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# incrcovariance

> Compute an [unbiased sample covariance][covariance] incrementally.

<section class="intro">

For unknown population means, the [unbiased sample covariance][covariance] is defined as

<!-- <equation class="equation" label="eq:unbiased_sample_covariance_unknown_means" align="center" raw="\operatorname{cov_n} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x}_n)(y_i - \bar{y}_n)" alt="Equation for the unbiased sample covariance for unknown population means."> -->

<div class="equation" align="center" data-raw-text="\operatorname{cov_n} = \frac{1}{n-1} \sum_{i=0}^{n-1} (x_i - \bar{x}_n)(y_i - \bar{y}_n)" data-equation="eq:unbiased_sample_covariance_unknown_means">
    <img src="https://cdn.rawgit.com/stdlib-js/stdlib/27e2a43c70db648bb5bbc3fd0cdee050c25adc0b/lib/node_modules/@stdlib/stats/incr/covariance/docs/img/equation_unbiased_sample_covariance_unknown_means.svg" alt="Equation for the unbiased sample covariance for unknown population means.">
    <br>
</div>

<!-- </equation> -->

For known population means, the [unbiased sample covariance][covariance] is defined as

<!-- <equation class="equation" label="eq:unbiased_sample_covariance_known_means" align="center" raw="\operatorname{cov_n} = \frac{1}{n} \sum_{i=0}^{n-1} (x_i - \mu_x)(y_i - \mu_y)" alt="Equation for the unbiased sample covariance for known population means."> -->

<div class="equation" align="center" data-raw-text="\operatorname{cov_n} = \frac{1}{n} \sum_{i=0}^{n-1} (x_i - \mu_x)(y_i - \mu_y)" data-equation="eq:unbiased_sample_covariance_known_means">
    <img src="https://cdn.rawgit.com/stdlib-js/stdlib/27e2a43c70db648bb5bbc3fd0cdee050c25adc0b/lib/node_modules/@stdlib/stats/incr/covariance/docs/img/equation_unbiased_sample_covariance_known_means.svg" alt="Equation for the unbiased sample covariance for known population means.">
    <br>
</div>

<!-- </equation> --> 

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var incrcovariance = require( '@stdlib/stats/incr/covariance' );
```

#### incrcovariance( \[mx, my] )

Returns an accumulator `function` which incrementally computes an [unbiased sample covariance][covariance].

```javascript
var accumulator = incrcovariance();
```

If the means are already known, provide `mx` and `my` arguments.

```javascript
var accumulator = incrcovariance( 3.0, -5.5 );
```

#### accumulator( \[x, y] )

If provided input values `x` and `y`, the accumulator function returns an updated [unbiased sample covariance][covariance]. If not provided input values `x` and `y`, the accumulator function returns the current [unbiased sample covariance][covariance].

```javascript
var accumulator = incrcovariance();

var v = accumulator( 2.0, 1.0 );
// returns 0.0

v = accumulator( 1.0, -5.0 );
// returns 3.0

v = accumulator( 3.0, 3.14 );
// returns 4.07

v = accumulator();
// returns 4.07
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   Input values are **not** type checked. If provided `NaN` or a value which, when used in computations, results in `NaN`, the accumulated value is `NaN` for **all** future invocations. If non-numeric inputs are possible, you are advised to type check and handle accordingly **before** passing the value to the accumulator function.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var randu = require( '@stdlib/random/base/randu' );
var incrcovariance = require( '@stdlib/stats/incr/covariance' );

var accumulator;
var x;
var y;
var i;

// Initialize an accumulator:
accumulator = incrcovariance();

// For each simulated datum, update the unbiased sample covariance...
for ( i = 0; i < 100; i++ ) {
    x = randu() * 100.0;
    y = randu() * 100.0;
    accumulator( x, y );
}
console.log( accumulator() );
```

</section>

<!-- /.examples -->

<section class="links">

[covariance]: https://en.wikipedia.org/wiki/Covariance

</section>

<!-- /.links -->
