lokeshh

require 'ruby-prof'
n = 100000
dv = Daru::Vector.new 1..n
RubyProf.start
100.times { dv.reject_values nil }
result = RubyProf.stop
printer = RubyProf::GraphHtmlPrinter.new(result)
printer.print(File.open('cache.html', 'w'))

lokeshh

require 'benchmark'

n = 10000
df = Daru::DataFrame.new({
  a: Statsample::Shorthand.rnorm(n),
  b: Statsample::Shorthand.rnorm(n),
  c: Statsample::Shorthand.rnorm(n),
  d: Statsample::Shorthand.rnorm(n),
})
df[:y] = df.a * 3 + df.b * 5 + df.c * -2 + df.d * 2

lokeshh

require 'benchmark'

n = 100000
dv = Daru::Vector.new 1..n

# For current branch
Benchmark.bm do |x|
  x.report do
    100.times { dv[0] = nil }
  end

lokeshh

ubuntu@ip-172-31-51-75:~$ irb
2.3.0 :001 > require 'daru'

Install the reportbuilder gem version ~>1.4 for using reportbuilder functions.

Install the spreadsheet gem version ~>1.1.1 for using spreadsheet functions.
NameError: uninitialized constant Daru::Accessors::ArrayWrapper::Forwardable
	from /home/ubuntu/.rvm/gems/ruby-2.3.0/gems/daru-0.1.4/lib/daru/accessors/array_wrapper.rb:6:in `<class:ArrayWrapper>'
	from /home/ubuntu/.rvm/gems/ruby-2.3.0/gems/daru-0.1.4/lib/daru/accessors/array_wrapper.rb:4:in `<module:Accessors>'

lokeshh

200000 24
1 1 1 0 0 0 0 0 1 1 0 1 0 0 1 1 1 1 0 0 1 1 1 1 
0 1 1 0 0 1 1 0 0 1 0 1 1 1 1 1 1 0 1 0 1 1 0 1 
0 1 1 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1 1 0 0 1 0 1 
1 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1 1 0 0 0 
0 1 0 0 1 1 1 0 1 0 1 1 0 0 1 1 1 1 1 0 0 1 0 0 
0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 1 
0 0 0 1 1 0 0 0 1 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 
0 0 0 0 1 0 0 0 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 0 
1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 1 1 1 1 1 1 0 0 1 

lokeshh

require 'benchmark'
require 'matrix'
require 'nmatrix'

class Matrix
  public :"[]=", :set_element, :set_component
end

m = Matrix.zero 1000 ;
n = NMatrix.new [1000, 1000], [0]*10**6

lokeshh

#language en = @PAGE@ = Please describe your City here.

== Teams == Please manually add a link to your team page below after creating it:

• Team1
• Team2

lokeshh

require 'daru'
require 'benchmark'


# Vector :b is not required here. Taking it only to compare the performance
# with second dataframe which also has two columns.
df1 = Daru::DataFrame.new({
  a: [1, 2, 3]*1000,
  b: [1, 2, 3]*1000},
  index: Daru::CategoricalIndex.new([:a, :b, :c, :d, :e, :f]*500)

lokeshh

array([[ 0.40576443,  0.17155695,  0.        ,  0.        ,  0.        ,
         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,
         0.        ],
       [ 0.20288222,  0.34311389,  0.17489069,  0.08206331,  0.0530504 ,
         0.03926702,  0.03141361,  0.0265252 ,  0.02344666,  0.02186134,
         0.02300929],
       [ 0.10144111,  0.17155695,  0.34978139,  0.16412661,  0.0795756 ,
         0.05235602,  0.03926702,  0.03183024,  0.02735444,  0.02498438,
         0.02588545],
       [ 0.06762741,  0.08577847,  0.17489069,  0.32825322,  0.15915119,

lokeshh

GSoC 2016 Summary, Adding categorical data support

Support for categorical data is important for any data analysis tool. This summer I added categorical data support to:

• to easily analyze categorical data in Daru
• visualize categorical data
• support regression with categorical variable in Statsample and Statsample-GLM

Here's my project page.

Lets talk about each of them in detail.