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手写体识别——Knn算法和logistic算法
Knn算法
导入手写体数据
from sklearn.datasets import load_digits
ldg = load_digits()
ldg
{'data': array([[ 0., 0., 5., ..., 0., 0., 0.],
[ 0., 0., 0., ..., 10., 0., 0.],
[ 0., 0., 0., ..., 16., 9., 0.],
...,
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 2., ..., 12., 0., 0.],
[ 0., 0., 10., ..., 12., 1., 0.]]),
'target': array([0, 1, 2, ..., 8, 9, 8]),
'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
'images': array([[[ 0., 0., 5., ..., 1., 0., 0.],
[ 0., 0., 13., ..., 15., 5., 0.],
[ 0., 3., 15., ..., 11., 8., 0.],
...,
[ 0., 4., 11., ..., 12., 7., 0.],
[ 0., 2., 14., ..., 12., 0., 0.],
[ 0., 0., 6., ..., 0., 0., 0.]],
[[ 0., 0., 0., ..., 5., 0., 0.],
[ 0., 0., 0., ..., 9., 0., 0.],
[ 0., 0., 3., ..., 6., 0., 0.],
...,
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 1., ..., 6., 0., 0.],
[ 0., 0., 0., ..., 10., 0., 0.]],
[[ 0., 0., 0., ..., 12., 0., 0.],
[ 0., 0., 3., ..., 14., 0., 0.],
[ 0., 0., 8., ..., 16., 0., 0.],
...,
[ 0., 9., 16., ..., 0., 0., 0.],
[ 0., 3., 13., ..., 11., 5., 0.],
[ 0., 0., 0., ..., 16., 9., 0.]],
...,
[[ 0., 0., 1., ..., 1., 0., 0.],
[ 0., 0., 13., ..., 2., 1., 0.],
[ 0., 0., 16., ..., 16., 5., 0.],
...,
[ 0., 0., 16., ..., 15., 0., 0.],
[ 0., 0., 15., ..., 16., 0., 0.],
[ 0., 0., 2., ..., 6., 0., 0.]],
[[ 0., 0., 2., ..., 0., 0., 0.],
[ 0., 0., 14., ..., 15., 1., 0.],
[ 0., 4., 16., ..., 16., 7., 0.],
...,
[ 0., 0., 0., ..., 16., 2., 0.],
[ 0., 0., 4., ..., 16., 2., 0.],
[ 0., 0., 5., ..., 12., 0., 0.]],
[[ 0., 0., 10., ..., 1., 0., 0.],
[ 0., 2., 16., ..., 1., 0., 0.],
[ 0., 0., 15., ..., 15., 0., 0.],
...,
[ 0., 4., 16., ..., 16., 6., 0.],
[ 0., 8., 16., ..., 16., 8., 0.],
[ 0., 1., 8., ..., 12., 1., 0.]]]),
'DESCR': ".. _digits_dataset:\n\nOptical recognition of handwritten digits dataset\n--------------------------------------------------\n\n**Data Set Characteristics:**\n\n :Number of Instances: 5620\n :Number of Attributes: 64\n :Attribute Information: 8x8 image of integer pixels in the range 0..16.\n :Missing Attribute Values: None\n :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\n :Date: July; 1998\n\nThis is a copy of the test set of the UCI ML hand-written digits datasets\nhttps://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\n\nThe data set contains images of hand-written digits: 10 classes where\neach class refers to a digit.\n\nPreprocessing programs made available by NIST were used to extract\nnormalized bitmaps of handwritten digits from a preprinted form. From a\ntotal of 43 people, 30 contributed to the training set and different 13\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\n4x4 and the number of on pixels are counted in each block. This generates\nan input matrix of 8x8 where each element is an integer in the range\n0..16. This reduces dimensionality and gives invariance to small\ndistortions.\n\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\n1994.\n\n.. topic:: References\n\n - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\n Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\n Graduate Studies in Science and Engineering, Bogazici University.\n - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\n - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\n Linear dimensionalityreduction using relevance weighted LDA. School of\n Electrical and Electronic Engineering Nanyang Technological University.\n 2005.\n - Claudio Gentile. A New Approximate Maximal Margin Classification\n Algorithm. NIPS. 2000."}
print(ldg.DESCR)
.. _digits_dataset:
Optical recognition of handwritten digits dataset
--------------------------------------------------
**Data Set Characteristics:**
:Number of Instances: 5620
:Number of Attributes: 64
:Attribute Information: 8x8 image of integer pixels in the range 0..16.
