Matrix Multiplication • Write a multiplication function that accepts two 2D numpy arrays, and returns the product of the two. This function should test that the arrays are compatible (recall that the number of columns in the first matrix must equal the number of rows in the second matrix). This function should test the array sizes before attempting the multiplication. If the arrays are not compatible, the function should return -1. solve in python

Matrix Multiplication • Write a multiplication function that accepts two 2D numpy arrays, and returns the product of the two. This function should test that the arrays are compatible (recall that the number of columns in the first matrix must equal the number of rows in the second matrix). This function should test the array sizes before attempting the multiplication. If the arrays are not compatible, the function should return -1. solve in python

C++ for Engineers and Scientists

4th Edition

ISBN:9781133187844

Author:Bronson, Gary J.

Publisher:Bronson, Gary J.

Chapter7: Arrays

Section: Chapter Questions

Problem 3PP: (Numerical) Given a one-dimensional array of integer numbers, write and test a function that...

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Matrix Addition• Write an addition function that accepts two 2D numpy arrays, andreturns the sum of the two (if they are the same size). This functionshould test that the arrays are the same size before performing theaddition. If the arrays are not the same size, the function shouldreturn -1. solve in python
Prompt: In Python language, write a function that applies the logistic sigmoid function to all elements of a NumPy array. Code: import numpy as np import math import matplotlib.pyplot as plt import pandas as pd def sigmoid(inputArray): modifiedArray = np.zeros(len(inputArray)) #YOUR CODE HERE: return(modifiedArray) def test(): inputs = np.arange(-100, 100, 0.5) outputs = sigmoid(inputs) plt.figure(1) plt.plot(inputs) plt.title('Input') plt.xlabel('Index') plt.ylabel('Value') plt.show() plt.figure(2) plt.plot(outputs,'Black') plt.title('Output') plt.xlabel('Index') plt.ylabel('Value') plt.show() test()
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Prompt: In Python language, wrte a function that applies the logistic sigmoid function to all elements of a NumPy array. Code: import numpy as np import math import matplotlib.pyplot as plt import pandas as pd def sigmoid(inputArray): modifiedArray = np.zeros(len(inputArray)) #YOUR CODE HERE: return(modifiedArray) def test(): inputs = np.arange(-100, 100, 0.5) outputs = sigmoid(inputs) plt.figure(1) plt.plot(inputs) plt.title('Input') plt.xlabel('Index') plt.ylabel('Value') plt.show() plt.figure(2) plt.plot(outputs,'Black') plt.title('Output') plt.xlabel('Index') plt.ylabel('Value') plt.show() test()
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Python Coding Exercise: Write a function that takes three parameters: a 2-D array arr, a row location i, a column location j and returns a float representing the fraction of neighbors around arr [i,j] # write your function here:def find_frac(arr,i,j):
Write a function that displays an n-by-n matrix using the following header: void printMatrix(int n)Each element is 0 or 1, which is generated randomly. Write a test program that prompts the user to enter n and displays an n-by-n matrix.
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