LDA与QDA分类数据的简单应用(python)

本文主要是介绍LDA与QDA分类数据的简单应用(python)，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

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Github链接：https://github.com/asd123pwj/pattern-recognition

LDA: Linear Discriminant Analysis
QDA: Quadratic Discriminant Analysis

需求

• python环境
• numpy库
• sklearn库 - 使用pip install -U scikit-learn安装
  • sklearn官网：https://scikit-learn.org/stable/modules/classes.html
• 数据集 - http://archive.ics.uci.edu/ml/datasets.php：

数据集

结果

• 下面的警告，好像是因为数据集太过离散导致的，表示这个分类器对这份数据不准确，最好换个。

代码

# 使用pip install -U scikit-learn 安装sklearn库
# 使用pip install numpy安装numpy库（可能内置）
# QDA与LDA库：
# https://scikit-learn.org/stable/modules/generated/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html#sklearn.discriminant_analysis.LinearDiscriminantAnalysis


import numpy as np
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA, QuadraticDiscriminantAnalysis as QDA


def get_list_letter(char, features, path):
    with open(path, 'r') as letter_train:
        content_letter_train = letter_train.readlines()
    for line in content_letter_train:
        temp = line.split(',')
        temp[-1] = list(temp[-1])[0]
        char.append(temp[0])
        features.append((temp[1::]))


def get_list_optdigits(dig, features, path):
    with open(path, 'r') as dig_train:
        content_dig_train = dig_train.readlines()
    for line in content_dig_train:
        temp = line.split(',')
        temp[-1] = list(temp[-1])[0]
        dig.append(temp[-1])
        features.append((temp[0:len(temp)-1:]))


def get_list_sat(sat, features, path):
    with open(path, 'r') as sat_train:
        content_sat_train = sat_train.readlines()
    for line in content_sat_train:
        temp = line.split(' ')
        temp[-1] = list(temp[-1])[0]
        sat.append(temp[-1])
        features.append((temp[0:len(temp)-1:]))


def get_list_vowel(vowel, features, path):
    with open(path, 'r') as vowel_train:
        content_vowel_train = vowel_train.readlines()
    for line in content_vowel_train:
        temp = line.split()
        temp[-1] = list(temp[-1])[0]
        vowel.append(temp[-1])
        features.append((temp[3:len(temp)-1:]))


def train_and_test(train_result, fetures_list, test_list, analysis_method):
    letter_x = np.array(fetures_list)
    letter_y = np.array(train_result)
    if analysis_method == "LDA":
        clf = LDA()
    elif analysis_method == "QDA":
        clf = QDA()
    else:
        print("错误的分类方法名。")
        return -1
    clf.fit(letter_x, letter_y)
    return list(clf.predict(test_list))


def convert_int(str_list):
    for row in range(0, len(str_list)):
        for col in range(0, len(str_list[row])):
            str_list[row][col] = int(str_list[row][col])


def convert_float(str_list):
    for row in range(0, len(str_list)):
        for col in range(0, len(str_list[row])):
            str_list[row][col] = float(str_list[row][col])


def analysis_accuracy(judge_result, test_char):
    sum = 0
    right_num = 0
    for pos in range(0, len(judge_result)):
        sum += 1
        if judge_result[pos] == test_char[pos]:
            right_num += 1
    return right_num / sum


# letter数据集初始化
letter_train_path = './dataset/letter.train'
letter_train_class = []
letter_train_features = []
letter_test_path = './dataset/letter.test'
letter_test_class = []
letter_test_features = []
get_list_letter(letter_train_class, letter_train_features, letter_train_path)
get_list_letter(letter_test_class, letter_test_features, letter_test_path)
convert_int(letter_train_features)
convert_int(letter_test_features)
# Letter数据集学习
letter_LDA_judge_result = train_and_test(letter_train_class, letter_train_features, letter_test_features, "LDA")
letter_LDA_judge_accuracy = analysis_accuracy(letter_LDA_judge_result, letter_test_class)
print('使用LDA对letter的', len(letter_train_features), '份数据学习后，对',
      len(letter_test_features), '份测试数据分类的准确率为：', letter_LDA_judge_accuracy)
letter_QDA_judge_result = train_and_test(letter_train_class, letter_train_features, letter_test_features, "QDA")
letter_QDA_judge_accuracy = analysis_accuracy(letter_QDA_judge_result, letter_test_class)
print('使用QDA对letter的', len(letter_train_features), '份数据学习后，对',
      len(letter_test_features), '份测试数据分类的准确率为：', letter_QDA_judge_accuracy)


