stdlib-js

stdlib-js/blas-ext-base-sapxsum

Adds a constant to each single-precision floating-point strided array element and computes the sum.

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Apache License 2.0
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Support the dependencies of stdlib-js/blas-ext-base-sapxsum

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Test if a value is an Error object.
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C utilities for retrieving Node-API add-on callback arguments.
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Convert a Node-API value to a single-precision floating-point number.
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Convert a Node-API value to a signed 64-bit integer.
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Convert a Node-API value representing a strided array to a single-precision floating-point array.
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C utilities for registering Node-API modules.
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Define a non-enumerable read-only property.
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Load a manifest for compiling source files.
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Wrap `require` in a try/catch block.
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Create a filled array according to a provided callback function.
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Check if the runtime is a web browser.
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Test if a double-precision floating-point numeric value is NaN.
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Exponential function.
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Discrete uniform distributed pseudorandom numbers.
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Uniformly distributed pseudorandom numbers.
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Return the minimum accessible index based on a set of provided strided array parameters.
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Return a typed array view having the same data type as a provided input typed array and starting at a specified index offset.
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Proxies nodejs require in order to allow overriding dependencies during testing.
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tap-producing test harness for node and browsers
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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
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Minimal TAP output formatter.
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Benchmark harness.
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Adds a constant to each single-precision floating-point strided array element and computes the sum using an improved Kahan–Babuška algorithm.

Support the repos that depend on stdlib-js/blas-ext-base-sapxsum

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Extended base (i.e., lower-level) basic linear algebra subroutines (BLAS).
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Calculate the arithmetic mean of a single-precision floating-point strided array using a one-pass trial mean algorithm.

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