 4)) + mat.set(1, row_vector) # Set the second row of mat to row_vector + + var view = row_vector.view() # create a view of row_vector + mat.set(2, view) # Set the third row of mat to the view + ``` + """ + if x >= self.shape[0] or x < -self.shape[0]: raise Error( String( "Error: Elements of `index` ({}) \n" @@ -422,7 +1221,7 @@ struct Matrix[dtype: DType = DType.float64]( if value.shape[0] != 1: raise Error( String( - "Error: The value should has only 1 row, " + "Error: The value should have only 1 row, " "but it has {} rows." ).format(value.shape[0]) ) @@ -435,67 +1234,503 @@ struct Matrix[dtype: DType = DType.float64]( ).format(self.shape[1], value.shape[1]) ) - var ptr = self._buf.ptr.offset(x * self.shape[1]) - memcpy(ptr, value._buf.ptr, value.size) + if self.flags.C_CONTIGUOUS: + if value.flags.C_CONTIGUOUS: + var dest_ptr = self._buf.ptr.offset(x * self.strides[0]) + memcpy(dest=dest_ptr, src=value._buf.ptr, count=self.shape[1]) + else: + for j in range(self.shape[1]): + self._store(x, j, value._load(0, j)) + + # For F-contiguous + else: + if value.flags.F_CONTIGUOUS: + for j in range(self.shape[1]): + self._buf.ptr.offset(x + j * self.strides[1]).store( + value._buf.ptr.load(j * value.strides[1]) + ) + else: + for j in range(self.shape[1]): + self._store(x, j, value._load(0, j)) + + fn __setitem__( + self, x: Slice, y: Int, value: MatrixImpl[dtype, **_] + ) raises: + """ + Assign values to a column in the matrix at the specified column index `y` + and row slice `x` with the given matrix. This method replaces the values + in the specified column and row slice with the data from the provided + `value` matrix. + + Args: + x: The row slice where the data will be assigned. Supports Python slice syntax (e.g., `start:stop:step`). + y: The column index where the data will be assigned. Negative indices + are supported and follow Python conventions (e.g., -1 refers to the + last column). + value: A `Matrix` instance representing the column vector to assign. + The `value` matrix must have the same number of rows as the + specified slice `x` and exactly one column. + + Raises: + Error: If the column index `y` is out of bounds. + Error: If the shape of the `value` matrix does not match the target + slice dimensions. + + Notes: + - Out of bound slice `x` is clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(4, 4)) + var col_vector = Matrix.ones(shape=(4, 1)) + mat[0:4, 2] = col_vector # Set the third column of mat to col_vector + ``` + """ + if y >= self.shape[1] or y < -self.shape[1]: + raise Error( + String("Index {} exceed the column number {}").format( + y, self.shape[1] + ) + ) + var y_norm = self.normalize(y, self.shape[1]) + var start_x: Int + var end_x: Int + var step_x: Int + start_x, end_x, step_x = x.indices(self.shape[0]) + var range_x = range(start_x, end_x, step_x) + var len_range_x: Int = len(range_x) + + if len_range_x != value.shape[0] or value.shape[1] != 1: + raise Error( + String( + "Shape mismatch when assigning to slice: " + "target shape ({}, {}) vs value shape ({}, {})" + ).format(len_range_x, 1, value.shape[0], value.shape[1]) + ) + + var row = 0 + for i in range_x: + self._store(i, y_norm, value._load(row, 0)) + row += 1 + + fn set(self, x: Slice, y: Int, value: MatrixImpl[dtype, **_]) raises: + """ + Assign values to a column in the matrix at the specified column index `y` + and row slice `x` with the given matrix. This method replaces the values + in the specified column and row slice with the data from the provided + `value` matrix. + + Args: + x: The row slice where the data will be assigned. Supports Python slice syntax (e.g., `start:stop:step`). + y: The column index where the data will be assigned. Negative indices + are supported and follow Python conventions (e.g., -1 refers to the + last column). + value: A `Matrix` instance representing the column vector to assign. + The `value` matrix must have the same number of rows as the + specified slice `x` and exactly one column. + + Raises: + Error: If the column index `y` is out of bounds. + Error: If the shape of the `value` matrix does not match the target + slice dimensions. + + Notes: + - Out of bound slice `x` is clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(4, 4)) + var col_vector = Matrix.ones(shape=(4, 1)) + mat.set(Slice(0, 4), 2, col_vector) # Set the third column of mat to col_vector + + var view = col_vector.view() # create a view of col_vector + mat.set(Slice(0, 4), 3, view) # Set the fourth column of mat to the view + ``` + """ + if y >= self.shape[1] or y < -self.shape[1]: + raise Error( + String("Index {} exceed the column number {}").format( + y, self.shape[1] + ) + ) + var y_norm = self.normalize(y, self.shape[1]) + var start_x: Int + var end_x: Int + var step_x: Int + start_x, end_x, step_x = x.indices(self.shape[0]) + var range_x = range(start_x, end_x, step_x) + var len_range_x: Int = len(range_x) + + if len_range_x != value.shape[0] or value.shape[1] != 1: + raise Error( + String( + "Shape mismatch when assigning to slice: " + "target shape ({}, {}) vs value shape ({}, {})" + ).format(len_range_x, 1, value.shape[0], value.shape[1]) + ) + + var row = 0 + for i in range_x: + self._store(i, y_norm, value._load(row, 0)) + row += 1 + + fn __setitem__( + self, x: Int, y: Slice, value: MatrixImpl[dtype, **_] + ) raises: + """ + Assign values to a row in the matrix at the specified row index `x` + and column slice `y` with the given matrix. This method replaces the values in the specified row and column slice with the data from the provided `value` matrix. + + Args: + x: The row index where the data will be assigned. Negative indices + are supported and follow Python conventions (e.g., -1 refers to the + last row). + y: The column slice where the data will be assigned. Supports Python slice syntax (e.g., `start:stop:step`). + value: A `Matrix` instance representing the row vector to assign. + The `value` matrix must have the same number of columns as the + specified slice `y` and exactly one row. + + Raises: + Error: If the row index `x` is out of bounds. + Error: If the shape of the `value` matrix does not match the target + slice dimensions. + + Notes: + - Out of bound slice `y` is clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(4, 4)) + var row_vector = Matrix.ones(shape=(1, 3)) + mat[1, 0:3] = row_vector # Set the second row, columns 0 to 2 of mat to row_vector + ``` + """ + if x >= self.shape[0] or x < -self.shape[0]: + raise Error( + String("Index {} exceed the row size {}").format( + x, self.shape[0] + ) + ) + var x_norm = self.normalize(x, self.shape[0]) + var start_y: Int + var end_y: Int + var step_y: Int + start_y, end_y, step_y = y.indices(self.shape[1]) + var range_y = range(start_y, end_y, step_y) + var len_range_y: Int = len(range_y) + + if len_range_y != value.shape[1] or value.shape[0] != 1: + raise Error( + String( + "Shape mismatch when assigning to slice: " + "target shape ({}, {}) vs value shape ({}, {})" + ).format(1, len_range_y, value.shape[0], value.shape[1]) + ) + + var col = 0 + for j in range_y: + self._store(x_norm, j, value._load(0, col)) + col += 1 + + fn set(self, x: Int, y: Slice, value: MatrixImpl[dtype, **_]) raises: + """ + Assign values to a row in the matrix at the specified row index `x` + and column slice `y` with the given matrix. This method replaces the values in the specified row and column slice with the data from the provided `value` matrix. + + Args: + x: The row index where the data will be assigned. Negative indices + are supported and follow Python conventions (e.g., -1 refers to the + last row). + y: The column slice where the data will be assigned. Supports Python slice syntax (e.g., `start:stop:step`). + value: A `Matrix` instance representing the row vector to assign. + The `value` matrix must have the same number of columns as the + specified slice `y` and exactly one row. + + Raises: + Error: If the row index `x` is out of bounds. + Error: If the shape of the `value` matrix does not match the target + slice dimensions. + + Notes: + - Out of bound slice `y` is clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(4, 4)) + var row_vector = Matrix.ones(shape=(1, 3)) + mat.set(1, Slice(0, 3), row_vector) # Set the second row, columns 0 to 2 of mat to row_vector + + var view = row_vector.view() # create a view of row_vector + mat.set(2, Slice(0, 3), view) # Set the third row, columns 0 to 2 of mat to the view + ``` + """ + if x >= self.shape[0] or x < -self.shape[0]: + raise Error( + String("Index {} exceed the row size {}").format( + x, self.shape[0] + ) + ) + var x_norm = self.normalize(x, self.shape[0]) + var start_y: Int + var end_y: Int + var step_y: Int + start_y, end_y, step_y = y.indices(self.shape[1]) + var range_y = range(start_y, end_y, step_y) + var len_range_y: Int = len(range_y) + + if len_range_y != value.shape[1] or value.shape[0] != 1: + raise Error( + String( + "Shape mismatch when assigning to slice: " + "target shape ({}, {}) vs value shape ({}, {})" + ).format(1, len_range_y, value.shape[0], value.shape[1]) + ) + + var col = 0 + for j in range_y: + self._store(x_norm, j, value._load(0, col)) + col += 1 + + fn __setitem__( + self, x: Slice, y: Slice, value: MatrixImpl[dtype, **_] + ) raises: + """ + Assign values to a submatrix of the matrix defined by row slice `x` and column slice `y` using the provided `value` matrix. This method replaces the elements in the specified row and column slices with the corresponding elements from `value`. + + Args: + x: Row slice specifying which rows to assign to. Supports Python slice syntax (e.g., `start:stop:step`). + y: Column slice specifying which columns to assign to. Supports Python slice syntax (e.g., `start:stop:step`). + value: A `Matrix` instance containing the values to assign. + + Raises: + Error: If the shape of `value` does not match the shape of the target slice. + + Notes: + - Out of bounds slices are clamped using the shape of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(4, 4)) + var submatrix = Matrix.ones(shape=(2, 2)) + mat[1:3, 1:3] = submatrix # Set the 2x2 submatrix starting at (1,1) to ones + ``` + """ + var start_x: Int + var end_x: Int + var step_x: Int + var start_y: Int + var end_y: Int + var step_y: Int + start_x, end_x, step_x = x.indices(self.shape[0]) + start_y, end_y, step_y = y.indices(self.shape[1]) + var range_x = range(start_x, end_x, step_x) + var range_y = range(start_y, end_y, step_y) + + if len(range_x) != value.shape[0] or len(range_y) != value.shape[1]: + raise Error( + String( + "Shape mismatch when assigning to slice: " + "target shape ({}, {}) vs value shape ({}, {})" + ).format( + len(range_x), len(range_y), value.shape[0], value.shape[1] + ) + ) + + var row = 0 + for i in range_x: + var col = 0 + for j in range_y: + self._store(i, j, value._load(row, col)) + col += 1 + row += 1 + + fn set(self, x: Slice, y: Slice, value: MatrixImpl[dtype, **_]) raises: + """ + Assign values to a submatrix of the matrix defined by row slice `x` and column slice `y` using the provided `value` matrix. This method replaces the elements in the specified row and column slices with the corresponding elements from `value`. + + Args: + x: Row slice specifying which rows to assign to. Supports Python slice syntax (e.g., `start:stop:step`). + y: Column slice specifying which columns to assign to. Supports Python slice syntax (e.g., `start:stop:step`). + value: A `Matrix` instance containing the values to assign. + + Raises: + Error: If the shape of `value` does not match the shape of the target slice. + + Notes: + - Out of bounds slices are clamped using the shape of the matrix. - fn _store[ - width: Int = 1 - ](mut self, x: Int, y: Int, simd: SIMD[dtype, width]): + Example: + ```mojo + from numojo.prelude import * + var mat = Matrix.zeros(shape=(4, 4)) + var submatrix = Matrix.ones(shape=(2, 2)) + mat.set(Slice(1, 3), Slice(1, 3), submatrix) # Set the 2x2 submatrix starting at (1,1) to ones + + var view = submatrix.view() # create a view of submatrix + mat.set(Slice(2, 4), Slice(2, 4), view + ) # Set the 2x2 submatrix starting at (2,2) to the view + ``` + """ + var start_x: Int + var end_x: Int + var step_x: Int + var start_y: Int + var end_y: Int + var step_y: Int + start_x, end_x, step_x = x.indices(self.shape[0]) + start_y, end_y, step_y = y.indices(self.shape[1]) + var range_x = range(start_x, end_x, step_x) + var range_y = range(start_y, end_y, step_y) + + if len(range_x) != value.shape[0] or len(range_y) != value.shape[1]: + raise Error( + String( + "Shape mismatch when assigning to slice: " + "target shape ({}, {}) vs value shape ({}, {})" + ).format( + len(range_x), len(range_y), value.shape[0], value.shape[1] + ) + ) + + var row = 0 + for i in range_x: + var col = 0 + for j in range_y: + self._store(i, j, value._load(row, col)) + col += 1 + row += 1 + + fn _store[width: Int = 1](self, x: Int, y: Int, simd: SIMD[dtype, width]): """ `__setitem__` with width. Unsafe: No boundary check! """ self._buf.ptr.store(x * self.strides[0] + y * self.strides[1], simd) + fn _store_idx[width: Int = 1](self, idx: Int, val: SIMD[dtype, width]): + """ + `__setitem__` with width. + Unsafe: No boundary check! + """ + self._buf.ptr.store(idx, val) + # ===-------------------------------------------------------------------===# # Other dunders and auxiliary methods # ===-------------------------------------------------------------------===# + fn view(ref self) -> MatrixView[dtype, MutOrigin.cast_from[origin]]: + """ + Return a non-owning view of the matrix. This method creates and returns a `MatrixView` that references the data of the original matrix. The view does not allocate new memory and directly points to the existing data buffer. Modifications to the view affect the original matrix. - fn __iter__(self) raises -> _MatrixIter[__origin_of(self), dtype]: - """Iterate over elements of the Matrix, returning copied value. + Returns: + A `MatrixView` referencing the original matrix data. Example: - ```mojo - from numojo import Matrix - var A = Matrix.rand((4,4)) - for i in A: - print(i) - ``` + ```mojo + from numojo import Matrix + var mat = Matrix.rand((4, 4)) + var mat_view = mat.view() # Create a view of the original matrix + ``` + """ + var new_data = DataContainer[dtype, MutOrigin.cast_from[origin]]( + ptr=self._buf.get_ptr().unsafe_origin_cast[ + MutOrigin.cast_from[origin] + ]() + ) + var matrix_view = MatrixView[dtype, MutOrigin.cast_from[origin]]( + shape=self.shape, + strides=self.strides, + data=new_data, + ) + return matrix_view^ - Returns: - An iterator of Matrix elements. + fn __iter__( + self, + ) -> Self.IteratorType[origin, origin_of(self), True] where ( + own_data == True + ): """ + Returns an iterator over the rows of the Matrix. Each iteration yields a MatrixView representing a single row. + + Returns: + Iterator that yields MatrixView objects for each row. - return _MatrixIter[__origin_of(self), dtype]( - matrix=self, - length=self.shape[0], + Example: + ```mojo + from numojo import Matrix + var mat = Matrix.rand((4, 4)) + for row in mat: + print(row) # Each row is a MatrixView + ``` + """ + return Self.IteratorType[origin, origin_of(self), True]( + index=0, + src=rebind[ + Pointer[ + MatrixImpl[dtype, own_data=True, origin=origin], + origin_of(self), + ] + ](Pointer(to=self)), ) fn __len__(self) -> Int: """ - Returns length of 0-th dimension. + Return the number of rows in the matrix (length of the first dimension). + + Returns: + The number of rows (self.shape[0]). + + Example: + ```mojo + from numojo import Matrix + var mat = Matrix.rand((4, 4)) + print(len(mat)) # Outputs: 4 + ``` """ return self.shape[0] fn __reversed__( - self, - ) raises -> _MatrixIter[__origin_of(self), dtype, forward=False]: - """Iterate backwards over elements of the Matrix, returning - copied value. - - Returns: - A reversed iterator of Matrix elements. + mut self, + ) raises -> Self.IteratorType[origin, origin_of(self), False] where ( + own_data == True + ): """ + Return an iterator that traverses the matrix rows in reverse order. - return _MatrixIter[__origin_of(self), dtype, forward=False]( - matrix=self, - length=self.shape[0], + Returns: + A reversed iterator over the rows of the matrix, yielding copies of each row. + """ + return Self.IteratorType[origin, origin_of(self), False]( + index=0, + src=rebind[ + Pointer[ + MatrixImpl[dtype, own_data=True, origin=origin], + origin_of(self), + ] + ](Pointer(to=self)), ) fn __str__(self) -> String: + """ + Return a string representation of the matrix. + + Returns: + A string showing the matrix contents, shape, strides, order, and ownership. + """ return String.write(self) fn write_to[W: Writer](self, mut writer: W): + """ + Write the string representation of the matrix to a writer. + + Args: + writer: The writer to output the matrix string to. + """ + fn print_row(self: Self, i: Int, sep: String) raises -> String: var result: String = String("[") var number_of_sep: Int = 1 @@ -551,10 +1786,8 @@ struct Matrix[dtype: DType = DType.float64]( + String(self.strides[0]) + "," + String(self.strides[1]) - + " C: " - + String(self.flags["C_CONTIGUOUS"]) - + " F: " - + String(self.flags["F_CONTIGUOUS"]) + + " order: " + + String("C" if self.flags["C_CONTIGUOUS"] else "F") + " Own: " + String(self.flags["OWNDATA"]) ) @@ -565,7 +1798,27 @@ struct Matrix[dtype: DType = DType.float64]( # Arithmetic dunder methods # ===-------------------------------------------------------------------===# - fn __add__(self, other: Self) raises -> Self: + fn __add__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: + """ + Add two matrices element-wise. + + Args: + other: Matrix to add to self. Must be broadcastable to self's shape. + + Returns: + A new Matrix containing the element-wise sum. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) + print(A + B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -577,36 +1830,71 @@ struct Matrix[dtype: DType = DType.float64]( ): return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__add__ - ](broadcast_to(self.copy(), other.shape, self.order()), other) + ](broadcast_to[dtype](self, other.shape, self.order()), other) else: return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__add__ - ](self, broadcast_to(other.copy(), self.shape, self.order())) + ](self, broadcast_to[dtype](other, self.shape, self.order())) + + fn __add__(self, other: Scalar[dtype]) raises -> Matrix[dtype]: + """ + Add a scalar to every element of the matrix. + + Args: + other: Scalar value to add. - fn __add__(self, other: Scalar[dtype]) raises -> Self: - """Add matrix to scalar. + Returns: + A new Matrix with the scalar added to each element. - ```mojo - from numojo import Matrix - var A = Matrix.ones(shape=(4, 4)) - print(A + 2) - ``` + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(A + 2) + ``` """ return self + broadcast_to[dtype](other, self.shape, self.order()) - fn __radd__(self, other: Scalar[dtype]) raises -> Self: + fn __radd__(self, other: Scalar[dtype]) raises -> Matrix[dtype]: """ - Right-add. + Add a matrix to a scalar (right-hand side). + + Args: + other: Scalar value to add. + + Returns: + A new Matrix with the scalar added to each element. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(2 + A) - ``` + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(2 + A) + ``` """ return broadcast_to[dtype](other, self.shape, self.order()) + self - fn __sub__(self, other: Self) raises -> Self: + fn __sub__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: + """ + Subtract two matrices element-wise. + + Args: + other: Matrix to subtract from self. Must be broadcastable to self's shape. + + Returns: + A new Matrix containing the element-wise difference. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) + print(A - B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -618,36 +1906,71 @@ struct Matrix[dtype: DType = DType.float64]( ): return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__sub__ - ](broadcast_to(self.copy(), other.shape, self.order()), other) + ](broadcast_to(self, other.shape, self.order()), other) else: return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__sub__ - ](self, broadcast_to(other.copy(), self.shape, self.order())) + ](self, broadcast_to(other, self.shape, self.order())) + + fn __sub__(self, other: Scalar[dtype]) raises -> Matrix[dtype]: + """ + Subtract a scalar from every element of the matrix. + + Args: + other: Scalar value to subtract. - fn __sub__(self, other: Scalar[dtype]) raises -> Self: - """Subtract matrix by scalar. + Returns: + A new Matrix with the scalar subtracted from each element. - ```mojo - from numojo import Matrix - A = Matrix(shape=(4, 4)) - print(A - 2) - ``` + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(A - 2) + ``` """ return self - broadcast_to[dtype](other, self.shape, self.order()) - fn __rsub__(self, other: Scalar[dtype]) raises -> Self: + fn __rsub__(self, other: Scalar[dtype]) raises -> Matrix[dtype]: """ - Right-sub. + Subtract a matrix from a scalar (right-hand side). + + Args: + other: Scalar value to subtract from. + + Returns: + A new Matrix with each element being the scalar minus the corresponding matrix element. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(2 - A) - ``` + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(2 - A) + ``` """ return broadcast_to[dtype](other, self.shape, self.order()) - self - fn __mul__(self, other: Self) raises -> Self: + fn __mul__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: + """ + Multiply two matrices element-wise. + + Args: + other: Matrix to multiply with self. Must be broadcastable to self's shape. + + Returns: + A new Matrix containing the element-wise product. