clean up sakana

README.md

@@ -73,12 +73,37 @@ output = model(x)
 
 ```
 
+- Fish School/Sakana is an all-new innovative approaches to machine learning that leverage the power of the Transformer model architecture. These systems are designed to mimic the behavior of a school of fish, where each fish represents an individual Transformer model. The goal is to optimize the performance of the entire school by learning from the best-performing fish.
+
+```python
+import torch
+from swarms_torch.fish_school import Fish, FishSchool
+
+# Create random source and target sequences
+src = torch.randn(10, 32, 512)
+tgt = torch.randn(10, 32, 512)
+
+# Create random labels
+labels = torch.randint(0, 512, (10, 32))
+
+# Create a fish and train it on the random data
+fish = Fish(512, 8, 6)
+fish.train(src, tgt, labels)
+print(fish.food)  # Print the fish's food
+
+# Create a fish school and optimize it on the random data
+school = FishSchool(10, 512, 8, 6, 100)
+school.forward(src, tgt, labels)
+print(school.fish[0].food)  # Print the first fish's food
+
+```
+
 # Documentation
 - [Click here for documentation](https://swarmstorch.readthedocs.io/en/latest/swarms/)
 
 
 # Todo
-Here are 10 swarming neural network algorithms, with brief overviews, algorithmic pseudocode, and potential use cases for each:
+10 swarming neural network algorithms, with brief overviews, algorithmic pseudocode, and potential use cases for each:
 
 1. **Particle Swarm Optimization (PSO)**
     - **Overview**: Simulates the social behavior of birds flocking or fish schooling. It adjusts trajectories of individual agents (particles) based on their own and their neighbors' best known positions.

playground/fish_school_example.py

@@ -0,0 +1,19 @@
+import torch
+from swarms_torch.fish_school import Fish, FishSchool
+
+# Create random source and target sequences
+src = torch.randn(10, 32, 512)
+tgt = torch.randn(10, 32, 512)
+
+# Create random labels
+labels = torch.randint(0, 512, (10, 32))
+
+# Create a fish and train it on the random data
+fish = Fish(512, 8, 6)
+fish.train(src, tgt, labels)
+print(fish.food)  # Print the fish's food
+
+# Create a fish school and optimize it on the random data
+school = FishSchool(10, 512, 8, 6, 100)
+school.forward(src, tgt, labels)
+print(school.fish[0].food)  # Print the first fish's food

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms-torch"
-version = "0.0.7"
+version = "0.0.9"
 description = "swarms-torch - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <[email protected]>"]
@@ -30,6 +30,8 @@ packages = [
 [tool.poetry.dependencies]
 python = "^3.6"
 torch = "*"
+einops = "*"
+
 
 
 [tool.poetry.dev-dependencies]

swarms_torch/fish_school.py

@@ -1,6 +1,5 @@
-import torch
 from torch import nn
-from torch.nn import Transformer, CrossEntropyLoss
+from torch.nn import CrossEntropyLoss, Transformer
 from torch.optim import Adam