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authorBenjamin Schönburg <lazybensch@gmail.com>2015-10-27 15:25:24 +0100
committerBenjamin Schönburg <lazybensch@gmail.com>2015-10-27 15:25:24 +0100
commit6f5efb6883cfc780cc38658be5336898b67aebc7 (patch)
treefd92e758bef4927f26173a0751b55bc28b5081ba
parent44ca091c73afe13ec8760021cfed1d77afc5e4a5 (diff)
update julia docs to 0.4
-rw-r--r--julia.html.markdown31
1 files changed, 13 insertions, 18 deletions
diff --git a/julia.html.markdown b/julia.html.markdown
index cba7cd45..8a300f79 100644
--- a/julia.html.markdown
+++ b/julia.html.markdown
@@ -8,7 +8,7 @@ filename: learnjulia.jl
Julia is a new homoiconic functional language focused on technical computing.
While having the full power of homoiconic macros, first-class functions, and low-level control, Julia is as easy to learn and use as Python.
-This is based on Julia 0.3.
+This is based on Julia 0.4.
```ruby
@@ -22,7 +22,7 @@ This is based on Julia 0.3.
## 1. Primitive Datatypes and Operators
####################################################
-# Everything in Julia is a expression.
+# Everything in Julia is an expression.
# There are several basic types of numbers.
3 # => 3 (Int64)
@@ -262,8 +262,8 @@ values(filled_dict)
# Note - Same as above regarding key ordering.
# Check for existence of keys in a dictionary with in, haskey
-in(("one", 1), filled_dict) # => true
-in(("two", 3), filled_dict) # => false
+in(("one" => 1), filled_dict) # => true
+in(("two" => 3), filled_dict) # => false
haskey(filled_dict, "one") # => true
haskey(filled_dict, 1) # => false
@@ -282,7 +282,7 @@ get(filled_dict,"four",4) # => 4
# Use Sets to represent collections of unordered, unique values
empty_set = Set() # => Set{Any}()
# Initialize a set with values
-filled_set = Set(1,2,2,3,4) # => Set{Int64}(1,2,3,4)
+filled_set = Set([1,2,2,3,4]) # => Set{Int64}(1,2,3,4)
# Add more values to a set
push!(filled_set,5) # => Set{Int64}(5,4,2,3,1)
@@ -292,7 +292,7 @@ in(2, filled_set) # => true
in(10, filled_set) # => false
# There are functions for set intersection, union, and difference.
-other_set = Set(3, 4, 5, 6) # => Set{Int64}(6,4,5,3)
+other_set = Set([3, 4, 5, 6]) # => Set{Int64}(6,4,5,3)
intersect(filled_set, other_set) # => Set{Int64}(3,4,5)
union(filled_set, other_set) # => Set{Int64}(1,2,3,4,5,6)
setdiff(Set(1,2,3,4),Set(2,3,5)) # => Set{Int64}(1,4)
@@ -404,12 +404,10 @@ varargs(1,2,3) # => (1,2,3)
# We just used it in a function definition.
# It can also be used in a fuction call,
# where it will splat an Array or Tuple's contents into the argument list.
-Set([1,2,3]) # => Set{Array{Int64,1}}([1,2,3]) # produces a Set of Arrays
-Set([1,2,3]...) # => Set{Int64}(1,2,3) # this is equivalent to Set(1,2,3)
+add([5,6]...) # this is equivalent to add(5,6)
-x = (1,2,3) # => (1,2,3)
-Set(x) # => Set{(Int64,Int64,Int64)}((1,2,3)) # a Set of Tuples
-Set(x...) # => Set{Int64}(2,3,1)
+x = (5,6) # => (5,6)
+add(x...) # this is equivalent to add(5,6)
# You can define functions with optional positional arguments
@@ -531,12 +529,8 @@ abstract Cat # just a name and point in the type hierarchy
# Abstract types cannot be instantiated, but can have subtypes.
# For example, Number is an abstract type
-subtypes(Number) # => 6-element Array{Any,1}:
- # Complex{Float16}
- # Complex{Float32}
- # Complex{Float64}
+subtypes(Number) # => 2-element Array{Any,1}:
# Complex{T<:Real}
- # ImaginaryUnit
# Real
subtypes(Cat) # => 0-element Array{Any,1}
@@ -554,10 +548,11 @@ subtypes(AbstractString) # 8-element Array{Any,1}:
# Every type has a super type; use the `super` function to get it.
typeof(5) # => Int64
super(Int64) # => Signed
-super(Signed) # => Real
+super(Signed) # => Integer
+super(Integer) # => Real
super(Real) # => Number
super(Number) # => Any
-super(super(Signed)) # => Number
+super(super(Signed)) # => Real
super(Any) # => Any
# All of these type, except for Int64, are abstract.
typeof("fire") # => ASCIIString