NumPy: Compute the covariance matrix of two given arrays
8. Covariance Matrix of Two Arrays
Write a NumPy program to compute the covariance matrix of two given arrays.
Sample Solution:
Python Code:
# Importing the NumPy library
import numpy as np
# Creating an array 'x' containing elements [0, 1, 2]
x = np.array([0, 1, 2])
# Creating an array 'y' containing elements [2, 1, 0]
y = np.array([2, 1, 0])
# Displaying the original array 'x'
print("\nOriginal array1:")
print(x)
# Displaying the original array 'y'
print("\nOriginal array1:")
print(y)
# Calculating the covariance matrix of arrays 'x' and 'y' using np.cov()
print("\nCovariance matrix of the said arrays:\n", np.cov(x, y))
Sample Output:
Original array1: [0 1 2] Original array1: [2 1 0] Covariance matrix of the said arrays: [[ 1. -1.] [-1. 1.]]
Explanation:
In the above exercise –
x = np.array([0, 1, 2]): This code creates a NumPy array x containing the values 0, 1, and 2.
y = np.array([2, 1, 0]): This code creates a NumPy array y containing the values 2, 1, and 0. print(np.cov(x, y)): This code calculates the covariance matrix of the arrays x and y using the np.cov() function, and then prints it to the console.
For more Practice: Solve these Related Problems:
- Write a function that calculates the covariance matrix of two 1D arrays using np.cov and ensures the matrix is symmetric.
- Create a program that computes the covariance of two 2D arrays after flattening them into 1D arrays.
- Implement a solution that normalizes two arrays before computing their covariance and compares the output with np.cov.
- Develop a method that computes covariance manually using the formula E[XY] - E[X]E[Y] and validates it against np.cov.
Python-Numpy Code Editor:
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Next: Write a NumPy program to compute cross-correlation of two given arrays.
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