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Author: kgryte

docs/user-guides/recipes/how_to_create_an_ndarray_filled_with_random_numbers.md

@@ -20,13 +20,13 @@ limitations under the License.
 
 # How to create an ndarray filled with random numbers
 
-> Create an ndarray of uniformly-distributed pseudorandom numbers (with optional reproducibility via a seed).
+> Create an ndarray of uniformly-distributed pseudorandom numbers and then regenerate the exact same ndarray using a seed or saved PRNG state.
 
 ## Introduction
 
-stdlib provides APIs and packages to help you create multi-dimensional arrays (a.k.a., ndarrays) containing pseudorandom numbers drawn from statistical distributions. These APIs allow you to specify seeds in order to generate reproducible sequences and to specify the output array shape and precision.
+stdlib provides APIs and packages to help you create [ndarrays][@stdlib/ndarray/ctor] (a.k.a., multi-dimensional arrays) containing pseudorandom numbers drawn from statistical distributions. These APIs allow you to specify seeds in order to generate reproducible sequences and to specify the output array shape and precision.
 
-In this recipe, we will use [`@stdlib/random/uniform`][@stdlib/random/uniform] to create ndarrays containing pseudorandom numbers drawn from a uniform distribution.
+In this recipe, we'll use [`@stdlib/random/uniform`][@stdlib/random/uniform] to create ndarrays containing pseudorandom numbers drawn from a uniform distribution.
 
 ## Setup
 
@@ -39,19 +39,19 @@ $ npm install @stdlib/stdlib
 ## Steps
 
 1.  Import all necessary packages for generating pseudorandom numbers.
-2.  Create a multi-dimensional array.
+2.  Generate an ndarray.
 3.  \[Optional] Regenerate a sequence by using a seed.
 
 ### 1. Import required packages
 
-For this recipe, we will use `@stdlib/stdlib` and its package [`@stdlib/random/uniform`][@stdlib/random/uniform]. We will also use the package [`@stdlib/ndarray/to-array`][@stdlib/ndarray/to-array] to convert an ndarray to a conventional array-of-arrays format.
+For this recipe, we'll use `@stdlib/stdlib` and its package [`@stdlib/random/uniform`][@stdlib/random/uniform]. We'll also use the package [`@stdlib/ndarray/to-array`][@stdlib/ndarray/to-array] to convert an ndarray to a conventional array-of-arrays format.
 
 ```javascript
 const uniform = require( '@stdlib/random/uniform' );
 const ndarray2array = require( '@stdlib/ndarray/to-array' );
 ```
 
-### 2. Create a multi-dimensional array
+### 2. Generate an ndarray
 
 Every stdlib interface for creating ndarrays of pseudorandom numbers drawn from a statistical distribution has two requirements. The first is defining the desired output shape. The second is defining the distribution parameters which control the shape of the sampled distribution.
 
@@ -60,10 +60,10 @@ For this recipe, the uniform distribution has two parameters:
 -   **a**: minimum support (inclusive).
 -   **b**: maximum support (exclusive).
 
-In the following code snippet, we generate a 3x3 matrix of pseudorandom numbers sampled from a uniform distribution with minimum support `-10` and maximum support `+10`.
+In the following code snippet, we generate a 3×3 matrix of pseudorandom numbers sampled from a uniform distribution with minimum support `-10` and maximum support `+10`.
 
 ```javascript
-const shape = [ 3, 3 ]; // 3x3
+const shape = [ 3, 3 ]; // 3×3
 const a = -10.0;        // minimum support
 const b = 10.0;         // maximum support
 
@@ -78,50 +78,69 @@ const xa = ndarray2array( x );
 // e.g., returns [ [ ~-2.41, ~3.08, ~5.09 ], [ ~5.73, ~-8.12, ~-8.99 ], [ ~0.11, ~-6.69, ~4.79 ] ]
 ```
 
-The output displayed in the above example is representative, and your values are likely to differ. However, notice that, even after running the example multiple times, the values always reside on the half-open interval `[-10,10)`, as expected based on the parameterization we used above.
+The output displayed in the above example is representative, and your values are likely to differ. However, notice that the values always reside on the half-open interval `[-10,10)`.
 
