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from numpy import *
import operator
import matplotlib.pyplot as plt
from os import listdir
def create_data_set():
group = array([[1.0, 1.1], [1.0, 1.0], [0, 1], [0, 0.1]])
labels = ['A', 'C', 'B', 'D']
return group, labels
def classify(in_x, data_set, labels, k):
"""
分类器
:param in_x: 用于分类的输入向量
:param data_set: 输入的训练样本集
:param labels: 标签向量
:param k: 用于选择最近邻居的数目
:return:
"""
data_set_size = data_set.shape[0]
diff_arr = tile(in_x, (data_set_size, 1)) - data_set
sq_diff_arr = diff_arr ** 2
sq_distinces = sq_diff_arr.sum(axis=1)
distinces = sq_distinces ** 0.5
sorted_dist_indices = distinces.argsort()
class_count = {}
for i in range(k):
vote_label = labels[sorted_dist_indices[i]]
class_count[vote_label] = class_count.get(vote_label, 0) + 1
sorted_class_count = sorted(class_count.items(),
key=operator.itemgetter(1), reverse=True)
return sorted_class_count[0][0]
def file2array(filename):
fr = open(filename)
array_lines = fr.readlines()
amount = len(array_lines)
return_array = zeros((amount, 3))
class_label_vector = []
index = 0
for line in array_lines:
line = line.strip()
list_from_line = line.split('\t')
return_array[index, :] = list_from_line[0:3]
class_label_vector.append(int(list_from_line[-1]))
index += 1
return return_array, class_label_vector
def show_data_in_chart():
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
dating_data_arr, dating_labels = file2array('resource/datingTestSet2.txt')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_title('Appointment Data Analysis')
plt.xlabel('每年获得的飞行常客里程数')
plt.ylabel('玩视频游戏所耗时间百分比')
length = dating_data_arr.shape[0]
x1 = []
y1 = []
x2 = []
y2 = []
x3 = []
y3 = []
for i in range(length):
if dating_labels[i] == 1:
x1.append(dating_data_arr[i, 0])
y1.append(dating_data_arr[i, 1])
elif dating_labels[i] == 2:
x2.append(dating_data_arr[i, 0])
y2.append(dating_data_arr[i, 1])
else:
x3.append(dating_data_arr[i, 0])
y3.append(dating_data_arr[i, 1])
type1 = ax.scatter(x1, y1, c='red')
type2 = ax.scatter(x2, y2, c='green')
type3 = ax.scatter(x3, y3, c='blue')
ax.legend([type1, type2, type3], ["not like", "general like", "very like"], loc=2)
plt.show()
def auto_norm(data_set):
"""
归一化特征值:自动将数据集转化为0到1区间内的值
由于里程数远远大于其他特征值,对结果影响过大
而Helen认为三者同等重要,故采用归一化处理
:param data_set:
:return:
"""
"""
>>> sh = array([
[[1, 1],[8, 18],[100, 3],[2, 4]],
[[1, 90],[21, 2],[11, 3],[19, 4]]
])
>>> shape(sh)
(2,4,2)
>>> sh.max()
100
>>> sh.min()
1
>>> sh.max(0)
array([[ 1, 90],
[ 21, 18],
[100, 3],
[ 19, 4]])
>>> sh.min(0)
array([[ 1, 1],
[ 8, 2],
[11, 3],
[ 2, 4]])
"""
min_vals = data_set.min(0)
max_vals = data_set.max(0)
ranges = max_vals - min_vals
length = data_set.shape[0]
norm_data_set = data_set - tile(min_vals, (length, 1))
norm_data_set = norm_data_set / tile(ranges, (length, 1))
return norm_data_set, ranges, min_vals
def dating_class_test():
"""
测试代码
:return:
"""
test_ratio = 0.10
dating_data_arr, dating_labels = file2array('resource/datingTestSet2.txt')
norm_arr, ranges, min_vals = auto_norm(dating_data_arr)
length = norm_arr.shape[0]
num_test_data = int(length * test_ratio)
error_count = 0.0
for i in range(num_test_data):
classifier_result = classify(norm_arr[i, :], norm_arr[num_test_data:length, :],
dating_labels[num_test_data:length], 3)
print('The classifier came back with: %d, the real answer is: %d'
% (classifier_result, dating_labels[i]))
if classifier_result != dating_labels[i]:
error_count += 1.0
print('The total error rate is: %f' % (error_count / num_test_data))
def classify_person():
"""
预测函数
:return:
"""
result_list = ['not like', 'general like', 'very like']
game_percent = float(input('percentage of time spent playing video games?'))
fly_miles = float(input('frequent flier miles earned per year?'))
how_much_ice_cream = float(input('liters of ice cream consumed per week?'))
dating_data_arr, dating_labels = file2array('resource/datingTestSet2.txt')
norm_arr, ranges, min_vals = auto_norm(dating_data_arr)
in_arr = array([fly_miles, game_percent, how_much_ice_cream])
classifier_result = classify((in_arr - min_vals) / ranges, norm_arr, dating_labels, 3)
print('You will probably like this person: %s' % result_list[classifier_result - 1])
def img2vector(filename):
"""
将32*32的二进制图像矩阵转化为1*1024的向量
:param filename:
:return:
"""
return_vector = zeros((1, 1024))
fr = open(filename)
for i in range(32):
line = fr.readline()
for j in range(32):
return_vector[0, 32*i+j] = int(line[j])
return return_vector
def handwriting_class_test():
handwriting_labels = []
training_file_list = listdir('resource/digits/trainingDigits')
length = len(training_file_list)
training_arr = zeros((length, 1024))
for i in range(length):
filename_str = training_file_list[i]
file_str = filename_str.split('.')[0]
class_num_str = int(file_str.split('_')[0])
handwriting_labels.append(class_num_str)
training_arr[i, :] = img2vector('resource/digits/trainingDigits/%s' % filename_str)
test_file_list = listdir('resource/digits/testDigits')
error_count = 0.0
length = len(test_file_list)
for i in range(length):
filename_str = test_file_list[i]
file_str = filename_str.split('.')[0]
class_num_str = int(file_str.split('_')[0])
vector_under_test = img2vector('resource/digits/testDigits/%s' % filename_str)
classifier_result = classify(vector_under_test, training_arr, handwriting_labels, 3)
print('The classifier came back with: %d,the real answer is: %d'
% (classifier_result, class_num_str))
if classifier_result != class_num_str:
error_count += 1.0
print('The total number of errors is: %d' % error_count)
print('The total error rate is: %f' % (error_count/length))
if __name__ == '__main__':
handwriting_class_test()
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