Module: Daru::Maths::Statistics::DataFrame

Included in:
DataFrame
Defined in:
lib/daru/maths/statistics/dataframe.rb

Instance Method Summary collapse

Instance Method Details

#acf ⇒ Object

Calculate Autocorrelation coefficient

Parameters:

  • max_lags (Integer) —

    (nil) Number of initial lags



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#correlation ⇒ Object Also known as: corr

Calculate the correlation between the numeric vectors.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 130

def correlation
  standard_deviation = std.to_matrix
  corr_arry = (cov
    .to_matrix
    .elementwise_division(standard_deviation.transpose * 
    standard_deviation)).to_a

  Daru::DataFrame.rows(corr_arry, index: numeric_vectors, order: numeric_vectors)
end

#count ⇒ Object

Count the number of non-nil values in each vector.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 21

def count
  compute_stats :count
end

#covariance ⇒ Object Also known as: cov

Calculate sample variance-covariance between the numeric vectors.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 104

def covariance
  cache={}
  vectors = self.numeric_vectors

  mat_rows = vectors.collect do |row|
    vectors.collect do |col|
      if row == col
        self[row].variance
      else
        if cache[[col,row]].nil?
          cov = vector_cov(self[row],self[col])
          cache[[row,col]] = cov
          cov
        else
          cache[[col,row]]
        end
      end
    end
  end

  Daru::DataFrame.rows(mat_rows, index: numeric_vectors, order: numeric_vectors)
end

#cumsum ⇒ Object

Calculate cumulative sum of each numeric Vector



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#describe(methods = nil) ⇒ Object

Create a summary of mean, standard deviation, count, max and min of each numeric vector in the dataframe in one shot.

Arguments

methods - An array with aggregation methods specified as symbols to be applied to numeric vectors. Default is [:count, :mean, :std, :max, :min]. Methods will be applied in the specified order.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 93

def describe methods=nil
  methods ||= [:count, :mean, :std, :min, :max]

  description_hash = {}
  numeric_vectors.each do |vec|
    description_hash[vec] = methods.map { |m| self[vec].send(m) }
  end
  Daru::DataFrame.new(description_hash, index: methods)
end

#ema ⇒ Object

Calculate exponential moving average.

Parameters:

  • n (Integer) —

    (10) Loopback length.

  • wilder (TrueClass, FalseClass, NilClass) —

    (false) If true, 1/n value is used for smoothing; if false, uses 2/(n+1) value.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#max(opts = {}) ⇒ Object

Calculate the maximum value of each numeric vector.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 26

def max opts={}
  if opts[:vector]
    self.row[*self[opts[:vector]].max_index.index.to_a]
  else
    compute_stats :max
  end
end

#mean ⇒ Object

Calculate mean of numeric vectors.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 6

def mean
  compute_stats :mean
end

#min ⇒ Object

Calculate the minimmum value of each numeric vector.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 35

def min
  compute_stats :min
end

#product ⇒ Object

Compute the product of each numeric vector.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 40

def product
  compute_stats :product
end

#rolling_count ⇒ Object

Calculate moving non-missing count

Parameters:

  • n (Integer) —

    (10) Loopback length. Default to 10.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#rolling_max ⇒ Object

Calculate moving max

Parameters:

  • n (Integer) —

    (10) Loopback length. Default to 10.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#rolling_mean ⇒ Object

Calculate moving averages

Parameters:

  • n (Integer) —

    (10) Loopback length. Default to 10.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#rolling_median ⇒ Object

Calculate moving median

Parameters:

  • n (Integer) —

    (10) Loopback length. Default to 10.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#rolling_min ⇒ Object

Calculate moving min

Parameters:

  • n (Integer) —

    (10) Loopback length. Default to 10.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#rolling_std ⇒ Object

Calculate moving standard deviation

Parameters:

  • n (Integer) —

    (10) Loopback length. Default to 10.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#rolling_variance ⇒ Object

Calculate moving variance

Parameters:

  • n (Integer) —

    (10) Loopback length. Default to 10.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#standardize ⇒ Object

Standardize each Vector



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# File 'lib/daru/maths/statistics/dataframe.rb', line 77

[:cumsum,:standardize,:acf,:ema,:rolling_mean,:rolling_median,:rolling_max,
 :rolling_min,:rolling_count,:rolling_std,:rolling_variance, :rolling_sum
].each do |meth|
  define_method(meth) do |*args|
    apply_method_to_numerics meth, *args
  end
end

#std ⇒ Object

Calculate sample standard deviation of numeric vectors.



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# File 'lib/daru/maths/statistics/dataframe.rb', line 11

def std
  compute_stats :std
end

#sum ⇒ Object

Calculate sum of numeric vectors



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# File 'lib/daru/maths/statistics/dataframe.rb', line 16

def sum
  compute_stats :sum
end