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lzh
2024-10-16 23:55:29 +08:00
parent b3608f44a8
commit 8719f435de
1 changed files with 42 additions and 48 deletions
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function.py
+42 -48
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@@ -1,6 +1,5 @@
# function.py
import numpy as np
from collections import Counter
import os
"""
@@ -41,10 +40,6 @@ name = "廖子豪"
"""
# 这里写自己的代码实现
import numpy as np
import os
from collections import Counter
def img_to_vector(filename):
"""
@@ -57,9 +52,7 @@ def img_to_vector(filename):
- np.array: 二值图像向量。
"""
with open(filename, 'r') as file:
lines = file.readlines()
vector = [int(pixel) for line in lines for pixel in line.strip()]
return np.array(vector)
return np.array([int(char) for line in file for char in line.strip()])
def load_dataset(directory):
"""
@@ -72,7 +65,7 @@ def load_dataset(directory):
- np.array: 特征数据。
- np.array: 标签数据。
"""
images = []
features = []
labels = []
for label_dir in os.listdir(directory):
label_dir_path = os.path.join(directory, label_dir)
@@ -80,9 +73,9 @@ def load_dataset(directory):
for filename in os.listdir(label_dir_path):
file_path = os.path.join(label_dir_path, filename)
image_vector = img_to_vector(file_path)
images.append(image_vector)
features.append(image_vector)
labels.append(int(label_dir))
return np.array(images), np.array(labels)
return np.array(features), np.array(labels)
def classify(input_vector, dataSet, labels, k=3):
"""
@@ -107,46 +100,47 @@ if __name__ == "__main__":
train_dir = "trainingDigits" # 训练集目录
test_dir = "testDigits" # 测试集目录
train_images, train_labels = load_dataset(train_dir)
test_images, test_labels = load_dataset(test_dir)
train_data, train_labels = load_dataset(train_dir)
test_data, test_labels = load_dataset(test_dir)
print("训练集特征形状：", train_images.shape)
print("训练集特征形状：", train_data.shape)
print("训练集标签形状：", train_labels.shape)
print("测试集特征形状：", test_images.shape)
print("测试集特征形状：", test_data.shape)
print("测试集标签形状：", test_labels.shape)
# 测试 classify 函数
test_data = np.array([int(char) for char in "00000000000000110000000000000000000" # 示例数据
"00000000000011111000000000000000"
"00000000000011111100000000000000"
"00000000001111111110000000000000"
"00000000001111111110000000000000"
"00000000001111111100000000000000"
"00000000011111111000000000000000"
"00000000111111111000000000000000"
"00000000111111110000000000000000"
"00000000111111110000000000000000"
"00000000111111110000000000000000"
"00000000111111110000000000000000"
"00000000111111100000000000000000"
"00000001111111100000000000000000"
"00000001111111100000000000000000"
"00000001111111100000000000000000"
"00000001111111111111000000000000"
"00000001111111111111111000000000"
"00000001111111111111111100000000"
"00000011111111111111111110000000"
"00000011111111111111111110000000"
"00000001111111111111111110000000"
"00000001111111111111111110000000"
"00000000111111111100111111100000"
"00000000111111111000111111100000"
"00000000011111111001111111100000"
"00000000011111111111111111000000"
"00000000001111111111111111000000"
"00000000000111111111111111000000"
"00000000000011111111111100000000"
"00000000000001111111110000000000"
"00000000000000000111100000000000"] * 4).reshape(1, 1024)
predicted_label = classify(test_data, train_images, train_labels)
test_data_vector = np.array([int(char) for char in (
"00000000000000110000000000000000"
"00000000000011111000000000000000"
"00000000000011111100000000000000"
"00000000001111111110000000000000"
"00000000001111111110000000000000"
"00000000001111111100000000000000"
"00000000011111111000000000000000"
"00000000111111111000000000000000"
"00000000111111110000000000000000"
"00000000111111110000000000000000"
"00000000111111110000000000000000"
"00000000111111110000000000000000"
"00000000111111100000000000000000"
"00000001111111100000000000000000"
"00000001111111100000000000000000"
"00000001111111100000000000000000"
"00000001111111111111000000000000"
"00000001111111111111111000000000"
"00000011111111111111111100000000"
"00000011111111111111111100000000"
"00000011111111111111111100000000"
"00000001111111111111111110000000"
"00000001111111111111111110000000"
"00000000111111111100111111100000"
"00000000111111111000111111100000"
"00000000011111111000111111110000"
"00000000011111111111111111000000"
"00000000001111111111111111000000"
"00000000000111111111111111000000"
"00000000000001111111111110000000"
"00000000000000000111100000000000"
)]).reshape(1, 1024)
predicted_label = classify(test_data_vector, train_data, train_labels)
print(f"预测的标签: {predicted_label}")