refactor: reduce number of arithmetic operations

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---

Author: kgryte

lib/node_modules/@stdlib/stats/base/dists/wald/mode/lib/main.js

@@ -55,6 +55,7 @@ var sqrt = require( '@stdlib/math/base/special/sqrt' );
 */
 function mode( mu, lambda ) {
 	var r;
+	var v;
 	if (
 		isnan( mu ) ||
 		isnan( lambda ) ||
@@ -64,7 +65,8 @@ function mode( mu, lambda ) {
 		return NaN;
 	}
 	r = mu / lambda;
-	return mu * ( sqrt( 1.0 + ( ( 3.0*r/2.0 ) * ( 3.0*r/2.0 ) ) ) - ( 3.0*r/2.0 ) );
+	v = 1.5 * r;
+	return mu * ( sqrt( 1.0 + ( v*v ) ) - v );
 }
 
 

lib/node_modules/@stdlib/stats/base/dists/wald/mode/src/main.c

@@ -33,6 +33,7 @@
 */
 double stdlib_base_dists_wald_mode( const double mu, const double lambda ) {
 	double r;
+	double v;
 	if (
 		stdlib_base_is_nan( mu ) ||
 		stdlib_base_is_nan( lambda ) ||
@@ -42,5 +43,6 @@ double stdlib_base_dists_wald_mode( const double mu, const double lambda ) {
 		return 0.0/0.0; // NaN
 	}
 	r = mu / lambda;
-	return mu * ( stdlib_base_sqrt( 1.0 + ( ( 3.0*r/2.0 ) * ( 3.0*r/2.0 ) ) ) - ( 3.0*r/2.0 ) );
+	v = 1.5 * r;
+	return mu * ( stdlib_base_sqrt( 1.0 + ( v*v ) ) - v );
 }

lib/node_modules/@stdlib/stats/base/dists/wald/mode/test/test.js

@@ -114,11 +114,7 @@ tape( 'the function returns the mode of a Wald distribution', function test( t )
 	lambda = data.lambda;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = mode( mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 2 ), 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 2 ), true, 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });

lib/node_modules/@stdlib/stats/base/dists/wald/mode/test/test.native.js

@@ -123,11 +123,7 @@ tape( 'the function returns the mode of a Wald distribution', opts, function tes
 	lambda = data.lambda;
 	for ( i = 0; i < mu.length; i++ ) {
 		y = mode( mu[i], lambda[i] );
-		if ( y === expected[i] ) {
-			t.strictEqual( y, expected[i], 'mu:'+mu[i]+', lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
-		} else {
-			t.ok( isAlmostSameValue( y, expected[i], 2 ), 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
-		}
+		t.strictEqual( isAlmostSameValue( y, expected[i], 2 ), true, 'within tolerance. mu: '+mu[i]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'.' );
 	}
 	t.end();
 });