Dominante kleuren weergeven

We hebben de volgende afbeelding geladen met de functie imread() van de image-klasse van matplotlib.

Om de dominante kleuren weer te geven, zet je de kleuren van de clustercentra om naar hun ruwe waarden en vervolgens naar het bereik 0-1 met de volgende formule: converted_pixel = standardized_pixel * pixel_std / 255

De RGB-waarden staan in een DataFrame, batman_df. De geschaalde RGB-waarden staan in de kolommen scaled_red, scaled_blue en scaled_green. De clustercentra staan in de variabele cluster_centers, die zijn gemaakt met de functie kmeans() met drie clusters.

Deze oefening maakt deel uit van de cursus

Clusteranalyse in Python

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Oefeninstructies

Haal de standaarddeviaties van elke kleur uit het DataFrame en sla ze op in r_std, g_std, b_std.
Zet voor elk clustercentrum de gestandaardiseerde RGB-waarden om naar geschaalde waarden in het bereik 0-1.
Geef de kleuren van de clustercentra weer.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Get standard deviations of each color
____, ____, ____ = batman_df[['red', 'green', 'blue']].___()

for cluster_center in cluster_centers:
    scaled_r, scaled_g, scaled_b = cluster_center
    # Convert each standardized value to scaled value
    colors.append((
        scaled_r * ____ / ____,
        scaled_g * ____ / ____,
        scaled_b * ____ / ____
    ))

# Display colors of cluster centers
plt.____(____)
plt.show()

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Clusteranalyse in Python

SkillTag.level.intermediateSkillTag.label

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Begin de cursus gratis

Before you are ready to classify news articles, you need to be introduced to the basics of clustering. This chapter familiarizes you with a class of machine learning algorithms called unsupervised learning and then introduces you to clustering, one of the popular unsupervised learning algorithms. You will know about two popular clustering techniques - hierarchical clustering and k-means clustering. The chapter concludes with basic pre-processing steps before you start clustering data.

Exercise 1: Unsupervised learning: basics Exercise 2: Unsupervised learning in real world Exercise 3: Pokémon sightings Exercise 4: Basics of cluster analysis Exercise 5: Pokémon sightings: hierarchical clustering Exercise 6: Pokémon sightings: k-means clustering Exercise 7: Data preparation for cluster analysis Exercise 8: Normalize basic list data Exercise 9: Visualize normalized data Exercise 10: Normalization of small numbers Exercise 11: FIFA 18: Normalize data

This chapter focuses on a popular clustering algorithm - hierarchical clustering - and its implementation in SciPy. In addition to the procedure to perform hierarchical clustering, it attempts to help you answer an important question - how many clusters are present in your data? The chapter concludes with a discussion on the limitations of hierarchical clustering and discusses considerations while using hierarchical clustering.

Exercise 1: Basics of hierarchical clustering Exercise 2: Hierarchical clustering: ward method Exercise 3: Hierarchical clustering: single method Exercise 4: Hierarchical clustering: complete method Exercise 5: Visualize clusters Exercise 6: Visualize clusters with matplotlib Exercise 7: Visualize clusters with seaborn Exercise 8: How many clusters?Exercise 9: Create a dendrogram Exercise 10: How many clusters in comic con data?Exercise 11: Limitations of hierarchical clustering Exercise 12: Timing run of hierarchical clustering Exercise 13: FIFA 18: exploring defenders

This chapter introduces a different clustering algorithm - k-means clustering - and its implementation in SciPy. K-means clustering overcomes the biggest drawback of hierarchical clustering that was discussed in the last chapter. As dendrograms are specific to hierarchical clustering, this chapter discusses one method to find the number of clusters before running k-means clustering. The chapter concludes with a discussion on the limitations of k-means clustering and discusses considerations while using this algorithm.

Exercise 1: Basics of k-means clustering Exercise 2: K-means clustering: first exercise Exercise 3: Runtime of k-means clustering Exercise 4: How many clusters?Exercise 5: Elbow method on distinct clusters Exercise 6: Elbow method on uniform data Exercise 7: Limitations of k-means clustering Exercise 8: Impact of seeds on distinct clusters Exercise 9: Uniform clustering patterns Exercise 10: FIFA 18: defenders revisited

Now that you are familiar with two of the most popular clustering techniques, this chapter helps you apply this knowledge to real-world problems. The chapter first discusses the process of finding dominant colors in an image, before moving on to the problem discussed in the introduction - clustering of news articles. The chapter concludes with a discussion on clustering with multiple variables, which makes it difficult to visualize all the data.

Exercise 1: Dominante kleuren in afbeeldingen Exercise 2: RGB-waarden uit een afbeelding halen Exercise 3: Hoeveel dominante kleuren?Exercise 4: Dominante kleuren weergeven

Huidige oefening

Exercise 5: Documentclustering Exercise 6: TF-IDF van filmplots Exercise 7: Toptermen in filmclusters Exercise 8: Clusteren met meerdere kenmerken Exercise 9: Clusteren met veel features Exercise 10: Basistests op clusters Exercise 11: FIFA 18: wat maakt een complete speler?Exercise 12: Tot ziens!