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apache / madlib / e77ec591d4a26d64eccd35326454e367051a768b / . / src / ports / postgres / modules / convex / test / mlp.sql_in
blob: d3e82f710351a62beb5cd087653dd8dadc53cce2 [file] [log] [blame]
/* ----------------------------------------------------------------------- *//**
*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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 distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*
*//* ----------------------------------------------------------------------- */
-- The data were collected by Anderson, Edgar (1935).
-- The irises of the Gaspe Peninsula, Bulletin of the American Iris Society, 59, 2–5.
-- Classification
SELECT setseed(0.6);
DROP TABLE IF EXISTS iris_data;
CREATE TABLE iris_data(
id integer,
attributes numeric[],
class_text varchar,
class integer,
grp text
);
INSERT INTO iris_data VALUES
(1,ARRAY[5.1,3.5,1.4,0.2],'Iris-setosa',1,'1'),
(2,ARRAY[4.9,3.0,1.4,0.2],'Iris-setosa',1,'1'),
(3,ARRAY[4.7,3.2,1.3,0.2],'Iris-setosa',1,'1'),
(4,ARRAY[4.6,3.1,1.5,0.2],'Iris-setosa',1,'1'),
(5,ARRAY[5.0,3.6,1.4,0.2],'Iris-setosa',1,'1'),
(6,ARRAY[5.4,3.9,1.7,0.4],'Iris-setosa',1,'1'),
(7,ARRAY[4.6,3.4,1.4,0.3],'Iris-setosa',1,'1'),
(8,ARRAY[5.0,3.4,1.5,0.2],'Iris-setosa',1,'1'),
(9,ARRAY[4.4,2.9,1.4,0.2],'Iris-setosa',1,'1'),
(10,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa',1,'1'),
(11,ARRAY[5.4,3.7,1.5,0.2],'Iris-setosa',1,'1'),
(12,ARRAY[4.8,3.4,1.6,0.2],'Iris-setosa',1,'1'),
(13,ARRAY[4.8,3.0,1.4,0.1],'Iris-setosa',1,'1'),
(14,ARRAY[4.3,3.0,1.1,0.1],'Iris-setosa',1,'1'),
(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa',1,'1'),
(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa',1,'1'),
(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa',1,'1'),
(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa',1,'1'),
(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa',1,'1'),
(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa',1,'1'),
(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa',1,'1'),
(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa',1,'1'),
(51,ARRAY[7.0,3.2,4.7,1.4],'Iris-versicolor',2,'1'),
(52,ARRAY[6.4,3.2,4.5,1.5],'Iris-versicolor',2,'1'),
(53,ARRAY[6.9,3.1,4.9,1.5],'Iris-versicolor',2,'1'),
(54,ARRAY[5.5,2.3,4.0,1.3],'Iris-versicolor',2,'1'),
(55,ARRAY[6.5,2.8,4.6,1.5],'Iris-versicolor',2,'1'),
(56,ARRAY[5.7,2.8,4.5,1.3],'Iris-versicolor',2,'1'),
(57,ARRAY[6.3,3.3,4.7,1.6],'Iris-versicolor',2,'1'),
(58,ARRAY[4.9,2.4,3.3,1.0],'Iris-versicolor',2,'1'),
(59,ARRAY[6.6,2.9,4.6,1.3],'Iris-versicolor',2,'1'),
(60,ARRAY[5.2,2.7,3.9,1.4],'Iris-versicolor',2,'1'),
(61,ARRAY[5.0,2.0,3.5,1.0],'Iris-versicolor',2,'1'),
(62,ARRAY[5.9,3.0,4.2,1.5],'Iris-versicolor',2,'1'),
(63,ARRAY[6.0,2.2,4.0,1.0],'Iris-versicolor',2,'1'),
(64,ARRAY[6.1,2.9,4.7,1.4],'Iris-versicolor',2,'1'),
(65,ARRAY[5.6,2.9,3.6,1.3],'Iris-versicolor',2,'1'),
(66,ARRAY[6.7,3.1,4.4,1.4],'Iris-versicolor',2,'1'),
(67,ARRAY[5.6,3.0,4.5,1.5],'Iris-versicolor',2,'1'),
(68,ARRAY[5.8,2.7,4.1,1.0],'Iris-versicolor',2,'1'),
(69,ARRAY[6.2,2.2,4.5,1.5],'Iris-versicolor',2,'1'),
(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor',2,'1'),
(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor',2,'1'),
(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor',2,'1'),
(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor',2,'1'),
(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor',2,'1'),
(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor',2,'1'),
(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor',2,'1'),
(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor',2,'1'),
(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor',2,'1'),
(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica',3,'1'),
(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica',3,'1'),
(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica',3,'1'),
(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica',3,'1'),
(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica',3,'1'),
(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica',3,'1'),
(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica',3,'1'),
(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica',3,'1'),
(109,ARRAY[6.7,2.5,5.8,1.8],'Iris-virginica',3,'1'),
(110,ARRAY[7.2,3.6,6.1,2.5],'Iris-virginica',3,'1'),
(111,ARRAY[6.5,3.2,5.1,2.0],'Iris-virginica',3,'1'),
(112,ARRAY[6.4,2.7,5.3,1.9],'Iris-virginica',3,'1'),
(113,ARRAY[6.8,3.0,5.5,2.1],'Iris-virginica',3,'1'),
(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica',3,'1'),
(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica',3,'1'),
(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica',3,'1'),
(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica',3,'1'),
(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica',3,'1'),
(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica',3,'1'),
(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica',3,'1'),
(27,ARRAY[5.0,3.4,1.6,0.4],'Iris-setosa',1,'2'),
(28,ARRAY[5.2,3.5,1.5,0.2],'Iris-setosa',1,'2'),
(29,ARRAY[5.2,3.4,1.4,0.2],'Iris-setosa',1,'2'),
(30,ARRAY[4.7,3.2,1.6,0.2],'Iris-setosa',1,'2'),
(31,ARRAY[4.8,3.1,1.6,0.2],'Iris-setosa',1,'2'),
(32,ARRAY[5.4,3.4,1.5,0.4],'Iris-setosa',1,'2'),
(33,ARRAY[5.2,4.1,1.5,0.1],'Iris-setosa',1,'2'),
(34,ARRAY[5.5,4.2,1.4,0.2],'Iris-setosa',1,'2'),
(35,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa',1,'2'),
(36,ARRAY[5.0,3.2,1.2,0.2],'Iris-setosa',1,'2'),
(37,ARRAY[5.5,3.5,1.3,0.2],'Iris-setosa',1,'2'),
(38,ARRAY[4.9,3.1,1.5,0.1],'Iris-setosa',1,'2'),
(39,ARRAY[4.4,3.0,1.3,0.2],'Iris-setosa',1,'2'),
(40,ARRAY[5.1,3.4,1.5,0.2],'Iris-setosa',1,'2'),
(41,ARRAY[5.0,3.5,1.3,0.3],'Iris-setosa',1,'2'),
(42,ARRAY[4.5,2.3,1.3,0.3],'Iris-setosa',1,'2'),
(43,ARRAY[4.4,3.2,1.3,0.2],'Iris-setosa',1,'2'),
(44,ARRAY[5.0,3.5,1.6,0.6],'Iris-setosa',1,'2'),
(45,ARRAY[5.1,3.8,1.9,0.4],'Iris-setosa',1,'2'),
(46,ARRAY[4.8,3.0,1.4,0.3],'Iris-setosa',1,'2'),
(47,ARRAY[5.1,3.8,1.6,0.2],'Iris-setosa',1,'2'),
(48,ARRAY[4.6,3.2,1.4,0.2],'Iris-setosa',1,'2'),
(49,ARRAY[5.3,3.7,1.5,0.2],'Iris-setosa',1,'2'),
(50,ARRAY[5.0,3.3,1.4,0.2],'Iris-setosa',1,'2'),
(75,ARRAY[6.4,2.9,4.3,1.3],'Iris-versicolor',2,'2'),
(76,ARRAY[6.6,3.0,4.4,1.4],'Iris-versicolor',2,'2'),
(77,ARRAY[6.8,2.8,4.8,1.4],'Iris-versicolor',2,'2'),
(78,ARRAY[6.7,3.0,5.0,1.7],'Iris-versicolor',2,'2'),
(79,ARRAY[6.0,2.9,4.5,1.5],'Iris-versicolor',2,'2'),
(80,ARRAY[5.7,2.6,3.5,1.0],'Iris-versicolor',2,'2'),
(81,ARRAY[5.5,2.4,3.8,1.1],'Iris-versicolor',2,'2'),
(82,ARRAY[5.5,2.4,3.7,1.0],'Iris-versicolor',2,'2'),
(83,ARRAY[5.8,2.7,3.9,1.2],'Iris-versicolor',2,'2'),
(84,ARRAY[6.0,2.7,5.1,1.6],'Iris-versicolor',2,'2'),
(85,ARRAY[5.4,3.0,4.5,1.5],'Iris-versicolor',2,'2'),
(86,ARRAY[6.0,3.4,4.5,1.6],'Iris-versicolor',2,'2'),
(87,ARRAY[6.7,3.1,4.7,1.5],'Iris-versicolor',2,'2'),
(88,ARRAY[6.3,2.3,4.4,1.3],'Iris-versicolor',2,'2'),
(89,ARRAY[5.6,3.0,4.1,1.3],'Iris-versicolor',2,'2'),
(90,ARRAY[5.5,2.5,4.0,1.3],'Iris-versicolor',2,'2'),
(91,ARRAY[5.5,2.6,4.4,1.2],'Iris-versicolor',2,'2'),
(92,ARRAY[6.1,3.0,4.6,1.4],'Iris-versicolor',2,'2'),
(93,ARRAY[5.8,2.6,4.0,1.2],'Iris-versicolor',2,'2'),
(94,ARRAY[5.0,2.3,3.3,1.0],'Iris-versicolor',2,'2'),
(95,ARRAY[5.6,2.7,4.2,1.3],'Iris-versicolor',2,'2'),
(96,ARRAY[5.7,3.0,4.2,1.2],'Iris-versicolor',2,'2'),
(97,ARRAY[5.7,2.9,4.2,1.3],'Iris-versicolor',2,'2'),
(98,ARRAY[6.2,2.9,4.3,1.3],'Iris-versicolor',2,'2'),
(99,ARRAY[5.1,2.5,3.0,1.1],'Iris-versicolor',2,'2'),
(100,ARRAY[5.7,2.8,4.1,1.3],'Iris-versicolor',2,'2'),
(101,ARRAY[6.3,3.3,6.0,2.5],'Iris-virginica',3,'2'),
(102,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica',3,'2'),
(103,ARRAY[7.1,3.0,5.9,2.1],'Iris-virginica',3,'2'),
(104,ARRAY[6.3,2.9,5.6,1.8],'Iris-virginica',3,'2'),
(105,ARRAY[6.5,3.0,5.8,2.2],'Iris-virginica',3,'2'),
(106,ARRAY[7.6,3.0,6.6,2.1],'Iris-virginica',3,'2'),
(107,ARRAY[4.9,2.5,4.5,1.7],'Iris-virginica',3,'2'),
(108,ARRAY[7.3,2.9,6.3,1.8],'Iris-virginica',3,'2'),
(138,ARRAY[6.4,3.1,5.5,1.8],'Iris-virginica',3,'2'),
(139,ARRAY[6.0,3.0,4.8,1.8],'Iris-virginica',3,'2'),
(140,ARRAY[6.9,3.1,5.4,2.1],'Iris-virginica',3,'2'),
(141,ARRAY[6.7,3.1,5.6,2.4],'Iris-virginica',3,'2'),
(142,ARRAY[6.9,3.1,5.1,2.3],'Iris-virginica',3,'2'),
(143,ARRAY[5.8,2.7,5.1,1.9],'Iris-virginica',3,'2'),
(144,ARRAY[6.8,3.2,5.9,2.3],'Iris-virginica',3,'2'),
(145,ARRAY[6.7,3.3,5.7,2.5],'Iris-virginica',3,'2'),
(146,ARRAY[6.7,3.0,5.2,2.3],'Iris-virginica',3,'2'),
(147,ARRAY[6.3,2.5,5.0,1.9],'Iris-virginica',3,'2'),
(148,ARRAY[6.5,3.0,5.2,2.0],'Iris-virginica',3,'2'),
(149,ARRAY[6.2,3.4,5.4,2.3],'Iris-virginica',3,'2'),
(150,ARRAY[5.9,3.0,5.1,1.8],'Iris-virginica',3,'2');
-- NOTE that the batch specific tables were created using:
-- madlib.minibatch_preprocessor(), with the regular source tables used in
-- this file.
-- Create preprocessed data that can be used with minibatch MLP:
DROP TABLE IF EXISTS iris_data_batch, iris_data_batch_summary, iris_data_batch_standardization;
CREATE TABLE iris_data_batch(
__id__ integer,
dependent_varname double precision[],
independent_varname double precision[]
);
COPY iris_data_batch (__id__, dependent_varname, independent_varname) FROM STDIN NULL '?' DELIMITER '|';
0 | {{0,1,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{1,0,0},{0,1,0},{1,0,0},{0,0,1},{0,0,1},{0,0,1},{1,0,0},{0,0,1},{0,0,1},{1,0,0},{1,0,0},{0,0,1},{0,1,0},{0,0,1},{0,0,1},{0,0,1},{0,0,1},{1,0,0},{0,1,0},{0,0,1},{0,0,1},{1,0,0}} | {{0.828881825720994,-0.314980522532101,0.363710790466334,0.159758615207397},{-1.08079689039279,-1.57669227467446,-0.229158821743702,-0.240110581430527},{-1.08079689039279,-1.32434992424599,0.482284712908341,0.692917544057962},{-1.46273263361555,0.442046528753317,-1.35561108494277,-1.30642843913166},{-0.0623015751321059,-0.567322872960574,0.245136868024327,0.159758615207397},{-0.189613489539692,-0.819665223389045,0.304423829245331,0.159758615207397},{0.701569911313408,-1.32434992424599,0.778719519013359,0.959497008483245},{-1.20810880480038,-0.0626381721036282,-1.35561108494277,-1.4397181713443},{-0.698861147170034,0.946731229610261,-1.35561108494277,-1.30642843913166},{-0.82617306157762,-1.32434992424599,-0.407019705406713,-0.106820849217886},{-0.698861147170034,2.71312768260957,-1.29632412372177,-1.4397181713443},{1.33812948335134,0.442046528753317,1.31230217000239,1.49265593733381},{0.319634168090651,-0.0626381721036282,0.660145596571352,0.826207276270604},{0.701569911313408,-1.32434992424599,0.778719519013359,0.959497008483245},{-0.698861147170034,1.19907358003873,-1.29632412372177,-1.30642843913166},{1.46544139775892,0.189704178324845,0.838006480234363,1.49265593733381},{1.21081756894375,-0.0626381721036282,0.897293441455367,1.49265593733381},{-0.444237318354863,1.70375828089568,-1.29632412372177,-1.30642843913166},{-0.82617306157762,1.95610063132415,-1.05917627883775,-1.03984897470638},{0.828881825720994,-0.819665223389045,0.95658040267637,0.959497008483245},{0.956193740128579,-0.567322872960574,0.541571674129345,0.42633807963268},{1.33812948335134,0.442046528753317,1.31230217000239,1.49265593733381},{0.574257996905822,0.946731229610261,1.01586736389737,1.49265593733381},{0.0650103392754793,-0.819665223389045,0.838006480234363,0.959497008483245},{0.0650103392754793,-0.819665223389045,0.838006480234363,0.959497008483245},{-1.46273263361555,0.442046528753317,-1.35561108494277,-1.30642843913166},{0.574257996905822,-2.08137697553141,0.482284712908341,0.42633807963268},{1.21081756894375,0.189704178324845,1.13444128633938,1.62594566954645},{1.97468905538926,-0.314980522532101,1.54945001488641,0.826207276270604},{-1.08079689039279,0.189704178324845,-1.29632412372177,-1.4397181713443}}
1 | {{0,1,0},{1,0,0},{0,1,0},{1,0,0},{1,0,0},{1,0,0},{1,0,0},{0,1,0},{0,0,1},{0,0,1},{1,0,0},{0,0,1},{1,0,0},{0,0,1},{0,1,0},{0,1,0},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{1,0,0},{0,1,0},{1,0,0},{0,0,1},{0,1,0},{0,1,0}} | {{-0.0623015751321059,-0.0626381721036282,0.304423829245331,0.0264688829947554},{-0.316925403947277,2.96547003303804,-1.35561108494277,-1.30642843913166},{0.319634168090651,-0.819665223389045,0.838006480234363,0.559627811845321},{-0.953484975985206,1.19907358003873,-1.41489804616377,-1.17313870691902},{-0.953484975985206,0.442046528753317,-1.47418500738478,-1.30642843913166},{-1.33542071920796,0.442046528753317,-1.41489804616377,-1.30642843913166},{-1.71735646243072,-0.0626381721036282,-1.41489804616377,-1.30642843913166},{0.446946082498236,-0.0626381721036282,0.541571674129345,0.293048347420038},{1.21081756894375,-1.32434992424599,1.25301520878139,0.826207276270604},{0.701569911313408,0.694388879181789,1.3715891312234,1.75923540175909},{-1.84466837683831,-0.0626381721036282,-1.53347196860578,-1.4397181713443},{1.84737714098168,1.45141593046721,1.4308760924444,1.75923540175909},{-0.82617306157762,1.19907358003873,-1.35561108494277,-1.30642843913166},{0.701569911313408,-0.314980522532101,1.13444128633938,0.826207276270604},{1.33812948335134,-0.567322872960574,0.660145596571352,0.293048347420038},{0.192322253683066,-0.0626381721036282,0.304423829245331,0.42633807963268},{-0.189613489539692,-0.819665223389045,0.304423829245331,0.159758615207397},{-1.46273263361555,0.189704178324845,-1.29632412372177,-1.30642843913166},{-1.71735646243072,0.442046528753317,-1.41489804616377,-1.30642843913166},{0.828881825720994,0.189704178324845,1.07515432511838,0.826207276270604},{0.0650103392754793,-1.07200757381752,0.185849906803323,0.0264688829947554},{-0.953484975985206,-2.58606167638835,-0.110584899301695,-0.240110581430527},{0.192322253683066,-0.0626381721036282,0.838006480234363,0.826207276270604},{-0.953484975985206,1.19907358003873,-1.23703716250076,-0.773269510281093},{-0.82617306157762,0.946731229610261,-1.29632412372177,-1.30642843913166},{0.319634168090651,0.946731229610261,0.482284712908341,0.559627811845321},{-0.953484975985206,0.694388879181789,-1.35561108494277,-1.30642843913166},{0.192322253683066,-0.0626381721036282,0.838006480234363,0.826207276270604},{0.446946082498236,-0.314980522532101,0.600858635350349,0.293048347420038},{-0.0623015751321059,-0.567322872960574,0.482284712908341,0.159758615207397}}