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) + print(A * B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -659,36 +1982,71 @@ struct Matrix[dtype: DType = DType.float64]( ): return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__mul__ - ](broadcast_to(self.copy(), other.shape, self.order()), other) + ](broadcast_to(self, other.shape, self.order()), other) else: return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__mul__ - ](self, broadcast_to(other.copy(), self.shape, self.order())) + ](self, broadcast_to(other, self.shape, self.order())) + + fn __mul__(self, other: Scalar[dtype]) raises -> Matrix[dtype]: + """ + Multiply matrix by scalar. + + Args: + other: Scalar value to multiply. - fn __mul__(self, other: Scalar[dtype]) raises -> Self: - """Mutiply matrix by scalar. + Returns: + A new Matrix with each element multiplied by the scalar. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(A * 2) - ``` + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(A * 2) + ``` """ return self * broadcast_to[dtype](other, self.shape, self.order()) - fn __rmul__(self, other: Scalar[dtype]) raises -> Self: + fn __rmul__(self, other: Scalar[dtype]) raises -> Matrix[dtype]: """ - Right-mul. + Multiply scalar by matrix (right-hand side). + + Args: + other: Scalar value to multiply. + + Returns: + A new Matrix with each element multiplied by the scalar. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(2 * A) - ``` + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(2 * A) + ``` """ return broadcast_to[dtype](other, self.shape, self.order()) * self - fn __truediv__(self, other: Self) raises -> Self: + fn __truediv__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: + """ + Divide two matrices element-wise. + + Args: + other: Matrix to divide self by. Must be broadcastable to self's shape. + + Returns: + A new Matrix containing the element-wise division result. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) + print(A / B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -700,25 +2058,76 @@ struct Matrix[dtype: DType = DType.float64]( ): return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__truediv__ - ](broadcast_to(self.copy(), other.shape, self.order()), other) + ](broadcast_to(self, other.shape, self.order()), other) else: return _arithmetic_func_matrix_matrix_to_matrix[ dtype, SIMD.__truediv__ - ](self, broadcast_to(other.copy(), self.shape, self.order())) + ](self, broadcast_to(other, self.shape, self.order())) + + fn __truediv__(self, other: Scalar[dtype]) raises -> Matrix[dtype]: + """ + Divide matrix by scalar. + + Args: + other: Scalar value to divide each element of the matrix by. + + Returns: + A new Matrix with each element divided by the scalar. - fn __truediv__(self, other: Scalar[dtype]) raises -> Self: - """Divide matrix by scalar.""" + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(A / 2) + ``` + """ return self / broadcast_to[dtype](other, self.shape, order=self.order()) - # Shouldn't we do the operation inplace? - fn __pow__(self, rhs: Scalar[dtype]) raises -> Self: - """Power of items.""" - var result: Self = self.copy() + fn __pow__(self, rhs: Scalar[dtype]) raises -> Matrix[dtype]: + """ + Raise each element of the matrix to the power of `rhs`. + + Args: + rhs: The scalar exponent to which each element of the matrix will be raised. + + Returns: + A new Matrix where each element is self[i] ** rhs. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(A ** 2) + ``` + """ + var result: Matrix[dtype] = Matrix[dtype]( + shape=self.shape, order=self.order() + ) for i in range(self.size): result._buf.ptr[i] = self._buf.ptr[i].__pow__(rhs) return result^ - fn __lt__(self, other: Self) raises -> Matrix[DType.bool]: + fn __lt__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[DType.bool]: + """ + Compare two matrices element-wise for less-than. + + Args: + other: Matrix to compare with self. Must be broadcastable to self's shape. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] < other[i, j], else False. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) * 2 + print(A < B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -729,25 +2138,53 @@ struct Matrix[dtype: DType = DType.float64]( self.shape[1] < other.shape[1] ): return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.lt]( - broadcast_to(self.copy(), other.shape, self.order()), other + broadcast_to(self, other.shape, self.order()), other ) else: return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.lt]( - self, broadcast_to(other.copy(), self.shape, self.order()) + self, broadcast_to(other, self.shape, self.order()) ) fn __lt__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: - """Matrix less than scalar. + """ + Compare each element of the matrix to a scalar for less-than. + + Args: + other: Scalar value to compare. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] < other, else False. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(A < 2) - ``` + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + print(A < 2) + ``` """ return self < broadcast_to[dtype](other, self.shape, self.order()) - fn __le__(self, other: Self) raises -> Matrix[DType.bool]: + fn __le__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[DType.bool]: + """ + Compare two matrices element-wise for less-than-or-equal. + + Args: + other: Matrix to compare with self. Must be broadcastable to self's shape. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] <= other[i, j], else False. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) * 2 + print(A <= B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -758,25 +2195,53 @@ struct Matrix[dtype: DType = DType.float64]( self.shape[1] < other.shape[1] ): return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.le]( - broadcast_to(self.copy(), other.shape, self.order()), other + broadcast_to(self, other.shape, self.order()), other ) else: return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.le]( - self, broadcast_to(other.copy(), self.shape, self.order()) + self, broadcast_to(other, self.shape, self.order()) ) fn __le__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: - """Matrix less than and equal to scalar. + """ + Compare each element of the matrix to a scalar for less-than-or-equal. + + Args: + other: Scalar value to compare. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(A <= 2) - ``` + Returns: + A new Matrix[bool] where each element is True if self[i, j] <= other, else False. + + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A <= 2) + ``` """ return self <= broadcast_to[dtype](other, self.shape, self.order()) - fn __gt__(self, other: Self) raises -> Matrix[DType.bool]: + fn __gt__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[DType.bool]: + """ + Compare two matrices element-wise for greater-than. + + Args: + other: Matrix to compare with self. Must be broadcastable to self's shape. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] > other[i, j], else False. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + B = Matrix.ones(shape=(4, 4)) * 2 + print(A > B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -787,25 +2252,53 @@ struct Matrix[dtype: DType = DType.float64]( self.shape[1] < other.shape[1] ): return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.gt]( - broadcast_to(self.copy(), other.shape, self.order()), other + broadcast_to(self, other.shape, self.order()), other ) else: return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.gt]( - self, broadcast_to(other.copy(), self.shape, self.order()) + self, broadcast_to(other, self.shape, self.order()) ) fn __gt__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: - """Matrix greater than scalar. + """ + Compare each element of the matrix to a scalar for greater-than. + + Args: + other: Scalar value to compare. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] > other, else False. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(A > 2) - ``` + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A > 2) + ``` """ return self > broadcast_to[dtype](other, self.shape, self.order()) - fn __ge__(self, other: Self) raises -> Matrix[DType.bool]: + fn __ge__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[DType.bool]: + """ + Compare two matrices element-wise for greater-than-or-equal. + + Args: + other: Matrix to compare with self. Must be broadcastable to self's shape. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] >= other[i, j], else False. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + B = Matrix.ones(shape=(4, 4)) * 2 + print(A >= B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -816,25 +2309,56 @@ struct Matrix[dtype: DType = DType.float64]( self.shape[1] < other.shape[1] ): return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ge]( - broadcast_to(self.copy(), other.shape, self.order()), other + broadcast_to(self, other.shape, self.order()), other ) else: return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ge]( - self, broadcast_to(other.copy(), self.shape, self.order()) + self, broadcast_to(other, self.shape, self.order()) ) fn __ge__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: - """Matrix greater than and equal to scalar. + """ + Compare each element of the matrix to a scalar for greater-than-or-equal. + + Args: + other: Scalar value to compare. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] >= other, else False. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(A >= 2) - ``` + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A >= 2) + ``` """ return self >= broadcast_to[dtype](other, self.shape, self.order()) - fn __eq__(self, other: Self) raises -> Matrix[DType.bool]: + fn __eq__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[DType.bool]: + """ + Compare two matrices element-wise for equality. + + Args: + other: Matrix to compare with self. Must be broadcastable to self's shape. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] == other[i, j], else False. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) + print(A == B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -845,25 +2369,53 @@ struct Matrix[dtype: DType = DType.float64]( self.shape[1] < other.shape[1] ): return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.eq]( - broadcast_to(self.copy(), other.shape, self.order()), other + broadcast_to(self, other.shape, self.order()), other ) else: return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.eq]( - self, broadcast_to(other.copy(), self.shape, self.order()) + self, broadcast_to(other, self.shape, self.order()) ) fn __eq__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: - """Matrix less than and equal to scalar. + """ + Compare each element of the matrix to a scalar for equality. + + Args: + other: Scalar value to compare. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] == other, else False. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(A == 2) - ``` + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A == 2) + ``` """ return self == broadcast_to[dtype](other, self.shape, self.order()) - fn __ne__(self, other: Self) raises -> Matrix[DType.bool]: + fn __ne__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[DType.bool]: + """ + Compare two matrices element-wise for inequality. + + Args: + other: Matrix to compare with self. Must be broadcastable to self's shape. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] != other[i, j], else False. + + Raises: + Error: If the shapes are not compatible for broadcasting. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 4)) + var B = Matrix.ones(shape=(4, 4)) + print(A != B) + ``` + """ if (self.shape[0] == other.shape[0]) and ( self.shape[1] == other.shape[1] ): @@ -874,164 +2426,406 @@ struct Matrix[dtype: DType = DType.float64]( self.shape[1] < other.shape[1] ): return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ne]( - broadcast_to(self.copy(), other.shape, self.order()), other + broadcast_to(self, other.shape, self.order()), other ) else: return _logic_func_matrix_matrix_to_matrix[dtype, SIMD.ne]( - self, broadcast_to(other.copy(), self.shape, self.order()) + self, broadcast_to(other, self.shape, self.order()) ) fn __ne__(self, other: Scalar[dtype]) raises -> Matrix[DType.bool]: - """Matrix less than and equal to scalar. + """ + Compare each element of the matrix to a scalar for inequality. + + Args: + other: Scalar value to compare. + + Returns: + A new Matrix[bool] where each element is True if self[i, j] != other, else False. - ```mojo - from numojo import Matrix - A = Matrix.ones(shape=(4, 4)) - print(A != 2) - ``` + Example: + ```mojo + from numojo import Matrix + A = Matrix.ones(shape=(4, 4)) + print(A != 2) + ``` """ return self != broadcast_to[dtype](other, self.shape, self.order()) - fn __matmul__(self, other: Self) raises -> Self: + fn __matmul__(self, other: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: + """ + Matrix multiplication using the @ operator. + + Args: + other: The matrix to multiply with self. + + Returns: + A new Matrix containing the result of matrix multiplication. + + Raises: + Error: If the shapes are not compatible for matrix multiplication. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.ones(shape=(4, 3)) + var B = Matrix.ones(shape=(3, 2)) + print(A @ B) + ``` + """ return numojo.linalg.matmul(self, other) - # ===-------------------------------------------------------------------===# - # Core methods - # ===-------------------------------------------------------------------===# + # # ===-------------------------------------------------------------------===# + # # Core methods + # # ===-------------------------------------------------------------------===# + # FIXME: These return types be Scalar[DType.bool] or Matrix[DType.bool] instead to match numpy. Fix the docstring examples too. fn all(self) -> Scalar[dtype]: """ - Test whether all array elements evaluate to True. + Returns True if all elements of the matrix evaluate to True. + + Returns: + Scalar[dtype]: True if all elements are True, otherwise False. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.fromlist(List[Float64](1, 1, 1, 1, 1), (5, 1)) + print(A.all()) # Outputs: True + var B = Matrix.fromlist(List[Float64](1, 0, 2, 3, 4), (5, 1)) + print(B.all()) # Outputs: False + ``` """ return numojo.logic.all(self) - fn all(self, axis: Int) raises -> Self: + fn all(self, axis: Int) raises -> Matrix[dtype]: """ - Test whether all array elements evaluate to True along axis. + Returns a matrix indicating whether all elements along the specified axis evaluate to True. + + Args: + axis: The axis along which to perform the test. + + Returns: + Matrix[dtype]: Matrix of boolean values for each slice along the axis. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.fromlist( + List[Float64](1, 1, 1, 0, 1, 3), (2, 3) + ) + print(A.all(axis=0)) # Outputs: [[0, 1, 1]] + print(A.all(axis=1)) # Outputs: [[1], [0]] + ``` """ - return numojo.logic.all(self, axis=axis) + return numojo.logic.all[dtype](self, axis=axis) fn any(self) -> Scalar[dtype]: """ - Test whether any array elements evaluate to True. + Returns True if any element of the matrix evaluates to True. + + Returns: + Scalar[dtype]: True if any element is True, otherwise False. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.fromlist(List[Float64](0, 0, 0, 0, 0), (5, 1)) + print(A.any()) # Outputs: False + var B = Matrix.fromlist(List[Float64](0, 2, 0, 0, 0), (5, 1)) + print(B.any()) # Outputs: True + ``` """ return numojo.logic.any(self) - fn any(self, axis: Int) raises -> Self: + fn any(self, axis: Int) raises -> Matrix[dtype]: """ - Test whether any array elements evaluate to True along axis. + Returns a matrix indicating whether any element along the specified axis evaluates to True. + + Args: + axis: The axis along which to perform the test. + + Returns: + Matrix[dtype]: Matrix of boolean values for each slice along the axis. """ return numojo.logic.any(self, axis=axis) - fn argmax(self) raises -> Scalar[DType.index]: + fn argmax(self) raises -> Scalar[DType.int]: """ - Index of the max. It is first flattened before sorting. + Returns the index of the maximum element in the flattened matrix. + + Returns: + Scalar[DType.int]: Index of the maximum element. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](1, 3, 2, 5, 4), (5, 1)) + print(A.argmax()) # Outputs: 3 + ``` """ return numojo.math.argmax(self) - fn argmax(self, axis: Int) raises -> Matrix[DType.index]: + fn argmax(self, axis: Int) raises -> Matrix[DType.int]: """ - Index of the max along the given axis. + Returns the indices of the maximum elements along the specified axis. + + Args: + axis: The axis along which to find the maximum. + + Returns: + Matrix[DType.int]: Indices of the maximum elements along the axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](1, 3, 2, 5, 4, 6), (2, 3)) + print(A.argmax(axis=0)) # Outputs: [[1, 1, 1]] + print(A.argmax(axis=1)) # Outputs: [[1], [2]] + ``` """ return numojo.math.argmax(self, axis=axis) - fn argmin(self) raises -> Scalar[DType.index]: + fn argmin(self) raises -> Scalar[DType.int]: """ - Index of the min. It is first flattened before sorting. + Returns the index of the minimum element in the flattened matrix. + + Returns: + Scalar[DType.int]: Index of the minimum element. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](3, 1, 4, 2, 5), (5, 1)) + print(A.argmin()) # Outputs: 1 + ``` """ return numojo.math.argmin(self) - fn argmin(self, axis: Int) raises -> Matrix[DType.index]: + fn argmin(self, axis: Int) raises -> Matrix[DType.int]: """ - Index of the min along the given axis. + Returns the indices of the minimum elements along the specified axis. + + Args: + axis: The axis along which to find the minimum. + + Returns: + Matrix[DType.int]: Indices of the minimum elements along the axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](3, 1, 4, 2, 5, 0), (2, 3)) + print(A.argmin(axis=0)) # Outputs: [[1, 1, 1]] + print(A.argmin(axis=1)) # Outputs: [[1], [2]] + ``` """ return numojo.math.argmin(self, axis=axis) - fn argsort(self) raises -> Matrix[DType.index]: + fn argsort(self) raises -> Matrix[DType.int]: """ - Argsort the Matrix. It is first flattened before sorting. + Returns the indices that would sort the flattened matrix. + + Returns: + Matrix[DType.int]: Indices that sort the flattened matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](3, 1, 4, 2), (4, 1)) + print(A.argsort()) # Outputs: [[1, 3, 0, 2]] + ``` """ return numojo.math.argsort(self) - fn argsort(self, axis: Int) raises -> Matrix[DType.index]: + fn argsort(self, axis: Int) raises -> Matrix[DType.int]: """ - Argsort the Matrix along the given axis. + Returns the indices that would sort the matrix along the specified axis. + + Args: + axis: The axis along which to sort. + + Returns: + Matrix[DType.int]: Indices that sort the matrix along the axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](3, 1, 4, 2, 5, 0), (2, 3)) + print(A.argsort(axis=0)) # Outputs: [[1, 1, 1], [0, 0, 0]] + print(A.argsort(axis=1)) # Outputs: [[1, 3, 0], [2, 0, 1]] + ``` """ - return numojo.math.argsort(self.copy(), axis=axis) + return numojo.math.argsort(self, axis=axis) fn astype[asdtype: DType](self) -> Matrix[asdtype]: """ - Copy of the matrix, cast to a specified type. - """ - var res = Matrix[asdtype]( + Returns a copy of the matrix cast to the specified data type. + + Parameters: + asdtype: The target data type to cast to. + + Returns: + Matrix[asdtype]: A new matrix with elements cast to the specified type. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.fromlist(List[Float32](1.5, 2.5, 3.5), (3, 1)) + var B = A.astype[i8]() + print(B) # Outputs a Matrix[i8] with values [[1], [2], [3]] + ``` + """ + var casted_matrix = Matrix[asdtype]( shape=(self.shape[0], self.shape[1]), order=self.order() ) for i in range(self.size): - res._buf.ptr[i] = self._buf.ptr[i].cast[asdtype]() - return res^ + casted_matrix._buf.ptr[i] = self._buf.ptr[i].cast[asdtype]() + return casted_matrix^ fn cumprod(self) raises -> Matrix[dtype]: """ - Cumprod of flattened matrix. + Compute the cumulative product of all elements in the matrix, flattened into a single dimension. + + Returns: + Matrix[dtype]: A matrix containing the cumulative product of the flattened input. Example: - ```mojo - from numojo import Matrix - var A = Matrix.rand(shape=(100, 100)) - print(A.cumprod()) - ``` + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.cumprod()) + ``` """ - return numojo.math.cumprod(self.copy()) + return numojo.math.cumprod(self) fn cumprod(self, axis: Int) raises -> Matrix[dtype]: """ - Cumprod of Matrix along the axis. + Compute the cumulative product of elements along a specified axis. Args: - axis: 0 or 1. + axis: The axis along which to compute the cumulative product (0 for rows, 1 for columns). + + Returns: + Matrix[dtype]: A matrix containing the cumulative product along the specified axis. Example: - ```mojo - from numojo import Matrix - var A = Matrix.rand(shape=(100, 100)) - print(A.cumprod(axis=0)) - print(A.cumprod(axis=1)) - ``` + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.cumprod(axis=0)) + print(A.cumprod(axis=1)) + ``` """ - return numojo.math.cumprod(self.copy(), axis=axis) + return numojo.math.cumprod(self, axis=axis) fn cumsum(self) raises -> Matrix[dtype]: - return numojo.math.cumsum(self.copy()) + """ + Compute the cumulative sum of all elements in the matrix, flattened into a single dimension. + + Returns: + Matrix[dtype]: A matrix containing the cumulative sum of the flattened input. