Crystal v0.34

README.md

@@ -3,7 +3,7 @@
 # stats
 
 An expressive implementation of statistical distributions.
-Compatible with crystal v0.27.0.
+Compatible with crystal v0.34.0.
 
 ## Installation
 
@@ -94,8 +94,6 @@ Math.factorial(4) # => 24
 
 ### Quartiles & Boxplot
 
-*Note: not big compatible yet*
-
 ```crystal
 [1, 3, 5].first_quartile  # => 2.0 (alias of lower_quartile)
 [1, 3, 5].second_quartile # => 3.0 (alias of median)
@@ -144,3 +142,4 @@ arr.upper_outliers(3) # => [1337]
 ## Contributors
 
 - [Nephos](https://github.com/Nephos) Arthur Poulet - creator, maintainer
+- [pyrokar](https://github.com/pyrokar) Gunter Solf - contributer

shard.yml

@@ -1,5 +1,5 @@
 name: stats
-version: 0.3.0
+version: 0.3.1
 
 authors:
   - Arthur Poulet <[email protected]>

spec/math/quartile.cr

@@ -13,18 +13,17 @@ module Math::Quartile
     arr.iqr.should eq 2.0
   end
 
-  # TODO
-  # it "big" do
-  #   arr = [BigInt.new(1), BigFloat.new(3), 5]
-  #
-  #   arr.first_quartile.should eq 2.0
-  #   arr.second_quartile.should eq 3.0
-  #   arr.third_quartile.should eq 4.0
-  #
-  #   arr.quartiles.should eq [2.0, 3.0, 4.0]
-  #
-  #   arr.iqr.should eq 2.0
-  # end
+  it "big" do
+    arr = [BigInt.new(1), BigFloat.new(3), 5]
+
+    arr.first_quartile.should eq 2.0
+    arr.second_quartile.should eq 3.0
+    arr.third_quartile.should eq 4.0
+
+    arr.quartiles.should eq [2.0, 3.0, 4.0]
+
+    arr.iqr.should eq 2.0
+  end
 
   it "odd size input" do
     arr = [6, 7, 15, 36, 39, 40, 41, 42, 43, 47, 49]

src/lib/math/factorial.cr

@@ -4,7 +4,7 @@ module Math
     def factorial(n : Number)
       raise Math::DomainError.new "The argument must be a natural (out of domain -- factorial)" if n < 0
       return (typeof(n)).new(1) if n == 0
-      return (1..n).to_a.reduce((typeof(n)).new(1)) { |a, b| a * b }
+      return (1..n).to_a.reduce((typeof(n)).new(1)) { |a, b| a &* b }
     end
   end
 end

src/lib/math/macd.cr

@@ -14,9 +14,9 @@ module Math::MACD
   # MACD computation if n is odd
   private def macd_odd(n)
     map_with_index do |_, i|
-      next if i < n / 2
-      next if i + n / 2 >= size
-      range = (i - n / 2)..(i + n / 2)
+      next if i < n // 2
+      next if i + n // 2 >= size
+      range = (i - n // 2)..(i + n // 2)
       self[range].mean
     end
   end

src/lib/math/median.cr

@@ -1,10 +1,10 @@
 module Math::Median
-  def median
+  def median : Number
     return 0.0_f64 if empty?
     sorted = sort
     size = size()
-    return sorted[(size - 1) / 2] / 1.0 if size.odd?
-    (sorted[(size / 2) - 1] + sorted[size / 2]) / 2.0
+    return sorted[(size - 1) // 2] / 1.0 if size.odd?
+    (sorted[(size // 2) - 1] + sorted[size // 2]) / 2.0
   end
 end
 

src/lib/math/quartile.cr

@@ -4,57 +4,57 @@
 # https://en.wikipedia.org/wiki/Quartile#Method_2
 #
 module Math::Quartile
-  def lower_quartile : Float64
-    return 0.0_f64 if empty?
+  def lower_quartile
+    return 0 if empty?
     m = self.median
     lower_half = self.select { |i| i <= m }
     lower_half.median
   end
 
   # alias
-  def first_quartile : Float64
+  def first_quartile
     lower_quartile
   end
 
   # alias
-  def second_quartile : Float64
+  def second_quartile
     median
   end
 
-  def upper_quartile : Float64
-    return 0.0_f64 if empty?
+  def upper_quartile
+    return 0 if empty?
     m = self.median
     upper_half = self.select { |i| i >= m }
     upper_half.median
   end
 
   # alias
-  def third_quartile : Float64
+  def third_quartile
     upper_quartile
   end
 
-  def quartiles : Array(Float64)
+  def quartiles
     [first_quartile, second_quartile, third_quartile]
   end
 
-  def iqr : Float64
+  def iqr
     third_quartile - first_quartile
   end
 
   # alias
-  def interquartile_range : Float64
+  def interquartile_range
     iqr
   end
 
-  def lower_fence(k : Number = 1.5) : Float64
+  def lower_fence(k : Number = 1.5)
     lower_quartile - k * iqr
   end
 
-  def upper_fence(k : Number = 1.5) : Float64
+  def upper_fence(k : Number = 1.5)
     upper_quartile + k * iqr
   end
 
-  def lower_outliers(k : Number = 1.5) : Array
+  def lower_outliers(k : Number = 1.5)
     lf = lower_fence k
     self.select { |i| i < lf }
   end