2 | {{1,0,0},{1,0,0},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{0,0,1},{0,1,0},{0,1,0},{0,1,0},{0,1,0},{1,0,0},{0,0,1},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{1,0,0},{0,0,1},{1,0,0},{1,0,0},{1,0,0},{1,0,0},{0,1,0},{1,0,0},{0,1,0},{0,0,1}} | {{-0.953484975985206,0.946731229610261,-1.23703716250076,-1.03984897470638},{-0.953484975985206,0.694388879181789,-1.35561108494277,-1.30642843913166},{1.21081756894375,0.694388879181789,1.19372824756038,1.75923540175909},{-1.20810880480038,0.946731229610261,-1.23703716250076,-1.30642843913166},{1.08350565453616,-0.314980522532101,0.541571674129345,0.159758615207397},{-0.189613489539692,-0.314980522532101,-0.0512979380806911,0.159758615207397},{-1.20810880480038,-0.0626381721036282,-1.35561108494277,-1.17313870691902},{-1.08079689039279,0.189704178324845,-1.29632412372177,-1.4397181713443},{0.956193740128579,-0.0626381721036282,0.897293441455367,1.09278674069589},{0.956193740128579,-0.0626381721036282,0.897293441455367,1.09278674069589},{1.46544139775892,0.189704178324845,0.719432557792356,0.42633807963268},{0.0650103392754793,-1.07200757381752,0.185849906803323,0.0264688829947554},{1.08350565453616,-0.0626381721036282,0.422997751687338,0.293048347420038},{0.319634168090651,-0.314980522532101,0.482284712908341,0.42633807963268},{-0.82617306157762,1.95610063132415,-1.23703716250076,-1.30642843913166},{0.956193740128579,-0.0626381721036282,1.25301520878139,1.35936620512117},{-0.0623015751321059,-1.07200757381752,-0.110584899301695,-0.240110581430527},{-0.571549232762449,1.70375828089568,-1.29632412372177,-1.30642843913166},{-0.571549232762449,1.70375828089568,-1.29632412372177,-1.30642843913166},{2.35662479861202,-0.0626381721036282,1.72731089854942,1.22607647290853},{-1.71735646243072,0.442046528753317,-1.41489804616377,-1.30642843913166},{1.72006522657409,-0.0626381721036282,1.31230217000239,1.22607647290853},{-0.953484975985206,0.946731229610261,-1.29632412372177,-1.30642843913166},{-1.46273263361555,0.946731229610261,-1.35561108494277,-1.17313870691902},{-1.08079689039279,-0.0626381721036282,-1.35561108494277,-1.30642843913166},{-0.953484975985206,1.45141593046721,-1.35561108494277,-1.30642843913166},{0.701569911313408,-1.82903462510294,0.422997751687338,0.159758615207397},{-0.444237318354863,2.20844298175262,-1.17775020127976,-1.03984897470638},{-0.0623015751321059,-0.314980522532101,0.304423829245331,0.159758615207397},{1.33812948335134,-0.0626381721036282,1.07515432511838,1.22607647290853}}
3 | {{0,1,0},{0,1,0},{0,1,0},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{0,1,0},{1,0,0},{0,1,0},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{1,0,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0},{0,0,1},{0,1,0},{0,0,1},{0,1,0},{0,1,0},{0,1,0}} | {{-0.953484975985206,-1.82903462510294,-0.229158821743702,-0.240110581430527},{0.319634168090651,-2.08137697553141,0.185849906803323,-0.240110581430527},{-0.189613489539692,-0.0626381721036282,0.482284712908341,0.42633807963268},{-0.316925403947277,-1.07200757381752,0.422997751687338,0.0264688829947554},{-0.953484975985206,1.19907358003873,-1.23703716250076,-0.773269510281093},{-0.316925403947277,1.19907358003873,-1.41489804616377,-1.30642843913166},{0.0650103392754793,-0.819665223389045,0.838006480234363,0.959497008483245},{0.446946082498236,-0.0626381721036282,0.541571674129345,0.293048347420038},{-0.444237318354863,0.946731229610261,-1.29632412372177,-1.03984897470638},{1.21081756894375,0.189704178324845,0.600858635350349,0.42633807963268},{-0.82617306157762,1.95610063132415,-1.23703716250076,-1.30642843913166},{-0.0623015751321059,-0.567322872960574,0.245136868024327,0.159758615207397},{-0.316925403947277,-1.82903462510294,0.185849906803323,0.159758615207397},{1.21081756894375,-0.0626381721036282,0.897293441455367,1.49265593733381},{-1.59004454802313,-1.82903462510294,-1.41489804616377,-1.17313870691902},{0.701569911313408,0.694388879181789,0.600858635350349,0.559627811845321},{-0.316925403947277,-1.57669227467446,0.00798902314031256,-0.240110581430527},{1.46544139775892,0.189704178324845,1.01586736389737,1.22607647290853},{-1.08079689039279,0.189704178324845,-1.29632412372177,-1.4397181713443},{-1.71735646243072,-0.314980522532101,-1.35561108494277,-1.30642843913166},{-0.444237318354863,-0.0626381721036282,0.482284712908341,0.42633807963268},{1.72006522657409,-0.0626381721036282,1.31230217000239,1.22607647290853},{-0.82617306157762,1.95610063132415,-1.05917627883775,-1.03984897470638},{1.21081756894375,-0.0626381721036282,0.778719519013359,0.692917544057962},{2.35662479861202,-0.0626381721036282,1.72731089854942,1.22607647290853},{-0.953484975985206,-1.82903462510294,-0.229158821743702,-0.240110581430527},{0.701569911313408,-0.314980522532101,1.13444128633938,0.826207276270604},{-0.698861147170034,-0.819665223389045,0.12656294558232,0.293048347420038},{-0.0623015751321059,-0.314980522532101,0.304423829245331,0.159758615207397},{0.574257996905822,-0.314980522532101,0.363710790466334,0.159758615207397}}
4 | {{0,0,1},{0,1,0},{0,0,1},{0,1,0},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{0,0,1},{0,1,0},{0,0,1},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{0,1,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0}} | {{1.21081756894375,0.694388879181789,1.19372824756038,1.75923540175909},{-0.82617306157762,-1.32434992424599,-0.407019705406713,-0.106820849217886},{0.701569911313408,0.694388879181789,1.3715891312234,1.75923540175909},{0.0650103392754793,-0.819665223389045,0.245136868024327,-0.240110581430527},{-1.20810880480038,0.189704178324845,-1.23703716250076,-1.30642843913166},{0.574257996905822,-0.314980522532101,0.363710790466334,0.159758615207397},{1.21081756894375,0.189704178324845,0.422997751687338,0.293048347420038},{1.97468905538926,-0.314980522532101,1.54945001488641,0.826207276270604},{-1.08079689039279,-1.32434992424599,0.482284712908341,0.692917544057962},{0.0650103392754793,-0.819665223389045,0.12656294558232,0.0264688829947554},{0.574257996905822,0.946731229610261,1.01586736389737,1.49265593733381},{0.956193740128579,0.442046528753317,0.838006480234363,1.09278674069589},{-1.20810880480038,-0.0626381721036282,-1.35561108494277,-1.17313870691902},{0.828881825720994,0.442046528753317,0.482284712908341,0.42633807963268},{-0.0623015751321059,-0.0626381721036282,0.304423829245331,0.0264688829947554},{-0.316925403947277,-1.57669227467446,0.0672759843613159,-0.106820849217886},{-0.189613489539692,-0.0626381721036282,0.245136868024327,0.159758615207397},{1.59275331216651,0.442046528753317,0.600858635350349,0.293048347420038},{0.956193740128579,-0.0626381721036282,1.25301520878139,1.35936620512117},{-1.33542071920796,0.442046528753317,-1.23703716250076,-1.30642843913166},{-0.316925403947277,-1.32434992424599,0.185849906803323,0.159758615207397}}
\.
-- Create the corresponding summary table for preprocessed data
CREATE TABLE iris_data_batch_summary(
source_table text,
output_table text,
dependent_varname text,
independent_varname text,
dependent_vartype text,
buffer_size integer,
class_values text[],
num_rows_processed integer,
num_rows_skipped integer,
grouping_cols text
);
-- The availability of the original source table should not be a condition for
-- MLP to work correctly. It should work fine even the original source table is
-- deleted (this basically ensures that all the necessary info is captured in
-- the summary table). So name the original source table as
-- 'iris_data_does_not_exist' instead of the original 'iris_data', to mimic the
-- scenario where the original source table is deleted and MLP is trained with
-- the preprocessed table.
INSERT INTO iris_data_batch_summary VALUES
('iris_data_does_not_exist', 'iris_data_batch', 'class::TEXT', 'attributes',
'text', 30, ARRAY[1,2,3], 141, 0, '');
-- Create the corresponding standardization table for preprocessed data
CREATE TABLE iris_data_batch_standardization(
mean double precision[],
std double precision[]
);
INSERT INTO iris_data_batch_standardization VALUES
-- -- TODO get real numbers by running preprocessor
(ARRAY[5.74893617021,3.02482269504,3.6865248227,1.18014184397],
ARRAY[0.785472439601,0.396287027644,1.68671151195,0.750245336531]);
-- Create grouping preprocessed data that can be used with minibatch MLP:
DROP TABLE IF EXISTS iris_data_batch_grp, iris_data_batch_grp_summary, iris_data_batch_grp_standardization;
CREATE TABLE iris_data_batch_grp(
grp text,
__id__ integer,
dependent_varname double precision[],
independent_varname double precision[]
);
COPY iris_data_batch_grp (grp, __id__, dependent_varname, independent_varname) FROM STDIN NULL '?' DELIMITER '|';
'1'|0 | {{0,1,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{1,0,0},{0,1,0},{1,0,0},{0,0,1},{0,0,1},{0,0,1},{1,0,0},{0,0,1},{0,0,1},{1,0,0},{1,0,0},{0,0,1},{0,1,0},{0,0,1},{0,0,1},{0,0,1},{0,0,1},{1,0,0},{0,1,0},{0,0,1},{0,0,1},{1,0,0}} | {{0.828881825720994,-0.314980522532101,0.363710790466334,0.159758615207397},{-1.08079689039279,-1.57669227467446,-0.229158821743702,-0.240110581430527},{-1.08079689039279,-1.32434992424599,0.482284712908341,0.692917544057962},{-1.46273263361555,0.442046528753317,-1.35561108494277,-1.30642843913166},{-0.0623015751321059,-0.567322872960574,0.245136868024327,0.159758615207397},{-0.189613489539692,-0.819665223389045,0.304423829245331,0.159758615207397},{0.701569911313408,-1.32434992424599,0.778719519013359,0.959497008483245},{-1.20810880480038,-0.0626381721036282,-1.35561108494277,-1.4397181713443},{-0.698861147170034,0.946731229610261,-1.35561108494277,-1.30642843913166},{-0.82617306157762,-1.32434992424599,-0.407019705406713,-0.106820849217886},{-0.698861147170034,2.71312768260957,-1.29632412372177,-1.4397181713443},{1.33812948335134,0.442046528753317,1.31230217000239,1.49265593733381},{0.319634168090651,-0.0626381721036282,0.660145596571352,0.826207276270604},{0.701569911313408,-1.32434992424599,0.778719519013359,0.959497008483245},{-0.698861147170034,1.19907358003873,-1.29632412372177,-1.30642843913166},{1.46544139775892,0.189704178324845,0.838006480234363,1.49265593733381},{1.21081756894375,-0.0626381721036282,0.897293441455367,1.49265593733381},{-0.444237318354863,1.70375828089568,-1.29632412372177,-1.30642843913166},{-0.82617306157762,1.95610063132415,-1.05917627883775,-1.03984897470638},{0.828881825720994,-0.819665223389045,0.95658040267637,0.959497008483245},{0.956193740128579,-0.567322872960574,0.541571674129345,0.42633807963268},{1.33812948335134,0.442046528753317,1.31230217000239,1.49265593733381},{0.574257996905822,0.946731229610261,1.01586736389737,1.49265593733381},{0.0650103392754793,-0.819665223389045,0.838006480234363,0.959497008483245},{0.0650103392754793,-0.819665223389045,0.838006480234363,0.959497008483245},{-1.46273263361555,0.442046528753317,-1.35561108494277,-1.30642843913166},{0.574257996905822,-2.08137697553141,0.482284712908341,0.42633807963268},{1.21081756894375,0.189704178324845,1.13444128633938,1.62594566954645},{1.97468905538926,-0.314980522532101,1.54945001488641,0.826207276270604},{-1.08079689039279,0.189704178324845,-1.29632412372177,-1.4397181713443}}
'1'|1 | {{0,1,0},{1,0,0},{0,1,0},{1,0,0},{1,0,0},{1,0,0},{1,0,0},{0,1,0},{0,0,1},{0,0,1},{1,0,0},{0,0,1},{1,0,0},{0,0,1},{0,1,0},{0,1,0},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{1,0,0},{0,1,0},{1,0,0},{0,0,1},{0,1,0},{0,1,0}} | {{-0.0623015751321059,-0.0626381721036282,0.304423829245331,0.0264688829947554},{-0.316925403947277,2.96547003303804,-1.35561108494277,-1.30642843913166},{0.319634168090651,-0.819665223389045,0.838006480234363,0.559627811845321},{-0.953484975985206,1.19907358003873,-1.41489804616377,-1.17313870691902},{-0.953484975985206,0.442046528753317,-1.47418500738478,-1.30642843913166},{-1.33542071920796,0.442046528753317,-1.41489804616377,-1.30642843913166},{-1.71735646243072,-0.0626381721036282,-1.41489804616377,-1.30642843913166},{0.446946082498236,-0.0626381721036282,0.541571674129345,0.293048347420038},{1.21081756894375,-1.32434992424599,1.25301520878139,0.826207276270604},{0.701569911313408,0.694388879181789,1.3715891312234,1.75923540175909},{-1.84466837683831,-0.0626381721036282,-1.53347196860578,-1.4397181713443},{1.84737714098168,1.45141593046721,1.4308760924444,1.75923540175909},{-0.82617306157762,1.19907358003873,-1.35561108494277,-1.30642843913166},{0.701569911313408,-0.314980522532101,1.13444128633938,0.826207276270604},{1.33812948335134,-0.567322872960574,0.660145596571352,0.293048347420038},{0.192322253683066,-0.0626381721036282,0.304423829245331,0.42633807963268},{-0.189613489539692,-0.819665223389045,0.304423829245331,0.159758615207397},{-1.46273263361555,0.189704178324845,-1.29632412372177,-1.30642843913166},{-1.71735646243072,0.442046528753317,-1.41489804616377,-1.30642843913166},{0.828881825720994,0.189704178324845,1.07515432511838,0.826207276270604},{0.0650103392754793,-1.07200757381752,0.185849906803323,0.0264688829947554},{-0.953484975985206,-2.58606167638835,-0.110584899301695,-0.240110581430527},{0.192322253683066,-0.0626381721036282,0.838006480234363,0.826207276270604},{-0.953484975985206,1.19907358003873,-1.23703716250076,-0.773269510281093},{-0.82617306157762,0.946731229610261,-1.29632412372177,-1.30642843913166},{0.319634168090651,0.946731229610261,0.482284712908341,0.559627811845321},{-0.953484975985206,0.694388879181789,-1.35561108494277,-1.30642843913166},{0.192322253683066,-0.0626381721036282,0.838006480234363,0.826207276270604},{0.446946082498236,-0.314980522532101,0.600858635350349,0.293048347420038},{-0.0623015751321059,-0.567322872960574,0.482284712908341,0.159758615207397}}
