Handgeschreven cijfers met `flexmix`

De digits_sample_2 is een data frame met 478 observaties, elk een binaire afbeelding van een handgeschreven cijfer opgebouwd uit 16x16 pixels. In deze oefening ga je (1) de gegevens verkennen, (2) een Bernoulli-mengmodel fitten met flexmix en (3) de resulterende clusters analyseren.

Deze oefening maakt deel uit van de cursus

Mixture Models in R

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# transform into matrix
digits_sample_2 <- ___

# dimension
___

# look to the first observation
show_digit(digits_sample_2[___,])

# look to the last observation
___(digits_sample_2[nrow(___),])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Mixture Models in R

SkillTag.level.intermediateSkillTag.label

4.8+

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: Number of clusters Exercise 3: Number of parameters Exercise 4: Univariate Gaussian Mixture Models with flexmix Exercise 5: Univariate case with flexmix Exercise 6: Extracting Parameters for Univariate Case Exercise 7: Visualizing Univariate Gaussian Mixture Model Exercise 8: Compare the results Exercise 9: Bivariate Gaussian Mixture Models Exercise 10: Cross-term from covariance matrix Exercise 11: Parameters in the bivariate case Exercise 12: Bivariate Gaussian Mixture Models with flexmix Exercise 13: Fit the model with cross-terms Exercise 14: Get the components Exercise 15: Create the ellipses Exercise 16: Visualize the 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-mengmodellen Exercise 2: Binaire afbeeldingen Exercise 3: Hoeveel waarden?Exercise 4: Bernoulli-mixturemodellen met flexmix Exercise 5: Handgeschreven cijfers met `flexmix`

Huidige oefening

Exercise 6: Poisson-mengmodellen Exercise 7: Ontdek de lambda Exercise 8: Steekproef uit een Poisson-verdeling Exercise 9: Poisson-mixturemodellen met flexmix Exercise 10: Crimes-gegevens met `flexmix`