update a lot, take a break

README.md

@@ -26,7 +26,7 @@ TensorNEAT is a JAX-based libaray for NeuroEvolution of Augmenting Topologies (N
 - JAX-based network for neuroevolution:
     - **Batch inference** across networks with different architectures, GPU-accelerated.
     - Evolve networks with **irregular structures** and **fully customize** their behavior.
-    - Visualize the network and represent it in **mathematical formulas**.
+    - Visualize the network and represent it in **mathematical formulas** or codes.
 
 - GPU-accelerated NEAT implementation:
     - Run NEAT and HyperNEAT on GPUs.
@@ -75,6 +75,60 @@ state, best = pipeline.auto_run(state)
 # show results
 pipeline.show(state, best)
 ```
+  Obtain result in a few generations:
+```
+Fitness limit reached!
+input: [0. 0. 0.], target: [0.], predict: [0.00066471]
+input: [0. 0. 1.], target: [1.], predict: [0.9992988]
+input: [0. 1. 0.], target: [1.], predict: [0.9988666]
+input: [0. 1. 1.], target: [0.], predict: [0.00107922]
+input: [1. 0. 0.], target: [1.], predict: [0.9987184]
+input: [1. 0. 1.], target: [0.], predict: [0.00093677]
+input: [1. 1. 0.], target: [0.], predict: [0.00060118]
+input: [1. 1. 1.], target: [1.], predict: [0.99927646]
+loss: 8.484730074087565e-07
+```
+4. **Visualize the best network**:
+```python
+network = algorithm.genome.network_dict(state, *best)
+algorithm.genome.visualize(network, save_path="./imgs/xor_network.svg")
+```
+<div style="text-align: center;">
+    <img src="./imgs/xor_network.svg" alt="Visualization of the policy"  width="200" height="200">
+</div>
+
+5. **Transform the network to latex formulas or python codes**:
+```python
+from tensorneat.common.sympy_tools import to_latex_code, to_python_code
+
+sympy_res = algorithm.genome.sympy_func(
+    state, network, sympy_output_transform=ACT.obtain_sympy(ACT.sigmoid)
+)
+latex_code = to_latex_code(*sympy_res)
+print(latex_code)
+
+python_code = to_python_code(*sympy_res)
+print(python_code)
+```
+Obtain latex formulas:
+```latex
+\begin{align}
+h_{0} &= \frac{1}{2.83 e^{5.66 h_{1} - 6.08 h_{2} - 3.03 i_{2}} + 1}\newline
+h_{1} &= \frac{1}{0.3 e^{- 4.8 h_{2} + 9.22 i_{0} + 8.09 i_{1} - 10.24 i_{2}} + 1}\newline
+h_{2} &= \frac{1}{0.27 e^{4.28 i_{1}} + 1}\newline
+o_{0} &= \frac{1}{0.68 e^{- 20.86 h_{0} + 11.12 h_{1} + 14.22 i_{0} - 1.96 i_{2}} + 1}\newline
+\end{align}
+```
+Obtain python codes:
+```python
+h = np.zeros(3)
+o = np.zeros(1)
+h[0] = 1/(2.825013*exp(5.660946*h[1] - 6.083459*h[2] - 3.033361*i[2]) + 1)
+h[1] = 1/(0.300038*exp(-4.802896*h[2] + 9.215506*i[0] + 8.091845*i[1] - 10.241107*i[2]) + 1)
+h[2] = 1/(0.269965*exp(4.279962*i[1]) + 1)
+o[0] = 1/(0.679321*exp(-20.860441*h[0] + 11.122242*h[1] + 14.216276*i[0] - 1.961642*i[2]) + 1)
+```
+
 
 ## Installation
 Install `tensorneat` from the GitHub source code: