Data enumerator lib

This library helps to manipulate the 2d arrays. User can execute functions like addition, substraction, multipliaction and other operations on square matrix and determinant.

Functionality:-

• Used to collect user data and process it according to user requirements.

• Can be used in create recommendation system.

• Used in deep learning model training.

• Used in unsupervised machine learning projects.

• Used in predicting data

Installation

• Run the following command

pip install num-enumerator

cd dataEnumerator

• Save the test file in this folder in order to import class and functions from enumerator

Docs :-

To add two matrix

from enumerator import matrix



A=[[1,2],

  [3,4]]

B=[[9,2],

  [3,8]]



C = matrix.add(A,B)



print(C)

Output :-

[[10,4],

[6,12]]

To substract two matrix

from enumerator import matrix



A=[[1,18],

  [3,14]]

B=[[9,2],

  [3,8]]



C = matrix.substract(A,B)



print(C)

Output :-

[[-8,16],

[0,6]]

To multiply two matrix

from enumerator import matrix



A=[[1,2],

  [3,4]]

B=[[0,1],

  [1,0]]



C = matrix.multiply(A,B)



print(C)

Output :-

[[1,2],

[3,4]]

To get transpose of matrix

from enumerator import matrix



A=[[1,2],

  [3,4]]



C = matrix.transpose(A)



print(C)

Output :-

[[1,3],

[2,4]]

To substract particular number with all the elements of determinant

from enumerator import determinant

A = [[2,4],

  [6,7]]



B = determinant.substractNum(2)

print(B)

Output :-

[[0,2],

[4,5]]

To add particular number with all the elements of determinant

from enumerator import determinant

A = [[2,4],

  [6,7]]



B = determinant.addNum(1)

print(B)

Output :-

[[3,5],

[7,8]]

To divide particular number with all the elements of determinant

from enumerator import determinant

A = [[3,9],

  [6,12]]



B = determinant.divideNum(2)

print(B)

Output :-

[[1,3],

[2,4]]

To multiply particular number with all the elements of determinant

from enumerator import determinant

A = [[2,4],

  [6,7]]



B = determinant.substractNum(5)

print(B)

Output :-

[[10,20],

[30,35]]

Graphical equation handling

x_data = [1, 2, 3, 4, 5]

y_data = [2, 4, 6, 8, 10]



graph_fitter = graphEQ(x_data, y_data)



graph_fitter.fit_linear()

print(graph_fitter.get_equation())  # Output: y = 2.00x



graph_fitter.fit_polynomial(2)

print(graph_fitter.get_equation())  # Output: y = 0.00x^2 + 2.00x

Languages used:

Developer

• abhineetraj1