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Author: felix-andreas

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]

README.md

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+<img src="data/icon/icon.svg" width="64" height="64" align="left"/>
+
+# hopfieldnetwork
+
+>A Hopfield network is a special kind of an artifical neural network. It implements a so called associative or content addressable memory. This means that memory contents are not reached via a memory address, but that the network responses
+to an input pattern with that stored pattern which has the highest similarity.
+
+**hopfieldnetwork** is a Python package which provides an implementation of a Hopfield network. The package also includes a graphical user interface.
+
+### Requirements
+    matplotlib
+    numpy
+
+
+### Usage
+Add the Package to your Python path and import the Hopfield network class:
+```python
+from hopfieldnetwork import HopfieldNetwork
+```
+
+Create a new Hopfield network of size _N_ = 100:
+```python
+hopfield_network1 = HopfieldNetwork(N=100)
+```
+
+Save / Train Images into the Hopfield network:
+```python
+hopfield_network1.train_pattern(input_pattern)
+```
+
+Start an asynchronous update with 5 iterations:
+```python
+hopfield_network1.update_neurons(iterations=5, mode=’async’)
+```
+
+Compute the energy function of a pattern:
+```python
+hopfield_network1.compute_energy(input_pattern)
+```
+
+Save a network as a file:
+```python
+hopfield_network1.save_network(’path/to/file’)
+```
+
+Open an already trained Hopfield network:
+```python
+hopfield_network2 = HopfieldNetwork(filepath=’network2.npz’)
+```
+
+### Graphical user interface
+
+![Hopfield network GUI](examples/project4/latex/images/gui_screenshot.png?raw=true)
+
+In the Hopfield network GUI, the one-dimensional vectors of the neuron states are visualized as a two-dimensional binary image. The user has the option to load different pictures/patterns into network and then start an asynchronous or synchronous update with or without finite temperatures. There are also prestored different networks in the examples tab.
+
+
+**Run the GUI with:**
+
+    python2/python3 start_gui.py
+
+### GUI Layout
+The Hopfield network GUI is divided into three frames:
+
+**Input frame**\
+The input frame (left) is the main point of interaction with the network. The user can change the state of an input neuron by a left click to +1, accordingly by to right-click to -1. This will only change the state of the input pattern not the state of the actual network. The input pattern can be transfered to the network with the buttons below:
+- **Set intial** sets the current input pattern as the start configuration of the neurons.
+- **Save / Train** stores / trains the current input pattern into the Hopfield network.
+- **Rand** sets a random input pattern.
+- **Clear** sets all points of the input pattern to -1.
+
+**Output frame**\
+The output frame (center) shows the current neuron configuration.
+- **Sync update** starts a synchronous update.
+- **Async update** starts an asynchronous update.
+- **Randomize** randomly flips the state of one tenth of the neurons.
+- **Set partial** sets the first half of the neurons to -1.
+- **Set random** sets a random neuron state.
+
+**Saved pattern frame**\
+The Saved pattern frame (right) shows the pattern currently saved in the network.
+- **Set initial** sets the currently displayed image as new neuron state.
+- **Set input** sets the currently displayed image as input pattern.
+- **Remove** removes the currently displayed image from the Hopfield network.
+
+**Menu bar**
+
+- In the **Network** tab, a new Hopfield network of any size can be initialized.
+In addition, it is possible to save the current network and load stored networks. Also, a raster graphic (JPG, PNG, GIF, TIF) can be added to the network or an entirly new network can be created out of multiple images.
+- In the **Options** tab, the update with finite temperatures can be (de)activated.
+- **View** offers options for visually changing the GUI.
+- In the **Examples** tab, different example networks can be loaded.