概率分布模型

Question

我正在尝试运行python在此数据科学站点上用作示例的脚本：https : //www.datascience.com/blog/introduction-to-bayesian-inference-learn-data-science-tutorialsimport numpy as npfrom scipy.misc import factorialimport matplotlib.pyplot as plt%matplotlib inlineplt.rcParams['figure.figsize'] = (16,7)def likelihood(theta, n, x):    """    likelihood function for a binomial distribution    n: [int] the number of experiments    x: [int] the number of successes    theta: [float] the proposed probability of success    """    return (factorial(n) / (factorial(x) * factorial(n - x))) \            * (theta ** x) * ((1 - theta) ** (n - x))# The number of impressions for our facebook-yellow-dress campaignn_impressions = 10# The number of clicks for our facebook-yellow-dress campaignn_clicks = 7# Observed click through ratectr = n_clicks / n_impressions# 0 to 1, all possible click through ratespossible_theta_values = map(lambda x: x/100., range(100))# Evaluate the likelihood function for possible click through rateslikelihoods = map(lambda theta: likelihood(theta, n, x)\                                , possible_theta_values)# Pick the best thetamle = possible_theta_values[np.argmax(likelihoods)]# Plotf, ax = plt.subplots(1)ax.plot(possible_theta_values, likelihoods)ax.axvline(mle, linestyle = "--")ax.set_xlabel("Theta")ax.set_ylabel("Likelihood")ax.grid()ax.set_title("Likelihood of Theta for New Campaign")plt.show()我正在使用 Spyder。我得到的错误包括：    %matplotlib inline    ^SyntaxError: invalid syntax也mle = possible_theta_values[np.argmax(likelihoods)]TypeError: 'map' object is not subscriptable和likelihoods = list(map(lambda theta: likelihood(theta, ctr, x)\NameError: name 'x' is not defined

Answer 1

对代码进行少量修改以使其工作。我不得不转换的map可迭代对象名单，让你的代码工作，你提到的原因：TypeError: 'map' object is not subscriptable。我还必须明确定义x数组才能在计算likelihoods.

# 0 to 1, all possible click through rates

possible_theta_values = list(map(lambda x: x/100., range(100)))

n = n_impressions

x = n_clicks

# Evaluate the likelihood function for possible click through rates

likelihoods = list(map(lambda theta: likelihood(theta, n, x)\

                                , possible_theta_values))