Instance methods inherited from class EvolveNet::Data

array_for_label(a_label) array_for_label, confusion_matrix(model) confusion_matrix, denormalize(x, xmin, xmax) denormalize, denormalize_outputs(outputs : Array(Number)) denormalize_outputs, inputs : Array(Array(Float32 | Float64 | Int32 | Int64)) inputs, inputs=(inputs : Array(Array(Float32 | Float64 | Int32 | Int64))) inputs=, label_encoder label_encoder, label_for_array(an_array) label_for_array, labels : Array(String) labels, labels=(label_array) labels=, normalize(x, xmin, xmax) normalize, normalize_inputs(inputs : Array(Number)) normalize_inputs, normalize_min_max normalize_min_max, normalize_outputs(outputs : Array(Number)) normalize_outputs, normalized_data normalized_data, normalized_inputs : Array(Array(Float64)) normalized_inputs, normalized_inputs=(normalized_inputs : Array(Array(Float64))) normalized_inputs=, normalized_outputs : Array(Array(Float64)) normalized_outputs, normalized_outputs=(normalized_outputs : Array(Array(Float64))) normalized_outputs=, one_hot_encoder one_hot_encoder, ordinal_encoder ordinal_encoder, outputs : Array(Array(Float32 | Float64 | Int32 | Int64)) outputs, outputs=(outputs : Array(Array(Float32 | Float64 | Int32 | Int64))) outputs=, raw_confusion_matrix(model) raw_confusion_matrix, raw_data raw_data, set_zero_to_average(cols = Array[Int32]) set_zero_to_average, size size, split(factor) split, to_onehot(data : Array(Array(Float64)), vector_size : Int32) to_onehot

Constructor methods inherited from class EvolveNet::Data

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

Class methods inherited from class EvolveNet::Data

new_with_csv_input_target(csv_file_path, input_column_range, target_column) new_with_csv_input_target