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更新 function.py
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cy
2024-10-15 20:46:54 +08:00
parent e002a736e6
commit d3bce57a27
1 changed files with 42 additions and 42 deletions
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function.py
+42 -42
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@@ -100,47 +100,47 @@ def load_dataset(directory):
if __name__ == "__main__":
# # 以下代码可用于测试，可自行随意修改
# train_data, train_labels = load_dataset("trainingDigits")
# 以下代码可用于测试，可自行随意修改
train_data, train_labels = load_dataset("trainingDigits")
# test_data = (
# "00000000000000110000000000000000"
# "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"
# )
# predicted_label = classify(
# np.array([int(char) for char in test_data]).reshape(1, 1024),
# train_data,
# train_labels,
# )
test_data = (
"00000000000000110000000000000000"
"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"
)
predicted_label = classify(
np.array([int(char) for char in test_data]).reshape(1, 1024),
train_data,
train_labels,
)
# print(f"预测的标签: {predicted_label}")
print(f"预测的标签: {predicted_label}")