feat: add Camera Work AI package

CameraAI~/Tools~/CWCameraWorker/_internal/sklearn/_loss/_loss.pxd

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+# Fused types for input like y_true, raw_prediction, sample_weights.
+ctypedef fused floating_in:
+    double
+    float
+
+
+# Fused types for output like gradient and hessian
+# We use a different fused types for input (floating_in) and output (floating_out), such
+# that input and output can have different dtypes in the same function call. A single
+# fused type can only take on one single value (type) for all arguments in one function
+# call.
+ctypedef fused floating_out:
+    double
+    float
+
+
+# Struct to return 2 doubles
+ctypedef struct double_pair:
+    double val1
+    double val2
+
+
+# C base class for loss functions
+cdef class CyLossFunction:
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfSquaredError(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyAbsoluteError(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyPinballLoss(CyLossFunction):
+    cdef readonly double quantile  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHuberLoss(CyLossFunction):
+    cdef public double delta  # public makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfPoissonLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfGammaLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfTweedieLoss(CyLossFunction):
+    cdef readonly double power  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfTweedieLossIdentity(CyLossFunction):
+    cdef readonly double power  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfBinomialLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyExponentialLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfMultinomialLoss():
+    cdef void cy_gradient(
+        self,
+        const floating_in y_true,
+        const floating_in[::1] raw_prediction,
+        const floating_in sample_weight,
+        floating_out[::1] gradient_out,
+    ) noexcept nogil

CameraAI~/Tools~/CWCameraWorker/_internal/sklearn/_loss/meson.build

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+# .pyx is generated, so this is needed to make Cython compilation work
+_loss_cython_tree = [
+  fs.copyfile('_loss.pxd')
+]
+
+_loss_pyx = custom_target(
+  '_loss_pyx',
+  output: '_loss.pyx',
+  input: '_loss.pyx.tp',
+  command: [tempita, '@INPUT@', '-o', '@OUTDIR@'],
+  # TODO in principle this should go in py.exension_module below. This is
+  # temporary work-around for dependency issue with .pyx.tp files. For more
+  # details, see https://github.com/mesonbuild/meson/issues/13212
+  depends: _loss_cython_tree,
+)
+
+py.extension_module(
+  '_loss',
+  cython_gen.process(_loss_pyx),
+  dependencies: [openmp_dep],
+  install: true,
+  subdir: 'sklearn/_loss',
+)