88 lines
2.7 KiB
Ruby
88 lines
2.7 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 by centering and scaling to unit variance.
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#
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# normalizer = SVMKit::Preprocessing::StandardScaler.new
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# new_training_samples = normalizer.fit_transform(training_samples)
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# new_testing_samples = normalizer.transform(testing_samples)
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class StandardScaler
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include Base::BaseEstimator
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include Base::Transformer
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# The vector consists of the mean value for each feature.
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attr_reader :mean_vec # :nodoc:
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# The vector consists of the standard deviation for each feature.
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attr_reader :std_vec # :nodoc:
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# Create a new normalizer for centering and scaling to unit variance.
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#
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# :call-seq:
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# new() -> StandardScaler
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def initialize(_params = {})
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@mean_vec = nil
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@std_vec = nil
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end
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# Calculate the mean value and standard deviation of each feature for scaling.
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#
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# :call-seq:
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# fit(x) -> StandardScaler
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#
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# * *Arguments* :
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# - +x+ (NMatrix, shape: [n_samples, n_features]) -- The samples to calculate the mean values and standard deviations.
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# * *Returns* :
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# - StandardScaler
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def fit(x, _y = nil)
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@mean_vec = x.mean(0)
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@std_vec = x.std(0)
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self
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end
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# Calculate the mean values and standard deviations, and then normalize samples using them.
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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 the mean values and standard deviations.
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# * *Returns* :
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# - The scaled samples (NMatrix)
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def fit_transform(x, _y = nil)
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fit(x).transform(x)
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end
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# Perform standardization the given samples.
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#
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# call-seq:
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# 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 be scaled.
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# * *Returns* :
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# - The scaled samples (NMatrix)
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def transform(x)
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n_samples, = x.shape
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(x - @mean_vec.repeat(n_samples, 0)) / @std_vec.repeat(n_samples, 0)
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end
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# Serializes object through Marshal#dump.
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def marshal_dump # :nodoc:
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{ mean_vec: Utils.dump_nmatrix(@mean_vec),
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std_vec: Utils.dump_nmatrix(@std_vec) }
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end
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# Deserialize object through Marshal#load.
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def marshal_load(obj) # :nodoc:
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@mean_vec = Utils.restore_nmatrix(obj[:mean_vec])
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@std_vec = Utils.restore_nmatrix(obj[:std_vec])
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nil
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end
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end
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end
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end
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