Fix a bunch of codacy code-style issues
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RELEASE.md
12
RELEASE.md
@ -1,9 +1,11 @@
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# Release 0.1.1-dev0
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# ProtoTorch Releases
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## Includes
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- Bugfixes.
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- 100% test coverage.
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## Release 0.1.1-dev0
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# Release 0.1.0-dev0
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### Includes
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- Bugfixes.
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- 100% test coverage.
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## Release 0.1.0-dev0
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Initial public release of ProtoTorch.
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@ -21,6 +21,7 @@ x_train = scaler.transform(x_train)
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# Define the GLVQ model
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class Model(torch.nn.Module):
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def __init__(self, **kwargs):
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"""GLVQ model."""
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super().__init__()
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self.p1 = Prototypes1D(input_dim=2,
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prototypes_per_class=1,
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@ -16,16 +16,17 @@ def register_activation(f):
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@register_activation
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# @torch.jit.script
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def identity(x, beta=torch.tensor([0])):
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""":math:`f(x) = x`"""
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""":math:`f(x) = x`."""
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return x
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@register_activation
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# @torch.jit.script
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def sigmoid_beta(x, beta=torch.tensor([10])):
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""":math:`f(x) = \\frac{1}{1 + e^{-\\beta x}}`
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r""":math:`f(x) = \\frac{1}{1 + e^{-\\beta x}}`.
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Keyword Arguments:
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__________________
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beta (float): Parameter :math:`\\beta`
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"""
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out = torch.reciprocal(1.0 + torch.exp(-int(beta.item()) * x))
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@ -35,9 +36,10 @@ def sigmoid_beta(x, beta=torch.tensor([10])):
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@register_activation
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# @torch.jit.script
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def swish_beta(x, beta=torch.tensor([10])):
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""":math:`f(x) = \\frac{x}{1 + e^{-\\beta x}}`
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r""":math:`f(x) = \\frac{x}{1 + e^{-\\beta x}}`.
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Keyword Arguments:
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__________________
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beta (float): Parameter :math:`\\beta`
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"""
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out = x * sigmoid_beta(x, beta=beta)
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@ -4,7 +4,8 @@ import torch
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def squared_euclidean_distance(x, y):
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"""Compute the squared Euclidean distance between :math:`x` and :math:`y`.
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"""
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Compute the squared Euclidean distance between :math:`x` and :math:`y`.
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Expected dimension of x is 2.
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Expected dimension of y is 2.
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@ -17,7 +18,8 @@ def squared_euclidean_distance(x, y):
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def euclidean_distance(x, y):
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"""Compute the Euclidean distance between :math:`x` and :math:`y`.
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"""
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Compute the Euclidean distance between :math:`x` and :math:`y`.
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Expected dimension of x is 2.
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Expected dimension of y is 2.
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@ -28,7 +30,8 @@ def euclidean_distance(x, y):
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def lpnorm_distance(x, y, p):
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"""Compute :math:`{\\langle x, y \\rangle}_p`.
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"""
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Compute :math:`{\\langle x, y \\rangle}_p`.
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Expected dimension of x is 2.
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Expected dimension of y is 2.
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@ -38,7 +41,8 @@ def lpnorm_distance(x, y, p):
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def omega_distance(x, y, omega):
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"""Omega distance.
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"""
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Omega distance.
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Compute :math:`{\\langle \\Omega x, \\Omega y \\rangle}_p`
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@ -53,7 +57,8 @@ def omega_distance(x, y, omega):
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def lomega_distance(x, y, omegas):
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"""Localized Omega distance.
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"""
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Localized Omega distance.
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Compute :math:`{\\langle \\Omega_k x, \\Omega_k y_k \\rangle}_p`
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@ -7,7 +7,6 @@ from prototorch.functions.losses import glvq_loss
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class GLVQLoss(torch.nn.Module):
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"""GLVQ Loss."""
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def __init__(self, margin=0.0, squashing='identity', beta=10, **kwargs):
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super().__init__(**kwargs)
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self.margin = margin
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