Class: SVMKit::Preprocessing::L2Normalizer

Inherits:
Object
  • Object
show all
Includes:
Base::BaseEstimator, Base::Transformer
Defined in:
lib/svmkit/preprocessing/l2_normalizer.rb

Overview

Normalize samples to unit L2-norm.

Examples:

normalizer = SVMKit::Preprocessing::StandardScaler.new
new_samples = normalizer.fit_transform(samples)

Instance Attribute Summary collapse

Attributes included from Base::BaseEstimator

#params

Instance Method Summary collapse

Constructor Details

#new ⇒ L2Normalizer

Create a new normalizer for normaliing to unit L2-norm.



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# File 'lib/svmkit/preprocessing/l2_normalizer.rb', line 23

def initialize(_params = {})
  @norm_vec = nil
end

Instance Attribute Details

#norm_vec ⇒ NMatrix (readonly)

Return the vector consists of L2-norm for each sample.

Returns:

  • (NMatrix) —

    (shape: [1, n_samples])



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# File 'lib/svmkit/preprocessing/l2_normalizer.rb', line 18

def norm_vec
  @norm_vec
end

Instance Method Details

#fit(x) ⇒ L2Normalizer

Calculate L2-norms of each sample.

Parameters:

  • x (NMatrix) —

    (shape: [n_samples, n_features]) The samples to calculate L2-norms.

Returns:



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# File 'lib/svmkit/preprocessing/l2_normalizer.rb', line 33

def fit(x, _y = nil)
  n_samples, = x.shape
  @norm_vec = NMatrix.new([1, n_samples],
                          Array.new(n_samples) { |n| x.row(n).norm2 })
  self
end

#fit_transform(x) ⇒ NMatrix

Calculate L2-norms of each sample, and then normalize samples to unit L2-norm.

Parameters:

  • x (NMatrix) —

    (shape: [n_samples, n_features]) The samples to calculate L2-norms.

Returns:

  • (NMatrix) —

    The normalized samples.



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# File 'lib/svmkit/preprocessing/l2_normalizer.rb', line 46

def fit_transform(x, _y = nil)
  fit(x)
  x / @norm_vec.transpose.repeat(x.shape[1], 1)
end