'1'|2 | {{1,0,0},{1,0,0},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{0,0,1},{0,1,0},{0,1,0},{0,1,0},{0,1,0},{1,0,0},{0,0,1},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{1,0,0},{0,0,1},{1,0,0},{1,0,0},{1,0,0},{1,0,0},{0,1,0},{1,0,0},{0,1,0},{0,0,1}} | {{-0.953484975985206,0.946731229610261,-1.23703716250076,-1.03984897470638},{-0.953484975985206,0.694388879181789,-1.35561108494277,-1.30642843913166},{1.21081756894375,0.694388879181789,1.19372824756038,1.75923540175909},{-1.20810880480038,0.946731229610261,-1.23703716250076,-1.30642843913166},{1.08350565453616,-0.314980522532101,0.541571674129345,0.159758615207397},{-0.189613489539692,-0.314980522532101,-0.0512979380806911,0.159758615207397},{-1.20810880480038,-0.0626381721036282,-1.35561108494277,-1.17313870691902},{-1.08079689039279,0.189704178324845,-1.29632412372177,-1.4397181713443},{0.956193740128579,-0.0626381721036282,0.897293441455367,1.09278674069589},{0.956193740128579,-0.0626381721036282,0.897293441455367,1.09278674069589},{1.46544139775892,0.189704178324845,0.719432557792356,0.42633807963268},{0.0650103392754793,-1.07200757381752,0.185849906803323,0.0264688829947554},{1.08350565453616,-0.0626381721036282,0.422997751687338,0.293048347420038},{0.319634168090651,-0.314980522532101,0.482284712908341,0.42633807963268},{-0.82617306157762,1.95610063132415,-1.23703716250076,-1.30642843913166},{0.956193740128579,-0.0626381721036282,1.25301520878139,1.35936620512117},{-0.0623015751321059,-1.07200757381752,-0.110584899301695,-0.240110581430527},{-0.571549232762449,1.70375828089568,-1.29632412372177,-1.30642843913166},{-0.571549232762449,1.70375828089568,-1.29632412372177,-1.30642843913166},{2.35662479861202,-0.0626381721036282,1.72731089854942,1.22607647290853},{-1.71735646243072,0.442046528753317,-1.41489804616377,-1.30642843913166},{1.72006522657409,-0.0626381721036282,1.31230217000239,1.22607647290853},{-0.953484975985206,0.946731229610261,-1.29632412372177,-1.30642843913166},{-1.46273263361555,0.946731229610261,-1.35561108494277,-1.17313870691902},{-1.08079689039279,-0.0626381721036282,-1.35561108494277,-1.30642843913166},{-0.953484975985206,1.45141593046721,-1.35561108494277,-1.30642843913166},{0.701569911313408,-1.82903462510294,0.422997751687338,0.159758615207397},{-0.444237318354863,2.20844298175262,-1.17775020127976,-1.03984897470638},{-0.0623015751321059,-0.314980522532101,0.304423829245331,0.159758615207397},{1.33812948335134,-0.0626381721036282,1.07515432511838,1.22607647290853}}
'2'|3 | {{0,1,0},{0,1,0},{0,1,0},{0,1,0},{1,0,0},{1,0,0},{0,0,1},{0,1,0},{1,0,0},{0,1,0},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{1,0,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0},{0,0,1},{0,1,0},{0,0,1},{0,1,0},{0,1,0},{0,1,0}} | {{-0.953484975985206,-1.82903462510294,-0.229158821743702,-0.240110581430527},{0.319634168090651,-2.08137697553141,0.185849906803323,-0.240110581430527},{-0.189613489539692,-0.0626381721036282,0.482284712908341,0.42633807963268},{-0.316925403947277,-1.07200757381752,0.422997751687338,0.0264688829947554},{-0.953484975985206,1.19907358003873,-1.23703716250076,-0.773269510281093},{-0.316925403947277,1.19907358003873,-1.41489804616377,-1.30642843913166},{0.0650103392754793,-0.819665223389045,0.838006480234363,0.959497008483245},{0.446946082498236,-0.0626381721036282,0.541571674129345,0.293048347420038},{-0.444237318354863,0.946731229610261,-1.29632412372177,-1.03984897470638},{1.21081756894375,0.189704178324845,0.600858635350349,0.42633807963268},{-0.82617306157762,1.95610063132415,-1.23703716250076,-1.30642843913166},{-0.0623015751321059,-0.567322872960574,0.245136868024327,0.159758615207397},{-0.316925403947277,-1.82903462510294,0.185849906803323,0.159758615207397},{1.21081756894375,-0.0626381721036282,0.897293441455367,1.49265593733381},{-1.59004454802313,-1.82903462510294,-1.41489804616377,-1.17313870691902},{0.701569911313408,0.694388879181789,0.600858635350349,0.559627811845321},{-0.316925403947277,-1.57669227467446,0.00798902314031256,-0.240110581430527},{1.46544139775892,0.189704178324845,1.01586736389737,1.22607647290853},{-1.08079689039279,0.189704178324845,-1.29632412372177,-1.4397181713443},{-1.71735646243072,-0.314980522532101,-1.35561108494277,-1.30642843913166},{-0.444237318354863,-0.0626381721036282,0.482284712908341,0.42633807963268},{1.72006522657409,-0.0626381721036282,1.31230217000239,1.22607647290853},{-0.82617306157762,1.95610063132415,-1.05917627883775,-1.03984897470638},{1.21081756894375,-0.0626381721036282,0.778719519013359,0.692917544057962},{2.35662479861202,-0.0626381721036282,1.72731089854942,1.22607647290853},{-0.953484975985206,-1.82903462510294,-0.229158821743702,-0.240110581430527},{0.701569911313408,-0.314980522532101,1.13444128633938,0.826207276270604},{-0.698861147170034,-0.819665223389045,0.12656294558232,0.293048347420038},{-0.0623015751321059,-0.314980522532101,0.304423829245331,0.159758615207397},{0.574257996905822,-0.314980522532101,0.363710790466334,0.159758615207397}}
'2'|4 | {{0,0,1},{0,1,0},{0,0,1},{0,1,0},{1,0,0},{0,1,0},{0,1,0},{0,0,1},{0,0,1},{0,1,0},{0,0,1},{0,0,1},{1,0,0},{0,1,0},{0,1,0},{0,1,0},{0,1,0},{0,1,0},{0,0,1},{1,0,0},{0,1,0}} | {{1.21081756894375,0.694388879181789,1.19372824756038,1.75923540175909},{-0.82617306157762,-1.32434992424599,-0.407019705406713,-0.106820849217886},{0.701569911313408,0.694388879181789,1.3715891312234,1.75923540175909},{0.0650103392754793,-0.819665223389045,0.245136868024327,-0.240110581430527},{-1.20810880480038,0.189704178324845,-1.23703716250076,-1.30642843913166},{0.574257996905822,-0.314980522532101,0.363710790466334,0.159758615207397},{1.21081756894375,0.189704178324845,0.422997751687338,0.293048347420038},{1.97468905538926,-0.314980522532101,1.54945001488641,0.826207276270604},{-1.08079689039279,-1.32434992424599,0.482284712908341,0.692917544057962},{0.0650103392754793,-0.819665223389045,0.12656294558232,0.0264688829947554},{0.574257996905822,0.946731229610261,1.01586736389737,1.49265593733381},{0.956193740128579,0.442046528753317,0.838006480234363,1.09278674069589},{-1.20810880480038,-0.0626381721036282,-1.35561108494277,-1.17313870691902},{0.828881825720994,0.442046528753317,0.482284712908341,0.42633807963268},{-0.0623015751321059,-0.0626381721036282,0.304423829245331,0.0264688829947554},{-0.316925403947277,-1.57669227467446,0.0672759843613159,-0.106820849217886},{-0.189613489539692,-0.0626381721036282,0.245136868024327,0.159758615207397},{1.59275331216651,0.442046528753317,0.600858635350349,0.293048347420038},{0.956193740128579,-0.0626381721036282,1.25301520878139,1.35936620512117},{-1.33542071920796,0.442046528753317,-1.23703716250076,-1.30642843913166},{-0.316925403947277,-1.32434992424599,0.185849906803323,0.159758615207397}}
\.
-- Create the corresponding summary table for preprocessed data
CREATE TABLE iris_data_batch_grp_summary(
source_table text,
output_table text,
dependent_varname text,
independent_varname text,
dependent_vartype text,
buffer_size integer,
class_values text[],
num_rows_processed integer,
num_rows_skipped integer,
grouping_cols text
);
-- The availability of the original source table should not be a condition for
-- MLP to work correctly. It should work fine even the original source table is
-- deleted (this basically ensures that all the necessary info is captured in
-- the summary table). So name the original source table as
-- 'iris_data_does_not_exist' instead of the original 'iris_data', to mimic the
-- scenario where the original source table is deleted and MLP is trained with
-- the preprocessed table.
INSERT INTO iris_data_batch_grp_summary VALUES
('iris_data_does_not_exist', 'iris_data_batch_grp', 'class::TEXT', 'attributes',
'text', 30, ARRAY['foo','bar','baaz'] , 141, 0, 'grp');
-- Create the corresponding standardization table for preprocessed data
CREATE TABLE iris_data_batch_grp_standardization(
grp text,
mean double precision[],
std double precision[]
);
INSERT INTO iris_data_batch_grp_standardization VALUES
-- -- TODO get real numbers by running preprocessor
('1',ARRAY[5.74893617021,3.02482269504,3.6865248227,1.18014184397],ARRAY[0.785472439601,0.396287027644,1.68671151195,0.750245336531]),
('2',ARRAY[5.74893617021,3.02482269504,3.6865248227,1.18014184397],ARRAY[0.785472439601,0.396287027644,1.68671151195,0.750245336531]);
DROP TABLE IF EXISTS mlp_class, mlp_class_summary, mlp_class_standardization;
SELECT mlp_classification(
'iris_data', -- Source table
'mlp_class', -- Destination table
'attributes', -- Input features
'class', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
n_tries=3,
tolerance=0',
'sigmoid',
'',
False,
False,
'grp'
);
DROP TABLE IF EXISTS mlp_prediction_output;
SELECT mlp_predict(
'mlp_class',
'iris_data',
'id',
'mlp_prediction_output',
'output');
-- Test for passing NUMERIC row_weights with grouping
DROP TABLE IF EXISTS iris_data_row_weight;
CREATE TABLE iris_data_row_weight
AS SELECT *, id::NUMERIC AS row_weight FROM iris_data;
DROP TABLE IF EXISTS mlp_class, mlp_class_summary, mlp_class_standardization;
SELECT mlp_classification(
'iris_data_row_weight', -- Source table
'mlp_class', -- Destination table
'attributes', -- Input features
'class', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
n_tries=3,
tolerance=0',
'sigmoid',
'row_weight',
False,
False,
'grp'
);
DROP TABLE IF EXISTS mlp_prediction_output;
SELECT mlp_predict(
'mlp_class',
'iris_data',
'id',
'mlp_prediction_output',
'output');
-- Test for passing NUMERIC row_weights without grouping
DROP TABLE IF EXISTS iris_data_row_weight;
CREATE TABLE iris_data_row_weight
AS SELECT *, id::NUMERIC AS row_weight FROM iris_data;
DROP TABLE IF EXISTS mlp_class, mlp_class_summary, mlp_class_standardization;
SELECT mlp_classification(
'iris_data_row_weight', -- Source table
'mlp_class', -- Destination table
'attributes', -- Input features
'class', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
n_tries=3,
tolerance=0',
'sigmoid',
'row_weight'
);
DROP TABLE IF EXISTS mlp_prediction_output;
SELECT mlp_predict(
'mlp_class',
'iris_data',
'id',
'mlp_prediction_output',
'output');
-- minibatch without grouping and without warm_start
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
SELECT mlp_classification(
'iris_data_batch', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=1,
n_tries=3,
tolerance=0,
n_epochs=20',
'sigmoid',
'',
False,
False
);
SELECT assert
(
source_table = 'iris_data_batch' AND
independent_varname = 'independent_varname' AND
dependent_varname = 'dependent_varname' AND
original_source_table = 'iris_data_does_not_exist' AND
original_independent_varname = 'attributes' AND
original_dependent_varname = 'class::TEXT' AND
original_dependent_vartype = 'text' AND
tolerance = 0 AND
learning_rate_init = 0.1 AND
learning_rate_policy = 'constant' AND
n_iterations = 1 AND
n_tries = 3 AND
layer_sizes = ARRAY[4,5,3] AND
activation = 'sigmoid' AND
is_classification = 't' AND
classes = '{1,2,3}' AND
weights = '1' AND
grouping_col = NULL,
'Summary Validation failed for special chars. Actual:' || __to_char(summary)
) from (select * from mlp_class_batch_summary order by classes) summary;
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_batch_output_summary, mlp_prediction_batch_output_standardization;
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'output');
-- minibatch with grouping and without warm_start
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
SELECT mlp_classification(
'iris_data_batch_grp', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
n_tries=3,
tolerance=0,
n_epochs=20',
'sigmoid',
'',
False,
False,
'grp'
);
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_output;
-- See prediction accuracy for training data
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'output');
-- minibatch without grouping and with warm_start
SELECT mlp_classification(
'iris_data_batch', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
tolerance=0,
batch_size=5,
n_epochs=15',
'sigmoid',
'',
True, -- Warm start
True
);
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_batch_output_summary, mlp_prediction_batch_output_standardization;
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'response');
