More cosmetic changes
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@ -3,8 +3,8 @@
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## Release 0.1.1-dev0
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## Release 0.1.1-dev0
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### Includes
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### Includes
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- Minor bugfixes.
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- Minor bugfixes.
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- 100% line coverage.
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- 100% line coverage.
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## Release 0.1.0-dev0
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## Release 0.1.0-dev0
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@ -8,9 +8,10 @@ ACTIVATIONS = dict()
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# def register_activation(scriptf):
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# def register_activation(scriptf):
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# ACTIVATIONS[scriptf.name] = scriptf
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# ACTIVATIONS[scriptf.name] = scriptf
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# return scriptf
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# return scriptf
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def register_activation(f):
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def register_activation(function):
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ACTIVATIONS[f.__name__] = f
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"""Add the activation function to the registry."""
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return f
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ACTIVATIONS[function.__name__] = function
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return function
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@register_activation
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@register_activation
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@ -55,6 +56,7 @@ def swish_beta(x, beta=torch.tensor([10])):
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def get_activation(funcname):
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def get_activation(funcname):
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"""Deserialize the activation function."""
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if callable(funcname):
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if callable(funcname):
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return funcname
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return funcname
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if funcname in ACTIVATIONS:
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if funcname in ACTIVATIONS:
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@ -4,8 +4,7 @@ import torch
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def squared_euclidean_distance(x, y):
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def squared_euclidean_distance(x, y):
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"""
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"""Compute the squared Euclidean distance between :math:`x` and :math:`y`.
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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 x is 2.
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Expected dimension of y is 2.
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Expected dimension of y is 2.
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@ -18,8 +17,7 @@ def squared_euclidean_distance(x, y):
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def euclidean_distance(x, y):
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def euclidean_distance(x, y):
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"""
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"""Compute the Euclidean distance between :math:`x` and :math:`y`.
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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 x is 2.
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Expected dimension of y is 2.
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Expected dimension of y is 2.
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@ -30,8 +28,7 @@ def euclidean_distance(x, y):
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def lpnorm_distance(x, y, p):
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def lpnorm_distance(x, y, p):
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r"""
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r"""Compute :math:`{\langle x, y \rangle}_p`.
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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 x is 2.
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Expected dimension of y is 2.
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Expected dimension of y is 2.
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@ -41,8 +38,7 @@ def lpnorm_distance(x, y, p):
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def omega_distance(x, y, omega):
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def omega_distance(x, y, omega):
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r"""
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r"""Omega distance.
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Omega distance.
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Compute :math:`{\langle \Omega x, \Omega y \rangle}_p`
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Compute :math:`{\langle \Omega x, \Omega y \rangle}_p`
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@ -57,8 +53,7 @@ def omega_distance(x, y, omega):
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def lomega_distance(x, y, omegas):
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def lomega_distance(x, y, omegas):
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r"""
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r"""Localized Omega distance.
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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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Compute :math:`{\langle \Omega_k x, \Omega_k y_k \rangle}_p`
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@ -7,9 +7,10 @@ import torch
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INITIALIZERS = dict()
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INITIALIZERS = dict()
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def register_initializer(func):
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def register_initializer(function):
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INITIALIZERS[func.__name__] = func
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"""Add the initializer to the registry."""
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return func
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INITIALIZERS[function.__name__] = function
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return function
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def labels_from(distribution):
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def labels_from(distribution):
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@ -84,6 +85,7 @@ def stratified_random(x_train, y_train, prototype_distribution):
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def get_initializer(funcname):
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def get_initializer(funcname):
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"""Deserialize the initializer."""
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if callable(funcname):
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if callable(funcname):
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return funcname
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return funcname
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if funcname in INITIALIZERS:
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if funcname in INITIALIZERS:
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