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.cumsum()) + ``` + """ + return numojo.math.cumsum(self) fn cumsum(self, axis: Int) raises -> Matrix[dtype]: - return numojo.math.cumsum(self.copy(), axis=axis) + """ + Compute the cumulative sum of elements along a specified axis. + + Args: + axis: The axis along which to compute the cumulative sum (0 for rows, 1 for columns). + + Returns: + Matrix[dtype]: A matrix containing the cumulative sum along the specified axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.cumsum(axis=0)) + print(A.cumsum(axis=1)) + ``` + """ + return numojo.math.cumsum(self, axis=axis) fn fill(self, fill_value: Scalar[dtype]): """ - Fill the matrix with value. + Fill the matrix with the specified value. This method sets every element of the matrix to `fill_value`. + + Args: + fill_value: The value to assign to every element of the matrix. - See also function `mat.creation.full`. + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3)) + A.fill(5) + print(A) + ``` + + See also: `Matrix.full` """ for i in range(self.size): self._buf.ptr[i] = fill_value - fn flatten(self) -> Self: + # * Make it inplace? + fn flatten(self) -> Matrix[dtype]: """ - Return a flattened copy of the matrix. - """ - var res = Self(shape=(1, self.size), order=self.order()) - memcpy(res._buf.ptr, self._buf.ptr, res.size) + Return a flattened copy of the matrix. This method returns a new matrix containing all elements of the original matrix in a single row (shape (1, size)), preserving the order. + + Returns: + Matrix[dtype]: A new matrix with shape (1, self.size) containing the flattened data. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((2, 3)) + print(A.flatten()) + ``` + """ + var res = Matrix[dtype](shape=(1, self.size), order=self.order()) + memcpy(dest=res._buf.ptr, src=self._buf.ptr, count=res.size) return res^ - fn inv(self) raises -> Self: + fn inv(self) raises -> Matrix[dtype]: """ - Inverse of matrix. + Compute the inverse of the matrix. + + Returns: + Matrix[dtype]: The inverse of the matrix. + + Raises: + Error: If the matrix is not square or not invertible. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3)) + print(A.inv()) + ``` """ return numojo.linalg.inv(self) fn order(self) -> String: """ - Returns the order. + Return the memory layout order of the matrix. + + Returns: + String: "C" if the matrix is C-contiguous, "F" if F-contiguous. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3), order="F") + print(A.order()) # "F" + ``` """ var order: String = "F" if self.flags.C_CONTIGUOUS: @@ -1040,13 +2834,39 @@ struct Matrix[dtype: DType = DType.float64]( fn max(self) raises -> Scalar[dtype]: """ - Find max item. It is first flattened before sorting. + Return the maximum element in the matrix. + + The matrix is flattened before finding the maximum. + + Returns: + Scalar[dtype]: The maximum value in the matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3)) + print(A.max()) + ``` """ return numojo.math.extrema.max(self) - fn max(self, axis: Int) raises -> Self: + fn max(self, axis: Int) raises -> Matrix[dtype]: """ - Find max item along the given axis. + Return the maximum values along the specified axis. + + Args: + axis: The axis along which to compute the maximum (0 for rows, 1 for columns). + + Returns: + Matrix[dtype]: A matrix containing the maximum values along the given axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3)) + print(A.max(axis=0)) # Max of each column + print(A.max(axis=1)) # Max of each row + ``` """ return numojo.math.extrema.max(self, axis=axis) @@ -1054,7 +2874,17 @@ struct Matrix[dtype: DType = DType.float64]( returned_dtype: DType = DType.float64 ](self) raises -> Scalar[returned_dtype]: """ - Calculate the arithmetic average of all items in the Matrix. + Compute the arithmetic mean of all elements in the matrix. + + Returns: + Scalar[returned_dtype]: The mean value of all elements. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.mean()) + ``` """ return numojo.statistics.mean[returned_dtype](self) @@ -1062,51 +2892,121 @@ struct Matrix[dtype: DType = DType.float64]( returned_dtype: DType = DType.float64 ](self, axis: Int) raises -> Matrix[returned_dtype]: """ - Calculate the arithmetic average of a Matrix along the axis. + Compute the arithmetic mean along the specified axis. Args: - axis: 0 or 1. + axis: The axis along which to compute the mean (0 for rows, 1 for columns). + + Returns: + Matrix[returned_dtype]: The mean values along the given axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.mean(axis=0)) + print(A.mean(axis=1)) + ``` """ return numojo.statistics.mean[returned_dtype](self, axis=axis) fn min(self) raises -> Scalar[dtype]: """ - Find min item. It is first flattened before sorting. + Return the minimum element in the matrix. + + The matrix is flattened before finding the minimum. + + Returns: + Scalar[dtype]: The minimum value in the matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3)) + print(A.min()) + ``` """ return numojo.math.extrema.min(self) - fn min(self, axis: Int) raises -> Self: + fn min(self, axis: Int) raises -> Matrix[dtype]: """ - Find min item along the given axis. + Return the minimum values along the specified axis. + + Args: + axis: The axis along which to compute the minimum (0 for rows, 1 for columns). + + Returns: + Matrix[dtype]: The minimum values along the given axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3)) + print(A.min(axis=0)) # Min of each column + print(A.min(axis=1)) # Min of each row + ``` """ return numojo.math.extrema.min(self, axis=axis) fn prod(self) -> Scalar[dtype]: """ - Product of all items in the Matrix. + Compute the product of all elements in the matrix. + + Returns: + Scalar[dtype]: The product of all elements. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.rand(shape=(100, 100)) + print(A.prod()) + ``` """ return numojo.math.prod(self) - fn prod(self, axis: Int) raises -> Self: + fn prod(self, axis: Int) raises -> Matrix[dtype]: """ - Product of items in a Matrix along the axis. + Compute the product of elements along the specified axis. Args: - axis: 0 or 1. + axis: The axis along which to compute the product (0 for rows, 1 for columns). + + Returns: + Matrix[dtype]: The product values along the given axis. Example: - ```mojo - from numojo import Matrix - var A = Matrix.rand(shape=(100, 100)) - print(A.prod(axis=0)) - print(A.prod(axis=1)) - ``` + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.prod(axis=0)) + print(A.prod(axis=1)) + ``` """ return numojo.math.prod(self, axis=axis) - fn reshape(self, shape: Tuple[Int, Int]) raises -> Self: + fn reshape( + self, shape: Tuple[Int, Int], order: String = "C" + ) raises -> Matrix[dtype]: """ - Change shape and size of matrix and return a new matrix. + Return a new matrix with the specified shape containing the same data. + + Args: + shape: Tuple of (rows, columns) specifying the new shape. + order: Memory layout order of the new matrix. "C" for C-contiguous, "F" for F-contiguous. Default is "C". + + Returns: + Matrix[dtype]: A new matrix with the requested shape. + + Raises: + Error: If the total number of elements does not match the original matrix size. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(4, 4)) + var B = A.reshape((2, 8)) + print(B) + ``` """ if shape[0] * shape[1] != self.size: raise Error( @@ -1114,36 +3014,81 @@ struct Matrix[dtype: DType = DType.float64]( "Cannot reshape matrix of size {} into shape ({}, {})." ).format(self.size, shape[0], shape[1]) ) - var res = Self(shape=shape, order="C") - if self.flags.F_CONTIGUOUS: - var temp = self.reorder_layout() - memcpy(res._buf.ptr, temp._buf.ptr, res.size) - res = res.reorder_layout() + var res = Matrix[dtype](shape=shape, order=order) + + if self.flags.C_CONTIGUOUS and order == "F": + for i in range(shape[0]): + for j in range(shape[1]): + var flat_idx = i * shape[1] + j + res._buf[ + j * res.strides[1] + i * res.strides[0] + ] = self._buf[flat_idx] + elif self.flags.F_CONTIGUOUS and order == "C": + var k = 0 + for row in range(self.shape[0]): + for col in range(self.shape[1]): + var val = self._buf.ptr[ + row * self.strides[0] + col * self.strides[1] + ] + var dest_row = Int(k // shape[1]) + var dest_col = k % shape[1] + res._buf.ptr[ + dest_row * res.strides[0] + dest_col * res.strides[1] + ] = val + k += 1 else: - memcpy(res._buf.ptr, self._buf.ptr, res.size) + memcpy(dest=res._buf.ptr, src=self._buf.ptr, count=res.size) return res^ - fn resize(mut self, shape: Tuple[Int, Int]) raises: + # NOTE: not sure if `where` clause works correctly here yet. + fn resize(mut self, shape: Tuple[Int, Int]) raises where own_data == True: """ - Change shape and size of matrix in-place. + Change the shape and size of the matrix in-place. + + Args: + shape: Tuple of (rows, columns) specifying the new shape. + + Raises: + Error: If the new shape requires more elements than the current matrix can hold and memory allocation fails. + + Notes: + - If the new shape is larger, the matrix is reallocated and new elements are zero-initialized. + - If the new shape is smaller, the matrix shape and strides are updated without reallocating memory. + - Only allowed for matrices with own_data=True. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(2, 3)) + A.resize((4, 5)) + print(A) + ``` """ if shape[0] * shape[1] > self.size: - var other = Self(shape=shape) + var other = MatrixImpl[dtype, own_data=own_data, origin=origin]( + shape=shape, order=self.order() + ) if self.flags.C_CONTIGUOUS: - memcpy(other._buf.ptr, self._buf.ptr, self.size) + memcpy(dest=other._buf.ptr, src=self._buf.ptr, count=self.size) for i in range(self.size, other.size): other._buf.ptr[i] = 0 else: - var idx = 0 - for i in range(other.size): - other._buf.ptr.store(i, 0.0) - if idx < self.size: - other._buf.ptr[i] = self._buf.ptr[ - (i % self.shape[1]) * self.shape[0] - + (i // self.shape[1]) + var min_rows = min(self.shape[0], shape[0]) + var min_cols = min(self.shape[1], shape[1]) + + for j in range(min_cols): + for i in range(min_rows): + other._buf.ptr[i + j * shape[0]] = self._buf.ptr[ + i + j * self.shape[0] ] - idx += 1 - other = other.reorder_layout() + for i in range(min_rows, shape[0]): + other._buf.ptr[i + j * shape[0]] = 0 + + # Zero the additional columns + for j in range(min_cols, shape[1]): + for i in range(shape[0]): + other._buf.ptr[i + j * shape[0]] = 0 + self = other^ else: self.shape[0] = shape[0] @@ -1155,17 +3100,44 @@ struct Matrix[dtype: DType = DType.float64]( else: self.strides[1] = shape[0] - fn round(self, decimals: Int) raises -> Self: - return numojo.math.rounding.round(self.copy(), decimals=decimals) + fn round(self, decimals: Int) raises -> Matrix[dtype]: + """ + Round each element of the matrix to the specified number of decimals. + + Args: + decimals: Number of decimal places to round to. + + Returns: + Matrix[dtype]: A new matrix with rounded values. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.fromlist(List[Float64](1.12345, 2.67891, 3.14159), (3, 1)) + var B = A.round(2) + print(B) # Outputs a Matrix[Float64] with values [[1.12], [2.68], [3.14]] + ``` + """ + return numojo.math.rounding.round(self, decimals=decimals) fn std[ returned_dtype: DType = DType.float64 ](self, ddof: Int = 0) raises -> Scalar[returned_dtype]: """ - Compute the standard deviation. + Compute the standard deviation of all elements in the matrix. Args: - ddof: Delta degree of freedom. + ddof: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N is the number of elements. + + Returns: + Scalar[returned_dtype]: The standard deviation of the matrix. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.rand(shape=(100, 100)) + print(A.std()) + ``` """ return numojo.statistics.std[returned_dtype](self, ddof=ddof) @@ -1173,79 +3145,168 @@ struct Matrix[dtype: DType = DType.float64]( returned_dtype: DType = DType.float64 ](self, axis: Int, ddof: Int = 0) raises -> Matrix[returned_dtype]: """ - Compute the standard deviation along axis. + Compute the standard deviation along the specified axis. Args: - axis: 0 or 1. - ddof: Delta degree of freedom. + axis: Axis along which to compute the standard deviation (0 for rows, 1 for columns). + ddof: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N is the number of elements along the axis. + + Returns: + Matrix[returned_dtype]: The standard deviation along the given axis. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.rand(shape=(100, 100)) + print(A.std(axis=0)) + print(A.std(axis=1)) + ``` """ return numojo.statistics.std[returned_dtype](self, axis=axis, ddof=ddof) fn sum(self) -> Scalar[dtype]: """ - Sum up all items in the Matrix. + Compute the sum of all elements in the matrix. + + Returns: + Scalar[dtype]: The sum of all elements. Example: - ```mojo - from numojo import Matrix - var A = Matrix.rand(shape=(100, 100)) - print(A.sum()) - ``` + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.sum()) + ``` """ return numojo.math.sum(self) - fn sum(self, axis: Int) raises -> Self: + fn sum(self, axis: Int) raises -> Matrix[dtype]: """ - Sum up the items in a Matrix along the axis. + Compute the sum of elements along the specified axis. Args: - axis: 0 or 1. + axis: Axis along which to sum (0 for rows, 1 for columns). + + Returns: + Matrix[dtype]: The sum along the given axis. Example: - ```mojo - from numojo import Matrix - var A = Matrix.rand(shape=(100, 100)) - print(A.sum(axis=0)) - print(A.sum(axis=1)) - ``` + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.sum(axis=0)) + print(A.sum(axis=1)) + ``` """ return numojo.math.sum(self, axis=axis) fn trace(self) raises -> Scalar[dtype]: """ - Trace of matrix. + Compute the trace of the matrix (sum of diagonal elements). + + Returns: + Scalar[dtype]: The trace value. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.fromlist( + List[Float64](1, 2, 3, 4, 5, 6, 7, 8, 9), (3, 3) + ) + print(A.trace()) # Outputs: 15.0 + ``` """ return numojo.linalg.trace(self) fn issymmetric(self) -> Bool: """ - Transpose of matrix. + Check if the matrix is symmetric (equal to its transpose). + + Returns: + Bool: True if the matrix is symmetric, False otherwise. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](1, 2, 2, 1), (2, 2)) + print(A.issymmetric()) # Outputs: True + var B = Matrix.fromlist(List[Float64](1, 2, 3, 4), (2, 2)) + print(B.issymmetric()) # Outputs: False + ``` """ return issymmetric(self) - fn transpose(self) -> Self: + fn transpose(self) -> Matrix[dtype]: """ - Transpose of matrix. + Return the transpose of the matrix. + + Returns: + Matrix[dtype]: The transposed matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](1, 2, 3, 4), (2, 2)) + print(A.transpose()) # Outputs: [[1, 3], [2, 4]] + ``` """ return transpose(self) - fn reorder_layout(self) raises -> Self: + # TODO: we should only allow this for owndata. not for views, it'll lead to weird origin behaviours. + fn reorder_layout(self) raises -> Matrix[dtype]: """ - Reorder_layout matrix. + Reorder the memory layout of the matrix to match its current order ("C" or "F"). This method returns a new matrix with the same data but stored in the requested memory layout. Only allowed for matrices with own_data=True. + + Returns: + Matrix[dtype]: A new matrix with reordered memory layout. + + Raises: + Error: If the matrix does not have its own data. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand((3, 3), order="F") + var B = A.reorder_layout() + print(B.order()) # Outputs: "F" + ``` """ return reorder_layout(self) - fn T(self) -> Self: + fn T(self) -> Matrix[dtype]: + """ + Return the transpose of the matrix. + + Returns: + Matrix[dtype]: The transposed matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.fromlist(List[Float64](1, 2, 3, 4), (2, 2)) + print(A.T()) # Outputs: [[1, 3], [2, 4]] + ``` + """ return transpose(self) fn variance[ returned_dtype: DType = DType.float64 ](self, ddof: Int = 0) raises -> Scalar[returned_dtype]: """ - Compute the variance. + Compute the variance of all elements in the matrix. Args: - ddof: Delta degree of freedom. + ddof: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N is the number of elements. + + Returns: + Scalar[returned_dtype]: The variance of the matrix. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.variance()) + ``` """ return numojo.statistics.variance[returned_dtype](self, ddof=ddof) @@ -1253,35 +3314,75 @@ struct Matrix[dtype: DType = DType.float64]( returned_dtype: DType = DType.float64 ](self, axis: Int, ddof: Int = 0) raises -> Matrix[returned_dtype]: """ - Compute the variance along axis. + Compute the variance along the specified axis. Args: - axis: 0 or 1. - ddof: Delta degree of freedom. + axis: Axis along which to compute the variance (0 for rows, 1 for columns). + ddof: Delta degrees of freedom. The divisor used in calculations is N - ddof, where N is the number of elements along the axis. + + Returns: + Matrix[returned_dtype]: The variance along the given axis. + + Example: + ```mojo + from numojo import Matrix + var A = Matrix.rand(shape=(100, 100)) + print(A.variance(axis=0)) + print(A.variance(axis=1)) + ``` """ return numojo.statistics.variance[returned_dtype]( self, axis=axis, ddof=ddof ) - # ===-------------------------------------------------------------------===# - # To other data types - # ===-------------------------------------------------------------------===# + # # ===-------------------------------------------------------------------===# + # # To other data types + # # ===-------------------------------------------------------------------===# fn to_ndarray(self) raises -> NDArray[dtype]: """Create `NDArray` from `Matrix`. - It makes a copy of the buffer of the matrix. + Returns a new NDArray with the same shape and data as the Matrix. + The buffer is copied, so changes to the NDArray do not affect the original Matrix. + + Returns: + NDArray[dtype]: A new NDArray containing the same data as the Matrix. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.rand((3, 3)) + var ndarray_A = A.to_ndarray() + print(ndarray_A) + ``` """ var ndarray: NDArray[dtype] = NDArray[dtype]( - shape=List[Int](self.shape[0], self.shape[1]), order="C" + shape=List[Int](self.shape[0], self.shape[1]), order=self.order() ) - memcpy(ndarray._buf.ptr, self._buf.ptr, ndarray.size) + memcpy(dest=ndarray._buf.ptr, src=self._buf.ptr, count=ndarray.size) return ndarray^ - fn to_numpy(self) raises -> PythonObject: - """See `numojo.core.utility.to_numpy`.""" + fn to_numpy(self) raises -> PythonObject where own_data == True: + """ + Convert the Matrix to a NumPy ndarray. + + Returns: + PythonObject: A NumPy ndarray containing the same data as the Matrix. + + Notes: + - The returned NumPy array is a copy of the Matrix data. + - The dtype and memory order are matched as closely as possible. + + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.rand((3, 3)) + var np_A = A.to_numpy() + print(np_A) + ``` + """ try: var np = Python.import_module("numpy") @@ -1316,7 +3417,7 @@ struct Matrix[dtype: DType = DType.float64]( np_dtype = np.uint8 elif dtype == DType.bool: np_dtype = np.bool_ - elif dtype == DType.index: + elif dtype == DType.int: np_dtype = np.int64 var order = "C" if self.flags.C_CONTIGUOUS else "F" @@ -1324,7 +3425,7 @@ struct Matrix[dtype: DType = DType.float64]( var pointer_d = numpyarray.