TSNE color each cluster
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At first, I use the LDA model to separate my data into ten topics:
from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=10,max_iter=10, n_jobs=-1,random_state=95865)
lda_trans = lda.fit_transform(X_tfidf)
In order to visualize the topic, I use TSNE to make my data into 2D,
topic_proportions = lda.transform(X_tfidf)[:1000]
from sklearn.manifold import TSNE
tsne_model = TSNE(n_components =2, learning_rate=800,angle=.99, init='pca')
topic_tsne_lda = tsne_model.fit_transform(topic_proportions)
plt.scatter(topic_tsne_lda[:,0],topic_tsne_lda[:,1])
And the scattered picture looks like this:
My Question is: how to color my cluster into different colors? Thanks!!
python scikit-learn lda
asked Nov 11 at 0:29
Shin Yu Wu
827
add a comment |
up vote
0
down vote
favorite
At first, I use the LDA model to separate my data into ten topics:
from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=10,max_iter=10, n_jobs=-1,random_state=95865)
lda_trans = lda.fit_transform(X_tfidf)
In order to visualize the topic, I use TSNE to make my data into 2D,
topic_proportions = lda.transform(X_tfidf)[:1000]
from sklearn.manifold import TSNE
tsne_model = TSNE(n_components =2, learning_rate=800,angle=.99, init='pca')
topic_tsne_lda = tsne_model.fit_transform(topic_proportions)
plt.scatter(topic_tsne_lda[:,0],topic_tsne_lda[:,1])
And the scattered picture looks like this:
My Question is: how to color my cluster into different colors? Thanks!!
python scikit-learn lda
asked Nov 11 at 0:29
Shin Yu Wu
827
1
Learn from the docs
– sascha
Nov 11 at 0:37
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
At first, I use the LDA model to separate my data into ten topics:
from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=10,max_iter=10, n_jobs=-1,random_state=95865)
lda_trans = lda.fit_transform(X_tfidf)
In order to visualize the topic, I use TSNE to make my data into 2D,
topic_proportions = lda.transform(X_tfidf)[:1000]
from sklearn.manifold import TSNE
tsne_model = TSNE(n_components =2, learning_rate=800,angle=.99, init='pca')
topic_tsne_lda = tsne_model.fit_transform(topic_proportions)
plt.scatter(topic_tsne_lda[:,0],topic_tsne_lda[:,1])
And the scattered picture looks like this:
My Question is: how to color my cluster into different colors? Thanks!!
python scikit-learn lda
asked Nov 11 at 0:29
Shin Yu Wu
827
At first, I use the LDA model to separate my data into ten topics:
from sklearn.decomposition import LatentDirichletAllocation
lda = LatentDirichletAllocation(n_components=10,max_iter=10, n_jobs=-1,random_state=95865)
lda_trans = lda.fit_transform(X_tfidf)
In order to visualize the topic, I use TSNE to make my data into 2D,
topic_proportions = lda.transform(X_tfidf)[:1000]
from sklearn.manifold import TSNE
tsne_model = TSNE(n_components =2, learning_rate=800,angle=.99, init='pca')
topic_tsne_lda = tsne_model.fit_transform(topic_proportions)
plt.scatter(topic_tsne_lda[:,0],topic_tsne_lda[:,1])
And the scattered picture looks like this:
My Question is: how to color my cluster into different colors? Thanks!!
python scikit-learn lda
python scikit-learn lda
asked Nov 11 at 0:29
Shin Yu Wu
827
asked Nov 11 at 0:29
Shin Yu Wu
827
asked Nov 11 at 0:29
Shin Yu Wu
827
asked Nov 11 at 0:29
Shin Yu Wu
827
asked Nov 11 at 0:29
Shin Yu Wu
827
827
1
Learn from the docs
– sascha
Nov 11 at 0:37
add a comment |
1
Learn from the docs
– sascha
Nov 11 at 0:37
1
1
Learn from the docs
– sascha
Nov 11 at 0:37
Learn from the docs
– sascha
Nov 11 at 0:37
add a comment |
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Learn from the docs
– sascha
Nov 11 at 0:37