Task: Element-wise operations





Java: import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
import java.util.function.BiFunction;
import java.util.stream.Stream;

@SuppressWarnings("serial")
public class ElementWiseOp {
	static final Map<String, BiFunction<Double, Double, Double>> OPERATIONS = new HashMap<String, BiFunction<Double, Double, Double>>() {
		{
			put("add", (a, b) -> a + b);
			put("sub", (a, b) -> a - b);
			put("mul", (a, b) -> a * b);
			put("div", (a, b) -> a / b);
			put("pow", (a, b) -> Math.pow(a, b));
			put("mod", (a, b) -> a % b);
		}
	};
	public static Double[][] scalarOp(String op, Double[][] matr, Double scalar) {
		BiFunction<Double, Double, Double> operation = OPERATIONS.getOrDefault(op, (a, b) -> a);
		Double[][] result = new Double[matr.length][matr[0].length];
		for (int i = 0; i < matr.length; i++) {
			for (int j = 0; j < matr[i].length; j++) {
				result[i][j] = operation.apply(matr[i][j], scalar);
			}
		}
		return result;
	}
	public static Double[][] matrOp(String op, Double[][] matr, Double[][] scalar) {
		BiFunction<Double, Double, Double> operation = OPERATIONS.getOrDefault(op, (a, b) -> a);
		Double[][] result = new Double[matr.length][Stream.of(matr).mapToInt(a -> a.length).max().getAsInt()];
		for (int i = 0; i < matr.length; i++) {
			for (int j = 0; j < matr[i].length; j++) {
				result[i][j] = operation.apply(matr[i][j], scalar[i % scalar.length][j
						% scalar[i % scalar.length].length]);
			}
		}
		return result;
	}
	public static void printMatrix(Double[][] matr) {
		Stream.of(matr).map(Arrays::toString).forEach(System.out::println);
	}
	public static void main(String[] args) {
		printMatrix(scalarOp("mul", new Double[][] {
				{ 1.0, 2.0, 3.0 }, 
				{ 4.0, 5.0, 6.0 }, 
				{ 7.0, 8.0, 9.0 }
		}, 3.0));

		printMatrix(matrOp("div", new Double[][] {
				{ 1.0, 2.0, 3.0 }, 
				{ 4.0, 5.0, 6.0 }, 
				{ 7.0, 8.0, 9.0 }
		}, new Double[][] {
				{ 1.0, 2.0}, 
				{ 3.0, 4.0} 
		}));
	}
}



Python: >>> import random
>>> from operator import add, sub, mul, floordiv
>>> from pprint import pprint as pp
>>> 
>>> def ewise(matrix1, matrix2, op):
	return [[op(e1,e2) for e1,e2 in zip(row1, row2)] for row1,row2 in zip(matrix1, matrix2)]

>>> m,n = 3,4 	
>>> a0 = [[random.randint(1,9) for y in range(n)] for x in range(m)]
>>> a1 = [[random.randint(1,9) for y in range(n)] for x in range(m)]
>>> pp(a0); pp(a1)
[[7, 8, 7, 4], [4, 9, 4, 1], [2, 3, 6, 4]]
[[4, 5, 1, 6], [6, 8, 3, 4], [2, 2, 6, 3]]
>>> pp(ewise(a0, a1, add))
[[11, 13, 8, 10], [10, 17, 7, 5], [4, 5, 12, 7]]
>>> pp(ewise(a0, a1, sub))
[[3, 3, 6, -2], [-2, 1, 1, -3], [0, 1, 0, 1]]
>>> pp(ewise(a0, a1, mul))
[[28, 40, 7, 24], [24, 72, 12, 4], [4, 6, 36, 12]]
>>> pp(ewise(a0, a1, floordiv))
[[1, 1, 7, 0], [0, 1, 1, 0], [1, 1, 1, 1]]
>>> pp(ewise(a0, a1, pow))
[[2401, 32768, 7, 4096], [4096, 43046721, 64, 1], [4, 9, 46656, 64]]
>>> pp(ewise(a0, a1, lambda x, y:2*x - y))
[[10, 11, 13, 2], [2, 10, 5, -2], [2, 4, 6, 5]]
>>> 
>>> def s_ewise(scalar1, matrix1, op):
	return [[op(scalar1, e1) for e1 in row1] for row1 in matrix1]

>>> scalar = 10
>>> a0
[[7, 8, 7, 4], [4, 9, 4, 1], [2, 3, 6, 4]]
>>> for op in ( add, sub, mul, floordiv, pow, lambda x, y:2*x - y ):
	print("%10s :" % op.__name__, s_ewise(scalar, a0, op))

	
       add : [[17, 18, 17, 14], [14, 19, 14, 11], [12, 13, 16, 14]]
       sub : [[3, 2, 3, 6], [6, 1, 6, 9], [8, 7, 4, 6]]
       mul : [[70, 80, 70, 40], [40, 90, 40, 10], [20, 30, 60, 40]]
  floordiv : [[1, 1, 1, 2], [2, 1, 2, 10], [5, 3, 1, 2]]
       pow : [[10000000, 100000000, 10000000, 10000], [10000, 1000000000, 10000, 10], [100, 1000, 1000000, 10000]]
  <lambda> : [[13, 12, 13, 16], [16, 11, 16, 19], [18, 17, 14, 16]]
>>>