我正在实现高斯朴素贝叶斯算法:
# importing modules
import pandas as pd
import numpy as np
# create an empty dataframe
data = pd.DataFrame()
# create our target variable
data["gender"] = ["male","male","male","male",
"female","female","female","female"]
# create our feature variables
data["height"] = [6,5.92,5.58,5.92,5,5.5,5.42,5.75]
data["weight"] = [180,190,170,165,100,150,130,150]
data["foot_size"] = [12,11,12,10,6,8,7,9]
# view the data
print(data)
# create an empty dataframe
person = pd.DataFrame()
# create some feature values for this single row
person["height"] = [6]
person["weight"] = [130]
person["foot_size"] = [8]
# view the data
print(person)
# Priors can be calculated either constants or probability distributions.
# In our example, this is simply the probability of being a gender.
# calculating prior now
# number of males
n_male = data["gender"][data["gender"] == "male"].count()
# number of females
n_female = data["gender"][data["gender"] == "female"].count()
# total people
total_ppl = data["gender"].count()
print ("Male count =",n_male,"and Female count =",n_female)
print ("Total number of persons =",total_ppl)
# number of males divided by the total rows
p_male = n_male / total_ppl
# number of females divided by the total rows
p_female = n_female / total_ppl
print ("Probability of MALE =",p_male,"and FEMALE =",p_female)
# group the data by gender and calculate the means of each feature
data_means = data.groupby("gender").mean()
# view the values
data_means
# group the data by gender and calculate the variance of each feature
data_variance = data.groupby("gender").var()
# view the values
data_variance
data_variance = data.groupby("gender").var()
data_variance["foot_size"][data_variance.index == "male"].values[0]
# means for male
male_height_mean=data_means["height"][data_means.index=="male"].values[0]
male_weight_mean=data_means["weight"][data_means.index=="male"].values[0]
male_footsize_mean=data_means["foot_size"][data_means.index=="male"].values[0]
print (male_height_mean,male_weight_mean,male_footsize_mean)
# means for female
female_height_mean=data_means["height"][data_means.index=="female"].values[0]
female_weight_mean=data_means["weight"][data_means.index=="female"].values[0]
female_footsize_mean=data_means["foot_size"][data_means.index=="female"].values[0]
print (female_height_mean,female_weight_mean,female_footsize_mean)
# variance for male
male_height_var=data_variance["height"][data_variance.index=="male"].values[0]
male_weight_var=data_variance["weight"][data_variance.index=="male"].values[0]
male_footsize_var=data_variance["foot_size"][data_variance.index=="male"].values[0]
print (male_height_var,male_weight_var,male_footsize_var)
# variance for female
female_height_var=data_variance["height"][data_variance.index=="female"].values[0]
female_weight_var=data_variance["weight"][data_variance.index=="female"].values[0]
female_footsize_var=data_variance["foot_size"][data_variance.index=="female"].values[0]
print (female_height_var,female_weight_var,female_footsize_var)
# create a function that calculates p(x | y):
def p_x_given_y(x,mean_y,variance_y):
# input the arguments into a probability density function
p = 1 / (np.sqrt(2 * np.pi * variance_y)) * \
np.exp((-(x - mean_y) ** 2) / (2 * variance_y))
# return p
return p
# numerator of the posterior if the unclassified observation is a male
posterior_numerator_male = p_male * \
p_x_given_y(person["height"][0],male_height_mean,male_height_var) * \
p_x_given_y(person["weight"][0],male_weight_mean,male_weight_var) * \
p_x_given_y(person["foot_size"][0],male_footsize_mean,male_footsize_var)
# numerator of the posterior if the unclassified observation is a female
posterior_numerator_female = p_female * \
p_x_given_y(person["height"][0],female_height_mean,female_height_var) * \
p_x_given_y(person["weight"][0],female_weight_mean,female_weight_var) * \
p_x_given_y(person["foot_size"][0],female_footsize_mean,female_footsize_var)
print ("Numerator of Posterior MALE =",posterior_numerator_male)
print ("Numerator of Posterior FEMALE =",posterior_numerator_female)
if (posterior_numerator_male >= posterior_numerator_female):
print ("Predicted gender is MALE")
else:
print ("Predicted gender is FEMALE")
当我们计算概率时，我们使用高斯 PDF 计算它:
$$ P(x) =\frac{1}{\sqrt {2\pi {\sigma}^2}} e^{\frac{-(x-\mu)^2}{2 {\西格玛}^2}}$$
我的问题是上面的等式是 PDF 的等式。要计算概率，我们必须在区域 dx 上对其进行积分。
$\int_{x0}^{x1} P(x)dx $
但在上面的程序中，我们将 x 的值插入并计算概率。那是对的吗？为什么？我有 seen most of the articles calculating概率以同样的方式。
如果这是朴素贝叶斯分类器中计算概率的错误方法，那么正确的方法是什么？
请您参考如下方法:
方法正确。 pdf函数是概率密度，即衡量处于某个值的邻域中的概率除以该邻域的“大小”的函数，其中“大小”是维度的长度1、2中的面积、3中的体积等
在连续概率中，准确获得任何给定结果的概率为 0，这就是使用密度代替的原因。因此，我们不处理诸如 P(X=x) 之类的表达式。但是用P(|X-x| < Δ(x)) , 代表 X 的概率亲近x .
让我简化符号并写成P(X~x)对于 P(|X-x| < Δ(x)) .
如果你在这里应用贝叶斯法则，你会得到
P(X~x|W~w) = P(W~w|X~x)*P(X~x)/P(W~w)
因为我们处理的是概率。如果我们现在引入密度:
pdf(x|w)*Δ(x) = pdf(w|x)Δ(w)*pdf(x)Δ(x)/(pdf(w)*Δ(w))
因为probability = density*neighborhood_size .由于所有 Δ(·)在上面的表达式中取消，我们得到
pdf(x|w) = pdf(w|x)*pdf(x)/pdf(w)
这是密度的贝叶斯规则。
结论是，鉴于贝叶斯规则也适用于密度，在处理连续随机变量时使用相同的方法用密度代替概率是合法的。