------------------------------------------------ Regression ------------------------------------------------------------
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
-- Set class_values column value to NULL so that encoding info is not captured, to test
-- case where dependent variable is an array for classification.
UPDATE iris_data_batch_summary SET class_values = NULL WHERE source_table='iris_data';
SELECT mlp_classification(
'iris_data_batch', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
tolerance=0,
n_epochs=20',
'sigmoid',
'',
False,
False
);
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_output;
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'response');
SELECT * FROM mlp_prediction_batch_output;
DROP TABLE IF EXISTS mlp_prediction_batch_output;
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'prob');
SELECT * FROM mlp_prediction_batch_output;
-- assert to test if the newly created column name exists and matches the count
SELECT assert(count(*) =3, 'Column Names does not exists')
from pg_attribute
where attrelid = 'mlp_prediction_batch_output'::regclass
and attname in( 'prob_1', 'prob_2','prob_3');
DROP TABLE IF EXISTS mlp_prediction_batch_output;
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
DROP TABLE IF EXISTS lin_housing_wi CASCADE;
CREATE TABLE lin_housing_wi (id serial, x float8[], grp int, y double precision[]);
COPY lin_housing_wi (x, grp, y) FROM STDIN NULL '?' DELIMITER '|';
{1,0.00632,18.00,2.310,0,0.5380,6.5750,65.20,4.0900,1,296.0,15.30,396.90,4.98} | 1 | {24.00}
{1,0.02731,0.00,7.070,0,0.4690,6.4210,78.90,4.9671,2,242.0,17.80,396.90,9.14} | 1 | {21.60}
{1,0.02729,0.00,7.070,0,0.4690,7.1850,61.10,4.9671,2,242.0,17.80,392.83,4.03} | 1 | {34.70}
{1,0.03237,0.00,2.180,0,0.4580,6.9980,45.80,6.0622,3,222.0,18.70,394.63,2.94} | 1 | {33.40}
{1,0.06905,0.00,2.180,0,0.4580,7.1470,54.20,6.0622,3,222.0,18.70,396.90,5.33} | 1 | {36.20}
{1,0.02985,0.00,2.180,0,0.4580,6.4300,58.70,6.0622,3,222.0,18.70,394.12,5.21} | 1 | {28.70}
{1,0.08829,12.50,7.870,0,0.5240,6.0120,66.60,5.5605,5,311.0,15.20,395.60,12.43} | 1 | {22.90}
{1,0.14455,12.50,7.870,0,0.5240,6.1720,96.10,5.9505,5,311.0,15.20,396.90,19.15} | 1 | {27.10}
{1,0.21124,12.50,7.870,0,0.5240,5.6310,100.00,6.0821,5,311.0,15.20,386.63,29.93} | 1 | {16.50}
{1,0.17004,12.50,7.870,0,0.5240,6.0040,85.90,6.5921,5,311.0,15.20,386.71,17.10} | 1 | {18.90}
{1,0.22489,12.50,7.870,0,0.5240,6.3770,94.30,6.3467,5,311.0,15.20,392.52,20.45} | 1 | {15.00}
{1,0.11747,12.50,7.870,0,0.5240,6.0090,82.90,6.2267,5,311.0,15.20,396.90,13.27} | 1 | {18.90}
{1,0.09378,12.50,7.870,0,0.5240,5.8890,39.00,5.4509,5,311.0,15.20,390.50,15.71} | 1 | {21.70}
{1,0.62976,0.00,8.140,0,0.5380,5.9490,61.80,4.7075,4,307.0,21.00,396.90,8.26} | 1 | {20.40}
{1,0.63796,0.00,8.140,0,0.5380,6.0960,84.50,4.4619,4,307.0,21.00,380.02,10.26} | 1 | {18.20}
{1,0.62739,0.00,8.140,0,0.5380,5.8340,56.50,4.4986,4,307.0,21.00,395.62,8.47} | 1 | {19.90}
{1,1.05393,0.00,8.140,0,0.5380,5.9350,29.30,4.4986,4,307.0,21.00,386.85,6.58} | 1 | {23.10}
{1,0.78420,0.00,8.140,0,0.5380,5.9900,81.70,4.2579,4,307.0,21.00,386.75,14.67} | 1 | {17.50}
{1,0.80271,0.00,8.140,0,0.5380,5.4560,36.60,3.7965,4,307.0,21.00,288.99,11.69} | 1 | {20.20}
{1,0.72580,0.00,8.140,0,0.5380,5.7270,69.50,3.7965,4,307.0,21.00,390.95,11.28} | 1 | {18.20}
{1,1.25179,0.00,8.140,0,0.5380,5.5700,98.10,3.7979,4,307.0,21.00,376.57,21.02} | 1 | {13.60}
{1,0.85204,0.00,8.140,0,0.5380,5.9650,89.20,4.0123,4,307.0,21.00,392.53,13.83} | 1 | {19.60}
{1,1.23247,0.00,8.140,0,0.5380,6.1420,91.70,3.9769,4,307.0,21.00,396.90,18.72} | 1 | {15.20}
{1,0.98843,0.00,8.140,0,0.5380,5.8130,100.00,4.0952,4,307.0,21.00,394.54,19.88} | 1 | {14.50}
{1,0.75026,0.00,8.140,0,0.5380,5.9240,94.10,4.3996,4,307.0,21.00,394.33,16.30} | 1 | {15.60}
{1,0.84054,0.00,8.140,0,0.5380,5.5990,85.70,4.4546,4,307.0,21.00,303.42,16.51} | 1 | {13.90}
{1,0.67191,0.00,8.140,0,0.5380,5.8130,90.30,4.6820,4,307.0,21.00,376.88,14.81} | 1 | {16.60}
{1,0.95577,0.00,8.140,0,0.5380,6.0470,88.80,4.4534,4,307.0,21.00,306.38,17.28} | 1 | {14.80}
{1,0.77299,0.00,8.140,0,0.5380,6.4950,94.40,4.4547,4,307.0,21.00,387.94,12.80} | 1 | {18.40}
{1,1.00245,0.00,8.140,0,0.5380,6.6740,87.30,4.2390,4,307.0,21.00,380.23,11.98} | 1 | {21.00}
{1,1.13081,0.00,8.140,0,0.5380,5.7130,94.10,4.2330,4,307.0,21.00,360.17,22.60} | 1 | {12.70}
{1,1.35472,0.00,8.140,0,0.5380,6.0720,100.00,4.1750,4,307.0,21.00,376.73,13.04} | 1 | {14.50}
{1,1.38799,0.00,8.140,0,0.5380,5.9500,82.00,3.9900,4,307.0,21.00,232.60,27.71} | 1 | {13.20}
{1,1.15172,0.00,8.140,0,0.5380,5.7010,95.00,3.7872,4,307.0,21.00,358.77,18.35} | 1 | {13.10}
{1,1.61282,0.00,8.140,0,0.5380,6.0960,96.90,3.7598,4,307.0,21.00,248.31,20.34} | 1 | {13.50}
{1,0.06417,0.00,5.960,0,0.4990,5.9330,68.20,3.3603,5,279.0,19.20,396.90,9.68} | 1 | {18.90}
{1,0.09744,0.00,5.960,0,0.4990,5.8410,61.40,3.3779,5,279.0,19.20,377.56,11.41} | 1 | {20.00}
{1,0.08014,0.00,5.960,0,0.4990,5.8500,41.50,3.9342,5,279.0,19.20,396.90,8.77} | 1 | {21.00}
{1,0.17505,0.00,5.960,0,0.4990,5.9660,30.20,3.8473,5,279.0,19.20,393.43,10.13} | 1 | {24.70}
{1,0.02763,75.00,2.950,0,0.4280,6.5950,21.80,5.4011,3,252.0,18.30,395.63,4.32} | 1 | {30.80}
{1,0.03359,75.00,2.950,0,0.4280,7.0240,15.80,5.4011,3,252.0,18.30,395.62,1.98} | 1 | {34.90}
{1,0.12744,0.00,6.910,0,0.4480,6.7700,2.90,5.7209,3,233.0,17.90,385.41,4.84} | 1 | {26.60}
{1,0.14150,0.00,6.910,0,0.4480,6.1690,6.60,5.7209,3,233.0,17.90,383.37,5.81} | 1 | {25.30}
{1,0.15936,0.00,6.910,0,0.4480,6.2110,6.50,5.7209,3,233.0,17.90,394.46,7.44} | 1 | {24.70}
{1,0.12269,0.00,6.910,0,0.4480,6.0690,40.00,5.7209,3,233.0,17.90,389.39,9.55} | 1 | {21.20}
{1,0.17142,0.00,6.910,0,0.4480,5.6820,33.80,5.1004,3,233.0,17.90,396.90,10.21} | 1 | {19.30}
{1,0.18836,0.00,6.910,0,0.4480,5.7860,33.30,5.1004,3,233.0,17.90,396.90,14.15} | 1 | {20.00}
{1,0.22927,0.00,6.910,0,0.4480,6.0300,85.50,5.6894,3,233.0,17.90,392.74,18.80} | 1 | {16.60}
{1,0.25387,0.00,6.910,0,0.4480,5.3990,95.30,5.8700,3,233.0,17.90,396.90,30.81} | 1 | {14.40}
{1,0.21977,0.00,6.910,0,0.4480,5.6020,62.00,6.0877,3,233.0,17.90,396.90,16.20} | 1 | {19.40}
{1,0.08873,21.00,5.640,0,0.4390,5.9630,45.70,6.8147,4,243.0,16.80,395.56,13.45} | 1 | {19.70}
{1,0.04337,21.00,5.640,0,0.4390,6.1150,63.00,6.8147,4,243.0,16.80,393.97,9.43} | 1 | {20.50}
{1,0.05360,21.00,5.640,0,0.4390,6.5110,21.10,6.8147,4,243.0,16.80,396.90,5.28} | 1 | {25.00}
{1,0.04981,21.00,5.640,0,0.4390,5.9980,21.40,6.8147,4,243.0,16.80,396.90,8.43} | 1 | {23.40}
{1,0.01360,75.00,4.000,0,0.4100,5.8880,47.60,7.3197,3,469.0,21.10,396.90,14.80} | 1 | {18.90}
{1,0.01311,90.00,1.220,0,0.4030,7.2490,21.90,8.6966,5,226.0,17.90,395.93,4.81} | 1 | {35.40}
{1,0.02055,85.00,0.740,0,0.4100,6.3830,35.70,9.1876,2,313.0,17.30,396.90,5.77} | 1 | {24.70}
{1,0.01432,100.00,1.320,0,0.4110,6.8160,40.50,8.3248,5,256.0,15.10,392.90,3.95} | 1 | {31.60}
{1,0.15445,25.00,5.130,0,0.4530,6.1450,29.20,7.8148,8,284.0,19.70,390.68,6.86} | 1 | {23.30}
{1,0.10328,25.00,5.130,0,0.4530,5.9270,47.20,6.9320,8,284.0,19.70,396.90,9.22} | 1 | {19.60}
{1,0.14932,25.00,5.130,0,0.4530,5.7410,66.20,7.2254,8,284.0,19.70,395.11,13.15} | 1 | {18.70}
{1,0.17171,25.00,5.130,0,0.4530,5.9660,93.40,6.8185,8,284.0,19.70,378.08,14.44} | 1 | {16.00}
{1,0.11027,25.00,5.130,0,0.4530,6.4560,67.80,7.2255,8,284.0,19.70,396.90,6.73} | 1 | {22.20}
{1,0.12650,25.00,5.130,0,0.4530,6.7620,43.40,7.9809,8,284.0,19.70,395.58,9.50} | 1 | {25.00}
{1,0.01951,17.50,1.380,0,0.4161,7.1040,59.50,9.2229,3,216.0,18.60,393.24,8.05} | 1 | {33.00}
{1,0.03584,80.00,3.370,0,0.3980,6.2900,17.80,6.6115,4,337.0,16.10,396.90,4.67} | 1 | {23.50}
{1,0.04379,80.00,3.370,0,0.3980,5.7870,31.10,6.6115,4,337.0,16.10,396.90,10.24} | 1 | {19.40}
{1,0.05789,12.50,6.070,0,0.4090,5.8780,21.40,6.4980,4,345.0,18.90,396.21,8.10} | 1 | {22.00}
{1,0.13554,12.50,6.070,0,0.4090,5.5940,36.80,6.4980,4,345.0,18.90,396.90,13.09} | 1 | {17.40}
{1,0.12816,12.50,6.070,0,0.4090,5.8850,33.00,6.4980,4,345.0,18.90,396.90,8.79} | 1 | {20.90}
{1,0.08826,0.00,10.810,0,0.4130,6.4170,6.60,5.2873,4,305.0,19.20,383.73,6.72} | 1 | {24.20}
{1,0.15876,0.00,10.810,0,0.4130,5.9610,17.50,5.2873,4,305.0,19.20,376.94,9.88} | 1 | {21.70}
{1,0.09164,0.00,10.810,0,0.4130,6.0650,7.80,5.2873,4,305.0,19.20,390.91,5.52} | 1 | {22.80}
{1,0.19539,0.00,10.810,0,0.4130,6.2450,6.20,5.2873,4,305.0,19.20,377.17,7.54} | 1 | {23.40}
{1,0.07896,0.00,12.830,0,0.4370,6.2730,6.00,4.2515,5,398.0,18.70,394.92,6.78} | 1 | {24.10}
{1,0.09512,0.00,12.830,0,0.4370,6.2860,45.00,4.5026,5,398.0,18.70,383.23,8.94} | 1 | {21.40}
{1,0.10153,0.00,12.830,0,0.4370,6.2790,74.50,4.0522,5,398.0,18.70,373.66,11.97} | 1 | {20.00}
{1,0.08707,0.00,12.830,0,0.4370,6.1400,45.80,4.0905,5,398.0,18.70,386.96,10.27} | 1 | {20.80}
{1,0.05646,0.00,12.830,0,0.4370,6.2320,53.70,5.0141,5,398.0,18.70,386.40,12.34} | 1 | {21.20}
{1,0.08387,0.00,12.830,0,0.4370,5.8740,36.60,4.5026,5,398.0,18.70,396.06,9.10} | 1 | {20.30}
{1,0.04113,25.00,4.860,0,0.4260,6.7270,33.50,5.4007,4,281.0,19.00,396.90,5.29} | 1 | {28.00}
{1,0.04462,25.00,4.860,0,0.4260,6.6190,70.40,5.4007,4,281.0,19.00,395.63,7.22} | 1 | {23.90}
{1,0.03659,25.00,4.860,0,0.4260,6.3020,32.20,5.4007,4,281.0,19.00,396.90,6.72} | 1 | {24.80}
{1,0.03551,25.00,4.860,0,0.4260,6.1670,46.70,5.4007,4,281.0,19.00,390.64,7.51} | 1 | {22.90}
{1,0.05059,0.00,4.490,0,0.4490,6.3890,48.00,4.7794,3,247.0,18.50,396.90,9.62} | 1 | {23.90}
{1,0.05735,0.00,4.490,0,0.4490,6.6300,56.10,4.4377,3,247.0,18.50,392.30,6.53} | 1 | {26.60}
{1,0.05188,0.00,4.490,0,0.4490,6.0150,45.10,4.4272,3,247.0,18.50,395.99,12.86} | 1 | {22.50}
{1,0.07151,0.00,4.490,0,0.4490,6.1210,56.80,3.7476,3,247.0,18.50,395.15,8.44} | 1 | {22.20}
{1,0.05660,0.00,3.410,0,0.4890,7.0070,86.30,3.4217,2,270.0,17.80,396.90,5.50} | 1 | {23.60}
{1,0.05302,0.00,3.410,0,0.4890,7.0790,63.10,3.4145,2,270.0,17.80,396.06,5.70} | 1 | {28.70}
{1,0.04684,0.00,3.410,0,0.4890,6.4170,66.10,3.0923,2,270.0,17.80,392.18,8.81} | 1 | {22.60}
{1,0.03932,0.00,3.410,0,0.4890,6.4050,73.90,3.0921,2,270.0,17.80,393.55,8.20} | 1 | {22.00}
{1,0.04203,28.00,15.040,0,0.4640,6.4420,53.60,3.6659,4,270.0,18.20,395.01,8.16} | 1 | {22.90}
{1,0.02875,28.00,15.040,0,0.4640,6.2110,28.90,3.6659,4,270.0,18.20,396.33,6.21} | 1 | {25.00}
{1,0.04294,28.00,15.040,0,0.4640,6.2490,77.30,3.6150,4,270.0,18.20,396.90,10.59} | 1 | {20.60}
{1,0.12204,0.00,2.890,0,0.4450,6.6250,57.80,3.4952,2,276.0,18.00,357.98,6.65} | 1 | {28.40}
{1,0.11504,0.00,2.890,0,0.4450,6.1630,69.60,3.4952,2,276.0,18.00,391.83,11.34} | 1 | {21.40}
{1,0.12083,0.00,2.890,0,0.4450,8.0690,76.00,3.4952,2,276.0,18.00,396.90,4.21} | 1 | {38.70}
{1,0.08187,0.00,2.890,0,0.4450,7.8200,36.90,3.4952,2,276.0,18.00,393.53,3.57} | 1 | {43.80}
{1,0.06860,0.00,2.890,0,0.4450,7.4160,62.50,3.4952,2,276.0,18.00,396.90,6.19} | 1 | {33.20}
{1,0.14866,0.00,8.560,0,0.5200,6.7270,79.90,2.7778,5,384.0,20.90,394.76,9.42} | 1 | {27.50}
{1,0.11432,0.00,8.560,0,0.5200,6.7810,71.30,2.8561,5,384.0,20.90,395.58,7.67} | 1 | {26.50}
{1,0.22876,0.00,8.560,0,0.5200,6.4050,85.40,2.7147,5,384.0,20.90,70.80,10.63} | 1 | {18.60}
{1,0.21161,0.00,8.560,0,0.5200,6.1370,87.40,2.7147,5,384.0,20.90,394.47,13.44} | 1 | {19.30}
{1,0.13960,0.00,8.560,0,0.5200,6.1670,90.00,2.4210,5,384.0,20.90,392.69,12.33} | 1 | {20.10}
{1,0.13262,0.00,8.560,0,0.5200,5.8510,96.70,2.1069,5,384.0,20.90,394.05,16.47} | 1 | {19.50}
{1,0.17120,0.00,8.560,0,0.5200,5.8360,91.90,2.2110,5,384.0,20.90,395.67,18.66} | 1 | {19.50}
{1,0.13117,0.00,8.560,0,0.5200,6.1270,85.20,2.1224,5,384.0,20.90,387.69,14.09} | 1 | {20.40}
{1,0.12802,0.00,8.560,0,0.5200,6.4740,97.10,2.4329,5,384.0,20.90,395.24,12.27} | 1 | {19.80}
{1,0.26363,0.00,8.560,0,0.5200,6.2290,91.20,2.5451,5,384.0,20.90,391.23,15.55} | 1 | {19.40}
{1,0.10793,0.00,8.560,0,0.5200,6.1950,54.40,2.7778,5,384.0,20.90,393.49,13.00} | 1 | {21.70}
{1,0.10084,0.00,10.010,0,0.5470,6.7150,81.60,2.6775,6,432.0,17.80,395.59,10.16} | 1 | {22.80}
{1,0.12329,0.00,10.010,0,0.5470,5.9130,92.90,2.3534,6,432.0,17.80,394.95,16.21} | 1 | {18.80}
{1,0.22212,0.00,10.010,0,0.5470,6.0920,95.40,2.5480,6,432.0,17.80,396.90,17.09} | 1 | {18.70}
{1,0.14231,0.00,10.010,0,0.5470,6.2540,84.20,2.2565,6,432.0,17.80,388.74,10.45} | 1 | {18.50}
{1,0.17134,0.00,10.010,0,0.5470,5.9280,88.20,2.4631,6,432.0,17.80,344.91,15.76} | 1 | {18.30}
{1,0.13158,0.00,10.010,0,0.5470,6.1760,72.50,2.7301,6,432.0,17.80,393.30,12.04} | 1 | {21.20}
{1,0.15098,0.00,10.010,0,0.5470,6.0210,82.60,2.7474,6,432.0,17.80,394.51,10.30} | 1 | {19.20}
{1,0.13058,0.00,10.010,0,0.5470,5.8720,73.10,2.4775,6,432.0,17.80,338.63,15.37} | 1 | {20.40}
{1,0.14476,0.00,10.010,0,0.5470,5.7310,65.20,2.7592,6,432.0,17.80,391.50,13.61} | 1 | {19.30}
{1,0.06899,0.00,25.650,0,0.5810,5.8700,69.70,2.2577,2,188.0,19.10,389.15,14.37} | 1 | {22.00}
{1,0.07165,0.00,25.650,0,0.5810,6.0040,84.10,2.1974,2,188.0,19.10,377.67,14.27} | 1 | {20.30}
{1,0.09299,0.00,25.650,0,0.5810,5.9610,92.90,2.0869,2,188.0,19.10,378.09,17.93} | 1 | {20.50}
{1,0.15038,0.00,25.650,0,0.5810,5.8560,97.00,1.9444,2,188.0,19.10,370.31,25.41} | 1 | {17.30}
{1,0.09849,0.00,25.650,0,0.5810,5.8790,95.80,2.0063,2,188.0,19.10,379.38,17.58} | 1 | {18.80}
{1,0.16902,0.00,25.650,0,0.5810,5.9860,88.40,1.9929,2,188.0,19.10,385.02,14.81} | 1 | {21.40}
{1,0.38735,0.00,25.650,0,0.5810,5.6130,95.60,1.7572,2,188.0,19.10,359.29,27.26} | 1 | {15.70}
