added deepsad base code

Deep-SAD-PyTorch/src/networks/inference/distributions.py

@@ -0,0 +1,41 @@
+import math
+import torch
+import torch.nn.functional as F
+
+
+# Acknowledgements: https://github.com/wohlert/semi-supervised-pytorch
+def log_standard_gaussian(x):
+    """
+    Evaluates the log pdf of a standard normal distribution at x.
+
+    :param x: point to evaluate
+    :return: log N(x|0,I)
+    """
+    return torch.sum(-0.5 * math.log(2 * math.pi) - x ** 2 / 2, dim=-1)
+
+
+def log_gaussian(x, mu, log_var):
+    """
+    Evaluates the log pdf of a normal distribution parametrized by mu and log_var at x.
+
+    :param x: point to evaluate
+    :param mu: mean
+    :param log_var: log variance
+    :return: log N(x|µ,σI)
+    """
+    log_pdf = -0.5 * math.log(2 * math.pi) - log_var / 2 - (x - mu)**2 / (2 * torch.exp(log_var))
+    return torch.sum(log_pdf, dim=-1)
+
+
+def log_standard_categorical(p):
+    """
+    Computes the cross-entropy between a (one-hot) categorical vector and a standard (uniform) categorical distribution.
+    :param p: one-hot categorical distribution
+    :return: H(p,u)
+    """
+    eps = 1e-8
+    prior = F.softmax(torch.ones_like(p), dim=1)  # Uniform prior over y
+    prior.requires_grad = False
+    cross_entropy = -torch.sum(p * torch.log(prior + eps), dim=1)
+
+    return cross_entropy