add stuff

tree.py

@@ -14,6 +14,8 @@ ETA = 0.5
 NBNEURON = 10
 ACTIVATION = lambda x : (1/(1  +  np.exp(-x)))
 
+per_lst = []
+
 def getNewWeight(weight: float, result: float, neuron_value: float, expected_value: int) -> float:
     return weight + ETA * (expected_value - result) * result * (1 - result) * neuron_value
 
@@ -23,6 +25,14 @@ def getSlope(weights: list[float]) -> Tuple[float, float]:
 
     return pente, origine
 
+def neuron(data: pd.DataFrame, weights: list[float], activation):
+    for i in range(NBNEURON):
+            perceptron(data, weights, activation)
+
+def gradient():
+    #somme (t-o)o(1-o)x
+    pass
+
 def perceptron(data: pd.DataFrame, weights: list[float], activation) -> list[float]:
     predicted = data["success"].tolist()
     for __ in range(0, 2000):