lib/darkext/statistics.rb in darkhelmet-darkext-0.11.0 vs lib/darkext/statistics.rb in darkhelmet-darkext-0.11.1

@@ -35,11 +35,11 @@
       return self.sort[self.size / 2]
     end
   end
 
   # Generates a histogram hash for the array
-  def histogram(bins = nil)
+  def histogram
     self.sort.inject({}) do |a,x|
       a[x] = a[x].to_i + 1
       a
     end
   end
@@ -51,38 +51,22 @@
     max = map.values.max
     map.keys.select { |x| map[x] == max }
   end
 
   # Variance
-  def population_variance
+  def variance
     raise ArgumentError.new('Array size must be > 0') if self.size.zero?
     self.sum_of_squares.to_f / (self.size).to_f
   end
 
-  def sample_variance
-    raise ArgumentError.new('Array size must be > 0') if self.size.zero?
-    self.sum_of_squares.to_f / (self.size - 1).to_f
-  end
-
   # Standard deviation
-  def population_deviation
+  def standard_deviation
     raise ArgumentError.new('Array size must be > 0') if self.size.zero?
-    self.population_variance.abs.sqrt
+    self.variance.abs.sqrt
   end
+  alias :stddev :standard_deviation
 
-  def sample_deviation
-    raise ArgumentError.new('Array size must be > 0') if self.size.zero?
-    self.sample_variance.abs.sqrt
-  end
-
-  def geometric_deviation
-    raise ArgumentError.new('Array size must be > 0') if self.size.zero?
-    gmean = self.g_mean
-    Math.exp((self.map { |x| (x.ln - gmean.ln).square }.sum.to_f / self.size.to_f).sqrt)
-  end
-  alias :gstddev :geometric_deviation
-
   # Randomly samples n elements
   def sample(n = 1)
     (1..n).collect { self[rand(self.size)] }
   end
 
@@ -115,30 +99,19 @@
   end
 
   # Destructive standardize
   def standardize!
     m = self.mean.to_f
-    rho = self.sample_deviation.to_f
+    rho = self.standard_deviation.to_f
     self.map! { |v| (v.to_f - m) / rho }
   end
 
   def sum_of_squares
     raise ArgumentError.new('Array size must be > 0') if self.size.zero?
     m = self.mean
     self.map { |v| v - m }.squares.sum
   end
-
-  # Normalize the array
-  def normalize
-    self.clone.normalize!
-  end
-
-  # Normalize the array destructively
-  def normalize!
-    m = self.mean.to_f
-    self.map! { |v| v / m }
-  end
 end
 
 module Darkext
   module Darkext::Statistics
     # Finds the probability of a z-score
@@ -161,17 +134,20 @@
       end
       return zscore
     end
 
     # Finds a two tail p-val for a high/low array
+    # can't remember how to use this
+=begin
     def self.p_val(r, n = 30, rho = 1, mu = r.mean)
       probs = r.map do |x|
         (x - mu) / (rho / n.sqrt)
       end.map do |x|
         Statistics::prob(x)
       end
       return 1 - (probs[1] - probs[0])
     end
+=end
 
     module Darkext::Statistics::Regression
       # Do a least squares linear regression on the two sets of x's and y's
       # Returns a hash containing many relevant values
       # * n (:n)