HackerRank Map Reduce Advanced - Matrix Multiplication problem solution

In this HackerRank Map Reduce Advanced - Matrix Multiplication problem solution Mappers and Reducers

Here's a quick but comprehensive introduction to the idea of splitting tasks into a MapReduce model. The four important functions involved are:

Map (the mapper function)  

EmitIntermediate(the intermediate key,value pairs emitted by the mapper functions)  

Reduce (the reducer function)  

Emit (the final output, after summarization from the Reduce functions)

We provide you with a single system, single thread version of a basic MapReduce implementation.

Task

The input is a number of test cases with two matrices each. A single test case will look like:

[#Rows_Matrix_1] [#Columns_Matrix_1]

[Row_1_Matrix_1]

.

.

[Row_N_Matrix_1]

[#Rows_Matrix_2] [#Columns_Matrix_2]

[Row_1_Matrix_2]

.

.

[Row_N_Matrix_2]

The required output is to print the product of the two matrices in the format shown. The code for the MapReduce class, parts related to IO etc. has already been provided. However, the mapper and reducer functions are incomplete. Your task is to fill up the mapper and reducer functions appropriately, such that the program works, and outputs the product of the two matrices, in row-wise manner.

Also, this program outputs certain information to the error stream. This information has been logged to help beginners gain a better understanding of the the intermediate steps in a map-reduce process.

Problem solution in Python.

import sys
from collections import OrderedDict
class MapReduce:
    def __init__(self):
        self.intermediate = OrderedDict()
        self.result = []

    def emitIntermediate(self, key, value):
        self.intermediate.setdefault(key, [])       
        self.intermediate[key].append(value)

    def emit(self, value):
        self.result[value[0]][value[1]] = value[2] 

    def execute(self, matrix1, matrix2, mapper, reducer):
        n = len(matrix1)
        m = len(matrix2[0])
        for i in xrange(0,n):
            self.result.append([0]*m)
        
        mapper(matrix1, matrix2)

        for key in self.intermediate:
            reducer(key, self.intermediate[key])

        for i in xrange(0,n):
            row = ""
            for j in xrange(0,m):
                 row += str(self.result[i][j]) + " "
            print row

mapReducer = None

def mapper(matrix1, matrix2):
    for i in xrange(0, len(matrix1)):
        for j in xrange(0, len(matrix2[0])):
            for k in xrange(0, len(matrix2)):
                mapReducer.emitIntermediate((i, j), matrix1[i][k] * matrix2[k][j])
    #Start writing the Map code here

def reducer(key, list_of_values):
    s = 0
    for i in list_of_values:
        s += i
    mapReducer.emit([key[0], key[1], s])
    #Start writing the Reduce code here
    
    
if __name__ == '__main__':
    testcases = int(sys.stdin.readline().strip())
    for t in xrange(0,testcases):
        mapReducer = MapReduce()
        dimensions = sys.stdin.readline().strip().split(" ")
        row = int(dimensions[0])
        column = int(dimensions[1])
        matrix1 = []
        matrix2 = []
        for i in range(0, row):
            read_row = sys.stdin.readline().strip()
            matrix1.append([])
            row_elems = read_row.strip().split()
            for j in range(0, len(row_elems)):
                matrix1[i].append(int(row_elems[j]))
        dimensions = sys.stdin.readline().strip().split(" ")
        row = int(dimensions[0])
        column = int(dimensions[1])
        for i in range(0, row):
            read_row = sys.stdin.readline().strip()
            matrix2.append([])
            row_elems = read_row.strip().split()
            for j in range(0, len(row_elems)):
                matrix2[i].append(int(row_elems[j]))
        mapReducer.execute(matrix1, matrix2 , mapper, reducer)