__array_interface__["data"][ 0 ].unsafe_get_as_pointer[dtype]() - memcpy(pointer_d, self._buf.ptr, self.size) + memcpy(dest=pointer_d, src=self._buf.get_ptr(), count=self.size) return numpyarray^ @@ -1338,72 +3439,106 @@ struct Matrix[dtype: DType = DType.float64]( @staticmethod fn full[ - dtype: DType = DType.float64 + datatype: DType = DType.float64 ]( shape: Tuple[Int, Int], - fill_value: Scalar[dtype] = 0, + fill_value: Scalar[datatype] = 0, order: String = "C", - ) -> Matrix[dtype]: - """Return a matrix with given shape and filled value. + ) -> Matrix[datatype]: + """ + Create a matrix of the specified shape, filled with the given value. + + Args: + shape: Tuple specifying the matrix dimensions (rows, columns). + fill_value: Value to fill every element of the matrix. + order: Memory layout order, "C" (row-major) or "F" (column-major). + + Returns: + Matrix[datatype]: Matrix filled with `fill_value`. Example: - ```mojo - from numojo import Matrix - var A = Matrix.full(shape=(10, 10), fill_value=100) - ``` + ```mojo + from numojo.prelude import * + var A = Matrix.full[f32](shape=(10, 10), fill_value=100) + ``` """ - var matrix = Matrix[dtype](shape, order) + var matrix = Matrix[datatype](shape, order) for i in range(shape[0] * shape[1]): - matrix._buf.ptr.store(i, fill_value) + matrix._buf.store[width=1](i, fill_value) return matrix^ @staticmethod fn zeros[ - dtype: DType = DType.float64 - ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[dtype]: - """Return a matrix with given shape and filled with zeros. + datatype: DType = DType.float64 + ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[datatype]: + """ + Create a matrix of the specified shape, filled with zeros. + + Args: + shape: Tuple specifying the matrix dimensions (rows, columns). + order: Memory layout order, "C" (row-major) or "F" (column-major). + + Returns: + Matrix[datatype]: Matrix filled with zeros. Example: - ```mojo - from numojo import Matrix - var A = Matrix.ones(shape=(10, 10)) - ``` + ```mojo + from numojo.prelude import * + var A = Matrix.zeros[i32](shape=(10, 10)) + ``` """ - var M = Matrix[dtype](shape, order) - memset_zero(M._buf.ptr, M.size) - return M^ + var res = Matrix[datatype](shape, order) + memset_zero(res._buf.ptr, res.size) + return res^ @staticmethod fn ones[ - dtype: DType = DType.float64 - ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[dtype]: - """Return a matrix with given shape and filled with ones. + datatype: DType = DType.float64 + ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[datatype]: + """ + Create a matrix of the specified shape, filled with ones. + + Args: + shape: Tuple specifying the matrix dimensions (rows, columns). + order: Memory layout order, "C" (row-major) or "F" (column-major). + + Returns: + Matrix[datatype]: Matrix filled with ones. Example: - ```mojo - from numojo import Matrix - var A = Matrix.ones(shape=(10, 10)) - ``` + ```mojo + from numojo.prelude import * + var A = Matrix.ones[f64](shape=(10, 10)) + ``` """ - return Matrix.full[dtype](shape=shape, fill_value=1) + return Matrix.full[datatype](shape=shape, fill_value=1) @staticmethod fn identity[ - dtype: DType = DType.float64 - ](len: Int, order: String = "C") -> Matrix[dtype]: - """Return an identity matrix with given size. + datatype: DType = DType.float64 + ](len: Int, order: String = "C") -> Matrix[datatype]: + """ + Create an identity matrix of the given size. + + Args: + len: Size of the identity matrix (number of rows and columns). + order: Memory layout order, "C" (row-major) or "F" (column-major). + + Returns: + Matrix[datatype]: Identity matrix of shape (len, len). Example: - ```mojo - from numojo import Matrix - var A = Matrix.identity(12) - ``` + ```mojo + from numojo.prelude import * + var A = Matrix.identity[f16](12) + print(A) + ``` """ - var matrix = Matrix.zeros[dtype]((len, len), order) + var matrix = Matrix.zeros[datatype]((len, len), order) for i in range(len): matrix._buf.ptr.store( i * matrix.strides[0] + i * matrix.strides[1], 1 @@ -1412,51 +3547,59 @@ struct Matrix[dtype: DType = DType.float64]( @staticmethod fn rand[ - dtype: DType = DType.float64 - ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[dtype]: - """Return a matrix with random values uniformed distributed between 0 and 1. + datatype: DType = DType.float64 + ](shape: Tuple[Int, Int], order: String = "C") -> Matrix[datatype]: + """ + Create a matrix of the specified shape, filled with random values uniformly distributed between 0 and 1. - Example: - ```mojo - from numojo import Matrix - var A = Matrix.rand((12, 12)) - ``` + Args: + shape: Tuple specifying the matrix dimensions (rows, columns). + order: Memory layout order, "C" (row-major) or "F" (column-major). - Parameters: - dtype: The data type of the NDArray elements. + Returns: + Matrix[datatype]: Matrix filled with random values. - Args: - shape: The shape of the Matrix. - order: The order of the Matrix. "C" or "F". + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.rand[f64]((12, 12)) + ``` """ - var result = Matrix[dtype](shape, order) + var result = Matrix[datatype](shape, order) for i in range(result.size): - result._buf.ptr.store(i, random_float64(0, 1).cast[dtype]()) + result._buf.ptr.store(i, random_float64(0, 1).cast[datatype]()) return result^ @staticmethod fn fromlist[ - dtype: DType + datatype: DType = DType.float64 ]( - object: List[Scalar[dtype]], + object: List[Scalar[datatype]], shape: Tuple[Int, Int] = (0, 0), order: String = "C", - ) raises -> Matrix[dtype]: - """Create a matrix from a 1-dimensional list into given shape. + ) raises -> Matrix[datatype]: + """ + Create a matrix from a 1-dimensional list and reshape to the given shape. + + Args: + object: List of values to populate the matrix. + shape: Tuple specifying the matrix dimensions (rows, columns). If not provided, creates a row vector. + order: Memory layout order, "C" (row-major) or "F" (column-major). - If no shape is passed, the return matrix will be a row vector. + Returns: + Matrix[datatype]: Matrix containing the values from the list. Example: - ```mojo - from numojo import Matrix - fn main() raises: - print(Matrix.fromlist(List[Float64](1, 2, 3, 4, 5), (5, 1))) - ``` + ```mojo + from numojo.prelude import * + var a = Matrix.fromlist(List[Float64](1, 2, 3, 4, 5), (5, 1)) + print(a) + ``` """ if (shape[0] == 0) and (shape[1] == 0): - var M = Matrix[dtype](shape=(1, len(object))) - memcpy(M._buf.ptr, object.unsafe_ptr(), M.size) + var M = Matrix[datatype](shape=(1, len(object))) + memcpy(dest=M._buf.ptr, src=object.unsafe_ptr(), count=M.size) return M^ if shape[0] * shape[1] != len(object): @@ -1464,48 +3607,49 @@ struct Matrix[dtype: DType = DType.float64]( "The input has {} elements, but the target has the shape {}x{}" ).format(len(object), shape[0], shape[1]) raise Error(message) - var M = Matrix[dtype](shape=shape, order="C") - memcpy(M._buf.ptr, object.unsafe_ptr(), M.size) + var M = Matrix[datatype](shape=shape, order="C") + memcpy(dest=M._buf.ptr, src=object.unsafe_ptr(), count=M.size) if order == "F": M = M.reorder_layout() return M^ @staticmethod fn fromstring[ - dtype: DType = DType.float64 + datatype: DType = DType.float64 ]( text: String, shape: Tuple[Int, Int] = (0, 0), order: String = "C" - ) raises -> Matrix[dtype]: - """Matrix initialization from string representation of an matrix. - - Comma, right brackets, and whitespace are treated as seperators of numbers. - Digits, underscores, and minus signs are treated as a part of the numbers. - - If now shape is passed, the return matrix will be a row vector. + ) raises -> Matrix[datatype]: + """ + Create a Matrix from a string representation of its elements. - Example: - ```mojo - from numojo.prelude import * - from numojo import Matrix - fn main() raises: - var A = Matrix.fromstring[f32]( - "1 2 .3 4 5 6.5 7 1_323.12 9 10, 11.12, 12 13 14 15 16", (4, 4)) - ``` - ```console - [[1.0 2.0 0.30000001192092896 4.0] - [5.0 6.5 7.0 1323.1199951171875] - [9.0 10.0 11.119999885559082 12.0] - [13.0 14.0 15.0 16.0]] - Size: 4x4 DType: float32 - ``` + The input string should contain numbers separated by commas, right brackets, or whitespace. Digits, underscores, decimal points, and minus signs are treated as part of numbers. If no shape is provided, the returned matrix will be a row vector. Args: - text: String representation of a matrix. - shape: Shape of the matrix. - order: Order of the matrix. "C" or "F". - """ + text: String containing the matrix elements. + shape: Tuple specifying the matrix dimensions (rows, columns). If not provided, creates a row vector. + order: Memory layout order, "C" (row-major) or "F" (column-major). + + Returns: + Matrix[datatype]: Matrix constructed from the string data. - var data = List[Scalar[dtype]]() + Example: + ```mojo + from numojo.prelude import * + var A = Matrix.fromstring[f32]("1 2 .3 4 5 6.5 7 1_323.12 9 10, 11.12, 12 13 14 15 16", (4, 4)) + print(A) + ``` + + Output: + ``` + [[1.0 2.0 0.30000001192092896 4.0] + [5.0 6.5 7.0 1323.1199951171875] + [9.0 10.0 11.119999885559082 12.0] + [13.0 14.0 15.0 16.0]] + Size: 4x4 datatype: float32 + ``` + """ + + var data = List[Scalar[datatype]]() var bytes = text.as_bytes() var number_as_str: String = "" var size = shape[0] * shape[1] @@ -1519,7 +3663,7 @@ struct Matrix[dtype: DType = DType.float64]( ): number_as_str = number_as_str + chr(Int(b)) if i == len(bytes) - 1: # Last byte - var number = atof(number_as_str).cast[dtype]() + var number = atof(number_as_str).cast[datatype]() data.append(number) # Add the number to the data buffer number_as_str = "" # Clean the number cache if ( @@ -1528,7 +3672,7 @@ struct Matrix[dtype: DType = DType.float64]( or (chr(Int(b)) == " ") ): if number_as_str != "": - var number = atof(number_as_str).cast[dtype]() + var number = atof(number_as_str).cast[datatype]() data.append(number) # Add the number to the data buffer number_as_str = "" # Clean the number cache @@ -1542,91 +3686,153 @@ struct Matrix[dtype: DType = DType.float64]( ).format(len(data), shape[0], shape[1]) raise Error(message) - var result = Matrix[dtype](shape=shape) + var result = Matrix[datatype](shape=shape) for i in range(len(data)): result._buf.ptr[i] = data[i] return result^ -# ===-----------------------------------------------------------------------===# -# MatrixIter struct -# ===-----------------------------------------------------------------------===# +# # ===-----------------------------------------------------------------------===# +# # MatrixIter struct +# # ===-----------------------------------------------------------------------===# -# ! Should the iterator be mutable or not? struct _MatrixIter[ is_mutable: Bool, //, - lifetime: Origin[is_mutable], dtype: DType, + matrix_origin: MutOrigin, + iterator_origin: Origin[is_mutable], forward: Bool = True, -](Copyable, Movable): - """Iterator for Matrix. +](ImplicitlyCopyable, Movable): + """ + Iterator for Matrix that yields row views. + + This struct provides iteration over the rows of a Matrix, returning a MatrixView for each row. It supports both forward and backward iteration. Parameters: - is_mutable: Whether the iterator is mutable. - lifetime: The lifetime of the underlying Matrix data. - dtype: The data type of the item. - forward: The iteration direction. `False` is backwards. + is_mutable: Whether the iterator allows mutable access to the matrix. + dtype: The data type of the matrix elements. + matrix_origin: The origin of the underlying Matrix data. + iterator_origin: The origin of the iterator itself. + forward: The iteration direction. If True, iterates forward; if False, iterates backward. """ + comptime Element = MatrixView[dtype, Self.matrix_origin] + """The type of elements yielded by the iterator (MatrixView). """ + var index: Int - var matrix: Matrix[dtype] - var length: Int + """Current index in the iteration.""" + + var matrix_ptr: Pointer[ + MatrixImpl[dtype, own_data=True, origin = Self.matrix_origin], + Self.iterator_origin, + ] + """Pointer to the source Matrix being iterated over.""" fn __init__( out self, - matrix: Matrix[dtype], - length: Int, + index: Int, + src: Pointer[ + MatrixImpl[dtype, own_data=True, origin = Self.matrix_origin], + Self.iterator_origin, + ], ): - self.index = 0 if forward else length - self.length = length - self.matrix = matrix.copy() + """Initialize the iterator. + + Args: + index: The starting index for iteration. + src: Pointer to the source Matrix. + """ + self.index = index + self.matrix_ptr = src - fn __iter__(self) -> Self: + @always_inline + fn __iter__(ref self) -> Self: + """Return a copy of the iterator for iteration protocol.""" return self.copy() - fn __next__(mut self) raises -> Matrix[dtype]: + @always_inline + fn __has_next__(self) -> Bool: + """Check if there are more rows to iterate over. + + Returns: + Bool: True if there are more rows to iterate, False otherwise. + """ + + @parameter + if Self.forward: + return self.index < self.matrix_ptr[].shape[0] + else: + return self.index > 0 + + fn __next__( + mut self, + ) raises -> MatrixView[dtype, MutOrigin.cast_from[Self.iterator_origin]]: + """Return a view of the next row. + + Returns: + MatrixView: A view representing the next row in the iteration. + """ + @parameter - if forward: + if Self.forward: var current_index = self.index self.index += 1 - return self.matrix[current_index] + return self.matrix_ptr[].get(current_index) else: - var current_index = self.index + var current_idx = self.index self.index -= 1 - return self.matrix[current_index] + return self.matrix_ptr[].get(current_idx) @always_inline - fn __has_next__(self) -> Bool: - @parameter - if forward: - return self.index < self.length - else: - return self.index > 0 + fn bounds(self) -> Tuple[Int, Optional[Int]]: + """Return the iteration bounds. + + Returns: + Tuple[Int, Optional[Int]]: Number of remaining rows and an optional value of the same. + """ + var remaining_rows: Int - fn __len__(self) -> Int: @parameter - if forward: - return self.length - self.index + if Self.forward: + remaining_rows = self.matrix_ptr[].shape[0] - self.index else: - return self.index + remaining_rows = self.index + + return (remaining_rows, {remaining_rows}) -# ===-----------------------------------------------------------------------===# -# Backend fucntions using SMID functions -# ===-----------------------------------------------------------------------===# +# # ===-----------------------------------------------------------------------===# +# # Backend fucntions using SMID functions +# # ===-----------------------------------------------------------------------===# +# TODO: we can move the checks in these functions to the caller functions to avoid redundant checks. fn _arithmetic_func_matrix_matrix_to_matrix[ dtype: DType, simd_func: fn[type: DType, simd_width: Int] ( SIMD[type, simd_width], SIMD[type, simd_width] ) -> SIMD[type, simd_width], -](A: Matrix[dtype], B: Matrix[dtype]) raises -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], B: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ - Matrix[dtype] & Matrix[dtype] -> Matrix[dtype] + Perform element-wise arithmetic operation between two matrices using a SIMD function. + + Parameters: + dtype: The data type of the matrix elements. + simd_func: A SIMD function that takes two SIMD vectors and returns a SIMD vector, representing the desired arithmetic operation (e.g., addition, subtraction). - For example: `__add__`, `__sub__`, etc. + Args: + A: The first input matrix. + B: The second input matrix. + + Returns: + Matrix[dtype]: A new matrix containing the result of applying the SIMD function element-wise to A and B. + + Raises: + Error: If the matrix orders or shapes do not match. + + Notes: + - Only for internal purposes. """ alias simd_width = simd_width_of[dtype]() if A.order() != B.order(): @@ -1643,11 +3849,11 @@ fn _arithmetic_func_matrix_matrix_to_matrix[ ) ) - var C = Matrix[dtype](shape=A.shape, order=A.order()) + var res = Matrix[dtype](shape=A.shape, order=A.order()) @parameter fn vec_func[simd_width: Int](i: Int): - C._buf.ptr.store( + res._buf.ptr.store( i, simd_func( A._buf.ptr.load[width=simd_width](i), @@ -1656,8 +3862,7 @@ fn _arithmetic_func_matrix_matrix_to_matrix[ ) vectorize[vec_func, simd_width](A.size) - - return C^ + return res^ fn _arithmetic_func_matrix_to_matrix[ @@ -1667,9 +3872,20 @@ fn _arithmetic_func_matrix_to_matrix[ ) -> SIMD[type, simd_width], ](A: Matrix[dtype]) -> Matrix[dtype]: """ - Matrix[dtype] -> Matrix[dtype] + Apply a unary SIMD function element-wise to a matrix. + + Parameters: + dtype: The data type of the matrix elements. + simd_func: A SIMD function that takes a SIMD vector and returns a SIMD vector representing + + Args: + A: Input matrix of type Matrix[dtype]. - For example: `sin`, `cos`, etc. + Returns: + Matrix[dtype]: A new matrix containing the result of applying the SIMD function to each element of the input matrix. + + Notes: + - Only for internal purposes. """ alias simd_width: Int = simd_width_of[dtype]() @@ -1689,9 +3905,29 @@ fn _logic_func_matrix_matrix_to_matrix[ simd_func: fn[type: DType, simd_width: Int] ( SIMD[type, simd_width], SIMD[type, simd_width] ) -> SIMD[DType.bool, simd_width], -](A: Matrix[dtype], B: Matrix[dtype]) raises -> Matrix[DType.bool]: +](A: MatrixImpl[dtype, **_], B: MatrixImpl[dtype, **_]) raises -> Matrix[ + DType.bool +]: """ - Matrix[dtype] & Matrix[dtype] -> Matrix[bool] + Perform element-wise logical comparison between two matrices using a SIMD function. + + Parameters: + dtype: The data type of the input matrices. + simd_func: A SIMD function that takes two SIMD vectors of dtype and returns a SIMD vector of bools. + + Args: + A: The first input matrix. + B: The second input matrix. + + Returns: + Matrix[DType.bool]: A new matrix of bools containing the result of the element-wise logical comparison. + + Raises: + Error: If the matrix orders or shapes do not match. + + Notes: + - Only for internal purposes. + - The output matrix has the same shape and order as the input matrices. """ alias width = simd_width_of[dtype]() @@ -1713,25 +3949,32 @@ fn _logic_func_matrix_matrix_to_matrix[ var t1 = A.shape[1] var C = Matrix[DType.bool](shape=A.shape, order=A.order()) - @parameter - fn calculate_CC(m: Int): - @parameter - fn vec_func[simd_width: Int](n: Int): - C._store[simd_width]( - m, - n, - simd_func(A._load[simd_width](m, n), B._load[simd_width](m, n)), - ) - - vectorize[vec_func, width](t1) - - parallelize[calculate_CC](t0, t0) + # FIXME: Since the width is calculated for dtype (which could be some int or float type), the same width doesn't apply to DType.bool. Hence the following parallelization/vectorization code doesn't work as expected with misaligned widths. Need to figure out a better way to handle this. Till then, use a simple nested for loop. + # @parameter + # fn calculate_CC(m: Int): + # @parameter + # fn vec_func[simd_width: Int](n: Int): + # C._store[simd_width]( + # m, + # n, + # simd_func(A._load[simd_width](m, n), B._load[simd_width](m, n)), + # ) + + # vectorize[vec_func, width](t1) + + # parallelize[calculate_CC](t0, t0) + if A.flags.C_CONTIGUOUS: + for i in range(t0): + for j in range(t1): + C._store[1](i, j, simd_func(A._load[1](i, j), B._load[1](i, j))) + else: + for j in range(t1): + for i in range(t0): + C._store[1](i, j, simd_func(A._load[1](i, j), B._load[1](i, j))) var _t0 = t0 var _t1 = t1 - var _A = ( - A.copy() - ) # ! perhaps remove this explicit copy if we don't need to extend it's lifetime. + var _A = A.copy() var _B = B.copy() return C^ diff --git a/numojo/core/ndarray.mojo b/numojo/core/ndarray.mojo index 696b259a..0a872411 100644 --- a/numojo/core/ndarray.mojo +++ b/numojo/core/ndarray.mojo @@ -39,7 +39,7 @@ # TODO: Return views that points to the buffer of the raw array. # This requires enhancement of functionalities of traits from Mojo's side. # The data buffer can implement an ArrayData trait (RawData or RefData) -# RawData type is just a wrapper of `UnsafePointer`. +# RawData type is just a wrapper of `LegacyUnsafePointer`. # RefData type has an extra property `indices`: getitem(i) -> A[I[i]]. # TODO: Rename some variables or methods that should not be exposed to users. # TODO: Special checks for 0d array (numojo scalar). @@ -54,7 +54,7 @@ import builtin.math as builtin_math from builtin.type_aliases import Origin from collections.optional import Optional from math import log10 -from memory import UnsafePointer, memset_zero, memcpy +from memory import LegacyUnsafePointer, memset_zero, memcpy from python import PythonObject from sys import simd_width_of from utils import Variant @@ -277,7 +277,7 @@ struct NDArray[dtype: DType = DType.float64]( fn __init__( out self, shape: NDArrayShape, - ref buffer: UnsafePointer[Scalar[dtype]], + ref buffer: LegacyUnsafePointer[Scalar[dtype]], offset: Int, strides: NDArrayStrides, ) raises: @@ -315,7 +315,7 @@ struct NDArray[dtype: DType = DType.float64]( self.size = other.size self.strides = other.strides self._buf = DataContainer[dtype](self.size) - memcpy(self._buf.ptr, other._buf.ptr, other.size) + memcpy(dest=self._buf.ptr, src=other._buf.ptr, count=other.size) self.flags = Flags( c_contiguous=other.flags.C_CONTIGUOUS, f_contiguous=other.flags.F_CONTIGUOUS, @@ -371,7 +371,7 @@ struct NDArray[dtype: DType = DType.float64]( # fn __getitem__(self, *slices: Variant[Slice, Int]) raises -> Self # Get by mix of slices/ints # # 4. Advanced Indexing - # fn __getitem__(self, indices: NDArray[DType.index]) raises -> Self # Get by index array + # fn __getitem__(self, indices: NDArray[DType.int]) raises -> Self # Get by index array # fn __getitem__(self, indices: List[Int]) raises -> Self # Get by list of indices # fn __getitem__(self, mask: NDArray[DType.bool]) raises -> Self # Get by boolean mask # fn __getitem__(self, mask: List[Bool]) raises -> Self # Get by boolean list @@ -611,18 +611,22 @@ struct NDArray[dtype: DType = DType.float64]( # Fast path for C-contiguous arrays if self.flags.C_CONTIGUOUS: var block = self.size // self.shape[0] - memcpy(result._buf.ptr, self._buf.ptr + norm * block, block) + memcpy( + dest=result._buf.ptr, + src=self._buf.ptr + norm * block, + count=block, + ) return result^ # (F-order or arbitrary stride layout) # TODO: Optimize this further (multi-axis unrolling / smarter linear index without div/mod) - self._copy_first_axis_slice[dtype](self, norm, result) + self._copy_first_axis_slice(self, norm, result) return result^ # perhaps move these to a utility module - fn _copy_first_axis_slice[ - dtype: DType - ](self, src: NDArray[dtype], norm_idx: Int, mut dst: NDArray[dtype]): + fn _copy_first_axis_slice( + self, src: NDArray[dtype], norm_idx: Int, mut dst: NDArray[dtype] + ): """Generic stride-based copier for first-axis slice (works for any layout). """ var out_ndim = dst.ndim @@ -1177,7 +1181,7 @@ struct NDArray[dtype: DType = DType.float64]( narr = self.__getitem__(slice_list^) return narr^ - fn __getitem__(self, indices: NDArray[DType.index]) raises -> Self: + fn __getitem__(self, indices: NDArray[DType.int]) raises -> Self: """ Get items from 0-th dimension of an ndarray of indices. If the original array is of shape (i,j,k) and @@ -1243,14 +1247,14 @@ struct NDArray[dtype: DType = DType.float64]( " ({})." ).format(self.shape[0]), location=String( - "NDArray.__getitem__(indices: NDArray[DType.index])" + "NDArray.__getitem__(indices: NDArray[DType.int])" ), ) ) memcpy( - result._buf.ptr + i * size_per_item, - self._buf.ptr + indices.item(i) * size_per_item, - size_per_item, + dest=result._buf.ptr + i * size_per_item, + src=self._buf.ptr + indices.item(i) * size_per_item, + count=size_per_item, ) return result^ @@ -1259,7 +1263,7 @@ struct NDArray[dtype: DType = DType.float64]( # TODO: Use trait IntLike when it is supported by Mojo. """ Get items from 0-th dimension of an array. It is an overload of - `__getitem__(self, indices: NDArray[DType.index]) raises -> Self`. + `__getitem__(self, indices: NDArray[DType.int]) raises -> Self`. Args: indices: A list of Int. @@ -1299,7 +1303,7 @@ struct NDArray[dtype: DType = DType.float64]( ```. """ - var indices_array = NDArray[DType.index](shape=Shape(len(indices))) + var indices_array = NDArray[DType.int](shape=Shape(len(indices))) for i in range(len(indices)): (indices_array._buf.ptr + i).init_pointee_copy(indices[i]) @@ -1393,9 +1397,9 @@ struct NDArray[dtype: DType = DType.float64]( for i in range(mask.size): if mask.item(i): memcpy( - result._buf.ptr + offset * size_per_item, - self._buf.ptr + i * size_per_item, - size_per_item, + dest=result._buf.ptr + offset * size_per_item, + src=self._buf.ptr + i * size_per_item, + count=size_per_item, ) offset += 1 @@ -1806,7 +1810,7 @@ struct NDArray[dtype: DType = DType.float64]( # fn __setitem__(mut self, *slices: Variant[Slice, Int], val: Self) raises # Set by mix of slices/ints # Index-based Setters - # fn __setitem__(self, indices: NDArray[DType.index], val: NDArray) raises # Set by index array + # fn __setitem__(self, indices: NDArray[DType.int], val: NDArray) raises # Set by index array # fn __setitem__(mut self, mask: NDArray[DType.bool], val: NDArray[dtype]) # Set by boolean mask array # Helper Methods @@ -1943,16 +1947,18 @@ struct NDArray[dtype: DType = DType.float64]( # Fast path for C-contiguous arrays (single block) if self.flags.C_CONTIGUOUS and val.flags.C_CONTIGUOUS: var block = self.size // self.shape[0] - memcpy(self._buf.ptr + norm * block, val._buf.ptr, block) + memcpy( + dest=self._buf.ptr + norm * block, src=val._buf.ptr, count=block + ) return # Generic stride path (F-order or irregular) - self._write_first_axis_slice[dtype](self, norm, val) + self._write_first_axis_slice(self, norm, val) # perhaps move these to a utility module - fn _write_first_axis_slice[ - dtype: DType - ](self, dst: NDArray[dtype], norm_idx: Int, src: NDArray[dtype]): + fn _write_first_axis_slice( + self, dst: NDArray[dtype], norm_idx: Int, src: NDArray[dtype] + ): var out_ndim = src.ndim var total = src.size if total == 0: @@ -2317,7 +2323,7 @@ struct NDArray[dtype: DType = DType.float64]( # TODO: fix this setter, add bound checks. Not sure about it's use case. fn __setitem__( - mut self, index: NDArray[DType.index], val: NDArray[dtype] + mut self, index: NDArray[DType.int], val: NDArray[dtype] ) raises: """ Returns the items of the array from an array of indices. @@ -2352,7 +2358,7 @@ struct NDArray[dtype: DType = DType.float64]( " each axis separately." ), location=String( - "NDArray.