 > 💡 Tip
 >
-> By default, `uniform` returns an ndarray having a `float64` data type (i.e., an ndarray of double-precision floating-point numbers). To return an ndarray having either a `float32` or `generic` data type, you can provide a `dtype` option when calling `uniform`.
+> By default, `uniform` returns an ndarray having a `float64` data type (i.e., an ndarray of double-precision floating-point numbers). To return an ndarray having either a `float32` or `generic` data type, you can provide a `dtype` option when calling `uniform`, as displayed in the following code snippet.
 >
 > ```javascript
 > const x32 = uniform( shape, a, b, {
 >     'dtype': 'float32'
 > });
 > ```
 
-At this point, we have finished creating an ndarray of pseudorandom numbers, and, now, we will reproduce the same sequence of numbers by using a seed.
+At this point, we've finished creating an ndarray of pseudorandom numbers, and now we'll reproduce the same sequence of numbers by using a seed.
 
 ### 3. \[Optional] Regenerate a sequence by using a seed
 
-A **seed** is a value which is used to initialize a pseudorandom number generator, and a pseudorandom number generator's sequence is completely determined by that seed. Accordingly, if another pseudorandom number generator instance is initialized with the same seed, that instance will produce the same sequence of numbers.
+A **seed** is a value which initializes a pseudorandom number generator, and a pseudorandom number generator's sequence is completely determined by that seed. Accordingly, if another pseudorandom number generator instance is initialized with the same seed, that instance will produce the same sequence of numbers.
 
 In the following code snippet, we pass the seed from the `uniform` pseudorandom number generator used above to a function which returns a new `uniform` instance. When calling that instance with the same distribution parameters `a` and `b` that we used above, we generate an ndarray containing the same set of pseudorandomly generated numbers.
 
 ```javascript
-const random = uniform.factory({
+const r1 = uniform.factory({
     'seed': uniform.seed
 });
 
-const y = random( shape, a, b );
+const y = r1( shape, a, b );
 // returns <ndarray>
 
 const ya = ndarray2array( y );
 // e.g., returns [ [ ~-2.41, ~3.08, ~5.09 ], [ ~5.73, ~-8.12, ~-8.99 ], [ ~0.11, ~-6.69, ~4.79 ] ]
 ```
 
-Congratulations! You have successfully used stdlib to generate an ndarray of pseudorandom numbers.
+Need to configure a `uniform` instance to continue from the current generator state, instead of starting over? Pass a state when creating a new `uniform` instance, as done in the following code snippet.
+
+```javascript
+const r2 = uniform.factory({
+    'state': uniform.state,
+    'copy': true              // set to `false` to share state
+});
+
+// Generate the same sequence of values as if calling `uniform` a second ndarray:
+const z = r2( shape, a, b );
+// returns <ndarray>
+```
+
+Congratulations! You have successfully used stdlib to generate (and re-generate) an ndarray of pseudorandom numbers.
 
 ## Learn More
 
 Check out our other [recipes][stdlib-user-guides-recipes] to continue your learning!
 
+Sampling other distributions? See [`@stdlib/random`][@stdlib/random] for all of what stdlib has to offer!
+
 <!-- links -->
 
 <section class="links">
 
+[@stdlib/random]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/random
+
 [@stdlib/random/uniform]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/random/uniform
 
+[@stdlib/ndarray/ctor]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/ctor
+
 [@stdlib/ndarray/to-array]: https://github.com/stdlib-js/stdlib/tree/develop/lib/node_modules/%40stdlib/ndarray/to-array
 
 [stdlib-user-guides-recipes]: https://github.com/stdlib-js/stdlib/tree/develop/docs/user-guides/recipes