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  1. class MyLogisticRegression02:
  2.  
  3.     def __init__(self, learning_rate=0.01, max_iter=10000):
  4.         self.learning_rate = learning_rate
  5.         self.max_iter = max_iter
  6.         self.weights = None  
  7.         self.bias = 0  
  8.  
  9.     def initialize_weights(self, n_features):
  10.         self.weights = np.zeros(n_features)
  11.         self.bias = 0  
  12.  
  13.     def sigmoid(self, z):
  14.         z = np.clip(z, -500, 500)
  15.         return 1 / (1 + np.exp(-z))
  16.  
  17.     def get_gradient(self, x, y):
  18.  
  19.         m = x.shape[0]
  20.         predictions = np.matmul(x, self.weights) + self.bias 
  21.  
  22.         errors = self.sigmoid(predictions) - y
  23.  
  24.         weight_gradient = np.matmul(x.T, errors)
  25.         bias_gradient = np.sum(errors)
  26.  
  27.         return weight_gradient / m, bias_gradient / m
  28.  
  29.  
  30.     def fit(self, x_train, y_train):
  31.         rows = x_train.shape[0]
  32.         cols = x_train.shape[1]
  33.  
  34.         if self.weights is None: 
  35.             self.initialize_weights(cols)
  36.  
  37.         for i in range(self.max_iter):
  38.             gradient_weights, gradient_bias = self.get_gradient(x_train, y_train)
  39.             self.weights = self.weights - self.learning_rate * gradient_weights
  40.             self.bias = self.bias - self.learning_rate * gradient_bias
  41.  
  42.     def predict(self, x_test):  
  43.         prediction =  np.matmul(x_test, self.weights) + self.bias
  44.         binary_predictions = self.sigmoid(prediction)
  45.         return (binary_predictions >= 0.5).reshape(-1).astype(int)
  46.  
  47.     def get_weights(self):
  48.         print("Bias (intercept):", self.bias)
  49.         print("Weights (coefficients):", self.weights)
Success #stdin #stdout 0.1s 14148KB
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https://ideone.com/7kFBir
language:
Python 3 (python  3.12)
created:
2 days ago
visibility:
public

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