__setitem__(index: NDArray[DType.index], val:" + "NDArray.__setitem__(index: NDArray[DType.int], val:" " NDArray)" ), ) @@ -2370,7 +2376,7 @@ struct NDArray[dtype: DType = DType.float64]( " first dimension ({})." ).format(self.shape[0]), location=String( - "NDArray.__setitem__(index: NDArray[DType.index], val:" + "NDArray.__setitem__(index: NDArray[DType.int], val:" " NDArray)" ), ) @@ -2397,7 +2403,7 @@ struct NDArray[dtype: DType = DType.float64]( " ({})." ).format(self.shape[0]), location=String( - "NDArray.__setitem__(index: NDArray[DType.index]," + "NDArray.__setitem__(index: NDArray[DType.int]," " val: NDArray)" ), ) @@ -3819,7 +3825,7 @@ struct NDArray[dtype: DType = DType.float64]( fn __iter__( self, - ) raises -> _NDArrayIter[__origin_of(self), dtype]: + ) raises -> _NDArrayIter[origin_of(self), dtype]: """ Iterates over elements of the NDArray and return sub-arrays as view. @@ -3843,14 +3849,14 @@ struct NDArray[dtype: DType = DType.float64]( ```. """ - return _NDArrayIter[__origin_of(self), dtype]( + return _NDArrayIter[origin_of(self), dtype]( self, dimension=0, ) fn __reversed__( self, - ) raises -> _NDArrayIter[__origin_of(self), dtype, forward=False]: + ) raises -> _NDArrayIter[origin_of(self), dtype, forward=False]: """ Iterates backwards over elements of the NDArray, returning copied value. @@ -3859,7 +3865,7 @@ struct NDArray[dtype: DType = DType.float64]( A reversed iterator of NDArray elements. """ - return _NDArrayIter[__origin_of(self), dtype, forward=False]( + return _NDArrayIter[origin_of(self), dtype, forward=False]( self, dimension=0, ) @@ -4177,33 +4183,33 @@ struct NDArray[dtype: DType = DType.float64]( vectorize[vectorized_any, self.width](self.size) return result - fn argmax(self) raises -> Scalar[DType.index]: + fn argmax(self) raises -> Scalar[DType.int]: """Returns the indices of the maximum values along an axis. When no axis is specified, the array is flattened. See `numojo.argmax()` for more details. """ return searching.argmax(self) - fn argmax(self, axis: Int) raises -> NDArray[DType.index]: + fn argmax(self, axis: Int) raises -> NDArray[DType.int]: """Returns the indices of the maximum values along an axis. See `numojo.argmax()` for more details. """ return searching.argmax(self, axis=axis) - fn argmin(self) raises -> Scalar[DType.index]: + fn argmin(self) raises -> Scalar[DType.int]: """Returns the indices of the minimum values along an axis. When no axis is specified, the array is flattened. See `numojo.argmin()` for more details. """ return searching.argmin(self) - fn argmin(self, axis: Int) raises -> NDArray[DType.index]: + fn argmin(self, axis: Int) raises -> NDArray[DType.int]: """Returns the indices of the minimum values along an axis. See `numojo.argmin()` for more details. """ return searching.argmin(self, axis=axis) - fn argsort(mut self) raises -> NDArray[DType.index]: + fn argsort(mut self) raises -> NDArray[DType.int]: """ Sort the NDArray and return the sorted indices. See `numojo.argsort()` for more details. @@ -4214,7 +4220,7 @@ struct NDArray[dtype: DType = DType.float64]( return numojo.sorting.argsort(self) - fn argsort(mut self, axis: Int) raises -> NDArray[DType.index]: + fn argsort(mut self, axis: Int) raises -> NDArray[DType.int]: """ Sort the NDArray and return the sorted indices. See `numojo.argsort()` for more details. @@ -4253,17 +4259,12 @@ struct NDArray[dtype: DType = DType.float64]( return numojo.clip(self, a_min, a_max) - fn compress[ - dtype: DType - ](self, condition: NDArray[DType.bool], axis: Int) raises -> Self: + fn compress(self, condition: NDArray[DType.bool], axis: Int) raises -> Self: # TODO: @forFudan try using parallelization for this function """ Return selected slices of an array along given axis. If no axis is provided, the array is flattened before use. - Parameters: - dtype: DType. - Args: condition: 1-D array of booleans that selects which entries to return. If length of condition is less than the size of the array along the @@ -4283,17 +4284,12 @@ struct NDArray[dtype: DType = DType.float64]( return numojo.compress(condition=condition, a=self, axis=axis) - fn compress[ - dtype: DType - ](self, condition: NDArray[DType.bool]) raises -> Self: + fn compress(self, condition: NDArray[DType.bool]) raises -> Self: """ Return selected slices of an array along given axis. If no axis is provided, the array is flattened before use. This is a function ***OVERLOAD***. - Parameters: - dtype: DType. - Args: condition: 1-D array of booleans that selects which entries to return. If length of condition is less than the size of the array along the @@ -4392,23 +4388,20 @@ struct NDArray[dtype: DType = DType.float64]( """ return numojo.math.cumsum[dtype](self.copy(), axis=axis) - fn diagonal[dtype: DType](self, offset: Int = 0) raises -> Self: + fn diagonal(self, offset: Int = 0) raises -> Self: """ Returns specific diagonals. Currently supports only 2D arrays. - Raises: - Error: If the array is not 2D. - Error: If the offset is beyond the shape of the array. - - Parameters: - dtype: Data type of the array. - Args: offset: Offset of the diagonal from the main diagonal. Returns: The diagonal of the NDArray. + + Raises: + Error: If the array is not 2D. + Error: If the offset is beyond the shape of the array. """ return numojo.linalg.diagonal(self, offset=offset) @@ -4438,7 +4431,7 @@ struct NDArray[dtype: DType = DType.float64]( fn iter_along_axis[ forward: Bool = True ](self, axis: Int, order: String = "C") raises -> _NDAxisIter[ - __origin_of(self), dtype, forward + origin_of(self), dtype, forward ]: """ Returns an iterator yielding 1-d array slices along the given axis. @@ -4531,7 +4524,7 @@ struct NDArray[dtype: DType = DType.float64]( ).format(axis, -self.ndim, self.ndim) ) - return _NDAxisIter[__origin_of(self), dtype, forward]( + return _NDAxisIter[origin_of(self), dtype, forward]( self, axis=normalized_axis, order=order, @@ -4540,7 +4533,7 @@ struct NDArray[dtype: DType = DType.float64]( fn iter_over_dimension[ forward: Bool = True ](read self, dimension: Int) raises -> _NDArrayIter[ - __origin_of(self), dtype, forward + origin_of(self), dtype, forward ]: """ Returns an iterator yielding `ndim-1` arrays over the given dimension. @@ -4570,7 +4563,7 @@ struct NDArray[dtype: DType = DType.float64]( ).format(dimension, -self.ndim, self.ndim) ) - return _NDArrayIter[__origin_of(self), dtype, forward]( + return _NDArrayIter[origin_of(self), dtype, forward]( a=self, dimension=normalized_dim, ) @@ -4728,7 +4721,7 @@ struct NDArray[dtype: DType = DType.float64]( return numojo.math.min(self, axis=axis) - fn nditer(self) raises -> _NDIter[__origin_of(self), dtype]: + fn nditer(self) raises -> _NDIter[origin_of(self), dtype]: """ ***Overload*** Return an iterator yielding the array elements according to the memory layout of the array. @@ -4759,7 +4752,7 @@ struct NDArray[dtype: DType = DType.float64]( return self.nditer(order=order) - fn nditer(self, order: String) raises -> _NDIter[__origin_of(self), dtype]: + fn nditer(self, order: String) raises -> _NDIter[origin_of(self), dtype]: """ Return an iterator yielding the array elements according to the order. @@ -4798,7 +4791,7 @@ struct NDArray[dtype: DType = DType.float64]( else: axis = 0 - return _NDIter[__origin_of(self), dtype](a=self, order=order, axis=axis) + return _NDIter[origin_of(self), dtype](a=self, order=order, axis=axis) fn num_elements(self) -> Int: """ @@ -4908,7 +4901,7 @@ struct NDArray[dtype: DType = DType.float64]( if shape.size_of_array() > self.size: var other = Self(shape=shape, order=order) - memcpy(other._buf.ptr, self._buf.ptr, self.size) + memcpy(dest=other._buf.ptr, src=self._buf.ptr, count=self.size) for i in range(self.size, other.size): (other._buf.ptr + i).init_pointee_copy(0) self = other^ @@ -5164,10 +5157,10 @@ struct NDArray[dtype: DType = DType.float64]( fn unsafe_ptr( ref self, - ) -> UnsafePointer[ + ) -> LegacyUnsafePointer[ Scalar[dtype], - mut = Origin(__origin_of(self)).mut, - origin = __origin_of(self), + mut = Origin(origin_of(self)).mut, + origin = origin_of(self), ]: """ Retreive pointer without taking ownership. @@ -5176,9 +5169,9 @@ struct NDArray[dtype: DType = DType.float64]( Unsafe pointer to the data buffer. """ - return self._buf.ptr.origin_cast[ - Origin(__origin_of(self)).mut, __origin_of(self) - ]() + return self._buf.ptr.mut_cast[ + Origin(origin_of(self)).mut + ]().unsafe_origin_cast[origin_of(self)]() fn variance[ returned_dtype: DType = DType.float64 @@ -5320,7 +5313,7 @@ struct _NDArrayIter[ """ var index: Int - var ptr: UnsafePointer[Scalar[dtype]] + var ptr: LegacyUnsafePointer[Scalar[dtype]] var dimension: Int var length: Int var shape: NDArrayShape @@ -5502,7 +5495,7 @@ struct _NDAxisIter[ ``` """ - var ptr: UnsafePointer[Scalar[dtype]] + var ptr: LegacyUnsafePointer[Scalar[dtype]] var axis: Int var order: String var length: Int @@ -5628,9 +5621,9 @@ struct _NDAxisIter[ ): # The memory layout is C-contiguous or F-contiguous memcpy( - res._buf.ptr, - self.ptr + _get_offset(item, self.strides), - self.size_of_item, + dest=res._buf.ptr, + src=self.ptr + _get_offset(item, self.strides), + count=self.size_of_item, ) else: @@ -5690,9 +5683,9 @@ struct _NDAxisIter[ ): # The memory layout is C-contiguous or F-contiguous memcpy( - elements._buf.ptr, - self.ptr + _get_offset(item, self.strides), - self.size_of_item, + dest=elements._buf.ptr, + src=self.ptr + _get_offset(item, self.strides), + count=self.size_of_item, ) else: for j in range(self.size_of_item): @@ -5705,7 +5698,7 @@ struct _NDAxisIter[ fn ith_with_offsets( self, index: Int - ) raises -> Tuple[NDArray[DType.index], NDArray[dtype]]: + ) raises -> Tuple[NDArray[DType.int], NDArray[dtype]]: """ Gets the i-th 1-d array of the iterator and the offsets (in C-order) of its elements. @@ -5717,7 +5710,7 @@ struct _NDAxisIter[ Offsets (in C-order) and elements of the i-th 1-d array of the iterator. """ - var offsets: NDArray[DType.index] = NDArray[DType.index]( + var offsets: NDArray[DType.int] = NDArray[DType.int]( Shape(self.size_of_item) ) var elements: NDArray[dtype] = NDArray[dtype](Shape(self.size_of_item)) @@ -5746,9 +5739,9 @@ struct _NDAxisIter[ ): # The memory layout is C-contiguous memcpy( - elements._buf.ptr, - self.ptr + _get_offset(item, self.strides), - self.size_of_item, + dest=elements._buf.ptr, + src=self.ptr + _get_offset(item, self.strides), + count=self.size_of_item, ) var begin_offset = _get_offset(item, new_strides) for j in range(self.size_of_item): @@ -5759,9 +5752,9 @@ struct _NDAxisIter[ ): # The memory layout is F-contiguous memcpy( - elements._buf.ptr, - self.ptr + _get_offset(item, self.strides), - self.size_of_item, + dest=elements._buf.ptr, + src=self.ptr + _get_offset(item, self.strides), + count=self.size_of_item, ) for j in range(self.size_of_item): (offsets._buf.ptr + j).init_pointee_copy( @@ -5790,7 +5783,7 @@ struct _NDIter[is_mutable: Bool, //, origin: Origin[is_mutable], dtype: DType]( It can be constructed by `NDArray.nditer()` method. """ - var ptr: UnsafePointer[Scalar[dtype]] + var ptr: LegacyUnsafePointer[Scalar[dtype]] var length: Int var ndim: Int var shape: NDArrayShape diff --git a/numojo/core/ndshape.mojo b/numojo/core/ndshape.mojo index e4c4b1dc..65831193 100644 --- a/numojo/core/ndshape.mojo +++ b/numojo/core/ndshape.mojo @@ -8,7 +8,8 @@ Implements NDArrayShape type. """ -from memory import UnsafePointer, memcpy, memcmp +from memory import memcpy, memcmp +from memory import LegacyUnsafePointer as UnsafePointer from numojo.core.error import IndexError, ShapeError, ValueError @@ -388,7 +389,7 @@ struct NDArrayShape( """ self.ndim = shape.ndim self._buf = UnsafePointer[Scalar[Self._type]]().alloc(shape.ndim) - memcpy(self._buf, shape._buf, shape.ndim) + memcpy(dest=self._buf, src=shape._buf, count=shape.ndim) for i in range(self.ndim): (self._buf + i).init_pointee_copy(shape._buf[i]) @@ -491,7 +492,7 @@ struct NDArrayShape( """ self.ndim = other.ndim self._buf = UnsafePointer[Scalar[Self._type]]().alloc(other.ndim) - memcpy(self._buf, other._buf, other.ndim) + memcpy(dest=self._buf, src=other._buf, count=other.ndim) fn __del__(deinit self): """ @@ -547,7 +548,7 @@ struct NDArrayShape( @always_inline("nodebug") fn _compute_slice_params( self, slice_index: Slice - ) raises -> (Int, Int, Int): + ) raises -> Tuple[Int, Int, Int]: var n = self.ndim if n == 0: return (0, 1, 0) @@ -771,7 +772,7 @@ struct NDArrayShape( """ var res = Self(ndim=self.ndim, initialized=False) - memcpy(res._buf, self._buf, self.ndim) + memcpy(dest=res._buf, src=self._buf, count=self.ndim) return res fn join(self, *shapes: Self) raises -> Self: @@ -1057,7 +1058,7 @@ struct _ShapeIter[ else: return self.index > 0 - fn __next__(mut self) raises -> Scalar[DType.index]: + fn __next__(mut self) raises -> Scalar[DType.int]: @parameter if forward: var current_index = self.index diff --git a/numojo/core/ndstrides.mojo b/numojo/core/ndstrides.mojo index c8994cb6..83f0db33 100644 --- a/numojo/core/ndstrides.mojo +++ b/numojo/core/ndstrides.mojo @@ -8,7 +8,8 @@ Implements NDArrayStrides type. """ -from memory import UnsafePointer, memcmp, memcpy +from memory import memcmp, memcpy +from memory import LegacyUnsafePointer as UnsafePointer from numojo.core.error import IndexError, ValueError @@ -133,7 +134,7 @@ struct NDArrayStrides( self.ndim = strides.ndim self._buf = UnsafePointer[Scalar[Self._type]]().alloc(self.ndim) - memcpy(self._buf, strides._buf, strides.ndim) + memcpy(dest=self._buf, src=strides._buf, count=strides.ndim) @always_inline("nodebug") fn __init__( @@ -339,7 +340,7 @@ struct NDArrayStrides( """ self.ndim = other.ndim self._buf = UnsafePointer[Scalar[Self._type]]().alloc(other.ndim) - memcpy(self._buf, other._buf, other.ndim) + memcpy(dest=self._buf, src=other._buf, count=other.ndim) fn __del__(deinit self): """ @@ -394,7 +395,7 @@ struct NDArrayStrides( @always_inline("nodebug") fn _compute_slice_params( self, slice_index: Slice - ) raises -> (Int, Int, Int): + ) raises -> Tuple[Int, Int, Int]: """ Compute normalized slice parameters (start, step, length). @@ -644,7 +645,7 @@ struct NDArrayStrides( """ var res = Self(ndim=self.ndim, initialized=False) - memcpy(res._buf, self._buf, self.ndim) + memcpy(dest=res._buf, src=self._buf, count=self.ndim) return res fn swapaxes(self, axis1: Int, axis2: Int) raises -> Self: @@ -683,7 +684,7 @@ struct NDArrayStrides( ) var res = Self(ndim=self.ndim, initialized=False) - memcpy(res._buf, self._buf, self.ndim) + memcpy(dest=res._buf, src=self._buf, count=self.ndim) res[axis1] = self[axis2] res[axis2] = self[axis1] return res^ @@ -957,7 +958,7 @@ struct _StrideIter[ else: return self.index > 0 - fn __next__(mut self) raises -> Scalar[DType.index]: + fn __next__(mut self) raises -> Scalar[DType.int]: @parameter if forward: var current_index = self.index diff --git a/numojo/core/own_data.mojo b/numojo/core/own_data.mojo index 11cc4bcd..67f88115 100644 --- a/numojo/core/own_data.mojo +++ b/numojo/core/own_data.mojo @@ -11,8 +11,13 @@ struct OwnData(Buffered, ImplicitlyCopyable, Movable): fn __init__(out self): pass - fn is_own_data(self) -> Bool: + @staticmethod + fn is_own_data() -> Bool: return True - fn is_ref_data(self) -> Bool: + @staticmethod + fn is_ref_data() -> Bool: return False + + fn __str__(self) -> String: + return "OwnData" diff --git a/numojo/core/ref_data.mojo b/numojo/core/ref_data.mojo index f324fc9d..e268d8c8 100644 --- a/numojo/core/ref_data.mojo +++ b/numojo/core/ref_data.mojo @@ -17,8 +17,13 @@ struct RefData[is_mutable: Bool, //, origin: Origin[is_mutable]]( fn __init__(out self): pass - fn is_own_data(self) -> Bool: + @staticmethod + fn is_own_data() -> Bool: return False - fn is_ref_data(self) -> Bool: + @staticmethod + fn is_ref_data() -> Bool: return True + + fn __str__(self) -> String: + return "RefData" diff --git a/numojo/core/traits/buffered.mojo b/numojo/core/traits/buffered.mojo index 8a28911d..0675544f 100644 --- a/numojo/core/traits/buffered.mojo +++ b/numojo/core/traits/buffered.mojo @@ -16,8 +16,13 @@ trait Buffered(ImplicitlyCopyable, Movable): fn __init__(out self): ... - fn is_own_data(self) -> Bool: + @staticmethod + fn is_own_data() -> Bool: ... - fn is_ref_data(self) -> Bool: + @staticmethod + fn is_ref_data() -> Bool: + ... + + fn __str__(self) -> String: ... diff --git a/numojo/core/utility.mojo b/numojo/core/utility.mojo index e6c035e7..4407a748 100644 --- a/numojo/core/utility.mojo +++ b/numojo/core/utility.mojo @@ -21,7 +21,8 @@ Implements N-DIMENSIONAL ARRAY UTILITY FUNCTIONS from algorithm.functional import vectorize, parallelize from collections import Dict -from memory import UnsafePointer, memcpy +from memory import memcpy +from memory import LegacyUnsafePointer as UnsafePointer from python import Python, PythonObject from sys import simd_width_of @@ -169,7 +170,7 @@ fn _transfer_offset(offset: Int, strides: NDArrayStrides) raises -> Int: fn _traverse_buffer_according_to_shape_and_strides( - mut ptr: UnsafePointer[Scalar[DType.index]], + mut ptr: UnsafePointer[Scalar[DType.int]], shape: NDArrayShape, strides: NDArrayStrides, current_dim: Int = 0, @@ -194,7 +195,7 @@ fn _traverse_buffer_according_to_shape_and_strides( Example: ```console # A is a 2x3x4 array - var I = nm.NDArray[DType.index](nm.Shape(A.size)) + var I = nm.NDArray[DType.int](nm.Shape(A.size)) var ptr = I._buf _traverse_buffer_according_to_shape_and_strides( ptr, A.shape._flip(), A.strides._flip() @@ -399,7 +400,7 @@ fn to_numpy[dtype: DType](array: NDArray[dtype]) raises -> PythonObject: np_dtype = np.int16 elif dtype == DType.int8: np_dtype = np.int8 - elif dtype == DType.index: + elif dtype == DType.int: np_dtype = np.intp elif dtype == DType.uint64: np_dtype = np.uint64 @@ -417,7 +418,7 @@ fn to_numpy[dtype: DType](array: NDArray[dtype]) raises -> PythonObject: var pointer_d = numpyarray.