making more modular so we can develop our other methods too

cluster/main.py

@@ -0,0 +1,2 @@
+def main():
+    return

cluster/opts.py

@@ -0,0 +1,4 @@
+
+
+clustering_methods = {
+}

main.py

@@ -1,6 +1,5 @@
 import numpy as np
-
-import neural_network.main as nn
+from opts import options
 
 
 def random_dataset():
@@ -16,13 +15,16 @@ def random_dataset():
     )
 
 
-if __name__ == "__main__":
+def main():
     method = input("\nChoose a method to test: ").lower()
-    if method != "nn":
-        raise ValueError(f"Invalid method '{method}'. Choose 'nn' instead.")
+    try:
+        func = options[method]
+    except KeyError:
+        raise f"Invalid method \"{method}\". Try one of these\n{list(options.keys())}"
 
     X, y = random_dataset()
-    nn.main(
-        X=X,
-        y=y,
-    )
+    result = func(X, y)
+
+
+if __name__ == "__main__":
+    main()

neural_network/activation.py

@@ -4,12 +4,14 @@ import numpy as np
 def sigmoid(x: float) -> float:
     return 1.0 / (1.0 + np.exp(-x))
 
+
 def sigmoid_prime(x: float) -> float:
-    return sigmoid(x) / (1.0 - sigmoid())
+    return sigmoid(x) / (1.0 - sigmoid(x))
+
 
 def relu(x):
     return np.maximum(x, 0)
 
+
 def relu_prime(x):
     return np.where(x > 0, 1, 0)
-

neural_network/backprop.py

@@ -21,20 +21,18 @@ def bp(X_train: np.array, y_train: np.array, wb: dict, args: dict):
         # right now this is just the weights,
         # we should also update the biases
         dw2 = np.dot(
-            node1.T, 
+            node1.T,
             error * func_prime(y_hat),
         )
         dw1 = np.dot(
-            X_train.T, 
+            X_train.T,
             np.dot(error * func_prime(y_hat), w2.T) * func_prime(node1),
         )
 
         # update weights & biases
         w1 -= lr * dw1
         w2 -= lr * dw2
-    
+
 
 def compute_node(X, w, b, func):
     return func(np.dot(X, w) + b)
-
-

neural_network/main.py

@@ -31,19 +31,16 @@ def init(X: np.array, y: np.array, hidden_size: int) -> dict:
 
 
 def main(
-    X: np.array,
-    y: np.array,
+        X: np.array,
+        y: np.array,
 ) -> None:
-
     args = get_args()
     wb = init(X, y, args["hidden_size"])
     X_train, X_test, y_train, y_test = train_test_split(
-        X, 
-        y, 
-        test_size=0.3, 
+        X,
+        y,
+        test_size=0.3,
         random_state=8675309
     )
 
     results = bp(X_train, y_train, wb, args)
-
-

neural_network/opts.py

@@ -1,6 +1,5 @@
 from neural_network.activation import *
 
-
 activation = {
     "relu": {
         "main": relu,

opts.py

@@ -0,0 +1,7 @@
+from neural_network.main import main as nn
+from cluster.main import main as clust
+
+options = {
+    "nn": nn,
+    "cluster": clust,
+}