{1,0.25915,0.00,21.890,0,0.6240,5.6930,96.00,1.7883,4,437.0,21.20,392.11,17.19} | 1 | {16.20}
{1,0.32543,0.00,21.890,0,0.6240,6.4310,98.80,1.8125,4,437.0,21.20,396.90,15.39} | 1 | {18.00}
{1,0.88125,0.00,21.890,0,0.6240,5.6370,94.70,1.9799,4,437.0,21.20,396.90,18.34} | 1 | {14.30}
{1,0.34006,0.00,21.890,0,0.6240,6.4580,98.90,2.1185,4,437.0,21.20,395.04,12.60} | 1 | {19.20}
{1,1.19294,0.00,21.890,0,0.6240,6.3260,97.70,2.2710,4,437.0,21.20,396.90,12.26} | 1 | {19.60}
{1,0.59005,0.00,21.890,0,0.6240,6.3720,97.90,2.3274,4,437.0,21.20,385.76,11.12} | 1 | {23.00}
{1,0.32982,0.00,21.890,0,0.6240,5.8220,95.40,2.4699,4,437.0,21.20,388.69,15.03} | 1 | {18.40}
{1,0.97617,0.00,21.890,0,0.6240,5.7570,98.40,2.3460,4,437.0,21.20,262.76,17.31} | 1 | {15.60}
{1,0.55778,0.00,21.890,0,0.6240,6.3350,98.20,2.1107,4,437.0,21.20,394.67,16.96} | 1 | {18.10}
{1,0.32264,0.00,21.890,0,0.6240,5.9420,93.50,1.9669,4,437.0,21.20,378.25,16.90} | 1 | {17.40}
{1,0.35233,0.00,21.890,0,0.6240,6.4540,98.40,1.8498,4,437.0,21.20,394.08,14.59} | 1 | {17.10}
{1,0.24980,0.00,21.890,0,0.6240,5.8570,98.20,1.6686,4,437.0,21.20,392.04,21.32} | 1 | {13.30}
{1,0.54452,0.00,21.890,0,0.6240,6.1510,97.90,1.6687,4,437.0,21.20,396.90,18.46} | 1 | {17.80}
{1,0.29090,0.00,21.890,0,0.6240,6.1740,93.60,1.6119,4,437.0,21.20,388.08,24.16} | 1 | {14.00}
{1,1.62864,0.00,21.890,0,0.6240,5.0190,100.00,1.4394,4,437.0,21.20,396.90,34.41} | 1 | {14.40}
{1,3.32105,0.00,19.580,1,0.8710,5.4030,100.00,1.3216,5,403.0,14.70,396.90,26.82} | 1 | {13.40}
{1,4.09740,0.00,19.580,0,0.8710,5.4680,100.00,1.4118,5,403.0,14.70,396.90,26.42} | 1 | {15.60}
{1,2.77974,0.00,19.580,0,0.8710,4.9030,97.80,1.3459,5,403.0,14.70,396.90,29.29} | 1 | {11.80}
{1,2.37934,0.00,19.580,0,0.8710,6.1300,100.00,1.4191,5,403.0,14.70,172.91,27.80} | 1 | {13.80}
{1,2.15505,0.00,19.580,0,0.8710,5.6280,100.00,1.5166,5,403.0,14.70,169.27,16.65} | 1 | {15.60}
{1,2.36862,0.00,19.580,0,0.8710,4.9260,95.70,1.4608,5,403.0,14.70,391.71,29.53} | 1 | {14.60}
{1,2.33099,0.00,19.580,0,0.8710,5.1860,93.80,1.5296,5,403.0,14.70,356.99,28.32} | 1 | {17.80}
{1,2.73397,0.00,19.580,0,0.8710,5.5970,94.90,1.5257,5,403.0,14.70,351.85,21.45} | 1 | {15.40}
{1,1.65660,0.00,19.580,0,0.8710,6.1220,97.30,1.6180,5,403.0,14.70,372.80,14.10} | 1 | {21.50}
{1,1.49632,0.00,19.580,0,0.8710,5.4040,100.00,1.5916,5,403.0,14.70,341.60,13.28} | 1 | {19.60}
{1,1.12658,0.00,19.580,1,0.8710,5.0120,88.00,1.6102,5,403.0,14.70,343.28,12.12} | 1 | {15.30}
{1,2.14918,0.00,19.580,0,0.8710,5.7090,98.50,1.6232,5,403.0,14.70,261.95,15.79} | 1 | {19.40}
{1,1.41385,0.00,19.580,1,0.8710,6.1290,96.00,1.7494,5,403.0,14.70,321.02,15.12} | 1 | {17.00}
{1,3.53501,0.00,19.580,1,0.8710,6.1520,82.60,1.7455,5,403.0,14.70,88.01,15.02} | 1 | {15.60}
{1,2.44668,0.00,19.580,0,0.8710,5.2720,94.00,1.7364,5,403.0,14.70,88.63,16.14} | 1 | {13.10}
{1,1.22358,0.00,19.580,0,0.6050,6.9430,97.40,1.8773,5,403.0,14.70,363.43,4.59} | 1 | {41.30}
{1,1.34284,0.00,19.580,0,0.6050,6.0660,100.00,1.7573,5,403.0,14.70,353.89,6.43} | 1 | {24.30}
{1,1.42502,0.00,19.580,0,0.8710,6.5100,100.00,1.7659,5,403.0,14.70,364.31,7.39} | 1 | {23.30}
{1,1.27346,0.00,19.580,1,0.6050,6.2500,92.60,1.7984,5,403.0,14.70,338.92,5.50} | 1 | {27.00}
{1,1.46336,0.00,19.580,0,0.6050,7.4890,90.80,1.9709,5,403.0,14.70,374.43,1.73} | 1 | {50.00}
{1,1.83377,0.00,19.580,1,0.6050,7.8020,98.20,2.0407,5,403.0,14.70,389.61,1.92} | 1 | {50.00}
{1,1.51902,0.00,19.580,1,0.6050,8.3750,93.90,2.1620,5,403.0,14.70,388.45,3.32} | 1 | {50.00}
{1,2.24236,0.00,19.580,0,0.6050,5.8540,91.80,2.4220,5,403.0,14.70,395.11,11.64} | 1 | {22.70}
{1,2.92400,0.00,19.580,0,0.6050,6.1010,93.00,2.2834,5,403.0,14.70,240.16,9.81} | 1 | {25.00}
{1,2.01019,0.00,19.580,0,0.6050,7.9290,96.20,2.0459,5,403.0,14.70,369.30,3.70} | 1 | {50.00}
{1,1.80028,0.00,19.580,0,0.6050,5.8770,79.20,2.4259,5,403.0,14.70,227.61,12.14} | 1 | {23.80}
{1,2.30040,0.00,19.580,0,0.6050,6.3190,96.10,2.1000,5,403.0,14.70,297.09,11.10} | 1 | {23.80}
{1,2.44953,0.00,19.580,0,0.6050,6.4020,95.20,2.2625,5,403.0,14.70,330.04,11.32} | 1 | {22.30}
{1,1.20742,0.00,19.580,0,0.6050,5.8750,94.60,2.4259,5,403.0,14.70,292.29,14.43} | 1 | {17.40}
{1,2.31390,0.00,19.580,0,0.6050,5.8800,97.30,2.3887,5,403.0,14.70,348.13,12.03} | 1 | {19.10}
{1,0.13914,0.00,4.050,0,0.5100,5.5720,88.50,2.5961,5,296.0,16.60,396.90,14.69} | 1 | {23.10}
{1,0.09178,0.00,4.050,0,0.5100,6.4160,84.10,2.6463,5,296.0,16.60,395.50,9.04} | 1 | {23.60}
{1,0.08447,0.00,4.050,0,0.5100,5.8590,68.70,2.7019,5,296.0,16.60,393.23,9.64} | 1 | {22.60}
{1,0.06664,0.00,4.050,0,0.5100,6.5460,33.10,3.1323,5,296.0,16.60,390.96,5.33} | 1 | {29.40}
{1,0.07022,0.00,4.050,0,0.5100,6.0200,47.20,3.5549,5,296.0,16.60,393.23,10.11} | 1 | {23.20}
{1,0.05425,0.00,4.050,0,0.5100,6.3150,73.40,3.3175,5,296.0,16.60,395.60,6.29} | 1 | {24.60}
{1,0.06642,0.00,4.050,0,0.5100,6.8600,74.40,2.9153,5,296.0,16.60,391.27,6.92} | 1 | {29.90}
{1,0.05780,0.00,2.460,0,0.4880,6.9800,58.40,2.8290,3,193.0,17.80,396.90,5.04} | 1 | {37.20}
{1,0.06588,0.00,2.460,0,0.4880,7.7650,83.30,2.7410,3,193.0,17.80,395.56,7.56} | 1 | {39.80}
{1,0.06888,0.00,2.460,0,0.4880,6.1440,62.20,2.5979,3,193.0,17.80,396.90,9.45} | 1 | {36.20}
{1,0.09103,0.00,2.460,0,0.4880,7.1550,92.20,2.7006,3,193.0,17.80,394.12,4.82} | 1 | {37.90}
{1,0.10008,0.00,2.460,0,0.4880,6.5630,95.60,2.8470,3,193.0,17.80,396.90,5.68} | 1 | {32.50}
{1,0.08308,0.00,2.460,0,0.4880,5.6040,89.80,2.9879,3,193.0,17.80,391.00,13.98} | 1 | {26.40}
{1,0.06047,0.00,2.460,0,0.4880,6.1530,68.80,3.2797,3,193.0,17.80,387.11,13.15} | 1 | {29.60}
{1,0.05602,0.00,2.460,0,0.4880,7.8310,53.60,3.1992,3,193.0,17.80,392.63,4.45} | 1 | {50.00}
{1,0.07875,45.00,3.440,0,0.4370,6.7820,41.10,3.7886,5,398.0,15.20,393.87,6.68} | 1 | {32.00}
{1,0.12579,45.00,3.440,0,0.4370,6.5560,29.10,4.5667,5,398.0,15.20,382.84,4.56} | 1 | {29.80}
{1,0.08370,45.00,3.440,0,0.4370,7.1850,38.90,4.5667,5,398.0,15.20,396.90,5.39} | 1 | {34.90}
{1,0.09068,45.00,3.440,0,0.4370,6.9510,21.50,6.4798,5,398.0,15.20,377.68,5.10} | 1 | {37.00}
{1,0.06911,45.00,3.440,0,0.4370,6.7390,30.80,6.4798,5,398.0,15.20,389.71,4.69} | 1 | {30.50}
{1,0.04590,52.50,5.320,0,0.4050,6.3150,45.60,7.3172,6,293.0,16.60,396.90,7.60} | 2 | {22.30}
{1,0.04297,52.50,5.320,0,0.4050,6.5650,22.90,7.3172,6,293.0,16.60,371.72,9.51} | 2 | {24.80}
{1,0.03502,80.00,4.950,0,0.4110,6.8610,27.90,5.1167,4,245.0,19.20,396.90,3.33} | 2 | {28.50}
{1,0.07886,80.00,4.950,0,0.4110,7.1480,27.70,5.1167,4,245.0,19.20,396.90,3.56} | 2 | {37.30}
{1,0.03615,80.00,4.950,0,0.4110,6.6300,23.40,5.1167,4,245.0,19.20,396.90,4.70} | 2 | {27.90}
{1,0.08265,0.00,13.920,0,0.4370,6.1270,18.40,5.5027,4,289.0,16.00,396.90,8.58} | 2 | {23.90}
{1,0.08199,0.00,13.920,0,0.4370,6.0090,42.30,5.5027,4,289.0,16.00,396.90,10.40} | 2 | {21.70}
{1,0.12932,0.00,13.920,0,0.4370,6.6780,31.10,5.9604,4,289.0,16.00,396.90,6.27} | 2 | {28.60}
{1,0.05372,0.00,13.920,0,0.4370,6.5490,51.00,5.9604,4,289.0,16.00,392.85,7.39} | 2 | {27.10}
{1,0.14103,0.00,13.920,0,0.4370,5.7900,58.00,6.3200,4,289.0,16.00,396.90,15.84} | 2 | {20.30}
{1,0.06466,70.00,2.240,0,0.4000,6.3450,20.10,7.8278,5,358.0,14.80,368.24,4.97} | 2 | {22.50}
{1,0.05561,70.00,2.240,0,0.4000,7.0410,10.00,7.8278,5,358.0,14.80,371.58,4.74} | 2 | {29.00}
{1,0.04417,70.00,2.240,0,0.4000,6.8710,47.40,7.8278,5,358.0,14.80,390.86,6.07} | 2 | {24.80}
{1,0.03537,34.00,6.090,0,0.4330,6.5900,40.40,5.4917,7,329.0,16.10,395.75,9.50} | 2 | {22.00}
{1,0.09266,34.00,6.090,0,0.4330,6.4950,18.40,5.4917,7,329.0,16.10,383.61,8.67} | 2 | {26.40}
{1,0.10000,34.00,6.090,0,0.4330,6.9820,17.70,5.4917,7,329.0,16.10,390.43,4.86} | 2 | {33.10}
{1,0.05515,33.00,2.180,0,0.4720,7.2360,41.10,4.0220,7,222.0,18.40,393.68,6.93} | 2 | {36.10}
{1,0.05479,33.00,2.180,0,0.4720,6.6160,58.10,3.3700,7,222.0,18.40,393.36,8.93} | 2 | {28.40}
{1,0.07503,33.00,2.180,0,0.4720,7.4200,71.90,3.0992,7,222.0,18.40,396.90,6.47} | 2 | {33.40}
{1,0.04932,33.00,2.180,0,0.4720,6.8490,70.30,3.1827,7,222.0,18.40,396.90,7.53} | 2 | {28.20}
{1,0.49298,0.00,9.900,0,0.5440,6.6350,82.50,3.3175,4,304.0,18.40,396.90,4.54} | 2 | {22.80}
{1,0.34940,0.00,9.900,0,0.5440,5.9720,76.70,3.1025,4,304.0,18.40,396.24,9.97} | 2 | {20.30}
{1,2.63548,0.00,9.900,0,0.5440,4.9730,37.80,2.5194,4,304.0,18.40,350.45,12.64} | 2 | {16.10}
{1,0.79041,0.00,9.900,0,0.5440,6.1220,52.80,2.6403,4,304.0,18.40,396.90,5.98} | 2 | {22.10}
{1,0.26169,0.00,9.900,0,0.5440,6.0230,90.40,2.8340,4,304.0,18.40,396.30,11.72} | 2 | {19.40}
{1,0.26938,0.00,9.900,0,0.5440,6.2660,82.80,3.2628,4,304.0,18.40,393.39,7.90} | 2 | {21.60}
{1,0.36920,0.00,9.900,0,0.5440,6.5670,87.30,3.6023,4,304.0,18.40,395.69,9.28} | 2 | {23.80}
{1,0.25356,0.00,9.900,0,0.5440,5.7050,77.70,3.9450,4,304.0,18.40,396.42,11.50} | 2 | {16.20}
{1,0.31827,0.00,9.900,0,0.5440,5.9140,83.20,3.9986,4,304.0,18.40,390.70,18.33} | 2 | {17.80}
{1,0.24522,0.00,9.900,0,0.5440,5.7820,71.70,4.0317,4,304.0,18.40,396.90,15.94} | 2 | {19.80}
{1,0.40202,0.00,9.900,0,0.5440,6.3820,67.20,3.5325,4,304.0,18.40,395.21,10.36} | 2 | {23.10}
{1,0.47547,0.00,9.900,0,0.5440,6.1130,58.80,4.0019,4,304.0,18.40,396.23,12.73} | 2 | {21.00}
{1,0.16760,0.00,7.380,0,0.4930,6.4260,52.30,4.5404,5,287.0,19.60,396.90,7.20} | 2 | {23.80}
{1,0.18159,0.00,7.380,0,0.4930,6.3760,54.30,4.5404,5,287.0,19.60,396.90,6.87} | 2 | {23.10}
{1,0.35114,0.00,7.380,0,0.4930,6.0410,49.90,4.7211,5,287.0,19.60,396.90,7.70} | 2 | {20.40}
{1,0.28392,0.00,7.380,0,0.4930,5.7080,74.30,4.7211,5,287.0,19.60,391.13,11.74} | 2 | {18.50}
{1,0.34109,0.00,7.380,0,0.4930,6.4150,40.10,4.7211,5,287.0,19.60,396.90,6.12} | 2 | {25.00}
{1,0.19186,0.00,7.380,0,0.4930,6.4310,14.70,5.4159,5,287.0,19.60,393.68,5.08} | 2 | {24.60}
{1,0.30347,0.00,7.380,0,0.4930,6.3120,28.90,5.4159,5,287.0,19.60,396.90,6.15} | 2 | {23.00}
{1,0.24103,0.00,7.380,0,0.4930,6.0830,43.70,5.4159,5,287.0,19.60,396.90,12.79} | 2 | {22.20}
{1,0.06617,0.00,3.240,0,0.4600,5.8680,25.80,5.2146,4,430.0,16.90,382.44,9.97} | 2 | {19.30}
{1,0.06724,0.00,3.240,0,0.4600,6.3330,17.20,5.2146,4,430.0,16.90,375.21,7.34} | 2 | {22.60}
{1,0.04544,0.00,3.240,0,0.4600,6.1440,32.20,5.8736,4,430.0,16.90,368.57,9.09} | 2 | {19.80}
{1,0.05023,35.00,6.060,0,0.4379,5.7060,28.40,6.6407,1,304.0,16.90,394.02,12.43} | 2 | {17.10}
{1,0.03466,35.00,6.060,0,0.4379,6.0310,23.30,6.6407,1,304.0,16.90,362.25,7.83} | 2 | {19.40}
{1,0.05083,0.00,5.190,0,0.5150,6.3160,38.10,6.4584,5,224.0,20.20,389.71,5.68} | 2 | {22.20}
{1,0.03738,0.00,5.190,0,0.5150,6.3100,38.50,6.4584,5,224.0,20.20,389.40,6.75} | 2 | {20.70}
{1,0.03961,0.00,5.190,0,0.5150,6.0370,34.50,5.9853,5,224.0,20.20,396.90,8.01} | 2 | {21.10}
{1,0.03427,0.00,5.190,0,0.5150,5.8690,46.30,5.2311,5,224.0,20.20,396.90,9.80} | 2 | {19.50}
{1,0.03041,0.00,5.190,0,0.5150,5.8950,59.60,5.6150,5,224.0,20.20,394.81,10.56} | 2 | {18.50}
{1,0.03306,0.00,5.190,0,0.5150,6.0590,37.30,4.8122,5,224.0,20.20,396.14,8.51} | 2 | {20.60}
{1,0.05497,0.00,5.190,0,0.5150,5.9850,45.40,4.8122,5,224.0,20.20,396.90,9.74} | 2 | {19.00}
{1,0.06151,0.00,5.190,0,0.5150,5.9680,58.50,4.8122,5,224.0,20.20,396.90,9.29} | 2 | {18.70}
{1,0.01301,35.00,1.520,0,0.4420,7.2410,49.30,7.0379,1,284.0,15.50,394.74,5.49} | 2 | {32.70}
{1,0.02498,0.00,1.890,0,0.5180,6.5400,59.70,6.2669,1,422.0,15.90,389.96,8.65} | 2 | {16.50}
{1,0.02543,55.00,3.780,0,0.4840,6.6960,56.40,5.7321,5,370.0,17.60,396.90,7.18} | 2 | {23.90}
{1,0.03049,55.00,3.780,0,0.4840,6.8740,28.10,6.4654,5,370.0,17.60,387.97,4.61} | 2 | {31.20}
{1,0.03113,0.00,4.390,0,0.4420,6.0140,48.50,8.0136,3,352.0,18.80,385.64,10.53} | 2 | {17.50}
{1,0.06162,0.00,4.390,0,0.4420,5.8980,52.30,8.0136,3,352.0,18.80,364.61,12.67} | 2 | {17.20}
{1,0.01870,85.00,4.150,0,0.4290,6.5160,27.70,8.5353,4,351.0,17.90,392.43,6.36} | 2 | {23.10}
{1,0.01501,80.00,2.010,0,0.4350,6.6350,29.70,8.3440,4,280.0,17.00,390.94,5.99} | 2 | {24.50}
{1,0.02899,40.00,1.250,0,0.4290,6.9390,34.50,8.7921,1,335.0,19.70,389.85,5.89} | 2 | {26.60}
{1,0.06211,40.00,1.250,0,0.4290,6.4900,44.40,8.7921,1,335.0,19.70,396.90,5.98} | 2 | {22.90}
{1,0.07950,60.00,1.690,0,0.4110,6.5790,35.90,10.7103,4,411.0,18.30,370.78,5.49} | 2 | {24.10}
{1,0.07244,60.00,1.690,0,0.4110,5.8840,18.50,10.7103,4,411.0,18.30,392.33,7.79} | 2 | {18.60}
{1,0.01709,90.00,2.020,0,0.4100,6.7280,36.10,12.1265,5,187.0,17.00,384.46,4.50} | 2 | {30.10}
{1,0.04301,80.00,1.910,0,0.4130,5.6630,21.90,10.5857,4,334.0,22.00,382.80,8.05} | 2 | {18.20}
{1,0.10659,80.00,1.910,0,0.4130,5.9360,19.50,10.5857,4,334.0,22.00,376.04,5.57} | 2 | {20.60}
{1,8.98296,0.00,18.100,1,0.7700,6.2120,97.40,2.1222,24,666.0,20.20,377.73,17.60} | 2 | {17.80}
{1,3.84970,0.00,18.100,1,0.7700,6.3950,91.00,2.5052,24,666.0,20.20,391.34,13.27} | 2 | {21.70}
{1,5.20177,0.00,18.100,1,0.7700,6.1270,83.40,2.7227,24,666.0,20.20,395.43,11.48} | 2 | {22.70}
{1,4.26131,0.00,18.100,0,0.7700,6.1120,81.30,2.5091,24,666.0,20.20,390.74,12.67} | 2 | {22.60}
{1,4.54192,0.00,18.100,0,0.7700,6.3980,88.00,2.5182,24,666.0,20.20,374.56,7.79} | 2 | {25.00}
{1,3.83684,0.00,18.100,0,0.7700,6.2510,91.10,2.2955,24,666.0,20.20,350.65,14.19} | 2 | {19.90}
{1,3.67822,0.00,18.100,0,0.7700,5.3620,96.20,2.1036,24,666.0,20.20,380.79,10.19} | 2 | {20.80}
{1,4.22239,0.00,18.100,1,0.7700,5.8030,89.00,1.9047,24,666.0,20.20,353.04,14.64} | 2 | {16.80}
{1,3.47428,0.00,18.100,1,0.7180,8.7800,82.90,1.9047,24,666.0,20.20,354.55,5.29} | 2 | {21.90}
{1,4.55587,0.00,18.100,0,0.7180,3.5610,87.90,1.6132,24,666.0,20.20,354.70,7.12} | 2 | {27.50}
