if HAS_ALGIB
module Statsample
module Regression
module Multiple
# Class for Multiple Regression Analysis
# Requires Alglib gem and uses a listwise aproach.
# Faster than GslEngine on massive prediction use, because process is c-based.
# Prefer GslEngine if you need good memory use.
# If you need pairwise, use RubyEngine
# Example:
#
#   @a=[1,3,2,4,3,5,4,6,5,7].to_vector(:scale)
#   @b=[3,3,4,4,5,5,6,6,4,4].to_vector(:scale)
#   @c=[11,22,30,40,50,65,78,79,99,100].to_vector(:scale)
#   @y=[3,4,5,6,7,8,9,10,20,30].to_vector(:scale)
#   ds={'a'=>@a,'b'=>@b,'c'=>@c,'y'=>@y}.to_dataset
#   lr=Statsample::Regression::Multiple::AlglibEngine.new(ds,'y')
#            
class AlglibEngine < BaseEngine
    def initialize(ds,y_var)
        @ds=ds.dup_only_valid
        @ds_valid=@ds
        @y_var=y_var
        @dy=@ds[@y_var]
        @ds_indep=ds.dup(ds.fields-[y_var])
        # Create a custom matrix
        columns=[]
        @fields=[]
        @ds.fields.each{|f|
            if f!=@y_var
                columns.push(@ds[f].to_a)
                @fields.push(f)
            end
        }
        @dep_columns=columns.dup
        columns.push(@ds[@y_var])
        matrix=Matrix.columns(columns)
        @lr_s=nil
        @lr=::Alglib::LinearRegression.build_from_matrix(matrix)
    end
    
    def _dump(i)
        Marshal.dump({'ds'=>@ds,'y_var'=>@y_var})
    end
    def self._load(data)
        h=Marshal.load(data)
        self.new(h['ds'], h['y_var'])
    end
    
    def coeffs
        assign_names(@lr.coeffs)
    end
    # Coefficients using a constant
    # Based on http://www.xycoon.com/ols1.htm
    def matrix_resolution
        mse_p=mse
        columns=@dep_columns.dup.map {|xi| xi.map{|i| i.to_f}}
        columns.unshift([1.0]*@ds.cases)
        y=Matrix.columns([@dy.data.map  {|i| i.to_f}])
        x=Matrix.columns(columns)
        xt=x.t
        matrix=((xt*x)).inverse*xt
        matrix*y
    end
    def r2
        r**2
    end
    def r
        Bivariate::pearson(@dy,predicted)
    end  
    def sst
        @dy.ss
    end
    def constant
        @lr.constant
    end
    def standarized_coeffs
        l=lr_s
        assign_names(l.coeffs)
    end
    def lr_s
        if @lr_s.nil?
            build_standarized
        end
        @lr_s
    end
    def build_standarized
        @ds_s=@ds.standarize
        columns=[]
        @ds_s.fields.each{|f|
            columns.push(@ds_s[f].to_a) unless f==@y_var
        }
        @dep_columns_s=columns.dup
        columns.push(@ds_s[@y_var])
        matrix=Matrix.columns(columns)
        @lr_s=Alglib::LinearRegression.build_from_matrix(matrix)
    end
    def process(v)
        @lr.process(v)
    end
    def process_s(v)
        lr_s.process(v)
    end
    # ???? Not equal to SPSS output
    def standarized_residuals
        res=residuals
        red_sd=residuals.sds
        res.collect {|v|
            v.quo(red_sd)
        }.to_vector(:scale)
    end
end
end
end
end # for Statsample
end # for if