74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
"""
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Module to perform integration for continuous convolution.
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"""
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import torch
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class Integral:
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"""
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Class allowing integration for continous convolution.
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"""
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def __init__(self, param):
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"""
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Initializzation of the :class:`Integral` class.
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:param param: The type of continuous convolution.
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:type param: string
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:raises TypeError: If the parameter is neither ``discrete``
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nor ``continuous``.
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"""
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if param == "discrete":
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self.make_integral = self.integral_param_disc
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elif param == "continuous":
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self.make_integral = self.integral_param_cont
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else:
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raise TypeError
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def __call__(self, *args, **kwds):
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"""
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Call the integral function
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:param list args: Arguments for the integral function.
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:param dict kwds: Keyword arguments for the integral function.
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:return: The integral of the input.
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:rtype: torch.tensor
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"""
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return self.make_integral(*args, **kwds)
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def _prepend_zero(self, x):
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"""
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Create bins to perform integration.
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:param torch.Tensor x: The input tensor.
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:return: The bins for the integral.
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:rtype: torch.Tensor
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"""
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return torch.cat((torch.zeros(1, dtype=x.dtype, device=x.device), x))
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def integral_param_disc(self, x, y, idx):
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"""
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Perform discrete integration with discrete parameters.
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:param torch.Tensor x: The first input tensor.
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:param torch.Tensor y: The second input tensor.
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:param list[int] idx: The indices for different strides.
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:return: The discrete integral.
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:rtype: torch.Tensor
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"""
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cs_idxes = self._prepend_zero(torch.cumsum(torch.tensor(idx), 0))
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cs = self._prepend_zero(torch.cumsum(x.flatten() * y.flatten(), 0))
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return cs[cs_idxes[1:]] - cs[cs_idxes[:-1]]
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def integral_param_cont(self, x, y, idx):
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"""
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Perform continuous integration with continuous parameters.
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:param torch.Tensor x: The first input tensor.
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:param torch.Tensor y: The second input tensor.
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:param list[int] idx: The indices for different strides.
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:raises NotImplementedError: The method is not implemented.
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"""
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raise NotImplementedError
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