def freivalds(N, A, B, C):
Z = N * [0] # vector fila de ceros
for I in range(N):
Z[I] = Random(0, 1) # construimos vector aleatorio
Y = multiply(N, B, Z)
X1 = multiply(N, A, Y)
X2 = multiply(N, C, Z)
return X1 == X2
def partition_randomized(values,sortBy, p, r):
pivot_index = Random(p, r)
pivot1 = sortBy[pivot_index]
pivot2 = values[pivot_index]
sortBy[pivot_index] = sortBy[r]
values[pivot_index] = values[r]
values[r] = pivot2
sortBy[r] = pivot1
return partition_with_pivot(values, sortBy, p, r, pivot1)
def freivalds(n, A, B, C):
def multiply(n, A, Z):
# Crea el vector a retornar
R = n * [0]
# Recorre los elementos del vector R y las filas de la matriz A
for i in range(n):
# Recorre los elementos del vector Z y los elementos de la fila i de A
for j in range(n):
R[i] = R[i] + (A[i][j] * Z[j])
return R
# Genera un vector Z lleno de ceros y unos
Z = n * [n]
for i in range(n):
Z[i] = Random(0, 1)
# Multiplica B x Z, luego A x (B x Z) y C x Z
# Obteniendo 2 vectores x1 y x2 de largo n
Y = multiply(n, B, Z)
x1 = multiply(n, A, Y)
x2 = multiply(n, C, Z)
# Chequea si A x (B x Z) = C x Z
return x1 == x2
def partition_randomized(A, p, r):
pivot_index = Random(p, r)
pivot = A[pivot_index]
A[pivot_index], A[r] = A[r], A[pivot_index]
return partition_with_pivot(A, p, r, pivot)
B = mergesort(A)
print B
R = False
for i in range(len(B) - 1):
start = i + 1
end = len(B) - 1
while start < end:
mid = (start + end) / 2
if B[mid] + B[i] == x:
R = True
break
elif B[mid] + B[i] < x:
start = mid + 1
elif B[mid] + B[i] > x:
end = mid - 1
if B[start] + B[i] == x:
R = True
return R
A = [Random(0, 2) for i in range(100)]
x = 71
#print A
print mergesort(A)
#print problema_3_8(A,x)