{1,3.69695,0.00,18.100,0,0.7180,4.9630,91.40,1.7523,24,666.0,20.20,316.03,14.00} | 2 | {21.90}
{1,13.52220,0.00,18.100,0,0.6310,3.8630,100.00,1.5106,24,666.0,20.20,131.42,13.33} | 2 | {23.10}
{1,4.89822,0.00,18.100,0,0.6310,4.9700,100.00,1.3325,24,666.0,20.20,375.52,3.26} | 2 | {50.00}
{1,5.66998,0.00,18.100,1,0.6310,6.6830,96.80,1.3567,24,666.0,20.20,375.33,3.73} | 2 | {50.00}
{1,6.53876,0.00,18.100,1,0.6310,7.0160,97.50,1.2024,24,666.0,20.20,392.05,2.96} | 2 | {50.00}
{1,9.23230,0.00,18.100,0,0.6310,6.2160,100.00,1.1691,24,666.0,20.20,366.15,9.53} | 2 | {50.00}
{1,8.26725,0.00,18.100,1,0.6680,5.8750,89.60,1.1296,24,666.0,20.20,347.88,8.88} | 2 | {50.00}
{1,11.10810,0.00,18.100,0,0.6680,4.9060,100.00,1.1742,24,666.0,20.20,396.90,34.77} | 2 | {13.80}
{1,18.49820,0.00,18.100,0,0.6680,4.1380,100.00,1.1370,24,666.0,20.20,396.90,37.97} | 2 | {13.80}
{1,19.60910,0.00,18.100,0,0.6710,7.3130,97.90,1.3163,24,666.0,20.20,396.90,13.44} | 2 | {15.00}
{1,15.28800,0.00,18.100,0,0.6710,6.6490,93.30,1.3449,24,666.0,20.20,363.02,23.24} | 2 | {13.90}
{1,9.82349,0.00,18.100,0,0.6710,6.7940,98.80,1.3580,24,666.0,20.20,396.90,21.24} | 2 | {13.30}
{1,23.64820,0.00,18.100,0,0.6710,6.3800,96.20,1.3861,24,666.0,20.20,396.90,23.69} | 2 | {13.10}
{1,17.86670,0.00,18.100,0,0.6710,6.2230,100.00,1.3861,24,666.0,20.20,393.74,21.78} | 2 | {10.20}
{1,88.97620,0.00,18.100,0,0.6710,6.9680,91.90,1.4165,24,666.0,20.20,396.90,17.21} | 2 | {10.40}
{1,15.87440,0.00,18.100,0,0.6710,6.5450,99.10,1.5192,24,666.0,20.20,396.90,21.08} | 2 | {10.90}
{1,9.18702,0.00,18.100,0,0.7000,5.5360,100.00,1.5804,24,666.0,20.20,396.90,23.60} | 2 | {11.30}
{1,7.99248,0.00,18.100,0,0.7000,5.5200,100.00,1.5331,24,666.0,20.20,396.90,24.56} | 2 | {12.30}
{1,20.08490,0.00,18.100,0,0.7000,4.3680,91.20,1.4395,24,666.0,20.20,285.83,30.63} | 2 | {8.80}
{1,16.81180,0.00,18.100,0,0.7000,5.2770,98.10,1.4261,24,666.0,20.20,396.90,30.81} | 2 | {7.20}
{1,24.39380,0.00,18.100,0,0.7000,4.6520,100.00,1.4672,24,666.0,20.20,396.90,28.28} | 2 | {10.50}
{1,22.59710,0.00,18.100,0,0.7000,5.0000,89.50,1.5184,24,666.0,20.20,396.90,31.99} | 2 | {7.40}
{1,14.33370,0.00,18.100,0,0.7000,4.8800,100.00,1.5895,24,666.0,20.20,372.92,30.62} | 2 | {10.20}
{1,8.15174,0.00,18.100,0,0.7000,5.3900,98.90,1.7281,24,666.0,20.20,396.90,20.85} | 2 | {11.50}
{1,6.96215,0.00,18.100,0,0.7000,5.7130,97.00,1.9265,24,666.0,20.20,394.43,17.11} | 2 | {15.10}
{1,5.29305,0.00,18.100,0,0.7000,6.0510,82.50,2.1678,24,666.0,20.20,378.38,18.76} | 2 | {23.20}
{1,11.57790,0.00,18.100,0,0.7000,5.0360,97.00,1.7700,24,666.0,20.20,396.90,25.68} | 2 | {9.70}
{1,8.64476,0.00,18.100,0,0.6930,6.1930,92.60,1.7912,24,666.0,20.20,396.90,15.17} | 2 | {13.80}
{1,13.35980,0.00,18.100,0,0.6930,5.8870,94.70,1.7821,24,666.0,20.20,396.90,16.35} | 2 | {12.70}
{1,8.71675,0.00,18.100,0,0.6930,6.4710,98.80,1.7257,24,666.0,20.20,391.98,17.12} | 2 | {13.10}
{1,5.87205,0.00,18.100,0,0.6930,6.4050,96.00,1.6768,24,666.0,20.20,396.90,19.37} | 2 | {12.50}
{1,7.67202,0.00,18.100,0,0.6930,5.7470,98.90,1.6334,24,666.0,20.20,393.10,19.92} | 2 | {8.50}
{1,38.35180,0.00,18.100,0,0.6930,5.4530,100.00,1.4896,24,666.0,20.20,396.90,30.59} | 2 | {5.00}
{1,9.91655,0.00,18.100,0,0.6930,5.8520,77.80,1.5004,24,666.0,20.20,338.16,29.97} | 2 | {6.30}
{1,25.04610,0.00,18.100,0,0.6930,5.9870,100.00,1.5888,24,666.0,20.20,396.90,26.77} | 2 | {5.60}
{1,14.23620,0.00,18.100,0,0.6930,6.3430,100.00,1.5741,24,666.0,20.20,396.90,20.32} | 2 | {7.20}
{1,9.59571,0.00,18.100,0,0.6930,6.4040,100.00,1.6390,24,666.0,20.20,376.11,20.31} | 2 | {12.10}
{1,24.80170,0.00,18.100,0,0.6930,5.3490,96.00,1.7028,24,666.0,20.20,396.90,19.77} | 2 | {8.30}
{1,41.52920,0.00,18.100,0,0.6930,5.5310,85.40,1.6074,24,666.0,20.20,329.46,27.38} | 2 | {8.50}
{1,67.92080,0.00,18.100,0,0.6930,5.6830,100.00,1.4254,24,666.0,20.20,384.97,22.98} | 2 | {5.00}
{1,20.71620,0.00,18.100,0,0.6590,4.1380,100.00,1.1781,24,666.0,20.20,370.22,23.34} | 2 | {11.90}
{1,11.95110,0.00,18.100,0,0.6590,5.6080,100.00,1.2852,24,666.0,20.20,332.09,12.13} | 2 | {27.90}
{1,7.40389,0.00,18.100,0,0.5970,5.6170,97.90,1.4547,24,666.0,20.20,314.64,26.40} | 2 | {17.20}
{1,14.43830,0.00,18.100,0,0.5970,6.8520,100.00,1.4655,24,666.0,20.20,179.36,19.78} | 2 | {27.50}
{1,51.13580,0.00,18.100,0,0.5970,5.7570,100.00,1.4130,24,666.0,20.20,2.60,10.11} | 2 | {15.00}
{1,14.05070,0.00,18.100,0,0.5970,6.6570,100.00,1.5275,24,666.0,20.20,35.05,21.22} | 2 | {17.20}
{1,18.81100,0.00,18.100,0,0.5970,4.6280,100.00,1.5539,24,666.0,20.20,28.79,34.37} | 2 | {17.90}
{1,28.65580,0.00,18.100,0,0.5970,5.1550,100.00,1.5894,24,666.0,20.20,210.97,20.08} | 2 | {16.30}
{1,45.74610,0.00,18.100,0,0.6930,4.5190,100.00,1.6582,24,666.0,20.20,88.27,36.98} | 2 | {7.00}
{1,18.08460,0.00,18.100,0,0.6790,6.4340,100.00,1.8347,24,666.0,20.20,27.25,29.05} | 2 | {7.20}
{1,10.83420,0.00,18.100,0,0.6790,6.7820,90.80,1.8195,24,666.0,20.20,21.57,25.79} | 2 | {7.50}
{1,25.94060,0.00,18.100,0,0.6790,5.3040,89.10,1.6475,24,666.0,20.20,127.36,26.64} | 2 | {10.40}
{1,73.53410,0.00,18.100,0,0.6790,5.9570,100.00,1.8026,24,666.0,20.20,16.45,20.62} | 2 | {8.80}
{1,11.81230,0.00,18.100,0,0.7180,6.8240,76.50,1.7940,24,666.0,20.20,48.45,22.74} | 2 | {8.40}
{1,11.08740,0.00,18.100,0,0.7180,6.4110,100.00,1.8589,24,666.0,20.20,318.75,15.02} | 2 | {16.70}
{1,7.02259,0.00,18.100,0,0.7180,6.0060,95.30,1.8746,24,666.0,20.20,319.98,15.70} | 2 | {14.20}
{1,12.04820,0.00,18.100,0,0.6140,5.6480,87.60,1.9512,24,666.0,20.20,291.55,14.10} | 2 | {20.80}
{1,7.05042,0.00,18.100,0,0.6140,6.1030,85.10,2.0218,24,666.0,20.20,2.52,23.29} | 2 | {13.40}
{1,8.79212,0.00,18.100,0,0.5840,5.5650,70.60,2.0635,24,666.0,20.20,3.65,17.16} | 2 | {11.70}
{1,15.86030,0.00,18.100,0,0.6790,5.8960,95.40,1.9096,24,666.0,20.20,7.68,24.39} | 2 | {8.30}
{1,12.24720,0.00,18.100,0,0.5840,5.8370,59.70,1.9976,24,666.0,20.20,24.65,15.69} | 2 | {10.20}
{1,37.66190,0.00,18.100,0,0.6790,6.2020,78.70,1.8629,24,666.0,20.20,18.82,14.52} | 2 | {10.90}
{1,7.36711,0.00,18.100,0,0.6790,6.1930,78.10,1.9356,24,666.0,20.20,96.73,21.52} | 2 | {11.00}
{1,9.33889,0.00,18.100,0,0.6790,6.3800,95.60,1.9682,24,666.0,20.20,60.72,24.08} | 2 | {9.50}
{1,8.49213,0.00,18.100,0,0.5840,6.3480,86.10,2.0527,24,666.0,20.20,83.45,17.64} | 2 | {14.50}
{1,10.06230,0.00,18.100,0,0.5840,6.8330,94.30,2.0882,24,666.0,20.20,81.33,19.69} | 2 | {14.10}
{1,6.44405,0.00,18.100,0,0.5840,6.4250,74.80,2.2004,24,666.0,20.20,97.95,12.03} | 2 | {16.10}
{1,5.58107,0.00,18.100,0,0.7130,6.4360,87.90,2.3158,24,666.0,20.20,100.19,16.22} | 2 | {14.30}
{1,13.91340,0.00,18.100,0,0.7130,6.2080,95.00,2.2222,24,666.0,20.20,100.63,15.17} | 2 | {11.70}
{1,11.16040,0.00,18.100,0,0.7400,6.6290,94.60,2.1247,24,666.0,20.20,109.85,23.27} | 2 | {13.40}
{1,14.42080,0.00,18.100,0,0.7400,6.4610,93.30,2.0026,24,666.0,20.20,27.49,18.05} | 2 | {9.60}
{1,15.17720,0.00,18.100,0,0.7400,6.1520,100.00,1.9142,24,666.0,20.20,9.32,26.45} | 2 | {8.70}
{1,13.67810,0.00,18.100,0,0.7400,5.9350,87.90,1.8206,24,666.0,20.20,68.95,34.02} | 2 | {8.40}
{1,9.39063,0.00,18.100,0,0.7400,5.6270,93.90,1.8172,24,666.0,20.20,396.90,22.88} | 2 | {12.80}
{1,22.05110,0.00,18.100,0,0.7400,5.8180,92.40,1.8662,24,666.0,20.20,391.45,22.11} | 2 | {10.50}
{1,9.72418,0.00,18.100,0,0.7400,6.4060,97.20,2.0651,24,666.0,20.20,385.96,19.52} | 2 | {17.10}
{1,5.66637,0.00,18.100,0,0.7400,6.2190,100.00,2.0048,24,666.0,20.20,395.69,16.59} | 2 | {18.40}
{1,9.96654,0.00,18.100,0,0.7400,6.4850,100.00,1.9784,24,666.0,20.20,386.73,18.85} | 2 | {15.40}
{1,12.80230,0.00,18.100,0,0.7400,5.8540,96.60,1.8956,24,666.0,20.20,240.52,23.79} | 2 | {10.80}
{1,10.67180,0.00,18.100,0,0.7400,6.4590,94.80,1.9879,24,666.0,20.20,43.06,23.98} | 2 | {11.80}
{1,6.28807,0.00,18.100,0,0.7400,6.3410,96.40,2.0720,24,666.0,20.20,318.01,17.79} | 2 | {14.90}
{1,9.92485,0.00,18.100,0,0.7400,6.2510,96.60,2.1980,24,666.0,20.20,388.52,16.44} | 2 | {12.60}
{1,9.32909,0.00,18.100,0,0.7130,6.1850,98.70,2.2616,24,666.0,20.20,396.90,18.13} | 2 | {14.10}
{1,7.52601,0.00,18.100,0,0.7130,6.4170,98.30,2.1850,24,666.0,20.20,304.21,19.31} | 2 | {13.00}
{1,6.71772,0.00,18.100,0,0.7130,6.7490,92.60,2.3236,24,666.0,20.20,0.32,17.44} | 2 | {13.40}
{1,5.44114,0.00,18.100,0,0.7130,6.6550,98.20,2.3552,24,666.0,20.20,355.29,17.73} | 2 | {15.20}
{1,5.09017,0.00,18.100,0,0.7130,6.2970,91.80,2.3682,24,666.0,20.20,385.09,17.27} | 2 | {16.10}
{1,8.24809,0.00,18.100,0,0.7130,7.3930,99.30,2.4527,24,666.0,20.20,375.87,16.74} | 2 | {17.80}
{1,9.51363,0.00,18.100,0,0.7130,6.7280,94.10,2.4961,24,666.0,20.20,6.68,18.71} | 2 | {14.90}
{1,4.75237,0.00,18.100,0,0.7130,6.5250,86.50,2.4358,24,666.0,20.20,50.92,18.13} | 2 | {14.10}
{1,4.66883,0.00,18.100,0,0.7130,5.9760,87.90,2.5806,24,666.0,20.20,10.48,19.01} | 2 | {12.70}
{1,8.20058,0.00,18.100,0,0.7130,5.9360,80.30,2.7792,24,666.0,20.20,3.50,16.94} | 2 | {13.50}
{1,7.75223,0.00,18.100,0,0.7130,6.3010,83.70,2.7831,24,666.0,20.20,272.21,16.23} | 2 | {14.90}
{1,6.80117,0.00,18.100,0,0.7130,6.0810,84.40,2.7175,24,666.0,20.20,396.90,14.70} | 2 | {20.00}
{1,4.81213,0.00,18.100,0,0.7130,6.7010,90.00,2.5975,24,666.0,20.20,255.23,16.42} | 2 | {16.40}
{1,3.69311,0.00,18.100,0,0.7130,6.3760,88.40,2.5671,24,666.0,20.20,391.43,14.65} | 2 | {17.70}
{1,6.65492,0.00,18.100,0,0.7130,6.3170,83.00,2.7344,24,666.0,20.20,396.90,13.99} | 2 | {19.50}
{1,5.82115,0.00,18.100,0,0.7130,6.5130,89.90,2.8016,24,666.0,20.20,393.82,10.29} | 2 | {20.20}
{1,7.83932,0.00,18.100,0,0.6550,6.2090,65.40,2.9634,24,666.0,20.20,396.90,13.22} | 2 | {21.40}
{1,3.16360,0.00,18.100,0,0.6550,5.7590,48.20,3.0665,24,666.0,20.20,334.40,14.13} | 2 | {19.90}
{1,3.77498,0.00,18.100,0,0.6550,5.9520,84.70,2.8715,24,666.0,20.20,22.01,17.15} | 2 | {19.00}
{1,4.42228,0.00,18.100,0,0.5840,6.0030,94.50,2.5403,24,666.0,20.20,331.29,21.32} | 2 | {19.10}
{1,15.57570,0.00,18.100,0,0.5800,5.9260,71.00,2.9084,24,666.0,20.20,368.74,18.13} | 2 | {19.10}
{1,13.07510,0.00,18.100,0,0.5800,5.7130,56.70,2.8237,24,666.0,20.20,396.90,14.76} | 2 | {20.10}
{1,4.34879,0.00,18.100,0,0.5800,6.1670,84.00,3.0334,24,666.0,20.20,396.90,16.29} | 2 | {19.90}
{1,4.03841,0.00,18.100,0,0.5320,6.2290,90.70,3.0993,24,666.0,20.20,395.33,12.87} | 2 | {19.60}
{1,3.56868,0.00,18.100,0,0.5800,6.4370,75.00,2.8965,24,666.0,20.20,393.37,14.36} | 2 | {23.20}
{1,4.64689,0.00,18.100,0,0.6140,6.9800,67.60,2.5329,24,666.0,20.20,374.68,11.66} | 2 | {29.80}
{1,8.05579,0.00,18.100,0,0.5840,5.4270,95.40,2.4298,24,666.0,20.20,352.58,18.14} | 2 | {13.80}
{1,6.39312,0.00,18.100,0,0.5840,6.1620,97.40,2.2060,24,666.0,20.20,302.76,24.10} | 2 | {13.30}
{1,4.87141,0.00,18.100,0,0.6140,6.4840,93.60,2.3053,24,666.0,20.20,396.21,18.68} | 2 | {16.70}
{1,15.02340,0.00,18.100,0,0.6140,5.3040,97.30,2.1007,24,666.0,20.20,349.48,24.91} | 2 | {12.00}
{1,10.23300,0.00,18.100,0,0.6140,6.1850,96.70,2.1705,24,666.0,20.20,379.70,18.03} | 2 | {14.60}
{1,14.33370,0.00,18.100,0,0.6140,6.2290,88.00,1.9512,24,666.0,20.20,383.32,13.11} | 2 | {21.40}
{1,5.82401,0.00,18.100,0,0.5320,6.2420,64.70,3.4242,24,666.0,20.20,396.90,10.74} | 2 | {23.00}
{1,5.70818,0.00,18.100,0,0.5320,6.7500,74.90,3.3317,24,666.0,20.20,393.07,7.74} | 2 | {23.70}
{1,5.73116,0.00,18.100,0,0.5320,7.0610,77.00,3.4106,24,666.0,20.20,395.28,7.01} | 2 | {25.00}
{1,2.81838,0.00,18.100,0,0.5320,5.7620,40.30,4.0983,24,666.0,20.20,392.92,10.42} | 2 | {21.80}
{1,2.37857,0.00,18.100,0,0.5830,5.8710,41.90,3.7240,24,666.0,20.20,370.73,13.34} | 2 | {20.60}
{1,3.67367,0.00,18.100,0,0.5830,6.3120,51.90,3.9917,24,666.0,20.20,388.62,10.58} | 2 | {21.20}
{1,5.69175,0.00,18.100,0,0.5830,6.1140,79.80,3.5459,24,666.0,20.20,392.68,14.98} | 2 | {19.10}
{1,4.83567,0.00,18.100,0,0.5830,5.9050,53.20,3.1523,24,666.0,20.20,388.22,11.45} | 2 | {20.60}
{1,0.15086,0.00,27.740,0,0.6090,5.4540,92.70,1.8209,4,711.0,20.10,395.09,18.06} | 2 | {15.20}
{1,0.18337,0.00,27.740,0,0.6090,5.4140,98.30,1.7554,4,711.0,20.10,344.05,23.97} | 2 | {7.00}
{1,0.20746,0.00,27.740,0,0.6090,5.0930,98.00,1.8226,4,711.0,20.10,318.43,29.68} | 2 | {8.10}
{1,0.10574,0.00,27.740,0,0.6090,5.9830,98.80,1.8681,4,711.0,20.10,390.11,18.07} | 2 | {13.60}
{1,0.11132,0.00,27.740,0,0.6090,5.9830,83.50,2.1099,4,711.0,20.10,396.90,13.35} | 2 | {20.10}
{1,0.17331,0.00,9.690,0,0.5850,5.7070,54.00,2.3817,6,391.0,19.20,396.90,12.01} | 2 | {21.80}
{1,0.27957,0.00,9.690,0,0.5850,5.9260,42.60,2.3817,6,391.0,19.20,396.90,13.59} | 2 | {24.50}
{1,0.17899,0.00,9.690,0,0.5850,5.6700,28.80,2.7986,6,391.0,19.20,393.29,17.60} | 2 | {23.10}
{1,0.28960,0.00,9.690,0,0.5850,5.3900,72.90,2.7986,6,391.0,19.20,396.90,21.14} | 2 | {19.70}
{1,0.26838,0.00,9.690,0,0.5850,5.7940,70.60,2.8927,6,391.0,19.20,396.90,14.10} | 2 | {18.30}
{1,0.23912,0.00,9.690,0,0.5850,6.0190,65.30,2.4091,6,391.0,19.20,396.90,12.92} | 2 | {21.20}
{1,0.17783,0.00,9.690,0,0.5850,5.5690,73.50,2.3999,6,391.0,19.20,395.77,15.10} | 2 | {17.50}
{1,0.22438,0.00,9.690,0,0.5850,6.0270,79.70,2.4982,6,391.0,19.20,396.90,14.33} | 2 | {16.80}
{1,0.06263,0.00,11.930,0,0.5730,6.5930,69.10,2.4786,1,273.0,21.00,391.99,9.67} | 2 | {22.40}
{1,0.04527,0.00,11.930,0,0.5730,6.1200,76.70,2.2875,1,273.0,21.00,396.90,9.08} | 2 | {20.60}
{1,0.06076,0.00,11.930,0,0.5730,6.9760,91.00,2.1675,1,273.0,21.00,396.90,5.64} | 2 | {23.90}
{1,0.10959,0.00,11.930,0,0.5730,6.7940,89.30,2.3889,1,273.0,21.00,393.45,6.48} | 2 | {22.00}
{1,0.04741,0.00,11.930,0,0.5730,6.0300,80.80,2.5050,1,273.0,21.00,396.90,7.88} | 2 | {11.90}
\.
