58 lines
1.6 KiB
Ruby
58 lines
1.6 KiB
Ruby
require 'svmkit/base/base_estimator'
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require 'svmkit/base/transformer'
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module SVMKit
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# This module consists of the classes that perform preprocessings.
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module Preprocessing
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# Normalize samples to unit L2-norm.
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#
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# normalizer = SVMKit::Preprocessing::StandardScaler.new
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# new_samples = normalizer.fit_transform(samples)
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class L2Normalizer
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include Base::BaseEstimator
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include Base::Transformer
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# The vector consists of norms of each sample.
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attr_reader :norm_vec # :nodoc:
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# Create a new normalizer for normaliing to unit L2-norm.
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#
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# :call-seq:
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# new() -> L2Normalizer
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def initialize(_params = {})
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@norm_vec = nil
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end
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# Calculate L2 norms of each sample.
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#
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# :call-seq:
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# fit(x) -> L2Normalizer
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#
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# * *Arguments* :
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# - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to calculate L2-norms.
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# * *Returns* :
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# - L2Normalizer
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def fit(x, _y = nil)
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n_samples, = x.shape
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@norm_vec = NMatrix.new([1, n_samples],
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Array.new(n_samples) { |n| x.row(n).norm2 })
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self
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end
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# Calculate L2 norms of each sample, and then normalize samples to unit L2-norm.
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#
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# :call-seq:
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# fit_transform(x) -> NMatrix
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#
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# * *Arguments* :
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# - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to calculate L2-norms.
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# * *Returns* :
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# - The normalized samples (NMatrix)
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def fit_transform(x, _y = nil)
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fit(x)
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x / @norm_vec.transpose.repeat(x.shape[1], 1)
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end
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end
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end
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end
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