Package Exports

numpy-matrix-js

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Readme

numpy-matrix-js

A numpy-like Matrix/Array module for Node JS. Browser support coming soon.

Installation & Set Up

NPM

In your terminal, type this command to install the module.

npm install numpy-matrix-js

After that, add this following code to the top of your file to import it.

const np = require('numpy-matrix-js')

After this, you can now use the functions available in the module.

Browser

Add this following script tag to your html file.

<script type="module">
     import np from "https://unpkg.com/numpy-matrix-js@1.0.8/src/html/index.js"
</script>

Documentation

Initializing

np.zeros(rows, cols)

Creates a new matrix/2d array filled with 0 If you only give 1 parameter, it creates a 1d array.

Parameters	What it is	Required
rows	number of rows in your array	yes
cols	number of columns in your array	no

np.random.rand(rows, cols)

Creates a new matrix/2d array of random values

Parameters	What it is	Required
rows	number of rows in your array	yes
cols	number of columns in your array	yes

Math

np.matmul(a, b)

Multiplies 2 matrices together and returns the result.

Note: Like linear algebra, the columns of the first matrix must match the rows of the second matrix.

Parameters	What it is	Required
a	First matrix	Yes
b	Second matrix	Yes

np.add(a,b)

Adds 2 matrices together and returns the result.

Note: Like linear algebra, the dimensions of both matrices needs to match.

Parameters	What it is	Required
a	First matrix	Yes
b	Second matrix	Yes

np.subtract(a,b)

Subtracts 2 matrices and returns the result.

Note: Like linear algebra, the dimensions of both matrices needs to match.

Parameters	What it is	Required
a	First matrix	Yes
b	Second matrix	Yes

np.transpose(a)

Returns the transposed version of the matrix "a".

Parameters	What it is	Required
a	Input matrix which needs to be transposed	Yes

Built-in Math Functions

Sigmoid

np.sigmoid(x)

Returns the sigmoid value of whatever "x" value you input

Dsigmoid (Derivative of sigmoid)

np.dsigmoid(x)

Returns the dsigmoid value of whatever "x" value you input

Tanh (Hyperbolic Tangent)

np.tanh(x)

Returns the hyperbolic tangent value of whatever "x" value you input

Softmax

np.softmax(inputs)

Applies the softmax function to the inputs array and returns the new result as an array.