class Statsample::Vector

# File lib/statsample/vector.rb, line 104
def self.[](*args)
  values=[]
  args.each do |a|
    case a
    when Array
      values.concat a.flatten
    when Statsample::Vector
      values.concat a.to_a
    when Range
      values.concat  a.to_a
    else
      values << a
    end
  end
  vector=new(values)
  vector.type=:scale if vector.can_be_scale?
  vector
end

# File lib/statsample/vector.rb, line 71
def initialize(data=[], type=:nominal, opts=Hash.new)
  @data=data.is_a?(Array) ? data : data.to_a
  @type=type
  opts_default={
    :missing_values=>[],
    :today_values=>['NOW','TODAY', :NOW, :TODAY],
    :labels=>{},
    :name=>nil
  }
  @opts=opts_default.merge(opts)
  if  @opts[:name].nil?
    @@n_table||=0
    @@n_table+=1
    @opts[:name]="Vector #{@@n_table}"
  end
  @missing_values=@opts[:missing_values]
  @labels=@opts[:labels]
  @today_values=@opts[:today_values]
  @name=@opts[:name]
  @valid_data=[]
  @data_with_nils=[]
  @date_data_with_nils=[]
  @missing_data=[]
  @has_missing_data=nil
  @scale_data=nil
  set_valid_data
  self.type=type
end

# File lib/statsample/vector.rb, line 127
def self.new_scale(n,val=nil, &block)
  if block
    vector=n.times.map {|i| block.call(i)}.to_scale
  else
    vector=n.times.map { val}.to_scale
  end
  vector.type=:scale
  vector
end

# File lib/statsample/vector.rb, line 424
def *(v)
  _vector_ari("*",v)
end

# File lib/statsample/vector.rb, line 410
def +(v)
_vector_ari("+",v)
end

# File lib/statsample/vector.rb, line 420
def -(v)
_vector_ari("-",v)
end

# File lib/statsample/vector.rb, line 221
def ==(v2)
  raise TypeError,"Argument should be a Vector" unless v2.instance_of? Statsample::Vector
  @data==v2.data and @missing_values==v2.missing_values and @type==v2.type and @labels==v2.labels
end

# File lib/statsample/vector.rb, line 367
def [](i)
  @data[i]
end

# File lib/statsample/vector.rb, line 372
def []=(i,v)
  @data[i]=v
end

# File lib/statsample/vector.rb, line 287
def add(v,update_valid=true)
  @data.push(v)
  set_valid_data if update_valid
end

# File lib/statsample/vector.rb, line 947
def average_deviation_population( m = nil )
  check_type :scale
  m ||= mean
  ( @scale_data.inject( 0 ) { |a, x| ( x - m ).abs + a } ).quo( n_valid )
end

# File lib/statsample/vector.rb, line 538
def bootstrap(estimators, nr, s=nil)
  s||=n
  
  h_est, es, bss= prepare_bootstrap(estimators)
  
 
  nr.times do |i|
    bs=sample_with_replacement(s)
    es.each do |estimator|          
      # Add bootstrap
      bss[estimator].push(h_est[estimator].call(bs))
    end
  end
  
  es.each do |est|
    bss[est]=bss[est].to_scale
    bss[est].type=:scale
  end
  bss.to_dataset
  
end

# File lib/statsample/vector.rb, line 692
def can_be_date?
if @data.find {|v|       
!v.nil? and !v.is_a? Date and !v.is_a? Time and (v.is_a? String and !@today_values.include? v) and (v.is_a? String and !(v=~/\d{4,4}[-\/]\d{1,2}[-\/]\d{1,2}/))}
  false
else
  true
end
end

# File lib/statsample/vector.rb, line 701
def can_be_scale?
  if @data.find {|v| !v.nil? and !v.is_a? Numeric and !@missing_values.include? v}
    false
  else
    true
  end
end

# File lib/statsample/vector.rb, line 150
def check_type(t)
  Statsample::STATSAMPLE__.check_type(self,t)
end

# File lib/statsample/vector.rb, line 1019
def coefficient_of_variation
    check_type :scale
    standard_deviation_sample.quo(mean)
end

# File lib/statsample/vector.rb, line 665
def count(x=false)
if block_given?
  r=@data.inject(0) {|s, i|
    r=yield i
    s+(r ? 1 : 0)
  }
  r.nil? ? 0 : r
else
  frequencies[x].nil? ? 0 : frequencies[x]
end
end

# File lib/statsample/vector.rb, line 679
def db_type(dbs='mysql')
# first, detect any character not number
if @data.find {|v|  v.to_s=~/\d{2,2}-\d{2,2}-\d{4,4}/} or @data.find {|v|  v.to_s=~/\d{4,4}-\d{2,2}-\d{2,2}/}
  return "DATE"
elsif @data.find {|v|  v.to_s=~/[^0-9e.-]/ }
  return "VARCHAR (255)"
elsif @data.find {|v| v.to_s=~/\./}
  return "DOUBLE"
else
  return "INTEGER"
end
end