-- NOTE that the batch specific tables were created using:
-- madlib.minibatch_preprocessor(), with the regular source tables used in this file.
-- Create preprocessed data that can be used with minibatch MLP:
DROP TABLE IF EXISTS lin_housing_wi_batch, lin_housing_wi_batch_summary, lin_housing_wi_batch_standardization;
CREATE TABLE lin_housing_wi_batch(
__id__ integer,
dependent_varname double precision[],
independent_varname double precision[]
);
COPY lin_housing_wi_batch (__id__, dependent_varname, independent_varname) FROM STDIN NULL '?' DELIMITER '|';
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8 | {{15.4},{13.8},{9.5},{10.9},{18.8},{23.3},{20.4},{21.8},{12.3},{22.5}} | {{0,0.593702903366415,-0.384945784500547,0.827294500187726,-0.194870940739048,1.43405415480209,0.531308284694902,1.02768826260825,-0.764662799396233,1.4404472143815,1.31654026747368,0.670201693636395,0.373109646066125,0.726039750181548},{0,-0.215829225317888,-0.384945784500547,1.04213296667209,-0.194870940739048,2.55473009343937,-0.0480047735883218,1.02768826260825,-1.03981067795046,-0.604583549242574,-0.193779824463889,-2.16420344350645,-1.76849304818519,1.97481069657669},{0,0.526734477521742,-0.384945784500547,0.827294500187726,-0.194870940739048,0.912212687192357,0.359962168864652,0.86361611618313,-0.769680693837952,1.4404472143815,1.31654026747368,0.670201693636395,-2.89217842031153,1.45576847081245},{0,1.22405434813772,-0.384945784500547,0.827294500187726,-0.194870940739048,0.843774461932065,0.629220350883616,0.994128050839476,-0.990566439360657,1.4404472143815,1.31654026747368,0.670201693636395,0.474971489675403,1.03718603068},{0,-0.45918927325515,-0.384945784500547,1.92326100150999,-0.194870940739048,0.0738444277537763,-0.457603583811109,0.871073941020636,-0.750937382246827,-0.927483143499006,-1.42845214296799,0.103320666207828,0.29949267944585,0.548839850525477},{0,-0.453218504338926,0.895084804673483,-1.05544530407052,-0.194870940739048,-1.0211671764109,-0.0235267570411439,-1.61238172986868,2.10655672213766,-0.281683954986141,-0.87715659610104,0.412528499350682,0.412672505678382,-0.946894735547813},{0,-0.45570241249421,-0.384945784500547,-0.557542641880405,-0.194870940739048,-0.447997039855952,-0.0529003768977577,0.475809224632848,-0.693821936689619,-0.604583549242574,-0.302890401447972,1.03094416563639,0.382724923338977,0.0618889451713929},{0,-0.451206197302472,-0.384945784500547,-0.393510569497073,-0.194870940739048,0.108063540383922,-0.738284840218754,-0.687611450018002,-0.566259188774168,-0.496950351157096,-0.262691767822257,0.154855305064969,0.474971489675403,-0.22832821332044},{0,0.383076454353533,-0.384945784500547,0.827294500187726,-0.194870940739048,1.09186302850062,-1.04344411317358,1.02768826260825,-0.983728328307727,1.4404472143815,1.31654026747368,0.670201693636395,0.474971489675403,1.52274166123365},{0,-0.464162424420003,-0.384945784500547,-1.14834842471241,-0.194870940739048,-1.05538628904105,-0.23566956711669,-1.01948465528699,0.440025426964572,-0.819849945413529,-1.08963508812268,-0.205887166935029,0.465857008093845,-0.109729855282912}}
\.
-- Create the corresponding summary table for preprocessed data
CREATE TABLE lin_housing_wi_batch_summary(
source_table text,
output_table text,
dependent_varname text,
independent_varname text,
dependent_vartype text,
buffer_size integer,
class_values text[],
num_rows_processed integer,
num_rows_skipped integer,
grouping_cols text
);
INSERT INTO lin_housing_wi_batch_summary VALUES
('lin_housing_wi_does_not_exist','lin_housing_wi_batch','y','x','float8',50,NULL,410,0,'');
-- Create the corresponding standardization table for preprocessed data
CREATE TABLE lin_housing_wi_batch_standardization(
mean double precision[],
std double precision[]
);
INSERT INTO lin_housing_wi_batch_standardization VALUES
-- TODO get real numbers by running preprocessor
(ARRAY[1,4.40216212195,7.51829268293,12.4008536585,0.0365853658537,0.57236804878,6.125941707317,72.44,3.5327492689,10.6170731707,436.743902439,18.8995121951,349.478317073,13.6464390244], ARRAY[1,9.37232721366,19.5307832574,6.88889668698,0.187741516077,0.116893738398,0.612794748753,26.8174708253,2.03272510382,9.29081378039,174.135271989,1.94044243285,99.8411145886,7.16704694791]);
-- Create preprocessed data that can be used with minibatch MLP:
DROP TABLE IF EXISTS lin_housing_wi_batch_grp, lin_housing_wi_batch_grp_summary, lin_housing_wi_batch_grp_standardization;
CREATE TABLE lin_housing_wi_batch_grp(
grp int,
__id__ integer,
dependent_varname double precision[],
independent_varname double precision[]
);
COPY lin_housing_wi_batch_grp (grp, __id__, dependent_varname, independent_varname) FROM STDIN NULL '?' DELIMITER '|';
1 | 0 | {{15},{15.7},{19.6},{10.9},{21.4},{50},{30.8},{18},{23},{20.5},{15.6},{9.6},{20.6},{29.8},{22.7},{19.3},{16.2},{7.5},{13.6},{20.4},{26.6},{24.7},{27.1},{17.9},{6.3},{28.7},{18.5},{27.9},{20},{17.5},{37.2},{18.3},{10.2},{32.5},{17.1},{23.1},{21.9},{18.7},{19.4},{20.3},{18.3},{19.3},{21.2},{50},{17.3},{21.7},{19.8},{15.2},{27.5},{14.6}} | 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2 | 8 | {{15.4},{13.8},{9.5},{10.9},{18.8},{23.3},{20.4},{21.8},{12.3},{22.5}} | {{0,0.593702903366415,-0.384945784500547,0.827294500187726,-0.194870940739048,1.43405415480209,0.531308284694902,1.02768826260825,-0.764662799396233,1.4404472143815,1.31654026747368,0.670201693636395,0.373109646066125,0.726039750181548},{0,-0.215829225317888,-0.384945784500547,1.04213296667209,-0.194870940739048,2.55473009343937,-0.0480047735883218,1.02768826260825,-1.03981067795046,-0.604583549242574,-0.193779824463889,-2.16420344350645,-1.76849304818519,1.97481069657669},{0,0.526734477521742,-0.384945784500547,0.827294500187726,-0.194870940739048,0.912212687192357,0.359962168864652,0.86361611618313,-0.769680693837952,1.4404472143815,1.31654026747368,0.670201693636395,-2.89217842031153,1.45576847081245},{0,1.22405434813772,-0.384945784500547,0.827294500187726,-0.194870940739048,0.843774461932065,0.629220350883616,0.994128050839476,-0.990566439360657,1.4404472143815,1.31654026747368,0.670201693636395,0.474971489675403,1.03718603068},{0,-0.45918927325515,-0.384945784500547,1.92326100150999,-0.194870940739048,0.0738444277537763,-0.457603583811109,0.871073941020636,-0.750937382246827,-0.927483143499006,-1.42845214296799,0.103320666207828,0.29949267944585,0.548839850525477},{0,-0.453218504338926,0.895084804673483,-1.05544530407052,-0.194870940739048,-1.0211671764109,-0.0235267570411439,-1.61238172986868,2.10655672213766,-0.281683954986141,-0.87715659610104,0.412528499350682,0.412672505678382,-0.946894735547813},{0,-0.45570241249421,-0.384945784500547,-0.557542641880405,-0.194870940739048,-0.447997039855952,-0.0529003768977577,0.475809224632848,-0.693821936689619,-0.604583549242574,-0.302890401447972,1.03094416563639,0.382724923338977,0.0618889451713929},{0,-0.451206197302472,-0.384945784500547,-0.393510569497073,-0.194870940739048,0.108063540383922,-0.738284840218754,-0.687611450018002,-0.566259188774168,-0.496950351157096,-0.262691767822257,0.154855305064969,0.474971489675403,-0.22832821332044},{0,0.383076454353533,-0.384945784500547,0.827294500187726,-0.194870940739048,1.09186302850062,-1.04344411317358,1.02768826260825,-0.983728328307727,1.4404472143815,1.31654026747368,0.670201693636395,0.474971489675403,1.52274166123365},{0,-0.464162424420003,-0.384945784500547,-1.14834842471241,-0.194870940739048,-1.05538628904105,-0.23566956711669,-1.01948465528699,0.440025426964572,-0.819849945413529,-1.08963508812268,-0.205887166935029,0.465857008093845,-0.109729855282912}}
\.
