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Principal component analysis (PCA)

from pyspark.mllib.linalg import Vectors
from pyspark.mllib.linalg.distributed import RowMatrix

rows = sc.parallelize([
    Vectors.sparse(5, {1: 1.0, 3: 7.0}),
    Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
    Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
])

mat = RowMatrix(rows)
# Compute the top 4 principal components.
# Principal components are stored in a local dense matrix.
pc = mat.computePrincipalComponents(4)

# Project the rows to the linear space spanned by the top 4 principal components.
projected = mat.multiply(pc)

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Principal component analysis (PCA)

from pyspark.mllib.linalg import Vectors
from pyspark.mllib.linalg.distributed import RowMatrix

rows = sc.parallelize([
    Vectors.sparse(5, {1: 1.0, 3: 7.0}),
    Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
    Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
])

mat = RowMatrix(rows)
# Compute the top 4 principal components.
# Principal components are stored in a local dense matrix.
pc = mat.computePrincipalComponents(4)

# Project the rows to the linear space spanned by the top 4 principal components.
projected = mat.multiply(pc)

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Principal component analysis (PCA)

Principal component analysis (PCA)

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