Haal de componenten op

Het getrainde model uit de vorige oefening staat in het object fit_with_covariance. Het doel is hier om de gemiddelden en de covariantiematrices voor beide clusters te extraheren.

Deze oefening maakt deel uit van de cursus

Mixture Models in R

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Oefeninstructies

Gebruik de functie parameters() met het bijbehorende componentnummer om de gemiddelden en de covariantiematrices voor elke cluster te extraheren. Sla de resultaten op in de objecten comp_number.
De eerste twee waarden van comp_number komen overeen met het gemiddelde. Sla ze op in de objecten mean_comp_number.
De laatste vier waarden van comp_number komen overeen met de covariantiematrix. Sla deze waarden op in een 2x2-matrix.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Get the parameters
comp_1 <- ___(___, component = 1)
comp_2 <- parameters(fit_with_covariance, component = ___)

# The means
mean_comp_1 <- ___[1:2]
mean_comp_1
mean_comp_2 <- comp_2[___]
mean_comp_2

# The covariance matrices
covariance_comp_1 <- matrix(comp_1[___], nrow = 2)
covariance_comp_1
covariance_comp_2 <- matrix(comp_2[___], nrow = ___)
covariance_comp_2

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Deze oefening maakt deel uit van de cursus

Mixture Models in R

SkillTag.level.intermediateSkillTag.label

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Begin de cursus gratis

In this chapter, you will be introduced to fundamental concepts in model-based clustering and how this approach differs from other clustering techniques. You will learn the generating process of Gaussian Mixture Models as well as how to visualize the clusters.

Exercise 1: Introduction to model-based clustering Exercise 2: Clustering approaches Exercise 3: Explore gender data Exercise 4: Gaussian distribution Exercise 5: Sampling a Gaussian distribution Exercise 6: (not so good) Estimations of the mean and sd Exercise 7: Gaussian mixture models (GMM)Exercise 8: Simulate a mixture of two Gaussian distributions Exercise 9: Plot histogram of Gaussian Mixture Exercise 10: Mixture of three Gaussian distributions

In this chapter, you will be introduced to the main structure of Mixture Models, how to address different data with this approach and how to estimate the parameters involved. To accomplish the estimation, you will learn an iterative method called Expectation-Maximization algorithm.

Exercise 1: Structure of mixture models Exercise 2: Which probability distribution?Exercise 3: Handwritten digits dataset Exercise 4: Parameters estimation Exercise 5: Estimation given the probabilities Exercise 6: Calculating the probabilities Exercise 7: EM algorithm Exercise 8: Expectation function Exercise 9: Maximization function Exercise 10: Apply the two steps Exercise 11: Plot the estimated clusters

This chapter shows how to fit Gaussian Mixture Models in 1 and 2 dimensions with `flexmix` package. The data used is formed by 10.000 observations of people with their weight, height, body mass index and informed gender.

Exercise 1: Univariate Gaussian Mixture Models Exercise 2: Aantal clusters Exercise 3: Aantal parameters Exercise 4: Univariate Gaussiaanse mengmodellen met flexmix Exercise 5: Univariate geval met flexmix Exercise 6: Parameters extraheren voor univariate geval Exercise 7: Een univariate Gaussian Mixture Model visualiseren Exercise 8: Vergelijk de resultaten Exercise 9: Bivariate Gaussiaanse mengmodellen Exercise 10: Kruis-term uit de covariantiematrix Exercise 11: Parameters in het bivariate geval Exercise 12: Bivariate Gaussiaanse mengmodellen met flexmix Exercise 13: Het model met kruistermen fitten Exercise 14: Haal de componenten op

Huidige oefening

Exercise 15: Maak de ellipsen Exercise 16: Visualiseer de clusters

In this module, you will learn how Mixture Models extends to consider probability distributions different from the Gaussian and how these models are fitted with `flexmix`. The datasets used are handwritten digits images and the number of crimes in Chicago city. For the first dataset you will find clusters that summarize the handwritten digits and for the second dataset, you will find clusters of communities where is more or less dangerous to live in.

Exercise 1: Bernoulli Mixture Models Exercise 2: Binary images Exercise 3: How many values?Exercise 4: Bernoulli Mixture Models with flexmix Exercise 5: Handwritten digits with `flexmix`Exercise 6: Poisson Mixture Models Exercise 7: Discover the lambda Exercise 8: Sample from Poisson distribution Exercise 9: Poisson Mixture Models with flexmix Exercise 10: Crimes data with `flexmix`