PR3_Skriptsprachen_GruppeD_SL1/s1_a51.py

import collections

import numpy as np
from scipy import sparse


class Matrix:
    def __init__(self):
        self.__matrix = collections.defaultdict(dict)

    def set(self, column, row, value):
        if value == 0:
            if self.__matrix.get(column) and self.__matrix.get(column).get(row):
                del self.__matrix[column][row]
        else:
            self.__matrix[column][row] = value

    def get(self, column, row):
        if self.__matrix.get(column) and self.__matrix.get(column).get(row):
            return self.__matrix[column][row]
        else:
            return 0

    def print(self):
        print(self.__matrix)


if __name__ == "__main__":
    # Aufgabe 1b
    input_matrix = [[3, 0, -2, 11],
                    [0, 0, 9, 0],
                    [0, 7, 0, 0],
                    [0, 0, 0, 0]]
    sparse_matrix = Matrix()
    for column in range(len(input_matrix)):
        for row in range(len(input_matrix[0])):
            sparse_matrix.set(column, row, input_matrix[row][column])

    sparse_matrix.print()
    # Aufgabe 1c
    # print("\nThe sparce matrix computed without numpy\n" + str(sparse_matrix))

    # faster way with numpy
    input_matrix = np.array(input_matrix)
    rows, columns = input_matrix.shape
    sparse_matrix = sparse.csr_matrix(input_matrix)
    print("\nThe sparce matrix computed with numpy\n" + str(sparse_matrix))