class

EvolveNet::Data

Inherits Reference < Object

Constants

Log = ::Log.for(self)

Constructors

new(raw_inputs : Array(Array(Float64)), raw_outputs : Array(Array(Float64)))
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new(inputs : Array(Array(Number)), outputs : Array(Array(Number)))
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new(data : Array(Array(Array(Number))))
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Class methods

new_with_csv_input_target(csv_file_path, input_column_range, target_column)
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Instance methods

array_for_label(a_label)

Takes a label as a String and returns the corresponding output array

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confusion_matrix(model)
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denormalize(x, xmin, xmax)
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denormalize_outputs(outputs : Array(Number))
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inputs
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inputs=(inputs : Array(Array(Float32 | Float64 | Int32 | Int64)))
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label_encoder
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label_for_array(an_array)

Takes an output array of 0,1s and returns the corresponding label

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labels
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labels=(label_array)

Receives an array of labels (String or Symbol) and sets them for this Data object

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normalize(x, xmin, xmax)
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normalize_inputs(inputs : Array(Number))
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normalize_min_max
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normalize_outputs(outputs : Array(Number))
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normalized_data
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normalized_inputs
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normalized_inputs=(normalized_inputs : Array(Array(Float64)))
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normalized_outputs
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normalized_outputs=(normalized_outputs : Array(Array(Float64)))
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one_hot_encoder
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ordinal_encoder
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outputs
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outputs=(outputs : Array(Array(Float32 | Float64 | Int32 | Int64)))
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raw_confusion_matrix(model)
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raw_data
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set_zero_to_average(cols = Array[Int32])
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size
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split(factor)

Splits the receiver in a TrainingData and a TestData object according to factor

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to_onehot(data : Array(Array(Float64)), vector_size : Int32)
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