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‎lib/node_modules/@stdlib/stats/base/dists/poisson/logpmf/README.md‎

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@@ -171,15 +171,7 @@ for ( i = 0; i < 10; i++ ) {
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#### stdlib_base_dists_poisson_logpmf( x, lambda )
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<<<<<<< HEAD
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Evaluates the natural logarithm of the probability mass function (PMF) for a Poisson distribution with mean parameter `lambda`.
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=======
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Evaluates the natural logarithm of the probability mass function (PMF) for a Poisson distribution with mean parameter `lambda` at a value `x`.
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>>>>>>> b4ecbe5ab00 (feat: add c implementation for /base/dists/poisson/logpmf)
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=======
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Evaluates the natural logarithm of the probability mass function (PMF) for a Poisson distribution with mean parameter `lambda`.
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>>>>>>> 2dfae310be5 (chore: updates and refractor some file)
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Evaluates the natural logarithm of the [probability mass function][pmf] (PMF) for a [Poisson][poisson-distribution] distribution with mean parameter `lambda`.
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```c
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double out = stdlib_base_dists_poisson_logpmf( 4.0, 3.0 );

‎lib/node_modules/@stdlib/stats/base/dists/poisson/logpmf/lib/native.js‎

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@@ -28,6 +28,7 @@ var addon = require( './../src/addon.node' );
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/**
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* Evaluates the natural logarithm of the probability mass function (PMF) for a Poisson distribution with mean parameter `lambda` at a value `x`.
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*
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* @private
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* @param {number} x - input value
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* @param {NonNegativeNumber} lambda - mean parameter
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* @returns {number} evaluated logPMF

‎lib/node_modules/@stdlib/stats/base/dists/poisson/logpmf/manifest.json‎

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@@ -41,7 +41,6 @@
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"@stdlib/math/base/napi/binary",
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"@stdlib/math/base/assert/is-nonnegative-integer",
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"@stdlib/math/base/special/factorialln",
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"@stdlib/math/base/special/ceil",
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"@stdlib/math/base/special/ln",
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"@stdlib/math/base/assert/is-nan",
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"@stdlib/constants/float64/pinf",
@@ -64,6 +63,7 @@
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"@stdlib/math/base/assert/is-nonnegative-integer",
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"@stdlib/math/base/special/factorialln",
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"@stdlib/math/base/special/ceil",
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"@stdlib/math/base/special/ln",
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"@stdlib/constants/float64/pinf",
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"@stdlib/constants/float64/ninf"
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]
@@ -84,6 +84,7 @@
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"@stdlib/math/base/assert/is-nonnegative-integer",
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"@stdlib/math/base/special/factorialln",
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"@stdlib/math/base/special/ceil",
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"@stdlib/math/base/special/ln",
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"@stdlib/constants/float64/pinf",
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"@stdlib/constants/float64/ninf"
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]

‎lib/node_modules/@stdlib/stats/base/dists/poisson/logpmf/test/test.logpmf.js‎

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Original file line numberDiff line numberDiff line change
@@ -128,13 +128,9 @@ tape( 'the function evaluates the logpmf for `x` given a small parameter `lambda
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lambda = smallMean.lambda;
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for ( i = 0; i < x.length; i++ ) {
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y = logpmf( x[i], lambda[i] );
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if ( y === expected[i] ) {
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t.equal( y, expected[i], 'x: '+x[i]+'. lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
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} else {
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delta = abs( y - expected[ i ] );
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tol = 20.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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delta = abs( y - expected[ i ] );
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tol = 20.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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t.end();
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});
@@ -153,13 +149,9 @@ tape( 'the function evaluates the logpmf for `x` given a medium parameter `lambd
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lambda = mediumMean.lambda;
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for ( i = 0; i < x.length; i++ ) {
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y = logpmf( x[i], lambda[i] );
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if ( y === expected[i] ) {
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t.equal( y, expected[i], 'x: '+x[i]+'. lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
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} else {
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delta = abs( y - expected[ i ] );
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tol = 30.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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delta = abs( y - expected[ i ] );
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tol = 30.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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t.end();
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});
@@ -178,13 +170,9 @@ tape( 'the function evaluates the logpmf for `x` given a large parameter `lambda
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lambda = largeMean.lambda;
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for ( i = 0; i < x.length; i++ ) {
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y = logpmf( x[i], lambda[i] );
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if ( y === expected[i] ) {
182-
t.equal( y, expected[i], 'x: '+x[i]+'. lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
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} else {
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delta = abs( y - expected[ i ] );
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tol = 80.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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delta = abs( y - expected[ i ] );
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tol = 80.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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t.end();
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});

‎lib/node_modules/@stdlib/stats/base/dists/poisson/logpmf/test/test.native.js‎

Lines changed: 9 additions & 21 deletions
Original file line numberDiff line numberDiff line change
@@ -137,13 +137,9 @@ tape( 'the function evaluates the logpmf for `x` given a small parameter `lambda
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lambda = smallMean.lambda;
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for ( i = 0; i < x.length; i++ ) {
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y = logpmf( x[i], lambda[i] );
140-
if ( y === expected[i] ) {
141-
t.equal( y, expected[i], 'x: '+x[i]+'. lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
142-
} else {
143-
delta = abs( y - expected[ i ] );
144-
tol = 20.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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delta = abs( y - expected[ i ] );
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tol = 20.0 * EPS * abs( expected[ i ] );
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t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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t.end();
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});
@@ -162,13 +158,9 @@ tape( 'the function evaluates the logpmf for `x` given a medium parameter `lambd
162158
lambda = mediumMean.lambda;
163159
for ( i = 0; i < x.length; i++ ) {
164160
y = logpmf( x[i], lambda[i] );
165-
if ( y === expected[i] ) {
166-
t.equal( y, expected[i], 'x: '+x[i]+'. lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
167-
} else {
168-
delta = abs( y - expected[ i ] );
169-
tol = 30.0 * EPS * abs( expected[ i ] );
170-
t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
171-
}
161+
delta = abs( y - expected[ i ] );
162+
tol = 30.0 * EPS * abs( expected[ i ] );
163+
t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
173165
t.end();
174166
});
@@ -187,13 +179,9 @@ tape( 'the function evaluates the logpmf for `x` given a large parameter `lambda
187179
lambda = largeMean.lambda;
188180
for ( i = 0; i < x.length; i++ ) {
189181
y = logpmf( x[i], lambda[i] );
190-
if ( y === expected[i] ) {
191-
t.equal( y, expected[i], 'x: '+x[i]+'. lambda: '+lambda[i]+', y: '+y+', expected: '+expected[i] );
192-
} else {
193-
delta = abs( y - expected[ i ] );
194-
tol = 80.0 * EPS * abs( expected[ i ] );
195-
t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
196-
}
182+
delta = abs( y - expected[ i ] );
183+
tol = 80.0 * EPS * abs( expected[ i ] );
184+
t.ok( delta <= tol, 'within tolerance. x: '+x[ i ]+'. lambda: '+lambda[i]+'. y: '+y+'. E: '+expected[ i ]+'. Δ: '+delta+'. tol: '+tol+'.' );
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}
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t.end();
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});

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