# File lib/statsample/vector.rb, line 257
def dichotomize(low=nil)
  fs=factors
  low||=factors.min
  @data_with_nils.collect{|x|
    if x.nil?
      nil
    elsif x>low
      1
    else
      0
    end
  }.to_scale
end

# File lib/statsample/vector.rb, line 139
def dup
  Vector.new(@data.dup,@type, :missing_values => @missing_values.dup, :labels => @labels.dup, :name=>@name)
end

# File lib/statsample/vector.rb, line 144
def dup_empty
  Vector.new([],@type, :missing_values => @missing_values.dup, :labels => @labels.dup, :name=> @name)
end

# File lib/statsample/vector.rb, line 273
def each
  @data.each{|x| yield(x) }
end

# File lib/statsample/vector.rb, line 278
def each_index
(0...@data.size).each {|i|
  yield(i)
}
end

# File lib/statsample/vector.rb, line 726
def factors
  if @type==:scale
    @scale_data.uniq.sort
  elsif @type==:date
    @date_data_with_nils.uniq.sort
  else
    @valid_data.uniq.sort
  end
end

# File lib/statsample/vector.rb, line 738
def frequencies
  Statsample::STATSAMPLE__.frequencies(@valid_data)
end

# File lib/statsample/vector.rb, line 338
def has_missing_data?
  @has_missing_data
end

# File lib/statsample/vector.rb, line 994
def histogram(bins=10)
  check_type :scale
  
  if bins.is_a? Array
    #h=Statsample::Histogram.new(self, bins)
    h=Statsample::Histogram.alloc(bins)                        
  else
    # ugly patch. The upper limit for a bin has the form
    # x < range
    #h=Statsample::Histogram.new(self, bins)
    min,max=Statsample::Util.nice(@valid_data.min,@valid_data.max)
    # fix last data
    if max==@valid_data.max
      max+=1e-10
    end
    h=Statsample::Histogram.alloc(bins,[min,max])
    # Fix last bin

  end
  h.increment(@valid_data)
  h
end

# File lib/statsample/vector.rb, line 722
def inspect
  self.to_s
end

# File lib/statsample/vector.rb, line 376
def is_valid?(x)
  !(x.nil? or @missing_values.include? x)
end

# File lib/statsample/vector.rb, line 577
def jacknife(estimators, k=1)
  raise "n should be divisible by k:#{k}" unless n%k==0
  
  nb=(n / k).to_i
  
  
  h_est, es, ps= prepare_bootstrap(estimators)

  est_n=es.inject({}) {|h,v|
    h[v]=h_est[v].call(self)
    h
  }
  
  
  nb.times do |i|
    other=@data_with_nils.dup
    other.slice!(i*k,k)
    other=other.to_scale
    es.each do |estimator|
      # Add pseudovalue
      ps[estimator].push( nb * est_n[estimator] - (nb-1) * h_est[estimator].call(other))
    end
  end
  
  
  es.each do |est|
    ps[est]=ps[est].to_scale
    ps[est].type=:scale
  end
  ps.to_dataset
end

# File lib/statsample/vector.rb, line 978
def kurtosis(m=nil)
    check_type :scale
    m||=mean
    fo=@scale_data.inject(0){|a,x| a+((x-m)**4)}
    fo.quo((@scale_data.size)*sd(m)**4)-3
    
end

# File lib/statsample/vector.rb, line 345
def labeling(x)
  @labels.has_key?(x) ? @labels[x].to_s : x.to_s
end

# File lib/statsample/vector.rb, line 864
def max
  check_type :ordinal
  @valid_data.max
end

# File lib/statsample/vector.rb, line 910
def mean
  check_type :scale
  sum.to_f.quo(n_valid)
end

# File lib/statsample/vector.rb, line 854
def median
  check_type :ordinal
  percentil(50)
end

# File lib/statsample/vector.rb, line 952
def median_absolute_deviation
  med=median
  recode {|x| (x-med).abs}.median
end

# File lib/statsample/vector.rb, line 859
def min 
  check_type :ordinal
  @valid_data.min
end

# File lib/statsample/vector.rb, line 381
def missing_values=(vals)
  @missing_values = vals
  set_valid_data
end

# File lib/statsample/vector.rb, line 757
def mode
  frequencies.max{|a,b| a[1]<=>b[1]}.first
end

# File lib/statsample/vector.rb, line 761
def n_valid
  @valid_data.size
end

# File lib/statsample/vector.rb, line 832
def percentil(q)
  check_type :ordinal
  sorted=@valid_data.sort
  v= (n_valid * q).quo(100)
  if(v.to_i!=v)
    sorted[v.to_i]
  else
    (sorted[(v-0.5).to_i].to_f + sorted[(v+0.5).to_i]).quo(2)
  end
end