-- Create the corresponding summary table for preprocessed data
CREATE TABLE lin_housing_wi_batch_grp_summary(
source_table text,
output_table text,
dependent_varname text,
independent_varname text,
dependent_vartype text,
buffer_size integer,
class_values text[],
num_rows_processed integer,
num_rows_skipped integer,
grouping_cols text
);
INSERT INTO lin_housing_wi_batch_grp_summary VALUES
('lin_housing_wi_does_not_exist','lin_housing_wi_batch_grp','y','x','float8',50,NULL,410,0,'grp');
-- Create the corresponding standardization table for preprocessed data
CREATE TABLE lin_housing_wi_batch_grp_standardization(
grp int,
mean double precision[],
std double precision[]
);
INSERT INTO lin_housing_wi_batch_grp_standardization VALUES
-- TODO get real numbers by running preprocessor
(1,ARRAY[1,4.40216212195,7.51829268293,12.4008536585,0.0365853658537,0.57236804878,6.125941707317,72.44,3.5327492689,10.6170731707,436.743902439,18.8995121951,349.478317073,13.6464390244], ARRAY[1,9.37232721366,19.5307832574,6.88889668698,0.187741516077,0.116893738398,0.612794748753,26.8174708253,2.03272510382,9.29081378039,174.135271989,1.94044243285,99.8411145886,7.16704694791]),
(2,ARRAY[1,4.40216212195,7.51829268293,12.4008536585,0.0365853658537,0.57236804878,6.15941707317,72.44,3.53274926829,10.6170731707,436.743902439,18.8995121951,349.478317073,13.6464390244], ARRAY[1,9.37232721366,19.5307832574,6.88889668698,0.187741516077,0.116893738398,0.612794748753,26.8174708253,2.03272510382,9.29081378039,174.135271989,1.94044243285,99.8411145886,7.16704694791]);
SELECT setseed(0);
-- without minibatch with grouping without warm start
DROP TABLE IF EXISTS mlp_regress, mlp_regress_summary, mlp_regress_standardization;
SELECT mlp_regression(
'lin_housing_wi', -- Source table
'mlp_regress', -- Destination table
'x', -- Input features
'y', -- Dependent variable
ARRAY[40], -- Number of units per layer
'learning_rate_init=0.015,
learning_rate_policy=inv,
n_iterations=5, n_tries=3,
tolerance=0',
'sigmoid',
'',
False,
False,
'grp');
DROP TABLE IF EXISTS mlp_prediction_regress;
SELECT mlp_predict(
'mlp_regress',
'lin_housing_wi',
'id',
'mlp_prediction_regress',
'output');
SELECT assert(
__to_char(pg_typeof(estimated_y)) = 'double precision[]',
'Estimated y should be an array. Actual ' || __to_char(pg_typeof(estimated_y))
)
FROM mlp_prediction_regress LIMIT 1;
-- with weights with grouping without minibatch without warm start
CREATE TABLE lin_housing_wi_with_row_weight AS SELECT *, (id%3) +1 AS row_weight FROM lin_housing_wi;
DROP TABLE IF EXISTS mlp_regress, mlp_regress_summary, mlp_regress_standardization;
SELECT mlp_regression(
'lin_housing_wi_with_row_weight', -- Source table
'mlp_regress', -- Destination table
'x', -- Input features
'y', -- Dependent variable
ARRAY[40], -- Number of units per layer
'learning_rate_init=0.015,
learning_rate_policy=inv,
n_iterations=5, n_tries=3,
tolerance=0',
'sigmoid',
'row_weight',
False,
False,
'grp');
DROP TABLE IF EXISTS mlp_prediction_regress;
SELECT mlp_predict(
'mlp_regress',
'lin_housing_wi',
'id',
'mlp_prediction_regress',
'output');
SELECT assert(
__to_char(pg_typeof(estimated_y)) = 'double precision[]',
'Estimated y should be an array. Actual ' || __to_char(pg_typeof(estimated_y))
)
FROM mlp_prediction_regress LIMIT 1;
-- with weights without grouping without minibatch without warm start
DROP TABLE IF EXISTS mlp_regress, mlp_regress_summary, mlp_regress_standardization;
SELECT mlp_regression(
'lin_housing_wi_with_row_weight', -- Source table
'mlp_regress', -- Destination table
'x', -- Input features
'y', -- Dependent variable
ARRAY[40], -- Number of units per layer
'learning_rate_init=0.015,
learning_rate_policy=inv,
n_iterations=5, n_tries=3,
tolerance=0',
'sigmoid',
'row_weight',
False,
False);
DROP TABLE IF EXISTS mlp_prediction_regress;
SELECT mlp_predict(
'mlp_regress',
'lin_housing_wi',
'id',
'mlp_prediction_regress',
'output');
SELECT assert(
__to_char(pg_typeof(estimated_y)) = 'double precision[]',
'Estimated y should be an array. Actual ' || __to_char(pg_typeof(estimated_y))
)
FROM mlp_prediction_regress LIMIT 1;
-- minibatch without grouping and without warm start
DROP TABLE IF EXISTS mlp_regress_batch, mlp_regress_batch_summary, mlp_regress_batch_standardization;
SELECT mlp_regression(
'lin_housing_wi_batch', -- Source table
'mlp_regress_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Dependent variable
ARRAY[10], -- Number of units per layer
'learning_rate_init=0.025,
learning_rate_policy=step,
lambda=0.001,
n_iterations=5,
tolerance=0,
batch_size=25, n_epochs=10',
'sigmoid',
'',
False,
TRUE);
DROP TABLE IF EXISTS mlp_prediction_regress_batch;
SELECT mlp_predict(
'mlp_regress_batch',
'lin_housing_wi',
'id',
'mlp_prediction_regress_batch',
'output');
SELECT assert(
__to_char(pg_typeof(estimated_y)) = 'double precision',
'Estimated y should be a scalar. Actual ' || __to_char(pg_typeof(estimated_y))
)
FROM mlp_prediction_regress_batch LIMIT 1;
-- minibatch with grouping and without warm start
DROP TABLE IF EXISTS mlp_regress_batch, mlp_regress_batch_summary, mlp_regress_batch_standardization;
SELECT mlp_regression(
'lin_housing_wi_batch_grp', -- Source table
'mlp_regress_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Dependent variable
ARRAY[10], -- Number of units per layer
'learning_rate_init=0.025,
learning_rate_policy=step,
lambda=0.001,
n_iterations=5,
tolerance=0,
batch_size=25, n_epochs=10',
'sigmoid',
'',
False,
TRUE,
'grp');
DROP TABLE IF EXISTS mlp_prediction_regress_batch;
SELECT mlp_predict(
'mlp_regress_batch',
'lin_housing_wi',
'id',
'mlp_prediction_regress_batch',
'output');
------------------------------------------------ Momentum ------------------------------------------------------------
-- regression momentum without nesterov momentum
DROP TABLE IF EXISTS mlp_regress, mlp_regress_summary, mlp_regress_standardization;
SELECT mlp_regression(
'lin_housing_wi', -- Source table
'mlp_regress', -- Destination table
'x', -- Input features
'y', -- Dependent variable
ARRAY[40], -- Number of units per layer
'learning_rate_init=0.015,
momentum = 0.5, nesterov = False,
learning_rate_policy=inv,
n_iterations=5, tolerance=0', 'sigmoid',
'',
False,
False,
'grp');
DROP TABLE IF EXISTS mlp_prediction_regress;
SELECT mlp_predict(
'mlp_regress',
'lin_housing_wi',
'id',
'mlp_prediction_regress',
'output');
-- assert that the summary table has momentum and nesterov
SELECT assert
(
source_table = 'lin_housing_wi' AND
independent_varname = 'x' AND
dependent_varname = 'y' AND
dependent_vartype = 'double precision[]' AND
tolerance = 0 AND
learning_rate_init = 0.015 AND
learning_rate_policy = 'inv' AND
momentum = 0.5 AND
nesterov = False AND
n_iterations = 5 AND
n_tries = 1 AND
layer_sizes = '{14,40,1}' AND
activation = 'sigmoid' AND
is_classification = False AND
classes = '{}' AND
weights = '1' AND
grouping_col = 'grp',
'Summary Validation failed. Actual:' || __to_char(summary)
) from (select * from mlp_regress_summary) summary;
-- regression momentum with nesterov momentum
DROP TABLE IF EXISTS mlp_regress_batch, mlp_regress_batch_summary, mlp_regress_batch_standardization;
SELECT mlp_regression(
'lin_housing_wi_batch', -- Source table
'mlp_regress_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Dependent variable
ARRAY[10], -- Number of units per layer
'learning_rate_init=0.025,
momentum = 0.5, nesterov = True,
learning_rate_policy=step,
lambda=0.001,
n_iterations=5,
tolerance=0,
batch_size=25, n_epochs=10',
'sigmoid',
'',
False,
TRUE);
DROP TABLE IF EXISTS mlp_prediction_regress_batch;
SELECT mlp_predict(
'mlp_regress_batch',
'lin_housing_wi',
'id',
'mlp_prediction_regress_batch',
'output');
-- classification momentum without nesterov momentum
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
SELECT mlp_classification(
'iris_data_batch', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
momentum = 0.1, nesterov = False,
learning_rate_policy=constant,
n_iterations=5,
n_tries=3,
tolerance=0,
n_epochs=20',
'sigmoid',
'',
False,
False
);
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_output;
-- See prediction accuracy for training data
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'output');
-- classification momentum with nesterov momentum
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
SELECT mlp_classification(
'iris_data_batch_grp', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
momentum = 0.1, nesterov = True,
learning_rate_policy=constant,
n_iterations=5,
n_tries=3,
tolerance=0,
n_epochs=20',
'sigmoid',
'',
False,
False,
'grp'
);
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_output;
-- See prediction accuracy for training data
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'output');
-- Assert of all input tables still exist, to make sure we have not dropped
-- anything in the code.
-- Classification minibatch tables
SELECT assert(
count(*)=1,
'Input table iris_data_batch is dropped.'
)
FROM
pg_catalog.pg_class c
JOIN pg_catalog.pg_namespace n
ON n.oid=c.relnamespace
WHERE c.relname = 'iris_data_batch' AND c.relkind='r' AND nspname=current_schema();
SELECT assert(
count(*)=1,
'Input table iris_data_batch_summary is dropped.'
)
FROM
pg_catalog.pg_class c
JOIN pg_catalog.pg_namespace n
ON n.oid=c.relnamespace
WHERE c.relname = 'iris_data_batch_summary' AND c.relkind='r' AND nspname=current_schema();
SELECT assert(
count(*)=1,
'Input table iris_data_batch_standardization is dropped.'
)
FROM
pg_catalog.pg_class c
JOIN pg_catalog.pg_namespace n
ON n.oid=c.relnamespace
WHERE c.relname = 'iris_data_batch_standardization' AND c.relkind='r' AND nspname=current_schema();
-- Regression minibatch tables
SELECT assert(
count(*)=1,
'Input table lin_housing_wi_batch is dropped.'
)
FROM
pg_catalog.pg_class c
JOIN pg_catalog.pg_namespace n
ON n.oid=c.relnamespace
WHERE c.relname = 'lin_housing_wi_batch' AND c.relkind='r' AND nspname=current_schema();
SELECT assert(
count(*)=1,
'Input table lin_housing_wi_batch_summary is dropped.'
)
FROM
pg_catalog.pg_class c
JOIN pg_catalog.pg_namespace n
ON n.oid=c.relnamespace
WHERE c.relname = 'lin_housing_wi_batch_summary' AND c.relkind='r' AND nspname=current_schema();
SELECT assert(
count(*)=1,
'Input table lin_housing_wi_batch_standardization is dropped.'
)
FROM
pg_catalog.pg_class c
JOIN pg_catalog.pg_namespace n
ON n.oid=c.relnamespace
WHERE c.relname = 'lin_housing_wi_batch_standardization' AND c.relkind='r' AND nspname=current_schema();
------------------------ Test special characters --------------------------------
DROP TABLE IF EXISTS iris_data_special_char;
CREATE TABLE iris_data_special_char(
id INTEGER,
"se$$''x" TEXT,
"len$$'%*Ж!#'()gth" DOUBLE PRECISION[],
"rinЖ!#'gs" INTEGER);
INSERT INTO iris_data_special_char VALUES
(1, 'M''M',ARRAY[0.66, 0.5],6),
(2, '''M''M''',ARRAY[0.66, 0.5],6),
(3, 'M|$$M',ARRAY[0.66, 0.5],6),
(4, 'M,M',ARRAY[0.66, 0.5],6),
(5, 'M@[}(:*;M',ARRAY[0.66, 0.5],6),
(6, 'M"M',ARRAY[0.66, 0.5],6),
(7, 'MЖM',ARRAY[0.66, 0.5],6);
DROP TABLE IF EXISTS mlp_model, mlp_model_summary, mlp_model_standardization;
-- Set seed so results are reproducible
SELECT setseed(0);
SELECT mlp_classification(
'iris_data_special_char', -- Source table
'mlp_model_special_char', -- Destination table
'"len$$''%*Ж!#''()gth"', -- Input features
'"se$$''''x"', -- Dependent variable
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.003,
n_iterations=10,
tolerance=0', -- Optimizer params
'tanh', -- Activation function
NULL, -- Default weight (1)
FALSE, -- No warm start
FALSE -- Not verbose
);
SELECT assert
(
source_table = 'iris_data_special_char' AND
independent_varname = '"len$$''%*Ж!#''()gth"' AND
dependent_varname = '"se$$''''x"' AND
tolerance = 0 AND
learning_rate_init = 0.003 AND
learning_rate_policy = 'constant' AND
n_iterations = 10 AND
n_tries = 1 AND
layer_sizes = ARRAY[2,5,7] AND
activation = 'tanh' AND
is_classification = 't' AND
classes = $__madlib__${ 'M'M', M\"M, M'M, "M,M", "M@[}(:*;M", "M|$$M", MЖM }$__madlib__$ AND
weights = '1' AND
grouping_col = NULL,
'Summary Validation failed for special chars. Actual:' || __to_char(summary)
) from (select * from mlp_model_special_char_summary order by classes) summary;
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_batch_output_summary, mlp_prediction_batch_output_standardization;
SELECT mlp_predict(
'mlp_model_special_char',
'iris_data_special_char',
'id',
'mlp_prediction_batch_output',
'output');
-- Test special chars with minibatch enabled
DROP TABLE IF EXISTS iris_data_special_char_batch, iris_data_special_char_batch_summary, iris_data_special_char_batch_standardization;
CREATE TABLE iris_data_special_char_batch(
__id__ integer,
dependent_varname double precision[],
independent_varname double precision[]
);
COPY iris_data_special_char_batch (__id__, dependent_varname, independent_varname) FROM STDIN NULL '?' DELIMITER '|';
0 | {{0,0,0,1,0,0,0},{0,1,0,0,0,0,0},{0,0,1,0,0,0,0},{0,0,0,0,1,0,0}} | {{0,0},{0,0},{0,0},{0,0}}
1 | {{0,0,0,0,0,0,1},{1,0,0,0,0,0,0},{0,0,0,0,0,1,0}} | {{0,0},{0,0},{0,0}}
\.
-- Create the corresponding summary table for preprocessed data
CREATE TABLE iris_data_special_char_batch_summary(
source_table text,
output_table text,
dependent_varname text,
independent_varname text,
dependent_vartype text,
buffer_size integer,
class_values text[],
num_rows_processed integer,
num_rows_skipped integer,
grouping_cols text
);
-- The availability of the original source table should not be a condition for
-- MLP to work correctly. It should work fine even the original source table is
-- deleted (this basically ensures that all the necessary info is captured in
-- the summary table). So name the original source table as
-- 'iris_data_does_not_exist' instead of the original 'iris_data', to mimic the
-- scenario where the original source table is deleted and MLP is trained with
-- the preprocessed table.
INSERT INTO iris_data_special_char_batch_summary VALUES
('minibatch_preprocessing_input', 'minibatch_preprocessing_out', '"se$$''x"', '"len$$''%*Ж!#''()gth"',
'text', 4, $__madlib__${'M'M',"M\"M",M'M,"M,M","M@[}(:*;M",M|$$M,MЖM}$__madlib__$, 7, 0, '');
-- Create the corresponding standardization table for preprocessed data
CREATE TABLE iris_data_special_char_batch_standardization(
mean double precision[],
std double precision[]
);
INSERT INTO iris_data_special_char_batch_standardization VALUES
(ARRAY[0.66,0.5], ARRAY[1,1]);
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
SELECT mlp_classification(
'iris_data_special_char_batch', -- Source table
'mlp_class_special_char_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=1,
n_tries=1,
tolerance=0,
n_epochs=20,
solver=sgd',
'sigmoid',
'',
False,
False
);
SELECT assert
(
source_table = 'iris_data_special_char_batch' AND
independent_varname = 'independent_varname' AND
dependent_varname = 'dependent_varname' AND
original_source_table = 'minibatch_preprocessing_input' AND
original_independent_varname = '"len$$''%*Ж!#''()gth"' AND
original_dependent_varname = '"se$$''x"' AND
original_dependent_vartype = 'text' AND
tolerance = 0 AND
learning_rate_init = 0.1 AND
learning_rate_policy = 'constant' AND
n_iterations = 1 AND
n_tries = 1 AND
layer_sizes = ARRAY[2,5,7] AND
activation = 'sigmoid' AND
is_classification = 't' AND
classes = $__madlib__${ 'M'M', M\"M, M'M, "M,M", "M@[}(:*;M", "M|$$M", MЖM }$__madlib__$ AND
weights = '1' AND
grouping_col = NULL,
'Summary Validation failed for special chars. Actual:' || __to_char(summary)
) from (select * from mlp_class_special_char_batch_summary order by classes) summary;
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_batch_output_summary, mlp_prediction_batch_output_standardization;
SELECT mlp_predict(
'mlp_class_special_char_batch',
'iris_data_special_char',
'id',
'mlp_prediction_batch_output',
'output');
-- Test rmsprop
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
SELECT mlp_classification(
'iris_data_batch', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
tolerance=0,
batch_size=5,
n_epochs=15,
rho=0.9,
solver=rmsprop,
eps=0.00000001',
'sigmoid',
'',
FALSE, -- Warm start
FALSE
);
SELECT assert
(
source_table = 'iris_data_batch' AND
independent_varname = 'independent_varname' AND
dependent_varname = 'dependent_varname' AND
original_source_table = 'iris_data_does_not_exist' AND
original_independent_varname = 'attributes' AND
original_dependent_varname = 'class::TEXT' AND
original_dependent_vartype = 'text' AND
tolerance = 0 AND
learning_rate_init = 0.1 AND
learning_rate_policy = 'constant' AND
layer_sizes = ARRAY[4,5,3] AND
activation = 'sigmoid' AND
is_classification = 't' AND
classes = '{1,2,3}' AND
weights = '1' AND
grouping_col = NULL AND
solver = 'rmsprop' AND
rho = 0.9 AND
eps = 0.00000001,
'Summary Validation failed for special chars. Actual:' || __to_char(summary)
) from (select * from mlp_class_batch_summary order by classes) summary;
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_batch_output_summary, mlp_prediction_batch_output_standardization;
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'output');
DROP TABLE IF EXISTS mlp_regress_batch, mlp_regress_batch_summary, mlp_regress_batch_standardization;
SELECT mlp_regression(
'lin_housing_wi_batch', -- Source table
'mlp_regress_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Dependent variable
ARRAY[10], -- Number of units per layer
'learning_rate_init=0.025,
learning_rate_policy=step,
lambda=0.001,
n_iterations=5,
tolerance=0,
batch_size=25,
n_epochs=10,
rho=0.9,
solver=rmsprop,
eps=0.00000001',
'sigmoid',
'',
False,
TRUE);
DROP TABLE IF EXISTS mlp_prediction_regress_batch;
SELECT mlp_predict(
'mlp_regress_batch',
'lin_housing_wi',
'id',
'mlp_prediction_regress_batch',
'output');
-- Test Adam
DROP TABLE IF EXISTS mlp_class_batch, mlp_class_batch_summary, mlp_class_batch_standardization;
SELECT mlp_classification(
'iris_data_batch', -- Source table
'mlp_class_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Label
ARRAY[5], -- Number of units per layer
'learning_rate_init=0.1,
learning_rate_policy=constant,
n_iterations=5,
tolerance=0,
batch_size=5,
n_epochs=15,
solver=adam,
beta1=0.9,
beta2=0.999',
'sigmoid',
'',
FALSE, -- Warm start
FALSE
);
SELECT assert
(
source_table = 'iris_data_batch' AND
independent_varname = 'independent_varname' AND
dependent_varname = 'dependent_varname' AND
original_source_table = 'iris_data_does_not_exist' AND
original_independent_varname = 'attributes' AND
original_dependent_varname = 'class::TEXT' AND
original_dependent_vartype = 'text' AND
tolerance = 0 AND
learning_rate_init = 0.1 AND
learning_rate_policy = 'constant' AND
layer_sizes = ARRAY[4,5,3] AND
activation = 'sigmoid' AND
is_classification = 't' AND
classes = '{1,2,3}' AND
weights = '1' AND
grouping_col = NULL AND
solver = 'adam' AND
beta1 = 0.9 AND
beta2 = 0.999,
'Summary Validation failed for special chars. Actual:' || __to_char(summary)
) from (select * from mlp_class_batch_summary order by classes) summary;
DROP TABLE IF EXISTS mlp_prediction_batch_output, mlp_prediction_batch_output_summary, mlp_prediction_batch_output_standardization;
SELECT mlp_predict(
'mlp_class_batch',
'iris_data',
'id',
'mlp_prediction_batch_output',
'output');
DROP TABLE IF EXISTS mlp_regress_batch, mlp_regress_batch_summary, mlp_regress_batch_standardization;
SELECT mlp_regression(
'lin_housing_wi_batch', -- Source table
'mlp_regress_batch', -- Destination table
'independent_varname', -- Input features
'dependent_varname', -- Dependent variable
ARRAY[10], -- Number of units per layer
'learning_rate_init=0.025,
learning_rate_policy=step,
lambda=0.001,
n_iterations=5,
tolerance=0,
batch_size=25,
n_epochs=10,
solver=adam,
beta1=0.9,
beta2=0.999',
'sigmoid',
'',
False,
TRUE);
DROP TABLE IF EXISTS mlp_prediction_regress_batch;
SELECT mlp_predict(
'mlp_regress_batch',
'lin_housing_wi',
'id',
'mlp_prediction_regress_batch',
'output');