__array_interface__["data"][ 0 ].unsafe_get_as_pointer[dtype]() - memcpy(pointer_d, array.unsafe_ptr(), array.size) + memcpy(dest=pointer_d, src=array.unsafe_ptr(), count=array.size) _ = array return numpyarray^ diff --git a/numojo/routines/creation.mojo b/numojo/routines/creation.mojo index 284afb16..770a89dd 100644 --- a/numojo/routines/creation.mojo +++ b/numojo/routines/creation.mojo @@ -2556,7 +2556,7 @@ fn array[ np_dtype = np.int16 elif dtype == DType.int8: np_dtype = np.int8 - elif dtype == DType.index: + elif dtype == DType.int: np_dtype = np.intp elif dtype == DType.uint64: np_dtype = np.uint64 @@ -2575,7 +2575,7 @@ fn array[ dtype ]() var A: NDArray[dtype] = NDArray[dtype](array_shape, order) - memcpy[Scalar[dtype]](A._buf.ptr, pointer, A.size) + memcpy[Scalar[dtype]](dest=A._buf.ptr, src=pointer, count=A.size) return A^ @@ -2634,7 +2634,7 @@ fn array[ np_dtype = np.int16 elif dtype == DType.int8: np_dtype = np.int8 - elif dtype == DType.index: + elif dtype == DType.int: np_dtype = np.intp elif dtype == DType.uint64: np_dtype = np.uint64 @@ -2657,8 +2657,10 @@ fn array[ 0 ].unsafe_get_as_pointer[dtype]() var A: ComplexNDArray[cdtype] = ComplexNDArray[cdtype](array_shape, order) - memcpy[Scalar[dtype]](A._re._buf.ptr, pointer, A._re.size) - memcpy[Scalar[dtype]](A._im._buf.ptr, pointer_imag, A._im.size) + memcpy[Scalar[dtype]](dest=A._re._buf.ptr, src=pointer, count=A._re.size) + memcpy[Scalar[dtype]]( + dest=A._im._buf.ptr, src=pointer_imag, count=A._im.size + ) return A^ diff --git a/numojo/routines/functional.mojo b/numojo/routines/functional.mojo index b5eb8ac5..cafdbc60 100644 --- a/numojo/routines/functional.mojo +++ b/numojo/routines/functional.mojo @@ -82,12 +82,12 @@ fn apply_along_axis[ fn apply_along_axis[ dtype: DType, func1d: fn[dtype_func: DType] (NDArray[dtype_func]) raises -> Scalar[ - DType.index + DType.int ], -](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.index]: +](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.int]: """ Applies a function to a NDArray by axis and reduce that dimension. - The returned data type is DType.index. + The returned data type is DType.int. When the array is 1-d, the returned array will be a 0-d array. Parameters: @@ -105,14 +105,14 @@ fn apply_along_axis[ # The iterator along the axis var iterator = a.iter_along_axis(axis=axis) # The final output array will have 1 less dimension than the input array - var res: NDArray[DType.index] + var res: NDArray[DType.int] if a.ndim == 1: - res = numojo.creation._0darray[DType.index](0) + res = numojo.creation._0darray[DType.int](0) (res._buf.ptr).init_pointee_copy(func1d[dtype](a)) else: - res = NDArray[DType.index](a.shape._pop(axis=axis)) + res = NDArray[DType.int](a.shape._pop(axis=axis)) @parameter fn parallelized_func(i: Int): @@ -221,9 +221,9 @@ fn apply_along_axis[ try: var elements: NDArray[dtype] = func1d[dtype](iterator.ith(i)) memcpy( - result._buf.ptr + i * elements.size, - elements._buf.ptr, - elements.size, + dest=result._buf.ptr + i * elements.size, + src=elements._buf.ptr, + count=elements.size, ) except e: print("Error in parallelized_func", e) @@ -236,7 +236,7 @@ fn apply_along_axis[ fn parallelized_func(i: Int): try: # The indices of the input array in each iteration - var indices: NDArray[DType.index] + var indices: NDArray[DType.int] # The elements of the input array in each iteration var elements: NDArray[dtype] # The array after applied the function @@ -293,9 +293,9 @@ fn apply_along_axis[ var elements: NDArray[dtype] = iterator.ith(i) func1d[dtype](elements) memcpy( - a._buf.ptr + i * elements.size, - elements._buf.ptr, - elements.size, + dest=a._buf.ptr + i * elements.size, + src=elements._buf.ptr, + count=elements.size, ) except e: print("Error in parallelized_func", e) @@ -308,7 +308,7 @@ fn apply_along_axis[ fn parallelized_func(i: Int): try: # The indices of the input array in each iteration - var indices: NDArray[DType.index] + var indices: NDArray[DType.int] # The elements of the input array in each iteration var elements: NDArray[dtype] # The array after applied the function @@ -333,9 +333,9 @@ fn apply_along_axis[ fn apply_along_axis[ dtype: DType, func1d: fn[dtype_func: DType] (NDArray[dtype_func]) raises -> NDArray[ - DType.index + DType.int ], -](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.index]: +](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.int]: """ Applies a function to a NDArray by axis without reducing that dimension. The resulting array will have the same shape as the input array. @@ -357,20 +357,20 @@ fn apply_along_axis[ # The iterator along the axis var iterator = a.iter_along_axis(axis=axis) # The final output array will have the same shape as the input array - var res = NDArray[DType.index](a.shape) + var res = NDArray[DType.int](a.shape) if a.flags.C_CONTIGUOUS and (axis == a.ndim - 1): # The memory layout is C-contiguous @parameter fn parallelized_func_c(i: Int): try: - var elements: NDArray[DType.index] = func1d[dtype]( + var elements: NDArray[DType.int] = func1d[dtype]( iterator.ith(i) ) memcpy( - res._buf.ptr + i * elements.size, - elements._buf.ptr, - elements.size, + dest=res._buf.ptr + i * elements.size, + src=elements._buf.ptr, + count=elements.size, ) except e: print("Error in parallelized_func", e) @@ -383,7 +383,7 @@ fn apply_along_axis[ fn parallelized_func(i: Int): try: # The indices of the input array in each iteration - var indices: NDArray[DType.index] + var indices: NDArray[DType.int] # The elements of the input array in each iteration var elements: NDArray[dtype] # The array after applied the function @@ -391,9 +391,7 @@ fn apply_along_axis[ indices = indices_elements[0].copy() elements = indices_elements[1].copy() - var res_along_axis: NDArray[DType.index] = func1d[dtype]( - elements - ) + var res_along_axis: NDArray[DType.int] = func1d[dtype](elements) for j in range(a.shape[axis]): (res._buf.ptr + Int(indices[j])).init_pointee_copy( diff --git a/numojo/routines/indexing.mojo b/numojo/routines/indexing.mojo index 4e928391..aefdd23b 100644 --- a/numojo/routines/indexing.mojo +++ b/numojo/routines/indexing.mojo @@ -25,7 +25,7 @@ import numojo.core.utility as utility # ===----------------------------------------------------------------------=== # -fn where[ +fn `where`[ dtype: DType ]( mut x: NDArray[dtype], scalar: SIMD[dtype, 1], mask: NDArray[DType.bool] @@ -48,7 +48,7 @@ fn where[ # TODO: do it with vectorization -fn where[ +fn `where`[ dtype: DType ](mut x: NDArray[dtype], y: NDArray[dtype], mask: NDArray[DType.bool]) raises: """ @@ -237,7 +237,7 @@ fn compress[ fn take_along_axis[ dtype: DType, //, ]( - arr: NDArray[dtype], indices: NDArray[DType.index], axis: Int = 0 + arr: NDArray[dtype], indices: NDArray[DType.int], axis: Int = 0 ) raises -> NDArray[dtype]: """ Takes values from the input array along the given axis based on indices. @@ -303,7 +303,7 @@ fn take_along_axis[ # except along the axis var broadcasted_indices: NDArray[ - DType.index + DType.int ] = indices.copy() # make this owned and don't copy if arr.shape != indices.shape: @@ -337,15 +337,15 @@ fn take_along_axis[ indices_slice ] memcpy( - result._buf.ptr + i * result.shape[normalized_axis], - arr_slice_after_applying_indices._buf.ptr, - result.shape[normalized_axis], + dest=result._buf.ptr + i * result.shape[normalized_axis], + src=arr_slice_after_applying_indices._buf.ptr, + count=result.shape[normalized_axis], ) else: # If axis is not the last axis, the data is not contiguous. for i in range(length_of_iterator): - var indices_slice_offsets: NDArray[DType.index] - var indices_slice: NDArray[DType.index] + var indices_slice_offsets: NDArray[DType.int] + var indices_slice: NDArray[DType.int] var indices_slice_offsets_slice = indices_iterator.ith_with_offsets( i ) diff --git a/numojo/routines/io/files.mojo b/numojo/routines/io/files.mojo index f9781b6b..dc774c44 100644 --- a/numojo/routines/io/files.mojo +++ b/numojo/routines/io/files.mojo @@ -79,7 +79,7 @@ fn load[ # return "' Tuple[Matrix[dtype], Matrix[dtype]]: +](A: MatrixImpl[dtype, **_]) raises -> Tuple[Matrix[dtype], Matrix[dtype]]: """ Perform LU (lower-upper) decomposition for matrix. """ @@ -226,8 +227,8 @@ fn lu_decomposition[ var n: Int = A.shape[0] # Initiate upper and lower triangular matrices - var U: Matrix[dtype] = Matrix.full[dtype](shape=(n, n), order=A.order()) - var L: Matrix[dtype] = Matrix.full[dtype](shape=(n, n), order=A.order()) + var U: Matrix[dtype] = Matrix.zeros[dtype](shape=(n, n), order=A.order()) + var L: Matrix[dtype] = Matrix.zeros[dtype](shape=(n, n), order=A.order()) # Fill in L and U for i in range(0, n): @@ -305,32 +306,36 @@ fn partial_pivoting[ fn partial_pivoting[ dtype: DType -](var A: Matrix[dtype]) raises -> Tuple[Matrix[dtype], Matrix[dtype], Int]: +](A: MatrixImpl[dtype, **_]) raises -> Tuple[Matrix[dtype], Matrix[dtype], Int]: """ Perform partial pivoting for matrix. """ var n = A.shape[0] - var P = Matrix.identity[dtype](n) - if A.flags.F_CONTIGUOUS: - A = A.reorder_layout() - var s: Int = 0 # Number of exchanges, for determinant + # Work on a copy that preserves the original layout + var result = A.create_copy() + var P = Matrix.identity[dtype](n, order=A.order()) + var s: Int = 0 # Number of row exchanges + for col in range(n): - var max_p = abs(A[col, col]) + var max_p = abs(result[col, col]) var max_p_row = col for row in range(col + 1, n): - if abs(A[row, col]) > max_p: - max_p = abs(A[row, col]) + if abs(result[row, col]) > max_p: + max_p = abs(result[row, col]) max_p_row = row - A[col], A[max_p_row] = A[max_p_row], A[col] - P[col], P[max_p_row] = P[max_p_row], P[col] if max_p_row != col: + # Swap rows in result and permutation matrix using element-wise swap + for j in range(n): + var t = result._load(col, j) + result._store(col, j, result._load(max_p_row, j)) + result._store(max_p_row, j, t) + var tp = P._load(col, j) + P._store(col, j, P._load(max_p_row, j)) + P._store(max_p_row, j, tp) s = s + 1 - if A.flags.F_CONTIGUOUS: - A = A.reorder_layout() - P = P.reorder_layout() - return Tuple(A^, P^, s) + return Tuple(result^, P^, s) fn qr[ @@ -376,7 +381,10 @@ fn qr[ if reorder: R = A.reorder_layout() else: - R = A.copy() + R = Matrix.zeros[dtype](shape=(m, n), order="F") + for i in range(m): + for j in range(n): + R._store(i, j, A._load(i, j)) var H = Matrix.zeros[dtype](shape=(m, min_n), order="F") @@ -392,16 +400,25 @@ fn qr[ _apply_householder(H, i, Q, i, i) if reorder: - Q = Q.reorder_layout() + var Q_reordered = Q.reorder_layout() if reduce: - R = R[:inner, :].reorder_layout() + var R_reduced = Matrix.zeros[dtype](shape=(inner, n), order="C") + for i in range(inner): + for j in range(n): + R_reduced._store(i, j, R._load(i, j)) + return Q_reordered^, R_reduced^ else: - R = R.reorder_layout() + var R_reordered = R.reorder_layout() + return Q_reordered^, R_reordered^ else: if reduce: - R = R[:inner, :] - - return Q^, R^ + var R_reduced = Matrix.zeros[dtype](shape=(inner, n), order="F") + for i in range(inner): + for j in range(n): + R_reduced._store(i, j, R._load(i, j)) + return Q^, R_reduced^ + else: + return Q^, R^ # ===----------------------------------------------------------------------=== # diff --git a/numojo/routines/linalg/misc.mojo b/numojo/routines/linalg/misc.mojo index f45776b2..a1e5662b 100644 --- a/numojo/routines/linalg/misc.mojo +++ b/numojo/routines/linalg/misc.mojo @@ -13,6 +13,7 @@ from sys import simd_width_of from algorithm import parallelize, vectorize from numojo.core.ndarray import NDArray +from numojo.core.matrix import MatrixImpl fn diagonal[ @@ -67,7 +68,9 @@ fn diagonal[ fn issymmetric[ dtype: DType ]( - A: Matrix[dtype], rtol: Scalar[dtype] = 1e-5, atol: Scalar[dtype] = 1e-8 + A: MatrixImpl[dtype, **_], + rtol: Scalar[dtype] = 1e-5, + atol: Scalar[dtype] = 1e-8, ) -> Bool: """ Returns True if A is symmetric, False otherwise. diff --git a/numojo/routines/linalg/norms.mojo b/numojo/routines/linalg/norms.mojo index 21fd5f5d..8312b8fb 100644 --- a/numojo/routines/linalg/norms.mojo +++ b/numojo/routines/linalg/norms.mojo @@ -3,7 +3,7 @@ # ===----------------------------------------------------------------------=== # from numojo.core.ndarray import NDArray -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl from numojo.routines.linalg.decompositions import ( lu_decomposition, partial_pivoting, @@ -121,7 +121,7 @@ fn trace[ fn trace[ dtype: DType -](A: Matrix[dtype], offset: Int = 0) raises -> Scalar[dtype]: +](A: MatrixImpl[dtype, **_], offset: Int = 0) raises -> Scalar[dtype]: """ Return the sum along diagonals of the array. diff --git a/numojo/routines/linalg/products.mojo b/numojo/routines/linalg/products.mojo index 64961039..7f54bb41 100644 --- a/numojo/routines/linalg/products.mojo +++ b/numojo/routines/linalg/products.mojo @@ -15,7 +15,7 @@ from memory import memcpy import numojo.routines.math._math_funcs as _mf from numojo.core.ndarray import NDArray from numojo.core.ndshape import NDArrayShape, Shape -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl from numojo.routines.creation import zeros from numojo.routines.math.sums import sum @@ -339,36 +339,37 @@ fn matmul[ for i in range(result.size // result_sub_matrix.size): memcpy( - A_sub_matrix._buf.ptr, - A._buf.ptr + (i * A_sub_matrix.size), - A_sub_matrix.size, + dest=A_sub_matrix._buf.ptr, + src=A._buf.ptr + (i * A_sub_matrix.size), + count=A_sub_matrix.size, ) memcpy( - B_sub_matrix._buf.ptr, - B._buf.ptr + (i * B_sub_matrix.size), - B_sub_matrix.size, + dest=B_sub_matrix._buf.ptr, + src=B._buf.ptr + (i * B_sub_matrix.size), + count=B_sub_matrix.size, ) result_sub_matrix = matmul_2darray(A_sub_matrix, B_sub_matrix) memcpy( - result._buf.ptr + (i * result_sub_matrix.size), - result_sub_matrix._buf.ptr, - result_sub_matrix.size, + dest=result._buf.ptr + (i * result_sub_matrix.size), + src=result_sub_matrix._buf.ptr, + count=result_sub_matrix.size, ) return result^ fn matmul[ dtype: DType -](A: Matrix[dtype], B: Matrix[dtype]) raises -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], B: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ Matrix multiplication. Example: ```mojo from numojo import Matrix + from numojo.routines.linalg import matmul var A = Matrix.rand(shape=(1000, 1000)) var B = Matrix.rand(shape=(1000, 1000)) - var result = mat.matmul(A, B) + var result = matmul(A, B) ``` """ @@ -448,8 +449,6 @@ fn matmul[ else: result = matmul(A.reorder_layout(), B) - # var _A = A - # var _B = B return result^ diff --git a/numojo/routines/linalg/solving.mojo b/numojo/routines/linalg/solving.mojo index 6303f7c8..9b0909ac 100644 --- a/numojo/routines/linalg/solving.mojo +++ b/numojo/routines/linalg/solving.mojo @@ -13,11 +13,15 @@ Provides: from algorithm import parallelize from numojo.core.ndarray import NDArray +from numojo.core.own_data import OwnData from numojo.core.item import Item import numojo.core.matrix as matrix -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl from numojo.routines.creation import zeros, eye, full -from numojo.routines.linalg.decompositions import partial_pivoting +from numojo.routines.linalg.decompositions import ( + partial_pivoting, + lu_decomposition, +) fn forward_substitution[ @@ -113,7 +117,7 @@ fn inv[dtype: DType](A: NDArray[dtype]) raises -> NDArray[dtype]: return solve(A, I) -fn inv[dtype: DType](A: Matrix[dtype]) raises -> Matrix[dtype]: +fn inv[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ Inverse of matrix. """ @@ -208,13 +212,13 @@ fn lstsq[ """Caclulate the OLS estimates. Example: - ```mojo + ```text from numojo import Matrix X = Matrix.rand((1000000, 5)) y = Matrix.rand((1000000, 1)) - print(mat.lstsq(X, y)) + print(lstsq(X, y)) ``` - ```console + ```text [[0.18731374756029967] [0.18821352688798607] [0.18717162200411439] @@ -369,7 +373,7 @@ fn solve[ fn solve[ dtype: DType -](A: Matrix[dtype], Y: Matrix[dtype]) raises -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], Y: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ Solve `AX = Y` using LUP decomposition. """ @@ -382,10 +386,12 @@ fn solve[ var A_pivoted_Pair: Tuple[ Matrix[dtype], Matrix[dtype], Int ] = partial_pivoting(A.copy()) - A_pivoted = A_pivoted_Pair[0].copy() - P = A_pivoted_Pair[1].copy() + + var pivoted_A = A_pivoted_Pair[0].copy() + var P = A_pivoted_Pair[1].copy() + var L_U: Tuple[Matrix[dtype], Matrix[dtype]] = lu_decomposition[dtype]( - A_pivoted + pivoted_A ) L = L_U[0].copy() U = L_U[1].copy() @@ -393,9 +399,8 @@ fn solve[ var m: Int = A.shape[0] var n: Int = Y.shape[1] - var Z: Matrix[dtype] = Matrix.full[dtype]((m, n), order=A.order()) - var X: Matrix[dtype] = Matrix.full[dtype]((m, n), order=A.order()) - + var Z: Matrix[dtype] = Matrix.zeros[dtype]((m, n), order=A.order()) + var X: Matrix[dtype] = Matrix.zeros[dtype]((m, n), order=A.order()) var PY = P @ Y @parameter @@ -432,7 +437,7 @@ fn solve[ fn solve_lu[ dtype: DType -](A: Matrix[dtype], Y: Matrix[dtype]) raises -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], Y: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ Solve `AX = Y` using LU decomposition. """ @@ -457,8 +462,6 @@ fn solve_lu[ _temp = _temp - L._load(i, j) * Z._load(j, col) _temp = _temp / L._load(i, i) Z._store(i, col, _temp) - - # Solve `UZ = Z` for `X` for each col for i in range(m - 1, -1, -1): var _temp2 = Z._load(i, col) for j in range(i + 1, m): diff --git a/numojo/routines/logic/truth.mojo b/numojo/routines/logic/truth.mojo index 0a5c5cac..f5188d08 100644 --- a/numojo/routines/logic/truth.mojo +++ b/numojo/routines/logic/truth.mojo @@ -8,10 +8,11 @@ from sys import simd_width_of import numojo.routines.math._math_funcs as _mf from numojo.core.ndarray import NDArray -from numojo.core.matrix import Matrix +from numojo.core.own_data import OwnData +from numojo.core.matrix import Matrix, MatrixImpl -fn all[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: +fn all[dtype: DType](A: MatrixImpl[dtype, **_]) -> Scalar[dtype]: """ Test whether all array elements evaluate to True. @@ -29,7 +30,9 @@ fn all[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: return res -fn all[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +fn all[ + dtype: DType +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Test whether all array elements evaluate to True along axis. """ @@ -121,7 +124,7 @@ fn any(array: NDArray[DType.bool]) raises -> Scalar[DType.bool]: return result -fn any[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: +fn any[dtype: DType](A: MatrixImpl[dtype, **_]) -> Scalar[dtype]: """ Test whether any array elements evaluate to True. @@ -139,7 +142,9 @@ fn any[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: return res -fn any[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +fn any[ + dtype: DType +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Test whether any array elements evaluate to True along axis. """ diff --git a/numojo/routines/manipulation.mojo b/numojo/routines/manipulation.mojo index a1030705..d5d6caa4 100644 --- a/numojo/routines/manipulation.mojo +++ b/numojo/routines/manipulation.mojo @@ -10,6 +10,7 @@ Array manipulation routines. """ from memory import UnsafePointer, memcpy +from memory import LegacyUnsafePointer from sys import simd_width_of from algorithm import vectorize @@ -18,7 +19,7 @@ from numojo.core.complex import ComplexNDArray from numojo.core.ndshape import NDArrayShape, Shape from numojo.core.ndstrides import NDArrayStrides import numojo.core.matrix as matrix -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl from numojo.core.utility import _list_of_flipped_range, _get_offset # ===----------------------------------------------------------------------=== # @@ -207,7 +208,7 @@ fn ravel[ # TODO: Remove this one if the following function is working well: # `numojo.core.utility._traverse_buffer_according_to_shape_and_strides` fn _set_values_according_to_shape_and_strides( - mut I: NDArray[DType.index], + mut I: NDArray[DType.int], mut index: Int, current_dim: Int, previous_sum: Int, @@ -285,8 +286,8 @@ fn transpose[ new_strides._buf[i] = A.strides[axes[i]] var array_order: String = "C" if A.flags.C_CONTIGUOUS else "F" - var I = NDArray[DType.index](Shape(A.size), order=array_order) - var ptr: UnsafePointer[Scalar[DType.index]] = I._buf.ptr + var I = NDArray[DType.int](Shape(A.size), order=array_order) + var ptr: LegacyUnsafePointer[Scalar[DType.int]] = I._buf.ptr numojo.core.utility._traverse_buffer_according_to_shape_and_strides( ptr, new_shape, new_strides ) @@ -310,7 +311,7 @@ fn transpose[dtype: DType](A: NDArray[dtype]) raises -> NDArray[dtype]: var array_order = "C" if A.flags.C_CONTIGUOUS else "F" var B = NDArray[dtype](Shape(A.shape[1], A.shape[0]), order=array_order) if A.shape[0] == 1 or A.shape[1] == 1: - memcpy(B._buf.ptr, A._buf.ptr, A.size) + memcpy(dest=B._buf.ptr, src=A._buf.ptr, count=A.size) else: for i in range(B.shape[0]): for j in range(B.shape[1]): @@ -324,7 +325,7 @@ fn transpose[dtype: DType](A: NDArray[dtype]) raises -> NDArray[dtype]: return transpose(A, axes=flipped_axes) -fn transpose[dtype: DType](A: Matrix[dtype]) -> Matrix[dtype]: +fn transpose[dtype: DType](A: MatrixImpl[dtype, **_]) -> Matrix[dtype]: """ Transpose of matrix. """ @@ -335,7 +336,7 @@ fn transpose[dtype: DType](A: Matrix[dtype]) -> Matrix[dtype]: var B = Matrix[dtype](Tuple(A.shape[1], A.shape[0]), order=order) if A.shape[0] == 1 or A.shape[1] == 1: - memcpy(B._buf.ptr, A._buf.ptr, A.size) + memcpy(dest=B._buf.ptr, src=A._buf.ptr, count=A.size) else: for i in range(B.shape[0]): for j in range(B.shape[1]): @@ -343,7 +344,9 @@ fn transpose[dtype: DType](A: Matrix[dtype]) -> Matrix[dtype]: return B^ -fn reorder_layout[dtype: DType](A: Matrix[dtype]) raises -> Matrix[dtype]: +fn reorder_layout[ + dtype: DType +](A: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ Create a new Matrix with the opposite layout from A: if A is C-contiguous, then create a new F-contiguous matrix of the same shape. @@ -368,16 +371,15 @@ fn reorder_layout[dtype: DType](A: Matrix[dtype]) raises -> Matrix[dtype]: ) ) - var B: Matrix[dtype] = Matrix[dtype](Tuple(rows, cols), new_order) - + var B = Matrix[dtype](Tuple(rows, cols), new_order) if new_order == "C": for i in range(rows): for j in range(cols): - B._buf.ptr[i * cols + j] = A._buf.ptr[i + j * rows] + B._buf[i * cols + j] = A._buf[i + j * rows] else: for j in range(cols): for i in range(rows): - B._buf.ptr[j * rows + i] = A._buf.ptr[i * cols + j] + B._buf[j * rows + i] = A._buf[i * cols + j] return B^ @@ -447,7 +449,9 @@ fn broadcast_to[ fn broadcast_to[ dtype: DType ]( - var A: Matrix[dtype], shape: Tuple[Int, Int], override_order: String = "" + A: MatrixImpl[dtype, **_], + shape: Tuple[Int, Int], + override_order: String = "", ) raises -> Matrix[dtype]: """ Broadcasts the vector to the given shape. @@ -485,11 +489,11 @@ fn broadcast_to[ else: ord = override_order - var B = Matrix[dtype](shape, order=ord) + var B: Matrix[dtype] = Matrix[dtype](shape, order=ord) if (A.shape[0] == shape[0]) and (A.shape[1] == shape[1]): - return A^ + memcpy(dest=B._buf.ptr, src=A._buf.ptr, count=A.size) elif (A.shape[0] == 1) and (A.shape[1] == 1): - B = Matrix.full[dtype](shape, A[0, 0], order=ord) + B = Matrix[dtype].full(shape, A[0, 0], order=ord) elif (A.shape[0] == 1) and (A.shape[1] == shape[1]): for i in range(shape[0]): memcpy( @@ -518,7 +522,7 @@ fn broadcast_to[ Broadcasts the scalar to the given shape. """ - var B: Matrix[dtype] = Matrix.full[dtype](shape, A, order=order) + var B: Matrix[dtype] = Matrix[dtype].full(shape, A, order=order) return B^ @@ -614,7 +618,7 @@ fn flip[ String("Invalid index: index out of bound [0, {}).").format(A.ndim) ) - var I = NDArray[DType.index](Shape(A.size)) + var I = NDArray[DType.int](Shape(A.size)) var ptr = I._buf.ptr numojo.core.utility._traverse_buffer_according_to_shape_and_strides( diff --git a/numojo/routines/math/_math_funcs.mojo b/numojo/routines/math/_math_funcs.mojo index c99b08d6..ae2fe8af 100644 --- a/numojo/routines/math/_math_funcs.mojo +++ b/numojo/routines/math/_math_funcs.mojo @@ -418,7 +418,7 @@ struct Vectorized(Backend): fn bool_simd_store[ simd_width: Int ]( - ptr: UnsafePointer[Scalar[DType.bool]], + ptr: LegacyUnsafePointer[Scalar[DType.bool]], start: Int, val: SIMD[DType.bool, simd_width], ): diff --git a/numojo/routines/math/extrema.mojo b/numojo/routines/math/extrema.mojo index 85b2abee..0d16ab0d 100644 --- a/numojo/routines/math/extrema.mojo +++ b/numojo/routines/math/extrema.mojo @@ -1,4 +1,4 @@ -# ===----------------------------------------------------------------------=== # +# views ===----------------------------------------------------------------------=== # # Distributed under the Apache 2.0 License with LLVM Exceptions. # See LICENSE and the LLVM License for more information. # https://github.com/Mojo-Numerics-and-Algorithms-group/NuMojo/blob/main/LICENSE @@ -27,9 +27,10 @@ from builtin.math import min as builtin_min from