Answer:
import numpy as np
a = int(input ("Enter a"))
b = int(input ("Enter b"))
c = int(input ("Enter c"))
d = int(input ("Enter d"))
c1 = int(input ("Enter c1"))
c2 = int(input ("Enter c2"))
array1 =[[a, b],[c, d]]
A = np.array (array1)
B = np.array ([c1, c2])
X = np.linalg.inv (A).dot (B)
print (X)
Explanation:
let ax + by =c1
cx + dy =c2
We have used the above NumPy library that has the methods for matrix calculation, and here we have used matrix multiplication, and the inverse of a matrix to find the value of x and y.
We know AX=B
X = inv A. B
And this we have used above. We can calculate inv A and do matrix multiplication using NumPy. And thus we get the above solution.
Answer:
elaborative rehearsal
Explanation:
when it comes to storage of information or data into long term memory then elaborative rehearsal plays an important role.
Elaborative rehearsal is a technique which focuses on thinking about the piece of information or data's meaning which is to be stored in long term memory and linking it with the information or data which is already present or stored.
