# Problem 2 – Part (b)
import sys
import numpy as np
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# generate_samples(init_prob, evid_seq, T, E, n, size)
# 1. Randomly generate a number of sampels.
# Parameters:
# 1. init_prob: Initial probabilities
# 2. evid_seq: Evidence seqeuence
# 3. T: T matrix
# 4. E: E matrix
# 5. n: Number of samples to be generated
# 6. size: Number of states in a sample
def generate_samples(init_prob, evid_seq, T, E, n, size):
# cal_weighted_prob_from_samples(samples)
# 1. Calculate the probability, based on the samples.
# Parameters:
# 1. samples: Generated samples.
def cal_weighted_prob_from_samples(samples):
# cal_average_prob_and_var(probs)
# 1. Calculate the average probability, and its variance.
# Parameters:
# 1. probs: A sequence of calculated probabilities.
def cal_average_prob_and_var(probs):
def main():
n = int(sys.argv[1])
size = int(sys.argv[2])
evid_seq = []
for i in range(size):
evid_seq.append(int(sys.argv[i+3]))
init_prob = [0.5, 0.5]
T = np.matrix([[0.7, 0.3],[0.3, 0.7]])
E = np.matrix([[0.9, 0.1],[0.2, 0.8]])
probs = []
for i in range(10):
samples = generate_samples(init_prob, evid_seq, T, E, n, size)
probs.append(cal_weighted_prob_from_samples(samples))
prob_mean, var = cal_average_prob_and_var(probs)
print(“Estimated probability: “, prob_mean)
print(“Variance of the estimation: “, var)
if __name__ == “__main__”:
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