collections.optional import Optional from sys import simd_width_of -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl import numojo.core.matrix as matrix from numojo.core.ndarray import NDArray +from numojo.core.own_data import OwnData import numojo.core.utility as utility from numojo.routines.creation import full from numojo.routines.sorting import binary_sort @@ -144,7 +145,7 @@ fn max[dtype: DType](a: NDArray[dtype], axis: Int) raises -> NDArray[dtype]: @always_inline fn matrix_extrema[ dtype: DType, find_max: Bool -](A: Matrix[dtype]) raises -> Scalar[dtype]: +](A: MatrixImpl[dtype, **_]) raises -> Scalar[dtype]: """ Generic implementation for finding global min/max in a matrix. Works with any memory layout (row-major or column-major). @@ -167,7 +168,7 @@ fn matrix_extrema[ @always_inline fn matrix_extrema_axis[ dtype: DType, find_max: Bool -](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Generic implementation for finding min/max along an axis in a matrix. Works with any memory layout (row-major or column-major). @@ -213,14 +214,16 @@ fn matrix_extrema_axis[ return B^ -fn max[dtype: DType](A: Matrix[dtype]) raises -> Scalar[dtype]: +fn max[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Scalar[dtype]: """ Find max item. It is first flattened before sorting. """ return matrix_extrema[dtype, True](A) -fn max[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +fn max[ + dtype: DType +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Find max item along the given axis. """ @@ -230,7 +233,7 @@ fn max[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: fn _max[ dtype: DType ](A: Matrix[dtype], start: Int, end: Int) raises -> Tuple[ - Scalar[dtype], Scalar[DType.index] + Scalar[dtype], Scalar[DType.int] ]: """ Auxiliary function that find the max value in a range of the buffer. @@ -243,7 +246,7 @@ fn _max[ ).format(start, end, A.size) ) - var max_index: Scalar[DType.index] = start + var max_index: Scalar[DType.int] = start var rows = A.shape[0] var cols = A.shape[1] @@ -333,14 +336,16 @@ fn min[dtype: DType](a: NDArray[dtype], axis: Int) raises -> NDArray[dtype]: ) -fn min[dtype: DType](A: Matrix[dtype]) raises -> Scalar[dtype]: +fn min[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Scalar[dtype]: """ Find min item. """ return matrix_extrema[dtype, False](A) -fn min[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +fn min[ + dtype: DType +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Find min item along the given axis. """ @@ -350,7 +355,7 @@ fn min[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: fn _min[ dtype: DType ](A: Matrix[dtype], start: Int, end: Int) raises -> Tuple[ - Scalar[dtype], Scalar[DType.index] + Scalar[dtype], Scalar[DType.int] ]: """ Auxiliary function that find the min value in a range of the buffer. @@ -363,7 +368,7 @@ fn _min[ ).format(start, end, A.size) ) - var min_index: Scalar[DType.index] = start + var min_index: Scalar[DType.int] = start var rows = A.shape[0] var cols = A.shape[1] diff --git a/numojo/routines/math/products.mojo b/numojo/routines/math/products.mojo index 38268dea..27614301 100644 --- a/numojo/routines/math/products.mojo +++ b/numojo/routines/math/products.mojo @@ -1,9 +1,11 @@ from algorithm.functional import parallelize, vectorize from sys import simd_width_of +from memory import UnsafePointer, memcpy, memset_zero from numojo.core.ndarray import NDArray +from numojo.core.own_data import OwnData import numojo.core.matrix as matrix -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl from numojo.routines.creation import ones @@ -81,7 +83,7 @@ fn prod[ return result^ -fn prod[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: +fn prod[dtype: DType](A: MatrixImpl[dtype, **_]) -> Scalar[dtype]: """ Product of all items in the Matrix. @@ -99,7 +101,9 @@ fn prod[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: return res -fn prod[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +fn prod[ + dtype: DType +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Product of items in a Matrix along the axis. @@ -205,7 +209,7 @@ fn cumprod[ String("Invalid index: index out of bound [0, {}).").format(A.ndim) ) - var I = NDArray[DType.index](Shape(A.size)) + var I = NDArray[DType.int](Shape(A.size)) var ptr = I._buf.ptr var _shape = B.shape._move_axis_to_end(axis) @@ -222,7 +226,7 @@ fn cumprod[ return B^ -fn cumprod[dtype: DType](var A: Matrix[dtype]) raises -> Matrix[dtype]: +fn cumprod[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ Cumprod of flattened matrix. @@ -236,25 +240,26 @@ fn cumprod[dtype: DType](var A: Matrix[dtype]) raises -> Matrix[dtype]: print(mat.cumprod(A)) ``` """ - var reorder = False - if A.flags.F_CONTIGUOUS: - reorder = True - A = A.reorder_layout() + alias width: Int = simd_width_of[dtype]() + var result: Matrix[dtype] = Matrix.zeros[dtype](A.shape, "C") - A.resize(shape=(1, A.size)) + if A.flags.C_CONTIGUOUS: + memcpy(dest=result._buf.ptr, src=A._buf.ptr, count=A.size) + else: + for i in range(A.shape[0]): + for j in range(A.shape[1]): + result[i, j] = A[i, j] for i in range(1, A.size): - A._buf.ptr[i] *= A._buf.ptr[i - 1] - - if reorder: - A = A.reorder_layout() + result._buf.ptr[i] *= result._buf.ptr[i - 1] - return A^ + result.resize(shape=(1, result.size)) + return result^ fn cumprod[ dtype: DType -](var A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Cumprod of Matrix along the axis. @@ -271,6 +276,19 @@ fn cumprod[ ``` """ alias width: Int = simd_width_of[dtype]() + var order: String = "C" if A.flags.C_CONTIGUOUS else "F" + var result: Matrix[dtype] = Matrix.zeros[dtype](A.shape, order) + + if order == "C": + memcpy(dest=result._buf.ptr, src=A._buf.ptr, count=A.size) + else: + for j in range(result.shape[1]): + + @parameter + fn copy_col[width: Int](i: Int): + result._store[width](i, j, A._load[width](i, j)) + + vectorize[copy_col, width](A.shape[0]) if axis == 0: if A.flags.C_CONTIGUOUS: @@ -278,34 +296,40 @@ fn cumprod[ @parameter fn cal_vec_row[width: Int](j: Int): - A._store[width]( - i, j, A._load[width](i - 1, j) * A._load[width](i, j) + result._store[width]( + i, + j, + result._load[width](i - 1, j) + * result._load[width](i, j), ) vectorize[cal_vec_row, width](A.shape[1]) - return A^ + return result^ else: for j in range(A.shape[1]): for i in range(1, A.shape[0]): - A[i, j] = A[i - 1, j] * A[i, j] - return A^ + result[i, j] = result[i - 1, j] * result[i, j] + return result^ elif axis == 1: if A.flags.C_CONTIGUOUS: for i in range(A.shape[0]): for j in range(1, A.shape[1]): - A[i, j] = A[i, j - 1] * A[i, j] - return A^ + result[i, j] = result[i, j - 1] * result[i, j] + return result^ else: for j in range(1, A.shape[1]): @parameter fn cal_vec_column[width: Int](i: Int): - A._store[width]( - i, j, A._load[width](i, j - 1) * A._load[width](i, j) + result._store[width]( + i, + j, + result._load[width](i, j - 1) + * result._load[width](i, j), ) vectorize[cal_vec_column, width](A.shape[0]) - return A^ + return result^ else: raise Error(String("The axis can either be 1 or 0!")) diff --git a/numojo/routines/math/rounding.mojo b/numojo/routines/math/rounding.mojo index bb45c538..5c1fba19 100644 --- a/numojo/routines/math/rounding.mojo +++ b/numojo/routines/math/rounding.mojo @@ -11,20 +11,19 @@ from utils.numerics import nextafter as builtin_nextafter import numojo.routines.math._math_funcs as _mf from numojo.core.ndarray import NDArray import numojo.core.matrix as matrix -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl fn round[ dtype: DType -](var A: Matrix[dtype], decimals: Int = 0) -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], decimals: Int = 0) -> Matrix[dtype]: # FIXME # The built-in `round` function is not working now. # It will be fixed in future. - + var res = Matrix.zeros[dtype](A.shape) for i in range(A.size): - A._buf.ptr[i] = builtin_math.round(A._buf.ptr[i], ndigits=decimals) - - return A^ + res._buf.ptr[i] = builtin_math.round(A._buf.ptr[i], ndigits=decimals) + return res^ fn tabs[ diff --git a/numojo/routines/math/sums.mojo b/numojo/routines/math/sums.mojo index 0d1ee799..c59ada7e 100644 --- a/numojo/routines/math/sums.mojo +++ b/numojo/routines/math/sums.mojo @@ -1,8 +1,10 @@ from sys import simd_width_of from algorithm import parallelize, vectorize +from memory import UnsafePointer, memset_zero, memcpy from numojo.core.ndarray import NDArray -from numojo.core.matrix import Matrix +from numojo.core.own_data import OwnData +from numojo.core.matrix import Matrix, MatrixImpl from numojo.routines.creation import zeros @@ -108,7 +110,7 @@ fn sum[dtype: DType](A: NDArray[dtype], axis: Int) raises -> NDArray[dtype]: return result^ -fn sum[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: +fn sum[dtype: DType](A: MatrixImpl[dtype, **_]) -> Scalar[dtype]: """ Sum up all items in the Matrix. @@ -133,7 +135,9 @@ fn sum[dtype: DType](A: Matrix[dtype]) -> Scalar[dtype]: return res -fn sum[dtype: DType](A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +fn sum[ + dtype: DType +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Sum up the items in a Matrix along the axis. @@ -263,7 +267,7 @@ fn cumsum[ String("Invalid index: index out of bound [0, {}).").format(A.ndim) ) - var I = NDArray[DType.index](Shape(A.size)) + var I = NDArray[DType.int](Shape(A.size)) var ptr = I._buf.ptr var _shape = B.shape._move_axis_to_end(axis) @@ -282,7 +286,7 @@ fn cumsum[ return B^ -fn cumsum[dtype: DType](var A: Matrix[dtype]) raises -> Matrix[dtype]: +fn cumsum[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Matrix[dtype]: """ Cumsum of flattened matrix. @@ -297,24 +301,28 @@ fn cumsum[dtype: DType](var A: Matrix[dtype]) raises -> Matrix[dtype]: ``` """ var reorder = False + var order = "C" if A.flags.C_CONTIGUOUS else "F" + var result: Matrix[dtype] = Matrix.zeros[dtype](A.shape, order) + memcpy(dest=result._buf.ptr, src=A._buf.ptr, count=A.size) + if A.flags.F_CONTIGUOUS: reorder = True - A = A.reorder_layout() + result = result.reorder_layout() - A.resize(shape=(1, A.size)) + result.resize(shape=(1, A.size)) for i in range(1, A.size): - A._buf.ptr[i] += A._buf.ptr[i - 1] + result._buf.ptr[i] += result._buf.ptr[i - 1] if reorder: - A = A.reorder_layout() + result = result.reorder_layout() - return A^ + return result^ fn cumsum[ dtype: DType -](var A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[dtype]: """ Cumsum of Matrix along the axis. @@ -332,41 +340,50 @@ fn cumsum[ """ alias width: Int = simd_width_of[dtype]() + var order = "C" if A.flags.C_CONTIGUOUS else "F" + var result: Matrix[dtype] = Matrix.zeros[dtype](A.shape, order) + memcpy(dest=result._buf.ptr, src=A._buf.ptr, count=A.size) if axis == 0: - if A.flags.C_CONTIGUOUS: + if result.flags.C_CONTIGUOUS: for i in range(1, A.shape[0]): @parameter fn cal_vec_sum_column[width: Int](j: Int): - A._store[width]( - i, j, A._load[width](i - 1, j) + A._load[width](i, j) + result._store[width]( + i, + j, + result._load[width](i - 1, j) + + result._load[width](i, j), ) - vectorize[cal_vec_sum_column, width](A.shape[1]) - return A^ + vectorize[cal_vec_sum_column, width](result.shape[1]) + return result^ else: for j in range(A.shape[1]): for i in range(1, A.shape[0]): - A[i, j] = A[i - 1, j] + A[i, j] - return A^ + result[i, j] = result[i - 1, j] + result[i, j] + return result^ elif axis == 1: if A.flags.C_CONTIGUOUS: for i in range(A.shape[0]): for j in range(1, A.shape[1]): - A[i, j] = A[i, j - 1] + A[i, j] - return A^ + result[i, j] = result[i, j - 1] + result[i, j] + return result^ else: for j in range(1, A.shape[1]): @parameter fn cal_vec_sum_row[width: Int](i: Int): - A._store[width]( - i, j, A._load[width](i, j - 1) + A._load[width](i, j) + result._store[width]( + i, + j, + result._load[width](i, j - 1) + + result._load[width](i, j), ) vectorize[cal_vec_sum_row, width](A.shape[0]) - return A^ + return result^ else: raise Error(String("The axis can either be 1 or 0!")) diff --git a/numojo/routines/searching.mojo b/numojo/routines/searching.mojo index a6bb2fe2..bf26bb22 100644 --- a/numojo/routines/searching.mojo +++ b/numojo/routines/searching.mojo @@ -11,13 +11,13 @@ from collections.optional import Optional from numojo.core.ndarray import NDArray from numojo.core.ndshape import NDArrayShape import numojo.core.matrix as matrix -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl from numojo.core.utility import is_inttype, is_floattype from numojo.routines.sorting import binary_sort from numojo.routines.math.extrema import _max, _min -fn argmax_1d[dtype: DType](a: NDArray[dtype]) raises -> Scalar[DType.index]: +fn argmax_1d[dtype: DType](a: NDArray[dtype]) raises -> Scalar[DType.int]: """Returns the index of the maximum value in the buffer. Regardless of the shape of input, it is treated as a 1-d array. @@ -44,7 +44,7 @@ fn argmax_1d[dtype: DType](a: NDArray[dtype]) raises -> Scalar[DType.index]: return result -fn argmin_1d[dtype: DType](a: NDArray[dtype]) raises -> Scalar[DType.index]: +fn argmin_1d[dtype: DType](a: NDArray[dtype]) raises -> Scalar[DType.int]: """Returns the index of the minimum value in the buffer. Regardless of the shape of input, it is treated as a 1-d array. @@ -71,7 +71,7 @@ fn argmin_1d[dtype: DType](a: NDArray[dtype]) raises -> Scalar[DType.index]: return result -fn argmax[dtype: DType, //](a: NDArray[dtype]) raises -> Scalar[DType.index]: +fn argmax[dtype: DType, //](a: NDArray[dtype]) raises -> Scalar[DType.int]: """Returns the indices of the maximum values of the array along an axis. When no axis is specified, the array is flattened. @@ -98,7 +98,7 @@ fn argmax[dtype: DType, //](a: NDArray[dtype]) raises -> Scalar[DType.index]: fn argmax[ dtype: DType, // -](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.index]: +](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.int]: """Returns the indices of the maximum values of the array along an axis. When no axis is specified, the array is flattened. @@ -161,11 +161,11 @@ fn argmax[ @always_inline fn find_extrema_index[ dtype: DType, find_max: Bool -](A: Matrix[dtype]) raises -> Scalar[DType.index]: +](A: MatrixImpl[dtype, **_]) raises -> Scalar[DType.int, **_]: """Find index of min/max value, either in whole matrix or along an axis.""" var extreme_val = A[0, 0] - var extreme_idx: Scalar[DType.index] = 0 + var extreme_idx: Scalar[DType.int] = 0 for i in range(A.shape[0]): for j in range(A.shape[1]): @@ -187,13 +187,13 @@ fn find_extrema_index[ @always_inline fn find_extrema_index[ dtype: DType, find_max: Bool -](A: Matrix[dtype], axis: Optional[Int]) raises -> Matrix[DType.index]: +](A: MatrixImpl[dtype, **_], axis: Optional[Int]) raises -> Matrix[DType.int]: """Find index of min/max value, either in whole matrix or along an axis.""" if axis != 0 and axis != 1: raise Error(String("The axis can either be 1 or 0!")) - var B = Matrix[DType.index]( + var B = Matrix[DType.int]( shape=(A.shape[0], 1) if axis == 1 else (1, A.shape[1]) ) @@ -237,19 +237,19 @@ fn find_extrema_index[ return B^ -fn argmax[dtype: DType](A: Matrix[dtype]) raises -> Scalar[DType.index]: +fn argmax[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Scalar[DType.int]: """Find index of max value in a flattened matrix.""" return find_extrema_index[dtype, True](A) fn argmax[ dtype: DType -](A: Matrix[dtype], axis: Int) raises -> Matrix[DType.index]: +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[DType.int]: """Find indices of max values along the given axis.""" return find_extrema_index[dtype, True](A, axis) -fn argmin[dtype: DType, //](a: NDArray[dtype]) raises -> Scalar[DType.index]: +fn argmin[dtype: DType, //](a: NDArray[dtype]) raises -> Scalar[DType.int]: """Returns the indices of the minimum values of the array along an axis. When no axis is specified, the array is flattened. @@ -276,7 +276,7 @@ fn argmin[dtype: DType, //](a: NDArray[dtype]) raises -> Scalar[DType.index]: fn argmin[ dtype: DType, // -](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.index]: +](a: NDArray[dtype], axis: Int) raises -> NDArray[DType.int]: """Returns the indices of the minimum values of the array along an axis. When no axis is specified, the array is flattened. @@ -309,7 +309,7 @@ fn argmin[ return numojo.apply_along_axis[func1d=argmin_1d](a=a, axis=normalized_axis) -fn argmin[dtype: DType](A: Matrix[dtype]) raises -> Scalar[DType.index]: +fn argmin[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Scalar[DType.int]: """ Index of the min. It is first flattened before sorting. """ @@ -318,7 +318,7 @@ fn argmin[dtype: DType](A: Matrix[dtype]) raises -> Scalar[DType.index]: fn argmin[ dtype: DType -](A: Matrix[dtype], axis: Int) raises -> Matrix[DType.index]: +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[DType.int]: """ Index of the min along the given axis. """ diff --git a/numojo/routines/sorting.mojo b/numojo/routines/sorting.mojo index 54408959..979cdf5c 100644 --- a/numojo/routines/sorting.mojo +++ b/numojo/routines/sorting.mojo @@ -23,9 +23,10 @@ import math from algorithm import vectorize from numojo.core.ndarray import NDArray +from numojo.core.own_data import OwnData from numojo.core.ndshape import NDArrayShape import numojo.core.matrix as matrix -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl import numojo.core.utility as utility from numojo.routines.manipulation import ravel, transpose @@ -149,7 +150,7 @@ fn sort[dtype: DType](A: Matrix[dtype]) raises -> Matrix[dtype]: """ Sort the Matrix. It is first flattened before sorting. """ - var I = Matrix.zeros[DType.index](shape=A.shape) + var I = Matrix[DType.int].zeros(shape=A.shape) var B = A.flatten() _quick_sort_inplace(B, I, 0, A.size - 1) @@ -167,7 +168,7 @@ fn sort[dtype: DType](var A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: for i in range(A.shape[0]): var row = Matrix[dtype](shape=(1, A.shape[1]), order="C") - var indices = Matrix.zeros[DType.index]( + var indices = Matrix[DType.int].zeros( shape=(1, A.shape[1]), order="C" ) @@ -186,7 +187,7 @@ fn sort[dtype: DType](var A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: for j in range(A.shape[1]): var col = Matrix[dtype](shape=(A.shape[0], 1), order="C") - var indices = Matrix.zeros[DType.index]( + var indices = Matrix[DType.int].zeros( shape=(A.shape[0], 1), order="C" ) @@ -203,7 +204,7 @@ fn sort[dtype: DType](var A: Matrix[dtype], axis: Int) raises -> Matrix[dtype]: raise Error(String("The axis can either be 1 or 0!")) -fn argsort[dtype: DType](a: NDArray[dtype]) raises -> NDArray[DType.index]: +fn argsort[dtype: DType](a: NDArray[dtype]) raises -> NDArray[DType.int]: """ Returns the indices that would sort an array. It is not guaranteed to be unstable. @@ -224,7 +225,7 @@ fn argsort[dtype: DType](a: NDArray[dtype]) raises -> NDArray[DType.index]: else: a_flattened = ravel(a) - var indices = arange[DType.index](a_flattened.size) + var indices = arange[DType.int](a_flattened.size) _quick_sort_inplace(a_flattened, indices) @@ -233,7 +234,7 @@ fn argsort[dtype: DType](a: NDArray[dtype]) raises -> NDArray[DType.index]: fn argsort[ dtype: DType -](mut a: NDArray[dtype], axis: Int) raises -> NDArray[DType.index]: +](mut a: NDArray[dtype], axis: Int) raises -> NDArray[DType.int]: """ Returns the indices that would sort an array. It is not guaranteed to be unstable. @@ -272,11 +273,11 @@ fn argsort[ ) -fn argsort[dtype: DType](A: Matrix[dtype]) raises -> Matrix[DType.index]: +fn argsort[dtype: DType](A: MatrixImpl[dtype, **_]) raises -> Matrix[DType.int]: """ Argsort the Matrix. It is first flattened before sorting. """ - var I = Matrix[DType.index](shape=(1, A.size), order=A.order()) + var I = Matrix[DType.int](shape=(1, A.size), order=A.order()) for i in range(I.size): I._buf.ptr[i] = i var B: Matrix[dtype] @@ -291,18 +292,18 @@ fn argsort[dtype: DType](A: Matrix[dtype]) raises -> Matrix[DType.index]: fn argsort[ dtype: DType -](var A: Matrix[dtype], axis: Int) raises -> Matrix[DType.index]: +](A: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[DType.int]: """ Argsort the Matrix along the given axis. """ var order = A.order() if axis == 1: - var result = Matrix[DType.index](shape=A.shape, order=order) + var result = Matrix[DType.int](shape=A.shape, order=order) for i in range(A.shape[0]): var row = Matrix[dtype](shape=(1, A.shape[1]), order="C") - var idx = Matrix[DType.index](shape=(1, A.shape[1]), order="C") + var idx = Matrix[DType.int](shape=(1, A.shape[1]), order="C") for j in range(A.shape[1]): row._store(0, j, A._load(i, j)) @@ -316,11 +317,11 @@ fn argsort[ return result^ elif axis == 0: - var result = Matrix[DType.index](shape=A.shape, order=order) + var result = Matrix[DType.int](shape=A.shape, order=order) for j in range(A.shape[1]): var col = Matrix[dtype](shape=(A.shape[0], 1), order="C") - var idx = Matrix[DType.index](shape=(A.shape[0], 1), order="C") + var idx = Matrix[DType.int](shape=(A.shape[0], 1), order="C") for i in range(A.shape[0]): col._store(i, 0, A._load(i, j)) @@ -542,7 +543,7 @@ fn quick_sort_stable_inplace_1d[dtype: DType](mut a: NDArray[dtype]) raises: fn argsort_quick_sort_1d[ dtype: DType -](a: NDArray[dtype]) raises -> NDArray[DType.index]: +](a: NDArray[dtype]) raises -> NDArray[DType.int]: """ Returns the indices that would sort the buffer of an array. Regardless of the shape of input, it is treated as a 1-d array. @@ -559,7 +560,7 @@ fn argsort_quick_sort_1d[ """ var result: NDArray[dtype] = a.copy() - var indices = arange[DType.index](result.size) + var indices = arange[DType.int](result.size) _quick_sort_inplace(result, indices) return indices^ @@ -831,7 +832,7 @@ fn _quick_sort_inplace[dtype: DType](mut A: NDArray[dtype]) raises: fn _quick_sort_inplace[ dtype: DType -](mut A: NDArray[dtype], mut I: NDArray[DType.index]) raises: +](mut A: NDArray[dtype], mut I: NDArray[DType.int]) raises: """ Sort in-place array's buffer using quick sort method. The indices are also sorted. diff --git a/numojo/routines/statistics/averages.mojo b/numojo/routines/statistics/averages.mojo index d8f5e406..05d4b339 100644 --- a/numojo/routines/statistics/averages.mojo +++ b/numojo/routines/statistics/averages.mojo @@ -15,8 +15,9 @@ from collections.optional import Optional import math as mt from numojo.core.ndarray import NDArray +from numojo.core.own_data import OwnData import numojo.core.matrix as matrix -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl import numojo.core.utility as utility from numojo.routines.logic.comparison import greater, less from numojo.routines.manipulation import broadcast_to, _broadcast_back_to @@ -102,7 +103,7 @@ fn mean[ fn mean[ dtype: DType, //, returned_dtype: DType = DType.float64 -](a: Matrix[dtype]) -> Scalar[returned_dtype]: +](a: MatrixImpl[dtype, **_]) -> Scalar[returned_dtype]: """ Calculate the arithmetic average of all items in the Matrix. @@ -122,7 +123,7 @@ fn mean[ fn mean[ dtype: DType, //, returned_dtype: DType = DType.float64 -](a: Matrix[dtype], axis: Int) raises -> Matrix[returned_dtype]: +](a: MatrixImpl[dtype, **_], axis: Int) raises -> Matrix[returned_dtype]: """ Calculate the arithmetic average of a Matrix along the axis. @@ -373,7 +374,7 @@ fn std[ fn std[ dtype: DType, //, returned_dtype: DType = DType.float64 -](A: Matrix[dtype], ddof: Int = 0) raises -> Scalar[returned_dtype]: +](A: MatrixImpl[dtype, **_], ddof: Int = 0) raises -> Scalar[returned_dtype]: """ Compute the standard deviation. @@ -398,7 +399,9 @@ fn std[ fn std[ dtype: DType, //, returned_dtype: DType = DType.float64 -](A: Matrix[dtype], axis: Int, ddof: Int = 0) raises -> Matrix[returned_dtype]: +](A: MatrixImpl[dtype, **_], axis: Int, ddof: Int = 0) raises -> Matrix[ + returned_dtype +]: """ Compute the standard deviation along axis. @@ -505,7 +508,7 @@ fn variance[ fn variance[ dtype: DType, //, returned_dtype: DType = DType.float64 -](A: Matrix[dtype], ddof: Int = 0) raises -> Scalar[returned_dtype]: +](A: MatrixImpl[dtype, **_], ddof: Int = 0) raises -> Scalar[returned_dtype]: """ Compute the variance. @@ -533,7 +536,9 @@ fn variance[ fn variance[ dtype: DType, //, returned_dtype: DType = DType.float64 -](A: Matrix[dtype], axis: Int, ddof: Int = 0) raises -> Matrix[returned_dtype]: +](A: MatrixImpl[dtype, **_], axis: Int, ddof: Int = 0) raises -> Matrix[ + returned_dtype +]: """ Compute the variance along axis. diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 00000000..f1a05552 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,5546 @@ +version: 6 +environments: + default: + channels: + - 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[package.host-dependencies] -modular = ">=25.6.1,<26" +modular = ">=25.7.0,<26" [package.build-dependencies] -modular = ">=25.6.1,<26" +modular = ">=25.7.0,<26" [package.run-dependencies] -modular = ">=25.6.1,<26" +modular = ">=25.7.0,<26" [tasks] # compile the package and copy it to the tests folder @@ -50,22 +51,48 @@ p = "clear && pixi run package" # format the package format = "pixi run mojo format ./" -# test whether tests pass on the built package -test = "pixi run package && pixi run mojo test tests -I tests/ && rm tests/numojo.mojopkg" -t = "clear && pixi run test" - -# run individual tests to avoid overheat -test_core = "pixi run package && pixi run mojo test tests/core -I tests/ && rm tests/numojo.mojopkg" -test_creation = "pixi run package && pixi run mojo test tests/routines/test_creation.mojo -I tests/ && rm tests/numojo.mojopkg" -test_functional = "pixi run package && pixi run mojo test tests/routines/test_functional.mojo -I tests/ && rm tests/numojo.mojopkg" -test_indexing = "pixi run package && pixi run mojo test tests/routines/test_indexing.mojo -I tests/ && rm tests/numojo.mojopkg" -test_linalg = "pixi run package && pixi run mojo test tests/routines/test_linalg.mojo -I tests/ && rm tests/numojo.mojopkg" -test_manipulation = "pixi run package && pixi run mojo test tests/routines/test_manipulation.mojo -I tests/ && rm tests/numojo.mojopkg" -test_math = "pixi run package && pixi run mojo test tests/routines/test_math.mojo -I tests/ && rm tests/numojo.mojopkg" -test_random = "pixi run package && pixi run mojo test tests/routines/test_random.mojo -I tests/ && rm tests/numojo.mojopkg" -test_statistics = "pixi run package && pixi run mojo test tests/routines/test_statistics.mojo -I tests/ && rm tests/numojo.mojopkg" -test_sorting = "pixi run package && pixi run mojo test tests/routines/test_sorting.mojo -I tests/ && rm tests/numojo.mojopkg" -test_searching = "pixi run package && pixi run mojo test tests/routines/test_searching.mojo -I tests/ && rm tests/numojo.mojopkg" +# to run individual test files +run-test = { cmd = "pixi run mojo run -I tests/ $TEST_FILE", env = { TEST_FILE = "" } } + +# Test core category +test_core = """ +pixi run package && \ +pixi run mojo run -I tests/ tests/core/test_array_indexing_and_slicing.mojo && \ +pixi run mojo run -I tests/ tests/core/test_array_methods.mojo && \ +pixi run mojo run -I tests/ tests/core/test_bool_masks.mojo && \ +pixi run mojo run -I tests/ tests/core/test_complexArray.mojo && \ +pixi run mojo run -I tests/ tests/core/test_complexSIMD.mojo && \ +pixi run mojo run -I tests/ tests/core/test_matrix.mojo && \ +pixi run mojo run -I tests/ -D F_CONTIGUOUS tests/core/test_matrix.mojo && \ +pixi run mojo run -I tests/ tests/core/test_shape_strides_item.mojo && \ +rm tests/numojo.mojopkg +""" + +# Test routines category +test_routines = """ +pixi run package && \ +pixi run mojo run -I tests/ tests/routines/test_creation.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_functional.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_indexing.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_io.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_linalg.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_manipulation.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_math.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_random.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_statistics.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_sorting.mojo && \ +pixi run mojo run -I tests/ tests/routines/test_searching.mojo && \ +rm tests/numojo.mojopkg +""" + +# Test science category +test_signal = "pixi run package && pixi run mojo run -I tests/ tests/science/test_signal.mojo && rm tests/numojo.mojopkg" + +test = """ +pixi run test_core && \ +pixi run test_routines && \ +pixi run test_signal +""" # run all final checks before a commit final = "pixi run format && pixi run test" @@ -78,7 +105,7 @@ doc_pages = "mojo doc numojo/ -o docs.json" release = "clear && pixi run final && pixi run doc_pages" [dependencies] -python = ">=3.13.9,<3.14" -numpy = ">=2.3.3,<3" -scipy = ">=1.16.2,<2" -modular = ">=25.6.1,<26" +python = ">=3.14.0,<3.15" +numpy = ">=2.3.5,<3" +scipy = ">=1.16.3,<2" +modular = ">=25.7.0,<26" diff --git a/tests/core/test_array_indexing_and_slicing.mojo b/tests/core/test_array_indexing_and_slicing.mojo index 8c08098f..4e9ed527 100644 --- a/tests/core/test_array_indexing_and_slicing.mojo +++ b/tests/core/test_array_indexing_and_slicing.mojo @@ -3,6 +3,7 @@ from numojo.prelude import * from testing.testing import assert_true, assert_almost_equal, assert_equal from utils_for_test import check, check_is_close from python import Python +from testing import TestSuite def test_getitem_scalar(): @@ -620,3 +621,7 @@ def test_3d_array_basic_slicing(): # nm_slice3 = nm_arr[::2, 1::2] # np_sliced3 = np_arr[::2, 1::2] # check(nm_slice3, np_sliced3, "F-order step [::2, 1::2] failed") + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/core/test_array_methods.mojo b/tests/core/test_array_methods.mojo index c49a79df..6d072a8a 100644 --- a/tests/core/test_array_methods.mojo +++ b/tests/core/test_array_methods.mojo @@ -3,6 +3,7 @@ from python import Python from numojo.prelude import * from testing.testing import assert_true, assert_almost_equal, assert_equal from utils_for_test import check, check_is_close, check_values_close +from testing import TestSuite def test_constructors(): @@ -148,3 +149,7 @@ def test_iterator(): fnp_nditer_f.__next__(), "`_NDIter` or `nditer()` of F array by order F breaks", ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/core/test_bool_masks.mojo b/tests/core/test_bool_masks.mojo index 33c99dab..aa65b7bb 100644 --- a/tests/core/test_bool_masks.mojo +++ b/tests/core/test_bool_masks.mojo @@ -3,6 +3,7 @@ from numojo import * from testing.testing import assert_true, assert_almost_equal, assert_equal from utils_for_test import check, check from python import Python +from testing import TestSuite # TODO: there's something wrong with bool comparision even though result looks same. @@ -65,3 +66,7 @@ def test_bool_masks_eq(): var np_mask = np_A[np_A > 10] var mask = A[A > Scalar[nm.i16](10)] check(mask, np_mask, "Masked array") + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/core/test_complexArray.mojo b/tests/core/test_complexArray.mojo index 1c260695..9d2fcf39 100644 --- a/tests/core/test_complexArray.mojo +++ b/tests/core/test_complexArray.mojo @@ -1,5 +1,6 @@ from testing import assert_equal, assert_almost_equal from numojo import * +from testing import TestSuite # TODO: Added getter and setter tests @@ -104,3 +105,7 @@ fn test_complex_array_div() raises: assert_almost_equal(quot.item(0).re, 0.44, "div failed") assert_almost_equal(quot.item(0).im, 0.08, "div failed") + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/core/test_complexSIMD.mojo b/tests/core/test_complexSIMD.mojo index fa0a9ec0..74f9bc66 100644 --- a/tests/core/test_complexSIMD.mojo +++ b/tests/core/test_complexSIMD.mojo @@ -1,5 +1,6 @@ from testing import assert_equal, assert_almost_equal from numojo import * +from testing import TestSuite fn test_complex_init() raises: @@ -69,3 +70,7 @@ fn test_complex_div() raises: var quot = c1 / c2 assert_almost_equal(quot.re, 0.44, " division failed") assert_almost_equal(quot.im, 0.08, " division failed") + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/core/test_matrix.mojo b/tests/core/test_matrix.mojo index c22a4a23..6ae8ca4b 100644 --- a/tests/core/test_matrix.mojo +++ b/tests/core/test_matrix.mojo @@ -1,9 +1,10 @@ import numojo as nm from numojo.prelude import * -from numojo.core.matrix import Matrix +from numojo.core.matrix import Matrix, MatrixImpl from python import Python, PythonObject from testing.testing import assert_raises, assert_true from sys import is_defined +from testing import assert_equal, TestSuite alias order: String = String("F") if is_defined["F_CONTIGUOUS"]() else String( "C" @@ -58,8 +59,6 @@ def test_manipulation(): "Reshape is broken", ) - _ = A.resize((1000, 100)) - _ = Anp.resize(1000, 100) check_matrices_equal( A, Anp, @@ -124,6 +123,7 @@ def test_arithmetic(): check_matrices_close(A**0.5, np.power(Ap, 0.5), "Pow (to int) is broken") +# FIXME: the gt, lt tests are failing when run together with all other tests even though they pass in isolation. weird behaviour. Commmenting it out temporarily and fix later. def test_logic(): var np = Python.import_module("numpy") var A = Matrix.ones((5, 1), order=order) @@ -135,8 +135,13 @@ def test_logic(): var Bnp = np.matrix(B.to_numpy()) var Lnp = np.matrix(L.to_numpy()) - check_matrices_equal(A > B, Anp > Bnp, "gt is broken") - check_matrices_equal(A < B, Anp < Bnp, "lt is broken") + var gt_res = A > B + var gt_res_np = Anp > Bnp + var lt_res = A < B + var lt_res_np = Anp < Bnp + check_matrices_equal(gt_res, gt_res_np, "gt is broken") + check_matrices_equal(lt_res, lt_res_np, "lt is broken") + assert_true( np.equal(nm.all(L), np.all(Lnp)), "`all` is broken", @@ -346,9 +351,6 @@ def test_eigen_decomposition(): namedtuple = np.linalg.eig(Anp) np_eigenvalues = namedtuple.eigenvalues - print(np_eigenvalues) - print(Lambda.to_numpy()) - print(np.diag(Lambda.to_numpy())) # Sort eigenvalues and eigenvectors for comparison (numpy doesn't guarantee order) var np_sorted_eigenvalues = np.sort(np_eigenvalues) @@ -369,7 +371,6 @@ def test_eigen_decomposition(): # Check that A = Q * Lambda * Q^T (eigendecomposition property) var A_reconstructed = Q @ Lambda @ Q.transpose() - print(A_reconstructed - A) assert_true( np.allclose(A_reconstructed.to_numpy(), Anp, atol=1e-10), "A ≠ Q * Lambda * Q^T", @@ -437,19 +438,19 @@ def test_math(): ) check_matrices_close( - nm.cumsum(A.copy()), + nm.cumsum(A), np.cumsum(Anp), "`cumsum` is broken", ) for i in range(2): check_matrices_close( - nm.cumsum(A.copy(), axis=i), + nm.cumsum(A, axis=i), np.cumsum(Anp, axis=i), String("`cumsum` by axis {i} is broken"), ) check_matrices_close( - nm.cumprod(A.copy()), + nm.cumprod(A), np.cumprod(Anp), "`cumprod` is broken", ) @@ -565,3 +566,7 @@ def test_searching(): np.argmin(Anp, axis=i), String("`argmin` by axis {} is broken").format(i), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/core/test_shape_strides_item.mojo b/tests/core/test_shape_strides_item.mojo index 163b65da..e96d712f 100644 --- a/tests/core/test_shape_strides_item.mojo +++ b/tests/core/test_shape_strides_item.mojo @@ -1,6 +1,7 @@ from numojo.prelude import * from testing.testing import assert_true, assert_almost_equal, assert_equal from utils_for_test import check, check_is_close +from testing import TestSuite def test_shape(): @@ -29,3 +30,7 @@ def test_item(): A[-1] == 4, msg=String("`NDArrayStrides.__getitem__()` fails: may overflow"), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_creation.mojo b/tests/routines/test_creation.mojo index e32060ac..99e0894f 100644 --- a/tests/routines/test_creation.mojo +++ b/tests/routines/test_creation.mojo @@ -10,6 +10,7 @@ from testing.testing import ( from python import Python, PythonObject import random as builtin_random from utils_for_test import check, check_is_close +from testing import TestSuite def test_arange(): @@ -143,26 +144,28 @@ def test_eye(): ) -def test_fromstring(): - var A = nm.fromstring("[[[1,2],[3,4]],[[5,6],[7,8]]]") - var B = nm.array[DType.int32](String("[0.1, -2.3, 41.5, 19.29145, -199]")) - print(A) - print(B) +# TODO: modify these tests to use assert_equal and check function +# def test_fromstring(): +# var A = nm.fromstring("[[[1,2],[3,4]],[[5,6],[7,8]]]") +# var B = nm.array[DType.int32](String("[0.1, -2.3, 41.5, 19.29145, -199]")) +# # print(A) +# # print(B) -def test_fromstring_complicated(): - var s = """ - [[[[1,2,10], - [3,4,2]], - [[5,6,4], - [7,8,10]]], - [[[1,2,12], - [3,4,41]], - [[5,6,12], - [7,8,99]]]] - """ - var A = nm.fromstring(s) - print(A) +# TODO: modify these tests to use assert_equal and check function +# def test_fromstring_complicated(): +# var s = """ +# [[[[1,2,10], +# [3,4,2]], +# [[5,6,4], +# [7,8,10]]], +# [[[1,2,12], +# [3,4,41]], +# [[5,6,12], +# [7,8,99]]]] +# """ +# var A = nm.fromstring(s) +# print(A) def test_diag(): @@ -357,3 +360,7 @@ def test_arr_manipulation(): # assert_equal( # image == image_converted_via_array, True, "Tensor conversion is broken" # ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_functional.mojo b/tests/routines/test_functional.mojo index e6fef22b..592d9207 100644 --- a/tests/routines/test_functional.mojo +++ b/tests/routines/test_functional.mojo @@ -10,6 +10,7 @@ Test functional programming module `numojo.routines.functional`. from python import Python from testing.testing import assert_true, assert_almost_equal, assert_equal from utils_for_test import check, check_is_close +from testing import TestSuite from numojo.prelude import * @@ -36,3 +37,7 @@ fn test_apply_along_axis() raises: "`apply_along_axis` F-order array along axis {} is broken" ).format(i), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_indexing.mojo b/tests/routines/test_indexing.mojo index 72c44e2c..0a2ea4d7 100644 --- a/tests/routines/test_indexing.mojo +++ b/tests/routines/test_indexing.mojo @@ -10,6 +10,7 @@ Test indexing module `numojo.routines.indexing`. from python import Python from testing.testing import assert_true, assert_almost_equal, assert_equal from utils_for_test import check, check_is_close +from testing import TestSuite from numojo.prelude import * @@ -298,3 +299,7 @@ fn test_take_along_axis_fortran_order() raises: " array is broken" ), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_io.mojo b/tests/routines/test_io.mojo index 4c81167d..4b5895eb 100644 --- a/tests/routines/test_io.mojo +++ b/tests/routines/test_io.mojo @@ -2,6 +2,7 @@ from numojo.routines.io.files import load, save, loadtxt, savetxt from numojo import ones, full from python import Python import os +from testing import TestSuite fn test_save_and_load() raises: @@ -32,3 +33,7 @@ fn test_savetxt_and_loadtxt() raises: np.allclose(arr2.to_numpy(), arr.to_numpy()) # Clean up os.remove(fname) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_linalg.mojo b/tests/routines/test_linalg.mojo index 81f03f8d..3de07f31 100644 --- a/tests/routines/test_linalg.mojo +++ b/tests/routines/test_linalg.mojo @@ -2,6 +2,7 @@ import numojo as nm from numojo.prelude import * from python import Python, PythonObject from utils_for_test import check, check_is_close, check_values_close +from testing import TestSuite # ===-----------------------------------------------------------------------===# # Matmul @@ -117,3 +118,7 @@ def test_misc(): np.diagonal(np_arr, offset=i), String("`diagonal` by axis {} is broken").format(i), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_manipulation.mojo b/tests/routines/test_manipulation.mojo index ef0716b7..fc784cc3 100644 --- a/tests/routines/test_manipulation.mojo +++ b/tests/routines/test_manipulation.mojo @@ -3,6 +3,7 @@ from numojo import * from testing.testing import assert_true, assert_almost_equal, assert_equal from utils_for_test import check, check_is_close from python import Python +from testing import TestSuite fn test_arr_manipulation() raises: @@ -137,3 +138,7 @@ def test_broadcast(): np.broadcast_to(a.to_numpy(), Python.tuple(2, 2, 2, 3)), "`broadcast_to` fails.", ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_math.mojo b/tests/routines/test_math.mojo index aa230a67..a73b62d0 100644 --- a/tests/routines/test_math.mojo +++ b/tests/routines/test_math.mojo @@ -8,6 +8,7 @@ from utils_for_test import ( check_values_close, check_with_dtype, ) +from testing import TestSuite # ===-----------------------------------------------------------------------===# # Sums, products, differences @@ -444,3 +445,7 @@ fn test_misc() raises: np.clip(cfnp, 0.02, -0.01), String("`clip` 3d f-order is broken"), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_random.mojo b/tests/routines/test_random.mojo index 9c741000..d192e553 100644 --- a/tests/routines/test_random.mojo +++ b/tests/routines/test_random.mojo @@ -4,6 +4,7 @@ from numojo.prelude import * from python import Python, PythonObject from utils_for_test import check, check_is_close from testing.testing import assert_true, assert_almost_equal +from testing import TestSuite def test_rand(): @@ -36,8 +37,8 @@ def test_randminmax(): def test_randint(): """Test random int array generation with min and max values.""" - var arr_low_high = nm.random.randint(Shape(10, 10, 10), 0, 10) - var arr_high = nm.random.randint(Shape(10, 10, 10), 6) + var arr_low_high = nm.random.randint(Shape(30, 30, 30), 0, 10) + var arr_high = nm.random.randint(Shape(30, 30, 30), 6) var arr_low_high_mean = nm.mean(arr_low_high) var arr_high_mean = nm.mean(arr_high) assert_almost_equal( @@ -215,3 +216,7 @@ def test_rand_exponential(): arr_list._buf.ptr[i] >= 0, "Exponential distribution should only produce non-negative values", ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_searching.mojo b/tests/routines/test_searching.mojo index 54816c80..ea77c2b9 100644 --- a/tests/routines/test_searching.mojo +++ b/tests/routines/test_searching.mojo @@ -1,6 +1,7 @@ from numojo.prelude import * from python import Python, PythonObject from utils_for_test import check, check_is_close, check_values_close +from testing import TestSuite fn test_argmax() raises: @@ -223,3 +224,7 @@ fn test_take_along_axis_with_argmax_argmin() raises: np.take_along_axis(a2d_np, reshaped_min_indices_np, axis=1), "`take_along_axis` with argmin is broken", ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_sorting.mojo b/tests/routines/test_sorting.mojo index 501b8683..68b53188 100644 --- a/tests/routines/test_sorting.mojo +++ b/tests/routines/test_sorting.mojo @@ -1,6 +1,7 @@ import numojo as nm from python import Python, PythonObject from utils_for_test import check, check_is_close +from testing import TestSuite fn test_sorting() raises: @@ -125,3 +126,7 @@ fn test_sorting() raises: np.sort(S.to_numpy(), axis=i, stable=True), String("`sort` 6d stably by axis {} is broken").format(i), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/routines/test_statistics.mojo b/tests/routines/test_statistics.mojo index 17e75845..11567091 100644 --- a/tests/routines/test_statistics.mojo +++ b/tests/routines/test_statistics.mojo @@ -4,6 +4,7 @@ from numojo.core.matrix import Matrix from python import Python, PythonObject from testing.testing import assert_raises, assert_true from utils_for_test import check, check_is_close +from testing import TestSuite # ===-----------------------------------------------------------------------===# # Statistics @@ -74,3 +75,7 @@ def test_mean_median_var_std(): np.std(Anp, axis), String("`std` is broken for axis {}").format(axis), ) + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run() diff --git a/tests/science/test_signal.mojo b/tests/science/test_signal.mojo index 46e2d496..7249fc17 100644 --- a/tests/science/test_signal.mojo +++ b/tests/science/test_signal.mojo @@ -3,6 +3,7 @@ from numojo.prelude import * from python import Python, PythonObject from utils_for_test import check, check_is_close from testing.testing import assert_raises +from testing import TestSuite def test_convolve2d(): @@ -16,3 +17,7 @@ def test_convolve2d(): res1 = nm.science.signal.convolve2d(in1, in2) res2 = sp.signal.convolve2d(npin1, npin2, mode="valid") check(res1, res2, "test_convolve2d failed #2\n") + + +def main(): + TestSuite.discover_tests[__functions_in_module()]().run()