myui

/*******************************************************************************
 * Copyright (c) 2010 Haifeng Li
 *   
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *  
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software

myui

/*
 * Hivemall: Hive scalable Machine Learning Library
 *
 * Copyright (C) 2015 Makoto YUI
 * Copyright (C) 2013-2015 National Institute of Advanced Industrial Science and Technology (AIST)
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *

myui

myui

@relation weather.symbolic

@attribute outlook {sunny, overcast, rainy}
@attribute temperature {hot, mild, cool}
@attribute humidity {high, normal}
@attribute windy {TRUE, FALSE}
@attribute play {yes, no}

@data
sunny,hot,high,FALSE,no

myui

%
% As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
% using instance-based learning with encoding length selection. In Progress
% in Connectionist-Based Information Systems. Singapore: Springer-Verlag.
%
% Deleted "vendor" attribute to make data consistent with with what we
% used in the data mining book.
%
@relation 'cpu'
@attribute MYCT real

myui

% 1. Title: Iris Plants Database
% 
% 2. Sources:
%      (a) Creator: R.A. Fisher
%      (b) Donor: Michael Marshall (MARSHALL%[email protected])
%      (c) Date: July, 1988
% 
% 3. Past Usage:
%    - Publications: too many to mention!!!  Here are a few.
%    1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"

myui

push x[10]; push 10.0; ifeq 205; push x[5]; push 275.5; ifle 68; push x[1]; push 7.0; ifeq 11; push 1; goto last; push x[15]; push 2.0; ifeq 26; push x[14]; push 2.5; ifle 24; push x[0]; push 49.5; ifle 22; push 0; goto last; push 1; goto last; push 1; goto last; push x[11]; push 327.5; ifle 66; push x[11]; push 265.5; ifle 64; push x[1]; push 6.0; ifeq 37; push 0; goto last; push x[11]; push 87.5; ifle 42; push 0; goto last; push x[11]; push 190.0; ifle 62; push x[9]; push 15.0; ifle 60; push x[3]; push 0.0; ifeq 58; push x[13]; push 264.0; ifle 56; push 1; goto last; push 0; goto last; push 0; goto last; push 1; goto last; push 0; goto last; push 1; goto last; push 0; goto last; push x[9]; push 18.5; ifle 128; push x[0]; push 25.0; ifle 76; push 1; goto last; push x[2]; push 2.0; ifeq 96; push x[11]; push 619.0; ifle 94; push x[6]; push 0.0; ifeq 87; push 1; goto last; push x[14]; push 3.5; ifle 92; push 0; goto last; push 1; goto last; push 0; goto last; push x[11]; push 153.0; ifle 101; push 0; goto last;

myui

_______1_______
___2_______2___
_4___4___4___4_
8_8_8_8_8_8_8_8

myui

/*
 * Hivemall: Hive scalable Machine Learning Library
 *
 * Copyright (C) 2015 Makoto YUI
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0

myui

set hivevar:k=11;

create table similarities
as
WITH test_rnd as (
select
  rand(31) as rnd,
  id,
  features
from