Python亲和性分析示例-学习笔记

《Python数据挖掘入门与实践》Robert Layton 人民邮电出版社

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#加载数据集
import numpy as np  #导入模块numpy并以np作为别名，np可自行定义
dataset_filename = "affinity_dataset.txt"  #设置数据集的路径和文件名。例如C:/Dataset/name.txt
X=np.loadtxt(dataset_filename)  #loadtxt读取数据集
n_samples, n_features = X.shape  #shape读取矩阵形状，二维矩阵返回行列
print("This dataset has {0} samples and {1} features".format(n_samples, n_features))  #format字符串格式化 

#打几行看看数据集的样子
print (X[:5])  #[:5]即编号分别为0-4的前五行数据

# The names of the features, for your reference.
features = ["bread", "milk", "cheese", "apples", "bananas"]  #列表

In our first example, we will compute the Support and Confidence of the rule “If a person buys Apples, they also buy Bananas”.

# First, how many rows contain our premise: that a person is buying apples
num_apple_purchases = 0  #购买苹果的总人数，初值为0
for sample in X:  #for...in语句，sample遍历X的每一行
    if sample[3] == 1:  #一行中的第四个特征值为1代表此人购买了苹果
        num_apple_purchases += 1
print("{0} people bought Apples".format(num_apple_purchases))

# How many of the cases that