# optdigits数据集初始化
optdigits_train_path = './dataset/optdigits.train'
optdigits_train_class = []
optdigits_train_features = []
optdigits_test_path = './dataset/optdigits.test'
optdigits_test_class = []
optdigits_test_features = []
get_list_optdigits(optdigits_train_class, optdigits_train_features, optdigits_train_path)
convert_int(optdigits_train_features)
get_list_optdigits(optdigits_test_class, optdigits_test_features, optdigits_test_path)
convert_int(optdigits_test_features)
# optdigits数据集学习
optdigits_LDA_judge_result = train_and_test(optdigits_train_class, optdigits_train_features, optdigits_test_features, "LDA")
optdigits_LDA_judge_accuracy = analysis_accuracy(optdigits_LDA_judge_result, optdigits_test_class)
print('使用LDA对optdigits的', len(optdigits_train_features), '份数据学习后，对',
      len(optdigits_test_features), '份测试数据分类的准确率为：', optdigits_LDA_judge_accuracy)
optdigits_QDA_judge_result = train_and_test(optdigits_train_class, optdigits_train_features, optdigits_test_features, "QDA")
optdigits_QDA_judge_accuracy = analysis_accuracy(optdigits_QDA_judge_result, optdigits_test_class)
print('使用QDA对optdigits的', len(optdigits_train_features), '份数据学习后，对',
      len(optdigits_test_features), '份测试数据分类的准确率为：', optdigits_QDA_judge_accuracy)


# sat数据集初始化
sat_train_path = './dataset/sat.train'
sat_train_class = []
sat_train_features = []
sat_test_path = './dataset/sat.test'
sat_test_class = []
sat_test_features = []
get_list_sat(sat_train_class, sat_train_features, sat_train_path)
convert_int(sat_train_features)
get_list_sat(sat_test_class, sat_test_features, sat_test_path)
convert_int(sat_test_features)
# sat数据集学习
sat_LDA_judge_result = train_and_test(sat_train_class, sat_train_features, sat_test_features, "LDA")
sat_LDA_judge_accuracy = analysis_accuracy(sat_LDA_judge_result, sat_test_class)
print('使用LDA对sat的', len(sat_train_features), '份数据学习后，对',
      len(sat_test_features), '份测试数据分类的准确率为：', sat_LDA_judge_accuracy)
sat_QDA_judge_result = train_and_test(sat_train_class, sat_train_features, sat_test_features, "QDA")
sat_QDA_judge_accuracy = analysis_accuracy(sat_QDA_judge_result, sat_test_class)
print('使用QDA对sat的', len(sat_train_features), '份数据学习后，对',
      len(sat_test_features), '份测试数据分类的准确率为：', sat_QDA_judge_accuracy)


# vowel数据集初始化
vowel_train_path = './dataset/vowel.train'
vowel_train_class = []
vowel_train_features = []
vowel_test_path = './dataset/vowel.test'
vowel_test_class = []
vowel_test_features = []
get_list_vowel(vowel_train_class, vowel_train_features, vowel_train_path)
convert_float(vowel_train_features)
get_list_vowel(vowel_test_class, vowel_test_features, vowel_test_path)
convert_float(vowel_test_features)
# vowel数据集学习
vowel_LDA_judge_result = train_and_test(vowel_train_class, vowel_train_features, vowel_test_features, "LDA")
vowel_LDA_judge_accuracy = analysis_accuracy(vowel_LDA_judge_result, vowel_test_class)
print('使用LDA对vowel的', len(vowel_train_features), '份数据学习后，对',
      len(vowel_test_features), '份测试数据分类的准确率为：', vowel_LDA_judge_accuracy)
vowel_QDA_judge_result = train_and_test(vowel_train_class, vowel_train_features, vowel_test_features, "QDA")
vowel_QDA_judge_accuracy = analysis_accuracy(vowel_QDA_judge_result, vowel_test_class)
print('使用QDA对vowel的', len(vowel_train_features), '份数据学习后，对',
      len(vowel_test_features), '份测试数据分类的准确率为：', vowel_QDA_judge_accuracy)

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