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lib/Algorithm/RegressionTree.pm  view on Meta::CPAN

    $self->{_YVector}  =  undef;
    $self->{_dependent_var} = $dependent_var_column_heading;
    $self->{_dependent_var_values} = \@dependent_var_values;
    $self->{_feature_names} = \@feature_names;
    $self->{_samples_dependent_var_val_hash}  = \%dependent_var_value_for_sample_hash;
    $self->{_training_data_hash}  =  \%feature_values_for_samples_hash;
    $self->{_features_and_values_hash}  = \%features_and_values_hash;
    $self->{_features_and_unique_values_hash}  =  \%features_and_unique_values_hash;
    $self->{_numeric_features_valuerange_hash} = \%numeric_features_valuerange_hash;
    $self->{_feature_values_how_many_uniques_hash} = \%feature_values_how_many_uniques_hash;
    $self->SUPER::calculate_first_order_probabilities();
}

sub construct_XMatrix_and_YVector_all_data {
    my $self = shift;
    my $matrix_rows_as_lists =  [ map {my @arr = @$_; [map substr($_,index($_,'=')+1), @arr] } map {$self->{_training_data_hash}->{$_}} sort {sample_index($a) <=> sample_index($b)} keys %{$self->{_training_data_hash}} ];
    map {push @$_, 1} @{$matrix_rows_as_lists};
    map {print "XMatrix row: @$_\n"} @{$matrix_rows_as_lists} if $self->{_debug1_r};
    my $XMatrix = Math::GSL::Matrix->new(scalar @{$matrix_rows_as_lists}, scalar @{$matrix_rows_as_lists->[0]});
    pairmap {$XMatrix->set_row($a,$b)} ( 0..@{$matrix_rows_as_lists}-1, @{$matrix_rows_as_lists} )
                       [ map { $_, $_ + @{$matrix_rows_as_lists} } ( 0 .. @{$matrix_rows_as_lists}-1 ) ];