input: Write a function to implement a basic machine learning model in Python that performs binary classification on the given data. The function should return the accuracy of the model on the training data.
  
  def binary_classification_model(X, y):
      from sklearn import linear_model
      from sklearn.metrics import accuracy_score
      
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      
      return accuracy
  
  assert binary_classification_model([[0.3, 0.2, 0.6], [0.3, 0.1, 0.4], [0.2, 0.3, 0.4], [0.7, 0.4, 0.2], [0.5, 0.2, 0.1]], [1, 0, 0, 1, 0]) == 1.0
  assert binary_classification_model([[0.4, 0.3, 0.2], [0.5, 0.1, 0.6], [0.1, 0.2, 0.3]], [0, 1, 1]) == 0.6666666666666666
  assert binary_classification_model([[0.1, 0.2, 0.3]], [0]) == 1.0
output: 
  def binary_classification_model(X, y):
      from sklearn import linear_model
      from sklearn.metrics import accuracy_score
      
      model = linear_model.LogisticRegression()
      model.fit(X, y)
      
      y_pred = model.predict(X)
      accuracy = accuracy_score(y, y_pred)
      
      return accuracy
  