# Hybrid Routing Algorithm

## Description

The new technique presented is effectively a hybrid of EM and dynamic routing algorithms, with the coupling coefficients changed using both the EM update rule and the dynamic routing update rule. The code and explanations are enclosed

## Files

## Steps to reproduce

The hybrid routing algorithm is a machine learning technique that combines elements of the Expectation-Maximization (EM) routing and dynamic routing algorithms. The algorithm is implemented in Python and can be executed using a Jupyter Notebook or a Python script. In order to execute the algorithm, you will need to have the following packages installed: NumPy Once you have the required packages installed, you can simply run the algorithm by executing the Python script that contains the implementation. The algorithm takes in three inputs: inputs: a matrix of shape (batch_size, num_inputs) that represents the input to the network num_classes: an integer representing the number of classes to predict num_routing_iterations: the number of iterations to use for the dynamic routing procedure (default is 3) The algorithm returns the output of the network, which is a matrix of shape (batch_size, num_classes). It is important to note that the implementation provided is a simple example and may need to be modified or extended in order to work with real-world data. If you wish to redistribute the hybrid routing algorithm, you must comply with the terms of the open-source license under which it is released (if applicable). You may also need to attribute the original authors of the code and any contributors.