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monteCarloMatrixSparse2.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from random import randint
+from scipy import optimize
+from time import time
+def monteCarloMatrixMultiply2(A,B):
+    n = len(A)
+    C = [[{} for j in range(n)] for i in range(n)]
+    D = [[{} for j in range(n)] for i in range(n)]
+    AB = [[0  for j in range(n)] for i in range(n)]
+    for i in range(n):
+        for j in range(n):
+            C[i][j]['size'] = 0
+            D[i][j]['size'] = 0
+    while True:
+        for i in range(n):
+            for j in range(n):
+                k = randint(0,n-1)
+                if j in C[i][k] and i in D[k][j]:
+                    pass
+                else: 
+                    if A[i][k] != 0 or B[k][j] != 0:
+                        AB[i][j] = AB[i][j] + A[i][k]*B[k][j]
+                    C[i][k][j] = True
+                    C[i][k]['size'] = C[i][k]['size'] + 1
+                    D[k][j][i] = True
+                    D[k][j]['size'] = D[k][j]['size'] + 1
+        flag = True
+        for i in range(n):
+            for j in range(n):
+                flag = flag and C[i][j]['size'] == n and D[i][j]['size'] == n
+        if flag: break
+    return AB
+def testPoly(x,a,b,c):
+    return a*x**2 + b*x + c
+setparams = [5,105,5]
+experi2Time = [k for k in range(setparams[0],setparams[1],setparams[2])]
+xaxis = [k for k in range(setparams[0],setparams[1],setparams[2])]
+for k in range(len(xaxis)):
+    n1 = xaxis[k]
+    A = [[randint(5,10) for i in range(n1)] for j in range(n1)]
+    B = [[randint(5,10) for i in range(n1)] for j in range(n1)]
+    start = time()
+    monteCarloMatrixMultiply2(A,B)
+    end = time()
+    experi2Time[k] = end - start
+    print('size = ',xaxis[k],' --> done ...')
+np.random.seed(0)
+params, params_covariance = optimize.curve_fit(
+    testPoly,xaxis,experi2Time,
+    p0=[1, 1, 1])
+print(
+    '(',params[0],')x^2 + (',params[1],')x +',params[2]
+    )