mirror of https://gitee.com/bigwinds/arangodb
359 lines
10 KiB
JavaScript
359 lines
10 KiB
JavaScript
/*jshint globalstrict:false, strict:false */
|
|
/*global assertTrue, assertEqual */
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief test the random document selector
|
|
///
|
|
/// @file
|
|
///
|
|
/// DISCLAIMER
|
|
///
|
|
/// Copyright 2010-2012 triagens GmbH, Cologne, Germany
|
|
///
|
|
/// Licensed under the Apache License, Version 2.0 (the "License");
|
|
/// you may not use this file except in compliance with the License.
|
|
/// You may obtain a copy of the License at
|
|
///
|
|
/// http://www.apache.org/licenses/LICENSE-2.0
|
|
///
|
|
/// Unless required by applicable law or agreed to in writing, software
|
|
/// 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.
|
|
///
|
|
/// Copyright holder is triAGENS GmbH, Cologne, Germany
|
|
///
|
|
/// @author Jan Steemann
|
|
/// @author Copyright 2012, triAGENS GmbH, Cologne, Germany
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
var jsunity = require("jsunity");
|
|
|
|
var arangodb = require("@arangodb");
|
|
var db = arangodb.db;
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief test suite
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
function AnySuite () {
|
|
'use strict';
|
|
var cn = "example";
|
|
var c;
|
|
|
|
var statsExpected = function (list, N) {
|
|
// list is a list of numbers, we assume that we draw N times one number
|
|
// from list with a uniform distribution, we compute the expected value
|
|
// of the average and the variance, as well as the variance
|
|
// of these two:
|
|
|
|
// First the expected value and the standard deviation of the uniform
|
|
// distribution:
|
|
var E = 0;
|
|
var n = list.length;
|
|
var i;
|
|
for (i = 0; i < n; i++) {
|
|
E += list[i];
|
|
}
|
|
E /= n;
|
|
var V = 0;
|
|
for (i = 0; i < n; i++) {
|
|
V += Math.pow(list[i] - E, 2);
|
|
}
|
|
V = V / n;
|
|
|
|
// Now we apply the central limit theorem to the random variable
|
|
// Y = (X - E)^2, first compute its expected value and standard
|
|
// deviation:
|
|
var EY = V;
|
|
var VY = 0;
|
|
for (i = 0; i < n; i++) {
|
|
VY += Math.pow(Math.pow(list[i] - E, 2) - EY, 2);
|
|
}
|
|
VY = VY / n;
|
|
// Now EY is V and sY is its variance, by the central limit theorem
|
|
// taking the average of a sample of size N of Y values will be close
|
|
// to the normal distribution with expected value EY and variance
|
|
// VY / N
|
|
|
|
return { average: E, variance: V,
|
|
averageStddev: Math.sqrt(V) / Math.sqrt(N),
|
|
varianceStddev: Math.sqrt(VY) / Math.sqrt(N) };
|
|
};
|
|
|
|
var statsFound = function (dist) {
|
|
var v;
|
|
var sum = 0;
|
|
var count = 0;
|
|
|
|
for (v in dist) {
|
|
if (dist.hasOwnProperty(v)) {
|
|
sum += dist[v] * Number(v);
|
|
count += dist[v];
|
|
}
|
|
}
|
|
|
|
var avg = sum / count;
|
|
var sum2 = 0;
|
|
|
|
for (v in dist) {
|
|
if (dist.hasOwnProperty(v)) {
|
|
var d = Number(v) - avg;
|
|
sum2 += d * d * dist[v];
|
|
}
|
|
}
|
|
|
|
return { average: avg, count: count, variance : sum2 / (count-1) };
|
|
};
|
|
|
|
var getDistribution = function (n, rng) {
|
|
var dist = { };
|
|
var i;
|
|
|
|
for (i = 0; i < n; ++i) {
|
|
var pick = rng();
|
|
|
|
if (dist.hasOwnProperty(pick)) {
|
|
dist[pick]++;
|
|
}
|
|
else {
|
|
dist[pick] = 1;
|
|
}
|
|
}
|
|
|
|
return dist;
|
|
};
|
|
|
|
return {
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief set up
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
setUp : function () {
|
|
db._drop(cn);
|
|
c = db._create(cn);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief tear down
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
tearDown : function () {
|
|
db._drop(cn);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief check entropy of Math.random()
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
testCheckEntropyNative : function () {
|
|
var i, n, l;
|
|
|
|
n = 100;
|
|
|
|
l = [];
|
|
for (i = 0; i < n; ++i) {
|
|
c.save({ value: i });
|
|
l.push(i);
|
|
}
|
|
|
|
var dist = getDistribution(n * 100, function () {
|
|
return parseInt(Math.random() * 100, 10);
|
|
});
|
|
|
|
var statsExp = statsExpected(l, n * 100);
|
|
var stats = statsFound(dist);
|
|
assertEqual(stats.count, n * 100);
|
|
assertTrue(Math.abs(stats.average - statsExp.average)
|
|
< statsExp.averageStddev * 3);
|
|
assertTrue(Math.abs(stats.variance - statsExp.variance)
|
|
< statsExp.varianceStddev * 3);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief check entropy of any(), just one document
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
