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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-tutorials

import numpy as np

from scipy.misc import factorial

import matplotlib.pyplot as plt

%matplotlib inline

plt.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 campaign

n_impressions = 10

# The number of clicks for our facebook-yellow-dress campaign

n_clicks = 7

# Observed click through rate

ctr = n_clicks / n_impressions

# 0 to 1, all possible click through rates

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

# Evaluate the likelihood function for possible click through rates

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

                                , possible_theta_values)

# Pick the best theta

mle = possible_theta_values[np.argmax(likelihoods)]

# Plot

f, 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 ratespossible_theta_values = list(map(lambda x: x/100., range(100)))n = n_impressionsx = n_clicks# Evaluate the likelihood function for possible click through rateslikelihoods = list(map(lambda theta: likelihood(theta, n, x)\                                , possible_theta_values))