# File lib/statsample/vector.rb, line 987
def product
    check_type :scale
    @scale_data.inject(1){|a,x| a*x }
end

# File lib/statsample/vector.rb, line 773
def proportion(v=1)
    frequencies[v].quo(@valid_data.size)
end

# File lib/statsample/vector.rb, line 813
def proportion_confidence_interval_t(n_poblation,margin=0.95,v=1)
  Statsample::proportion_confidence_interval_t(proportion(v), @valid_data.size, n_poblation, margin)
end

# File lib/statsample/vector.rb, line 816
def proportion_confidence_interval_z(n_poblation,margin=0.95,v=1)
  Statsample::proportion_confidence_interval_z(proportion(v), @valid_data.size, n_poblation, margin)
end

# File lib/statsample/vector.rb, line 766
def proportions
    frequencies.inject({}){|a,v|
        a[v[0]] = v[1].quo(n_valid)
        a
    }
end

# File lib/statsample/vector.rb, line 250
def push(v)
  @data.push(v)
  set_valid_data
end

# File lib/statsample/vector.rb, line 900
def range; 
  check_type :scale
  @scale_data.max - @scale_data.min
end

# File lib/statsample/vector.rb, line 843
def ranked(type=:ordinal)
  check_type :ordinal
  i=0
  r=frequencies.sort.inject({}){|a,v|
    a[v[0]]=(i+1 + i+v[1]).quo(2)
    i+=v[1]
    a
  }
  @data.collect {|c| r[c] }.to_vector(type)
end

# File lib/statsample/vector.rb, line 236
def recode(type=nil)
  type||=@type
  @data.collect{|x|
    yield x
  }.to_vector(type)
end

# File lib/statsample/vector.rb, line 244
def recode!
@data.collect!{|x|
  yield x
}
set_valid_data
end

# File lib/statsample/vector.rb, line 776
def report_building(b)
  b.section(:name=>name) do |s|
    s.text _("n :%d") % n        
    s.text _("n valid:%d") % n_valid
    if @type==:nominal
      s.text  _("factors:%s") % factors.join(",") 
      s.text   _("mode: %s") % mode 
      
      s.table(:name=>_("Distribution")) do |t|
        frequencies.sort.each do |k,v|
          key=labels.has_key?(k) ? labels[k]:k
          t.row [key, v , ("%0.2f%%" % (v.quo(n_valid)*100))]
        end
      end
    end
    
    s.text _("median: %s") % median.to_s if(@type==:ordinal or @type==:scale)
    if(@type==:scale)
      s.text _("mean: %0.4f") % mean
      if sd
        s.text _("std.dev.: %0.4f") % sd
        s.text _("std.err.: %0.4f") % se
        s.text _("skew: %0.4f") % skew
        s.text _("kurtosis: %0.4f") % kurtosis
      end
    end
  end
end

# File lib/statsample/vector.rb, line 639
def sample_with_replacement(sample=1)
  vds=@valid_data.size
  (0...sample).collect{ @valid_data[rand(vds)] }
end

# File lib/statsample/vector.rb, line 650
def sample_without_replacement(sample=1)
  raise ArgumentError, "Sample size couldn't be greater than n" if sample>@valid_data.size
  out=[]
  size=@valid_data.size
  while out.size<sample
    value=rand(size)
    out.push(value) if !out.include?value
  end
  out.collect{|i| @data[i]}
end

# File lib/statsample/vector.rb, line 306
def set_valid_data
  @valid_data.clear
  @missing_data.clear
  @data_with_nils.clear
  @date_data_with_nils.clear
  set_valid_data_intern
  set_scale_data if(@type==:scale)
  set_date_data if(@type==:date)
end

# File lib/statsample/vector.rb, line 361
def size
  @data.size
end

# File lib/statsample/vector.rb, line 971
def skew(m=nil)
    check_type :scale
    m||=mean
    th=@scale_data.inject(0){|a,x| a+((x-m)**3)}
    th.quo((@scale_data.size)*sd(m)**3)
end

# File lib/statsample/vector.rb, line 493
def split_by_separator(sep=Statsample::SPLIT_TOKEN)
split_data=splitted(sep)
factors=split_data.flatten.uniq.compact
out=factors.inject({}) {|a,x|
  a[x]=[]
  a
}
split_data.each do |r|
  if r.nil?
    factors.each do |f|
      out[f].push(nil)
    end
  else
    factors.each do |f|
      out[f].push(r.include?(f) ? 1:0) 
    end
  end
end
out.inject({}){|s,v|
  s[v[0]]=Vector.new(v[1],:nominal)
  s
}
end