testCheckEntropyCollectionOne : function () {
|
|
c.save({ value: 1 });
|
|
|
|
var dist = getDistribution(100, function () {
|
|
return c.any().value;
|
|
});
|
|
|
|
var statsExp = statsExpected([1], 100);
|
|
var stats = statsFound(dist);
|
|
assertEqual(stats.count, 100);
|
|
assertTrue(Math.abs(stats.average - statsExp.average) < 0.000001);
|
|
assertTrue(Math.abs(stats.variance - statsExp.variance) < 0.000001);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief check entropy of any(), few picks
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
testCheckEntropyCollectionFew1 : function () {
|
|
var i, n, l;
|
|
|
|
n = 3;
|
|
|
|
l = [];
|
|
for (i = 0; i < n; ++i) {
|
|
c.save({ value: i });
|
|
l.push(i);
|
|
}
|
|
|
|
var dist = getDistribution(n * 200, function () {
|
|
return c.any().value;
|
|
});
|
|
|
|
var statsExp = statsExpected(l, n * 200);
|
|
var stats = statsFound(dist);
|
|
assertEqual(stats.count, n * 200);
|
|
assertTrue(Math.abs(stats.average - statsExp.average)
|
|
< statsExp.averageStddev * 4);
|
|
assertTrue(Math.abs(stats.variance - statsExp.variance)
|
|
< statsExp.varianceStddev * 4);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief check entropy of any(), few picks
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
testCheckEntropyCollectionFew2 : function () {
|
|
var i, n, l;
|
|
|
|
n = 10;
|
|
|
|
l = [];
|
|
for (i = 0; i < n; ++i) {
|
|
c.save({ value: i });
|
|
l.push(i);
|
|
}
|
|
|
|
var dist = getDistribution(n * 100, function () {
|
|
return c.any().value;
|
|
});
|
|
|
|
var statsExp = statsExpected(l, n * 100);
|
|
var stats = statsFound(dist);
|
|
assertEqual(stats.count, n * 100);
|
|
assertTrue(Math.abs(stats.average - statsExp.average)
|
|
< statsExp.averageStddev * 4);
|
|
assertTrue(Math.abs(stats.variance - statsExp.variance)
|
|
< statsExp.varianceStddev * 4);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief check entropy of any(), more picks
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
testCheckEntropyCollectionMore : function () {
|
|
var i, n, l;
|
|
|
|
n = 500;
|
|
|
|
l = [];
|
|
for (i = 0; i < n; ++i) {
|
|
c.save({ value: i });
|
|
l.push(i);
|
|
}
|
|
|
|
var dist = getDistribution(n * 100, function () {
|
|
return c.any().value;
|
|
});
|
|
|
|
|
|
var statsExp = statsExpected(l, n * 100);
|
|
var stats = statsFound(dist);
|
|
assertEqual(stats.count, n * 100);
|
|
assertTrue(Math.abs(stats.average - statsExp.average)
|
|
< statsExp.averageStddev * 3);
|
|
assertTrue(Math.abs(stats.variance - statsExp.variance)
|
|
< statsExp.varianceStddev * 3);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief check entropy of any(), with many documents deleted
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
testCheckEntropyCollectionHalf : function () {
|
|
var i, n, l;
|
|
|
|
n = 500;
|
|
|
|
for (i = 0; i < n; ++i) {
|
|
c.save({ value: i });
|
|
}
|
|
|
|
// remove 50 % of entries
|
|
var d = Math.round(n * 0.5);
|
|
for (i = 0; i < d; ++i) {
|
|
c.remove(c.any());
|
|
}
|
|
|
|
l = db._query(`FOR d IN ${cn} RETURN d.value`).toArray();
|
|
|
|
var dist = getDistribution(n * 50, function () {
|
|
return c.any().value;
|
|
});
|
|
|
|
var statsExp = statsExpected(l, n * 50);
|
|
var stats = statsFound(dist);
|
|
assertEqual(stats.count, n * 50);
|
|
assertTrue(Math.abs(stats.average - statsExp.average)
|
|
< statsExp.averageStddev * 3);
|
|
assertTrue(Math.abs(stats.variance - statsExp.variance)
|
|
< statsExp.varianceStddev * 3);
|
|
},
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief check entropy of any(), with most documents deleted
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
testCheckEntropyCollectionSparse : function () {
|
|
var i, n, l;
|
|
|
|
n = 500;
|
|
|
|
for (i = 0; i < n; ++i) {
|
|
c.save({ value: i });
|
|
}
|
|
|
|
// remove 50 % of entries
|
|
var d = Math.round(n * 0.95);
|
|
for (i = 0; i < d; ++i) {
|
|
c.remove(c.any());
|
|
}
|
|
|
|
var dist = getDistribution(n * 5, function () {
|
|
return c.any().value;
|
|
});
|
|
|
|
l = db._query(`FOR d IN ${cn} RETURN d.value`).toArray();
|
|
|
|
var statsExp = statsExpected(l, n * 5);
|
|
var stats = statsFound(dist);
|
|
assertEqual(stats.count, n * 5);
|
|
assertTrue(Math.abs(stats.average - statsExp.average)
|
|
< statsExp.averageStddev * 3);
|
|
assertTrue(Math.abs(stats.variance - statsExp.variance)
|
|
< statsExp.varianceStddev * 3);
|
|
}
|
|
|
|
};
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
/// @brief executes the test suite
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
|
|
jsunity.run(AnySuite);
|
|
|
|
return jsunity.done();
|
|
|