Borsa için bir t-SNE haritası

t-SNE, tek tek örneklere etiket verilebildiğinde harika görselleştirmeler sunar. Bu egzersizde t-SNE'yi şirket hisse senedi fiyat verilerine uygulayacaksın. Ortaya çıkan t-SNE özelliklerinin dağılım grafiğini, şirket adlarıyla etiketleyerek çizdiğinde, borsanın bir haritasını elde edersin! Her şirketin hisse fiyatı hareketleri normalized_movements dizisi olarak mevcut (senin için zaten normalize edildi). companies listesi her şirketin adını veriyor. PyPlot (plt) senin için içe aktarıldı.

Bu egzersiz

Python'da Unsupervised Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

sklearn.manifold içinden TSNEyi içe aktar.
learning_rate=50 ile model adlı bir TSNE örneği oluştur.
modelin .fit_transform() metodunu normalized_movements üzerine uygula. Sonucu tsne_features değişkenine ata.
tsne_features dizisinin 0 ve 1 numaralı sütunlarını seç.
t-SNE özellikleri xs ve ys için bir saçılım grafiği çiz. Ek anahtar argüman olarak alpha=0.5 belirt.
Her noktayı şirket adıyla etiketlemek için plt.annotate() kullanan kod hazır. Görselleştirmeyi görmek için sadece gönder!

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import TSNE
____

# Create a TSNE instance: model
model = ____

# Apply fit_transform to normalized_movements: tsne_features
tsne_features = ____

# Select the 0th feature: xs
xs = ____

# Select the 1th feature: ys
ys = tsne_features[:,1]

# Scatter plot
____

# Annotate the points
for x, y, company in zip(xs, ys, companies):
    plt.annotate(company, (x, y), fontsize=5, alpha=0.75)
plt.show()

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Unsupervised Learning

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: How many clusters?Exercise 3: Clustering 2D points Exercise 4: Inspect your clustering Exercise 5: Evaluating a clustering Exercise 6: How many clusters of grain?Exercise 7: Evaluating the grain clustering Exercise 8: Transforming features for better clusterings Exercise 9: Scaling fish data for clustering Exercise 10: Clustering the fish data Exercise 11: Clustering stocks using KMeans Exercise 12: Which stocks move together?

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Hiyerarşileri görselleştirme Exercise 2: Kaç birleştirme?Exercise 3: Tahıl verilerinin hiyerarşik kümelemesi Exercise 4: Hisse senedi hiyerarşileri Exercise 5: Hiyerarşik kümelemede küme etiketleri Exercise 6: Hangi kümeler daha yakın?Exercise 7: Farklı bağlantı, farklı hiyerarşik kümeleme!Exercise 8: Ara kümelemeler Exercise 9: Küme etiketlerini çıkarma Exercise 10: 2 boyutlu haritalar için t-SNE Exercise 11: t-SNE ile tahıl veri kümesinin görselleştirilmesi Exercise 12: Borsa için bir t-SNE haritası

Geçerli Egzersiz

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Non-negative matrix factorization (NMF)Exercise 2: Non-negative data Exercise 3: NMF applied to Wikipedia articles Exercise 4: NMF features of the Wikipedia articles Exercise 5: NMF reconstructs samples Exercise 6: NMF learns interpretable parts Exercise 7: NMF learns topics of documents Exercise 8: Explore the LED digits dataset Exercise 9: NMF learns the parts of images Exercise 10: PCA doesn't learn parts Exercise 11: Building recommender systems using NMF Exercise 12: Which articles are similar to 'Cristiano Ronaldo'?Exercise 13: Recommend musical artists part I Exercise 14: Recommend musical artists part II Exercise 15: Final thoughts