import numpy as np

from girth import ability_mle

class Rasch:

  def __init__(self, model_params, kwargs):
    self.model_params = model_params
    self.b_param = kwargs['b_param']
    self.e = 2.71828
    self.theta = kwargs['theta']

  def result(self):
    return 0.0

  @classmethod
  def ability_estimate(self, items) -> float:
    # we'll likely have to change this to something more robust
    # when we get into more complex response types
    responses = np.array([[int(item.response)] for item in items])
    difficulty = np.array([item.b_param for item in items])
    discrimination = np.linspace(1, 1, len(difficulty))
    return ability_mle(responses, difficulty, discrimination)