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/**
* @license Apache-2.0
*
* Copyright (c) 2018 The Stdlib Authors.
*
* 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.
*/
'use strict';
// MODULES //
var tape = require( 'tape' );
var isnan = require( '@stdlib/math/base/assert/is-nan' );
var abs = require( '@stdlib/math/base/special/abs' );
var PINF = require( '@stdlib/constants/float64/pinf' );
var NINF = require( '@stdlib/constants/float64/ninf' );
var EPS = require( '@stdlib/constants/float64/eps' );
var mgf = require( './../lib' );
// FIXTURES //
var positiveMean = require( './fixtures/julia/positive_mean.json' );
var negativeMean = require( './fixtures/julia/negative_mean.json' );
var largeVariance = require( './fixtures/julia/large_variance.json' );
// TESTS //
tape( 'main export is a function', function test( t ) {
t.ok( true, __filename );
t.equal( typeof mgf, 'function', 'main export is a function' );
t.end();
});
tape( 'if provided `NaN` for any parameter, the function returns `NaN`', function test( t ) {
var y = mgf( NaN, 0.0, 1.0 );
t.equal( isnan( y ), true, 'returns NaN' );
y = mgf( 0.0, NaN, 1.0 );
t.equal( isnan( y ), true, 'returns NaN' );
y = mgf( 0.0, 1.0, NaN );
t.equal( isnan( y ), true, 'returns NaN' );
t.end();
});
tape( 'if provided a negative `s`, the function returns `NaN`', function test( t ) {
var y;
y = mgf( 2.0, 2.0, -1.0 );
t.equal( isnan( y ), true, 'returns NaN' );
y = mgf( 0.0, 2.0, -1.0 );
t.equal( isnan( y ), true, 'returns NaN' );
y = mgf( 2.0, 1.0, NINF );
t.equal( isnan( y ), true, 'returns NaN' );
y = mgf( 2.0, PINF, NINF );
t.equal( isnan( y ), true, 'returns NaN' );
y = mgf( 2.0, NINF, NINF );
t.equal( isnan( y ), true, 'returns NaN' );
y = mgf( 2.0, NaN, NINF );
t.equal( isnan( y ), true, 'returns NaN' );
t.end();
});
tape( 'the function evaluates the MGF for `x` given positive `mu`', function test( t ) {
var expected;
var delta;
var tol;
var mu;
var x;
var s;
var y;
var i;
expected = positiveMean.expected;
x = positiveMean.x;
mu = positiveMean.mu;
s = positiveMean.s;
for ( i = 0; i < x.length; i++ ) {
y = mgf( x[i], mu[i], s[i] );
if ( expected[i] !== null) {
if ( y === expected[i] ) {
t.equal( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
} else {
delta = abs( y - expected[ i ] );
tol = 2.0 * EPS * abs( expected[ i ] );
t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. s: '+s[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
}
}
}
t.end();
});
tape( 'the function evaluates the MGF for `x` given negative `mu`', function test( t ) {
var expected;
var delta;
var tol;
var mu;
var x;
var s;
var y;
var i;
expected = negativeMean.expected;
x = negativeMean.x;
mu = negativeMean.mu;
s = negativeMean.s;
for ( i = 0; i < x.length; i++ ) {
y = mgf( x[i], mu[i], s[i] );
if ( expected[i] !== null ) {
if ( y === expected[i] ) {
t.equal( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
} else {
delta = abs( y - expected[ i ] );
tol = 2.0 * EPS * abs( expected[ i ] );
t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. s: '+s[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
}
}
}
t.end();
});
tape( 'the function evaluates the MGF for `x` given large variance ( = large `s` )', function test( t ) {
var expected;
var delta;
var tol;
var mu;
var x;
var s;
var y;
var i;
expected = largeVariance.expected;
x = largeVariance.x;
mu = largeVariance.mu;
s = largeVariance.s;
for ( i = 0; i < x.length; i++ ) {
y = mgf( x[i], mu[i], s[i] );
if ( expected[i] !== null ) {
if ( y === expected[i] ) {
t.equal( y, expected[i], 'x: '+x[i]+', mu: '+mu[i]+', s: '+s[i]+', y: '+y+', expected: '+expected[i] );
} else {
delta = abs( y - expected[ i ] );
tol = 2.0 * EPS * abs( expected[ i ] );
t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. mu: '+mu[i]+'. s: '+s[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
}
}
}
t.end();
});
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