text: /**
  * @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 factory = require( './../lib/factory.js' );
  
  
  // 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 factory, 'function', 'main export is a function' );
  	t.end();
  });
  
  tape( 'the function returns a function', function test( t ) {
  	var pdf = factory( 0.0, 1.0 );
  	t.equal( typeof pdf, 'function', 'returns a function' );
  	t.end();
  });
  
  tape( 'if provided `NaN` for any parameter, the created function returns `NaN`', function test( t ) {
  	var pdf;
  	var y;
  
  	pdf = factory( 0.0, 1.0 );
  	y = pdf( NaN );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( NaN, 1.0 );
  	y = pdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( 1.0, NaN );
  	y = pdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( NaN, NaN );
  	y = pdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( NaN, NaN );
  	y = pdf( NaN );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	t.end();
  });
  
  tape( 'if provided a finite `mu` and `s`, the function returns a function which returns `0` when provided `x` greater than `mu + sigma`', function test( t ) {
  	var pdf;
  	var y;
  
  	pdf = factory( 0.0, 1.0 );
  
  	y = pdf( PINF );
  	t.equal( y, 0.0, 'returns 0' );
  
  	y = pdf( 2.0 );
  	t.equal( y, 0.0, 'returns 0' );
  
  	y = pdf( 20.0 );
  	t.equal( y, 0.0, 'returns 0' );
  
  	t.end();
  });
  
  tape( 'if provided a finite `mu` and `s`, the function returns a function which returns `0` when provided `x` smaller than `mu - sigma`', function test( t ) {
  	var pdf;
  	var y;
  
  	pdf = factory( 0.0, 1.0 );
  	y = pdf( NINF );
  	t.equal( y, 0.0, 'returns 0' );
  
  	y = pdf( -2.0 );
  	t.equal( y, 0.0, 'returns 0' );
  
  	y = pdf( -20.0 );
  	t.equal( y, 0.0, 'returns 0' );
  
  	t.end();
  });
  
  tape( 'if provided a negative `s`, the created function always returns `NaN`', function test( t ) {
  	var pdf;
  	var y;
  
  	pdf = factory( 0.0, -1.0 );
  
  	y = pdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	y = pdf( 0.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( 0.0, NINF );
  	y = pdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( PINF, NINF );
  	y = pdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( NINF, NINF );
  	y = pdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	pdf = factory( NaN, NINF );
  	y = pdf( 2.0 );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	t.end();
  });
  
  tape( 'if `s` equals `0`, the created function evaluates a degenerate distribution centered at `mu`', function test( t ) {
  	var pdf;
  	var y;
  
  	pdf = factory( 2.0, 0.0 );
  
  	y = pdf( 2.0 );
  	t.equal( y, PINF, 'returns +infinity for x equal to mu' );
  
  	y = pdf( 1.0 );
  	t.equal( y, 0.0, 'returns 0' );
  
  	y = pdf( PINF );
  	t.equal( y, 0.0, 'returns 0' );
  
  	y = pdf( NINF );
  	t.equal( y, 0.0, 'returns 0' );
  
  	y = pdf( NaN );
  	t.equal( isnan( y ), true, 'returns NaN' );
  
  	t.end();
  });
  
  tape( 'the created function evaluates the pdf for `x` given positive `mu`', function test( t ) {
  	var expected;
  	var delta;
  	var pdf;
  	var tol;
  	var mu;
  	var s;
  	var x;
  	var y;
  	var i;
  
  	expected = positiveMean.expected;
  	x = positiveMean.x;
  	mu = positiveMean.mu;
  	s = positiveMean.s;
  	for ( i = 0; i < x.length; i++ ) {
  		pdf = factory( mu[i], s[i] );
  		y = pdf( x[i] );
  		if ( expected[i] !== null && !(expected[i] === 0.0 && y < EPS ) ) {
  			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 = 3.0 * 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 created function evaluates the pdf for `x` given negative `mu`', function test( t ) {
  	var expected;
  	var delta;
  	var pdf;
  	var tol;
  	var mu;
  	var s;
  	var x;
  	var y;
  	var i;
  
  	expected = negativeMean.expected;
  	x = negativeMean.x;
  	mu = negativeMean.mu;
  	s = negativeMean.s;
  	for ( i = 0; i < x.length; i++ ) {
  		pdf = factory( mu[i], s[i] );
  		y = pdf( x[i] );
  		if ( expected[i] !== null && !(expected[i] === 0.0 && y < EPS ) ) {
  			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 = 4.0 * 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 created function evaluates the pdf for `x` given large variance ( = large `s`)', function test( t ) {
  	var expected;
  	var delta;
  	var pdf;
  	var tol;
  	var mu;
  	var s;
  	var x;
  	var y;
  	var i;
  
  	expected = largeVariance.expected;
  	x = largeVariance.x;
  	mu = largeVariance.mu;
  	s = largeVariance.s;
  	for ( i = 0; i < x.length; i++ ) {
  		pdf = factory( mu[i], s[i] );
  		y = pdf( x[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 = 3.0 * 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();
  });
  