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Practice2

练习 2

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Practice2

中文版：练习 2

Import NumPy using np as an alias (refer to Week 9: lab4 02.0202 and 02.07)

import numpy as np
  1. Create a 6×6 matrix X (with all elements being unique, e.g., 1 to 36), and extract the 3rd and 5th columns from X to form a new matrix Y.
# Step 1: Create a 6x6 matrix with unique elements (e.g., 1 to 36)
# to complete
X =
print("Matrix X (6x6):")
print(X)
 
# Step 2: Extract the 3rd and 5th columns (indices 2 and 4 in 0-based indexing)
# to complete
Y =
print("\nMatrix Y (6x2, 3rd & 4th columns of X):")
print(Y)
  1. Now we have an array X_1 of shape 6x6, predict the result of X+X_1
# to complete
X_1=
# run for test
Z=X+X_1
print(Z)
  1. Predict the result of X + Y, and use code to perform the corresponding calculation
Z=X+Y # is this correct?

The rest of this practice may include next week’s lecture material, you can have a look or leave these questions for next week and continue the Practice3

  1. Calculate the maximum value in each row of X, and the minimum value in each column of Y.
# Max in each row of X
# to complete
row_max_X =
print("Max per row of X:", row_max_X)
 
# Min in each column of Y
# to complete
col_min_Y =
print("Min per column of Y:", col_min_Y)
  1. Create an 8×8 array filled with zeros. Randomly select 25 positions and fill them with random numbers greater than 0 using ‘np.random.uniform’. Print the resulting matrix W.
# Step 1: Create an 8x8 array of zeros
W =
 
# Step 2: Randomly select 25 unique positions (indices)
 
# Step 3: Fill selected positions with random numbers > 0
 
# Print the resulting matrix W
print("Matrix W (8x8 with 25 random non-zero entries):")
print(W)
  1. Calculate the average value of the elements in the matrix and store it in a variable named mean_W. Replace all the zeros in W with the value of mean_W.
# Calculate the mean of all elements in W (excluding zeros if needed)
mean_W =
 
# Replace zeros with mean_W
W_no_zeros =
# Alternatively, modify W in-place:
# W[W == 0] = mean_W
 
print("Mean value (mean_W):", mean_W)
print("\nMatrix W with zeros replaced by mean_W:")
print(W_no_zeros)
  1. Replace all values in the matrix that are smaller than mean_W with -1, and print how many -1 values are in the matrix.
# Calculate mean_W (as previously defined)
mean_W =
 
# Replace values < mean_W with -1
W_replaced =
 
# Count the number of -1 values
count_minus_1 =
 
# Print results
print(f"Mean value (mean_W): {mean_W:.4f}")
print(f"Number of -1 values: {count_minus_1}")
print("\nMatrix after replacement:")
print(W_replaced)
  1. Compute the transpose of matrix W and store it in Z. Combine W and Z to form a 3-dimensional matrix V, and then flatten Z to get a new 1-dimensional array U.
# Step 1: Compute transpose of W and store in Z
Z =
 
# Step 2: Combine W and Z to form a 3D matrix V (shape: 2 x rows x cols)
V =
 
# Step 3: Flatten Z to get a 1D array U
U =
 
# Print results
print("Transpose Z:")
print(Z)
print("\n3D Matrix V (shape: {}):".format(V.shape))
print(V)
print("\nFlattened array U:")
print(U)
  1. Find the third largest value in matrix W, denoted as Max3. Then, obtain a Boolean matrix B from W where all elements greater than or equal to Max3 are marked as True, and the rest as False.
# Step 1: Find the third largest value (Max3)
 
# Step 2: Create Boolean matrix B
B =
 
# Print results
print(f"Third largest value (Max3): {Max3}")
print("\nBoolean matrix B:")
print(B)
  1. Print the elements in Z that correspond to the True positions in B and calculate their average.
# Step 1: Get elements in Z where B is True
selected_elements =
 
# Step 2: Calculate the average of these elements
average_selected =
 
# Print results
print("Elements in Z corresponding to True positions in B:")
print(selected_elements)
print(f"\nAverage of these elements: {average_selected}")
  1. Combine A and B into a 2x4 matrix C. Then, combine A and B into an 4x2 matrix D. Finally, print out the elements of C row by row., print out the elements of D column by column.
# Combine into C (2x4) by horizontal stacking
C =
 
# Combine into D (4x2) by vertical stacking
D =
 
print(C)
print(D)
# Print C row by row
print("Matrix C (row by row):")
for row in C:
 print(row)
 
# Print D column by column
print("\nMatrix D (column by column):")
for col_idx in range(D.shape[1]): # Iterate over the list colomn index
 column = D[:, col_idx] # get the current column
 print(column)

Graph View