# File lib/statsample/vector.rb, line 516
def split_by_separator_freq(sep=Statsample::SPLIT_TOKEN)
  split_by_separator(sep).inject({}) {|a,v|
    a[v[0]]=v[1].inject {|s,x| s+x.to_i}
    a
  }
end

# File lib/statsample/vector.rb, line 469
def splitted(sep=Statsample::SPLIT_TOKEN)
@data.collect{|x|
  if x.nil?
    nil
  elsif (x.respond_to? :split)
    x.split(sep)
  else
    [x]
  end
}
end

# File lib/statsample/vector.rb, line 939
def standard_deviation_population(m=nil)
  check_type :scale
  Math::sqrt( variance_population(m) )
end

# File lib/statsample/vector.rb, line 965
def standard_deviation_sample(m=nil)
    check_type :scale
    m||=mean
    Math::sqrt(variance_sample(m))
end

# File lib/statsample/vector.rb, line 1025
def standard_error
  standard_deviation_sample.quo(Math.sqrt(valid_data.size))
end

# File lib/statsample/vector.rb, line 905
def sum
  check_type :scale
  @scale_data.inject(0){|a,x|x+a} ; 
end

# File lib/statsample/vector.rb, line 924
def sum_of_squared_deviation
  check_type :scale
  @scale_data.inject(0) {|a,x| x.square+a} - (sum.square.quo(n_valid))
end

# File lib/statsample/vector.rb, line 918
def sum_of_squares(m=nil)
  check_type :scale
  m||=mean
  @scale_data.inject(0){|a,x| a+(x-m).square}
end

# File lib/statsample/rserve_extension.rb, line 6
def to_REXP
  Rserve::REXP::Wrapper.wrap(data_with_nils)
end

# File lib/statsample/vector.rb, line 396
def to_a
  if @data.is_a? Array
    @data.dup
  else
    @data.to_a
  end
end

# File lib/statsample/vector.rb, line 714
def to_matrix(dir=:horizontal)
  case dir
  when :horizontal
    Matrix[@data]
  when :vertical
    Matrix.columns([@data])
  end
end

# File lib/statsample/vector.rb, line 709
def to_s
  sprintf("Vector(type:%s, n:%d)[%s]",@type.to_s,@data.size, @data.collect{|d| d.nil? ? "nil":d}.join(","))
end

# File lib/statsample/vector.rb, line 386
def today_values=(vals)
  @today_values = vals
  set_valid_data
end

# File lib/statsample/vector.rb, line 391
def type=(t)
  @type=t   
  set_scale_data if(t==:scale)
  set_date_data if (t==:date)
end

# File lib/statsample/vector.rb, line 930
def variance_population(m=nil)
  check_type :scale
  m||=mean
  squares=@scale_data.inject(0){|a,x| x.square+a}
  squares.quo(n_valid) - m.square
end

# File lib/statsample/vector.rb, line 806
def variance_proportion(n_poblation, v=1)
  Statsample::proportion_variance_sample(self.proportion(v), @valid_data.size, n_poblation)
end

# File lib/statsample/vector.rb, line 958
def variance_sample(m=nil)
  check_type :scale
  m||=mean
  sum_of_squares(m).quo(n_valid - 1)
end

# File lib/statsample/vector.rb, line 810
def variance_total(n_poblation, v=1)
  Statsample::total_variance_sample(self.proportion(v), @valid_data.size, n_poblation)
end

# File lib/statsample/vector.rb, line 184
def vector_centered
  check_type :scale
  m=mean
  return ([nil]*size).to_scale if mean.nil?
  vector=vector_centered_compute(m)
  vector.name=_("%s(centered)") % @name
  vector
end

# File lib/statsample/vector.rb, line 350
def vector_labeled
  d=@data.collect{|x|
    if @labels.has_key? x
      @labels[x]
    else
      x
    end
  }
  Vector.new(d,@type)
end

# File lib/statsample/vector.rb, line 197
def vector_percentil
  check_type :ordinal
  c=@valid_data.size
  vector=ranked.map {|i| i.nil? ? nil : (i.quo(c)*100).to_f }.to_vector(@type)
  vector.name=_("%s(percentil)")  % @name
  vector
end

# File lib/statsample/vector.rb, line 171
def vector_standarized(use_population=false)
  check_type :scale
  m=mean
  sd=use_population ? sdp : sds
  return ([nil]*size).to_scale if mean.nil? or sd==0.0 
  vector=vector_standarized_compute(m,sd)
  vector.name=_("%s(standarized)")  % @name
  vector
end

# File lib/statsample/vector.rb, line 429
def verify
h={}
(0...@data.size).to_a.each{|i|
  if !(yield @data[i])
    h[i]=@data[i]
  end
}
h
end