stdlib-js/ndarray-base-numel

stdlib TypeScript type declarations.

Load a manifest for compiling source files.

Round a double-precision floating-point number toward negative infinity.

Discrete uniform distributed pseudorandom numbers.

Uniformly distributed pseudorandom numbers between 0 and 1.

tap-producing test harness for node and browsers

Yet another JS code coverage tool that computes statement, line, function and branch coverage with module loader hooks to transparently add coverage when running tests. Supports all JS coverage use cases including unit tests, server side functional tests and browser tests. Built for scale

Minimal TAP output formatter.

Benchmark harness.

Multidimensional array constructor.

Return a view of an input ndarray.

Convert an ndarray-like object to an object likely to have the same "shape".

Apply a unary callback to elements in a input ndarray and assign results to elements in an output ndarray.

Determine if an array is compatible with a single memory segment.

Create a zero-filled ndarray having a specified shape and data type.

Assign elements in a input ndarray to elements in an output ndarray.

Flatten an n-dimensional nested array.

Multiple dispatch for unary mathematical functions.

Multidimensional arrays.

Compute a sample Pearson product-moment correlation distance matrix incrementally.

Invoke a callback function once for each ndarray element.

Create an iterator which returns indices for use indexing into an ndarray having a specified shape.

Create an iterator which returns [index, row] pairs for each row in a matrix (or stack of matrices).

Create an iterator which iterates over each matrix in a stack of matrices.

Create an uninitialized ndarray having a specified shape and data type.

Apply a unary function to each element retrieved from a input ndarray according to a callback function and assign results to elements in an output ndarray.

Flatten an n-dimensional nested array according to a callback function.

Create an iterator which returns [index, value] pairs for each element in a provided ndarray.

Create an iterator which returns [index, matrix] pairs for each matrix in a stack of matrices.

Create an iterator which iterates over each column in a matrix (or stack of matrices).

Create an iterator which returns [index, column] pairs for each column in a matrix (or stack of matrices).

Create a zero-filled ndarray having the same shape and data type as a provided ndarray.

Compute a sample Pearson product-moment correlation matrix incrementally.

Create an uninitialized ndarray having the same shape and data type as a provided ndarray.

Create an iterator which returns individual elements from a provided ndarray.

Compute an unbiased sample covariance matrix incrementally.

Apply a nullary function and assign results to elements in an output ndarray.

Determine if a buffer length is compatible with a provided shape array.

Create an iterator which iterates over each row in a matrix (or stack of matrices).

Create a zero-filled ndarray having a specified shape and data type.

Incrementally perform binary classification using stochastic gradient descent (SGD).

Create an uninitialized ndarray having a specified shape and data type.

Perform a chi-square independence test.

Create an uninitialized ndarray having the same shape and data type as a provided ndarray.

Base ndarray.