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, col, ro, value):
        if value == 0:
            if self.matrix.get(col) and self.matrix.get(col).get(ro):
                del self.matrix[col][ro]
        else:
            self.matrix[col][ro] = value

    def get(self, col, ro):
        if self.matrix.get(col) and self.matrix.get(col).get(row):
            return self.matrix[col][row]
        else:
            return 0


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

    print(mar.matrix)
    # 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))