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Maximization function

We saw that the EM algorithm is an iterative method between two steps: the expectation and the maximization. In the last exercise, you created the expectation function. Now, create the maximization function which takes the data frame with the probabilities and outputs the estimations of the means and proportions.

Bu egzersiz

Mixture Models in R

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

Create the function maximization by completing the sample code.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

maximization <- function(___){
  means_estimates <- data_with_probs %>%
    summarise(mean_1 = sum(x * ___) / ___(prob_cluster1),
              mean_2 = sum(x * ___) / ___(prob_cluster2)) %>% 
    as.numeric()
  props_estimates <- data_with_probs %>% 
    summarise(proportion_1 = ___(prob_cluster1),
              proportion_2 = 1 - ___) %>% 
    as.numeric()
  list(means_estimates, props_estimates)   
}
Kodu Düzenle ve Çalıştır