test commit

Author: kreininmv

code/irt/.ipynb_checkpoints/distributions-checkpoint.py

@@ -0,0 +1,72 @@
+import torch
+from torch.distributions import Distribution
+
+### alternative infromation
+
+
+# Define a custom Normal distribution class that inherits from PyTorch's Distribution class
+class Normal(Distribution):
+    # Indicates that the distribution supports reparameterized sampling
+    has_rsample = True
+
+    def __init__(self, loc: torch.Tensor, scale: torch.Tensor, generator: torch.Generator = None) -> None:
+        """
+        Initializes the Normal distribution with a given mean (loc) and standard deviation (scale).
+
+        Args:
+            loc (Tensor): Mean of the normal distribution. This defines the central tendency of the distribution.
+            scale (Tensor): Standard deviation of the normal distribution. This defines the spread or width of the distribution.
+            generator (torch.Generator, optional): A random number generator for reproducible sampling.
+        """
+        self.loc = loc  # Mean of the distribution
+        self.scale = scale  # Standard deviation of the distribution
+        self.generator = generator  # Optional random number generator for reproducibility
+        super(Distribution).__init__()  # Initialize the base Distribution class
+
+    def transform(self, z: torch.Tensor) -> torch.Tensor:
+        """
+        Transforms the input tensor `z` to the standard normal form using the distribution's mean and scale.
+
+        Args:
+            z (Tensor): Input tensor to be transformed.
+
+        Returns:
+            Tensor: The transformed tensor, which is normalized to have mean 0 and standard deviation 1.
+        """
+        return (z - self.loc) / self.scale
+
+    def d_transform_d_z(self) -> torch.Tensor:
+        """
+        Computes the derivative of the transform function with respect to the input tensor `z`.
+
+        Returns:
+            Tensor: The derivative, which is the reciprocal of the scale. This is used for reparameterization.
+        """
+        return 1 / self.scale
+
+    def sample(self) -> torch.Tensor:
+        """
+        Generates a sample from the Normal distribution using PyTorch's `torch.normal` function.
+
+        Returns:
+            Tensor: A tensor containing a sample from the distribution. The `detach()` method is used to prevent
+                    gradients from being tracked during sampling.
+        """
+        return torch.normal(self.loc, self.scale, generator=self.generator).detach()
+
+    def rsample(self) -> torch.Tensor:
+        """
+        Generates a reparameterized sample from the Normal distribution, which is useful for gradient-based optimization.
+
+        The `rsample` method generates a sample `x`, applies a transformation, and creates a surrogate sample
+        that allows gradients to flow through the sampling process.
+
+        Returns:
+            Tensor: A reparameterized sample tensor, which allows gradient backpropagation.
+        """
+        x = self.sample()  # Sample from the distribution
+        
+        transform = self.transform(x)  # Transform the sample to standard normal form
+        surrogate_x = -transform / self.d_transform_d_z().detach()  # Compute the surrogate for backpropagation
+        # Return the sample adjusted to allow gradient flow
+        return x + (surrogate_x - surrogate_x.detach())

code/irt/distributions.py

@@ -1,6 +1,9 @@
 import torch
 from torch.distributions import Distribution
 
+### alternative infromation
+
+
 # Define a custom Normal distribution class that inherits from PyTorch's Distribution class
 class Normal(Distribution):
     # Indicates that the distribution supports reparameterized sampling