Added codes 1.1, 1.2, 1.3 and 1.5

Codes/Code-1.2.py

@@ -0,0 +1,57 @@
+import numpy as np
+
+# Constants for PageRank
+threshold = 1e-13
+beta = 0.85
+
+# Spider Trap Network represented as adjacency matrix
+A = [
+    [0, 0, 1, 0],
+    [1, 0, 0, 0],
+    [1, 1, 0, 0],
+    [1, 1, 0, 1]
+]
+
+# Convert adjacency matrix to a numpy array
+arr = np.array(A, dtype=float)
+
+# Calculate summation of columns
+s = []
+for i in range(len(A)):
+    s.append(np.sum(arr[:, i]))
+
+print("Summation of columns: ", s)
+
+# Create the column stochastic probability matrix, M
+M = arr.copy()
+for j in range(len(A)):
+    if s[j] != 0:  # Prevent division by zero
+        M[:, j] = M[:, j] / s[j]
+
+print("Column stochastic probability matrix, M:")
+print(M)
+
+# Initialize rank vector
+r = (1.0 + np.zeros([len(M), 1])) / len(M)
+print("Initial rank vector:")
+print(r)
+
+# Calculate the uniform rank contribution
+uniformR = (1.0 - beta) * r
+r_prev = r.copy()
+
+# PageRank iterations
+for i in range(1, 1001):
+    print("Iteration: ", i)
+    r = beta * np.matmul(M, r_prev) + uniformR
+    print("The rank vector: ")
+    print(r)
+
+    diff = np.sum(abs(r - r_prev))
+    if diff < threshold:
+        break
+    r_prev = r.copy()
+
+# Display the final rank vector
+print("The final rank vector: ")
+print(r[:, 0])