KeyError: 0 when calculate likelihood in EM algorithm

Question

I have a model EM algorithm. The model should select random columns and then calculate the likelihood. For example, the dataset has 39 attributes, the model selects 19 attributes only and displays likelihood.

define several functions : initialize_model_params, expected_hidden_vars , update_model, likelihood

the problem when calculating likelihood I got the error

KeyError: 0

It's related to the index

function likelihood

 def calc_likelihood(data, means, covariances, weights):
 
  n_samples, n_features = data.shape
  n_components = len(means)

  # Initialize likelihood to 0
  likelihood = 0
  
  # Loop over each sample in the data
  for i in range(n_samples):
    sample_likelihood = 0
    # Loop over each component in the mixture
    for j in range(n_components):
      # Calculate the probability of the sample given the component
      component_prob = weights[j] * multivariate_normal.pdf(data[i], mean=means[j], cov=covariances[j])
      # Add the component probability to the sample likelihood
      sample_likelihood += component_prob
    # Add the sample likelihood to the overall likelihood
    likelihood += np.log(sample_likelihood)
    
  return likelihood

function em

    def em(data, columns, num_iterations):
  # Initialize the model parameters
  model_params = initialize_model_params(data, columns)
# Select a random subset of columns
  selected_columns = random.sample(list(columns), k=len(columns) // 2)
  print(selected_columns) 

  for i in range(num_iterations):
    
    # E step: calculate the expected value of the hidden variables using the selected columns
    expected_hidden_vars = calculate_expected_hidden_vars(data, selected_columns, model_params)

    # M step: update the estimates of the model parameters using the selected columns and the expected hidden variables
    model_params = update_model_params(data, selected_columns, expected_hidden_vars)
    
    means, covariances, weights = model_params
    print('params', model_params)
    
    # Calculate the likelihood of the data given the current model parameters
    likelihood = calc_likelihood(data, means, covariances, weights)
    print('likelihood : ' , likelihood)
    

  # Return the maximum likelihood estimates of the model parameters
  return model_params

how to fix it. can anyone help me?