:Missing Attribute Values: None
:Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)
:Date: July; 1998
This is a copy of the test set of the UCI ML hand-written digits datasets
https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits
The data set contains images of hand-written digits: 10 classes where
each class refers to a digit.
Preprocessing programs made available by NIST were used to extract
normalized bitmaps of handwritten digits from a preprinted form. From a
total of 43 people, 30 contributed to the training set and different 13
to the test set. 32x32 bitmaps are divided into nonoverlapping blocks of
4x4 and the number of on pixels are counted in each block. This generates
an input matrix of 8x8 where each element is an integer in the range
0..16. This reduces dimensionality and gives invariance to small
distortions.
For info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.
T. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.
L. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,
1994.
.. topic:: References
- C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their
Applications to Handwritten Digit Recognition, MSc Thesis, Institute of
Graduate Studies in Science and Engineering, Bogazici University.
- E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.
- Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.
Linear dimensionalityreduction using relevance weighted LDA. School of
Electrical and Electronic Engineering Nanyang Technological University.
2005.
- Claudio Gentile. A New Approximate Maximal Margin Classification
Algorithm. NIPS. 2000.
一共5620个样本，样本属性64个，8 * 8像素矩阵
随机划分训练集、测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(ldg.data, ldg.target, test_size=0.25)
X_train
array([[ 0., 0., 2., ..., 16., 16., 7.],
[ 0., 0., 8., ..., 13., 2., 0.],
[ 0., 0., 10., ..., 7., 0., 0.],
...,
[ 0., 0., 3., ..., 0., 0., 0.],
[ 0., 0., 15., ..., 0., 0., 0.],
[ 0., 0., 12., ..., 3., 0., 0.]])
导入画图函数
import matplotlib.pyplot as plt
plt.imshow(X_train[0].reshape(8, 8))
<matplotlib.image.AxesImage at 0x1aa99e30>
导入knn算法并训练
from sklearn.neighbors import KNeighborsClassifier
from sklearn.externals import joblib
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
joblib.dump(knn, './knn.kpl')
['./knn.kpl']
Knn算法运行时间
knn = joblib.load('./knn.kpl')
%time y_p_kn = knn.predict(X_test)
Wall time: 394 ms
Knn算法准确率
round(knn.score(X_test, y_test), 3)
0.987
knn算法运算时间长，精度略高
plt.figure(figsize=(10, 16)) # 画布大小
for i in range(100):
axes = plt.subplot(10, 10, i+1)
data = X_test[i].reshape(8, 8)
plt.imshow(data, cmap='gray') # 灰度
t = y_test[i]
p = y_p_kn[i]
title = 'T:' + str(t) + '\nP:' + str(p)
axes.set_title(title)
axes.axis('off')
logistic算法
导入logistic算法并训练
from sklearn.linear_model import LogisticRegression
lgst = LogisticRegression(C=0.1)
lgst.fit(X_train, y_train)
joblib.dump(lgst, './lgst.kpl')
logistic算法运行时间
lgst = joblib.load('./lgst.kpl')
%time y_p_lg = lgst.predict(X_test)
Wall time: 1.99 ms
logistic算法准确率
round(lgst.score(X_test, y_test), 3)
0.969
logistic算法速度块，精度略低
plt.figure(figsize=(10, 16))
for i in range(100):
axes = plt.subplot(10, 10, i+1)
data = X_test[i].reshape(8, 8)
plt.imshow(data, cmap='gray')
t = y_test[i]
p = y_p_lg[i]
title = 'T:' + str(t) + '\nP:' + str(p)
axes.set_title(title)
axes.axis('off')
总结
knn算法和logistic算法均能进行分类问题的监督式学习，knn算法精度略高，但运算较慢。
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